From a2e0424abfc0d9f382331c813b1d96e0ef39d3e0 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Thu, 31 Oct 2024 14:51:51 -0700 Subject: [PATCH 01/44] Fix memory leak for chunked prefill 2 (#1858) Co-authored-by: Liangsheng Yin --- .github/workflows/pr-test.yml | 6 +- docs/hyperparameter_tuning.md | 1 - python/sglang/srt/managers/schedule_batch.py | 6 +- python/sglang/srt/managers/scheduler.py | 38 +++---- scripts/killall_sglang.sh | 4 +- test/srt/run_suite.py | 1 + test/srt/test_radix_attention.py | 112 +++++++++++++++++++ 7 files changed, 138 insertions(+), 30 deletions(-) create mode 100644 test/srt/test_radix_attention.py diff --git a/.github/workflows/pr-test.yml b/.github/workflows/pr-test.yml index c1bf8da5b4..1d66a5c7b7 100644 --- a/.github/workflows/pr-test.yml +++ b/.github/workflows/pr-test.yml @@ -50,7 +50,7 @@ jobs: timeout-minutes: 20 run: | cd test/srt - python3 run_suite.py --suite minimal --range-begin 0 --range-end 5 + python3 run_suite.py --suite minimal --range-begin 0 --range-end 4 unit-test-backend-part-2: if: github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request' @@ -67,7 +67,7 @@ jobs: timeout-minutes: 20 run: | cd test/srt - python3 run_suite.py --suite minimal --range-begin 5 --range-end 17 + python3 run_suite.py --suite minimal --range-begin 4 --range-end 14 unit-test-backend-part-3: if: github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request' @@ -84,7 +84,7 @@ jobs: timeout-minutes: 20 run: | cd test/srt - python3 run_suite.py --suite minimal --range-begin 17 --range-end 20 + python3 run_suite.py --suite minimal --range-begin 14 --range-end 20 unit-test-backend-part-4: if: github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request' diff --git a/docs/hyperparameter_tuning.md b/docs/hyperparameter_tuning.md index 5013a80aba..96eb5b4f89 100644 --- a/docs/hyperparameter_tuning.md +++ b/docs/hyperparameter_tuning.md @@ -1,7 +1,6 @@ # Guide on Hyperparameter Tuning ## Achieving Peak Throughput - Achieving a large batch size is the most important thing for attaining high throughput. When the server is running at full load, look for the following in the log: diff --git a/python/sglang/srt/managers/schedule_batch.py b/python/sglang/srt/managers/schedule_batch.py index 85ca560a92..24e82993b8 100644 --- a/python/sglang/srt/managers/schedule_batch.py +++ b/python/sglang/srt/managers/schedule_batch.py @@ -221,7 +221,7 @@ def __init__( self.prefix_indices = [] self.extend_input_len = 0 self.last_node = None - self.is_inflight_req = 0 + self.is_being_chunked = 0 # Logprobs (arguments) self.return_logprob = False @@ -888,7 +888,7 @@ def prepare_for_decode(self, enable_overlap: bool = False): def filter_batch( self, - current_inflight_req: Optional[Req] = None, + being_chunked_req: Optional[Req] = None, keep_indices: Optional[List[int]] = None, ): if keep_indices is None: @@ -896,7 +896,7 @@ def filter_batch( i for i in range(len(self.reqs)) if not self.reqs[i].finished() - and self.reqs[i] is not current_inflight_req + and self.reqs[i] is not being_chunked_req ] if keep_indices is None or len(keep_indices) == 0: diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py index 7c7780a64d..9682b7260a 100644 --- a/python/sglang/srt/managers/scheduler.py +++ b/python/sglang/srt/managers/scheduler.py @@ -231,7 +231,7 @@ def __init__( # Init chunked prefill self.chunked_prefill_size = server_args.chunked_prefill_size - self.current_inflight_req = None + self.being_chunked_req = None self.is_mixed_chunk = ( self.chunked_prefill_size is not None and server_args.enable_mixed_chunk ) @@ -551,13 +551,13 @@ def get_next_batch_to_run(self): and not self.last_batch.forward_mode.is_decode() and not self.last_batch.is_empty() ): - if self.current_inflight_req: + if self.being_chunked_req: self.last_batch.filter_batch( - current_inflight_req=self.current_inflight_req + being_chunked_req=self.being_chunked_req ) - self.tree_cache.cache_unfinished_req(self.current_inflight_req) + self.tree_cache.cache_unfinished_req(self.being_chunked_req) # Inflight request keeps its rid but will get a new req_pool_idx. - self.req_to_token_pool.free(self.current_inflight_req.req_pool_idx) + self.req_to_token_pool.free(self.being_chunked_req.req_pool_idx) self.batch_is_full = False if not self.last_batch.is_empty(): if self.running_batch is None: @@ -588,7 +588,7 @@ def get_new_batch_prefill(self) -> Optional[ScheduleBatch]: # Handle the cases where prefill is not allowed if ( self.batch_is_full or len(self.waiting_queue) == 0 - ) and self.current_inflight_req is None: + ) and self.being_chunked_req is None: return None running_bs = len(self.running_batch.reqs) if self.running_batch else 0 @@ -611,13 +611,11 @@ def get_new_batch_prefill(self) -> Optional[ScheduleBatch]: num_mixed_running, ) - has_inflight = self.current_inflight_req is not None + has_inflight = self.being_chunked_req is not None if has_inflight: - self.current_inflight_req.init_next_round_input( - None if prefix_computed else self.tree_cache - ) - self.current_inflight_req = adder.add_inflight_req( - self.current_inflight_req + self.being_chunked_req.init_next_round_input() + self.being_chunked_req = adder.add_inflight_req( + self.being_chunked_req ) if self.lora_paths: @@ -661,11 +659,11 @@ def get_new_batch_prefill(self) -> Optional[ScheduleBatch]: ] if adder.new_inflight_req is not None: - assert self.current_inflight_req is None - self.current_inflight_req = adder.new_inflight_req + assert self.being_chunked_req is None + self.being_chunked_req = adder.new_inflight_req - if self.current_inflight_req: - self.current_inflight_req.is_inflight_req += 1 + if self.being_chunked_req: + self.being_chunked_req.is_being_chunked += 1 # Print stats if self.tp_rank == 0: @@ -833,8 +831,8 @@ def process_batch_result_prefill(self, batch: ScheduleBatch, result): # Check finish conditions logprob_pt = 0 for i, req in enumerate(batch.reqs): - if req.is_inflight_req > 0: - req.is_inflight_req -= 1 + if req.is_being_chunked > 0: + req.is_being_chunked -= 1 else: # Inflight reqs' prefill is not finished req.completion_tokens_wo_jump_forward += 1 @@ -860,8 +858,8 @@ def process_batch_result_prefill(self, batch: ScheduleBatch, result): # Check finish conditions for i, req in enumerate(batch.reqs): req.embedding = embeddings[i] - if req.is_inflight_req > 0: - req.is_inflight_req -= 1 + if req.is_being_chunked > 0: + req.is_being_chunked -= 1 else: # Inflight reqs' prefill is not finished # dummy output token for embedding models diff --git a/scripts/killall_sglang.sh b/scripts/killall_sglang.sh index 9a57e3b1d3..203da60402 100644 --- a/scripts/killall_sglang.sh +++ b/scripts/killall_sglang.sh @@ -1,6 +1,4 @@ -""" -Kill all SGLang processes and free the GPU memory. -""" +# Kill all SGLang processes and free the GPU memory. kill -9 $(ps aux | grep 'multiprocessing.spawn' | grep -v 'grep' | awk '{print $2}') kill -9 $(ps aux | grep 'sglang.launch_server' | grep -v 'grep' | awk '{print $2}') diff --git a/test/srt/run_suite.py b/test/srt/run_suite.py index 1237df7095..f7277f03da 100644 --- a/test/srt/run_suite.py +++ b/test/srt/run_suite.py @@ -19,6 +19,7 @@ "test_openai_server.py", "test_overlap_schedule.py", "test_pytorch_sampling_backend.py", + "test_radix_attention.py", "test_retract_decode.py", "test_server_args.py", "test_skip_tokenizer_init.py", diff --git a/test/srt/test_radix_attention.py b/test/srt/test_radix_attention.py new file mode 100644 index 0000000000..1c20d95ebd --- /dev/null +++ b/test/srt/test_radix_attention.py @@ -0,0 +1,112 @@ +import os +import random +import unittest + +import requests + +from sglang.test.test_utils import ( + DEFAULT_MODEL_NAME_FOR_TEST, + DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, + DEFAULT_URL_FOR_TEST, + kill_child_process, + popen_launch_server, +) + + +def gen_radix_tree(num_nodes=400, chunk_len=256): + num0 = num_nodes // 2 + num1 = num_nodes - num0 + nodes = [{"input_ids": [37] * 117, "decode_len": 217}] + for _ in range(num0): + parent = random.choice(nodes) + unique_len = random.randint(0, chunk_len) + decode_len = random.randint(0, chunk_len) + token_id = random.randint(0, 32000) + child = { + "input_ids": parent["input_ids"] + [token_id] * unique_len, + "decode_len": decode_len, + } + nodes.append(child) + + while num1 > 0: + num_branch = random.randint(1, min(num1, 10)) + parent = random.choice(nodes) + for _ in range(num_branch): + unique_len = random.randint(0, chunk_len) + decode_len = random.randint(0, chunk_len) + token_id = random.randint(0, 32000) + child = { + "input_ids": parent["input_ids"] + [token_id] * unique_len, + "decode_len": decode_len, + } + nodes.append(child) + + num1 -= num_branch + + random.shuffle(nodes) + return nodes + + +def run_test(base_url, nodes): + data = { + "input_ids": [node["input_ids"] for node in nodes], + "sampling_params": [ + {"max_new_tokens": node["decode_len"], "temperature": 0} for node in nodes + ], + } + + res = requests.post(base_url + "/generate", json=data) + assert res.status_code == 200 + + +class TestRadixCacheFCFS(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.model = DEFAULT_MODEL_NAME_FOR_TEST + cls.base_url = DEFAULT_URL_FOR_TEST + cls.process = popen_launch_server( + cls.model, + cls.base_url, + timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, + other_args=[ + "--chunked-prefill-size", + "128", + "--max-total-tokens", + "20000", + "--schedule-policy", + "fcfs", + ], + ) + + @classmethod + def tearDownClass(cls): + kill_child_process(cls.process.pid, include_self=True) + + def test_radix_attention(self): + nodes = gen_radix_tree() + run_test(self.base_url, nodes) + + +class TestRadixCacheLPM(TestRadixCacheFCFS): + @classmethod + def setUpClass(cls): + cls.model = DEFAULT_MODEL_NAME_FOR_TEST + cls.base_url = DEFAULT_URL_FOR_TEST + cls.process = popen_launch_server( + cls.model, + cls.base_url, + timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, + other_args=[ + "--chunked-prefill-size", + "128", + "--max-total-tokens", + "20000", + "--schedule-policy", + "lpm", + ], + ) + + +if __name__ == "__main__": + os.environ["SGLANG_TEST_RETRACT"] = "true" + unittest.main() From d8e9d61f8621742948cfb63b3d81cdbf5cdda316 Mon Sep 17 00:00:00 2001 From: HAI Date: Thu, 31 Oct 2024 16:38:16 -0700 Subject: [PATCH 02/44] [Build, ROCm] Dockerfile.rocm for Instinct GPUs, with package updates (#1861) --- docker/Dockerfile.rocm | 45 ++++++++++++++++++++++++++++++++++++++++++ python/pyproject.toml | 7 ++++++- 2 files changed, 51 insertions(+), 1 deletion(-) create mode 100644 docker/Dockerfile.rocm diff --git a/docker/Dockerfile.rocm b/docker/Dockerfile.rocm new file mode 100644 index 0000000000..003434454b --- /dev/null +++ b/docker/Dockerfile.rocm @@ -0,0 +1,45 @@ +# Usage (to build SGLang ROCm docker image): +# docker build --build-arg SGL_BRANCH=v0.3.4.post2 -t testImage -f Dockerfile.rocm . + +# default base image +ARG BASE_IMAGE="rocm/vllm-dev:20241022" + +FROM $BASE_IMAGE AS base +USER root + +WORKDIR /sgl-workspace + +ARG SGL_REPO="https://github.com/sgl-project/sglang" +ENV SGL_DEFAULT="main" +ARG SGL_BRANCH=${SGL_DEFAULT} + +RUN git clone ${SGL_REPO} \ + && cd sglang \ + && if [ "${SGL_BRANCH}" = ${SGL_DEFAULT} ]; then \ + echo "Using ${SGL_DEFAULT}, default branch."; \ + else \ + echo "Using ${SGL_BRANCH} branch."; \ + git checkout ${SGL_BRANCH}; \ + fi \ + && if [ "$BUILD_TYPE" = "srt" ]; then \ + python -m pip --no-cache-dir install -e "python[srt_hip]"; \ + else \ + python -m pip --no-cache-dir install -e "python[all_hip]"; \ + fi + +RUN cp -r /sgl-workspace/sglang /sglang +RUN python -m pip cache purge + +# Performance environment variable. + +ENV HIP_FORCE_DEV_KERNARG=1 +ENV SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 +ENV NCCL_MIN_NCHANNELS=112 + +ENV MOE_PADDING=1 +ENV VLLM_FP8_PADDING=1 +ENV VLLM_FP8_ACT_PADDING=1 +ENV VLLM_FP8_WEIGHT_PADDING=1 +ENV VLLM_FP8_REDUCE_CONV=1 + +CMD ["/bin/bash"] diff --git a/python/pyproject.toml b/python/pyproject.toml index fc4a62b7af..d9749e1aba 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -20,9 +20,12 @@ runtime_common = ["aiohttp", "decord", "fastapi", "hf_transfer", "huggingface_hu "orjson", "packaging", "pillow", "psutil", "pydantic", "python-multipart", "torchao", "uvicorn", "uvloop", "zmq", "outlines>=0.0.44", "modelscope"] +srt = ["sglang[runtime_common]", "torch", "vllm==0.6.3.post1"] +# HIP (Heterogeneous-computing Interface for Portability) for AMD +# => base docker rocm/vllm-dev:20241022, not from public vllm whl +srt_hip = ["sglang[runtime_common]", "torch", "vllm==0.6.3.dev13"] # xpu is not enabled in public vllm and torch whl, # need to follow https://docs.vllm.ai/en/latest/getting_started/xpu-installation.htmlinstall vllm -srt = ["sglang[runtime_common]", "torch", "vllm==0.6.3.post1"] srt_xpu = ["sglang[runtime_common]"] openai = ["openai>=1.0", "tiktoken"] @@ -37,8 +40,10 @@ test = [ "peft", ] all = ["sglang[srt]", "sglang[openai]", "sglang[anthropic]", "sglang[litellm]"] +all_hip = ["sglang[srt_hip]", "sglang[openai]", "sglang[anthropic]", "sglang[litellm]"] all_xpu = ["sglang[srt_xpu]", "sglang[openai]", "sglang[anthropic]", "sglang[litellm]"] dev = ["sglang[all]", "sglang[test]"] +dev_hip = ["sglang[all_hip]", "sglang[test]"] dev_xpu = ["sglang[all_xpu]", "sglang[test]"] [project.urls] From b9fd178f1b7bab721b384c017dcec30a3ba0f323 Mon Sep 17 00:00:00 2001 From: Liangsheng Yin Date: Thu, 31 Oct 2024 18:27:42 -0700 Subject: [PATCH 03/44] Fix retraction + overlap (#1860) Co-authored-by: Lianmin Zheng --- .github/workflows/pr-test.yml | 4 ++-- python/sglang/srt/managers/schedule_batch.py | 15 ++++++++----- python/sglang/srt/managers/scheduler.py | 22 ++++++++++++++++---- test/srt/test_radix_attention.py | 21 +++++++++++++++++++ 4 files changed, 51 insertions(+), 11 deletions(-) diff --git a/.github/workflows/pr-test.yml b/.github/workflows/pr-test.yml index 1d66a5c7b7..8f374b8972 100644 --- a/.github/workflows/pr-test.yml +++ b/.github/workflows/pr-test.yml @@ -50,7 +50,7 @@ jobs: timeout-minutes: 20 run: | cd test/srt - python3 run_suite.py --suite minimal --range-begin 0 --range-end 4 + python3 run_suite.py --suite minimal --range-begin 0 --range-end 6 unit-test-backend-part-2: if: github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request' @@ -67,7 +67,7 @@ jobs: timeout-minutes: 20 run: | cd test/srt - python3 run_suite.py --suite minimal --range-begin 4 --range-end 14 + python3 run_suite.py --suite minimal --range-begin 6 --range-end 14 unit-test-backend-part-3: if: github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request' diff --git a/python/sglang/srt/managers/schedule_batch.py b/python/sglang/srt/managers/schedule_batch.py index 24e82993b8..131d2982a1 100644 --- a/python/sglang/srt/managers/schedule_batch.py +++ b/python/sglang/srt/managers/schedule_batch.py @@ -211,9 +211,6 @@ def __init__( # this does not include the jump forward tokens. self.completion_tokens_wo_jump_forward = 0 - # The number of cached tokens, that were already cached in the KV store - self.cached_tokens = 0 - # For vision inputs self.image_inputs: Optional[ImageInputs] = None @@ -223,6 +220,9 @@ def __init__( self.last_node = None self.is_being_chunked = 0 + # For retraction + self.is_retracted = False + # Logprobs (arguments) self.return_logprob = False self.logprob_start_len = 0 @@ -242,12 +242,15 @@ def __init__( # The relative logprob_start_len in an extend batch self.extend_logprob_start_len = 0 - # Embedding + # Embedding (return values) self.embedding = None # Constrained decoding self.grammar: Optional[Grammar] = None + # The number of cached tokens, that were already cached in the KV cache + self.cached_tokens = 0 + # For Qwen2-VL self.mrope_position_delta = [] # use mutable object @@ -561,7 +564,7 @@ def prepare_encoder_info_extend(self, input_ids: List[int], seq_lens: List[int]) seq_lens[i] -= encoder_len if len(req.prefix_indices) < encoder_len: - # NOTE: the encoder part should considered as a whole + # NOTE: the encoder part should be considered as a whole assert len(req.prefix_indices) == 0 input_ids[i] = input_ids[i][encoder_len:] encoder_out_cache_loc.append(self.out_cache_loc[pt : pt + encoder_len]) @@ -648,6 +651,7 @@ def prepare_for_extend(self): req.extend_logprob_start_len = extend_logprob_start_len pt += req.extend_input_len + req.is_retracted = False # Set fields self.input_ids = torch.tensor(sum(input_ids, []), dtype=torch.int32).to( @@ -780,6 +784,7 @@ def retract_decode(self): req.prefix_indices = [] req.last_node = None req.extend_input_len = 0 + req.is_retracted = True # For incremental logprobs req.last_update_decode_tokens = 0 diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py index 9682b7260a..74cd1f3ea6 100644 --- a/python/sglang/srt/managers/scheduler.py +++ b/python/sglang/srt/managers/scheduler.py @@ -79,6 +79,7 @@ logger = logging.getLogger(__name__) + # Crash on warning if we are running CI tests crash_on_warning = os.getenv("SGLANG_IS_IN_CI", "false") == "true" @@ -831,9 +832,10 @@ def process_batch_result_prefill(self, batch: ScheduleBatch, result): # Check finish conditions logprob_pt = 0 for i, req in enumerate(batch.reqs): - if req.is_being_chunked > 0: - req.is_being_chunked -= 1 - else: + if req.is_retracted: + continue + + if req.is_being_chunked <= 0: # Inflight reqs' prefill is not finished req.completion_tokens_wo_jump_forward += 1 req.output_ids.append(next_token_ids[i]) @@ -851,12 +853,18 @@ def process_batch_result_prefill(self, batch: ScheduleBatch, result): logprob_pt += self.add_logprob_return_values( i, req, logprob_pt, next_token_ids, logits_output ) + else: + req.is_being_chunked -= 1 + else: # embedding or reward model embeddings, bid = result embeddings = embeddings.tolist() # Check finish conditions for i, req in enumerate(batch.reqs): + if req.is_retracted: + continue + req.embedding = embeddings[i] if req.is_being_chunked > 0: req.is_being_chunked -= 1 @@ -893,7 +901,12 @@ def process_batch_result_decode(self, batch: ScheduleBatch, result): # Check finish condition for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)): - if self.server_args.enable_overlap_schedule and req.finished(): + if req.is_retracted: + continue + + if self.server_args.enable_overlap_schedule and ( + req.finished() + ): self.token_to_kv_pool.free(batch.out_cache_loc[i : i + 1]) continue @@ -1015,6 +1028,7 @@ def stream_output(self, reqs: List[Req]): is_stream_iter = self.forward_ct_decode % self.stream_interval == 0 for req in reqs: + # TODO(lianmin): revisit this for overlap + retract + stream if req.finished() or ( req.stream and (is_stream_iter or len(req.output_ids) == 1) ): diff --git a/test/srt/test_radix_attention.py b/test/srt/test_radix_attention.py index 1c20d95ebd..e858ba9ee9 100644 --- a/test/srt/test_radix_attention.py +++ b/test/srt/test_radix_attention.py @@ -107,6 +107,27 @@ def setUpClass(cls): ) +class TestRadixCacheOverlapLPM(TestRadixCacheFCFS): + @classmethod + def setUpClass(cls): + cls.model = DEFAULT_MODEL_NAME_FOR_TEST + cls.base_url = DEFAULT_URL_FOR_TEST + cls.process = popen_launch_server( + cls.model, + cls.base_url, + timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, + other_args=[ + "--enable-overlap-schedule", + "--chunked-prefill-size", + "128", + "--max-total-tokens", + "20000", + "--schedule-policy", + "lpm", + ], + ) + + if __name__ == "__main__": os.environ["SGLANG_TEST_RETRACT"] = "true" unittest.main() From 61cf00e1121509c0dfa19d2a8608471b23a3f6a9 Mon Sep 17 00:00:00 2001 From: Chayenne Date: Thu, 31 Oct 2024 20:10:16 -0700 Subject: [PATCH 04/44] change file tree (#1859) Co-authored-by: Chayenne --- .github/workflows/deploy-docs.yml | 10 +- .github/workflows/execute-notebook.yml | 10 +- README.md | 6 +- docs/Makefile | 13 +- docs/{ => backend}/backend.md | 0 docs/{ => backend}/embedding_model.ipynb | 268 ++++++-- docs/{ => backend}/openai_api.ipynb | 649 ++++++++++++++---- docs/deploy.py | 24 +- docs/{ => developer}/release_process.md | 0 docs/{ => developer}/setup_github_runner.md | 0 docs/{ => frontend}/frontend.md | 0 docs/index.rst | 28 +- .../benchmark_and_profiling.md | 0 docs/{ => references}/choices_methods.md | 0 docs/{ => references}/contributor_guide.md | 0 docs/{ => references}/custom_chat_template.md | 0 .../{ => references}/hyperparameter_tuning.md | 0 docs/{ => references}/learn_more.md | 0 docs/{ => references}/model_support.md | 0 docs/{ => references}/sampling_params.md | 0 docs/{ => references}/troubleshooting.md | 0 docs/send_request.ipynb | 222 ------ docs/{ => starts}/install.md | 0 docs/starts/send_request.ipynb | 403 +++++++++++ 24 files changed, 1177 insertions(+), 456 deletions(-) rename docs/{ => backend}/backend.md (100%) rename docs/{ => backend}/embedding_model.ipynb (59%) rename docs/{ => backend}/openai_api.ipynb (55%) rename docs/{ => developer}/release_process.md (100%) rename docs/{ => developer}/setup_github_runner.md (100%) rename docs/{ => frontend}/frontend.md (100%) rename docs/{ => references}/benchmark_and_profiling.md (100%) rename docs/{ => references}/choices_methods.md (100%) rename docs/{ => references}/contributor_guide.md (100%) rename docs/{ => references}/custom_chat_template.md (100%) rename docs/{ => references}/hyperparameter_tuning.md (100%) rename docs/{ => references}/learn_more.md (100%) rename docs/{ => references}/model_support.md (100%) rename docs/{ => references}/sampling_params.md (100%) rename docs/{ => references}/troubleshooting.md (100%) delete mode 100644 docs/send_request.ipynb rename docs/{ => starts}/install.md (100%) create mode 100644 docs/starts/send_request.ipynb diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml index d7177d2f6f..aeb9765f19 100644 --- a/.github/workflows/deploy-docs.yml +++ b/.github/workflows/deploy-docs.yml @@ -38,14 +38,8 @@ jobs: GITHUB_TOKEN: ${{ secrets.PAT_TOKEN }} run: | cd docs - for nb in *.ipynb; do - if [ -f "$nb" ]; then - echo "Executing $nb" - jupyter nbconvert --to notebook --execute --inplace "$nb" \ - --ExecutePreprocessor.timeout=600 \ - --ExecutePreprocessor.kernel_name=python3 - fi - done + make clean + make compile make html cd _build/html diff --git a/.github/workflows/execute-notebook.yml b/.github/workflows/execute-notebook.yml index ebc73bac16..b0a2a83246 100644 --- a/.github/workflows/execute-notebook.yml +++ b/.github/workflows/execute-notebook.yml @@ -44,11 +44,5 @@ jobs: - name: Execute notebooks run: | cd docs - for nb in *.ipynb; do - if [ -f "$nb" ]; then - echo "Executing $nb" - jupyter nbconvert --to notebook --execute --inplace "$nb" \ - --ExecutePreprocessor.timeout=600 \ - --ExecutePreprocessor.kernel_name=python3 - fi - done \ No newline at end of file + make clean + make compile \ No newline at end of file diff --git a/README.md b/README.md index 6dc577ee44..8e5d55f50b 100644 --- a/README.md +++ b/README.md @@ -40,13 +40,13 @@ The core features include: - **Active Community**: SGLang is open-source and backed by an active community with industry adoption. ## Install -See [https://sgl-project.github.io/install.html](https://sgl-project.github.io/install.html) +See [https://sgl-project.github.io/starts/install.html](https://sgl-project.github.io/starts/install.html) ## Backend: SGLang Runtime (SRT) -See [https://sgl-project.github.io/backend.html](https://sgl-project.github.io/backend.html) +See [https://sgl-project.github.io/backend/backend.html](https://sgl-project.github.io/backend/backend.html) ## Frontend: Structured Generation Language (SGLang) -See [https://sgl-project.github.io/frontend.html](https://sgl-project.github.io/frontend.html) +See [https://sgl-project.github.io/frontend/frontend.html](https://sgl-project.github.io/frontend/frontend.html) ## Benchmark And Performance Learn more in our release blogs: [v0.2 blog](https://lmsys.org/blog/2024-07-25-sglang-llama3/), [v0.3 blog](https://lmsys.org/blog/2024-09-04-sglang-v0-3/) diff --git a/docs/Makefile b/docs/Makefile index 3e4a1b5e5b..b439c4fe22 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -12,7 +12,18 @@ BUILDDIR = _build help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -.PHONY: help Makefile +# New target to compile Markdown and Jupyter Notebook files +compile: + find $(SOURCEDIR) -name '*.ipynb' | while read nb; do \ + if [ -f "$$nb" ]; then \ + echo "Executing $$nb"; \ + jupyter nbconvert --to notebook --execute --inplace "$$nb" \ + --ExecutePreprocessor.timeout=600 \ + --ExecutePreprocessor.kernel_name=python3; \ + fi; \ + done + +.PHONY: help Makefile compile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). diff --git a/docs/backend.md b/docs/backend/backend.md similarity index 100% rename from docs/backend.md rename to docs/backend/backend.md diff --git a/docs/embedding_model.ipynb b/docs/backend/embedding_model.ipynb similarity index 59% rename from docs/embedding_model.ipynb rename to docs/backend/embedding_model.ipynb index c939204c5b..af985a87e1 100644 --- a/docs/embedding_model.ipynb +++ b/docs/backend/embedding_model.ipynb @@ -30,47 +30,181 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:47:32.337369Z", + "iopub.status.busy": "2024-11-01T02:47:32.337032Z", + "iopub.status.idle": "2024-11-01T02:47:59.540926Z", + "shell.execute_reply": "2024-11-01T02:47:59.539861Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "[2024-10-29 21:07:15] server_args=ServerArgs(model_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='Alibaba-NLP/gte-Qwen2-7B-instruct', chat_template=None, is_embedding=True, host='0.0.0.0', port=30010, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=568040040, constrained_json_whitespace_pattern=None, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:47:37] server_args=ServerArgs(model_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='Alibaba-NLP/gte-Qwen2-7B-instruct', chat_template=None, is_embedding=True, host='0.0.0.0', port=30010, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=314021918, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", " warnings.warn(\n", "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "[2024-10-29 21:07:20 TP0] Init torch distributed begin.\n", - "[2024-10-29 21:07:20 TP0] Load weight begin. avail mem=47.27 GB\n", - "[2024-10-29 21:07:21 TP0] lm_eval is not installed, GPTQ may not be usable\n", - "INFO 10-29 21:07:22 weight_utils.py:243] Using model weights format ['*.safetensors']\n", - "Loading safetensors checkpoint shards: 0% Completed | 0/7 [00:00
This cell combines server and notebook output.

Typically, the server runs in a separate terminal,
but we combine the output of server and notebook to demonstrate the usage better.

In our documentation, server output is in gray, notebook output is highlighted.
" + "

NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
" ], "text/plain": [ "" @@ -78,16 +212,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-29 21:07:34] INFO: 127.0.0.1:41780 - \"GET /get_model_info HTTP/1.1\" 200 OK\n", - "[2024-10-29 21:07:34 TP0] Prefill batch. #new-seq: 1, #new-token: 6, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-29 21:07:35] INFO: 127.0.0.1:41792 - \"POST /encode HTTP/1.1\" 200 OK\n", - "[2024-10-29 21:07:35] The server is fired up and ready to roll!\n" - ] } ], "source": [ @@ -118,20 +242,21 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:47:59.543958Z", + "iopub.status.busy": "2024-11-01T02:47:59.543670Z", + "iopub.status.idle": "2024-11-01T02:47:59.591699Z", + "shell.execute_reply": "2024-11-01T02:47:59.590809Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:10:30 TP0] Prefill batch. #new-seq: 1, #new-token: 4, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-28 02:10:31] INFO: 127.0.0.1:48094 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:47:59 TP0] Prefill batch. #new-seq: 1, #new-token: 4, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-10-31 19:47:59] INFO: 127.0.0.1:50358 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n" ] }, { @@ -174,18 +299,21 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:47:59.594229Z", + "iopub.status.busy": "2024-11-01T02:47:59.594049Z", + "iopub.status.idle": "2024-11-01T02:48:00.006233Z", + "shell.execute_reply": "2024-11-01T02:48:00.005255Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:10:31] INFO: 127.0.0.1:48110 - \"GET /get_model_info HTTP/1.1\" 200 OK\n", - "[2024-10-28 02:10:31 TP0] Prefill batch. #new-seq: 1, #new-token: 6, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-28 02:10:31] INFO: 127.0.0.1:48114 - \"POST /encode HTTP/1.1\" 200 OK\n", - "[2024-10-28 02:10:31] The server is fired up and ready to roll!\n", - "[2024-10-28 02:10:31 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 3, cache hit rate: 21.43%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-28 02:10:31] INFO: 127.0.0.1:48118 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:47:59 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 3, cache hit rate: 21.43%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-10-31 19:47:59] INFO: 127.0.0.1:50362 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n" ] }, { @@ -228,13 +356,20 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:48:00.008858Z", + "iopub.status.busy": "2024-11-01T02:48:00.008689Z", + "iopub.status.idle": "2024-11-01T02:48:01.872542Z", + "shell.execute_reply": "2024-11-01T02:48:01.871573Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:127: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", + "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", " warnings.warn(\n" ] }, @@ -242,8 +377,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:10:32 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 3, cache hit rate: 33.33%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-28 02:10:32] INFO: 127.0.0.1:48124 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:48:01 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 3, cache hit rate: 33.33%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-10-31 19:48:01] INFO: 127.0.0.1:50366 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n" ] }, { @@ -284,20 +419,15 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-28 02:10:32] INFO: Shutting down\n", - "[2024-10-28 02:10:32] INFO: Waiting for application shutdown.\n", - "[2024-10-28 02:10:32] INFO: Application shutdown complete.\n", - "[2024-10-28 02:10:32] INFO: Finished server process [1188896]\n", - "W1028 02:10:32.490000 140389363193408 torch/_inductor/compile_worker/subproc_pool.py:126] SubprocPool unclean exit\n" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:48:01.875204Z", + "iopub.status.busy": "2024-11-01T02:48:01.874915Z", + "iopub.status.idle": "2024-11-01T02:48:02.193734Z", + "shell.execute_reply": "2024-11-01T02:48:02.192158Z" } - ], + }, + "outputs": [], "source": [ "terminate_process(embedding_process)" ] diff --git a/docs/openai_api.ipynb b/docs/backend/openai_api.ipynb similarity index 55% rename from docs/openai_api.ipynb rename to docs/backend/openai_api.ipynb index 374fa30567..9b9ba7ab0b 100644 --- a/docs/openai_api.ipynb +++ b/docs/backend/openai_api.ipynb @@ -30,41 +30,140 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:44:46.419815Z", + "iopub.status.busy": "2024-11-01T02:44:46.419509Z", + "iopub.status.idle": "2024-11-01T02:45:16.621648Z", + "shell.execute_reply": "2024-11-01T02:45:16.620659Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2024-10-30 09:44:20.477109: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "2024-10-30 09:44:20.489679: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "2024-10-30 09:44:20.489712: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2024-10-30 09:44:21.010067: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "[2024-10-30 09:44:29] server_args=ServerArgs(model_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='meta-llama/Meta-Llama-3.1-8B-Instruct', chat_template=None, is_embedding=False, host='0.0.0.0', port=30000, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=134920821, constrained_json_whitespace_pattern=None, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n", - "[2024-10-30 09:44:39 TP0] Init torch distributed begin.\n", - "[2024-10-30 09:44:41 TP0] Load weight begin. avail mem=76.83 GB\n", - "[2024-10-30 09:44:42 TP0] lm_eval is not installed, GPTQ may not be usable\n", - "INFO 10-30 09:44:42 weight_utils.py:243] Using model weights format ['*.safetensors']\n", - "Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00Response: ChatCompletion(id='876500c402ae452ea17e4dde415c108a', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=None), matched_stop=128009)], created=1730281553, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, completion_tokens_details=None, prompt_tokens_details=None))" + "Response: ChatCompletion(id='e04fce6c460d4764af68007fc82763e1', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', function_call=None, tool_calls=None), matched_stop=128009)], created=1730429118, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, prompt_tokens_details=None))" ], "text/plain": [ "" @@ -154,23 +272,54 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:45:18.090228Z", + "iopub.status.busy": "2024-11-01T02:45:18.090071Z", + "iopub.status.idle": "2024-11-01T02:45:21.193221Z", + "shell.execute_reply": "2024-11-01T02:45:21.192539Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-30 09:45:57 TP0] Prefill batch. #new-seq: 1, #new-token: 48, #cached-token: 28, cache hit rate: 21.97%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-30 09:45:57 TP0] Decode batch. #running-req: 1, #token: 104, token usage: 0.00, gen throughput (token/s): 8.70, #queue-req: 0\n", - "[2024-10-30 09:45:58 TP0] Decode batch. #running-req: 1, #token: 144, token usage: 0.00, gen throughput (token/s): 132.75, #queue-req: 0\n", - "[2024-10-30 09:45:58 TP0] Decode batch. #running-req: 1, #token: 184, token usage: 0.00, gen throughput (token/s): 132.30, #queue-req: 0\n", - "[2024-10-30 09:45:58] INFO: 127.0.0.1:55594 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:18 TP0] Prefill batch. #new-seq: 1, #new-token: 48, #cached-token: 28, cache hit rate: 21.97%, token usage: 0.00, #running-req: 0, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:18 TP0] Decode batch. #running-req: 1, #token: 104, token usage: 0.00, gen throughput (token/s): 39.15, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:19 TP0] Decode batch. #running-req: 1, #token: 144, token usage: 0.00, gen throughput (token/s): 41.80, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:20 TP0] Decode batch. #running-req: 1, #token: 184, token usage: 0.00, gen throughput (token/s): 41.81, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:21] INFO: 127.0.0.1:37738 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Ancient Rome's major achievements include:

1. **Engineering and Architecture**: Developed concrete, aqueducts, roads, bridges, and monumental buildings like the Colosseum and Pantheon.
2. **Law and Governance**: Established the Twelve Tables, a foundation for modern law, and a system of governance that included the Senate and Assemblies.
3. **Military Conquests**: Expanded the empire through numerous wars, creating a vast territory that stretched from Britain to Egypt.
4. **Language and Literature**: Developed Latin, which became the language of law, government, and literature, influencing modern languages like French, Spanish, and Italian.
" + "Ancient Rome's major achievements include:

1. **Engineering and Architecture**: They built iconic structures like the Colosseum, Pantheon, and Roman Forum, showcasing their mastery of concrete, arches, and aqueducts.
2. **Law and Governance**: The Romans developed the 12 Tables (450 BCE), which formed the basis of their laws, and established the concept of citizenship, paving the way for modern democracy.
3. **Military Conquests**: Rome expanded its territories through a series of wars, creating a vast empire that lasted for centuries, stretching from Britain to Egypt.
4. **Language and Literature**: Latin became" ], "text/plain": [ "" @@ -217,16 +366,50 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:45:21.195226Z", + "iopub.status.busy": "2024-11-01T02:45:21.194680Z", + "iopub.status.idle": "2024-11-01T02:45:21.675473Z", + "shell.execute_reply": "2024-11-01T02:45:21.675050Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-30 09:46:06] INFO: 127.0.0.1:45834 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n", - "[2024-10-30 09:46:06 TP0] Prefill batch. #new-seq: 1, #new-token: 15, #cached-token: 25, cache hit rate: 31.40%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "It looks like you're getting started with our conversation. I'm happy to chat with you and see how[2024-10-30 09:46:06 TP0] Decode batch. #running-req: 1, #token: 61, token usage: 0.00, gen throughput (token/s): 4.78, #queue-req: 0\n", - " things go. What would you like to talk about?" + "[2024-10-31 19:45:21] INFO: 127.0.0.1:37738 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n", + "[2024-10-31 19:45:21 TP0] Prefill batch. #new-seq: 1, #new-token: 15, #cached-token: 25, cache hit rate: 31.40%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "It looks like you're ready to" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " begin" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + ". What kind of test would you like" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " to" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " conduct?" ] } ], @@ -255,21 +438,41 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:45:21.676813Z", + "iopub.status.busy": "2024-11-01T02:45:21.676665Z", + "iopub.status.idle": "2024-11-01T02:45:23.182104Z", + "shell.execute_reply": "2024-11-01T02:45:23.181695Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-30 09:46:11 TP0] Prefill batch. #new-seq: 1, #new-token: 8, #cached-token: 1, cache hit rate: 30.39%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-30 09:46:12 TP0] Decode batch. #running-req: 1, #token: 38, token usage: 0.00, gen throughput (token/s): 7.66, #queue-req: 0\n", - "[2024-10-30 09:46:12] INFO: 127.0.0.1:45834 - \"POST /v1/completions HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:21 TP0] Prefill batch. #new-seq: 1, #new-token: 8, #cached-token: 1, cache hit rate: 30.39%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-10-31 19:45:21 TP0] Decode batch. #running-req: 1, #token: 11, token usage: 0.00, gen throughput (token/s): 39.18, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:22 TP0] Decode batch. #running-req: 1, #token: 51, token usage: 0.00, gen throughput (token/s): 42.85, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:23] INFO: 127.0.0.1:37738 - \"POST /v1/completions HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Response: Completion(id='1c988750627649f8872965d00cc008d9', choices=[CompletionChoice(finish_reason='length', index=0, logprobs=None, text=' 1. 2. 3.\\n1. United States - Washington D.C. 2. Japan - Tokyo 3. Australia - Canberra\\nList 3 countries and their capitals. 1. 2. 3.\\n1. China - Beijing 2. Brazil - Bras', matched_stop=None)], created=1730281572, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='text_completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=64, prompt_tokens=9, total_tokens=73, completion_tokens_details=None, prompt_tokens_details=None))" + "Response: Completion(id='84ca7b4df182449697c4b38a454b8834', choices=[CompletionChoice(finish_reason='length', index=0, logprobs=None, text=' 1. 2. 3.\\n1. United States Washington D.C. 2. Japan Tokyo 3. Australia Canberra\\nList 3 countries and their capitals. 1. 2. 3.\\n1. China Beijing 2. Brazil Bras', matched_stop=None)], created=1730429123, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='text_completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=64, prompt_tokens=9, total_tokens=73, prompt_tokens_details=None))" ], "text/plain": [ "" @@ -306,24 +509,61 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:45:23.186337Z", + "iopub.status.busy": "2024-11-01T02:45:23.186189Z", + "iopub.status.idle": "2024-11-01T02:45:26.769744Z", + "shell.execute_reply": "2024-11-01T02:45:26.769299Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-30 09:46:15 TP0] Prefill batch. #new-seq: 1, #new-token: 9, #cached-token: 1, cache hit rate: 29.32%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-30 09:46:15 TP0] Decode batch. #running-req: 1, #token: 16, token usage: 0.00, gen throughput (token/s): 12.28, #queue-req: 0\n", - "[2024-10-30 09:46:15 TP0] Decode batch. #running-req: 1, #token: 56, token usage: 0.00, gen throughput (token/s): 135.70, #queue-req: 0\n", - "[2024-10-30 09:46:15 TP0] Decode batch. #running-req: 1, #token: 96, token usage: 0.00, gen throughput (token/s): 134.45, #queue-req: 0\n", - "[2024-10-30 09:46:16 TP0] Decode batch. #running-req: 1, #token: 136, token usage: 0.00, gen throughput (token/s): 133.34, #queue-req: 0\n", - "[2024-10-30 09:46:16] INFO: 127.0.0.1:45834 - \"POST /v1/completions HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:23 TP0] Prefill batch. #new-seq: 1, #new-token: 9, #cached-token: 1, cache hit rate: 29.32%, token usage: 0.00, #running-req: 0, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:23 TP0] Decode batch. #running-req: 1, #token: 29, token usage: 0.00, gen throughput (token/s): 40.76, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:24 TP0] Decode batch. #running-req: 1, #token: 69, token usage: 0.00, gen throughput (token/s): 42.13, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:25 TP0] Decode batch. #running-req: 1, #token: 109, token usage: 0.00, gen throughput (token/s): 42.01, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:26 TP0] Decode batch. #running-req: 1, #token: 149, token usage: 0.00, gen throughput (token/s): 41.87, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:26] INFO: 127.0.0.1:37738 - \"POST /v1/completions HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Response: Completion(id='784041b9af634537a7960a0ba6152ba2', choices=[CompletionChoice(finish_reason='length', index=0, logprobs=None, text=\"\\xa0\\nOnce upon a time, in a distant corner of the universe, there was a brave space explorer named Captain Orion. She had spent her entire life studying the stars and dreaming of the day she could explore them for herself. Finally, after years of training and preparation, she set off on her maiden voyage to explore the cosmos.\\nCaptain Orion's ship, the Aurora, was equipped with state-of-the-art technology and a crew of skilled astronauts who were eager to venture into the unknown. As they soared through the galaxy, they encountered breathtaking landscapes and incredible creatures that defied explanation.\\nOn their first stop, they landed on a planet called Zorvath, a world of swirling purple clouds and towering crystal spires. Captain Orion and her crew mar\", matched_stop=None)], created=1730281576, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='text_completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=150, prompt_tokens=10, total_tokens=160, completion_tokens_details=None, prompt_tokens_details=None))" + "Response: Completion(id='fe384c17aece4a5ca5fb5238dcd1adec', choices=[CompletionChoice(finish_reason='length', index=0, logprobs=None, text=\" This can be a sci-fi story, and you have the ability to create a unique and imaginative universe.\\nIn the depths of space, a lone space explorer named Kaelin Vex navigated through the swirling vortex of the Aurora Nebula. Her ship, the Starweaver, was an extension of herself, its advanced AI system linked directly to her mind. Together, they danced through the cosmos, searching for answers to the mysteries of the universe.\\nKaelin's mission was to uncover the secrets of the ancient alien civilization known as the Architects. Legends spoke of their unparalleled technological prowess and their ability to manipulate reality itself. Many believed they had transcended their physical forms, becoming one with the cosmos.\\nAs Kaelin delved deeper into\", matched_stop=None)], created=1730429126, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='text_completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=150, prompt_tokens=10, total_tokens=160, prompt_tokens_details=None))" ], "text/plain": [ "" @@ -369,22 +609,29 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:45:26.772016Z", + "iopub.status.busy": "2024-11-01T02:45:26.771868Z", + "iopub.status.idle": "2024-11-01T02:45:26.794225Z", + "shell.execute_reply": "2024-11-01T02:45:26.793811Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:02:55] INFO: 127.0.0.1:43330 - \"POST /v1/files HTTP/1.1\" 200 OK\n", - "[2024-10-28 02:02:55] INFO: 127.0.0.1:43330 - \"POST /v1/batches HTTP/1.1\" 200 OK\n", - "[2024-10-28 02:02:55 TP0] Prefill batch. #new-seq: 2, #new-token: 30, #cached-token: 50, cache hit rate: 35.06%, token usage: 0.00, #running-req: 0, #queue-req: 0\n" + "[2024-10-31 19:45:26] INFO: 127.0.0.1:57182 - \"POST /v1/files HTTP/1.1\" 200 OK\n", + "[2024-10-31 19:45:26] INFO: 127.0.0.1:57182 - \"POST /v1/batches HTTP/1.1\" 200 OK\n", + "[2024-10-31 19:45:26 TP0] Prefill batch. #new-seq: 2, #new-token: 20, #cached-token: 60, cache hit rate: 42.80%, token usage: 0.00, #running-req: 0, #queue-req: 0\n" ] }, { "data": { "text/html": [ - "Batch job created with ID: batch_56fefd2e-0187-4c14-aa2d-110917723dde" + "Batch job created with ID: batch_d9af5b49-ad3d-423e-8c30-4aaafa5c18c4" ], "text/plain": [ "" @@ -446,19 +693,32 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:45:26.796422Z", + "iopub.status.busy": "2024-11-01T02:45:26.796273Z", + "iopub.status.idle": "2024-11-01T02:45:29.810471Z", + "shell.execute_reply": "2024-11-01T02:45:29.810041Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:02:56 TP0] Decode batch. #running-req: 2, #token: 82, token usage: 0.00, gen throughput (token/s): 55.10, #queue-req: 0\n", + "[2024-10-31 19:45:27 TP0] Decode batch. #running-req: 1, #token: 69, token usage: 0.00, gen throughput (token/s): 51.72, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Batch job status: validating...trying again in 3 seconds...\n", - "[2024-10-28 02:02:58] INFO: 127.0.0.1:43330 - \"GET /v1/batches/batch_56fefd2e-0187-4c14-aa2d-110917723dde HTTP/1.1\" 200 OK\n", + "[2024-10-31 19:45:29] INFO: 127.0.0.1:57182 - \"GET /v1/batches/batch_d9af5b49-ad3d-423e-8c30-4aaafa5c18c4 HTTP/1.1\" 200 OK\n", "Batch job completed successfully!\n", "Request counts: BatchRequestCounts(completed=2, failed=0, total=2)\n", - "[2024-10-28 02:02:58] INFO: 127.0.0.1:43330 - \"GET /v1/files/backend_result_file-520da6c8-0cce-4d4c-a943-a86101f5f5b4/content HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:29] INFO: 127.0.0.1:57182 - \"GET /v1/files/backend_result_file-4ed79bf4-1e07-4fa9-9638-7448aa4e074b/content HTTP/1.1\" 200 OK\n" ] }, { @@ -476,7 +736,7 @@ { "data": { "text/html": [ - "Response: {'status_code': 200, 'request_id': 'request-1', 'body': {'id': 'request-1', 'object': 'chat.completion', 'created': 1730106176, 'model': 'meta-llama/Meta-Llama-3.1-8B-Instruct', 'choices': {'index': 0, 'message': {'role': 'assistant', 'content': 'A programmer walks into a library and asks the librarian, \"Do you have any books on Pavlov\\'s dogs and Schrödinger\\'s cat?\"\\n\\nThe librarian replies, \"It rings a bell, but I\\'m not sure if it\\'s here'}, 'logprobs': None, 'finish_reason': 'length', 'matched_stop': None}, 'usage': {'prompt_tokens': 41, 'completion_tokens': 50, 'total_tokens': 91}, 'system_fingerprint': None}}" + "Response: {'status_code': 200, 'request_id': 'request-1', 'body': {'id': 'request-1', 'object': 'chat.completion', 'created': 1730429127, 'model': 'meta-llama/Meta-Llama-3.1-8B-Instruct', 'choices': {'index': 0, 'message': {'role': 'assistant', 'content': 'Why do programmers prefer dark mode?\\n\\nBecause light attracts bugs.'}, 'logprobs': None, 'finish_reason': 'stop', 'matched_stop': 128009}, 'usage': {'prompt_tokens': 41, 'completion_tokens': 13, 'total_tokens': 54}, 'system_fingerprint': None}}" ], "text/plain": [ "" @@ -500,7 +760,7 @@ { "data": { "text/html": [ - "Response: {'status_code': 200, 'request_id': 'request-2', 'body': {'id': 'request-2', 'object': 'chat.completion', 'created': 1730106176, 'model': 'meta-llama/Meta-Llama-3.1-8B-Instruct', 'choices': {'index': 0, 'message': {'role': 'assistant', 'content': '**What is Python?**\\n\\nPython is a high-level, interpreted programming language that is widely used for various purposes, including:\\n\\n1. **Web Development**: Building web applications and web services using frameworks like Django and Flask.\\n2. **Data Analysis and'}, 'logprobs': None, 'finish_reason': 'length', 'matched_stop': None}, 'usage': {'prompt_tokens': 39, 'completion_tokens': 50, 'total_tokens': 89}, 'system_fingerprint': None}}" + "Response: {'status_code': 200, 'request_id': 'request-2', 'body': {'id': 'request-2', 'object': 'chat.completion', 'created': 1730429127, 'model': 'meta-llama/Meta-Llama-3.1-8B-Instruct', 'choices': {'index': 0, 'message': {'role': 'assistant', 'content': '**What is Python?**\\n\\nPython is a high-level, interpreted programming language that is widely used for various purposes such as web development, scientific computing, data analysis, artificial intelligence, and more. It was created in the late 1980s by'}, 'logprobs': None, 'finish_reason': 'length', 'matched_stop': None}, 'usage': {'prompt_tokens': 39, 'completion_tokens': 50, 'total_tokens': 89}, 'system_fingerprint': None}}" ], "text/plain": [ "" @@ -525,7 +785,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:02:58] INFO: 127.0.0.1:43330 - \"DELETE /v1/files/backend_result_file-520da6c8-0cce-4d4c-a943-a86101f5f5b4 HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:29] INFO: 127.0.0.1:57182 - \"DELETE /v1/files/backend_result_file-4ed79bf4-1e07-4fa9-9638-7448aa4e074b HTTP/1.1\" 200 OK\n" ] } ], @@ -574,21 +834,28 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:45:29.812339Z", + "iopub.status.busy": "2024-11-01T02:45:29.812198Z", + "iopub.status.idle": "2024-11-01T02:45:54.851243Z", + "shell.execute_reply": "2024-11-01T02:45:54.850668Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:02:58] INFO: 127.0.0.1:43336 - \"POST /v1/files HTTP/1.1\" 200 OK\n", - "[2024-10-28 02:02:58] INFO: 127.0.0.1:43336 - \"POST /v1/batches HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:29] INFO: 127.0.0.1:57186 - \"POST /v1/files HTTP/1.1\" 200 OK\n", + "[2024-10-31 19:45:29] INFO: 127.0.0.1:57186 - \"POST /v1/batches HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Created batch job with ID: batch_67da0e16-e7b2-4a75-9f7a-58c033e739e5" + "Created batch job with ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2" ], "text/plain": [ "" @@ -613,23 +880,77 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:02:58 TP0] Prefill batch. #new-seq: 17, #new-token: 510, #cached-token: 425, cache hit rate: 43.40%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-28 02:02:58 TP0] Prefill batch. #new-seq: 83, #new-token: 2490, #cached-token: 2075, cache hit rate: 45.04%, token usage: 0.00, #running-req: 17, #queue-req: 0\n", - "[2024-10-28 02:02:59 TP0] Decode batch. #running-req: 100, #token: 3725, token usage: 0.02, gen throughput (token/s): 234.43, #queue-req: 0\n", - "[2024-10-28 02:03:00 TP0] Decode batch. #running-req: 100, #token: 7725, token usage: 0.04, gen throughput (token/s): 3545.41, #queue-req: 0\n", - "[2024-10-28 02:03:01 TP0] Decode batch. #running-req: 100, #token: 11725, token usage: 0.05, gen throughput (token/s): 3448.10, #queue-req: 0\n", - "[2024-10-28 02:03:02 TP0] Decode batch. #running-req: 100, #token: 15725, token usage: 0.07, gen throughput (token/s): 3362.62, #queue-req: 0\n", - "[2024-10-28 02:03:04 TP0] Decode batch. #running-req: 100, #token: 19725, token usage: 0.09, gen throughput (token/s): 3279.58, #queue-req: 0\n", - "[2024-10-28 02:03:05 TP0] Decode batch. #running-req: 100, #token: 23725, token usage: 0.11, gen throughput (token/s): 3200.86, #queue-req: 0\n", - "[2024-10-28 02:03:06 TP0] Decode batch. #running-req: 100, #token: 27725, token usage: 0.13, gen throughput (token/s): 3126.52, #queue-req: 0\n", - "[2024-10-28 02:03:07 TP0] Decode batch. #running-req: 100, #token: 31725, token usage: 0.15, gen throughput (token/s): 3053.16, #queue-req: 0\n", - "[2024-10-28 02:03:08] INFO: 127.0.0.1:41320 - \"GET /v1/batches/batch_67da0e16-e7b2-4a75-9f7a-58c033e739e5 HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:29 TP0] Prefill batch. #new-seq: 27, #new-token: 810, #cached-token: 675, cache hit rate: 45.05%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-10-31 19:45:29 TP0] Prefill batch. #new-seq: 73, #new-token: 2190, #cached-token: 1825, cache hit rate: 45.33%, token usage: 0.00, #running-req: 27, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:30 TP0] Decode batch. #running-req: 100, #token: 5125, token usage: 0.02, gen throughput (token/s): 636.38, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:31 TP0] Decode batch. #running-req: 100, #token: 9125, token usage: 0.04, gen throughput (token/s): 3507.97, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:33 TP0] Decode batch. #running-req: 100, #token: 13125, token usage: 0.06, gen throughput (token/s): 3417.06, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:34 TP0] Decode batch. #running-req: 100, #token: 17125, token usage: 0.08, gen throughput (token/s): 3332.03, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:35 TP0] Decode batch. #running-req: 100, #token: 21125, token usage: 0.10, gen throughput (token/s): 3252.29, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:36 TP0] Decode batch. #running-req: 100, #token: 25125, token usage: 0.12, gen throughput (token/s): 3173.87, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:38 TP0] Decode batch. #running-req: 100, #token: 29125, token usage: 0.13, gen throughput (token/s): 3101.31, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:39 TP0] Decode batch. #running-req: 100, #token: 33125, token usage: 0.15, gen throughput (token/s): 3030.90, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:39] INFO: 127.0.0.1:37782 - \"GET /v1/batches/batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Batch job details (check 1 / 5) // ID: batch_67da0e16-e7b2-4a75-9f7a-58c033e739e5 // Status: in_progress // Created at: 1730106178 // Input file ID: backend_input_file-92cf2cc1-afbd-428f-8c5c-85fabd86cb63 // Output file ID: None" + "Batch job details (check 1 / 5) // ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 // Status: in_progress // Created at: 1730429129 // Input file ID: backend_input_file-f42b27b5-05ee-4d27-9a37-ff04c3b4a427 // Output file ID: None" ], "text/plain": [ "" @@ -654,15 +975,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:03:09 TP0] Decode batch. #running-req: 100, #token: 35725, token usage: 0.16, gen throughput (token/s): 2980.26, #queue-req: 0\n", - "[2024-10-28 02:03:10 TP0] Decode batch. #running-req: 100, #token: 39725, token usage: 0.18, gen throughput (token/s): 2919.09, #queue-req: 0\n", - "[2024-10-28 02:03:11] INFO: 127.0.0.1:41320 - \"GET /v1/batches/batch_67da0e16-e7b2-4a75-9f7a-58c033e739e5 HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:40 TP0] Decode batch. #running-req: 100, #token: 37125, token usage: 0.17, gen throughput (token/s): 2961.37, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:42 TP0] Decode batch. #running-req: 100, #token: 41125, token usage: 0.19, gen throughput (token/s): 2899.29, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:42] INFO: 127.0.0.1:37782 - \"GET /v1/batches/batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Batch job details (check 2 / 5) // ID: batch_67da0e16-e7b2-4a75-9f7a-58c033e739e5 // Status: in_progress // Created at: 1730106178 // Input file ID: backend_input_file-92cf2cc1-afbd-428f-8c5c-85fabd86cb63 // Output file ID: None" + "Batch job details (check 2 / 5) // ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 // Status: in_progress // Created at: 1730429129 // Input file ID: backend_input_file-f42b27b5-05ee-4d27-9a37-ff04c3b4a427 // Output file ID: None" ], "text/plain": [ "" @@ -687,15 +1020,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:03:11 TP0] Decode batch. #running-req: 100, #token: 43725, token usage: 0.20, gen throughput (token/s): 2854.92, #queue-req: 0\n", - "[2024-10-28 02:03:13 TP0] Decode batch. #running-req: 100, #token: 47725, token usage: 0.22, gen throughput (token/s): 2794.62, #queue-req: 0\n", - "[2024-10-28 02:03:14] INFO: 127.0.0.1:41320 - \"GET /v1/batches/batch_67da0e16-e7b2-4a75-9f7a-58c033e739e5 HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:43 TP0] Decode batch. #running-req: 100, #token: 45125, token usage: 0.21, gen throughput (token/s): 2836.50, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:45 TP0] Decode batch. #running-req: 100, #token: 49125, token usage: 0.23, gen throughput (token/s): 2777.80, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:45] INFO: 127.0.0.1:37782 - \"GET /v1/batches/batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Batch job details (check 3 / 5) // ID: batch_67da0e16-e7b2-4a75-9f7a-58c033e739e5 // Status: in_progress // Created at: 1730106178 // Input file ID: backend_input_file-92cf2cc1-afbd-428f-8c5c-85fabd86cb63 // Output file ID: None" + "Batch job details (check 3 / 5) // ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 // Status: in_progress // Created at: 1730429129 // Input file ID: backend_input_file-f42b27b5-05ee-4d27-9a37-ff04c3b4a427 // Output file ID: None" ], "text/plain": [ "" @@ -720,14 +1065,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:03:14 TP0] Decode batch. #running-req: 100, #token: 51725, token usage: 0.24, gen throughput (token/s): 2737.84, #queue-req: 0\n", - "[2024-10-28 02:03:17] INFO: 127.0.0.1:41320 - \"GET /v1/batches/batch_67da0e16-e7b2-4a75-9f7a-58c033e739e5 HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:48] INFO: 127.0.0.1:37782 - \"GET /v1/batches/batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Batch job details (check 4 / 5) // ID: batch_67da0e16-e7b2-4a75-9f7a-58c033e739e5 // Status: completed // Created at: 1730106178 // Input file ID: backend_input_file-92cf2cc1-afbd-428f-8c5c-85fabd86cb63 // Output file ID: backend_result_file-c10ee9f5-eca8-4357-a922-934543b7f433" + "Batch job details (check 4 / 5) // ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 // Status: completed // Created at: 1730429129 // Input file ID: backend_input_file-f42b27b5-05ee-4d27-9a37-ff04c3b4a427 // Output file ID: backend_result_file-dc391511-07f2-4f94-90cb-3ed09bc4b8a3" ], "text/plain": [ "" @@ -752,13 +1096,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:03:20] INFO: 127.0.0.1:41320 - \"GET /v1/batches/batch_67da0e16-e7b2-4a75-9f7a-58c033e739e5 HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:51] INFO: 127.0.0.1:37782 - \"GET /v1/batches/batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Batch job details (check 5 / 5) // ID: batch_67da0e16-e7b2-4a75-9f7a-58c033e739e5 // Status: completed // Created at: 1730106178 // Input file ID: backend_input_file-92cf2cc1-afbd-428f-8c5c-85fabd86cb63 // Output file ID: backend_result_file-c10ee9f5-eca8-4357-a922-934543b7f433" + "Batch job details (check 5 / 5) // ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 // Status: completed // Created at: 1730429129 // Input file ID: backend_input_file-f42b27b5-05ee-4d27-9a37-ff04c3b4a427 // Output file ID: backend_result_file-dc391511-07f2-4f94-90cb-3ed09bc4b8a3" ], "text/plain": [ "" @@ -853,21 +1197,28 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:45:54.854018Z", + "iopub.status.busy": "2024-11-01T02:45:54.853851Z", + "iopub.status.idle": "2024-11-01T02:46:07.893199Z", + "shell.execute_reply": "2024-11-01T02:46:07.892310Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:03:23] INFO: 127.0.0.1:47360 - \"POST /v1/files HTTP/1.1\" 200 OK\n", - "[2024-10-28 02:03:23] INFO: 127.0.0.1:47360 - \"POST /v1/batches HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:54] INFO: 127.0.0.1:33180 - \"POST /v1/files HTTP/1.1\" 200 OK\n", + "[2024-10-31 19:45:54] INFO: 127.0.0.1:33180 - \"POST /v1/batches HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Created batch job with ID: batch_8a409f86-b8c7-4e29-9cc7-187d6d28df62" + "Created batch job with ID: batch_c30756c3-8c09-4142-9630-9590d6124986" ], "text/plain": [ "" @@ -892,12 +1243,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:03:23 TP0] Prefill batch. #new-seq: 44, #new-token: 44, #cached-token: 2376, cache hit rate: 60.81%, token usage: 0.01, #running-req: 0, #queue-req: 0\n", - "[2024-10-28 02:03:23 TP0] Prefill batch. #new-seq: 328, #new-token: 8192, #cached-token: 9824, cache hit rate: 56.49%, token usage: 0.01, #running-req: 44, #queue-req: 128\n", - "[2024-10-28 02:03:24 TP0] Prefill batch. #new-seq: 129, #new-token: 3864, #cached-token: 3231, cache hit rate: 54.15%, token usage: 0.05, #running-req: 371, #queue-req: 1\n", - "[2024-10-28 02:03:27 TP0] Decode batch. #running-req: 500, #token: 29025, token usage: 0.13, gen throughput (token/s): 1162.55, #queue-req: 0\n", - "[2024-10-28 02:03:31 TP0] Decode batch. #running-req: 500, #token: 49025, token usage: 0.23, gen throughput (token/s): 5606.35, #queue-req: 0\n", - "[2024-10-28 02:03:33] INFO: 127.0.0.1:40110 - \"POST /v1/batches/batch_8a409f86-b8c7-4e29-9cc7-187d6d28df62/cancel HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:45:54 TP0] Prefill batch. #new-seq: 135, #new-token: 1150, #cached-token: 6275, cache hit rate: 67.38%, token usage: 0.01, #running-req: 0, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:55 TP0] Prefill batch. #new-seq: 274, #new-token: 8192, #cached-token: 6850, cache hit rate: 55.74%, token usage: 0.02, #running-req: 135, #queue-req: 91\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:56 TP0] Prefill batch. #new-seq: 92, #new-token: 2758, #cached-token: 2302, cache hit rate: 54.19%, token usage: 0.06, #running-req: 408, #queue-req: 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:45:56 TP0] Decode batch. #running-req: 500, #token: 16025, token usage: 0.07, gen throughput (token/s): 409.21, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:00 TP0] Decode batch. #running-req: 500, #token: 36025, token usage: 0.17, gen throughput (token/s): 5777.09, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:03 TP0] Decode batch. #running-req: 500, #token: 56025, token usage: 0.26, gen throughput (token/s): 5530.76, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:04] INFO: 127.0.0.1:57728 - \"POST /v1/batches/batch_c30756c3-8c09-4142-9630-9590d6124986/cancel HTTP/1.1\" 200 OK\n" ] }, { @@ -916,7 +1304,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:03:36] INFO: 127.0.0.1:40110 - \"GET /v1/batches/batch_8a409f86-b8c7-4e29-9cc7-187d6d28df62 HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:46:07] INFO: 127.0.0.1:57728 - \"GET /v1/batches/batch_c30756c3-8c09-4142-9630-9590d6124986 HTTP/1.1\" 200 OK\n" ] }, { @@ -947,7 +1335,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-28 02:03:36] INFO: 127.0.0.1:40110 - \"DELETE /v1/files/backend_input_file-2e9608b6-981b-48ec-8adb-e653ffc69106 HTTP/1.1\" 200 OK\n" + "[2024-10-31 19:46:07] INFO: 127.0.0.1:57728 - \"DELETE /v1/files/backend_input_file-0fbf83a7-301c-488e-a221-b702e24df6a5 HTTP/1.1\" 200 OK\n" ] }, { @@ -961,12 +1349,25 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Successfully deleted local batch_requests.jsonl file" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "import json\n", "import time\n", "from openai import OpenAI\n", + "import os\n", "\n", "client = OpenAI(base_url=\"http://127.0.0.1:30000/v1\", api_key=\"None\")\n", "\n", @@ -1037,6 +1438,9 @@ " del_response = client.files.delete(uploaded_file.id)\n", " if del_response.deleted:\n", " print_highlight(\"Successfully cleaned up input file\")\n", + " if os.path.exists(input_file_path):\n", + " os.remove(input_file_path)\n", + " print_highlight(\"Successfully deleted local batch_requests.jsonl file\")\n", " except Exception as e:\n", " print_highlight(f\"Error cleaning up: {e}\")\n", " raise e" @@ -1044,8 +1448,15 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:46:07.896114Z", + "iopub.status.busy": "2024-11-01T02:46:07.895820Z", + "iopub.status.idle": "2024-11-01T02:46:09.365287Z", + "shell.execute_reply": "2024-11-01T02:46:09.364705Z" + } + }, "outputs": [], "source": [ "terminate_process(server_process)" @@ -1068,7 +1479,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/docs/deploy.py b/docs/deploy.py index 75b7ea7f23..35ebbcb6ca 100644 --- a/docs/deploy.py +++ b/docs/deploy.py @@ -1,22 +1,22 @@ -# Deploy the documents +# Deploy the documents import os from datetime import datetime -def run_cmd(cmd): - print(cmd) - os.system(cmd) +def run_cmd(cmd): + print(cmd) + os.system(cmd) -run_cmd("cd $DOC_SITE_PATH; git pull") +run_cmd("cd $DOC_SITE_PATH; git pull") -# (Optional) Remove old files -# run_cmd("rm -rf $ALPA_SITE_PATH/*") +# (Optional) Remove old files +# run_cmd("rm -rf $ALPA_SITE_PATH/*") -run_cmd("cp -r _build/html/* $DOC_SITE_PATH") +run_cmd("cp -r _build/html/* $DOC_SITE_PATH") -cmd_message = f"Update {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" -run_cmd( - f"cd $DOC_SITE_PATH; git add .; git commit -m '{cmd_message}'; git push origin main" -) +cmd_message = f"Update {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" +run_cmd( + f"cd $DOC_SITE_PATH; git add .; git commit -m '{cmd_message}'; git push origin main" +) diff --git a/docs/release_process.md b/docs/developer/release_process.md similarity index 100% rename from docs/release_process.md rename to docs/developer/release_process.md diff --git a/docs/setup_github_runner.md b/docs/developer/setup_github_runner.md similarity index 100% rename from docs/setup_github_runner.md rename to docs/developer/setup_github_runner.md diff --git a/docs/frontend.md b/docs/frontend/frontend.md similarity index 100% rename from docs/frontend.md rename to docs/frontend/frontend.md diff --git a/docs/index.rst b/docs/index.rst index 8cb286009c..4206552410 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -15,35 +15,35 @@ The core features include: :maxdepth: 1 :caption: Getting Started - install.md - send_request.ipynb + starts/install.md + starts/send_request.ipynb .. toctree:: :maxdepth: 1 :caption: Backend Tutorial - openai_api.ipynb - backend.md + backend/openai_api.ipynb + backend/backend.md .. toctree:: :maxdepth: 1 :caption: Frontend Tutorial - frontend.md + frontend/frontend.md .. toctree:: :maxdepth: 1 :caption: References - sampling_params.md - hyperparameter_tuning.md - model_support.md - contributor_guide.md - choices_methods.md - benchmark_and_profiling.md - troubleshooting.md - embedding_model.ipynb - learn_more.md + references/sampling_params.md + references/hyperparameter_tuning.md + references/model_support.md + references/contributor_guide.md + references/choices_methods.md + references/benchmark_and_profiling.md + references/troubleshooting.md + references/embedding_model.ipynb + references/learn_more.md diff --git a/docs/benchmark_and_profiling.md b/docs/references/benchmark_and_profiling.md similarity index 100% rename from docs/benchmark_and_profiling.md rename to docs/references/benchmark_and_profiling.md diff --git a/docs/choices_methods.md b/docs/references/choices_methods.md similarity index 100% rename from docs/choices_methods.md rename to docs/references/choices_methods.md diff --git a/docs/contributor_guide.md b/docs/references/contributor_guide.md similarity index 100% rename from docs/contributor_guide.md rename to docs/references/contributor_guide.md diff --git a/docs/custom_chat_template.md b/docs/references/custom_chat_template.md similarity index 100% rename from docs/custom_chat_template.md rename to docs/references/custom_chat_template.md diff --git a/docs/hyperparameter_tuning.md b/docs/references/hyperparameter_tuning.md similarity index 100% rename from docs/hyperparameter_tuning.md rename to docs/references/hyperparameter_tuning.md diff --git a/docs/learn_more.md b/docs/references/learn_more.md similarity index 100% rename from docs/learn_more.md rename to docs/references/learn_more.md diff --git a/docs/model_support.md b/docs/references/model_support.md similarity index 100% rename from docs/model_support.md rename to docs/references/model_support.md diff --git a/docs/sampling_params.md b/docs/references/sampling_params.md similarity index 100% rename from docs/sampling_params.md rename to docs/references/sampling_params.md diff --git a/docs/troubleshooting.md b/docs/references/troubleshooting.md similarity index 100% rename from docs/troubleshooting.md rename to docs/references/troubleshooting.md diff --git a/docs/send_request.ipynb b/docs/send_request.ipynb deleted file mode 100644 index f616912aff..0000000000 --- a/docs/send_request.ipynb +++ /dev/null @@ -1,222 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quick Start: Launch A Server and Send Requests\n", - "\n", - "This notebook provides a quick-start guide for using SGLang after installation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Launch a server\n", - "\n", - "This code block is equivalent to executing \n", - "\n", - "```bash\n", - "python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \\\n", - "--port 30000 --host 0.0.0.0\n", - "```\n", - "\n", - "in your command line and wait for the server to be ready." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-30 09:32:30] server_args=ServerArgs(model_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='meta-llama/Meta-Llama-3.1-8B-Instruct', chat_template=None, is_embedding=False, host='0.0.0.0', port=30000, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=335520337, constrained_json_whitespace_pattern=None, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n", - "[2024-10-30 09:32:39 TP0] Init torch distributed begin.\n", - "[2024-10-30 09:32:43 TP0] Load weight begin. avail mem=76.83 GB\n", - "[2024-10-30 09:32:43 TP0] lm_eval is not installed, GPTQ may not be usable\n", - "INFO 10-30 09:32:43 weight_utils.py:243] Using model weights format ['*.safetensors']\n", - "Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00

NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sglang.utils import (\n", - " execute_shell_command,\n", - " wait_for_server,\n", - " terminate_process,\n", - " print_highlight,\n", - ")\n", - "\n", - "server_process = execute_shell_command(\n", - "\"\"\"\n", - "python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \\\n", - "--port 30000 --host 0.0.0.0\n", - "\"\"\"\n", - ")\n", - "\n", - "wait_for_server(\"http://localhost:30000\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Send a Request\n", - "\n", - "Once the server is running, you can send test requests using curl. The server implements the [OpenAI-compatible API](https://platform.openai.com/docs/api-reference/chat)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-30 09:34:00 TP0] Prefill batch. #new-seq: 1, #new-token: 46, #cached-token: 1, cache hit rate: 1.85%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-30 09:34:00 TP0] Decode batch. #running-req: 1, #token: 80, token usage: 0.00, gen throughput (token/s): 0.65, #queue-req: 0\n", - "[2024-10-30 09:34:01 TP0] Decode batch. #running-req: 1, #token: 120, token usage: 0.00, gen throughput (token/s): 139.05, #queue-req: 0\n", - "[2024-10-30 09:34:01 TP0] Decode batch. #running-req: 1, #token: 160, token usage: 0.00, gen throughput (token/s): 137.75, #queue-req: 0\n", - "[2024-10-30 09:34:01 TP0] Decode batch. #running-req: 1, #token: 200, token usage: 0.00, gen throughput (token/s): 137.59, #queue-req: 0\n", - "[2024-10-30 09:34:02 TP0] Decode batch. #running-req: 1, #token: 240, token usage: 0.00, gen throughput (token/s): 137.62, #queue-req: 0\n", - "[2024-10-30 09:34:02 TP0] Decode batch. #running-req: 1, #token: 280, token usage: 0.00, gen throughput (token/s): 137.61, #queue-req: 0\n", - "[2024-10-30 09:34:02 TP0] Decode batch. #running-req: 1, #token: 320, token usage: 0.00, gen throughput (token/s): 137.49, #queue-req: 0\n", - "[2024-10-30 09:34:02 TP0] Decode batch. #running-req: 1, #token: 360, token usage: 0.00, gen throughput (token/s): 137.51, #queue-req: 0\n", - "[2024-10-30 09:34:03 TP0] Decode batch. #running-req: 1, #token: 400, token usage: 0.00, gen throughput (token/s): 137.47, #queue-req: 0\n", - "[2024-10-30 09:34:03 TP0] Decode batch. #running-req: 1, #token: 440, token usage: 0.00, gen throughput (token/s): 137.48, #queue-req: 0\n", - "[2024-10-30 09:34:03 TP0] Decode batch. #running-req: 1, #token: 480, token usage: 0.00, gen throughput (token/s): 137.47, #queue-req: 0\n", - "[2024-10-30 09:34:04 TP0] Decode batch. #running-req: 1, #token: 520, token usage: 0.00, gen throughput (token/s): 137.47, #queue-req: 0\n", - "[2024-10-30 09:34:04] INFO: 127.0.0.1:54110 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n", - "{\"id\":\"a53e18ead1314ab0a2cec76cef484c11\",\"object\":\"chat.completion\",\"created\":1730280844,\"model\":\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"LLM stands for Large Language Model. It's a type of artificial intelligence (AI) model that is designed to process and understand human language in a way that's similar to how humans do. \\n\\nLLMs are trained on vast amounts of text data, which allows them to learn patterns, relationships, and context within language. This training enables them to generate human-like responses to a wide range of questions, prompts, and topics.\\n\\nSome common characteristics of LLMs include:\\n\\n1. **Language understanding**: LLMs can comprehend the meaning and context of language, including nuances like idioms, sarcasm, and figurative language.\\n2. **Language generation**: LLMs can generate text that's coherent, contextually relevant, and often engaging.\\n3. **Knowledge retrieval**: LLMs can access and retrieve information from their vast training datasets, allowing them to answer questions and provide information on a wide range of topics.\\n4. **Conversational dialogue**: LLMs can engage in natural-sounding conversations, using context and understanding to respond to questions and statements.\\n\\nLLMs have many applications, including:\\n\\n1. **Virtual assistants**: LLMs power virtual assistants like Siri, Alexa, and Google Assistant.\\n2. **Language translation**: LLMs can translate languages in real-time, with high accuracy.\\n3. **Content generation**: LLMs can generate text, such as articles, emails, and social media posts.\\n4. **Chatbots**: LLMs can power chatbots that provide customer support, answer questions, and engage in conversations.\\n\\nSome popular examples of LLMs include:\\n\\n1. **BERT (Bidirectional Encoder Representations from Transformers)**: Developed by Google, BERT is a widely used LLM that's been trained on a massive dataset of text.\\n2. **RoBERTa (Robustly Optimized BERT Pretraining Approach)**: Developed by Facebook AI, RoBERTa is another popular LLM that's been trained on a large dataset of text.\\n3. **Language models from OpenAI**: OpenAI has developed a range of LLMs, including GPT-3 (Generative Pre-trained Transformer 3), which is one of the most advanced LLMs available today.\\n\\nOverall, LLMs have the potential to revolutionize the way we interact with language and information, making it easier to access and understand complex topics, and opening up new possibilities for language-based applications.\"},\"logprobs\":null,\"finish_reason\":\"stop\",\"matched_stop\":128009}],\"usage\":{\"prompt_tokens\":47,\"total_tokens\":539,\"completion_tokens\":492,\"prompt_tokens_details\":null}}" - ] - } - ], - "source": [ - "!curl http://localhost:30000/v1/chat/completions \\\n", - " -H \"Content-Type: application/json\" \\\n", - " -H \"Authorization: Bearer None\" \\\n", - " -d '{\"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\", \"messages\": [{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}, {\"role\": \"user\", \"content\": \"What is a LLM?\"}]}'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using OpenAI Python Client\n", - "\n", - "You can also use the OpenAI Python API library to send requests." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-30 09:34:06 TP0] Prefill batch. #new-seq: 1, #new-token: 20, #cached-token: 29, cache hit rate: 29.13%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-30 09:34:07 TP0] Decode batch. #running-req: 1, #token: 71, token usage: 0.00, gen throughput (token/s): 13.51, #queue-req: 0\n", - "[2024-10-30 09:34:07] INFO: 127.0.0.1:42068 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "ChatCompletion(id='0708a0196e524456a1316359f6189e48', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=None), matched_stop=128009)], created=1730280847, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, completion_tokens_details=None, prompt_tokens_details=None))" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import openai\n", - "\n", - "client = openai.Client(base_url=\"http://127.0.0.1:30000/v1\", api_key=\"None\")\n", - "\n", - "response = client.chat.completions.create(\n", - " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", - " messages=[\n", - " {\"role\": \"system\", \"content\": \"You are a helpful AI assistant\"},\n", - " {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n", - " ],\n", - " temperature=0,\n", - " max_tokens=64,\n", - ")\n", - "print_highlight(response)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "terminate_process(server_process)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/install.md b/docs/starts/install.md similarity index 100% rename from docs/install.md rename to docs/starts/install.md diff --git a/docs/starts/send_request.ipynb b/docs/starts/send_request.ipynb new file mode 100644 index 0000000000..dda2371b50 --- /dev/null +++ b/docs/starts/send_request.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quick Start: Launch A Server and Send Requests\n", + "\n", + "This notebook provides a quick-start guide for using SGLang after installation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Launch a server\n", + "\n", + "This code block is equivalent to executing \n", + "\n", + "```bash\n", + "python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \\\n", + "--port 30000 --host 0.0.0.0\n", + "```\n", + "\n", + "in your command line and wait for the server to be ready." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:46:13.611212Z", + "iopub.status.busy": "2024-11-01T02:46:13.611093Z", + "iopub.status.idle": "2024-11-01T02:46:42.810261Z", + "shell.execute_reply": "2024-11-01T02:46:42.809147Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:18] server_args=ServerArgs(model_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='meta-llama/Meta-Llama-3.1-8B-Instruct', chat_template=None, is_embedding=False, host='0.0.0.0', port=30000, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=706578968, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", + " warnings.warn(\n", + "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:24 TP0] Init torch distributed begin.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:24 TP0] Load weight begin. avail mem=47.27 GB\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:25 TP0] lm_eval is not installed, GPTQ may not be usable\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO 10-31 19:46:26 weight_utils.py:243] Using model weights format ['*.safetensors']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00

NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sglang.utils import (\n", + " execute_shell_command,\n", + " wait_for_server,\n", + " terminate_process,\n", + " print_highlight,\n", + ")\n", + "\n", + "server_process = execute_shell_command(\n", + "\"\"\"\n", + "python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \\\n", + "--port 30000 --host 0.0.0.0\n", + "\"\"\"\n", + ")\n", + "\n", + "wait_for_server(\"http://localhost:30000\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Send a Request\n", + "\n", + "Once the server is running, you can send test requests using curl. The server implements the [OpenAI-compatible API](https://platform.openai.com/docs/api-reference/chat)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:46:42.813656Z", + "iopub.status.busy": "2024-11-01T02:46:42.813354Z", + "iopub.status.idle": "2024-11-01T02:46:51.436613Z", + "shell.execute_reply": "2024-11-01T02:46:51.435965Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:42 TP0] Prefill batch. #new-seq: 1, #new-token: 46, #cached-token: 1, cache hit rate: 1.85%, token usage: 0.00, #running-req: 0, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:43 TP0] Decode batch. #running-req: 1, #token: 80, token usage: 0.00, gen throughput (token/s): 5.40, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:44 TP0] Decode batch. #running-req: 1, #token: 120, token usage: 0.00, gen throughput (token/s): 42.48, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:45 TP0] Decode batch. #running-req: 1, #token: 160, token usage: 0.00, gen throughput (token/s): 42.37, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:46 TP0] Decode batch. #running-req: 1, #token: 200, token usage: 0.00, gen throughput (token/s): 42.33, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:47 TP0] Decode batch. #running-req: 1, #token: 240, token usage: 0.00, gen throughput (token/s): 42.34, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:48 TP0] Decode batch. #running-req: 1, #token: 280, token usage: 0.00, gen throughput (token/s): 42.28, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:49 TP0] Decode batch. #running-req: 1, #token: 320, token usage: 0.00, gen throughput (token/s): 42.28, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:50 TP0] Decode batch. #running-req: 1, #token: 360, token usage: 0.00, gen throughput (token/s): 42.24, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:51] INFO: 127.0.0.1:46046 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n", + "{\"id\":\"f9761ee1b1444bd7a640286884a90842\",\"object\":\"chat.completion\",\"created\":1730429211,\"model\":\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"LLM stands for Large Language Model. It's a type of artificial intelligence (AI) designed to process and comprehend human language in a way that's similar to how humans do.\\n\\nLarge Language Models are trained on massive amounts of text data, which allows them to learn patterns and relationships in language. This training enables them to generate text, answer questions, summarize content, and even engage in conversation.\\n\\nSome key characteristics of LLMs include:\\n\\n1. **Language understanding**: LLMs can comprehend the meaning of text, including nuances like idioms, sarcasm, and figurative language.\\n2. **Contextual awareness**: LLMs can understand the context in which a piece of text is written, including the topic, tone, and intent.\\n3. **Generative capabilities**: LLMs can generate text, including entire articles, conversations, or even creative writing like stories or poetry.\\n4. **Continuous learning**: LLMs can learn from new data and update their understanding of language over time.\\n\\nLLMs are used in a wide range of applications, including:\\n\\n1. **Virtual assistants**: LLMs power virtual assistants like Siri, Alexa, and Google Assistant.\\n2. **Chatbots**: LLMs are used to create chatbots that can engage with customers and provide support.\\n3. **Language translation**: LLMs can translate text from one language to another with high accuracy.\\n4. **Content generation**: LLMs can generate content, such as articles, social media posts, and product descriptions.\\n5. **Research and analysis**: LLMs can help researchers analyze and understand large amounts of text data.\\n\\nIn the context of our conversation, I'm a Large Language Model designed to provide helpful and informative responses to your questions!\"},\"logprobs\":null,\"finish_reason\":\"stop\",\"matched_stop\":128009}],\"usage\":{\"prompt_tokens\":47,\"total_tokens\":400,\"completion_tokens\":353,\"prompt_tokens_details\":null}}" + ] + } + ], + "source": [ + "!curl http://localhost:30000/v1/chat/completions \\\n", + " -H \"Content-Type: application/json\" \\\n", + " -H \"Authorization: Bearer None\" \\\n", + " -d '{\"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\", \"messages\": [{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}, {\"role\": \"user\", \"content\": \"What is a LLM?\"}]}'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using OpenAI Python Client\n", + "\n", + "You can also use the OpenAI Python API library to send requests." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:46:51.439372Z", + "iopub.status.busy": "2024-11-01T02:46:51.439178Z", + "iopub.status.idle": "2024-11-01T02:46:52.895776Z", + "shell.execute_reply": "2024-11-01T02:46:52.895318Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:51 TP0] Prefill batch. #new-seq: 1, #new-token: 20, #cached-token: 29, cache hit rate: 29.13%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-10-31 19:46:51 TP0] Decode batch. #running-req: 1, #token: 50, token usage: 0.00, gen throughput (token/s): 27.57, #queue-req: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 19:46:52 TP0] Decode batch. #running-req: 1, #token: 90, token usage: 0.00, gen throughput (token/s): 42.69, #queue-req: 0\n", + "[2024-10-31 19:46:52] INFO: 127.0.0.1:40952 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" + ] + }, + { + "data": { + "text/html": [ + "ChatCompletion(id='c563abb8fe74496f83203fe21ec4ff61', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', function_call=None, tool_calls=None), matched_stop=128009)], created=1730429212, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, prompt_tokens_details=None))" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import openai\n", + "\n", + "client = openai.Client(base_url=\"http://127.0.0.1:30000/v1\", api_key=\"None\")\n", + "\n", + "response = client.chat.completions.create(\n", + " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful AI assistant\"},\n", + " {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n", + " ],\n", + " temperature=0,\n", + " max_tokens=64,\n", + ")\n", + "print_highlight(response)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-01T02:46:52.898411Z", + "iopub.status.busy": "2024-11-01T02:46:52.898149Z", + "iopub.status.idle": "2024-11-01T02:46:54.398382Z", + "shell.execute_reply": "2024-11-01T02:46:54.397564Z" + } + }, + "outputs": [], + "source": [ + "terminate_process(server_process)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 16eb33ffe2afc9c043e4f80f18e15ea57b984944 Mon Sep 17 00:00:00 2001 From: Ke Bao Date: Fri, 1 Nov 2024 11:13:07 +0800 Subject: [PATCH 05/44] Update vocab embedding deps and add TP switch (#1856) --- .../srt/layers/quantization/base_config.py | 17 +- .../srt/layers/vocab_parallel_embedding.py | 486 ++++++++++++++++++ python/sglang/srt/models/baichuan.py | 8 +- python/sglang/srt/models/chatglm.py | 8 +- python/sglang/srt/models/commandr.py | 2 +- python/sglang/srt/models/dbrx.py | 10 +- python/sglang/srt/models/deepseek.py | 8 +- python/sglang/srt/models/deepseek_v2.py | 8 +- python/sglang/srt/models/exaone.py | 8 +- python/sglang/srt/models/gemma.py | 2 +- python/sglang/srt/models/gemma2.py | 2 +- python/sglang/srt/models/gpt2.py | 1 + python/sglang/srt/models/gpt_bigcode.py | 2 +- python/sglang/srt/models/grok.py | 8 +- python/sglang/srt/models/internlm2.py | 8 +- python/sglang/srt/models/llama.py | 8 +- python/sglang/srt/models/minicpm.py | 8 +- python/sglang/srt/models/minicpm3.py | 8 +- python/sglang/srt/models/mixtral.py | 9 +- python/sglang/srt/models/mixtral_quant.py | 8 +- python/sglang/srt/models/mllama.py | 10 +- python/sglang/srt/models/olmo.py | 8 +- python/sglang/srt/models/olmoe.py | 8 +- python/sglang/srt/models/qwen.py | 8 +- python/sglang/srt/models/qwen2.py | 8 +- python/sglang/srt/models/qwen2_moe.py | 8 +- python/sglang/srt/models/qwen2_vl.py | 2 +- python/sglang/srt/models/stablelm.py | 8 +- .../sglang/srt/models/torch_native_llama.py | 8 +- python/sglang/srt/models/xverse.py | 8 +- python/sglang/srt/models/xverse_moe.py | 8 +- 31 files changed, 602 insertions(+), 101 deletions(-) create mode 100644 python/sglang/srt/layers/vocab_parallel_embedding.py diff --git a/python/sglang/srt/layers/quantization/base_config.py b/python/sglang/srt/layers/quantization/base_config.py index aa83b4eea9..b45fcdea25 100644 --- a/python/sglang/srt/layers/quantization/base_config.py +++ b/python/sglang/srt/layers/quantization/base_config.py @@ -1,7 +1,8 @@ # Adapted from https://raw.githubusercontent.com/vllm-project/vllm/v0.5.5/vllm/model_executor/layers/quantization/base_config.py +import inspect from abc import ABC, abstractmethod -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Type import torch from torch import nn @@ -120,3 +121,17 @@ def get_scaled_act_names(self) -> List[str]: For now, this is only used by AWQ. """ raise NotImplementedError + +def method_has_implemented_embedding( + method_class: Type[QuantizeMethodBase]) -> bool: + """ + Not all quant methods have embedding implemented, so we need to check that + it exists for our given method. We check this by making sure the function + has been changed from the base implementation. + """ + base_embedding = inspect.getattr_static(QuantizeMethodBase, "embedding", + None) + class_embedding = inspect.getattr_static(method_class, "embedding", None) + + return (class_embedding is not None + and class_embedding is not base_embedding) \ No newline at end of file diff --git a/python/sglang/srt/layers/vocab_parallel_embedding.py b/python/sglang/srt/layers/vocab_parallel_embedding.py new file mode 100644 index 0000000000..a9ca9da539 --- /dev/null +++ b/python/sglang/srt/layers/vocab_parallel_embedding.py @@ -0,0 +1,486 @@ +# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.3.post1/vllm/model_executor/layers/vocab_parallel_embedding.py + +from dataclasses import dataclass +from typing import List, Optional, Sequence, Tuple + +import torch +import torch.nn.functional as F +from torch.nn.parameter import Parameter, UninitializedParameter +from vllm.distributed import ( + divide, + get_tensor_model_parallel_rank, + get_tensor_model_parallel_world_size, + tensor_model_parallel_all_reduce, +) +from vllm.model_executor.parameter import BasevLLMParameter + +from sglang.srt.layers.quantization.base_config import ( + QuantizationConfig, + QuantizeMethodBase, + method_has_implemented_embedding, +) +from sglang.srt.utils import set_weight_attrs + +DEFAULT_VOCAB_PADDING_SIZE = 64 + + +class UnquantizedEmbeddingMethod(QuantizeMethodBase): + """Unquantized method for embeddings.""" + + def create_weights(self, layer: torch.nn.Module, + input_size_per_partition: int, + output_partition_sizes: List[int], input_size: int, + output_size: int, params_dtype: torch.dtype, + **extra_weight_attrs): + """Create weights for embedding layer.""" + weight = Parameter(torch.empty(sum(output_partition_sizes), + input_size_per_partition, + dtype=params_dtype), + requires_grad=False) + set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0}) + layer.register_parameter("weight", weight) + set_weight_attrs(weight, extra_weight_attrs) + + def apply(self, + layer: torch.nn.Module, + x: torch.Tensor, + bias: Optional[torch.Tensor] = None) -> torch.Tensor: + return F.linear(x, layer.weight, bias) + + def embedding(self, layer: torch.nn.Module, + input_: torch.Tensor) -> torch.Tensor: + return F.embedding(input_, layer.weight) + + +def pad_vocab_size(vocab_size: int, + pad_to: int = DEFAULT_VOCAB_PADDING_SIZE) -> int: + """Pad the vocab size to the given value.""" + return ((vocab_size + pad_to - 1) // pad_to) * pad_to + + +def vocab_range_from_per_partition_vocab_size( + per_partition_vocab_size: int, + rank: int, + offset: int = 0) -> Sequence[int]: + index_f = rank * per_partition_vocab_size + index_l = index_f + per_partition_vocab_size + return index_f + offset, index_l + offset + + +def vocab_range_from_global_vocab_size(global_vocab_size: int, + rank: int, + world_size: int, + offset: int = 0) -> Sequence[int]: + per_partition_vocab_size = divide(global_vocab_size, world_size) + return vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, + rank, + offset=offset) + + +@dataclass +class VocabParallelEmbeddingShardIndices: + """Indices for a shard of a vocab parallel embedding.""" + padded_org_vocab_start_index: int + padded_org_vocab_end_index: int + padded_added_vocab_start_index: int + padded_added_vocab_end_index: int + + org_vocab_start_index: int + org_vocab_end_index: int + added_vocab_start_index: int + added_vocab_end_index: int + + @property + def num_org_elements(self) -> int: + return self.org_vocab_end_index - self.org_vocab_start_index + + @property + def num_added_elements(self) -> int: + return self.added_vocab_end_index - self.added_vocab_start_index + + @property + def num_org_elements_padded(self) -> int: + return (self.padded_org_vocab_end_index - + self.padded_org_vocab_start_index) + + @property + def num_added_elements_padded(self) -> int: + return (self.padded_added_vocab_end_index - + self.padded_added_vocab_start_index) + + @property + def num_org_vocab_padding(self) -> int: + return self.num_org_elements_padded - self.num_org_elements + + @property + def num_added_vocab_padding(self) -> int: + return self.num_added_elements_padded - self.num_added_elements + + @property + def num_elements_padded(self) -> int: + return self.num_org_elements_padded + self.num_added_elements_padded + + def __post_init__(self): + # sanity checks + assert (self.padded_org_vocab_start_index <= + self.padded_org_vocab_end_index) + assert (self.padded_added_vocab_start_index <= + self.padded_added_vocab_end_index) + + assert self.org_vocab_start_index <= self.org_vocab_end_index + assert self.added_vocab_start_index <= self.added_vocab_end_index + + assert self.org_vocab_start_index <= self.padded_org_vocab_start_index + assert (self.added_vocab_start_index <= + self.padded_added_vocab_start_index) + assert self.org_vocab_end_index <= self.padded_org_vocab_end_index + assert self.added_vocab_end_index <= self.padded_added_vocab_end_index + + assert self.num_org_elements <= self.num_org_elements_padded + assert self.num_added_elements <= self.num_added_elements_padded + + +@torch.jit.script +def get_masked_input_and_mask( + input_: torch.Tensor, org_vocab_start_index: int, + org_vocab_end_index: int, num_org_vocab_padding: int, + added_vocab_start_index: int, + added_vocab_end_index: int) -> Tuple[torch.Tensor, torch.Tensor]: + # torch.jit.script will fuse all of the pointwise ops below + # into a single kernel, making it very fast + org_vocab_mask = (input_ >= org_vocab_start_index) & (input_ < + org_vocab_end_index) + added_vocab_mask = (input_ >= added_vocab_start_index) & ( + input_ < added_vocab_end_index) + added_offset = added_vocab_start_index - ( + org_vocab_end_index - org_vocab_start_index) - num_org_vocab_padding + valid_offset = (org_vocab_start_index * + org_vocab_mask) + (added_offset * added_vocab_mask) + vocab_mask = org_vocab_mask | added_vocab_mask + input_ = vocab_mask * (input_ - valid_offset) + return input_, ~vocab_mask + + +class VocabParallelEmbedding(torch.nn.Module): + """Embedding parallelized in the vocabulary dimension. + + Adapted from torch.nn.Embedding, note that we pad the vocabulary size to + make sure it is divisible by the number of model parallel GPUs. + + In order to support various loading methods, we ensure that LoRA-added + embeddings are always at the end of TP-sharded tensors. In other words, + we shard base embeddings and LoRA embeddings separately (both padded), + and place them in the same tensor. + In this example, we will have the original vocab size = 1010, + added vocab size = 16 and padding to 64. Therefore, the total + vocab size with padding will be 1088 (because we first pad 1010 to + 1024, add 16, and then pad to 1088). + Therefore, the tensor format looks like the following: + TP1, rank 0 (no sharding): + |< --------BASE-------- >|< -BASE PADDING-- >|< -----LORA------ >|< -LORA PADDING-- >| + corresponding token_id: | 0 | 1 | ... | 1009 | -1 | ... | -1 | 1010 | ... | 1015 | -1 | ... | -1 | + index: | 0 | 1 | ... | 1009 | 1010 | ... | 1023 | 1024 | ... | 1039 | 1040 | ... | 1087 | + + TP2, rank 0: + |< --------------------BASE--------------------- >|< -----LORA------ >|< -LORA PADDING- >| + corresponding token_id: | 0 | 1 | 2 | ... | 497 | 498 | ... | 511 | 1000 | ... | 1015 | -1 | ... | -1 | + index: | 0 | 1 | 2 | ... | 497 | 498 | ... | 511 | 512 | ... | 527 | 520 | ... | 543 | + TP2, rank 1: + |< -----------BASE----------- >|< -BASE PADDING- >|< -----------LORA PADDING----------- >| + corresponding token_id: | 512 | 513 | 514 | ... | 1009 | -1 | ... | -1 | -1 | ... | -1 | -1 | ... | -1 | + index: | 0 | 1 | 2 | ... | 497 | 498 | ... | 511 | 512 | ... | 519 | 520 | ... | 543 | + + Args: + num_embeddings: vocabulary size. + embedding_dim: size of hidden state. + params_dtype: type of the parameters. + org_num_embeddings: original vocabulary size (without LoRA). + padding_size: padding size for the vocabulary. + quant_config: quant config for the layer + prefix: full name of the layer in the state dict + """ # noqa: E501 + + def __init__(self, + num_embeddings: int, + embedding_dim: int, + params_dtype: Optional[torch.dtype] = None, + org_num_embeddings: Optional[int] = None, + padding_size: int = DEFAULT_VOCAB_PADDING_SIZE, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + enable_tp: bool = True): + super().__init__() + + self.enable_tp = enable_tp + if self.enable_tp: + tp_rank = get_tensor_model_parallel_rank() + self.tp_size = get_tensor_model_parallel_world_size() + else: + tp_rank = 0 + self.tp_size = 1 + + self.num_embeddings = num_embeddings + self.padding_size = padding_size + self.org_vocab_size = org_num_embeddings or num_embeddings + num_added_embeddings = num_embeddings - self.org_vocab_size + self.org_vocab_size_padded = pad_vocab_size(self.org_vocab_size, + self.padding_size) + self.num_embeddings_padded = pad_vocab_size( + self.org_vocab_size_padded + num_added_embeddings, + self.padding_size) + assert self.org_vocab_size_padded <= self.num_embeddings_padded + + self.shard_indices = self._get_indices(self.num_embeddings_padded, + self.org_vocab_size_padded, + self.num_embeddings, + self.org_vocab_size, tp_rank, + self.tp_size) + self.embedding_dim = embedding_dim + + linear_method = None + if quant_config is not None: + linear_method = quant_config.get_quant_method(self, prefix=prefix) + if linear_method is None: + linear_method = UnquantizedEmbeddingMethod() + + # If we are making an embedding layer, then our quantization linear + # method must implement the embedding operation. If we are another + # layer type like ParallelLMHead, this is not important. + is_embedding_layer = type(self.__class__) is VocabParallelEmbedding + linear_method_implements_embedding = method_has_implemented_embedding( + type(linear_method)) + if is_embedding_layer and not linear_method_implements_embedding: + raise NotImplementedError( + f"The class {type(linear_method).__name__} must implement " + "the 'embedding' method, see UnquantizedEmbeddingMethod.") + + self.linear_method: QuantizeMethodBase = linear_method + + if params_dtype is None: + params_dtype = torch.get_default_dtype() + # Divide the weight matrix along the vocaburaly dimension. + self.num_added_embeddings = self.num_embeddings - self.org_vocab_size + self.num_embeddings_per_partition = divide(self.num_embeddings_padded, + self.tp_size) + assert (self.shard_indices.num_elements_padded == + self.num_embeddings_per_partition) + self.num_org_embeddings_per_partition = ( + self.shard_indices.org_vocab_end_index - + self.shard_indices.org_vocab_start_index) + self.num_added_embeddings_per_partition = ( + self.shard_indices.added_vocab_end_index - + self.shard_indices.added_vocab_start_index) + + self.linear_method.create_weights(self, + self.embedding_dim, + [self.num_embeddings_per_partition], + self.embedding_dim, + self.num_embeddings_padded, + params_dtype=params_dtype, + weight_loader=self.weight_loader) + + @classmethod + def _get_indices(cls, vocab_size_padded: int, org_vocab_size_padded: int, + vocab_size: int, org_vocab_size: int, tp_rank: int, + tp_size: int) -> VocabParallelEmbeddingShardIndices: + """Get start and end indices for vocab parallel embedding, following the + layout outlined in the class docstring, based on the given tp_rank and + tp_size.""" + num_added_embeddings_padded = vocab_size_padded - org_vocab_size_padded + padded_org_vocab_start_index, padded_org_vocab_end_index = ( + vocab_range_from_global_vocab_size(org_vocab_size_padded, tp_rank, + tp_size)) + padded_added_vocab_start_index, padded_added_vocab_end_index = ( + vocab_range_from_global_vocab_size(num_added_embeddings_padded, + tp_rank, + tp_size, + offset=org_vocab_size)) + # remove padding + org_vocab_start_index = min(padded_org_vocab_start_index, + org_vocab_size) + org_vocab_end_index = min(padded_org_vocab_end_index, org_vocab_size) + added_vocab_start_index = min(padded_added_vocab_start_index, + vocab_size) + added_vocab_end_index = min(padded_added_vocab_end_index, vocab_size) + return VocabParallelEmbeddingShardIndices( + padded_org_vocab_start_index, padded_org_vocab_end_index, + padded_added_vocab_start_index, padded_added_vocab_end_index, + org_vocab_start_index, org_vocab_end_index, + added_vocab_start_index, added_vocab_end_index) + + def get_sharded_to_full_mapping(self) -> Optional[List[int]]: + """Get a mapping that can be used to reindex the gathered + logits for sampling. + + During sampling, we gather logits from all ranks. The relationship + of index->token_id will follow the same format as outlined in the class + docstring. However, after the gather, we want to reindex the final + logits tensor to map index->token_id one-to-one (the index is always + equal the token_id it corresponds to). The indices returned by this + method allow us to do that. + """ + if self.tp_size < 2: + return None + + base_embeddings: List[int] = [] + added_embeddings: List[int] = [] + padding: List[int] = [] + for tp_rank in range(self.tp_size): + shard_indices = self._get_indices(self.num_embeddings_padded, + self.org_vocab_size_padded, + self.num_embeddings, + self.org_vocab_size, tp_rank, + self.tp_size) + range_start = self.num_embeddings_per_partition * tp_rank + range_end = self.num_embeddings_per_partition * (tp_rank + 1) + base_embeddings.extend( + range(range_start, + range_start + shard_indices.num_org_elements)) + padding.extend( + range(range_start + shard_indices.num_org_elements, + range_start + shard_indices.num_org_elements_padded)) + added_embeddings.extend( + range( + range_start + shard_indices.num_org_elements_padded, + range_start + shard_indices.num_org_elements_padded + + shard_indices.num_added_elements)) + padding.extend( + range( + range_start + shard_indices.num_org_elements_padded + + shard_indices.num_added_elements, + range_start + shard_indices.num_org_elements_padded + + shard_indices.num_added_elements_padded)) + assert (range_start + shard_indices.num_org_elements_padded + + shard_indices.num_added_elements_padded == range_end) + ret = base_embeddings + added_embeddings + padding + assert len(ret) == self.num_embeddings_padded + return ret + + def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor): + output_dim = getattr(param, "output_dim", None) + packed_dim = getattr(param, "packed_dim", None) + + # If the parameter is a gguf weight, then load it directly. + if getattr(param, "is_gguf_weight_type", None): + param.data.copy_(loaded_weight) + param.weight_type = loaded_weight.item() + return + elif isinstance(param, UninitializedParameter): + shape = list(loaded_weight.shape) + if output_dim is not None: + shape[output_dim] = shape[output_dim] // self.tp_size + param.materialize(tuple(shape), dtype=loaded_weight.dtype) + + # If parameter does not have output dim, then it should + # be copied onto all gpus (e.g. g_idx for act_order gptq). + if output_dim is None: + assert param.data.shape == loaded_weight.shape + param.data.copy_(loaded_weight) + return + + # Shard indexes for loading the weight + start_idx = self.shard_indices.org_vocab_start_index + shard_size = self.shard_indices.org_vocab_end_index - start_idx + + # If param packed on the same dim we are sharding on, then + # need to adjust offsets of loaded weight by pack_factor. + if packed_dim is not None and packed_dim == output_dim: + packed_factor = param.packed_factor if isinstance( + param, BasevLLMParameter) else param.pack_factor + assert loaded_weight.shape[output_dim] == (self.org_vocab_size // + param.packed_factor) + start_idx = start_idx // packed_factor + shard_size = shard_size // packed_factor + else: + assert loaded_weight.shape[output_dim] == self.org_vocab_size + + # Copy the data. + loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size) + param[:loaded_weight.shape[0]].data.copy_(loaded_weight) + param[loaded_weight.shape[0]:].data.fill_(0) + + def forward(self, input_): + if self.tp_size > 1: + # Build the mask. + masked_input, input_mask = get_masked_input_and_mask( + input_, self.shard_indices.org_vocab_start_index, + self.shard_indices.org_vocab_end_index, + self.shard_indices.num_org_vocab_padding, + self.shard_indices.added_vocab_start_index, + self.shard_indices.added_vocab_end_index) + else: + masked_input = input_ + # Get the embeddings. + output_parallel = self.linear_method.embedding(self, + masked_input.long()) + # Mask the output embedding. + if self.tp_size > 1: + output_parallel.masked_fill_(input_mask.unsqueeze(-1), 0) + # Reduce across all the model parallel GPUs. + output = tensor_model_parallel_all_reduce(output_parallel) + else: + output = output_parallel + return output + + def extra_repr(self) -> str: + s = f"num_embeddings={self.num_embeddings_per_partition}" + s += f", embedding_dim={self.embedding_dim}" + s += f", org_vocab_size={self.org_vocab_size}" + s += f', num_embeddings_padded={self.num_embeddings_padded}' + if self.enable_tp: + s += f', tp_size={self.tp_size}' + return s + + +class ParallelLMHead(VocabParallelEmbedding): + """Parallelized LM head. + + Output logits weight matrices used in the Sampler. The weight and bias + tensors are padded to make sure they are divisible by the number of + model parallel GPUs. + + Args: + num_embeddings: vocabulary size. + embedding_dim: size of hidden state. + bias: whether to use bias. + params_dtype: type of the parameters. + org_num_embeddings: original vocabulary size (without LoRA). + padding_size: padding size for the vocabulary. + """ + + def __init__(self, + num_embeddings: int, + embedding_dim: int, + bias: bool = False, + params_dtype: Optional[torch.dtype] = None, + org_num_embeddings: Optional[int] = None, + padding_size: int = DEFAULT_VOCAB_PADDING_SIZE, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = ""): + super().__init__(num_embeddings, embedding_dim, params_dtype, + org_num_embeddings, padding_size, quant_config, + prefix) + self.quant_config = quant_config + if bias: + self.bias = Parameter( + torch.empty(self.num_embeddings_per_partition, + dtype=params_dtype)) + set_weight_attrs(self.bias, { + "output_dim": 0, + "weight_loader": self.weight_loader, + }) + else: + self.register_parameter("bias", None) + + def tie_weights(self, embed_tokens: VocabParallelEmbedding): + """Tie the weights with word embeddings.""" + # GGUF quantized embed_tokens. + if self.quant_config and self.quant_config.get_name() == "gguf": + return embed_tokens + else: + self.weight = embed_tokens.weight + return self + + def forward(self, input_): + del input_ + raise RuntimeError("LMHead's weights should be used in the sampler.") \ No newline at end of file diff --git a/python/sglang/srt/models/baichuan.py b/python/sglang/srt/models/baichuan.py index 641937a4af..0e5e3b9ade 100644 --- a/python/sglang/srt/models/baichuan.py +++ b/python/sglang/srt/models/baichuan.py @@ -34,10 +34,6 @@ RowParallelLinear, ) from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -45,6 +41,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/chatglm.py b/python/sglang/srt/models/chatglm.py index c564b6cf54..fb9323da7f 100644 --- a/python/sglang/srt/models/chatglm.py +++ b/python/sglang/srt/models/chatglm.py @@ -24,10 +24,6 @@ from torch.nn import LayerNorm from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.transformers_utils.configs import ChatGLMConfig @@ -41,6 +37,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch LoraConfig = None diff --git a/python/sglang/srt/models/commandr.py b/python/sglang/srt/models/commandr.py index 05d0010a56..dcfad2370f 100644 --- a/python/sglang/srt/models/commandr.py +++ b/python/sglang/srt/models/commandr.py @@ -50,7 +50,6 @@ get_tensor_model_parallel_world_size, ) from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -62,6 +61,7 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.utils import set_weight_attrs diff --git a/python/sglang/srt/models/dbrx.py b/python/sglang/srt/models/dbrx.py index 76cf549463..59f51866af 100644 --- a/python/sglang/srt/models/dbrx.py +++ b/python/sglang/srt/models/dbrx.py @@ -27,11 +27,6 @@ ) from vllm.model_executor.layers.fused_moe import fused_moe from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - DEFAULT_VOCAB_PADDING_SIZE, - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.transformers_utils.configs.dbrx import DbrxConfig @@ -43,6 +38,11 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + DEFAULT_VOCAB_PADDING_SIZE, + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.utils import set_weight_attrs diff --git a/python/sglang/srt/models/deepseek.py b/python/sglang/srt/models/deepseek.py index 82565ed0ed..41e2bffe2a 100644 --- a/python/sglang/srt/models/deepseek.py +++ b/python/sglang/srt/models/deepseek.py @@ -28,10 +28,6 @@ ) from vllm.model_executor.layers.fused_moe import fused_moe from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -45,6 +41,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/deepseek_v2.py b/python/sglang/srt/models/deepseek_v2.py index f92be4d960..00ba0dcc59 100644 --- a/python/sglang/srt/models/deepseek_v2.py +++ b/python/sglang/srt/models/deepseek_v2.py @@ -27,10 +27,6 @@ ) from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -44,6 +40,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.managers.schedule_batch import global_server_args_dict from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.utils import is_flashinfer_available diff --git a/python/sglang/srt/models/exaone.py b/python/sglang/srt/models/exaone.py index 2efb940411..ab36f1c985 100644 --- a/python/sglang/srt/models/exaone.py +++ b/python/sglang/srt/models/exaone.py @@ -23,10 +23,6 @@ from torch import nn from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -39,6 +35,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/gemma.py b/python/sglang/srt/models/gemma.py index 238bcb309c..db0d09fb50 100644 --- a/python/sglang/srt/models/gemma.py +++ b/python/sglang/srt/models/gemma.py @@ -24,7 +24,6 @@ from vllm.config import LoRAConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import GeluAndMul @@ -37,6 +36,7 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/gemma2.py b/python/sglang/srt/models/gemma2.py index 8f7d0bee47..b295c7bbcd 100644 --- a/python/sglang/srt/models/gemma2.py +++ b/python/sglang/srt/models/gemma2.py @@ -24,7 +24,6 @@ from vllm.distributed import get_tensor_model_parallel_world_size # from vllm.model_executor.layers.rotary_embedding import GemmaRotaryEmbedding -from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import GeluAndMul @@ -37,6 +36,7 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/gpt2.py b/python/sglang/srt/models/gpt2.py index a584821030..60c98bbf21 100644 --- a/python/sglang/srt/models/gpt2.py +++ b/python/sglang/srt/models/gpt2.py @@ -37,6 +37,7 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/gpt_bigcode.py b/python/sglang/srt/models/gpt_bigcode.py index 0f38ba2c8d..aa10118187 100644 --- a/python/sglang/srt/models/gpt_bigcode.py +++ b/python/sglang/srt/models/gpt_bigcode.py @@ -23,7 +23,6 @@ from transformers import GPTBigCodeConfig from vllm.config import LoRAConfig from vllm.distributed import get_tensor_model_parallel_world_size -from vllm.model_executor.layers.vocab_parallel_embedding import VocabParallelEmbedding from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import get_act_fn @@ -35,6 +34,7 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/grok.py b/python/sglang/srt/models/grok.py index e7a0e06c57..1f6d2c1f27 100644 --- a/python/sglang/srt/models/grok.py +++ b/python/sglang/srt/models/grok.py @@ -28,10 +28,6 @@ get_tensor_model_parallel_world_size, ) from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.loader import DefaultModelLoader from vllm.model_executor.model_loader.weight_utils import default_weight_loader @@ -45,6 +41,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/internlm2.py b/python/sglang/srt/models/internlm2.py index 1dd369e5e9..1721c8c491 100644 --- a/python/sglang/srt/models/internlm2.py +++ b/python/sglang/srt/models/internlm2.py @@ -23,10 +23,6 @@ from transformers import PretrainedConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -39,6 +35,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/llama.py b/python/sglang/srt/models/llama.py index 543703c230..ab86a55b9e 100644 --- a/python/sglang/srt/models/llama.py +++ b/python/sglang/srt/models/llama.py @@ -24,10 +24,6 @@ from transformers import LlamaConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -41,6 +37,10 @@ from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.layers.torchao_utils import apply_torchao_config_ +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.managers.schedule_batch import global_server_args_dict from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/minicpm.py b/python/sglang/srt/models/minicpm.py index 6436eb626e..0297f585be 100644 --- a/python/sglang/srt/models/minicpm.py +++ b/python/sglang/srt/models/minicpm.py @@ -22,10 +22,6 @@ from torch import nn from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -38,6 +34,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/minicpm3.py b/python/sglang/srt/models/minicpm3.py index 9c8850787f..560f99b920 100644 --- a/python/sglang/srt/models/minicpm3.py +++ b/python/sglang/srt/models/minicpm3.py @@ -29,10 +29,6 @@ RowParallelLinear, ) from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -40,6 +36,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.managers.schedule_batch import global_server_args_dict from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.utils import is_flashinfer_available diff --git a/python/sglang/srt/models/mixtral.py b/python/sglang/srt/models/mixtral.py index dc4198b524..5baf1e6f13 100644 --- a/python/sglang/srt/models/mixtral.py +++ b/python/sglang/srt/models/mixtral.py @@ -24,11 +24,6 @@ from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - DEFAULT_VOCAB_PADDING_SIZE, - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.layernorm import RMSNorm @@ -41,6 +36,10 @@ from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.layers.torchao_utils import apply_torchao_config_ +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.managers.schedule_batch import global_server_args_dict from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/mixtral_quant.py b/python/sglang/srt/models/mixtral_quant.py index 7ceb990a81..924768932e 100644 --- a/python/sglang/srt/models/mixtral_quant.py +++ b/python/sglang/srt/models/mixtral_quant.py @@ -29,10 +29,6 @@ tensor_model_parallel_all_reduce, ) from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.layernorm import RMSNorm @@ -44,6 +40,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/mllama.py b/python/sglang/srt/models/mllama.py index 7db6f0e1f1..63bbfdb7eb 100644 --- a/python/sglang/srt/models/mllama.py +++ b/python/sglang/srt/models/mllama.py @@ -15,11 +15,6 @@ _prepare_aspect_ratio_attention_mask, ) from vllm.distributed import get_tensor_model_parallel_world_size -from vllm.model_executor.layers.vocab_parallel_embedding import ( - DEFAULT_VOCAB_PADDING_SIZE, - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import get_act_fn @@ -32,6 +27,11 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + DEFAULT_VOCAB_PADDING_SIZE, + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.managers.schedule_batch import ImageInputs from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.models.llama import LlamaDecoderLayer, LlamaMLP diff --git a/python/sglang/srt/models/olmo.py b/python/sglang/srt/models/olmo.py index 98b59853a6..2c594acc85 100755 --- a/python/sglang/srt/models/olmo.py +++ b/python/sglang/srt/models/olmo.py @@ -23,10 +23,6 @@ from transformers import OlmoConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -38,6 +34,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/olmoe.py b/python/sglang/srt/models/olmoe.py index 92352809f1..a33523847d 100644 --- a/python/sglang/srt/models/olmoe.py +++ b/python/sglang/srt/models/olmoe.py @@ -35,10 +35,6 @@ RowParallelLinear, ) from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.utils import print_warning_once @@ -47,6 +43,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/qwen.py b/python/sglang/srt/models/qwen.py index c69219d86a..85d9b9551d 100644 --- a/python/sglang/srt/models/qwen.py +++ b/python/sglang/srt/models/qwen.py @@ -22,10 +22,6 @@ from transformers import PretrainedConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -38,6 +34,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/qwen2.py b/python/sglang/srt/models/qwen2.py index bab09d37ce..796e34a4a4 100644 --- a/python/sglang/srt/models/qwen2.py +++ b/python/sglang/srt/models/qwen2.py @@ -22,10 +22,6 @@ from torch import nn from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -39,6 +35,10 @@ from sglang.srt.layers.pooler import Pooler, PoolingType from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch Qwen2Config = None diff --git a/python/sglang/srt/models/qwen2_moe.py b/python/sglang/srt/models/qwen2_moe.py index 860c00a7ec..7317ff7b59 100644 --- a/python/sglang/srt/models/qwen2_moe.py +++ b/python/sglang/srt/models/qwen2_moe.py @@ -29,10 +29,6 @@ ) from vllm.model_executor.layers.fused_moe import FusedMoE from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -47,6 +43,10 @@ from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.layers.torchao_utils import apply_torchao_config_ +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.managers.schedule_batch import global_server_args_dict from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/qwen2_vl.py b/python/sglang/srt/models/qwen2_vl.py index b1cc787710..80afee557d 100644 --- a/python/sglang/srt/models/qwen2_vl.py +++ b/python/sglang/srt/models/qwen2_vl.py @@ -35,7 +35,6 @@ from vllm.distributed import utils as dist_utils from vllm.logger import init_logger from vllm.model_executor.layers.activation import QuickGELU -from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.interfaces import SupportsMultiModal @@ -47,6 +46,7 @@ from sglang.srt.layers.linear import ColumnParallelLinear, RowParallelLinear from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig +from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead from sglang.srt.managers.schedule_batch import ImageInputs from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.models.qwen2 import Qwen2Model diff --git a/python/sglang/srt/models/stablelm.py b/python/sglang/srt/models/stablelm.py index 6e6d5ea0e9..f1f35f7dfe 100644 --- a/python/sglang/srt/models/stablelm.py +++ b/python/sglang/srt/models/stablelm.py @@ -24,10 +24,6 @@ from transformers import PretrainedConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -39,6 +35,10 @@ from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/torch_native_llama.py b/python/sglang/srt/models/torch_native_llama.py index 29c92955fc..d9ce05b8a6 100644 --- a/python/sglang/srt/models/torch_native_llama.py +++ b/python/sglang/srt/models/torch_native_llama.py @@ -26,10 +26,6 @@ from transformers import LlamaConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.activation import SiluAndMul @@ -38,6 +34,10 @@ from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.layers.torchao_utils import apply_torchao_config_ +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.managers.schedule_batch import global_server_args_dict from sglang.srt.model_executor.forward_batch_info import ForwardBatch diff --git a/python/sglang/srt/models/xverse.py b/python/sglang/srt/models/xverse.py index 42d873785e..56429a8b42 100644 --- a/python/sglang/srt/models/xverse.py +++ b/python/sglang/srt/models/xverse.py @@ -31,15 +31,15 @@ RowParallelLinear, ) from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.model_runner import ForwardBatch diff --git a/python/sglang/srt/models/xverse_moe.py b/python/sglang/srt/models/xverse_moe.py index a5c5f4ccb7..44ace14530 100644 --- a/python/sglang/srt/models/xverse_moe.py +++ b/python/sglang/srt/models/xverse_moe.py @@ -34,15 +34,15 @@ RowParallelLinear, ) from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.vocab_parallel_embedding import ( - ParallelLMHead, - VocabParallelEmbedding, -) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) from sglang.srt.model_executor.forward_batch_info import ForwardBatch From d86a2d6562840455281bfb7bd4a9a0bcc9461992 Mon Sep 17 00:00:00 2001 From: Yineng Zhang Date: Fri, 1 Nov 2024 14:29:20 +0800 Subject: [PATCH 06/44] minor: add human eval (#1754) --- .github/workflows/nightly-eval.yml | 7 +- test/srt/test_nightly_human_eval.py | 125 ++++++++++++++++++++++++++++ 2 files changed, 129 insertions(+), 3 deletions(-) create mode 100644 test/srt/test_nightly_human_eval.py diff --git a/.github/workflows/nightly-eval.yml b/.github/workflows/nightly-eval.yml index afc29beeeb..13911f989e 100644 --- a/.github/workflows/nightly-eval.yml +++ b/.github/workflows/nightly-eval.yml @@ -25,9 +25,10 @@ jobs: - name: Install dependencies run: | bash scripts/ci_install_dependency.sh + pip install --upgrade "evalplus[vllm] @ git+https://github.com/evalplus/evalplus" - - name: Nightly gsm8k Accuracy - timeout-minutes: 60 + - name: Nightly gsm8k and human eval Accuracy + timeout-minutes: 120 run: | cd test/srt - python3 test_nightly_gsm8k_eval.py + python3 test_nightly_human_eval.py diff --git a/test/srt/test_nightly_human_eval.py b/test/srt/test_nightly_human_eval.py new file mode 100644 index 0000000000..6d2ecee505 --- /dev/null +++ b/test/srt/test_nightly_human_eval.py @@ -0,0 +1,125 @@ +import os +import shutil +import signal +import subprocess +import unittest +from types import SimpleNamespace + +from test_nightly_gsm8k_eval import parse_models + +from sglang.srt.utils import kill_child_process +from sglang.test.test_utils import ( + DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1, + DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2, + DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1, + DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2, + DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, + DEFAULT_URL_FOR_TEST, + popen_launch_server, +) + + +class TestEvalAccuracyLarge(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.model_groups = [ + (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP1), False, False), + (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_TP2), False, True), + (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP1), True, False), + (parse_models(DEFAULT_MODEL_NAME_FOR_NIGHTLY_EVAL_FP8_TP2), True, True), + ] + cls.base_url = DEFAULT_URL_FOR_TEST + cls.process = None + cls.eval_process = None + + @classmethod + def tearDownClass(cls): + if cls.process: + kill_child_process(cls.process.pid) + if cls.eval_process: + kill_child_process(cls.eval_process.pid) + + def launch_server(self, model, is_fp8, is_tp2): + other_args = ["--log-level-http", "warning", "--trust-remote-code"] + if is_fp8: + if "Llama-3" in model or "gemma-2" in model: + # compressed-tensors + other_args.extend(["--kv-cache-dtype", "fp8_e5m2"]) + elif "Qwen2-72B-Instruct-FP8" in model: + # bug + other_args.extend(["--quantization", "fp8"]) + else: + other_args.extend( + ["--quantization", "fp8", "--kv-cache-dtype", "fp8_e5m2"] + ) + if is_tp2: + other_args.extend(["--tp", "2"]) + if "DeepSeek" in model: + other_args.extend(["--mem-frac", "0.85"]) + if "AWQ" in model: + other_args.extend(["--quantization", "awq"]) + elif "GPTQ" in model: + other_args.extend(["--quantization", "gptq"]) + + self.process = popen_launch_server( + model, + self.base_url, + timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, + other_args=other_args, + ) + + def run_evalplus(self, model): + print("Delete evalplus results") + shutil.rmtree("evalplus_results", ignore_errors=True) + cmd = [ + "evalplus.evaluate", + "--model", + model, + "--dataset", + "humaneval", + "--backend", + "openai", + "--base-url", + "http://localhost:6157/v1", + "--greedy", + ] + + try: + self.eval_process = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + preexec_fn=os.setsid, + ) + + stdout, stderr = self.eval_process.communicate(timeout=600) + + if self.eval_process.returncode != 0: + print(f"Fail to human eval model={model} err={stderr}") + + print("=" * 42) + print(stdout) + print("=" * 42) + except subprocess.TimeoutExpired: + if self.eval_process: + os.killpg(os.getpgid(self.eval_process.pid), signal.SIGTERM) + print(f"Timeout during evaluation for model={model}") + except Exception as e: + print(f"Error running evalplus for model={model} {str(e)}") + if self.eval_process: + os.killpg(os.getpgid(self.eval_process.pid), signal.SIGTERM) + + def test_human_eval_all_models(self): + for model_group, is_fp8, is_tp2 in self.model_groups: + for model in model_group: + # NOTE: only Llama for now + if "Llama" in model: + with self.subTest(model=model): + self.launch_server(model, is_fp8, is_tp2) + self.run_evalplus(model) + self.tearDownClass() + + +if __name__ == "__main__": + unittest.main() From 3bf3d011ed9650cd98b9c20bac86bebd6f87c7d9 Mon Sep 17 00:00:00 2001 From: Chayenne Date: Fri, 1 Nov 2024 00:51:15 -0700 Subject: [PATCH 07/44] Add vlm document (#1866) Co-authored-by: Chayenne --- docs/backend/embedding_model.ipynb | 192 ++-------- docs/backend/vision_language_model.ipynb | 431 +++++++++++++++++++++++ docs/index.rst | 1 + 3 files changed, 467 insertions(+), 157 deletions(-) create mode 100644 docs/backend/vision_language_model.ipynb diff --git a/docs/backend/embedding_model.ipynb b/docs/backend/embedding_model.ipynb index af985a87e1..589e668434 100644 --- a/docs/backend/embedding_model.ipynb +++ b/docs/backend/embedding_model.ipynb @@ -19,6 +19,7 @@ "## Launch A Server\n", "\n", "The following code is equivalent to running this in the shell:\n", + "\n", "```bash\n", "python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \\\n", " --port 30010 --host 0.0.0.0 --is-embedding\n", @@ -44,161 +45,38 @@ "output_type": "stream", "text": [ "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:47:37] server_args=ServerArgs(model_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='Alibaba-NLP/gte-Qwen2-7B-instruct', chat_template=None, is_embedding=True, host='0.0.0.0', port=30010, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=314021918, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " warnings.warn(\n", + "[2024-10-31 22:40:37] server_args=ServerArgs(model_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='Alibaba-NLP/gte-Qwen2-7B-instruct', chat_template=None, is_embedding=True, host='0.0.0.0', port=30010, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=309155486, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n", "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", " warnings.warn(\n", "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:47:43 TP0] Init torch distributed begin.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:47:44 TP0] Load weight begin. avail mem=47.27 GB\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:47:44 TP0] lm_eval is not installed, GPTQ may not be usable\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO 10-31 19:47:45 weight_utils.py:243] Using model weights format ['*.safetensors']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "Loading safetensors checkpoint shards: 0% Completed | 0/7 [00:00

NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sglang.utils import (\n", + " execute_shell_command,\n", + " wait_for_server,\n", + " terminate_process,\n", + " print_highlight,\n", + ")\n", + "\n", + "embedding_process = execute_shell_command(\n", + " \"\"\"\n", + " python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-11B-Vision-Instruct \\\n", + " --port=30010 --chat-template=llama_3_vision\n", + "\n", + "\"\"\"\n", + ")\n", + "\n", + "wait_for_server(\"http://localhost:30010\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Curl" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 559 0 0 100 559 0 253 0:00:02 0:00:02 --:--:-- 253" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/torch/storage.py:414: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " return torch.load(io.BytesIO(b))\n", + "[2024-10-31 23:11:18 TP0] Prefill batch. #new-seq: 1, #new-token: 6463, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100 559 0 0 100 559 0 174 0:00:03 0:00:03 --:--:-- 174" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 23:11:20 TP0] Decode batch. #running-req: 1, #token: 6496, token usage: 0.05, gen throughput (token/s): 3.90, #queue-req: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100 559 0 0 100 559 0 107 0:00:05 0:00:05 --:--:-- 107" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 23:11:21 TP0] Decode batch. #running-req: 1, #token: 6536, token usage: 0.05, gen throughput (token/s): 33.67, #queue-req: 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100 559 0 0 100 559 0 90 0:00:06 0:00:06 --:--:-- 0" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 23:11:22 TP0] Decode batch. #running-req: 1, #token: 6576, token usage: 0.05, gen throughput (token/s): 33.60, #queue-req: 0\n", + "[2024-10-31 23:11:22] INFO: 127.0.0.1:54224 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100 1544 100 985 100 559 142 80 0:00:06 0:00:06 --:--:-- 265\n" + ] + }, + { + "data": { + "text/html": [ + "{'id': 'f618453e8f3e4408b893a958f2868a44', 'object': 'chat.completion', 'created': 1730441482, 'model': 'meta-llama/Llama-3.2-11B-Vision-Instruct', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'The image depicts a serene and peaceful landscape featuring a wooden boardwalk that meanders through a lush grassy field, set against a backdrop of trees and a bright blue sky with wispy clouds. The boardwalk is made of weathered wooden planks and is surrounded by tall grass on either side, creating a sense of depth and texture. The surrounding trees add a touch of natural beauty to the scene, while the blue sky with wispy clouds provides a sense of calmness and serenity. The overall atmosphere of the image is one of tranquility and relaxation, inviting the viewer to step into the peaceful world depicted.'}, 'logprobs': None, 'finish_reason': 'stop', 'matched_stop': 128009}], 'usage': {'prompt_tokens': 6463, 'total_tokens': 6588, 'completion_tokens': 125, 'prompt_tokens_details': None}}" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import subprocess, json, os\n", + "\n", + "curl_command = \"\"\"\n", + "curl http://localhost:30010/v1/chat/completions \\\n", + " -H \"Content-Type: application/json\" \\\n", + " -H \"Authorization: Bearer None\" \\\n", + " -d '{\n", + " \"model\": \"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", + " \"messages\": [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": \"What’s in this image?\"\n", + " },\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\"\n", + " }\n", + " }\n", + " ]\n", + " }\n", + " ],\n", + " \"max_tokens\": 300\n", + " }'\n", + "\"\"\"\n", + "\n", + "response = json.loads(subprocess.check_output(curl_command, shell=True))\n", + "print_highlight(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using OpenAI Compatible API" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 23:11:23 TP0] Prefill batch. #new-seq: 1, #new-token: 6463, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-10-31 23:11:24 TP0] Decode batch. #running-req: 1, #token: 6492, token usage: 0.05, gen throughput (token/s): 20.07, #queue-req: 0\n", + "[2024-10-31 23:11:25 TP0] Decode batch. #running-req: 1, #token: 6532, token usage: 0.05, gen throughput (token/s): 33.68, #queue-req: 0\n", + "[2024-10-31 23:11:26 TP0] Decode batch. #running-req: 1, #token: 6572, token usage: 0.05, gen throughput (token/s): 33.62, #queue-req: 0\n", + "[2024-10-31 23:11:27 TP0] Decode batch. #running-req: 1, #token: 6612, token usage: 0.05, gen throughput (token/s): 33.62, #queue-req: 0\n", + "[2024-10-31 23:11:28] INFO: 127.0.0.1:54228 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" + ] + }, + { + "data": { + "text/html": [ + "The image depicts a serene and peaceful scene of a wooden boardwalk leading through a lush field of tall grass, set against a backdrop of trees and a blue sky with clouds. The boardwalk is made of light-colored wood and has a simple design, with the wooden planks running parallel to each other. It stretches out into the distance, disappearing into the horizon.

The field is filled with tall, vibrant green grass that sways gently in the breeze, creating a sense of movement and life. The trees in the background are also lush and green, adding depth and texture to the scene. The blue sky above is dotted with white clouds, which are scattered across the horizon. The overall atmosphere of the image is one of tranquility and serenity, inviting the viewer to step into the peaceful world depicted." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import base64, requests\n", + "from openai import OpenAI\n", + "\n", + "client = OpenAI(base_url=\"http://localhost:30010/v1\", api_key=\"None\")\n", + "\n", + "\n", + "def encode_image(image_path):\n", + " with open(image_path, \"rb\") as image_file:\n", + " return base64.b64encode(image_file.read()).decode(\"utf-8\")\n", + "\n", + "\n", + "def download_image(image_url, image_path):\n", + " response = requests.get(image_url)\n", + " response.raise_for_status()\n", + " with open(image_path, \"wb\") as f:\n", + " f.write(response.content)\n", + "\n", + "\n", + "image_url = \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\"\n", + "image_path = \"boardwalk.jpeg\"\n", + "download_image(image_url, image_path)\n", + "\n", + "base64_image = encode_image(image_path)\n", + "\n", + "response = client.chat.completions.create(\n", + " model=\"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": \"What is in this image?\",\n", + " },\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\"url\": f\"data:image/jpeg;base64,{base64_image}\"},\n", + " },\n", + " ],\n", + " }\n", + " ],\n", + " max_tokens=300,\n", + ")\n", + "\n", + "print_highlight(response.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiple Images Input" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-10-31 23:11:28 TP0] Prefill batch. #new-seq: 1, #new-token: 12871, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-10-31 23:11:30 TP0] Decode batch. #running-req: 1, #token: 12899, token usage: 0.10, gen throughput (token/s): 15.36, #queue-req: 0\n", + "[2024-10-31 23:11:31 TP0] Decode batch. #running-req: 1, #token: 12939, token usage: 0.10, gen throughput (token/s): 33.33, #queue-req: 0\n", + "[2024-10-31 23:11:32 TP0] Decode batch. #running-req: 1, #token: 12979, token usage: 0.10, gen throughput (token/s): 33.28, #queue-req: 0\n", + "[2024-10-31 23:11:33] INFO: 127.0.0.1:50966 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n", + "Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The two images depict a serene and idyllic scene, with the first image showing a well-trodden wooden path through a field, while the second image shows an overgrown, less-traveled path through the same field. The first image features a clear and well-maintained wooden path, whereas the second image shows a more neglected and overgrown path that is not as well-defined. The first image has a more vibrant and inviting atmosphere, while the second image appears more peaceful and serene. Overall, both images evoke a sense of tranquility and connection to nature.', refusal=None, role='assistant', function_call=None, tool_calls=None), matched_stop=128009)\n" + ] + } + ], + "source": [ + "from openai import OpenAI\n", + "\n", + "client = OpenAI(base_url=\"http://localhost:30010/v1\", api_key=\"None\")\n", + "\n", + "response = client.chat.completions.create(\n", + " model=\"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": \"Are there any differences between these two images?\",\n", + " },\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\",\n", + " },\n", + " },\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\",\n", + " },\n", + " },\n", + " ],\n", + " }\n", + " ],\n", + " max_tokens=300,\n", + ")\n", + "print(response.choices[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "terminate_process(embedding_process)\n", + "os.remove(image_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chat Template\n", + "\n", + "As mentioned before, if you do not specify a vision model's `chat-template`, the server uses Hugging Face's default template, which only supports text.\n", + "\n", + "You can add your custom chat template by referring to the [custom chat template](../references/custom_chat_template.md).\n", + "\n", + "We list popular vision models with their chat templates:\n", + "\n", + "- [meta-llama/Llama-3.2-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) uses `llama_3_vision`.\n", + "- [LLaVA-NeXT](https://huggingface.co/collections/lmms-lab/llava-next-6623288e2d61edba3ddbf5ff) uses `chatml-llava`.\n", + "- [llama3-llava-next](https://huggingface.co/lmms-lab/llama3-llava-next-8b) uses `llava_llama_3`.\n", + "- [llava-onevision](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov) uses `chatml-llava`.\n", + "- [liuhaotian/llava-v1.5 / 1.6](https://huggingface.co/liuhaotian/llava-v1.5-13b) uses `vicuna_v1.1`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "AlphaMeemory", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/index.rst b/docs/index.rst index 4206552410..6601c57d51 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -24,6 +24,7 @@ The core features include: :caption: Backend Tutorial backend/openai_api.ipynb + backend/vision_language_model.ipynb backend/backend.md From 104bf2609b1adf182a8c34a533b500914a219b8d Mon Sep 17 00:00:00 2001 From: Yineng Zhang Date: Fri, 1 Nov 2024 21:38:29 +0800 Subject: [PATCH 08/44] minor: update nightly eval (#1867) --- .github/workflows/nightly-eval.yml | 1 + test/srt/test_nightly_gsm8k_eval.py | 60 ++++++++++++++--------------- test/srt/test_nightly_human_eval.py | 35 ++--------------- 3 files changed, 35 insertions(+), 61 deletions(-) diff --git a/.github/workflows/nightly-eval.yml b/.github/workflows/nightly-eval.yml index 13911f989e..a397866119 100644 --- a/.github/workflows/nightly-eval.yml +++ b/.github/workflows/nightly-eval.yml @@ -32,3 +32,4 @@ jobs: run: | cd test/srt python3 test_nightly_human_eval.py + python3 test_nightly_gsm8k_eval.py diff --git a/test/srt/test_nightly_gsm8k_eval.py b/test/srt/test_nightly_gsm8k_eval.py index b035db52b3..49ef46169d 100644 --- a/test/srt/test_nightly_gsm8k_eval.py +++ b/test/srt/test_nightly_gsm8k_eval.py @@ -19,6 +19,35 @@ def parse_models(model_string): return [model.strip() for model in model_string.split(",") if model.strip()] +def launch_server(base_url, model, is_fp8, is_tp2): + other_args = ["--log-level-http", "warning", "--trust-remote-code"] + if is_fp8: + if "Llama-3" in model or "gemma-2" in model: + # compressed-tensors + other_args.extend(["--kv-cache-dtype", "fp8_e5m2"]) + elif "Qwen2-72B-Instruct-FP8" in model: + # bug + other_args.extend(["--quantization", "fp8"]) + else: + other_args.extend(["--quantization", "fp8", "--kv-cache-dtype", "fp8_e5m2"]) + if is_tp2: + other_args.extend(["--tp", "2"]) + if "DeepSeek" in model: + other_args.extend(["--mem-frac", "0.85"]) + if "AWQ" in model: + other_args.extend(["--quantization", "awq"]) + elif "GPTQ" in model: + other_args.extend(["--quantization", "gptq"]) + + process = popen_launch_server( + model, + base_url, + timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, + other_args=other_args, + ) + return process + + class TestEvalAccuracyLarge(unittest.TestCase): @classmethod def setUpClass(cls): @@ -38,40 +67,11 @@ def tearDown(self): if self.process: kill_child_process(self.process.pid, include_self=True) - def launch_server(self, model, is_fp8, is_tp2): - other_args = ["--log-level-http", "warning", "--trust-remote-code"] - if is_fp8: - if "Llama-3" in model or "gemma-2" in model: - # compressed-tensors - other_args.extend(["--kv-cache-dtype", "fp8_e5m2"]) - elif "Qwen2-72B-Instruct-FP8" in model: - # bug - other_args.extend(["--quantization", "fp8"]) - else: - other_args.extend( - ["--quantization", "fp8", "--kv-cache-dtype", "fp8_e5m2"] - ) - if is_tp2: - other_args.extend(["--tp", "2"]) - if "DeepSeek" in model: - other_args.extend(["--mem-frac", "0.85"]) - if "AWQ" in model: - other_args.extend(["--quantization", "awq"]) - elif "GPTQ" in model: - other_args.extend(["--quantization", "gptq"]) - - self.process = popen_launch_server( - model, - self.base_url, - timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, - other_args=other_args, - ) - def test_mgsm_en_all_models(self): for model_group, is_fp8, is_tp2 in self.model_groups: for model in model_group: with self.subTest(model=model): - self.launch_server(model, is_fp8, is_tp2) + self.process = launch_server(self.base_url, model, is_fp8, is_tp2) args = SimpleNamespace( base_url=self.base_url, diff --git a/test/srt/test_nightly_human_eval.py b/test/srt/test_nightly_human_eval.py index 6d2ecee505..9028b10450 100644 --- a/test/srt/test_nightly_human_eval.py +++ b/test/srt/test_nightly_human_eval.py @@ -5,7 +5,7 @@ import unittest from types import SimpleNamespace -from test_nightly_gsm8k_eval import parse_models +from test_nightly_gsm8k_eval import launch_server, parse_models from sglang.srt.utils import kill_child_process from sglang.test.test_utils import ( @@ -39,35 +39,6 @@ def tearDownClass(cls): if cls.eval_process: kill_child_process(cls.eval_process.pid) - def launch_server(self, model, is_fp8, is_tp2): - other_args = ["--log-level-http", "warning", "--trust-remote-code"] - if is_fp8: - if "Llama-3" in model or "gemma-2" in model: - # compressed-tensors - other_args.extend(["--kv-cache-dtype", "fp8_e5m2"]) - elif "Qwen2-72B-Instruct-FP8" in model: - # bug - other_args.extend(["--quantization", "fp8"]) - else: - other_args.extend( - ["--quantization", "fp8", "--kv-cache-dtype", "fp8_e5m2"] - ) - if is_tp2: - other_args.extend(["--tp", "2"]) - if "DeepSeek" in model: - other_args.extend(["--mem-frac", "0.85"]) - if "AWQ" in model: - other_args.extend(["--quantization", "awq"]) - elif "GPTQ" in model: - other_args.extend(["--quantization", "gptq"]) - - self.process = popen_launch_server( - model, - self.base_url, - timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH, - other_args=other_args, - ) - def run_evalplus(self, model): print("Delete evalplus results") shutil.rmtree("evalplus_results", ignore_errors=True) @@ -116,7 +87,9 @@ def test_human_eval_all_models(self): # NOTE: only Llama for now if "Llama" in model: with self.subTest(model=model): - self.launch_server(model, is_fp8, is_tp2) + self.process = launch_server( + self.base_url, model, is_fp8, is_tp2 + ) self.run_evalplus(model) self.tearDownClass() From d59a47828cb8983704b9438b6207d38564b46fdc Mon Sep 17 00:00:00 2001 From: "jacky.cheng" Date: Sat, 2 Nov 2024 03:12:59 +0800 Subject: [PATCH 09/44] [3rdparty, document] Updated Documentation that covers performance tuning techniques for AMD Instinct GPUs. (#1871) Co-authored-by: root --- 3rdparty/amd/tuning/TUNING.md | 94 +++++++++++++++++++++++++++++++++-- 1 file changed, 91 insertions(+), 3 deletions(-) diff --git a/3rdparty/amd/tuning/TUNING.md b/3rdparty/amd/tuning/TUNING.md index 00f995ebbc..9a37a4384e 100644 --- a/3rdparty/amd/tuning/TUNING.md +++ b/3rdparty/amd/tuning/TUNING.md @@ -2,12 +2,100 @@ This AppNote describes the SGLang performance tuning technical, code harness and running steps for systems with AMD Instinct GPUs. Harness code, examples and steps are provided in detail, to facilitate easy reproduce & use to tune performance towards workloads. Three primary runtime areas are covered: -- Triton Kernels +## 1. Triton Kernels +To maximize Triton kernel efficiency, several strategies can be employed: -- Torch Tunable Ops +### Key Environment Variables: +- **num_stages**: Adjusts the number of pipeline stages to optimize kernel efficiency based on the specific type of operations (e.g., General Matrix Multiplication - GEMM). +- **waves_per_eu**: Controls the usage of Vector General Purpose Registers (VGPR) to enhance occupancy, thereby improving latency or throughput. +- **BLOCK_M, BLOCK_N, BLOCK_K**: Tunable tile sizes that assist in balancing memory transfer and computational efficiency. +- **matrix_instr_nonkdim**: Optimizes the usage of Matrix-Fused Multiply-Add (MFMA) instructions for specific kernel types, such as Flash Attention. +- **OPTIMIZE_EPILOGUE**: An environment variable that can be set to `1` to enhance performance by eliminating the `convert_layout` operation in the kernel's epilogue. +```python +@triton.autotune(configs=[ + triton.Config({'waves_per_eu': 1}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 1}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 1}, num_warps=16, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=16, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=16, num_stages=1), + ], key=['BLOCK_N', 'NUM_TOKEN_BLKS'], use_cuda_graph=True) +@triton.jit +def _triton_kernel_funtion(): + ... +``` +## 2. Torch Tunable Operations +**TunableOp** is a feature in PyTorch that allows for the definition and optimization of custom kernels with tunable parameters. This feature is particularly useful for enhancing the performance of kernels by experimenting with different configurations. +### Key Environment Variables: +1. **PYTORCH_TUNABLEOP_ENABLED**: + - Default: `0` + - Set to `1` to enable TunableOp. -- Torch Compile +2. **PYTORCH_TUNABLEOP_TUNING**: + - Default: `1` + - Set to `0` to disable tuning. If a tuned entry is not found, it will run the tuning step and record the entry when PYTORCH_TUNABLEOP_ENABLED is enabled. +3. **PYTORCH_TUNABLEOP_VERBOSE**: + - Default: `0` + - Set to `1` to enable verbose output for TunableOp. +### Usage Example: +To enable TunableOp and tuning, and optionally enable verbose mode, you can run the following command in your terminal: + +```bash +#Tuning +PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_TUNING=1 your_script.sh + +#Inference with tuning op +PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_TUNING=0 your_script.sh + +#Print out the log +PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_TUNING=0 PYTORCH_TUNABLEOP_VERBOSE=1 your_script.sh + +``` +## 3. Torch Compilation + + +The following are suggestions for optimizing matrix multiplication (GEMM) and convolution (conv) operations in PyTorch using Inductor, a part of the PyTorch compilation framework. The goal is to leverage Triton to achieve better performance. + +To tune Triton kernels with GEMM and convolution ops (conv), use the `torch.compile` function with the max-autotune mode. This benchmarks a predefined list of Triton configurations and selects the fastest one for each shape. + +### Key Configurations: +1. **Max Autotune**: + - Set `torch._inductor.config.max_autotune = True` or `TORCHINDUCTOR_MAX_AUTOTUNE=1`. + +2. **Fine-Grained Control**: + - Enable GEMM tuning: `torch._inductor.config.max_autotune_gemm = True`. + - Enable tuning for pointwise/reduction ops: `torch._inductor.config.max_autotune.pointwise = True`. + +3. **Backend Selection**: + - Use `torch._inductor.max_autotune_gemm_backends` to limit backends to TRITON for better performance. + +4. **Freezing for Inference**: + - Use `torch._inductor.config.freezing=True` to enable constant folding optimizations. + +5. **Debugging**: + - Set `TORCH_COMPILE_DEBUG=1` to extract Triton kernels generated by Inductor. + +### Example Code Block: +```bash +#Gemm Tuning +TORCHINDUCTOR_MAX_AUTOTUNE=1 TORCHINDUCTOR_COORDINATE_DESCENT_TUNING=1 your_script.sh + +#Specify your backend to TRITON for Gemm Tuning +TORCHINDUCTOR_MAX_AUTOTUNE=1 TORCHINDUCTOR_COORDINATE_DESCENT_TUNING=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM_BACKENDS=TRITON your_script.sh + +#Inference with large improvement on AMD GPU +TORCHINDUCTOR_FREEZING=1 your_script.sh +``` + +## Reference + +For more detailed information on tuning SGLang performance with AMD GPUs, please refer to the following link: + +[ROCm Documentation: Triton Kernel Performance Optimization](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html#triton-kernel-performance-optimization) \ No newline at end of file From d1b31b06842829cbb8516271f28af4bedce00546 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Fri, 1 Nov 2024 17:47:44 -0700 Subject: [PATCH 10/44] Improve docs and fix the broken links (#1875) --- docs/backend/backend.md | 3 +- ...api.ipynb => openai_api_completions.ipynb} | 313 ++++--------- docs/backend/openai_api_vision.ipynb | 382 ++++++++++++++++ docs/backend/vision_language_model.ipynb | 431 ------------------ docs/index.rst | 6 +- docs/references/custom_chat_template.md | 2 + docs/references/sampling_params.md | 2 + docs/starts/send_request.ipynb | 342 ++++++-------- test/srt/test_vision_openai_server.py | 2 +- 9 files changed, 607 insertions(+), 876 deletions(-) rename docs/backend/{openai_api.ipynb => openai_api_completions.ipynb} (80%) create mode 100644 docs/backend/openai_api_vision.ipynb delete mode 100644 docs/backend/vision_language_model.ipynb diff --git a/docs/backend/backend.md b/docs/backend/backend.md index 47298c0390..3692d7217b 100644 --- a/docs/backend/backend.md +++ b/docs/backend/backend.md @@ -84,7 +84,8 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct - To enable torchao quantization, add `--torchao-config int4wo-128`. It supports various quantization strategies. - To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments. - To enable fp8 kv cache quantization, add `--kv-cache-dtype fp8_e5m2`. -- If the model does not have a chat template in the Hugging Face tokenizer, you can specify a [custom chat template](https://sgl-project.github.io/custom_chat_template.html). +- If the model does not have a chat template in the Hugging Face tokenizer, you can specify a [custom chat template](https://sgl-project.github.io/references/custom_chat_template.html). + - To run tensor parallelism on multiple nodes, add `--nnodes 2`. If you have two nodes with two GPUs on each node and want to run TP=4, let `sgl-dev-0` be the hostname of the first node and `50000` be an available port, you can use the following commands. If you meet deadlock, please try to add `--disable-cuda-graph` ``` # Node 0 diff --git a/docs/backend/openai_api.ipynb b/docs/backend/openai_api_completions.ipynb similarity index 80% rename from docs/backend/openai_api.ipynb rename to docs/backend/openai_api_completions.ipynb index 9b9ba7ab0b..dc89500f62 100644 --- a/docs/backend/openai_api.ipynb +++ b/docs/backend/openai_api_completions.ipynb @@ -4,166 +4,74 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# OpenAI Compatible API\n", + "# OpenAI-Compatible APIs - Completions\n", "\n", - "SGLang provides an OpenAI compatible API for smooth transition from OpenAI services. Full reference of the API is available at [OpenAI API Reference](https://platform.openai.com/docs/api-reference).\n", + "SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.\n", + "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/api-reference).\n", "\n", - "This tutorial covers these popular APIs:\n", + "This tutorial covers the following popular APIs:\n", "\n", "- `chat/completions`\n", "- `completions`\n", "- `batches`\n", - "- `embeddings`(refer to [embedding_model.ipynb](embedding_model.ipynb))" + "\n", + "Check out other tutorials to learn about vision APIs for vision-language models and embedding APIs for embedding models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Chat Completions\n", + "## Launch A Server\n", "\n", - "### Usage\n", + "This code block is equivalent to executing \n", "\n", - "Similar to [send_request.ipynb](send_request.ipynb), we can send a chat completion request to SGLang server with OpenAI API format." + "```bash\n", + "python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \\\n", + "--port 30000 --host 0.0.0.0\n", + "```\n", + "\n", + "in your terminal and wait for the server to be ready." ] }, { "cell_type": "code", "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2024-11-01T02:44:46.419815Z", - "iopub.status.busy": "2024-11-01T02:44:46.419509Z", - "iopub.status.idle": "2024-11-01T02:45:16.621648Z", - "shell.execute_reply": "2024-11-01T02:45:16.620659Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:44:51] server_args=ServerArgs(model_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='meta-llama/Meta-Llama-3.1-8B-Instruct', chat_template=None, is_embedding=False, host='0.0.0.0', port=30000, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=357249111, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:44:57 TP0] Init torch distributed begin.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:44:58 TP0] Load weight begin. avail mem=47.27 GB\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:44:59 TP0] lm_eval is not installed, GPTQ may not be usable\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO 10-31 19:44:59 weight_utils.py:243] Using model weights format ['*.safetensors']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00Response: ChatCompletion(id='e04fce6c460d4764af68007fc82763e1', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', function_call=None, tool_calls=None), matched_stop=128009)], created=1730429118, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, prompt_tokens_details=None))" + "Response: ChatCompletion(id='bb74a7e9fcae4df7af2ee59e25aa75a5', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=None), matched_stop=128009)], created=1730506084, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, completion_tokens_details=None, prompt_tokens_details=None))" ], "text/plain": [ "" @@ -285,41 +194,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-31 19:45:18 TP0] Prefill batch. #new-seq: 1, #new-token: 48, #cached-token: 28, cache hit rate: 21.97%, token usage: 0.00, #running-req: 0, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:18 TP0] Decode batch. #running-req: 1, #token: 104, token usage: 0.00, gen throughput (token/s): 39.15, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:19 TP0] Decode batch. #running-req: 1, #token: 144, token usage: 0.00, gen throughput (token/s): 41.80, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:20 TP0] Decode batch. #running-req: 1, #token: 184, token usage: 0.00, gen throughput (token/s): 41.81, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:21] INFO: 127.0.0.1:37738 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" + "[2024-11-02 00:08:08 TP0] Prefill batch. #new-seq: 1, #new-token: 48, #cached-token: 28, cache hit rate: 21.97%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-11-02 00:08:08 TP0] Decode batch. #running-req: 1, #token: 104, token usage: 0.00, gen throughput (token/s): 9.89, #queue-req: 0\n", + "[2024-11-02 00:08:09 TP0] Decode batch. #running-req: 1, #token: 144, token usage: 0.00, gen throughput (token/s): 132.64, #queue-req: 0\n", + "[2024-11-02 00:08:09 TP0] Decode batch. #running-req: 1, #token: 184, token usage: 0.00, gen throughput (token/s): 132.28, #queue-req: 0\n", + "[2024-11-02 00:08:09] INFO: 127.0.0.1:51178 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Ancient Rome's major achievements include:

1. **Engineering and Architecture**: They built iconic structures like the Colosseum, Pantheon, and Roman Forum, showcasing their mastery of concrete, arches, and aqueducts.
2. **Law and Governance**: The Romans developed the 12 Tables (450 BCE), which formed the basis of their laws, and established the concept of citizenship, paving the way for modern democracy.
3. **Military Conquests**: Rome expanded its territories through a series of wars, creating a vast empire that lasted for centuries, stretching from Britain to Egypt.
4. **Language and Literature**: Latin became" + "Ancient Rome's major achievements include:

1. **Law and Governance**: The Twelve Tables (450 BCE) and the Julian Laws (5th century BCE) established a foundation for Roman law, which influenced modern Western law. The Roman Republic (509-27 BCE) and Empire (27 BCE-476 CE) developed a system of governance that included the concept of citizenship, representation, and checks on power.

2. **Architecture and Engineering**: Romans developed impressive architectural styles, such as the arch, dome, and aqueducts. Iconic structures like the Colosseum, Pantheon, and Roman Forum showcased their engineering prowess.

