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 set architectures equal to none 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 set architectures equal to none
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 = "sgugger/tiny-distilbert-classification"
        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)
        
        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())