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|
# Owner(s): ["module: inductor"]
import os
import unittest
import torch
from torch._inductor.runtime.benchmarking import benchmarker
from torch._inductor.test_case import run_tests, TestCase
from torch._inductor.utils import run_and_get_code
from torch.testing._internal.common_utils import skipIfXpu
from torch.testing._internal.inductor_utils import GPU_TYPE, HAS_GPU
@skipIfXpu(msg="Segmentation fault on CI machine")
class B2BGEMMTest(TestCase):
device = GPU_TYPE
@torch._dynamo.config.patch(cache_size_limit=32)
@torch._inductor.config.patch(b2b_gemm_pass=True)
def test_b2b_gemm_left_assoc_good_shape(self):
"""
left_assoc means the pattern is (subgraph(A @ B) @ C)
good_shape means the sizes are good for b2b_gemm
"""
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
g = torch.nn.GELU()
return torch.mm(g(torch.mm(m1, m2)), m3)
def f_32(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
"""
When the optimization is applied,
the Triton kernel is more precise than the above f,
because it internally uses float32 for accumulation while the above f uses float16.
To ensure a fair comparison,
we promote the baseline f to float32 for precision comparison.
This actually reduced some atol's in the tests from 0.2 to 0.1.
"""
m1 = m1.to(torch.float32)
m2 = m2.to(torch.float32)
m3 = m3.to(torch.float32)
return f(m1, m2, m3).to(torch.float16)
f_opt = torch.compile(f)
A = torch.randn((256, 32), device=GPU_TYPE, dtype=torch.float16)
B = torch.randn((32, 256), device=GPU_TYPE, dtype=torch.float16)
C = torch.randn((256, 32), device=GPU_TYPE, dtype=torch.float16)
res, (code,) = run_and_get_code(f_opt, A, B, C)
self.assertTrue(torch.allclose(f_32(A, B, C), res, atol=0.1, rtol=0.01))
self.assertTrue("B2B_GEMM_LEFT_TRITON_ENTRANCE" in code)
@torch._dynamo.config.patch(cache_size_limit=32)
@torch._inductor.config.patch(b2b_gemm_pass=True)
def test_b2b_gemm_right_assoc_good_shape(self):
"""
right_assoc means the pattern is (A @ subgraph(B @ C))
good_shape means the sizes are good for b2b_gemm
"""
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
g = torch.nn.ReLU()
return torch.mm(m1, g(torch.mm(m2, m3)))
def f_32(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
m1 = m1.to(torch.float32)
m2 = m2.to(torch.float32)
m3 = m3.to(torch.float32)
return f(m1, m2, m3).to(torch.float16)
f_opt = torch.compile(f)
A = torch.randn((32, 256), device=GPU_TYPE, dtype=torch.float16)
B = torch.randn((256, 32), device=GPU_TYPE, dtype=torch.float16)
C = torch.randn((32, 256), device=GPU_TYPE, dtype=torch.float16)
res, (code,) = run_and_get_code(f_opt, A, B, C)
self.assertTrue(torch.allclose(f_32(A, B, C), res, atol=0.1, rtol=0.01))
self.assertTrue("B2B_GEMM_RIGHT_TRITON_ENTRANCE" in code)
@torch._dynamo.config.patch(cache_size_limit=32)
@torch._inductor.config.patch(b2b_gemm_pass=True)
def test_b2b_gemm_trivial_left_assoc_good_shape(self):
"""
trivial_left_assoc means the pattern is ((A @ B) @ C)
good_shape means the sizes are good for b2b_gemm
"""
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
return torch.mm(torch.mm(m1, m2), m3)
def f_32(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
m1 = m1.to(torch.float32)
m2 = m2.to(torch.float32)
m3 = m3.to(torch.float32)
return f(m1, m2, m3).to(torch.float16)
f_opt = torch.compile(f)
A = torch.randn((256, 32), device=GPU_TYPE, dtype=torch.float16)
B = torch.randn((32, 256), device=GPU_TYPE, dtype=torch.float16)
C = torch.randn((256, 32), device=GPU_TYPE, dtype=torch.float16)
res, (code,) = run_and_get_code(f_opt, A, B, C)
self.assertTrue(torch.allclose(f_32(A, B, C), res, atol=0.1, rtol=0.01))
self.assertTrue("B2B_GEMM_LEFT_TRITON_ENTRANCE" in code)
@torch._dynamo.config.patch(cache_size_limit=32)
@torch._inductor.config.patch(b2b_gemm_pass=True)
def test_b2b_gemm_trivial_right_assoc_good_shape(self):
"""
trivial_right_assoc means the pattern is (A @ (B @ C))
