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# Copyright 2020 The HuggingFace Team. 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 os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class BenchmarkTest(unittest.TestCase):
def check_results_dict_not_empty(self, results):
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["bs"], model_result["ss"]):
result = model_result["result"][batch_size][sequence_length]
self.assertIsNotNone(result)
def test_inference_no_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_no_configs_only_pretrain(self):
MODEL_ID = "sgugger/tiny-distilbert-classification"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
only_pretrain_model=True,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_torchscript(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
torchscript=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_inference_fp16(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
fp16=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_no_model_no_architectures(self):
MODEL_ID = "sshleifer/tiny-gpt2"
config = AutoConfig.from_pretrained(MODEL_ID)
# set architectures equal to `None`
config.architectures = None
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_train_no_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
@unittest.skipIf(torch_device == "cpu", "Can't do half precision")
def test_train_no_configs_fp16(self):
MODEL_ID = "sshleifer/tiny-gpt2"
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
fp16=True,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def test_inference_with_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_inference_encoder_decoder_with_configs(self):
MODEL_ID = "sshleifer/tinier_bart"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=False,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result)
self.check_results_dict_not_empty(results.memory_inference_result)
def test_train_with_configs(self):
MODEL_ID = "sshleifer/tiny-gpt2"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=False,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def test_train_encoder_decoder_with_configs(self):
MODEL_ID = "sshleifer/tinier_bart"
config = AutoConfig.from_pretrained(MODEL_ID)
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=True,
sequence_lengths=[8],
batch_sizes=[1],
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
results = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result)
self.check_results_dict_not_empty(results.memory_train_result)
def test_save_csv_files(self):
MODEL_ID = "sshleifer/tiny-gpt2"
with tempfile.TemporaryDirectory() as tmp_dir:
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
inference=True,
save_to_csv=True,
sequence_lengths=[8],
batch_sizes=[1],
inference_time_csv_file=os.path.join(tmp_dir, "inf_time.csv"),
train_memory_csv_file=os.path.join(tmp_dir, "train_mem.csv"),
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"),
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
benchmark.run()
self.assertTrue(Path(os.path.join(tmp_dir, "inf_time.csv")).exists())
self.assertTrue(Path(os.path.join(tmp_dir, "train_time.csv")).exists())
self.assertTrue(Path(os.path.join(tmp_dir, "inf_mem.csv")).exists())
self.assertTrue(Path(os.path.join(tmp_dir, "train_mem.csv")).exists())
self.assertTrue(Path(os.path.join(tmp_dir, "env.csv")).exists())
def test_trace_memory(self):
MODEL_ID = "sshleifer/tiny-gpt2"
def _check_summary_is_not_empty(summary):
self.assertTrue(hasattr(summary, "sequential"))
self.assertTrue(hasattr(summary, "cumulative"))
self.assertTrue(hasattr(summary, "current"))
self.assertTrue(hasattr(summary, "total"))
with tempfile.TemporaryDirectory() as tmp_dir:
benchmark_args = PyTorchBenchmarkArguments(
models=[MODEL_ID],
training=True,
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,
multi_process=False,
)
benchmark = PyTorchBenchmark(benchmark_args)
result = benchmark.run()
_check_summary_is_not_empty(result.inference_summary)
_check_summary_is_not_empty(result.train_summary)
self.assertTrue(Path(os.path.join(tmp_dir, "log.txt")).exists())
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