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import argparse
import datetime
import subprocess
import sys
import time
import torch
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(f"[system] {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 f"{OUTPUT_DIR}prof_{rnn_tag}_{size_tag}_{date_tag}.nvvp"
def nvprof(cmd, outpath):
return system(f"nvprof -o {outpath} {cmd}")
def full_profile(rnns, **args):
profile_args = []
for k, v in args.items():
profile_args.append(f"--{k}={v}")
profile_args.append(f"--rnns {' '.join(rnns)}")
profile_args.append("--internal-run")
outpath = nvprof_output_filename(rnns, **args)
cmd = f"{sys.executable} -m fastrnns.profile {' '.join(profile_args)}"
rc, stdout, stderr = nvprof(cmd, outpath)
if rc != 0:
raise RuntimeError(f"stderr: {stderr}\nstdout: {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", "--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",
"--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))
|
