memoize dataset length for eval sample packing

examples/llama-3/qlora-1b.yml

@@ -0,0 +1,77 @@
+base_model: meta-llama/Llama-3.2-1B
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: teknium/GPT4-LLM-Cleaned
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.1
+output_dir: ./outputs/qlora-out
+
+adapter: qlora
+lora_model_dir:
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: true
+pad_to_sequence_len: true
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+loss_watchdog_threshold: 5.0
+loss_watchdog_patience: 3
+
+warmup_steps: 10
+evals_per_epoch: 4
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<|end_of_text|>"

src/axolotl/utils/samplers/multipack.py

@@ -133,6 +133,8 @@ def __init__(
         self.eff_total_used = 0
         self.eff_total_slots = 0
 
+        self.len_across_ranks = None
+
     def set_epoch(self, epoch: int):
         self.epoch = epoch
 
@@ -195,15 +197,14 @@ def calc_min_len(estimates: list[(int, float)]):
             LOG.info(f"gather_len_batches: {repr(estimates)}")
             return math.floor(0.998 * min(estimates))
 
-        min_len_batches = reduce_and_broadcast(
-            lambda: num,
-            calc_min_len,
-        )
+        min_len_batches = reduce_and_broadcast(lambda: num, calc_min_len)
         return min_len_batches
 
     def __len__(self):
-        len_batches = self.num_batches()
-        return self.gather_len_batches(len_batches)
+        if not self.len_across_ranks:
+            len_batches = self.num_batches()
+            self.len_across_ranks = self.gather_len_batches(len_batches)
+        return self.len_across_ranks
 
     def _len_est(self):
         efficiency = (

tests/e2e/multigpu/test_eval.py

@@ -0,0 +1,155 @@
+"""
+E2E tests for multigpu eval
+"""
+import logging
+import os
+import unittest
+from pathlib import Path
+
+import yaml
+from accelerate.test_utils import execute_subprocess_async
+
+from axolotl.utils.dict import DictDefault
+
+from ..utils import with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
+os.environ["WANDB_DISABLED"] = "true"
+
+AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
+
+
+class TestMultiGPUEval(unittest.TestCase):
+    """
+    Test case for MultiGPU Eval Sample Packing
+    """
+
+    @with_temp_dir
+    def test_eval_sample_packing(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "load_in_8bit": False,
+                "load_in_4bit": True,
+                "strict": False,
+                "sequence_len": 2048,
+                "adapter": "qlora",
+                "sample_packing": True,
+                "eval_sample_packing": True,
+                "pad_to_sequence_len": True,
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "lora_modules_to_save": ["embed_tokens", "lm_head"],
+                "val_set_size": 0.1,
+                "special_tokens": {"pad_token": "<|end_of_text|>"},
+                "datasets": [
+                    {
+                        "path": "teknium/GPT4-LLM-Cleaned",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "loss_watchdog_threshold": 5.0,
+                "loss_watchdog_patience": 3,
+                "bf16": "auto",
+                "warmup_steps": 1,
+                "evals_per_epoch": 2,
+                "eval_max_new_tokens": 128,
+                "saves_per_epoch": 1,
+                "logging_steps": 1,
+                "weight_decay": 0.0,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "accelerate",
+                "launch",
+                "--num-processes",
+                "2",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )
+
+    @with_temp_dir
+    def test_eval(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "load_in_8bit": False,
+                "load_in_4bit": True,
+                "strict": False,
+                "sequence_len": 2048,
+                "adapter": "qlora",
+                "sample_packing": True,
+                "eval_sample_packing": False,
+                "pad_to_sequence_len": True,
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "lora_modules_to_save": ["embed_tokens", "lm_head"],
+                "val_set_size": 0.1,
+                "special_tokens": {"pad_token": "<|end_of_text|>"},
+                "datasets": [
+                    {
+                        "path": "teknium/GPT4-LLM-Cleaned",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "loss_watchdog_threshold": 5.0,
+                "loss_watchdog_patience": 3,
+                "bf16": "auto",
+                "warmup_steps": 1,
+                "evals_per_epoch": 2,
+                "eval_max_new_tokens": 128,
+                "saves_per_epoch": 1,
+                "logging_steps": 1,
+                "weight_decay": 0.0,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "accelerate",
+                "launch",
+                "--num-processes",
+                "2",
+                "-m",
+                "axolotl.cli.train",
+                str(Path(temp_dir) / "config.yaml"),
+            ]
+        )