tests / test_benchmark.py

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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 = "sshleifer/tiny-distilbert-base-uncased-finetuned-sst-2-english"
	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())