good_shape means the sizes are good for b2b_gemm
"""
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
return torch.mm(m1, torch.mm(m2, m3))
def f_32(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
m1 = m1.to(torch.float32)
m2 = m2.to(torch.float32)
m3 = m3.to(torch.float32)
return f(m1, m2, m3).to(torch.float16)
f_opt = torch.compile(f)
A = torch.randn((32, 256), device=GPU_TYPE, dtype=torch.float16)
B = torch.randn((256, 32), device=GPU_TYPE, dtype=torch.float16)
C = torch.randn((32, 256), device=GPU_TYPE, dtype=torch.float16)
res, (code,) = run_and_get_code(f_opt, A, B, C)
self.assertTrue(torch.allclose(f_32(A, B, C), res, atol=0.1, rtol=0.01))
self.assertTrue("B2B_GEMM_RIGHT_TRITON_ENTRANCE" in code)
@torch._dynamo.config.patch(cache_size_limit=32)
@torch._inductor.config.patch(b2b_gemm_pass=True)
def test_b2b_gemm_bad_pattern_good_shape(self):
"""
bad_pattern means the code does not contain the supported patterns
"""
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
mm1 = torch.mm(m1, m2)
mm2 = torch.mm(mm1, m3)
return torch.mm(mm1, mm2)
f_opt = torch.compile(f)
A = torch.randn((256, 32), device=GPU_TYPE, dtype=torch.float16)
B = torch.randn((32, 256), device=GPU_TYPE, dtype=torch.float16)
C = torch.randn((256, 32), device=GPU_TYPE, dtype=torch.float16)
res, (code,) = run_and_get_code(f_opt, A, B, C)
self.assertTrue(torch.allclose(f(A, B, C), res, atol=0.1, rtol=0.01))
self.assertTrue("B2B_GEMM_LEFT_TRITON_ENTRANCE" not in code)
self.assertTrue("B2B_GEMM_RIGHT_TRITON_ENTRANCE" not in code)
@torch._dynamo.config.patch(cache_size_limit=32)
@torch._inductor.config.patch(b2b_gemm_pass=True)
def test_b2b_gemm_good_pattern_bad_shape(self):
"""
bad_shape means the sizes are not good for b2b_gemm
"""
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
return torch.mm(torch.mm(m1, m2), m3)
f_opt = torch.compile(f)
A = torch.randn((100, 100), device=GPU_TYPE, dtype=torch.float16)
B = torch.randn((100, 100), device=GPU_TYPE, dtype=torch.float16)
C = torch.randn((100, 100), device=GPU_TYPE, dtype=torch.float16)
res, (code,) = run_and_get_code(f_opt, A, B, C)
self.assertTrue(torch.allclose(f(A, B, C), res, atol=0.1, rtol=0.01))
self.assertTrue("B2B_GEMM_LEFT_TRITON_ENTRANCE" not in code)
self.assertTrue("B2B_GEMM_RIGHT_TRITON_ENTRANCE" not in code)
@unittest.skipIf(
not (os.environ.get("DO_PERF_TEST") == "1"), "Perf test not enabled"
)
@torch._dynamo.config.patch(cache_size_limit=32)
def test_plain_b2b_gemm_performance(self):
"""compare torch.compile(f, b2b_gemm = off) with torch.compile(f, b2b_gemm = on)"""
def run_with_b2b_gemm_off(
m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor
) -> float:
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
return torch.mm(torch.mm(m1, m2), m3)
f_opt = torch.compile(f, dynamic=False)
return benchmarker.benchmark(f_opt, (m1, m2, m3), {}, warmup=100, rep=500)
@torch._inductor.config.patch(b2b_gemm_pass=True)
def run_with_b2b_gemm_on(
m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor
) -> float:
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
return torch.mm(torch.mm(m1, m2), m3)
f_opt = torch.compile(f, dynamic=False)
return benchmarker.benchmark(f_opt, (m1, m2, m3), {}, warmup=100, rep=500)
Ms = [128, 256, 300, 400, 512]
Ns = [16, 20, 32, 40, 50, 64]
speedups = []
print("Perf Test for Plain B2B-GEMM:")
print("Speedups".ljust(10), end="")
for N in Ns:
print(f"N = {N}".ljust(10), end="")
print()
for M in Ms:
print(f"M = {M}".ljust(10), end="")
for N in Ns:
O, P = M, N
A = torch.randn((M, N), device=GPU_TYPE, dtype=torch.float16)
B = torch.randn((N, O), device=GPU_TYPE, dtype=torch.float16)
C = torch.randn((O, P), device=GPU_TYPE, dtype=torch.float16)
speedup = run_with_b2b_gemm_off(A, B, C) / run_with_b2b_gemm_on(A, B, C)
print(f"{round(speedup, 3)}".ljust(10), end="")
speedups.append(speedup)
print()
average_speedup = 1.0
for s in speedups:
average_speedup *= s
