mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-02 11:42:49 +08:00
133 lines
3.9 KiB
Python
133 lines
3.9 KiB
Python
import gc
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import os
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import torch
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from typing import TYPE_CHECKING, Tuple
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from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList
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try:
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from transformers.utils import (
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is_torch_bf16_cpu_available,
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is_torch_bf16_gpu_available,
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is_torch_cuda_available,
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is_torch_npu_available
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)
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_is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
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_is_bf16_available = is_torch_bf16_gpu_available() or is_torch_bf16_cpu_available()
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except ImportError:
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_is_fp16_available = torch.cuda.is_available()
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try:
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_is_bf16_available = torch.cuda.is_bf16_supported()
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except:
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_is_bf16_available = False
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if TYPE_CHECKING:
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from transformers import HfArgumentParser
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from llmtuner.hparams import ModelArguments
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class AverageMeter:
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r"""
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Computes and stores the average and current value.
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"""
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def __init__(self):
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
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r"""
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Returns the number of trainable parameters and number of all parameters in the model.
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"""
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trainable_params, all_param = 0, 0
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for param in model.parameters():
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num_params = param.numel()
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# if using DS Zero 3 and the weights are initialized empty
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if num_params == 0 and hasattr(param, "ds_numel"):
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num_params = param.ds_numel
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# Due to the design of 4bit linear layers from bitsandbytes, multiply the number of parameters by 2
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if param.__class__.__name__ == "Params4bit":
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num_params = num_params * 2
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all_param += num_params
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if param.requires_grad:
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trainable_params += num_params
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return trainable_params, all_param
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def get_current_device() -> torch.device:
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import accelerate
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if accelerate.utils.is_xpu_available():
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device = "xpu:{}".format(os.environ.get("LOCAL_RANK", "0"))
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elif accelerate.utils.is_npu_available():
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device = "npu:{}".format(os.environ.get("LOCAL_RANK", "0"))
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elif torch.cuda.is_available():
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device = "cuda:{}".format(os.environ.get("LOCAL_RANK", "0"))
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else:
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device = "cpu"
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return torch.device(device)
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def get_logits_processor() -> "LogitsProcessorList":
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r"""
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Gets logits processor that removes NaN and Inf logits.
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"""
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logits_processor = LogitsProcessorList()
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logits_processor.append(InfNanRemoveLogitsProcessor())
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return logits_processor
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def infer_optim_dtype(model_dtype: torch.dtype) -> torch.dtype:
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r"""
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Infers the optimal dtype according to the model_dtype and device compatibility.
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"""
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if _is_bf16_available and model_dtype == torch.bfloat16:
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return torch.bfloat16
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elif _is_fp16_available:
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return torch.float16
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else:
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return torch.float32
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def torch_gc() -> None:
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r"""
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Collects GPU memory.
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"""
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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def try_download_model_from_ms(model_args: "ModelArguments") -> None:
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if not use_modelscope() or os.path.exists(model_args.model_name_or_path):
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return
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try:
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from modelscope import snapshot_download # type: ignore
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revision = "master" if model_args.model_revision == "main" else model_args.model_revision
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model_args.model_name_or_path = snapshot_download(
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model_args.model_name_or_path,
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revision=revision,
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cache_dir=model_args.cache_dir
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)
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except ImportError:
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raise ImportError("Please install modelscope via `pip install modelscope -U`")
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def use_modelscope() -> bool:
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return bool(int(os.environ.get("USE_MODELSCOPE_HUB", "0")))
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