LLM-Drop_superweights_change_v2/src/llmtuner/model/utils.py

import inspect
from typing import TYPE_CHECKING, Dict, List

import torch
from transformers import PreTrainedModel
from transformers.utils import cached_file

from ..extras.constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from ..extras.logging import get_logger
from ..extras.misc import get_current_device


if TYPE_CHECKING:
    from transformers import PretrainedConfig, PreTrainedTokenizer

    from ..hparams import ModelArguments


logger = get_logger(__name__)


def dispatch_model(model: "PreTrainedModel") -> "PreTrainedModel":
    r"""
    Dispatches a pre-trained model to GPUs with balanced memory when the GPU is available.
    Borrowed from: https://github.com/huggingface/transformers/blob/v4.36.2/src/transformers/modeling_utils.py#L3570
    """
    if getattr(model, "quantization_method", None):  # already set on current device
        return model

    if (
        torch.cuda.device_count() > 1
        and isinstance(model, PreTrainedModel)
        and model._no_split_modules is not None
        and model.config.model_type != "chatglm"
    ):
        from accelerate import dispatch_model
        from accelerate.utils import get_balanced_memory, infer_auto_device_map

        kwargs = {"dtype": model.dtype, "no_split_module_classes": model._get_no_split_modules("auto")}
        max_memory = get_balanced_memory(model, **kwargs)
        # Make sure tied weights are tied before creating the device map.
        model.tie_weights()
        device_map = infer_auto_device_map(model, max_memory=max_memory, **kwargs)
        device_map_kwargs = {"device_map": device_map, "offload_dir": "offload"}
        if "skip_keys" in inspect.signature(dispatch_model).parameters:
            device_map_kwargs["skip_keys"] = model._skip_keys_device_placement
        return dispatch_model(model, **device_map_kwargs)
    else:
        return model.to(device=get_current_device())


def find_all_linear_modules(model: "PreTrainedModel") -> List[str]:
    r"""
    Finds all available modules to apply lora.
    """
    quantization_method = getattr(model, "quantization_method", None)
    if quantization_method is None:
        linear_cls = torch.nn.Linear
    elif quantization_method == "bitsandbytes":
        import bitsandbytes as bnb

        linear_cls = bnb.nn.Linear4bit if getattr(model, "is_loaded_in_4bit", False) else bnb.nn.Linear8bitLt
    else:
        raise ValueError("Finding linear modules for {} models is not supported.".format(quantization_method))

    output_layer_names = ["lm_head"]
    if model.config.model_type == "chatglm":
        output_layer_names.append("output_layer")

    module_names = set()
    for name, module in model.named_modules():
        if isinstance(module, linear_cls) and not any(output_layer in name for output_layer in output_layer_names):
            module_names.add(name.split(".")[-1])

    logger.info("Found linear modules: {}".format(",".join(module_names)))
    return list(module_names)


def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], num_layer_trainable: int) -> List[str]:
    r"""
    Finds the modules in the expanded blocks to apply lora.
    """
    num_layers = getattr(model.config, "num_hidden_layers", None)
    if not num_layers:
        raise ValueError("Model was not supported.")

    if num_layers % num_layer_trainable != 0:
        raise ValueError(
            "`num_layers` {} should be divisible by `num_layer_trainable` {}.".format(num_layers, num_layer_trainable)
        )

    stride = num_layers // num_layer_trainable
    trainable_layer_ids = range(stride - 1, num_layers + stride - 1, stride)
    trainable_layers = [".{:d}.".format(idx) for idx in trainable_layer_ids]
    module_names = []
    for name, _ in model.named_modules():
        if any(target_module in name for target_module in target_modules) and any(
            trainable_layer in name for trainable_layer in trainable_layers
        ):
            module_names.append(name)

    logger.info("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
    return module_names


def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") -> Dict[str, torch.Tensor]:
    r"""
    Loads value head parameters from Hugging Face Hub or local disk.

    Returns: dict with keys `v_head.summary.weight` and `v_head.summary.bias`.
    """
    kwargs = {"path_or_repo_id": path_or_repo_id, "cache_dir": model_args.cache_dir, "token": model_args.hf_hub_token}

    try:
        from safetensors import safe_open

        vhead_file = cached_file(filename=V_HEAD_SAFE_WEIGHTS_NAME, **kwargs)
        with safe_open(vhead_file, framework="pt", device="cpu") as f:
            return {key: f.get_tensor(key) for key in f.keys()}
    except Exception as err:
        logger.info("Failed to load {}: {}".format(V_HEAD_SAFE_WEIGHTS_NAME, str(err)))

    try:
        vhead_file = cached_file(filename=V_HEAD_WEIGHTS_NAME, **kwargs)
        return torch.load(vhead_file, map_location="cpu")
    except Exception as err:
        logger.info("Failed to load {}: {}".format(V_HEAD_WEIGHTS_NAME, str(err)))

    logger.info("Provided path ({}) does not contain value head weights.".format(path_or_repo_id))
    logger.info("Ignore these messages if you are not resuming the training of a value head model.")
    return None


def register_autoclass(config: "PretrainedConfig", model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer"):
    if "AutoConfig" in getattr(config, "auto_map", {}):
        config.__class__.register_for_auto_class()
    if "AutoModelForCausalLM" in getattr(config, "auto_map", {}):
        model.__class__.register_for_auto_class()
    if "AutoTokenizer" in tokenizer.init_kwargs.get("auto_map", {}):
        tokenizer.__class__.register_for_auto_class()