Bug fixes (#308)
* Update rms_layernorm.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Update gemma.py * Fix Gemma merging * Update rms_layernorm.py * Update gemma.py * Update pyproject.toml * Layernorms * Gemma precision * Update gemma.py * sqrt * Update gemma.py * Update save.py * RoPE and Gemma precision * Update rms_layernorm.py * Fix warning * Update chat_templates.py * Update chat_templates.py * Update save.py * Update save.py * Update save.py * Update chat_templates.py * Update llama.py * model_name * Update loader.py * Tokenizer overwritten * Update llama.py * Update llama.py * Update llama.py * Update save.py * Accuracy * Revert * Update save.py * Update fast_lora.py * Update fast_lora.py * Update fast_lora.py * Update fast_lora.py * Update fast_lora.py * Update chat_templates.py * Update save.py * Update save.py * Update llama.py * Update 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 * Update pyproject.toml * Update pyproject.toml * Update pyproject.toml * Update rope_embedding.py * Update rope_embedding.py * Fix bugs * Update fast_lora.py * Update fast_lora.py * Update README.md * Update README.md * GGUF * Update save.py * Update save.py * Update save.py * Update save.py * Update README.md * Update README.md * Bugs * Update fast_lora.py * Update pyproject.toml * Update fast_lora.py * Update __init__.py * Update fast_lora.py * dtype * Update llama.py * Update llama.py * Update llama.py * dtype * Update mistral.py * trust_remote_code * lm_head * Update llama.py * save_pretrained_settings * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * state_dict * Update save.py * whoami * Update llama.py * Update save.py * Update llama.py * Patch tokenizer * Update chat_templates.py * Heal tokenizers * Update chat_templates.py * Update mapper.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update chat_templates.py * tokenizer patching * patch_tokenizer * Update chat_templates.py * Update tokenizer_utils.py * Update chat_templates.py * Update chat_templates.py * Update chat_templates.py * Update tokenizer_utils.py * Edit * Update mistral.py * Update mistral.py * Stats * Update mistral.py * attention_mask * Update llama.py * Update llama.py * batch * Temp fix batch inference * Update llama.py * Update gemma.py * Fix inference * swiglu * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update mistral.py * Update llama.py * fast inference * model * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update utils.py * Update llama.py * Update utils.py * inference * Update llama.py * Update llama.py * Update llama.py * overhead * Update llama.py * Update llama.py * compile * Update gemma.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update utils.py * Update utils.py * lora mamtul * Update llama.py * Update llama.py * Update llama.py * offloaded checkpointing * Update llama.py * Update llama.py * Update _utils.py * Update _utils.py * Update _utils.py * Update llama.py * Update llama.py * Update gemma.py * Revert "Update gemma.py" This reverts commit e3c3c5f3fa3d04a87f854056f6b547ced610d712. * Update _utils.py * Update _utils.py * Update _utils.py * Saving * sentencepiece_model_pb2 * Update llama.py * Update save.py * Update llama.py * padding side * Update tokenizer_utils.py * cache dir * Update tokenizer_utils.py * Update tokenizer_utils.py * Update pyproject.toml * Update pyproject.toml * Update tokenizer_utils.py * Update tokenizer_utils.py * Update llama.py
This commit is contained in:
Daniel Han
GitHub
parent
4290c8a278
commit
cb7924e9db
4 changed files with 80 additions and 21 deletions
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@ -44,6 +44,7 @@ huggingface = [
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"accelerate>=0.26.1",
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"trl>=0.7.9",
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"peft>=0.7.1",
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"protobuf<4.0.0",
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]
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cu118only = [
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"xformers @ https://download.pytorch.org/whl/cu118/xformers-0.0.22.post7%2Bcu118-cp39-cp39-manylinux2014_x86_64.whl ; python_version=='3.9'",
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@ -170,6 +171,7 @@ colab-new = [
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"psutil",
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"wheel>=0.42.0",
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"numpy",
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"protobuf<4.0.0",
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]
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colab-no-deps = [
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"accelerate>=0.26.1",
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@ -177,6 +179,7 @@ colab-no-deps = [
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"peft>=0.7.1",
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"xformers",
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"bitsandbytes",
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"protobuf<4.0.0",
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]
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colab = [
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"unsloth[cu121]",
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@ -1068,17 +1068,39 @@ class FastLlamaModel:
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# https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/discussions/12
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# RoPE Scaling's max_position_embeddings must be updated
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max_position_embeddings = max(max_seq_length, model_max_seq_length)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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device_map = device_map,
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torch_dtype = dtype,
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quantization_config = bnb_config,
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token = token,
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rope_scaling = rope_scaling,
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max_position_embeddings = max_position_embeddings,
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trust_remote_code = trust_remote_code,
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**kwargs,
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)
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try:
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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device_map = device_map,
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torch_dtype = dtype,
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quantization_config = bnb_config,
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token = token,
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rope_scaling = rope_scaling,
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max_position_embeddings = max_position_embeddings,
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trust_remote_code = trust_remote_code,
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**kwargs,
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)
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except Exception as error:
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if "rope_scaling" in str(error):
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if rope_scaling is not None:
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raise TypeError("Unsloth: {model_name} does not support rope_scaling.")
