#!/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 csv import json import logging import time from collections import OrderedDict from contextlib import suppress from functools import partial import torch import torch.nn as nn import torch.nn.parallel from timm.data import resolve_data_config from timm.layers import set_fast_norm from timm.models import create_model, is_model, list_models from timm.optim import create_optimizer_v2 from timm.utils import setup_default_logging, set_jit_fuser, decay_batch_step, check_batch_size_retry, ParseKwargs 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 try: from deepspeed.profiling.flops_profiler import get_model_profile has_deepspeed_profiling = True except ImportError as e: has_deepspeed_profiling = False try: from fvcore.nn import FlopCountAnalysis, flop_count_str, ActivationCountAnalysis has_fvcore_profiling = True except ImportError as e: FlopCountAnalysis = None has_fvcore_profiling = False try: from functorch.compile import memory_efficient_fusion has_functorch = True except ImportError as e: has_functorch = False has_compile = hasattr(torch, 'compile') if torch.cuda.is_available(): torch.backends.cuda.matmul.allow_tf32 = True 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('--no-retry', action='store_true', default=False, help='Do not decay batch size and retry on error.') parser.add_argument('--results-file', default='', type=str, help='Output csv file for validation results (summary)') parser.add_argument('--results-format', default='csv', type=str, help='Format for results file one of (csv, json) (default: csv).') parser.add_argument('--num-warm-iter', default=10, type=int, help='Number of warmup iterations (default: 10)') parser.add_argument('--num-bench-iter', default=40, type=int, help='Number of benchmark iterations (default: 40)') parser.add_argument('--device', default='cuda', type=str, help="device to run benchmark on") # 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('--grad-checkpointing', action='store_true', default=False, help='Enable gradient checkpointing through model blocks/stages') parser.add_argument('--amp', action='store_true', default=False, help='use PyTorch Native AMP for mixed precision training. Overrides --precision arg.') parser.add_argument('--amp-dtype', default='float16', type=str, help='lower precision AMP dtype (default: float16). Overrides --precision arg if args.amp True.') parser.add_argument('--precision', default='float32', type=str, help='Numeric precision. One of (amp, float32, float16, bfloat16, tf32)') parser.add_argument('--fuser', default='', type=str, help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')") parser.add_argument('--fast-norm', default=False, action='store_true', help='enable experimental fast-norm') parser.add_argument('--model-kwargs', nargs='*', default={}, action=ParseKwargs) # codegen (model compilation) options scripting_group = parser.add_mutually_exclusive_group() scripting_group.add_argument('--torchscript', dest='torchscript', action='store_true', help='convert model torchscript for inference') scripting_group.add_argument('--torchcompile', nargs='?', type=str, default=None, const='inductor', help="Enable compilation w/ specified backend (default: inductor).") scripting_group.add_argument('--aot-autograd', default=False, action='store_true', help="Enable AOT Autograd optimization.") # 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', 'amp_bfloat16', 'float16', 'bfloat16', 'float32') amp_dtype = None # amp disabled model_dtype = torch.float32 data_dtype = torch.float32 if precision == 'amp': amp_dtype = torch.float16 elif precision == 'amp_bfloat16': amp_dtype = torch.bfloat16 elif precision == 'float16': model_dtype = torch.float16 data_dtype = torch.float16 elif precision == 'bfloat16': model_dtype = torch.bfloat16 data_dtype = torch.bfloat16 return amp_dtype, model_dtype, data_dtype def profile_deepspeed(model, input_size=(3, 224, 224), batch_size=1, detailed=False): _, macs, _ = get_model_profile( model=model, input_shape=(batch_size,) + input_size, # input shape/resolution print_profile=detailed, # prints the model graph with the measured profile attached to each module detailed=detailed, # 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, 0 # no activation count in DS def profile_fvcore(model, input_size=(3, 224, 224), batch_size=1, detailed=False, force_cpu=False): if force_cpu: model = model.to('cpu') device, dtype = next(model.parameters()).device, next(model.parameters()).dtype example_input = torch.ones((batch_size,) + input_size, device=device, dtype=dtype) fca = FlopCountAnalysis(model, example_input) aca = ActivationCountAnalysis(model, example_input) if detailed: fcs = flop_count_str(fca) print(fcs) return fca.total(), aca.total() class BenchmarkRunner: def __init__( self, model_name, detail=False, device='cuda', torchscript=False, torchcompile=None, aot_autograd=False, precision='float32', fuser='', 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.amp_dtype, self.model_dtype, self.data_dtype = resolve_precision(precision) self.channels_last = kwargs.pop('channels_last', False) if self.amp_dtype is not None: self.amp_autocast = partial(torch.cuda.amp.autocast, dtype=self.amp_dtype) else: self.amp_autocast = suppress if fuser: set_jit_fuser(fuser) self.model = create_model( model_name, num_classes=kwargs.pop('num_classes', None), in_chans=3, global_pool=kwargs.pop('gp', 'fast'), scriptable=torchscript, drop_rate=kwargs.pop('drop', 0.), drop_path_rate=kwargs.pop('drop_path', None), drop_block_rate=kwargs.pop('drop_block', None), **kwargs.pop('model_kwargs', {}), ) 