| import time |
| from collections import namedtuple |
| from torch.utils import ThroughputBenchmark |
| |
| NUM_LOOP_ITERS = 1000 |
| BenchmarkConfig = namedtuple('BenchmarkConfig', 'num_warmup_iters num_iters') |
| ModuleConfig = namedtuple('ModuleConfig', 'pt_fn c2_op num_params graph_mode') |
| |
| def ms_to_us(time_ms): |
| return (time_ms * 1e3) |
| |
| def secs_to_us(time_s): |
| return (time_s * 1e6) |
| |
| def secs_to_ms(time_s): |
| return (time_s * 1e3) |
| |
| def benchmark_using_throughput_benchmark(config, module): |
| print("Benchmarking via ThroughputBenchmark") |
| bench = ThroughputBenchmark(module.module) |
| bench.add_input(*module.tensor_inputs) |
| stats = bench.benchmark(1, config.num_warmup_iters, config.num_iters) |
| return stats.latency_avg_ms / NUM_LOOP_ITERS |
| |
| def benchmark_module(config, module, use_throughput_benchmark=False): |
| if use_throughput_benchmark: |
| return benchmark_using_throughput_benchmark(config, module) |
| module.forward(config.num_warmup_iters) |
| print("Running module for {} iterations".format(config.num_iters)) |
| start = time.time() |
| module.forward(config.num_iters) |
| end = time.time() |
| time_elapsed_s = (end - start) |
| return (secs_to_ms(time_elapsed_s) / config.num_iters / NUM_LOOP_ITERS) |
