2020 the huggingface team 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 orglicenseslicense2 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 2020 the huggingface team 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_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf

    from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class TFBenchmarkTest(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_eager(self):
        MODEL_ID = "sshleifer/tiny-gpt2"
        benchmark_args = TensorFlowBenchmarkArguments(
            models=[MODEL_ID],
            training=False,
            inference=True,
            sequence_lengths=[8],
            batch_sizes=[1],
            eager_mode=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_inference_no_configs_only_pretrain(self):
        MODEL_ID = "sgugger/tiny-distilbert-classification"
        benchmark_args = TensorFlowBenchmarkArguments(
            models=[MODEL_ID],
            training=False,
            inference=True,
            sequence_lengths=[8],
            batch_sizes=[1],
            multi_process=False,
            only_pretrain_model=True,
        )
        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_inference_no_configs_graph(self):
        MODEL_ID = "sshleifer/tiny-gpt2"
        benchmark_args = TensorFlowBenchmarkArguments(
            models=[MODEL_ID],
            training=False,
            inference=True,
            sequence_lengths=[8],
            batch_sizes=[1],
            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_inference_with_configs_eager(self):
        MODEL_ID = "sshleifer/tiny-gpt2"
        config = AutoConfig.from_pretrained(MODEL_ID)
        benchmark_args = TensorFlowBenchmarkArguments(
            models=[MODEL_ID],
            training=False,
            inference=True,
            sequence_lengths=[8],
            batch_sizes=[1],
            eager_mode=True,
            multi_process=False,
        )
        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)

    def test_inference_with_configs_graph(self):
        MODEL_ID = "sshleifer/tiny-gpt2"
        config = AutoConfig.from_pretrained(MODEL_ID)
        benchmark_args = TensorFlowBenchmarkArguments(
            models=[MODEL_ID],
            training=False,
            inference=True,
            sequence_lengths=[8],
            batch_sizes=[1],
            multi_process=False,
        )
        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)

    def test_train_no_configs(self):
        MODEL_ID = "sshleifer/tiny-gpt2"
        benchmark_args = TensorFlowBenchmarkArguments(
            models=[MODEL_ID],
            training=True,
            inference=False,
            sequence_lengths=[8],
            batch_sizes=[1],
            multi_process=False,
        )
        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)

    def test_train_with_configs(self):
        MODEL_ID = "sshleifer/tiny-gpt2"
        config = AutoConfig.from_pretrained(MODEL_ID)
        benchmark_args = TensorFlowBenchmarkArguments(
            models=[MODEL_ID],
            training=True,
            inference=False,
            sequence_lengths=[8],
            batch_sizes=[1],
            multi_process=False,
        )
        benchmark = TensorFlowBenchmark(benchmark_args, [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_inference_encoder_decoder_with_configs(self):
        MODEL_ID = "patrickvonplaten/t5-tiny-random"
        config = AutoConfig.from_pretrained(MODEL_ID)
        benchmark_args = TensorFlowBenchmarkArguments(
            models=[MODEL_ID],
            training=False,
            inference=True,
            sequence_lengths=[8],
            batch_sizes=[1],
            multi_process=False,
        )
        benchmark = TensorFlowBenchmark(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)

    @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())