mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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319 lines
11 KiB
Python
319 lines
11 KiB
Python
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
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#
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# This code is inspired by the HuggingFace's PEFT library.
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# https://github.com/huggingface/peft/blob/v0.10.0/src/peft/peft_model.py
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import os
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import socket
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from typing import TYPE_CHECKING, Any, Literal, Union
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import torch
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import torch.distributed as dist
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import transformers.dynamic_module_utils
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from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList
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from transformers.dynamic_module_utils import get_relative_imports
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from transformers.utils import (
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is_torch_bf16_gpu_available,
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is_torch_cuda_available,
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is_torch_mps_available,
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is_torch_npu_available,
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is_torch_xpu_available,
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)
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from transformers.utils.versions import require_version
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from . import logging
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_is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
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try:
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_is_bf16_available = is_torch_bf16_gpu_available() or (is_torch_npu_available() and torch.npu.is_bf16_supported())
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except Exception:
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_is_bf16_available = False
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if TYPE_CHECKING:
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from numpy.typing import NDArray
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from ..hparams import ModelArguments
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logger = logging.get_logger(__name__)
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class AverageMeter:
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r"""Compute and store the average and current value."""
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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 check_version(requirement: str, mandatory: bool = False) -> None:
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r"""Optionally check the package version."""
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if is_env_enabled("DISABLE_VERSION_CHECK") and not mandatory:
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logger.warning_rank0_once("Version checking has been disabled, may lead to unexpected behaviors.")
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return
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if "gptmodel" in requirement or "autoawq" in requirement:
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pip_command = f"pip install {requirement} --no-build-isolation"
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else:
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pip_command = f"pip install {requirement}"
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if mandatory:
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hint = f"To fix: run `{pip_command}`."
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else:
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hint = f"To fix: run `{pip_command}` or set `DISABLE_VERSION_CHECK=1` to skip this check."
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require_version(requirement, hint)
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def check_dependencies() -> None:
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r"""Check the version of the required packages."""
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check_version("transformers>=4.49.0,<=4.52.4,!=4.52.0")
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check_version("datasets>=2.16.0,<=3.6.0")
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check_version("accelerate>=1.3.0,<=1.7.0")
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check_version("peft>=0.14.0,<=0.15.2")
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check_version("trl>=0.8.6,<=0.9.6")
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def calculate_tps(dataset: list[dict[str, Any]], metrics: dict[str, float], stage: Literal["sft", "rm"]) -> float:
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r"""Calculate effective tokens per second."""
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effective_token_num = 0
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for data in dataset:
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if stage == "sft":
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effective_token_num += len(data["input_ids"])
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elif stage == "rm":
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effective_token_num += len(data["chosen_input_ids"]) + len(data["rejected_input_ids"])
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result = effective_token_num * metrics["epoch"] / metrics["train_runtime"]
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return result / dist.get_world_size() if dist.is_initialized() else result
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def count_parameters(model: "torch.nn.Module") -> tuple[int, int]:
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r"""Return the number of trainable parameters and number of all parameters in the model."""
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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 itemsize
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if param.__class__.__name__ == "Params4bit":
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if hasattr(param, "quant_storage") and hasattr(param.quant_storage, "itemsize"):
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num_bytes = param.quant_storage.itemsize
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elif hasattr(param, "element_size"): # for older pytorch version
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num_bytes = param.element_size()
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else:
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num_bytes = 1
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num_params = num_params * 2 * num_bytes
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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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r"""Get the current available device."""
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if is_torch_xpu_available():
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device = "xpu:{}".format(os.getenv("LOCAL_RANK", "0"))
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elif is_torch_npu_available():
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device = "npu:{}".format(os.getenv("LOCAL_RANK", "0"))
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elif is_torch_mps_available():
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device = "mps:{}".format(os.getenv("LOCAL_RANK", "0"))
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elif is_torch_cuda_available():
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device = "cuda:{}".format(os.getenv("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_device_count() -> int:
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r"""Get the number of available devices."""
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if is_torch_xpu_available():
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return torch.xpu.device_count()
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elif is_torch_npu_available():
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return torch.npu.device_count()
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elif is_torch_mps_available():
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return torch.mps.device_count()
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elif is_torch_cuda_available():
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return torch.cuda.device_count()
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else:
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return 0
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def get_logits_processor() -> "LogitsProcessorList":
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r"""Get logits processor that removes NaN and Inf logits."""
