mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 20:52:59 +08:00
341 lines
15 KiB
Python
341 lines
15 KiB
Python
import math
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import os
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import random
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from contextlib import nullcontext
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from types import MethodType
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
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import torch
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from datasets import load_dataset
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from peft import PeftModel
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from transformers import BitsAndBytesConfig, GPTQConfig, PreTrainedModel, PreTrainedTokenizerBase
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from transformers.integrations import is_deepspeed_zero3_enabled
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from transformers.utils.versions import require_version
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from ..extras.constants import FILEEXT2TYPE, LAYERNORM_NAMES
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from ..extras.logging import get_logger
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from ..extras.misc import get_current_device, infer_optim_dtype
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from ..extras.packages import is_flash_attn2_available
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from ..extras.patches.llama_patch import apply_llama_patch
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from ..extras.patches.mixtral_patch import patch_mixtral_replace_moe_impl
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if TYPE_CHECKING:
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from transformers import PretrainedConfig, PreTrainedTokenizer
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from trl import AutoModelForCausalLMWithValueHead
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from ..hparams import ModelArguments
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logger = get_logger(__name__)
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SUPPORTED_CLASS_FOR_S2ATTN = ["llama"]
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def _noisy_mean_initialization(embed_weight: torch.Tensor, num_new_tokens: int):
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embedding_dim = embed_weight.size(1)
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avg_weight = embed_weight[:-num_new_tokens].mean(dim=0, keepdim=True)
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noise_weight = torch.empty_like(embed_weight[-num_new_tokens:])
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noise_weight.normal_(mean=0, std=(1.0 / math.sqrt(embedding_dim)))
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embed_weight[-num_new_tokens:] = avg_weight + noise_weight
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def _resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer") -> None:
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r"""
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Resize token embeddings.
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"""
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if is_deepspeed_zero3_enabled():
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import deepspeed # type: ignore
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params = [model.get_input_embeddings().weight]
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if model.get_output_embeddings() is not None and not model.config.tie_word_embeddings:
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params.append(model.get_output_embeddings().weight)
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context_maybe_zero3 = deepspeed.zero.GatheredParameters(params, modifier_rank=0)
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else:
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context_maybe_zero3 = nullcontext()
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with context_maybe_zero3:
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current_embedding_size = model.get_input_embeddings().weight.size(0)
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if len(tokenizer) > current_embedding_size:
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if not isinstance(model.get_output_embeddings(), torch.nn.Linear):
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logger.warning("Current model does not support resizing token embeddings.")
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return
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model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=64)
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with context_maybe_zero3:
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new_embedding_size = model.get_input_embeddings().weight.size(0)
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num_new_tokens = new_embedding_size - current_embedding_size
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_noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens)
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_noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens)
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logger.info("Resized token embeddings from {} to {}.".format(current_embedding_size, new_embedding_size))
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def _get_quantization_dataset(tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments") -> List[str]:
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r"""
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Inspired by: https://github.com/huggingface/optimum/blob/v1.16.0/optimum/gptq/data.py#L133
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TODO: remove tokenizer.decode() https://github.com/huggingface/optimum/pull/1600
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"""
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if os.path.isfile(model_args.export_quantization_dataset):
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data_path = FILEEXT2TYPE.get(model_args.export_quantization_dataset.split(".")[-1], None)
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data_files = model_args.export_quantization_dataset
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else:
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data_path = model_args.export_quantization_dataset
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data_files = None
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dataset = load_dataset(path=data_path, data_files=data_files, split="train", cache_dir=model_args.cache_dir)
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maxlen = model_args.export_quantization_maxlen
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samples = []
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for _ in range(model_args.export_quantization_nsamples):
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while True:
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sample_idx = random.randint(0, len(dataset) - 1)
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sample: Dict[str, torch.Tensor] = tokenizer(dataset[sample_idx]["text"], return_tensors="pt")
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if sample["input_ids"].size(1) >= maxlen:
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break # TODO: fix large maxlen
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word_idx = random.randint(0, sample["input_ids"].size(1) - maxlen - 1)
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input_ids = sample["input_ids"][:, word_idx : word_idx + maxlen]
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samples.append(tokenizer.decode(input_ids[0].tolist(), skip_special_tokens=True))
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return samples
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def _configure_attn_implementation(model_args: "ModelArguments", init_kwargs: Dict[str, Any]) -> None:
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if model_args.flash_attn:
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if is_flash_attn2_available():
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logger.info("Using FlashAttention-2 for faster training and inference.")
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init_kwargs["attn_implementation"] = "flash_attention_2"
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else:
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logger.warning("FlashAttention2 is not installed.")
