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LLaMA-Factory-310P3
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update patcher

This commit is contained in:
hiyouga 2023-12-23 15:24:27 +08:00
parent 0bbf7118df
commit e44b82ee24
6 changed files with 135 additions and 130 deletions
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2
README.md
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@ -214,7 +214,7 @@ huggingface-cli login
| Method | Bits | 7B | 13B | 30B | 65B | 8x7B |
| ------ | ---- | ----- | ----- | ----- | ------ | ------ |
| Full | 16 | 160GB | 320GB | 600GB | 1200GB | 1000GB |
| Full | 16 | 160GB | 320GB | 600GB | 1200GB | 900GB |
| Freeze | 16 | 20GB | 40GB | 120GB | 240GB | 200GB |
| LoRA | 16 | 16GB | 32GB | 80GB | 160GB | 120GB |
| QLoRA | 8 | 10GB | 16GB | 40GB | 80GB | 80GB |

2
README_zh.md
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@ -214,7 +214,7 @@ huggingface-cli login
| 训练方法 | 精度 | 7B | 13B | 30B | 65B | 8x7B |
| ------- | ---- | ----- | ----- | ----- | ------ | ------ |
| 全参数 | 16 | 160GB | 320GB | 600GB | 1200GB | 1000GB |
| 全参数 | 16 | 160GB | 320GB | 600GB | 1200GB | 900GB |
| 部分参数 | 16 | 20GB | 40GB | 120GB | 240GB | 200GB |
| LoRA | 16 | 16GB | 32GB | 80GB | 160GB | 120GB |
| QLoRA | 8 | 10GB | 16GB | 40GB | 80GB | 80GB |

4
src/llmtuner/hparams/model_args.py
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@ -22,6 +22,10 @@ class ModelArguments:
default=False,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}
)
resize_vocab: Optional[bool] = field(
default=False,
metadata={"help": "Whether to resize the tokenizer vocab and the embedding layers."}
)
split_special_tokens: Optional[bool] = field(
default=False,
metadata={"help": "Whether or not the special tokens should be split during the tokenization process."}

8
src/llmtuner/model/loader.py
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@ -8,9 +8,7 @@ from llmtuner.extras.logging import get_logger
from llmtuner.extras.misc import count_parameters, get_current_device, try_download_model_from_ms
from llmtuner.model.adapter import init_adapter
from llmtuner.model.patcher import patch_config, patch_tokenizer, patch_model, patch_valuehead_model
from llmtuner.model.utils import (
load_valuehead_params, prepare_model_for_training, resize_embedding_layer, register_autoclass
)
from llmtuner.model.utils import load_valuehead_params, prepare_model_for_training, register_autoclass
if TYPE_CHECKING:
from transformers import PreTrainedModel, PreTrainedTokenizer
@ -94,10 +92,8 @@ def load_model_and_tokenizer(
)
model = model.to(model_args.compute_dtype) if not getattr(model, "quantization_method", None) else model
patch_model(model)
patch_model(model, tokenizer, model_args)
register_autoclass(config, model, tokenizer)
if not is_deepspeed_zero3_enabled():
resize_embedding_layer(model, tokenizer)
model = prepare_model_for_training(model=model, finetuning_args=finetuning_args) if is_trainable else model
model = init_adapter(model, model_args, finetuning_args, is_trainable)