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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 get_current_memory() -> tuple[int, int]:
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r"""Get the available and total memory for the current device (in Bytes)."""
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if is_torch_xpu_available():
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return torch.xpu.mem_get_info()
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elif is_torch_npu_available():
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return torch.npu.mem_get_info()
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elif is_torch_mps_available():
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return torch.mps.current_allocated_memory(), torch.mps.recommended_max_memory()
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elif is_torch_cuda_available():
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return torch.cuda.mem_get_info()
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else:
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return 0, -1
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def get_peak_memory() -> tuple[int, int]:
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r"""Get the peak memory usage (allocated, reserved) for the current device (in Bytes)."""
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if is_torch_xpu_available():
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return torch.xpu.max_memory_allocated(), torch.xpu.max_memory_reserved()
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elif is_torch_npu_available():
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return torch.npu.max_memory_allocated(), torch.npu.max_memory_reserved()
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elif is_torch_mps_available():
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return torch.mps.current_allocated_memory(), -1
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elif is_torch_cuda_available():
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return torch.cuda.max_memory_allocated(), torch.cuda.max_memory_reserved()
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else:
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return 0, -1
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def has_tokenized_data(path: "os.PathLike") -> bool:
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r"""Check if the path has a tokenized dataset."""
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return os.path.isdir(path) and len(os.listdir(path)) > 0
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def infer_optim_dtype(model_dtype: "torch.dtype") -> "torch.dtype":
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r"""Infer the optimal dtype according to the model_dtype and device compatibility."""
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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 is_accelerator_available() -> bool:
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r"""Check if the accelerator is available."""
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return (
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is_torch_xpu_available() or is_torch_npu_available() or is_torch_mps_available() or is_torch_cuda_available()
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)
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def is_env_enabled(env_var: str, default: str = "0") -> bool:
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r"""Check if the environment variable is enabled."""
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return os.getenv(env_var, default).lower() in ["true", "y", "1"]
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def numpify(inputs: Union["NDArray", "torch.Tensor"]) -> "NDArray":
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r"""Cast a torch tensor or a numpy array to a numpy array."""
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if isinstance(inputs, torch.Tensor):
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inputs = inputs.cpu()
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if inputs.dtype == torch.bfloat16: # numpy does not support bfloat16 until 1.21.4
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inputs = inputs.to(torch.float32)
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inputs = inputs.numpy()
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return inputs
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def skip_check_imports() -> None:
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r"""Avoid flash attention import error in custom model files."""
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if not is_env_enabled("FORCE_CHECK_IMPORTS"):
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transformers.dynamic_module_utils.check_imports = get_relative_imports
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def torch_gc() -> None:
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r"""Collect the device memory."""
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gc.collect()
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if is_torch_xpu_available():
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torch.xpu.empty_cache()
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elif is_torch_npu_available():
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torch.npu.empty_cache()
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elif is_torch_mps_available():
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torch.mps.empty_cache()
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elif is_torch_cuda_available():
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torch.cuda.empty_cache()
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def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
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if (not use_modelscope() and not use_openmind()) or os.path.exists(model_args.model_name_or_path):
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return model_args.model_name_or_path
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if use_modelscope():
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check_version("modelscope>=1.11.0", mandatory=True)
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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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return 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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if use_openmind():
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check_version("openmind>=0.8.0", mandatory=True)
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from openmind.utils.hub import snapshot_download # type: ignore
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return snapshot_download(
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model_args.model_name_or_path,
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revision=model_args.model_revision,
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cache_dir=model_args.cache_dir,
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)
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def use_modelscope() -> bool:
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return is_env_enabled("USE_MODELSCOPE_HUB")
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def use_openmind() -> bool:
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return is_env_enabled("USE_OPENMIND_HUB")
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def use_ray() -> bool:
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return is_env_enabled("USE_RAY")
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def find_available_port() -> int:
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r"""Find an available port on the local machine."""
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sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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sock.bind(("", 0))
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port = sock.getsockname()[1]
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sock.close()
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return port
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def fix_proxy(ipv6_enabled: bool = False) -> None:
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r"""Fix proxy settings for gradio ui."""
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os.environ["no_proxy"] = "localhost,127.0.0.1,0.0.0.0"
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if ipv6_enabled:
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for name in ("http_proxy", "https_proxy", "HTTP_PROXY", "HTTPS_PROXY"):
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os.environ.pop(name, None)
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