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bench.py
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bench.py
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import argparse
import copy
import gc
import json
import sys
import time
from collections import namedtuple
import torch
from torch.autograd.profiler import record_function
from .fuser import set_fuser
from .runner import get_nn_runners
BenchResult = namedtuple(
"BenchResult",
[
"name",
"avg_fwd",
"std_fwd",
"info_fwd",
"avg_bwd",
"std_bwd",
"info_bwd",
],
)
def fit_str(string, colwidth=16):
if len(string) < colwidth:
return (colwidth - len(string)) * " " + string
else:
return string[:colwidth]
def to_str(item):
if isinstance(item, float):
return f"{item:.4g}"
return str(item)
def print_header(colwidth=16, sep=" "):
items = []
for item in BenchResult._fields:
items.append(fit_str(item))
return sep.join(items)
def pretty_print(benchresult, colwidth=16, sep=" "):
items = []
for thing in benchresult:
items.append(fit_str(to_str(thing)))
return sep.join(items)
# shim for torch.cuda.Event when running on cpu
class Event:
def __init__(self, enable_timing):
pass
def record(self):
self.time = time.perf_counter()
def elapsed_time(self, end_event):
assert isinstance(end_event, Event)
return end_event.time - self.time
def trainbench(
name,
rnn_creator,
nloops=100,
warmup=10,
seqLength=100,
numLayers=1,
inputSize=512,
hiddenSize=512,
miniBatch=64,
device="cuda",
seed=None,
):
def train_batch(modeldef):
# CUDA events for timing
if device == "cuda":
timer_class = torch.cuda.Event
else:
timer_class = Event
fwd_start_event = timer_class(enable_timing=True)
fwd_end_event = timer_class(enable_timing=True)
bwd_start_event = timer_class(enable_timing=True)
bwd_end_event = timer_class(enable_timing=True)
gc.collect()
fwd_start_event.record()
with record_function("## forward ##"):
forward_output = modeldef.forward(*modeldef.inputs)
fwd_end_event.record()
# XXX: Use if need to print something
# print(modeldef.forward.graph_for(*modeldef.inputs))
if modeldef.backward_setup is not None:
backward_input = modeldef.backward_setup(forward_output)
else:
backward_input = forward_output
gc.collect()
bwd_start_event.record()
if modeldef.backward is not None:
modeldef.backward(*backward_input)
bwd_end_event.record()
if modeldef.backward is not None:
with torch.no_grad():
for param in modeldef.params:
assert param.grad is not None
param.grad.zero_()
if device == "cuda":
torch.cuda.synchronize()
fwd_time = fwd_start_event.elapsed_time(fwd_end_event)
bwd_time = bwd_start_event.elapsed_time(bwd_end_event)
return fwd_time, bwd_time
creator_args = creator_args = {
"seqLength": seqLength,
"numLayers": numLayers,
"inputSize": inputSize,
"hiddenSize": hiddenSize,
"miniBatch": miniBatch,
"device": device,
"seed": seed,
}
modeldef = rnn_creator(**creator_args)
[train_batch(modeldef) for _ in range(warmup)]
results = [train_batch(modeldef) for _ in range(nloops)]
fwd_times, bwd_times = zip(*results)
fwd_times = torch.tensor(fwd_times)
bwd_times = torch.tensor(bwd_times)
return BenchResult(
name=name,
avg_fwd=fwd_times.mean().item(),
std_fwd=fwd_times.std().item(),
info_fwd=fwd_times,
avg_bwd=bwd_times.mean().item(),
std_bwd=bwd_times.std().item(),
info_bwd=bwd_times,
)
def print_stderr(*args, **kwargs):
kwargs["file"] = sys.stderr
return print(*args, **kwargs)
def print_json_oss_format(results):
oss_results = {}
for group_name, group_val in results.items():
oss_results[group_name] = {}
for model_name, run_time in group_val.items():
# Output for OSS
oss_results[group_name][model_name] = run_time["avg"]
print(json.dumps(oss_results))
def print_json_pep_format(results):
# print the AI-PEP format json string for each model
for group_name, group_val in results.items():
for model_name, run_time in group_val.items():
# Output for AI-PEP
num_iters = len(run_time["info"])
info = run_time["info"].tolist()
for i in range(num_iters):
print(
"Caffe2Observer "
+ json.dumps(
{
"type": "NET",
"metric": group_name + "-" + model_name,
"unit": "ms",
"value": str(info[i]),
}
)
)
def bench(rnn_runners, group_name, print_json=False, sep=" ", **params):
print_stderr(print_header(sep=sep))
results = {}
for name, creator, context in rnn_runners:
with context():
try:
result = trainbench(name, creator, **params)
# Replace the value of info_fwd and info_bwd to None
result_with_no_info = result._replace(info_fwd="None", info_bwd="None")
print_stderr(pretty_print(result_with_no_info, sep=sep))
results[name] = result
except Exception as e:
if not print_json:
raise
return {
group_name: {
k: {"avg": v.avg_fwd, "std": v.std_fwd, "info": v.info_fwd}
for k, v in results.items()
},
group_name
+ "-backward": {
k: {"avg": v.avg_bwd, "std": v.std_bwd, "info": v.info_bwd}
for k, v in results.items()
},
}
def bench_group(model_list, bench_name, bench_group, bench_args):
print_stderr(f"Benchmarking {bench_name}s...")
