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import argparse
import subprocess
import sys
import time
import torch
import datetime
from .runner import get_nn_runners
def run_rnn(name, rnn_creator, nloops=5,
seqLength=100, numLayers=1, inputSize=512, hiddenSize=512,
miniBatch=64, device='cuda', seed=None):
def run_iter(modeldef):
# Forward
forward_output = modeldef.forward(*modeldef.inputs)
# "loss computation" and backward
if modeldef.backward_setup is not None:
backward_input = modeldef.backward_setup(forward_output)
else:
backward_input = forward_output
if modeldef.backward is not None:
modeldef.backward(*backward_input)
# "Update" parameters
if modeldef.backward is not None:
with torch.no_grad():
for param in modeldef.params:
param.grad.zero_()
torch.cuda.synchronize()
assert device == 'cuda'
creator_args = dict(seqLength=seqLength, numLayers=numLayers,
inputSize=inputSize, hiddenSize=hiddenSize,
miniBatch=miniBatch, device=device, seed=seed)
modeldef = rnn_creator(**creator_args)
[run_iter(modeldef) for _ in range(nloops)]
def profile(rnns, sleep_between_seconds=1, nloops=5,
internal_run=True, # Unused, get rid of this TODO
seqLength=100, numLayers=1, inputSize=512, hiddenSize=512,
miniBatch=64, device='cuda', seed=None):
params = dict(seqLength=seqLength, numLayers=numLayers,
inputSize=inputSize, hiddenSize=hiddenSize,
miniBatch=miniBatch, device=device, seed=seed)
for name, creator, context in get_nn_runners(*rnns):
with context():
run_rnn(name, creator, nloops, **params)
time.sleep(sleep_between_seconds)
def system(command):
"""Returns (return-code, stdout, stderr)"""
print('[system] {}'.format(command))
p = subprocess.Popen(command, stdout=subprocess.PIPE,
stderr=subprocess.PIPE, shell=True)
output, err = p.communicate()
rc = p.returncode
output = output.decode("ascii")
err = err.decode("ascii")
return rc, output, err
def describe_sizes(**sizes):
# seqLength, numLayers, inputSize, hiddenSize, miniBatch
return 's{}-l{}-i{}-h{}-b{}'.format(
sizes['seqLength'],
sizes['numLayers'],
sizes['inputSize'],
sizes['hiddenSize'],
sizes['miniBatch'],
)
OUTPUT_DIR = '~/profout/'
def nvprof_output_filename(rnns, **params):
rnn_tag = '-'.join(rnns)
size_tag = describe_sizes(**params)
date_tag = datetime.datetime.now().strftime("%m%d%y-%H%M")
return '{}prof_{}_{}_{}.nvvp'.format(OUTPUT_DIR, rnn_tag,
size_tag, date_tag)
def nvprof(cmd, outpath):
return system('nvprof -o {} {}'.format(outpath, cmd))
def full_profile(rnns, **args):
profile_args = []
for k, v in args.items():
profile_args.append('--{}={}'.format(k, v))
profile_args.append('--rnns {}'.format(' '.join(rnns)))
profile_args.append('--internal_run')
outpath = nvprof_output_filename(rnns, **args)
cmd = '{} -m fastrnns.profile {}'.format(
sys.executable, ' '.join(profile_args))
rc, stdout, stderr = nvprof(cmd, outpath)
if rc != 0:
raise RuntimeError('stderr: {}\nstdout: {}'.format(stderr, stdout))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Profile RNNs')
parser.add_argument('--seqLength', default='100', type=int)
parser.add_argument('--numLayers', default='1', type=int)
parser.add_argument('--inputSize', default='512', type=int)
parser.add_argument('--hiddenSize', default='512', type=int)
parser.add_argument('--miniBatch', default='64', type=int)
parser.add_argument('--sleep_between_seconds', default='1', type=int)
parser.add_argument('--nloops', default='5', type=int)
parser.add_argument('--rnns', nargs='*',
help='What to run. cudnn, aten, jit, etc')
# if internal_run, we actually run the rnns.
# if not internal_run, we shell out to nvprof with internal_run=T
parser.add_argument('--internal_run', default=False, action='store_true',
help='Don\'t use this')
args = parser.parse_args()
if args.rnns is None:
args.rnns = ['cudnn', 'aten', 'jit']
print(args)
if args.internal_run:
profile(**vars(args))
else:
full_profile(**vars(args))
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