227 lines
8.8 KiB
Python
227 lines
8.8 KiB
Python
# 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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)
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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)
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self.check_results_dict_not_empty(results.memory_inference_result)
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@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU")) == 0, "Cannot do xla on CPU.")
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def test_inference_no_configs_xla(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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use_xla=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_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 = TensorFlowBenchmarkArguments(
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models=[MODEL_ID],
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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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inference_memory_csv_file=os.path.join(tmp_dir, "inf_mem.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 = TensorFlowBenchmark(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, "inf_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 = TensorFlowBenchmarkArguments(
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models=[MODEL_ID],
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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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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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result = benchmark.run()
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_check_summary_is_not_empty(result.inference_summary)
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self.assertTrue(Path(os.path.join(tmp_dir, "log.txt")).exists())
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