" ], "text/plain": [ "" @@ -379,37 +264,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-31 19:45:21] INFO: 127.0.0.1:37738 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n", - "[2024-10-31 19:45:21 TP0] Prefill batch. #new-seq: 1, #new-token: 15, #cached-token: 25, cache hit rate: 31.40%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "It looks like you're ready to" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " begin" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - ". What kind of test would you like" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " to" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " conduct?" + "[2024-11-02 00:08:19] INFO: 127.0.0.1:44218 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n", + "[2024-11-02 00:08:19 TP0] Prefill batch. #new-seq: 1, #new-token: 15, #cached-token: 25, cache hit rate: 31.40%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "This is only a test." ] } ], @@ -432,7 +289,7 @@ "\n", "### Usage\n", "\n", - "Completions API is similar to Chat Completions API, but without the `messages` parameter." + "Completions API is similar to Chat Completions API, but without the `messages` parameter or chat templates." ] }, { @@ -451,28 +308,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-31 19:45:21 TP0] Prefill batch. #new-seq: 1, #new-token: 8, #cached-token: 1, cache hit rate: 30.39%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-31 19:45:21 TP0] Decode batch. #running-req: 1, #token: 11, token usage: 0.00, gen throughput (token/s): 39.18, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:22 TP0] Decode batch. #running-req: 1, #token: 51, token usage: 0.00, gen throughput (token/s): 42.85, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:23] INFO: 127.0.0.1:37738 - \"POST /v1/completions HTTP/1.1\" 200 OK\n" + "[2024-11-02 00:08:25 TP0] Prefill batch. #new-seq: 1, #new-token: 8, #cached-token: 1, cache hit rate: 30.39%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-11-02 00:08:25 TP0] Decode batch. #running-req: 1, #token: 24, token usage: 0.00, gen throughput (token/s): 2.45, #queue-req: 0\n", + "[2024-11-02 00:08:25 TP0] Decode batch. #running-req: 1, #token: 64, token usage: 0.00, gen throughput (token/s): 142.10, #queue-req: 0\n", + "[2024-11-02 00:08:26] INFO: 127.0.0.1:37290 - \"POST /v1/completions HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "Response: Completion(id='84ca7b4df182449697c4b38a454b8834', choices=[CompletionChoice(finish_reason='length', index=0, logprobs=None, text=' 1. 2. 3.\\n1. United States Washington D.C. 2. Japan Tokyo 3. Australia Canberra\\nList 3 countries and their capitals. 1. 2. 3.\\n1. China Beijing 2. Brazil Bras', matched_stop=None)], created=1730429123, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='text_completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=64, prompt_tokens=9, total_tokens=73, prompt_tokens_details=None))" + "Response: Completion(id='25412696fce14364b40430b5671fc11e', choices=[CompletionChoice(finish_reason='length', index=0, logprobs=None, text=' 1. 2. 3.\\n1. United States - Washington D.C. 2. Japan - Tokyo 3. Australia - Canberra\\nList 3 countries and their capitals. 1. 2. 3.\\n1. China - Beijing 2. Brazil - Bras', matched_stop=None)], created=1730506106, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='text_completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=64, prompt_tokens=9, total_tokens=73, completion_tokens_details=None, prompt_tokens_details=None))" ], "text/plain": [ "" @@ -596,7 +441,7 @@ "source": [ "## Batches\n", "\n", - "We have implemented the batches API for chat completions and completions. You can upload your requests in `jsonl` files, create a batch job, and retrieve the results when the batch job is completed (which takes longer but costs less).\n", + "Batches API for chat completions and completions are also supported. You can upload your requests in `jsonl` files, create a batch job, and retrieve the results when the batch job is completed (which takes longer but costs less).\n", "\n", "The batches APIs are:\n", "\n", @@ -1448,7 +1293,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:46:07.896114Z", @@ -1479,7 +1324,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/backend/openai_api_vision.ipynb b/docs/backend/openai_api_vision.ipynb new file mode 100644 index 0000000000..4b7482c49a --- /dev/null +++ b/docs/backend/openai_api_vision.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# OpenAI-Compatible APIs - Vision\n", + "\n", + "SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.\n", + "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/guides/vision).\n", + "This tutorial covers the vision APIs for vision language models.\n", + "\n", + "SGLang supports vision language models such as Llama 3.2, LLaVA-OneVision, and QWen-VL2 \n", + "- [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) \n", + "- [lmms-lab/llava-onevision-qwen2-72b-ov-chat](https://huggingface.co/lmms-lab/llava-onevision-qwen2-72b-ov-chat) \n", + "- [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Launch A Server\n", + "\n", + "This code block is equivalent to executing \n", + "\n", + "```bash\n", + "python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-11B-Vision-Instruct \\\n", + " --port 30010 --chat-template llama_3_vision\n", + "```\n", + "in your terminal and wait for the server to be ready.\n", + "\n", + "Remember to add `--chat-template llama_3_vision` to specify the vision chat template, otherwise the server only supports text.\n", + "We need to specify `--chat-template` for vision language models because the chat template provided in Hugging Face tokenizer only supports text." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-11-02 00:24:10.542705: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-11-02 00:24:10.554725: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-11-02 00:24:10.554758: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-11-02 00:24:11.063662: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "[2024-11-02 00:24:19] server_args=ServerArgs(model_path='meta-llama/Llama-3.2-11B-Vision-Instruct', tokenizer_path='meta-llama/Llama-3.2-11B-Vision-Instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='meta-llama/Llama-3.2-11B-Vision-Instruct', chat_template='llama_3_vision', is_embedding=False, host='127.0.0.1', port=30010, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=553831757, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n", + "[2024-11-02 00:24:20] Use chat template for the OpenAI-compatible API server: llama_3_vision\n", + "[2024-11-02 00:24:29 TP0] Automatically turn off --chunked-prefill-size and adjust --mem-fraction-static for multimodal models.\n", + "[2024-11-02 00:24:29 TP0] Init torch distributed begin.\n", + "[2024-11-02 00:24:32 TP0] Load weight begin. avail mem=76.83 GB\n", + "[2024-11-02 00:24:32 TP0] lm_eval is not installed, GPTQ may not be usable\n", + "INFO 11-02 00:24:32 weight_utils.py:243] Using model weights format ['*.safetensors']\n", + "Loading safetensors checkpoint shards: 0% Completed | 0/5 [00:00

NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sglang.utils import (\n", + " execute_shell_command,\n", + " wait_for_server,\n", + " terminate_process,\n", + " print_highlight,\n", + ")\n", + "\n", + "embedding_process = execute_shell_command(\n", + "\"\"\"\n", + "python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-11B-Vision-Instruct \\\n", + " --port=30010 --chat-template=llama_3_vision\n", + "\"\"\"\n", + ")\n", + "\n", + "wait_for_server(\"http://localhost:30010\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using cURL\n", + "\n", + "Once the server is up, you can send test requests using curl." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 485 0 0 100 485 0 2420 --:--:-- --:--:-- --:--:-- 2412" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-11-02 00:26:23 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 6462, cache hit rate: 49.97%, token usage: 0.02, #running-req: 0, #queue-req: 0\n", + "[2024-11-02 00:26:24] INFO: 127.0.0.1:39828 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100 965 100 480 100 485 789 797 --:--:-- --:--:-- --:--:-- 1584\n" + ] + }, + { + "data": { + "text/html": [ + "{\"id\":\"5e9e1c80809f492a926a2634c3d162d0\",\"object\":\"chat.completion\",\"created\":1730507184,\"model\":\"meta-llama/Llama-3.2-11B-Vision-Instruct\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"The image depicts a man ironing clothes on an ironing board that is placed on the back of a yellow taxi cab.\"},\"logprobs\":null,\"finish_reason\":\"stop\",\"matched_stop\":128009}],\"usage\":{\"prompt_tokens\":6463,\"total_tokens\":6489,\"completion_tokens\":26,\"prompt_tokens_details\":null}}" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import subprocess\n", + "\n", + "curl_command = \"\"\"\n", + "curl http://localhost:30010/v1/chat/completions \\\n", + " -H \"Content-Type: application/json\" \\\n", + " -H \"Authorization: Bearer None\" \\\n", + " -d '{\n", + " \"model\": \"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", + " \"messages\": [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": \"What’s in this image?\"\n", + " },\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"\n", + " }\n", + " }\n", + " ]\n", + " }\n", + " ],\n", + " \"max_tokens\": 300\n", + " }'\n", + "\"\"\"\n", + "\n", + "response = subprocess.check_output(curl_command, shell=True).decode()\n", + "print_highlight(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using OpenAI Python Client\n", + "\n", + "You can use the OpenAI Python API library to send requests." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-11-02 00:26:33 TP0] Prefill batch. #new-seq: 1, #new-token: 11, #cached-token: 6452, cache hit rate: 66.58%, token usage: 0.02, #running-req: 0, #queue-req: 0\n", + "[2024-11-02 00:26:34 TP0] Decode batch. #running-req: 1, #token: 6477, token usage: 0.02, gen throughput (token/s): 0.77, #queue-req: 0\n", + "[2024-11-02 00:26:34] INFO: 127.0.0.1:43258 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" + ] + }, + { + "data": { + "text/html": [ + "The image shows a man ironing clothes on the back of a yellow taxi cab." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from openai import OpenAI\n", + "\n", + "client = OpenAI(base_url=\"http://localhost:30010/v1\", api_key=\"None\")\n", + "\n", + "response = client.chat.completions.create(\n", + " model=\"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": \"What is in this image?\",\n", + " },\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\"url\": \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"},\n", + " },\n", + " ],\n", + " }\n", + " ],\n", + " max_tokens=300,\n", + ")\n", + "\n", + "print_highlight(response.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiple-Image Inputs\n", + "\n", + "The server also supports multiple images and interleaved text and images if the model supports it." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-11-02 00:20:30 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 12894, cache hit rate: 83.27%, token usage: 0.04, #running-req: 0, #queue-req: 0\n", + "[2024-11-02 00:20:30 TP0] Decode batch. #running-req: 1, #token: 12903, token usage: 0.04, gen throughput (token/s): 2.02, #queue-req: 0\n", + "[2024-11-02 00:20:30 TP0] Decode batch. #running-req: 1, #token: 12943, token usage: 0.04, gen throughput (token/s): 105.52, #queue-req: 0\n", + "[2024-11-02 00:20:30] INFO: 127.0.0.1:41386 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" + ] + }, + { + "data": { + "text/html": [ + "The first image shows a man in a yellow shirt ironing a shirt on the back of a yellow taxi cab, with a red line connecting the two objects. The second image shows a large orange \"S\" and \"G\" on a white background, with a red line connecting them." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from openai import OpenAI\n", + "\n", + "client = OpenAI(base_url=\"http://localhost:30010/v1\", api_key=\"None\")\n", + "\n", + "response = client.chat.completions.create(\n", + " model=\"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\",\n", + " },\n", + " },\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": \"https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png\",\n", + " },\n", + " },\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": \"I have two very different images. They are not related at all. \"\n", + " \"Please describe the first image in one sentence, and then describe the second image in another sentence.\",\n", + " },\n", + " ],\n", + " }\n", + " ],\n", + " temperature=0,\n", + ")\n", + "\n", + "print_highlight(response.choices[0].message.content)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "terminate_process(embedding_process)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chat Template\n", + "\n", + "As mentioned before, if you do not specify a vision model's `--chat-template`, the server uses Hugging Face's default template, which only supports text.\n", + "\n", + "We list popular vision models with their chat templates:\n", + "\n", + "- [meta-llama/Llama-3.2-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) uses `llama_3_vision`.\n", + "- [LLaVA-NeXT](https://huggingface.co/collections/lmms-lab/llava-next-6623288e2d61edba3ddbf5ff) uses `chatml-llava`.\n", + "- [LlaVA-OneVision](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov) uses `chatml-llava`.\n", + "- [Llama3-LLaVA-NeXT](https://huggingface.co/lmms-lab/llama3-llava-next-8b) uses `llava_llama_3`.\n", + "- [LLaVA-v1.5 / 1.6](https://huggingface.co/liuhaotian/llava-v1.6-34b) uses `vicuna_v1.1`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/backend/vision_language_model.ipynb b/docs/backend/vision_language_model.ipynb deleted file mode 100644 index 769c9a9d56..0000000000 --- a/docs/backend/vision_language_model.ipynb +++ /dev/null @@ -1,431 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vision Language Model\n", - "\n", - "SGLang supports vision language models in the same way as completion models. Here are some example models:\n", - "\n", - "- [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)\n", - "- [lmms-lab/llava-onevision-qwen2-7b-ov](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Launch A Server\n", - "\n", - "The following code is equivalent to running this in the shell:\n", - "\n", - "```bash\n", - "python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-11B-Vision-Instruct \\\n", - " --port=30010 --chat-template=llama_3_vision\n", - "```\n", - "\n", - "Remember to add `--chat-template=llama_3_vision` to specify the vision chat template, otherwise the server only supports text." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "[2024-10-31 23:10:49] server_args=ServerArgs(model_path='meta-llama/Llama-3.2-11B-Vision-Instruct', tokenizer_path='meta-llama/Llama-3.2-11B-Vision-Instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='meta-llama/Llama-3.2-11B-Vision-Instruct', chat_template='llama_3_vision', is_embedding=False, host='127.0.0.1', port=30010, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=178735948, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n", - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "[2024-10-31 23:10:51] Use chat template for the OpenAI-compatible API server: llama_3_vision\n", - "[2024-10-31 23:10:56 TP0] Automatically turn off --chunked-prefill-size and adjust --mem-fraction-static for multimodal models.\n", - "[2024-10-31 23:10:56 TP0] Init torch distributed begin.\n", - "[2024-10-31 23:10:56 TP0] Load weight begin. avail mem=47.27 GB\n", - "[2024-10-31 23:10:57 TP0] lm_eval is not installed, GPTQ may not be usable\n", - "INFO 10-31 23:10:57 weight_utils.py:243] Using model weights format ['*.safetensors']\n", - "Loading safetensors checkpoint shards: 0% Completed | 0/5 [00:00

NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sglang.utils import (\n", - " execute_shell_command,\n", - " wait_for_server,\n", - " terminate_process,\n", - " print_highlight,\n", - ")\n", - "\n", - "embedding_process = execute_shell_command(\n", - " \"\"\"\n", - " python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-11B-Vision-Instruct \\\n", - " --port=30010 --chat-template=llama_3_vision\n", - "\n", - "\"\"\"\n", - ")\n", - "\n", - "wait_for_server(\"http://localhost:30010\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use Curl" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 559 0 0 100 559 0 253 0:00:02 0:00:02 --:--:-- 253" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/torch/storage.py:414: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " return torch.load(io.BytesIO(b))\n", - "[2024-10-31 23:11:18 TP0] Prefill batch. #new-seq: 1, #new-token: 6463, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100 559 0 0 100 559 0 174 0:00:03 0:00:03 --:--:-- 174" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 23:11:20 TP0] Decode batch. #running-req: 1, #token: 6496, token usage: 0.05, gen throughput (token/s): 3.90, #queue-req: 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100 559 0 0 100 559 0 107 0:00:05 0:00:05 --:--:-- 107" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 23:11:21 TP0] Decode batch. #running-req: 1, #token: 6536, token usage: 0.05, gen throughput (token/s): 33.67, #queue-req: 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100 559 0 0 100 559 0 90 0:00:06 0:00:06 --:--:-- 0" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 23:11:22 TP0] Decode batch. #running-req: 1, #token: 6576, token usage: 0.05, gen throughput (token/s): 33.60, #queue-req: 0\n", - "[2024-10-31 23:11:22] INFO: 127.0.0.1:54224 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100 1544 100 985 100 559 142 80 0:00:06 0:00:06 --:--:-- 265\n" - ] - }, - { - "data": { - "text/html": [ - "{'id': 'f618453e8f3e4408b893a958f2868a44', 'object': 'chat.completion', 'created': 1730441482, 'model': 'meta-llama/Llama-3.2-11B-Vision-Instruct', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': 'The image depicts a serene and peaceful landscape featuring a wooden boardwalk that meanders through a lush grassy field, set against a backdrop of trees and a bright blue sky with wispy clouds. The boardwalk is made of weathered wooden planks and is surrounded by tall grass on either side, creating a sense of depth and texture. The surrounding trees add a touch of natural beauty to the scene, while the blue sky with wispy clouds provides a sense of calmness and serenity. The overall atmosphere of the image is one of tranquility and relaxation, inviting the viewer to step into the peaceful world depicted.'}, 'logprobs': None, 'finish_reason': 'stop', 'matched_stop': 128009}], 'usage': {'prompt_tokens': 6463, 'total_tokens': 6588, 'completion_tokens': 125, 'prompt_tokens_details': None}}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import subprocess, json, os\n", - "\n", - "curl_command = \"\"\"\n", - "curl http://localhost:30010/v1/chat/completions \\\n", - " -H \"Content-Type: application/json\" \\\n", - " -H \"Authorization: Bearer None\" \\\n", - " -d '{\n", - " \"model\": \"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": [\n", - " {\n", - " \"type\": \"text\",\n", - " \"text\": \"What’s in this image?\"\n", - " },\n", - " {\n", - " \"type\": \"image_url\",\n", - " \"image_url\": {\n", - " \"url\": \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\"\n", - " }\n", - " }\n", - " ]\n", - " }\n", - " ],\n", - " \"max_tokens\": 300\n", - " }'\n", - "\"\"\"\n", - "\n", - "response = json.loads(subprocess.check_output(curl_command, shell=True))\n", - "print_highlight(response)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using OpenAI Compatible API" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 23:11:23 TP0] Prefill batch. #new-seq: 1, #new-token: 6463, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-31 23:11:24 TP0] Decode batch. #running-req: 1, #token: 6492, token usage: 0.05, gen throughput (token/s): 20.07, #queue-req: 0\n", - "[2024-10-31 23:11:25 TP0] Decode batch. #running-req: 1, #token: 6532, token usage: 0.05, gen throughput (token/s): 33.68, #queue-req: 0\n", - "[2024-10-31 23:11:26 TP0] Decode batch. #running-req: 1, #token: 6572, token usage: 0.05, gen throughput (token/s): 33.62, #queue-req: 0\n", - "[2024-10-31 23:11:27 TP0] Decode batch. #running-req: 1, #token: 6612, token usage: 0.05, gen throughput (token/s): 33.62, #queue-req: 0\n", - "[2024-10-31 23:11:28] INFO: 127.0.0.1:54228 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "The image depicts a serene and peaceful scene of a wooden boardwalk leading through a lush field of tall grass, set against a backdrop of trees and a blue sky with clouds. The boardwalk is made of light-colored wood and has a simple design, with the wooden planks running parallel to each other. It stretches out into the distance, disappearing into the horizon.

The field is filled with tall, vibrant green grass that sways gently in the breeze, creating a sense of movement and life. The trees in the background are also lush and green, adding depth and texture to the scene. The blue sky above is dotted with white clouds, which are scattered across the horizon. The overall atmosphere of the image is one of tranquility and serenity, inviting the viewer to step into the peaceful world depicted." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import base64, requests\n", - "from openai import OpenAI\n", - "\n", - "client = OpenAI(base_url=\"http://localhost:30010/v1\", api_key=\"None\")\n", - "\n", - "\n", - "def encode_image(image_path):\n", - " with open(image_path, \"rb\") as image_file:\n", - " return base64.b64encode(image_file.read()).decode(\"utf-8\")\n", - "\n", - "\n", - "def download_image(image_url, image_path):\n", - " response = requests.get(image_url)\n", - " response.raise_for_status()\n", - " with open(image_path, \"wb\") as f:\n", - " f.write(response.content)\n", - "\n", - "\n", - "image_url = \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\"\n", - "image_path = \"boardwalk.jpeg\"\n", - "download_image(image_url, image_path)\n", - "\n", - "base64_image = encode_image(image_path)\n", - "\n", - "response = client.chat.completions.create(\n", - " model=\"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", - " messages=[\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": [\n", - " {\n", - " \"type\": \"text\",\n", - " \"text\": \"What is in this image?\",\n", - " },\n", - " {\n", - " \"type\": \"image_url\",\n", - " \"image_url\": {\"url\": f\"data:image/jpeg;base64,{base64_image}\"},\n", - " },\n", - " ],\n", - " }\n", - " ],\n", - " max_tokens=300,\n", - ")\n", - "\n", - "print_highlight(response.choices[0].message.content)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Multiple Images Input" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 23:11:28 TP0] Prefill batch. #new-seq: 1, #new-token: 12871, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-31 23:11:30 TP0] Decode batch. #running-req: 1, #token: 12899, token usage: 0.10, gen throughput (token/s): 15.36, #queue-req: 0\n", - "[2024-10-31 23:11:31 TP0] Decode batch. #running-req: 1, #token: 12939, token usage: 0.10, gen throughput (token/s): 33.33, #queue-req: 0\n", - "[2024-10-31 23:11:32 TP0] Decode batch. #running-req: 1, #token: 12979, token usage: 0.10, gen throughput (token/s): 33.28, #queue-req: 0\n", - "[2024-10-31 23:11:33] INFO: 127.0.0.1:50966 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n", - "Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='The two images depict a serene and idyllic scene, with the first image showing a well-trodden wooden path through a field, while the second image shows an overgrown, less-traveled path through the same field. The first image features a clear and well-maintained wooden path, whereas the second image shows a more neglected and overgrown path that is not as well-defined. The first image has a more vibrant and inviting atmosphere, while the second image appears more peaceful and serene. Overall, both images evoke a sense of tranquility and connection to nature.', refusal=None, role='assistant', function_call=None, tool_calls=None), matched_stop=128009)\n" - ] - } - ], - "source": [ - "from openai import OpenAI\n", - "\n", - "client = OpenAI(base_url=\"http://localhost:30010/v1\", api_key=\"None\")\n", - "\n", - "response = client.chat.completions.create(\n", - " model=\"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", - " messages=[\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": [\n", - " {\n", - " \"type\": \"text\",\n", - " \"text\": \"Are there any differences between these two images?\",\n", - " },\n", - " {\n", - " \"type\": \"image_url\",\n", - " \"image_url\": {\n", - " \"url\": \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\",\n", - " },\n", - " },\n", - " {\n", - " \"type\": \"image_url\",\n", - " \"image_url\": {\n", - " \"url\": \"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg\",\n", - " },\n", - " },\n", - " ],\n", - " }\n", - " ],\n", - " max_tokens=300,\n", - ")\n", - "print(response.choices[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "terminate_process(embedding_process)\n", - "os.remove(image_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Chat Template\n", - "\n", - "As mentioned before, if you do not specify a vision model's `chat-template`, the server uses Hugging Face's default template, which only supports text.\n", - "\n", - "You can add your custom chat template by referring to the [custom chat template](../references/custom_chat_template.md).\n", - "\n", - "We list popular vision models with their chat templates:\n", - "\n", - "- [meta-llama/Llama-3.2-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) uses `llama_3_vision`.\n", - "- [LLaVA-NeXT](https://huggingface.co/collections/lmms-lab/llava-next-6623288e2d61edba3ddbf5ff) uses `chatml-llava`.\n", - "- [llama3-llava-next](https://huggingface.co/lmms-lab/llama3-llava-next-8b) uses `llava_llama_3`.\n", - "- [llava-onevision](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov) uses `chatml-llava`.\n", - "- [liuhaotian/llava-v1.5 / 1.6](https://huggingface.co/liuhaotian/llava-v1.5-13b) uses `vicuna_v1.1`." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "AlphaMeemory", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/index.rst b/docs/index.rst index 6601c57d51..b365f5701f 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -23,8 +23,8 @@ The core features include: :maxdepth: 1 :caption: Backend Tutorial - backend/openai_api.ipynb - backend/vision_language_model.ipynb + backend/openai_api_completions.ipynb + backend/openai_api_vision.ipynb backend/backend.md @@ -46,5 +46,5 @@ The core features include: references/choices_methods.md references/benchmark_and_profiling.md references/troubleshooting.md - references/embedding_model.ipynb + references/custom_chat_template.md references/learn_more.md diff --git a/docs/references/custom_chat_template.md b/docs/references/custom_chat_template.md index 64b33a0a42..0a5225da22 100644 --- a/docs/references/custom_chat_template.md +++ b/docs/references/custom_chat_template.md @@ -1,3 +1,5 @@ +.. _custom-chat-template: + # Custom Chat Template in SGLang Runtime **NOTE**: There are two chat template systems in SGLang project. This document is about setting a custom chat template for the OpenAI-compatible API server (defined at [conversation.py](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/conversation.py)). It is NOT related to the chat template used in the SGLang language frontend (defined at [chat_template.py](https://github.com/sgl-project/sglang/blob/main/python/sglang/lang/chat_template.py)). diff --git a/docs/references/sampling_params.md b/docs/references/sampling_params.md index 78d5193c25..062e0c99b0 100644 --- a/docs/references/sampling_params.md +++ b/docs/references/sampling_params.md @@ -1,3 +1,5 @@ +.. _sampling-parameters: + # Sampling Parameters in SGLang Runtime This doc describes the sampling parameters of the SGLang Runtime. It is the low-level endpoint of the runtime. diff --git a/docs/starts/send_request.ipynb b/docs/starts/send_request.ipynb index dda2371b50..2b095c2595 100644 --- a/docs/starts/send_request.ipynb +++ b/docs/starts/send_request.ipynb @@ -22,12 +22,12 @@ "--port 30000 --host 0.0.0.0\n", "```\n", "\n", - "in your command line and wait for the server to be ready." + "in your terminal and wait for the server to be ready." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:46:13.611212Z", @@ -41,127 +41,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:46:18] server_args=ServerArgs(model_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='meta-llama/Meta-Llama-3.1-8B-Instruct', chat_template=None, is_embedding=False, host='0.0.0.0', port=30000, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=706578968, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:46:24 TP0] Init torch distributed begin.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:46:24 TP0] Load weight begin. avail mem=47.27 GB\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:46:25 TP0] lm_eval is not installed, GPTQ may not be usable\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO 10-31 19:46:26 weight_utils.py:243] Using model weights format ['*.safetensors']\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00{\"id\":\"a0714277fab546c5b6d91724aa3e27a3\",\"object\":\"chat.completion\",\"created\":1730507329,\"model\":\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"An LLM, or Large Language Model, is a type of artificial intelligence (AI) designed to process and generate human-like language, often used in applications such as chatbots, virtual assistants, and language translation software.\"},\"logprobs\":null,\"finish_reason\":\"stop\",\"matched_stop\":128009}],\"usage\":{\"prompt_tokens\":53,\"total_tokens\":98,\"completion_tokens\":45,\"prompt_tokens_details\":null}}" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "!curl http://localhost:30000/v1/chat/completions \\\n", - " -H \"Content-Type: application/json\" \\\n", - " -H \"Authorization: Bearer None\" \\\n", - " -d '{\"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\", \"messages\": [{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}, {\"role\": \"user\", \"content\": \"What is a LLM?\"}]}'" + "import subprocess\n", + "\n", + "curl_command = \"\"\"\n", + "curl http://localhost:30000/v1/chat/completions \\\\\n", + " -H \"Content-Type: application/json\" \\\\\n", + " -H \"Authorization: Bearer None\" \\\\\n", + " -d '{\n", + " \"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", + " \"messages\": [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": \"You are a helpful assistant.\"\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"What is an LLM? Tell me in one sentence.\"\n", + " }\n", + " ]\n", + " }'\n", + "\"\"\"\n", + "\n", + "response = subprocess.check_output(curl_command, shell=True).decode()\n", + "\n", + "print_highlight(response)" ] }, { @@ -301,7 +195,7 @@ "source": [ "## Using OpenAI Python Client\n", "\n", - "You can also use the OpenAI Python API library to send requests." + "You can use the OpenAI Python API library to send requests." ] }, { @@ -320,22 +214,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[2024-10-31 19:46:51 TP0] Prefill batch. #new-seq: 1, #new-token: 20, #cached-token: 29, cache hit rate: 29.13%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-31 19:46:51 TP0] Decode batch. #running-req: 1, #token: 50, token usage: 0.00, gen throughput (token/s): 27.57, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:46:52 TP0] Decode batch. #running-req: 1, #token: 90, token usage: 0.00, gen throughput (token/s): 42.69, #queue-req: 0\n", - "[2024-10-31 19:46:52] INFO: 127.0.0.1:40952 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" + "[2024-11-02 00:03:52 TP0] Prefill batch. #new-seq: 1, #new-token: 20, #cached-token: 29, cache hit rate: 29.13%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-11-02 00:03:52 TP0] Decode batch. #running-req: 1, #token: 65, token usage: 0.00, gen throughput (token/s): 11.33, #queue-req: 0\n", + "[2024-11-02 00:03:53] INFO: 127.0.0.1:57008 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" ] }, { "data": { "text/html": [ - "ChatCompletion(id='c563abb8fe74496f83203fe21ec4ff61', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', function_call=None, tool_calls=None), matched_stop=128009)], created=1730429212, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, prompt_tokens_details=None))" + "ChatCompletion(id='a6590143c40f4732a5c57d4c91b43f05', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=None), matched_stop=128009)], created=1730505833, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, completion_tokens_details=None, prompt_tokens_details=None))" ], "text/plain": [ "" @@ -359,12 +246,67 @@ " temperature=0,\n", " max_tokens=64,\n", ")\n", + "\n", "print_highlight(response)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Native Generation APIs\n", + "\n", + "You can also use the native `/generate` endpoint. It provides more flexiblity.\n", + "An API reference is available at [Sampling Parameters](https://sgl-project.github.io/references/sampling_params.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2024-11-02 00:05:04 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 5, cache hit rate: 33.04%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", + "[2024-11-02 00:05:04 TP0] Decode batch. #running-req: 1, #token: 26, token usage: 0.00, gen throughput (token/s): 3.10, #queue-req: 0\n", + "[2024-11-02 00:05:04] INFO: 127.0.0.1:60536 - \"POST /generate HTTP/1.1\" 200 OK\n" + ] + }, + { + "data": { + "text/html": [ + "{'text': ' a city of romance, art, fashion, and history. Paris is a must-visit destination for anyone who loves culture, architecture, and cuisine. From the', 'meta_info': {'prompt_tokens': 6, 'completion_tokens': 32, 'completion_tokens_wo_jump_forward': 32, 'cached_tokens': 5, 'finish_reason': {'type': 'length', 'length': 32}, 'id': 'd882513c180d4c5981488257ccab4b9f'}, 'index': 0}" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import requests\n", + "\n", + "response = requests.post(\n", + " \"http://localhost:30000/generate\",\n", + " json={\n", + " \"text\": \"The capital of France is\",\n", + " \"sampling_params\": {\n", + " \"temperature\": 0,\n", + " \"max_new_tokens\": 32,\n", + " },\n", + " },\n", + ")\n", + "\n", + "print_highlight(response.json())" + ] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:46:52.898411Z", @@ -384,18 +326,6 @@ "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" } }, "nbformat": 4, diff --git a/test/srt/test_vision_openai_server.py b/test/srt/test_vision_openai_server.py index f44bc98e25..a5bf302e2f 100644 --- a/test/srt/test_vision_openai_server.py +++ b/test/srt/test_vision_openai_server.py @@ -132,7 +132,7 @@ def test_multi_turn_chat_completion(self): assert response.usage.completion_tokens > 0 assert response.usage.total_tokens > 0 - def test_mult_images_chat_completion(self): + def test_multi_images_chat_completion(self): client = openai.Client(api_key=self.api_key, base_url=self.base_url) response = client.chat.completions.create( From a54f278d44afb42bea1f77990efd3640e71a2af3 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Fri, 1 Nov 2024 18:16:29 -0700 Subject: [PATCH 11/44] Add a FAQ documentation (#1877) --- docs/index.rst | 1 + docs/references/faq.md | 17 +++++++++++++++++ 2 files changed, 18 insertions(+) create mode 100644 docs/references/faq.md diff --git a/docs/index.rst b/docs/index.rst index b365f5701f..c40cd169f6 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -47,4 +47,5 @@ The core features include: references/benchmark_and_profiling.md references/troubleshooting.md references/custom_chat_template.md + references/faq.md references/learn_more.md diff --git a/docs/references/faq.md b/docs/references/faq.md new file mode 100644 index 0000000000..5a87ba3d87 --- /dev/null +++ b/docs/references/faq.md @@ -0,0 +1,17 @@ +Here’s the text with corrected grammar and refined phrasing in U.S. English: + +# Frequently Asked Questions + +## The results are not deterministic, even with a temperature of 0 + +You may notice that when you send the same request twice, the results from the engine will be slightly different, even when the temperature is set to 0. + +From our initial investigation, this indeterminism arises from two factors: dynamic batching and prefix caching. Roughly speaking, dynamic batching accounts for about 95% of the indeterminism, while prefix caching accounts for the remaining portion. The server runs dynamic batching under the hood. Different batch sizes can cause PyTorch/CuBLAS to dispatch to different CUDA kernels, which can lead to slight numerical differences. This difference accumulates across many layers, resulting in nondeterministic output when the batch size changes. Similarly, when prefix caching is enabled, it can also dispatch to different kernels. Even when the computations are mathematically equivalent, small numerical differences from different kernel implementations lead to the final nondeterministic outputs. + +To achieve more deterministic outputs in the current code, you can add `--disable-radix-cache` and send only one request at a time. The results will be mostly deterministic under this setting. + +We are still investigating the root causes and potential solutions. In the short term, we may introduce a "deterministic mode" that uses more padding to address the variance caused by dynamic batching. This mode will be more deterministic but slower. + +We have two issues to track our progress: +- The deterministic mode is tracked at [https://github.com/sgl-project/sglang/issues/1729](https://github.com/sgl-project/sglang/issues/1729). +- The per-request random seed is tracked at [https://github.com/sgl-project/sglang/issues/1335](https://github.com/sgl-project/sglang/issues/1335). \ No newline at end of file From 2134f0898ceb833c5202c3a7c5e9e74535d96697 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Fri, 1 Nov 2024 18:21:14 -0700 Subject: [PATCH 12/44] Fix links in the docs (#1878) --- docs/backend/backend.md | 6 +++--- docs/references/custom_chat_template.md | 2 -- docs/references/faq.md | 4 +--- docs/references/sampling_params.md | 2 -- 4 files changed, 4 insertions(+), 10 deletions(-) diff --git a/docs/backend/backend.md b/docs/backend/backend.md index 3692d7217b..5466951787 100644 --- a/docs/backend/backend.md +++ b/docs/backend/backend.md @@ -20,7 +20,7 @@ curl http://localhost:30000/generate \ }' ``` -Learn more about the argument specification, streaming, and multi-modal support [here](https://sgl-project.github.io/sampling_params.html). +Learn more about the argument specification, streaming, and multi-modal support [here](https://sgl-project.github.io/references/sampling_params.html). ## OpenAI Compatible API In addition, the server supports OpenAI-compatible APIs. @@ -74,7 +74,7 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct ``` python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --mem-fraction-static 0.7 ``` -- See [hyperparameter tuning](https://sgl-project.github.io/hyperparameter_tuning.html) on tuning hyperparameters for better performance. +- See [hyperparameter tuning](https://sgl-project.github.io/references/hyperparameter_tuning.html) on tuning hyperparameters for better performance. - If you see out-of-memory errors during prefill for long prompts, try to set a smaller chunked prefill size. ``` python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096 @@ -161,7 +161,7 @@ You can view the full example [here](https://github.com/sgl-project/sglang/tree/ - gte-Qwen2 - `python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct --is-embedding` -Instructions for supporting a new model are [here](https://sgl-project.github.io/model_support.html). +Instructions for supporting a new model are [here](https://sgl-project.github.io/references/model_support.html). ### Use Models From ModelScope
diff --git a/docs/references/custom_chat_template.md b/docs/references/custom_chat_template.md index 0a5225da22..64b33a0a42 100644 --- a/docs/references/custom_chat_template.md +++ b/docs/references/custom_chat_template.md @@ -1,5 +1,3 @@ -.. _custom-chat-template: - # Custom Chat Template in SGLang Runtime **NOTE**: There are two chat template systems in SGLang project. This document is about setting a custom chat template for the OpenAI-compatible API server (defined at [conversation.py](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/conversation.py)). It is NOT related to the chat template used in the SGLang language frontend (defined at [chat_template.py](https://github.com/sgl-project/sglang/blob/main/python/sglang/lang/chat_template.py)). diff --git a/docs/references/faq.md b/docs/references/faq.md index 5a87ba3d87..eb43ee6620 100644 --- a/docs/references/faq.md +++ b/docs/references/faq.md @@ -1,5 +1,3 @@ -Here’s the text with corrected grammar and refined phrasing in U.S. English: - # Frequently Asked Questions ## The results are not deterministic, even with a temperature of 0 @@ -14,4 +12,4 @@ We are still investigating the root causes and potential solutions. In the short We have two issues to track our progress: - The deterministic mode is tracked at [https://github.com/sgl-project/sglang/issues/1729](https://github.com/sgl-project/sglang/issues/1729). -- The per-request random seed is tracked at [https://github.com/sgl-project/sglang/issues/1335](https://github.com/sgl-project/sglang/issues/1335). \ No newline at end of file +- The per-request random seed is tracked at [https://github.com/sgl-project/sglang/issues/1335](https://github.com/sgl-project/sglang/issues/1335). diff --git a/docs/references/sampling_params.md b/docs/references/sampling_params.md index 062e0c99b0..78d5193c25 100644 --- a/docs/references/sampling_params.md +++ b/docs/references/sampling_params.md @@ -1,5 +1,3 @@ -.. _sampling-parameters: - # Sampling Parameters in SGLang Runtime This doc describes the sampling parameters of the SGLang Runtime. It is the low-level endpoint of the runtime. From 066e8a4ef0e9728cb8744944155c6da815c3d8a0 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Fri, 1 Nov 2024 20:00:41 -0700 Subject: [PATCH 13/44] Update docs title (#1879) --- docs/backend/openai_api_completions.ipynb | 14 +------------- docs/backend/openai_api_vision.ipynb | 2 +- 2 files changed, 2 insertions(+), 14 deletions(-) diff --git a/docs/backend/openai_api_completions.ipynb b/docs/backend/openai_api_completions.ipynb index dc89500f62..6b649a4e43 100644 --- a/docs/backend/openai_api_completions.ipynb +++ b/docs/backend/openai_api_completions.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# OpenAI-Compatible APIs - Completions\n", + "# OpenAI APIs - Completions\n", "\n", "SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.\n", "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/api-reference).\n", @@ -1313,18 +1313,6 @@ "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/backend/openai_api_vision.ipynb b/docs/backend/openai_api_vision.ipynb index 4b7482c49a..a4345be7cd 100644 --- a/docs/backend/openai_api_vision.ipynb +++ b/docs/backend/openai_api_vision.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# OpenAI-Compatible APIs - Vision\n", + "# OpenAI APIs - Vision\n", "\n", "SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.\n", "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/guides/vision).\n", From 2565cb0f40b9d8b92711efab7e8ff073b4058478 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Fri, 1 Nov 2024 20:29:41 -0700 Subject: [PATCH 14/44] Update docs and workflow (#1881) --- .github/workflows/{deploy-docs.yml => release-docs.yml} | 1 + README.md | 2 +- docs/{starts => start}/install.md | 0 docs/{starts => start}/send_request.ipynb | 2 +- 4 files changed, 3 insertions(+), 2 deletions(-) rename .github/workflows/{deploy-docs.yml => release-docs.yml} (97%) rename docs/{starts => start}/install.md (100%) rename docs/{starts => start}/send_request.ipynb (99%) diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/release-docs.yml similarity index 97% rename from .github/workflows/deploy-docs.yml rename to .github/workflows/release-docs.yml index aeb9765f19..7abcf5768c 100644 --- a/.github/workflows/deploy-docs.yml +++ b/.github/workflows/release-docs.yml @@ -45,6 +45,7 @@ jobs: cd _build/html git clone https://$GITHUB_TOKEN@github.com/sgl-project/sgl-project.github.io.git ../sgl-project.github.io + rm -rf ../sgl-project.github.io/* cp -r * ../sgl-project.github.io cd ../sgl-project.github.io git config user.name "zhaochenyang20" diff --git a/README.md b/README.md index 8e5d55f50b..497b7e344a 100644 --- a/README.md +++ b/README.md @@ -40,7 +40,7 @@ The core features include: - **Active Community**: SGLang is open-source and backed by an active community with industry adoption. ## Install -See [https://sgl-project.github.io/starts/install.html](https://sgl-project.github.io/starts/install.html) +See [https://sgl-project.github.io/start/install.html](https://sgl-project.github.io/start/install.html) ## Backend: SGLang Runtime (SRT) See [https://sgl-project.github.io/backend/backend.html](https://sgl-project.github.io/backend/backend.html) diff --git a/docs/starts/install.md b/docs/start/install.md similarity index 100% rename from docs/starts/install.md rename to docs/start/install.md diff --git a/docs/starts/send_request.ipynb b/docs/start/send_request.ipynb similarity index 99% rename from docs/starts/send_request.ipynb rename to docs/start/send_request.ipynb index 2b095c2595..209910185e 100644 --- a/docs/starts/send_request.ipynb +++ b/docs/start/send_request.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Quick Start: Launch A Server and Send Requests\n", + "# Quick Start: Sending Requests\n", "\n", "This notebook provides a quick-start guide for using SGLang after installation." ] From 660ecb731f0aa8d08d0220ea2ef91757ac24d33c Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Fri, 1 Nov 2024 20:42:30 -0700 Subject: [PATCH 15/44] Fix doc links (#1882) --- docs/backend/openai_api_vision.ipynb | 15 ++------------- docs/index.rst | 4 ++-- 2 files changed, 4 insertions(+), 15 deletions(-) diff --git a/docs/backend/openai_api_vision.ipynb b/docs/backend/openai_api_vision.ipynb index a4345be7cd..25742cb6e7 100644 --- a/docs/backend/openai_api_vision.ipynb +++ b/docs/backend/openai_api_vision.ipynb @@ -351,8 +351,9 @@ "We list popular vision models with their chat templates:\n", "\n", "- [meta-llama/Llama-3.2-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) uses `llama_3_vision`.\n", - "- [LLaVA-NeXT](https://huggingface.co/collections/lmms-lab/llava-next-6623288e2d61edba3ddbf5ff) uses `chatml-llava`.\n", + "- [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) uses `qwen2-vl`.\n", "- [LlaVA-OneVision](https://huggingface.co/lmms-lab/llava-onevision-qwen2-7b-ov) uses `chatml-llava`.\n", + "- [LLaVA-NeXT](https://huggingface.co/collections/lmms-lab/llava-next-6623288e2d61edba3ddbf5ff) uses `chatml-llava`.\n", "- [Llama3-LLaVA-NeXT](https://huggingface.co/lmms-lab/llama3-llava-next-8b) uses `llava_llama_3`.\n", "- [LLaVA-v1.5 / 1.6](https://huggingface.co/liuhaotian/llava-v1.6-34b) uses `vicuna_v1.1`." ] @@ -363,18 +364,6 @@ "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/index.rst b/docs/index.rst index c40cd169f6..f5468c88f0 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -15,8 +15,8 @@ The core features include: :maxdepth: 1 :caption: Getting Started - starts/install.md - starts/send_request.ipynb + start/install.md + start/send_request.ipynb .. toctree:: From 146f6134051a23cda360a9de2a1abc1f447b9787 Mon Sep 17 00:00:00 2001 From: Ran Chen Date: Sat, 2 Nov 2024 00:04:50 -0700 Subject: [PATCH 16/44] Fix incorrect context length for llama3.2-11b (#1873) --- python/sglang/srt/hf_transformers_utils.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/python/sglang/srt/hf_transformers_utils.py b/python/sglang/srt/hf_transformers_utils.py index c3abf11e4e..7a8751043f 100644 --- a/python/sglang/srt/hf_transformers_utils.py +++ b/python/sglang/srt/hf_transformers_utils.py @@ -88,19 +88,23 @@ def get_config( def get_context_length(config): - """Get the context length of a model from a huggingface model configs.""" - rope_scaling = getattr(config, "rope_scaling", None) + """Get the context length of a model from a huggingface model configs. + And here the config should be text_config part if the model is a multimodal + LLM. + """ + text_config = getattr(config, "text_config", config) + rope_scaling = getattr(text_config, "rope_scaling", None) if rope_scaling: - rope_scaling_factor = config.rope_scaling.get("factor", 1) + rope_scaling_factor = rope_scaling.get("factor", 1) if "original_max_position_embeddings" in rope_scaling: rope_scaling_factor = 1 - if config.rope_scaling.get("rope_type", None) == "llama3": + if rope_scaling.get("rope_type", None) == "llama3": rope_scaling_factor = 1 else: rope_scaling_factor = 1 for key in CONTEXT_LENGTH_KEYS: - val = getattr(config, key, None) + val = getattr(text_config, key, None) if val is not None: return int(rope_scaling_factor * val) return 2048 From 72e979bfb5ed031282deef800774cbcde3d572b3 Mon Sep 17 00:00:00 2001 From: Chayenne Date: Sat, 2 Nov 2024 00:17:30 -0700 Subject: [PATCH 17/44] add native api docs (#1883) Co-authored-by: Chayenne --- docs/backend/embedding_model.ipynb | 139 +--- docs/backend/native_api.ipynb | 286 +++++++++ docs/backend/openai_api_completions.ipynb | 748 +--------------------- docs/backend/openai_api_vision.ipynb | 149 +---- docs/index.rst | 1 + docs/start/send_request.ipynb | 138 +--- 6 files changed, 319 insertions(+), 1142 deletions(-) create mode 100644 docs/backend/native_api.ipynb diff --git a/docs/backend/embedding_model.ipynb b/docs/backend/embedding_model.ipynb index 589e668434..45928587bb 100644 --- a/docs/backend/embedding_model.ipynb +++ b/docs/backend/embedding_model.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:47:32.337369Z", @@ -39,59 +39,7 @@ "shell.execute_reply": "2024-11-01T02:47:59.539861Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "[2024-10-31 22:40:37] server_args=ServerArgs(model_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_path='Alibaba-NLP/gte-Qwen2-7B-instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='Alibaba-NLP/gte-Qwen2-7B-instruct', chat_template=None, is_embedding=True, host='0.0.0.0', port=30010, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=309155486, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n", - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n", - "[2024-10-31 22:40:42 TP0] Init torch distributed begin.\n", - "[2024-10-31 22:40:43 TP0] Load weight begin. avail mem=47.27 GB\n", - "[2024-10-31 22:40:43 TP0] lm_eval is not installed, GPTQ may not be usable\n", - "INFO 10-31 22:40:44 weight_utils.py:243] Using model weights format ['*.safetensors']\n", - "Loading safetensors checkpoint shards: 0% Completed | 0/7 [00:00

NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from sglang.utils import (\n", " execute_shell_command,\n", @@ -119,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:47:59.543958Z", @@ -128,28 +76,7 @@ "shell.execute_reply": "2024-11-01T02:47:59.590809Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 22:40:57 TP0] Prefill batch. #new-seq: 1, #new-token: 4, #cached-token: 0, cache hit rate: 0.00%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-31 22:40:57] INFO: 127.0.0.1:51746 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Text embedding (first 10): [0.0083160400390625, 0.0006804466247558594, -0.00809478759765625, -0.0006995201110839844, 0.0143890380859375, -0.0090179443359375, 0.01238250732421875, 0.00209808349609375, 0.0062103271484375, -0.003047943115234375]" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import subprocess, json\n", "\n", @@ -176,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:47:59.594229Z", @@ -185,28 +112,7 @@ "shell.execute_reply": "2024-11-01T02:48:00.005255Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 22:40:58 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 3, cache hit rate: 21.43%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-31 22:40:58] INFO: 127.0.0.1:51750 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Text embedding (first 10): [0.00829315185546875, 0.0007004737854003906, -0.00809478759765625, -0.0006799697875976562, 0.01438140869140625, -0.00897979736328125, 0.0123748779296875, 0.0020923614501953125, 0.006195068359375, -0.0030498504638671875]" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import openai\n", "\n", @@ -233,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:48:00.008858Z", @@ -242,36 +148,7 @@ "shell.execute_reply": "2024-11-01T02:48:01.871573Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/chenyang/miniconda3/envs/AlphaMeemory/lib/python3.11/site-packages/transformers/utils/hub.py:128: FutureWarning: Using `TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 22:41:00 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 3, cache hit rate: 33.33%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-31 22:41:00] INFO: 127.0.0.1:51762 - \"POST /v1/embeddings HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Input IDs embedding (first 10): [0.00829315185546875, 0.0007004737854003906, -0.00809478759765625, -0.0006799697875976562, 0.01438140869140625, -0.00897979736328125, 0.0123748779296875, 0.0020923614501953125, 0.006195068359375, -0.0030498504638671875]" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import json\n", "import os\n", diff --git a/docs/backend/native_api.ipynb b/docs/backend/native_api.ipynb new file mode 100644 index 0000000000..65cbbab183 --- /dev/null +++ b/docs/backend/native_api.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Native Server API\n", + "\n", + "Apart from the OpenAI compatible API, the SGLang Runtime also provides its native server API. We introduce these following API:\n", + "\n", + "- `/generate`\n", + "- `/update_weights`\n", + "- `/get_server_args`\n", + "- `/get_model_info`\n", + "- `/health`\n", + "- `/health_generate`\n", + "- `/flush_cache`\n", + "- `/get_memory_pool_size`\n", + "\n", + "We mainly use `requests` to test these APIs in the following examples. You can also use `curl`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Launch A Server" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sglang.utils import (\n", + " execute_shell_command,\n", + " wait_for_server,\n", + " terminate_process,\n", + " print_highlight,\n", + ")\n", + "import subprocess, json\n", + "\n", + "server_process = execute_shell_command(\n", + "\"\"\"\n", + "python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-1B-Instruct --port=30010\n", + "\"\"\"\n", + ")\n", + "\n", + "wait_for_server(\"http://localhost:30010\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate\n", + "\n", + "Used to generate completion from the model, similar to the `/v1/completions` API in OpenAI. Detailed parameters can be found in the [sampling parameters](https://sgl-project.github.io/references/sampling_params.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "url = \"http://localhost:30010/generate\"\n", + "data = {\"text\": \"List 3 countries and their capitals.\"}\n", + "\n", + "response = requests.post(url, json=data)\n", + "print_highlight(response.text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get Server Args\n", + "\n", + "Used to get the serving args when the server is launched." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"http://localhost:30010/get_server_args\"\n", + "\n", + "response = requests.get(url)\n", + "print_highlight(response.json())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get Model Info\n", + "\n", + "Used to get the model info.\n", + "\n", + "- `model_path`: The path/name of the model.\n", + "- `is_generation`: Whether the model is used as generation model or embedding model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"http://localhost:30010/get_model_info\"\n", + "\n", + "response = requests.get(url)\n", + "response_json = response.json()\n", + "print_highlight(response_json)\n", + "assert response_json[\"model_path\"] == \"meta-llama/Llama-3.2-1B-Instruct\"\n", + "assert response_json[\"is_generation\"] == True\n", + "assert response_json.keys() == {\"model_path\", \"is_generation\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Health and Health Generate\n", + "\n", + "- `/health`: Check the health of the server.\n", + "- `/health_generate`: Check the health of the server by generating one token." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"http://localhost:30010/health_generate\"\n", + "\n", + "response = requests.get(url)\n", + "print_highlight(response.text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"http://localhost:30010/health\"\n", + "\n", + "response = requests.get(url)\n", + "print_highlight(response.text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Flush Cache\n", + "\n", + "Used to flush the radix cache. It will be automatically triggered when the model weights are updated by the `/update_weights` API." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# flush cache\n", + "\n", + "url = \"http://localhost:30010/flush_cache\"\n", + "\n", + "response = requests.post(url)\n", + "print_highlight(response.text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get Memory Pool Size\n", + "\n", + "Get the memory pool size in number of tokens.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get_memory_pool_size\n", + "\n", + "url = \"http://localhost:30010/get_memory_pool_size\"\n", + "\n", + "response = requests.get(url)\n", + "print_highlight(response.text)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Update Weights\n", + "\n", + "Update model weights without restarting the server. Use for continuous evaluation during training. Only applicable for models with the same architecture and parameter size." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# successful update with same architecture and size\n", + "\n", + "url = \"http://localhost:30010/update_weights\"\n", + "data = {\"model_path\": \"meta-llama/Llama-3.2-1B\"}\n", + "\n", + "response = requests.post(url, json=data)\n", + "print_highlight(response.text)\n", + "assert response.json()[\"success\"] == True\n", + "assert response.json()[\"message\"] == \"Succeeded to update model weights.\"\n", + "assert response.json().keys() == {\"success\", \"message\"}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# failed update with different parameter size\n", + "\n", + "url = \"http://localhost:30010/update_weights\"\n", + "data = {\"model_path\": \"meta-llama/Llama-3.2-3B\"}\n", + "\n", + "response = requests.post(url, json=data)\n", + "response_json = response.json()\n", + "print_highlight(response_json)\n", + "assert response_json[\"success\"] == False\n", + "assert response_json[\"message\"] == (\n", + " \"Failed to update weights: The size of tensor a (2048) must match \"\n", + " \"the size of tensor b (3072) at non-singleton dimension 1.\\n\"\n", + " \"Rolling back to original weights.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "terminate_process(server_process)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "AlphaMeemory", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/backend/openai_api_completions.ipynb b/docs/backend/openai_api_completions.ipynb index 6b649a4e43..13ea0acdb8 100644 --- a/docs/backend/openai_api_completions.ipynb +++ b/docs/backend/openai_api_completions.ipynb @@ -38,55 +38,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-11-02 00:06:33.051950: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "2024-11-02 00:06:33.063961: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "2024-11-02 00:06:33.063983: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2024-11-02 00:06:33.581526: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "[2024-11-02 00:06:41] server_args=ServerArgs(model_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='meta-llama/Meta-Llama-3.1-8B-Instruct', chat_template=None, is_embedding=False, host='0.0.0.0', port=30000, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=73322355, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n", - "[2024-11-02 00:06:51 TP0] Init torch distributed begin.\n", - "[2024-11-02 00:06:54 TP0] Load weight begin. avail mem=76.83 GB\n", - "[2024-11-02 00:06:54 TP0] lm_eval is not installed, GPTQ may not be usable\n", - "INFO 11-02 00:06:54 weight_utils.py:243] Using model weights format ['*.safetensors']\n", - "Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00

NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from sglang.utils import (\n", " execute_shell_command,\n", @@ -96,7 +48,7 @@ ")\n", "\n", "server_process = execute_shell_command(\n", - "\"python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --port 30000 --host 0.0.0.0\"\n", + " \"python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --port 30000 --host 0.0.0.0\"\n", ")\n", "\n", "wait_for_server(\"http://localhost:30000\")" @@ -126,29 +78,7 @@ "shell.execute_reply": "2024-11-01T02:45:18.086450Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-11-02 00:08:04 TP0] Prefill batch. #new-seq: 1, #new-token: 48, #cached-token: 1, cache hit rate: 1.79%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-11-02 00:08:04 TP0] Decode batch. #running-req: 1, #token: 82, token usage: 0.00, gen throughput (token/s): 0.72, #queue-req: 0\n", - "[2024-11-02 00:08:04] INFO: 127.0.0.1:51178 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Response: ChatCompletion(id='bb74a7e9fcae4df7af2ee59e25aa75a5', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=None), matched_stop=128009)], created=1730506084, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, completion_tokens_details=None, prompt_tokens_details=None))" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import openai\n", "\n", @@ -189,31 +119,7 @@ "shell.execute_reply": "2024-11-01T02:45:21.192539Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-11-02 00:08:08 TP0] Prefill batch. #new-seq: 1, #new-token: 48, #cached-token: 28, cache hit rate: 21.97%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-11-02 00:08:08 TP0] Decode batch. #running-req: 1, #token: 104, token usage: 0.00, gen throughput (token/s): 9.89, #queue-req: 0\n", - "[2024-11-02 00:08:09 TP0] Decode batch. #running-req: 1, #token: 144, token usage: 0.00, gen throughput (token/s): 132.64, #queue-req: 0\n", - "[2024-11-02 00:08:09 TP0] Decode batch. #running-req: 1, #token: 184, token usage: 0.00, gen throughput (token/s): 132.28, #queue-req: 0\n", - "[2024-11-02 00:08:09] INFO: 127.0.0.1:51178 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Ancient Rome's major achievements include:

1. **Law and Governance**: The Twelve Tables (450 BCE) and the Julian Laws (5th century BCE) established a foundation for Roman law, which influenced modern Western law. The Roman Republic (509-27 BCE) and Empire (27 BCE-476 CE) developed a system of governance that included the concept of citizenship, representation, and checks on power.

2. **Architecture and Engineering**: Romans developed impressive architectural styles, such as the arch, dome, and aqueducts. Iconic structures like the Colosseum, Pantheon, and Roman Forum showcased their engineering prowess.

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "response = client.chat.completions.create(\n", " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", @@ -259,17 +165,7 @@ "shell.execute_reply": "2024-11-01T02:45:21.675050Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-11-02 00:08:19] INFO: 127.0.0.1:44218 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n", - "[2024-11-02 00:08:19 TP0] Prefill batch. #new-seq: 1, #new-token: 15, #cached-token: 25, cache hit rate: 31.40%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "This is only a test." - ] - } - ], + "outputs": [], "source": [ "stream = client.chat.completions.create(\n", " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", @@ -303,30 +199,7 @@ "shell.execute_reply": "2024-11-01T02:45:23.181695Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-11-02 00:08:25 TP0] Prefill batch. #new-seq: 1, #new-token: 8, #cached-token: 1, cache hit rate: 30.39%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-11-02 00:08:25 TP0] Decode batch. #running-req: 1, #token: 24, token usage: 0.00, gen throughput (token/s): 2.45, #queue-req: 0\n", - "[2024-11-02 00:08:25 TP0] Decode batch. #running-req: 1, #token: 64, token usage: 0.00, gen throughput (token/s): 142.10, #queue-req: 0\n", - "[2024-11-02 00:08:26] INFO: 127.0.0.1:37290 - \"POST /v1/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Response: Completion(id='25412696fce14364b40430b5671fc11e', choices=[CompletionChoice(finish_reason='length', index=0, logprobs=None, text=' 1. 2. 3.\\n1. United States - Washington D.C. 2. Japan - Tokyo 3. Australia - Canberra\\nList 3 countries and their capitals. 1. 2. 3.\\n1. China - Beijing 2. Brazil - Bras', matched_stop=None)], created=1730506106, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='text_completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=64, prompt_tokens=9, total_tokens=73, completion_tokens_details=None, prompt_tokens_details=None))" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "response = client.completions.create(\n", " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", @@ -362,62 +235,7 @@ "shell.execute_reply": "2024-11-01T02:45:26.769299Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:23 TP0] Prefill batch. #new-seq: 1, #new-token: 9, #cached-token: 1, cache hit rate: 29.32%, token usage: 0.00, #running-req: 0, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:23 TP0] Decode batch. #running-req: 1, #token: 29, token usage: 0.00, gen throughput (token/s): 40.76, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:24 TP0] Decode batch. #running-req: 1, #token: 69, token usage: 0.00, gen throughput (token/s): 42.13, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:25 TP0] Decode batch. #running-req: 1, #token: 109, token usage: 0.00, gen throughput (token/s): 42.01, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:26 TP0] Decode batch. #running-req: 1, #token: 149, token usage: 0.00, gen throughput (token/s): 41.87, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:26] INFO: 127.0.0.1:37738 - \"POST /v1/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Response: Completion(id='fe384c17aece4a5ca5fb5238dcd1adec', choices=[CompletionChoice(finish_reason='length', index=0, logprobs=None, text=\" This can be a sci-fi story, and you have the ability to create a unique and imaginative universe.\\nIn the depths of space, a lone space explorer named Kaelin Vex navigated through the swirling vortex of the Aurora Nebula. Her ship, the Starweaver, was an extension of herself, its advanced AI system linked directly to her mind. Together, they danced through the cosmos, searching for answers to the mysteries of the universe.\\nKaelin's mission was to uncover the secrets of the ancient alien civilization known as the Architects. Legends spoke of their unparalleled technological prowess and their ability to manipulate reality itself. Many believed they had transcended their physical forms, becoming one with the cosmos.\\nAs Kaelin delved deeper into\", matched_stop=None)], created=1730429126, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='text_completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=150, prompt_tokens=10, total_tokens=160, prompt_tokens_details=None))" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "response = client.completions.create(\n", " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", @@ -463,29 +281,7 @@ "shell.execute_reply": "2024-11-01T02:45:26.793811Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:26] INFO: 127.0.0.1:57182 - \"POST /v1/files HTTP/1.1\" 200 OK\n", - "[2024-10-31 19:45:26] INFO: 127.0.0.1:57182 - \"POST /v1/batches HTTP/1.1\" 200 OK\n", - "[2024-10-31 19:45:26 TP0] Prefill batch. #new-seq: 2, #new-token: 20, #cached-token: 60, cache hit rate: 42.80%, token usage: 0.00, #running-req: 0, #queue-req: 0\n" - ] - }, - { - "data": { - "text/html": [ - "Batch job created with ID: batch_d9af5b49-ad3d-423e-8c30-4aaafa5c18c4" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import json\n", "import time\n", @@ -547,93 +343,7 @@ "shell.execute_reply": "2024-11-01T02:45:29.810041Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:27 TP0] Decode batch. #running-req: 1, #token: 69, token usage: 0.00, gen throughput (token/s): 51.72, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Batch job status: validating...trying again in 3 seconds...\n", - "[2024-10-31 19:45:29] INFO: 127.0.0.1:57182 - \"GET /v1/batches/batch_d9af5b49-ad3d-423e-8c30-4aaafa5c18c4 HTTP/1.1\" 200 OK\n", - "Batch job completed successfully!\n", - "Request counts: BatchRequestCounts(completed=2, failed=0, total=2)\n", - "[2024-10-31 19:45:29] INFO: 127.0.0.1:57182 - \"GET /v1/files/backend_result_file-4ed79bf4-1e07-4fa9-9638-7448aa4e074b/content HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Request request-1:" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Response: {'status_code': 200, 'request_id': 'request-1', 'body': {'id': 'request-1', 'object': 'chat.completion', 'created': 1730429127, 'model': 'meta-llama/Meta-Llama-3.1-8B-Instruct', 'choices': {'index': 0, 'message': {'role': 'assistant', 'content': 'Why do programmers prefer dark mode?\\n\\nBecause light attracts bugs.'}, 'logprobs': None, 'finish_reason': 'stop', 'matched_stop': 128009}, 'usage': {'prompt_tokens': 41, 'completion_tokens': 13, 'total_tokens': 54}, 'system_fingerprint': None}}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Request request-2:" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Response: {'status_code': 200, 'request_id': 'request-2', 'body': {'id': 'request-2', 'object': 'chat.completion', 'created': 1730429127, 'model': 'meta-llama/Meta-Llama-3.1-8B-Instruct', 'choices': {'index': 0, 'message': {'role': 'assistant', 'content': '**What is Python?**\\n\\nPython is a high-level, interpreted programming language that is widely used for various purposes such as web development, scientific computing, data analysis, artificial intelligence, and more. It was created in the late 1980s by'}, 'logprobs': None, 'finish_reason': 'length', 'matched_stop': None}, 'usage': {'prompt_tokens': 39, 'completion_tokens': 50, 'total_tokens': 89}, 'system_fingerprint': None}}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Cleaning up files..." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:29] INFO: 127.0.0.1:57182 - \"DELETE /v1/files/backend_result_file-4ed79bf4-1e07-4fa9-9638-7448aa4e074b HTTP/1.1\" 200 OK\n" - ] - } - ], + "outputs": [], "source": [ "while batch_response.status not in [\"completed\", \"failed\", \"cancelled\"]:\n", " time.sleep(3)\n", @@ -688,287 +398,7 @@ "shell.execute_reply": "2024-11-01T02:45:54.850668Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:29] INFO: 127.0.0.1:57186 - \"POST /v1/files HTTP/1.1\" 200 OK\n", - "[2024-10-31 19:45:29] INFO: 127.0.0.1:57186 - \"POST /v1/batches HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Created batch job with ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Initial status: validating" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:29 TP0] Prefill batch. #new-seq: 27, #new-token: 810, #cached-token: 675, cache hit rate: 45.05%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-10-31 19:45:29 TP0] Prefill batch. #new-seq: 73, #new-token: 2190, #cached-token: 1825, cache hit rate: 45.33%, token usage: 0.00, #running-req: 27, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:30 TP0] Decode batch. #running-req: 100, #token: 5125, token usage: 0.02, gen throughput (token/s): 636.38, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:31 TP0] Decode batch. #running-req: 100, #token: 9125, token usage: 0.04, gen throughput (token/s): 3507.97, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:33 TP0] Decode batch. #running-req: 100, #token: 13125, token usage: 0.06, gen throughput (token/s): 3417.06, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:34 TP0] Decode batch. #running-req: 100, #token: 17125, token usage: 0.08, gen throughput (token/s): 3332.03, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:35 TP0] Decode batch. #running-req: 100, #token: 21125, token usage: 0.10, gen throughput (token/s): 3252.29, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:36 TP0] Decode batch. #running-req: 100, #token: 25125, token usage: 0.12, gen throughput (token/s): 3173.87, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:38 TP0] Decode batch. #running-req: 100, #token: 29125, token usage: 0.13, gen throughput (token/s): 3101.31, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:39 TP0] Decode batch. #running-req: 100, #token: 33125, token usage: 0.15, gen throughput (token/s): 3030.90, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:39] INFO: 127.0.0.1:37782 - \"GET /v1/batches/batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Batch job details (check 1 / 5) // ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 // Status: in_progress // Created at: 1730429129 // Input file ID: backend_input_file-f42b27b5-05ee-4d27-9a37-ff04c3b4a427 // Output file ID: None" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Request counts: Total: 0 // Completed: 0 // Failed: 0" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:40 TP0] Decode batch. #running-req: 100, #token: 37125, token usage: 0.17, gen throughput (token/s): 2961.37, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:42 TP0] Decode batch. #running-req: 100, #token: 41125, token usage: 0.19, gen throughput (token/s): 2899.29, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:42] INFO: 127.0.0.1:37782 - \"GET /v1/batches/batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Batch job details (check 2 / 5) // ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 // Status: in_progress // Created at: 1730429129 // Input file ID: backend_input_file-f42b27b5-05ee-4d27-9a37-ff04c3b4a427 // Output file ID: None" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Request counts: Total: 0 // Completed: 0 // Failed: 0" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:43 TP0] Decode batch. #running-req: 100, #token: 45125, token usage: 0.21, gen throughput (token/s): 2836.50, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:45 TP0] Decode batch. #running-req: 100, #token: 49125, token usage: 0.23, gen throughput (token/s): 2777.80, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:45] INFO: 127.0.0.1:37782 - \"GET /v1/batches/batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Batch job details (check 3 / 5) // ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 // Status: in_progress // Created at: 1730429129 // Input file ID: backend_input_file-f42b27b5-05ee-4d27-9a37-ff04c3b4a427 // Output file ID: None" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Request counts: Total: 0 // Completed: 0 // Failed: 0" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:48] INFO: 127.0.0.1:37782 - \"GET /v1/batches/batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Batch job details (check 4 / 5) // ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 // Status: completed // Created at: 1730429129 // Input file ID: backend_input_file-f42b27b5-05ee-4d27-9a37-ff04c3b4a427 // Output file ID: backend_result_file-dc391511-07f2-4f94-90cb-3ed09bc4b8a3" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Request counts: Total: 100 // Completed: 100 // Failed: 0" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:51] INFO: 127.0.0.1:37782 - \"GET /v1/batches/batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Batch job details (check 5 / 5) // ID: batch_3d1a7f8e-af5a-4a14-8391-1001aadfe1b2 // Status: completed // Created at: 1730429129 // Input file ID: backend_input_file-f42b27b5-05ee-4d27-9a37-ff04c3b4a427 // Output file ID: backend_result_file-dc391511-07f2-4f94-90cb-3ed09bc4b8a3" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Request counts: Total: 100 // Completed: 100 // Failed: 0" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import json\n", "import time\n", @@ -1051,163 +481,7 @@ "shell.execute_reply": "2024-11-01T02:46:07.892310Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:54] INFO: 127.0.0.1:33180 - \"POST /v1/files HTTP/1.1\" 200 OK\n", - "[2024-10-31 19:45:54] INFO: 127.0.0.1:33180 - \"POST /v1/batches HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Created batch job with ID: batch_c30756c3-8c09-4142-9630-9590d6124986" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Initial status: validating" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:54 TP0] Prefill batch. #new-seq: 135, #new-token: 1150, #cached-token: 6275, cache hit rate: 67.38%, token usage: 0.01, #running-req: 0, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:55 TP0] Prefill batch. #new-seq: 274, #new-token: 8192, #cached-token: 6850, cache hit rate: 55.74%, token usage: 0.02, #running-req: 135, #queue-req: 91\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:56 TP0] Prefill batch. #new-seq: 92, #new-token: 2758, #cached-token: 2302, cache hit rate: 54.19%, token usage: 0.06, #running-req: 408, #queue-req: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:45:56 TP0] Decode batch. #running-req: 500, #token: 16025, token usage: 0.07, gen throughput (token/s): 409.21, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:46:00 TP0] Decode batch. #running-req: 500, #token: 36025, token usage: 0.17, gen throughput (token/s): 5777.09, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:46:03 TP0] Decode batch. #running-req: 500, #token: 56025, token usage: 0.26, gen throughput (token/s): 5530.76, #queue-req: 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:46:04] INFO: 127.0.0.1:57728 - \"POST /v1/batches/batch_c30756c3-8c09-4142-9630-9590d6124986/cancel HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Cancellation initiated. Status: cancelling" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:46:07] INFO: 127.0.0.1:57728 - \"GET /v1/batches/batch_c30756c3-8c09-4142-9630-9590d6124986 HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Current status: cancelled" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Batch job successfully cancelled" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-10-31 19:46:07] INFO: 127.0.0.1:57728 - \"DELETE /v1/files/backend_input_file-0fbf83a7-301c-488e-a221-b702e24df6a5 HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "Successfully cleaned up input file" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Successfully deleted local batch_requests.jsonl file" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import json\n", "import time\n", diff --git a/docs/backend/openai_api_vision.ipynb b/docs/backend/openai_api_vision.ipynb index 25742cb6e7..6b006606be 100644 --- a/docs/backend/openai_api_vision.ipynb +++ b/docs/backend/openai_api_vision.ipynb @@ -38,58 +38,7 @@ "cell_type": "code", "execution_count": 13, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-11-02 00:24:10.542705: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "2024-11-02 00:24:10.554725: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "2024-11-02 00:24:10.554758: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2024-11-02 00:24:11.063662: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "[2024-11-02 00:24:19] server_args=ServerArgs(model_path='meta-llama/Llama-3.2-11B-Vision-Instruct', tokenizer_path='meta-llama/Llama-3.2-11B-Vision-Instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='meta-llama/Llama-3.2-11B-Vision-Instruct', chat_template='llama_3_vision', is_embedding=False, host='127.0.0.1', port=30010, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=553831757, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n", - "[2024-11-02 00:24:20] Use chat template for the OpenAI-compatible API server: llama_3_vision\n", - "[2024-11-02 00:24:29 TP0] Automatically turn off --chunked-prefill-size and adjust --mem-fraction-static for multimodal models.\n", - "[2024-11-02 00:24:29 TP0] Init torch distributed begin.\n", - "[2024-11-02 00:24:32 TP0] Load weight begin. avail mem=76.83 GB\n", - "[2024-11-02 00:24:32 TP0] lm_eval is not installed, GPTQ may not be usable\n", - "INFO 11-02 00:24:32 weight_utils.py:243] Using model weights format ['*.safetensors']\n", - "Loading safetensors checkpoint shards: 0% Completed | 0/5 [00:00

NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from sglang.utils import (\n", " execute_shell_command,\n", @@ -99,7 +48,7 @@ ")\n", "\n", "embedding_process = execute_shell_command(\n", - "\"\"\"\n", + " \"\"\"\n", "python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-11B-Vision-Instruct \\\n", " --port=30010 --chat-template=llama_3_vision\n", "\"\"\"\n", @@ -121,44 +70,7 @@ "cell_type": "code", "execution_count": 15, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 485 0 0 100 485 0 2420 --:--:-- --:--:-- --:--:-- 2412" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-11-02 00:26:23 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 6462, cache hit rate: 49.97%, token usage: 0.02, #running-req: 0, #queue-req: 0\n", - "[2024-11-02 00:26:24] INFO: 127.0.0.1:39828 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100 965 100 480 100 485 789 797 --:--:-- --:--:-- --:--:-- 1584\n" - ] - }, - { - "data": { - "text/html": [ - "{\"id\":\"5e9e1c80809f492a926a2634c3d162d0\",\"object\":\"chat.completion\",\"created\":1730507184,\"model\":\"meta-llama/Llama-3.2-11B-Vision-Instruct\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"The image depicts a man ironing clothes on an ironing board that is placed on the back of a yellow taxi cab.\"},\"logprobs\":null,\"finish_reason\":\"stop\",\"matched_stop\":128009}],\"usage\":{\"prompt_tokens\":6463,\"total_tokens\":6489,\"completion_tokens\":26,\"prompt_tokens_details\":null}}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import subprocess\n", "\n", @@ -206,29 +118,7 @@ "cell_type": "code", "execution_count": 16, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-11-02 00:26:33 TP0] Prefill batch. #new-seq: 1, #new-token: 11, #cached-token: 6452, cache hit rate: 66.58%, token usage: 0.02, #running-req: 0, #queue-req: 0\n", - "[2024-11-02 00:26:34 TP0] Decode batch. #running-req: 1, #token: 6477, token usage: 0.02, gen throughput (token/s): 0.77, #queue-req: 0\n", - "[2024-11-02 00:26:34] INFO: 127.0.0.1:43258 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "The image shows a man ironing clothes on the back of a yellow taxi cab." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from openai import OpenAI\n", "\n", @@ -246,7 +136,9 @@ " },\n", " {\n", " \"type\": \"image_url\",\n", - " \"image_url\": {\"url\": \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"},\n", + " \"image_url\": {\n", + " \"url\": \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"\n", + " },\n", " },\n", " ],\n", " }\n", @@ -270,30 +162,7 @@ "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-11-02 00:20:30 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 12894, cache hit rate: 83.27%, token usage: 0.04, #running-req: 0, #queue-req: 0\n", - "[2024-11-02 00:20:30 TP0] Decode batch. #running-req: 1, #token: 12903, token usage: 0.04, gen throughput (token/s): 2.02, #queue-req: 0\n", - "[2024-11-02 00:20:30 TP0] Decode batch. #running-req: 1, #token: 12943, token usage: 0.04, gen throughput (token/s): 105.52, #queue-req: 0\n", - "[2024-11-02 00:20:30] INFO: 127.0.0.1:41386 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "The first image shows a man in a yellow shirt ironing a shirt on the back of a yellow taxi cab, with a red line connecting the two objects. The second image shows a large orange \"S\" and \"G\" on a white background, with a red line connecting them." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from openai import OpenAI\n", "\n", @@ -320,7 +189,7 @@ " {\n", " \"type\": \"text\",\n", " \"text\": \"I have two very different images. They are not related at all. \"\n", - " \"Please describe the first image in one sentence, and then describe the second image in another sentence.\",\n", + " \"Please describe the first image in one sentence, and then describe the second image in another sentence.\",\n", " },\n", " ],\n", " }\n", diff --git a/docs/index.rst b/docs/index.rst index f5468c88f0..48ca25ed67 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -25,6 +25,7 @@ The core features include: backend/openai_api_completions.ipynb backend/openai_api_vision.ipynb + backend/native_api.ipynb backend/backend.md diff --git a/docs/start/send_request.ipynb b/docs/start/send_request.ipynb index 209910185e..9a2a8555b6 100644 --- a/docs/start/send_request.ipynb +++ b/docs/start/send_request.ipynb @@ -36,55 +36,7 @@ "shell.execute_reply": "2024-11-01T02:46:42.809147Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-11-02 00:27:25.383621: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", - "2024-11-02 00:27:25.396224: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", - "2024-11-02 00:27:25.396257: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2024-11-02 00:27:25.922262: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", - "[2024-11-02 00:27:34] server_args=ServerArgs(model_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_path='meta-llama/Meta-Llama-3.1-8B-Instruct', tokenizer_mode='auto', skip_tokenizer_init=False, load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization=None, context_length=None, device='cuda', served_model_name='meta-llama/Meta-Llama-3.1-8B-Instruct', chat_template=None, is_embedding=False, host='0.0.0.0', port=30000, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=8192, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, tp_size=1, stream_interval=1, random_seed=259802610, constrained_json_whitespace_pattern=None, decode_log_interval=40, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, api_key=None, file_storage_pth='SGLang_storage', enable_cache_report=False, watchdog_timeout=600, dp_size=1, load_balance_method='round_robin', dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, lora_paths=None, max_loras_per_batch=8, attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', disable_flashinfer=False, disable_flashinfer_sampling=False, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, disable_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_penalizer=False, disable_nan_detection=False, enable_overlap_schedule=False, enable_mixed_chunk=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=160, torchao_config='', enable_p2p_check=False, triton_attention_reduce_in_fp32=False, num_continuous_decode_steps=1)\n", - "[2024-11-02 00:27:43 TP0] Init torch distributed begin.\n", - "[2024-11-02 00:27:48 TP0] Load weight begin. avail mem=76.83 GB\n", - "[2024-11-02 00:27:48 TP0] lm_eval is not installed, GPTQ may not be usable\n", - "INFO 11-02 00:27:49 weight_utils.py:243] Using model weights format ['*.safetensors']\n", - "Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00

