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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)
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