#!/usr/bin/env python3 """ Model Benchmark Script An inference and train step benchmark script for timm models. Hacked together by Ross Wightman (https://github.com/rwightman) """ import argparse import os import csv import json import time import logging import torch import torch.nn as nn import torch.nn.parallel from collections import OrderedDict from contextlib import suppress from functools import partial try: from deepspeed.profiling.flops_profiler import get_model_profile except ImportError as e: get_model_profile = None from timm.models import create_model, is_model, list_models from timm.optim import create_optimizer_v2 from timm.data import resolve_data_config from timm.utils import AverageMeter, setup_default_logging has_apex = False try: from apex import amp has_apex = True except ImportError: pass has_native_amp = False try: if getattr(torch.cuda.amp, 'autocast') is not None: has_native_amp = True except AttributeError: pass torch.backends.cudnn.benchmark = True _logger = logging.getLogger('validate') parser = argparse.ArgumentParser(description='PyTorch Benchmark') # benchmark specific args parser.add_argument('--model-list', metavar='NAME', default='', help='txt file based list of model names to benchmark') parser.add_argument('--bench', default='both', type=str, help="Benchmark mode. One of 'inference', 'train', 'both'. Defaults to 'both'") parser.add_argument('--detail', action='store_true', default=False, help='Provide train fwd/bwd/opt breakdown detail if True. Defaults to False') parser.add_argument('--results-file', default='', type=str, metavar='FILENAME', help='Output csv file for validation results (summary)') parser.add_argument('--num-warm-iter', default=10, type=int, metavar='N', help='Number of warmup iterations (default: 10)') parser.add_argument('--num-bench-iter', default=40, type=int, metavar='N', help='Number of benchmark iterations (default: 40)') # common inference / train args parser.add_argument('--model', '-m', metavar='NAME', default='resnet50', help='model architecture (default: resnet50)') parser.add_argument('-b', '--batch-size', default=256, type=int, metavar='N', help='mini-batch size (default: 256)') parser.add_argument('--img-size', default=None, type=int, metavar='N', help='Input image dimension, uses model default if empty') parser.add_argument('--input-size', default=None, nargs=3, type=int, metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty') parser.add_argument('--use-train-size', action='store_true', default=False, help='Run inference at train size, not test-input-size if it exists.') parser.add_argument('--num-classes', type=int, default=None, help='Number classes in dataset') parser.add_argument('--gp', default=None, type=str, metavar='POOL', help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.') parser.add_argument('--channels-last', action='store_true', default=False, help='Use channels_last memory layout') parser.add_argument('--amp', action='store_true', default=False, help='use PyTorch Native AMP for mixed precision training. Overrides --precision arg.') parser.add_argument('--precision', default='float32', type=str, help='Numeric precision. One of (amp, float32, float16, bfloat16, tf32)') parser.add_argument('--torchscript', dest='torchscript', action='store_true', help='convert model torchscript for inference') # train optimizer parameters parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER', help='Optimizer (default: "sgd"') parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON', help='Optimizer Epsilon (default: None, use opt default)') parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA', help='Optimizer Betas (default: None, use opt default)') parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='Optimizer momentum (default: 0.9)') parser.add_argument('--weight-decay', type=float, default=0.0001, help='weight decay (default: 0.0001)') parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM', help='Clip gradient norm (default: None, no clipping)') parser.add_argument('--clip-mode', type=str, default='norm', help='Gradient clipping