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|
# Owner(s): ["module: inductor"]
import logging
import torch
import torch._inductor
from torch._dynamo.utils import counters
from torch._inductor.fx_passes.decompose_mem_bound_mm import check_device
from torch._inductor.test_case import run_tests, TestCase
from torch._inductor.utils import run_and_get_code
from torch.testing import FileCheck
from torch.testing._internal.common_utils import (
instantiate_parametrized_tests,
parametrize,
skipIfXpu,
)
from torch.testing._internal.inductor_utils import GPU_TYPE, HAS_CUDA
from torch.testing._internal.triton_utils import requires_gpu
class MyModule(torch.nn.Module):
def __init__(
self, n_input: int, n_output: int, has_bias: bool, device=GPU_TYPE
) -> None:
super().__init__()
self.linear = torch.nn.Linear(n_input, n_output, bias=has_bias)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.linear(x)
class MyModule2(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(self, input1, input2):
output = torch.bmm(input1, input2)
return output
class MyModule3(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(self, input1, input2):
output = torch.mm(input1, input2)
return output
@requires_gpu
@skipIfXpu(
msg="Intel GPU has not enabled decompose_mem_bound_mm PASS in "
"torch/_inductor/fx_passes/decompose_mem_bound_mm.py"
)
@torch._inductor.config.patch(
post_grad_fusion_options={
"decompose_mm_pass": {},
}
)
@instantiate_parametrized_tests
class TestDecomposeMemMM(TestCase):
def compare_dict_tensors(self, ref_dict, res_dict, rtol=1e-3, atol=1e-3):
if len(set(ref_dict.keys())) != len(set(res_dict.keys())):
return False
for key1 in ref_dict.keys():
key2 = "_orig_mod." + key1
assert key2 in res_dict, f"{key1} does not exist in traced module"
if not torch.allclose(ref_dict[key1], res_dict[key2], rtol=rtol, atol=atol):
return False
return True
def compare_pred(self, module, traced, input, rtol=1e-3, atol=1e-3):
ref = module(*input)
res = traced(*input)
self.assertEqual(ref, res, rtol=rtol, atol=atol)
def compare_parameters(self, module, traced, rtol=1e-3, atol=1e-3):
ref_params = dict(module.named_parameters())
res_params = dict(traced.named_parameters())
self.assertTrue(self.compare_dict_tensors(ref_params, res_params, rtol, atol))
def compare_gradients(self, module, traced, rtol=1e-3, atol=1e-3):
ref_grad = {key: param.grad for key, param in module.named_parameters()}
res_grad = {key: param.grad for key, param in traced.named_parameters()}
self.assertTrue(
self.compare_dict_tensors(ref_grad, res_grad, rtol=rtol, atol=atol)
)
@parametrize(
"b,m,k,n,should_decompose",
[(10240, 2, 2, 2, True), (10240, 2, 32, 32, False), (2000, 2, 2, 2, False)],
)
def test_decompose_bmm(self, b, m, n, k, should_decompose):
torch._logging.set_logs(inductor=logging.DEBUG)
mat1 = torch.randn(b, m, k, device=GPU_TYPE).requires_grad_(True)
mat2 = torch.randn(b, k, n, device=GPU_TYPE).requires_grad_(True)
counters.clear()
module = MyModule2().to(GPU_TYPE)
traced = torch.compile(module)
input = [mat1, mat2]
ref = module(*input)
res = traced(*input)
self.compare_pred(module, traced, input)
expected_val = 1 if should_decompose and HAS_CUDA else 0
self.assertEqual(
counters["inductor"]["decompose_bmm"],
expected_val,
)
ref.sum().backward()
res.sum().backward()
self.compare_parameters(module, traced)
self.compare_gradients(module, traced)
expected_val = 3 if should_decompose and HAS_CUDA else 0
self.assertEqual(
counters["inductor"]["decompose_bmm"],
expected_val,
)
counters.clear()
@parametrize(
"b,m,k,n,should_decompose",
[(1, 2, 2, 2, True), (2, 2, 2, 2, False)],
)
def test_decompose_bmm_cpu(self, b, m, n, k, should_decompose):
torch._logging.set_logs(inductor=logging.DEBUG)
