public/gpt-2/transformers/benchmark/benchmark_args_utils.py

# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import dataclasses
import json
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


logger = logging.get_logger(__name__)


def list_field(default=None, metadata=None):
    return field(default_factory=lambda: default, metadata=metadata)


@dataclass
class BenchmarkArguments:
    """
    BenchMarkArguments are arguments we use in our benchmark scripts **which relate to the training loop itself**.

    Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command
    line.
    """

    models: List[str] = list_field(
        default=[],
        metadata={
            "help": "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version of all available models"
        },
    )

    batch_sizes: List[int] = list_field(
        default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}
    )

    sequence_lengths: List[int] = list_field(
        default=[8, 32, 128, 512],
        metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"},
    )

    inference: bool = field(
        default=True,
        metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."},
    )
    cuda: bool = field(
        default=True,
        metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."},
    )
    tpu: bool = field(
        default=True, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}
    )
    fp16: bool = field(default=False, metadata={"help": "Use FP16 to accelerate inference."})
    training: bool = field(default=False, metadata={"help": "Benchmark training of model"})
    verbose: bool = field(default=False, metadata={"help": "Verbose memory tracing"})
    speed: bool = field(
        default=True,
        metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."},
    )
    memory: bool = field(
        default=True,
        metadata={
            "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
        },
    )
    trace_memory_line_by_line: bool = field(default=False, metadata={"help": "Trace memory line by line"})
    save_to_csv: bool = field(default=False, metadata={"help": "Save result to a CSV file"})
    log_print: bool = field(default=False, metadata={"help": "Save all print statements in a log file"})
    env_print: bool = field(default=False, metadata={"help": "Whether to print environment information"})
    multi_process: bool = field(
        default=True,
        metadata={
            "help": "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled for debugging / testing and on TPU."
        },
    )
    inference_time_csv_file: str = field(
        default=f"inference_time_{round(time())}.csv",
        metadata={"help": "CSV filename used if saving time results to csv."},
    )
    inference_memory_csv_file: str = field(
        default=f"inference_memory_{round(time())}.csv",
        metadata={"help": "CSV filename used if saving memory results to csv."},
    )
    train_time_csv_file: str = field(
        default=f"train_time_{round(time())}.csv",
        metadata={"help": "CSV filename used if saving time results to csv for training."},
    )
    train_memory_csv_file: str = field(
        default=f"train_memory_{round(time())}.csv",
        metadata={"help": "CSV filename used if saving memory results to csv for training."},
    )
    env_info_csv_file: str = field(
        default=f"env_info_{round(time())}.csv",
        metadata={"help": "CSV filename used if saving environment information."},
    )
    log_filename: str = field(
        default=f"log_{round(time())}.csv",
        metadata={"help": "Log filename used if print statements are saved in log."},
    )
    repeat: int = field(default=3, metadata={"help": "Times an experiment will be run."})
    only_pretrain_model: bool = field(
        default=False,
        metadata={
            "help": "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain model weights."
        },
    )

    def to_json_string(self):
        """
        Serializes this instance to a JSON string.
        """
        return json.dumps(dataclasses.asdict(self), indent=2)

    @property
    def model_names(self):
        assert (
            len(self.models) > 0
        ), "Please make sure you provide at least one model name / model identifier, *e.g.* `--models bert-base-cased` or `args.models = ['bert-base-cased']."
        return self.models

    @property
    def do_multi_processing(self):
        if not self.multi_process:
            return False
        elif self.is_tpu:
            logger.info("Multiprocessing is currently not possible on TPU.")
            return False
        else:
            return True