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#!/usr/bin/env python3
import argparse
import inspect
import sys
import numpy as np
import tabulate
import torch
import torch._inductor
from torch._dynamo.backends.cudagraphs import cudagraphs_inner
from torch._dynamo.testing import same
from torch._inductor.compile_fx import compile_fx
from torch._inductor.utils import timed
aten = torch.ops.aten
try:
import test.test_torchinductor as tti
except ImportError:
tti = None
def compute_speedups(args, models, example_inputs):
expected = models[0](*example_inputs)
for model in models[1:]:
actual = model(*example_inputs)
assert same(actual, expected), expected[0] - actual[0]
timings = np.zeros((args.repeat, len(models)), np.float64)
for rep in range(args.repeat):
# interleave the runs to handle frequency scaling and load changes
for m, model in enumerate(models):
timings[rep, m] = timed(model, example_inputs)
median = np.median(timings, axis=0)
return (median[0] / median[1:]).tolist()
def microbenchmark(args, model, example_inputs):
compiled_fn = compile_fx(torch.fx.symbolic_trace(model), example_inputs)
cudagraphs_eager = cudagraphs_inner(model, example_inputs, copy_outputs=False)
cudagraphs_jit = cudagraphs_inner(
torch.jit.trace(model, example_inputs), example_inputs, copy_outputs=False
)
return compute_speedups(
args,
[cudagraphs_eager, cudagraphs_jit, compiled_fn],
example_inputs,
)
class MyModel1(torch.nn.Module):
def __init__(self):
super().__init__()
self.model = torch.nn.Sequential(
torch.nn.Linear(1024, 1024),
torch.nn.ReLU(),
)
def forward(self, input):
# return (self.model(input) + 1,)
return (self.model(input),)
class MyModel2(torch.nn.Module):
def forward(self, x, y):
# return x / (torch.abs(x) + 1.0),
return (x + y,)
class MicroBenchmarks:
@staticmethod
def add(a, b):
return (a + b,)
@staticmethod
def scale(x, m, d):
return ((x - m) / torch.clip(d, 1e-4),)
@staticmethod
def abs_norm(x):
return (x / (torch.abs(x) + 1),)
@staticmethod
def add_relu_softmax(x, a):
return (torch.softmax(torch.relu(x + a), -1),)
@staticmethod
def sum(a, b):
return ((a + b).sum(),)
@staticmethod
def view(x):
return (aten.alias(x),)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--filter", "-k", action="append", help="filter benchmarks with regexp"
)
parser.add_argument(
"--exclude", "-x", action="append", help="filter benchmarks with regexp"
)
parser.add_argument("--devices", "-d", action="append", help="cpu or cuda")
parser.add_argument("--size", "-s", action="append", help="cpu or cuda")
parser.add_argument(
"--repeat", "-n", type=int, default=30, help="number of timing runs"
)
parser.add_argument(
"--threads", "-t", type=int, help="number of threads to use for eager"
)
parser.add_argument(
"--verbose", "-v", action="store_true", help="enable verbose debug printouts"
)
parser.add_argument(
"--nvfuser", action="store_true", help="enable nvfuser globally"
)
parser.add_argument("--transpose", action="store_true", help="transpose one input")
parser.add_argument("--broadcast", action="store_true", help="broadcast one input")
args = parser.parse_args()
# defaults
args.devices = args.devices or ["cpu", "cuda"]
args.filter = args.filter or [r"."]
args.exclude = args.exclude or [r"^$"]
args.size = args.size or [64, 256, 1024, 4096, 8192]
if args.nvfuser:
torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(False)
torch._C._jit_set_texpr_fuser_enabled(False)
torch._C._jit_set_nvfuser_enabled(True)
else:
torch._C._jit_override_can_fuse_on_cpu(torch._C._llvm_enabled())
torch._C._jit_override_can_fuse_on_gpu(True)
torch._C._jit_set_texpr_fuser_enabled(True)
if torch.cuda.is_available():
torch._C._jit_set_nvfuser_enabled(False)
if args.threads:
torch.set_num_threads(args.threads)
torch._inductor.config.cpp.threads = args.threads
if args.verbose:
torch._inductor.config.debug = True
torch._inductor.config.triton.autotune_pointwise = True
rows = []
for model in (MicroBenchmarks.sum, MicroBenchmarks.view):
nargs = len(inspect.signature(model).parameters)
for device in args.devices:
for n in args.size:
n = int(n)
sys.stdout.write(f"{model.__name__:10} {device:4} {n:5} ")
sys.stdout.flush()
inputs = [torch.rand((n, n), device=device) for _ in range(nargs)]
if args.broadcast:
inputs[-1] = torch.rand((1, n), device=device)
if args.transpose:
inputs[-1] = inputs[-1].transpose(0, 1)
result = microbenchmark(args, model, inputs)
rows.append([model.__name__, device, str(n)] + result)
print(" ".join(f"{v:.2f}x" for v in result))
print(
tabulate.tabulate(
rows,
headers=[
"model",
"dev",
"n",
"ts",
"inductor",
],
)
)
if __name__ == "__main__":
main()
|
