LLaMA-Factory/src/llmtuner/extras/misc.py

import gc
import os
from typing import TYPE_CHECKING, Dict, Tuple

import torch
from peft import PeftModel
from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList, PreTrainedModel
from transformers.utils import (
    SAFE_WEIGHTS_NAME,
    WEIGHTS_NAME,
    is_torch_bf16_gpu_available,
    is_torch_cuda_available,
    is_torch_npu_available,
    is_torch_xpu_available,
)

from .constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from .logging import get_logger


_is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
try:
    _is_bf16_available = is_torch_bf16_gpu_available()
except Exception:
    _is_bf16_available = False


if TYPE_CHECKING:
    from trl import AutoModelForCausalLMWithValueHead

    from llmtuner.hparams import ModelArguments


logger = get_logger(__name__)


class AverageMeter:
    r"""
    Computes and stores the average and current value.
    """

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
    r"""
    Returns the number of trainable parameters and number of all parameters in the model.
    """
    trainable_params, all_param = 0, 0
    for param in model.parameters():
        num_params = param.numel()
        # if using DS Zero 3 and the weights are initialized empty
        if num_params == 0 and hasattr(param, "ds_numel"):
            num_params = param.ds_numel

        # Due to the design of 4bit linear layers from bitsandbytes, multiply the number of parameters by 2
        if param.__class__.__name__ == "Params4bit":
            num_params = num_params * 2

        all_param += num_params
        if param.requires_grad:
            trainable_params += num_params

    return trainable_params, all_param


def fix_valuehead_checkpoint(
    model: "AutoModelForCausalLMWithValueHead", output_dir: str, safe_serialization: bool
) -> None:
    r"""
    The model is already unwrapped.

    There are three cases:
    1. full tuning without ds_zero3: state_dict = {"model.layers.*": ..., "v_head.summary.*": ...}
    2. lora tuning without ds_zero3: state_dict = {"v_head.summary.*": ...}
    3. under deepspeed zero3: state_dict = {"pretrained_model.model.layers.*": ..., "v_head.summary.*": ...}

    We assume `stage3_gather_16bit_weights_on_model_save=true`.
    """
    if not isinstance(model.pretrained_model, (PreTrainedModel, PeftModel)):
        return

    if safe_serialization:
        from safetensors import safe_open
        from safetensors.torch import save_file

        path_to_checkpoint = os.path.join(output_dir, SAFE_WEIGHTS_NAME)
        with safe_open(path_to_checkpoint, framework="pt", device="cpu") as f:
            state_dict: Dict[str, torch.Tensor] = {key: f.get_tensor(key) for key in f.keys()}
    else:
        path_to_checkpoint = os.path.join(output_dir, WEIGHTS_NAME)
        state_dict: Dict[str, torch.Tensor] = torch.load(path_to_checkpoint, map_location="cpu")

    decoder_state_dict = {}
    v_head_state_dict = {}
    for name, param in state_dict.items():
        if name.startswith("v_head."):
            v_head_state_dict[name] = param
        else:
            decoder_state_dict[name.replace("pretrained_model.", "")] = param

    os.remove(path_to_checkpoint)
    model.pretrained_model.save_pretrained(
        output_dir, state_dict=decoder_state_dict or None, safe_serialization=safe_serialization
    )

    if safe_serialization:
        save_file(v_head_state_dict, os.path.join(output_dir, V_HEAD_SAFE_WEIGHTS_NAME), metadata={"format": "pt"})
    else:
        torch.save(v_head_state_dict, os.path.join(output_dir, V_HEAD_WEIGHTS_NAME))

    logger.info("Value head model saved at: {}".format(output_dir))


def get_current_device() -> torch.device:
    r"""
    Gets the current available device.
    """
    if is_torch_xpu_available():
        device = "xpu:{}".format(os.environ.get("LOCAL_RANK", "0"))
    elif is_torch_npu_available():
        device = "npu:{}".format(os.environ.get("LOCAL_RANK", "0"))
    elif is_torch_cuda_available():
        device = "cuda:{}".format(os.environ.get("LOCAL_RANK", "0"))
    else:
        device = "cpu"

    return torch.device(device)


def get_device_count() -> int:
    return torch.cuda.device_count()


def get_logits_processor() -> "LogitsProcessorList":
    r"""
    Gets logits processor that removes NaN and Inf logits.
    """
    logits_processor = LogitsProcessorList()
    logits_processor.append(InfNanRemoveLogitsProcessor())
    return logits_processor


def infer_optim_dtype(model_dtype: torch.dtype) -> torch.dtype:
    r"""
    Infers the optimal dtype according to the model_dtype and device compatibility.
    """
    if _is_bf16_available and model_dtype == torch.bfloat16:
        return torch.bfloat16
    elif _is_fp16_available:
        return torch.float16
    else:
        return torch.float32


def torch_gc() -> None:
    r"""
    Collects GPU memory.
    """
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        torch.cuda.ipc_collect()


def try_download_model_from_ms(model_args: "ModelArguments") -> None:
    if not use_modelscope() or os.path.exists(model_args.model_name_or_path):
        return

    try:
        from modelscope import snapshot_download

        revision = "master" if model_args.model_revision == "main" else model_args.model_revision
        model_args.model_name_or_path = snapshot_download(
            model_args.model_name_or_path, revision=revision, cache_dir=model_args.cache_dir
        )
    except ImportError:
        raise ImportError("Please install modelscope via `pip install modelscope -U`")


def use_modelscope() -> bool:
    return bool(int(os.environ.get("USE_MODELSCOPE_HUB", "0")))