137 lines
4.6 KiB
Python
137 lines
4.6 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 cached_property, is_tf_available, logging, requires_backends
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from .benchmark_args_utils import BenchmarkArguments
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if is_tf_available():
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import tensorflow as tf
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logger = logging.get_logger(__name__)
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@dataclass
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class TensorFlowBenchmarkArguments(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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kwargs[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.tpu_name = kwargs.pop("tpu_name", self.tpu_name)
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self.device_idx = kwargs.pop("device_idx", self.device_idx)
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self.eager_mode = kwargs.pop("eager_mode", self.eager_mode)
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self.use_xla = kwargs.pop("use_xla", self.use_xla)
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super().__init__(**kwargs)
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tpu_name: str = field(
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default=None,
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metadata={"help": "Name of TPU"},
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)
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device_idx: int = field(
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default=0,
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metadata={"help": "CPU / GPU device index. Defaults to 0."},
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)
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eager_mode: bool = field(default=False, metadata={"help": "Benchmark models in eager model."})
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use_xla: bool = field(
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default=False,
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metadata={
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"help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
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},
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)
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@cached_property
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def _setup_tpu(self) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
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requires_backends(self, ["tf"])
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tpu = None
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if self.tpu:
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try:
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if self.tpu_name:
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tpu = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name)
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else:
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tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
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except ValueError:
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tpu = None
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return tpu
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@cached_property
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def _setup_strategy(self) -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
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requires_backends(self, ["tf"])
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if self.is_tpu:
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tf.config.experimental_connect_to_cluster(self._setup_tpu)
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tf.tpu.experimental.initialize_tpu_system(self._setup_tpu)
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strategy = tf.distribute.TPUStrategy(self._setup_tpu)
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else:
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# currently no multi gpu is allowed
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if self.is_gpu:
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# TODO: Currently only single GPU is supported
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tf.config.set_visible_devices(self.gpu_list[self.device_idx], "GPU")
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strategy = tf.distribute.OneDeviceStrategy(device=f"/gpu:{self.device_idx}")
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else:
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tf.config.set_visible_devices([], "GPU") # disable GPU
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strategy = tf.distribute.OneDeviceStrategy(device=f"/cpu:{self.device_idx}")
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return strategy
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@property
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def is_tpu(self) -> bool:
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requires_backends(self, ["tf"])
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return self._setup_tpu is not None
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@property
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def strategy(self) -> "tf.distribute.Strategy":
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requires_backends(self, ["tf"])
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return self._setup_strategy
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@property
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def gpu_list(self):
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requires_backends(self, ["tf"])
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return tf.config.list_physical_devices("GPU")
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@property
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def n_gpu(self) -> int:
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requires_backends(self, ["tf"])
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if self.cuda:
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return len(self.gpu_list)
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return 0
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@property
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def is_gpu(self) -> bool:
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return self.n_gpu > 0
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