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160 lines
6.3 KiB
160 lines
6.3 KiB
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import torch._C
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def format_time(time_us=None, time_ms=None, time_s=None):
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"""Define time formatting."""
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assert sum([time_us is not None, time_ms is not None, time_s is not None]) == 1
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US_IN_SECOND = 1e6
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US_IN_MS = 1e3
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if time_us is None:
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if time_ms is not None:
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time_us = time_ms * US_IN_MS
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elif time_s is not None:
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time_us = time_s * US_IN_SECOND
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else:
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raise AssertionError("Shouldn't reach here :)")
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if time_us >= US_IN_SECOND:
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return f'{time_us / US_IN_SECOND:.3f}s'
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if time_us >= US_IN_MS:
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return f'{time_us / US_IN_MS:.3f}ms'
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return f'{time_us:.3f}us'
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class ExecutionStats:
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def __init__(self, c_stats, benchmark_config):
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self._c_stats = c_stats
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self.benchmark_config = benchmark_config
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@property
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def latency_avg_ms(self):
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return self._c_stats.latency_avg_ms
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@property
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def num_iters(self):
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return self._c_stats.num_iters
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@property
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def iters_per_second(self):
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"""Return total number of iterations per second across all calling threads."""
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return self.num_iters / self.total_time_seconds
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@property
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def total_time_seconds(self):
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return self.num_iters * (
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self.latency_avg_ms / 1000.0) / self.benchmark_config.num_calling_threads
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def __str__(self):
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return '\n'.join([
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"Average latency per example: " + format_time(time_ms=self.latency_avg_ms),
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f"Total number of iterations: {self.num_iters}",
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f"Total number of iterations per second (across all threads): {self.iters_per_second:.2f}",
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"Total time: " + format_time(time_s=self.total_time_seconds)
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])
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class ThroughputBenchmark:
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"""
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This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
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This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
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for executing a PyTorch module (nn.Module or ScriptModule) under an inference
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server like load. It can emulate multiple calling threads to a single module
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provided. In the future we plan to enhance this component to support inter and
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intra-op parallelism as well as multiple models running in a single process.
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Please note that even though nn.Module is supported, it might incur an overhead
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from the need to hold GIL every time we execute Python code or pass around
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inputs as Python objects. As soon as you have a ScriptModule version of your
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model for inference deployment it is better to switch to using it in this
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benchmark.
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Example::
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>>> # xdoctest: +SKIP("undefined vars")
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>>> from torch.utils import ThroughputBenchmark
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>>> bench = ThroughputBenchmark(my_module)
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>>> # Pre-populate benchmark's data set with the inputs
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>>> for input in inputs:
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... # Both args and kwargs work, same as any PyTorch Module / ScriptModule
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... bench.add_input(input[0], x2=input[1])
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>>> # Inputs supplied above are randomly used during the execution
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>>> stats = bench.benchmark(
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... num_calling_threads=4,
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... num_warmup_iters = 100,
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... num_iters = 1000,
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... )
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>>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
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>>> print("Number of iterations: {}".format(stats.num_iters))
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"""
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def __init__(self, module):
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if isinstance(module, torch.jit.ScriptModule):
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self._benchmark = torch._C.ThroughputBenchmark(module._c)
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else:
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self._benchmark = torch._C.ThroughputBenchmark(module)
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def run_once(self, *args, **kwargs):
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"""
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Given input id (input_idx) run benchmark once and return prediction.
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This is useful for testing that benchmark actually runs the module you
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want it to run. input_idx here is an index into inputs array populated
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by calling add_input() method.
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"""
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return self._benchmark.run_once(*args, **kwargs)
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def add_input(self, *args, **kwargs):
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"""
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Store a single input to a module into the benchmark memory and keep it there.
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During the benchmark execution every thread is going to pick up a
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random input from the all the inputs ever supplied to the benchmark via
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this function.
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"""
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self._benchmark.add_input(*args, **kwargs)
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def benchmark(
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self,
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num_calling_threads=1,
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num_warmup_iters=10,
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num_iters=100,
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profiler_output_path=""):
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"""
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Run a benchmark on the module.
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Args:
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num_warmup_iters (int): Warmup iters are used to make sure we run a module
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a few times before actually measuring things. This way we avoid cold
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caches and any other similar problems. This is the number of warmup
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iterations for each of the thread in separate
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num_iters (int): Number of iterations the benchmark should run with.
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This number is separate from the warmup iterations. Also the number is
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shared across all the threads. Once the num_iters iterations across all
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the threads is reached, we will stop execution. Though total number of
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iterations might be slightly larger. Which is reported as
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stats.num_iters where stats is the result of this function
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profiler_output_path (str): Location to save Autograd Profiler trace.
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If not empty, Autograd Profiler will be enabled for the main benchmark
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execution (but not the warmup phase). The full trace will be saved
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into the file path provided by this argument
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This function returns BenchmarkExecutionStats object which is defined via pybind11.
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It currently has two fields:
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- num_iters - number of actual iterations the benchmark have made
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- avg_latency_ms - average time it took to infer on one input example in milliseconds
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"""
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config = torch._C.BenchmarkConfig()
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config.num_calling_threads = num_calling_threads
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config.num_warmup_iters = num_warmup_iters
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config.num_iters = num_iters
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config.profiler_output_path = profiler_output_path
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c_stats = self._benchmark.benchmark(config)
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return ExecutionStats(c_stats, config)
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