diff --git a/.gitignore b/.gitignore index 2677418..ceb9e82 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,8 @@ examples/grammars/debug examples/grammars/wip examples/jupyter +profiling +debug # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/README.md b/README.md index 883abf5..a4ac086 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,7 @@ ## Latest News +**Support for Unicode(multilingual) grammars** (2024-02-29) **Integration with Text-Generation-WebUI** (2023-12-17) We are thrilled to announce that `transformers_cfg` has been used in the [Text-Generation-WebUI](https://github.com/oobabooga/text-generation-webui) project. @@ -85,6 +86,7 @@ if __name__ == "__main__": - We support EBNF grammar description format - We offer the same grammar interface as llama-cpp project, allowing you to drop-in replace llama-cpp with transformers-CFG. - We allow you to use any of the models in the πŸ€— Transformers library, including the ones that are not supported by llama-cpp. +- We support multilingual grammars, you can use any character from any language in your grammar, e.g. δΈ­ζ–‡, ζ—₯本θͺž, ν•œκ΅­μ–΄, ΰ€Ήΰ€Ώΰ€¨ΰ₯ΰ€¦ΰ₯€, Ψ§Ω„ΨΉΨ±Ψ¨ΩŠΨ©, Χ’Χ‘Χ¨Χ™Χͺ, or emoji πŸ€—. ## What is grammar ? @@ -116,6 +118,12 @@ You can use it to describe very simple but useful things, like a valid email add phone_number ::= "+" [0-9]+ ``` +You can also force it to [generate only emojis](examples/generate_emoji.py) or [generate only korean characters](examples/generate_korean.py). +``` +['Describe your feeling with emoji: πŸ™ŒπŸ™‚πŸ˜πŸ˜―πŸ˜…πŸ™πŸ™‡πŸ™ˆπŸ™ŠπŸ™‹πŸ™ƒπŸ™†πŸ™…πŸ™„πŸ™πŸ™‚πŸ™€πŸ™‰πŸ™ŽπŸ™ŠπŸ™‹πŸ™ƒπŸ™†πŸ™…πŸ™„πŸ™πŸ™‚πŸ™€πŸ™‰πŸ™ŽπŸ™ŠπŸ™‹πŸ™ƒπŸ™†', 'Write a poem with emoji: πŸ™πŸ˜πŸ™πŸ™πŸ™ŒπŸ™πŸ™πŸ™πŸ™πŸ˜πŸ˜…πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™‡πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™‹πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™'] +``` + + More details can be found in this [doc from llama-cpp](https://github.com/ggerganov/llama.cpp/tree/master/grammars) Advanced grammar debugging guide can be found [here](docs/debugging_custom_grammars.md) diff --git a/dev/HF_top_100_TGI_model_2024_01_25.json b/dev/HF_top_100_TGI_model_2024_01_25.json new file mode 100644 index 0000000..e2d9e23 --- /dev/null +++ b/dev/HF_top_100_TGI_model_2024_01_25.json @@ -0,0 +1,502 @@ +[ + { + "id": "gpt2", + "likes": 1611, + "downloads": 19783506 + }, + { + "id": "baffo32/decapoda-research-llama-7B-hf", + "likes": 20, + "downloads": 6589820 + }, + { + "id": "distilgpt2", + "likes": 310, + "downloads": 2089200 + }, + { + "id": "tiiuae/falcon-40b-instruct", + "likes": 1139, + "downloads": 1301880 + }, + { + "id": "bigscience/bloom-560m", + "likes": 304, + "downloads": 1180667 + }, + { + "id": "meta-llama/Llama-2-7b-chat-hf", + "likes": 2499, + "downloads": 772056 + }, + { + "id": "mistralai/Mistral-7B-v0.1", + "likes": 2644, + "downloads": 646365 + }, + { + "id": "teknium/OpenHermes-2-Mistral-7B", + "likes": 230, + "downloads": 636989 + }, + { + "id": 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warnings +from pprint import pprint +from collections import defaultdict +from typing import List + +from transformers import AutoTokenizer + + +def get_tokenizer_class(tokenizer): + return f"{tokenizer.__class__.__module__}.{tokenizer.__class__.__name__}" + + +def read_json(file_path): + with open(file_path, "r") as f: + return json.load(f) + + +class TokenizerDict: + def __init__(self, tokenizer_ids: List[str]): + self.unique_tokenizers = {} # Stores tokenizer hash: tokenizer object + self.hashes = set() # Keeps track of existing hashes for quick lookup + self.tokenizers = {} # Stores hash: list of tokenizer objects + self.models_by_tokenizer_class = {} + self.models_by_hash = {} + + if tokenizer_ids: + for tokenizer_id in tokenizer_ids: + if type(tokenizer_id) == str: + try: + tokenizer = AutoTokenizer.from_pretrained(tokenizer_id) + except Exception as e: + # print warning + warnings.warn(f"Can't load tokenizer for '{tokenizer_id}': {e}") + continue + else: + raise ValueError( + f"Tokenizer id should be a string, but got {type(tokenizer_id)}" + ) + self.add_tokenizer(tokenizer, model_id=tokenizer_id) + + def hash_tokenizer(self, tokenizer): + # Your hash_tokenizer function implementation here + """ + Hash the tokenizer based on the vocabulary. + Two tokenizers with the same vocabulary will have the same hash as they represent the same model. + If a tokenizer is slightly different from another, such as adding a new token, the hash will be different. + This makes sense as the two tokenizers represent different models. + c.f. https://stackoverflow.com/a/5884123/12234753 + """ + if type(tokenizer) == str: + try: + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained(tokenizer) + except OSError: + # print warning + warnings.warn(f"Can't load tokenizer for '{tokenizer}'") + return None + vocab = tokenizer.get_vocab() + frozen_vocab = frozenset(vocab.items()) + return hash(frozen_vocab) + + def add_tokenizer(self, tokenizer, model_id): + hash_value = self.hash_tokenizer(tokenizer) + if hash_value in self.hashes: + print(f"Tokenizer already exists in the collection. Skipping.") + self.tokenizers[hash_value].append(tokenizer) + self.models_by_hash[hash_value].append(model_id) + + else: + self.unique_tokenizers[hash_value] = tokenizer + self.hashes.add(hash_value) + self.models_by_hash[hash_value] = [model_id] + self.tokenizers[hash_value] = [tokenizer] + print(f"Tokenizer added to the collection.") + self.tokenizers[hash_value].append(tokenizer) + tokenizer_cls = get_tokenizer_class(tokenizer) + if tokenizer_cls not in self.models_by_tokenizer_class: + self.models_by_tokenizer_class[tokenizer_cls] = [model_id] + else: + self.models_by_tokenizer_class[tokenizer_cls].append(model_id) + + def get_tokenizer_by_hash(self, hash_value): + return self.unique_tokenizers.get(hash_value, None) + + def is_duplicate(self, tokenizer): + hash_value = self.hash_tokenizer(tokenizer) + return hash_value in self.hashes + + +if __name__ == "__main__": + + most_popular_models: dict = read_json("HF_top_100_TGI_model_2024_01_25.json") + + tokenizers = [model_info["id"] for model_info in most_popular_models] + + td = TokenizerDict(tokenizers) + + pprint(td.models_by_tokenizer_class) + + # output to yaml + import yaml + + with open("../docs/supported_models.yaml", "w") as file: + documents = yaml.dump( + td.models_by_tokenizer_class, + file, + indent=4, + sort_keys=True, + default_flow_style=False, + ) diff --git a/docs/debugging_custom_grammars.md b/docs/debugging_custom_grammars.md index 55346cb..4542206 100644 --- a/docs/debugging_custom_grammars.md +++ b/docs/debugging_custom_grammars.md @@ -87,6 +87,7 @@ If the grammar can be parsed, it means that it is syntactically correct. After you have checked that the grammar can be parsed, you can test it with a simple input to see if it can generate the expected output. We provide a simple script to do this: + ```python from transformers_cfg.parser import parse_ebnf from transformers_cfg.recognizer import GrammarRecognizer @@ -100,7 +101,7 @@ recognizer = GrammarRecognizer(parsed_grammar.grammar_encoding, start_rule_id) # Test the grammar with a simple input json_input = '{"foo": "bar", "baz": "bat"}' -is_accepted = recognizer._accept_string(json_input, recognizer.stacks) +is_accepted = recognizer._accept_prefix(json_input, recognizer.stacks) print(is_accepted) ``` @@ -108,14 +109,17 @@ If the above script returns `True`, it means that the grammar can recognize the If it returns `False`, it means that the grammar cannot recognize the input string. In this case, you need to check in which step the input string is rejected. N.B. the recognizer can accept partial input, so you can try the following: + ```python json_input = '{"foo": "bar"' -is_accepted = recognizer._accept_string(json_input, recognizer.stacks) +is_accepted = recognizer._accept_prefix(json_input, recognizer.stacks) print(is_accepted) ``` This helps you to see where the grammar fails to recognize the input string. +If you want to check if the sentence is complete or not, you can use `_accept_string` method, which returns `True` if the input string is complete and `False` otherwise. + ## DEBUG mode You can enable the DEBUG mode to see the parsing process of the input string. diff --git a/docs/troubleshoot.md b/docs/troubleshoot.md new file mode 100644 index 0000000..79c1631 --- /dev/null +++ b/docs/troubleshoot.md @@ -0,0 +1,3 @@ +# Trouble Shooting + +## The generation doesn't seem to be conforming to the json format? diff --git a/error_refactor.patch b/error_refactor.patch new file mode 100644 index 0000000..ca08aba --- /dev/null +++ b/error_refactor.patch @@ -0,0 +1,199 @@ +Subject: [PATCH] error refactor +--- +Index: tests/_tokenizer_common.py +IDEA additional info: +Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP +<+>UTF-8 +=================================================================== +diff --git a/tests/_tokenizer_common.py b/tests/_tokenizer_common.py +--- a/tests/_tokenizer_common.py (revision 3b964653167363f8a58533a5fa9ac91a55361696) ++++ b/tests/_tokenizer_common.py (date 1707474468902) +@@ -97,7 +97,7 @@ + # + # start_rule_id = parsed_grammar.symbol_table["root"] + # +- # recognizer = GrammarRecognizer(parsed_grammar.grammar_encoding, start_rule_id) ++ # recognizer = GrammarRecognizer.init_from_src(parsed_grammar.grammar_encoding, start_rule_id) + # + # self.assertTrue(recognizer._accept_string(emoji, recognizer.stacks)) + +Index: transformers_cfg/recognizer.py +IDEA additional info: +Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP +<+>UTF-8 +=================================================================== +diff --git a/transformers_cfg/recognizer.py b/transformers_cfg/recognizer.py +--- a/transformers_cfg/recognizer.py (revision 3b964653167363f8a58533a5fa9ac91a55361696) ++++ b/transformers_cfg/recognizer.py (date 1707476820625) +@@ -12,22 +12,37 @@ + + + class GrammarRecognizer: +- def __init__(self, grammar_encoding: List[int], start_rule_id: int): +- # strictly speaking, we don't need to copy grammar_encoding because we don't modify it +- # but we do it anyway to be safe +- # in case where the grammar is very large, we can consider not copying it +- self.grammar_encoding = copy.deepcopy(grammar_encoding) +- self.rule_offsets: List[int] = self.init_rules(start_rule_id) +- # each stack is a list of indices into grammar_encoding +- # each index points to a rule's +- self.stacks: List[List[int]] = self.init_stack(start_rule_id) ++ # def __init__(self, grammar_encoding: List[int], start_rule_id: int): ++ # # strictly speaking, we don't need to copy grammar_encoding because we don't modify it ++ # # but we do it anyway to be safe ++ # # in case where the grammar is very large, we can consider not copying it ++ # self.grammar_encoding = copy.deepcopy(grammar_encoding) ++ # self.rule_offsets: List[int] = self.init_rules(start_rule_id) ++ # # each stack is a list of indices into grammar_encoding ++ # # each index points to a rule's ++ # self.stacks: List[List[int]] = self.init_stack(start_rule_id) ++ ++ def __init__(self, grammar_encoding, rule_offsets: List[int], stacks: List[List[int]]): ++ self.grammar_encoding = grammar_encoding ++ self.rule_offsets = rule_offsets ++ self.stacks = stacks + +- def init_rules(self, start_rule_id: int) -> List[int]: ++ @classmethod ++ def init_from_src(cls, grammar_encoding: List[int], start_rule_id: int): ++ rule_offsets = cls.init_rules(grammar_encoding, start_rule_id) ++ import pdb; pdb.set_trace() ++ # Create an instance of cls ++ instance = cls(rule_offsets, [[]]) ++ instance.stacks = instance.init_stack(start_rule_id) ++ return instance ++ ++ @staticmethod ++ def init_rules(grammar_encoding: List[int], start_rule_id: int) -> List[int]: + _rule_offset = 0 + rule_offsets = [] + # Build `rules` as an array of rule IDs to their positions in `grammar_src` +- while self.grammar_encoding[_rule_offset] != 0xFFFF: +- rule_id = self.grammar_encoding[_rule_offset] ++ while grammar_encoding[_rule_offset] != 0xFFFF: ++ rule_id = grammar_encoding[_rule_offset] + # store the offset idx + if len(rule_offsets) <= rule_id: + rule_offsets.extend([-1] * (rule_id - len(rule_offsets) + 1)) +@@ -38,16 +53,16 @@ + simple_rhs_offset = _rule_offset + 1 + + # Skip rule alternates +- while self.grammar_encoding[simple_rhs_offset] != END_OF_RULE_MARKER: ++ while grammar_encoding[simple_rhs_offset] != END_OF_RULE_MARKER: + simple_rhs_offset = ( +- simple_rhs_offset + 1 + self.grammar_encoding[simple_rhs_offset] ++ simple_rhs_offset + 1 + grammar_encoding[simple_rhs_offset] + ) + + # Skip 0 denoting