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)) 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.compiled = False if torchscript: self.model = torch.jit.script(self.model) self.compiled = True elif torchcompile: assert has_compile, 'A version of torch w/ torch.compile() is required, possibly a nightly.' torch._dynamo.reset() self.model = torch.compile(self.model, backend=torchcompile) self.compiled = True elif aot_autograd: assert has_functorch, "functorch is needed for --aot-autograd" self.model = memory_efficient_fusion(self.model) self.compiled = True 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), ) retries = 0 if self.compiled else 2 # skip profiling if model is scripted while retries: retries -= 1 try: if has_deepspeed_profiling: macs, _ = profile_deepspeed(self.model, self.input_size) results['gmacs'] = round(macs / 1e9, 2) elif has_fvcore_profiling: macs, activations = profile_fvcore(self.model, self.input_size, force_cpu=not retries) results['gmacs'] = round(macs / 1e9, 2) results['macts'] = round(activations / 1e6, 2) except RuntimeError as e: pass _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() 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)) if kwargs.pop('grad_checkpointing', False): self.model.set_grad_checkpointing() 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 class ProfileRunner(BenchmarkRunner): def __init__(self, model_name, device='cuda', profiler='', **kwargs): super().__init__(model_name=model_name, device=device, **kwargs) if not profiler: if has_deepspeed_profiling: profiler = 'deepspeed' elif has_fvcore_profiling: profiler = 'fvcore' assert profiler, "One of deepspeed or fvcore needs to be installed for profiling to work." self.profiler = profiler self.model.eval() def run(self): _logger.info( f'Running profiler on {self.model_name} w/ ' f'input size {self.input_size} and batch size {self.batch_size}.') macs = 0 activations = 0 if self.profiler == 'deepspeed': macs, _ = profile_deepspeed(self.model, self.input_size, batch_size=self.batch_size, detailed=True) elif self.profiler == 'fvcore': macs, activations = profile_fvcore(self.model, self.input_size, batch_size=self.batch_size, detailed=True) results = dict( gmacs=round(macs / 1e9, 2), macts=round(activations / 1e6, 2), batch_size=self.batch_size, img_size=self.input_size[-1], param_count=round(self.param_count / 1e6, 2), ) _logger.info( f"Profile of {self.model_name} done. " f"{results['gmacs']:.2f} GMACs, {results['param_count']:.2f} M params.") return results def _try_run( model_name, bench_fn, bench_kwargs, initial_batch_size, no_batch_size_retry=False ): batch_size = initial_batch_size results = dict() error_str = 'Unknown' while batch_size: try: torch.cuda.empty_cache() bench = bench_fn(model_name=model_name, batch_size=batch_size, **bench_kwargs) results = bench.run() return results except RuntimeError as e: error_str = str(e) _logger.error(f'"{error_str}" while running benchmark.') if not check_batch_size_retry(error_str): _logger.error(f'Unrecoverable error encountered while benchmarking {model_name}, skipping.') break if no_batch_size_retry: break batch_size = decay_batch_step(batch_size) _logger.warning(f'Reducing batch size to {batch_size} for retry.') results['error'] = error_str return results def benchmark(args): if args.amp: _logger.warning("Overriding precision to 'amp' since --amp flag set.") args.precision = 'amp' if args.amp_dtype == 'float16' else '_'.join(['amp', args.amp_dtype]) _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', elif args.bench.startswith('profile'): # specific profiler used if included in bench mode string, otherwise default to deepspeed, fallback to fvcore if 'deepspeed' in args.bench: assert has_deepspeed_profiling, "deepspeed must be installed to use deepspeed flop counter" bench_kwargs['profiler'] = 'deepspeed' elif 'fvcore' in args.bench: assert has_fvcore_profiling, "fvcore must be installed to use fvcore flop counter" bench_kwargs['profiler'] = 'fvcore' bench_fns = ProfileRunner, batch_size = 1 model_results = OrderedDict(model=model) for prefix, bench_fn in zip(prefixes, bench_fns): run_results = _try_run( model, bench_fn, bench_kwargs=bench_kwargs, initial_batch_size=batch_size, no_batch_size_retry=args.no_retry, ) if prefix and 'error' not in run_results: run_results = {'_'.join([prefix, k]): v for k, v in run_results.items()} model_results.update(run_results) if 'error' in run_results: break if 'error' not in model_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.fast_norm: set_fast_norm() 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): _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) if r: results.append(r) time.sleep(10) except KeyboardInterrupt as e: pass sort_key = 'infer_samples_per_sec' if 'train' in args.bench: sort_key = 'train_samples_per_sec' elif 'profile' in args.bench: sort_key = 'infer_gmacs' results = filter(lambda x: sort_key in x, results) results = sorted(results, key=lambda x: x[sort_key], reverse=True) else: results = benchmark(args) if args.results_file: write_results(args.results_file, results, format=args.results_format) # output results in JSON to stdout w/ delimiter for runner script print(f'--result\n{json.dumps(results, indent=4)}') def write_results(results_file, results, format='csv'): with open(results_file, mode='w') as cf: if format == 'json': json.dump(results, cf, indent=4) else: if not isinstance(results, (list, tuple)): results = [results] if not results: return dw = csv.DictWriter(cf, fieldnames=results[0].keys()) dw.writeheader() for r in results: dw.writerow(r) cf.flush() if __name__ == '__main__': main()