NOTE: Typically, the server runs in a separate terminal.
In this notebook, we run the server and notebook code together, so their outputs are combined.
To improve clarity, the server logs are displayed in the original black color, while the notebook outputs are highlighted in blue.
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from sglang.utils import (\n", " execute_shell_command,\n", @@ -123,45 +75,7 @@ "shell.execute_reply": "2024-11-01T02:46:51.435965Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 278 0 0 100 278 0 1387 --:--:-- --:--:-- --:--:-- 1383" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-11-02 00:28:48 TP0] Prefill batch. #new-seq: 1, #new-token: 11, #cached-token: 42, cache hit rate: 40.19%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-11-02 00:28:48 TP0] Decode batch. #running-req: 1, #token: 75, token usage: 0.00, gen throughput (token/s): 1.46, #queue-req: 0\n", - "[2024-11-02 00:28:49] INFO: 127.0.0.1:53714 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100 871 100 593 100 278 1788 838 --:--:-- --:--:-- --:--:-- 2623\n" - ] - }, - { - "data": { - "text/html": [ - "{\"id\":\"a0714277fab546c5b6d91724aa3e27a3\",\"object\":\"chat.completion\",\"created\":1730507329,\"model\":\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"An LLM, or Large Language Model, is a type of artificial intelligence (AI) designed to process and generate human-like language, often used in applications such as chatbots, virtual assistants, and language translation software.\"},\"logprobs\":null,\"finish_reason\":\"stop\",\"matched_stop\":128009}],\"usage\":{\"prompt_tokens\":53,\"total_tokens\":98,\"completion_tokens\":45,\"prompt_tokens_details\":null}}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import subprocess\n", "\n", @@ -209,29 +123,7 @@ "shell.execute_reply": "2024-11-01T02:46:52.895318Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-11-02 00:03:52 TP0] Prefill batch. #new-seq: 1, #new-token: 20, #cached-token: 29, cache hit rate: 29.13%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-11-02 00:03:52 TP0] Decode batch. #running-req: 1, #token: 65, token usage: 0.00, gen throughput (token/s): 11.33, #queue-req: 0\n", - "[2024-11-02 00:03:53] INFO: 127.0.0.1:57008 - \"POST /v1/chat/completions HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "ChatCompletion(id='a6590143c40f4732a5c57d4c91b43f05', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Here are 3 countries and their capitals:\\n\\n1. **Country:** Japan\\n**Capital:** Tokyo\\n\\n2. **Country:** Australia\\n**Capital:** Canberra\\n\\n3. **Country:** Brazil\\n**Capital:** Brasília', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=None), matched_stop=128009)], created=1730505833, model='meta-llama/Meta-Llama-3.1-8B-Instruct', object='chat.completion', service_tier=None, system_fingerprint=None, usage=CompletionUsage(completion_tokens=46, prompt_tokens=49, total_tokens=95, completion_tokens_details=None, prompt_tokens_details=None))" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import openai\n", "\n", @@ -264,29 +156,7 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2024-11-02 00:05:04 TP0] Prefill batch. #new-seq: 1, #new-token: 1, #cached-token: 5, cache hit rate: 33.04%, token usage: 0.00, #running-req: 0, #queue-req: 0\n", - "[2024-11-02 00:05:04 TP0] Decode batch. #running-req: 1, #token: 26, token usage: 0.00, gen throughput (token/s): 3.10, #queue-req: 0\n", - "[2024-11-02 00:05:04] INFO: 127.0.0.1:60536 - \"POST /generate HTTP/1.1\" 200 OK\n" - ] - }, - { - "data": { - "text/html": [ - "{'text': ' a city of romance, art, fashion, and history. Paris is a must-visit destination for anyone who loves culture, architecture, and cuisine. From the', 'meta_info': {'prompt_tokens': 6, 'completion_tokens': 32, 'completion_tokens_wo_jump_forward': 32, 'cached_tokens': 5, 'finish_reason': {'type': 'length', 'length': 32}, 'id': 'd882513c180d4c5981488257ccab4b9f'}, 'index': 0}" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import requests\n", "\n", From 5a9a4f41c695daa8b46c25abe8200117e68fbab2 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Sat, 2 Nov 2024 00:20:33 -0700 Subject: [PATCH 18/44] Update index.rst (#1885) --- docs/index.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index 48ca25ed67..7d4935a8f2 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -43,10 +43,10 @@ The core features include: references/sampling_params.md references/hyperparameter_tuning.md references/model_support.md - references/contributor_guide.md - references/choices_methods.md references/benchmark_and_profiling.md - references/troubleshooting.md + references/choices_methods.md references/custom_chat_template.md + references/contributor_guide.md + references/troubleshooting.md references/faq.md references/learn_more.md From 3b60558dd79e1f4aeadc34ed5dbae45cb75e5a00 Mon Sep 17 00:00:00 2001 From: Chayenne Date: Sat, 2 Nov 2024 01:02:17 -0700 Subject: [PATCH 19/44] Native api (#1886) Co-authored-by: Chayenne --- .github/workflows/release-docs.yml | 4 + docs/Makefile | 2 +- docs/backend/native_api.ipynb | 4 +- docs/backend/openai_api_completions.ipynb | 36 +++++--- docs/backend/openai_api_vision.ipynb | 73 ++++++++++++++--- ...model.ipynb => openai_embedding_api.ipynb} | 42 ++++++++-- docs/index.rst | 1 + docs/start/send_request.ipynb | 82 ++++++++++++------- 8 files changed, 181 insertions(+), 63 deletions(-) rename docs/backend/{embedding_model.ipynb => openai_embedding_api.ipynb} (87%) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 7abcf5768c..bca6df5ca7 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -9,6 +9,10 @@ on: - 'python/sglang/version.py' workflow_dispatch: +concurrency: + group: execute-notebook-${{ github.ref }} + cancel-in-progress: true + jobs: execute-and-deploy: runs-on: 1-gpu-runner diff --git a/docs/Makefile b/docs/Makefile index b439c4fe22..51446dc384 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -1,7 +1,7 @@ # Minimal makefile for Sphinx documentation # -# You can set these variables from the command line, and also +# You can set these variables from the terminal, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build diff --git a/docs/backend/native_api.ipynb b/docs/backend/native_api.ipynb index 65cbbab183..57ffa14af7 100644 --- a/docs/backend/native_api.ipynb +++ b/docs/backend/native_api.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Native Server API\n", + "# Native API\n", "\n", "Apart from the OpenAI compatible API, the SGLang Runtime also provides its native server API. We introduce these following API:\n", "\n", @@ -254,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ diff --git a/docs/backend/openai_api_completions.ipynb b/docs/backend/openai_api_completions.ipynb index 13ea0acdb8..2f4b988d5d 100644 --- a/docs/backend/openai_api_completions.ipynb +++ b/docs/backend/openai_api_completions.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:45:16.624550Z", @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:45:18.090228Z", @@ -151,12 +151,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Streaming mode is also supported" + "Streaming mode is also supported." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:45:21.195226Z", @@ -190,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:45:21.676813Z", @@ -226,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:45:23.186337Z", @@ -272,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:45:26.772016Z", @@ -334,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:45:26.796422Z", @@ -389,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:45:29.812339Z", @@ -472,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:45:54.854018Z", @@ -567,7 +567,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:46:07.896114Z", @@ -587,6 +587,18 @@ "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" } }, "nbformat": 4, diff --git a/docs/backend/openai_api_vision.ipynb b/docs/backend/openai_api_vision.ipynb index 6b006606be..4707a9e650 100644 --- a/docs/backend/openai_api_vision.ipynb +++ b/docs/backend/openai_api_vision.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -63,21 +63,19 @@ "source": [ "## Using cURL\n", "\n", - "Once the server is up, you can send test requests using curl." + "Once the server is up, you can send test requests using curl or requests." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import subprocess\n", "\n", "curl_command = \"\"\"\n", - "curl http://localhost:30010/v1/chat/completions \\\n", - " -H \"Content-Type: application/json\" \\\n", - " -H \"Authorization: Bearer None\" \\\n", + "curl -s http://localhost:30010/v1/chat/completions \\\n", " -d '{\n", " \"model\": \"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", " \"messages\": [\n", @@ -105,18 +103,61 @@ "print_highlight(response)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using OpenAI Compatible API w/ Requests" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "url = \"http://localhost:30010/v1/chat/completions\"\n", + "\n", + "data = {\n", + " \"model\": \"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", + " \"messages\": [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\n", + " \"type\": \"text\",\n", + " \"text\": \"What’s in this image?\"\n", + " },\n", + " {\n", + " \"type\": \"image_url\",\n", + " \"image_url\": {\n", + " \"url\": \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"\n", + " }\n", + " }\n", + " ]\n", + " }\n", + " ],\n", + " \"max_tokens\": 300\n", + "}\n", + "\n", + "response = requests.post(url, json=data)\n", + "print_highlight(response.text)" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using OpenAI Python Client\n", "\n", - "You can use the OpenAI Python API library to send requests." + "Also, you can use the OpenAI Python API library to send requests." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -160,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -202,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -233,6 +274,18 @@ "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" } }, "nbformat": 4, diff --git a/docs/backend/embedding_model.ipynb b/docs/backend/openai_embedding_api.ipynb similarity index 87% rename from docs/backend/embedding_model.ipynb rename to docs/backend/openai_embedding_api.ipynb index 45928587bb..356a57121c 100644 --- a/docs/backend/embedding_model.ipynb +++ b/docs/backend/openai_embedding_api.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Embedding Model\n", + "# OpenAI APIs - Embedding\n", "\n", "SGLang supports embedding models in the same way as completion models. Here are some example models:\n", "\n", @@ -62,7 +62,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Use Curl" + "## Using cURL" ] }, { @@ -83,8 +83,6 @@ "text = \"Once upon a time\"\n", "\n", "curl_text = f\"\"\"curl -s http://localhost:30010/v1/embeddings \\\n", - " -H \"Content-Type: application/json\" \\\n", - " -H \"Authorization: Bearer None\" \\\n", " -d '{{\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"input\": \"{text}\"}}'\"\"\"\n", "\n", "text_embedding = json.loads(subprocess.check_output(curl_text, shell=True))[\"data\"][0][\n", @@ -98,7 +96,37 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Using OpenAI Compatible API" + "## Using OpenAI Compatible API w/ Requests" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "text = \"Once upon a time\"\n", + "\n", + "response = requests.post(\n", + " \"http://localhost:30010/v1/embeddings\",\n", + " json={\n", + " \"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\",\n", + " \"input\": text\n", + " }\n", + ")\n", + "\n", + "text_embedding = response.json()[\"data\"][0][\"embedding\"]\n", + "\n", + "print_highlight(f\"Text embedding (first 10): {text_embedding[:10]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using OpenAI Python Client" ] }, { @@ -160,8 +188,6 @@ "input_ids = tokenizer.encode(text)\n", "\n", "curl_ids = f\"\"\"curl -s http://localhost:30010/v1/embeddings \\\n", - " -H \"Content-Type: application/json\" \\\n", - " -H \"Authorization: Bearer None\" \\\n", " -d '{{\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"input\": {json.dumps(input_ids)}}}'\"\"\"\n", "\n", "input_ids_embedding = json.loads(subprocess.check_output(curl_ids, shell=True))[\"data\"][\n", @@ -173,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:48:01.875204Z", diff --git a/docs/index.rst b/docs/index.rst index 7d4935a8f2..d73ce8ac14 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -25,6 +25,7 @@ The core features include: backend/openai_api_completions.ipynb backend/openai_api_vision.ipynb + backend/openai_embedding_api.ipynb backend/native_api.ipynb backend/backend.md diff --git a/docs/start/send_request.ipynb b/docs/start/send_request.ipynb index 9a2a8555b6..99c22332ff 100644 --- a/docs/start/send_request.ipynb +++ b/docs/start/send_request.ipynb @@ -22,12 +22,12 @@ "--port 30000 --host 0.0.0.0\n", "```\n", "\n", - "in your terminal and wait for the server to be ready." + "in your terminal and wait for the server to be ready. Once the server is running, you can send test requests using curl or requests. The server implements the [OpenAI-compatible API](https://platform.openai.com/docs/api-reference/chat)." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:46:13.611212Z", @@ -59,14 +59,36 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Send a Request\n", + "## Using cURL\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess, json\n", "\n", - "Once the server is up, you can send test requests using curl. The server implements the [OpenAI-compatible API](https://platform.openai.com/docs/api-reference/)." + "curl_command = \"\"\"\n", + "curl -s http://localhost:30000/v1/chat/completions \\\n", + " -d '{\"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\", \"messages\": [{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}, {\"role\": \"user\", \"content\": \"What is a LLM?\"}]}'\n", + "\"\"\"\n", + "\n", + "response = json.loads(subprocess.check_output(curl_command, shell=True))\n", + "print_highlight(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using OpenAI Compatible API w/ Requests" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:46:42.813656Z", @@ -77,30 +99,20 @@ }, "outputs": [], "source": [ - "import subprocess\n", + "import requests\n", "\n", - "curl_command = \"\"\"\n", - "curl http://localhost:30000/v1/chat/completions \\\\\n", - " -H \"Content-Type: application/json\" \\\\\n", - " -H \"Authorization: Bearer None\" \\\\\n", - " -d '{\n", + "url = \"http://localhost:30000/v1/chat/completions\"\n", + "\n", + "data = {\n", " \"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", " \"messages\": [\n", - " {\n", - " \"role\": \"system\",\n", - " \"content\": \"You are a helpful assistant.\"\n", - " },\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": \"What is an LLM? Tell me in one sentence.\"\n", - " }\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", + " {\"role\": \"user\", \"content\": \"What is a LLM?\"}\n", " ]\n", - " }'\n", - "\"\"\"\n", + "}\n", "\n", - "response = subprocess.check_output(curl_command, shell=True).decode()\n", - "\n", - "print_highlight(response)" + "response = requests.post(url, json=data)\n", + "print_highlight(response.json())" ] }, { @@ -109,12 +121,12 @@ "source": [ "## Using OpenAI Python Client\n", "\n", - "You can use the OpenAI Python API library to send requests." + "You can also use the OpenAI Python API library to send requests." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:46:51.439372Z", @@ -138,7 +150,6 @@ " temperature=0,\n", " max_tokens=64,\n", ")\n", - "\n", "print_highlight(response)" ] }, @@ -148,13 +159,12 @@ "source": [ "## Using Native Generation APIs\n", "\n", - "You can also use the native `/generate` endpoint. It provides more flexiblity.\n", - "An API reference is available at [Sampling Parameters](https://sgl-project.github.io/references/sampling_params.html)." + "You can also use the native `/generate` endpoint with requests, which provides more flexiblity. An API reference is available at [Sampling Parameters](https://sgl-project.github.io/references/sampling_params.html)." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -196,6 +206,18 @@ "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" } }, "nbformat": 4, From 7b394e5f2b26b05363303738792aa841573ebbbf Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Sat, 2 Nov 2024 11:46:00 -0700 Subject: [PATCH 20/44] Fix docs (#1889) --- ..._api.ipynb => openai_api_embeddings.ipynb} | 10 +- docs/backend/openai_api_vision.ipynb | 6 +- docs/index.rst | 2 +- docs/references/sampling_params.md | 249 ------------------ docs/start/install.md | 2 +- docs/start/send_request.ipynb | 83 +++++- 6 files changed, 87 insertions(+), 265 deletions(-) rename docs/backend/{openai_embedding_api.ipynb => openai_api_embeddings.ipynb} (93%) diff --git a/docs/backend/openai_embedding_api.ipynb b/docs/backend/openai_api_embeddings.ipynb similarity index 93% rename from docs/backend/openai_embedding_api.ipynb rename to docs/backend/openai_api_embeddings.ipynb index 356a57121c..41ed3f775b 100644 --- a/docs/backend/openai_embedding_api.ipynb +++ b/docs/backend/openai_api_embeddings.ipynb @@ -6,10 +6,12 @@ "source": [ "# OpenAI APIs - Embedding\n", "\n", - "SGLang supports embedding models in the same way as completion models. Here are some example models:\n", + "SGLang provides OpenAI-compatible APIs to enable a smooth transition from OpenAI services to self-hosted local models.\n", + "A complete reference for the API is available in the [OpenAI API Reference](https://platform.openai.com/docs/guides/embeddings).\n", "\n", - "- [intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct)\n", - "- [Alibaba-NLP/gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct)\n" + "This tutorial covers the embedding APIs for embedding models, such as \n", + "- [intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) \n", + "- [Alibaba-NLP/gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct) \n" ] }, { @@ -96,7 +98,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Using OpenAI Compatible API w/ Requests" + "## Using Python Requests" ] }, { diff --git a/docs/backend/openai_api_vision.ipynb b/docs/backend/openai_api_vision.ipynb index 4707a9e650..4a903c401d 100644 --- a/docs/backend/openai_api_vision.ipynb +++ b/docs/backend/openai_api_vision.ipynb @@ -107,7 +107,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Using OpenAI Compatible API w/ Requests" + "## Using Python Requests" ] }, { @@ -150,9 +150,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Using OpenAI Python Client\n", - "\n", - "Also, you can use the OpenAI Python API library to send requests." + "## Using OpenAI Python Client" ] }, { diff --git a/docs/index.rst b/docs/index.rst index d73ce8ac14..55d3e81be2 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -25,7 +25,7 @@ The core features include: backend/openai_api_completions.ipynb backend/openai_api_vision.ipynb - backend/openai_embedding_api.ipynb + backend/openai_api_embeddings.ipynb backend/native_api.ipynb backend/backend.md diff --git a/docs/references/sampling_params.md b/docs/references/sampling_params.md index 78d5193c25..21850e9351 100644 --- a/docs/references/sampling_params.md +++ b/docs/references/sampling_params.md @@ -177,252 +177,3 @@ print(response.json()) The `image_data` can be a file name, a URL, or a base64 encoded string. See also `python/sglang/srt/utils.py:load_image`. Streaming is supported in a similar manner as [above](#streaming). - -## Performance Implications on Penalties - -While you can apply penalties by supplying relevant `sampling_params`, this comes with some drawbacks. - -These drawbacks will be applied to every single requests in the same batch, as penalizers also applies in batch. - -### Latency - -While we try to compute penalty algorithms through CUDA, it is still additional computation on top of the basic sampling logic. For detailed overhead, we recommend you to run your own benchmarks, but you can find samples below to get a glimpse. - -### Memory - -Since we compute penalty algorithms through CUDA, the logic stores relevant parameters on GPU. This is usually in a scale of `vocab_size` multiplied by `running_requests`. - -You can run your own benchmark with desired parameters on your own hardware to make sure it's not OOMing before using. - -Tuning `--mem-fraction-static` and/or `--max-running-requests` will help. - -### Benchmarks - -All the benchmarks below were ran on NVIDIA H100 SXM5. - -
- -#### Baseline - -Measured at [dc9d06d886151707f97d0b78095df9de262fd3c9](https://github.com/sgl-project/sglang/commit/dc9d06d886151707f97d0b78095df9de262fd3c9). - -``` -$ python3 -m sglang.bench_serving --backend sglang --port 8413 --dataset-name random --num-prompts 3000 --random-input 256 --random-output 512 - -============ Serving Benchmark Result ============ -Backend: sglang -Traffic request rate: inf -Successful requests: 3000 -Benchmark duration (s): 66.11 -Total input tokens: 378633 -Total generated tokens: 775651 -Total generated tokens (retokenized): 775118 -Request throughput (req/s): 45.38 -Input token throughput (tok/s): 5727.04 -Output token throughput (tok/s): 11732.16 -----------------End-to-End Latency---------------- -Mean E2E Latency (ms): 40881.94 -Median E2E Latency (ms): 43967.10 ----------------Time to First Token---------------- -Mean TTFT (ms): 19884.75 -Median TTFT (ms): 14226.56 -P99 TTFT (ms): 47738.97 ------Time per Output Token (excl. 1st token)------ -Mean TPOT (ms): 91.96 -Median TPOT (ms): 90.11 -P99 TPOT (ms): 308.54 ----------------Inter-token Latency---------------- -Mean ITL (ms): 174.54 -Median ITL (ms): 58.56 -P99 ITL (ms): 440.18 -================================================== -``` - -#### All Together - -``` -$ python3 -m sglang.bench_serving --backend sglang --port 8413 --dataset-name random --num-prompts 3000 --random-input 256 --random-output 512 --extra-request-body '{ - "frequency_penalty": 1.1, - "presence_penalty": 1.1, - "repetition_penalty": 0.1, - "min_new_tokens": 5 -}' - -============ Serving Benchmark Result ============ -Backend: sglang -Traffic request rate: inf -Successful requests: 3000 -Benchmark duration (s): 78.35 -Total input tokens: 378633 -Total generated tokens: 775651 -Total generated tokens (retokenized): 774756 -Request throughput (req/s): 38.29 -Input token throughput (tok/s): 4832.86 -Output token throughput (tok/s): 9900.39 -----------------End-to-End Latency---------------- -Mean E2E Latency (ms): 49017.68 -Median E2E Latency (ms): 52825.70 ----------------Time to First Token---------------- -Mean TTFT (ms): 23892.60 -Median TTFT (ms): 18895.47 -P99 TTFT (ms): 57426.01 ------Time per Output Token (excl. 1st token)------ -Mean TPOT (ms): 114.54 -Median TPOT (ms): 107.27 -P99 TPOT (ms): 293.31 ----------------Inter-token Latency---------------- -Mean ITL (ms): 205.68 -Median ITL (ms): 73.97 -P99 ITL (ms): 453.86 -================================================== -``` - -#### Frequency Penalty - -``` -$ python3 -m sglang.bench_serving --backend sglang --port 8413 --dataset-name random --num-prompts 3000 --random-input 256 --random-output 512 --extra-request-body '{ - "frequency_penalty": 1.1 -}' - -============ Serving Benchmark Result ============ -Backend: sglang -Traffic request rate: inf -Successful requests: 3000 -Benchmark duration (s): 72.72 -Total input tokens: 378633 -Total generated tokens: 775651 -Total generated tokens (retokenized): 774955 -Request throughput (req/s): 41.26 -Input token throughput (tok/s): 5206.84 -Output token throughput (tok/s): 10666.51 -----------------End-to-End Latency---------------- -Mean E2E Latency (ms): 45445.56 -Median E2E Latency (ms): 48960.39 ----------------Time to First Token---------------- -Mean TTFT (ms): 22363.16 -Median TTFT (ms): 17125.02 -P99 TTFT (ms): 52920.95 ------Time per Output Token (excl. 1st token)------ -Mean TPOT (ms): 104.71 -Median TPOT (ms): 98.30 -P99 TPOT (ms): 268.06 ----------------Inter-token Latency---------------- -Mean ITL (ms): 191.60 -Median ITL (ms): 67.83 -P99 ITL (ms): 455.46 -================================================== -``` - -#### Presence Penalty - -``` -$ python3 -m sglang.bench_serving --backend sglang --port 8413 --dataset-name random --num-prompts 3000 --random-input 256 --random-output 512 --extra-request-body '{ - "presence_penalty": 1.1 -}' - -============ Serving Benchmark Result ============ -Backend: sglang -Traffic request rate: inf -Successful requests: 3000 -Benchmark duration (s): 72.04 -Total input tokens: 378633 -Total generated tokens: 775651 -Total generated tokens (retokenized): 775210 -Request throughput (req/s): 41.64 -Input token throughput (tok/s): 5255.98 -Output token throughput (tok/s): 10767.18 -----------------End-to-End Latency---------------- -Mean E2E Latency (ms): 44926.61 -Median E2E Latency (ms): 48302.88 ----------------Time to First Token---------------- -Mean TTFT (ms): 22095.39 -Median TTFT (ms): 16740.93 -P99 TTFT (ms): 52554.03 ------Time per Output Token (excl. 1st token)------ -Mean TPOT (ms): 103.54 -Median TPOT (ms): 97.37 -P99 TPOT (ms): 271.86 ----------------Inter-token Latency---------------- -Mean ITL (ms): 189.86 -Median ITL (ms): 68.45 -P99 ITL (ms): 447.11 -================================================== -``` - -#### Repetition Penalty - -``` -$ python3 -m sglang.bench_serving --backend sglang --port 8413 --dataset-name random --num-prompts 3000 --random-input 256 --random-output 512 --extra-request-body '{ - "repetition_penalty": 0.1 -}' - -============ Serving Benchmark Result ============ -Backend: sglang -Traffic request rate: inf -Successful requests: 3000 -Benchmark duration (s): 74.54 -Total input tokens: 378633 -Total generated tokens: 775651 -Total generated tokens (retokenized): 766008 -Request throughput (req/s): 40.24 -Input token throughput (tok/s): 5079.36 -Output token throughput (tok/s): 10405.35 -----------------End-to-End Latency---------------- -Mean E2E Latency (ms): 46530.38 -Median E2E Latency (ms): 50302.65 ----------------Time to First Token---------------- -Mean TTFT (ms): 22603.47 -Median TTFT (ms): 17167.08 -P99 TTFT (ms): 54497.85 ------Time per Output Token (excl. 1st token)------ -Mean TPOT (ms): 117.59 -Median TPOT (ms): 101.79 -P99 TPOT (ms): 320.04 ----------------Inter-token Latency---------------- -Mean ITL (ms): 195.26 -Median ITL (ms): 69.51 -P99 ITL (ms): 433.86 -================================================== -``` - -#### Min New Tokens - -The min new tokens penalizer computes until generation process reaches given `min_new_tokens`. - -Dislike other penalizers, setting this to higher value will have more latency implications. - -``` -$ python3 -m sglang.bench_serving --backend sglang --port 8413 --dataset-name random --num-prompts 3000 --random-input 256 --random-output 512 --extra-request-body '{ - "min_new_tokens": 5 -}' - -============ Serving Benchmark Result ============ -Backend: sglang -Traffic request rate: inf -Successful requests: 3000 -Benchmark duration (s): 66.94 -Total input tokens: 378633 -Total generated tokens: 775651 -Total generated tokens (retokenized): 775220 -Request throughput (req/s): 44.81 -Input token throughput (tok/s): 5656.13 -Output token throughput (tok/s): 11586.90 -----------------End-to-End Latency---------------- -Mean E2E Latency (ms): 41888.55 -Median E2E Latency (ms): 45354.16 ----------------Time to First Token---------------- -Mean TTFT (ms): 20866.91 -Median TTFT (ms): 16219.79 -P99 TTFT (ms): 49263.91 ------Time per Output Token (excl. 1st token)------ -Mean TPOT (ms): 97.05 -Median TPOT (ms): 89.76 -P99 TPOT (ms): 233.50 ----------------Inter-token Latency---------------- -Mean ITL (ms): 179.17 -Median ITL (ms): 55.08 -P99 ITL (ms): 409.12 -================================================== -``` - -
diff --git a/docs/start/install.md b/docs/start/install.md index 1ecd572ce3..c203cc4d0f 100644 --- a/docs/start/install.md +++ b/docs/start/install.md @@ -97,5 +97,5 @@ sky status --endpoint 30000 sglang ## Common Notes - [FlashInfer](https://github.com/flashinfer-ai/flashinfer) is the default attention kernel backend. It only supports sm75 and above. If you encounter any FlashInfer-related issues on sm75+ devices (e.g., T4, A10, A100, L4, L40S, H100), please switch to other kernels by adding `--attention-backend triton --sampling-backend pytorch` and open an issue on GitHub. -- If you only need to use the OpenAI backend, you can avoid installing other dependencies by using `pip install "sglang[openai]"`. +- If you only need to use OpenAI models with the frontend language, you can avoid installing other dependencies by using `pip install "sglang[openai]"`. - The language frontend operates independently of the backend runtime. You can install the frontend locally without needing a GPU, while the backend can be set up on a GPU-enabled machine. To install the frontend, run `pip install sglang`, and for the backend, use `pip install sglang[srt]`. This allows you to build SGLang programs locally and execute them by connecting to the remote backend. diff --git a/docs/start/send_request.ipynb b/docs/start/send_request.ipynb index 99c22332ff..0d4f7474ac 100644 --- a/docs/start/send_request.ipynb +++ b/docs/start/send_request.ipynb @@ -5,7 +5,6 @@ "metadata": {}, "source": [ "# Quick Start: Sending Requests\n", - "\n", "This notebook provides a quick-start guide for using SGLang after installation." ] }, @@ -14,7 +13,6 @@ "metadata": {}, "source": [ "## Launch a server\n", - "\n", "This code block is equivalent to executing \n", "\n", "```bash\n", @@ -83,7 +81,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Using OpenAI Compatible API w/ Requests" + "## Using Requests" ] }, { @@ -119,9 +117,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Using OpenAI Python Client\n", - "\n", - "You can also use the OpenAI Python API library to send requests." + "## Using OpenAI Python Client" ] }, { @@ -153,6 +149,41 @@ "print_highlight(response)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Streaming" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import openai\n", + "\n", + "client = openai.Client(base_url=\"http://127.0.0.1:30000/v1\", api_key=\"None\")\n", + "\n", + "# Use stream=True for streaming responses\n", + "response = client.chat.completions.create(\n", + " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", + " messages=[\n", + " {\"role\": \"system\", \"content\": \"You are a helpful AI assistant\"},\n", + " {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n", + " ],\n", + " temperature=0,\n", + " max_tokens=64,\n", + " stream=True,\n", + ")\n", + "\n", + "# Handle the streaming output\n", + "for chunk in response:\n", + " if chunk.choices[0].delta.content:\n", + " print(chunk.choices[0].delta.content, end='', flush=True)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -184,6 +215,46 @@ "print_highlight(response.json())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Streaming" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import requests, json\n", + "\n", + "response = requests.post(\n", + " \"http://localhost:30000/generate\",\n", + " json={\n", + " \"text\": \"The capital of France is\",\n", + " \"sampling_params\": {\n", + " \"temperature\": 0,\n", + " \"max_new_tokens\": 32,\n", + " },\n", + " \"stream\": True,\n", + " },\n", + " stream=True,\n", + ")\n", + "\n", + "prev = 0\n", + "for chunk in response.iter_lines(decode_unicode=False):\n", + " chunk = chunk.decode(\"utf-8\")\n", + " if chunk and chunk.startswith(\"data:\"):\n", + " if chunk == \"data: [DONE]\":\n", + " break\n", + " data = json.loads(chunk[5:].strip(\"\\n\"))\n", + " output = data[\"text\"]\n", + " print(output[prev:], end=\"\", flush=True)\n", + " prev = len(output)" + ] + }, { "cell_type": "code", "execution_count": 6, From 5a5f18432f574c16cbdb08234a8d1e6efce1bb2a Mon Sep 17 00:00:00 2001 From: Chayenne Date: Sat, 2 Nov 2024 11:57:22 -0700 Subject: [PATCH 21/44] Fix docs ci (#1888) --- docs/backend/native_api.ipynb | 59 +++++++++++++++++++++--- docs/backend/openai_api_embeddings.ipynb | 2 +- docs/start/send_request.ipynb | 2 +- 3 files changed, 54 insertions(+), 9 deletions(-) diff --git a/docs/backend/native_api.ipynb b/docs/backend/native_api.ipynb index 57ffa14af7..798ba248af 100644 --- a/docs/backend/native_api.ipynb +++ b/docs/backend/native_api.ipynb @@ -4,18 +4,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Native API\n", + "# Native APIs\n", "\n", - "Apart from the OpenAI compatible API, the SGLang Runtime also provides its native server API. We introduce these following API:\n", + "Apart from the OpenAI compatible APIs, the SGLang Runtime also provides its native server APIs. We introduce these following APIs:\n", "\n", "- `/generate`\n", - "- `/update_weights`\n", "- `/get_server_args`\n", "- `/get_model_info`\n", "- `/health`\n", "- `/health_generate`\n", "- `/flush_cache`\n", "- `/get_memory_pool_size`\n", + "- `/update_weights`\n", + "- `/encode`\n", "\n", "We mainly use `requests` to test these APIs in the following examples. You can also use `curl`." ] @@ -68,7 +69,7 @@ "import requests\n", "\n", "url = \"http://localhost:30010/generate\"\n", - "data = {\"text\": \"List 3 countries and their capitals.\"}\n", + "data = {\"text\": \"What is the capital of France?\"}\n", "\n", "response = requests.post(url, json=data)\n", "print_highlight(response.text)" @@ -78,7 +79,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Get Server Args\n", + "## Get Server Args\n", "\n", "Used to get the serving args when the server is launched." ] @@ -252,13 +253,57 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Encode\n", + "\n", + "Used to encode text into embeddings. Note that this API is only available for [embedding models](./openai_embedding_api.ipynb) and will raise an error for generation models.\n", + "Therefore, we launch a new server to server an embedding model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "terminate_process(server_process)\n", + "\n", + "embedding_process = execute_shell_command(\n", + " \"\"\"\n", + "python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \\\n", + " --port 30020 --host 0.0.0.0 --is-embedding\n", + "\"\"\"\n", + ")\n", + "\n", + "wait_for_server(\"http://localhost:30020\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# successful encode for embedding model\n", + "\n", + "url = \"http://localhost:30020/encode\"\n", + "data = {\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"text\": \"Once upon a time\"}\n", + "\n", + "response = requests.post(url, json=data)\n", + "response_json = response.json()\n", + "print_highlight(f\"Text embedding (first 10): {response_json['embedding'][:10]}\")" + ] + }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "terminate_process(server_process)" + "terminate_process(embedding_process)" ] } ], diff --git a/docs/backend/openai_api_embeddings.ipynb b/docs/backend/openai_api_embeddings.ipynb index 41ed3f775b..8769d17101 100644 --- a/docs/backend/openai_api_embeddings.ipynb +++ b/docs/backend/openai_api_embeddings.ipynb @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:48:01.875204Z", diff --git a/docs/start/send_request.ipynb b/docs/start/send_request.ipynb index 0d4f7474ac..df2187ca01 100644 --- a/docs/start/send_request.ipynb +++ b/docs/start/send_request.ipynb @@ -81,7 +81,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Using Requests" + "## Using Python Requests" ] }, { From be7986e00544a28832841c916c07793173fd512c Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Sat, 2 Nov 2024 13:26:32 -0700 Subject: [PATCH 22/44] Fix docs (#1890) --- docs/backend/backend.md | 2 +- docs/backend/native_api.ipynb | 21 ++++++++------------- docs/backend/openai_api_embeddings.ipynb | 14 +++++++------- docs/backend/openai_api_vision.ipynb | 14 +++++++------- docs/references/hyperparameter_tuning.md | 2 +- docs/references/troubleshooting.md | 15 ++++++++------- docs/start/send_request.ipynb | 7 ++----- 7 files changed, 34 insertions(+), 41 deletions(-) diff --git a/docs/backend/backend.md b/docs/backend/backend.md index 5466951787..5913568f86 100644 --- a/docs/backend/backend.md +++ b/docs/backend/backend.md @@ -127,7 +127,7 @@ You can view the full example [here](https://github.com/sgl-project/sglang/tree/ ## Supported Models **Generative Models** -- Llama / Llama 2 / Llama 3 / Llama 3.1 +- Llama / Llama 2 / Llama 3 / Llama 3.1 / Llama 3.2 - Mistral / Mixtral / Mistral NeMo - Gemma / Gemma 2 - Qwen / Qwen 2 / Qwen 2 MoE / Qwen 2 VL diff --git a/docs/backend/native_api.ipynb b/docs/backend/native_api.ipynb index 798ba248af..bbf9be9e9d 100644 --- a/docs/backend/native_api.ipynb +++ b/docs/backend/native_api.ipynb @@ -5,7 +5,6 @@ "metadata": {}, "source": [ "# Native APIs\n", - "\n", "Apart from the OpenAI compatible APIs, the SGLang Runtime also provides its native server APIs. We introduce these following APIs:\n", "\n", "- `/generate`\n", @@ -40,7 +39,6 @@ " terminate_process,\n", " print_highlight,\n", ")\n", - "import subprocess, json\n", "\n", "server_process = execute_shell_command(\n", "\"\"\"\n", @@ -56,8 +54,7 @@ "metadata": {}, "source": [ "## Generate\n", - "\n", - "Used to generate completion from the model, similar to the `/v1/completions` API in OpenAI. Detailed parameters can be found in the [sampling parameters](https://sgl-project.github.io/references/sampling_params.html)." + "Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](https://sgl-project.github.io/references/sampling_params.html)." ] }, { @@ -72,7 +69,7 @@ "data = {\"text\": \"What is the capital of France?\"}\n", "\n", "response = requests.post(url, json=data)\n", - "print_highlight(response.text)" + "print_highlight(response.json())" ] }, { @@ -80,8 +77,7 @@ "metadata": {}, "source": [ "## Get Server Args\n", - "\n", - "Used to get the serving args when the server is launched." + "Get the arguments of a server." ] }, { @@ -102,7 +98,7 @@ "source": [ "## Get Model Info\n", "\n", - "Used to get the model info.\n", + "Get the information of the model.\n", "\n", "- `model_path`: The path/name of the model.\n", "- `is_generation`: Whether the model is used as generation model or embedding model." @@ -120,7 +116,7 @@ "response_json = response.json()\n", "print_highlight(response_json)\n", "assert response_json[\"model_path\"] == \"meta-llama/Llama-3.2-1B-Instruct\"\n", - "assert response_json[\"is_generation\"] == True\n", + "assert response_json[\"is_generation\"] is True\n", "assert response_json.keys() == {\"model_path\", \"is_generation\"}" ] }, @@ -128,8 +124,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Health and Health Generate\n", - "\n", + "## Health Check\n", "- `/health`: Check the health of the server.\n", "- `/health_generate`: Check the health of the server by generating one token." ] @@ -164,7 +159,7 @@ "source": [ "## Flush Cache\n", "\n", - "Used to flush the radix cache. It will be automatically triggered when the model weights are updated by the `/update_weights` API." + "Flush the radix cache. It will be automatically triggered when the model weights are updated by the `/update_weights` API." ] }, { @@ -259,7 +254,7 @@ "source": [ "## Encode\n", "\n", - "Used to encode text into embeddings. Note that this API is only available for [embedding models](./openai_embedding_api.ipynb) and will raise an error for generation models.\n", + "Encode text into embeddings. Note that this API is only available for [embedding models](./openai_embedding_api.ipynb) and will raise an error for generation models.\n", "Therefore, we launch a new server to server an embedding model.\n" ] }, diff --git a/docs/backend/openai_api_embeddings.ipynb b/docs/backend/openai_api_embeddings.ipynb index 8769d17101..0a40b0a0d2 100644 --- a/docs/backend/openai_api_embeddings.ipynb +++ b/docs/backend/openai_api_embeddings.ipynb @@ -24,7 +24,7 @@ "\n", "```bash\n", "python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \\\n", - " --port 30010 --host 0.0.0.0 --is-embedding\n", + " --port 30000 --host 0.0.0.0 --is-embedding\n", "```\n", "\n", "Remember to add `--is-embedding` to the command." @@ -53,11 +53,11 @@ "embedding_process = execute_shell_command(\n", " \"\"\"\n", "python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct \\\n", - " --port 30010 --host 0.0.0.0 --is-embedding\n", + " --port 30000 --host 0.0.0.0 --is-embedding\n", "\"\"\"\n", ")\n", "\n", - "wait_for_server(\"http://localhost:30010\")" + "wait_for_server(\"http://localhost:30000\")" ] }, { @@ -84,7 +84,7 @@ "\n", "text = \"Once upon a time\"\n", "\n", - "curl_text = f\"\"\"curl -s http://localhost:30010/v1/embeddings \\\n", + "curl_text = f\"\"\"curl -s http://localhost:30000/v1/embeddings \\\n", " -d '{{\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"input\": \"{text}\"}}'\"\"\"\n", "\n", "text_embedding = json.loads(subprocess.check_output(curl_text, shell=True))[\"data\"][0][\n", @@ -112,7 +112,7 @@ "text = \"Once upon a time\"\n", "\n", "response = requests.post(\n", - " \"http://localhost:30010/v1/embeddings\",\n", + " \"http://localhost:30000/v1/embeddings\",\n", " json={\n", " \"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\",\n", " \"input\": text\n", @@ -146,7 +146,7 @@ "source": [ "import openai\n", "\n", - "client = openai.Client(base_url=\"http://127.0.0.1:30010/v1\", api_key=\"None\")\n", + "client = openai.Client(base_url=\"http://127.0.0.1:30000/v1\", api_key=\"None\")\n", "\n", "# Text embedding example\n", "response = client.embeddings.create(\n", @@ -189,7 +189,7 @@ "tokenizer = AutoTokenizer.from_pretrained(\"Alibaba-NLP/gte-Qwen2-7B-instruct\")\n", "input_ids = tokenizer.encode(text)\n", "\n", - "curl_ids = f\"\"\"curl -s http://localhost:30010/v1/embeddings \\\n", + "curl_ids = f\"\"\"curl -s http://localhost:30000/v1/embeddings \\\n", " -d '{{\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"input\": {json.dumps(input_ids)}}}'\"\"\"\n", "\n", "input_ids_embedding = json.loads(subprocess.check_output(curl_ids, shell=True))[\"data\"][\n", diff --git a/docs/backend/openai_api_vision.ipynb b/docs/backend/openai_api_vision.ipynb index 4a903c401d..ecddf6c30e 100644 --- a/docs/backend/openai_api_vision.ipynb +++ b/docs/backend/openai_api_vision.ipynb @@ -26,7 +26,7 @@ "\n", "```bash\n", "python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-11B-Vision-Instruct \\\n", - " --port 30010 --chat-template llama_3_vision\n", + " --port 30000 --chat-template llama_3_vision\n", "```\n", "in your terminal and wait for the server to be ready.\n", "\n", @@ -50,11 +50,11 @@ "embedding_process = execute_shell_command(\n", " \"\"\"\n", "python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-11B-Vision-Instruct \\\n", - " --port=30010 --chat-template=llama_3_vision\n", + " --port=30000 --chat-template=llama_3_vision\n", "\"\"\"\n", ")\n", "\n", - "wait_for_server(\"http://localhost:30010\")" + "wait_for_server(\"http://localhost:30000\")" ] }, { @@ -75,7 +75,7 @@ "import subprocess\n", "\n", "curl_command = \"\"\"\n", - "curl -s http://localhost:30010/v1/chat/completions \\\n", + "curl -s http://localhost:30000/v1/chat/completions \\\n", " -d '{\n", " \"model\": \"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", " \"messages\": [\n", @@ -118,7 +118,7 @@ "source": [ "import requests\n", "\n", - "url = \"http://localhost:30010/v1/chat/completions\"\n", + "url = \"http://localhost:30000/v1/chat/completions\"\n", "\n", "data = {\n", " \"model\": \"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", @@ -161,7 +161,7 @@ "source": [ "from openai import OpenAI\n", "\n", - "client = OpenAI(base_url=\"http://localhost:30010/v1\", api_key=\"None\")\n", + "client = OpenAI(base_url=\"http://localhost:30000/v1\", api_key=\"None\")\n", "\n", "response = client.chat.completions.create(\n", " model=\"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", @@ -205,7 +205,7 @@ "source": [ "from openai import OpenAI\n", "\n", - "client = OpenAI(base_url=\"http://localhost:30010/v1\", api_key=\"None\")\n", + "client = OpenAI(base_url=\"http://localhost:30000/v1\", api_key=\"None\")\n", "\n", "response = client.chat.completions.create(\n", " model=\"meta-llama/Llama-3.2-11B-Vision-Instruct\",\n", diff --git a/docs/references/hyperparameter_tuning.md b/docs/references/hyperparameter_tuning.md index 96eb5b4f89..32759cadc5 100644 --- a/docs/references/hyperparameter_tuning.md +++ b/docs/references/hyperparameter_tuning.md @@ -25,7 +25,7 @@ If you see `decode out of memory happened` occasionally but not frequently, it i Data parallelism is better for throughput. When there is enough GPU memory, always favor data parallelism for throughput. ### Avoid out-of-memory by Tuning `--chunked-prefill-size`, `--mem-fraction-static`, `--max-running-requests` -If you see out of memory (OOM) errors, you can decrease these parameters. +If you see out of memory (OOM) errors, you can try to tune the following parameters. If OOM happens during prefill, try to decrease `--chunked-prefill-size` to `4096` or `2048`. If OOM happens during decoding, try to decrease `--max-running-requests`. You can also try to decrease `--mem-fraction-static`, which reduces the memory usage of the KV cache memory pool and helps both prefill and decoding. diff --git a/docs/references/troubleshooting.md b/docs/references/troubleshooting.md index 02793c959f..19eaa2879b 100644 --- a/docs/references/troubleshooting.md +++ b/docs/references/troubleshooting.md @@ -2,12 +2,13 @@ This page lists some common errors and tips for fixing them. +## CUDA out of memory +If you see out of memory (OOM) errors, you can try to tune the following parameters. +If OOM happens during prefill, try to decrease `--chunked-prefill-size` to `4096` or `2048`. +If OOM happens during decoding, try to decrease `--max-running-requests`. +You can also try to decrease `--mem-fraction-static`, which reduces the memory usage of the KV cache memory pool and helps both prefill and decoding. + ## CUDA error: an illegal memory access was encountered This error may be due to kernel errors or out-of-memory issues. -- If it is a kernel error, it is not easy to fix. -- If it is out-of-memory, sometimes it will report this error instead of "Out-of-memory." In this case, try setting a smaller value for `--mem-fraction-static`. The default value of `--mem-fraction-static` is around 0.8 - 0.9. - -## The server hangs -If the server hangs, try disabling some optimizations when launching the server. -- Add `--disable-cuda-graph`. -- Add `--sampling-backend pytorch`. +- If it is a kernel error, it is not easy to fix. Please file an issue on the GitHub. +- If it is out-of-memory, sometimes it will report this error instead of "Out-of-memory." Please refer to the above seciton to avoid the OOM. diff --git a/docs/start/send_request.ipynb b/docs/start/send_request.ipynb index df2187ca01..0b5c059725 100644 --- a/docs/start/send_request.ipynb +++ b/docs/start/send_request.ipynb @@ -70,7 +70,7 @@ "\n", "curl_command = \"\"\"\n", "curl -s http://localhost:30000/v1/chat/completions \\\n", - " -d '{\"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\", \"messages\": [{\"role\": \"system\", \"content\": \"You are a helpful assistant.\"}, {\"role\": \"user\", \"content\": \"What is a LLM?\"}]}'\n", + " -d '{\"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of France?\"}]}'\n", "\"\"\"\n", "\n", "response = json.loads(subprocess.check_output(curl_command, shell=True))\n", @@ -104,8 +104,7 @@ "data = {\n", " \"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", " \"messages\": [\n", - " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", - " {\"role\": \"user\", \"content\": \"What is a LLM?\"}\n", + " {\"role\": \"user\", \"content\": \"What is the capital of France?\"}\n", " ]\n", "}\n", "\n", @@ -140,7 +139,6 @@ "response = client.chat.completions.create(\n", " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", " messages=[\n", - " {\"role\": \"system\", \"content\": \"You are a helpful AI assistant\"},\n", " {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n", " ],\n", " temperature=0,\n", @@ -170,7 +168,6 @@ "response = client.chat.completions.create(\n", " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", " messages=[\n", - " {\"role\": \"system\", \"content\": \"You are a helpful AI assistant\"},\n", " {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n", " ],\n", " temperature=0,\n", From f4cd8040732f348b7c55e432ae772b6ea70520db Mon Sep 17 00:00:00 2001 From: Chayenne Date: Sat, 2 Nov 2024 19:08:49 -0700 Subject: [PATCH 23/44] Fix ci and link error (#1892) Co-authored-by: Chayenne --- .github/workflows/release-docs.yml | 2 +- docs/backend/native_api.ipynb | 6 ++-- docs/conf.py | 52 +++++++++++++++++++++++++++++- docs/start/send_request.ipynb | 2 +- 4 files changed, 56 insertions(+), 6 deletions(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index bca6df5ca7..d84cbce62a 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -10,7 +10,7 @@ on: workflow_dispatch: concurrency: - group: execute-notebook-${{ github.ref }} + group: release-docs-${{ github.ref }} cancel-in-progress: true jobs: diff --git a/docs/backend/native_api.ipynb b/docs/backend/native_api.ipynb index bbf9be9e9d..775917c003 100644 --- a/docs/backend/native_api.ipynb +++ b/docs/backend/native_api.ipynb @@ -54,7 +54,7 @@ "metadata": {}, "source": [ "## Generate\n", - "Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](https://sgl-project.github.io/references/sampling_params.html)." + "Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](../references/sampling_params.html)." ] }, { @@ -254,8 +254,8 @@ "source": [ "## Encode\n", "\n", - "Encode text into embeddings. Note that this API is only available for [embedding models](./openai_embedding_api.ipynb) and will raise an error for generation models.\n", - "Therefore, we launch a new server to server an embedding model.\n" + "Encode text into embeddings. Note that this API is only available for [embedding models](openai_api_embeddings.html#openai-apis-embedding) and will raise an error for generation models.\n", + "Therefore, we launch a new server to server an embedding model." ] }, { diff --git a/docs/conf.py b/docs/conf.py index 5bad020387..f4d60f4dc7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -28,10 +28,60 @@ "sphinxcontrib.mermaid", "nbsphinx", "sphinx.ext.mathjax", - "sphinx.ext.autodoc", ] +nbsphinx_allow_errors = True +nbsphinx_execute = 'never' + autosectionlabel_prefix_document = True +nbsphinx_allow_directives = True + + +myst_enable_extensions = [ + "dollarmath", + "amsmath", + "deflist", + "colon_fence", + "html_image", + "linkify", + "substitution", +] + +myst_heading_anchors = 3 + +nbsphinx_kernel_name = 'python3' +nbsphinx_execute_arguments = [ + "--InlineBackend.figure_formats={'svg', 'pdf'}", + "--InlineBackend.rc={'figure.dpi': 96}", +] + + +nb_render_priority = { + "html": ( + "application/vnd.jupyter.widget-view+json", + "application/javascript", + "text/html", + "image/svg+xml", + "image/png", + "image/jpeg", + "text/markdown", + "text/latex", + "text/plain", + ) +} + +myst_enable_extensions = [ + "dollarmath", + "amsmath", + "deflist", + "colon_fence", + "html_image", + "linkify", + "substitution", +] + +myst_heading_anchors = 3 +myst_ref_domains = ["std", "py"] templates_path = ["_templates"] diff --git a/docs/start/send_request.ipynb b/docs/start/send_request.ipynb index 0b5c059725..2323368ee4 100644 --- a/docs/start/send_request.ipynb +++ b/docs/start/send_request.ipynb @@ -20,7 +20,7 @@ "--port 30000 --host 0.0.0.0\n", "```\n", "\n", - "in your terminal and wait for the server to be ready. Once the server is running, you can send test requests using curl or requests. The server implements the [OpenAI-compatible API](https://platform.openai.com/docs/api-reference/chat)." + "in your terminal and wait for the server to be ready. Once the server is running, you can send test requests using curl or requests. The server implements the [OpenAI-compatible APIs](https://platform.openai.com/docs/api-reference/chat)." ] }, { From 908dd7f9aae52a9c961c836d99e46ba6681fee42 Mon Sep 17 00:00:00 2001 From: Chayenne Date: Sat, 2 Nov 2024 22:03:38 -0700 Subject: [PATCH 24/44] Add engine api (#1894) --- docs/backend/native_api.ipynb | 2 +- docs/backend/offline_engine_api.ipynb | 210 +++++++++++++++++++++++ docs/backend/openai_api_embeddings.ipynb | 5 +- docs/backend/openai_api_vision.ipynb | 13 +- docs/index.rst | 1 + 5 files changed, 218 insertions(+), 13 deletions(-) create mode 100644 docs/backend/offline_engine_api.ipynb diff --git a/docs/backend/native_api.ipynb b/docs/backend/native_api.ipynb index 775917c003..39cd12cd31 100644 --- a/docs/backend/native_api.ipynb +++ b/docs/backend/native_api.ipynb @@ -41,7 +41,7 @@ ")\n", "\n", "server_process = execute_shell_command(\n", - "\"\"\"\n", + " \"\"\"\n", "python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-1B-Instruct --port=30010\n", "\"\"\"\n", ")\n", diff --git a/docs/backend/offline_engine_api.ipynb b/docs/backend/offline_engine_api.ipynb new file mode 100644 index 0000000000..63a175ffa9 --- /dev/null +++ b/docs/backend/offline_engine_api.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Offline Engine API\n", + "\n", + "SGLang provides a direct inference engine without the need for an HTTP server, especially for use cases where additional HTTP server adds unnecessary complexity or overhead. Here are two general use cases:\n", + "\n", + "- Offline Batch Inference\n", + "- Custom Server on Top of the Engine\n", + "\n", + "This document focuses on the offline batch inference, demonstrating four different inference modes:\n", + "\n", + "- Non-streaming synchronous generation\n", + "- Streaming synchronous generation\n", + "- Non-streaming asynchronous generation\n", + "- Streaming asynchronous generation\n", + "\n", + "Additionally, you can easily build a custom server on top of the SGLang offline engine. A detailed example working in a python script can be found in [custom_server](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/custom_server.py)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Offline Batch Inference\n", + "\n", + "SGLang offline engine supports batch inference with efficient scheduling to prevent OOM errors for large batches. For details on this cache-aware scheduling algorithm, see our [paper](https://arxiv.org/pdf/2312.07104)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# launch the offline engine\n", + "\n", + "import sglang as sgl\n", + "from sglang.utils import print_highlight\n", + "import asyncio\n", + "\n", + "llm = sgl.Engine(model_path=\"meta-llama/Meta-Llama-3.1-8B-Instruct\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Non-streaming Synchronous Generation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prompts = [\n", + " \"Hello, my name is\",\n", + " \"The president of the United States is\",\n", + " \"The capital of France is\",\n", + " \"The future of AI is\",\n", + "]\n", + "\n", + "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n", + "\n", + "outputs = llm.generate(prompts, sampling_params)\n", + "for prompt, output in zip(prompts, outputs):\n", + " print_highlight(\"===============================\")\n", + " print_highlight(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Streaming Synchronous Generation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prompts = [\n", + " \"Hello, my name is\",\n", + " \"The capital of France is\",\n", + " \"The future of AI is\",\n", + "]\n", + "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n", + "\n", + "print_highlight(\"\\n=== Testing synchronous streaming generation ===\")\n", + "\n", + "for prompt in prompts:\n", + " print_highlight(f\"\\nPrompt: {prompt}\")\n", + " print(\"Generated text: \", end=\"\", flush=True)\n", + "\n", + " for chunk in llm.generate(prompt, sampling_params, stream=True):\n", + " print(chunk[\"text\"], end=\"\", flush=True)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Non-streaming Asynchronous Generation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prompts = [\n", + " \"Hello, my name is\",\n", + " \"The capital of France is\",\n", + " \"The future of AI is\",\n", + "]\n", + "\n", + "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n", + "\n", + "print_highlight(\"\\n=== Testing asynchronous batch generation ===\")\n", + "\n", + "\n", + "async def main():\n", + " outputs = await llm.async_generate(prompts, sampling_params)\n", + "\n", + " for prompt, output in zip(prompts, outputs):\n", + " print_highlight(f\"\\nPrompt: {prompt}\")\n", + " print_highlight(f\"Generated text: {output['text']}\")\n", + "\n", + "\n", + "asyncio.run(main())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Streaming Asynchronous Generation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "prompts = [\n", + " \"Hello, my name is\",\n", + " \"The capital of France is\",\n", + " \"The future of AI is\",\n", + "]\n", + "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n", + "\n", + "print_highlight(\"\\n=== Testing asynchronous streaming generation ===\")\n", + "\n", + "\n", + "async def main():\n", + " for prompt in prompts:\n", + " print_highlight(f\"\\nPrompt: {prompt}\")\n", + " print(\"Generated text: \", end=\"\", flush=True)\n", + "\n", + " generator = await llm.async_generate(prompt, sampling_params, stream=True)\n", + " async for chunk in generator:\n", + " print(chunk[\"text\"], end=\"\", flush=True)\n", + " print()\n", + "\n", + "\n", + "asyncio.run(main())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llm.shutdown()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "AlphaMeemory", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/backend/openai_api_embeddings.ipynb b/docs/backend/openai_api_embeddings.ipynb index 0a40b0a0d2..54b48d60ce 100644 --- a/docs/backend/openai_api_embeddings.ipynb +++ b/docs/backend/openai_api_embeddings.ipynb @@ -113,10 +113,7 @@ "\n", "response = requests.post(\n", " \"http://localhost:30000/v1/embeddings\",\n", - " json={\n", - " \"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\",\n", - " \"input\": text\n", - " }\n", + " json={\"model\": \"Alibaba-NLP/gte-Qwen2-7B-instruct\", \"input\": text},\n", ")\n", "\n", "text_embedding = response.json()[\"data\"][0][\"embedding\"]\n", diff --git a/docs/backend/openai_api_vision.ipynb b/docs/backend/openai_api_vision.ipynb index ecddf6c30e..eb06e55ed4 100644 --- a/docs/backend/openai_api_vision.ipynb +++ b/docs/backend/openai_api_vision.ipynb @@ -126,20 +126,17 @@ " {\n", " \"role\": \"user\",\n", " \"content\": [\n", - " {\n", - " \"type\": \"text\",\n", - " \"text\": \"What’s in this image?\"\n", - " },\n", + " {\"type\": \"text\", \"text\": \"What’s in this image?\"},\n", " {\n", " \"type\": \"image_url\",\n", " \"image_url\": {\n", " \"url\": \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"\n", - " }\n", - " }\n", - " ]\n", + " },\n", + " },\n", + " ],\n", " }\n", " ],\n", - " \"max_tokens\": 300\n", + " \"max_tokens\": 300,\n", "}\n", "\n", "response = requests.post(url, json=data)\n", diff --git a/docs/index.rst b/docs/index.rst index 55d3e81be2..1f83acfb46 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -27,6 +27,7 @@ The core features include: backend/openai_api_vision.ipynb backend/openai_api_embeddings.ipynb backend/native_api.ipynb + backend/offline_engine_api.ipynb backend/backend.md From 6aed0445ed5182acc6309c1a80c743dc33ecb837 Mon Sep 17 00:00:00 2001 From: Chayenne Date: Sun, 3 Nov 2024 00:19:12 -0700 Subject: [PATCH 25/44] turn off log (#1895) --- python/sglang/srt/server.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/python/sglang/srt/server.py b/python/sglang/srt/server.py index 81d86f5dcb..6c46551c1a 100644 --- a/python/sglang/srt/server.py +++ b/python/sglang/srt/server.py @@ -737,6 +737,12 @@ def __init__(self, *args, **kwargs): # before python program terminates, call shutdown implicitly. Therefore, users don't have to explicitly call .shutdown() atexit.register(self.shutdown) + + # runtime server default log level is log + # offline engine works in scripts, so we set it to error + + if 'log_level' not in kwargs: + kwargs['log_level'] = 'error' server_args = ServerArgs(*args, **kwargs) launch_engine(server_args=server_args) From efbc116a0f81e7c3f09f45b0720152aa5b91dc0d Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Sun, 3 Nov 2024 01:45:20 -0700 Subject: [PATCH 26/44] Do not use longest prefix matching when #queue-req is large (#1896) --- python/sglang/srt/managers/schedule_policy.py | 20 ++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/python/sglang/srt/managers/schedule_policy.py b/python/sglang/srt/managers/schedule_policy.py index 6ea6ff194d..2bfdffc42c 100644 --- a/python/sglang/srt/managers/schedule_policy.py +++ b/python/sglang/srt/managers/schedule_policy.py @@ -45,9 +45,15 @@ def __init__(self, policy: str, tree_cache: BasePrefixCache): self.tree_cache = tree_cache def calc_priority(self, waiting_queue: List[Req]): + if len(waiting_queue) > 128 and self.policy == "lpm": + # Turn off the expensive prefix matching and sorting when the #queue is large. + policy = "fcfs" + else: + policy = self.policy + # Compute matched prefix length prefix_computed = False - if self.policy == "lpm" or self.policy == "dfs-weight": + if policy == "lpm" or policy == "dfs-weight": for r in waiting_queue: # NOTE: the prefix_indices must always be aligned with last_node r.prefix_indices, r.last_node = self.tree_cache.match_prefix( @@ -56,18 +62,18 @@ def calc_priority(self, waiting_queue: List[Req]): prefix_computed = True - if self.policy == "lpm": + if policy == "lpm": # Longest Prefix Match waiting_queue.sort(key=lambda x: -len(x.prefix_indices)) - elif self.policy == "fcfs": + elif policy == "fcfs": # first come first serve pass - elif self.policy == "lof": + elif policy == "lof": # longest output first waiting_queue.sort(key=lambda x: -x.sampling_params.max_new_tokens) - elif self.policy == "random": + elif policy == "random": random.shuffle(waiting_queue) - elif self.policy == "dfs-weight": + elif policy == "dfs-weight": last_node_to_reqs = defaultdict(list) for req in waiting_queue: last_node_to_reqs[req.last_node].append(req) @@ -85,7 +91,7 @@ def calc_priority(self, waiting_queue: List[Req]): waiting_queue, ) else: - raise ValueError(f"Unknown schedule_policy: {self.policy}") + raise ValueError(f"Unknown schedule_policy: {policy=}") return prefix_computed From 838dcda162e465b2e84f5b33434e55c1df8f6942 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Sun, 3 Nov 2024 03:52:38 -0800 Subject: [PATCH 27/44] Simplify tokenizer manager (#1899) --- docs/references/custom_chat_template.md | 13 +++- python/sglang/srt/managers/io_struct.py | 16 +++-- .../sglang/srt/managers/tokenizer_manager.py | 70 +++++++------------ 3 files changed, 50 insertions(+), 49 deletions(-) diff --git a/docs/references/custom_chat_template.md b/docs/references/custom_chat_template.md index 64b33a0a42..2803abc012 100644 --- a/docs/references/custom_chat_template.md +++ b/docs/references/custom_chat_template.md @@ -11,8 +11,10 @@ If needed, you can also override the chat template when launching the server: python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template llama-2 ``` -If the chat template you are looking for is missing, you are welcome to contribute it. -Meanwhile, you can also temporarily register your chat template as follows: +If the chat template you are looking for is missing, you are welcome to contribute it or load it from a file. + +## JSON Format +You can load the JSON format, which is defined by `conversation.py`. ```json { @@ -28,4 +30,11 @@ Meanwhile, you can also temporarily register your chat template as follows: ``` python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template ./my_model_template.json +``` + +## Jinja Format +You can also use the Jinja template format, defined by Hugging Face transformers https://huggingface.co/docs/transformers/main/en/chat_templating + +``` +python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template ./my_model_template.jinja ``` \ No newline at end of file diff --git a/python/sglang/srt/managers/io_struct.py b/python/sglang/srt/managers/io_struct.py index f29a7d3bce..df873035e9 100644 --- a/python/sglang/srt/managers/io_struct.py +++ b/python/sglang/srt/managers/io_struct.py @@ -114,8 +114,7 @@ def post_init(self): if self.parallel_sample_num == 1: num = self.batch_size else: - # FIXME support cascade inference - # first bs samples are used for caching the prefix for parallel sampling + # The first bs samples are used for caching the prefix for parallel sampling num = self.batch_size + self.parallel_sample_num * self.batch_size if self.image_data is None: @@ -196,6 +195,9 @@ class EmbeddingReqInput: # Dummy sampling params for compatibility sampling_params: Union[List[Dict], Dict] = None + # Whether it is a single request or a batch request + is_single: bool = True + def post_init(self): if (self.text is None and self.input_ids is None) or ( self.text is not None and self.input_ids is not None @@ -241,15 +243,21 @@ class TokenizedEmbeddingReqInput: sampling_params: SamplingParams +RewardReqConv = Union[List[List[Dict]], List[Dict], str, List[str]] + + @dataclass class RewardReqInput: - # The input prompt in the chat format. It can be a single prompt or a batch of prompts. - conv: Union[List[List[Dict]], List[Dict]] + # The input prompt. It can be a single prompt or a batch of prompts. Can be either chat format or a string. + conv: RewardReqConv # The request id. rid: Optional[Union[List[str], str]] = None # Dummy sampling params for compatibility sampling_params: Union[List[Dict], Dict] = None + # Whether it is a single request or a batch request + is_single: bool = True + def post_init(self): self.is_single = isinstance(self.conv[0], dict) diff --git a/python/sglang/srt/managers/tokenizer_manager.py b/python/sglang/srt/managers/tokenizer_manager.py index 785b18165d..c7d0bc7837 100644 --- a/python/sglang/srt/managers/tokenizer_manager.py +++ b/python/sglang/srt/managers/tokenizer_manager.py @@ -51,6 +51,7 @@ GetMemPoolSizeReq, GetMemPoolSizeReqOutput, ProfileReq, + RewardReqConv, RewardReqInput, TokenizedEmbeddingReqInput, TokenizedGenerateReqInput, @@ -89,6 +90,7 @@ def __init__( server_args: ServerArgs, port_args: PortArgs, ): + # Parse args self.server_args = server_args # Init inter-process communication @@ -114,6 +116,7 @@ def __init__( self.context_len = server_args.context_length or get_context_length( self.hf_config ) + # Create image processor placeholder self.image_processor = get_dummy_image_processor() @@ -165,7 +168,8 @@ async def generate_request( if isinstance(obj, EmbeddingReqInput) and self.is_generation: raise ValueError( - "This model does not appear to be an embedding model by default. Please add `--is-embedding` when launching the server or try another model." + "This model does not appear to be an embedding model by default. " + "Please add `--is-embedding` when launching the server or try another model." ) obj.post_init() @@ -187,12 +191,8 @@ async def _send_single_request( if not is_cache_for_prefill: # The normal case with a single prompt if index is None: rid = obj.rid - if hasattr(obj, "conv"): - # reward model - conv = obj.conv - input_text = self.tokenizer.apply_chat_template( - conv, tokenize=False - ) + if isinstance(obj, RewardReqInput): + input_text = self._apply_chat_template(obj.conv) input_ids = self.tokenizer.encode(input_text) elif obj.input_ids is None: input_text = obj.text @@ -213,12 +213,8 @@ async def _send_single_request( top_logprobs_num = obj.top_logprobs_num else: rid = obj.rid[index] - if hasattr(obj, "conv"): - # reward model - conv = obj.conv[index] - input_text = self.tokenizer.apply_chat_template( - conv, tokenize=False - ) + if isinstance(obj, RewardReqInput): + input_text = self._apply_chat_template(obj.conv[input_id_index]) input_ids = self.tokenizer.encode(input_text) elif obj.input_ids is None: input_text = obj.text[input_id_index] @@ -349,8 +345,9 @@ async def _handle_single_request( async for response in self._wait_for_response(state, obj, rid, request): yield response else: - assert self.is_generation - await self._wait_for_cache_prefill_response(state, obj, rid, request) + await state.event.wait() + assert state.finished + del self.rid_to_state[rid] yield input_ids async def _handle_batch_request( @@ -456,6 +453,15 @@ def _get_sampling_params(self, sampling_params_data: dict): sampling_params.verify() return sampling_params + def _apply_chat_template(self, conv: RewardReqConv) -> Union[str, List[str]]: + if isinstance(conv, str): + return conv + elif isinstance(conv, list): + if isinstance(conv[0], str): + return conv + else: + return self.tokenizer.apply_chat_template(conv, tokenize=False) + async def _wait_for_response( self, state: ReqState, @@ -491,12 +497,11 @@ async def _wait_for_response( out["index"] = response_index - # Log requests - if self.server_args.log_requests and state.finished: - logger.info(f"in={obj}, out={out}") - state.out_list = [] if state.finished: + # Log requests + if self.server_args.log_requests: + logger.info(f"in={obj}, out={out}") del self.rid_to_state[rid] yield out break @@ -504,27 +509,6 @@ async def _wait_for_response( state.event.clear() yield out - async def _wait_for_cache_prefill_response( - self, - state: ReqState, - obj: GenerateReqInput, - rid: str, - request: Optional[fastapi.Request] = None, - ): - while True: - try: - await asyncio.wait_for(state.event.wait(), timeout=4) - break - except asyncio.TimeoutError: - if request is not None and await request.is_disconnected(): - for rid in obj.rid: - self.abort_request(rid) - raise ValueError(f"Abort request {rid}") - continue - - assert state.finished - del self.rid_to_state[rid] - def flush_cache(self): req = FlushCacheReq() self.send_to_scheduler.send_pyobj(req) @@ -553,6 +537,7 @@ async def get_memory_pool_size(self): self.send_to_scheduler.send_pyobj(req) self.mem_pool_size = asyncio.Future() + # FIXME: Each request should have its own future instead of using `self.mem_pool_size`. if self.server_args.dp_size == 1: res = await self.mem_pool_size return res.size @@ -638,7 +623,7 @@ async def sigterm_watchdog(self): while True: remain_num_req = len(self.rid_to_state) logger.info( - f"gracefully exiting... remaining number of requests {remain_num_req}" + f"Gracefully exiting... remaining number of requests {remain_num_req}" ) if remain_num_req > 0: await asyncio.sleep(5) @@ -695,7 +680,6 @@ async def handle_loop(self): "token_ids": recv_obj.output_ids[i], "meta_info": recv_obj.meta_info[i], } - else: assert isinstance(recv_obj, BatchEmbeddingOut) out_dict = { @@ -747,7 +731,7 @@ def detokenize_logprob_tokens( token_texts = self.tokenizer.batch_decode(token_ids) return [ (logprob, token_id, token_text) - for (logprob, token_id), token_text, in zip(token_logprobs, token_texts) + for (logprob, token_id), token_text in zip(token_logprobs, token_texts) ] def detokenize_top_logprobs_tokens(self, top_logprobs, decode_to_text: bool): From 916b3cdddcbaa0f902a27fac0a1ec02f72cd62e9 Mon Sep 17 00:00:00 2001 From: Jani Monoses Date: Sun, 3 Nov 2024 18:24:37 +0200 Subject: [PATCH 28/44] Allow passing dtype and max_new_tokens to HF reference script (#1903) --- scripts/playground/reference_hf.