mat1 = torch.randn(b, m, k)
mat2 = torch.randn(b, k, n)
counters.clear()
module = MyModule2()
traced = torch.compile(module)
input = [mat1, mat2]
self.compare_pred(module, traced, input)
expected_val = 1 if should_decompose else 0
self.assertEqual(
counters["inductor"]["decompose_bmm"],
expected_val,
)
counters.clear()
@parametrize(
"m,k,n, should_decompose",
[(20480, 5, 2, True), (20480, 32, 2, False), (2048, 2, 2, False)],
)
@parametrize("has_bias", [True, False])
def test_decompose_linear(self, m, n, k, has_bias, should_decompose):
torch._logging.set_logs(inductor=logging.DEBUG)
input = torch.randn(m, k, device=GPU_TYPE).requires_grad_(True)
counters.clear()
module = MyModule(k, n, has_bias).to(GPU_TYPE)
traced = torch.compile(module)
input = [input]
ref = module(*input)
res = traced(*input)
self.compare_pred(module, traced, input)
expected_val = 1 if should_decompose and HAS_CUDA else 0
if has_bias:
self.assertEqual(
counters["inductor"]["decompose_addmm"],
expected_val,
)
else:
self.assertEqual(
counters["inductor"]["decompose_mm"],
expected_val,
)
decompose_mm_fwd = counters["inductor"]["decompose_mm"]
ref.sum().backward()
res.sum().backward()
self.compare_parameters(module, traced)
self.compare_gradients(module, traced)
self.assertEqual(
counters["inductor"]["decompose_mm"] - decompose_mm_fwd,
expected_val,
)
counters.clear()
@parametrize(
"m,k,n, should_decompose",
[(20480, 5, 2, True), (20480, 32, 2, False), (2048, 2, 2, False)],
)
@parametrize("has_bias", [True, False])
def test_decompose_linear_mixed_precision(
self, m, n, k, has_bias, should_decompose
):
with torch.amp.autocast(device_type=GPU_TYPE, dtype=torch.bfloat16):
torch._logging.set_logs(inductor=logging.DEBUG)
input = torch.randn(m, k, device=GPU_TYPE).requires_grad_(True)
counters.clear()
module = MyModule(k, n, has_bias).to(GPU_TYPE)
traced = torch.compile(module)
input = [input]
ref = module(*input)
res = traced(*input)
self.compare_pred(module, traced, input)
expected_val = 1 if should_decompose and HAS_CUDA else 0
if has_bias:
self.assertEqual(
counters["inductor"]["decompose_addmm"],
expected_val,
)
else:
self.assertEqual(
counters["inductor"]["decompose_mm"],
expected_val,
)
decompose_mm_fwd = counters["inductor"]["decompose_mm"]
ref.sum().backward()
res.sum().backward()
self.compare_parameters(module, traced)
self.compare_gradients(module, traced)
self.assertEqual(
counters["inductor"]["decompose_mm"] - decompose_mm_fwd,
expected_val,
)
counters.clear()
@parametrize(
"m,k,n, should_decompose",
[(20480, 5, 2, True), (20480, 32, 2, False), (2048, 2, 2, False)],
)
@parametrize("has_bias", [True, False])
def test_decompose_mm(self, m, n, k, has_bias, should_decompose):
torch._logging.set_logs(inductor=logging.DEBUG)
mat1 = torch.randn(m, k, device=GPU_TYPE).requires_grad_(True)
mat2 = torch.randn(k, n, device=GPU_TYPE).requires_grad_(True)
counters.clear()
module = MyModule3().to(GPU_TYPE)
traced = torch.compile(module)
input = [mat1, mat2]
ref = module(*input)
res = traced(*input)
self.compare_pred(module, traced, input)
expected_val = 1 if should_decompose and HAS_CUDA else 0
self.assertEqual(
counters["inductor"]["decompose_mm"],
expected_val,
)
decompose_mm_fwd = counters["inductor"]["decompose_mm"]
ref.sum().backward()
res.sum().backward()
self.compare_parameters(module, traced)
self.compare_gradients(module, traced)
expected_val = 1 if should_decompose and HAS_CUDA else 0
self.assertEqual(
counters["inductor"]["decompose_mm"] - decompose_mm_fwd,
expected_val,
)
counters.clear()
@parametrize(
"m,k,n, should_decompose",
[(1, 64, 16, True), (2, 64, 16, False), (1, 64, 32, False)],
)
def test_decompose_mm_cpu(self, m, n, k, should_decompose):
