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20ac736200
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44735 Reviewed By: mruberry Differential Revision: D23731306 Pulled By: ezyang fbshipit-source-id: 0ba009a99e475ddbe22981be8ac636f8a1c8b02f
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
import time
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from collections import namedtuple
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from torch.utils import ThroughputBenchmark
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NUM_LOOP_ITERS = 1000
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BenchmarkConfig = namedtuple('BenchmarkConfig', 'num_warmup_iters num_iters')
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ModuleConfig = namedtuple('ModuleConfig', 'pt_fn c2_op num_params graph_mode')
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def ms_to_us(time_ms):
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return (time_ms * 1e3)
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def secs_to_us(time_s):
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return (time_s * 1e6)
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def secs_to_ms(time_s):
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return (time_s * 1e3)
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def benchmark_using_throughput_benchmark(config, module):
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print("Benchmarking via ThroughputBenchmark")
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bench = ThroughputBenchmark(module.module)
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bench.add_input(*module.tensor_inputs)
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stats = bench.benchmark(1, config.num_warmup_iters, config.num_iters)
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return stats.latency_avg_ms / NUM_LOOP_ITERS
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def benchmark_module(config, module, use_throughput_benchmark=False):
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if use_throughput_benchmark:
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return benchmark_using_throughput_benchmark(config, module)
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module.forward(config.num_warmup_iters)
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print("Running module for {} iterations".format(config.num_iters))
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start = time.time()
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module.forward(config.num_iters)
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end = time.time()
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time_elapsed_s = (end - start)
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return (secs_to_ms(time_elapsed_s) / config.num_iters / NUM_LOOP_ITERS)
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