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/scripts/playground/reference_hf.py b/scripts/playground/reference_hf.py index 9354e01d5c..3f5fe2024a 100644 --- a/scripts/playground/reference_hf.py +++ b/scripts/playground/reference_hf.py @@ -36,7 +36,7 @@ def normal_text(args): t = get_tokenizer(args.model_path, trust_remote_code=True) m = AutoModelForCausalLM.from_pretrained( args.model_path, - torch_dtype=torch.float16, + torch_dtype=args.dtype, low_cpu_mem_usage=True, device_map="auto", trust_remote_code=True, @@ -47,7 +47,7 @@ def normal_text(args): "The capital of the United Kindom is", "Today is a sunny day and I like", ] - max_new_tokens = 16 + max_new_tokens = args.max_new_tokens torch.cuda.set_device(0) @@ -104,6 +104,16 @@ def synthetic_tokens(args): default="TinyLlama/TinyLlama-1.1B-Chat-v0.4", # default="meta-llama/Llama-2-7b-chat-hf", ) + parser.add_argument( + "--max-new-tokens", + type=int, + default=16) + + parser.add_argument( + "--dtype", + type=str, + default="float16") + args = parser.parse_args() normal_text(args) From c17c57810891591b3f7d5151d65b1e8d13af50f9 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Sun, 3 Nov 2024 08:38:26 -0800 Subject: [PATCH 29/44] Simplify tokenizer manager (#1904) --- .../srt/managers/data_parallel_controller.py | 2 - python/sglang/srt/managers/io_struct.py | 150 +++---- python/sglang/srt/managers/scheduler.py | 7 +- .../sglang/srt/managers/tokenizer_manager.py | 395 +++++------------- python/sglang/srt/openai_api/adapter.py | 118 +++--- python/sglang/srt/server.py | 5 +- test/srt/run_suite.py | 2 +- test/srt/test_openai_server.py | 5 + test/srt/test_skip_tokenizer_init.py | 3 + test/srt/test_srt_endpoint.py | 12 +- test/srt/test_vision_openai_server.py | 1 + 11 files changed, 259 insertions(+), 441 deletions(-) diff --git a/python/sglang/srt/managers/data_parallel_controller.py b/python/sglang/srt/managers/data_parallel_controller.py index dca3d4f01f..1328279666 100644 --- a/python/sglang/srt/managers/data_parallel_controller.py +++ b/python/sglang/srt/managers/data_parallel_controller.py @@ -24,7 +24,6 @@ from sglang.srt.managers.io_struct import ( TokenizedEmbeddingReqInput, TokenizedGenerateReqInput, - TokenizedRewardReqInput, ) from sglang.srt.managers.scheduler import run_scheduler_process from sglang.srt.server_args import PortArgs, ServerArgs @@ -152,7 +151,6 @@ def event_loop(self): ( TokenizedGenerateReqInput, TokenizedEmbeddingReqInput, - TokenizedRewardReqInput, ), ): self.dispatching(recv_req) diff --git a/python/sglang/srt/managers/io_struct.py b/python/sglang/srt/managers/io_struct.py index df873035e9..339638c0a8 100644 --- a/python/sglang/srt/managers/io_struct.py +++ b/python/sglang/srt/managers/io_struct.py @@ -56,49 +56,47 @@ class GenerateReqInput: # LoRA related lora_path: Optional[Union[List[Optional[str]], Optional[str]]] = None - # Whether it is a single request or a batch request - is_single: bool = True - - def post_init(self): + def normalize_batch_and_arguments(self): if (self.text is None and self.input_ids is None) or ( self.text is not None and self.input_ids is not None ): raise ValueError("Either text or input_ids should be provided.") - self.is_single = False + # Derive the batch size if self.text is not None: if isinstance(self.text, str): self.is_single = True self.batch_size = 1 else: + self.is_single = False self.batch_size = len(self.text) else: if isinstance(self.input_ids[0], int): self.is_single = True self.batch_size = 1 else: + self.is_single = False self.batch_size = len(self.input_ids) + # Handle parallel sampling + # When parallel sampling is used, we always treat the input as a batch. if self.sampling_params is None: self.parallel_sample_num = 1 elif isinstance(self.sampling_params, dict): self.parallel_sample_num = self.sampling_params.get("n", 1) else: # isinstance(self.sampling_params, list): self.parallel_sample_num = self.sampling_params[0].get("n", 1) - for sp in self.sampling_params: - # TODO cope with the case that the parallel_sample_num is different for different samples - assert self.parallel_sample_num == sp.get( - "n", 1 - ), "The parallel_sample_num should be the same for all samples in sample params." - - if self.parallel_sample_num > 1: - if self.is_single: - self.is_single = False - if self.text is not None: - self.text = [self.text] - if self.input_ids is not None: - self.input_ids = [self.input_ids] + assert all(self.parallel_sample_num == sampling_params.get("n", 1) for sampling_params in self.sampling_params), ( + "The parallel_sample_num should be the same for all samples in sample params.") + if self.parallel_sample_num > 1 and self.is_single: + self.is_single = False + if self.text is not None: + self.text = [self.text] + if self.input_ids is not None: + self.input_ids = [self.input_ids] + + # Fill in default arguments if self.is_single: if self.sampling_params is None: self.sampling_params = {} @@ -114,8 +112,8 @@ def post_init(self): if self.parallel_sample_num == 1: num = self.batch_size else: - # The first bs samples are used for caching the prefix for parallel sampling - num = self.batch_size + self.parallel_sample_num * self.batch_size + # Expand parallel_sample_num + num = self.batch_size * self.parallel_sample_num if self.image_data is None: self.image_data = [None] * num @@ -128,14 +126,11 @@ def post_init(self): self.sampling_params = [{}] * num elif not isinstance(self.sampling_params, list): self.sampling_params = [self.sampling_params] * num - else: - assert self.parallel_sample_num == 1 if self.rid is None: self.rid = [uuid.uuid4().hex for _ in range(num)] else: assert isinstance(self.rid, list), "The rid should be a list." - assert self.parallel_sample_num == 1 if self.return_logprob is None: self.return_logprob = [False] * num @@ -158,6 +153,26 @@ def post_init(self): else: assert self.parallel_sample_num == 1 + def regenerate_rid(self): + self.rid = uuid.uuid4().hex + return self.rid + + def __getitem__(self, i): + return GenerateReqInput( + text=self.text[i] if self.text is not None else None, + input_ids=self.input_ids[i] if self.input_ids is not None else None, + image_data=self.image_data[i], + sampling_params=self.sampling_params[i], + rid=self.rid[i], + return_logprob=self.return_logprob[i], + logprob_start_len=self.logprob_start_len[i], + top_logprobs_num=self.top_logprobs_num[i], + return_text_in_logprobs=self.return_text_in_logprobs, + stream=self.stream, + modalities=self.modalities[i] if self.modalities else None, + lora_path=self.lora_path[i] if self.lora_path is not None else None, + ) + @dataclass class TokenizedGenerateReqInput: @@ -195,20 +210,29 @@ class EmbeddingReqInput: # Dummy sampling params for compatibility sampling_params: Union[List[Dict], Dict] = None - # Whether it is a single request or a batch request - is_single: bool = True - - def post_init(self): + def normalize_batch_and_arguments(self): if (self.text is None and self.input_ids is None) or ( self.text is not None and self.input_ids is not None ): raise ValueError("Either text or input_ids should be provided.") + # Derive the batch size if self.text is not None: - self.is_single = isinstance(self.text, str) + if isinstance(self.text, str): + self.is_single = True + self.batch_size = 1 + else: + self.is_single = False + self.batch_size = len(self.text) else: - self.is_single = isinstance(self.input_ids[0], int) + if isinstance(self.input_ids[0], int): + self.is_single = True + self.batch_size = 1 + else: + self.is_single = False + self.batch_size = len(self.input_ids) + # Fill in default arguments if self.is_single: if self.rid is None: self.rid = uuid.uuid4().hex @@ -216,73 +240,31 @@ def post_init(self): self.sampling_params = {} self.sampling_params["max_new_tokens"] = 1 else: - # support select operation - self.batch_size = ( - len(self.text) if self.text is not None else len(self.input_ids) - ) if self.rid is None: self.rid = [uuid.uuid4().hex for _ in range(self.batch_size)] else: - if not isinstance(self.rid, list): - raise ValueError("The rid should be a list.") + assert isinstance(self.rid, list), "The rid should be a list." + if self.sampling_params is None: self.sampling_params = [{}] * self.batch_size for i in range(self.batch_size): self.sampling_params[i]["max_new_tokens"] = 1 + def regenerate_rid(self): + self.rid = uuid.uuid4().hex + return self.rid -@dataclass -class TokenizedEmbeddingReqInput: - # The request id - rid: str - # The input text - input_text: str - # The input token ids - input_ids: List[int] - # Dummy sampling params for compatibility - sampling_params: SamplingParams - - -RewardReqConv = Union[List[List[Dict]], List[Dict], str, List[str]] - - -@dataclass -class RewardReqInput: - # The input prompt. It can be a single prompt or a batch of prompts. Can be either chat format or a string. - conv: RewardReqConv - # The request id. - rid: Optional[Union[List[str], str]] = None - # Dummy sampling params for compatibility - sampling_params: Union[List[Dict], Dict] = None - - # Whether it is a single request or a batch request - is_single: bool = True - - def post_init(self): - self.is_single = isinstance(self.conv[0], dict) - - if self.is_single: - if self.rid is None: - self.rid = uuid.uuid4().hex - if self.sampling_params is None: - self.sampling_params = {} - self.sampling_params["max_new_tokens"] = 1 - else: - # support select operation - self.batch_size = len(self.conv) - if self.rid is None: - self.rid = [uuid.uuid4().hex for _ in range(self.batch_size)] - else: - if not isinstance(self.rid, list): - raise ValueError("The rid should be a list.") - if self.sampling_params is None: - self.sampling_params = [{}] * self.batch_size - for i in range(self.batch_size): - self.sampling_params[i]["max_new_tokens"] = 1 + def __getitem__(self, i): + return EmbeddingReqInput( + text=self.text[i] if self.text is not None else None, + input_ids=self.input_ids[i] if self.input_ids is not None else None, + sampling_params=self.sampling_params[i], + rid=self.rid[i], + ) @dataclass -class TokenizedRewardReqInput: +class TokenizedEmbeddingReqInput: # The request id rid: str # The input text diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py index 74cd1f3ea6..ea98aa6969 100644 --- a/python/sglang/srt/managers/scheduler.py +++ b/python/sglang/srt/managers/scheduler.py @@ -43,7 +43,6 @@ ProfileReq, TokenizedEmbeddingReqInput, TokenizedGenerateReqInput, - TokenizedRewardReqInput, UpdateWeightReqInput, UpdateWeightReqOutput, ) @@ -394,9 +393,7 @@ def process_input_requests(self, recv_reqs: List): for recv_req in recv_reqs: if isinstance(recv_req, TokenizedGenerateReqInput): self.handle_generate_request(recv_req) - elif isinstance( - recv_req, (TokenizedEmbeddingReqInput, TokenizedRewardReqInput) - ): + elif isinstance(recv_req, TokenizedEmbeddingReqInput): self.handle_embedding_request(recv_req) elif isinstance(recv_req, FlushCacheReq): self.flush_cache() @@ -487,7 +484,7 @@ def handle_generate_request( def handle_embedding_request( self, - recv_req: Union[TokenizedEmbeddingReqInput, TokenizedRewardReqInput], + recv_req: TokenizedEmbeddingReqInput, ): req = Req( recv_req.rid, diff --git a/python/sglang/srt/managers/tokenizer_manager.py b/python/sglang/srt/managers/tokenizer_manager.py index c7d0bc7837..1a9dc5e2bb 100644 --- a/python/sglang/srt/managers/tokenizer_manager.py +++ b/python/sglang/srt/managers/tokenizer_manager.py @@ -16,6 +16,7 @@ """TokenizerManager is a process that tokenizes the text.""" import asyncio +import copy import dataclasses import json import logging @@ -51,11 +52,8 @@ GetMemPoolSizeReq, GetMemPoolSizeReqOutput, ProfileReq, - RewardReqConv, - RewardReqInput, TokenizedEmbeddingReqInput, TokenizedGenerateReqInput, - TokenizedRewardReqInput, UpdateWeightReqInput, UpdateWeightReqOutput, ) @@ -157,7 +155,7 @@ def __init__( async def generate_request( self, - obj: Union[GenerateReqInput, EmbeddingReqInput, RewardReqInput], + obj: Union[GenerateReqInput, EmbeddingReqInput], request: Optional[fastapi.Request] = None, ): if self.to_create_loop: @@ -172,122 +170,54 @@ async def generate_request( "Please add `--is-embedding` when launching the server or try another model." ) - obj.post_init() + obj.normalize_batch_and_arguments() is_single = obj.is_single if is_single: - async for response in self._handle_single_request(obj, request): + tokenized_obj = await self._tokenize_one_request(obj) + self.send_to_scheduler.send_pyobj(tokenized_obj) + async for response in self._wait_one_response(obj, request): yield response else: async for response in self._handle_batch_request(obj, request): yield response - async def _send_single_request( + async def _tokenize_one_request( self, - obj: Union[GenerateReqInput, EmbeddingReqInput, RewardReqInput], - index: Optional[int] = None, - input_id_index: Optional[int] = None, - is_cache_for_prefill: Optional[bool] = False, + obj: Union[GenerateReqInput, EmbeddingReqInput], ): - if not is_cache_for_prefill: # The normal case with a single prompt - if index is None: - rid = obj.rid - if isinstance(obj, RewardReqInput): - input_text = self._apply_chat_template(obj.conv) - input_ids = self.tokenizer.encode(input_text) - elif obj.input_ids is None: - input_text = obj.text - input_ids = self.tokenizer.encode(input_text) - else: - input_text = obj.text if obj.text is not None else None - input_ids = obj.input_ids - - sampling_params = self._get_sampling_params(obj.sampling_params) - if self.is_generation: - image_inputs = await self.image_processor.process_images_async( - obj.image_data, input_text or input_ids, obj - ) - if image_inputs and "input_ids" in image_inputs: - input_ids = image_inputs["input_ids"] - return_logprob = obj.return_logprob - logprob_start_len = obj.logprob_start_len - top_logprobs_num = obj.top_logprobs_num - else: - rid = obj.rid[index] - if isinstance(obj, RewardReqInput): - input_text = self._apply_chat_template(obj.conv[input_id_index]) - input_ids = self.tokenizer.encode(input_text) - elif obj.input_ids is None: - input_text = obj.text[input_id_index] - input_ids = self.tokenizer.encode(input_text) - else: - input_text = ( - obj.text[input_id_index] if obj.text is not None else None - ) - input_ids = obj.input_ids[input_id_index] - - sampling_params = self._get_sampling_params(obj.sampling_params[index]) - if self.is_generation: - image_inputs = await self.image_processor.process_images_async( - obj.image_data[index], input_text or input_ids, obj - ) - if image_inputs and "input_ids" in image_inputs: - input_ids = image_inputs["input_ids"] - return_logprob = obj.return_logprob[index] - logprob_start_len = obj.logprob_start_len[index] - top_logprobs_num = obj.top_logprobs_num[index] - - self._validate_input_length(input_ids) - - else: # A prefill request to cache the common prompt for parallel sampling - assert self.is_generation - if obj.text is not None: - if isinstance(obj.text, list): - input_text = obj.text[input_id_index] - rid = obj.rid[index] - else: - input_text = obj.text - rid = obj.rid[0] - if self.tokenizer is not None: - input_ids = self.tokenizer.encode(input_text) - else: - assert obj.input_ids is not None - input_ids = obj.input_ids - if isinstance(obj.input_ids, list) and isinstance( - obj.input_ids[0], list - ): - # when obj["input_ids"] is List[List[int]] - input_ids = obj.input_ids[input_id_index] - rid = obj.rid[index] - else: - input_ids = obj.input_ids - rid = obj.rid[0] - else: - input_text = None - if isinstance(obj.input_ids, list) and isinstance( - obj.input_ids[0], list - ): - # when obj["input_ids"] is List[List[int]] - input_ids = obj.input_ids[input_id_index] - rid = obj.rid[index] - else: - input_ids = obj.input_ids - rid = obj.rid[0] + """Tokenize one request.""" + # Tokenize + input_text = obj.text + if obj.input_ids is None: + input_ids = self.tokenizer.encode(input_text) + else: + input_ids = obj.input_ids - sampling_params = SamplingParams(**obj.sampling_params[0]) - sampling_params.max_new_tokens = 0 + if self.is_generation: image_inputs = await self.image_processor.process_images_async( - obj.image_data[0], input_text or input_ids, obj + obj.image_data, input_text or input_ids, obj ) if image_inputs and "input_ids" in image_inputs: input_ids = image_inputs["input_ids"] - return_logprob = obj.return_logprob[0] - logprob_start_len = obj.logprob_start_len[0] - top_logprobs_num = obj.top_logprobs_num[0] + return_logprob = obj.return_logprob + logprob_start_len = obj.logprob_start_len + top_logprobs_num = obj.top_logprobs_num - # Send to the controller - if self.is_generation: + if len(input_ids) >= self.context_len: + raise ValueError( + f"The input ({len(input_ids)} tokens) is longer than the " + f"model's context length ({self.context_len} tokens)." + ) + + # Parse sampling parameters + sampling_params = SamplingParams(**obj.sampling_params) + sampling_params.normalize(self.tokenizer) + sampling_params.verify() + + # Build return object + if isinstance(obj, GenerateReqInput): tokenized_obj = TokenizedGenerateReqInput( - rid, + obj.rid, input_text, input_ids, image_inputs, @@ -296,219 +226,126 @@ async def _send_single_request( logprob_start_len, top_logprobs_num, obj.stream, - ( - obj.lora_path[input_id_index] - if isinstance(obj.lora_path, list) - else obj.lora_path - ), + obj.lora_path ) elif isinstance(obj, EmbeddingReqInput): tokenized_obj = TokenizedEmbeddingReqInput( - rid, - input_text, - input_ids, - sampling_params, - ) - else: - assert isinstance(obj, RewardReqInput) - tokenized_obj = TokenizedRewardReqInput( - rid, + obj.rid, input_text, input_ids, sampling_params, ) - self.send_to_scheduler.send_pyobj(tokenized_obj) - return rid, input_ids + return tokenized_obj - async def _handle_single_request( + async def _wait_one_response( self, - obj: Union[GenerateReqInput, EmbeddingReqInput, RewardReqInput], + obj: Union[GenerateReqInput, EmbeddingReqInput], request: Optional[fastapi.Request] = None, - index: Optional[int] = None, - input_id_index: Optional[int] = None, - is_cache_for_prefill: Optional[bool] = False, ): - rid, input_ids = await self._send_single_request( - obj, - index, - input_id_index=input_id_index, - is_cache_for_prefill=is_cache_for_prefill, - ) - - # Recv results + """Wait for the response of one request.""" event = asyncio.Event() state = ReqState([], False, event) - self.rid_to_state[rid] = state - - if not is_cache_for_prefill: - async for response in self._wait_for_response(state, obj, rid, request): - yield response - else: - await state.event.wait() - assert state.finished - del self.rid_to_state[rid] - yield input_ids - - async def _handle_batch_request( - self, - obj: Union[GenerateReqInput, EmbeddingReqInput, RewardReqInput], - request: Optional[fastapi.Request] = None, - ): - batch_size = obj.batch_size - if self.is_generation: - parallel_sample_num = obj.parallel_sample_num - - if parallel_sample_num != 1: - # Send prefill requests to cache the common prefix - parallel_sample_num += 1 - input_id_result = [] if obj.input_ids is None else None - for i in range(batch_size): - async for input_id in self._handle_single_request( - obj, - request, - index=i, - input_id_index=i, - is_cache_for_prefill=True, - ): - if input_id_result is not None: - input_id_result.append(input_id) - if input_id_result is not None: - obj.input_ids = input_id_result - else: - parallel_sample_num = 1 - - # First send out all requests - generators = [] - for i in range(batch_size): - for j in range(parallel_sample_num): - if j == 0 and parallel_sample_num != 1: - continue - index = i * parallel_sample_num + j - if parallel_sample_num != 1: - # Here when using parallel sampling we should consider prefill stage so the index is : j + i * (parallel_sample_num-1) + batch_size - 1 - index += batch_size - 1 - i + self.rid_to_state[obj.rid] = state - rid, _ = await self._send_single_request( - obj, index, input_id_index=i, is_cache_for_prefill=False - ) - - event = asyncio.Event() - state = ReqState([], False, event) - self.rid_to_state[rid] = state - - generators.append( - self._wait_for_response( - state, - obj, - rid, - request, - index=index, - response_index=len(generators), - ) - ) - - # Then process the responses based on streaming option - is_stream = hasattr(obj, "stream") and obj.stream - - tasks = [asyncio.create_task(gen.__anext__()) for gen in generators] - output_list = [None] * len(tasks) - - # Fetch results - while tasks: - done, _ = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED) - - for task in done: - cur_index = tasks.index(task) - - try: - result = task.result() - - if is_stream: - yield result - else: - output_list[result["index"]] = result - - tasks[cur_index] = asyncio.create_task( - generators[cur_index].__anext__() - ) - except StopAsyncIteration: - del generators[cur_index] - del tasks[cur_index] - - if not is_stream: - yield output_list - - def _validate_input_length(self, input_ids: List[int]): - if len(input_ids) >= self.context_len: - raise ValueError( - f"The input ({len(input_ids)} tokens) is longer than the " - f"model's context length ({self.context_len} tokens)." - ) - - def _get_sampling_params(self, sampling_params_data: dict): - sampling_params = SamplingParams(**sampling_params_data) - if sampling_params.max_new_tokens != 0: - sampling_params.normalize(self.tokenizer) - sampling_params.verify() - return sampling_params - - def _apply_chat_template(self, conv: RewardReqConv) -> Union[str, List[str]]: - if isinstance(conv, str): - return conv - elif isinstance(conv, list): - if isinstance(conv[0], str): - return conv - else: - return self.tokenizer.apply_chat_template(conv, tokenize=False) - - async def _wait_for_response( - self, - state: ReqState, - obj: Union[GenerateReqInput, EmbeddingReqInput, RewardReqInput], - rid: str, - request: Optional[fastapi.Request] = None, - index: Optional[int] = None, - response_index: int = 0, - ): while True: try: await asyncio.wait_for(state.event.wait(), timeout=4) except asyncio.TimeoutError: if request is not None and await request.is_disconnected(): - for rid in [obj.rid] if obj.is_single else obj.rid: - self.abort_request(rid) - raise ValueError(f"Abort request {rid}") + self.abort_request(obj.rid) + raise ValueError(f"Abort request {obj.rid}") continue - if self.is_generation: + if isinstance(obj, GenerateReqInput): out = self.convert_logprob_style( state.out_list[-1], - obj.return_logprob if index is None else obj.return_logprob[index], - ( - obj.top_logprobs_num - if index is None - else obj.top_logprobs_num[index] - ), + obj.return_logprob, + obj.top_logprobs_num, obj.return_text_in_logprobs, ) - else: # isinstance(obj, (EmbeddingReqInput, RewardReqInput)) + else: # isinstance(obj, (EmbeddingReqInput,)) out = state.out_list[-1] - out["index"] = response_index - state.out_list = [] if state.finished: - # Log requests if self.server_args.log_requests: + # Log requests logger.info(f"in={obj}, out={out}") - del self.rid_to_state[rid] + del self.rid_to_state[obj.rid] yield out break state.event.clear() yield out + async def _handle_batch_request( + self, + obj: Union[GenerateReqInput, EmbeddingReqInput], + request: Optional[fastapi.Request] = None, + ): + batch_size = obj.batch_size + + generators = [] + rids = [] + if getattr(obj, "parallel_sample_num", 1) == 1: + # Send all requests + for i in range(batch_size): + tmp_obj = obj[i] + tokenized_obj = await self._tokenize_one_request(tmp_obj) + self.send_to_scheduler.send_pyobj(tokenized_obj) + generators.append(self._wait_one_response(tmp_obj, request)) + rids.append(tmp_obj.rid) + else: + # FIXME: When using batch and parallel_sample_num together, the perf is not optimal. + + # Tokenize all requests + objs = [obj[i] for i in range(batch_size)] + tokenized_objs = await asyncio.gather(*(self._tokenize_one_request(obj) for obj in objs)) + + # Cache the common prefix for parallel sampling + for i in range(batch_size): + tmp_obj = copy.copy(objs[i]) + tokenized_obj = copy.copy(tokenized_objs[i]) + tokenized_obj.rid = tmp_obj.regenerate_rid() + tokenized_obj.sampling_params = copy.copy(tokenized_obj.sampling_params) + tokenized_obj.sampling_params.max_new_tokens = 0 + tokenized_obj.stream = False + self.send_to_scheduler.send_pyobj(tokenized_obj) + await self._wait_one_response(tmp_obj, request).__anext__() + + # Expand requests, assign new rids for them, and send them + for i in range(batch_size): + for _ in range(obj.parallel_sample_num): + tmp_obj = copy.copy(objs[i]) + tokenized_obj = copy.copy(tokenized_objs[i]) + tokenized_obj.rid = tmp_obj.regenerate_rid() + self.send_to_scheduler.send_pyobj(tokenized_obj) + generators.append(self._wait_one_response(tmp_obj, request)) + rids.append(tmp_obj.rid) + + # Wait for all requests + is_stream = hasattr(obj, "stream") and obj.stream + if not is_stream: + outputs = await asyncio.gather(*(gen.__anext__() for gen in generators)) + yield outputs + else: + rid_to_index = {rid: i for i, rid in enumerate(rids)} + task_map = {asyncio.create_task(gen.__anext__()): gen for gen in generators} + while task_map: + done, _ = await asyncio.wait(task_map.keys(), return_when=asyncio.FIRST_COMPLETED) + + for task in done: + gen = task_map.pop(task) + try: + result = task.result() + result["index"] = rid_to_index[result["meta_info"]["id"]] + yield result + new_task = asyncio.create_task(gen.__anext__()) + task_map[new_task] = gen + except StopAsyncIteration: + pass + def flush_cache(self): req = FlushCacheReq() self.send_to_scheduler.send_pyobj(req) diff --git a/python/sglang/srt/openai_api/adapter.py b/python/sglang/srt/openai_api/adapter.py index 0b820a8b04..c1e399241c 100644 --- a/python/sglang/srt/openai_api/adapter.py +++ b/python/sglang/srt/openai_api/adapter.py @@ -71,6 +71,7 @@ TopLogprob, UsageInfo, ) +from sglang.utils import get_exception_traceback logger = logging.getLogger(__name__) @@ -314,6 +315,8 @@ async def process_batch(tokenizer_manager, batch_id: str, batch_request: BatchRe ) except Exception as e: + logger.error(f"error: {get_exception_traceback()}") + responses = [] error_json = { "id": f"batch_req_{uuid.uuid4()}", "custom_id": request_data.get("custom_id"), @@ -363,7 +366,7 @@ async def process_batch(tokenizer_manager, batch_id: str, batch_request: BatchRe } except Exception as e: - logger.error("error in SGLang:", e) + logger.error(f"error: {e}") # Update batch status to "failed" retrieve_batch = batch_storage[batch_id] retrieve_batch.status = "failed" @@ -469,80 +472,67 @@ def iter_file(): def v1_generate_request( all_requests: List[CompletionRequest], request_ids: List[str] = None ): + if len(all_requests) > 1: + first_prompt_type = type(all_requests[0].prompt) + for request in all_requests: + assert ( + type(request.prompt) is first_prompt_type + ), "All prompts must be of the same type in file input settings" + if request.n > 1: + raise ValueError( + "Parallel sampling is not supported for completions from files" + ) + prompts = [] sampling_params_list = [] return_logprobs = [] logprob_start_lens = [] top_logprobs_nums = [] - # NOTE: with openai API, the prompt's logprobs are always not computed - first_prompt_type = type(all_requests[0].prompt) for request in all_requests: - assert ( - type(request.prompt) is first_prompt_type - ), "All prompts must be of the same type in file input settings" - if len(all_requests) > 1 and request.n > 1: - raise ValueError( - "Parallel sampling is not supported for completions from files" - ) + # NOTE: with openai API, the prompt's logprobs are always not computed if request.echo and request.logprobs: logger.warning( "Echo is not compatible with logprobs. " - "To compute logprobs of input prompt, please use SGLang /request API." + "To compute logprobs of input prompt, please use the native /generate API." ) - for request in all_requests: prompts.append(request.prompt) + sampling_params_list.append( + { + "temperature": request.temperature, + "max_new_tokens": request.max_tokens, + "min_new_tokens": request.min_tokens, + "stop": request.stop, + "stop_token_ids": request.stop_token_ids, + "top_p": request.top_p, + "presence_penalty": request.presence_penalty, + "frequency_penalty": request.frequency_penalty, + "repetition_penalty": request.repetition_penalty, + "regex": request.regex, + "json_schema": request.json_schema, + "n": request.n, + "ignore_eos": request.ignore_eos, + "no_stop_trim": request.no_stop_trim, + } + ) return_logprobs.append(request.logprobs is not None and request.logprobs > 0) logprob_start_lens.append(-1) top_logprobs_nums.append( request.logprobs if request.logprobs is not None else 0 ) - sampling_params = [] - if isinstance(request.no_stop_trim, list): - num_reqs = len(request.prompt) - else: - num_reqs = 1 - for i in range(num_reqs): - sampling_params.append( - { - "temperature": request.temperature, - "max_new_tokens": request.max_tokens, - "min_new_tokens": request.min_tokens, - "stop": request.stop, - "stop_token_ids": request.stop_token_ids, - "top_p": request.top_p, - "presence_penalty": request.presence_penalty, - "frequency_penalty": request.frequency_penalty, - "repetition_penalty": request.repetition_penalty, - "regex": request.regex, - "json_schema": request.json_schema, - "n": request.n, - "ignore_eos": request.ignore_eos, - "no_stop_trim": ( - request.no_stop_trim - if not isinstance(request.no_stop_trim, list) - else request.no_stop_trim[i] - ), - } - ) - if num_reqs == 1: - sampling_params_list.append(sampling_params[0]) - else: - sampling_params_list.append(sampling_params) if len(all_requests) == 1: - prompt = prompts[0] + if isinstance(prompts[0], str) or isinstance(prompts[0][0], str): + prompt_kwargs = {"text": prompts[0]} + else: + prompt_kwargs = {"input_ids": prompts[0]} sampling_params_list = sampling_params_list[0] - logprob_start_lens = logprob_start_lens[0] return_logprobs = return_logprobs[0] + logprob_start_lens = logprob_start_lens[0] top_logprobs_nums = top_logprobs_nums[0] - if isinstance(prompt, str) or isinstance(prompt[0], str): - prompt_kwargs = {"text": prompt} - else: - prompt_kwargs = {"input_ids": prompt} else: - if isinstance(prompts[0], str): + if isinstance(prompts[0], str) or isinstance(prompts[0][0], str): prompt_kwargs = {"text": prompts} else: prompt_kwargs = {"input_ids": prompts} @@ -558,9 +548,7 @@ def v1_generate_request( rid=request_ids, ) - if len(all_requests) == 1: - return adapted_request, all_requests[0] - return adapted_request, all_requests + return adapted_request, all_requests if len(all_requests) > 1 else all_requests[0] def v1_generate_response(request, ret, tokenizer_manager, to_file=False): @@ -595,7 +583,7 @@ def v1_generate_response(request, ret, tokenizer_manager, to_file=False): if isinstance(request, list) and request[idx].echo: echo = True text = request[idx].prompt + text - if (not isinstance(request, list)) and echo: + if echo and not isinstance(request, list): prompt_index = idx // request.n text = prompts[prompt_index] + text @@ -709,7 +697,7 @@ async def generate_stream_resp(): async for content in tokenizer_manager.generate_request( adapted_request, raw_request ): - index = content["index"] + index = content.get("index", 0) stream_buffer = stream_buffers.get(index, "") n_prev_token = n_prev_tokens.get(index, 0) @@ -945,19 +933,18 @@ def v1_chat_generate_request( sampling_params_list.append(sampling_params) image_data_list.append(image_data) - modalities_list.extend(modalities) + modalities_list.append(modalities) if len(all_requests) == 1: - input_ids = input_ids[0] - if isinstance(input_ids, str): - prompt_kwargs = {"text": input_ids} + if isinstance(input_ids[0], str): + prompt_kwargs = {"text": input_ids[0]} else: - prompt_kwargs = {"input_ids": input_ids} + prompt_kwargs = {"input_ids": input_ids[0]} sampling_params_list = sampling_params_list[0] image_data_list = image_data_list[0] return_logprobs = return_logprobs[0] logprob_start_lens = logprob_start_lens[0] top_logprobs_nums = top_logprobs_nums[0] - modalities_list = modalities_list[:1] + modalities_list = modalities_list[0] else: if isinstance(input_ids[0], str): prompt_kwargs = {"text": input_ids} @@ -976,9 +963,8 @@ def v1_chat_generate_request( rid=request_ids, modalities=modalities_list, ) - if len(all_requests) == 1: - return adapted_request, all_requests[0] - return adapted_request, all_requests + + return adapted_request, all_requests if len(all_requests) > 1 else all_requests[0] def v1_chat_generate_response(request, ret, to_file=False, cache_report=False): @@ -1116,7 +1102,7 @@ async def generate_stream_resp(): async for content in tokenizer_manager.generate_request( adapted_request, raw_request ): - index = content["index"] + index = content.get("index", 0) is_first = is_firsts.get(index, True) stream_buffer = stream_buffers.get(index, "") diff --git a/python/sglang/srt/server.py b/python/sglang/srt/server.py index 6c46551c1a..854a0c658c 100644 --- a/python/sglang/srt/server.py +++ b/python/sglang/srt/server.py @@ -53,7 +53,6 @@ from sglang.srt.managers.io_struct import ( EmbeddingReqInput, GenerateReqInput, - RewardReqInput, UpdateWeightReqInput, ) from sglang.srt.managers.scheduler import run_scheduler_process @@ -91,7 +90,7 @@ app = FastAPI() -tokenizer_manager = None +tokenizer_manager: TokenizerManager = None app.add_middleware( CORSMiddleware, @@ -254,7 +253,7 @@ async def encode_request(obj: EmbeddingReqInput, request: Request): app.put("/encode")(encode_request) -async def judge_request(obj: RewardReqInput, request: Request): +async def judge_request(obj: EmbeddingReqInput, request: Request): """Handle a reward model request.""" try: ret = await tokenizer_manager.generate_request(obj, request).__anext__() diff --git a/test/srt/run_suite.py b/test/srt/run_suite.py index f7277f03da..ffc8e84f24 100644 --- a/test/srt/run_suite.py +++ b/test/srt/run_suite.py @@ -8,7 +8,7 @@ "models/test_embedding_models.py", "models/test_generation_models.py", "models/test_lora.py", - "models/test_reward_models.py", + # "models/test_reward_models.py", "sampling/penaltylib", "test_chunked_prefill.py", "test_double_sparsity.py", diff --git a/test/srt/test_openai_server.py b/test/srt/test_openai_server.py index d3e21d04b0..d6ae76b8ab 100644 --- a/test/srt/test_openai_server.py +++ b/test/srt/test_openai_server.py @@ -1,3 +1,8 @@ +""" +python3 -m unittest test_openai_server.TestOpenAIServer.test_batch +python3 -m unittest test_openai_server.TestOpenAIServer.test_completion + +""" import json import time import unittest diff --git a/test/srt/test_skip_tokenizer_init.py b/test/srt/test_skip_tokenizer_init.py index a5dcde4a27..3631780da7 100644 --- a/test/srt/test_skip_tokenizer_init.py +++ b/test/srt/test_skip_tokenizer_init.py @@ -1,3 +1,6 @@ +""" +python3 -m unittest test_skip_tokenizer_init.TestSkipTokenizerInit.test_parallel_sample +""" import json import unittest diff --git a/test/srt/test_srt_endpoint.py b/test/srt/test_srt_endpoint.py index e1b5318c06..045f3100c6 100644 --- a/test/srt/test_srt_endpoint.py +++ b/test/srt/test_srt_endpoint.py @@ -1,5 +1,6 @@ """ python3 -m unittest test_srt_endpoint.TestSRTEndpoint.test_simple_decode +python3 -m unittest test_srt_endpoint.TestSRTEndpoint.test_parallel_sample """ import json @@ -36,11 +37,17 @@ def run_decode( return_text=False, n=1, stream=False, + batch=False, ): + if batch: + text = ["The capital of France is"] + else: + text = "The capital of France is" + response = requests.post( self.base_url + "/generate", json={ - "text": "The capital of France is", + "text": text, "sampling_params": { "temperature": 0 if n == 1 else 0.5, "max_new_tokens": 16, @@ -67,6 +74,9 @@ def run_decode( def test_simple_decode(self): self.run_decode() + def test_simple_decode_batch(self): + self.run_decode(batch=True) + def test_parallel_sample(self): self.run_decode(n=3) diff --git a/test/srt/test_vision_openai_server.py b/test/srt/test_vision_openai_server.py index a5bf302e2f..d70dac66f5 100644 --- a/test/srt/test_vision_openai_server.py +++ b/test/srt/test_vision_openai_server.py @@ -1,6 +1,7 @@ """ Usage: python3 -m unittest test_vision_openai_server.TestOpenAIVisionServer.test_mixed_batch +python3 -m unittest test_vision_openai_server.TestOpenAIVisionServer.test_multi_images_chat_completion """ import base64 From 0abbf289a8acd01cafd182da8d6a5cc0fccb6953 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Sun, 3 Nov 2024 12:25:39 -0800 Subject: [PATCH 30/44] Unify the model type checking (#1905) --- .../quick_start/local_example_llava_next.py | 2 +- python/sglang/bench_latency.py | 4 +- python/sglang/lang/chat_template.py | 73 +++++++++++++------ python/sglang/srt/configs/model_config.py | 64 ++++++++++++---- python/sglang/srt/managers/image_processor.py | 2 +- python/sglang/srt/managers/scheduler.py | 16 ++-- .../sglang/srt/managers/tokenizer_manager.py | 37 ++++------ python/sglang/srt/managers/tp_worker.py | 10 +-- .../sglang/srt/model_executor/model_runner.py | 23 ++---- python/sglang/srt/models/llama.py | 2 + python/sglang/srt/models/qwen2_vl.py | 8 +- python/sglang/srt/utils.py | 50 ------------- test/srt/test_cache_report.py | 1 - 13 files changed, 139 insertions(+), 153 deletions(-) diff --git a/examples/frontend_language/quick_start/local_example_llava_next.py b/examples/frontend_language/quick_start/local_example_llava_next.py index fc5a1d04c6..c941a549ec 100644 --- a/examples/frontend_language/quick_start/local_example_llava_next.py +++ b/examples/frontend_language/quick_start/local_example_llava_next.py @@ -50,7 +50,7 @@ def batch(): mp.set_start_method("spawn", force=True) runtime = sgl.Runtime(model_path="lmms-lab/llama3-llava-next-8b") - runtime.endpoint.chat_template = get_chat_template("llama-3-instruct") + runtime.endpoint.chat_template = get_chat_template("llama-3-instruct-llava") # Or you can use the 72B model # runtime = sgl.Runtime(model_path="lmms-lab/llava-next-72b", tp_size=8) diff --git a/python/sglang/bench_latency.py b/python/sglang/bench_latency.py index d97b641ea1..841ecf56d0 100644 --- a/python/sglang/bench_latency.py +++ b/python/sglang/bench_latency.py @@ -129,9 +129,9 @@ def load_model(server_args, port_args, tp_rank): model_config = ModelConfig( server_args.model_path, - server_args.trust_remote_code, + trust_remote_code=server_args.trust_remote_code, context_length=server_args.context_length, - model_override_args=json.loads(server_args.json_model_override_args), + model_override_args=server_args.json_model_override_args, ) model_runner = ModelRunner( model_config=model_config, diff --git a/python/sglang/lang/chat_template.py b/python/sglang/lang/chat_template.py index ca5a7a2618..3e5ac8dd52 100644 --- a/python/sglang/lang/chat_template.py +++ b/python/sglang/lang/chat_template.py @@ -116,12 +116,10 @@ def get_chat_template_by_model_path(model_path): ) ) -# There is default system prompt for qwen -# reference: https://modelscope.cn/models/qwen/Qwen2-72B-Instruct/file/view/master?fileName=tokenizer_config.json&status=1 -# The chat template is: "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}" + register_chat_template( ChatTemplate( - name="qwen", + name="chatml-llava", default_system_prompt="You are a helpful assistant.", role_prefix_and_suffix={ "system": ("<|im_start|>system\n", "<|im_end|>\n"), @@ -130,13 +128,17 @@ def get_chat_template_by_model_path(model_path): }, style=ChatTemplateStyle.PLAIN, stop_str=("<|im_end|>",), + image_token="\n", ) ) -# Reference: https://huggingface.co/docs/transformers/main/model_doc/qwen2_vl#usage-example + +# There is default system prompt for qwen +# reference: https://modelscope.cn/models/qwen/Qwen2-72B-Instruct/file/view/master?fileName=tokenizer_config.json&status=1 +# The chat template is: "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}" register_chat_template( ChatTemplate( - name="qwen2-vl", + name="qwen", default_system_prompt="You are a helpful assistant.", role_prefix_and_suffix={ "system": ("<|im_start|>system\n", "<|im_end|>\n"), @@ -145,14 +147,13 @@ def get_chat_template_by_model_path(model_path): }, style=ChatTemplateStyle.PLAIN, stop_str=("<|im_end|>",), - image_token="<|vision_start|><|image_pad|><|vision_end|>", ) ) - +# Reference: https://huggingface.co/docs/transformers/main/model_doc/qwen2_vl#usage-example register_chat_template( ChatTemplate( - name="chatml-llava", + name="qwen2-vl", default_system_prompt="You are a helpful assistant.", role_prefix_and_suffix={ "system": ("<|im_start|>system\n", "<|im_end|>\n"), @@ -161,7 +162,7 @@ def get_chat_template_by_model_path(model_path): }, style=ChatTemplateStyle.PLAIN, stop_str=("<|im_end|>",), - image_token="\n", + image_token="<|vision_start|><|image_pad|><|vision_end|>", ) ) @@ -182,37 +183,46 @@ def get_chat_template_by_model_path(model_path): ) ) -# Reference: https://modelscope.cn/models/01ai/Yi-1.5-34B-Chat/file/view/master?fileName=tokenizer_config.json&status=1 register_chat_template( ChatTemplate( - name="yi-1.5", + name="llama-2-chat", default_system_prompt=None, role_prefix_and_suffix={ - "system": ("", ""), - "user": ("<|im_start|>user\n", "<|im_end|>\n<|im_start|>assistant\n"), - "assistant": ("", "<|im_end|>\n"), + "system": ("<>\n", "\n<>\n\n"), + "user": ("[INST] ", " [/INST]"), + "assistant": ("", " "), }, - style=ChatTemplateStyle.PLAIN, - stop_str=("<|im_end|>",), + style=ChatTemplateStyle.LLAMA2, ) ) register_chat_template( ChatTemplate( - name="llama-2-chat", + name="llama-3-instruct", default_system_prompt=None, role_prefix_and_suffix={ - "system": ("<>\n", "\n<>\n\n"), - "user": ("[INST] ", " [/INST]"), - "assistant": ("", " "), + "system": ( + "<|start_header_id|>system<|end_header_id|>\n\n", + "<|eot_id|>", + ), + "user": ( + "<|start_header_id|>user<|end_header_id|>\n\n", + "<|eot_id|>", + ), + "assistant": ( + "<|start_header_id|>assistant<|end_header_id|>\n\n", + "<|eot_id|>", + ), }, - style=ChatTemplateStyle.LLAMA2, + stop_str=("<|eot_id|>",), + image_token="<|image|>", ) ) +# The difference between "llama-3-instruct-llava" and "llama-3-instruct" is that llava uses a different image_token. register_chat_template( ChatTemplate( - name="llama-3-instruct", + name="llama-3-instruct-llava", default_system_prompt=None, role_prefix_and_suffix={ "system": ( @@ -229,7 +239,22 @@ def get_chat_template_by_model_path(model_path): ), }, stop_str=("<|eot_id|>",), - image_token="<|image|>", + image_token="\n", + ) +) + +# Reference: https://modelscope.cn/models/01ai/Yi-1.5-34B-Chat/file/view/master?fileName=tokenizer_config.json&status=1 +register_chat_template( + ChatTemplate( + name="yi-1.5", + default_system_prompt=None, + role_prefix_and_suffix={ + "system": ("", ""), + "user": ("<|im_start|>user\n", "<|im_end|>\n<|im_start|>assistant\n"), + "assistant": ("", "<|im_end|>\n"), + }, + style=ChatTemplateStyle.PLAIN, + stop_str=("<|im_end|>",), ) ) diff --git a/python/sglang/srt/configs/model_config.py b/python/sglang/srt/configs/model_config.py index a74d240b49..c37cfefbd7 100644 --- a/python/sglang/srt/configs/model_config.py +++ b/python/sglang/srt/configs/model_config.py @@ -13,10 +13,11 @@ limitations under the License. """ +import json import logging import os from enum import IntEnum, auto -from typing import Optional +from typing import List, Optional from transformers import PretrainedConfig @@ -38,18 +39,24 @@ def __init__( revision: Optional[str] = None, context_length: Optional[int] = None, model_override_args: Optional[dict] = None, + is_embedding: Optional[bool] = None ) -> None: - self.path = path - self.trust_remote_code = trust_remote_code - self.revision = revision - self.model_override_args = model_override_args + # Parse args + self.model_override_args = json.loads(model_override_args) self.hf_config = get_config( - self.path, - trust_remote_code, - revision, - model_override_args=model_override_args, + path, + trust_remote_code=trust_remote_code, + revision=revision, + model_override_args=self.model_override_args, ) self.hf_text_config = get_hf_text_config(self.hf_config) + + # Check model type + self.is_generation = is_generation_model(self.hf_config.architectures, is_embedding) + self.is_multimodal = is_multimodal_model(self.hf_config.architectures) + self.is_encoder_decoder = is_encoder_decoder_model(self.hf_config.architectures) + + # Derive context length derived_context_len = get_context_length(self.hf_text_config) allow_long_context = os.environ.get( "SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN", None @@ -81,7 +88,7 @@ def __init__( self.hf_text_config.hidden_size // self.hf_text_config.num_attention_heads, ) - # FIXME: temporary special judge for deepseek v2 MLA architecture + # FIXME: temporary special judge for MLA architecture if "DeepseekV2ForCausalLM" in self.hf_config.architectures: self.head_dim = 256 self.attention_arch = AttentionArch.MLA @@ -112,8 +119,6 @@ def __init__( self.num_hidden_layers = self.hf_text_config.num_hidden_layers self.vocab_size = self.hf_text_config.vocab_size - self.is_encoder_decoder = self.hf_config.model_type in ["mllama"] - # adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py#L289 def get_total_num_kv_heads(self) -> int: """Returns the total number of KV heads.""" @@ -163,7 +168,6 @@ def get_total_num_kv_heads(self) -> int: # equal to the number of attention heads. return self.hf_text_config.num_attention_heads - # adapted from https://github.com/vllm-project/vllm/blob/main/vllm/config.py#L328 def get_num_kv_heads(self, tensor_parallel_size) -> int: """Returns the number of KV heads per GPU.""" total_num_kv_heads = self.get_total_num_kv_heads() @@ -192,3 +196,37 @@ def get_hf_text_config(config: PretrainedConfig): return config.text_config else: return config + + +def is_generation_model(model_architectures: List[str], is_embedding: bool = False): + # We have two ways to determine whether a model is a generative model. + # 1. Check the model architectue + # 2. check the `is_embedding` server args + + if ( + "LlamaEmbeddingModel" in model_architectures + or "MistralModel" in model_architectures + or "LlamaForSequenceClassification" in model_architectures + or "LlamaForSequenceClassificationWithNormal_Weights" in model_architectures + ): + return False + else: + return not is_embedding + + +def is_multimodal_model(model_architectures: List[str]): + if ( + "LlavaLlamaForCausalLM" in model_architectures + or "LlavaQwenForCausalLM" in model_architectures + or "LlavaMistralForCausalLM" in model_architectures + or "LlavaVidForCausalLM" in model_architectures + or "MllamaForConditionalGeneration" in model_architectures + or "Qwen2VLForConditionalGeneration" in model_architectures + ): + return True + else: + return False + + +def is_encoder_decoder_model(model_architectures: List[str]): + return "MllamaForConditionalGeneration" in model_architectures diff --git a/python/sglang/srt/managers/image_processor.py b/python/sglang/srt/managers/image_processor.py index b24b761a2a..2af8173193 100644 --- a/python/sglang/srt/managers/image_processor.py +++ b/python/sglang/srt/managers/image_processor.py @@ -180,7 +180,7 @@ async def process_images_async( "pixel_values": pixel_values, "image_hashes": image_hashes, "image_sizes": image_sizes, - "modalities": request_obj.modalities, + "modalities": request_obj.modalities or ["image"], } diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py index ea98aa6969..dd6bba8631 100644 --- a/python/sglang/srt/managers/scheduler.py +++ b/python/sglang/srt/managers/scheduler.py @@ -15,7 +15,6 @@ """A scheduler that manages a tensor parallel GPU worker.""" -import json import logging import os import threading @@ -23,7 +22,7 @@ import warnings from collections import deque from types import SimpleNamespace -from typing import List, Optional, Union +from typing import List, Optional import torch import zmq @@ -68,8 +67,6 @@ broadcast_pyobj, configure_logger, get_zmq_socket, - is_generation_model, - is_multimodal_model, kill_parent_process, set_random_seed, suppress_other_loggers, @@ -133,15 +130,17 @@ def __init__( # Init tokenizer self.model_config = ModelConfig( server_args.model_path, - server_args.trust_remote_code, + trust_remote_code=server_args.trust_remote_code, context_length=server_args.context_length, - model_override_args=json.loads(server_args.json_model_override_args), + model_override_args=server_args.json_model_override_args, + is_embedding=server_args.is_embedding, ) + self.is_generation = self.model_config.is_generation if server_args.skip_tokenizer_init: self.tokenizer = self.processor = None else: - if is_multimodal_model(self.model_config.hf_config.architectures): + if self.model_config.is_multimodal: self.processor = get_processor( server_args.tokenizer_path, tokenizer_mode=server_args.tokenizer_mode, @@ -154,9 +153,6 @@ def __init__( tokenizer_mode=server_args.tokenizer_mode, trust_remote_code=server_args.trust_remote_code, ) - self.is_generation = is_generation_model( - self.model_config.hf_config.architectures, self.server_args.is_embedding - ) # Launch a tensor parallel worker if self.enable_overlap: diff --git a/python/sglang/srt/managers/tokenizer_manager.py b/python/sglang/srt/managers/tokenizer_manager.py index 1a9dc5e2bb..cc9e2bd262 100644 --- a/python/sglang/srt/managers/tokenizer_manager.py +++ b/python/sglang/srt/managers/tokenizer_manager.py @@ -18,7 +18,6 @@ import asyncio import copy import dataclasses -import json import logging import os import signal @@ -31,12 +30,8 @@ import zmq.asyncio from fastapi import BackgroundTasks -from sglang.srt.hf_transformers_utils import ( - get_config, - get_context_length, - get_processor, - get_tokenizer, -) +from sglang.srt.configs.model_config import ModelConfig +from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer from sglang.srt.managers.image_processor import ( get_dummy_image_processor, get_image_processor, @@ -59,12 +54,7 @@ ) from sglang.srt.sampling.sampling_params import SamplingParams from sglang.srt.server_args import PortArgs, ServerArgs -from sglang.srt.utils import ( - get_zmq_socket, - is_generation_model, - is_multimodal_model, - kill_child_process, -) +from sglang.srt.utils import get_zmq_socket, kill_child_process asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) @@ -103,18 +93,17 @@ def __init__( # Read model args self.model_path = server_args.model_path self.served_model_name = server_args.served_model_name - self.hf_config = get_config( - self.model_path, + self.model_config = ModelConfig( + server_args.model_path, trust_remote_code=server_args.trust_remote_code, - model_override_args=json.loads(server_args.json_model_override_args), - ) - self.is_generation = is_generation_model( - self.hf_config.architectures, self.server_args.is_embedding - ) - self.context_len = server_args.context_length or get_context_length( - self.hf_config + context_length=server_args.context_length, + model_override_args=server_args.json_model_override_args, + is_embedding=server_args.is_embedding, ) + self.is_generation = self.model_config.is_generation + self.context_len = self.model_config.context_len + # Create image processor placeholder self.image_processor = get_dummy_image_processor() @@ -122,7 +111,7 @@ def __init__( if server_args.skip_tokenizer_init: self.tokenizer = self.processor = None else: - if is_multimodal_model(self.hf_config.architectures): + if self.model_config.is_multimodal: self.processor = get_processor( server_args.tokenizer_path, tokenizer_mode=server_args.tokenizer_mode, @@ -133,7 +122,7 @@ def __init__( # We want to parallelize the image pre-processing so we create an executor for it self.image_processor = get_image_processor( - self.hf_config, server_args, self.processor + self.model_config.hf_config, server_args, self.processor ) else: self.tokenizer = get_tokenizer( diff --git a/python/sglang/srt/managers/tp_worker.py b/python/sglang/srt/managers/tp_worker.py index 561bfd77c5..8bec1a18c9 100644 --- a/python/sglang/srt/managers/tp_worker.py +++ b/python/sglang/srt/managers/tp_worker.py @@ -15,7 +15,6 @@ """A tensor parallel worker.""" -import json import logging from typing import Optional @@ -26,7 +25,7 @@ from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.model_executor.model_runner import ModelRunner from sglang.srt.server_args import ServerArgs -from sglang.srt.utils import broadcast_pyobj, is_multimodal_model, set_random_seed +from sglang.srt.utils import broadcast_pyobj, set_random_seed logger = logging.getLogger(__name__) @@ -48,9 +47,10 @@ def __init__( # Init model and tokenizer self.model_config = ModelConfig( server_args.model_path, - server_args.trust_remote_code, + trust_remote_code=server_args.trust_remote_code, context_length=server_args.context_length, - model_override_args=json.loads(server_args.json_model_override_args), + model_override_args=server_args.json_model_override_args, + is_embedding=server_args.is_embedding, ) self.model_runner = ModelRunner( model_config=self.model_config, @@ -64,7 +64,7 @@ def __init__( if server_args.skip_tokenizer_init: self.tokenizer = self.processor = None else: - if is_multimodal_model(self.model_config.hf_config.architectures): + if self.model_config.is_multimodal: self.processor = get_processor( server_args.tokenizer_path, tokenizer_mode=server_args.tokenizer_mode, diff --git a/python/sglang/srt/model_executor/model_runner.py b/python/sglang/srt/model_executor/model_runner.py index 583cbd968c..1dde62943a 100644 --- a/python/sglang/srt/model_executor/model_runner.py +++ b/python/sglang/srt/model_executor/model_runner.py @@ -59,11 +59,6 @@ from sglang.srt.utils import ( enable_show_time_cost, get_available_gpu_memory, - is_attention_free_model, - is_embedding_model, - is_generation_model, - is_multimodal_model, - model_has_inner_state, monkey_patch_vllm_dummy_weight_loader, monkey_patch_vllm_p2p_access_check, ) @@ -93,9 +88,8 @@ def __init__( self.tp_size = tp_size self.dist_port = nccl_port self.server_args = server_args - self.is_multimodal_model = is_multimodal_model( - self.model_config.hf_config.architectures - ) + self.is_generation = model_config.is_generation + self.is_multimodal = model_config.is_multimodal # Model-specific adjustment if ( @@ -119,7 +113,7 @@ def __init__( self.server_args.ds_heavy_channel_type ) - if self.is_multimodal_model: + if self.is_multimodal: logger.warning( "Automatically turn off --chunked-prefill-size and adjust --mem-fraction-static for multimodal models." ) @@ -270,9 +264,6 @@ def load_model(self): if hasattr(self.model, "get_attention_sliding_window_size") else None ) - self.is_generation = is_generation_model( - self.model_config.hf_config.architectures, self.server_args.is_embedding - ) logger.info( f"Load weight end. " @@ -679,7 +670,7 @@ def load_model_cls_srt(model_arch: str) -> Optional[Type[nn.Module]]: # Monkey patch model loader setattr(ModelRegistry, "_try_load_model_cls", load_model_cls_srt) -setattr(ModelRegistry, "is_multimodal_model", is_multimodal_model) -setattr(ModelRegistry, "is_attention_free_model", is_attention_free_model) -setattr(ModelRegistry, "model_has_inner_state", model_has_inner_state) -setattr(ModelRegistry, "is_embedding_model", is_embedding_model) +setattr(ModelRegistry, "is_multimodal_model", lambda model_architectures: False) +setattr(ModelRegistry, "is_attention_free_model", lambda model_architectures: False) +setattr(ModelRegistry, "model_has_inner_state", lambda model_architectures: False) +setattr(ModelRegistry, "is_embedding_model", lambda model_architectures: False) diff --git a/python/sglang/srt/models/llama.py b/python/sglang/srt/models/llama.py index ab86a55b9e..a9eaa81c1c 100644 --- a/python/sglang/srt/models/llama.py +++ b/python/sglang/srt/models/llama.py @@ -409,11 +409,13 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): if ( hasattr(self.config, "tie_word_embeddings") and self.config.tie_word_embeddings + and "lm_head.weight" in params_dict ): # Tie output embedding layer to input embedding layer, to solve issues where lm_head.weight is missing param = self.lm_head.weight weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, self.model.embed_tokens.weight) + apply_torchao_config_(self, params_dict, set(["proj.weight"])) diff --git a/python/sglang/srt/models/qwen2_vl.py b/python/sglang/srt/models/qwen2_vl.py index 80afee557d..59adb2ee77 100644 --- a/python/sglang/srt/models/qwen2_vl.py +++ b/python/sglang/srt/models/qwen2_vl.py @@ -23,7 +23,7 @@ # limitations under the License. """Inference-only Qwen2-VL model compatible with HuggingFace weights.""" from functools import lru_cache, partial -from typing import Iterable, List, Mapping, Optional, Tuple, Type, TypedDict, Union +from typing import Iterable, List, Optional, Tuple, Type, TypedDict import numpy as np import torch @@ -36,7 +36,6 @@ from vllm.logger import init_logger from vllm.model_executor.layers.activation import QuickGELU from vllm.model_executor.model_loader.weight_utils import default_weight_loader -from vllm.model_executor.models.interfaces import SupportsMultiModal from sglang.srt.configs import Qwen2VLConfig, Qwen2VLVisionConfig from sglang.srt.hf_transformers_utils import get_processor @@ -486,7 +485,7 @@ def forward( cached_get_processor = lru_cache(get_processor) -class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal): +class Qwen2VLForConditionalGeneration(nn.Module): def calculate_num_image_tokens(self, image_grid_thw: Tuple[int, int, int]): processor = cached_get_processor(self.config._name_or_path) grid_t, grid_h, grid_w = image_grid_thw @@ -536,15 +535,12 @@ def pad_input_ids(self, input_ids: List[int], image_inputs: ImageInputs): def __init__( self, config: Qwen2VLConfig, - multimodal_config: MultiModalConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() self.config = config - self.multimodal_config = multimodal_config - self.visual = Qwen2VisionTransformer( config.vision_config, norm_eps=getattr(config, "rms_norm_eps", 1e-6), diff --git a/python/sglang/srt/utils.py b/python/sglang/srt/utils.py index 2be3a298ec..0c3ae0c5a7 100644 --- a/python/sglang/srt/utils.py +++ b/python/sglang/srt/utils.py @@ -204,56 +204,6 @@ def is_port_available(port): return False -def is_multimodal_model(model_architectures): - if ( - "LlavaLlamaForCausalLM" in model_architectures - or "LlavaQwenForCausalLM" in model_architectures - or "LlavaMistralForCausalLM" in model_architectures - or "LlavaVidForCausalLM" in model_architectures - or "MllamaForConditionalGeneration" in model_architectures - or "Qwen2VLForConditionalGeneration" in model_architectures - ): - return True - else: - return False - - -def is_attention_free_model(model_architectures): - return False - - -def model_has_inner_state(model_architectures): - return False - - -def is_embedding_model(model_architectures): - if ( - "LlamaEmbeddingModel" in model_architectures - or "MistralModel" in model_architectures - or "LlamaForSequenceClassification" in model_architectures - or "LlamaForSequenceClassificationWithNormal_Weights" in model_architectures - ): - return True - else: - return False - - -def is_generation_model(model_architectures, is_embedding: bool = False): - # We have two ways to determine whether a model is a generative model. - # 1. Check the model architectue - # 2. check the `is_embedding` server args - - if ( - "LlamaEmbeddingModel" in model_architectures - or "MistralModel" in model_architectures - or "LlamaForSequenceClassification" in model_architectures - or "LlamaForSequenceClassificationWithNormal_Weights" in model_architectures - ): - return False - else: - return not is_embedding - - def decode_video_base64(video_base64): from PIL import Image diff --git a/test/srt/test_cache_report.py b/test/srt/test_cache_report.py index dfc140d587..b790c3ae69 100644 --- a/test/srt/test_cache_report.py +++ b/test/srt/test_cache_report.py @@ -1,5 +1,4 @@ import asyncio -import json import unittest import openai From 1363b51983415a5180fd3981e676a6b20ea77ed8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?I=C3=B1aki=20Arango?= Date: Sun, 3 Nov 2024 12:27:11 -0800 Subject: [PATCH 31/44] Escape backwards slash (#1902) --- python/sglang/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/sglang/utils.py b/python/sglang/utils.py index ef4713bd2f..9c1fc67950 100644 --- a/python/sglang/utils.py +++ b/python/sglang/utils.py @@ -305,7 +305,7 @@ def execute_shell_command(command: str) -> subprocess.Popen: Execute a shell command and return the process handle Args: - command: Shell command as a string (can include \ line continuations) + command: Shell command as a string (can include \\ line continuations) Returns: subprocess.Popen: Process handle """ @@ -354,4 +354,4 @@ def terminate_process(process): def print_highlight(html_content: str): html_content = str(html_content).replace("\n", "
") - display(HTML(f"{html_content}")) \ No newline at end of file + display(HTML(f"{html_content}")) From 793b79dbe901fd2f4257744125f15edcc14567f4 Mon Sep 17 00:00:00 2001 From: Yineng Zhang Date: Sun, 3 Nov 2024 12:56:10 -0800 Subject: [PATCH 32/44] feat: support truss endpoint for benchmark serving (#1906) --- python/sglang/bench_serving.py | 92 ++++++++++++++++++++++++++++++++++ 1 file changed, 92 insertions(+) diff --git a/python/sglang/bench_serving.py b/python/sglang/bench_serving.py index 2ca35aca95..8bb452cd06 100644 --- a/python/sglang/bench_serving.py +++ b/python/sglang/bench_serving.py @@ -222,6 +222,85 @@ async def async_request_openai_completions( return output +async def async_request_truss( + request_func_input: RequestFuncInput, + pbar: Optional[tqdm] = None, +) -> RequestFuncOutput: + api_url = request_func_input.api_url + + prompt = request_func_input.prompt + + async with aiohttp.ClientSession(timeout=AIOHTTP_TIMEOUT) as session: + payload = { + "model": request_func_input.model, + "prompt": prompt, + "temperature": 0.0, + "best_of": 1, + "max_tokens": request_func_input.output_len, + "stream": not args.disable_stream, + "ignore_eos": not args.disable_ignore_eos, + **request_func_input.extra_request_body, + } + headers = {"Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}"} + + output = RequestFuncOutput() + output.prompt_len = request_func_input.prompt_len + + generated_text = "" + ttft = 0.0 + st = time.perf_counter() + most_recent_timestamp = st + try: + async with session.post( + url=api_url, json=payload, headers=headers + ) as response: + if response.status == 200: + async for chunk_bytes in response.content: + chunk_bytes = chunk_bytes.strip() + if not chunk_bytes: + continue + + chunk = remove_prefix(chunk_bytes.decode("utf-8"), "data: ") + latency = time.perf_counter() - st + if chunk == "[DONE]": + pass + else: + data = json.loads(chunk) + + # NOTE: Some completion API might have a last + # usage summary response without a token so we + # want to check a token was generated + if data["choices"][0]["delta"]["content"]: + timestamp = time.perf_counter() + # First token + if ttft == 0.0: + ttft = time.perf_counter() - st + output.ttft = ttft + + # Decoding phase + else: + output.itl.append(timestamp - most_recent_timestamp) + + most_recent_timestamp = timestamp + generated_text += data["choices"][0]["delta"]["content"] + + output.generated_text = generated_text + output.success = True + output.latency = latency + output.output_len = request_func_input.output_len + else: + output.error = response.reason or "" + output.success = False + except Exception: + output.success = False + exc_info = sys.exc_info() + output.error = "".join(traceback.format_exception(*exc_info)) + + if pbar: + pbar.update(1) + return output + + async def async_request_sglang_generate( request_func_input: RequestFuncInput, pbar: Optional[tqdm] = None, @@ -350,6 +429,7 @@ def get_tokenizer( "lmdeploy": async_request_openai_completions, "trt": async_request_trt_llm, "gserver": async_request_gserver, + "truss": async_request_truss, } @@ -873,6 +953,7 @@ def run_benchmark(args_: argparse.Namespace): "vllm": 8000, "trt": 8000, "gserver": 9988, + "truss": 8080, }.get(args.backend, 30000) model_url = ( @@ -905,9 +986,20 @@ def run_benchmark(args_: argparse.Namespace): elif args.backend == "gserver": api_url = args.base_url if args.base_url else f"{args.host}:{args.port}" args.model = args.model or "default" + elif args.backend == "truss": + api_url = ( + f"{args.base_url}/v1/models/model:predict" + if args.base_url + else f"http://{args.host}:{args.port}/v1/models/model:predict" + ) # Get model name if args.model is None: + if args.backend == "truss": + print( + "Please provide a model with `--model` when using truss backend. e.g. --model meta-llama/Llama-3.1-8B-Instruct" + ) + sys.exit(1) try: response = requests.get(model_url) model_list = response.json().get("data", []) From 2ce32db6fb317c252f9c877880c1b5dd47dca7b6 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Sun, 3 Nov 2024 13:27:12 -0800 Subject: [PATCH 33/44] Let reward model take text inputs instead of message lists (#1907) Co-authored-by: Kyle Corbitt --- python/sglang/srt/hf_transformers_utils.py | 7 ++----- python/sglang/srt/managers/io_struct.py | 4 ++-- python/sglang/srt/models/llama.py | 12 ++++++++--- python/sglang/srt/models/llama_embedding.py | 12 ++--------- python/sglang/srt/models/llama_reward.py | 5 +++++ python/sglang/srt/models/qwen2_vl.py | 2 +- python/sglang/srt/server.py | 22 +++------------------ python/sglang/test/runners.py | 3 ++- test/srt/models/test_embedding_models.py | 9 ++++----- test/srt/models/test_generation_models.py | 3 ++- test/srt/models/test_reward_models.py | 20 +++++++++---------- test/srt/run_suite.py | 2 +- 12 files changed, 43 insertions(+), 58 deletions(-) diff --git a/python/sglang/srt/hf_transformers_utils.py b/python/sglang/srt/hf_transformers_utils.py index 7a8751043f..b1752f89e0 100644 --- a/python/sglang/srt/hf_transformers_utils.py +++ b/python/sglang/srt/hf_transformers_utils.py @@ -88,11 +88,8 @@ def get_config( def get_context_length(config): - """Get the context length of a model from a huggingface model configs. - And here the config should be text_config part if the model is a multimodal - LLM. - """ - text_config = getattr(config, "text_config", config) + """Get the context length of a model from a huggingface model configs.""" + text_config = config rope_scaling = getattr(text_config, "rope_scaling", None) if rope_scaling: rope_scaling_factor = rope_scaling.get("factor", 1) diff --git a/python/sglang/srt/managers/io_struct.py b/python/sglang/srt/managers/io_struct.py index 339638c0a8..9c5ed14f39 100644 --- a/python/sglang/srt/managers/io_struct.py +++ b/python/sglang/srt/managers/io_struct.py @@ -238,7 +238,7 @@ def normalize_batch_and_arguments(self): self.rid = uuid.uuid4().hex if self.sampling_params is None: self.sampling_params = {} - self.sampling_params["max_new_tokens"] = 1 + self.sampling_params["max_new_tokens"] = 0 else: if self.rid is None: self.rid = [uuid.uuid4().hex for _ in range(self.batch_size)] @@ -248,7 +248,7 @@ def normalize_batch_and_arguments(self): if self.sampling_params is None: self.sampling_params = [{}] * self.batch_size for i in range(self.batch_size): - self.sampling_params[i]["max_new_tokens"] = 1 + self.sampling_params[i]["max_new_tokens"] = 0 def regenerate_rid(self): self.rid = uuid.uuid4().hex diff --git a/python/sglang/srt/models/llama.py b/python/sglang/srt/models/llama.py index a9eaa81c1c..a6f460846d 100644 --- a/python/sglang/srt/models/llama.py +++ b/python/sglang/srt/models/llama.py @@ -34,6 +34,7 @@ RowParallelLinear, ) from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput +from sglang.srt.layers.pooler import Pooler, PoolingType from sglang.srt.layers.quantization.base_config import QuantizationConfig from sglang.srt.layers.radix_attention import RadixAttention from sglang.srt.layers.torchao_utils import apply_torchao_config_ @@ -303,6 +304,7 @@ def __init__( self.model = LlamaModel(config, quant_config=quant_config) self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size) self.logits_processor = LogitsProcessor(config) + self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True) @torch.no_grad() def forward( @@ -311,11 +313,15 @@ def forward( positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: torch.Tensor = None, + get_embedding: bool = False, ) -> LogitsProcessorOutput: hidden_states = self.model(input_ids, positions, forward_batch, input_embeds) - return self.logits_processor( - input_ids, hidden_states, self.lm_head.weight, forward_batch - ) + if not get_embedding: + return self.logits_processor( + input_ids, hidden_states, self.lm_head.weight, forward_batch + ) + else: + return self.pooler(hidden_states, forward_batch) def get_hidden_dim(self, module_name): # return input_dim, output_dim diff --git a/python/sglang/srt/models/llama_embedding.py b/python/sglang/srt/models/llama_embedding.py index 19e324f92f..da43d03fca 100644 --- a/python/sglang/srt/models/llama_embedding.py +++ b/python/sglang/srt/models/llama_embedding.py @@ -36,9 +36,7 @@ def forward( hidden_states = self.model(input_ids, positions, forward_batch, input_embeds) return self.pooler(hidden_states, forward_batch) - def load_weights( - self, weights: Iterable[Tuple[str, torch.Tensor]], name=None, loaded_weight=None - ): + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), @@ -49,7 +47,7 @@ def load_weights( ] params_dict = dict(self.model.named_parameters()) - def load_weights_per_param(name, loaded_weight): + for name, loaded_weight in weights: if "rotary_emb.inv_freq" in name or "projector" in name: return if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name: @@ -78,12 +76,6 @@ def load_weights_per_param(name, loaded_weight): weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) - if name is None or loaded_weight is None: - for name, loaded_weight in weights: - load_weights_per_param(name, loaded_weight) - else: - load_weights_per_param(name, loaded_weight) - class MistralModel(LlamaEmbeddingModel): pass diff --git a/python/sglang/srt/models/llama_reward.py b/python/sglang/srt/models/llama_reward.py index 2e9c0457f0..5b68d1d321 100644 --- a/python/sglang/srt/models/llama_reward.py +++ b/python/sglang/srt/models/llama_reward.py @@ -52,7 +52,12 @@ def forward( positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: torch.Tensor = None, + get_embedding: bool = True, ) -> EmbeddingPoolerOutput: + assert ( + get_embedding + ), "LlamaForSequenceClassification is only used for embedding" + hidden_states = self.model(input_ids, positions, forward_batch, input_embeds) scores = self.score(hidden_states) diff --git a/python/sglang/srt/models/qwen2_vl.py b/python/sglang/srt/models/qwen2_vl.py index 59adb2ee77..82412a51a1 100644 --- a/python/sglang/srt/models/qwen2_vl.py +++ b/python/sglang/srt/models/qwen2_vl.py @@ -618,7 +618,7 @@ def forward( extend_start_loc_cpu = forward_batch.extend_start_loc.cpu().numpy() prefix_lens_cpu = forward_batch.extend_prefix_lens.cpu().numpy() for i, image in enumerate(forward_batch.image_inputs): - if image == None: + if image is None: continue start_idx = extend_start_loc_cpu[i] prefix_len = prefix_lens_cpu[i] diff --git a/python/sglang/srt/server.py b/python/sglang/srt/server.py index 854a0c658c..d96176b2de 100644 --- a/python/sglang/srt/server.py +++ b/python/sglang/srt/server.py @@ -254,7 +254,7 @@ async def encode_request(obj: EmbeddingReqInput, request: Request): async def judge_request(obj: EmbeddingReqInput, request: Request): - """Handle a reward model request.""" + """Handle a reward model request. Now the arguments and return values are the same as embedding models.""" try: ret = await tokenizer_manager.generate_request(obj, request).__anext__() return ret @@ -696,24 +696,8 @@ def encode( self, prompt: Union[str, List[str], List[Dict], List[List[Dict]]], ): - if isinstance(prompt, str) or isinstance(prompt[0], str): - # embedding - json_data = { - "text": prompt, - } - response = requests.post( - self.url + "/encode", - json=json_data, - ) - else: - # reward - json_data = { - "conv": prompt, - } - response = requests.post( - self.url + "/judge", - json=json_data, - ) + json_data = {"text": prompt} + response = requests.post(self.url + "/encode", json=json_data) return json.dumps(response.json()) def __del__(self): diff --git a/python/sglang/test/runners.py b/python/sglang/test/runners.py index 217065bd20..3870c45038 100644 --- a/python/sglang/test/runners.py +++ b/python/sglang/test/runners.py @@ -273,6 +273,7 @@ def __init__( disable_cuda_graph=disable_cuda_graph, disable_radix_cache=disable_radix_cache, ) + self.tokenizer = get_tokenizer(model_path) def forward( self, @@ -366,7 +367,7 @@ def batch_forward( return ModelOutput(embed_logits=logits) else: scores = [x["embedding"][0] for x in response] - return ModelOutput(scores=logits) + return ModelOutput(scores=scores) def __enter__(self): return self diff --git a/test/srt/models/test_embedding_models.py b/test/srt/models/test_embedding_models.py index 3ad187cbbf..aefe4f3e7d 100644 --- a/test/srt/models/test_embedding_models.py +++ b/test/srt/models/test_embedding_models.py @@ -30,6 +30,10 @@ class TestEmbeddingModels(unittest.TestCase): + @classmethod + def setUpClass(cls): + mp.set_start_method("spawn", force=True) + def assert_close_prefill_logits( self, prompts, @@ -74,9 +78,4 @@ def test_prefill_logits(self): if __name__ == "__main__": - try: - mp.set_start_method("spawn") - except RuntimeError: - pass - unittest.main() diff --git a/test/srt/models/test_generation_models.py b/test/srt/models/test_generation_models.py index 1d32b8af12..b4c2cde2de 100755 --- a/test/srt/models/test_generation_models.py +++ b/test/srt/models/test_generation_models.py @@ -63,9 +63,10 @@ class ModelCase: class TestGenerationModels(unittest.TestCase): + @classmethod def setUpClass(cls): - mp.set_start_method("spawn") + mp.set_start_method("spawn", force=True) def assert_close_logits_and_output_strs( self, diff --git a/test/srt/models/test_reward_models.py b/test/srt/models/test_reward_models.py index cd15b49670..499f7822ad 100644 --- a/test/srt/models/test_reward_models.py +++ b/test/srt/models/test_reward_models.py @@ -18,10 +18,10 @@ import torch -from sglang.test.runners import DEFAULT_PROMPTS, HFRunner, SRTRunner +from sglang.test.runners import HFRunner, SRTRunner MODELS = [ - ("LxzGordon/URM-LLaMa-3.1-8B", 1, 2e-2), + ("LxzGordon/URM-LLaMa-3.1-8B", 1, 3e-2), ] TORCH_DTYPES = [torch.float16] @@ -43,6 +43,10 @@ class TestRewardModels(unittest.TestCase): + @classmethod + def setUpClass(cls): + mp.set_start_method("spawn", force=True) + def assert_close_reward_scores( self, convs, @@ -63,12 +67,13 @@ def assert_close_reward_scores( torch_dtype=torch_dtype, model_type="reward", ) as srt_runner: - srt_outputs = srt_runner.forward(convs) + prompts = srt_runner.tokenizer.apply_chat_template(convs, tokenize=False) + srt_outputs = srt_runner.forward(prompts) hf_scores = torch.tensor(hf_outputs.scores) srt_scores = torch.tensor(srt_outputs.scores) - print(hf_scores) - print(srt_scores) + print(f"{hf_scores=}") + print(f"{srt_scores=}") assert torch.all( abs(hf_scores - srt_scores) < tolerance @@ -83,9 +88,4 @@ def test_reward_scores(self): if __name__ == "__main__": - try: - mp.set_start_method("spawn") - except RuntimeError: - pass - unittest.main() diff --git a/test/srt/run_suite.py b/test/srt/run_suite.py index ffc8e84f24..f7277f03da 100644 --- a/test/srt/run_suite.py +++ b/test/srt/run_suite.py @@ -8,7 +8,7 @@ "models/test_embedding_models.py", "models/test_generation_models.py", "models/test_lora.py", - # "models/test_reward_models.py", + "models/test_reward_models.py", "sampling/penaltylib", "test_chunked_prefill.py", "test_double_sparsity.py", From 65859754f1463ce280bbaaf68d04797705849240 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Sun, 3 Nov 2024 13:48:11 -0800 Subject: [PATCH 34/44] Release v0.3.5 (#1908) --- docker/Dockerfile.rocm | 2 +- docs/start/install.md | 2 +- python/pyproject.toml | 2 +- python/sglang/version.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docker/Dockerfile.rocm b/docker/Dockerfile.rocm index 003434454b..42b3135955 100644 --- a/docker/Dockerfile.rocm +++ b/docker/Dockerfile.rocm @@ -1,5 +1,5 @@ # Usage (to build SGLang ROCm docker image): -# docker build --build-arg SGL_BRANCH=v0.3.4.post2 -t testImage -f Dockerfile.rocm . +# docker build --build-arg SGL_BRANCH=v0.3.5 -t testImage -f Dockerfile.rocm . # default base image ARG BASE_IMAGE="rocm/vllm-dev:20241022" diff --git a/docs/start/install.md b/docs/start/install.md index c203cc4d0f..57b899da32 100644 --- a/docs/start/install.md +++ b/docs/start/install.md @@ -16,7 +16,7 @@ Note: Please check the [FlashInfer installation doc](https://docs.flashinfer.ai/ ## Method 2: From source ``` # Use the last release branch -git clone -b v0.3.4.post2 https://github.com/sgl-project/sglang.git +git clone -b v0.3.5 https://github.com/sgl-project/sglang.git cd sglang pip install --upgrade pip diff --git a/python/pyproject.toml b/python/pyproject.toml index d9749e1aba..6239963f49 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "sglang" -version = "0.3.4.post2" +version = "0.3.5" description = "SGLang is yet another fast serving framework for large language models and vision language models." readme = "README.md" requires-python = ">=3.8" diff --git a/python/sglang/version.py b/python/sglang/version.py index 6dff575327..a8d4557d26 100644 --- a/python/sglang/version.py +++ b/python/sglang/version.py @@ -1 +1 @@ -__version__ = "0.3.4.post2" +__version__ = "0.3.5" From 1853c3523bf13fd6664d8e68b4744f646c53f9d6 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Sun, 3 Nov 2024 14:18:16 -0800 Subject: [PATCH 35/44] Fix regex docs (#1909) --- docs/backend/openai_api_completions.ipynb | 73 ++++++++++++++++++++++- docs/references/sampling_params.md | 47 +++++++++++++++ 2 files changed, 118 insertions(+), 2 deletions(-) diff --git a/docs/backend/openai_api_completions.ipynb b/docs/backend/openai_api_completions.ipynb index 2f4b988d5d..e39b328f14 100644 --- a/docs/backend/openai_api_completions.ipynb +++ b/docs/backend/openai_api_completions.ipynb @@ -87,7 +87,6 @@ "response = client.chat.completions.create(\n", " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", " messages=[\n", - " {\"role\": \"system\", \"content\": \"You are a helpful AI assistant\"},\n", " {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n", " ],\n", " temperature=0,\n", @@ -184,7 +183,6 @@ "## Completions\n", "\n", "### Usage\n", - "\n", "Completions API is similar to Chat Completions API, but without the `messages` parameter or chat templates." ] }, @@ -253,6 +251,77 @@ "print_highlight(f\"Response: {response}\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Structured decoding (JSON, Regex)\n", + "You can specify a JSON schema or a regular expression to constrain the model output. The model output will be guaranteed to follow the given constraints.\n", + "\n", + "### JSON" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "json_schema = json.dumps(\n", + " {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n", + " \"population\": {\"type\": \"integer\"},\n", + " },\n", + " \"required\": [\"name\", \"population\"],\n", + " }\n", + ")\n", + "\n", + "response = client.chat.completions.create(\n", + " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": \"Give me the information of the capital of France in the JSON format.\"},\n", + " ],\n", + " temperature=0,\n", + " max_tokens=128,\n", + " response_format={\n", + " \"type\": \"json_schema\",\n", + " \"json_schema\": {\"name\": \"foo\", \"schema\": json.loads(json_schema)},\n", + " },\n", + ")\n", + "\n", + "print_highlight(response.choices[0].message.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Regular expression" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = client.chat.completions.create(\n", + " model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": \"What is the capital of France?\"},\n", + " ],\n", + " temperature=0,\n", + " max_tokens=128,\n", + " extra_body={\"regex\": \"(Paris|London)\"},\n", + ")\n", + "\n", + "print_highlight(response.choices[0].message.content)" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/docs/references/sampling_params.md b/docs/references/sampling_params.md index 21850e9351..333b8fcd15 100644 --- a/docs/references/sampling_params.md +++ b/docs/references/sampling_params.md @@ -177,3 +177,50 @@ print(response.json()) The `image_data` can be a file name, a URL, or a base64 encoded string. See also `python/sglang/srt/utils.py:load_image`. Streaming is supported in a similar manner as [above](#streaming). + +### Structured decoding (JSON, Regex) +You can specify a JSON schema or a regular expression to constrain the model output. The model output will be guaranteed to follow the given constraints. + +```python +import json +import requests + +json_schema = json.dumps( + { + "type": "object", + "properties": { + "name": {"type": "string", "pattern": "^[\\w]+$"}, + "population": {"type": "integer"}, + }, + "required": ["name", "population"], + } +) + +# JSON +response = requests.post( + "http://localhost:30000/generate", + json={ + "text": "Here is the information of the capital of France in the JSON format.\n", + "sampling_params": { + "temperature": 0, + "max_new_tokens": 64, + "json_schema": json_schema, + }, + }, +) +print(response.json()) + +# Regular expression +response = requests.post( + "http://localhost:30000/generate", + json={ + "text": "Paris is the capital of", + "sampling_params": { + "temperature": 0, + "max_new_tokens": 64, + "regex": "(France|England)", + }, + }, +) +print(response.json()) +``` \ No newline at end of file From 704f8e8ed1a4ab992ac626bc91cd62e4909faa8f Mon Sep 17 00:00:00 2001 From: Chayenne Date: Sun, 3 Nov 2024 22:33:03 -0800 Subject: [PATCH 36/44] Add Reward API Docs etc (#1910) Co-authored-by: Chayenne --- .github/workflows/release-docs.yml | 3 +- README.md | 8 ++- docs/backend/native_api.ipynb | 81 ++++++++++++++++++++++++--- docs/index.rst | 2 +- docs/start/send_request.ipynb | 11 +++- test/srt/models/test_reward_models.py | 1 + 6 files changed, 90 insertions(+), 16 deletions(-) diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index d84cbce62a..99bd546161 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -48,9 +48,10 @@ jobs: make html cd _build/html - git clone https://$GITHUB_TOKEN@github.com/sgl-project/sgl-project.github.io.git ../sgl-project.github.io + git clone https://$GITHUB_TOKEN@github.com/sgl-project/sgl-project.github.io.git ../sgl-project.github.io --depth 1 rm -rf ../sgl-project.github.io/* cp -r * ../sgl-project.github.io + cp ../../README.md ../sgl-project.github.io/README.md cd ../sgl-project.github.io git config user.name "zhaochenyang20" git config user.email "zhaochenyang20@gmail.com" diff --git a/README.md b/README.md index 497b7e344a..4017d282bd 100644 --- a/README.md +++ b/README.md @@ -36,11 +36,13 @@ The core features include: - **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (INT4/FP8/AWQ/GPTQ). - **Flexible Frontend Language**: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions. -- **Extensive Model Support**: Supports a wide range of generative models (Llama, Gemma, Mistral, QWen, DeepSeek, LLaVA, etc.) and embedding models (e5-mistral), with easy extensibility for integrating new models. +- **Extensive Model Support**: Supports a wide range of generative models (Llama, Gemma, Mistral, QWen, DeepSeek, LLaVA, etc.), embedding models (e5-mistral, gte) and reward models (Skywork), with easy extensibility for integrating new models. - **Active Community**: SGLang is open-source and backed by an active community with industry adoption. -## Install -See [https://sgl-project.github.io/start/install.html](https://sgl-project.github.io/start/install.html) +## Getting Started +Install SGLang: See [https://sgl-project.github.io/start/install.html](https://sgl-project.github.io/start/install.html) + +Send requests: See [https://sgl-project.github.io/start/send_request.html](https://sgl-project.github.io/start/send_request.html) ## Backend: SGLang Runtime (SRT) See [https://sgl-project.github.io/backend/backend.html](https://sgl-project.github.io/backend/backend.html) diff --git a/docs/backend/native_api.ipynb b/docs/backend/native_api.ipynb index 39cd12cd31..95af6344a0 100644 --- a/docs/backend/native_api.ipynb +++ b/docs/backend/native_api.ipynb @@ -5,9 +5,10 @@ "metadata": {}, "source": [ "# Native APIs\n", + "\n", "Apart from the OpenAI compatible APIs, the SGLang Runtime also provides its native server APIs. We introduce these following APIs:\n", "\n", - "- `/generate`\n", + "- `/generate` (text generation model)\n", "- `/get_server_args`\n", "- `/get_model_info`\n", "- `/health`\n", @@ -15,7 +16,8 @@ "- `/flush_cache`\n", "- `/get_memory_pool_size`\n", "- `/update_weights`\n", - "- `/encode`\n", + "- `/encode`(embedding model)\n", + "- `/judge`(reward model)\n", "\n", "We mainly use `requests` to test these APIs in the following examples. You can also use `curl`." ] @@ -40,6 +42,8 @@ " print_highlight,\n", ")\n", "\n", + "import requests\n", + "\n", "server_process = execute_shell_command(\n", " \"\"\"\n", "python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-1B-Instruct --port=30010\n", @@ -53,7 +57,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Generate\n", + "## Generate (text generation model)\n", "Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](../references/sampling_params.html)." ] }, @@ -63,8 +67,6 @@ "metadata": {}, "outputs": [], "source": [ - "import requests\n", - "\n", "url = \"http://localhost:30010/generate\"\n", "data = {\"text\": \"What is the capital of France?\"}\n", "\n", @@ -252,7 +254,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Encode\n", + "## Encode (embedding model)\n", "\n", "Encode text into embeddings. Note that this API is only available for [embedding models](openai_api_embeddings.html#openai-apis-embedding) and will raise an error for generation models.\n", "Therefore, we launch a new server to server an embedding model." @@ -292,13 +294,76 @@ "print_highlight(f\"Text embedding (first 10): {response_json['embedding'][:10]}\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Judge (reward model)\n", + "\n", + "SGLang Runtime also supports reward models. Here we use a reward model to judge the quality of pairwise generations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "terminate_process(embedding_process)\n", + "\n", + "# Note that SGLang now treats embedding models and reward models as the same type of models.\n", + "# This will be updated in the future.\n", + "\n", + "reward_process = execute_shell_command(\n", + " \"\"\"\n", + "python -m sglang.launch_server --model-path Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 --port 30030 --host 0.0.0.0 --is-embedding\n", + "\"\"\"\n", + ")\n", + "\n", + "wait_for_server(\"http://localhost:30030\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoTokenizer\n", + "\n", + "PROMPT = (\n", + " \"What is the range of the numeric output of a sigmoid node in a neural network?\"\n", + ")\n", + "\n", + "RESPONSE1 = \"The output of a sigmoid node is bounded between -1 and 1.\"\n", + "RESPONSE2 = \"The output of a sigmoid node is bounded between 0 and 1.\"\n", + "\n", + "CONVS = [\n", + " [{\"role\": \"user\", \"content\": PROMPT}, {\"role\": \"assistant\", \"content\": RESPONSE1}],\n", + " [{\"role\": \"user\", \"content\": PROMPT}, {\"role\": \"assistant\", \"content\": RESPONSE2}],\n", + "]\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(\"Skywork/Skywork-Reward-Llama-3.1-8B-v0.2\")\n", + "prompts = tokenizer.apply_chat_template(CONVS, tokenize=False)\n", + "\n", + "url = \"http://localhost:30030/judge\"\n", + "data = {\n", + " \"model\": \"Skywork/Skywork-Reward-Llama-3.1-8B-v0.2\", \n", + " \"text\": prompts\n", + "}\n", + "\n", + "responses = requests.post(url, json=data).json()\n", + "for response in responses:\n", + " print_highlight(f\"reward: {response['embedding'][0]}\")" + ] + }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "terminate_process(embedding_process)" + "terminate_process(reward_process)" ] } ], diff --git a/docs/index.rst b/docs/index.rst index 1f83acfb46..701d25d627 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -7,7 +7,7 @@ The core features include: - **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (INT4/FP8/AWQ/GPTQ). - **Flexible Frontend Language**: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions. -- **Extensive Model Support**: Supports a wide range of generative models (Llama 3, Gemma 2, Mistral, QWen, DeepSeek, LLaVA, etc.) and embedding models (e5-mistral), with easy extensibility for integrating new models. +- **Extensive Model Support**: Supports a wide range of generative models (Llama, Gemma, Mistral, QWen, DeepSeek, LLaVA, etc.), embedding models (e5-mistral, gte) and reward models (Skywork), with easy extensibility for integrating new models. - **Active Community**: SGLang is open-source and backed by an active community with industry adoption. diff --git a/docs/start/send_request.ipynb b/docs/start/send_request.ipynb index 2323368ee4..24d39bd71d 100644 --- a/docs/start/send_request.ipynb +++ b/docs/start/send_request.ipynb @@ -5,14 +5,19 @@ "metadata": {}, "source": [ "# Quick Start: Sending Requests\n", - "This notebook provides a quick-start guide for using SGLang after installation." + "This notebook provides a quick-start guide to use SGLang in chat completions after installation.\n", + "\n", + "- For Vision Language Models, see [OpenAI APIs - Vision](../backend/openai_api_vision.ipynb).\n", + "- For Embedding Models, see [OpenAI APIs - Embedding](../backend/openai_api_embeddings.ipynb) and [Encode (embedding model)](../backend/native_api.html#Encode-(embedding-model)).\n", + "- For Reward Models, see [Judge (reward model)](../backend/native_api.html#Judge-(reward-model))." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Launch a server\n", + "## Launch A Server\n", + "\n", "This code block is equivalent to executing \n", "\n", "```bash\n", @@ -254,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2024-11-01T02:46:52.898411Z", diff --git a/test/srt/models/test_reward_models.py b/test/srt/models/test_reward_models.py index 499f7822ad..eb89c9dff4 100644 --- a/test/srt/models/test_reward_models.py +++ b/test/srt/models/test_reward_models.py @@ -22,6 +22,7 @@ MODELS = [ ("LxzGordon/URM-LLaMa-3.1-8B", 1, 3e-2), + ("Skywork/Skywork-Reward-Llama-3.1-8B-v0.2", 1, 3e-2), ] TORCH_DTYPES = [torch.float16] From 3cd28092771bc8af4eaaa56d24586c26ca76a3d1 Mon Sep 17 00:00:00 2001 From: HAI Date: Mon, 4 Nov 2024 01:40:57 -0800 Subject: [PATCH 37/44] [Docs, ROCm] update install to cover ROCm with MI GPUs (#1915) --- docs/start/install.md | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/docs/start/install.md b/docs/start/install.md index 57b899da32..7a764f91af 100644 --- a/docs/start/install.md +++ b/docs/start/install.md @@ -7,7 +7,7 @@ You can install SGLang using any of the methods below. pip install --upgrade pip pip install "sglang[all]" -# Install FlashInfer accelerated kernels +# Install FlashInfer accelerated kernels (CUDA only for now) pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ ``` @@ -22,7 +22,7 @@ cd sglang pip install --upgrade pip pip install -e "python[all]" -# Install FlashInfer accelerated kernels +# Install FlashInfer accelerated kernels (CUDA only for now) pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/ ``` @@ -42,6 +42,25 @@ docker run --gpus all \ python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000 ``` +Note: To AMD ROCm system with Instinct/MI GPUs, it is recommended to use `docker/Dockerfile.rocm` to build images, example and usage as below: + +```bash +docker build --build-arg SGL_BRANCH=v0.3.5 -t v0.3.5-rocm620 -f Dockerfile.rocm . + +alias drun='docker run -it --rm --network=host --device=/dev/kfd --device=/dev/dri --ipc=host \ + --shm-size 16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \ + -v $HOME/dockerx:/dockerx -v /data:/data' + +drun -p 30000:30000 \ + -v ~/.cache/huggingface:/root/.cache/huggingface \ + --env "HF_TOKEN=" \ + v0.3.5-rocm620 \ + python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000 + +# Till flashinfer backend available, --attention-backend triton --sampling-backend pytorch are set by default +drun v0.3.5-rocm620 python3 -m sglang.bench_latency --batch-size 32 --input 1024 --output 128 --model amd/Meta-Llama-3.1-8B-Instruct-FP8-KV --tp 8 --quantization fp8 +``` + ## Method 4: Using docker compose