mode. One of ("norm", "value", "agc")') # model regularization / loss params that impact model or loss fn parser.add_argument('--smoothing', type=float, default=0.1, help='Label smoothing (default: 0.1)') parser.add_argument('--drop', type=float, default=0.0, metavar='PCT', help='Dropout rate (default: 0.)') parser.add_argument('--drop-path', type=float, default=None, metavar='PCT', help='Drop path rate (default: None)') parser.add_argument('--drop-block', type=float, default=None, metavar='PCT', help='Drop block rate (default: None)') def timestamp(sync=False): return time.perf_counter() def cuda_timestamp(sync=False, device=None): if sync: torch.cuda.synchronize(device=device) return time.perf_counter() def count_params(model: nn.Module): return sum([m.numel() for m in model.parameters()]) def resolve_precision(precision: str): assert precision in ('amp', 'float16', 'bfloat16', 'float32') use_amp = False model_dtype = torch.float32 data_dtype = torch.float32 if precision == 'amp': use_amp = True elif precision == 'float16': model_dtype = torch.float16 data_dtype = torch.float16 elif precision == 'bfloat16': model_dtype = torch.bfloat16 data_dtype = torch.bfloat16 return use_amp, model_dtype, data_dtype def profile(model, input_size=(3, 224, 224)): batch_size = 1 macs, params = get_model_profile( model=model, input_res=(batch_size,) + input_size, # input shape or input to the input_constructor input_constructor=None, # if specified, a constructor taking input_res is used as input to the model print_profile=False, # prints the model graph with the measured profile attached to each module detailed=False, # print the detailed profile warm_up=10, # the number of warm-ups before measuring the time of each module as_string=False, # print raw numbers (e.g. 1000) or as human-readable strings (e.g. 1k) output_file=None, # path to the output file. If None, the profiler prints to stdout. ignore_modules=None) # the list of modules to ignore in the profiling return macs class BenchmarkRunner: def __init__( self, model_name, detail=False, device='cuda', torchscript=False, precision='float32', num_warm_iter=10, num_bench_iter=50, use_train_size=False, **kwargs): self.model_name = model_name self.detail = detail self.device = device self.use_amp, self.model_dtype, self.data_dtype = resolve_precision(precision) self.channels_last = kwargs.pop('channels_last', False) self.amp_autocast = torch.cuda.amp.autocast if self.use_amp else suppress self.model = create_model( model_name, num_classes=kwargs.pop('num_classes', None), in_chans=3, global_pool=kwargs.pop('gp', 'fast'), scriptable=torchscript) self.model.to( device=self.device, dtype=self.model_dtype, memory_format=torch.channels_last if self.channels_last else None) self.num_classes = self.model.num_classes self.param_count = count_params(self.model) _logger.info('Model %s created, param count: %d' % (model_name, self.param_count)) if torchscript: self.model = torch.jit.script(self.model) data_config = resolve_data_config(kwargs, model=self.model, use_test_size=not use_train_size) self.input_size = data_config['input_size'] self.batch_size = kwargs.pop('batch_size', 256) self.example_inputs = None self.num_warm_iter = num_warm_iter self.num_bench_iter = num_bench_iter self.log_freq = num_bench_iter // 5 if 'cuda' in self.device: self.time_fn = partial(cuda_timestamp, device=self.device) else: self.time_fn = timestamp def _init_input(self): self.example_inputs = torch.randn( (self.batch_size,) + self.input_size, device=self.device, dtype=self.data_dtype) if self.channels_last: self.example_inputs = self.example_inputs.contiguous(memory_format=torch.channels_last) class InferenceBenchmarkRunner(BenchmarkRunner): def __init__(self, model_name, device='cuda', torchscript=False, **kwargs): super().