torch._logging.set_logs(inductor=logging.DEBUG)
mat1 = torch.randn(m, k)
mat2 = torch.randn(k, n)
counters.clear()
module = MyModule3()
traced = torch.compile(module)
input = [mat1, mat2]
self.compare_pred(module, traced, input)
expected_val = 1 if should_decompose else 0
self.assertEqual(
counters["inductor"]["decompose_mm"],
expected_val,
)
counters.clear()
@parametrize(
"m,k,n, should_decompose",
[(20480, 5, 2, True), (20480, 32, 2, False), (2048, 2, 2, False)],
)
@parametrize("has_bias", [True, False])
def test_decompose_mm_mixed_precision(self, m, n, k, has_bias, should_decompose):
with torch.amp.autocast(device_type=GPU_TYPE, dtype=torch.bfloat16):
torch._logging.set_logs(inductor=logging.DEBUG)
mat1 = torch.randn(m, k, device=GPU_TYPE).requires_grad_(True)
mat2 = torch.randn(k, n, device=GPU_TYPE).requires_grad_(True)
counters.clear()
module = MyModule3().to(GPU_TYPE)
traced = torch.compile(module)
input = [mat1, mat2]
ref = module(*input)
res = traced(*input)
self.compare_pred(module, traced, input)
expected_val = 1 if should_decompose and HAS_CUDA else 0
self.assertEqual(
counters["inductor"]["decompose_mm"],
expected_val,
)
decompose_mm_fwd = counters["inductor"]["decompose_mm"]
ref.sum().backward()
res.sum().backward()
self.compare_parameters(module, traced)
self.compare_gradients(module, traced)
expected_val = 1 if should_decompose and HAS_CUDA else 0
self.assertEqual(
counters["inductor"]["decompose_mm"] - decompose_mm_fwd,
expected_val,
)
counters.clear()
@parametrize("m,k,n, should_decompose", [(20480, 5, 2, True)])
@parametrize("has_bias", [True, False])
def test_dynamic_shape(self, m, n, k, has_bias, should_decompose):
torch._logging.set_logs(inductor=logging.DEBUG)
input = torch.randn(m, k, device=GPU_TYPE).requires_grad_(True)
counters.clear()
module = MyModule(k, n, has_bias).to(GPU_TYPE)
traced = torch.compile(module, dynamic=True)
input = [input]
ref = module(*input)
res = traced(*input)
self.compare_pred(module, traced, input)
expected_val = 1 if should_decompose and HAS_CUDA else 0
if has_bias:
self.assertEqual(
counters["inductor"]["decompose_addmm"],
expected_val,
)
ref.sum().backward()
res.sum().backward()
self.compare_parameters(module, traced)
self.compare_gradients(module, traced)
expected_val = 0
if HAS_CUDA:
expected_val = 1 if has_bias else 2
self.assertEqual(
counters["inductor"]["decompose_mm"],
expected_val,
)
counters.clear()
def test_realize_input(self):
m = 20480
k = 5
n = 2
torch._logging.set_logs(inductor=logging.DEBUG)
input1 = torch.randn(m, k, device=GPU_TYPE).T.contiguous()
input2 = torch.randn(k, n, device=GPU_TYPE)
@torch.compile()
def foo(x, y):
return x.T.contiguous() @ y
out, code = run_and_get_code(foo, input1, input2)
if GPU_TYPE == "xpu":
# only 1 kernel generated on the XPU stack
FileCheck().check_count(".run(", 1, exactly=True).run(code[0])
else:
# two kernels generated
FileCheck().check_count(".run(", 2, exactly=True).run(code[0])
def test_check_device(self):
m = 5
k = 5
n = 2
torch._logging.set_logs(inductor=logging.DEBUG)
input1 = torch.randn(m, k, device=GPU_TYPE)
input2 = torch.randn(k, n, device=GPU_TYPE)
self.assertTrue(check_device(input1, input2))
self.assertFalse(check_device(input1, input2, device="cpu"))
input1 = torch.randn(m, k)
input2 = torch.randn(k, n)
self.assertTrue(check_device(input1, input2, device="cpu"))
self.assertFalse(check_device(input1, input2))
input1 = torch.randn(m, k, device=GPU_TYPE)
input2 = torch.randn(k, n)
self.assertFalse(check_device(input1, input2, device="gpu"))
self.assertFalse(check_device(input1, input2, device="cpu"))
self.assertFalse(check_device(input1, input2, device="mtia"))
if __name__ == "__main__":
run_tests()
|