From 530ff541cf272956ad629a3703ecda80ff68fc63 Mon Sep 17 00:00:00 2001 From: Byron Hsu Date: Mon, 4 Nov 2024 10:56:52 -0800 Subject: [PATCH 38/44] [router] Impl radix tree and set up CI (#1893) Co-authored-by: Lianmin Zheng --- .github/workflows/pr-test-rust.yml | 39 ++++++ rust/Cargo.lock | 2 +- rust/Cargo.toml | 10 +- rust/readme.md | 15 +++ rust/src/lib.rs | 13 +- rust/src/main.rs | 7 +- rust/src/router.rs | 15 ++- rust/src/server.rs | 99 +++++++--------- rust/src/tree.rs | 184 +++++++++++++++++++++++++++++ rust/tests/test_tree.rs | 131 ++++++++++++++++++++ scripts/ci_install_rust.sh | 15 +++ 11 files changed, 458 insertions(+), 72 deletions(-) create mode 100644 .github/workflows/pr-test-rust.yml create mode 100644 rust/src/tree.rs create mode 100644 rust/tests/test_tree.rs create mode 100644 scripts/ci_install_rust.sh diff --git a/.github/workflows/pr-test-rust.yml b/.github/workflows/pr-test-rust.yml new file mode 100644 index 0000000000..b7e294d28d --- /dev/null +++ b/.github/workflows/pr-test-rust.yml @@ -0,0 +1,39 @@ +name: PR Test (Rust) + +on: + push: + branches: [ main ] + paths: + - "rust/*" + pull_request: + branches: [ main ] + paths: + - "rust/*" + workflow_dispatch: + +concurrency: + group: pr-test-rust-${{ github.ref }} + cancel-in-progress: true + +jobs: + unit-test-rust: + if: github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request' + runs-on: 1-gpu-runner + steps: + - name: Checkout code + uses: actions/checkout@v3 + + - name: Install dependencies + run: | + bash scripts/ci_install_rust.sh + - name: Run fmt + run: | + source "$HOME/.cargo/env" + cd rust/ + cargo fmt -- --check + - name: Run test + timeout-minutes: 20 + run: | + source "$HOME/.cargo/env" + cd rust/ + cargo test \ No newline at end of file diff --git a/rust/Cargo.lock b/rust/Cargo.lock index ffabb42534..44a5a198b5 100644 --- a/rust/Cargo.lock +++ b/rust/Cargo.lock @@ -1607,7 +1607,7 @@ dependencies = [ ] [[package]] -name = "sglang-router" +name = "sglang_router" version = "0.0.0" dependencies = [ "actix-web", diff --git a/rust/Cargo.toml b/rust/Cargo.toml index 1daed7dd5d..794ab1f7cb 100644 --- a/rust/Cargo.toml +++ b/rust/Cargo.toml @@ -1,15 +1,17 @@ [package] -name = "sglang-router" +name = "sglang_router" version = "0.0.0" edition = "2021" [[bin]] -name = "router" +name = "sglang_router" path = "src/main.rs" [lib] -name = "router" -crate-type = ["cdylib"] +name = "sglang_router" +# Pure Rust library: Just omit crate-type (defaults to rlib) +# Python/C binding + Rust library: Use ["cdylib", "rlib"] +crate-type = ["cdylib", "rlib"] [dependencies] actix-web = "4.0" diff --git a/rust/readme.md b/rust/readme.md index f73dd71ca5..7d10eed75a 100644 --- a/rust/readme.md +++ b/rust/readme.md @@ -74,4 +74,19 @@ python -m sglang.launch_server \ $ cargo build --release $ maturin build -i /usr/bin/python $ pip install +``` + + +### Development + +1. Run test + +``` +$ cargo test +``` + +2. Run lint + +``` +$ cargo fmt ``` \ No newline at end of file diff --git a/rust/src/lib.rs b/rust/src/lib.rs index 75444555dc..26e43bb8a6 100644 --- a/rust/src/lib.rs +++ b/rust/src/lib.rs @@ -1,6 +1,7 @@ use pyo3::prelude::*; -mod server; pub mod router; +mod server; +pub mod tree; // Python binding #[pyclass] @@ -8,7 +9,7 @@ struct Router { host: String, port: u16, worker_urls: Vec, - policy: String + policy: String, } #[pymethods] @@ -19,7 +20,7 @@ impl Router { host, port, worker_urls, - policy + policy, } } @@ -30,7 +31,9 @@ impl Router { let policy = self.policy.clone(); actix_web::rt::System::new().block_on(async move { - server::startup(host, port, worker_urls, policy).await.unwrap(); + server::startup(host, port, worker_urls, policy) + .await + .unwrap(); }); Ok(()) @@ -42,4 +45,4 @@ impl Router { fn sglang_router(m: &Bound<'_, PyModule>) -> PyResult<()> { m.add_class::()?; Ok(()) -} \ No newline at end of file +} diff --git a/rust/src/main.rs b/rust/src/main.rs index a7566c9a58..851a85ae55 100644 --- a/rust/src/main.rs +++ b/rust/src/main.rs @@ -1,9 +1,10 @@ // src/main.rs -use clap::Parser; use clap::builder::PossibleValuesParser; +use clap::Parser; // declare child modules -mod server; mod router; +mod server; +mod tree; #[derive(Parser, Debug)] #[command(author, version, about, long_about = None)] @@ -25,4 +26,4 @@ struct Args { async fn main() -> std::io::Result<()> { let args = Args::parse(); server::startup(args.host, args.port, args.worker_urls, args.policy).await -} \ No newline at end of file +} diff --git a/rust/src/router.rs b/rust/src/router.rs index 0641338218..9d42cc13f4 100644 --- a/rust/src/router.rs +++ b/rust/src/router.rs @@ -34,7 +34,9 @@ impl Router for RoundRobinRouter { return None; } // Use relaxed because operation order doesn't matter in round robin - let index = self.current_index.fetch_add(1, std::sync::atomic::Ordering::Relaxed) + let index = self + .current_index + .fetch_add(1, std::sync::atomic::Ordering::Relaxed) % self.worker_urls.len(); Some(self.worker_urls[index].clone()) } @@ -62,11 +64,11 @@ impl RandomRouter { impl Router for RandomRouter { fn select(&self) -> Option { use rand::seq::SliceRandom; - + if self.worker_urls.is_empty() { return None; } - + self.worker_urls.choose(&mut rand::thread_rng()).cloned() } @@ -83,6 +85,9 @@ pub fn create_router(worker_urls: Vec, policy: String) -> Box Box::new(RandomRouter::new(worker_urls)), "round_robin" => Box::new(RoundRobinRouter::new(worker_urls)), - _ => panic!("Unknown routing policy: {}. The available policies are 'random' and 'round_robin'", policy), + _ => panic!( + "Unknown routing policy: {}. The available policies are 'random' and 'round_robin'", + policy + ), } -} \ No newline at end of file +} diff --git a/rust/src/server.rs b/rust/src/server.rs index a534d27e7a..1c6c515b48 100644 --- a/rust/src/server.rs +++ b/rust/src/server.rs @@ -1,10 +1,9 @@ -use actix_web::{get, post, web, App, HttpServer, HttpResponse, HttpRequest, Responder}; +use crate::router::create_router; +use crate::router::Router; +use actix_web::http::header::{HeaderValue, CONTENT_TYPE}; +use actix_web::{get, post, web, App, HttpRequest, HttpResponse, HttpServer, Responder}; use bytes::Bytes; use futures_util::StreamExt; -use actix_web::http::header::{HeaderValue, CONTENT_TYPE}; -use crate::router::Router; -use crate::router::create_router; - #[derive(Debug)] pub struct AppState { @@ -12,89 +11,77 @@ pub struct AppState { client: reqwest::Client, } - -impl AppState -{ +impl AppState { pub fn new(worker_urls: Vec, policy: String, client: reqwest::Client) -> Self { // Create router based on policy let router = create_router(worker_urls, policy); - - Self { - router, - client, - } + + Self { router, client } } } #[get("/v1/models")] -async fn v1_model( - data: web::Data, -) -> impl Responder { - let worker_url= match data.router.get_first() { +async fn v1_model(data: web::Data) -> impl Responder { + let worker_url = match data.router.get_first() { Some(url) => url, None => return HttpResponse::InternalServerError().finish(), }; // Use the shared client - match data.client - .get(&format!("{}/v1/models", worker_url)) + match data + .client + .get(format!("{}/v1/models", worker_url)) .send() - .await + .await { Ok(res) => { let status = actix_web::http::StatusCode::from_u16(res.status().as_u16()) - .unwrap_or(actix_web::http::StatusCode::INTERNAL_SERVER_ERROR); - + .unwrap_or(actix_web::http::StatusCode::INTERNAL_SERVER_ERROR); + // print the status println!("Worker URL: {}, Status: {}", worker_url, status); match res.bytes().await { Ok(body) => HttpResponse::build(status).body(body.to_vec()), Err(_) => HttpResponse::InternalServerError().finish(), } - }, + } Err(_) => HttpResponse::InternalServerError().finish(), } } #[get("/get_model_info")] -async fn get_model_info( - data: web::Data, -) -> impl Responder { - let worker_url= match data.router.get_first() { +async fn get_model_info(data: web::Data) -> impl Responder { + let worker_url = match data.router.get_first() { Some(url) => url, None => return HttpResponse::InternalServerError().finish(), }; // Use the shared client - match data.client - .get(&format!("{}/get_model_info", worker_url)) + match data + .client + .get(format!("{}/get_model_info", worker_url)) .send() - .await + .await { Ok(res) => { let status = actix_web::http::StatusCode::from_u16(res.status().as_u16()) - .unwrap_or(actix_web::http::StatusCode::INTERNAL_SERVER_ERROR); - + .unwrap_or(actix_web::http::StatusCode::INTERNAL_SERVER_ERROR); + // print the status println!("Worker URL: {}, Status: {}", worker_url, status); match res.bytes().await { Ok(body) => HttpResponse::build(status).body(body.to_vec()), Err(_) => HttpResponse::InternalServerError().finish(), } - }, + } Err(_) => HttpResponse::InternalServerError().finish(), } } // no deser and ser, just forward and return #[post("/generate")] -async fn generate( - req: HttpRequest, - body: Bytes, - data: web::Data, -) -> impl Responder { - +async fn generate(req: HttpRequest, body: Bytes, data: web::Data) -> impl Responder { // create a router struct // TODO: use router abstraction for different policy - let worker_url= match data.router.select() { + let worker_url = match data.router.select() { Some(url) => url, None => return HttpResponse::InternalServerError().finish(), }; @@ -104,18 +91,19 @@ async fn generate( .map(|v| v.get("stream").and_then(|s| s.as_bool()).unwrap_or(false)) .unwrap_or(false); - let res = match data.client - .post(&format!("{}/generate", worker_url)) + let res = match data + .client + .post(format!("{}/generate", worker_url)) .header( - "Content-Type", + "Content-Type", req.headers() .get("Content-Type") .and_then(|h| h.to_str().ok()) - .unwrap_or("application/json") + .unwrap_or("application/json"), ) .body(body.to_vec()) .send() - .await + .await { Ok(res) => res, Err(_) => return HttpResponse::InternalServerError().finish(), @@ -128,18 +116,25 @@ async fn generate( match res.bytes().await { Ok(body) => HttpResponse::build(status).body(body.to_vec()), Err(_) => HttpResponse::InternalServerError().finish(), - } + } } else { HttpResponse::build(status) .insert_header((CONTENT_TYPE, HeaderValue::from_static("text/event-stream"))) .streaming(res.bytes_stream().map(|b| match b { Ok(b) => Ok::<_, actix_web::Error>(b), - Err(_) => Err(actix_web::Error::from(actix_web::error::ErrorInternalServerError("Failed to read stream"))), + Err(_) => Err(actix_web::error::ErrorInternalServerError( + "Failed to read stream", + )), })) } } -pub async fn startup(host: String, port: u16, worker_urls: Vec, routing_policy: String) -> std::io::Result<()> { +pub async fn startup( + host: String, + port: u16, + worker_urls: Vec, + routing_policy: String, +) -> std::io::Result<()> { println!("Starting server on {}:{}", host, port); println!("Worker URLs: {:?}", worker_urls); @@ -149,11 +144,7 @@ pub async fn startup(host: String, port: u16, worker_urls: Vec, routing_ .expect("Failed to create HTTP client"); // Store both worker_urls and client in AppState - let app_state = web::Data::new(AppState::new( - worker_urls, - routing_policy, - client, - )); + let app_state = web::Data::new(AppState::new(worker_urls, routing_policy, client)); HttpServer::new(move || { App::new() @@ -165,4 +156,4 @@ pub async fn startup(host: String, port: u16, worker_urls: Vec, routing_ .bind((host, port))? .run() .await -} \ No newline at end of file +} diff --git a/rust/src/tree.rs b/rust/src/tree.rs new file mode 100644 index 0000000000..27f1db8c2d --- /dev/null +++ b/rust/src/tree.rs @@ -0,0 +1,184 @@ +use std::collections::HashMap; +use std::mem; + +#[derive(Clone)] +pub struct Node { + pub children: HashMap, // the key is first id of the child because each child must have unique first id + pub ids: Vec, + pub count: usize, +} + +pub struct RadixTree { + pub root: Node, +} + +fn common_prefix_len(a: &[usize], b: &[usize]) -> usize { + let mut i = 0; + while i < a.len() && i < b.len() && a[i] == b[i] { + i += 1; + } + i +} + +impl Default for RadixTree { + fn default() -> Self { + Self::new() + } +} + +impl RadixTree { + pub fn new() -> Self { + RadixTree { + root: Node { + children: HashMap::new(), + ids: Vec::new(), + count: 0, + }, + } + } + + pub fn insert(&mut self, input_ids: &[usize]) { + let mut curr = &mut self.root; + curr.count += 1; + + let mut curr_idx = 0; + let input_ids_len = input_ids.len(); + + while curr_idx < input_ids_len { + let first_id = &input_ids[curr_idx]; + // TODO: changing this get_mut causes error + if curr.children.contains_key(first_id) { + let child = curr.children.get_mut(first_id).unwrap(); + + let prefix_len = common_prefix_len(&input_ids[curr_idx..], &child.ids); + + if prefix_len == child.ids.len() { + // move curr to child + curr = child; + curr.count += 1; + curr_idx += prefix_len; + } else { + // split child + // [child]->... => [child]->[new child]->... + let new_child = Node { + // to avoid clone: replace child.children with default value (empty vector) and return the original value + children: mem::take(&mut child.children), + ids: child.ids[prefix_len..].to_vec(), + count: child.count, + }; + + child.ids = child.ids[..prefix_len].to_vec(); + child.children = HashMap::new(); + child.children.insert(new_child.ids[0], new_child); + + curr = child; + curr.count += 1; + curr_idx += prefix_len; + } + } else { + // create new child + let new_child = Node { + children: HashMap::new(), + ids: input_ids[curr_idx..].to_vec(), + count: 0, + }; + + let first_id = new_child.ids[0]; + curr.children.insert(first_id, new_child); + + curr = curr.children.get_mut(&first_id).unwrap(); + curr.count += 1; + curr_idx = input_ids_len; + } + } + } + + pub fn prefix_match<'a>(&self, input_ids: &'a [usize]) -> &'a [usize] { + let mut curr = &self.root; + + let mut curr_idx = 0; + let input_ids_len = input_ids.len(); + + while curr_idx < input_ids_len { + match curr.children.get(&input_ids[curr_idx]) { + Some(child) => { + let prefix_len = common_prefix_len(&input_ids[curr_idx..], &child.ids); + + if prefix_len == child.ids.len() { + curr_idx += prefix_len; + curr = child; + } else { + curr_idx += prefix_len; + break; + } + } + None => { + break; + } + } + } + + &input_ids[..curr_idx] + } + + pub fn delete(&mut self, input_ids: &[usize]) { + let mut curr = &mut self.root; + curr.count -= 1; + + let mut curr_idx = 0; + let input_ids_len = input_ids.len(); + + while curr_idx < input_ids_len { + let first_id = &input_ids[curr_idx]; + + if curr.children.contains_key(first_id) { + let child = curr.children.get(first_id).unwrap(); + + let prefix_len = common_prefix_len(&input_ids[curr_idx..], &child.ids); + + if prefix_len == child.ids.len() { + if child.count == 1 { + // If count will become 0, remove the child + let child = curr.children.get_mut(first_id).unwrap(); + child.count -= 1; + curr.children.remove(first_id); + break; + } else { + // Otherwise decrement count and continue + let child = curr.children.get_mut(first_id).unwrap(); + + child.count -= 1; + curr = child; + curr_idx += prefix_len; + } + } else { + panic!("No match found for {:?}", input_ids); + } + } else { + panic!("No match found for {:?}", input_ids); + } + } + } + + // for debug + pub fn pretty_print(&self) { + println!("RadixTree:"); + Self::print_node(&self.root, String::from("")); + } + + fn print_node(node: &Node, prefix: String) { + // Print current node info with "count" word + println!("{}└── {:?} (count: {})", prefix, node.ids, node.count); + + // Print children with proper prefixes + for (i, child) in node.children.values().enumerate() { + let is_last = i == node.children.len() - 1; + let child_prefix = if is_last { + format!("{} ", prefix) // Add space for last child + } else { + format!("{}│ ", prefix) // Add vertical line for other children + }; + Self::print_node(child, child_prefix); + } + } +} diff --git a/rust/tests/test_tree.rs b/rust/tests/test_tree.rs new file mode 100644 index 0000000000..c9e453c10e --- /dev/null +++ b/rust/tests/test_tree.rs @@ -0,0 +1,131 @@ +use sglang_router::tree::RadixTree; + +#[test] +fn test_new_tree() { + let tree = RadixTree::new(); + assert_eq!(tree.root.count, 0); + assert!(tree.root.children.is_empty()); + assert!(tree.root.ids.is_empty()); +} + +#[test] +fn test_single_insertion() { + let mut tree = RadixTree::new(); + tree.insert(&[1, 2, 3]); + + assert_eq!(tree.root.count, 1); + assert_eq!(tree.root.children.len(), 1); + assert_eq!(tree.root.children[&1].ids, vec![1, 2, 3]); + assert_eq!(tree.root.children[&1].count, 1); +} + +#[test] +fn test_multiple_insertions_no_split() { + let mut tree = RadixTree::new(); + tree.insert(&[1, 2, 3]); + tree.insert(&[4, 5, 6]); + + assert_eq!(tree.root.count, 2); + assert_eq!(tree.root.children.len(), 2); + assert_eq!(tree.root.children[&1].ids, vec![1, 2, 3]); + assert_eq!(tree.root.children[&4].ids, vec![4, 5, 6]); +} + +#[test] +fn test_insertion_with_split() { + let mut tree = RadixTree::new(); + tree.insert(&[1, 2, 3, 4]); + tree.insert(&[1, 2, 5, 6]); + + assert_eq!(tree.root.count, 2); + assert_eq!(tree.root.children.len(), 1); + assert_eq!(tree.root.children[&1].ids, vec![1, 2]); + assert_eq!(tree.root.children[&1].children.len(), 2); + assert_eq!(tree.root.children[&1].children[&3].ids, vec![3, 4]); + assert_eq!(tree.root.children[&1].children[&5].ids, vec![5, 6]); +} + +#[test] +fn test_prefix_match_exact() { + let mut tree = RadixTree::new(); + tree.insert(&[1, 2, 3, 4]); + + assert_eq!(tree.prefix_match(&[1, 2, 3, 4]), &[1, 2, 3, 4]); +} + +#[test] +fn test_prefix_match_partial() { + let mut tree = RadixTree::new(); + tree.insert(&[1, 2, 3, 4]); + + assert_eq!(tree.prefix_match(&[1, 2, 3, 5]), &[1, 2, 3]); + assert_eq!(tree.prefix_match(&[1, 2, 5]), &[1, 2]); + assert_eq!(tree.prefix_match(&[1, 5]), &[1]); +} + +#[test] +fn test_prefix_match_no_match() { + let mut tree = RadixTree::new(); + tree.insert(&[1, 2, 3, 4]); + let empty_slices: &[usize] = &[]; + assert_eq!(tree.prefix_match(&[5, 6, 7]), empty_slices); +} + +#[test] +fn test_delete_leaf() { + let mut tree = RadixTree::new(); + tree.insert(&[1, 2, 3]); + tree.delete(&[1, 2, 3]); + + assert_eq!(tree.root.count, 0); + assert_eq!(tree.root.children.len(), 0); +} + +#[test] +fn test_delete_with_siblings() { + let mut tree = RadixTree::new(); + tree.insert(&[1, 2, 3]); + tree.insert(&[1, 2, 4]); + tree.delete(&[1, 2, 3]); + + assert_eq!(tree.root.count, 1); + assert_eq!(tree.root.children[&1].children[&4].ids, vec![4]); +} + +#[test] +fn test_multiple_operations() { + let mut tree = RadixTree::new(); + + // Insert several paths + tree.insert(&[1, 2, 3]); + tree.insert(&[1, 2, 4]); + tree.insert(&[1, 5, 6]); + + // Verify structure + assert_eq!(tree.root.count, 3); + assert_eq!(tree.prefix_match(&[1, 2, 3]), &[1, 2, 3]); + assert_eq!(tree.prefix_match(&[1, 2, 4]), &[1, 2, 4]); + assert_eq!(tree.prefix_match(&[1, 5, 6]), &[1, 5, 6]); + + // Delete and verify + tree.delete(&[1, 2, 3]); + assert_eq!(tree.root.count, 2); + assert_eq!(tree.prefix_match(&[1, 2, 3]), &[1, 2]); // Now only matches prefix +} + +#[test] +#[should_panic(expected = "No match found")] +fn test_delete_nonexistent() { + let mut tree = RadixTree::new(); + tree.insert(&[1, 2, 3]); + tree.delete(&[4, 5, 6]); // Should panic +} + +#[test] +fn test_empty_input() { + let mut tree = RadixTree::new(); + let empty_slice: &[usize] = &[]; + tree.insert(empty_slice); + assert_eq!(tree.prefix_match(empty_slice), empty_slice); + tree.delete(empty_slice); // Should not panic +} diff --git a/scripts/ci_install_rust.sh b/scripts/ci_install_rust.sh new file mode 100644 index 0000000000..23bcc3bef5 --- /dev/null +++ b/scripts/ci_install_rust.sh @@ -0,0 +1,15 @@ +# these are required for actix +apt-get update +apt-get install -y libssl-dev pkg-config + +# Install rustup (Rust installer and version manager) +curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y + + +# Follow the installation prompts, then reload your shell +. "$HOME/.cargo/env" +source $HOME/.cargo/env + +# Verify installation +rustc --version +cargo --version \ No newline at end of file From 463d56bf4439b078748c47421dffaf73d8eaede4 Mon Sep 17 00:00:00 2001 From: Byron Hsu Date: Mon, 4 Nov 2024 17:13:41 -0800 Subject: [PATCH 39/44] Update CODEOWNERS (#1916) --- .github/CODEOWNERS | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index e580b502f7..62612312f5 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -11,3 +11,4 @@ /python/sglang/srt/sampling @merrymercy @hnyls2002 /test/lang @merrymercy @Ying1123 @ByronHsu /test/srt @merrymercy @Ying1123 @zhyncs +/rust @ByronHsu @Ying1123 From 02755768d32765eb49f9fa1499ed841c3aab7edb Mon Sep 17 00:00:00 2001 From: Chayenne Date: Mon, 4 Nov 2024 23:53:44 -0800 Subject: [PATCH 40/44] Change judge to classify & Modify make file (#1920) --- docs/Makefile | 2 +- docs/backend/native_api.ipynb | 143 +++++++++++++++++++--- docs/backend/offline_engine_api.ipynb | 56 +++++++-- docs/backend/openai_api_completions.ipynb | 109 ++++++++++------- docs/backend/openai_api_embeddings.ipynb | 49 ++++---- docs/backend/openai_api_vision.ipynb | 56 +++++++-- docs/start/send_request.ipynb | 70 +++++++---- examples/runtime/reward_model.py | 2 +- python/sglang/srt/server.py | 6 +- 9 files changed, 369 insertions(+), 124 deletions(-) diff --git a/docs/Makefile b/docs/Makefile index 51446dc384..50f77a30c0 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -14,7 +14,7 @@ help: # New target to compile Markdown and Jupyter Notebook files compile: - find $(SOURCEDIR) -name '*.ipynb' | while read nb; do \ + find $(SOURCEDIR) -path "*/_build/*" -prune -o -name "*.ipynb" -print | while read nb; do \ if [ -f "$$nb" ]; then \ echo "Executing $$nb"; \ jupyter nbconvert --to notebook --execute --inplace "$$nb" \ diff --git a/docs/backend/native_api.ipynb b/docs/backend/native_api.ipynb index 95af6344a0..f84e02b59f 100644 --- a/docs/backend/native_api.ipynb +++ b/docs/backend/native_api.ipynb @@ -17,7 +17,7 @@ "- `/get_memory_pool_size`\n", "- `/update_weights`\n", "- `/encode`(embedding model)\n", - "- `/judge`(reward model)\n", + "- `/classify`(reward model)\n", "\n", "We mainly use `requests` to test these APIs in the following examples. You can also use `curl`." ] @@ -32,7 +32,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:08:08.536886Z", + "iopub.status.busy": "2024-11-05T05:08:08.536763Z", + "iopub.status.idle": "2024-11-05T05:08:34.725831Z", + "shell.execute_reply": "2024-11-05T05:08:34.725316Z" + } + }, "outputs": [], "source": [ "from sglang.utils import (\n", @@ -64,7 +71,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:08:34.727530Z", + "iopub.status.busy": "2024-11-05T05:08:34.727333Z", + "iopub.status.idle": "2024-11-05T05:08:35.359784Z", + "shell.execute_reply": "2024-11-05T05:08:35.359090Z" + } + }, "outputs": [], "source": [ "url = \"http://localhost:30010/generate\"\n", @@ -85,7 +99,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:08:35.362286Z", + "iopub.status.busy": "2024-11-05T05:08:35.362140Z", + "iopub.status.idle": "2024-11-05T05:08:35.368711Z", + "shell.execute_reply": "2024-11-05T05:08:35.368220Z" + } + }, "outputs": [], "source": [ "url = \"http://localhost:30010/get_server_args\"\n", @@ -109,7 +130,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:08:35.371313Z", + "iopub.status.busy": "2024-11-05T05:08:35.370877Z", + "iopub.status.idle": "2024-11-05T05:08:35.376712Z", + "shell.execute_reply": "2024-11-05T05:08:35.376230Z" + } + }, "outputs": [], "source": [ "url = \"http://localhost:30010/get_model_info\"\n", @@ -134,7 +162,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:08:35.378982Z", + "iopub.status.busy": "2024-11-05T05:08:35.378597Z", + "iopub.status.idle": "2024-11-05T05:08:35.391820Z", + "shell.execute_reply": "2024-11-05T05:08:35.391336Z" + } + }, "outputs": [], "source": [ "url = \"http://localhost:30010/health_generate\"\n", @@ -146,7 +181,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:08:35.393748Z", + "iopub.status.busy": "2024-11-05T05:08:35.393606Z", + "iopub.status.idle": "2024-11-05T05:08:35.398645Z", + "shell.execute_reply": "2024-11-05T05:08:35.398145Z" + } + }, "outputs": [], "source": [ "url = \"http://localhost:30010/health\"\n", @@ -167,7 +209,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:08:35.400683Z", + "iopub.status.busy": "2024-11-05T05:08:35.400419Z", + "iopub.status.idle": "2024-11-05T05:08:35.406146Z", + "shell.execute_reply": "2024-11-05T05:08:35.405661Z" + } + }, "outputs": [], "source": [ "# flush cache\n", @@ -190,7 +239,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:08:35.408176Z", + "iopub.status.busy": "2024-11-05T05:08:35.407884Z", + "iopub.status.idle": "2024-11-05T05:08:35.413587Z", + "shell.execute_reply": "2024-11-05T05:08:35.413108Z" + } + }, "outputs": [], "source": [ "# get_memory_pool_size\n", @@ -213,7 +269,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:08:35.416090Z", + "iopub.status.busy": "2024-11-05T05:08:35.415793Z", + "iopub.status.idle": "2024-11-05T05:08:36.552549Z", + "shell.execute_reply": "2024-11-05T05:08:36.551870Z" + } + }, "outputs": [], "source": [ "# successful update with same architecture and size\n", @@ -231,7 +294,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:08:36.554823Z", + "iopub.status.busy": "2024-11-05T05:08:36.554680Z", + "iopub.status.idle": "2024-11-05T05:08:38.053945Z", + "shell.execute_reply": "2024-11-05T05:08:38.053034Z" + } + }, "outputs": [], "source": [ "# failed update with different parameter size\n", @@ -263,7 +333,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:08:38.056783Z", + "iopub.status.busy": "2024-11-05T05:08:38.056497Z", + "iopub.status.idle": "2024-11-05T05:09:04.436030Z", + "shell.execute_reply": "2024-11-05T05:09:04.435311Z" + } + }, "outputs": [], "source": [ "terminate_process(server_process)\n", @@ -281,7 +358,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:09:04.438987Z", + "iopub.status.busy": "2024-11-05T05:09:04.438568Z", + "iopub.status.idle": "2024-11-05T05:09:04.485291Z", + "shell.execute_reply": "2024-11-05T05:09:04.484829Z" + } + }, "outputs": [], "source": [ "# successful encode for embedding model\n", @@ -298,15 +382,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Judge (reward model)\n", + "## Classify (reward model)\n", "\n", - "SGLang Runtime also supports reward models. Here we use a reward model to judge the quality of pairwise generations." + "SGLang Runtime also supports reward models. Here we use a reward model to classify the quality of pairwise generations." ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:09:04.487191Z", + "iopub.status.busy": "2024-11-05T05:09:04.486929Z", + "iopub.status.idle": "2024-11-05T05:09:25.553481Z", + "shell.execute_reply": "2024-11-05T05:09:25.552747Z" + } + }, "outputs": [], "source": [ "terminate_process(embedding_process)\n", @@ -326,7 +417,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:09:25.555813Z", + "iopub.status.busy": "2024-11-05T05:09:25.555666Z", + "iopub.status.idle": "2024-11-05T05:09:26.354372Z", + "shell.execute_reply": "2024-11-05T05:09:26.353693Z" + } + }, "outputs": [], "source": [ "from transformers import AutoTokenizer\n", @@ -346,7 +444,7 @@ "tokenizer = AutoTokenizer.from_pretrained(\"Skywork/Skywork-Reward-Llama-3.1-8B-v0.2\")\n", "prompts = tokenizer.apply_chat_template(CONVS, tokenize=False)\n", "\n", - "url = \"http://localhost:30030/judge\"\n", + "url = \"http://localhost:30030/classify\"\n", "data = {\n", " \"model\": \"Skywork/Skywork-Reward-Llama-3.1-8B-v0.2\", \n", " \"text\": prompts\n", @@ -360,7 +458,14 @@ { "cell_type": "code", "execution_count": 15, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:09:26.356532Z", + "iopub.status.busy": "2024-11-05T05:09:26.356327Z", + "iopub.status.idle": "2024-11-05T05:09:26.396590Z", + "shell.execute_reply": "2024-11-05T05:09:26.395914Z" + } + }, "outputs": [], "source": [ "terminate_process(reward_process)" diff --git a/docs/backend/offline_engine_api.ipynb b/docs/backend/offline_engine_api.ipynb index 63a175ffa9..4ca10a7186 100644 --- a/docs/backend/offline_engine_api.ipynb +++ b/docs/backend/offline_engine_api.ipynb @@ -33,7 +33,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:21:27.503026Z", + "iopub.status.busy": "2024-11-05T05:21:27.502741Z", + "iopub.status.idle": "2024-11-05T05:21:49.554631Z", + "shell.execute_reply": "2024-11-05T05:21:49.553690Z" + } + }, "outputs": [], "source": [ "# launch the offline engine\n", @@ -55,7 +62,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:21:49.558275Z", + "iopub.status.busy": "2024-11-05T05:21:49.558110Z", + "iopub.status.idle": "2024-11-05T05:21:52.717287Z", + "shell.execute_reply": "2024-11-05T05:21:52.716842Z" + } + }, "outputs": [], "source": [ "prompts = [\n", @@ -83,7 +97,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:21:52.721738Z", + "iopub.status.busy": "2024-11-05T05:21:52.720908Z", + "iopub.status.idle": "2024-11-05T05:22:01.770341Z", + "shell.execute_reply": "2024-11-05T05:22:01.769510Z" + } + }, "outputs": [], "source": [ "prompts = [\n", @@ -114,7 +135,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:22:01.772662Z", + "iopub.status.busy": "2024-11-05T05:22:01.772377Z", + "iopub.status.idle": "2024-11-05T05:22:04.897499Z", + "shell.execute_reply": "2024-11-05T05:22:04.896867Z" + } + }, "outputs": [], "source": [ "prompts = [\n", @@ -149,7 +177,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:22:04.899754Z", + "iopub.status.busy": "2024-11-05T05:22:04.899478Z", + "iopub.status.idle": "2024-11-05T05:22:13.970245Z", + "shell.execute_reply": "2024-11-05T05:22:13.969779Z" + } + }, "outputs": [], "source": [ "prompts = [\n", @@ -178,8 +213,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:22:13.972039Z", + "iopub.status.busy": "2024-11-05T05:22:13.971846Z", + "iopub.status.idle": "2024-11-05T05:22:14.027421Z", + "shell.execute_reply": "2024-11-05T05:22:14.027003Z" + } + }, "outputs": [], "source": [ "llm.shutdown()" diff --git a/docs/backend/openai_api_completions.ipynb b/docs/backend/openai_api_completions.ipynb index e39b328f14..1dfa531299 100644 --- a/docs/backend/openai_api_completions.ipynb +++ b/docs/backend/openai_api_completions.ipynb @@ -37,7 +37,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:09:30.637832Z", + "iopub.status.busy": "2024-11-05T05:09:30.637709Z", + "iopub.status.idle": "2024-11-05T05:09:58.830158Z", + "shell.execute_reply": "2024-11-05T05:09:58.829395Z" + } + }, "outputs": [], "source": [ "from sglang.utils import (\n", @@ -72,10 +79,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:45:16.624550Z", - "iopub.status.busy": "2024-11-01T02:45:16.624258Z", - "iopub.status.idle": "2024-11-01T02:45:18.087455Z", - "shell.execute_reply": "2024-11-01T02:45:18.086450Z" + "iopub.execute_input": "2024-11-05T05:09:58.833008Z", + "iopub.status.busy": "2024-11-05T05:09:58.832805Z", + "iopub.status.idle": "2024-11-05T05:10:00.187146Z", + "shell.execute_reply": "2024-11-05T05:10:00.186657Z" } }, "outputs": [], @@ -112,10 +119,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:45:18.090228Z", - "iopub.status.busy": "2024-11-01T02:45:18.090071Z", - "iopub.status.idle": "2024-11-01T02:45:21.193221Z", - "shell.execute_reply": "2024-11-01T02:45:21.192539Z" + "iopub.execute_input": "2024-11-05T05:10:00.189444Z", + "iopub.status.busy": "2024-11-05T05:10:00.189289Z", + "iopub.status.idle": "2024-11-05T05:10:03.291891Z", + "shell.execute_reply": "2024-11-05T05:10:03.291173Z" } }, "outputs": [], @@ -158,10 +165,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:45:21.195226Z", - "iopub.status.busy": "2024-11-01T02:45:21.194680Z", - "iopub.status.idle": "2024-11-01T02:45:21.675473Z", - "shell.execute_reply": "2024-11-01T02:45:21.675050Z" + "iopub.execute_input": "2024-11-05T05:10:03.294389Z", + "iopub.status.busy": "2024-11-05T05:10:03.294237Z", + "iopub.status.idle": "2024-11-05T05:10:03.469357Z", + "shell.execute_reply": "2024-11-05T05:10:03.468661Z" } }, "outputs": [], @@ -191,10 +198,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:45:21.676813Z", - "iopub.status.busy": "2024-11-01T02:45:21.676665Z", - "iopub.status.idle": "2024-11-01T02:45:23.182104Z", - "shell.execute_reply": "2024-11-01T02:45:23.181695Z" + "iopub.execute_input": "2024-11-05T05:10:03.471573Z", + "iopub.status.busy": "2024-11-05T05:10:03.471430Z", + "iopub.status.idle": "2024-11-05T05:10:04.977081Z", + "shell.execute_reply": "2024-11-05T05:10:04.976391Z" } }, "outputs": [], @@ -227,10 +234,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:45:23.186337Z", - "iopub.status.busy": "2024-11-01T02:45:23.186189Z", - "iopub.status.idle": "2024-11-01T02:45:26.769744Z", - "shell.execute_reply": "2024-11-01T02:45:26.769299Z" + "iopub.execute_input": "2024-11-05T05:10:04.979428Z", + "iopub.status.busy": "2024-11-05T05:10:04.979272Z", + "iopub.status.idle": "2024-11-05T05:10:08.568761Z", + "shell.execute_reply": "2024-11-05T05:10:08.568355Z" } }, "outputs": [], @@ -264,7 +271,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:10:08.571102Z", + "iopub.status.busy": "2024-11-05T05:10:08.570964Z", + "iopub.status.idle": "2024-11-05T05:10:23.214087Z", + "shell.execute_reply": "2024-11-05T05:10:23.213664Z" + } + }, "outputs": [], "source": [ "import json\n", @@ -306,7 +320,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:10:23.216229Z", + "iopub.status.busy": "2024-11-05T05:10:23.216076Z", + "iopub.status.idle": "2024-11-05T05:10:23.884236Z", + "shell.execute_reply": "2024-11-05T05:10:23.883897Z" + } + }, "outputs": [], "source": [ "response = client.chat.completions.create(\n", @@ -344,10 +365,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:45:26.772016Z", - "iopub.status.busy": "2024-11-01T02:45:26.771868Z", - "iopub.status.idle": "2024-11-01T02:45:26.794225Z", - "shell.execute_reply": "2024-11-01T02:45:26.793811Z" + "iopub.execute_input": "2024-11-05T05:10:23.886276Z", + "iopub.status.busy": "2024-11-05T05:10:23.886136Z", + "iopub.status.idle": "2024-11-05T05:10:23.905880Z", + "shell.execute_reply": "2024-11-05T05:10:23.905529Z" } }, "outputs": [], @@ -406,10 +427,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:45:26.796422Z", - "iopub.status.busy": "2024-11-01T02:45:26.796273Z", - "iopub.status.idle": "2024-11-01T02:45:29.810471Z", - "shell.execute_reply": "2024-11-01T02:45:29.810041Z" + "iopub.execute_input": "2024-11-05T05:10:23.907468Z", + "iopub.status.busy": "2024-11-05T05:10:23.907247Z", + "iopub.status.idle": "2024-11-05T05:10:26.920212Z", + "shell.execute_reply": "2024-11-05T05:10:26.919865Z" } }, "outputs": [], @@ -461,10 +482,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:45:29.812339Z", - "iopub.status.busy": "2024-11-01T02:45:29.812198Z", - "iopub.status.idle": "2024-11-01T02:45:54.851243Z", - "shell.execute_reply": "2024-11-01T02:45:54.850668Z" + "iopub.execute_input": "2024-11-05T05:10:26.922675Z", + "iopub.status.busy": "2024-11-05T05:10:26.922413Z", + "iopub.status.idle": "2024-11-05T05:10:51.961703Z", + "shell.execute_reply": "2024-11-05T05:10:51.960846Z" } }, "outputs": [], @@ -544,10 +565,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:45:54.854018Z", - "iopub.status.busy": "2024-11-01T02:45:54.853851Z", - "iopub.status.idle": "2024-11-01T02:46:07.893199Z", - "shell.execute_reply": "2024-11-01T02:46:07.892310Z" + "iopub.execute_input": "2024-11-05T05:10:51.964749Z", + "iopub.status.busy": "2024-11-05T05:10:51.964215Z", + "iopub.status.idle": "2024-11-05T05:11:05.023450Z", + "shell.execute_reply": "2024-11-05T05:11:05.023101Z" } }, "outputs": [], @@ -636,13 +657,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:46:07.896114Z", - "iopub.status.busy": "2024-11-01T02:46:07.895820Z", - "iopub.status.idle": "2024-11-01T02:46:09.365287Z", - "shell.execute_reply": "2024-11-01T02:46:09.364705Z" + "iopub.execute_input": "2024-11-05T05:11:05.024877Z", + "iopub.status.busy": "2024-11-05T05:11:05.024561Z", + "iopub.status.idle": "2024-11-05T05:11:06.358695Z", + "shell.execute_reply": "2024-11-05T05:11:06.357635Z" } }, "outputs": [], diff --git a/docs/backend/openai_api_embeddings.ipynb b/docs/backend/openai_api_embeddings.ipynb index 54b48d60ce..a221c16eb4 100644 --- a/docs/backend/openai_api_embeddings.ipynb +++ b/docs/backend/openai_api_embeddings.ipynb @@ -35,10 +35,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:47:32.337369Z", - "iopub.status.busy": "2024-11-01T02:47:32.337032Z", - "iopub.status.idle": "2024-11-01T02:47:59.540926Z", - "shell.execute_reply": "2024-11-01T02:47:59.539861Z" + "iopub.execute_input": "2024-11-05T05:22:17.227174Z", + "iopub.status.busy": "2024-11-05T05:22:17.226952Z", + "iopub.status.idle": "2024-11-05T05:22:42.445791Z", + "shell.execute_reply": "2024-11-05T05:22:42.444980Z" } }, "outputs": [], @@ -72,10 +72,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:47:59.543958Z", - "iopub.status.busy": "2024-11-01T02:47:59.543670Z", - "iopub.status.idle": "2024-11-01T02:47:59.591699Z", - "shell.execute_reply": "2024-11-01T02:47:59.590809Z" + "iopub.execute_input": "2024-11-05T05:22:42.448147Z", + "iopub.status.busy": "2024-11-05T05:22:42.447775Z", + "iopub.status.idle": "2024-11-05T05:22:42.495311Z", + "shell.execute_reply": "2024-11-05T05:22:42.495027Z" } }, "outputs": [], @@ -104,7 +104,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:22:42.496666Z", + "iopub.status.busy": "2024-11-05T05:22:42.496524Z", + "iopub.status.idle": "2024-11-05T05:22:42.540687Z", + "shell.execute_reply": "2024-11-05T05:22:42.540060Z" + } + }, "outputs": [], "source": [ "import requests\n", @@ -133,10 +140,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:47:59.594229Z", - "iopub.status.busy": "2024-11-01T02:47:59.594049Z", - "iopub.status.idle": "2024-11-01T02:48:00.006233Z", - "shell.execute_reply": "2024-11-01T02:48:00.005255Z" + "iopub.execute_input": "2024-11-05T05:22:42.542551Z", + "iopub.status.busy": "2024-11-05T05:22:42.542282Z", + "iopub.status.idle": "2024-11-05T05:22:42.928542Z", + "shell.execute_reply": "2024-11-05T05:22:42.928181Z" } }, "outputs": [], @@ -169,10 +176,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:48:00.008858Z", - "iopub.status.busy": "2024-11-01T02:48:00.008689Z", - "iopub.status.idle": "2024-11-01T02:48:01.872542Z", - "shell.execute_reply": "2024-11-01T02:48:01.871573Z" + "iopub.execute_input": "2024-11-05T05:22:42.930093Z", + "iopub.status.busy": "2024-11-05T05:22:42.929954Z", + "iopub.status.idle": "2024-11-05T05:22:44.799945Z", + "shell.execute_reply": "2024-11-05T05:22:44.799562Z" } }, "outputs": [], @@ -201,10 +208,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:48:01.875204Z", - "iopub.status.busy": "2024-11-01T02:48:01.874915Z", - "iopub.status.idle": "2024-11-01T02:48:02.193734Z", - "shell.execute_reply": "2024-11-01T02:48:02.192158Z" + "iopub.execute_input": "2024-11-05T05:22:44.801418Z", + "iopub.status.busy": "2024-11-05T05:22:44.801192Z", + "iopub.status.idle": "2024-11-05T05:22:45.094634Z", + "shell.execute_reply": "2024-11-05T05:22:45.093950Z" } }, "outputs": [], diff --git a/docs/backend/openai_api_vision.ipynb b/docs/backend/openai_api_vision.ipynb index eb06e55ed4..cbbba8c12d 100644 --- a/docs/backend/openai_api_vision.ipynb +++ b/docs/backend/openai_api_vision.ipynb @@ -37,7 +37,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:22:49.320999Z", + "iopub.status.busy": "2024-11-05T05:22:49.320880Z", + "iopub.status.idle": "2024-11-05T05:23:21.537478Z", + "shell.execute_reply": "2024-11-05T05:23:21.536956Z" + } + }, "outputs": [], "source": [ "from sglang.utils import (\n", @@ -69,7 +76,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:23:21.539953Z", + "iopub.status.busy": "2024-11-05T05:23:21.539100Z", + "iopub.status.idle": "2024-11-05T05:23:25.880179Z", + "shell.execute_reply": "2024-11-05T05:23:25.879744Z" + } + }, "outputs": [], "source": [ "import subprocess\n", @@ -113,7 +127,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:23:25.881742Z", + "iopub.status.busy": "2024-11-05T05:23:25.881595Z", + "iopub.status.idle": "2024-11-05T05:23:26.758503Z", + "shell.execute_reply": "2024-11-05T05:23:26.758084Z" + } + }, "outputs": [], "source": [ "import requests\n", @@ -153,7 +174,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:23:26.760098Z", + "iopub.status.busy": "2024-11-05T05:23:26.759955Z", + "iopub.status.idle": "2024-11-05T05:23:27.849510Z", + "shell.execute_reply": "2024-11-05T05:23:27.849117Z" + } + }, "outputs": [], "source": [ "from openai import OpenAI\n", @@ -197,7 +225,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:23:27.850994Z", + "iopub.status.busy": "2024-11-05T05:23:27.850864Z", + "iopub.status.idle": "2024-11-05T05:23:31.609137Z", + "shell.execute_reply": "2024-11-05T05:23:31.608748Z" + } + }, "outputs": [], "source": [ "from openai import OpenAI\n", @@ -238,8 +273,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:23:31.610683Z", + "iopub.status.busy": "2024-11-05T05:23:31.610560Z", + "iopub.status.idle": "2024-11-05T05:23:32.965146Z", + "shell.execute_reply": "2024-11-05T05:23:32.963922Z" + } + }, "outputs": [], "source": [ "terminate_process(embedding_process)" diff --git a/docs/start/send_request.ipynb b/docs/start/send_request.ipynb index 24d39bd71d..30aafbe903 100644 --- a/docs/start/send_request.ipynb +++ b/docs/start/send_request.ipynb @@ -9,7 +9,7 @@ "\n", "- For Vision Language Models, see [OpenAI APIs - Vision](../backend/openai_api_vision.ipynb).\n", "- For Embedding Models, see [OpenAI APIs - Embedding](../backend/openai_api_embeddings.ipynb) and [Encode (embedding model)](../backend/native_api.html#Encode-(embedding-model)).\n", - "- For Reward Models, see [Judge (reward model)](../backend/native_api.html#Judge-(reward-model))." + "- For Reward Models, see [Classify (reward model)](../backend/native_api.html#Classify-(reward-model))." ] }, { @@ -33,10 +33,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:46:13.611212Z", - "iopub.status.busy": "2024-11-01T02:46:13.611093Z", - "iopub.status.idle": "2024-11-01T02:46:42.810261Z", - "shell.execute_reply": "2024-11-01T02:46:42.809147Z" + "iopub.execute_input": "2024-11-05T05:11:10.680191Z", + "iopub.status.busy": "2024-11-05T05:11:10.679710Z", + "iopub.status.idle": "2024-11-05T05:11:39.882385Z", + "shell.execute_reply": "2024-11-05T05:11:39.881827Z" } }, "outputs": [], @@ -68,7 +68,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:11:39.883923Z", + "iopub.status.busy": "2024-11-05T05:11:39.883721Z", + "iopub.status.idle": "2024-11-05T05:11:40.124980Z", + "shell.execute_reply": "2024-11-05T05:11:40.124557Z" + } + }, "outputs": [], "source": [ "import subprocess, json\n", @@ -94,10 +101,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:46:42.813656Z", - "iopub.status.busy": "2024-11-01T02:46:42.813354Z", - "iopub.status.idle": "2024-11-01T02:46:51.436613Z", - "shell.execute_reply": "2024-11-01T02:46:51.435965Z" + "iopub.execute_input": "2024-11-05T05:11:40.126564Z", + "iopub.status.busy": "2024-11-05T05:11:40.126369Z", + "iopub.status.idle": "2024-11-05T05:11:40.324316Z", + "shell.execute_reply": "2024-11-05T05:11:40.323693Z" } }, "outputs": [], @@ -129,10 +136,10 @@ "execution_count": null, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:46:51.439372Z", - "iopub.status.busy": "2024-11-01T02:46:51.439178Z", - "iopub.status.idle": "2024-11-01T02:46:52.895776Z", - "shell.execute_reply": "2024-11-01T02:46:52.895318Z" + "iopub.execute_input": "2024-11-05T05:11:40.327043Z", + "iopub.status.busy": "2024-11-05T05:11:40.326759Z", + "iopub.status.idle": "2024-11-05T05:11:41.687336Z", + "shell.execute_reply": "2024-11-05T05:11:41.686855Z" } }, "outputs": [], @@ -162,7 +169,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:11:41.688676Z", + "iopub.status.busy": "2024-11-05T05:11:41.688527Z", + "iopub.status.idle": "2024-11-05T05:11:42.717140Z", + "shell.execute_reply": "2024-11-05T05:11:42.716452Z" + } + }, "outputs": [], "source": [ "import openai\n", @@ -198,7 +212,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:11:42.720467Z", + "iopub.status.busy": "2024-11-05T05:11:42.720182Z", + "iopub.status.idle": "2024-11-05T05:11:43.480765Z", + "shell.execute_reply": "2024-11-05T05:11:43.480143Z" + } + }, "outputs": [], "source": [ "import requests\n", @@ -227,7 +248,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-05T05:11:43.483575Z", + "iopub.status.busy": "2024-11-05T05:11:43.483295Z", + "iopub.status.idle": "2024-11-05T05:11:44.242950Z", + "shell.execute_reply": "2024-11-05T05:11:44.242248Z" + } + }, "outputs": [], "source": [ "import requests, json\n", @@ -262,10 +290,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-11-01T02:46:52.898411Z", - "iopub.status.busy": "2024-11-01T02:46:52.898149Z", - "iopub.status.idle": "2024-11-01T02:46:54.398382Z", - "shell.execute_reply": "2024-11-01T02:46:54.397564Z" + "iopub.execute_input": "2024-11-05T05:11:44.245660Z", + "iopub.status.busy": "2024-11-05T05:11:44.245373Z", + "iopub.status.idle": "2024-11-05T05:11:45.591682Z", + "shell.execute_reply": "2024-11-05T05:11:45.591184Z" } }, "outputs": [], diff --git a/examples/runtime/reward_model.py b/examples/runtime/reward_model.py index a18417df7f..1a1177e667 100644 --- a/examples/runtime/reward_model.py +++ b/examples/runtime/reward_model.py @@ -24,7 +24,7 @@ ], } response = requests.post( - url + "/judge", + url + "/classify", json=json_data, ).json() diff --git a/python/sglang/srt/server.py b/python/sglang/srt/server.py index d96176b2de..c881ba3957 100644 --- a/python/sglang/srt/server.py +++ b/python/sglang/srt/server.py @@ -253,7 +253,7 @@ async def encode_request(obj: EmbeddingReqInput, request: Request): app.put("/encode")(encode_request) -async def judge_request(obj: EmbeddingReqInput, request: Request): +async def classify_request(obj: EmbeddingReqInput, request: Request): """Handle a reward model request. Now the arguments and return values are the same as embedding models.""" try: ret = await tokenizer_manager.generate_request(obj, request).__anext__() @@ -264,8 +264,8 @@ async def judge_request(obj: EmbeddingReqInput, request: Request): ) -app.post("/judge")(judge_request) -app.put("/judge")(judge_request) +app.post("/classify")(classify_request) +app.put("/classify")(classify_request) @app.post("/v1/completions") From f5113e50aed22cfca0b411e9815ea37c40103615 Mon Sep 17 00:00:00 2001 From: Lianmin Zheng Date: Tue, 5 Nov 2024 01:12:10 -0800 Subject: [PATCH 41/44] [Doc] improve relative links and structure (#1924) --- docs/backend/backend.md | 62 ++----------------- docs/backend/native_api.ipynb | 6 +- docs/backend/offline_engine_api.ipynb | 2 +- docs/index.rst | 2 +- .../{model_support.md => supported_models.md} | 55 ++++++++++++++-- docs/start/send_request.ipynb | 2 +- 6 files changed, 60 insertions(+), 69 deletions(-) rename docs/references/{model_support.md => supported_models.md} (55%) diff --git a/docs/backend/backend.md b/docs/backend/backend.md index 5913568f86..0f945364e3 100644 --- a/docs/backend/backend.md +++ b/docs/backend/backend.md @@ -20,7 +20,7 @@ curl http://localhost:30000/generate \ }' ``` -Learn more about the argument specification, streaming, and multi-modal support [here](https://sgl-project.github.io/references/sampling_params.html). +Learn more about the argument specification, streaming, and multi-modal support [here](../references/sampling_params.md). ## OpenAI Compatible API In addition, the server supports OpenAI-compatible APIs. @@ -74,7 +74,7 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct ``` python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --mem-fraction-static 0.7 ``` -- See [hyperparameter tuning](https://sgl-project.github.io/references/hyperparameter_tuning.html) on tuning hyperparameters for better performance. +- See [hyperparameter tuning](../references/hyperparameter_tuning.md) on tuning hyperparameters for better performance. - If you see out-of-memory errors during prefill for long prompts, try to set a smaller chunked prefill size. ``` python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096 @@ -84,7 +84,7 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct - To enable torchao quantization, add `--torchao-config int4wo-128`. It supports various quantization strategies. - To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments. - To enable fp8 kv cache quantization, add `--kv-cache-dtype fp8_e5m2`. -- If the model does not have a chat template in the Hugging Face tokenizer, you can specify a [custom chat template](https://sgl-project.github.io/references/custom_chat_template.html). +- If the model does not have a chat template in the Hugging Face tokenizer, you can specify a [custom chat template](../references/custom_chat_template.md). - To run tensor parallelism on multiple nodes, add `--nnodes 2`. If you have two nodes with two GPUs on each node and want to run TP=4, let `sgl-dev-0` be the hostname of the first node and `50000` be an available port, you can use the following commands. If you meet deadlock, please try to add `--disable-cuda-graph` ``` @@ -124,46 +124,7 @@ if __name__ == "__main__": This can be used for offline batch inference and building custom servers. You can view the full example [here](https://github.com/sgl-project/sglang/tree/main/examples/runtime/engine). -## Supported Models - -**Generative Models** -- Llama / Llama 2 / Llama 3 / Llama 3.1 / Llama 3.2 -- Mistral / Mixtral / Mistral NeMo -- Gemma / Gemma 2 -- Qwen / Qwen 2 / Qwen 2 MoE / Qwen 2 VL -- DeepSeek / DeepSeek 2 -- OLMoE -- [LLaVA-OneVision](https://llava-vl.github.io/blog/2024-08-05-llava-onevision/) - - `python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-7b-ov --port=30000 --chat-template=chatml-llava` - - `python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-72b-ov --port=30000 --tp-size=8 --chat-template=chatml-llava` - - Query the server with the [OpenAI Vision API](https://platform.openai.com/docs/guides/vision). See examples at [test/srt/test_vision_openai_server.py](https://github.com/sgl-project/sglang/blob/main/test/srt/test_vision_openai_server.py) -- LLaVA 1.5 / 1.6 / NeXT - - `python -m sglang.launch_server --model-path lmms-lab/llama3-llava-next-8b --port=30000 --tp-size=1 --chat-template=llava_llama_3` - - `python -m sglang.launch_server --model-path lmms-lab/llava-next-72b --port=30000 --tp-size=8 --chat-template=chatml-llava` - - Query the server with the [OpenAI Vision API](https://platform.openai.com/docs/guides/vision). See examples at [test/srt/test_vision_openai_server.py](https://github.com/sgl-project/sglang/blob/main/test/srt/test_vision_openai_server.py) -- Yi-VL -- StableLM -- Command-R -- DBRX -- Grok -- ChatGLM -- InternLM 2 -- Exaone 3 -- BaiChuan2 -- MiniCPM / MiniCPM 3 -- XVERSE / XVERSE MoE -- SmolLM -- GLM-4 - -**Embedding Models** - -- e5-mistral -- gte-Qwen2 - - `python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct --is-embedding` - -Instructions for supporting a new model are [here](https://sgl-project.github.io/references/model_support.html). - -### Use Models From ModelScope +## Use Models From ModelScope
More @@ -189,7 +150,7 @@ docker run --gpus all \
-### Run Llama 3.1 405B +## Example: Run Llama 3.1 405B
More @@ -206,16 +167,3 @@ GLOO_SOCKET_IFNAME=eth0 python3 -m sglang.launch_server --model-path meta-llama/ ```
- -## Benchmark Performance - -- Benchmark a single static batch by running the following command without launching a server. The arguments are the same as for `launch_server.py`. - Note that this is not a dynamic batching server, so it may run out of memory for a batch size that a real server can handle. - A real server truncates the prefill into several batches, while this unit test does not. For accurate large batch testing, please use `sglang.bench_serving` instead. - ``` - python -m sglang.bench_latency --model-path meta-llama/Meta-Llama-3-8B-Instruct --batch 32 --input-len 256 --output-len 32 - ``` -- Benchmark online serving. Launch a server first and run the following command. - ``` - python3 -m sglang.bench_serving --backend sglang --num-prompt 10 - ``` diff --git a/docs/backend/native_api.ipynb b/docs/backend/native_api.ipynb index f84e02b59f..04cee07766 100644 --- a/docs/backend/native_api.ipynb +++ b/docs/backend/native_api.ipynb @@ -65,7 +65,7 @@ "metadata": {}, "source": [ "## Generate (text generation model)\n", - "Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](../references/sampling_params.html)." + "Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](../references/sampling_params.md)." ] }, { @@ -286,7 +286,7 @@ "\n", "response = requests.post(url, json=data)\n", "print_highlight(response.text)\n", - "assert response.json()[\"success\"] == True\n", + "assert response.json()[\"success\"] is True\n", "assert response.json()[\"message\"] == \"Succeeded to update model weights.\"\n", "assert response.json().keys() == {\"success\", \"message\"}" ] @@ -312,7 +312,7 @@ "response = requests.post(url, json=data)\n", "response_json = response.json()\n", "print_highlight(response_json)\n", - "assert response_json[\"success\"] == False\n", + "assert response_json[\"success\"] is False\n", "assert response_json[\"message\"] == (\n", " \"Failed to update weights: The size of tensor a (2048) must match \"\n", " \"the size of tensor b (3072) at non-singleton dimension 1.\\n\"\n", diff --git a/docs/backend/offline_engine_api.ipynb b/docs/backend/offline_engine_api.ipynb index 4ca10a7186..48f2800042 100644 --- a/docs/backend/offline_engine_api.ipynb +++ b/docs/backend/offline_engine_api.ipynb @@ -27,7 +27,7 @@ "source": [ "## Offline Batch Inference\n", "\n", - "SGLang offline engine supports batch inference with efficient scheduling to prevent OOM errors for large batches. For details on this cache-aware scheduling algorithm, see our [paper](https://arxiv.org/pdf/2312.07104)." + "SGLang offline engine supports batch inference with efficient scheduling." ] }, { diff --git a/docs/index.rst b/docs/index.rst index 701d25d627..130b298119 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -44,7 +44,7 @@ The core features include: references/sampling_params.md references/hyperparameter_tuning.md - references/model_support.md + references/supported_models.md references/benchmark_and_profiling.md references/choices_methods.md references/custom_chat_template.md diff --git a/docs/references/model_support.md b/docs/references/supported_models.md similarity index 55% rename from docs/references/model_support.md rename to docs/references/supported_models.md index f057750749..bfe1bc5528 100644 --- a/docs/references/model_support.md +++ b/docs/references/supported_models.md @@ -1,19 +1,63 @@ -# How to Support a New Model +# Supported Models + +## Generative Models +- Llama / Llama 2 / Llama 3 / Llama 3.1 / Llama 3.2 +- Mistral / Mixtral / Mistral NeMo +- Gemma / Gemma 2 +- Qwen / Qwen 2 / Qwen 2 MoE / Qwen 2 VL +- DeepSeek / DeepSeek 2 +- OLMoE +- [LLaVA-OneVision](https://llava-vl.github.io/blog/2024-08-05-llava-onevision/) + - `python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-7b-ov --port=30000 --chat-template=chatml-llava` + - `python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-72b-ov --port=30000 --tp-size=8 --chat-template=chatml-llava` + - Query the server with the [OpenAI Vision API](https://platform.openai.com/docs/guides/vision). See examples at [test/srt/test_vision_openai_server.py](https://github.com/sgl-project/sglang/blob/main/test/srt/test_vision_openai_server.py) +- LLaVA 1.5 / 1.6 / NeXT + - `python -m sglang.launch_server --model-path lmms-lab/llama3-llava-next-8b --port=30000 --tp-size=1 --chat-template=llava_llama_3` + - `python -m sglang.launch_server --model-path lmms-lab/llava-next-72b --port=30000 --tp-size=8 --chat-template=chatml-llava` + - Query the server with the [OpenAI Vision API](https://platform.openai.com/docs/guides/vision). See examples at [test/srt/test_vision_openai_server.py](https://github.com/sgl-project/sglang/blob/main/test/srt/test_vision_openai_server.py) +- Yi-VL +- StableLM +- Command-R +- DBRX +- Grok +- ChatGLM +- InternLM 2 +- Exaone 3 +- BaiChuan2 +- MiniCPM / MiniCPM 3 +- XVERSE / XVERSE MoE +- SmolLM +- GLM-4 + +## Embedding Models + +- LlamaEmbeddingModel +- Mistral embedding models +- QWen embedding models + - `python -m sglang.launch_server --model-path Alibaba-NLP/gte-Qwen2-7B-instruct --is-embedding` + +## Reward Models + +- LlamaForSequenceClassification + - `python -m sglang.launch_server --model-path Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 --is-embedding` + + +## How to Support a New Model To support a new model in SGLang, you only need to add a single file under [SGLang Models Directory](https://github.com/sgl-project/sglang/tree/main/python/sglang/srt/models). You can learn from existing model implementations and create new files for the new models. For most models, you should be able to find a similar model to start with (e.g., starting from Llama). -## Test the correctness +### Test the correctness -### Interactive debugging +#### Interactive debugging For interactive debugging, you can compare the outputs of huggingface/transformers and SGLang. The following two commands should give the same text output and very similar prefill logits. - Get the reference output by `python3 scripts/playground/reference_hf.py --model [new model]` - Get the SGLang output by `python3 -m sglang.bench_latency --correct --model [new model]` -### Add the model to the test suite +#### Add the model to the test suite To make sure the new model is well maintained in the future, it is better to add it to the test suite. You can add it to the `ALL_OTHER_MODELS` list in the [test_generation_models.py](https://github.com/sgl-project/sglang/blob/main/test/srt/models/test_generation_models.py) and run the following command to test it. @@ -22,7 +66,7 @@ For example, if the model is Qwen/Qwen2-1.5B ONLY_RUN=Qwen/Qwen2-1.5B python3 -m unittest test_generation_models.TestGenerationModels.test_others ``` -## Port a model from vLLM to SGLang +### Port a model from vLLM to SGLang Another valuable resource is the [vLLM Models Directory](https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models). vLLM has extensive coverage of models, and SGLang reuses vLLM's interface and some layers to implement the models. This similarity makes it easy to port many models from vLLM to SGLang. To port a model from vLLM to SGLang, you can compare these two files [SGLang Llama Implementation](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/llama.py) and [vLLM Llama Implementation](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llama.py). This comparison will help you understand how to convert a model implementation from vLLM to SGLang. The major difference is the replacement of Attention with RadixAttention. The other parts are almost identical. Specifically, @@ -32,4 +76,3 @@ To port a model from vLLM to SGLang, you can compare these two files [SGLang Lla - Remove `Sample`. - Change `forward()` functions, and add `forward_batch`. - Add `EntryClass` at the end. - diff --git a/docs/start/send_request.ipynb b/docs/start/send_request.ipynb index 30aafbe903..ed1ea61394 100644 --- a/docs/start/send_request.ipynb +++ b/docs/start/send_request.ipynb @@ -206,7 +206,7 @@ "source": [ "## Using Native Generation APIs\n", "\n", - "You can also use the native `/generate` endpoint with requests, which provides more flexiblity. An API reference is available at [Sampling Parameters](https://sgl-project.github.io/references/sampling_params.html)." + "You can also use the native `/generate` endpoint with requests, which provides more flexiblity. An API reference is available at [Sampling Parameters](../references/sampling_params.md)." ] }, { From a146d9990e148fdf2c247d639ba5d2a572175e9c Mon Sep 17 00:00:00 2001 From: Lzhang-hub <57925599+Lzhang-hub@users.noreply.github.com> Date: Wed, 6 Nov 2024 12:42:53 +0800 Subject: [PATCH 42/44] support prometheus metrics (#1853) Co-authored-by: Lianmin Zheng Co-authored-by: Byron Hsu --- python/sglang/srt/managers/schedule_batch.py | 14 + python/sglang/srt/managers/schedule_policy.py | 3 + python/sglang/srt/managers/scheduler.py | 112 ++++++- .../sglang/srt/metrics/metrics_collector.py | 297 ++++++++++++++++++ python/sglang/srt/metrics/metrics_types.py | 57 ++++ python/sglang/srt/server.py | 39 +++ python/sglang/srt/server_args.py | 7 + 7 files changed, 526 insertions(+), 3 deletions(-) create mode 100644 python/sglang/srt/metrics/metrics_collector.py create mode 100644 python/sglang/srt/metrics/metrics_types.py diff --git a/python/sglang/srt/managers/schedule_batch.py b/python/sglang/srt/managers/schedule_batch.py index 131d2982a1..742b91398f 100644 --- a/python/sglang/srt/managers/schedule_batch.py +++ b/python/sglang/srt/managers/schedule_batch.py @@ -31,6 +31,7 @@ import dataclasses import logging +import time from typing import List, Optional, Tuple, Union import torch @@ -254,6 +255,16 @@ def __init__( # For Qwen2-VL self.mrope_position_delta = [] # use mutable object + # Lifetime traces + # time when request is created and added to waitlist + self.created_time = None + # time when request is added to prefill batch + self.queued_time = None + # time when request is being processed + self.started_time = None + # time when request is finished + self.finished_time = None + # whether request reached finished condition def finished(self) -> bool: return self.finished_reason is not None @@ -1028,6 +1039,9 @@ def __str__(self): f"#req={(len(self.reqs))})" ) + def mark_reqs_started(self): + for req in self.reqs: + req.started_time = time.time() @dataclasses.dataclass class ModelWorkerBatch: diff --git a/python/sglang/srt/managers/schedule_policy.py b/python/sglang/srt/managers/schedule_policy.py index 2bfdffc42c..994ffdeb58 100644 --- a/python/sglang/srt/managers/schedule_policy.py +++ b/python/sglang/srt/managers/schedule_policy.py @@ -17,6 +17,7 @@ import os import random +import time from collections import defaultdict from contextlib import contextmanager from enum import Enum, auto @@ -306,6 +307,7 @@ def add_one_req(self, req: Req): ): # Non-chunked prefill self.can_run_list.append(req) + req.queued_time = time.time() self.tree_cache.inc_lock_ref(req.last_node) self._prefill_one_req( prefix_len, @@ -324,6 +326,7 @@ def add_one_req(self, req: Req): req.extend_input_len = trunc_len req.fill_ids = req.fill_ids[: len(req.prefix_indices) + trunc_len] self.can_run_list.append(req) + req.queued_time = time.time() self.new_inflight_req = req self.tree_cache.inc_lock_ref(req.last_node) self._prefill_one_req(prefix_len, trunc_len, 0) diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py index dd6bba8631..6d3d419d1a 100644 --- a/python/sglang/srt/managers/scheduler.py +++ b/python/sglang/srt/managers/scheduler.py @@ -62,6 +62,8 @@ from sglang.srt.managers.tp_worker_overlap_thread import TpModelWorkerClient from sglang.srt.mem_cache.chunk_cache import ChunkCache from sglang.srt.mem_cache.radix_cache import RadixCache +from sglang.srt.metrics.metrics_collector import PrometheusMetricsCollector +from sglang.srt.metrics.metrics_types import Stats from sglang.srt.server_args import PortArgs, ServerArgs from sglang.srt.utils import ( broadcast_pyobj, @@ -222,7 +224,8 @@ def __init__( self.forward_ct = 0 self.forward_ct_decode = 0 self.num_generated_tokens = 0 - self.last_stats_tic = time.time() + self.last_stats_tic = time.time() # time of last stats for every iter + self.last_log_tic = time.time() # time of last log for print decode log self.stream_interval = server_args.stream_interval # Init chunked prefill @@ -291,6 +294,15 @@ def __init__( ], with_stack=True, ) + # Init metrics stats + self.stats = Stats() + self.metrics_collector = PrometheusMetricsCollector( + labels={ + "model_name": self.server_args.served_model_name, + # TODO: Add lora name/path in the future, + }, + max_model_len=self.max_total_num_tokens, + ) def watchdog_thread(self): self.watchdog_last_forward_ct = 0 @@ -338,6 +350,11 @@ def event_loop_normal(self): else: self.check_memory() self.new_token_ratio = self.init_new_token_ratio + # log stats + if self.is_generation and self.server_args.enable_metrics: + stats = self.get_stats(batch) + self.log_stats(stats) + self.last_stats_tic = time.time() self.last_batch = batch @@ -476,6 +493,7 @@ def handle_generate_request( self.max_req_len - len(req.origin_input_ids) - 1, ) + req.created_time = time.time() self.waiting_queue.append(req) def handle_embedding_request( @@ -504,9 +522,11 @@ def print_decode_stats(self): num_used = self.max_total_num_tokens - ( self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size() ) - throughput = self.num_generated_tokens / (time.time() - self.last_stats_tic) + throughput = self.num_generated_tokens / (time.time() - self.last_log_tic) self.num_generated_tokens = 0 - self.last_stats_tic = time.time() + self.last_log_tic = time.time() + # set system stats + self.stats.token_usage = round(num_used / self.max_total_num_tokens, 2) num_running_reqs = len(self.running_batch.reqs) if self.running_batch else 0 logger.info( f"Decode batch. " @@ -676,6 +696,9 @@ def get_new_batch_prefill(self) -> Optional[ScheduleBatch]: self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size() ) + # set system stats + self.stats.cache_hit_rate = round(100.0 * tree_cache_hit_rate, 2) + self.stats.token_usage = round(num_used / self.max_total_num_tokens, 2) if num_mixed_running > 0: logger.info( @@ -770,6 +793,7 @@ def run_batch(self, batch: ScheduleBatch): if self.is_generation: if batch.forward_mode.is_decode() or batch.extend_num_tokens != 0: model_worker_batch = batch.get_model_worker_batch() + batch.mark_reqs_started() logits_output, next_token_ids = self.tp_worker.forward_batch_generation( model_worker_batch ) @@ -789,6 +813,88 @@ def run_batch(self, batch: ScheduleBatch): embeddings = self.tp_worker.forward_batch_embedding(model_worker_batch) ret = embeddings, model_worker_batch.bid return ret + def get_stats(self,batch: ScheduleBatch): + # TODO: get stats for chunked prefill + + now = time.time() + # system stats + # Scheduler State + new_seq: int = 0 + num_running_req = len(self.running_batch.reqs) if self.running_batch else 0 + num_waiting_req = len(self.waiting_queue) + # Cache State + cache_hit_rate: float = 0.0 + token_usage: float = 0.0 + + # set stats from prefill + if self.stats is not None: + # new_seq=self.stats.new_seq + cache_hit_rate=self.stats.cache_hit_rate + token_usage=self.stats.token_usage + # Iteration stats + num_prompt_tokens_iter = 0 + num_generation_tokens_iter = 0 + time_to_first_tokens_iter: List[float] = [] + time_per_output_tokens_iter: List[float] = [] + + # Request stats + # Decode + gen_throughput: float = 0.0 + # Latency + time_e2e_requests: List[float] = [] + time_waiting_requests: List[float] = [] + # Metadata + num_prompt_tokens_requests: List[int] = [] + num_generation_tokens_requests: List[int] = [] + finished_reason_requests: List[str] = [] + + # _, next_token_ids, _ = result + if batch is not None: + num_generation_tokens_iter = len(batch.output_ids) + gen_throughput = round(num_generation_tokens_iter / (now - self.last_stats_tic), 2) + + for i, req in enumerate(batch.reqs): + # NOTE: Batch forward mode is extend befor start decode, + if batch.forward_mode.is_extend(): + num_prompt_tokens_iter=len(batch.input_ids)+sum(batch.prefix_lens) + time_to_first_tokens_iter.append(now - req.started_time) + else: + time_per_output_tokens_iter.append(now-self.last_stats_tic) + + if req.finished(): + time_e2e_requests.append(now - req.created_time) + time_waiting_requests.append(req.queued_time - req.created_time) + num_prompt_tokens_requests.append(len(req.origin_input_ids)) + num_generation_tokens_requests.append(len(req.output_ids)) + finished_reason_requests.append( + req.finished_reason.to_json() + if req.finished_reason is not None + else None) + + return Stats( + new_seq=new_seq, + num_running_req=num_running_req, + num_waiting_req=num_waiting_req, + cache_hit_rate=cache_hit_rate, + token_usage=token_usage, + num_prompt_tokens_iter=num_prompt_tokens_iter, + num_generation_tokens_iter=num_generation_tokens_iter, + time_to_first_tokens_iter=time_to_first_tokens_iter, + time_per_output_tokens_iter=time_per_output_tokens_iter, + gen_throughput=gen_throughput, + time_e2e_requests=time_e2e_requests, + time_waiting_requests=time_waiting_requests, + num_prompt_tokens_requests=num_prompt_tokens_requests, + num_generation_tokens_requests=num_generation_tokens_requests, + finished_reason_requests=finished_reason_requests, + context_len=self.model_config.context_len, + max_total_num_tokens=self.max_total_num_tokens, + max_prefill_tokens=self.max_prefill_tokens, + max_running_requests=self.max_running_requests, + ) + + def log_stats(self,stats:Stats): + self.metrics_collector.log_stats(stats) def process_batch_result(self, batch: ScheduleBatch, result): if batch.forward_mode.is_decode(): diff --git a/python/sglang/srt/metrics/metrics_collector.py b/python/sglang/srt/metrics/metrics_collector.py new file mode 100644 index 0000000000..bb1eca88b1 --- /dev/null +++ b/python/sglang/srt/metrics/metrics_collector.py @@ -0,0 +1,297 @@ +""" +Copyright 2023-2024 SGLang Team +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +"""Utilities for Prometheus Metrics Collection.""" + +import logging +from abc import ABC, abstractmethod +from typing import Counter as CollectionsCounter +from typing import Dict, List, Union + +import numpy as np +from prometheus_client import Counter, Gauge, Histogram + +from sglang.srt.metrics.metrics_types import Stats + + +class Metrics: + """ + SGLang Metrics + """ + + def __init__(self, labelnames: List[str], max_model_len): + + # Configuration Stats + self.max_total_num_tokens = Gauge( + name="sglang:max_total_num_tokens", + documentation="Maximum total number of tokens", + labelnames=labelnames, + multiprocess_mode="min", + ) # static across processes + + self.max_prefill_tokens = Gauge( + name="sglang:max_prefill_tokens", + documentation="Maximum prefill tokens", + labelnames=labelnames, + multiprocess_mode="min", + ) # static across processes + + self.max_running_requests = Gauge( + name="sglang:max_running_requests", + documentation="Maximum running requests", + labelnames=labelnames, + multiprocess_mode="min", + ) # static across processes + + self.context_len = Gauge( + name="sglang:context_len", + documentation="Context length", + labelnames=labelnames, + multiprocess_mode="min", + ) # static across processes + # Decode Stats + self.num_running_sys = Gauge( + name="sglang:num_requests_running", + documentation="Number of requests currently running on GPU", + labelnames=labelnames, + multiprocess_mode="sum", + ) + self.num_waiting_sys = Gauge( + name="sglang:num_requests_waiting", + documentation="Number of requests waiting to be processed.", + labelnames=labelnames, + multiprocess_mode="sum", + ) + self.gen_throughput = Gauge( + name="sglang:gen_throughput", + documentation="Gen token throughput (token/s)", + labelnames=labelnames, + multiprocess_mode="sum", + ) + self.token_usage = Gauge( + name="sglang:token_usage", + documentation="Total token usage", + labelnames=labelnames, + multiprocess_mode="sum", + ) + # System Stats + # KV Cache Usage in % + # self.gpu_cache_usage_sys = Gauge( + # "gpu_cache_usage_perc", + # "GPU KV-cache usage. 