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init_kwargs["attn_implementation"] = None
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else:
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init_kwargs["attn_implementation"] = "eager"
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def _configure_rope(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
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if not hasattr(config, "rope_scaling"):
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logger.warning("Current model does not support RoPE scaling.")
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return
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if is_trainable:
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if model_args.rope_scaling == "dynamic":
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logger.warning(
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"Dynamic NTK scaling may not work well with fine-tuning. "
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"See: https://github.com/huggingface/transformers/pull/24653"
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)
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current_max_length = getattr(config, "max_position_embeddings", None)
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if current_max_length and model_args.model_max_length > current_max_length:
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scaling_factor = float(math.ceil(model_args.model_max_length / current_max_length))
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else:
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logger.warning("Input length is smaller than max length. Consider increase input length.")
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scaling_factor = 1.0
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else:
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scaling_factor = 2.0
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setattr(config, "rope_scaling", {"type": model_args.rope_scaling, "factor": scaling_factor})
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logger.info(
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"Using {} scaling strategy and setting scaling factor to {}".format(model_args.rope_scaling, scaling_factor)
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)
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def _configure_longlora(config: "PretrainedConfig") -> None:
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if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN:
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setattr(config, "group_size_ratio", 0.25)
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apply_llama_patch()
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logger.info("Using shift short attention with group_size_ratio=1/4.")
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else:
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logger.warning("Current model does not support shift short attention.")
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def _configure_quantization(
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config: "PretrainedConfig",
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tokenizer: "PreTrainedTokenizer",
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model_args: "ModelArguments",
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init_kwargs: Dict[str, Any],
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) -> None:
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r"""
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Priority: PTQ-quantized (training) > AutoGPTQ (export) > Bitsandbytes (training)
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"""
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if getattr(config, "quantization_config", None): # gptq
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if is_deepspeed_zero3_enabled():
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raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")
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quantization_config: Dict[str, Any] = getattr(config, "quantization_config", None)
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if quantization_config.get("quant_method", None) == "gptq" and quantization_config.get("bits", -1) == 4:
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quantization_config["use_exllama"] = False # disable exllama
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if quantization_config.get("quant_method", None) == "aqlm":
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require_version(
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"transformers>=4.39.0.dev0", "To fix: pip install git+https://github.com/huggingface/transformers.git"
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)
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quantization_config["bits"] = 2
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logger.info(
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"Loading {}-bit {}-quantized model.".format(
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quantization_config.get("bits", "?"), str(quantization_config.get("quant_method", "")).upper()
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)
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)
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elif model_args.export_quantization_bit is not None: # auto-gptq
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require_version("optimum>=1.16.0", "To fix: pip install optimum>=1.16.0")
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require_version("auto_gptq>=0.5.0", "To fix: pip install auto_gptq>=0.5.0")
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from accelerate.utils import get_max_memory
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if getattr(config, "model_type", None) == "chatglm":
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raise ValueError("ChatGLM model is not supported.")
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init_kwargs["quantization_config"] = GPTQConfig(
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bits=model_args.export_quantization_bit,
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tokenizer=tokenizer,
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dataset=_get_quantization_dataset(tokenizer, model_args),
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)
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init_kwargs["device_map"] = "auto"
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init_kwargs["max_memory"] = get_max_memory()
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logger.info("Quantizing model to {} bit.".format(model_args.export_quantization_bit))
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elif model_args.quantization_bit is not None: # bnb
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if is_deepspeed_zero3_enabled():
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raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")
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if model_args.quantization_bit == 8:
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require_version("bitsandbytes>=0.37.0", "To fix: pip install bitsandbytes>=0.37.0")
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init_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
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elif model_args.quantization_bit == 4:
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require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
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init_kwargs["quantization_config"] = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=model_args.compute_dtype,
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bnb_4bit_use_double_quant=model_args.double_quantization,
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bnb_4bit_quant_type=model_args.quantization_type,
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)
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logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
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def _prepare_model_for_training(
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model: "PreTrainedModel", model_args: "ModelArguments", output_layer_name: Optional[str] = "lm_head"
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) -> None:
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r"""
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Includes:
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(1) cast the layernorm in fp32
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(2) make output embedding layer require grads
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(3) add the upcasting of the lm_head in fp32
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Inspired by: https://github.com/huggingface/peft/blob/v0.7.1/src/peft/utils/other.py#L72
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"""
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if model_args.upcast_layernorm:
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for name, param in model.named_parameters():
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if param.ndim == 1 and any(ln_name in name for ln_name in LAYERNORM_NAMES):
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param.data = param.data.to(torch.float32)
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logger.info("Upcasting layernorm weights in float32.")