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pass
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# Counteract missing rope_scaling
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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device_map = device_map,
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torch_dtype = dtype,
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quantization_config = bnb_config,
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token = token,
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max_position_embeddings = max_position_embeddings,
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trust_remote_code = trust_remote_code,
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**kwargs,
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)
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else:
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raise error
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pass
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pass
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# Counteract saved tokenizers
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tokenizer_name = model_name if tokenizer_name is None else tokenizer_name
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@ -684,7 +684,7 @@ pass
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def install_llama_cpp_make_non_blocking():
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env = { **os.environ, "LLAMA_CUBLAS": "1", }
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env = { **os.environ, "LLAMA_CUDA": "1", }
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n_jobs = max(int(psutil.cpu_count()*1.5), 1)
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# Force make clean
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os.system("make clean -C llama.cpp")
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@ -752,7 +752,7 @@ pass
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def install_llama_cpp_blocking(use_cuda = True):
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use_cuda = "LLAMA_CUBLAS=1" if use_cuda else ""
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use_cuda = "LLAMA_CUDA=1" if use_cuda else ""
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commands = [
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"git clone https://github.com/ggerganov/llama.cpp",
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@ -937,7 +937,7 @@ def save_to_gguf(
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"You do not need to close this Python program. Run the following commands in a new terminal:\n"\
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"You must run this in the same folder as you're saving your model.\n"\
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"git clone https://github.com/ggerganov/llama.cpp\n"\
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"cd llama.cpp && make clean && LLAMA_CUBLAS=1 make all -j\n"\
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"cd llama.cpp && make clean && LLAMA_CUDA=1 make all -j\n"\
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"Once that's done, redo the quantization."
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)
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pass
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@ -966,7 +966,7 @@ def save_to_gguf(
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"You do not need to close this Python program. Run the following commands in a new terminal:\n"\
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"You must run this in the same folder as you're saving your model.\n"\
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"git clone https://github.com/ggerganov/llama.cpp\n"\
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"cd llama.cpp && make clean && LLAMA_CUBLAS=1 make all -j\n"\
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"cd llama.cpp && make clean && LLAMA_CUDA=1 make all -j\n"\
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"Once that's done, redo the quantization."
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)
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pass
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@ -31,6 +31,12 @@ IGNORED_TOKENIZER_CHECKING = frozenset((
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"CodeLlamaTokenizer",
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))
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# Check environments
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keynames = "\n" + "\n".join(os.environ.keys())
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IS_COLAB_ENVIRONMENT = "\nCOLAB_" in keynames
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IS_KAGGLE_ENVIRONMENT = "\nKAGGLE_" in keynames
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del keynames
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def try_fix_tokenizer(tokenizer, prepend = True):
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@ -179,10 +185,19 @@ def assert_same_tokenization(slow_tokenizer, fast_tokenizer):
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if x.endswith("_token") and x.count("_") == 1
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)))
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all_special_tokens = list(set(special_tokens + slow_tokenizer.all_special_tokens))
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string = "\n".join(all_special_tokens) + \
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"A quick brown fox jumps over the lazy dog!!\n\n" + \
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"".join(all_special_tokens)
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return slow_tokenizer(string).input_ids == fast_tokenizer(string).input_ids
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try:
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string = "\n".join(all_special_tokens) + \
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"A quick brown fox jumps over the lazy dog!!\n\n" + \
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"".join(all_special_tokens)
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return slow_tokenizer(string).input_ids == fast_tokenizer(string).input_ids
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except:
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# For eg see https://github.com/unslothai/unsloth/issues/292
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# Sometimes tokenizer has weird tokens, causing a combined tokenization to fail.
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# [TODO] We temporarily disable this for CodeLlama tokenizers
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if slow_tokenizer.__repr__().split("(", 1)[0] in IGNORED_TOKENIZER_CHECKING:
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return True
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else:
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return False
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pass
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@ -203,7 +218,6 @@ def fix_sentencepiece_tokenizer(
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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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@ -220,7 +234,11 @@ def fix_sentencepiece_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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# [TODO] Hack for Starling - try except
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try:
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tokenizer_piece = tokenizer_file.pieces[ids]
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except:
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continue
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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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@ -243,7 +261,14 @@ def load_correct_tokenizer(
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padding_side = "right",
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token = None,
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trust_remote_code = False,
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cache_dir = "huggingface_tokenizers_cache",
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):
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if IS_COLAB_ENVIRONMENT or IS_KAGGLE_ENVIRONMENT:
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cache_dir = cache_dir
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else:
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cache_dir = None
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pass
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slow_tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_name,
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model_max_length = model_max_length,
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@ -251,6 +276,7 @@ def load_correct_tokenizer(
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token = token,
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trust_remote_code = trust_remote_code,
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use_fast = False,
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cache_dir = cache_dir,
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)
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fast_tokenizer = AutoTokenizer.from_pretrained(
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tokenizer_name,
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@ -258,6 +284,7 @@ def load_correct_tokenizer(
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padding_side = padding_side,
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token = token,
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trust_remote_code = trust_remote_code,
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cache_dir = cache_dir,
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)
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fast_tokenizer.add_bos_token = slow_tokenizer.add_bos_token
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fast_tokenizer.add_eos_token = slow_tokenizer.add_eos_token
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@ -375,6 +402,12 @@ def check_tokenizer(
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)
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pass
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if IS_COLAB_ENVIRONMENT or IS_KAGGLE_ENVIRONMENT:
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cache_dir = "huggingface_tokenizers_cache"
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else:
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cache_dir = None
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pass
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# Try slow tokenizer which can fix things!
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tokenizer = AutoTokenizer.from_pretrained(
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model_name,
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@ -382,6 +415,7 @@ def check_tokenizer(
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padding_side = padding_side,
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token = token,
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use_fast = False,
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cache_dir = cache_dir,
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)
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return check_tokenizer(
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model = model,
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