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# 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
import warnings
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 __post_init__(self):
warnings.warn(
f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
" are deprecated in general and it is advised to use external Benchmarking libraries "
" to benchmark Transformer models.",
FutureWarning,
)
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) -> List[str]:
if len(self.models) <= 0:
raise ValueError(
"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
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