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from functools import partial
import numpy as np
import pandas as pd
import timeit
import torch
from functorch.compile import pointwise_operator
WRITE_CSV = False
CUDA = False
SIZES = [1, 512, 8192]
NUMBER = [100, 10, 1, 1]
REPEAT = 20
@pointwise_operator
def nnc_add(a, b):
return a + b
@pointwise_operator
def nnc_addnorm(a, b, mean, std):
return (a + b - mean) / std
def eager_addnorm(a, b, mean, std):
return (a + b - mean) / std
def inplace_addnorm(a, b, mean, std, out):
out = torch.add(a, b, out=out)
torch.sub(out, mean, out=out)
torch.div(out, std, out=out)
return out
ts_addnorm = torch.jit.script(eager_addnorm)
ts_ip_addnorm = torch.jit.script(inplace_addnorm)
def maybe_synced(fn):
if CUDA:
synchronize = torch.cuda.synchronize
synchronize() # warmup
def _fn():
result = fn()
synchronize()
return result
return _fn
return fn
def benchmark_loop(setup):
result = np.zeros((REPEAT, len(SIZES), 2), dtype=np.float64)
for s, n in enumerate(SIZES):
nnc, aten = setup(n)
nnc = maybe_synced(nnc)
aten = maybe_synced(aten)
for r in range(result.shape[0]):
result[r, s, 0] = timeit.timeit(nnc, number=NUMBER[s])
result[r, s, 1] = timeit.timeit(aten, number=NUMBER[s])
result = np.median(result, axis=0)
assert result.shape == (len(SIZES), 2)
result = result[:, 1] / result[:, 0]
print(result)
return result
def test(make_args, nnc=nnc_add, aten=torch.add):
def setup(n):
args = make_args(n)
result_aten = aten(*args)
result_nnc = nnc(*args)
assert result_nnc.dtype == result_aten.dtype
assert result_nnc.size() == result_aten.size()
assert result_nnc.stride() == result_aten.stride()
torch.testing.assert_allclose(result_aten, result_nnc)
return (lambda: nnc(*args), lambda: aten(*args))
return benchmark_loop(setup)
def test_inplace(make_args, nnc=nnc_add, aten=torch.add):
def inplace_setup(n):
a, b = make_args(n)
result_aten = torch.clone(a)
result_nnc = torch.clone(a)
nnc(result_nnc, b, out=result_nnc)
aten(result_aten, b, out=result_aten)
torch.testing.assert_allclose(result_aten, result_nnc)
return (lambda: nnc(a, b, out=a), lambda: aten(a, b, out=a))
return benchmark_loop(inplace_setup)
def test_out(make_args, out, nnc=nnc_add, aten=torch.add):
def out_setup(n):
args = make_args(n)
result_aten = out(n)
result_nnc = out(n)
aten(*args, out=result_aten)
nnc(*args, out=result_nnc)
torch.testing.assert_allclose(result_aten, result_nnc)
result = out(n)
return (lambda: nnc(*args, out=result), lambda: aten(*args, out=result))
return benchmark_loop(out_setup)
def test_backwards(make_args, nnc=nnc_add, aten=torch.add):
def backwards_setup(n):
args = make_args(n)
(grad_var,) = [a for a in args if a.requires_grad]
aten(*args).sum().backward()
correct = grad_var.grad.clone()
grad_var.grad.zero_()
nnc(*args).sum().backward()
torch.testing.assert_allclose(correct, grad_var.grad)
return (
lambda: nnc(*args).sum().backward(),
lambda: aten(*args).sum().backward(),
)
return benchmark_loop(backwards_setup)