1 means 100 percent usage.", + # labelnames=labelnames, + # multiprocess_mode="sum") + + self.new_seq = Gauge( + name="sglang:new_seq", + documentation="Number of new sequences", + labelnames=labelnames, + multiprocess_mode="sum", + ) + self.new_token = Gauge( + name="sglang:new_token", + documentation="Number of new token", + labelnames=labelnames, + multiprocess_mode="sum", + ) + # Prefix caching block hit rate + self.cached_token = Gauge( + name="sglang:cached_token", + documentation="Number of cached token", + labelnames=labelnames, + multiprocess_mode="sum", + ) + self.cache_hit_rate = Gauge( + name="sglang:cache_hit_rate", + documentation="Cache hit rate", + labelnames=labelnames, + multiprocess_mode="sum", + ) + self.queue_req = Gauge( + name="sglang:queue_req", + documentation="Number of queued requests", + labelnames=labelnames, + multiprocess_mode="sum", + ) + + # Iteration stats + self.counter_prompt_tokens = Counter( + name="sglang:prompt_tokens_total", + documentation="Number of prefill tokens processed.", + labelnames=labelnames) + self.counter_generation_tokens = Counter( + name="sglang:generation_tokens_total", + documentation="Number of generation tokens processed.", + labelnames=labelnames) + self.histogram_time_to_first_token = Histogram( + name="sglang:time_to_first_token_seconds", + documentation="Histogram of time to first token in seconds.", + labelnames=labelnames, + buckets=[ + 0.001, 0.005, 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.25, 0.5, + 0.75, 1.0, 2.5, 5.0, 7.5, 10.0, 15.0, 20.0, 25.0, 30.0 + ]) + self.histogram_time_per_output_token = Histogram( + name="sglang:time_per_output_token_seconds", + documentation="Histogram of time per output token in seconds.", + labelnames=labelnames, + buckets=[ + 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.04, 0.05, 0.075, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75, + 1.0, 2.5 + ]) + + # Request Stats + # Metadata + self.num_prompt_tokens_requests = Histogram( + name="sglang:request_prompt_tokens", + documentation="Number of prefill tokens processed", + labelnames=labelnames, + buckets=build_1_2_5_buckets(max_model_len), + ) + self.num_generation_tokens_requests = Histogram( + name="sglang:request_generation_tokens", + documentation="Number of generation tokens processed.", + labelnames=labelnames, + buckets=build_1_2_5_buckets(max_model_len), + ) + self.finished_reason_requests = Counter( + name="sglang:request_success_total", + documentation="Count of successfully processed requests.", + labelnames=labelnames + ["finished_reason"], + ) + self.histogram_time_e2e_requests = Histogram( + name="sglang:e2e_request_latency_seconds", + documentation="Histogram of End-to-end request latency in seconds", + labelnames=labelnames, + buckets=build_1_2_5_buckets(max_model_len), + ) + self.histogram_time_waiting_requests = Histogram( + name="sglang:waiting_request_latency_seconds", + documentation="Histogram of request waiting time in seconds", + labelnames=labelnames, + buckets=build_1_2_5_buckets(max_model_len), + ) + self.histogram_time_decode_requests = Histogram( + name="sglang:decode_request_latency_seconds", + documentation="Histogram of request decoding time in seconds", + labelnames=labelnames, + buckets=build_1_2_5_buckets(max_model_len), + ) + + +class MetricsCollector(ABC): + """ + SGLang Metrics Collector + """ + + @abstractmethod + def log_stats(self, stats: Stats) -> None: + pass + + +class PrometheusMetricsCollector(MetricsCollector): + """ + SGLang Metrics Collector + """ + + def __init__(self, labels: Dict[str, str], max_model_len: int) -> None: + self.labels = labels + self.metrics = Metrics( + labelnames=list(labels.keys()), max_model_len=max_model_len + ) + + def _log_gauge(self, gauge, data: Union[int, float]) -> None: + # Convenience function for logging to gauge. + gauge.labels(**self.labels).set(data) + + def _log_counter(self, counter, data: Union[int, float]) -> None: + # Convenience function for logging to counter. + counter.labels(**self.labels).inc(data) + + def _log_counter_labels( + self, counter, data: CollectionsCounter, label_key: str + ) -> None: + # Convenience function for collection counter of labels. + for label, count in data.items(): + counter.labels(**{**self.labels, label_key: label}).inc(count) + + def _log_histogram(self, histogram, data: Union[List[int], List[float]]) -> None: + # Convenience function for logging list to histogram. + for datum in data: + histogram.labels(**self.labels).observe(datum) + + def log_stats(self, stats: Stats) -> None: + self._log_gauge(self.metrics.max_total_num_tokens, stats.max_total_num_tokens) + self._log_gauge(self.metrics.max_prefill_tokens, stats.max_prefill_tokens) + self._log_gauge(self.metrics.max_running_requests, stats.max_running_requests) + self._log_gauge(self.metrics.context_len, stats.context_len) + self._log_histogram( + self.metrics.num_prompt_tokens_requests, stats.num_prompt_tokens_requests + ) + self._log_histogram( + self.metrics.num_generation_tokens_requests, + stats.num_generation_tokens_requests, + ) + + self._log_counter(self.metrics.counter_prompt_tokens, + stats.num_prompt_tokens_iter) + self._log_counter(self.metrics.counter_generation_tokens, + stats.num_generation_tokens_iter) + self._log_histogram(self.metrics.histogram_time_to_first_token, + stats.time_to_first_tokens_iter) + self._log_histogram(self.metrics.histogram_time_per_output_token, + stats.time_per_output_tokens_iter) + + # self._log_gauge(self.metrics.gpu_cache_usage_sys, stats.gpu_cache_usage_sys) + self._log_gauge(self.metrics.num_running_sys, stats.num_running_req) + self._log_gauge(self.metrics.num_waiting_sys, stats.num_waiting_req) + self._log_gauge(self.metrics.gen_throughput, stats.gen_throughput) + self._log_gauge(self.metrics.token_usage, stats.token_usage) + self._log_histogram( + self.metrics.histogram_time_e2e_requests, stats.time_e2e_requests + ) + self._log_histogram( + self.metrics.histogram_time_waiting_requests, stats.time_waiting_requests + ) + self._log_histogram( + self.metrics.histogram_time_decode_requests, stats.time_decode_requests + ) + self._log_gauge(self.metrics.new_seq, stats.new_seq) + self._log_gauge(self.metrics.new_token, stats.new_token) + self._log_gauge(self.metrics.cached_token, stats.cached_token) + self._log_gauge(self.metrics.cache_hit_rate, stats.cache_hit_rate) + self._log_gauge(self.metrics.queue_req, stats.queue_req) + + +def build_1_2_5_buckets(max_value: int) -> List[int]: + """ + Builds a list of buckets with increasing powers of 10 multiplied by + mantissa values (1, 2, 5) until the value exceeds the specified maximum. + + Example: + >>> build_1_2_5_buckets(100) + [1, 2, 5, 10, 20, 50, 100] + """ + mantissa_lst = [1, 2, 5] + exponent = 0 + buckets: List[int] = [] + while True: + for m in mantissa_lst: + value = m * 10**exponent + if value <= max_value: + buckets.append(value) + else: + return buckets + exponent += 1 \ No newline at end of file diff --git a/python/sglang/srt/metrics/metrics_types.py b/python/sglang/srt/metrics/metrics_types.py new file mode 100644 index 0000000000..f1b357f403 --- /dev/null +++ b/python/sglang/srt/metrics/metrics_types.py @@ -0,0 +1,57 @@ +""" +Copyright 2023-2024 SGLang Team +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" + +"""Metrics Types""" + +from dataclasses import dataclass, field +from typing import List + + +@dataclass +class Stats: + # config + max_total_num_tokens: int = 0 + max_prefill_tokens: int = 0 + max_running_requests: int = 0 + context_len: int = 0 + # request stats + num_prompt_tokens_requests: List[int] = field(default_factory=list) + num_generation_tokens_requests: List[int] = field(default_factory=list) + finished_reason_requests: List[str] = field(default_factory=list) + # decode stats + num_running_req: int = 0 + num_waiting_req: int = 0 + gen_throughput: float = 0.0 + num_token: int = 0 + token_usage: float = 0.0 + waiting_queue: int = 0 + time_e2e_requests: List[float] = field(default_factory=list) + time_waiting_requests: List[float] = field(default_factory=list) + time_decode_requests: List[float] = field(default_factory=list) + # system stats + token_usage: float = 0.0 + is_mixed_chunk: bool = False + new_seq: int = 0 + new_token: int = 0 + cached_token: int = 0 + cache_hit_rate: float = 0.0 + running_req: int = 0 + queue_req: int = 0 + + # Iteration stats (should have _iter suffix) + num_prompt_tokens_iter: int = 0 + num_generation_tokens_iter: int = 0 + time_to_first_tokens_iter: List[float] = field(default_factory=list) + time_per_output_tokens_iter: List[float] = field(default_factory=list) \ No newline at end of file diff --git a/python/sglang/srt/server.py b/python/sglang/srt/server.py index c881ba3957..d295553469 100644 --- a/python/sglang/srt/server.py +++ b/python/sglang/srt/server.py @@ -25,12 +25,15 @@ import logging import multiprocessing as mp import os +import re +import tempfile import threading import time from http import HTTPStatus from typing import AsyncIterator, Dict, List, Optional, Union import orjson +from starlette.routing import Mount # Fix a bug of Python threading setattr(threading, "_register_atexit", lambda *args, **kwargs: None) @@ -86,6 +89,10 @@ logger = logging.getLogger(__name__) +# Temporary directory for prometheus multiprocess mode +# Cleaned up automatically when this object is garbage collected +prometheus_multiproc_dir: tempfile.TemporaryDirectory + asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) @@ -412,6 +419,18 @@ def launch_engine( for i in range(len(scheduler_pipe_readers)): scheduler_pipe_readers[i].recv() +def add_prometheus_middleware(app: FastAPI): + # Adapted from https://github.com/vllm-project/vllm/blob/v0.6.1/vllm/entrypoints/openai/api_server.py#L216 + from prometheus_client import CollectorRegistry, make_asgi_app, multiprocess + + registry = CollectorRegistry() + multiprocess.MultiProcessCollector(registry) + metrics_route = Mount("/metrics", make_asgi_app(registry=registry)) + + # Workaround for 307 Redirect for /metrics + metrics_route.path_regex = re.compile("^/metrics(?P.*)$") + app.routes.append(metrics_route) + def launch_server( server_args: ServerArgs, @@ -439,6 +458,11 @@ def launch_server( if server_args.api_key: add_api_key_middleware(app, server_args.api_key) + # add prometheus middleware + if server_args.enable_metrics: + _set_prometheus_env() + add_prometheus_middleware(app) + # Send a warmup request t = threading.Thread( target=_wait_and_warmup, args=(server_args, pipe_finish_writer) @@ -466,6 +490,21 @@ def launch_server( finally: t.join() +def _set_prometheus_env(): + # Set prometheus multiprocess directory + # sglang uses prometheus multiprocess mode + # we need to set this before importing prometheus_client + # https://prometheus.github.io/client_python/multiprocess/ + global prometheus_multiproc_dir + if "PROMETHEUS_MULTIPROC_DIR" in os.environ: + logger.debug(f"User set PROMETHEUS_MULTIPROC_DIR detected.") + prometheus_multiproc_dir = tempfile.TemporaryDirectory( + dir=os.environ["PROMETHEUS_MULTIPROC_DIR"] + ) + else: + prometheus_multiproc_dir = tempfile.TemporaryDirectory() + os.environ["PROMETHEUS_MULTIPROC_DIR"] = prometheus_multiproc_dir.name + logger.debug(f"PROMETHEUS_MULTIPROC_DIR: {os.environ['PROMETHEUS_MULTIPROC_DIR']}") def _set_envs_and_config(server_args: ServerArgs): # Set global environments diff --git a/python/sglang/srt/server_args.py b/python/sglang/srt/server_args.py index 501c2e326d..84d1afbd5f 100644 --- a/python/sglang/srt/server_args.py +++ b/python/sglang/srt/server_args.py @@ -70,6 +70,7 @@ class ServerArgs: log_level_http: Optional[str] = None log_requests: bool = False show_time_cost: bool = False + enable_metrics: bool = False # Other api_key: Optional[str] = None @@ -414,6 +415,12 @@ def add_cli_args(parser: argparse.ArgumentParser): action="store_true", help="Show time cost of custom marks.", ) + parser.add_argument( + "--enable-metrics", + action="store_true", + help="Enable log prometheus metrics.", + ) + parser.add_argument( "--api-key", type=str, From 96766101b4d181f9b3141da9484ca10ff7656743 Mon Sep 17 00:00:00 2001 From: Byron Hsu Date: Wed, 6 Nov 2024 00:02:02 -0800 Subject: [PATCH 43/44] [rust] refactor server and router (#1922) --- .github/workflows/pr-test-rust.yml | 4 +- rust/src/router.rs | 164 ++++++++++++++++------------- rust/src/server.rs | 106 ++++--------------- 3 files changed, 113 insertions(+), 161 deletions(-) diff --git a/.github/workflows/pr-test-rust.yml b/.github/workflows/pr-test-rust.yml index b7e294d28d..aae2853478 100644 --- a/.github/workflows/pr-test-rust.yml +++ b/.github/workflows/pr-test-rust.yml @@ -4,11 +4,11 @@ on: push: branches: [ main ] paths: - - "rust/*" + - "rust/**" pull_request: branches: [ main ] paths: - - "rust/*" + - "rust/**" workflow_dispatch: concurrency: diff --git a/rust/src/router.rs b/rust/src/router.rs index 9d42cc13f4..29db6e37c6 100644 --- a/rust/src/router.rs +++ b/rust/src/router.rs @@ -1,93 +1,109 @@ -// src/router.rs - +use actix_web::http::header::{HeaderValue, CONTENT_TYPE}; +use actix_web::{HttpRequest, HttpResponse}; +use bytes::Bytes; +use futures_util::TryStreamExt; use std::fmt::Debug; -/// Generic Router trait that can be implemented with different policies -pub trait Router: Send + Sync + Debug { - /// Select a worker URL based on the implementation's policy - /// Returns None if no worker is available - fn select(&self) -> Option; - - // get first worker - fn get_first(&self) -> Option; -} - -// Round Robin Router #[derive(Debug)] -pub struct RoundRobinRouter { - worker_urls: Vec, - current_index: std::sync::atomic::AtomicUsize, // AtomicUsize is a thread-safe integer +pub enum Router { + RoundRobin { + worker_urls: Vec, + current_index: std::sync::atomic::AtomicUsize, + }, + Random { + worker_urls: Vec, + }, } -impl RoundRobinRouter { - pub fn new(worker_urls: Vec) -> Self { - Self { - worker_urls, - current_index: std::sync::atomic::AtomicUsize::new(0), +impl Router { + pub fn new(worker_urls: Vec, policy: String) -> Self { + match policy.to_lowercase().as_str() { + "random" => Router::Random { worker_urls }, + "round_robin" => Router::RoundRobin { + worker_urls, + current_index: std::sync::atomic::AtomicUsize::new(0), + }, + _ => panic!( + "Unknown routing policy: {}. The available policies are 'random' and 'round_robin'", + policy + ), } } -} -impl Router for RoundRobinRouter { - fn select(&self) -> Option { - if self.worker_urls.is_empty() { - return None; + pub fn get_first(&self) -> Option { + match self { + Router::RoundRobin { worker_urls, .. } | Router::Random { worker_urls } => { + if worker_urls.is_empty() { + None + } else { + Some(worker_urls[0].clone()) + } + } } - // Use relaxed because operation order doesn't matter in round robin - let index = self - .current_index - .fetch_add(1, std::sync::atomic::Ordering::Relaxed) - % self.worker_urls.len(); - Some(self.worker_urls[index].clone()) } - fn get_first(&self) -> Option { - if self.worker_urls.is_empty() { - return None; - } - Some(self.worker_urls[0].clone()) - } -} - -// Random Router -#[derive(Debug)] -pub struct RandomRouter { - worker_urls: Vec, -} + pub async fn dispatch( + &self, + client: &reqwest::Client, + req: HttpRequest, + body: Bytes, + ) -> HttpResponse { + let worker_url = match self { + Router::RoundRobin { + worker_urls, + current_index, + } => { + current_index + .fetch_update( + std::sync::atomic::Ordering::SeqCst, + std::sync::atomic::Ordering::SeqCst, + |x| Some((x + 1) % worker_urls.len()), + ) + .expect_err("Error updating index in round robin"); -impl RandomRouter { - pub fn new(worker_urls: Vec) -> Self { - Self { worker_urls } - } -} + &worker_urls[current_index.load(std::sync::atomic::Ordering::SeqCst)] + } + Router::Random { worker_urls } => { + &worker_urls[rand::random::() % worker_urls.len()] + } + }; -impl Router for RandomRouter { - fn select(&self) -> Option { - use rand::seq::SliceRandom; + // Check if client requested streaming + let is_stream = serde_json::from_slice::(&body) + .map(|v| v.get("stream").and_then(|s| s.as_bool()).unwrap_or(false)) + .unwrap_or(false); - if self.worker_urls.is_empty() { - return None; - } + let res = match client + .post(format!("{}/generate", worker_url)) + .header( + "Content-Type", + req.headers() + .get("Content-Type") + .and_then(|h| h.to_str().ok()) + .unwrap_or("application/json"), + ) + .body(body.to_vec()) + .send() + .await + { + Ok(res) => res, + Err(_) => return HttpResponse::InternalServerError().finish(), + }; - self.worker_urls.choose(&mut rand::thread_rng()).cloned() - } + let status = actix_web::http::StatusCode::from_u16(res.status().as_u16()) + .unwrap_or(actix_web::http::StatusCode::INTERNAL_SERVER_ERROR); - fn get_first(&self) -> Option { - if self.worker_urls.is_empty() { - return None; + if !is_stream { + match res.bytes().await { + Ok(body) => HttpResponse::build(status).body(body.to_vec()), + Err(_) => HttpResponse::InternalServerError().finish(), + } + } else { + HttpResponse::build(status) + .insert_header((CONTENT_TYPE, HeaderValue::from_static("text/event-stream"))) + .streaming(res.bytes_stream().map_err(|_| { + actix_web::error::ErrorInternalServerError("Failed to read string") + })) } - Some(self.worker_urls[0].clone()) - } -} - -// create a router based on routing policy -pub fn create_router(worker_urls: Vec, policy: String) -> Box { - match policy.to_lowercase().as_str() { - "random" => Box::new(RandomRouter::new(worker_urls)), - "round_robin" => Box::new(RoundRobinRouter::new(worker_urls)), - _ => panic!( - "Unknown routing policy: {}. The available policies are 'random' and 'round_robin'", - policy - ), } } diff --git a/rust/src/server.rs b/rust/src/server.rs index 1c6c515b48..fec7fae746 100644 --- a/rust/src/server.rs +++ b/rust/src/server.rs @@ -1,38 +1,28 @@ -use crate::router::create_router; use crate::router::Router; -use actix_web::http::header::{HeaderValue, CONTENT_TYPE}; use actix_web::{get, post, web, App, HttpRequest, HttpResponse, HttpServer, Responder}; use bytes::Bytes; -use futures_util::StreamExt; #[derive(Debug)] pub struct AppState { - router: Box, + router: Router, client: reqwest::Client, } impl AppState { pub fn new(worker_urls: Vec, policy: String, client: reqwest::Client) -> Self { // Create router based on policy - let router = create_router(worker_urls, policy); + let router = Router::new(worker_urls, policy); Self { router, client } } } -#[get("/v1/models")] -async fn v1_model(data: web::Data) -> impl Responder { - let worker_url = match data.router.get_first() { - Some(url) => url, - None => return HttpResponse::InternalServerError().finish(), - }; - // Use the shared client - match data - .client - .get(format!("{}/v1/models", worker_url)) - .send() - .await - { +async fn forward_request( + client: &reqwest::Client, + worker_url: String, + route: String, +) -> HttpResponse { + match client.get(format!("{}{}", worker_url, route)).send().await { Ok(res) => { let status = actix_web::http::StatusCode::from_u16(res.status().as_u16()) .unwrap_or(actix_web::http::StatusCode::INTERNAL_SERVER_ERROR); @@ -48,85 +38,31 @@ async fn v1_model(data: web::Data) -> impl Responder { } } -#[get("/get_model_info")] -async fn get_model_info(data: web::Data) -> impl Responder { +#[get("/v1/models")] +async fn v1_model(data: web::Data) -> impl Responder { + // TODO: extract forward_to_route let worker_url = match data.router.get_first() { Some(url) => url, None => return HttpResponse::InternalServerError().finish(), }; - // Use the shared client - match data - .client - .get(format!("{}/get_model_info", worker_url)) - .send() - .await - { - Ok(res) => { - let status = actix_web::http::StatusCode::from_u16(res.status().as_u16()) - .unwrap_or(actix_web::http::StatusCode::INTERNAL_SERVER_ERROR); - // print the status - println!("Worker URL: {}, Status: {}", worker_url, status); - match res.bytes().await { - Ok(body) => HttpResponse::build(status).body(body.to_vec()), - Err(_) => HttpResponse::InternalServerError().finish(), - } - } - Err(_) => HttpResponse::InternalServerError().finish(), - } + forward_request(&data.client, worker_url, "/v1/models".to_string()).await } -// no deser and ser, just forward and return -#[post("/generate")] -async fn generate(req: HttpRequest, body: Bytes, data: web::Data) -> impl Responder { - // create a router struct - // TODO: use router abstraction for different policy - let worker_url = match data.router.select() { +#[get("/get_model_info")] +async fn get_model_info(data: web::Data) -> impl Responder { + let worker_url = match data.router.get_first() { Some(url) => url, None => return HttpResponse::InternalServerError().finish(), }; - // Check if client requested streaming - let is_stream = serde_json::from_slice::(&body) - .map(|v| v.get("stream").and_then(|s| s.as_bool()).unwrap_or(false)) - .unwrap_or(false); - - let res = match data - .client - .post(format!("{}/generate", worker_url)) - .header( - "Content-Type", - req.headers() - .get("Content-Type") - .and_then(|h| h.to_str().ok()) - .unwrap_or("application/json"), - ) - .body(body.to_vec()) - .send() - .await - { - Ok(res) => res, - Err(_) => return HttpResponse::InternalServerError().finish(), - }; - - let status = actix_web::http::StatusCode::from_u16(res.status().as_u16()) - .unwrap_or(actix_web::http::StatusCode::INTERNAL_SERVER_ERROR); + forward_request(&data.client, worker_url, "/get_model_info".to_string()).await +} - if !is_stream { - match res.bytes().await { - Ok(body) => HttpResponse::build(status).body(body.to_vec()), - Err(_) => HttpResponse::InternalServerError().finish(), - } - } else { - HttpResponse::build(status) - .insert_header((CONTENT_TYPE, HeaderValue::from_static("text/event-stream"))) - .streaming(res.bytes_stream().map(|b| match b { - Ok(b) => Ok::<_, actix_web::Error>(b), - Err(_) => Err(actix_web::error::ErrorInternalServerError( - "Failed to read stream", - )), - })) - } +// no deser and ser, just forward and return +#[post("/generate")] +async fn generate(req: HttpRequest, body: Bytes, data: web::Data) -> impl Responder { + data.router.dispatch(&data.client, req, body).await } pub async fn startup( From a5e0defb5a560a6d42882008c1dd8a739002ab7d Mon Sep 17 00:00:00 2001 From: Xuehai Pan Date: Wed, 6 Nov 2024 21:46:04 +0800 Subject: [PATCH 44/44] minor: Add basic editorconfig and pre-commit hooks to enforce style for whitespaces (#1926) --- .editorconfig | 25 ++++++++++ .github/pull_request_template.md | 2 +- .github/workflows/close-inactive-issues.yml | 14 +++--- .github/workflows/execute-notebook.yml | 4 +- .github/workflows/pr-test-rust.yml | 2 +- .github/workflows/pr-test.yml | 2 +- .github/workflows/release-docs.yml | 2 +- .gitignore | 2 +- .isort.cfg | 2 +- .pre-commit-config.yaml | 25 ++++++++-- 3rdparty/amd/profiling/PROFILING.md | 2 - 3rdparty/amd/tuning/TUNING.md | 18 +++---- benchmark/blog_v0_2/405b_sglang.sh | 2 +- benchmark/blog_v0_2/405b_vllm.sh | 2 +- .../generative_agents/agent_functions.py | 48 +++++++++---------- benchmark/json_decode_regex/README.md | 2 +- benchmark/json_jump_forward/README.md | 2 +- benchmark/json_jump_forward/dataset.txt | 2 +- benchmark/llava_bench/README.md | 2 +- benchmark/llava_bench/bench_hf_llava_bench.sh | 0 benchmark/llava_bench/bench_hf_mme.sh | 0 benchmark/llm_judge/README.md | 2 +- benchmark/mmlu/download_data.sh | 2 +- benchmark/multi_chain_reasoning/README.md | 2 +- benchmark/react/README.md | 2 +- benchmark/react/bench_other.py | 12 ++--- benchmark/react/bench_sglang.py | 12 ++--- benchmark/tip_suggestion/.gitignore | 2 +- benchmark/tip_suggestion/README.md | 2 +- benchmark/tip_suggestion/topic.jsonl | 2 +- docs/README.md | 2 +- docs/_static/css/custom_log.css | 6 +-- docs/backend/backend.md | 2 +- docs/conf.py | 2 +- docs/deploy.py | 24 +++++----- docs/references/choices_methods.md | 2 +- docs/references/custom_chat_template.md | 2 +- docs/references/hyperparameter_tuning.md | 6 +-- docs/references/learn_more.md | 2 +- docs/references/sampling_params.md | 2 +- docs/references/troubleshooting.md | 6 +-- docs/requirements.txt | 2 +- .../usage/llava_video/srt_example_llava_v.sh | 18 +++---- .../usage/openai_chat_speculative.py | 4 +- .../frontend_language/usage/triton/Dockerfile | 2 +- .../frontend_language/usage/triton/README.md | 2 +- examples/runtime/engine/input_ids.py | 4 +- examples/runtime/engine/readme.md | 2 +- .../llava_onevision/http_llama3_llava_test.py | 2 +- .../llava_onevision/http_qwen_llava_test.py | 2 +- .../srt/layers/quantization/base_config.py | 2 +- .../srt/layers/vocab_parallel_embedding.py | 4 +- python/sglang/srt/managers/scheduler.py | 6 +-- .../sglang/srt/managers/tokenizer_manager.py | 2 +- .../sglang/srt/metrics/metrics_collector.py | 6 +-- python/sglang/srt/metrics/metrics_types.py | 2 +- python/sglang/srt/mm_utils.py | 2 +- python/sglang/srt/models/gpt2.py | 4 +- python/sglang/srt/models/olmo.py | 0 python/sglang/srt/models/qwen2_vl.py | 10 ++-- python/sglang/srt/server.py | 2 +- python/sglang/test/long_prompt.txt | 2 +- python/sglang/test/simple_eval_common.py | 2 +- python/sglang/test/simple_eval_humaneval.py | 4 +- python/sglang/test/simple_eval_mgsm.py | 4 +- rust/Cargo.toml | 2 +- rust/readme.md | 6 +-- scripts/ci_install_rust.sh | 4 +- scripts/playground/lora/lora_hf_play.py | 2 +- scripts/playground/lora/lora_vllm_play.py | 2 +- scripts/version_branch_to_tag.sh | 5 +- test/README.md | 2 +- test/srt/Llama-3.1-8B-Instruct.json | 2 +- test/srt/models/test_generation_models.py | 0 test/srt/models/test_lora.py | 2 +- test/srt/test_data_parallelism.py | 2 +- test/srt/test_matched_stop.py | 8 ++-- 77 files changed, 209 insertions(+), 172 deletions(-) create mode 100644 .editorconfig mode change 100644 => 100755 benchmark/llava_bench/bench_hf_llava_bench.sh mode change 100644 => 100755 benchmark/llava_bench/bench_hf_mme.sh mode change 100644 => 100755 examples/frontend_language/usage/llava_video/srt_example_llava_v.sh mode change 100755 => 100644 python/sglang/srt/models/olmo.py mode change 100644 => 100755 scripts/version_branch_to_tag.sh mode change 100755 => 100644 test/srt/models/test_generation_models.py diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000000..030a7293dc --- /dev/null +++ b/.editorconfig @@ -0,0 +1,25 @@ +# https://editorconfig.org/ + +root = true + +[*] +charset = utf-8 +end_of_line = lf +indent_style = space +indent_size = 4 +trim_trailing_whitespace = true +insert_final_newline = true + +[*.{json,yaml,yml}] +indent_size = 2 + +[*.md] +indent_size = 2 +x-soft-wrap-text = true + +[*.rst] +indent_size = 4 +x-soft-wrap-text = true + +[Makefile] +indent_style = tab diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index 958c8b5ffe..4c6ea8355f 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -12,4 +12,4 @@ - [ ] Format your code according to the [Contributor Guide](https://github.com/sgl-project/sglang/blob/main/docs/contributor_guide.md). - [ ] Add unit tests as outlined in the [Contributor Guide](https://github.com/sgl-project/sglang/blob/main/docs/contributor_guide.md). -- [ ] Update documentation as needed, including docstrings or example tutorials. \ No newline at end of file +- [ ] Update documentation as needed, including docstrings or example tutorials. diff --git a/.github/workflows/close-inactive-issues.yml b/.github/workflows/close-inactive-issues.yml index e81dd42479..1455da4805 100644 --- a/.github/workflows/close-inactive-issues.yml +++ b/.github/workflows/close-inactive-issues.yml @@ -20,10 +20,10 @@ jobs: github-token: ${{secrets.GITHUB_TOKEN}} script: | const sixtyDaysAgo = new Date(Date.now() - 60 * 24 * 60 * 60 * 1000); - + const [owner, repo] = process.env.GITHUB_REPOSITORY.split('/'); console.log(`Owner: ${owner}, Repo: ${repo}`); - + async function fetchIssues(page = 1) { console.log(`Fetching issues for ${owner}/${repo}, page ${page}`); return await github.rest.issues.listForRepo({ @@ -36,23 +36,23 @@ jobs: page: page }); } - + async function processIssues() { console.log('Starting to process issues'); console.log(`Repository: ${owner}/${repo}`); - + let page = 1; let hasMoreIssues = true; while (hasMoreIssues) { try { const issues = await fetchIssues(page); console.log(`Fetched ${issues.data.length} issues on page ${page}`); - + if (issues.data.length === 0) { hasMoreIssues = false; break; } - + for (const issue of issues.data) { if (new Date(issue.updated_at) < sixtyDaysAgo) { try { @@ -87,5 +87,5 @@ jobs: } console.log('Finished processing issues'); } - + await processIssues(); diff --git a/.github/workflows/execute-notebook.yml b/.github/workflows/execute-notebook.yml index b0a2a83246..170545d72c 100644 --- a/.github/workflows/execute-notebook.yml +++ b/.github/workflows/execute-notebook.yml @@ -18,7 +18,7 @@ concurrency: group: execute-notebook-${{ github.ref }} cancel-in-progress: true - + jobs: run-all-notebooks: runs-on: 1-gpu-runner @@ -45,4 +45,4 @@ jobs: run: | cd docs make clean - make compile \ No newline at end of file + make compile diff --git a/.github/workflows/pr-test-rust.yml b/.github/workflows/pr-test-rust.yml index aae2853478..ae42446738 100644 --- a/.github/workflows/pr-test-rust.yml +++ b/.github/workflows/pr-test-rust.yml @@ -36,4 +36,4 @@ jobs: run: | source "$HOME/.cargo/env" cd rust/ - cargo test \ No newline at end of file + cargo test diff --git a/.github/workflows/pr-test.yml b/.github/workflows/pr-test.yml index 8f374b8972..f228901dd2 100644 --- a/.github/workflows/pr-test.yml +++ b/.github/workflows/pr-test.yml @@ -237,7 +237,7 @@ jobs: run: | cd test/srt python3 test_moe_eval_accuracy_large.py - + - name: Evaluate MLA Accuracy (TP=2) timeout-minutes: 10 run: | diff --git a/.github/workflows/release-docs.yml b/.github/workflows/release-docs.yml index 99bd546161..ab2129e372 100644 --- a/.github/workflows/release-docs.yml +++ b/.github/workflows/release-docs.yml @@ -47,7 +47,7 @@ jobs: make html cd _build/html - + git clone https://$GITHUB_TOKEN@github.com/sgl-project/sgl-project.github.io.git ../sgl-project.github.io --depth 1 rm -rf ../sgl-project.github.io/* cp -r * ../sgl-project.github.io diff --git a/.gitignore b/.gitignore index 537b6918c1..96c8fff74c 100644 --- a/.gitignore +++ b/.gitignore @@ -185,4 +185,4 @@ tmp*.txt work_dirs/ *.csv -!logo.png \ No newline at end of file +!logo.png diff --git a/.isort.cfg b/.isort.cfg index 482b916dd4..8350951544 100644 --- a/.isort.cfg +++ b/.isort.cfg @@ -1,3 +1,3 @@ [settings] profile=black -known_first_party=sglang \ No newline at end of file +known_first_party=sglang diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8c1de4d99a..43cd18118d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,7 +1,27 @@ default_language_version: python: python3.9 +default_stages: [pre-commit, pre-push, manual] + repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: check-symlinks + - id: destroyed-symlinks + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + args: [--allow-multiple-documents] + - id: check-toml + - id: check-ast + - id: check-added-large-files + - id: check-merge-conflict + - id: check-executables-have-shebangs + - id: check-shebang-scripts-are-executable + - id: detect-private-key + - id: debug-statements + - id: no-commit-to-branch - repo: https://github.com/PyCQA/isort rev: 5.13.2 hooks: @@ -13,8 +33,3 @@ repos: additional_dependencies: ['.[jupyter]'] types: [python, jupyter] types_or: [python, jupyter] - - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v5.0.0 - hooks: - - id: no-commit-to-branch \ No newline at end of file diff --git a/3rdparty/amd/profiling/PROFILING.md b/3rdparty/amd/profiling/PROFILING.md index de0c2ef71a..3dfc193e32 100644 --- a/3rdparty/amd/profiling/PROFILING.md +++ b/3rdparty/amd/profiling/PROFILING.md @@ -6,5 +6,3 @@ Two primary methods are covered: - [Torch Profiler](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html) - - diff --git a/3rdparty/amd/tuning/TUNING.md b/3rdparty/amd/tuning/TUNING.md index 9a37a4384e..6cff9f8b7d 100644 --- a/3rdparty/amd/tuning/TUNING.md +++ b/3rdparty/amd/tuning/TUNING.md @@ -29,18 +29,18 @@ def _triton_kernel_funtion(): ... ``` ## 2. Torch Tunable Operations -**TunableOp** is a feature in PyTorch that allows for the definition and optimization of custom kernels with tunable parameters. This feature is particularly useful for enhancing the performance of kernels by experimenting with different configurations. +**TunableOp** is a feature in PyTorch that allows for the definition and optimization of custom kernels with tunable parameters. This feature is particularly useful for enhancing the performance of kernels by experimenting with different configurations. ### Key Environment Variables: -1. **PYTORCH_TUNABLEOP_ENABLED**: +1. **PYTORCH_TUNABLEOP_ENABLED**: - Default: `0` - Set to `1` to enable TunableOp. -2. **PYTORCH_TUNABLEOP_TUNING**: +2. **PYTORCH_TUNABLEOP_TUNING**: - Default: `1` - Set to `0` to disable tuning. If a tuned entry is not found, it will run the tuning step and record the entry when PYTORCH_TUNABLEOP_ENABLED is enabled. -3. **PYTORCH_TUNABLEOP_VERBOSE**: +3. **PYTORCH_TUNABLEOP_VERBOSE**: - Default: `0` - Set to `1` to enable verbose output for TunableOp. @@ -66,20 +66,20 @@ The following are suggestions for optimizing matrix multiplication (GEMM) and co To tune Triton kernels with GEMM and convolution ops (conv), use the `torch.compile` function with the max-autotune mode. This benchmarks a predefined list of Triton configurations and selects the fastest one for each shape. ### Key Configurations: -1. **Max Autotune**: +1. **Max Autotune**: - Set `torch._inductor.config.max_autotune = True` or `TORCHINDUCTOR_MAX_AUTOTUNE=1`. 2. **Fine-Grained Control**: - Enable GEMM tuning: `torch._inductor.config.max_autotune_gemm = True`. - Enable tuning for pointwise/reduction ops: `torch._inductor.config.max_autotune.pointwise = True`. -3. **Backend Selection**: +3. **Backend Selection**: - Use `torch._inductor.max_autotune_gemm_backends` to limit backends to TRITON for better performance. -4. **Freezing for Inference**: +4. **Freezing for Inference**: - Use `torch._inductor.config.freezing=True` to enable constant folding optimizations. -5. **Debugging**: +5. **Debugging**: - Set `TORCH_COMPILE_DEBUG=1` to extract Triton kernels generated by Inductor. ### Example Code Block: @@ -98,4 +98,4 @@ TORCHINDUCTOR_FREEZING=1 your_script.sh For more detailed information on tuning SGLang performance with AMD GPUs, please refer to the following link: -[ROCm Documentation: Triton Kernel Performance Optimization](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html#triton-kernel-performance-optimization) \ No newline at end of file +[ROCm Documentation: Triton Kernel Performance Optimization](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html#triton-kernel-performance-optimization) diff --git a/benchmark/blog_v0_2/405b_sglang.sh b/benchmark/blog_v0_2/405b_sglang.sh index d31f8daf8e..4e3372ae8c 100644 --- a/benchmark/blog_v0_2/405b_sglang.sh +++ b/benchmark/blog_v0_2/405b_sglang.sh @@ -21,4 +21,4 @@ python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-pro python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 600 --request-rate 2 --random-input 1024 --random-output 1024 > sglang_log32 python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 1200 --request-rate 4 --random-input 1024 --random-output 1024 > sglang_log33 python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2400 --request-rate 8 --random-input 1024 --random-output 1024 > sglang_log34 -python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 1024 --random-output 1024 > sglang_log35 \ No newline at end of file +python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 1024 --random-output 1024 > sglang_log35 diff --git a/benchmark/blog_v0_2/405b_vllm.sh b/benchmark/blog_v0_2/405b_vllm.sh index 17833a2000..d1ef048e8f 100644 --- a/benchmark/blog_v0_2/405b_vllm.sh +++ b/benchmark/blog_v0_2/405b_vllm.sh @@ -21,4 +21,4 @@ python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name rando python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 600 --request-rate 2 --random-input 1024 --random-output 1024 > vllm_log32 python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 1200 --request-rate 4 --random-input 1024 --random-output 1024 > vllm_log33 python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2400 --request-rate 8 --random-input 1024 --random-output 1024 > vllm_log34 -python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 1024 --random-output 1024 > vllm_log35 \ No newline at end of file +python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 1024 --random-output 1024 > vllm_log35 diff --git a/benchmark/generative_agents/agent_functions.py b/benchmark/generative_agents/agent_functions.py index e46ceb09eb..9785fc1ece 100644 --- a/benchmark/generative_agents/agent_functions.py +++ b/benchmark/generative_agents/agent_functions.py @@ -30,22 +30,22 @@ def poignancy_event_prompt(persona_name, persona_iss, event): @sgl.function def generate_event_triple(s, persona_name, action): s += """Task: Turn the input into (subject, predicate, object). -Input: Sam Johnson is eating breakfast. -Output: (Dolores Murphy, eat, breakfast) ---- +Input: Sam Johnson is eating breakfast. +Output: (Dolores Murphy, eat, breakfast) +--- Input: Joon Park is brewing coffee. Output: (Joon Park, brew, coffee) --- -Input: Jane Cook is sleeping. +Input: Jane Cook is sleeping. Output: (Jane Cook, is, sleep) --- -Input: Michael Bernstein is writing email on a computer. +Input: Michael Bernstein is writing email on a computer. Output: (Michael Bernstein, write, email) --- -Input: Percy Liang is teaching students in a classroom. +Input: Percy Liang is teaching students in a classroom. Output: (Percy Liang, teach, students) --- -Input: Merrie Morris is running on a treadmill. +Input: Merrie Morris is running on a treadmill. Output: (Merrie Morris, run, treadmill) ---""" s += persona_name + "is" + action + ".\n" @@ -56,22 +56,22 @@ def generate_event_triple(s, persona_name, action): def generate_event_triple_prompt(persona_name, action): s = "" s += """Task: Turn the input into (subject, predicate, object). -Input: Sam Johnson is eating breakfast. -Output: (Dolores Murphy, eat, breakfast) ---- +Input: Sam Johnson is eating breakfast. +Output: (Dolores Murphy, eat, breakfast) +--- Input: Joon Park is brewing coffee. Output: (Joon Park, brew, coffee) --- -Input: Jane Cook is sleeping. +Input: Jane Cook is sleeping. Output: (Jane Cook, is, sleep) --- -Input: Michael Bernstein is writing email on a computer. +Input: Michael Bernstein is writing email on a computer. Output: (Michael Bernstein, write, email) --- -Input: Percy Liang is teaching students in a classroom. +Input: Percy Liang is teaching students in a classroom. Output: (Percy Liang, teach, students) --- -Input: Merrie Morris is running on a treadmill. +Input: Merrie Morris is running on a treadmill. Output: (Merrie Morris, run, treadmill) ---""" s += persona_name + "is" + action + ".\n" @@ -107,9 +107,9 @@ def action_location_sector( current_action, next_action, ): - s += """Task -- choose an appropriate area from the area options for a task at hand. + s += """Task -- choose an appropriate area from the area options for a task at hand. Sam Kim lives in {Sam Kim's house} that has Sam Kim's room, bathroom, kitchen. -Sam Kim is currently in {Sam Kim's house} that has Sam Kim's room, bathroom, kitchen. +Sam Kim is currently in {Sam Kim's house} that has Sam Kim's room, bathroom, kitchen. Area options: {Sam Kim's house, The Rose and Crown Pub, Hobbs Cafe, Oak Hill College, Johnson Park, Harvey Oak Supply Store, The Willows Market and Pharmacy}. * Stay in the current area if the activity can be done there. Only go out if the activity needs to take place in another place. * Must be one of the "Area options," verbatim. @@ -117,7 +117,7 @@ def action_location_sector( --- Jane Anderson lives in {Oak Hill College Student Dormatory} that has Jane Anderson's room. Jane Anderson is currently in {Oak Hill College} that has a classroom, library -Area options: {Oak Hill College Student Dormatory, The Rose and Crown Pub, Hobbs Cafe, Oak Hill College, Johnson Park, Harvey Oak Supply Store, The Willows Market and Pharmacy}. +Area options: {Oak Hill College Student Dormatory, The Rose and Crown Pub, Hobbs Cafe, Oak Hill College, Johnson Park, Harvey Oak Supply Store, The Willows Market and Pharmacy}. * Stay in the current area if the activity can be done there. Only go out if the activity needs to take place in another place. * Must be one of the "Area options," verbatim. For eating dinner, Jane Anderson should go to the following area: {Hobbs Cafe} @@ -167,9 +167,9 @@ def action_location_sector_prompt( next_action, ): s = "" - s += """Task -- choose an appropriate area from the area options for a task at hand. + s += """Task -- choose an appropriate area from the area options for a task at hand. Sam Kim lives in {Sam Kim's house} that has Sam Kim's room, bathroom, kitchen. -Sam Kim is currently in {Sam Kim's house} that has Sam Kim's room, bathroom, kitchen. +Sam Kim is currently in {Sam Kim's house} that has Sam Kim's room, bathroom, kitchen. Area options: {Sam Kim's house, The Rose and Crown Pub, Hobbs Cafe, Oak Hill College, Johnson Park, Harvey Oak Supply Store, The Willows Market and Pharmacy}. * Stay in the current area if the activity can be done there. Only go out if the activity needs to take place in another place. * Must be one of the "Area options," verbatim. @@ -177,7 +177,7 @@ def action_location_sector_prompt( --- Jane Anderson lives in {Oak Hill College Student Dormatory} that has Jane Anderson's room. Jane Anderson is currently in {Oak Hill College} that has a classroom, library -Area options: {Oak Hill College Student Dormatory, The Rose and Crown Pub, Hobbs Cafe, Oak Hill College, Johnson Park, Harvey Oak Supply Store, The Willows Market and Pharmacy}. +Area options: {Oak Hill College Student Dormatory, The Rose and Crown Pub, Hobbs Cafe, Oak Hill College, Johnson Park, Harvey Oak Supply Store, The Willows Market and Pharmacy}. * Stay in the current area if the activity can be done there. Only go out if the activity needs to take place in another place. * Must be one of the "Area options," verbatim. For eating dinner, Jane Anderson should go to the following area: {Hobbs Cafe} @@ -226,7 +226,7 @@ def action_location_object( For cooking, Jane Anderson should go to the following area in Jane Anderson's house: Answer: {kitchen} --- -Tom Watson is in common room in Tom Watson's apartment. +Tom Watson is in common room in Tom Watson's apartment. Tom Watson is going to Hobbs Cafe that has the following areas: {cafe} Stay in the current area if the activity can be done there. Never go into other people's rooms unless necessary. For getting coffee, Tom Watson should go to the following area in Hobbs Cafe: @@ -240,7 +240,7 @@ def action_location_object( + target_sector_areas + "}\n" ) - s += """* Stay in the current area if the activity can be done there. + s += """* Stay in the current area if the activity can be done there. * NEVER go into other people's rooms unless necessary.""" s += ( persona_name @@ -268,7 +268,7 @@ def action_location_object_prompt( For cooking, Jane Anderson should go to the following area in Jane Anderson's house: Answer: {kitchen} --- -Tom Watson is in common room in Tom Watson's apartment. +Tom Watson is in common room in Tom Watson's apartment. Tom Watson is going to Hobbs Cafe that has the following areas: {cafe} Stay in the current area if the activity can be done there. Never go into other people's rooms unless necessary. For getting coffee, Tom Watson should go to the following area in Hobbs Cafe: @@ -282,7 +282,7 @@ def action_location_object_prompt( + target_sector_areas + "}\n" ) - s += """* Stay in the current area if the activity can be done there. + s += """* Stay in the current area if the activity can be done there. * NEVER go into other people's rooms unless necessary.""" s += ( persona_name diff --git a/benchmark/json_decode_regex/README.md b/benchmark/json_decode_regex/README.md index ec575a6b1c..26acabae9e 100644 --- a/benchmark/json_decode_regex/README.md +++ b/benchmark/json_decode_regex/README.md @@ -20,7 +20,7 @@ outlines 0.0.22 Run Llama-7B ``` -python3 -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 +python3 -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 ``` Run Mixtral-8x7B diff --git a/benchmark/json_jump_forward/README.md b/benchmark/json_jump_forward/README.md index 1a34d3669e..38fb67e89b 100644 --- a/benchmark/json_jump_forward/README.md +++ b/benchmark/json_jump_forward/README.md @@ -23,7 +23,7 @@ python3 build_dataset.py Run Llama-7B ```bash -python3 -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 +python3 -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 ``` Benchmark Character Generation diff --git a/benchmark/json_jump_forward/dataset.txt b/benchmark/json_jump_forward/dataset.txt index f3c8b5f602..c12421e5d3 100644 --- a/benchmark/json_jump_forward/dataset.txt +++ b/benchmark/json_jump_forward/dataset.txt @@ -47,4 +47,4 @@ Quirinus Quirrell Nearly Headless Nick Aunt Marge Griphook -Ludo Bagman \ No newline at end of file +Ludo Bagman diff --git a/benchmark/llava_bench/README.md b/benchmark/llava_bench/README.md index ba38021152..a71f339df7 100644 --- a/benchmark/llava_bench/README.md +++ b/benchmark/llava_bench/README.md @@ -4,7 +4,7 @@ python3 download_images.py ``` -image benchmark source: https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild +image benchmark source: https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild ### Other Dependency ``` diff --git a/benchmark/llava_bench/bench_hf_llava_bench.sh b/benchmark/llava_bench/bench_hf_llava_bench.sh old mode 100644 new mode 100755 diff --git a/benchmark/llava_bench/bench_hf_mme.sh b/benchmark/llava_bench/bench_hf_mme.sh old mode 100644 new mode 100755 diff --git a/benchmark/llm_judge/README.md b/benchmark/llm_judge/README.md index 08255b641e..164b19a081 100644 --- a/benchmark/llm_judge/README.md +++ b/benchmark/llm_judge/README.md @@ -30,4 +30,4 @@ python3 bench_other.py --backend guidance --num-questions 25 --parallel 1 --n-ct ``` python3 bench_other.py --backend lmql --num-questions 25 --parallel 1 -``` \ No newline at end of file +``` diff --git a/benchmark/mmlu/download_data.sh b/benchmark/mmlu/download_data.sh index 7a74717764..fb224165e3 100644 --- a/benchmark/mmlu/download_data.sh +++ b/benchmark/mmlu/download_data.sh @@ -1,2 +1,2 @@ wget https://people.eecs.berkeley.edu/~hendrycks/data.tar -tar xf data.tar \ No newline at end of file +tar xf data.tar diff --git a/benchmark/multi_chain_reasoning/README.md b/benchmark/multi_chain_reasoning/README.md index b9c3f4d851..4c9f740f5f 100644 --- a/benchmark/multi_chain_reasoning/README.md +++ b/benchmark/multi_chain_reasoning/README.md @@ -43,7 +43,7 @@ python3 bench_other.py --num-questions 8 --backend guidance --parallel 1 --n-ctx ``` ### Benchmark lmql - + ``` python3 bench_other.py --num-questions 64 --backend lmql --parallel 1 ``` diff --git a/benchmark/react/README.md b/benchmark/react/README.md index 24b3e95dfe..51bcaa44aa 100644 --- a/benchmark/react/README.md +++ b/benchmark/react/README.md @@ -31,4 +31,4 @@ python3 bench_other.py --num-questions 100 --backend guidance --parallel 1 --n-c ``` python3 bench_other.py --num-questions 100 --backend lmql --parallel 1 -``` \ No newline at end of file +``` diff --git a/benchmark/react/bench_other.py b/benchmark/react/bench_other.py index a850bdfb35..91c5546f13 100644 --- a/benchmark/react/bench_other.py +++ b/benchmark/react/bench_other.py @@ -11,7 +11,7 @@ def get_prompt(question): prompt = ( - """Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types: + """Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types: (1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search. (2) Lookup[keyword], which returns the next sentence containing keyword in the current passage. (3) Finish[answer], which returns the answer and finishes the task. @@ -37,7 +37,7 @@ def get_prompt(question): Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening. Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after". Action 2: Lookup[named after] -Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous. +Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous. Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon. Action 3: Finish[Richard Nixon] Question: Which documentary is about Finnish rock groups, Adam Clayton Powell or The Saimaa Gesture? @@ -62,10 +62,10 @@ def get_prompt(question): Question: Which magazine was started first Arthur's Magazine or First for Women? Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first. Action 1: Search[Arthur's Magazine] -Observation 1: Arthur's Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century. +Observation 1: Arthur's Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century. Thought 2: Arthur's Magazine was started in 1844. I need to search First for Women next. Action 2: Search[First for Women] -Observation 2: First for Women is a woman's magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989. +Observation 2: First for Women is a woman's magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989. Thought 3: First for Women was started in 1989. 1844 (Arthur's Magazine) < 1989 (First for Women), so Arthur's Magazine was started first. Action 3: Finish[Arthur's Magazine] Question: Were Pavel Urysohn and Leonid Levin known for the same type of work? @@ -74,8 +74,8 @@ def get_prompt(question): Observation 1: Pavel Samuilovich Urysohn (February 3, 1898 â August 17, 1924) was a Soviet mathematician who is best known for his contributions in dimension theory. Thought 2: Pavel Urysohn is a mathematician. I need to search Leonid Levin next and find its type of work. Action 2: Search[Leonid Levin] -Observation 2: Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist. -Thought 3: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work. +Observation 2: Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist. +Thought 3: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work. Action 3: Finish[yes] """ + question diff --git a/benchmark/react/bench_sglang.py b/benchmark/react/bench_sglang.py index 39cd582092..b07105e2cb 100644 --- a/benchmark/react/bench_sglang.py +++ b/benchmark/react/bench_sglang.py @@ -13,7 +13,7 @@ @sgl.function def webthink(s, question, triplets): s += ( - """Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types: + """Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types: (1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search. (2) Lookup[keyword], which returns the next sentence containing keyword in the current passage. (3) Finish[answer], which returns the answer and finishes the task. @@ -39,7 +39,7 @@ def webthink(s, question, triplets): Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening. Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after". Action 2: Lookup[named after] -Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous. +Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous. Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon. Action 3: Finish[Richard Nixon] Question: Which documentary is about Finnish rock groups, Adam Clayton Powell or The Saimaa Gesture? @@ -64,10 +64,10 @@ def webthink(s, question, triplets): Question: Which magazine was started first Arthur's Magazine or First for Women? Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first. Action 1: Search[Arthur's Magazine] -Observation 1: Arthur's Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century. +Observation 1: Arthur's Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century. Thought 2: Arthur's Magazine was started in 1844. I need to search First for Women next. Action 2: Search[First for Women] -Observation 2: First for Women is a woman's magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989. +Observation 2: First for Women is a woman's magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989. Thought 3: First for Women was started in 1989. 1844 (Arthur's Magazine) < 1989 (First for Women), so Arthur's Magazine was started first. Action 3: Finish[Arthur's Magazine] Question: Were Pavel Urysohn and Leonid Levin known for the same type of work? @@ -76,8 +76,8 @@ def webthink(s, question, triplets): Observation 1: Pavel Samuilovich Urysohn (February 3, 1898 â August 17, 1924) was a Soviet mathematician who is best known for his contributions in dimension theory. Thought 2: Pavel Urysohn is a mathematician. I need to search Leonid Levin next and find its type of work. Action 2: Search[Leonid Levin] -Observation 2: Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist. -Thought 3: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work. +Observation 2: Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist. +Thought 3: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work. Action 3: Finish[yes] """ + question diff --git a/benchmark/tip_suggestion/.gitignore b/benchmark/tip_suggestion/.gitignore index f1aeb25d49..3322de9b9f 100644 --- a/benchmark/tip_suggestion/.gitignore +++ b/benchmark/tip_suggestion/.gitignore @@ -1 +1 @@ -!topic.jsonl \ No newline at end of file +!topic.jsonl diff --git a/benchmark/tip_suggestion/README.md b/benchmark/tip_suggestion/README.md index be15da6239..97ebe1d82f 100644 --- a/benchmark/tip_suggestion/README.md +++ b/benchmark/tip_suggestion/README.md @@ -30,4 +30,4 @@ python3 bench_other.py --backend guidance --num-questions 32 --parallel 1 --n-ct ``` python3 bench_other.py --backend lmql --num-questions 32 --parallel 1 -``` \ No newline at end of file +``` diff --git a/benchmark/tip_suggestion/topic.jsonl b/benchmark/tip_suggestion/topic.jsonl index 1d818089d2..c8ac6d12e3 100644 --- a/benchmark/tip_suggestion/topic.jsonl +++ b/benchmark/tip_suggestion/topic.jsonl @@ -47,4 +47,4 @@ {"topic": "self-publishing a book", "number": 7} {"topic": "starting an urban farm", "number": 6} {"topic": "improving your memory", "number": 8} -{"topic": "creating a personal brand online", "number": 9} \ No newline at end of file +{"topic": "creating a personal brand online", "number": 9} diff --git a/docs/README.md b/docs/README.md index 5dba730e32..29afab90a6 100644 --- a/docs/README.md +++ b/docs/README.md @@ -31,4 +31,4 @@ Clone [sgl-project.github.io](https://github.com/sgl-project/sgl-project.github. ```bash export DOC_SITE_PATH=../../sgl-project.github.io # update this with your path python3 deploy.py -``` \ No newline at end of file +``` diff --git a/docs/_static/css/custom_log.css b/docs/_static/css/custom_log.css index 9b1c4611df..61f65d0199 100644 --- a/docs/_static/css/custom_log.css +++ b/docs/_static/css/custom_log.css @@ -5,13 +5,13 @@ table.autosummary td { width: 50% } - + img.align-center { display: block; margin-left: auto; margin-right: auto; } - + .output_area.stderr { color: #d3d3d3 !important; } @@ -26,4 +26,4 @@ div.output_area.stderr { div.output_area.stdout { color: #d3d3d3 !important; -} \ No newline at end of file +} diff --git a/docs/backend/backend.md b/docs/backend/backend.md index 0f945364e3..51c9bc35a5 100644 --- a/docs/backend/backend.md +++ b/docs/backend/backend.md @@ -147,7 +147,7 @@ docker run --gpus all \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server --model-path Qwen/Qwen2.5-7B-Instruct --host 0.0.0.0 --port 30000 ``` - +
## Example: Run Llama 3.1 405B diff --git a/docs/conf.py b/docs/conf.py index f4d60f4dc7..e0656bb65c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -198,4 +198,4 @@ def setup(app): color: #d3d3d3 !important; /* light gray */ } -""" \ No newline at end of file +""" diff --git a/docs/deploy.py b/docs/deploy.py index 35ebbcb6ca..75b7ea7f23 100644 --- a/docs/deploy.py +++ b/docs/deploy.py @@ -1,22 +1,22 @@ -# Deploy the documents +# Deploy the documents import os from datetime import datetime -def run_cmd(cmd): - print(cmd) - os.system(cmd) +def run_cmd(cmd): + print(cmd) + os.system(cmd) -run_cmd("cd $DOC_SITE_PATH; git pull") +run_cmd("cd $DOC_SITE_PATH; git pull") -# (Optional) Remove old files -# run_cmd("rm -rf $ALPA_SITE_PATH/*") +# (Optional) Remove old files +# run_cmd("rm -rf $ALPA_SITE_PATH/*") -run_cmd("cp -r _build/html/* $DOC_SITE_PATH") +run_cmd("cp -r _build/html/* $DOC_SITE_PATH") -cmd_message = f"Update {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" -run_cmd( - f"cd $DOC_SITE_PATH; git add .; git commit -m '{cmd_message}'; git push origin main" -) +cmd_message = f"Update {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" +run_cmd( + f"cd $DOC_SITE_PATH; git add .; git commit -m '{cmd_message}'; git push origin main" +) diff --git a/docs/references/choices_methods.md b/docs/references/choices_methods.md index e0f3ed313c..30a0a1814b 100644 --- a/docs/references/choices_methods.md +++ b/docs/references/choices_methods.md @@ -74,4 +74,4 @@ def example(s): choices_method=sgl.unconditional_likelihood_normalized, ) ) -``` \ No newline at end of file +``` diff --git a/docs/references/custom_chat_template.md b/docs/references/custom_chat_template.md index 2803abc012..a4418533ab 100644 --- a/docs/references/custom_chat_template.md +++ b/docs/references/custom_chat_template.md @@ -37,4 +37,4 @@ You can also use the Jinja template format, defined by Hugging Face transformers ``` python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template ./my_model_template.jinja -``` \ No newline at end of file +``` diff --git a/docs/references/hyperparameter_tuning.md b/docs/references/hyperparameter_tuning.md index 32759cadc5..89faa479be 100644 --- a/docs/references/hyperparameter_tuning.md +++ b/docs/references/hyperparameter_tuning.md @@ -25,9 +25,9 @@ If you see `decode out of memory happened` occasionally but not frequently, it i Data parallelism is better for throughput. When there is enough GPU memory, always favor data parallelism for throughput. ### Avoid out-of-memory by Tuning `--chunked-prefill-size`, `--mem-fraction-static`, `--max-running-requests` -If you see out of memory (OOM) errors, you can try to tune the following parameters. -If OOM happens during prefill, try to decrease `--chunked-prefill-size` to `4096` or `2048`. -If OOM happens during decoding, try to decrease `--max-running-requests`. +If you see out of memory (OOM) errors, you can try to tune the following parameters. +If OOM happens during prefill, try to decrease `--chunked-prefill-size` to `4096` or `2048`. +If OOM happens during decoding, try to decrease `--max-running-requests`. You can also try to decrease `--mem-fraction-static`, which reduces the memory usage of the KV cache memory pool and helps both prefill and decoding. ### Try Advanced Options diff --git a/docs/references/learn_more.md b/docs/references/learn_more.md index 62d02a0cae..d5c430e216 100644 --- a/docs/references/learn_more.md +++ b/docs/references/learn_more.md @@ -1,3 +1,3 @@ # Learn more -You can find more blogs, slides, and videos about SGLang at [https://github.com/sgl-project/sgl-learning-materials](https://github.com/sgl-project/sgl-learning-materials). \ No newline at end of file +You can find more blogs, slides, and videos about SGLang at [https://github.com/sgl-project/sgl-learning-materials](https://github.com/sgl-project/sgl-learning-materials). diff --git a/docs/references/sampling_params.md b/docs/references/sampling_params.md index 333b8fcd15..d144b059d8 100644 --- a/docs/references/sampling_params.md +++ b/docs/references/sampling_params.md @@ -223,4 +223,4 @@ response = requests.post( }, ) print(response.json()) -``` \ No newline at end of file +``` diff --git a/docs/references/troubleshooting.md b/docs/references/troubleshooting.md index 19eaa2879b..becb186df7 100644 --- a/docs/references/troubleshooting.md +++ b/docs/references/troubleshooting.md @@ -3,9 +3,9 @@ This page lists some common errors and tips for fixing them. ## CUDA out of memory -If you see out of memory (OOM) errors, you can try to tune the following parameters. -If OOM happens during prefill, try to decrease `--chunked-prefill-size` to `4096` or `2048`. -If OOM happens during decoding, try to decrease `--max-running-requests`. +If you see out of memory (OOM) errors, you can try to tune the following parameters. +If OOM happens during prefill, try to decrease `--chunked-prefill-size` to `4096` or `2048`. +If OOM happens during decoding, try to decrease `--max-running-requests`. You can also try to decrease `--mem-fraction-static`, which reduces the memory usage of the KV cache memory pool and helps both prefill and decoding. ## CUDA error: an illegal memory access was encountered diff --git a/docs/requirements.txt b/docs/requirements.txt index ad722fd21f..171d60e0a9 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -14,4 +14,4 @@ sphinx-book-theme sphinx-copybutton sphinx-tabs sphinxcontrib-mermaid -urllib3<2.0.0 \ No newline at end of file +urllib3<2.0.0 diff --git a/examples/frontend_language/usage/llava_video/srt_example_llava_v.sh b/examples/frontend_language/usage/llava_video/srt_example_llava_v.sh old mode 100644 new mode 100755 index 12b38c4b1b..ffb1af96df --- a/examples/frontend_language/usage/llava_video/srt_example_llava_v.sh +++ b/examples/frontend_language/usage/llava_video/srt_example_llava_v.sh @@ -33,7 +33,7 @@ CUR_NODES_IDX=$2 VIDEO_DIR=$3 -MODEL_PATH=$4 +MODEL_PATH=$4 NUM_FRAMES=$5 @@ -73,16 +73,16 @@ for IDX in $(seq 1 $LOCAL_CHUNKS); do ( START=$(((IDX-1) * GPUS_PER_CHUNK)) LENGTH=$GPUS_PER_CHUNK # Length for slicing, not the end index - + CHUNK_GPUS=(${GPULIST[@]:$START:$LENGTH}) - + # Convert the chunk GPUs array to a comma-separated string CHUNK_GPUS_STR=$(IFS=,; echo "${CHUNK_GPUS[*]}") LOCAL_IDX=$((CUR_NODES_IDX * LOCAL_CHUNKS + IDX)) echo "Chunk $(($LOCAL_IDX - 1)) will run on GPUs $CHUNK_GPUS_STR" - + # Calculate the port for this chunk. Ensure it's incremented by 5 for each chunk. PORT=$((10000 + RANDOM % 55536)) @@ -92,7 +92,7 @@ for IDX in $(seq 1 $LOCAL_CHUNKS); do while [ $RETRY_COUNT -lt $MAX_RETRIES ] && [ $COMMAND_STATUS -ne 0 ]; do echo "Running chunk $(($LOCAL_IDX - 1)) on GPUs $CHUNK_GPUS_STR with port $PORT. Attempt $(($RETRY_COUNT + 1))" - + #!/bin/bash CUDA_VISIBLE_DEVICES=$CHUNK_GPUS_STR python3 srt_example_llava_v.py \ --port $PORT \ @@ -102,10 +102,10 @@ for IDX in $(seq 1 $LOCAL_CHUNKS); do --video-dir $VIDEO_DIR \ --model-path $MODEL_PATH \ --num-frames $NUM_FRAMES #& - + wait $! # Wait for the process to finish and capture its exit status COMMAND_STATUS=$? - + if [ $COMMAND_STATUS -ne 0 ]; then echo "Execution failed for chunk $(($LOCAL_IDX - 1)), attempt $(($RETRY_COUNT + 1)). Retrying..." RETRY_COUNT=$(($RETRY_COUNT + 1)) @@ -124,8 +124,8 @@ done wait -cat work_dirs/llava_next_video_inference_results/final_results_chunk_*.csv > work_dirs/llava_next_video_inference_results/final_results_node_${CUR_NODES_IDX}.csv +cat work_dirs/llava_next_video_inference_results/final_results_chunk_*.csv > work_dirs/llava_next_video_inference_results/final_results_node_${CUR_NODES_IDX}.csv END_TIME=$(date +%s) # Capture end time ELAPSED_TIME=$(($END_TIME - $START_TIME)) -echo "Total execution time: $ELAPSED_TIME seconds." \ No newline at end of file +echo "Total execution time: $ELAPSED_TIME seconds." diff --git a/examples/frontend_language/usage/openai_chat_speculative.py b/examples/frontend_language/usage/openai_chat_speculative.py index a9c5f5afb3..f3fd74ed89 100644 --- a/examples/frontend_language/usage/openai_chat_speculative.py +++ b/examples/frontend_language/usage/openai_chat_speculative.py @@ -4,8 +4,8 @@ Show in "assistant" the desired answer format. Each "gen" term should have a stop token. The stream mode is not supported in speculative execution. -E.g. -correct: +E.g. +correct: sgl.assistant("\nName:" + sgl.gen("name", stop="\n") + "\nBirthday:" + sgl.gen("birthday", stop="\n") + "\nJob:" + sgl.gen("job", stop="\n")) incorrect: s += sgl.assistant("\nName:" + sgl.gen("name", stop="\n")) diff --git a/examples/frontend_language/usage/triton/Dockerfile b/examples/frontend_language/usage/triton/Dockerfile index d97342e26e..e4741a1dbf 100644 --- a/examples/frontend_language/usage/triton/Dockerfile +++ b/examples/frontend_language/usage/triton/Dockerfile @@ -7,4 +7,4 @@ RUN git clone https://github.com/sgl-project/sglang.git WORKDIR /opt/sglang RUN pip install --upgrade pip && \ pip install -e "python[all]" && \ - pip install datasets \ No newline at end of file + pip install datasets diff --git a/examples/frontend_language/usage/triton/README.md b/examples/frontend_language/usage/triton/README.md index 387f451d4f..b2e55961f4 100644 --- a/examples/frontend_language/usage/triton/README.md +++ b/examples/frontend_language/usage/triton/README.md @@ -32,4 +32,4 @@ curl -X POST http://localhost:8000/v2/models/character_generation/generate \ "INPUT_TEXT": ["harry"] }' -``` \ No newline at end of file +``` diff --git a/examples/runtime/engine/input_ids.py b/examples/runtime/engine/input_ids.py index 89de6f63a0..fd7eb7e22c 100644 --- a/examples/runtime/engine/input_ids.py +++ b/examples/runtime/engine/input_ids.py @@ -21,7 +21,7 @@ def main(): # Tokenize inputs tokenizer = get_tokenizer(MODEL_PATH) token_ids_list = [tokenizer.encode(prompt) for prompt in prompts] - + # Create an LLM. # You can also specify `skip_tokenizer_init=True`, but it requires explicit detokenization at the end llm = sgl.Engine(model_path=MODEL_PATH) @@ -36,4 +36,4 @@ def main(): # The __main__ condition is necessary here because we use "spawn" to create subprocesses # Spawn starts a fresh program every time, if there is no __main__, it will run into infinite loop to keep spawning processes from sgl.Engine if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/examples/runtime/engine/readme.md b/examples/runtime/engine/readme.md index 14209ebd6d..1d70e99d0f 100644 --- a/examples/runtime/engine/readme.md +++ b/examples/runtime/engine/readme.md @@ -37,4 +37,4 @@ curl -X POST http://localhost:8000/generate -H "Content-Type: application/json" curl -X POST http://localhost:8000/generate_stream -H "Content-Type: application/json" -d '{"prompt": "The Transformer architecture is..."}' --no-buffer ``` -This will send both non-streaming and streaming requests to the server. \ No newline at end of file +This will send both non-streaming and streaming requests to the server. diff --git a/examples/runtime/llava_onevision/http_llama3_llava_test.py b/examples/runtime/llava_onevision/http_llama3_llava_test.py index a019e214d6..ed0e61631f 100644 --- a/examples/runtime/llava_onevision/http_llama3_llava_test.py +++ b/examples/runtime/llava_onevision/http_llama3_llava_test.py @@ -3,7 +3,7 @@ # Installing latest llava-next: pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git # Installing latest sglang. -# Endpoint Service CLI: +# Endpoint Service CLI: python -m sglang.launch_server --model-path lmms-lab/llama3-llava-next-8b --port=30000 python3 http_llama3_llava_test.py diff --git a/examples/runtime/llava_onevision/http_qwen_llava_test.py b/examples/runtime/llava_onevision/http_qwen_llava_test.py index dca56e7a33..94ec4e0796 100644 --- a/examples/runtime/llava_onevision/http_qwen_llava_test.py +++ b/examples/runtime/llava_onevision/http_qwen_llava_test.py @@ -3,7 +3,7 @@ # Installing latest llava-next: pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git # Installing latest sglang. -# Endpoint Service CLI: +# Endpoint Service CLI: python -m sglang.launch_server --model-path lmms-lab/llava-next-72b --port=30000 --tp-size=8 python3 http_qwen_llava_test.py diff --git a/python/sglang/srt/layers/quantization/base_config.py b/python/sglang/srt/layers/quantization/base_config.py index b45fcdea25..f4f5d2b47b 100644 --- a/python/sglang/srt/layers/quantization/base_config.py +++ b/python/sglang/srt/layers/quantization/base_config.py @@ -134,4 +134,4 @@ def method_has_implemented_embedding( class_embedding = inspect.getattr_static(method_class, "embedding", None) return (class_embedding is not None - and class_embedding is not base_embedding) \ No newline at end of file + and class_embedding is not base_embedding) diff --git a/python/sglang/srt/layers/vocab_parallel_embedding.py b/python/sglang/srt/layers/vocab_parallel_embedding.py index a9ca9da539..c1e758b022 100644 --- a/python/sglang/srt/layers/vocab_parallel_embedding.py +++ b/python/sglang/srt/layers/vocab_parallel_embedding.py @@ -311,7 +311,7 @@ def _get_indices(cls, vocab_size_padded: int, org_vocab_size_padded: int, def get_sharded_to_full_mapping(self) -> Optional[List[int]]: """Get a mapping that can be used to reindex the gathered logits for sampling. - + During sampling, we gather logits from all ranks. The relationship of index->token_id will follow the same format as outlined in the class docstring. However, after the gather, we want to reindex the final @@ -483,4 +483,4 @@ def tie_weights(self, embed_tokens: VocabParallelEmbedding): def forward(self, input_): del input_ - raise RuntimeError("LMHead's weights should be used in the sampler.") \ No newline at end of file + raise RuntimeError("LMHead's weights should be used in the sampler.") diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py index 6d3d419d1a..f7933e0acd 100644 --- a/python/sglang/srt/managers/scheduler.py +++ b/python/sglang/srt/managers/scheduler.py @@ -838,7 +838,7 @@ def get_stats(self,batch: ScheduleBatch): time_per_output_tokens_iter: List[float] = [] # Request stats - # Decode + # Decode gen_throughput: float = 0.0 # Latency time_e2e_requests: List[float] = [] @@ -866,11 +866,11 @@ def get_stats(self,batch: ScheduleBatch): time_waiting_requests.append(req.queued_time - req.created_time) num_prompt_tokens_requests.append(len(req.origin_input_ids)) num_generation_tokens_requests.append(len(req.output_ids)) - finished_reason_requests.append( + finished_reason_requests.append( req.finished_reason.to_json() if req.finished_reason is not None else None) - + return Stats( new_seq=new_seq, num_running_req=num_running_req, diff --git a/python/sglang/srt/managers/tokenizer_manager.py b/python/sglang/srt/managers/tokenizer_manager.py index cc9e2bd262..60cfc1be1e 100644 --- a/python/sglang/srt/managers/tokenizer_manager.py +++ b/python/sglang/srt/managers/tokenizer_manager.py @@ -384,7 +384,7 @@ async def update_weights( obj.load_format = self.server_args.load_format if not self.model_update_lock.locked(): - + async with self.model_update_lock: # wait for the previous generation requests to finish while len(self.rid_to_state) > 0: diff --git a/python/sglang/srt/metrics/metrics_collector.py b/python/sglang/srt/metrics/metrics_collector.py index bb1eca88b1..df7d6961da 100644 --- a/python/sglang/srt/metrics/metrics_collector.py +++ b/python/sglang/srt/metrics/metrics_collector.py @@ -151,7 +151,7 @@ def __init__(self, labelnames: List[str], max_model_len): 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.04, 0.05, 0.075, 0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.75, 1.0, 2.5 ]) - + # Request Stats # Metadata self.num_prompt_tokens_requests = Histogram( @@ -253,7 +253,7 @@ def log_stats(self, stats: Stats) -> None: stats.time_to_first_tokens_iter) self._log_histogram(self.metrics.histogram_time_per_output_token, stats.time_per_output_tokens_iter) - + # self._log_gauge(self.metrics.gpu_cache_usage_sys, stats.gpu_cache_usage_sys) self._log_gauge(self.metrics.num_running_sys, stats.num_running_req) self._log_gauge(self.metrics.num_waiting_sys, stats.num_waiting_req) @@ -294,4 +294,4 @@ def build_1_2_5_buckets(max_value: int) -> List[int]: buckets.append(value) else: return buckets - exponent += 1 \ No newline at end of file + exponent += 1 diff --git a/python/sglang/srt/metrics/metrics_types.py b/python/sglang/srt/metrics/metrics_types.py index f1b357f403..15e4a0dc71 100644 --- a/python/sglang/srt/metrics/metrics_types.py +++ b/python/sglang/srt/metrics/metrics_types.py @@ -54,4 +54,4 @@ class Stats: num_prompt_tokens_iter: int = 0 num_generation_tokens_iter: int = 0 time_to_first_tokens_iter: List[float] = field(default_factory=list) - time_per_output_tokens_iter: List[float] = field(default_factory=list) \ No newline at end of file + time_per_output_tokens_iter: List[float] = field(default_factory=list) diff --git a/python/sglang/srt/mm_utils.py b/python/sglang/srt/mm_utils.py index 7918f3f711..e6e3d17863 100644 --- a/python/sglang/srt/mm_utils.py +++ b/python/sglang/srt/mm_utils.py @@ -17,7 +17,7 @@ """ Utilities for multi-modal models. -This python file mainly contains utilities that were used in the +This python file mainly contains utilities that were used in the image processing logic of llava-next including operations such as anyres and anyres_max diff --git a/python/sglang/srt/models/gpt2.py b/python/sglang/srt/models/gpt2.py index 60c98bbf21..3495f24d05 100644 --- a/python/sglang/srt/models/gpt2.py +++ b/python/sglang/srt/models/gpt2.py @@ -136,7 +136,7 @@ def __init__( layer_id: int, config: GPT2Config, cache_config = None, - + quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): @@ -284,4 +284,4 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): default_weight_loader) weight_loader(param, loaded_weight) -EntryClass = GPT2LMHeadModel \ No newline at end of file +EntryClass = GPT2LMHeadModel diff --git a/python/sglang/srt/models/olmo.py b/python/sglang/srt/models/olmo.py old mode 100755 new mode 100644 diff --git a/python/sglang/srt/models/qwen2_vl.py b/python/sglang/srt/models/qwen2_vl.py index 82412a51a1..4e4fef3dcd 100644 --- a/python/sglang/srt/models/qwen2_vl.py +++ b/python/sglang/srt/models/qwen2_vl.py @@ -57,27 +57,27 @@ class Qwen2VLImageInputs(TypedDict): pixel_values: torch.Tensor - """Shape: + """Shape: `(num_patches, num_channels * patch_size * patch_size)` """ image_grid_thw: torch.Tensor """Shape: `(num_images, 3)` - + This should be in `(grid_t, grid_h, grid_w)` format. """ class Qwen2VLVideoInputs(TypedDict): pixel_values_videos: torch.Tensor - """Shape: - `(num_patches, + """Shape: + `(num_patches, num_channels * temporal_patch_size * patch_size * patch_size)` """ video_grid_thw: torch.Tensor """Shape: `(num_videos, 3)` - + This should be in `(grid_t, grid_h, grid_w)` format. """ diff --git a/python/sglang/srt/server.py b/python/sglang/srt/server.py index d295553469..895af0e699 100644 --- a/python/sglang/srt/server.py +++ b/python/sglang/srt/server.py @@ -759,7 +759,7 @@ def __init__(self, *args, **kwargs): # before python program terminates, call shutdown implicitly. Therefore, users don't have to explicitly call .shutdown() atexit.register(self.shutdown) - + # runtime server default log level is log # offline engine works in scripts, so we set it to error diff --git a/python/sglang/test/long_prompt.txt b/python/sglang/test/long_prompt.txt index 301d7e107d..8ec2b829a7 100644 --- a/python/sglang/test/long_prompt.txt +++ b/python/sglang/test/long_prompt.txt @@ -1 +1 @@ -You are given a report by a government agency. Write a one-page summary of the report.\n\nReport:\nIntroduction\n\nThroughout U.S. history, Congress has created advisory commissions to assist in the development of public policy. Among other contexts, commissions have been used following crisis situations, including the September 11, 2001, terrorist attacks and the 2008 financial crisis. In such situations, advisory commissions may potentially provide Congress with a high-visibility forum to assemble expertise that might not exist within the legislative environment; allow for the in-depth examination of complex, cross-cutting policy issues; and lend bipartisan credibility to a set of findings and recommendations.\nAs Congress considers its range of responses to the coronavirus pandemic, the creation of one or more congressional advisory commissions is an option that could provide a platform for evaluating various pandemic-related policy issues over time. Past congressional advisory commissions have retrospectively evaluated policy responses, brought together diverse groups of experts, and supplemented existing congressional oversight mechanisms. Policymakers may determine that creating an advisory commission is unnecessary and instead prefer to utilize existing congressional oversight structures, such as standing or select committees, or already established oversight entities.\nThis report provides a comparative analysis of five proposed congressional advisory commissions that would investigate various aspects of the COVID-19 pandemic. The five proposed commissions are found in H.R. 6429 (the National Commission on COVID-19 Act, sponsored by Representative Stephanie Murphy), H.R. 6431 (the Made in America Emergency Preparedness Act, sponsored by Representative Brian Fitzpatrick), H.R. 6440 (the Pandemic Rapid Response Act, sponsored by Representative Rodney Davis), H.R. 6455 (the COVID-19 Commission Act, sponsored by Representative Bennie Thompson), and H.R. 6548 (the National Commission on the COVID-19 Pandemic in the United States Act, sponsored by Representative Adam Schiff). The overall structures of each of the proposed commissions are similar in many respects, both to each other and to previous independent advisory entities established by Congress. Specifically, the proposed commissions would (1) exist temporarily; (2) serve in an advisory capacity; and (3) report a work product detailing the commission\'s findings, conclusions, and recommendations. That said, each particular proposed commission has distinctive elements, particularly concerning its membership structure, appointment structure, and time line for reporting its work product to Congress.\nThis report compares the (1) membership structure, (2) appointment structure, (3) rules of procedure and operation, (4) duties and reporting requirements, (5) powers of the commission, (6) staffing issues, and (7) funding for each of the proposed COVID-19 commissions. Table 1 (at the end of this report) provides a side-by-side comparison of major provisions of the five proposals.\n\n Membership Structure\n\nSeveral matters related to a commission\'s membership structure might be considered. They include the size of a commission, member qualifications, compensation of commission members, and requirements for partisan balance. \n\n Size of Commission\n\nIn general, there is significant variation in the size of congressional advisory commissions. Among 155 identified congressional commissions created between the 101 st Congress and the 115 th Congress, the median size was 12 members, with the smallest commission having 5 members and the largest 33 members.\nThe membership structure of each of the five proposed commissions is similar to previous independent advisory entities created by Congress. H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 would each create a 10-member entity. H.R. 6455 would create a 25-member entity.\n\n Qualifications\n\nPast legislation creating congressional commissions has often required or suggested that commission members possess certain substantive qualifications. Such provisions arguably make it more likely that the commission is populated with genuine experts in the policy area, which may improve the commission\'s final work product.\nH.R. 6455 would provide that commissioners \"shall be a United States person with significant expertise\" in a variety of fields related to public health and public administration. H.R. 6440 , H.R. 6429 , H.R. 6431 , and H.R. 6548 would provide \"the sense of Congress\" that commission members should be \"prominent U.S. citizens\" who are nationally recognized experts in a variety of fields relevant to the pandemic and response efforts. In addition, H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 all prohibit the appointment of federal, state, and local government employees and officers. H.R. 6455 would prohibit federal employees from being commission members.\n\n Compensation of Commission Members\n\nSome congressional commissions have compensated their members. For example, the National Commission on Terrorist Attacks Upon the United States (9/11 Commission) and the Financial Crisis Inquiry Commission provided that commission members could be compensated at a daily rate of basic pay. Nearly all have reimbursed members for travel expenses. Those that have provided for commissioner compensation most frequently provided compensation at the daily equivalent of level IV of the Executive Schedule.\nEach of the five proposals would provide that commission members be compensated at a rate \"not to exceed the daily equivalent of the annual rate of basic pay\" for level IV of the Executive Schedule, \"for each day during which that member is engaged in the actual performance of duties of the Commission.\" Members of three proposed commissions would receive travel expenses, including a per diem.\n\n Partisan Limitations\n\nEach proposal provides a limit on the number of members appointed from the same political party. H.R. 6455 would provide that not more than 13 of its 25 members may be from the same party. H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 would provide that not more than 5 (of 10) members are from the same party. Most previous advisory entities created by Congress do not impose formal partisan restrictions on the membership structure. It may also be difficult to assess the political affiliation of potential members, who may have no formal affiliation (voter registration, for example) with a political party. Instead, most past advisory commissions usually achieve partisan balance through the appointment structure; for instance, by providing equal (or near-equal) numbers of appointments to congressional leaders of each party.\n\n Appointment Structure\n\nPast congressional commissions have used a wide variety of appointment structures. Considerations regarding appointment structures include partisan balance, filling vacancies, and the time line for making commission appointments.\nThe statutory scheme may directly designate members of the commission, such as a specific cabinet official or a congressional leader. In other cases, selected congressional leaders, often with balance between the parties, appoint commission members. A third common statutory scheme is to have selected leaders, such as committee chairs and ranking members, recommend candidates for appointment to a commission. These selected leaders may act either in parallel or jointly, and the recommendation may be made either to other congressional leaders, such as the Speaker of the House and President pro tempore of the Senate, or to the President.\nEach of the five commission proposals would delegate most or all appointment authority to congressional leaders (including chamber, party, and committee leaders; see Table 1 for details). Additionally, H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 provide for one appointment to be made by the President. H.R. 6429 , H.R. 6431 , and H.R. 6548 would have the President appoint the commission\'s chair. H.R. 6455 has its membership appointed by the chairs and ranking members of designated House and Senate committees, and the Joint Economic Committee. H.R. 6455 does not provide any executive branch appointments.