end of rule + # _rule_offset += 1 + _rule_offset = simple_rhs_offset + 1 + +- retrieved_start_rule_id = self.grammar_encoding[rule_offsets[start_rule_id]] ++ retrieved_start_rule_id = grammar_encoding[rule_offsets[start_rule_id]] + assert retrieved_start_rule_id == start_rule_id + + return rule_offsets +@@ -56,7 +71,7 @@ + + stacks = [] + # Loop over alternates of start rule to build initial stacks +- sub_rhs_offset = self.rule_offsets[start_rule_id] + 1 ++ sub_rhs_offset = self.grammar_encoding[start_rule_id] + 1 + while self.grammar_encoding[sub_rhs_offset]: + stack: List[int] = [] + # If alternate is nonempty, add to stack +@@ -83,7 +98,7 @@ + else: + ref_rule_id = self.grammar_encoding[cur_element_offset + 1] + # find the offset of the referenced rule +- ref_subrule_offset = self.rule_offsets[ref_rule_id] + 1 ++ ref_subrule_offset = self.grammar_encoding[ref_rule_id] + 1 + new_stacks: List[List[int]] = [] + # Loop over alternates of referenced rule to build new stacks + while self.grammar_encoding[ref_subrule_offset] != END_OF_RULE_MARKER: +@@ -214,7 +229,7 @@ + print(f"symbol_ids: \n{parsed_grammar.symbol_table}") + + start_rule_id = parsed_grammar.symbol_table["root"] +- recognizer = GrammarRecognizer(parsed_grammar.grammar_encoding, start_rule_id) ++ recognizer = GrammarRecognizer.init_from_src(parsed_grammar.grammar_encoding, start_rule_id) + res = recognizer._accept_string("12222", recognizer.stacks) + print(f"12222: {res}") + res = recognizer._accept_string("12222+", recognizer.stacks) +Index: tests/test_json_arr.py +IDEA additional info: +Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP +<+>UTF-8 +=================================================================== +diff --git a/tests/test_json_arr.py b/tests/test_json_arr.py +--- a/tests/test_json_arr.py (revision 3b964653167363f8a58533a5fa9ac91a55361696) ++++ b/tests/test_json_arr.py (date 1707474468922) +@@ -28,7 +28,7 @@ + + start_rule_id = parsed_grammar.symbol_table["root"] + +- recognizer = GrammarRecognizer(parsed_grammar.grammar_encoding, start_rule_id) ++ recognizer = GrammarRecognizer.init_from_src(parsed_grammar.grammar_encoding, start_rule_id) + + for json in jsons: + self.assertEqual( +Index: transformers_cfg/token_grammar_recognizer.py +IDEA additional info: +Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP +<+>UTF-8 +=================================================================== +diff --git a/transformers_cfg/token_grammar_recognizer.py b/transformers_cfg/token_grammar_recognizer.py +--- a/transformers_cfg/token_grammar_recognizer.py (revision 3b964653167363f8a58533a5fa9ac91a55361696) ++++ b/transformers_cfg/token_grammar_recognizer.py (date 1707474468914) +@@ -66,7 +66,7 @@ + assert len(self.mapping) == len( + self.token_trie + ), f"{len(self.mapping)}, {len(self.token_trie)}" +- self.grammar = GrammarRecognizer(grammar_encoding, self.start_rule_id) ++ self.grammar = GrammarRecognizer.init_from_src(grammar_encoding, self.start_rule_id) + + def _consume_token_id(self, token_id: int, stacks: List[List[int]]): + if self.grammar._must_stop(stacks): +Index: tests/test_unicode.py +IDEA additional info: +Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP +<+>UTF-8 +=================================================================== +diff --git a/tests/test_unicode.py b/tests/test_unicode.py +--- a/tests/test_unicode.py (revision 3b964653167363f8a58533a5fa9ac91a55361696) ++++ b/tests/test_unicode.py (date 1707474468926) +@@ -24,7 +24,7 @@ + + start_rule_id = parsed_grammar.symbol_table["root"] + +- recognizer = GrammarRecognizer(parsed_grammar.grammar_encoding, start_rule_id) ++ recognizer = GrammarRecognizer.init_from_src(parsed_grammar.grammar_encoding, start_rule_id) + + self.assertTrue(recognizer._accept_string(japanese, recognizer.stacks)) + +@@ -40,6 +40,6 @@ + + start_rule_id = parsed_grammar.symbol_table["root"] + +- recognizer = GrammarRecognizer(parsed_grammar.grammar_encoding, start_rule_id) ++ recognizer = GrammarRecognizer.init_from_src(parsed_grammar.grammar_encoding, start_rule_id) + + self.assertTrue(recognizer._accept_string(emoji, recognizer.stacks)) +Index: tests/test_json.py +IDEA additional info: +Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP +<+>UTF-8 +=================================================================== +diff --git a/tests/test_json.py b/tests/test_json.py +--- a/tests/test_json.py (revision 3b964653167363f8a58533a5fa9ac91a55361696) ++++ b/tests/test_json.py (date 1707474468922) +@@ -42,7 +42,7 @@ + + start_rule_id = parsed_grammar.symbol_table["root"] + +- self.recognizer = GrammarRecognizer( ++ self.recognizer = GrammarRecognizer.init_from_src( + parsed_grammar.grammar_encoding, start_rule_id + ) diff --git a/examples/generate_arabic.py b/examples/generate_arabic.py new file mode 100644 index 0000000..9ec06aa --- /dev/null +++ b/examples/generate_arabic.py @@ -0,0 +1,49 @@ +import cProfile +import pstats +import io + +from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers_cfg.grammar_utils import IncrementalGrammarConstraint +from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor + +import logging + +logging.basicConfig(level=logging.DEBUG) + + +def main(): + + # Load model and tokenizer + tokenizer = AutoTokenizer.from_pretrained("gpt2") # JackFram/llama-68m" + tokenizer.pad_token = tokenizer.eos_token + model = AutoModelForCausalLM.from_pretrained("gpt2") # Load model to defined device + + # Load grammar + with open("examples/grammars/arabic.ebnf", "r") as file: + grammar_str = file.read() + grammar = IncrementalGrammarConstraint(grammar_str, "root", tokenizer, unicode=True) + grammar_processor = GrammarConstrainedLogitsProcessor(grammar) + + # Generate + prefix1 = "English: coffee, Arabic: " + prefix2 = "English: dog, Arabic: " + input_ids = tokenizer( + [prefix1, prefix2], add_special_tokens=False, return_tensors="pt", padding=True + )["input_ids"] + + output = model.generate( + input_ids, + do_sample=False, + max_new_tokens=20, + logits_processor=[grammar_processor], + repetition_penalty=1.1, + num_return_sequences=1, + ) + # decode output + generations = tokenizer.batch_decode(output, skip_special_tokens=True) + print(output) + print(generations) + + +if __name__ == "__main__": + main() diff --git a/examples/generate_cIE.py b/examples/generate_cIE.py index f889f21..ef10255 100644 --- a/examples/generate_cIE.py +++ b/examples/generate_cIE.py @@ -10,16 +10,12 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") - model_id = "mistralai/Mistral-7B-v0.1" - # Load model and tokenizer - tokenizer = AutoTokenizer.from_pretrained(model_id) + tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer.pad_token = tokenizer.eos_token - - model = AutoModelForCausalLM.from_pretrained(model_id).to( + model = AutoModelForCausalLM.from_pretrained("gpt2").to( device ) # Load model to defined device - model.generation_config.pad_token_id = model.generation_config.eos_token_id # Load grammar with open("examples/grammars/cIE.ebnf", "r") as file: @@ -28,9 +24,10 @@ grammar_processor = GrammarConstrainedLogitsProcessor(grammar) # Generate - prefix1 = "This is a mock prompt for closed information extraction" + prefix1 = "This is a valid json string for http request:" + prefix2 = "This is a valid json string for shopping cart:" input_ids = tokenizer( - [prefix1], add_special_tokens=False, return_tensors="pt", padding=True + [prefix1, prefix2], add_special_tokens=False, return_tensors="pt", padding=True )["input_ids"].to( device ) # Move input_ids to the same device as model @@ -48,5 +45,6 @@ print(generations) """ - [s] entity1 [r] relation2 [o] entity3 [e] [s] entity4 [r] relation4 [o] entity4 [e] [ + 'This is a valid json string for http request:{ "request": { "method": "GET", "headers": [], "content": "Content","type": "application" }} + 'This is a valid json string for shopping cart:This is a valid json string for shopping cart:{ "name": "MyCart", "price": 0, "value": 1 } """ diff --git a/examples/generate_c_code.py b/examples/generate_c_code.py new file mode 100644 index 0000000..36a5d5d --- /dev/null +++ b/examples/generate_c_code.py @@ -0,0 +1,58 @@ +import cProfile +import pstats +import io +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers_cfg.grammar_utils import IncrementalGrammarConstraint +from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor + +############################################################ +# +# use llama to generate C code +# +############################################################ + + +def main(): + global device, tokenizer, tokenizer + # Detect if GPU is available, otherwise use CPU + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + # Load model and tokenizer + tokenizer = AutoTokenizer.from_pretrained("gpt2-large") + tokenizer.pad_token = tokenizer.eos_token + model = AutoModelForCausalLM.from_pretrained("gpt2-large").to( + device + ) # Load model to defined device + # Load grammar + with open("examples/grammars/c.ebnf", "r") as file: + grammar_str = file.read() + grammar = IncrementalGrammarConstraint(grammar_str, "root", tokenizer) + grammar_processor = GrammarConstrainedLogitsProcessor(grammar) + # Generate + prefix1 = "#include \n" + input_ids = tokenizer( + [prefix1], add_special_tokens=False, return_tensors="pt", padding=True + )["input_ids"].to( + device + ) # Move input_ids to the same device as model + output = model.generate( + input_ids, + do_sample=False, + max_new_tokens=20, + logits_processor=[grammar_processor], + repetition_penalty=3.0, + num_return_sequences=1, + ) + # decode output + generations = tokenizer.batch_decode(output, skip_special_tokens=True) + print(generations) + print(output) + """ + #include + int thresh_f(int n){return (1-threshold);} + """ + + +if __name__ == "__main__": + main() diff --git a/examples/generate_chinese.py b/examples/generate_chinese.py new file mode 100644 index 0000000..8ba1481 --- /dev/null +++ b/examples/generate_chinese.py @@ -0,0 +1,49 @@ +import cProfile +import pstats +import io + +from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers_cfg.grammar_utils import IncrementalGrammarConstraint +from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor + +import logging + +logging.basicConfig(level=logging.DEBUG) + + +def main(): + + # Load model and tokenizer + tokenizer = AutoTokenizer.from_pretrained("gpt2") # JackFram/llama-68m" + tokenizer.pad_token = tokenizer.eos_token + model = AutoModelForCausalLM.from_pretrained("gpt2") # Load model to defined device + + # Load grammar + with open("examples/grammars/chinese.ebnf", "r") as file: + grammar_str = file.read() + grammar = IncrementalGrammarConstraint(grammar_str, "root", tokenizer, unicode=True) + grammar_processor = GrammarConstrainedLogitsProcessor(grammar) + + # Generate + prefix1 = "English: coffee, Chinese: " + prefix2 = "English: dog, Chinese: " + input_ids = tokenizer( + [prefix1, prefix2], add_special_tokens=False, return_tensors="pt", padding=True + )["input_ids"] + + output = model.generate( + input_ids, + do_sample=False, + max_new_tokens=20, + logits_processor=[grammar_processor], + repetition_penalty=1.1, + num_return_sequences=1, + ) + # decode output + generations = tokenizer.batch_decode(output, skip_special_tokens=True) + print(output) + print(generations) + + +if __name__ == "__main__": + main() diff --git a/examples/generate_emoji.py b/examples/generate_emoji.py new file mode 100644 index 0000000..6257d87 --- /dev/null +++ b/examples/generate_emoji.py @@ -0,0 +1,54 @@ +import cProfile +import pstats +import io + +from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers_cfg.grammar_utils import IncrementalGrammarConstraint +from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor + +import logging + +logging.basicConfig(level=logging.DEBUG) + + +def main(): + + # Load model and tokenizer + tokenizer = AutoTokenizer.from_pretrained("gpt2") # JackFram/llama-68m" + tokenizer.pad_token = tokenizer.eos_token + model = AutoModelForCausalLM.from_pretrained("gpt2") # Load model to defined device + + # Load grammar + with open("examples/grammars/emoji.ebnf", "r") as file: + grammar_str = file.read() + grammar = IncrementalGrammarConstraint(grammar_str, "root", tokenizer, unicode=True) + grammar_processor = GrammarConstrainedLogitsProcessor(grammar) + + # Generate + prefix1 = "Describe your feeling with emoji: " + prefix2 = "Write a poem with emoji: " + input_ids = tokenizer( + [prefix1, prefix2], add_special_tokens=False, return_tensors="pt", padding=True + )["input_ids"] + + output = model.generate( + input_ids, + do_sample=False, + max_new_tokens=100, + logits_processor=[grammar_processor], + repetition_penalty=1.1, + num_return_sequences=1, + ) + # decode