Gemma GGUF chat templates work! (#246)
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llama.py * Account for DoRA * Update llama.py * Update save.py * GGUF incorrect * Update save.py * Update pyproject.toml * kaggle new * Update pyproject.toml * Update pyproject.toml * upcasting * Fix Colab * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update chat_templates.py * Update chat_templates.py * Update chat_templates.py * Update chat_templates.py * Update chat_templates.py
This commit is contained in:
Daniel Han
GitHub
parent
2c5c5bb4bb
commit
990c7a809c
1 changed files with 75 additions and 3 deletions
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@ -15,12 +15,15 @@
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__all__ = [
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"get_chat_template",
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"test_chat_templates",
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"fix_sentencepiece_tokenizer",
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]
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from transformers import StoppingCriteria, StoppingCriteriaList
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from torch import LongTensor, FloatTensor
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from transformers.models.llama.modeling_llama import logger
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from .models._utils import patch_tokenizer
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import os
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import shutil
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CHAT_TEMPLATES = {}
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@ -239,14 +242,75 @@ CHAT_TEMPLATES["gemma"] = (gemma_template, gemma_eos_token,)
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# Gemma with ChatML instead
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# We find using <eos> is still more appropriate!
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gemma_chatml_template = "{{ bos_token }}" + chatml_template
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gemma_chatml_eos_token = (
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{"<start_of_turn>" : "<|im_start|>", "<end_of_turn>" : "<|im_end|>"},
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{"<start_of_turn>" : "<|im_start|>", "<eos>" : "<|im_end|>"},
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"<|im_end|>",
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)
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CHAT_TEMPLATES["gemma_chatml"] = (gemma_chatml_template, gemma_chatml_eos_token,)
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def fix_sentencepiece_tokenizer(
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old_tokenizer,
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new_tokenizer,
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token_mapping,
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temporary_location = "_unsloth_sentencepiece_temp",
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):
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# From https://github.com/google/sentencepiece/issues/121
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# We need to manually edit the sentencepiece tokenizer!
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try:
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import sentencepiece.sentencepiece_model_pb2 as sentencepiece_model_pb2
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except:
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if not os.path.exists(temporary_location):
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os.system("git clone https://github.com/google/sentencepiece.git unsloth_sentencepiece_temp")
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os.system(f"cd {temporary_location}/src && protoc --python_out=. sentencepiece_model.proto")
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shutil.rmtree(temporary_location)
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pass
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import sentencepiece.sentencepiece_model_pb2 as sentencepiece_model_pb2
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pass
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if not os.path.exists(temporary_location):
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os.makedirs(temporary_location)
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pass
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# First save the old tokenizer
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old_tokenizer.save_pretrained(temporary_location)
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from sentencepiece import SentencePieceProcessor
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tokenizer_file = sentencepiece_model_pb2.ModelProto()
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tokenizer_file.ParseFromString(open(f"{temporary_location}/tokenizer.model", "rb").read())
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# Now save the new tokenizer
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new_tokenizer.save_pretrained(temporary_location)
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# Now correct the old tokenizer's .model file
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for old_token, new_token in token_mapping.items():
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ids = old_tokenizer([old_token], add_special_tokens = False).input_ids
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ids = ids[0]
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if (len(ids) != 1):
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# Skip this token!
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print(f"Skip mapping {old_token} to {new_token} since {new_token} is already in the tokenizer!")
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continue
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pass
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ids = ids[0]
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tokenizer_piece = tokenizer_file.pieces[ids]
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assert(tokenizer_piece.piece == old_token)
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tokenizer_piece.piece = new_token
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pass
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# And now write it
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with open(f"{temporary_location}/tokenizer.model", "wb") as file:
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file.write(tokenizer_file.SerializeToString())
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pass
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# And load it!
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(temporary_location, eos_token = new_tokenizer.eos_token)
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return tokenizer
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pass
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def get_chat_template(
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tokenizer,
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chat_template = "chatml",
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@ -290,11 +354,13 @@ def get_chat_template(
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string_vocab = tokenizer._tokenizer.to_str()
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skipped = 0
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for old_token, new_token in token_mapping.items():
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old_count = string_vocab.count(f'"{old_token}"')
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new_count = string_vocab.count(f'"{new_token}"')
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if new_count != 0:
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print(f"{new_token} is already a token. Skipping.")
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skipped += 1
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elif old_count == 0:
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raise RuntimeError(f"{old_token} was not part of the tokenizer!")
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else:
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@ -308,8 +374,14 @@ def get_chat_template(
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string_vocab = string_vocab.replace(tokenizer.eos_token, stop_word)
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pass
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new_tokenizer = tokenizer._tokenizer.from_str(string_vocab)
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tokenizer = tokenizer.__class__(tokenizer_object = new_tokenizer, eos_token = stop_word)
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if skipped != len(token_mapping):
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new_tokenizer = tokenizer._tokenizer.from_str(string_vocab)
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new_tokenizer = tokenizer.__class__(tokenizer_object = new_tokenizer, eos_token = stop_word)
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# Must fix the sentence piece tokenizer since there's no tokenizer.model file!
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tokenizer = fix_sentencepiece_tokenizer(tokenizer, new_tokenizer, token_mapping,)
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else:
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pass
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elif stop_word != "eos_token":
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logger.warning_once(f"Unsloth: Will map {stop_word} to EOS = {tokenizer.eos_token}.")
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