222
src/llmtuner/model/patcher.py
View File

@ -25,105 +25,34 @@ logger = get_logger(__name__)
SUPPORTED_CLASS_FOR_S2ATTN = [] # TODO: add llama
def _configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool):
if model_args.rope_scaling is not None:
if not hasattr(config, "rope_scaling"):
logger.warning("Current model does not support RoPE scaling.")
else:
if is_trainable:
if model_args.rope_scaling == "dynamic":
logger.warning(
"Dynamic NTK may not work well with fine-tuning. "
"See: https://github.com/huggingface/transformers/pull/24653"
)
current_max_length = getattr(config, "max_position_embeddings", None)
if current_max_length and model_args.model_max_length > current_max_length:
scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
else:
logger.warning("Input length is smaller than max length. Consider increase input length.")
scaling_factor = 1.0
else:
scaling_factor = 2.0
setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
logger.info("Using {} scaling strategy and setting scaling factor to {}".format(
model_args.rope_scaling, scaling_factor
))
def _noisy_mean_initialization(embed_weight: torch.Tensor, num_new_tokens: int):
embedding_dim = embed_weight.size(1)
avg_weight = embed_weight[:-num_new_tokens].mean(dim=0, keepdim=True)
noise_weight = torch.empty_like(avg_weight[-num_new_tokens:])
noise_weight.normal_(mean=0, std=(1.0 / math.sqrt(embedding_dim)))
embed_weight[-num_new_tokens:] = avg_weight + noise_weight
def _configure_flashattn(model_args: "ModelArguments", config_kwargs: Dict[str, Any]):
if model_args.flash_attn and is_flash_attn2_available():
config_kwargs["use_flash_attention_2"] = True
config_kwargs["torch_dtype"] = model_args.compute_dtype
logger.info("Using FlashAttention-2 for faster training and inference.")
def _configure_longlora(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool):
if is_trainable and model_args.shift_attn:
if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN:
setattr(config, "group_size_ratio", 0.25)
logger.info("Using shift short attention with group_size_ratio=1/4.")
else:
logger.warning("Current model does not support shift short attention.")
def _configure_quantization(
config: "PretrainedConfig",
tokenizer: "PreTrainedTokenizer",
model_args: "ModelArguments",
config_kwargs: Dict[str, Any]
):
def _resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer") -> None:
r"""
Priority: Pre-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
Resize token embeddings.
"""
if getattr(config, "quantization_config", None): # gptq or awq
if is_deepspeed_zero3_enabled():
raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")
current_embedding_size = model.get_input_embeddings().weight.size(0)
if len(tokenizer) > current_embedding_size:
if not isinstance(model.get_output_embeddings(), torch.nn.Linear):
logger.warning("Current model does not support resizing token embeddings.")
return
config_kwargs["device_map"] = {"": get_current_device()}
quantization_config = getattr(config, "quantization_config", None)
logger.info("Loading {}-bit pre-quantized model.".format(quantization_config.get("bits", -1)))
model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=64)
new_embedding_size = model.get_input_embeddings().weight.size(0)
num_new_tokens = new_embedding_size - current_embedding_size
_noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens)
_noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens)
elif model_args.export_quantization_bit is not None: # gptq
require_version("optimum>=1.16.0", "To fix: pip install optimum>=1.16.0")
require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
from accelerate.utils import get_max_memory
if getattr(config, "model_type", None) == "chatglm":
raise ValueError("ChatGLM model is not supported.")
config_kwargs["quantization_config"] = GPTQConfig(
bits=model_args.export_quantization_bit,
tokenizer=tokenizer,
dataset=get_quantization_dataset(tokenizer, model_args)
)
config_kwargs["device_map"] = "auto"
config_kwargs["max_memory"] = get_max_memory()
logger.info("Quantizing model to {} bit.".format(model_args.export_quantization_bit))
elif model_args.quantization_bit is not None: # bnb
if is_deepspeed_zero3_enabled():
raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")
if model_args.quantization_bit == 8:
require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
config_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
elif model_args.quantization_bit == 4:
require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
config_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=model_args.compute_dtype,
bnb_4bit_use_double_quant=model_args.double_quantization,
bnb_4bit_quant_type=model_args.quantization_type
)
config_kwargs["device_map"] = {"": get_current_device()}
logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
logger.info("Resized token embeddings from {} to {}.".format(current_embedding_size, new_embedding_size))
def get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> List[str]:
def _get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> List[str]:
r"""
Inspired by: https://github.com/huggingface/optimum/blob/v1.16.0/optimum/gptq/data.py#L133
TODO: remove tokenizer.decode() https://github.com/huggingface/optimum/pull/1600
@ -153,7 +82,105 @@ def get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "Mode
return samples
def patch_tokenizer(tokenizer: "PreTrainedTokenizer"):
def _configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
if model_args.rope_scaling is not None:
if not hasattr(config, "rope_scaling"):
logger.warning("Current model does not support RoPE scaling.")
else:
if is_trainable:
if model_args.rope_scaling == "dynamic":
logger.warning(
"Dynamic NTK scaling may not work well with fine-tuning. "
"See: https://github.com/huggingface/transformers/pull/24653"
)
current_max_length = getattr(config, "max_position_embeddings", None)
if current_max_length and model_args.model_max_length > current_max_length:
scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
else:
logger.warning("Input length is smaller than max length. Consider increase input length.")
scaling_factor = 1.0
else:
scaling_factor = 2.0
setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
logger.info("Using {} scaling strategy and setting scaling factor to {}".format(
model_args.rope_scaling, scaling_factor
))
def _configure_flashattn(model_args: "ModelArguments", config_kwargs: Dict[str, Any]) -> None:
if model_args.flash_attn and is_flash_attn2_available():
config_kwargs["use_flash_attention_2"] = True
config_kwargs["torch_dtype"] = model_args.compute_dtype
logger.info("Using FlashAttention-2 for faster training and inference.")
def _configure_longlora(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
if is_trainable and model_args.shift_attn:
if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN:
setattr(config, "group_size_ratio", 0.25)
logger.info("Using shift short attention with group_size_ratio=1/4.")
else:
logger.warning("Current model does not support shift short attention.")
def _configure_quantization(
config: "PretrainedConfig",
tokenizer: "PreTrainedTokenizer",
model_args: "ModelArguments",
config_kwargs: Dict[str, Any]
) -> None:
r"""
Priority: Pre-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
"""
if getattr(config, "quantization_config", None): # gptq or awq
if is_deepspeed_zero3_enabled():
raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")
config_kwargs["device_map"] = {"": get_current_device()}
quantization_config = getattr(config, "quantization_config", None)
logger.info("Loading {}-bit pre-quantized model.".format(quantization_config.get("bits", -1)))
elif model_args.export_quantization_bit is not None: # gptq
require_version("optimum>=1.16.0", "To fix: pip install optimum>=1.16.0")
require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
from accelerate.utils import get_max_memory
if getattr(config, "model_type", None) == "chatglm":
raise ValueError("ChatGLM model is not supported.")
config_kwargs["quantization_config"] = GPTQConfig(
bits=model_args.export_quantization_bit,
tokenizer=tokenizer,
dataset=_get_quantization_dataset(tokenizer, model_args)
)
config_kwargs["device_map"] = "auto"
config_kwargs["max_memory"] = get_max_memory()
logger.info("Quantizing model to {} bit.".format(model_args.export_quantization_bit))
elif model_args.quantization_bit is not None: # bnb
if is_deepspeed_zero3_enabled():
raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")
if model_args.quantization_bit == 8:
require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
config_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
elif model_args.quantization_bit == 4:
require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
config_kwargs["quantization_config"] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=model_args.compute_dtype,
bnb_4bit_use_double_quant=model_args.double_quantization,
bnb_4bit_quant_type=model_args.quantization_type
)
config_kwargs["device_map"] = {"": get_current_device()}
logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
def patch_tokenizer(tokenizer: "PreTrainedTokenizer") -> None:
if "PreTrainedTokenizerBase" not in str(tokenizer._pad.__func__):
tokenizer._pad = MethodType(PreTrainedTokenizerBase._pad, tokenizer)
@ -164,7 +191,7 @@ def patch_config(
model_args: "ModelArguments",
config_kwargs: Dict[str, Any],
is_trainable: bool
):
) -> None:
if model_args.compute_dtype is None: # priority: bf16 > fp16 > fp32
model_args.compute_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
setattr(config, "torch_dtype", model_args.compute_dtype)
@ -179,7 +206,7 @@ def patch_config(
_configure_quantization(config, tokenizer, model_args, config_kwargs)
def patch_model(model: "PreTrainedModel"):
def patch_model(model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> None:
if "GenerationMixin" not in str(model.generate.__func__):
model.generate = MethodType(PreTrainedModel.generate, model)
@ -187,8 +214,13 @@ def patch_model(model: "PreTrainedModel"):
setattr(model, "lm_head", model.transformer.output_layer)
setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
if model_args.resize_vocab:
if is_deepspeed_zero3_enabled():
raise ValueError("DeepSpeed ZeRO-3 is incompatible with vocab resizing.")
def patch_valuehead_model(model: "AutoModelForCausalLMWithValueHead"):
_resize_embedding_layer(model, tokenizer)
def patch_valuehead_model(model: "AutoModelForCausalLMWithValueHead") -> None:
def tie_weights(self: "AutoModelForCausalLMWithValueHead") -> None:
if isinstance(self.pretrained_model, PreTrainedModel):
self.pretrained_model.tie_weights()

