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[inductor][cpp] Add BMM kernel template for autotuning #129772

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[inductor][cpp] Add BMM kernel template for autotuning #129772

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473eec7
Squashed commit of the following:
frost-intel Dec 5, 2024
0e81a8a
Merge branch 'main' into bmm_microkernel
frost-intel Dec 5, 2024
0f9e245
lint
frost-intel Dec 6, 2024
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273 changes: 271 additions & 2 deletions test/inductor/test_cpu_select_algorithm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1295,8 +1295,8 @@ def forward(self, arg152_1):
atol=atol,
rtol=rtol,
)
self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 1)
self.assertEqual(counters["inductor"]["cpp_epilogue_fusion_counter"], 1)
self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 2)
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@leslie-fang-intel leslie-fang-intel Nov 9, 2024

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There is more than 1 bmm in this UT. Why the count only increase by 1? Is there some bmm fails the check to use bmm template?

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@frost-intel frost-intel Nov 12, 2024

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The two BMM ops have exactly the same problem dimensions, so the autotune counter is only triggered one time. Only the second BMM op is fused with epilogue nodes, so we only increase the fusion counter by one.

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@leslie-fang-intel leslie-fang-intel Nov 18, 2024

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I think there is no cache mechanism to bypass the autotune of same problem dimensions, cc @jgong5 is there?

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@frost-intel frost-intel Nov 18, 2024

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Correction: The first BMM has an input where stride is not 1 for last dim, so BMM template is not used.

self.assertEqual(counters["inductor"]["cpp_epilogue_fusion_counter"], 2)

@inductor_config.patch({"freezing": True})
@patches
Expand Down Expand Up @@ -1761,6 +1761,245 @@ def forward(self, x):
self.common(mod, (v,), atol=atol, rtol=rtol)
self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 1)

@patches
@torch.no_grad
@unittest.skipIf(not TEST_MKL, "Test requires MKL")
@parametrize("bs", (1, 50))
@parametrize("Mdim", (192,))
@parametrize("Kdim", (196,))
@parametrize("Ndim", (84, 385))
@dtypes(torch.float, torch.bfloat16, torch.half)
def test_bmm(self, dtype, bs, Mdim, Kdim, Ndim):
class M(torch.nn.Module):
def __init__(self):
super().__init__()

def forward(self, x, y):
return x @ y

counters.clear()
u = torch.randn(bs, Mdim, Kdim).to(dtype=dtype)
v = torch.randn(bs, Kdim, Ndim).to(dtype=dtype)
mod = M().to(dtype=dtype).eval()
with verify(dtype) as (atol, rtol):
self.common(mod, (u, v), atol=atol, rtol=rtol)
self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 1)

@patches
@torch.no_grad
@unittest.skipIf(not TEST_MKL, "Test requires MKL")
@parametrize("bs", (1,))
@parametrize("Mdim", (192,))
@parametrize("Kdim", (196,))
@parametrize("Ndim", (84,))
@dtypes(torch.float, torch.bfloat16, torch.half)
def test_bmm_amp(self, dtype, bs, Mdim, Kdim, Ndim):
class M(torch.nn.Module):
def __init__(self):
super().__init__()

def forward(self, x, y):
return x @ y

counters.clear()
u = torch.randn(bs, Mdim, Kdim).to(dtype=dtype)
v = torch.randn(bs, Kdim, Ndim).to(dtype=dtype)
mod = M().to(dtype=dtype).eval()
with verify(dtype) as (atol, rtol), torch.amp.autocast("cpu"):
self.common(mod, (u, v), atol=atol, rtol=rtol)
self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 1)

@inductor_config.patch({"freezing": True})
@patches
@torch.no_grad
@unittest.skipIf(not TEST_MKL, "Test requires MKL")
@parametrize("bs", (1,))
@parametrize("Mdim", (192,))
@parametrize("Kdim", (196,))
@parametrize("Ndim", (64, 65))
@dtypes(torch.float, torch.bfloat16, torch.half)
def test_bmm_freezing(self, dtype, bs, Mdim, Kdim, Ndim):
class M(torch.nn.Module):
def __init__(self, w):
super().__init__()
self.w = torch.nn.Parameter(w, requires_grad=False)

def forward(self, x):
return x @ self.w

counters.clear()
u = torch.randn(bs, Mdim, Kdim).to(dtype=dtype)
v = torch.randn(bs, Kdim, Ndim).to(dtype=dtype)
mod = M(v).to(dtype=dtype).eval()
with verify(dtype) as (atol, rtol):
self.common(mod, (u,), atol=atol, rtol=rtol)
self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 1)

