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Unverified Commit d2666136 authored by Felipe Curti's avatar Felipe Curti Committed by GitHub
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[Benchmarks] Change all args to from `no_...` to their positive form (#7075) 



* Changed name to all no_... arguments and all references to them, inverting the boolean condition

* Change benchmark tests to use new Benchmark Args

* Update src/transformers/benchmark/benchmark_args_utils.py
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>

* Update src/transformers/benchmark/benchmark.py
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>

* Fix Style. Add --no options in help

* fix some part of tests

* Update src/transformers/benchmark/benchmark_args_utils.py

* Update src/transformers/benchmark/benchmark_args_utils.py

* Update src/transformers/benchmark/benchmark_args_utils.py

* fix all tests

* make style

* add backwards compability

* make backwards compatible
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: default avatarfmcurti <fcurti@DESKTOP-RRQURBM.localdomain>
parent 8c697d58
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Showing with 1174 additions and 1066 deletions
examples/benchmarking/run_benchmark.py
View file @ d2666136
......@@ -20,7 +20,25 @@ from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArg
def main():
parser = HfArgumentParser(PyTorchBenchmarkArguments)
benchmark_args = parser.parse_args_into_dataclasses()[0]
try:
benchmark_args = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
arg_error_msg = "Arg --no_{0} is no longer used, please use --no-{0} instead."
begin_error_msg = " ".join(str(e).split(" ")[:-1])
full_error_msg = ""
depreciated_args = eval(str(e).split(" ")[-1])
wrong_args = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(arg)
if len(wrong_args) > 0:
full_error_msg = full_error_msg + begin_error_msg + str(wrong_args)
raise ValueError(full_error_msg)
benchmark = PyTorchBenchmark(args=benchmark_args)
benchmark.run()
......
examples/benchmarking/run_benchmark_tf.py
View file @ d2666136
......@@ -22,6 +22,24 @@ def main():
parser = HfArgumentParser(TensorFlowBenchmarkArguments)
benchmark_args = parser.parse_args_into_dataclasses()[0]
benchmark = TensorFlowBenchmark(args=benchmark_args)
try:
benchmark_args = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
arg_error_msg = "Arg --no_{0} is no longer used, please use --no-{0} instead."
begin_error_msg = " ".join(str(e).split(" ")[:-1])
full_error_msg = ""
depreciated_args = eval(str(e).split(" ")[-1])
wrong_args = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:])
else:
wrong_args.append(arg)
if len(wrong_args) > 0:
full_error_msg = full_error_msg + begin_error_msg + str(wrong_args)
raise ValueError(full_error_msg)
benchmark.run()
......
src/transformers/benchmark/benchmark.py
View file @ d2666136
......@@ -229,7 +229,7 @@ class PyTorchBenchmark(Benchmark):
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with `--no_memory` or `args.no_memory=True`"
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with `--no-memory` or `args.memory=False`"
)
elif self.args.is_gpu:
if not is_py3nvml_available():
......
src/transformers/benchmark/benchmark_args.py
View file @ d2666136
......@@ -34,6 +34,34 @@ logger = logging.get_logger(__name__)
@dataclass
class PyTorchBenchmarkArguments(BenchmarkArguments):
deprecated_args = [
"no_inference",
"no_cuda",
"no_tpu",
"no_speed",
"no_memory",
"no_env_print",
"no_multi_process",
]
def __init__(self, **kwargs):
"""This __init__ is there for legacy code. When removing
deprecated args completely, the class can simply be deleted
"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
positive_arg = deprecated_arg[3:]
setattr(self, positive_arg, not kwargs.pop(deprecated_arg))
logger.warning(
f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or {positive_arg}={kwargs[positive_arg]}"
)
self.torchscript = kwargs.pop("torchscript", self.torchscript)
self.torch_xla_tpu_print_metrics = kwargs.pop("torch_xla_tpu_print_metrics", self.torch_xla_tpu_print_metrics)
self.fp16_opt_level = kwargs.pop("fp16_opt_level", self.fp16_opt_level)
super().__init__(**kwargs)
torchscript: bool = field(default=False, metadata={"help": "Trace the models using torchscript"})
torch_xla_tpu_print_metrics: bool = field(default=False, metadata={"help": "Print Xla/PyTorch tpu metrics"})
fp16_opt_level: str = field(
