mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-24 23:08:57 +06:00
2cfb947f59
* add tpu and torchscipt for benchmark * fix name in tests * "fix email" * make style * better log message for tpu * add more print and info for tpu * allow possibility to print tpu metrics * correct cpu usage * fix test for non-install * remove bugus file * include psutil in testing * run a couple of times before tracing in torchscript * do not allow tpu memory tracing for now * make style * add torchscript to env * better name for torch tpu Co-authored-by: Patrick von Platen <patrick@huggingface.co>
170 lines
7.3 KiB
Python
170 lines
7.3 KiB
Python
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 .utils import require_torch
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if is_torch_available():
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from transformers import (
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PyTorchBenchmarkArguments,
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PyTorchBenchmark,
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)
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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], training=False, no_inference=False, sequence_lengths=[8], batch_sizes=[1]
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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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no_inference=False,
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torchscript=True,
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sequence_lengths=[8],
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batch_sizes=[1],
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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_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], training=True, no_inference=True, sequence_lengths=[8], batch_sizes=[1]
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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], training=False, no_inference=False, sequence_lengths=[8], batch_sizes=[1]
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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], training=False, no_inference=False, sequence_lengths=[8], batch_sizes=[1]
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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], training=True, no_inference=True, sequence_lengths=[8], batch_sizes=[1]
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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_with_configs_torchscript(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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no_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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)
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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], training=True, no_inference=True, sequence_lengths=[8], batch_sizes=[1]
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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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no_inference=False,
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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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)
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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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no_inference=False,
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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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)
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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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