[Test refactor 1/5] Per-folder tests reorganization (#15725)
* Per-folder tests reorganization Co-authored-by: sgugger <sylvain.gugger@gmail.com> Co-authored-by: Stas Bekman <stas@stason.org>
This commit is contained in:
Lysandre Debut
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tests/benchmark/__init__.py
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tests/benchmark/__init__.py
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tests/benchmark/test_benchmark.py
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tests/benchmark/test_benchmark.py
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import tempfile
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import unittest
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from pathlib import Path
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from transformers import AutoConfig, is_torch_available
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from transformers.testing_utils import require_torch, torch_device
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if is_torch_available():
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from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
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@require_torch
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class BenchmarkTest(unittest.TestCase):
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def check_results_dict_not_empty(self, results):
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for model_result in results.values():
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for batch_size, sequence_length in zip(model_result["bs"], model_result["ss"]):
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result = model_result["result"][batch_size][sequence_length]
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self.assertIsNotNone(result)
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def test_inference_no_configs(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args)
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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def test_inference_no_configs_only_pretrain(self):
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MODEL_ID = "sgugger/tiny-distilbert-classification"
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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only_pretrain_model=True,
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)
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benchmark = PyTorchBenchmark(benchmark_args)
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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def test_inference_torchscript(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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inference=True,
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torchscript=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args)
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
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def test_inference_fp16(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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inference=True,
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fp16=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args)
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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def test_inference_no_model_no_architectures(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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config = AutoConfig.from_pretrained(MODEL_ID)
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# set architectures equal to `None`
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config.architectures = None
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=True,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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def test_train_no_configs(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=True,
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inference=False,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args)
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_train_result)
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self.check_results_dict_not_empty(results.memory_train_result)
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@unittest.skipIf(torch_device == "cpu", "Can't do half precision")
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def test_train_no_configs_fp16(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=True,
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inference=False,
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sequence_lengths=[8],
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batch_sizes=[1],
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fp16=True,
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args)
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_train_result)
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self.check_results_dict_not_empty(results.memory_train_result)
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def test_inference_with_configs(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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config = AutoConfig.from_pretrained(MODEL_ID)
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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def test_inference_encoder_decoder_with_configs(self):
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MODEL_ID = "sshleifer/tinier_bart"
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config = AutoConfig.from_pretrained(MODEL_ID)
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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def test_train_with_configs(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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config = AutoConfig.from_pretrained(MODEL_ID)
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=True,
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inference=False,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_train_result)
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self.check_results_dict_not_empty(results.memory_train_result)
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def test_train_encoder_decoder_with_configs(self):
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MODEL_ID = "sshleifer/tinier_bart"
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config = AutoConfig.from_pretrained(MODEL_ID)
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=True,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args, configs=[config])
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_train_result)
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self.check_results_dict_not_empty(results.memory_train_result)
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def test_save_csv_files(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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with tempfile.TemporaryDirectory() as tmp_dir:
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=True,
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inference=True,
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save_to_csv=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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inference_time_csv_file=os.path.join(tmp_dir, "inf_time.csv"),
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train_memory_csv_file=os.path.join(tmp_dir, "train_mem.csv"),
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inference_memory_csv_file=os.path.join(tmp_dir, "inf_mem.csv"),
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train_time_csv_file=os.path.join(tmp_dir, "train_time.csv"),
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env_info_csv_file=os.path.join(tmp_dir, "env.csv"),
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args)
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benchmark.run()
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self.assertTrue(Path(os.path.join(tmp_dir, "inf_time.csv")).exists())
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self.assertTrue(Path(os.path.join(tmp_dir, "train_time.csv")).exists())
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self.assertTrue(Path(os.path.join(tmp_dir, "inf_mem.csv")).exists())
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self.assertTrue(Path(os.path.join(tmp_dir, "train_mem.csv")).exists())
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self.assertTrue(Path(os.path.join(tmp_dir, "env.csv")).exists())
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def test_trace_memory(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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def _check_summary_is_not_empty(summary):
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self.assertTrue(hasattr(summary, "sequential"))
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self.assertTrue(hasattr(summary, "cumulative"))
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self.assertTrue(hasattr(summary, "current"))
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self.assertTrue(hasattr(summary, "total"))
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with tempfile.TemporaryDirectory() as tmp_dir:
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benchmark_args = PyTorchBenchmarkArguments(
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models=[MODEL_ID],
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training=True,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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log_filename=os.path.join(tmp_dir, "log.txt"),
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log_print=True,
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trace_memory_line_by_line=True,
