Пример #1
0
    def test_trace_memory(self):
        MODEL_ID = "albert-base-v2"

        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],
                no_inference=False,
                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,
            )
            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())
Пример #2
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 def test_train_no_configs(self):
     MODEL_ID = "albert-base-v2"
     benchmark_args = TensorFlowBenchmarkArguments(
         models=[MODEL_ID],
         training=True,
         no_inference=True,
         sequence_lengths=[8],
         batch_sizes=[1],
         no_multi_process=True,
     )
     benchmark = TensorFlowBenchmark(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)
Пример #3
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 def test_inference_with_configs_graph(self):
     MODEL_ID = "albert-base-v2"
     config = AutoConfig.from_pretrained(MODEL_ID)
     benchmark_args = TensorFlowBenchmarkArguments(
         models=[MODEL_ID],
         training=False,
         no_inference=False,
         sequence_lengths=[8],
         batch_sizes=[1],
         no_multi_process=True,
     )
     benchmark = TensorFlowBenchmark(benchmark_args, [config])
     results = benchmark.run()
     self.check_results_dict_not_empty(results.time_inference_result)
     self.check_results_dict_not_empty(results.memory_inference_result)
Пример #4
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 def test_save_csv_files(self):
     MODEL_ID = "albert-base-v2"
     with tempfile.TemporaryDirectory() as tmp_dir:
         benchmark_args = TensorFlowBenchmarkArguments(
             models=[MODEL_ID],
             no_inference=False,
             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,
         )
         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())