def main():
torch.set_num_threads(1) # TODO(jansel): add parallel support
torch._C._jit_override_can_fuse_on_cpu(True)
device = "cuda" if CUDA else "cpu"
I = partial(torch.randint, 0, 100, device=device)
R = partial(torch.randn, device=device)
results = [
("add", test(lambda n: (R(n, n), R(n, n)))),
("broadcast1", test(lambda n: (R(n, n), R(1)))),
("broadcast2", test(lambda n: (R(n, n), R(n, 1)))),
("broadcast3", test(lambda n: (R(n, 1), R(1, n)))),
("inplace", test_inplace(lambda n: (R(n, n), R(n, 1)))),
("out=", test_out(lambda n: (R(n, n), R(n, n)), out=lambda n: R(n, n))),
("transposed1", test(lambda n: (R(n, n), R(n, n).transpose(0, 1)))),
(
"transposed2",
test(lambda n: (R(n, n).transpose(0, 1), R(n, n).transpose(0, 1))),
),
("slice1", test(lambda n: (R(n + 1, n + 1, 2)[:n, :n, 0], R(n, n)))),
("slice2", test(lambda n: (R(n, n, 2)[:, :, 0], R(n, n, 2)[:, :, 0]))),
(
"strided out",
test_out(
lambda n: (R(n, n), R(n, n)),
out=lambda n: R(n + 1, n + 1, 2)[:n, :n, 0],
),
),
(
"out convert",
test_out(
lambda n: (R(n, n), R(n, n)), out=lambda n: R(n, n, dtype=torch.float64)
),
),
("issue #57611 (n,32,32,2)", test(lambda n: (R(1, 32, 32, 2), R(n, 1, 1, 2)))),
("float+double", test(lambda n: (R(n, n), R(n, n, dtype=torch.float64)))),
(
"int+long",
test(
lambda n: (I([n, n], dtype=torch.int32), I([n, n], dtype=torch.int64))
),
),
(
"int+short",
test(
lambda n: (I([n, n], dtype=torch.int32), I([n, n], dtype=torch.int16))
),
),
(
"float+int",
test(
lambda n: (R([n, n], dtype=torch.float32), I([n, n], dtype=torch.int32))
),
),
(
"double+long",
test(
lambda n: (R([n, n], dtype=torch.float64), I([n, n], dtype=torch.int64))
),
),
(
"fused addnorm",
test(
lambda n: (R(n, n), R(n, n), R(n, n), R(n, n)),
nnc=nnc_addnorm,
aten=eager_addnorm,
),
),
(
"fused addnorm (vs TS)",
test(
lambda n: (R(n, n), R(n, n), R(n, n), R(n, n)),
nnc=nnc_addnorm,
aten=ts_addnorm,
),
),
(
"fused addnorm out=",
test_out(
lambda n: (R(n, n), R(n, n), R(n, n), R(n, n)),
nnc=nnc_addnorm,
aten=inplace_addnorm,
out=lambda n: R(n, n),
),
),
(
"fused addnorm out= (vs TS)",
test_out(
lambda n: (R(n, n), R(n, n), R(n, n), R(n, n)),
nnc=nnc_addnorm,
aten=ts_ip_addnorm,
out=lambda n: R(n, n),
),
),
(
"fused addnorm backward",
test_backwards(
lambda n: (R(n, n), R(n, n, requires_grad=True), R(n, n), R(n, n)),
nnc=nnc_addnorm,
aten=eager_addnorm,
),
),
(
"fused addnorm backward (vs TS)",
test_backwards(
lambda n: (R(n, n), R(n, n, requires_grad=True), R(n, n), R(n, n)),
nnc=nnc_addnorm,
aten=ts_addnorm,
),
),
]
df = pd.DataFrame(
np.stack([r for n, r in results]),
columns=[f"{n}x{n}".rjust(9) for n in SIZES],
index=[n for n, r in results],
)
if WRITE_CSV:
df.to_csv("../operator_authoring_results.csv")
print("wrote ../operator_authoring_results.csv")
print()
print("Speedups over aten")
pd.options.display.float_format = "{:.2f}x".format
print(df)
if __name__ == "__main__":
main()
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