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if not model_args.disable_gradient_checkpointing:
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if not getattr(model, "supports_gradient_checkpointing", False):
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logger.warning("Current model does not support gradient checkpointing.")
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else:
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# use_reentrant=False might increase VRAM usage (have not been empirically verified yet)
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# According to: https://github.com/huggingface/transformers/issues/28339
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model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True})
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model.enable_input_require_grads()
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model.config.use_cache = False # turn off when gradient checkpointing is enabled
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logger.info("Gradient checkpointing enabled.")
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if hasattr(model, output_layer_name) and model_args.upcast_lmhead_output:
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def fp32_forward_post_hook(module: torch.nn.Module, args: Tuple[torch.Tensor], output: torch.Tensor):
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return output.to(torch.float32)
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output_layer = getattr(model, output_layer_name)
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if isinstance(output_layer, torch.nn.Linear) and output_layer.weight.dtype != torch.float32:
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output_layer.register_forward_hook(fp32_forward_post_hook)
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def patch_tokenizer(tokenizer: "PreTrainedTokenizer") -> None:
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if "PreTrainedTokenizerBase" not in str(tokenizer._pad.__func__):
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tokenizer._pad = MethodType(PreTrainedTokenizerBase._pad, tokenizer)
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def patch_config(
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config: "PretrainedConfig",
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tokenizer: "PreTrainedTokenizer",
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model_args: "ModelArguments",
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init_kwargs: Dict[str, Any],
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is_trainable: bool,
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) -> None:
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if model_args.compute_dtype is None: # priority: bf16 > fp16 > fp32
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model_args.compute_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
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if getattr(config, "model_type", None) == "qwen":
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for dtype_name, dtype in [("fp16", torch.float16), ("bf16", torch.bfloat16), ("fp32", torch.float32)]:
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setattr(config, dtype_name, model_args.compute_dtype == dtype)
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_configure_attn_implementation(model_args, init_kwargs)
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if model_args.rope_scaling is not None:
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_configure_rope(config, model_args, is_trainable)
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if is_trainable and model_args.shift_attn:
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_configure_longlora(config)
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_configure_quantization(config, tokenizer, model_args, init_kwargs)
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init_kwargs["torch_dtype"] = model_args.compute_dtype
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if not is_deepspeed_zero3_enabled():
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init_kwargs["device_map"] = {"": get_current_device()}
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init_kwargs["low_cpu_mem_usage"] = True
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def patch_model(
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model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer", model_args: "ModelArguments", is_trainable: bool
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) -> None:
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if "GenerationMixin" not in str(model.generate.__func__):
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model.generate = MethodType(PreTrainedModel.generate, model)
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if getattr(model.config, "model_type", None) == "chatglm":
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setattr(model, "lm_head", model.transformer.output_layer)
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setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
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if model_args.resize_vocab:
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_resize_embedding_layer(model, tokenizer)
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if is_trainable:
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_prepare_model_for_training(model, model_args)
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if getattr(model.config, "model_type", None) == "mixtral" and is_deepspeed_zero3_enabled():
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require_version("deepspeed>=0.13.0", "To fix: pip install deepspeed>=0.13.0")
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from deepspeed.utils import set_z3_leaf_modules # type: ignore
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from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
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set_z3_leaf_modules(model, [MixtralSparseMoeBlock])
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if is_trainable:
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patch_mixtral_replace_moe_impl()
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try:
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model.add_model_tags(["llama-factory"])
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except Exception:
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logger.warning("Cannot properly tag the model.")
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def patch_valuehead_model(model: "AutoModelForCausalLMWithValueHead") -> None:
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def tie_weights(self: "AutoModelForCausalLMWithValueHead") -> None:
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if isinstance(self.pretrained_model, PreTrainedModel):
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self.pretrained_model.tie_weights()
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def get_input_embeddings(self: "AutoModelForCausalLMWithValueHead") -> torch.nn.Module:
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if isinstance(self.pretrained_model, PreTrainedModel):
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return self.pretrained_model.get_input_embeddings()
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def create_or_update_model_card(self: "AutoModelForCausalLMWithValueHead", output_dir: str) -> None:
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if isinstance(self.pretrained_model, PeftModel):
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self.pretrained_model.create_or_update_model_card(output_dir)
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ignore_modules = [name for name, _ in model.named_parameters() if "pretrained_model" in name]
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setattr(model, "_keys_to_ignore_on_save", ignore_modules)
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setattr(model, "tie_weights", MethodType(tie_weights, model))
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setattr(model, "get_input_embeddings", MethodType(get_input_embeddings, model))
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setattr(model, "create_or_update_model_card", MethodType(create_or_update_model_card, model))
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