mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-10-15 16:18:10 +08:00
119 lines
3.7 KiB
Python
119 lines
3.7 KiB
Python
import gc
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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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_is_bf16_available = torch.cuda.is_bf16_supported()
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if TYPE_CHECKING:
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from transformers.modeling_utils import PreTrainedModel
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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 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 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 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 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, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): # do nothing
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return model
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if torch.cuda.device_count() > 1:
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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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