......@@ -50,7 +78,7 @@ class PyTorchBenchmarkArguments(BenchmarkArguments):
@torch_required
def _setup_devices(self) -> Tuple["torch.device", int]:
logger.info("PyTorch: setting up devices")
if self.no_cuda:
if not self.cuda:
device = torch.device("cpu")
n_gpu = 0
elif is_torch_tpu_available():
......@@ -63,7 +91,7 @@ class PyTorchBenchmarkArguments(BenchmarkArguments):
@property
def is_tpu(self):
return is_torch_tpu_available() and not self.no_tpu
return is_torch_tpu_available() and self.tpu
@property
@torch_required
......
src/transformers/benchmark/benchmark_args_tf.py
View file @ d2666136
......@@ -31,6 +31,34 @@ logger = logging.get_logger(__name__)
@dataclass
class TensorFlowBenchmarkArguments(BenchmarkArguments):
deprecated_args = [
"no_inference",
"no_cuda",
"no_tpu",
"no_speed",
"no_memory",
"no_env_print",
"no_multi_process",
]
def __init__(self, **kwargs):
"""This __init__ is there for legacy code. When removing
deprecated args completely, the class can simply be deleted
"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
positive_arg = deprecated_arg[3:]
kwargs[positive_arg] = not kwargs.pop(deprecated_arg)
logger.warning(
f"{deprecated_arg} is depreciated. Please use --no-{positive_arg} or {positive_arg}={kwargs[positive_arg]}"
)
self.tpu_name = kwargs.pop("tpu_name", self.tpu_name)
self.device_idx = kwargs.pop("device_idx", self.device_idx)
self.eager_mode = kwargs.pop("eager_mode", self.eager_mode)
self.use_xla = kwargs.pop("use_xla", self.use_xla)
super().__init__(**kwargs)
tpu_name: str = field(
default=None,
metadata={"help": "Name of TPU"},
......@@ -50,7 +78,7 @@ class TensorFlowBenchmarkArguments(BenchmarkArguments):
@cached_property
@tf_required
def _setup_tpu(self) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
if not self.no_tpu:
if self.tpu:
try:
if self.tpu_name:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name)
......@@ -98,7 +126,7 @@ class TensorFlowBenchmarkArguments(BenchmarkArguments):
@property
@tf_required
def n_gpu(self) -> int:
if not self.no_cuda:
if self.cuda:
return len(self.gpu_list)
return 0
......
src/transformers/benchmark/benchmark_args_utils.py
View file @ d2666136
# 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"},
)
no_inference: bool = field(default=False, metadata={"help": "Don't benchmark inference of model"})
no_cuda: bool = field(default=False, metadata={"help": "Whether to run on available cuda devices"})
no_tpu: bool = field(default=False, metadata={"help": "Whether to run on available tpu devices"})
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"})
no_speed: bool = field(default=False, metadata={"help": "Don't perform speed measurements"})
no_memory: bool = field(default=False, metadata={"help": "Don't perform memory measurements"})
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"})
no_env_print: bool = field(default=False, metadata={"help": "Don't print environment information"})
no_multi_process: bool = field(
default=False,
metadata={
"help": "Don't 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 used 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 self.no_multi_process:
return False
elif self.is_tpu:
logger.info("Multiprocessing is currently not possible on TPU.")
return False
else:
return True
# 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
src/transformers/benchmark/benchmark_tf.py
View file @ d2666136
......@@ -248,7 +248,7 @@ class TensorFlowBenchmark(Benchmark):
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with `args.no_memory=True`"
"Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with `args.memory=False`"
)
elif self.args.is_gpu:
# gpu
......
tests/test_benchmark.py
View file @ d2666136
......@@ -24,10 +24,10 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
......@@ -39,10 +39,10 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
only_pretrain_model=True,
)
benchmark = PyTorchBenchmark(benchmark_args)
......@@ -55,11 +55,11 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
torchscript=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
......@@ -72,11 +72,11 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
fp16=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
......@@ -91,10 +91,10 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
......@@ -106,10 +106,10 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
no_inference=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
......@@ -122,11 +122,11 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