27
src/llmtuner/model/utils.py
View File

@ -123,14 +123,6 @@ def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") ->
return None
def noisy_mean_initialization(embed_weight: torch.Tensor, num_new_tokens: int):
embedding_dim = embed_weight.size(1)
avg_weight = embed_weight[:-num_new_tokens].mean(dim=0, keepdim=True)
noise_weight = torch.empty_like(avg_weight[-num_new_tokens:])
noise_weight.normal_(mean=0, std=(1.0 / math.sqrt(embedding_dim)))
embed_weight[-num_new_tokens:] = avg_weight + noise_weight
def prepare_model_for_training(
model: "PreTrainedModel",
finetuning_args: "FinetuningArguments",
@ -176,25 +168,6 @@ def prepare_model_for_training(
return model
def resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer") -> None:
r"""
Resize token embeddings.
"""
current_embedding_size = model.get_input_embeddings().weight.size(0)
if len(tokenizer) > current_embedding_size:
if not isinstance(model.get_output_embeddings(), torch.nn.Linear):
logger.warning("Current model does not support resizing token embeddings.")
return
model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=64)
new_embedding_size = model.get_input_embeddings().weight.size(0)
num_new_tokens = new_embedding_size - current_embedding_size
noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens)
noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens)
logger.info("Resized token embeddings from {} to {}.".format(current_embedding_size, new_embedding_size))
def register_autoclass(config: "PretrainedConfig", model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer"):
if "AutoConfig" in getattr(config, "auto_map", {}):
config.__class__.register_for_auto_class()