average_speedup = average_speedup ** (1 / len(speedups))
print(f"Average speedup: {round(average_speedup, 3)}")
# flaky test assertion: disabled
# self.assertTrue(average_speedup > 1)
@unittest.skipIf(
not (os.environ.get("DO_PERF_TEST") == "1"), "Perf test not enabled"
)
@torch._dynamo.config.patch(cache_size_limit=32)
def test_gelu_b2b_gemm_performance(self):
"""compare torch.compile(f, b2b_gemm = off) with torch.compile(f, b2b_gemm = on)"""
def run_with_b2b_gemm_off(
m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor
) -> float:
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
g = torch.nn.GELU()
return torch.mm(g(torch.mm(m1, m2)), m3)
f_opt = torch.compile(f, dynamic=False)
return benchmarker.benchmark(f_opt, (m1, m2, m3), {}, warmup=100, rep=500)
@torch._inductor.config.patch(b2b_gemm_pass=True)
def run_with_b2b_gemm_on(
m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor
) -> float:
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
g = torch.nn.GELU()
return torch.mm(g(torch.mm(m1, m2)), m3)
f_opt = torch.compile(f, dynamic=False)
return benchmarker.benchmark(f_opt, (m1, m2, m3), {}, warmup=100, rep=500)
Ms = [128, 256, 300, 400, 512]
Ns = [16, 20, 32, 40, 50, 64]
speedups = []
print("Perf Test for GELU B2B-GEMM:")
print("Speedups".ljust(10), end="")
for N in Ns:
print(f"N = {N}".ljust(10), end="")
print()
for M in Ms:
print(f"M = {M}".ljust(10), end="")
for N in Ns:
O, P = M, N
A = torch.randn((M, N), device=GPU_TYPE, dtype=torch.float16)
B = torch.randn((N, O), device=GPU_TYPE, dtype=torch.float16)
C = torch.randn((O, P), device=GPU_TYPE, dtype=torch.float16)
speedup = run_with_b2b_gemm_off(A, B, C) / run_with_b2b_gemm_on(A, B, C)
print(f"{round(speedup, 3)}".ljust(10), end="")
speedups.append(speedup)
print()
average_speedup = 1.0
for s in speedups:
average_speedup *= s
average_speedup = average_speedup ** (1 / len(speedups))
print(f"Average speedup: {round(average_speedup, 3)}")
# flaky test assertion: disabled
# self.assertTrue(average_speedup > 1)
@unittest.skipIf(
not (os.environ.get("DO_PERF_TEST") == "1"), "Perf test not enabled"
)
@torch._dynamo.config.patch(cache_size_limit=32)
def test_gelu_mlp_b2b_gemm_performance(self):
"""compare torch.compile(f, b2b_gemm = off) with torch.compile(f, b2b_gemm = on)"""
def run_with_b2b_gemm_off(
m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor
) -> float:
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
g = torch.nn.GELU()
return torch.mm(g(torch.mm(m1, m2)), m3)
f_opt = torch.compile(f, dynamic=False)
return benchmarker.benchmark(f_opt, (m1, m2, m3), {}, warmup=100, rep=500)
@torch._inductor.config.patch(b2b_gemm_pass=True)
def run_with_b2b_gemm_on(
m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor
) -> float:
def f(m1: torch.Tensor, m2: torch.Tensor, m3: torch.Tensor) -> torch.Tensor:
g = torch.nn.GELU()
return torch.mm(g(torch.mm(m1, m2)), m3)
f_opt = torch.compile(f, dynamic=False)
return benchmarker.benchmark(f_opt, (m1, m2, m3), {}, warmup=100, rep=500)
Ms = [128, 256, 300, 400, 512]
Ns = [16, 20, 32, 40, 50, 64]
speedups = []
print("Perf Test for GELU B2B-GEMM (MLP):")
print("Speedups".ljust(10), end="")
for N in Ns:
print(f"N = {N}".ljust(10), end="")
print()
for M in Ms:
print(f"M = {M}".ljust(10), end="")
for N in Ns:
O, P = N, N
A = torch.randn((M, N), device=GPU_TYPE, dtype=torch.float16)
B = torch.randn((N, O), device=GPU_TYPE, dtype=torch.float16)
C = torch.randn((O, P), device=GPU_TYPE, dtype=torch.float16)
speedup = run_with_b2b_gemm_off(A, B, C) / run_with_b2b_gemm_on(A, B, C)
print(f"{round(speedup, 3)}".ljust(10), end="")
speedups.append(speedup)
print()
average_speedup = 1.0
for s in speedups:
average_speedup *= s
average_speedup = average_speedup ** (1 / len(speedups))
print(f"Average speedup: {round(average_speedup, 3)}")
# flaky test assertion: disabled
# self.assertTrue(average_speedup > 1)
if __name__ == "__main__":
if HAS_GPU:
run_tests()
|
