CSS-LM

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# 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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import logging
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from dataclasses import dataclass, field
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from typing import Tuple
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from ..file_utils import cached_property, is_torch_available, is_torch_tpu_available, torch_required
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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_tpu_available():
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    import torch_xla.core.xla_model as xm
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logger = logging.getLogger(__name__)
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@dataclass
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class PyTorchBenchmarkArguments(BenchmarkArguments):
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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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    @torch_required
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    def _setup_devices(self) -> Tuple["torch.device", int]:
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        logger.info("PyTorch: setting up devices")
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        if self.no_cuda:
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            device = torch.device("cpu")
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            n_gpu = 0
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        elif is_torch_tpu_available():
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            device = xm.xla_device()
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            n_gpu = 0
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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_tpu_available() and not self.no_tpu
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    @property
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    @torch_required
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    def device_idx(self) -> int:
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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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    @torch_required
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    def device(self) -> "torch.device":
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        return self._setup_devices[0]
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    @property
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    @torch_required
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    def n_gpu(self):
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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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