#!/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
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
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
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_deepspeed(model, input_size=(3, 224, 224), batch_size=1, detailed=False):
macs, _ = 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=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, 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,
drop_rate=kwargs.pop('drop', 0.),
drop_path_rate=kwargs.pop('drop_path', None),
drop_block_rate=kwargs.pop('drop_block', None),
)
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))
self.scripted = False
if torchscript:
self.model = torch.jit.script(self.model)
self.scripted = True
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),
)
retries = 0 if self.scripted 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()
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
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 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:
e_str = str(e)
print(e_str)
if 'channels_last' in e_str:
print(f'Error: {model_name} not supported in channels_last, skipping.')
break
print(f'Error: "{e_str}" 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',
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, 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 if model_results['param_count'] else dict()
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)
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 = sorted(results, key=lambda x: x[sort_key], reverse=True)
if len(results):
write_results(results_file, results)
else:
results = benchmark(args)
json_str = json.dumps(results, indent=4)
print(json_str)
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()