mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-03 12:12:50 +08:00
196 lines
8.0 KiB
Python
196 lines
8.0 KiB
Python
import torch
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import inspect
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple
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from transformers.utils import cached_file
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from transformers.trainer import WEIGHTS_NAME, SAFE_WEIGHTS_NAME
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from llmtuner.extras.constants import LAYERNORM_NAMES
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from llmtuner.extras.logging import get_logger
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from llmtuner.hparams import ModelArguments, FinetuningArguments
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if TYPE_CHECKING:
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from transformers.modeling_utils import PreTrainedModel
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from transformers.tokenization_utils import PreTrainedTokenizer
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from llmtuner.hparams import DataArguments
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logger = get_logger(__name__)
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def dispatch_model(model: "PreTrainedModel") -> "PreTrainedModel":
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r"""
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Dispatches a pre-trained model to GPUs with balanced memory.
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Borrowed from: https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/modeling_utils.py#L2803
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"""
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if getattr(model, "quantization_method", None): # already set on current device
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return model
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if torch.cuda.device_count() > 1 and getattr(model.config, "model_type", None) != "chatglm":
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from accelerate import dispatch_model
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from accelerate.utils import infer_auto_device_map, get_balanced_memory
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if model._no_split_modules is None:
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raise ValueError("The model class needs to implement the `_no_split_modules` attribute.")
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kwargs = {"dtype": model.dtype, "no_split_module_classes": model._no_split_modules}
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max_memory = get_balanced_memory(model, **kwargs)
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# Make sure tied weights are tied before creating the device map.
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model.tie_weights()
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device_map = infer_auto_device_map(model, max_memory=max_memory, **kwargs)
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return dispatch_model(model, device_map)
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else:
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return model.cuda()
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def find_all_linear_modules(model: "PreTrainedModel") -> List[str]:
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r"""
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Finds all available modules to apply lora.
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"""
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quantization_method = getattr(model, "quantization_method", None)
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if quantization_method is None:
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linear_cls = torch.nn.Linear
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elif quantization_method == "bitsandbytes":
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import bitsandbytes as bnb
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linear_cls = bnb.nn.Linear4bit if getattr(model, "is_loaded_in_4bit", False) else bnb.nn.Linear8bitLt
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else:
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raise ValueError("Finding linear modules for {} models is not supported.".format(quantization_method))
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output_layer_names = ["lm_head"]
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if model.config.model_type == "chatglm":
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output_layer_names.append("output_layer")
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module_names = set()
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for name, module in model.named_modules():
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if (
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isinstance(module, linear_cls)
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and not any([output_layer in name for output_layer in output_layer_names])
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):
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module_names.add(name.split(".")[-1])
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logger.info("Found linear modules: {}".format(",".join(module_names)))
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return list(module_names)
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def get_modelcard_args(
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model_args: "ModelArguments",
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data_args: "DataArguments",
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finetuning_args: "FinetuningArguments"
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) -> Dict[str, Any]:
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return {
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"tasks": "text-generation",
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"license": "other",
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"finetuned_from": model_args.model_name_or_path,
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"dataset": [dataset.strip() for dataset in data_args.dataset.split(",")],
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"tags": ["llama-factory"] + (["lora"] if finetuning_args.finetuning_type == "lora" else [])
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}
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def load_valuehead_params(
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path_or_repo_id: str,
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model_args: "ModelArguments"
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) -> Dict[str, torch.Tensor]:
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r"""
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Loads value head parameters from Hugging Face Hub or local disk.
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Returns: dict with keys `v_head.summary.weight` and `v_head.summary.bias`.
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"""
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kwargs = {
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"path_or_repo_id": path_or_repo_id,
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"cache_dir": model_args.cache_dir
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}
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if "token" in inspect.signature(cached_file).parameters:
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kwargs["token"] = model_args.hf_hub_token
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elif "use_auth_token" in inspect.signature(cached_file).parameters: # for transformers==4.31.0
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kwargs["use_auth_token"] = model_args.hf_hub_token
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else:
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logger.warning("Ignore `hf_hub_token` since matched parameter is not found.")
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try:
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vhead_file = cached_file(filename=WEIGHTS_NAME, **kwargs)
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return torch.load(vhead_file, map_location="cpu")
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except Exception as err:
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logger.info("Failed to load {}: {}".format(WEIGHTS_NAME, str(err)))
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try:
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from safetensors import safe_open
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vhead_file = cached_file(filename=SAFE_WEIGHTS_NAME, **kwargs)
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with safe_open(vhead_file, framework="pt", device="cpu") as f:
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return {
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"v_head.summary.weight": f.get_tensor("v_head.summary.weight"),
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"v_head.summary.bias": f.get_tensor("v_head.summary.bias")
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}
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except Exception as err:
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logger.info("Failed to load {}: {}".format(SAFE_WEIGHTS_NAME, str(err)))
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logger.warning("Provided path ({}) does not contain valuehead weights.".format(path_or_repo_id))
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return None
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def prepare_model_for_training(
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model: "PreTrainedModel",
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finetuning_args: "FinetuningArguments",
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output_layer_name: Optional[str] = "lm_head",
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use_gradient_checkpointing: Optional[bool] = True,
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layernorm_names: Optional[Set[str]] = LAYERNORM_NAMES
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) -> "PreTrainedModel":
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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) upcast the lm_head to fp32
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Inspired by: https://github.com/huggingface/peft/blob/v0.2.0/src/peft/utils/other.py#L33
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"""
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if finetuning_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 weights in layernorm in float32.")
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if finetuning_args.neft_alpha > 1e-6:
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def neftune_forward_hook(module: torch.nn.Module, args: Tuple[torch.Tensor], output: torch.Tensor):
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if module.training:
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dims = torch.tensor(output.size(1) * output.size(2))
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mag_norm = finetuning_args.neft_alpha / torch.sqrt(dims)
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output = output + torch.zeros_like(output).uniform_(-mag_norm, mag_norm)
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return output
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model.get_input_embeddings().register_forward_hook(neftune_forward_hook)
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logger.info("Using noisy embedding with alpha={:.2f}".format(finetuning_args.neft_alpha))
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if use_gradient_checkpointing and getattr(model, "supports_gradient_checkpointing", False):
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if hasattr(model, "enable_input_require_grads"):
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model.enable_input_require_grads()
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else:
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def make_inputs_require_grad(module: torch.nn.Module, args: Tuple[torch.Tensor], output: torch.Tensor):
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output.requires_grad_(True)
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model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
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model.gradient_checkpointing_enable()
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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 finetuning_args.finetuning_type != "full" and hasattr(model, output_layer_name):
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output_layer = getattr(model, output_layer_name)
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if isinstance(output_layer, torch.nn.Linear):
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def fp32_forward_pre_hook(module: torch.nn.Module, args: Tuple[torch.Tensor]):
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return args[0].to(output_layer.weight.dtype)
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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.register_forward_pre_hook(fp32_forward_pre_hook)
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output_layer.register_forward_hook(fp32_forward_post_hook)
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return model
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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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old_vocab_size = model.get_input_embeddings().weight.size(0)
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new_vocab_size = len(tokenizer)
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if new_vocab_size != old_vocab_size:
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model.resize_token_embeddings(new_vocab_size, pad_to_multiple_of=64)
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logger.info("Resized embedding tokens from {} to {}.".format(old_vocab_size, new_vocab_size))
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