\nAttention to the proper balance between the number of members appointed by congressional leaders and by other individuals (such as the President), or to the number of Members of Congress required to be among the appointees, or to the qualifications of appointees, can be significant factors in enabling a commission to fulfill its congressional mandate.\nIn general, a commission\'s appointment scheme can impact both the commission\'s ability to fulfill its statutory duties and its final work product. For instance, if the scheme provides only for the appointment of Members of Congress to the commission, it arguably might not have the technical expertise or diversity of knowledge to complete its duties within the time given by statute. Similarly, if the appointment scheme includes qualifying provisos so specific that only a small set of private citizens could serve on the panel, the commission\'s final work product may arguably only represent a narrow range of viewpoints. None of the proposed COVID-19 commissions specify whether Members of Congress may serve on the commission.\n\n Partisan Balance in Appointment Authority\n\nMost previous congressional advisory commissions have been structured to be bipartisan, with an even (or near-even) split of appointments between leaders of the two major parties. By achieving a nonpartisan or bipartisan character, congressional commissions may make their findings and recommendations more politically acceptable to diverse viewpoints. The bipartisan or nonpartisan arrangement can give recommendations strong credibility, both in Congress and among the public, even when dealing with divisive public policy issues. Similarly, commission recommendations that are perceived as partisan may have difficulty gaining support in Congress.\nIn some cases, however, bipartisanship also can arguably impede a commission\'s ability to complete its mandate. In situations where a commission is tasked with studying divisive or partisan issues, the appointment of an equal number of majority and minority commissioners may serve to promote partisanship within the commission rather than suppress it, raising the possibility of deadlock where neither side can muster a majority to act.\nEach of the five proposals employs a structure where leaders in both the majority and minority parties in Congress would make appointments. H.R. 6429 , H.R. 6431 , and H.R. 6548 would provide for five majority and five minority appointments, including one for the President. H.R. 6440 would include two each by the Senate majority leader, the Senate minority leader, and the Speaker of the House, with one appointment by the House minority leader and one by the President, and the chair appointed by the Speaker and vice chair appointed by the Senate majority leader. H.R. 6455 would have 12 majority and 12 minority appointments made by the 12 committee chairs and ranking members and one member jointly appointed by the chair and vice chair of the Joint Economic Committee.\n\n Vacancies\n\nAll five proposals provide that vacancies on the commission will not affect its powers and would be filled in the same manner as the original appointment.\n\n Deadline for Appointments\n\nThree of the bills propose specific deadlines for the appointment of commissioners. H.R. 6429 and H.R. 6548 provide that appointments are made between specific dates in January or February 2021. Further, H.R. 6429 provides that commission members could be appointed in September 2020, if there is no longer a COVID-19 public health emergency in effect—as determined by the Secretary of Health and Human Services—as of August 31, 2020. H.R. 6440 would require all appointments be made by December 15, 2020. H.R. 6455 would require appointments to be made within 45 days after enactment. H.R. 6429 , H.R. 6440 , and H.R. 6548 would start the commission\'s work in early 2021, as the commission cannot operate without the appointment of members. H.R. 6429 , however would provide that the proposed commission\'s work would begin no later than October 31, 2020, if members are appointed in September 2020. H.R. 6431 does not specify a deadline for the appointment of members.\nTypically, deadlines for appointment can range from several weeks to several months. For example, the deadline for appointments to the Antitrust Modernization Commission was 60 days after the enactment of its establishing act. The deadline for appointment to the Commission on Wartime Contracting in Iraq and Afghanistan was 120 days from the date of enactment. The deadline for appointment to the 9/11 Commission was December 15, 2002, 18 days after enactment of the act.\n\n Rules of Procedure and Operations\n\nWhile most statutes that authorize congressional advisory commissions do not provide detailed procedures for how the commission should conduct its business, the statutory language may provide a general structure, including a mechanism for selecting a chair and procedures for creating rules. None of the five COVID-19 commission proposals contain language that directs the process for potentially adopting rules of procedure. For a comparison of each proposed commission\'s specified rules of procedures and operations, see Table 1 .\n\n Chair Selection\n\nEach bill provides for the selection of a chair and/or vice chair of the commission. H.R. 6429 , H.R. 6431 , and H.R. 6548 would have the chair appointed by the President and the vice chair appointed by congressional leaders of the political party opposite the President. H.R. 6440 would have the chair appointed by the Speaker of the House (in consultation with the Senate majority leader and the House minority leader) and the vice chair appointed by the Senate majority leader (in consultation with the Speaker of the House and the Senate minority leader). H.R. 6455 would have the chair and vice chair chosen from among commission members by a majority vote of the commission, and would require the chair and vice chair to have \"significant experience\" in areas to be studied by the commission.\n\n Initial Meeting Deadline\n\nAs with the timing of commission appointments, some authorizing statutes are prescriptive in when the commission\'s first meeting should take place. Three of the bills analyzed here provide specific time lines for the commission\'s first meeting. H.R. 6429 would require the first meeting to be no later than March 15, 2021, unless members are appointed in September 2020 (if no public health emergency exists). H.R. 6455 would require the first meeting within 45 days after the appointment of all commission members, which is—given the 45-day deadline for appointment—effectively a maximum of 90 days after enactment. H.R. 6548 would direct the commission to hold its initial meeting \"as soon as practicable,\" but not later than March 5, 2021. H.R. 6431 and H.R. 6440 do not provide for an initial meeting deadline. Instead, they direct the commission to meet \"as soon as practicable.\" \n\n Quorum\n\nMost commission statutes provide that a quorum will consist of a particular number of commissioners, usually a majority, but occasionally a supermajority. All five bills would provide for a quorum requirement. H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 would define a quorum as 6 (of 10) members. H.R. 6455 would provide that a quorum is 18 of 25 members (72%).\n\n Public Access\n\nAll five commission bills would require commission meetings to be open to the public. Each bill would also require that reports be made publicly available.\n\n Formulating Other Rules of Procedure and Operations\n\nAbsent statutory guidance (eithe r in general statutes or in individual statutes authorizing commissions), advisory entities vary widely in how they adopt their rules of procedure. In general, three models exist: formal written rules, informal rules, and the reliance on norms. Any individual advisory entity might make use of all three of these models for different types of decisionmaking. \nThe choice to adopt written rules or rely on informal norms to guide commission procedure may be based on a variety of factors, such as the entity\'s size, the frequency of meetings, member preferences regarding formality, the level of collegiality among members, and the amount of procedural guidance provided by the entity\'s authorizing statute. Regardless of how procedural issues are handled, protocol for decisionmaking regarding the following operational issues may be important for the commission to consider at the outset of its existence: eligibility to vote and proxy rules; staff hiring, compensation, and work assignments; hearings, meetings, and field visits; nonstaff expenditures and contracting; reports to Congress; budgeting; and procedures for future modification of rules. None of the five COVID-19 commission proposals specify that the proposed commission must adopt written rules.\n\n FACA Applicability\n\nThe Federal Advisory Committee Act (FACA) mandates certain structural and operational requirements, including formal reporting and oversight procedures, for certain federal advisory bodies that advise the executive branch. Three proposals ( H.R. 6429 , H.R. 6431 , and H.R. 6548 ) specifically exempt the proposed commission from FACA. Of the remaining two, FACA would also likely not apply to the commission proposed in H.R. 6455 because it would be appointed entirely by Members of Congress, although it only specifies that its final report is public, not whether it is specifically sent to Congress and/or the President. It is not clear that FACA would apply to the commission proposed in H.R. 6440 . Although it includes a presidential appointment and its report would be sent to both Congress and the President, its establishment clause specifies that the commission \"is established in the legislative branch,\" and a super-majority of its members would be appointed by Congress.\n\n Duties and Reporting Requirements\n\nMost congressional commissions are generally considered policy commissions—temporary bodies that study particular policy problems and report their findings to Congress or review a specific event. \n\n General Duties\n\nAll five of the proposed commissions would be tasked with duties that are analogous to those of past policy commissions. While the specific mandates differ somewhat, all proposed commissions are tasked with investigating aspects of the COVID-19 pandemic and submitting one or more reports that include the commission\'s findings, conclusions, and recommendations for legislative action. H.R. 6440 would specifically require the commission to avoid unnecessary duplication of work being conducted by the Government Accountability Office (GAO), congressional committees, and executive branch agency and independent commission investigations.\n\n Reports\n\nEach proposed commission would be tasked with issuing a final report detailing its findings, conclusions, and recommendations. H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 would provide that the commission \"may submit\" interim reports to Congress and the President, but do not provide time lines on when those reports might be submitted. In each case, the interim report would need to be agreed to by a majority of commission members. H.R. 6431 would also require the commission to submit a report on actions taken by the states and a report on essential products, materials, ingredients, and equipment required to fight pandemics.\nH.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 also specify that final reports shall be agreed to by a majority of commission members. H.R. 6455 does not specify a vote threshold for approval of its report.\nNone of the bills make specific provisions for the inclusion of minority viewpoints. Presumably this would leave each commission with discretion on whether to include or exclude minority viewpoints. Past advisory entities have been proposed or established with a variety of statutory reporting conditions, including the specification of majority or super-majority rules for report adoption and provisions requiring the inclusion of minority viewpoints. In practice, advisory bodies that are not given statutory direction on these matters have tended to work under simple-majority rules for report adoption.\n\n Report Deadlines\n\nH.R. 6429 would require a final report one year after the commission\'s initial meeting. H.R. 6431 and H.R. 6440 would require a final report not later than 18 months after enactment. H.R. 6455 would require a final report to be published not later than 18 months after the commission\'s first meeting. \nH.R. 6548 would require a final report by October 15, 2021. This deadline could be extended by 90 days upon a vote of no fewer than 8 (out of 10) commission members. The commission could vote to extend its final report deadline up to three times, and would be required to notify Congress, the President, and the public of any such extension.\nWhile such a deadline would potentially give the commission a defined period of time to complete its work, setting a particular date for report completion could potentially create unintended time constraints. Any delay in the passage of the legislation or in the appointment process would reduce the amount of time the commission has to complete its work, even with the opportunity for the commission to extend its own deadline up to three times.\nThe length of time a congressional commission has to complete its work is arguably one of the most consequential decisions when designing an advisory entity. If the entity has a short window of time, the quality of its work product may suffer or it may not be able to fulfill its statutory mandate on time.\nOn the other hand, if the commission is given a long period of time to complete its work, it may undermine one of a commission\'s primary legislative advantages, the timely production of expert advice on a current matter. A short deadline may also affect the process of standing up a new commission. The selection of commissioners, recruitment of staff, arrangement of office space, and other logistical matters may require expedited action if short deadlines need to be met.\n\n Report Submission\n\nOf the five proposed commissions, four ( H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 ) are directed to submit their reports to both Congress and the President. H.R. 6455 requires that the report is made public.\nMost congressional advisory commissions are required to submit their reports to Congress, and sometimes to the President or an executive department or agency head. For example, the National Commission on Severely Distressed Public Housing\'s final report was submitted to both Congress and the Secretary of Housing and Urban Development.\n\n Commission Termination\n\nCongressional commissions are usually statutorily mandated to terminate. Termination dates for most commissions are linked to either a fixed period of time after the establishment of the commission, the selection of members, or the date of submission of the commission\'s final report. Alternatively, some commissions are given fixed calendar termination dates.\nAll five commission proposals would provide for the commission to terminate within a certain period of time following submission of its final report. H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6455 would each direct the commission to terminate 60 days after the submission; H.R. 6548 specifies a time line of 90 days after submission.\n\n Commission Powers\n\nEach of the five proposals would provide the proposed commission with certain powers to carry out its mission (see Table 1 for specifics). One general issue for commissions is who is authorized to execute such powers. In some cases, the commission itself executes its powers, with the commission deciding whether to devise rules and procedures for the general use of such power. In other cases, the legislation specifically authorizes the commission to give discretionary power to subcommittees or individual commission members. Finally, the legislation itself might grant certain powers to individual members of the commission, such as the chair.\n\n Hearings and Evidence\n\nAll five bills would provide the proposed commission with the power to hold hearings, take testimony, and receive evidence. All five commissions would also be provided the power to administer oaths to witnesses.\n\n Subpoenas\n\nFour of the bills would provide the commission with subpoena power. H.R. 6440 would not provide subpoena power to the commission. H.R. 6429 , H.R. 6431 , and H.R. 6548 would provide that subpoenas could only be issued by either (1) agreement of the chair and vice chair, or (2) the affirmative vote of 6 (of 10) commission members. H.R. 6455 would require that a subpoena could only be issued by either agreement of the chair and vice chair or an affirmative vote of 18 (of 25) commission members. All four bills that would provide subpoena power contain substantially similar judicial methods of subpoena enforcement.\n\n Administrative Support\n\nAll five of the bills would provide that the commission receive administrative support from the General Services Administration (GSA). The GSA provides administrative support to dozens of federal entities, including congressional advisory commissions. Each of the five bills would provide that GSA be reimbursed for its services by the commission. Each bill also provides that other departments or agencies may provide funds, facilities, staff, and other services to the commission.\n\n Other Powers\n\nWithout explicit language authorizing certain activities, commissions often cannot gather information, enter into contracts, use the U.S. mail like an executive branch entity, or accept donations or gifts. \nAll five bills direct that federal agencies provide information to the commission upon request. H.R. 6429 , H.R. 6431 , and H.R. 6548 would also provide that the commission could use the U.S. mails in the same manner as any department or agency, enter into contracts, and accept gifts or donations of services or property.\n\n Staffing\n\nThe proposed COVID-19 commissions contain staffing provisions commonly found in congressional advisory commission legislation. Congressional advisory commissions are usually authorized to hire staff. Most statutes specify that the commission may hire a lead staffer, often referred to as a \"staff director,\" \"executive director,\" or another similar title, in addition to additional staff as needed. Rather than mandate a specific staff size, many commissions are instead authorized to appoint a staff director and other personnel as necessary, subject to the limitations of available funds.\nMost congressional commissions are also authorized to hire consultants, procure intermittent services, and request that federal agencies detail personnel to aid the work of the commission.\n\n Director and Commission Staff\n\nFour of the bills provide that the commission may hire staff without regard to certain laws regarding the competitive service; H.R. 6440 does not specifically exempt the commission from such laws. Four bills ( H.R. 6429 , H.R. 6431 , H.R. 6455 , and H.R. 6548 ) would authorize, but not require, the commission to hire a staff director and additional staff, as appropriate. Four proposals would limit staff salaries to level V of the executive schedule. Three of the bills would specifically designate staff as federal employees for the purposes of certain laws, such as workman\'s compensation, retirement, and other benefits.\n\n Detailees\n\nWhen authorized, some commissions can have federal agency staff detailed to the commission. All five bills would provide that federal employees could be detailed to the commission. Four bills would provide that the detailee would be without reimbursement to his or her home agency. H.R. 6440 would allow detailees on a reimbursable basis. \n\n Experts and Consultants\n\nAll five bills would provide the commission with the authority to hire experts and consultants. Four of the bills limit the rate of pay for consultants to level IV of the Executive Schedule. H.R. 6440 does not specify a specific limit.\n\n Security Clearances\n\nFour bills would provide that federal agencies and departments shall cooperate with the commission to provide members and staff appropriate security clearances. H.R. 6440 does not contain a security clearance provision.\n\n Funding and Costs\n\nCommissions generally require funding to help meet their statutory goals. When designing a commission, therefore, policymakers may consider both how the commission will be funded, and how much funding the commission will be authorized to receive. Four of the five proposals specify a funding mechanism for the commission.\nHow commissions are funded and the amounts that they receive vary considerably. Several factors can contribute to overall commission costs. These factors might include the cost of hiring staff, contracting with outside consultants, and engaging administrative support, among others. Additionally, most commissions reimburse the travel expenditures of commissioners and staff, and some compensate their members. The duration of a commission can also significantly affect its cost; past congressional commissions have been designed to last anywhere from several months to several years.\n\n Costs\n\nIt is difficult to estimate or predict the potential overall cost of any commission. Annual budgets for congressional advisory entities range from several hundred thousand dollars to millions of dollars annually. Overall expenses for any individual advisory entity depend on a variety of factors, the most important of which are the number of paid staff and the commission\'s duration and scope. Some commissions have few full-time staff; others employ large numbers, such as the National Commission on Terrorist Attacks Upon the United States, which had a full-time paid staff of nearly 80. Secondary factors that can affect commission costs include the number of commissioners, how often the commission meets or holds hearings, whether or not the commission travels or holds field hearings, and the publications the commission produces.\n\n Authorized Funding\n\nThree of the bills ( H.R. 6429 , H.R. 6440 , and H.R. 6548 ) would authorize the appropriation of \"such sums as may be necessary\" for the commission, to be derived in equal amounts from the contingent fund of the Senate and the applicable accounts of the House of Representatives. H.R. 6429 and H.R. 6548 would provide that funds are available until the commission terminates. H.R. 6455 would authorize the appropriation of $4 million for the commission, to remain available until the commission terminates. H.R. 6431 does not include an authorization of appropriations.\n\n Comparison of Proposals to Create a COVID-19 Commission\n\n Table 1 provides a side-by-side comparison of major provisions of the five proposals. For each bill, the membership structure, appointment structure, rules of procedure and operation, duties and reporting requirements, proposed commission powers, staffing provisions, and funding are compared.\n\nSummary:\n \ No newline at end of file +You are given a report by a government agency. Write a one-page summary of the report.\n\nReport:\nIntroduction\n\nThroughout U.S. history, Congress has created advisory commissions to assist in the development of public policy. Among other contexts, commissions have been used following crisis situations, including the September 11, 2001, terrorist attacks and the 2008 financial crisis. In such situations, advisory commissions may potentially provide Congress with a high-visibility forum to assemble expertise that might not exist within the legislative environment; allow for the in-depth examination of complex, cross-cutting policy issues; and lend bipartisan credibility to a set of findings and recommendations.\nAs Congress considers its range of responses to the coronavirus pandemic, the creation of one or more congressional advisory commissions is an option that could provide a platform for evaluating various pandemic-related policy issues over time. Past congressional advisory commissions have retrospectively evaluated policy responses, brought together diverse groups of experts, and supplemented existing congressional oversight mechanisms. Policymakers may determine that creating an advisory commission is unnecessary and instead prefer to utilize existing congressional oversight structures, such as standing or select committees, or already established oversight entities.\nThis report provides a comparative analysis of five proposed congressional advisory commissions that would investigate various aspects of the COVID-19 pandemic. The five proposed commissions are found in H.R. 6429 (the National Commission on COVID-19 Act, sponsored by Representative Stephanie Murphy), H.R. 6431 (the Made in America Emergency Preparedness Act, sponsored by Representative Brian Fitzpatrick), H.R. 6440 (the Pandemic Rapid Response Act, sponsored by Representative Rodney Davis), H.R. 6455 (the COVID-19 Commission Act, sponsored by Representative Bennie Thompson), and H.R. 6548 (the National Commission on the COVID-19 Pandemic in the United States Act, sponsored by Representative Adam Schiff). The overall structures of each of the proposed commissions are similar in many respects, both to each other and to previous independent advisory entities established by Congress. Specifically, the proposed commissions would (1) exist temporarily; (2) serve in an advisory capacity; and (3) report a work product detailing the commission\'s findings, conclusions, and recommendations. That said, each particular proposed commission has distinctive elements, particularly concerning its membership structure, appointment structure, and time line for reporting its work product to Congress.\nThis report compares the (1) membership structure, (2) appointment structure, (3) rules of procedure and operation, (4) duties and reporting requirements, (5) powers of the commission, (6) staffing issues, and (7) funding for each of the proposed COVID-19 commissions. Table 1 (at the end of this report) provides a side-by-side comparison of major provisions of the five proposals.\n\n Membership Structure\n\nSeveral matters related to a commission\'s membership structure might be considered. They include the size of a commission, member qualifications, compensation of commission members, and requirements for partisan balance. \n\n Size of Commission\n\nIn general, there is significant variation in the size of congressional advisory commissions. Among 155 identified congressional commissions created between the 101 st Congress and the 115 th Congress, the median size was 12 members, with the smallest commission having 5 members and the largest 33 members.\nThe membership structure of each of the five proposed commissions is similar to previous independent advisory entities created by Congress. H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 would each create a 10-member entity. H.R. 6455 would create a 25-member entity.\n\n Qualifications\n\nPast legislation creating congressional commissions has often required or suggested that commission members possess certain substantive qualifications. Such provisions arguably make it more likely that the commission is populated with genuine experts in the policy area, which may improve the commission\'s final work product.\nH.R. 6455 would provide that commissioners \"shall be a United States person with significant expertise\" in a variety of fields related to public health and public administration. H.R. 6440 , H.R. 6429 , H.R. 6431 , and H.R. 6548 would provide \"the sense of Congress\" that commission members should be \"prominent U.S. citizens\" who are nationally recognized experts in a variety of fields relevant to the pandemic and response efforts. In addition, H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 all prohibit the appointment of federal, state, and local government employees and officers. H.R. 6455 would prohibit federal employees from being commission members.\n\n Compensation of Commission Members\n\nSome congressional commissions have compensated their members. For example, the National Commission on Terrorist Attacks Upon the United States (9/11 Commission) and the Financial Crisis Inquiry Commission provided that commission members could be compensated at a daily rate of basic pay. Nearly all have reimbursed members for travel expenses. Those that have provided for commissioner compensation most frequently provided compensation at the daily equivalent of level IV of the Executive Schedule.\nEach of the five proposals would provide that commission members be compensated at a rate \"not to exceed the daily equivalent of the annual rate of basic pay\" for level IV of the Executive Schedule, \"for each day during which that member is engaged in the actual performance of duties of the Commission.\" Members of three proposed commissions would receive travel expenses, including a per diem.\n\n Partisan Limitations\n\nEach proposal provides a limit on the number of members appointed from the same political party. H.R. 6455 would provide that not more than 13 of its 25 members may be from the same party. H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 would provide that not more than 5 (of 10) members are from the same party. Most previous advisory entities created by Congress do not impose formal partisan restrictions on the membership structure. It may also be difficult to assess the political affiliation of potential members, who may have no formal affiliation (voter registration, for example) with a political party. Instead, most past advisory commissions usually achieve partisan balance through the appointment structure; for instance, by providing equal (or near-equal) numbers of appointments to congressional leaders of each party.\n\n Appointment Structure\n\nPast congressional commissions have used a wide variety of appointment structures. Considerations regarding appointment structures include partisan balance, filling vacancies, and the time line for making commission appointments.\nThe statutory scheme may directly designate members of the commission, such as a specific cabinet official or a congressional leader. In other cases, selected congressional leaders, often with balance between the parties, appoint commission members. A third common statutory scheme is to have selected leaders, such as committee chairs and ranking members, recommend candidates for appointment to a commission. These selected leaders may act either in parallel or jointly, and the recommendation may be made either to other congressional leaders, such as the Speaker of the House and President pro tempore of the Senate, or to the President.\nEach of the five commission proposals would delegate most or all appointment authority to congressional leaders (including chamber, party, and committee leaders; see Table 1 for details). Additionally, H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 provide for one appointment to be made by the President. H.R. 6429 , H.R. 6431 , and H.R. 6548 would have the President appoint the commission\'s chair. H.R. 6455 has its membership appointed by the chairs and ranking members of designated House and Senate committees, and the Joint Economic Committee. H.R. 6455 does not provide any executive branch appointments.\nAttention to the proper balance between the number of members appointed by congressional leaders and by other individuals (such as the President), or to the number of Members of Congress required to be among the appointees, or to the qualifications of appointees, can be significant factors in enabling a commission to fulfill its congressional mandate.\nIn general, a commission\'s appointment scheme can impact both the commission\'s ability to fulfill its statutory duties and its final work product. For instance, if the scheme provides only for the appointment of Members of Congress to the commission, it arguably might not have the technical expertise or diversity of knowledge to complete its duties within the time given by statute. Similarly, if the appointment scheme includes qualifying provisos so specific that only a small set of private citizens could serve on the panel, the commission\'s final work product may arguably only represent a narrow range of viewpoints. None of the proposed COVID-19 commissions specify whether Members of Congress may serve on the commission.\n\n Partisan Balance in Appointment Authority\n\nMost previous congressional advisory commissions have been structured to be bipartisan, with an even (or near-even) split of appointments between leaders of the two major parties. By achieving a nonpartisan or bipartisan character, congressional commissions may make their findings and recommendations more politically acceptable to diverse viewpoints. The bipartisan or nonpartisan arrangement can give recommendations strong credibility, both in Congress and among the public, even when dealing with divisive public policy issues. Similarly, commission recommendations that are perceived as partisan may have difficulty gaining support in Congress.\nIn some cases, however, bipartisanship also can arguably impede a commission\'s ability to complete its mandate. In situations where a commission is tasked with studying divisive or partisan issues, the appointment of an equal number of majority and minority commissioners may serve to promote partisanship within the commission rather than suppress it, raising the possibility of deadlock where neither side can muster a majority to act.\nEach of the five proposals employs a structure where leaders in both the majority and minority parties in Congress would make appointments. H.R. 6429 , H.R. 6431 , and H.R. 6548 would provide for five majority and five minority appointments, including one for the President. H.R. 6440 would include two each by the Senate majority leader, the Senate minority leader, and the Speaker of the House, with one appointment by the House minority leader and one by the President, and the chair appointed by the Speaker and vice chair appointed by the Senate majority leader. H.R. 6455 would have 12 majority and 12 minority appointments made by the 12 committee chairs and ranking members and one member jointly appointed by the chair and vice chair of the Joint Economic Committee.\n\n Vacancies\n\nAll five proposals provide that vacancies on the commission will not affect its powers and would be filled in the same manner as the original appointment.\n\n Deadline for Appointments\n\nThree of the bills propose specific deadlines for the appointment of commissioners. H.R. 6429 and H.R. 6548 provide that appointments are made between specific dates in January or February 2021. Further, H.R. 6429 provides that commission members could be appointed in September 2020, if there is no longer a COVID-19 public health emergency in effect—as determined by the Secretary of Health and Human Services—as of August 31, 2020. H.R. 6440 would require all appointments be made by December 15, 2020. H.R. 6455 would require appointments to be made within 45 days after enactment. H.R. 6429 , H.R. 6440 , and H.R. 6548 would start the commission\'s work in early 2021, as the commission cannot operate without the appointment of members. H.R. 6429 , however would provide that the proposed commission\'s work would begin no later than October 31, 2020, if members are appointed in September 2020. H.R. 6431 does not specify a deadline for the appointment of members.\nTypically, deadlines for appointment can range from several weeks to several months. For example, the deadline for appointments to the Antitrust Modernization Commission was 60 days after the enactment of its establishing act. The deadline for appointment to the Commission on Wartime Contracting in Iraq and Afghanistan was 120 days from the date of enactment. The deadline for appointment to the 9/11 Commission was December 15, 2002, 18 days after enactment of the act.\n\n Rules of Procedure and Operations\n\nWhile most statutes that authorize congressional advisory commissions do not provide detailed procedures for how the commission should conduct its business, the statutory language may provide a general structure, including a mechanism for selecting a chair and procedures for creating rules. None of the five COVID-19 commission proposals contain language that directs the process for potentially adopting rules of procedure. For a comparison of each proposed commission\'s specified rules of procedures and operations, see Table 1 .\n\n Chair Selection\n\nEach bill provides for the selection of a chair and/or vice chair of the commission. H.R. 6429 , H.R. 6431 , and H.R. 6548 would have the chair appointed by the President and the vice chair appointed by congressional leaders of the political party opposite the President. H.R. 6440 would have the chair appointed by the Speaker of the House (in consultation with the Senate majority leader and the House minority leader) and the vice chair appointed by the Senate majority leader (in consultation with the Speaker of the House and the Senate minority leader). H.R. 6455 would have the chair and vice chair chosen from among commission members by a majority vote of the commission, and would require the chair and vice chair to have \"significant experience\" in areas to be studied by the commission.\n\n Initial Meeting Deadline\n\nAs with the timing of commission appointments, some authorizing statutes are prescriptive in when the commission\'s first meeting should take place. Three of the bills analyzed here provide specific time lines for the commission\'s first meeting. H.R. 6429 would require the first meeting to be no later than March 15, 2021, unless members are appointed in September 2020 (if no public health emergency exists). H.R. 6455 would require the first meeting within 45 days after the appointment of all commission members, which is—given the 45-day deadline for appointment—effectively a maximum of 90 days after enactment. H.R. 6548 would direct the commission to hold its initial meeting \"as soon as practicable,\" but not later than March 5, 2021. H.R. 6431 and H.R. 6440 do not provide for an initial meeting deadline. Instead, they direct the commission to meet \"as soon as practicable.\" \n\n Quorum\n\nMost commission statutes provide that a quorum will consist of a particular number of commissioners, usually a majority, but occasionally a supermajority. All five bills would provide for a quorum requirement. H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 would define a quorum as 6 (of 10) members. H.R. 6455 would provide that a quorum is 18 of 25 members (72%).\n\n Public Access\n\nAll five commission bills would require commission meetings to be open to the public. Each bill would also require that reports be made publicly available.\n\n Formulating Other Rules of Procedure and Operations\n\nAbsent statutory guidance (eithe r in general statutes or in individual statutes authorizing commissions), advisory entities vary widely in how they adopt their rules of procedure. In general, three models exist: formal written rules, informal rules, and the reliance on norms. Any individual advisory entity might make use of all three of these models for different types of decisionmaking. \nThe choice to adopt written rules or rely on informal norms to guide commission procedure may be based on a variety of factors, such as the entity\'s size, the frequency of meetings, member preferences regarding formality, the level of collegiality among members, and the amount of procedural guidance provided by the entity\'s authorizing statute. Regardless of how procedural issues are handled, protocol for decisionmaking regarding the following operational issues may be important for the commission to consider at the outset of its existence: eligibility to vote and proxy rules; staff hiring, compensation, and work assignments; hearings, meetings, and field visits; nonstaff expenditures and contracting; reports to Congress; budgeting; and procedures for future modification of rules. None of the five COVID-19 commission proposals specify that the proposed commission must adopt written rules.\n\n FACA Applicability\n\nThe Federal Advisory Committee Act (FACA) mandates certain structural and operational requirements, including formal reporting and oversight procedures, for certain federal advisory bodies that advise the executive branch. Three proposals ( H.R. 6429 , H.R. 6431 , and H.R. 6548 ) specifically exempt the proposed commission from FACA. Of the remaining two, FACA would also likely not apply to the commission proposed in H.R. 6455 because it would be appointed entirely by Members of Congress, although it only specifies that its final report is public, not whether it is specifically sent to Congress and/or the President. It is not clear that FACA would apply to the commission proposed in H.R. 6440 . Although it includes a presidential appointment and its report would be sent to both Congress and the President, its establishment clause specifies that the commission \"is established in the legislative branch,\" and a super-majority of its members would be appointed by Congress.\n\n Duties and Reporting Requirements\n\nMost congressional commissions are generally considered policy commissions—temporary bodies that study particular policy problems and report their findings to Congress or review a specific event. \n\n General Duties\n\nAll five of the proposed commissions would be tasked with duties that are analogous to those of past policy commissions. While the specific mandates differ somewhat, all proposed commissions are tasked with investigating aspects of the COVID-19 pandemic and submitting one or more reports that include the commission\'s findings, conclusions, and recommendations for legislative action. H.R. 6440 would specifically require the commission to avoid unnecessary duplication of work being conducted by the Government Accountability Office (GAO), congressional committees, and executive branch agency and independent commission investigations.\n\n Reports\n\nEach proposed commission would be tasked with issuing a final report detailing its findings, conclusions, and recommendations. H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 would provide that the commission \"may submit\" interim reports to Congress and the President, but do not provide time lines on when those reports might be submitted. In each case, the interim report would need to be agreed to by a majority of commission members. H.R. 6431 would also require the commission to submit a report on actions taken by the states and a report on essential products, materials, ingredients, and equipment required to fight pandemics.\nH.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 also specify that final reports shall be agreed to by a majority of commission members. H.R. 6455 does not specify a vote threshold for approval of its report.\nNone of the bills make specific provisions for the inclusion of minority viewpoints. Presumably this would leave each commission with discretion on whether to include or exclude minority viewpoints. Past advisory entities have been proposed or established with a variety of statutory reporting conditions, including the specification of majority or super-majority rules for report adoption and provisions requiring the inclusion of minority viewpoints. In practice, advisory bodies that are not given statutory direction on these matters have tended to work under simple-majority rules for report adoption.\n\n Report Deadlines\n\nH.R. 6429 would require a final report one year after the commission\'s initial meeting. H.R. 6431 and H.R. 6440 would require a final report not later than 18 months after enactment. H.R. 6455 would require a final report to be published not later than 18 months after the commission\'s first meeting. \nH.R. 6548 would require a final report by October 15, 2021. This deadline could be extended by 90 days upon a vote of no fewer than 8 (out of 10) commission members. The commission could vote to extend its final report deadline up to three times, and would be required to notify Congress, the President, and the public of any such extension.\nWhile such a deadline would potentially give the commission a defined period of time to complete its work, setting a particular date for report completion could potentially create unintended time constraints. Any delay in the passage of the legislation or in the appointment process would reduce the amount of time the commission has to complete its work, even with the opportunity for the commission to extend its own deadline up to three times.\nThe length of time a congressional commission has to complete its work is arguably one of the most consequential decisions when designing an advisory entity. If the entity has a short window of time, the quality of its work product may suffer or it may not be able to fulfill its statutory mandate on time.\nOn the other hand, if the commission is given a long period of time to complete its work, it may undermine one of a commission\'s primary legislative advantages, the timely production of expert advice on a current matter. A short deadline may also affect the process of standing up a new commission. The selection of commissioners, recruitment of staff, arrangement of office space, and other logistical matters may require expedited action if short deadlines need to be met.\n\n Report Submission\n\nOf the five proposed commissions, four ( H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6548 ) are directed to submit their reports to both Congress and the President. H.R. 6455 requires that the report is made public.\nMost congressional advisory commissions are required to submit their reports to Congress, and sometimes to the President or an executive department or agency head. For example, the National Commission on Severely Distressed Public Housing\'s final report was submitted to both Congress and the Secretary of Housing and Urban Development.\n\n Commission Termination\n\nCongressional commissions are usually statutorily mandated to terminate. Termination dates for most commissions are linked to either a fixed period of time after the establishment of the commission, the selection of members, or the date of submission of the commission\'s final report. Alternatively, some commissions are given fixed calendar termination dates.\nAll five commission proposals would provide for the commission to terminate within a certain period of time following submission of its final report. H.R. 6429 , H.R. 6431 , H.R. 6440 , and H.R. 6455 would each direct the commission to terminate 60 days after the submission; H.R. 6548 specifies a time line of 90 days after submission.\n\n Commission Powers\n\nEach of the five proposals would provide the proposed commission with certain powers to carry out its mission (see Table 1 for specifics). One general issue for commissions is who is authorized to execute such powers. In some cases, the commission itself executes its powers, with the commission deciding whether to devise rules and procedures for the general use of such power. In other cases, the legislation specifically authorizes the commission to give discretionary power to subcommittees or individual commission members. Finally, the legislation itself might grant certain powers to individual members of the commission, such as the chair.\n\n Hearings and Evidence\n\nAll five bills would provide the proposed commission with the power to hold hearings, take testimony, and receive evidence. All five commissions would also be provided the power to administer oaths to witnesses.\n\n Subpoenas\n\nFour of the bills would provide the commission with subpoena power. H.R. 6440 would not provide subpoena power to the commission. H.R. 6429 , H.R. 6431 , and H.R. 6548 would provide that subpoenas could only be issued by either (1) agreement of the chair and vice chair, or (2) the affirmative vote of 6 (of 10) commission members. H.R. 6455 would require that a subpoena could only be issued by either agreement of the chair and vice chair or an affirmative vote of 18 (of 25) commission members. All four bills that would provide subpoena power contain substantially similar judicial methods of subpoena enforcement.\n\n Administrative Support\n\nAll five of the bills would provide that the commission receive administrative support from the General Services Administration (GSA). The GSA provides administrative support to dozens of federal entities, including congressional advisory commissions. Each of the five bills would provide that GSA be reimbursed for its services by the commission. Each bill also provides that other departments or agencies may provide funds, facilities, staff, and other services to the commission.\n\n Other Powers\n\nWithout explicit language authorizing certain activities, commissions often cannot gather information, enter into contracts, use the U.S. mail like an executive branch entity, or accept donations or gifts. \nAll five bills direct that federal agencies provide information to the commission upon request. H.R. 6429 , H.R. 6431 , and H.R. 6548 would also provide that the commission could use the U.S. mails in the same manner as any department or agency, enter into contracts, and accept gifts or donations of services or property.\n\n Staffing\n\nThe proposed COVID-19 commissions contain staffing provisions commonly found in congressional advisory commission legislation. Congressional advisory commissions are usually authorized to hire staff. Most statutes specify that the commission may hire a lead staffer, often referred to as a \"staff director,\" \"executive director,\" or another similar title, in addition to additional staff as needed. Rather than mandate a specific staff size, many commissions are instead authorized to appoint a staff director and other personnel as necessary, subject to the limitations of available funds.\nMost congressional commissions are also authorized to hire consultants, procure intermittent services, and request that federal agencies detail personnel to aid the work of the commission.\n\n Director and Commission Staff\n\nFour of the bills provide that the commission may hire staff without regard to certain laws regarding the competitive service; H.R. 6440 does not specifically exempt the commission from such laws. Four bills ( H.R. 6429 , H.R. 6431 , H.R. 6455 , and H.R. 6548 ) would authorize, but not require, the commission to hire a staff director and additional staff, as appropriate. Four proposals would limit staff salaries to level V of the executive schedule. Three of the bills would specifically designate staff as federal employees for the purposes of certain laws, such as workman\'s compensation, retirement, and other benefits.\n\n Detailees\n\nWhen authorized, some commissions can have federal agency staff detailed to the commission. All five bills would provide that federal employees could be detailed to the commission. Four bills would provide that the detailee would be without reimbursement to his or her home agency. H.R. 6440 would allow detailees on a reimbursable basis. \n\n Experts and Consultants\n\nAll five bills would provide the commission with the authority to hire experts and consultants. Four of the bills limit the rate of pay for consultants to level IV of the Executive Schedule. H.R. 6440 does not specify a specific limit.\n\n Security Clearances\n\nFour bills would provide that federal agencies and departments shall cooperate with the commission to provide members and staff appropriate security clearances. H.R. 6440 does not contain a security clearance provision.\n\n Funding and Costs\n\nCommissions generally require funding to help meet their statutory goals. When designing a commission, therefore, policymakers may consider both how the commission will be funded, and how much funding the commission will be authorized to receive. Four of the five proposals specify a funding mechanism for the commission.\nHow commissions are funded and the amounts that they receive vary considerably. Several factors can contribute to overall commission costs. These factors might include the cost of hiring staff, contracting with outside consultants, and engaging administrative support, among others. Additionally, most commissions reimburse the travel expenditures of commissioners and staff, and some compensate their members. The duration of a commission can also significantly affect its cost; past congressional commissions have been designed to last anywhere from several months to several years.\n\n Costs\n\nIt is difficult to estimate or predict the potential overall cost of any commission. Annual budgets for congressional advisory entities range from several hundred thousand dollars to millions of dollars annually. Overall expenses for any individual advisory entity depend on a variety of factors, the most important of which are the number of paid staff and the commission\'s duration and scope. Some commissions have few full-time staff; others employ large numbers, such as the National Commission on Terrorist Attacks Upon the United States, which had a full-time paid staff of nearly 80. Secondary factors that can affect commission costs include the number of commissioners, how often the commission meets or holds hearings, whether or not the commission travels or holds field hearings, and the publications the commission produces.\n\n Authorized Funding\n\nThree of the bills ( H.R. 6429 , H.R. 6440 , and H.R. 6548 ) would authorize the appropriation of \"such sums as may be necessary\" for the commission, to be derived in equal amounts from the contingent fund of the Senate and the applicable accounts of the House of Representatives. H.R. 6429 and H.R. 6548 would provide that funds are available until the commission terminates. H.R. 6455 would authorize the appropriation of $4 million for the commission, to remain available until the commission terminates. H.R. 6431 does not include an authorization of appropriations.\n\n Comparison of Proposals to Create a COVID-19 Commission\n\n Table 1 provides a side-by-side comparison of major provisions of the five proposals. For each bill, the membership structure, appointment structure, rules of procedure and operation, duties and reporting requirements, proposed commission powers, staffing provisions, and funding are compared.\n\nSummary:\n diff --git a/python/sglang/test/simple_eval_common.py b/python/sglang/test/simple_eval_common.py index d97d84de93..518e6245c0 100644 --- a/python/sglang/test/simple_eval_common.py +++ b/python/sglang/test/simple_eval_common.py @@ -320,7 +320,7 @@ def map_with_progress(f: callable, xs: List[Any], num_threads: int): _message_template = """
- {{ role }} + {{ role }} {% if variant %}({{ variant }}){% endif %}
diff --git a/python/sglang/test/simple_eval_humaneval.py b/python/sglang/test/simple_eval_humaneval.py index b0ad79d413..fe6f32f4d1 100644 --- a/python/sglang/test/simple_eval_humaneval.py +++ b/python/sglang/test/simple_eval_humaneval.py @@ -2,8 +2,8 @@ """ HumanEval: Evaluating Large Language Models Trained on Code -Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba -https://arxiv.org/abs/2107.03374 https://github.com/openai/human-eval/ +Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba +https://arxiv.org/abs/2107.03374 https://github.com/openai/human-eval/ """ import random diff --git a/python/sglang/test/simple_eval_mgsm.py b/python/sglang/test/simple_eval_mgsm.py index ce00a1ac76..0b0b72a20f 100644 --- a/python/sglang/test/simple_eval_mgsm.py +++ b/python/sglang/test/simple_eval_mgsm.py @@ -1,10 +1,10 @@ # Adapted from https://github.com/openai/simple-evals/ """ -MGSM: Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems. +MGSM: Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems. Language Models are Multilingual Chain-of-Thought Reasoners Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, Jason Wei -https://arxiv.org/abs/2210.03057 reference: https://github.com/google-research/url-nlp +https://arxiv.org/abs/2210.03057 reference: https://github.com/google-research/url-nlp """ import re diff --git a/rust/Cargo.toml b/rust/Cargo.toml index 794ab1f7cb..a23ac1e460 100644 --- a/rust/Cargo.toml +++ b/rust/Cargo.toml @@ -22,4 +22,4 @@ rand = "0.8.5" reqwest = { version = "0.12.8", features = ["stream"] } futures-util = "0.3" serde_json = "=1.0.1" -pyo3 = { version = "0.22.5", features = ["extension-module"] } \ No newline at end of file +pyo3 = { version = "0.22.5", features = ["extension-module"] } diff --git a/rust/readme.md b/rust/readme.md index 7d10eed75a..a6d1556a8e 100644 --- a/rust/readme.md +++ b/rust/readme.md @@ -36,7 +36,7 @@ Usage: router [OPTIONS] Options: --host [default: 127.0.0.1] --port [default: 3001] - --worker-urls + --worker-urls --policy [default: round_robin] [possible values: round_robin, random] -h, --help Print help -V, --version Print version @@ -82,11 +82,11 @@ $ pip install 1. Run test ``` -$ cargo test +$ cargo test ``` 2. Run lint ``` $ cargo fmt -``` \ No newline at end of file +``` diff --git a/scripts/ci_install_rust.sh b/scripts/ci_install_rust.sh index 23bcc3bef5..85b3e95697 100644 --- a/scripts/ci_install_rust.sh +++ b/scripts/ci_install_rust.sh @@ -7,9 +7,9 @@ curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y # Follow the installation prompts, then reload your shell -. "$HOME/.cargo/env" +. "$HOME/.cargo/env" source $HOME/.cargo/env # Verify installation rustc --version -cargo --version \ No newline at end of file +cargo --version diff --git a/scripts/playground/lora/lora_hf_play.py b/scripts/playground/lora/lora_hf_play.py index 127cea696d..0abddd2c13 100644 --- a/scripts/playground/lora/lora_hf_play.py +++ b/scripts/playground/lora/lora_hf_play.py @@ -10,7 +10,7 @@ prompt = """ ### Instruction: -Write a poem about the transformers Python library. +Write a poem about the transformers Python library. Mention the word "large language models" in that poem. ### Response: The Transformers are large language models, diff --git a/scripts/playground/lora/lora_vllm_play.py b/scripts/playground/lora/lora_vllm_play.py index 75762d739d..4f77d8beab 100644 --- a/scripts/playground/lora/lora_vllm_play.py +++ b/scripts/playground/lora/lora_vllm_play.py @@ -5,7 +5,7 @@ ADAPTER = "/home/ying/test_lora" prompt = """ ### Instruction: -Write a poem about the transformers Python library. +Write a poem about the transformers Python library. Mention the word "large language models" in that poem. ### Response: The Transformers are large language models, diff --git a/scripts/version_branch_to_tag.sh b/scripts/version_branch_to_tag.sh old mode 100644 new mode 100755 index dcac125e03..53272c1efe --- a/scripts/version_branch_to_tag.sh +++ b/scripts/version_branch_to_tag.sh @@ -1,8 +1,8 @@ #!/bin/bash # This script is used for release. -# It tags all remote branches starting with 'v' with the same name as the branch, -# deletes the corresponding branches from the remote, and pushes the tags to the remote repository. +# It tags all remote branches starting with 'v' with the same name as the branch, +# deletes the corresponding branches from the remote, and pushes the tags to the remote repository. git fetch origin --prune @@ -27,4 +27,3 @@ done git push --tags echo "All branches starting with 'v' have been tagged, deleted from remote, and pushed to the remote repository." - diff --git a/test/README.md b/test/README.md index b9cf63ff19..9825faf63b 100644 --- a/test/README.md +++ b/test/README.md @@ -1,6 +1,6 @@ # Run Unit Tests -SGLang uses the built-in library [unittest](https://docs.python.org/3/library/unittest.html) as the testing framework. +SGLang uses the built-in library [unittest](https://docs.python.org/3/library/unittest.html) as the testing framework. ## Test Backend Runtime ```bash diff --git a/test/srt/Llama-3.1-8B-Instruct.json b/test/srt/Llama-3.1-8B-Instruct.json index 359a2372c5..f1652ec96f 100644 --- a/test/srt/Llama-3.1-8B-Instruct.json +++ b/test/srt/Llama-3.1-8B-Instruct.json @@ -1 +1 @@ -{"model.layers.0.self_attn.q_proj": [[39, 106, 104, 102, 33, 95, 13, 44, 11, 29, 12, 10, 53, 126, 27, 114, 121, 8, 124, 113, 112, 15, 23, 89, 69, 111, 54, 80, 4, 1, 20, 24, 83, 63, 115, 122, 66, 42, 22, 110, 3, 73, 21, 61, 97, 19, 25, 88, 117, 119, 116, 85, 70, 5, 56, 118, 68, 123, 2, 86, 71, 127, 93, 49, 109, 50, 67, 52, 91, 40, 17, 108, 60, 55, 78, 62, 65, 47, 6, 87, 51, 84, 58, 82, 94, 79, 57, 103, 76, 48, 0, 18, 81, 96, 9, 31, 16, 92, 26, 43, 30, 37, 74, 100, 46, 38, 32, 14, 90, 36, 101, 75, 35, 125, 45, 7, 72, 41, 77, 105, 28, 59, 34, 99, 98, 107, 120, 64], [39, 104, 102, 106, 33, 44, 95, 53, 126, 111, 114, 110, 29, 14, 113, 54, 121, 112, 109, 80, 119, 12, 63, 25, 78, 123, 42, 10, 24, 127, 108, 115, 122, 60, 116, 97, 9, 93, 61, 17, 117, 56, 82, 91, 21, 124, 62, 0, 43, 19, 31, 26, 49, 52, 57, 59, 1, 50, 41, 15, 46, 86, 88, 81, 118, 125, 2, 3, 55, 105, 84, 75, 120, 70, 47, 85, 32, 51, 37, 16, 22, 23, 89, 87, 67, 30, 68, 69, 77, 48, 66, 27, 36, 107, 20, 76, 94, 79, 96, 83, 99, 38, 100, 18, 8, 35, 28, 101, 13, 72, 92, 7, 4, 98, 90, 103, 6, 34, 74, 11, 65, 40, 71, 45, 58, 64, 73, 5], [106, 104, 39, 102, 2, 44, 95, 53, 121, 3, 4, 33, 1, 0, 122, 111, 126, 115, 63, 71, 119, 112, 113, 70, 109, 29, 61, 52, 114, 54, 127, 21, 10, 72, 5, 110, 116, 49, 123, 11, 12, 55, 24, 45, 15, 19, 117, 73, 80, 50, 6, 23, 64, 124, 75, 69, 25, 83, 14, 77, 97, 42, 78, 13, 79, 85, 27, 56, 87, 86, 57, 17, 118, 91, 81, 74, 65, 76, 9, 41, 88, 18, 51, 22, 62, 93, 89, 67, 105, 84, 82, 38, 48, 120, 60, 47, 26, 20, 43, 68, 28, 66, 108, 59, 58, 8, 125, 98, 46, 31, 90, 36, 94, 92, 34, 107, 35, 7, 99, 100, 30, 32, 37, 101, 16, 96, 40, 103], [39, 104, 106, 102, 33, 44, 53, 111, 113, 95, 54, 109, 41, 116, 124, 63, 121, 122, 61, 56, 114, 110, 60, 108, 127, 112, 125, 43, 52, 126, 12, 80, 119, 59, 62, 82, 123, 97, 117, 25, 37, 0, 88, 29, 57, 120, 26, 91, 115, 78, 24, 21, 118, 49, 93, 1, 42, 14, 58, 107, 10, 84, 67, 7, 9, 86, 87, 3, 50, 51, 64, 98, 18, 20, 72, 66, 100, 17, 32, 81, 69, 79, 2, 101, 105, 85, 15, 77, 31, 22, 75, 70, 68, 90, 55, 19, 89, 11, 74, 76, 8, 38, 30, 4, 65, 5, 45, 92, 23, 46, 73, 13, 94, 47, 35, 36, 6, 48, 34, 28, 99, 27, 96, 103, 71, 83, 16, 40], [41, 103, 38, 33, 42, 93, 109, 95, 88, 44, 107, 17, 25, 20, 110, 21, 47, 40, 78, 116, 115, 126, 59, 61, 64, 57, 48, 83, 119, 12, 45, 125, 11, 18, 124, 118, 53, 56, 89, 70, 22, 77, 86, 50, 92, 4, 87, 24, 97, 65, 49, 73, 26, 62, 3, 16, 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120, 119, 109, 4, 65, 12, 118, 71, 62, 22, 24, 80, 14, 50, 74, 73, 51, 70, 3, 7, 121, 67, 83, 27, 111, 72, 5, 69, 76, 23, 45, 84, 29, 60, 9, 31, 10, 81, 26, 8, 87, 19, 13, 0, 40, 2, 56, 113, 85, 82, 127, 91, 43, 34, 108, 117, 114, 6, 15, 96, 106, 79, 75, 94, 102, 100, 101, 68, 98, 63, 58, 99, 105, 55, 30, 90, 36, 1, 35, 32, 46, 112, 28, 104, 39, 37, 92, 52, 54], [103, 41, 33, 38, 42, 93, 95, 107, 110, 25, 59, 17, 48, 116, 88, 109, 44, 57, 11, 83, 20, 125, 78, 21, 62, 86, 115, 70, 97, 77, 4, 118, 18, 47, 43, 64, 12, 24, 50, 126, 22, 124, 45, 3, 61, 72, 65, 39, 89, 84, 113, 53, 73, 29, 49, 120, 66, 80, 79, 27, 71, 122, 10, 117, 40, 9, 92, 16, 67, 81, 19, 23, 119, 123, 51, 105, 36, 8, 15, 55, 108, 111, 60, 90, 14, 26, 87, 104, 74, 56, 91, 75, 0, 28, 69, 68, 100, 2, 102, 99, 58, 52, 85, 6, 63, 34, 54, 94, 127, 96, 76, 32, 121, 30, 1, 31, 7, 114, 82, 46, 37, 13, 112, 35, 5, 101, 98, 106], [102, 41, 112, 101, 108, 103, 33, 104, 29, 27, 115, 30, 42, 38, 16, 45, 114, 20, 32, 100, 121, 89, 98, 111, 87, 62, 59, 126, 13, 63, 107, 11, 43, 88, 117, 75, 118, 25, 84, 34, 31, 50, 51, 55, 23, 95, 56, 58, 53, 78, 94, 97, 82, 92, 8, 18, 127, 113, 110, 19, 40, 21, 35, 39, 85, 119, 37, 125, 96, 54, 6, 72, 90, 91, 14, 26, 124, 105, 9, 44, 28, 17, 116, 120, 60, 48, 80, 57, 1, 123, 79, 77, 86, 24, 69, 73, 15, 122, 68, 99, 81, 74, 4, 52, 71, 67, 7, 65, 76, 66, 61, 109, 106, 46, 93, 2, 64, 12, 10, 49, 3, 47, 83, 70, 36, 22, 0, 5], [107, 104, 102, 33, 108, 45, 95, 92, 29, 42, 103, 21, 99, 43, 85, 101, 23, 50, 27, 28, 113, 98, 19, 15, 120, 83, 26, 46, 74, 97, 34, 12, 86, 123, 52, 127, 90, 79, 39, 7, 17, 41, 112, 49, 126, 77, 48, 38, 125, 89, 44, 61, 111, 18, 100, 115, 32, 25, 35, 119, 93, 62, 30, 60, 84, 96, 31, 80, 122, 87, 114, 53, 55, 47, 56, 110, 13, 57, 63, 37, 51, 54, 116, 117, 68, 124, 105, 78, 40, 81, 118, 88, 9, 91, 11, 109, 76, 24, 59, 71, 121, 82, 6, 20, 1, 72, 106, 16, 58, 73, 5, 94, 67, 10, 69, 14, 4, 22, 75, 2, 36, 70, 8, 66, 3, 65, 64, 0], [102, 103, 33, 29, 41, 42, 104, 45, 64, 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72, 36, 75, 31, 14, 9], [104, 103, 33, 43, 44, 111, 102, 90, 49, 89, 8, 41, 93, 37, 116, 113, 95, 122, 123, 23, 74, 42, 14, 17, 83, 68, 85, 106, 91, 92, 13, 78, 63, 127, 115, 81, 112, 87, 121, 3, 70, 48, 72, 120, 28, 124, 53, 71, 30, 19, 47, 86, 59, 51, 96, 56, 94, 114, 108, 118, 76, 79, 55, 38, 29, 110, 5, 82, 15, 21, 11, 20, 26, 66, 6, 84, 22, 50, 9, 27, 100, 65, 61, 64, 57, 25, 35, 7, 16, 101, 80, 45, 52, 36, 46, 109, 107, 24, 126, 2, 4, 12, 67, 125, 75, 10, 32, 117, 97, 1, 69, 60, 77, 88, 98, 99, 73, 105, 58, 18, 62, 54, 34, 31, 0, 119, 40, 39], [103, 104, 102, 33, 43, 44, 111, 90, 115, 106, 113, 89, 42, 74, 56, 93, 48, 95, 85, 127, 41, 72, 92, 37, 107, 71, 17, 87, 123, 112, 122, 50, 83, 22, 55, 57, 116, 14, 53, 124, 49, 114, 26, 47, 110, 23, 63, 70, 98, 100, 51, 108, 121, 59, 13, 35, 28, 81, 94, 79, 32, 120, 29, 76, 101, 105, 126, 38, 7, 82, 9, 6, 118, 24, 125, 91, 20, 88, 15, 99, 36, 109, 18, 62, 34, 46, 84, 19, 3, 61, 2, 30, 11, 27, 86, 52, 58, 75, 25, 68, 12, 31, 117, 69, 54, 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55, 47, 71, 39, 11, 72, 34, 50, 63, 116, 48, 107, 70, 121, 13, 120, 17, 127, 78, 83, 3, 22, 7, 6, 5, 8, 66, 74, 97, 69, 10, 68, 67, 64, 4, 65, 0, 1, 2], [39, 41, 101, 97, 29, 110, 24, 43, 32, 49, 121, 84, 120, 63, 3, 56, 44, 8, 59, 21, 38, 98, 40, 6, 60, 127, 53, 71, 2, 25, 52, 116, 99, 13, 62, 4, 125, 72, 46, 18, 23, 7, 31, 68, 85, 15, 92, 42, 79, 76, 115, 123, 87, 75, 109, 57, 108, 95, 102, 12, 54, 0, 36, 66, 61, 19, 50, 124, 94, 65, 111, 82, 69, 126, 77, 80, 83, 106, 113, 51, 33, 100, 114, 91, 14, 20, 78, 10, 47, 27, 117, 86, 48, 118, 67, 89, 11, 112, 58, 16, 35, 88, 30, 45, 28, 5, 17, 70, 93, 73, 90, 34, 105, 22, 26, 55, 64, 119, 107, 9, 122, 104, 103, 96, 81, 1, 74, 37], [39, 41, 97, 32, 101, 43, 29, 26, 116, 31, 54, 80, 99, 55, 125, 18, 21, 59, 24, 78, 73, 111, 23, 76, 71, 16, 87, 82, 119, 3, 25, 28, 11, 121, 112, 12, 123, 63, 98, 120, 85, 83, 47, 91, 94, 56, 13, 103, 7, 33, 17, 88, 51, 127, 4, 95, 104, 19, 38, 108, 60, 86, 100, 79, 124, 77, 30, 27, 109, 81, 52, 50, 84, 61, 67, 37, 93, 49, 92, 5, 10, 118, 46, 58, 69, 74, 110, 14, 53, 45, 122, 126, 57, 113, 114, 15, 42, 107, 68, 22, 72, 9, 62, 90, 89, 70, 115, 48, 102, 8, 75, 36, 40, 6, 117, 105, 2, 0, 96, 20, 106, 44, 35, 65, 66, 1, 34, 64], [6, 39, 41, 70, 101, 72, 8, 43, 74, 60, 108, 53, 92, 110, 98, 113, 50, 32, 44, 47, 61, 87, 62, 95, 28, 111, 26, 38, 58, 57, 67, 124, 18, 52, 56, 51, 99, 97, 112, 123, 118, 125, 116, 59, 109, 33, 40, 89, 120, 106, 19, 121, 30, 46, 14, 31, 68, 126, 88, 11, 4, 104, 24, 45, 2, 63, 127, 10, 119, 105, 80, 90, 107, 83, 34, 114, 36, 78, 3, 27, 69, 42, 55, 9, 20, 73, 122, 5, 15, 86, 25, 49, 48, 29, 21, 94, 66, 100, 22, 0, 54, 65, 75, 84, 17, 96, 93, 91, 35, 77, 12, 71, 115, 102, 103, 79, 7, 23, 81, 117, 37, 85, 16, 13, 64, 76, 1, 82], [41, 39, 70, 101, 10, 32, 19, 43, 87, 97, 11, 83, 46, 14, 60, 4, 123, 15, 125, 38, 44, 108, 56, 86, 16, 9, 118, 53, 18, 5, 109, 2, 92, 76, 78, 62, 61, 23, 120, 127, 24, 68, 49, 59, 42, 66, 84, 75, 21, 110, 8, 27, 106, 72, 63, 111, 47, 52, 121, 50, 73, 126, 28, 40, 17, 99, 67, 107, 79, 122, 89, 82, 33, 124, 105, 77, 58, 26, 1, 85, 25, 114, 119, 51, 22, 57, 98, 48, 31, 71, 94, 103, 115, 116, 13, 7, 102, 74, 113, 3, 20, 96, 6, 0, 81, 90, 69, 45, 104, 29, 34, 100, 12, 36, 91, 88, 80, 64, 54, 95, 30, 55, 35, 65, 93, 37, 112, 117], [40, 102, 31, 107, 22, 39, 78, 98, 9, 6, 73, 88, 12, 81, 17, 67, 76, 106, 25, 70, 48, 85, 86, 110, 91, 92, 114, 74, 82, 2, 58, 15, 83, 59, 14, 27, 108, 117, 127, 66, 79, 111, 47, 30, 51, 50, 5, 21, 57, 52, 109, 36, 89, 23, 35, 49, 101, 63, 72, 90, 122, 54, 100, 26, 84, 56, 124, 33, 18, 55, 62, 126, 29, 123, 11, 95, 119, 20, 96, 28, 77, 19, 24, 4, 87, 32, 115, 37, 99, 41, 53, 97, 125, 112, 8, 104, 71, 43, 93, 3, 116, 65, 16, 10, 64, 75, 94, 113, 120, 105, 68, 60, 46, 103, 7, 61, 69, 34, 13, 44, 0, 118, 80, 45, 121, 1, 42, 38], [40, 73, 70, 14, 39, 17, 110, 102, 114, 85, 82, 91, 51, 106, 99, 49, 48, 41, 98, 83, 36, 75, 115, 46, 88, 119, 92, 30, 69, 57, 28, 32, 15, 109, 126, 63, 86, 31, 123, 84, 6, 37, 72, 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13, 124, 3, 59, 47, 77, 91, 42, 96, 8, 57, 62, 99, 97, 72, 26, 46, 22, 69, 37, 5, 20, 74, 30, 100, 6, 86, 10, 33, 84, 90, 35, 36, 38, 101, 94, 102, 92], [121, 58, 126, 43, 122, 109, 49, 50, 55, 115, 56, 123, 103, 117, 45, 104, 93, 105, 98, 89, 40, 21, 107, 61, 85, 51, 0, 34, 64, 41, 118, 114, 25, 95, 23, 39, 73, 111, 31, 9, 127, 108, 53, 76, 52, 28, 27, 29, 87, 4, 116, 82, 44, 112, 78, 15, 113, 120, 48, 80, 24, 54, 106, 16, 124, 110, 18, 1, 2, 63, 68, 71, 88, 14, 12, 11, 83, 19, 17, 81, 66, 119, 65, 79, 67, 7, 46, 60, 3, 70, 77, 72, 47, 59, 99, 75, 32, 5, 22, 69, 91, 125, 96, 62, 42, 13, 57, 6, 84, 86, 26, 8, 30, 10, 36, 37, 74, 97, 33, 20, 94, 90, 100, 102, 35, 38, 101, 92], [121, 126, 58, 43, 49, 55, 122, 50, 109, 115, 123, 56, 103, 117, 104, 45, 105, 98, 93, 40, 21, 25, 107, 89, 61, 51, 41, 85, 0, 95, 111, 23, 34, 31, 39, 9, 64, 114, 118, 73, 29, 127, 53, 27, 4, 87, 113, 52, 44, 14, 78, 71, 116, 1, 76, 28, 24, 108, 16, 80, 48, 54, 66, 124, 82, 112, 68, 15, 18, 88, 3, 12, 2, 83, 81, 46, 79, 120, 17, 70, 47, 67, 11, 106, 7, 119, 63, 65, 72, 110, 59, 75, 42, 60, 125, 19, 32, 13, 77, 99, 26, 5, 62, 57, 91, 69, 20, 22, 84, 96, 86, 74, 6, 8, 37, 10, 97, 100, 36, 30, 90, 33, 101, 94, 102, 38, 35, 92], [121, 58, 126, 43, 122, 49, 109, 55, 50, 115, 56, 123, 104, 117, 103, 45, 93, 98, 21, 105, 25, 89, 40, 61, 107, 85, 41, 23, 34, 64, 51, 127, 31, 118, 0, 9, 39, 114, 95, 73, 111, 27, 68, 16, 79, 108, 4, 53, 82, 29, 52, 76, 87, 48, 44, 24, 12, 65, 78, 88, 28, 116, 14, 66, 46, 71, 18, 80, 113, 3, 11, 15, 112, 7, 2, 72, 1, 54, 124, 70, 106, 119, 81, 17, 120, 83, 67, 75, 63, 47, 60, 19, 99, 110, 5, 32, 77, 13, 22, 69, 59, 42, 26, 125, 57, 62, 84, 74, 10, 96, 91, 86, 30, 6, 33, 20, 37, 97, 8, 90, 100, 36, 38, 102, 94, 35, 101, 92], [121, 126, 58, 43, 109, 49, 122, 50, 55, 115, 56, 123, 103, 117, 45, 104, 93, 105, 61, 98, 40, 21, 89, 41, 107, 114, 51, 25, 127, 85, 118, 34, 23, 0, 95, 64, 31, 111, 53, 52, 9, 39, 108, 44, 73, 48, 27, 87, 76, 78, 82, 79, 16, 24, 29, 113, 4, 14, 1, 68, 17, 120, 28, 12, 110, 81, 88, 15, 71, 72, 124, 2, 54, 80, 11, 112, 66, 60, 63, 116, 65, 18, 46, 7, 106, 3, 83, 67, 47, 99, 62, 19, 59, 119, 70, 22, 75, 5, 125, 13, 69, 77, 100, 91, 6, 57, 96, 42, 84, 32, 26, 37, 20, 86, 33, 74, 36, 30, 10, 8, 102, 97, 94, 90, 101, 35, 38, 92], [121, 126, 58, 43, 122, 55, 49, 109, 50, 115, 56, 123, 117, 45, 103, 104, 93, 105, 98, 61, 64, 51, 89, 95, 21, 40, 41, 107, 85, 0, 25, 34, 118, 23, 31, 29, 114, 53, 108, 39, 9, 111, 52, 73, 127, 44, 87, 4, 27, 124, 16, 113, 116, 14, 68, 79, 76, 18, 67, 82, 120, 66, 78, 2, 28, 112, 63, 12, 48, 1, 106, 80, 72, 15, 24, 17, 88, 54, 65, 7, 47, 83, 110, 81, 71, 119, 3, 19, 11, 75, 6, 46, 125, 60, 57, 69, 99, 59, 5, 13, 77, 32, 70, 42, 26, 22, 62, 84, 96, 86, 91, 10, 97, 20, 8, 74, 33, 37, 30, 90, 101, 100, 36, 94, 102, 38, 35, 92], [121, 58, 126, 43, 122, 109, 49, 50, 55, 115, 56, 123, 117, 103, 45, 104, 93, 105, 98, 21, 40, 89, 61, 25, 107, 51, 41, 34, 85, 114, 118, 127, 95, 23, 31, 39, 111, 0, 108, 73, 64, 53, 29, 9, 27, 52, 87, 4, 113, 44, 28, 112, 76, 78, 14, 48, 79, 82, 88, 3, 80, 54, 71, 116, 16, 15, 110, 17, 18, 120, 1, 24, 7, 12, 124, 47, 63, 68, 67, 83, 2, 65, 81, 59, 60, 119, 19, 106, 11, 72, 6, 75, 66, 5, 125, 46, 77, 13, 69, 32, 99, 57, 22, 26, 96, 42, 62, 91, 20, 86, 74, 97, 84, 8, 10, 30, 70, 37, 33, 36, 100, 90, 38, 35, 94, 92, 101, 102], [121, 58, 126, 43, 122, 49, 109, 55, 50, 115, 123, 56, 103, 117, 104, 45, 93, 21, 98, 61, 40, 105, 23, 89, 25, 41, 107, 118, 34, 85, 127, 31, 51, 0, 111, 108, 114, 64, 95, 73, 39, 9, 52, 27, 53, 24, 113, 4, 76, 18, 15, 12, 54, 80, 79, 87, 120, 44, 16, 68, 78, 82, 48, 29, 28, 112, 6, 1, 14, 81, 71, 63, 88, 2, 65, 7, 17, 75, 66, 11, 124, 83, 116, 119, 72, 106, 47, 19, 46, 3, 67, 110, 69, 22, 59, 60, 99, 42, 125, 77, 32, 13, 26, 62, 5, 91, 74, 96, 8, 57, 86, 84, 100, 20, 33, 97, 10, 36, 30, 37, 70, 90, 94, 101, 102, 35, 38, 92], [121, 58, 126, 43, 122, 49, 109, 55, 50, 115, 56, 123, 103, 117, 104, 45, 93, 98, 40, 89, 21, 105, 41, 61, 85, 107, 51, 25, 127, 23, 34, 95, 118, 114, 0, 39, 9, 73, 31, 64, 53, 108, 111, 27, 52, 4, 113, 79, 87, 44, 48, 78, 12, 80, 28, 68, 24, 14, 76, 54, 3, 120, 29, 82, 15, 81, 110, 66, 65, 116, 18, 75, 63, 1, 112, 124, 7, 6, 88, 16, 11, 71, 17, 2, 83, 119, 106, 67, 19, 42, 60, 46, 22, 99, 47, 77, 62, 57, 96, 72, 8, 13, 59, 32, 5, 69, 125, 91, 70, 30, 20, 26, 97, 74, 86, 84, 37, 10, 33, 36, 100, 90, 101, 38, 102, 35, 94, 92], [121, 126, 58, 43, 122, 49, 109, 50, 55, 115, 56, 123, 103, 45, 117, 104, 89, 93, 98, 40, 105, 21, 85, 34, 107, 25, 114, 41, 23, 118, 95, 127, 51, 64, 31, 61, 39, 111, 29, 108, 0, 27, 112, 53, 73, 28, 87, 44, 9, 12, 113, 52, 68, 18, 78, 88, 24, 15, 82, 124, 116, 120, 54, 48, 14, 76, 60, 79, 16, 66, 1, 7, 75, 81, 65, 80, 4, 17, 63, 110, 11, 19, 2, 71, 106, 6, 119, 47, 59, 83, 99, 67, 3, 13, 46, 77, 42, 8, 125, 96, 22, 32, 57, 91, 5, 62, 70, 69, 20, 86, 37, 30, 74, 72, 84, 26, 36, 97, 100, 94, 38, 10, 33, 101, 90, 35, 102, 92], [121, 58, 126, 43, 122, 49, 109, 50, 55, 115, 56, 123, 103, 117, 104, 45, 93, 40, 98, 21, 89, 105, 127, 61, 25, 107, 23, 85, 34, 41, 111, 114, 51, 108, 73, 118, 39, 31, 95, 0, 29, 9, 64, 48, 28, 52, 68, 53, 27, 54, 112, 87, 24, 82, 12, 120, 113, 4, 78, 76, 44, 88, 16, 14, 79, 17, 80, 1, 15, 18, 116, 110, 81, 83, 63, 65, 2, 75, 7, 47, 59, 46, 66, 19, 71, 60, 8, 67, 119, 11, 106, 3, 13, 6, 42, 32, 99, 62, 125, 124, 5, 77, 26, 70, 69, 57, 86, 96, 37, 30, 91, 33, 72, 22, 97, 20, 84, 74, 100, 10, 36, 102, 94, 35, 90, 38, 101, 92], [121, 58, 126, 43, 122, 49, 109, 50, 55, 115, 56, 123, 103, 117, 104, 45, 93, 98, 21, 61, 89, 40, 105, 41, 85, 107, 23, 0, 25, 51, 34, 114, 64, 95, 31, 127, 111, 73, 118, 39, 9, 113, 108, 29, 24, 53, 52, 27, 68, 4, 120, 76, 79, 16, 18, 82, 28, 87, 12, 78, 44, 54, 116, 1, 124, 14, 15, 80, 7, 71, 48, 46, 3, 8, 66, 2, 75, 88, 67, 83, 112, 81, 110, 65, 63, 11, 17, 60, 70, 106, 99, 125, 47, 19, 22, 5, 119, 13, 59, 42, 77, 57, 32, 69, 62, 26, 74, 6, 37, 10, 97, 91, 96, 84, 100, 72, 20, 86, 33, 30, 36, 90, 94, 101, 102, 35, 38, 92], [121, 126, 58, 43, 122, 49, 109, 55, 50, 115, 56, 123, 103, 104, 117, 45, 93, 98, 105, 89, 21, 64, 40, 61, 85, 0, 107, 51, 25, 114, 118, 23, 41, 127, 9, 53, 34, 111, 95, 31, 73, 39, 108, 48, 113, 1, 68, 12, 52, 27, 44, 54, 87, 4, 29, 120, 14, 80, 18, 78, 70, 24, 66, 7, 8, 79, 28, 82, 15, 76, 63, 16, 112, 67, 71, 81, 124, 75, 46, 65, 88, 2, 3, 17, 110, 116, 69, 83, 11, 47, 106, 119, 19, 125, 59, 60, 77, 57, 32, 13, 99, 42, 62, 5, 26, 96, 10, 22, 33, 84, 86, 74, 97, 100, 20, 37, 91, 30, 36, 6, 72, 90, 102, 101, 94, 35, 92, 38], [121, 58, 126, 43, 122, 49, 50, 55, 109, 115, 56, 123, 103, 104, 117, 45, 93, 21, 40, 105, 98, 89, 61, 85, 127, 23, 41, 25, 107, 118, 114, 51, 34, 31, 39, 95, 73, 111, 9, 108, 64, 53, 0, 27, 29, 48, 12, 44, 79, 52, 82, 68, 87, 24, 54, 15, 18, 14, 113, 80, 78, 28, 16, 76, 120, 81, 70, 65, 112, 17, 116, 4, 88, 124, 75, 8, 83, 7, 110, 3, 1, 71, 11, 60, 19, 66, 63, 59, 106, 125, 47, 119, 2, 67, 13, 57, 69, 46, 77, 99, 5, 32, 96, 22, 42, 62, 20, 30, 26, 86, 10, 74, 91, 84, 97, 100, 33, 37, 36, 72, 94, 6, 90, 101, 102, 38, 35, 92], [121, 126, 58, 43, 122, 49, 109, 55, 50, 115, 56, 123, 104, 103, 117, 45, 98, 93, 89, 105, 61, 21, 40, 107, 41, 23, 25, 51, 127, 64, 34, 85, 0, 114, 39, 118, 31, 108, 73, 111, 9, 95, 4, 12, 53, 15, 87, 44, 29, 27, 113, 28, 79, 68, 52, 1, 78, 48, 14, 112, 80, 24, 16, 120, 116, 63, 76, 18, 65, 70, 2, 7, 54, 71, 67, 66, 81, 8, 82, 110, 11, 83, 124, 17, 3, 19, 47, 88, 75, 106, 46, 119, 125, 13, 60, 69, 59, 32, 5, 77, 99, 57, 62, 22, 96, 91, 6, 42, 86, 26, 30, 97, 72, 37, 20, 74, 84, 10, 90, 36, 94, 33, 100, 101, 102, 38, 35, 92], [121, 126, 58, 43, 122, 49, 50, 109, 55, 115, 123, 56, 103, 117, 104, 45, 93, 40, 89, 85, 105, 107, 98, 61, 21, 127, 23, 25, 114, 34, 118, 41, 95, 111, 51, 39, 9, 108, 29, 73, 31, 52, 53, 27, 12, 0, 24, 64, 18, 16, 120, 14, 113, 78, 87, 79, 54, 44, 112, 48, 4, 76, 15, 82, 80, 88, 68, 63, 28, 17, 1, 75, 81, 83, 119, 116, 8, 71, 11, 19, 7, 65, 106, 124, 2, 67, 3, 110, 66, 60, 70, 59, 5, 77, 13, 47, 96, 22, 62, 32, 99, 69, 46, 6, 42, 125, 57, 91, 86, 20, 26, 30, 72, 84, 74, 97, 10, 37, 36, 33, 94, 90, 100, 101, 35, 38, 102, 92]]} diff --git a/test/srt/models/test_generation_models.py b/test/srt/models/test_generation_models.py old mode 100755 new mode 100644 diff --git a/test/srt/models/test_lora.py b/test/srt/models/test_lora.py index 41ea8fc152..1dc2140e4d 100644 --- a/test/srt/models/test_lora.py +++ b/test/srt/models/test_lora.py @@ -45,7 +45,7 @@ PROMPTS = [ """ ### Instruction: -Write a poem about the transformers Python library. +Write a poem about the transformers Python library. Mention the word "large language models" in that poem. ### Response: The Transformers are large language models, diff --git a/test/srt/test_data_parallelism.py b/test/srt/test_data_parallelism.py index 0ac8b784c3..4efccd36c0 100644 --- a/test/srt/test_data_parallelism.py +++ b/test/srt/test_data_parallelism.py @@ -53,7 +53,7 @@ def test_update_weight(self): # pause a few seconds then send again time.sleep(5) - + response = requests.post( self.base_url + "/update_weights", json={"model_path": DEFAULT_MODEL_NAME_FOR_TEST}, diff --git a/test/srt/test_matched_stop.py b/test/srt/test_matched_stop.py index df37fa13c7..81d08b0913 100644 --- a/test/srt/test_matched_stop.py +++ b/test/srt/test_matched_stop.py @@ -11,9 +11,9 @@ ) MANY_NEW_TOKENS_PROMPT = """ -Please write an extremely detailed and vivid fantasy story, set in a world full of intricate magic systems, political intrigue, and complex characters. -Ensure that you thoroughly describe every scene, character's motivations, and the environment. Include long, engaging dialogues and elaborate on the inner thoughts of the characters. -Each section should be as comprehensive as possible to create a rich and immersive experience for the reader. +Please write an extremely detailed and vivid fantasy story, set in a world full of intricate magic systems, political intrigue, and complex characters. +Ensure that you thoroughly describe every scene, character's motivations, and the environment. Include long, engaging dialogues and elaborate on the inner thoughts of the characters. +Each section should be as comprehensive as possible to create a rich and immersive experience for the reader. The story should span multiple events, challenges, and character developments over time. Aim to make the story at least 3,000 words long. """ @@ -109,7 +109,7 @@ def test_finish_stop_eos(self): llama_format_prompt = """ <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|> - + What is 2 + 2?<|eot_id|><|start_header_id|>assistant<|end_header_id|> """ eos_token_id = 128009