@patches
@torch.no_grad
@unittest.skipIf(not TEST_MKL, "Test requires MKL")
@parametrize("Ndim", (64, 61))
@parametrize(
"order",
(
((0, 1, 2), (0, 2, 1)), # First BMM in hf_Reformer
((0, 1, 2), (1, 2, 0)), # First BMM in hf_DistilBert
((0, 1, 2), (1, 0, 2)), # Second BMM in hf_DistilBert, hf_T5
((1, 0, 2), (0, 1, 2)), # Third BMM in hf_Reformer
((1, 0, 2), (1, 2, 0)), # First in hf_T5
),
)
@dtypes(torch.float, torch.bfloat16, torch.half)
def test_bmm_2d_permute(self, Ndim, order, dtype):
# TODO: Support bmm with transposed X
dtype = torch.float
bs = 12
Mdim = 10
Kdim = 62
x_args = (bs, Mdim, Kdim)
w_args = (bs, Kdim, Ndim)
inverse_order = [torch.argsort(torch.tensor(o)).tolist() for o in order]

class M(torch.nn.Module):
def __init__(self):
super().__init__()

def forward(self, x, w):
if order[0] != (0, 1, 2):
x_order = [x_args[i] for i in inverse_order[0]]
x = x.reshape(x_order[0], x_order[1] * x_order[2]).clone()
x = x.reshape(*x_order).permute(*order[0])
if order[1] != (0, 1, 2):
w_order = [w_args[i] for i in inverse_order[1]]
w = w.reshape(w_order[0], w_order[1] * w_order[2]).clone()
w = w.reshape(*w_order).permute(*order[1])
y = x @ w
return y

counters.clear()
u = torch.randn(bs, Mdim, Kdim).to(dtype=dtype)
v = torch.randn(bs, Kdim, Ndim).to(dtype=dtype)
mod = M().to(dtype=dtype).eval()
with verify(dtype) as (atol, rtol):
self.common(mod, (u, v), atol=atol, rtol=rtol)
self.assertEqual(
counters["inductor"]["select_algorithm_autotune"],
1 if order[0] == (0, 1, 2) else 0,
)

@patches
@torch.no_grad
@unittest.skipIf(not TEST_MKL, "Test requires MKL")
@parametrize("bs", (5,))
@parametrize("Mdim", (64,))
@parametrize("Kdim", (96,))
@dtypes(torch.float, torch.float16, torch.bfloat16)
def test_bmm_self_permute(self, bs, Mdim, Kdim, dtype):
class M(torch.nn.Module):
def __init__(self):
super().__init__()

def forward(self, x):
return x @ x.permute(0, 2, 1)

counters.clear()
u = torch.randn(bs, Mdim, Kdim).to(dtype=dtype)
mod = M().to(dtype=dtype).eval()
with verify(dtype) as (atol, rtol):
self.common(mod, (u,), atol=atol, rtol=rtol)
self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 1)

@patches
@torch.no_grad
@unittest.skipIf(not TEST_MKL, "Test requires MKL")
@parametrize("bs", (5,))
@parametrize("Mdim", (64,))
@dtypes(torch.float)
def test_bmm_self_square(self, bs, Mdim, dtype):
class M(torch.nn.Module):
def __init__(self):
super().__init__()

def forward(self, x):
return x @ x

counters.clear()
u = torch.randn(bs, Mdim, Mdim).to(dtype=dtype)
mod = M().to(dtype=dtype).eval()
with verify(dtype) as (atol, rtol):
self.common(mod, (u,), atol=atol, rtol=rtol)
self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 1)

