mirror of
https://github.com/huggingface/transformers.git
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96f57c9ccb
* improve memory benchmarking * correct typo * fix current memory * check torch memory allocated * better pytorch function * add total cached gpu memory * add total gpu required * improve torch gpu usage * update memory usage * finalize memory tracing * save intermediate benchmark class * fix conflict * improve benchmark * improve benchmark * finalize * make style * improve benchmarking * correct typo * make train function more flexible * fix csv save * better repr of bytes * better print * fix __repr__ bug * finish plot script * rename plot file * delete csv and small improvements * fix in plot * fix in plot * correct usage of timeit * remove redundant line * remove redundant line * fix bug * add hf parser tests * add versioning and platform info * make style * add gpu information * ensure backward compatibility * finish adding all tests * Update src/transformers/benchmark/benchmark_args.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/benchmark/benchmark_args_utils.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * delete csv files * fix isort ordering * add out of memory handling * add better train memory handling Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
91 lines
3.9 KiB
Python
91 lines
3.9 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 GPT2Config, 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_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 = GPT2Config.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 = GPT2Config.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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