output + generations = tokenizer.batch_decode(output, skip_special_tokens=True) + print(output) + print(generations) + + """ + ['Describe your feeling with emoji: πŸ™ŒπŸ™‚πŸ˜πŸ˜―πŸ˜…πŸ™πŸ™‡πŸ™ˆπŸ™ŠπŸ™‹πŸ™ƒπŸ™†πŸ™…πŸ™„πŸ™πŸ™‚πŸ™€πŸ™‰πŸ™ŽπŸ™ŠπŸ™‹πŸ™ƒπŸ™†πŸ™…πŸ™„πŸ™πŸ™‚πŸ™€πŸ™‰πŸ™ŽπŸ™ŠπŸ™‹πŸ™ƒπŸ™†', 'Write a poem with emoji: πŸ™πŸ˜πŸ™πŸ™πŸ™ŒπŸ™πŸ™πŸ™πŸ™πŸ˜πŸ˜…πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™‡πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™‹πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™'] + + """ + + +if __name__ == "__main__": + main() diff --git a/examples/generate_japanese.py b/examples/generate_japanese.py new file mode 100644 index 0000000..3daf833 --- /dev/null +++ b/examples/generate_japanese.py @@ -0,0 +1,49 @@ +import cProfile +import pstats +import io + +from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers_cfg.grammar_utils import IncrementalGrammarConstraint +from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor + +import logging + +logging.basicConfig(level=logging.DEBUG) + + +def main(): + + # Load model and tokenizer + tokenizer = AutoTokenizer.from_pretrained("gpt2") # JackFram/llama-68m" + tokenizer.pad_token = tokenizer.eos_token + model = AutoModelForCausalLM.from_pretrained("gpt2") # Load model to defined device + + # Load grammar + with open("examples/grammars/japanese.ebnf", "r") as file: + grammar_str = file.read() + grammar = IncrementalGrammarConstraint(grammar_str, "root", tokenizer, unicode=True) + grammar_processor = GrammarConstrainedLogitsProcessor(grammar) + + # Generate + prefix1 = "English: coffee, Japanese: " + prefix2 = "English: dog, Japanese: " + input_ids = tokenizer( + [prefix1, prefix2], add_special_tokens=False, return_tensors="pt", padding=True + )["input_ids"] + + output = model.generate( + input_ids, + do_sample=False, + max_new_tokens=20, + logits_processor=[grammar_processor], + repetition_penalty=1.1, + num_return_sequences=1, + ) + # decode output + generations = tokenizer.batch_decode(output, skip_special_tokens=True) + print(output) + print(generations) + + +if __name__ == "__main__": + main() diff --git a/examples/generate_json.py b/examples/generate_json.py index 1f0fb4b..6aeeb15 100644 --- a/examples/generate_json.py +++ b/examples/generate_json.py @@ -10,7 +10,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") - model_id = "mistralai/Mistral-7B-v0.1" + model_id = "gpt2" # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id) diff --git a/examples/generate_json_array.py b/examples/generate_json_array.py index 5d7b7b7..70fe4cc 100644 --- a/examples/generate_json_array.py +++ b/examples/generate_json_array.py @@ -9,16 +9,12 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") - model_id = "mistralai/Mistral-7B-v0.1" - # Load model and tokenizer - tokenizer = AutoTokenizer.from_pretrained(model_id) + tokenizer = AutoTokenizer.from_pretrained("gpt2") tokenizer.pad_token = tokenizer.eos_token - - model = AutoModelForCausalLM.from_pretrained(model_id).to( + model = AutoModelForCausalLM.from_pretrained("gpt2").to( device ) # Load model to defined device - model.generation_config.pad_token_id = model.generation_config.eos_token_id # Load grammar with open("examples/grammars/json_arr.ebnf", "r") as file: @@ -28,7 +24,7 @@ # Generate prefix1 = "This is a valid json array for student records:" - prefix2 = "This is a valid json array for visiting records:" + prefix2 = "This is a valid json array for shopping cart:" input_ids = tokenizer( [prefix1, prefix2], add_special_tokens=False, return_tensors="pt", padding=True )["input_ids"].to( diff --git a/examples/generate_korean.py b/examples/generate_korean.py new file mode 100644 index 0000000..72c9dc1 --- /dev/null +++ b/examples/generate_korean.py @@ -0,0 +1,49 @@ +import cProfile +import pstats +import io + +from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers_cfg.grammar_utils import IncrementalGrammarConstraint +from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor + +import logging + +logging.basicConfig(level=logging.DEBUG) + + +def main(): + + # Load model and tokenizer + tokenizer = AutoTokenizer.from_pretrained("gpt2") # JackFram/llama-68m" + tokenizer.pad_token = tokenizer.eos_token + model = AutoModelForCausalLM.from_pretrained("gpt2") # Load model to defined device + + # Load grammar + with open("examples/grammars/korean.ebnf", "r") as file: + grammar_str = file.read() + grammar = IncrementalGrammarConstraint(grammar_str, "root", tokenizer, unicode=True) + grammar_processor = GrammarConstrainedLogitsProcessor(grammar) + + # Generate + prefix1 = "English: coffee, Korean: " + prefix2 = "English: dog, Korean: " + input_ids = tokenizer( + [prefix1, prefix2], add_special_tokens=False, return_tensors="pt", padding=True + )["input_ids"] + + output = model.generate( + input_ids, + do_sample=False, + max_new_tokens=20, + logits_processor=[grammar_processor], + repetition_penalty=1.1, + num_return_sequences=1, + ) + # decode output + generations = tokenizer.batch_decode(output, skip_special_tokens=True) + print(output) + print(generations) + + +if __name__ == "__main__": + main() diff --git a/examples/generate_russian.py b/examples/generate_russian.py new file mode 100644 index 0000000..5c486b8 --- /dev/null +++ b/examples/generate_russian.py @@ -0,0 +1,49 @@ +import cProfile +import pstats +import io + +from transformers import AutoModelForCausalLM, AutoTokenizer +from transformers_cfg.grammar_utils import IncrementalGrammarConstraint +from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor + +import logging + +logging.basicConfig(level=logging.DEBUG) + + +def main(): + + # Load model and tokenizer + tokenizer = AutoTokenizer.from_pretrained("gpt2") # JackFram/llama-68m" + tokenizer.pad_token = tokenizer.eos_token + model = AutoModelForCausalLM.from_pretrained("gpt2") # Load model to defined device + + # Load grammar + with open("examples/grammars/russian.ebnf", "r") as file: + grammar_str = file.read() + grammar = IncrementalGrammarConstraint(grammar_str, "root", tokenizer, unicode=True) + grammar_processor = GrammarConstrainedLogitsProcessor(grammar) + + # Generate + prefix1 = "English: coffee, Russian: " + prefix2 = "English: dog, Russian: " + input_ids = tokenizer( + [prefix1, prefix2], add_special_tokens=False, return_tensors="pt", padding=True + )["input_ids"] + + output = model.generate( + input_ids, + do_sample=False, + max_new_tokens=20, + logits_processor=[grammar_processor], + repetition_penalty=1.1, + num_return_sequences=1, + ) + # decode output + generations = tokenizer.batch_decode(output, skip_special_tokens=True) + print(output) + print(generations) + + +if __name__ == "__main__": + main() diff --git a/examples/grammars/arabic.ebnf b/examples/grammars/arabic.ebnf new file mode 100644 index 0000000..e9fb03f --- /dev/null +++ b/examples/grammars/arabic.ebnf @@ -0,0 +1,5 @@ +# A probably incorrect grammar for Arabic text +root ::= ar-char+ ([ \t\n] ar-char+)* +ar-char ::= arabic | punctuation +arabic ::= [Ω -Ω©] +punctuation ::= [،-Ϋ”] diff --git a/examples/grammars/balanced_parentheses.ebnf b/examples/grammars/balanced_parentheses.ebnf new file mode 100644 index 0000000..66f7043 --- /dev/null +++ b/examples/grammars/balanced_parentheses.ebnf @@ -0,0 +1 @@ +root ::= "(" root ")" | "" diff --git a/examples/grammars/chinese.ebnf b/examples/grammars/chinese.ebnf new file mode 100644 index 0000000..4ba1a4e --- /dev/null +++ b/examples/grammars/chinese.ebnf @@ -0,0 +1,5 @@ +# An overly simplified grammar for Chinese text +root ::= cn-char+ ([ \t\n] cn-char+)* +cn-char ::= chinese | punctuation +chinese ::= [δΈ€-ιΏΏ] +punctuation ::= [、-γ€Ύ] | [!?.,;:()"'`] diff --git a/examples/grammars/json_minimal.ebnf b/examples/grammars/json_minimal.ebnf new file mode 100644 index 0000000..71b22f4 --- /dev/null +++ b/examples/grammars/json_minimal.ebnf @@ -0,0 +1,16 @@ + + +root ::= object + +object ::= "{" ws ( string ":" ws value ("," ws string ":" ws value)* )? "}" + +value ::= object | array | string | number | ("true" | "false" | "null") ws + +array ::= "[" ws ( value ("," ws value)* )? "]" ws + +string ::= "\"" [a-zA-Z0-9]* "\"" ws + +number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws + + +ws ::= ([ \t\n] ws)? diff --git a/examples/grammars/korean.ebnf b/examples/grammars/korean.ebnf index b3ab65c..c119ca8 100644 --- a/examples/grammars/korean.ebnf +++ b/examples/grammars/korean.ebnf @@ -1,3 +1,4 @@ +# An overly simplified grammar for Korean root ::= kr-char+ ([ \t\n] kr-char+)* kr-char ::= hangul | hanja | punctuation hangul ::= [κ°€-힣] diff --git a/examples/grammars/russian.ebnf b/examples/grammars/russian.ebnf new file mode 100644 index 0000000..cd030cd --- /dev/null +++ b/examples/grammars/russian.ebnf @@ -0,0 +1,5 @@ +# An overly simplified grammar for Russian text +root ::= ru-char+ ([ \t\n] ru-char+)* +ru-char ::= cyrillic | punctuation +cyrillic ::= [А-Π―Π°-я] +punctuation ::= [、-γ€Ύ] | [!?.,;:()"'`] diff --git a/generate_japanese.patch b/generate_japanese.patch new file mode 100644 index 0000000..9d42a61 --- /dev/null +++ b/generate_japanese.patch @@ -0,0 +1,56 @@ +Subject: [PATCH] generate japanese +--- +Index: examples/generate_japanese.py +IDEA additional info: +Subsystem: com.intellij.openapi.diff.impl.patch.CharsetEP +<+>UTF-8 +=================================================================== +diff --git a/examples/generate_japanese.py b/examples/generate_japanese.py +new file mode 100644 +--- /dev/null (date 1707473435097) ++++ b/examples/generate_japanese.py (date 1707473435097) +@@ -0,0 +1,44 @@ ++from transformers import AutoModelForCausalLM, AutoTokenizer ++from transformers_cfg.grammar_utils import IncrementalGrammarConstraint ++from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor ++ ++import logging ++logging.basicConfig(level=logging.DEBUG) ++ ++ ++if __name__ == "__main__": ++ ++ # Load model and tokenizer ++ tokenizer = AutoTokenizer.from_pretrained("JackFram/llama-68m") ++ tokenizer.pad_token = tokenizer.eos_token ++ model = AutoModelForCausalLM.from_pretrained("JackFram/llama-68m") ++ ++ # Load grammar ++ with open("examples/grammars/japanese.ebnf", "r") as file: ++ grammar_str = file.read() ++ grammar = IncrementalGrammarConstraint(grammar_str, "root", tokenizer) ++ grammar_processor = GrammarConstrainedLogitsProcessor(grammar) ++ ++ # Generate ++ prefix1 = "Speak Japanese:" ++ prefix2 = "My name is Jack, what is my name in Japanese?" ++ input_ids = tokenizer( ++ [prefix1, prefix2], add_special_tokens=False, return_tensors="pt", padding=True ++ )["input_ids"] ++ ++ output = model.generate( ++ input_ids, ++ do_sample=False, ++ max_new_tokens=60, ++ logits_processor=[grammar_processor], ++ repetition_penalty=1.1, ++ num_return_sequences=1, ++ ) ++ # decode output ++ generations = tokenizer.batch_decode(output, skip_special_tokens=True) ++ print(generations) ++ ++ """ ++ 'This is a valid json string for http request:{ "request": { "method": "GET", "headers": [], "content": "Content","type": "application" }} ++ 'This is a valid json string for shopping cart:This is a valid json string for shopping cart:{ "name": "MyCart", "price": 0, "value": 1 } ++ 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0.000 0.000 0.000 /home/saibo/.virtualenvs/python3.8/transformers-CFG-dev-edit/lib/python3.8/site-packages/sympy/polys/polyerrors.py:151(MultivariatePolynomialError) + 1 0.000 0.000 0.000 0.000 /home/saibo/.virtualenvs/python3.8/transformers-CFG-dev-edit/lib/python3.8/site-packages/torch/_numpy/_dtypes.py:63(complexfloating) + 1 0.000 0.000 0.000 0.000 /home/saibo/.virtualenvs/python3.8/transformers-CFG-dev-edit/lib/python3.8/site-packages/regex/_regex_core.py:1894(has_simple_start) diff --git a/tests/_tokenizer_common.py b/tests/_tokenizer_common.py index 09b0b97..96453f3 100644 --- a/tests/_tokenizer_common.py +++ b/tests/_tokenizer_common.py @@ -2,9 +2,9 @@ import warnings from transformers import PreTrainedTokenizer -from transformers_cfg.token_grammar_recognizer import IncrementalTokenGrammarRecognizer +from transformers_cfg.token_grammar_recognizer import IncrementalTokenRecognizer -from transformers_cfg.recognizer import GrammarRecognizer +from transformers_cfg.recognizer import StringRecognizer from transformers_cfg.parser import parse_ebnf from transformers_cfg.utils import pprint_token_ids @@ -42,7 +42,7 @@ def test_json_parsable(self): # Test that we can load a JSON object with open("examples/grammars/json.ebnf", "r") as file: input_text = file.read() - JsontokenRecognizer = IncrementalTokenGrammarRecognizer( + JsontokenRecognizer = IncrementalTokenRecognizer( grammar_str=input_text, start_rule_name="root", tokenizer=self.tokenizer ) @@ -58,11 +58,50 @@ def test_json_parsable(self): ) return - stacks = JsontokenRecognizer._consume_token_ids( - token_ids, JsontokenRecognizer.grammar.stacks, as_string=False + acc_state = JsontokenRecognizer._consume_token_ids(token_ids, as_string=False) + # the json object is complete, so the stacks should be empty + self.assertTrue( + acc_state.stacks == [] or acc_state.stacks == [[]], + f"stacks: {acc_state.stacks}, not empty", + ) + + def test_balanced_parentheses(self): + # Test that we can recognize a balanced parentheses + with open("examples/grammars/balanced_parentheses.ebnf", "r") as file: + input_text = file.read() + recognizer = IncrementalTokenRecognizer( + grammar_str=input_text, start_rule_name="root", tokenizer=self.tokenizer ) + + balanced_parentheses = "((((((((()))))))))" + token_ids = self.tokenizer.encode(balanced_parentheses) + pprint_token_ids(self.tokenizer, token_ids) + + # check if there is unk token + for token_id in token_ids: + if token_id == self.tokenizer.unk_token_id: + warnings.warn( + f"unk token found in input_token_ids: {token_ids}, skipping test" + ) + return + + accept_state = recognizer._consume_token_ids(token_ids, as_string=False) # the json object is complete, so the stacks should be empty - self.assertTrue(stacks == [] or stacks == [[]], f"stacks: {stacks}, not empty") + self.assertTrue( + accept_state.stacks == [] or accept_state.stacks == [[]], + f"stacks: {accept_state.stacks}, not empty", + ) + + # inbalanced_parentheses = "((((((((()))))))))))))" + # token_ids = self.tokenizer.encode(inbalanced_parentheses) + # pprint_token_ids(self.tokenizer, token_ids) + # + # # check if there is unk token + # stacks = recognizer._consume_token_ids( + # token_ids, recognizer.grammar.stacks, as_string=False + # ) + # + # self.assertTrue(stacks != [] and stacks != [[]], f"stacks: {stacks}, empty") @unittest.skip("Not implemented") def test_emoji(self): @@ -73,7 +112,7 @@ def test_emoji(self): with open("examples/grammars/emoji.ebnf", "r") as file: input_text = file.read() - JsontokenRecognizer = IncrementalTokenGrammarRecognizer( + JsontokenRecognizer = IncrementalTokenRecognizer( grammar_str=input_text, start_rule_name="root", tokenizer=self.tokenizer ) @@ -90,7 +129,7 @@ def test_emoji(self): return stacks = JsontokenRecognizer._consume_token_ids( - token_ids, JsontokenRecognizer.grammar.stacks, as_string=False + token_ids, JsontokenRecognizer.string_recognizer.stacks, as_string=False ) # parsed_grammar = parse_ebnf(input_text) diff --git a/tests/test_grammar_constrained_decoding.py b/tests/test_grammar_constrained_decoding.py index 889d3d9..8b7eb27 100644 --- a/tests/test_grammar_constrained_decoding.py +++ b/tests/test_grammar_constrained_decoding.py @@ -1,7 +1,8 @@ +import unittest from unittest import TestCase from transformers import AutoModelForCausalLM, AutoTokenizer -from transformers_cfg.token_grammar_recognizer import IncrementalTokenGrammarRecognizer +from transformers_cfg.token_grammar_recognizer import IncrementalTokenRecognizer from transformers_cfg.generation import GrammarConstrainedLogitsProcessor MODEL_IDS = [ @@ -25,7 +26,7 @@ def test_grammar_constrained_decoding_greedy_w_number_grammar(self): tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.pad_token = tokenizer.eos_token - grammar = IncrementalTokenGrammarRecognizer( + grammar = IncrementalTokenRecognizer( grammar_str, start_rule_name="root", tokenizer=tokenizer ) grammar_processor = GrammarConstrainedLogitsProcessor(grammar) @@ -72,7 +73,7 @@ def test_grammar_constrained_decoding_greedy_w_balanced_parenthesis_grammar(self tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.pad_token = tokenizer.eos_token - grammar = IncrementalTokenGrammarRecognizer( + grammar = IncrementalTokenRecognizer( grammar_str, start_rule_name="root", tokenizer=tokenizer ) grammar_processor = GrammarConstrainedLogitsProcessor(grammar) @@ -98,8 +99,6 @@ def test_grammar_constrained_decoding_greedy_w_balanced_parenthesis_grammar(self early_stopping=True, ) - # generations = tokenizer.batch_decode(output, skip_special_tokens=True) - generation: str = tokenizer.batch_decode( output[:, input_ids.shape[1] :], skip_special_tokens=True )[0] @@ -123,6 +122,21 @@ def check_parentheses(generation): f"generations: {generation} is not a balanced parenthesis", ) + # raise correct error when the input is not a valid sequence + tentative = "((())" + input_ids = tokenizer( + [tentative], add_special_tokens=True, return_tensors="pt", padding=True + )["input_ids"] + + tokenRecognizer = IncrementalTokenRecognizer( + grammar_str=grammar_str, start_rule_name="root", tokenizer=tokenizer + ) + + accept_state = tokenRecognizer._consume_token_ids( + input_ids[0], as_string=False + ) + # generations = tokenizer.batch_decode(output, skip_special_tokens=True) + # # diff --git a/tests/test_hf_generation/test_repetition_penalty.py b/tests/test_hf_generation/test_repetition_penalty.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_mapping.py b/tests/test_mapping.py new file mode 100644 index 0000000..4dad869 --- /dev/null +++ b/tests/test_mapping.py @@ -0,0 +1,2 @@ +import unittest +from transformers_cfg.mapping import get_mapping diff --git a/tests/test_parsing/__init__.py b/tests/test_parsing/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_parsing.py b/tests/test_parsing/test_parsing.py similarity index 100% rename from tests/test_parsing.py rename to tests/test_parsing/test_parsing.py diff --git a/tests/test_string_recognizer/__init__.py b/tests/test_string_recognizer/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_decode_utf8.py b/tests/test_string_recognizer/test_decode_utf8.py similarity index 100% rename from tests/test_decode_utf8.py rename to tests/test_string_recognizer/test_decode_utf8.py diff --git a/tests/test_json.py b/tests/test_string_recognizer/test_json.py similarity index 80% rename from tests/test_json.py rename to tests/test_string_recognizer/test_json.py index e985dc2..769cd0d 100644 --- a/tests/test_json.py +++ b/tests/test_string_recognizer/test_json.py @@ -4,7 +4,7 @@ import json import logging from transformers_cfg.parser import parse_ebnf -from transformers_cfg.recognizer import GrammarRecognizer +from transformers_cfg.recognizer import StringRecognizer, AcceptState from tests.json_utils import is_json_parsable @@ -41,7 +41,7 @@ def setUp(self): start_rule_id = parsed_grammar.symbol_table["root"] - self.recognizer = GrammarRecognizer( + self.recognizer = StringRecognizer( parsed_grammar.grammar_encoding, start_rule_id ) @@ -51,16 +51,29 @@ def test_minimal_json_object(self): """ json = '{"foo": "bar", "baz": "bat"}' + # accept_state = AcceptState.empty_state() + self.assertEqual( is_json_parsable(json), - self.recognizer._accept_string(json, self.recognizer.stacks), + self.recognizer._accept_prefix(json), ) + self.assertEqual( + is_json_parsable(json), + self.recognizer._accept_string(json), + ) + + prefix_json = json[: len(json) // 2] + self.assertTrue(self.recognizer._accept_prefix(prefix_json)) + + self.assertFalse(self.recognizer._accept_string(prefix_json)) + def test_systematic_examples(self): for name, json_object in json_examples.items(): + # accept_state = AcceptState.empty_state() self.assertEqual( is_json_parsable(json_object), - self.recognizer._accept_string(json_object, self.recognizer.stacks), + self.recognizer._accept_prefix(json_object), msg=f"Failed on {name}, {json_object}", ) diff --git a/tests/test_json_arr.py b/tests/test_string_recognizer/test_json_arr.py similarity index 76% rename from tests/test_json_arr.py rename to tests/test_string_recognizer/test_json_arr.py index 6750dc9..3196795 100644 --- a/tests/test_json_arr.py +++ b/tests/test_string_recognizer/test_json_arr.py @@ -7,7 +7,7 @@ from tests.json_utils import is_json_parsable from transformers_cfg.parser import parse_ebnf -from transformers_cfg.recognizer import GrammarRecognizer +from transformers_cfg.recognizer import StringRecognizer, AcceptState class TestJsonArray(TestCase): @@ -28,11 +28,12 @@ def test_minimal_json_array(self): start_rule_id = parsed_grammar.symbol_table["root"] - recognizer = GrammarRecognizer(parsed_grammar.grammar_encoding, start_rule_id) + recognizer = StringRecognizer(parsed_grammar.grammar_encoding, start_rule_id) for json in jsons: + # accept_state = AcceptState.empty_state() self.assertEqual( is_json_parsable(json), - recognizer._accept_string(json, recognizer.stacks), + recognizer._accept_prefix(json), f"Failed on {json}", ) diff --git a/tests/test_unicode.py b/tests/test_string_recognizer/test_unicode.py similarity index 67% rename from tests/test_unicode.py rename to tests/test_string_recognizer/test_unicode.py index 27eb303..1e7b023 100644 --- a/tests/test_unicode.py +++ b/tests/test_string_recognizer/test_unicode.py @@ -1,15 +1,13 @@ import unittest from unittest import TestCase -from transformers_cfg.recognizer import GrammarRecognizer +from transformers_cfg.recognizer import StringRecognizer, AcceptState from transformers_cfg.parser import parse_ebnf from tests.json_utils import is_json_parsable import logging -logging.basicConfig(level=logging.DEBUG) - class TestUnicode(TestCase): def test_accept_japanese(self): @@ -24,9 +22,11 @@ def test_accept_japanese(self): start_rule_id = parsed_grammar.symbol_table["root"] - recognizer = GrammarRecognizer(parsed_grammar.grammar_encoding, start_rule_id) + recognizer = StringRecognizer(parsed_grammar.grammar_encoding, start_rule_id) + + # accept_state = AcceptState.empty_state() - self.assertTrue(recognizer._accept_string(japanese, recognizer.stacks)) + self.assertTrue(recognizer._accept_prefix(japanese)) def test_emoji(self): """ @@ -40,6 +40,6 @@ def test_emoji(self): start_rule_id = parsed_grammar.symbol_table["root"] - recognizer = GrammarRecognizer(parsed_grammar.grammar_encoding, start_rule_id) + recognizer = StringRecognizer(parsed_grammar.grammar_encoding, start_rule_id) - self.assertTrue(recognizer._accept_string(emoji, recognizer.stacks)) + self.assertTrue(recognizer._accept_prefix(emoji)) diff --git a/tests/test_token_sequence_recognizer/__init__.py b/tests/test_token_sequence_recognizer/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_token_sequence_recognizer/test_accept_unicode_bytes.py b/tests/test_token_sequence_recognizer/test_accept_unicode_bytes.py new file mode 100644 index 0000000..46d444e --- /dev/null +++ b/tests/test_token_sequence_recognizer/test_accept_unicode_bytes.py @@ -0,0 +1,93 @@ +import unittest +from unittest import TestCase + +from transformers_cfg.recognizer import StringRecognizer, AcceptState + +from transformers_cfg.parser import parse_ebnf +from tests.json_utils import is_json_parsable + +import logging + +from transformers_cfg.utf8_utils import PartialUTF8 + + +class TestUnicode(TestCase): + def test_accept_japanese(self): + """ + Test that we can accept japanese characters + """ + + japanese = "γ“γ‚“γ«γ‘γ―δΈ–η•Œ" + with open("examples/grammars/japanese.ebnf", "r") as file: + input_text = file.read() + parsed_grammar = parse_ebnf(input_text) + + start_rule_id = parsed_grammar.symbol_table["root"] + + recognizer = StringRecognizer(parsed_grammar.grammar_encoding, start_rule_id) + + bytes_japanese = bytes(japanese, "utf-8") + logging.debug( + f"bytes_japanese: {bytes_japanese} of length {len(bytes_japanese)}" + ) + # γ“γ‚“γ«γ‘γ―δΈ–η•Œ + + head_bytes = bytes_japanese[:8] + # partial_utf8 = PartialUTF8() + accept_state = recognizer._consume_bytes(head_bytes) + + # non empty stack means that the bytes were accepted + self.assertTrue(len(accept_state.stacks) > 0) + + def test_accept_japanese_progressive(self): + ####################### + # Now consider the case of progressive matching + ####################### + + japanese = "γ“γ‚“γ«γ‘γ―δΈ–η•Œ" + with open("examples/grammars/japanese.ebnf", "r") as file: + input_text = file.read() + parsed_grammar = parse_ebnf(input_text) + + start_rule_id = parsed_grammar.symbol_table["root"] + + recognizer = StringRecognizer(parsed_grammar.grammar_encoding, start_rule_id) + + bytes_japanese = bytes(japanese, "utf-8") + logging.debug( + f"bytes_japanese: {bytes_japanese} of length {len(bytes_japanese)}" + ) + + byte_tokens = [bytes_japanese[i] for i in range(len(bytes_japanese))] + # cast into bytes + byte_tokens = [bytes([byte]) for byte in byte_tokens] + + accept_state = recognizer.get_initial_accept_state() + + # accept_state = recognizer.init_accept_state + for i, byte in enumerate(byte_tokens): + accept_state = recognizer._consume_bytes(byte, accept_state) + self.assertTrue(len(accept_state.stacks) > 