@patches
@torch.no_grad
@unittest.skipIf(not TEST_MKL, "Test requires MKL")
@parametrize("bs", (5,))
@parametrize("Mdim", (384,))
@parametrize("Kdim", (96,))
@parametrize("Ndim", (64, 65))
@parametrize(
"epilogue",
(
"relu",
"add",
"sub",
"mul",
"div",
),
)
@dtypes(torch.float32, torch.bfloat16, torch.half)
def test_bmm_with_pointwise(self, bs, Mdim, Kdim, Ndim, epilogue, dtype):
class M(torch.nn.Module):
def __init__(self, epilogue, other):
super().__init__()
self.epilogue = _get_epilogue(epilogue, other)

def forward(self, x, w):
return self.epilogue(x @ w)

counters.clear()
x = torch.randn(bs, Mdim, Kdim).to(dtype=dtype)
w = torch.randn(bs, Kdim, Ndim).to(dtype=dtype)
other = torch.randn(bs, Mdim, Ndim).to(dtype=dtype)
mod = M(epilogue, other).to(dtype=dtype).eval()
with verify(dtype) as (atol, rtol):
self.common(mod, (x, w), atol=atol, rtol=rtol)
self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 1)
self.assertEqual(counters["inductor"]["cpp_epilogue_fusion_counter"], 1)

@patches
@torch.no_grad
@unittest.skipIf(not TEST_MKL, "Test requires MKL")
@dtypes(torch.float32, torch.bfloat16, torch.half)
def test_bmm_with_fused_epilogues(self, dtype):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.mul = torch.randn(8, 8, 3136, 8).as_strided(
(8, 8, 3136, 8), (200704, 8, 64, 1)
)

def forward(self, x, w):
x = torch.ops.aten.reshape.default(x, [64, 3137, 8])
w = torch.ops.aten.reshape.default(w, [64, 8, 8])
bmm = torch.ops.aten.bmm.default(x, w)
bmm = torch.ops.aten.reshape.default(bmm, [8, 8, 3137, 8])
constant_pad_nd = torch.ops.aten.constant_pad_nd.default(
self.mul, [0, 0, 1, 0, 0, 0], 0.0
)
mul_2 = torch.ops.aten.mul.Tensor(bmm, 0.3535533905932738)
add = torch.ops.aten.add.Tensor(mul_2, constant_pad_nd)
return add

counters.clear()
x = torch.randn(8, 8, 3137, 8).to(dtype=dtype)
w = torch.randn(8, 8, 8, 8).to(dtype=dtype)
mod = M().to(dtype=dtype).eval()
with verify(dtype) as (atol, rtol):
self.common(mod, (x, w), atol=atol, rtol=rtol)
self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 1)
self.assertEqual(counters["inductor"]["cpp_epilogue_fusion_counter"], 1)


@dynamo_config.patch({"dynamic_shapes": True, "assume_static_by_default": False})
class _DynamicShapesTestBase(BaseTestSelectAlgorithm):
Expand Down Expand Up @@ -1800,6 +2039,36 @@ class TestSelectAlgorithmDynamicShapes(_DynamicShapesTestBase):
TestSelectAlgorithm.test_linear_thread_factors
)

@patches
@torch.no_grad
@unittest.skipIf(not TEST_MKL, "Test requires MKL")
@parametrize("bs", (5,))
@parametrize("Mdim", (384,))
@parametrize("Kdim", (96,))
@parametrize("Ndim", (64, 65))
@dtypes(torch.float, torch.bfloat16, torch.half)
def test_bmm_with_pointwise_dynamic_shapes(self, bs, Mdim, Kdim, Ndim, dtype):
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.epilogue = torch.nn.ReLU()

def forward(self, x, other):
return self.epilogue(x @ other)

counters.clear()
u = torch.randn(bs, Mdim, Kdim).to(dtype=dtype)
v = torch.randn(bs, Kdim, Ndim).to(dtype=dtype)
torch._dynamo.mark_dynamic(u, 0)
torch._dynamo.mark_dynamic(u, 1)
torch._dynamo.mark_static(u, 2)
torch._dynamo.mark_static(v, 2)
mod = M().to(dtype=dtype).eval()
with verify(dtype) as (atol, rtol):
self.common(mod, (u, v), atol=atol, rtol=rtol)
self.assertEqual(counters["inductor"]["select_algorithm_autotune"], 1)
self.assertEqual(counters["inductor"]["cpp_epilogue_fusion_counter"], 1)


instantiate_device_type_tests(TestSelectAlgorithm, globals(), only_for="cpu")
instantiate_device_type_tests(
Expand Down
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