125 lines
4.0 KiB
Python
125 lines
4.0 KiB
Python
# coding=utf-8
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# Copyright 2018 The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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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from dataclasses import dataclass, field
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from typing import Tuple
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from ..utils import (
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cached_property,
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is_torch_available,
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is_torch_xla_available,
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is_torch_xpu_available,
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logging,
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requires_backends,
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)
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from .benchmark_args_utils import BenchmarkArguments
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if is_torch_available():
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import torch
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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logger = logging.get_logger(__name__)
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@dataclass
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class PyTorchBenchmarkArguments(BenchmarkArguments):
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deprecated_args = [
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"no_inference",
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"no_cuda",
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"no_tpu",
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"no_speed",
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"no_memory",
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"no_env_print",
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"no_multi_process",
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]
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def __init__(self, **kwargs):
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"""
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This __init__ is there for legacy code. When removing deprecated args completely, the class can simply be
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deleted
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"""
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for deprecated_arg in self.deprecated_args:
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if deprecated_arg in kwargs:
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positive_arg = deprecated_arg[3:]
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setattr(self, positive_arg, not kwargs.pop(deprecated_arg))
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logger.warning(
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f"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
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f" {positive_arg}={kwargs[positive_arg]}"
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)
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self.torchscript = kwargs.pop("torchscript", self.torchscript)
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self.torch_xla_tpu_print_metrics = kwargs.pop("torch_xla_tpu_print_metrics", self.torch_xla_tpu_print_metrics)
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self.fp16_opt_level = kwargs.pop("fp16_opt_level", self.fp16_opt_level)
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super().__init__(**kwargs)
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torchscript: bool = field(default=False, metadata={"help": "Trace the models using torchscript"})
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torch_xla_tpu_print_metrics: bool = field(default=False, metadata={"help": "Print Xla/PyTorch tpu metrics"})
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fp16_opt_level: str = field(
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default="O1",
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metadata={
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"help": (
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"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
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"See details at https://nvidia.github.io/apex/amp.html"
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)
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},
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)
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@cached_property
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def _setup_devices(self) -> Tuple["torch.device", int]:
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requires_backends(self, ["torch"])
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logger.info("PyTorch: setting up devices")
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if not self.cuda:
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device = torch.device("cpu")
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n_gpu = 0
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elif is_torch_xla_available():
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device = xm.xla_device()
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n_gpu = 0
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elif is_torch_xpu_available():
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device = torch.device("xpu")
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n_gpu = torch.xpu.device_count()
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else:
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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n_gpu = torch.cuda.device_count()
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return device, n_gpu
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@property
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def is_tpu(self):
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return is_torch_xla_available() and self.tpu
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@property
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def device_idx(self) -> int:
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requires_backends(self, ["torch"])
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# TODO(PVP): currently only single GPU is supported
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return torch.cuda.current_device()
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@property
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def device(self) -> "torch.device":
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requires_backends(self, ["torch"])
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return self._setup_devices[0]
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@property
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def n_gpu(self):
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requires_backends(self, ["torch"])
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return self._setup_devices[1]
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@property
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def is_gpu(self):
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return self.n_gpu > 0
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