no_inference=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
fp16=True,
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
......@@ -139,10 +139,10 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
......@@ -155,10 +155,10 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
......@@ -171,10 +171,10 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
no_inference=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
......@@ -187,10 +187,10 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
no_inference=True,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
......@@ -203,7 +203,7 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
no_inference=False,
inference=True,
save_to_csv=True,
sequence_lengths=[8],
batch_sizes=[1],
......@@ -212,7 +212,7 @@ class BenchmarkTest(unittest.TestCase):
inference_memory_csv_file=os.path.join(tmp_dir, "inf_mem.csv"),
train_time_csv_file=os.path.join(tmp_dir, "train_time.csv"),
env_info_csv_file=os.path.join(tmp_dir, "env.csv"),
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
benchmark.run()
......@@ -235,13 +235,13 @@ class BenchmarkTest(unittest.TestCase):
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
log_filename=os.path.join(tmp_dir, "log.txt"),
log_print=True,
trace_memory_line_by_line=True,
no_multi_process=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
result = benchmark.run()
......
tests/test_benchmark_tf.py
View file @ d2666136
......@@ -26,11 +26,11 @@ class TFBenchmarkTest(unittest.TestCase):
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
eager_mode=True,
no_multi_process=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
results = benchmark.run()
......@@ -42,10 +42,10 @@ class TFBenchmarkTest(unittest.TestCase):
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
only_pretrain_model=True,
)
benchmark = TensorFlowBenchmark(benchmark_args)
......@@ -58,10 +58,10 @@ class TFBenchmarkTest(unittest.TestCase):
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
results = benchmark.run()
......@@ -74,11 +74,11 @@ class TFBenchmarkTest(unittest.TestCase):
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
eager_mode=True,
no_multi_process=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args, [config])
results = benchmark.run()
......@@ -91,10 +91,10 @@ class TFBenchmarkTest(unittest.TestCase):
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args, [config])
results = benchmark.run()
......@@ -106,10 +106,10 @@ class TFBenchmarkTest(unittest.TestCase):
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=True,
no_inference=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
results = benchmark.run()
......@@ -122,10 +122,10 @@ class TFBenchmarkTest(unittest.TestCase):
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=True,
no_inference=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args, [config])
results = benchmark.run()
......@@ -138,10 +138,10 @@ class TFBenchmarkTest(unittest.TestCase):
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
no_multi_process=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
......@@ -154,11 +154,11 @@ class TFBenchmarkTest(unittest.TestCase):
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
training=False,
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
use_xla=True,
no_multi_process=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
results = benchmark.run()
......@@ -170,14 +170,14 @@ class TFBenchmarkTest(unittest.TestCase):
with tempfile.TemporaryDirectory() as tmp_dir:
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
no_inference=False,
inference=True,
save_to_csv=True,
sequence_lengths=[8],
batch_sizes=[1],
inference_time_csv_file=os.path.join(tmp_dir, "inf_time.csv"),
inference_memory_csv_file=os.path.join(tmp_dir, "inf_mem.csv"),
env_info_csv_file=os.path.join(tmp_dir, "env.csv"),
no_multi_process=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
benchmark.run()
......@@ -197,14 +197,14 @@ class TFBenchmarkTest(unittest.TestCase):
with tempfile.TemporaryDirectory() as tmp_dir:
benchmark_args = TensorFlowBenchmarkArguments(
models=[MODEL_ID],
no_inference=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
log_filename=os.path.join(tmp_dir, "log.txt"),
log_print=True,
trace_memory_line_by_line=True,
eager_mode=True,
no_multi_process=True,
multi_process=False,
)
benchmark = TensorFlowBenchmark(benchmark_args)
result = benchmark.run()
......
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