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multi_process=False,
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)
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benchmark = PyTorchBenchmark(benchmark_args)
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result = benchmark.run()
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_check_summary_is_not_empty(result.inference_summary)
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_check_summary_is_not_empty(result.train_summary)
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self.assertTrue(Path(os.path.join(tmp_dir, "log.txt")).exists())
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tests/benchmark/test_benchmark_tf.py
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tests/benchmark/test_benchmark_tf.py
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import tempfile
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import unittest
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from pathlib import Path
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from transformers import AutoConfig, is_tf_available
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from transformers.testing_utils import require_tf
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if is_tf_available():
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import tensorflow as tf
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from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
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@require_tf
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class TFBenchmarkTest(unittest.TestCase):
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def check_results_dict_not_empty(self, results):
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for model_result in results.values():
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for batch_size, sequence_length in zip(model_result["bs"], model_result["ss"]):
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result = model_result["result"][batch_size][sequence_length]
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self.assertIsNotNone(result)
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def test_inference_no_configs_eager(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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benchmark_args = TensorFlowBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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eager_mode=True,
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multi_process=False,
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)
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benchmark = TensorFlowBenchmark(benchmark_args)
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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def test_inference_no_configs_only_pretrain(self):
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MODEL_ID = "sgugger/tiny-distilbert-classification"
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benchmark_args = TensorFlowBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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only_pretrain_model=True,
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)
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benchmark = TensorFlowBenchmark(benchmark_args)
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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def test_inference_no_configs_graph(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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benchmark_args = TensorFlowBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = TensorFlowBenchmark(benchmark_args)
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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def test_inference_with_configs_eager(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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config = AutoConfig.from_pretrained(MODEL_ID)
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benchmark_args = TensorFlowBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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eager_mode=True,
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multi_process=False,
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)
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benchmark = TensorFlowBenchmark(benchmark_args, [config])
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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def test_inference_with_configs_graph(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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config = AutoConfig.from_pretrained(MODEL_ID)
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benchmark_args = TensorFlowBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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inference=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = TensorFlowBenchmark(benchmark_args, [config])
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_inference_result)
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self.check_results_dict_not_empty(results.memory_inference_result)
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def test_train_no_configs(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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benchmark_args = TensorFlowBenchmarkArguments(
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models=[MODEL_ID],
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training=True,
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inference=False,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = TensorFlowBenchmark(benchmark_args)
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_train_result)
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self.check_results_dict_not_empty(results.memory_train_result)
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def test_train_with_configs(self):
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MODEL_ID = "sshleifer/tiny-gpt2"
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config = AutoConfig.from_pretrained(MODEL_ID)
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benchmark_args = TensorFlowBenchmarkArguments(
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models=[MODEL_ID],
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training=True,
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inference=False,
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sequence_lengths=[8],
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batch_sizes=[1],
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multi_process=False,
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)
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benchmark = TensorFlowBenchmark(benchmark_args, [config])
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results = benchmark.run()
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self.check_results_dict_not_empty(results.time_train_result)
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self.check_results_dict_not_empty(results.memory_train_result)
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def test_inference_encoder_decoder_with_configs(self):
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MODEL_ID = "patrickvonplaten/t5-tiny-random"
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config = AutoConfig.from_pretrained(MODEL_ID)
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||||
benchmark_args = TensorFlowBenchmarkArguments(
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models=[MODEL_ID],
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training=False,
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||||
inference=True,
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||||
sequence_lengths=[8],
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||||
batch_sizes=[1],
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||||
multi_process=False,
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||||
)
|
||||
benchmark = TensorFlowBenchmark(benchmark_args, configs=[config])
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||||
results = benchmark.run()
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||||
self.check_results_dict_not_empty(results.time_inference_result)
|
||||
self.check_results_dict_not_empty(results.memory_inference_result)
|
||||
|
||||
@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU")) == 0, "Cannot do xla on CPU.")
|
||||
def test_inference_no_configs_xla(self):
|
||||
MODEL_ID = "sshleifer/tiny-gpt2"
|
||||
benchmark_args = TensorFlowBenchmarkArguments(
|
||||
models=[MODEL_ID],
|
||||
training=False,
|
||||
inference=True,
|
||||
sequence_lengths=[8],
|
||||
batch_sizes=[1],
|
||||
use_xla=True,
|
||||
multi_process=False,
|
||||
)
|
||||
benchmark = TensorFlowBenchmark(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_save_csv_files(self):
|
||||
MODEL_ID = "sshleifer/tiny-gpt2"
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
benchmark_args = TensorFlowBenchmarkArguments(
|
||||
models=[MODEL_ID],
|
||||
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"),
|
||||
multi_process=False,
|
||||
)
|
||||
benchmark = TensorFlowBenchmark(benchmark_args)
|
||||
benchmark.run()
|
||||
self.assertTrue(Path(os.path.join(tmp_dir, "inf_time.csv")).exists())
|
||||
self.assertTrue(Path(os.path.join(tmp_dir, "inf_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 = TensorFlowBenchmarkArguments(
|
||||
models=[MODEL_ID],
|
||||
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,
|
||||
multi_process=False,
|
||||
)
|
||||
benchmark = TensorFlowBenchmark(benchmark_args)
|
||||
result = benchmark.run()
|
||||
_check_summary_is_not_empty(result.inference_summary)
|
||||
self.assertTrue(Path(os.path.join(tmp_dir, "log.txt")).exists())
|
||||
Reference in New Issue
Block a user