nn_results = bench(get_nn_runners(*model_list), bench_group, **bench_args)
print_stderr("")
return nn_results
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Profile RNNs")
# groups help control which test group you want to run
# if you only want to run one/two benchmark, run it with
# e.g: python -m fastrnns.bench --rnns jit and --group rnns
default_groups = ["cnns", "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("--warmup", default="10", type=int)
parser.add_argument("--nloops", default="100", type=int)
parser.add_argument("--device", default="cuda", type=str)
parser.add_argument(
"--variable-lstms",
"--variable_lstms",
action="store_true",
help="Also benchmark variable sequence length lstms "
"Note that some of these run really slowly "
"and that the `seqLength` flag will be ignored.",
)
parser.add_argument("--sep", default=" ", type=str)
parser.add_argument("--print-json", nargs="?", default=None, const="oss")
parser.add_argument("--rnns", nargs="*", help="What to run. cudnn, aten, jit, etc")
parser.add_argument(
"--cnns", nargs="*", help="What to run. resnet18, resnet18_jit, resnet50, etc"
)
parser.add_argument(
"--group",
nargs="*",
default=default_groups,
help="Which group to run. cnns, rnns, etc.",
)
parser.add_argument(
"--fuser",
default="te",
type=str,
help="The fuser backend to use. One of: te, old, or none",
)
parser.add_argument(
"--executor",
default=None,
type=str,
help="The executor to use. One of: legacy, simple, profiling",
)
parser.add_argument(
"--cuda-pointwise-loop-level",
"--cuda_pointwise_loop_level",
default=None,
type=int,
)
parser.add_argument(
"--cuda-pointwise-block-count",
"--cuda_pointwise_block_count",
default=None,
type=int,
)
parser.add_argument(
"--cuda-pointwise-block-size",
"--cuda_pointwise_block_size",
default=None,
type=int,
)
args = parser.parse_args()
set_fuser(args.fuser, args.executor)
if args.cuda_pointwise_loop_level:
torch._C._jit_set_te_cuda_pointwise_loop_levels(args.cuda_pointwise_loop_level)
if args.cuda_pointwise_block_count:
torch._C._jit_set_te_cuda_pointwise_block_count(args.cuda_pointwise_block_count)
if args.cuda_pointwise_block_size:
torch._C._jit_set_te_cuda_pointwise_block_size(args.cuda_pointwise_block_size)
rnns = args.rnns or [
"cudnn",
"aten",
"jit",
"jit_premul",
"jit_premul_bias",
"jit_simple",
"jit_multilayer",
"py",
]
cnns = args.cnns or ["resnet18", "resnet18_jit", "resnet50", "resnet50_jit"]
# TODO: Maybe add a separate section for the layernorm/dropout lstms
# 'cudnn_layernorm', jit_layernorm', 'jit_layernom_decom',
# 'jit', 'jit_dropout', 'cudnn_dropout'
vlrnns = ["vl_cudnn", "vl_jit", "vl_py"]
if args.print_json:
print_stderr = lambda *args, **kwargs: None # noqa: E731,F811
print_stderr(args)
bench_args = copy.deepcopy(vars(args))
should_bench_varlen_lstms = args.variable_lstms
del bench_args["group"]
del bench_args["rnns"]
del bench_args["cnns"]
del bench_args["variable_lstms"]
del bench_args["fuser"]
del bench_args["executor"]
del bench_args["cuda_pointwise_loop_level"]
del bench_args["cuda_pointwise_block_count"]
del bench_args["cuda_pointwise_block_size"]
results = {}
if should_bench_varlen_lstms:
if args.nloops + args.warmup > 30:
print_stderr(
"WARNING: some of the variable sequence length lstms are "
"very unoptimized and therefore take forever to run."
)
results.update(
bench_group(vlrnns, "variable-length sequence LSTM", "vl_lstm", bench_args)
)
if "rnns" in args.group:
results.update(bench_group(rnns, "LSTM", "lstm", bench_args))
if "cnns" in args.group:
results.update(bench_group(cnns, "ResNet", "resnet", bench_args))
if args.print_json == "oss":
print_json_oss_format(results)
elif args.print_json == "pep":
print_json_pep_format(results)