0) + + def test_accept_emoji(self): + """ + Test that we can accept emoji + """ + + emoji = "πŸ˜€πŸ˜„πŸ˜‚" + with open("examples/grammars/emoji.ebnf", "r") as file: + input_text = file.read() + parsed_grammar = parse_ebnf(input_text) + + start_rule_id = parsed_grammar.symbol_table["root"] + + recognizer = StringRecognizer(parsed_grammar.grammar_encoding, start_rule_id) + + bytes_emoji = bytes(emoji, "utf-8") + logging.debug(f"bytes_emoji: {bytes_emoji} of length {len(bytes_emoji)}") + # πŸ˜€πŸ˜„πŸ˜‚ + + # partial_utf8 = PartialUTF8() + accept_state = recognizer._consume_bytes(bytes_emoji) + # non empty stack means that the bytes were accepted + self.assertTrue(len(accept_state.stacks) > 0) diff --git a/tests/test_tokenizers/test_bloom.py b/tests/test_tokenizers/test_bloom.py index 6253698..ebee494 100644 --- a/tests/test_tokenizers/test_bloom.py +++ b/tests/test_tokenizers/test_bloom.py @@ -7,6 +7,7 @@ import logging +# @unittest.skip("GPTNeoXTokenizerFast is not available for testing") class BloomTokenizerTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = BloomTokenizerFast diff --git a/tests/test_tokenizers/test_llama.py b/tests/test_tokenizers/test_llama.py index de30415..1f95e87 100644 --- a/tests/test_tokenizers/test_llama.py +++ b/tests/test_tokenizers/test_llama.py @@ -7,7 +7,7 @@ import logging -class GPT2TokenizerTest(TokenizerTesterMixin, unittest.TestCase): +class LlamaTokenizerTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = LlamaTokenizerFast pretrained_name = "saibo/llama-1B" diff --git a/tests/test_tokenizers/test_t5.py b/tests/test_tokenizers/test_t5.py index f4d6bbf..936bd39 100644 --- a/tests/test_tokenizers/test_t5.py +++ b/tests/test_tokenizers/test_t5.py @@ -7,6 +7,7 @@ import logging +@unittest.skip("T5Tokenizer's mapping is not well defined, not working") class T5TokenizerTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = T5TokenizerFast diff --git a/transformers_cfg/__init__.py b/transformers_cfg/__init__.py index ea02d19..e67e5c7 100644 --- a/transformers_cfg/__init__.py +++ b/transformers_cfg/__init__.py @@ -2,4 +2,4 @@ setup_logging() -__version__ = "0.1.3" +__version__ = "0.1.2" diff --git a/transformers_cfg/generation/logits_process.py b/transformers_cfg/generation/logits_process.py index cfca5e3..131b25a 100644 --- a/transformers_cfg/generation/logits_process.py +++ b/transformers_cfg/generation/logits_process.py @@ -18,7 +18,7 @@ class GrammarConstrainedLogitsProcessor(LogitsProcessor): def __init__(self, grammar_constraint, parse_start_index=None): self.last_size = None self.grammar_constraint = grammar_constraint - self.batch_stacks = None + self.batch_accept_states = None self.parse_start_index = None def mask_logits(self, logits, device): @@ -26,7 +26,7 @@ def mask_logits(self, logits, device): # indicating acceptance # acceptance = self.grammar_acceptor.filter_vocab(self.stacks, device) acceptance = self.grammar_constraint.batch_filter_vocab( - self.batch_stacks, device + self.batch_accept_states, device ) # acceptance is a tensor of shape (batch_size, vocab_size) # get the indices of the accepted tokens @@ -64,10 +64,12 @@ def process_logits(self, input_ids, scores): :return: """ # we dynamically create stacks at the first call, so that we know the batch size and beam size - if self.batch_stacks is None: - self.batch_stacks = [ + if self.batch_accept_states is None: + self.batch_accept_states = [ # self.grammar_constraint.init_stacks() - copy.deepcopy(self.grammar_constraint.grammar.stacks) + copy.deepcopy( + self.grammar_constraint.string_recognizer.get_initial_accept_state() + ) for _ in range(len(input_ids)) ] @@ -79,13 +81,16 @@ def process_logits(self, input_ids, scores): logger.debug("last_size: \n" + pprint.pformat(self.last_size)) logger.debug( "num of stacks: \n" - + pprint.pformat([len(stack) for stack in self.batch_stacks]) + + pprint.pformat( + [len(acc_state.stacks) for acc_state in self.batch_accept_states] + ) ) - logger.debug("stacks: \n" + pprint.pformat(self.batch_stacks)) + # logger.debug("stacks: \n" + pprint.pformat(self.batch_accept_states.stacks)) - self.batch_stacks = self.grammar_constraint.advance_token_ids( - input_ids, self.batch_stacks, self.parse_start_index + self.batch_accept_states = self.grammar_constraint.advance_token_ids( + input_ids, self.batch_accept_states, self.parse_start_index ) + logger.debug(f"input_ids: {input_ids}") self.mask_logits(scores, scores.device) return scores diff --git a/transformers_cfg/grammar_utils.py b/transformers_cfg/grammar_utils.py index 4f1c823..6c4c409 100644 --- a/transformers_cfg/grammar_utils.py +++ b/transformers_cfg/grammar_utils.py @@ -1,8 +1,8 @@ -from .token_grammar_recognizer import IncrementalTokenGrammarRecognizer +from .token_grammar_recognizer import IncrementalTokenRecognizer # Old class name, kept for backward compatibility -class IncrementalGrammarConstraint(IncrementalTokenGrammarRecognizer): +class IncrementalGrammarConstraint(IncrementalTokenRecognizer): def __init__(self, *args, **kwargs): # import warnings # warnings.warn( diff --git a/transformers_cfg/mapping.py b/transformers_cfg/mapping.py index 7567dfa..c193aec 100644 --- a/transformers_cfg/mapping.py +++ b/transformers_cfg/mapping.py @@ -1,29 +1,39 @@ -from transformers_cfg.utils import get_tokenizer_model_type +from typing import Dict, List +from transformers_cfg.utils import get_tokenizer_model_type, ints2bytes +from transformers import AutoTokenizer import logging log = logging.getLogger(__name__) -def get_mapping(tokenizer): +def get_mapping(tokenizer, unicode=False): log.debug(f"tokenizer type: {tokenizer.__class__.__name__}") log.debug(f"tokenizer model type: {get_tokenizer_model_type(tokenizer)}") - if ( - "gpt2" in tokenizer.__class__.__name__.lower() - or "bloom" in tokenizer.__class__.__name__.lower() - or "pretrainedtokenizer" in tokenizer.__class__.__name__.lower() - or "codegen" in tokenizer.__class__.__name__.lower() - or "gptneox" in tokenizer.__class__.__name__.lower() - ): - return BBPEMapping(tokenizer) - elif "t5" in tokenizer.__class__.__name__.lower(): - return BPEMapping(tokenizer) - elif "llama" in tokenizer.__class__.__name__.lower(): - return LlamaBPEMapping(tokenizer) - elif "xglm" in tokenizer.__class__.__name__.lower(): - return UniGramMapping(tokenizer) + if not unicode: + if ( + "gpt2" in tokenizer.__class__.__name__.lower() + or "bloom" in tokenizer.__class__.__name__.lower() + or "pretrainedtokenizer" in tokenizer.__class__.__name__.lower() + or "codegen" in tokenizer.__class__.__name__.lower() + or "gptneox" in tokenizer.__class__.__name__.lower() + ): + return BBPEMapping(tokenizer) + elif "t5" in tokenizer.__class__.__name__.lower(): + return BPEMapping(tokenizer) + elif "llama" in tokenizer.__class__.__name__.lower(): + return LlamaBPEMapping(tokenizer) + elif "xglm" in tokenizer.__class__.__name__.lower(): + return UniGramMapping(tokenizer) + else: + raise ValueError(f"Unknown tokenizer type: {tokenizer.__class__.__name__}") else: - raise ValueError(f"Unknown tokenizer type: {tokenizer.__class__.__name__}") + if "gpt2" in tokenizer.__class__.__name__.lower(): + return UnicodeBBPEMapping(tokenizer) + else: + raise NotImplementedError( + f"Unicode mapping for {tokenizer.__class__.__name__}" + ) class Mapping: @@ -43,12 +53,13 @@ def _map(self, token_id: int) -> str: # if token_id is tensor, convert it to int if hasattr(token_id, "item"): token_id = token_id.item() - raw_token = self.tokenizer.decode(token_id) + raw_token = self.tokenizer.convert_ids_to_tokens(token_id) return raw_token - def map(self, token_id: int) -> bytes: + def map(self, token_id: int, verbose=False) -> bytes: token = self._map(token_id) - log.debug(f"token_id: {token_id}, token: {token}") + if verbose: + log.debug(f"token_id: {token_id}, token: {token}") return bytes(token, "utf-8") @@ -63,6 +74,33 @@ def _map(self, token_id: int) -> str: return raw_token +class UnicodeBBPEMapping(Mapping): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.intermediate_encoding = UnicodeBBPEMapping.get_intermediate_encoding( + self.tokenizer + ) + + def _map(self, token_id: int, verbose=False) -> str: + raw_token = super()._map(token_id) + # if raw_token.startswith("Δ "): + # raw_token = raw_token.replace("Δ ", " ") + return raw_token + + def map(self, token_id: int, verbose=False) -> bytes: + raw_token = self._map(token_id, verbose) + if verbose: + log.debug(f"token_id: {token_id}, raw_token: {raw_token}") + return self.intermediate_encoding.token2bytes(raw_token) + + @staticmethod + def get_intermediate_encoding(tokenizer): + if "gpt2" in tokenizer.__class__.__name__.lower(): + return ByteEncoding(tokenizer) + else: + return None + + class BPEMapping(Mapping): def __init__(self, tokenizer): super().__init__(tokenizer) @@ -77,7 +115,6 @@ def _map(self, token_id: int) -> str: if self.last_token_id is not None and self.last_token_id == self.bos_token_id: at_bos = True self.last_token_id = token_id - if raw_token.startswith("▁"): raw_token = raw_token.replace("▁", " ") if at_bos: @@ -131,3 +168,72 @@ def __init__(self, tokenizer): super().__init__(tokenizer) self.bos_token_id = tokenizer.eos_token_id self.eos_token_id = None + + +class ByteEncoding: + def __init__(self, tokenizer): + # check if the tokenizer is fast, if so, convert it to slow + if tokenizer.is_fast: + tokenizer = AutoTokenizer.from_pretrained( + tokenizer.name_or_path, use_fast=False + ) + self.tokenizer = tokenizer + self.byte2char: Dict[int, str] = tokenizer.byte_encoder + self.char2byte: Dict[str, int] = tokenizer.byte_decoder + # code point to byte + self.cdp2byte: Dict[int, int] = {ord(c): b for c, b in self.char2byte.items()} + self.byte2cdp: Dict[int, int] = {v: k for k, v in self.cdp2byte.items()} + + def map(self, byte: int) -> int: + assert 0 <= byte < 256, f"byte: {byte} is not in the range [0, 256)" + return ord(self.byte2char[byte]) + + def token_ids2bytes(self, token_ids: List[int]) -> bytes: + tokens: List[str] = self.tokenizer.convert_ids_to_tokens(token_ids) + # for token id = BOS, the token should be empty string instead of + # TODO, this may cause issues because this means that special tokens like BOS can appear at any position + tokens = [ + "" if token in self.tokenizer.all_special_ids else token for token in tokens + ] + bytes: List[List[int]] = [self.token2bytes(token) for token in tokens] + # join the bytes + return ints2bytes(sum(bytes, [])) + + def token_id2bytes(self, token_id: int) -> bytes: + token: str = self.tokenizer.convert_ids_to_tokens(token_id) + return self.token2bytes(token) + + def token2bytes(self, token: str) -> bytes: + # import pdb; pdb.set_trace() + bytes_seq: List[int] = [self.char2byte[c] for c in token] + return bytes(bytes_seq) + + +if __name__ == "__main__": + gpt2_tokenizer = AutoTokenizer.from_pretrained("gpt2", use_fast=True) + + gpt2_tokenizer.encode("榴莲") + # [162, 99, 112, 164, 236, 110] + + mapping = get_mapping(gpt2_tokenizer) + + x = mapping.map(162) + # b'\xef\xbf\xbd' + x = mapping.map(99) + + ################################## + + gpt2_tokenizer = AutoTokenizer.from_pretrained("gpt2", use_fast=False) + + token_ids = gpt2_tokenizer.encode("榴莲") + + print(token_ids) + utf8_encoding = "榴莲".encode("utf-8") + print("utf8_encoding: ", utf8_encoding) + mapping = ByteEncoding(gpt2_tokenizer) + print(mapping.token_ids2bytes(token_ids)) + # b'\xef\xbf\xbd' + + ################################### + + gpt2_tokenizer.encode("Β΄") diff --git a/transformers_cfg/recognizer.py b/transformers_cfg/recognizer.py index f52e3d8..585ab68 100644 --- a/transformers_cfg/recognizer.py +++ b/transformers_cfg/recognizer.py @@ -13,17 +13,24 @@ from transformers_cfg.utils import intervals_intersect import logging -logging.basicConfig(level=logging.DEBUG) + +class AcceptState: + def __init__(self, stacks, partial_utf8): + self.stacks = stacks + self.partial_utf8 = partial_utf8 + + @staticmethod + def empty_state(): + return AcceptState([], PartialUTF8()) -class GrammarRecognizer: +class StringRecognizer: def __init__( self, grammar_encoding: List[int], start_rule_id: int = None, rule_offsets: List[int] = None, stacks: List[List[int]] = None, - partial_utf8: PartialUTF8 = None, ): # strictly speaking, we don't need to copy grammar_encoding because we don't modify it # but we do it anyway to be safe @@ -43,7 +50,7 @@ def __init__( if start_rule_id is None: raise ValueError("start_rule_id cannot be None if stacks is None") self.stacks: List[List[int]] = self.init_stack(start_rule_id) - self.partial_utf8 = partial_utf8 + self.start_rule_id = start_rule_id def init_rules(self, start_rule_id: int) -> List[int]: _rule_offset = 0 @@ -90,6 +97,12 @@ def init_stack(self, start_rule_id: int) -> List[List[int]]: sub_rhs_offset += 1 + self.grammar_encoding[sub_rhs_offset] return stacks + def get_initial_accept_state(self) -> AcceptState: + return AcceptState(self.init_stack(self.start_rule_id), PartialUTF8()) + + def get_termination_accept_state(self) -> AcceptState: + return AcceptState([], PartialUTF8()) + @lru_cache(maxsize=32768) def advance_stack(self, stack: Tuple[int]) -> List[List[int]]: stack = list(stack) @@ -130,31 +143,95 @@ def advance_stack(self, stack: Tuple[int]) -> List[List[int]]: return new_stacks - def _consume_byte_partial_match(self, byte: int, stacks: List[List[int]]): + def _consume_byte(self, byte: int, accept_state: AcceptState): # suppose we have code point δΈ€, ord('δΈ€') = 19968, we need to match 3 bytes # we need to match 3 bytes, so we need to call _consume_byte_partial_match 3 times - raise NotImplementedError + self._consume_bytes(bytes([byte]), accept_state) - def _consume_bytes_partial_match( - self, bytes: bytes, stacks: List[List[int]], partial_utf8: PartialUTF8 + # @lru_cache(maxsize=32768) + def _probe_bytes( + self, + byte_seq: bytes, + stacks: List[List[int]], + partial_utf8: PartialUTF8, + verbose=True, ): - code_points, new_partial_utf8 = decode_utf8(bytes, partial_utf8) - logging.debug( - f"code_points: {code_points}; new_partial_utf8: {new_partial_utf8}" - ) + if type(byte_seq) is list: + byte_seq = bytes(byte_seq) + code_points, new_partial_utf8 = decode_utf8(byte_seq, partial_utf8) + if verbose: + logging.debug( + f"code_points: {code_points}; new_partial_utf8: {new_partial_utf8}" + ) + new_stacks = self._consume_code_points(code_points, stacks) + + for stack in new_stacks: + + # stack is empty, meaning that the variables are all consumed + if len(stack) == 0: + return True + element_offset = stack[-1] + if self.partial_utf8_accept_at_element(element_offset, new_partial_utf8): + return True + return False + + def _consume_bytes( + self, + byte_seq: bytes, + accept_state: AcceptState = None, + verbose=True, + ): + if accept_state is None: + accept_state = self.get_initial_accept_state() + stacks = accept_state.stacks + partial_utf8 = accept_state.partial_utf8 + if type(byte_seq) is list: + byte_seq = bytes(byte_seq) + code_points, new_partial_utf8 = decode_utf8(byte_seq, partial_utf8) + if verbose: + logging.debug( + f"code_points: {code_points}; new_partial_utf8: {new_partial_utf8}" + ) new_stacks = self._consume_code_points(code_points, stacks) - # pdb.set_trace() new_new_stacks = [] for stack in new_stacks: if len(stack) == 0: continue element_offset = stack[-1] - if self.valid_partial_match(element_offset, new_partial_utf8): + if self.partial_utf8_accept_at_element(element_offset, new_partial_utf8): new_new_stacks.append(stack) - return new_new_stacks, new_partial_utf8 + return AcceptState(new_new_stacks, new_partial_utf8) + + ########################## + # + # Code point recognition + # + ########################## + + @lru_cache(maxsize=30000) + def _consume_code_point( + self, code_point: int, stacks: Tuple[Tuple[int]] + ) -> List[List[int]]: + """ + consume a character from the stack + char_code_point: can be a Unicode code point, including ascii code points which are in the range [0, 127] + """ + new_stacks = [] - def _consume_char_code_point(self, char_code_point: int, stacks: List[List[int]]): + stacks: List[List[int]] = list([list(stack) for stack in stacks]) + if code_point == 0: + return new_stacks + for stack in stacks: + new_stacks.extend( + self._consume_code_point_per_stack(code_point, tuple(stack)) + ) + return new_stacks + + @lru_cache(maxsize=30000) + def _consume_code_point_per_stack( + self, code_point: int, stack: Tuple[int] + ) -> List[List[int]]: """ consume a character from the stack char_code_point: can be a Unicode code point, including ascii code points which are in the range [0, 127] @@ -162,31 +239,53 @@ def _consume_char_code_point(self, char_code_point: int, stacks: List[List[int]] # TODO, the below code will raise an error when the stack is empty, but why is this happening? # if len(stacks) == 0: # raise ValueError("Stacks don't contain any stack, meaning that no character can be consumed") - # code_point = 0 is a special case for EOS token, which should be handled by the _consume_token method - if char_code_point == 0: - raise ValueError("byte cannot be 0") + # code_point = 0 is a special case when the uf8 sequence is not complete, we return an empty stack + # to indicate that the character is not accepted + stack = list(stack) new_stacks = [] - for stack in stacks: - # stack is empty - if not stack: - continue + if code_point == 0: + return new_stacks + # stack is empty + if len(stack) == 0: + return new_stacks - element_offset = stack[-1] + element_offset = stack[-1] - found = self.valid_char_code_point_match(char_code_point, element_offset) - if not found: - continue + found = self.accept_code_point_at_element(code_point, element_offset) + if not found: + return new_stacks - size = self.grammar_encoding[element_offset] - element_offset += size + 1 - new_stack = stack[:-1] - if self.grammar_encoding[element_offset]: - new_stack.append(element_offset) - new_stacks.extend(self.advance_stack(tuple(new_stack))) - return new_stacks + size = self.grammar_encoding[element_offset] + element_offset += size + 1 + new_stack = stack[:-1] + if self.grammar_encoding[element_offset]: + new_stack.append(element_offset) + return self.advance_stack(tuple(new_stack)) + + def _consume_code_points( + self, code_points: List[int], stacks: List[List[int]], verbose=False + ) -> List[List[int]]: + for i, code_point in enumerate(code_points): + # for lru_cache to work, we need to convert the list of stacks into a tuple of stacks + tuple_stacks: Tuple[Tuple[int]] = tuple([tuple(stack) for stack in stacks]) + stacks = self._consume_code_point(code_point, tuple_stacks) + if len(stacks) > 0 and verbose: + accepted_code_point = code_points[: i + 1] + corresponding_char = chr(code_point) + logging.debug( + f"code point {accepted_code_point} corresponding to {corresponding_char} is accepted" + ) + return stacks + + def _accept_code_points( + self, code_points: List[int], stacks: List[List[int]], verbose=False + ) -> bool: + stacks = self._consume_code_points(code_points, stacks, verbose) + return len(stacks) > 0 - def valid_char_code_point_match( - self, char_code_point: int, element_offset: int + @lru_cache(maxsize=30000) + def accept_code_point_at_element( + self, code_point: int, element_offset: int ) -> bool: size = self.grammar_encoding[element_offset] # to make idx point to the range_start of the first range @@ -194,13 +293,25 @@ def valid_char_code_point_match( for i in range(0, size, 2): if ( self.grammar_encoding[element_offset + i] - <= char_code_point + <= code_point <= self.grammar_encoding[element_offset + i + 1] ): return True return False - def valid_partial_match( + # def _accept_code_point(self, code_point: int, stacks: List[List[int]]): + # # for lru_cache to work, we need to convert the list of stacks into a tuple of stacks + # tuple_stacks: Tuple[Tuple[int]] = tuple([tuple(stack) for stack in stacks]) + # new_stacks: List[List[int]] = self._consume_code_point(code_point, tuple_stacks) + # return len(new_stacks) > 0 + + ############################# + # + # Partial UTF-8 recognition + # + ############################# + + def partial_utf8_accept_at_element( self, element_offset: int, partial_utf8: PartialUTF8 ) -> bool: # Extract the accumulated value and the number of remaining bytes from the partial_utf8 object. @@ -208,11 +319,11 @@ def valid_partial_match( n_remain = partial_utf8.n_remain # Return False if there are no remaining bytes to process or if it's an invalid UTF-8 sequence. - if n_remain < 0 or (n_remain == 1 and partial_value < 2): + if n_remain == 1 and partial_value < 2: return False # If there are no remaining bytes, this means we had already consumed a complete UTF-8 sequence. - if n_remain == 0: + if n_remain <= 0: return True # Calculate the lowest possible Unicode code point that can be formed with the remaining bytes. @@ -247,30 +358,32 @@ def valid_partial_match( # If no overlap is found with any of the ranges, return False, indicating no valid partial match. return False - def _accept_char_code_point(self, code_point: int, stacks: List[List[int]]): - new_stacks = self._consume_char_code_point(code_point, stacks) - return len(new_stacks) > 0 + ############################# + # + # String recognition + # + ############################# - def _consume_string(self, string: str, stacks: List[List[int]]): + def _consume_string(self, string: str, accept_state: AcceptState): # _bytes = bytes(string, "utf-8") code_points = [ord(char) for char in string] - stacks = self._consume_code_points(code_points, stacks) - return stacks - - def _consume_code_points(self, code_points: List[int], stacks: List[List[int]]): - for i, code_point in enumerate(code_points): - stacks = self._consume_char_code_point(code_point, stacks) - if len(stacks) > 0: - accepted_code_point = code_points[: i + 1] - corresponding_char = chr(code_point) - logging.debug( - f"code point {accepted_code_point} corresponding to {corresponding_char} is accepted" - ) - return stacks - - def _accept_string(self, string: str, stacks: List[List[int]]): - new_stacks = self._consume_string(string, stacks) - return len(new_stacks) > 0 + stacks = self._consume_code_points(code_points, accept_state.stacks) + return AcceptState(stacks, accept_state.partial_utf8) + + def _accept_prefix(self, string: str, accept_state: AcceptState = None): + if accept_state is None: + accept_state = self.get_initial_accept_state() + new_accept_state = self._consume_string(string, accept_state) + return len(new_accept_state.stacks) > 0 + + def _accept_string(self, string: str, accept_state: AcceptState = None): + if accept_state is None: + accept_state = self.get_initial_accept_state() + new_accept_state = self._consume_string(string, accept_state) + at_least_one_stack_is_empty = any( + len(stack) == 0 for stack in new_accept_state.stacks + ) + return at_least_one_stack_is_empty def _can_stop(self, stacks: List[List[int]]): # This happens in practice, but maybe it shouldn't? TODO @@ -286,6 +399,13 @@ def _can_stop(self, stacks: List[List[int]]): def _must_stop(self, stacks: List[List[int]]): return len(stacks) == 0 or all(len(stack) == 0 for stack in stacks) + ############################# + # + # Not Used + # + ############################# + + # For each sub-rule in the grammar, cache whether each byte is accepted. @lru_cache(maxsize=None) def char_acceptance_at_element(self, element_offset): """ @@ -311,6 +431,30 @@ def char_acceptance_at_element(self, element_offset): logging.debug(acceptance) return acceptance + def _consume_code_points_new( + self, code_points: List[int], stacks: List[List[int]], verbose=False + ) -> List[List[int]]: + new_stacks: List[List[int]] = [] + for stack in stacks: + new_stacks.extend( + self._consume_code_points_per_stack( + tuple(code_points), tuple(stack), verbose + ) + ) + return new_stacks + + @lru_cache(maxsize=30000) + def _consume_code_points_per_stack( + self, code_points: Tuple[int], stack: Tuple[int], verbose=False + ) -> List[List[int]]: + code_points = list(code_points) + stacks = (stack,) + for i, code_point in enumerate(code_points): + # for lru_cache to work, we need to convert the list of stacks into a tuple of stacks + stacks = self._consume_code_point(code_point, stacks) + stacks = tuple([tuple(stack) for stack in stacks]) + return [list(stack) for stack in stacks] + if __name__ == "__main__": # set logging level @@ -318,11 +462,10 @@ def char_acceptance_at_element(self, element_offset): with open("examples/grammars/japanese.ebnf", "r") as file: input_text = file.read() parsed_grammar = parse_ebnf(input_text) - parsed_grammar.logging.debug() logging.debug(f"symbol_ids: \n{parsed_grammar.symbol_table}") start_rule_id = parsed_grammar.symbol_table["root"] - recognizer = GrammarRecognizer(parsed_grammar.grammar_encoding, start_rule_id) + recognizer = StringRecognizer(parsed_grammar.grammar_encoding, start_rule_id) japanese = "γ“γ‚“γ«γ‘γ―δΈ–η•Œ" # "γ“γ‚“γ«γ‘γ―δΈ–η•Œ" doesn't work bytes_japanese = bytes(japanese, "utf-8") @@ -352,14 +495,11 @@ def char_acceptance_at_element(self, element_offset): # cast into bytes byte_tokens = [bytes([byte]) for byte in byte_tokens] - new_partial_utf8 = PartialUTF8() - new_stacks = recognizer.stacks + accept_state = AcceptState(recognizer.stacks, PartialUTF8()) for i, byte in enumerate(byte_tokens): - new_stacks, new_partial_utf8 = recognizer._consume_bytes_partial_match( - byte, new_stacks, new_partial_utf8 - ) - logging.debug(f"new partial utf8: {new_partial_utf8}") - if len(new_stacks) > 0: + new_accept_state = recognizer._consume_bytes(byte, accept_state) + logging.debug(f"new partial utf8: {new_accept_state.partial_utf8}") + if len(new_accept_state.stacks) > 0: logging.debug(f"byte {byte} is accepted") else: logging.debug(f"byte {byte} is not accepted") diff --git a/transformers_cfg/token_grammar_recognizer.py b/transformers_cfg/token_grammar_recognizer.py index 7a1f80a..c3246da 100644 --- a/transformers_cfg/token_grammar_recognizer.py +++ b/transformers_cfg/token_grammar_recognizer.py @@ -6,159 +6,166 @@ import torch -from transformers_cfg.recognizer import GrammarRecognizer +from transformers_cfg.recognizer import StringRecognizer, AcceptState from transformers_cfg.parser import parse_ebnf +from transformers_cfg.trie import ByteTrie +from transformers_cfg.utf8_utils import PartialUTF8 from .vocab_struct import LEAF, TokenTrie from transformers_cfg.mapping import get_mapping logger = logging.getLogger(__name__) -def check_token_acceptance_in_trie(trie, stacks, grammar, eos_token_id, accepts): - - for byte, next_trie in trie.items(): - if byte == LEAF: - token_id = next_trie - if token_id != eos_token_id: - # if the stacks is not empty, it means we can still continue to parse - # so we should accept the token - accepts[token_id] = bool(stacks) - continue - - new_stacks = [] - for stk in stacks: - if not stk: - continue - - next_element_offset = stk[-1] - num_chars = grammar.grammar_encoding[next_element_offset] - - if not grammar.char_acceptance_at_element(next_element_offset).get( - byte, False - ): - # if the current byte is not accepted by the current rule, we need to try next rule - continue - - next_element_offset += num_chars + 1 - new_stack = stk[:-1] - if grammar.grammar_encoding[next_element_offset]: - new_stack.append(next_element_offset) - new_stacks.extend(grammar.advance_stack(tuple(new_stack))) - - if new_stacks: - check_token_acceptance_in_trie( - next_trie, new_stacks, grammar, eos_token_id, accepts - ) - - return accepts - - -class AbsTokenGrammarRecognizer(ABC): - def __init__(self, grammar_str, tokenizer, start_rule_name="root"): +class AbsTokenRecognizer(ABC): + def __init__(self, grammar_str, tokenizer, start_rule_name="root", unicode=False): parsed_grammar = parse_ebnf(grammar_str) grammar_encoding = parsed_grammar.grammar_encoding self.start_rule_id = parsed_grammar.symbol_table.get(start_rule_name) + self.byte_encoding = unicode self.eos_token_id = tokenizer.eos_token_id - self.mapping = get_mapping(tokenizer) self.token_trie = TokenTrie(tokenizer) self.tokenizer = tokenizer + self.string_recognizer = StringRecognizer(grammar_encoding, self.start_rule_id) + self.unicode_trie = ByteTrie.from_tokenizer(tokenizer, unicode=unicode) + self.mapping = get_mapping(tokenizer, unicode=unicode) assert len(self.mapping) == len( self.token_trie ), f"{len(self.mapping)}, {len(self.token_trie)}" - self.grammar = GrammarRecognizer(grammar_encoding, self.start_rule_id) - def _consume_token_id(self, token_id: int, stacks: List[List[int]]): - if self.grammar._must_stop(stacks): + def _consume_token_id( + self, token_id: int, accept_state: AcceptState + ) -> AcceptState: + if self.string_recognizer._must_stop(accept_state.stacks): if token_id == self.eos_token_id: - return [] + return self.string_recognizer.get_termination_accept_state() else: raise ValueError( - f"All stacks are empty, so the only token accepted is EOS, but got {token_id}" + f"All stacks are empty, so the only token accepted is EOS({self.eos_token_id}), but got {token_id}" ) if token_id == self.eos_token_id: - if self.grammar._can_stop(stacks): + if self.string_recognizer._can_stop(accept_state.stacks): # if at least one of the stack is empty, we can stop # we clear all the stacks, meaning that we don't accept any token after EOS - return [] + return self.string_recognizer.get_termination_accept_state() else: raise ValueError( f"At least one of the stack should be empty when EOS is reached. However, " - f"the stacks are {stacks}" + f"the stacks are {accept_state.stacks}" ) - for byte in self.mapping.map(token_id): - stacks = self.grammar._consume_char_code_point(byte, stacks) - # check updated stacks - # TODO, I commented this out because it will fail when the stack is empty - # empty stack means the end of the grammar - # assert stacks != [] - return stacks + bytes_or_codepoints = self.mapping.map(token_id) + accept_state = self.string_recognizer._consume_bytes( + bytes_or_codepoints, accept_state + ) + return accept_state - # Newly added - # def accept_token_id(self, token_id: int, stacks: List[List[int]]): - # new_stacks = self._consume_token_id(token_id, stacks) - # return len(new_stacks) > 0 + def probe_token_id(self, token_id: int, accept_state: AcceptState) -> bool: + stacks = accept_state.stacks + if self.string_recognizer._must_stop(stacks): + if token_id == self.eos_token_id: + return True + else: + return False + if token_id == self.eos_token_id: + if self.string_recognizer._can_stop(stacks): + # if at least one of the stack is empty, we can stop + # we clear all the stacks, meaning that we don't accept any token after EOS + return True + else: + return False + # for code_point in self.mapping.map(token_id): + # stacks = self.grammar._consume_char_code_point(code_point, stacks) + bytes_or_codepoints = self.mapping.map(token_id, verbose=False) + new_acc_state = self.string_recognizer._consume_bytes( + bytes_or_codepoints, accept_state, verbose=False + ) + return len(new_acc_state.stacks) > 0 def advance_token_ids(self, *args, **kwargs): """Process a list of tokens according to the grammar rules.""" raise NotImplementedError - def batch_filter_vocab(self, batch_stacks, device) -> torch.Tensor: + def batch_filter_vocab(self, batch_accept_states, device) -> torch.Tensor: batch_acceptance = [] - for stacks in batch_stacks: - batch_acceptance.append(self.filter_vocab(stacks, device)) + for accept_state in batch_accept_states: + batch_acceptance.append(self.filter_vocab(accept_state, device)) return torch.stack(batch_acceptance) - def filter_vocab(self, stacks, device) -> torch.Tensor: - if not stacks: # Check if stacks is empty + def filter_vocab(self, accept_state, device) -> torch.Tensor: + if not accept_state.stacks: # Check if stacks is empty # Handle the empty case: for example, return a tensor of False # The size of the tensor should match the size of your vocabulary vocab_size = len(self.mapping) logger.debug(f"Empty stack, sum of acceptance: {0}") - return torch.zeros(vocab_size, dtype=torch.bool, device=device) + # size of the vocab + accepts = [False] * vocab_size + accepts[self.eos_token_id] = True + return torch.tensor(accepts, dtype=torch.bool, device=device) + acceptance = self.get_token_acceptance(accept_state, device) + + return acceptance + + def get_token_acceptance(self, accept_state, device) -> torch.Tensor: acceptance_matrix = torch.cat( - [self.token_acceptance_for_stack(tuple(stack), device) for stack in stacks] + [ + self.get_token_acceptance_array_for_stack( + tuple(stack), accept_state.partial_utf8, device + ) + for stack in accept_state.stacks + ] ) # Merge stacks: any True => True - acceptance = acceptance_matrix.reshape(len(stacks), -1).any(dim=0) - logger.debug(f"sum of acceptance: {acceptance.sum()}") + acceptance = acceptance_matrix.reshape(len(accept_state.stacks), -1).any(dim=0) return acceptance - # Probably this should be configurable. If the grammar has an exceedingly - # large number of states, the correct setting is a tradeoff between GPU - # RAM usage and recomputation time. - # - # The main variable that pushes usage up here is number of states in the - # grammar. @lru_cache(maxsize=32768) - def token_acceptance_for_stack(self, stack, device): - stack = list(stack) # needs to come in as a tuple for lru_cache + def get_token_acceptance_array_for_stack(self, stack, partial_utf8, device): + # stack = list(stack) # needs to come in as a tuple for lru_cache + assert isinstance(stack, tuple) + stack = list(stack) - # size of the vocab - accepts = [False] * len(self.mapping) - accepts[self.eos_token_id] = len(stack) == 0 - if len(stack) == 0: - logger.debug("empty stack") + if self.byte_encoding: - accepts = check_token_acceptance_in_trie( - self.token_trie.trie, [stack], self.grammar, self.eos_token_id, accepts - ) + accept_f = lambda x: self.string_recognizer._probe_bytes( + x, [stack], partial_utf8=partial_utf8 + ) + token_acceptance = self.unicode_trie.get_token_acceptance( + accept=accept_f, accept_eos=False, eos_token_id=self.eos_token_id + ) + else: + accepts = [False] * len(self.mapping) + token_acceptance = check_token_acceptance_in_trie( + self.token_trie.trie, + [stack], + self.string_recognizer, + self.eos_token_id, + accepts, + ) + x = torch.tensor(token_acceptance, dtype=torch.bool, device=device) + x_eos = self.validate_and_set_eos_acceptance(x) + return x_eos - x = torch.tensor(accepts, dtype=torch.bool, device=device) - return x + def validate_and_set_eos_acceptance(self, acceptance: torch.Tensor) -> torch.Tensor: + if torch.any(acceptance) == 0: + acceptance[self.eos_token_id] = True + else: + if acceptance[self.eos_token_id]: + raise ValueError() + acceptance[self.eos_token_id] = False + return acceptance -class IncrementalTokenGrammarRecognizer(AbsTokenGrammarRecognizer): - def __init__(self, grammar_str, start_rule_name, tokenizer): - super().__init__(grammar_str, tokenizer, start_rule_name) +class IncrementalTokenRecognizer(AbsTokenRecognizer): + def __init__(self, grammar_str, start_rule_name, tokenizer, unicode=False): + super().__init__(grammar_str, tokenizer, start_rule_name, unicode) self.last_size = None # if self.last_size is not set (which would be the case when processing the first token). # In this case, do nothing. - def advance_token_ids(self, input_ids, batch_stacks, parse_start_index=None): + def advance_token_ids(self, input_ids, batch_accept_states, parse_start_index=None): if self.last_size is None: prefix_to_parse = [ @@ -169,16 +176,18 @@ def advance_token_ids(self, input_ids, batch_stacks, parse_start_index=None): ] # self.grammar_acceptor.accept_token_ids(prefix_to_parse, self.stacks) - batch_stacks = [ - self._consume_token_ids(prefix, stack) - for prefix, stack in zip(prefix_to_parse, batch_stacks) + batch_accept_states = [ + self._consume_token_ids(prefix, accept_state) + for prefix, accept_state in zip(prefix_to_parse, batch_accept_states) ] # if the length of the current input IDs (input_ids[0]) is exactly one more than self.last_size. # This is expected in a scenario where inputs are processed incrementally, one token at a time. elif len(input_ids[0]) == self.last_size + 1: - batch_stacks = [ - self._consume_token_id(single_input_ids[-1], stack) - for single_input_ids, stack in zip(input_ids, batch_stacks) + batch_accept_states = [ + self._consume_token_id(single_input_ids[-1], accept_state) + for single_input_ids, accept_state in zip( + input_ids, batch_accept_states + ) ] # ensure that the input size is consistent with the expected incremental processing # (i.e., one token at a time). @@ -200,84 +209,102 @@ def advance_token_ids(self, input_ids, batch_stacks, parse_start_index=None): ) self.last_size = len(input_ids[0]) - return batch_stacks + return batch_accept_states def _consume_token_ids( - self, token_ids: List[int], stacks: List[List[int]], as_string=True + self, token_ids: List[int], accept_state: AcceptState = None, as_string=True ): + if accept_state is None: + accept_state = self.string_recognizer.get_initial_accept_state() if as_string: string = self.tokenizer.decode(token_ids) - stacks = self.grammar._consume_string(string, stacks) + accept_state = self.string_recognizer._consume_string(string, accept_state) else: for i, token_id in enumerate(token_ids): - stacks = self._consume_token_id(token_id, stacks) - if len(stacks) > 0: + accept_state = self._consume_token_id(token_id, accept_state) + if len(accept_state.stacks) > 0: cur_token_ids = token_ids[: i + 1] logging.debug(f"{cur_token_ids} is accepted") decoded_string = self.tokenizer.decode(cur_token_ids) logging.debug(f"The decoded string is {decoded_string}") - return stacks + return accept_state -class VanillaTokenGrammarRecognizer(AbsTokenGrammarRecognizer): - # TODO, this class may have been broken because of the recent refactoring, in particular, the stacks is bound to the - # parser class - def __init__(self, grammar_str, start_rule_name, tokenizer): - super().