__init__(model_name=model_name, device=device, torchscript=torchscript, **kwargs) self.model.eval() def run(self): def _step(): t_step_start = self.time_fn() with self.amp_autocast(): output = self.model(self.example_inputs) t_step_end = self.time_fn(True) return t_step_end - t_step_start _logger.info( f'Running inference benchmark on {self.model_name} for {self.num_bench_iter} steps w/ ' f'input size {self.input_size} and batch size {self.batch_size}.') with torch.no_grad(): self._init_input() for _ in range(self.num_warm_iter): _step() total_step = 0. num_samples = 0 t_run_start = self.time_fn() for i in range(self.num_bench_iter): delta_fwd = _step() total_step += delta_fwd num_samples += self.batch_size num_steps = i + 1 if num_steps % self.log_freq == 0: _logger.info( f"Infer [{num_steps}/{self.num_bench_iter}]." f" {num_samples / total_step:0.2f} samples/sec." f" {1000 * total_step / num_steps:0.3f} ms/step.") t_run_end = self.time_fn(True) t_run_elapsed = t_run_end - t_run_start results = dict( samples_per_sec=round(num_samples / t_run_elapsed, 2), step_time=round(1000 * total_step / self.num_bench_iter, 3), batch_size=self.batch_size, img_size=self.input_size[-1], param_count=round(self.param_count / 1e6, 2), ) if get_model_profile is not None: macs = profile(self.model, self.input_size) results['GMACs'] = round(macs / 1e9, 2) _logger.info( f"Inference benchmark of {self.model_name} done. " f"{results['samples_per_sec']:.2f} samples/sec, {results['step_time']:.2f} ms/step") return results class TrainBenchmarkRunner(BenchmarkRunner): def __init__(self, model_name, device='cuda', torchscript=False, **kwargs): super().__init__(model_name=model_name, device=device, torchscript=torchscript, **kwargs) self.model.train() if kwargs.pop('smoothing', 0) > 0: self.loss = nn.CrossEntropyLoss().to(self.device) else: self.loss = nn.CrossEntropyLoss().to(self.device) self.target_shape = tuple() self.optimizer = create_optimizer_v2( self.model, opt=kwargs.pop('opt', 'sgd'), lr=kwargs.pop('lr', 1e-4)) def _gen_target(self, batch_size): return torch.empty( (batch_size,) + self.target_shape, device=self.device, dtype=torch.long).random_(self.num_classes) def run(self): def _step(detail=False): self.optimizer.zero_grad() # can this be ignored? t_start = self.time_fn() t_fwd_end = t_start t_bwd_end = t_start with self.amp_autocast(): output = self.model(self.example_inputs) if isinstance(output, tuple): output = output[0] if detail: t_fwd_end = self.time_fn(True) target = self._gen_target(output.shape[0]) self.loss(output, target).backward() if detail: t_bwd_end = self.time_fn(True) self.optimizer.step() t_end = self.time_fn(True) if detail: delta_fwd = t_fwd_end - t_start delta_bwd = t_bwd_end - t_fwd_end delta_opt = t_end - t_bwd_end return delta_fwd, delta_bwd, delta_opt else: delta_step = t_end - t_start return delta_step _logger.info( f'Running train benchmark on {self.model_name} for {self.num_bench_iter} steps w/ ' f'input size {self.input_size} and batch size {self.batch_size}.') self._init_input() for _ in range(self.num_warm_iter): _step() t_run_start = self.time_fn() if self.detail: total_fwd = 0. total_bwd = 0. total_opt = 0. num_samples = 0 for i in range(self.num_bench_iter): delta_fwd, delta_bwd, delta_opt = _step(True) num_samples += self.batch_size total_fwd += delta_fwd total_bwd += delta_bwd total_opt += delta_opt num_steps = (i + 1) if num_steps % self.log_freq == 0: total_step = total_fwd + total_bwd + total_opt _logger.info( f"Train [{num_steps}/{self.num_bench_iter}]." f" {num_samples / total_step:0.2f} samples/sec." f" {1000 * total_fwd / num_steps:0.3f} ms/step fwd," f" {1000 * total_bwd / num_steps:0.3f} ms/step bwd," f" {1000 * total_opt / num_steps:0.3f} ms/step opt." ) total_step = total_fwd + total_bwd + total_opt t_run_elapsed = self.time_fn() - t_run_start results = dict( samples_per_sec=round(num_samples / t_run_elapsed, 2), step_time=round(1000 * total_step / self.num_bench_iter, 3), fwd_time=round(1000 * total_fwd / self.num_bench_iter, 3), bwd_time=round(1000 * total_bwd / self.num_bench_iter, 3), opt_time=round(1000 * total_opt / self.num_bench_iter, 3), batch_size=self.batch_size, img_size=self.input_size[-1], param_count=round(self.param_count / 1e6, 2), ) else: total_step = 0. num_samples = 0 for i in range(self.num_bench_iter): delta_step = _step(False) num_samples += self.batch_size total_step += delta_step num_steps = (i + 1) if num_steps % self.log_freq == 0: _logger.info( f"Train [{num_steps}/{self.num_bench_iter}]." f" {num_samples / total_step:0.2f} samples/sec." f" {1000 * total_step / num_steps:0.3f} ms/step.") t_run_elapsed = self.time_fn() - t_run_start results = dict( samples_per_sec=round(num_samples / t_run_elapsed, 2), step_time=round(1000 * total_step / self.num_bench_iter, 3), batch_size=self.batch_size, img_size=self.input_size[-1], param_count=round(self.param_count / 1e6, 2), ) _logger.info( f"Train benchmark of {self.model_name} done. " f"{results['samples_per_sec']:.2f} samples/sec, {results['step_time']:.2f} ms/sample") return results def decay_batch_exp(batch_size, factor=0.5, divisor=16): out_batch_size = batch_size * factor if out_batch_size > divisor: out_batch_size = (out_batch_size + 1) // divisor * divisor else: out_batch_size = batch_size - 1 return max(0, int(out_batch_size)) def _try_run(model_name, bench_fn, initial_batch_size, bench_kwargs): batch_size = initial_batch_size results = dict() while batch_size >= 1: torch.cuda.empty_cache() try: bench = bench_fn(model_name=model_name, batch_size=batch_size, **bench_kwargs) results = bench.run() return results except RuntimeError as e: print(f'Error: {str(e)} while running benchmark. Reducing batch size to {batch_size} for retry.') batch_size = decay_batch_exp(batch_size) return results def benchmark(args): if args.amp: _logger.warning("Overriding precision to 'amp' since --amp flag set.") args.precision = 'amp' _logger.info(f'Benchmarking in {args.precision} precision. ' f'{"NHWC" if args.channels_last else "NCHW"} layout. ' f'torchscript {"enabled" if args.torchscript else "disabled"}') bench_kwargs = vars(args).copy() bench_kwargs.pop('amp') model = bench_kwargs.pop('model') batch_size = bench_kwargs.pop('batch_size') bench_fns = (InferenceBenchmarkRunner,) prefixes = ('infer',) if args.bench == 'both': bench_fns = ( InferenceBenchmarkRunner, TrainBenchmarkRunner ) prefixes = ('infer', 'train') elif args.bench == 'train': bench_fns = TrainBenchmarkRunner, prefixes = 'train', model_results = OrderedDict(model=model) for prefix, bench_fn in zip(prefixes, bench_fns): run_results = _try_run(model, bench_fn, initial_batch_size=batch_size, bench_kwargs=bench_kwargs) if prefix: run_results = {'_'.join([prefix, k]): v for k, v in run_results.items()} model_results.update(run_results) param_count = model_results.pop('infer_param_count', model_results.pop('train_param_count', 0)) model_results.setdefault('param_count', param_count) model_results.pop('train_param_count', 0) return model_results def main(): setup_default_logging() args = parser.parse_args() model_cfgs = [] model_names = [] if args.model_list: args.model = '' with open(args.model_list) as f: model_names = [line.rstrip() for line in f] model_cfgs = [(n, None) for n in model_names] elif args.model == 'all': # validate all models in a list of names with pretrained checkpoints args.pretrained = True model_names = list_models(pretrained=True, exclude_filters=['*in21k']) model_cfgs = [(n, None) for n in model_names] elif not is_model(args.model): # model name doesn't exist, try as wildcard filter model_names = list_models(args.model) model_cfgs = [(n, None) for n in model_names] if len(model_cfgs): results_file = args.results_file or './benchmark.csv' _logger.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names))) results = [] try: for m, _ in model_cfgs: if not m: continue args.model = m r = benchmark(args) results.append(r) except KeyboardInterrupt as e: pass sort_key = 'train_samples_per_sec' if 'train' in args.bench else 'infer_samples_per_sec' results = sorted(results, key=lambda x: x[sort_key], reverse=True) if len(results): write_results(results_file, results) import json json_str = json.dumps(results, indent=4) print(json_str) else: benchmark(args) def write_results(results_file, results): with open(results_file, mode='w') as cf: dw = csv.DictWriter(cf, fieldnames=results[0].keys()) dw.writeheader() for r in results: dw.writerow(r) cf.flush() if __name__ == '__main__': main()