__init__(grammar_str, tokenizer, start_rule_name) - self.offset = None +def check_token_acceptance_in_trie(trie, stacks, grammar, eos_token_id, accepts): - def advance_token_ids(self, input_ids, batch_stacks, parse_start_index=None): - # By design, the batch_stacks should be empty at the beginning, thus it doesn't matter what we pass in. - if self.offset is None: - self.offset = ( - len(input_ids[0]) if parse_start_index is None else parse_start_index - ) + for byte, next_trie in trie.items(): + if byte == LEAF: + token_id = next_trie + if token_id != eos_token_id: + # if the stacks is not empty, it means we can still continue to parse + # so we should accept the token + accepts[token_id] = bool(stacks) + continue - # TODO: here may be broken, the deepcopy may not work - batch_stacks_from_scratch = [ - copy.deepcopy(self.grammar.stacks) for _ in range(len(input_ids)) - ] - - prefix_to_consume = [ - single_input_ids[self.offset :] for single_input_ids in input_ids - ] - # self.grammar_acceptor.accept_token_ids(prefix_to_consume, self.stacks) - advanced_batch_stacks = [ - self._consume_token_ids(prefix, stack, as_string=False) - for prefix, stack in zip(prefix_to_consume, batch_stacks_from_scratch) - ] - return advanced_batch_stacks + new_stacks = [] + for stk in stacks: + if not stk: + continue - def _consume_token_ids( - self, token_ids: List[int], stacks: List[List[int]], as_string=True - ): - if as_string: - string = self.tokenizer.decode(token_ids) - stacks = self.grammar._consume_string(string, stacks) - else: - for token_id in token_ids: - stacks = self._consume_token_id(token_id, stacks) - return stacks + next_element_offset = stk[-1] + num_chars = grammar.grammar_encoding[next_element_offset] + + if not grammar.char_acceptance_at_element(next_element_offset).get( + byte, False + ): + # if the current byte is not accepted by the current rule, we need to try next rule + continue + + next_element_offset += num_chars + 1 + new_stack = stk[:-1] + if grammar.grammar_encoding[next_element_offset]: + new_stack.append(next_element_offset) + new_stacks.extend(grammar.advance_stack(tuple(new_stack))) + + if new_stacks: + check_token_acceptance_in_trie( + next_trie, new_stacks, grammar, eos_token_id, accepts + ) + + return accepts if __name__ == "__main__": from transformers import AutoTokenizer - with open("examples/grammars/json.ebnf", "r") as file: + with open("examples/grammars/japanese.ebnf", "r") as file: input_text = file.read() parsed_grammar = parse_ebnf(input_text) parsed_grammar.print() tokenizer = AutoTokenizer.from_pretrained("gpt2") - tokenRecognizer = IncrementalTokenGrammarRecognizer( + tokenRecognizer = IncrementalTokenRecognizer( grammar_str=input_text, start_rule_name="root", tokenizer=tokenizer ) - valid_json = '{"foo": "bar", "baz": "bat"}' - token_ids = tokenizer.encode(valid_json) + japanese = "γƒˆγƒͺγƒΌγƒ " # "こんにけは" + token_ids = tokenizer.encode(japanese) + # 13298, 12675, 12045, 254 stacks = tokenRecognizer._consume_token_ids( - token_ids, tokenRecognizer.grammar.stacks, as_string=False + token_ids, tokenRecognizer.string_recognizer.stacks, as_string=False ) - # the json object is complete, so the stacks should be empty - assert stacks == [] or stacks == [[]], f"stacks: {stacks}, not empty" + + if stacks: + print("The Japanese input is accepted") + else: + print("The Japanese input is not accepted") + + korean = "μ•ˆλ…•ν•˜μ„Έμš”" + token_ids = tokenizer.encode(korean) + + try: + stacks = tokenRecognizer._consume_token_ids( + token_ids, tokenRecognizer.string_recognizer.stacks, as_string=False + ) + if stacks: + print("The Korean input is accepted") + else: + print("The Korean input is not accepted") + except ValueError as e: + print("The Korean input is not accepted") diff --git a/transformers_cfg/trie.py b/transformers_cfg/trie.py new file mode 100644 index 0000000..4682220 --- /dev/null +++ b/transformers_cfg/trie.py @@ -0,0 +1,204 @@ +import logging +from functools import lru_cache +from typing import Dict, List, Tuple +from collections import deque + +from transformers_cfg.mapping import get_mapping + +# from transformers_cfg.parser import parse_ebnf +# from transformers_cfg.recognizer import GrammarRecognizer +# from transformers_cfg.token_grammar_recognizer import IncrementalTokenGrammarRecognizer + +logger = logging.getLogger(__name__) + +# def check_token_acceptance_in_trie(trie, stacks, grammar, partial_utf8, accept_eos=True, eos_token_id=None) -> List[bool]: +# accept_f = lambda x: grammar._probe_bytes_partial_match(x, stack=stacks, partial_utf8=partial_utf8) +# accepts = trie.get_token_acceptance(accept=accept_f, accept_eos=accept_eos, eos_token_id=eos_token_id) +# return accepts + + +class TrieNode: + def __init__(self): + self.children = {} + self.is_end_of_word = False + self.token_id = None + + +class ByteTrie: + def __init__(self): + self.root = TrieNode() + + def insert(self, word, token_id=None): + node = self.root + for char in word: + if char not in node.children: + node.children[char] = TrieNode() + node = node.children[char] + node.is_end_of_word = True + node.token_id = token_id + + def search(self, word): + node = self.root + for char in word: + if char not in node.children: + return False + node = node.children[char] + return node.is_end_of_word + + def start_with_prefix(self, prefix): + node = self.root + for char in prefix: + if char not in node.children: + return False + node = node.children[char] + return True + + @classmethod + def from_tokenizer(cls, tokenizer, unicode=True): + vocab: Dict[str, int] = tokenizer.get_vocab() + trie = cls() + mapping = get_mapping(tokenizer, unicode=unicode) + for token_id in vocab.values(): + byte_repr = mapping.map(token_id) + trie.insert(byte_repr, token_id) + return trie + + @lru_cache(maxsize=128) + def __len__(self): + return len(self.dfs(verbose=False)) + + def dfs(self, accept=lambda x: True, verbose=False) -> List[Tuple[List[int], int]]: + result = [] + counter = {"visited": 0, "pruned": 0} + _dfs(self.root, [], result, accept, counter) + return result + + def bfs( + self, predicate=lambda x: True, verbose=False + ) -> List[Tuple[List[int], int]]: + queue = deque([(self.root, [])]) + valid_byte_seqs: List[Tuple[List[int], int]] = [] + counter = {"visited": 0, "pruned": 0} + + while queue: + counter["visited"] += 1 + node, byte_seq = queue.popleft() + if predicate(byte_seq): + if node.is_end_of_word: + valid_byte_seqs.append((byte_seq, node.token_id)) + for char, next_node in node.children.items(): + new_byte_seq: List[int] = byte_seq.copy() + new_byte_seq.append(char) + queue.append((next_node, new_byte_seq)) + else: + counter["pruned"] += 1 + return valid_byte_seqs + + def get_token_acceptance( + self, accept=lambda x: True, accept_eos=True, eos_token_id=None + ) -> List[bool]: + valid_byte_seqs: List[Tuple[List[int], int]] = self.bfs(accept, verbose=True) + valid_token_ids: List[int] = [token_id for _, token_id in valid_byte_seqs] + token_acceptance: List[bool] = [False] * (len(self)) + for token_id in valid_token_ids: + token_acceptance[token_id] = True + if not accept_eos: + # eos_token is mapped to an empty string, so it's always accepted regardless of the accept function + # this can be undesirable, so we can set it to False to ignore it + token_acceptance[eos_token_id] = False + return token_acceptance + + +def _dfs( + node, + cur_byte_seq: List[int], + result: List[Tuple[List[int], int]], + accept: callable, + counter: Dict[str, int], +): + counter["visited"] += 1 + if accept(cur_byte_seq): + if node.is_end_of_word: + result.append((cur_byte_seq, node.token_id)) + for char, next_node in node.children.items(): + new_byte_seq: List[int] = cur_byte_seq.copy() + new_byte_seq.append(char) + _dfs(next_node, new_byte_seq, result, accept, counter) + else: + # Skip the entire subtree if the predict function returns False + counter["pruned"] += 1 + return + + +def starts_with_prefix(prefix, target): + """ + Check if the given prefix is a valid start of the target word or if the target word is a valid start of the given prefix. + + Args: + prefix (str): The string prefix to be checked. + target (str): The target word to compare the prefix against. + + Returns: + bool: True if prefix is a valid start of target or if target is a valid start of prefix, False otherwise. + """ + + # Check if the target word starts with the given prefix. + # This covers the case where the prefix is shorter than the target word. + if target.startswith(prefix): + return True + + # Check if the given prefix starts with the target word. + # This covers the case where the prefix is longer than or equal to the target word. + if prefix.startswith(target): + return True + + # If neither of the above conditions are true, return False. + return False + + +if __name__ == "__main__": + import logging + + # Configure logging + logging.basicConfig(level=logging.INFO) + from transformers import AutoTokenizer + + tokenizer = AutoTokenizer.from_pretrained("gpt2", fast=True) + + trie = ByteTrie.from_tokenizer(tokenizer, unicode=True) + print(f"length of trie: {len(trie)}=={len(tokenizer.vocab.items())}") + + # + # print(trie.search("hello")) # Example, replace with actual words from the vocab + # print(trie.start_with_prefix("hell")) + # + # # Example Usage + # words = trie.dfs(accept=lambda x: len(x) > 0 and x[0] == 65 or len(x)==0) + # for word in words: + # print(bytes(word[0]).decode("utf-8")) + # + # # Example Usage + # words = trie.bfs(predicate=lambda x: len(x) > 0 and x[0] == 65 or len(x)==0) + # for word in words: + # print(bytes(word[0]).decode("utf-8")) + # + # token_acceptance = trie.get_token_acceptance(accept=lambda x: len(x) > 0 and x[0] == 65 or len(x)==0) + # print(sum(token_acceptance)) + # assert sum(token_acceptance) == len(words) + + ######################## + # UTF-8 + ######################## + + # from transformers import AutoTokenizer + # + # japanese = "γ“γ‚“γ«γ‘γ―δΈ–η•Œ" + # with open("examples/grammars/japanese.ebnf", "r") as file: + # input_text = file.read() + # parsed_grammar = parse_ebnf(input_text) + # + # start_rule_id = parsed_grammar.symbol_table["root"] + # + # recognizer = GrammarRecognizer(parsed_grammar.grammar_encoding, start_rule_id) + # accept_state = recognizer.init_accept_state() + # token_acc = trie.get_token_acceptance(accept=lambda x: recognizer._probe_bytes_partial_match(x, accept_state=accept_state)) diff --git a/transformers_cfg/utf8_utils.py b/transformers_cfg/utf8_utils.py index 8056f73..f82dbaf 100644 --- a/transformers_cfg/utf8_utils.py +++ b/transformers_cfg/utf8_utils.py @@ -27,10 +27,20 @@ class PartialUTF8: -1 ) # Default to -1, indicating no bytes are currently expected to complete the character + def __hash__(self): + return hash((self.value, self.n_remain)) + + def __eq__(self, other): + if not isinstance(other, PartialUTF8): + return NotImplemented + return self.value == other.value and self.n_remain == other.n_remain + from typing import List, Tuple +from functools import lru_cache +@lru_cache(maxsize=3000000) def decode_utf8( src: bytes, partial_start: PartialUTF8 ) -> Tuple[List[int], PartialUTF8]: @@ -47,9 +57,8 @@ def decode_utf8( # Check if the continuation byte format is correct (`10xxxxxx`) if (next_byte >> 6) != 2: # If not, it's an invalid sequence. Abort and return a special error state. - # code_points.append(0) - raise ValueError("Invalid UTF-8 sequence") - # return (code_points, PartialUTF8(0, -1)) + code_points = [0] + return code_points, PartialUTF8(0, -1) # Accumulate the value by shifting left and adding the relevant 6 bits value = (value << 6) + (next_byte & 0x3F) @@ -68,9 +77,9 @@ def decode_utf8( # If lookup returns an invalid number, it's an invalid sequence. Abort. if n_remain < 0: - raise ValueError("Invalid UTF-8 sequence") - # code_points = [0] - # return (code_points, PartialUTF8(0, n_remain)) + # raise ValueError("Invalid UTF-8 sequence") + code_points = [0] + return code_points, PartialUTF8(0, -1) # Calculate the mask to isolate significant bits from the first byte mask = (1 << (7 - n_remain)) - 1 @@ -91,6 +100,10 @@ def decode_utf8( # # Append a terminating value to indicate the end (following llama-cpp implementation) # code_points.append(0) + # the following line is crucial for LRU cache to work, as it reset to the initial state + if n_remain == 0: + n_remain = -1 + value = 0 # Return the decoded code points and the state of any partial decoding return code_points, PartialUTF8(value, n_remain) diff --git a/transformers_cfg/utils.py b/transformers_cfg/utils.py index 8d88a68..50bd045 100644 --- a/transformers_cfg/utils.py +++ b/transformers_cfg/utils.py @@ -1,9 +1,22 @@ import json import warnings +from typing import List from 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