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Revert "Add Python to CompositeImplicitAutograd (#82333)"

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This reverts commit 1a20c69385.

Reverted https://github.com/pytorch/pytorch/pull/82333 on behalf of https://github.com/osalpekar due to Failing executorch tests internally D38252636 due to changes in graph tracing
This commit is contained in:
PyTorch MergeBot 2022-07-29 00:46:27 +00:00
parent a7299647d3
commit e3243203b0
3 changed files with 61 additions and 44 deletions
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4
c10/core/DispatchKeySet.cpp
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@ -46,8 +46,8 @@ bool isBackendDispatchKey(DispatchKey t) {
// math_dispatch_keyset contains all keys in backend_dispatch_keyset and
// autograd_dispatch_keyset Alias key DispatchKey::CompositeImplicitAutograd
// maps to [math_dispatch_keyset x full_backend_mask]
constexpr DispatchKeySet math_dispatch_keyset = backend_dispatch_keyset |
autograd_dispatch_keyset | DispatchKeySet(DispatchKey::Python);
constexpr DispatchKeySet math_dispatch_keyset =
backend_dispatch_keyset | autograd_dispatch_keyset;
DispatchKeySet getRuntimeDispatchKeySet(DispatchKey t) {
TORCH_INTERNAL_ASSERT(t != DispatchKey::Undefined);

2
test/test_decomp.py
View File

@ -279,8 +279,6 @@ CROSS_REF_EXCLUDE_SET = {
# CompositeAutogradImplicit
# See https://github.com/pytorch/pytorch/issues/81669
(None, None, "nn.functional.relu6"),
(None, None, "nn.functional.mish"),
(None, None, "nn.functional.silu"),
}

99
test/test_functionalization.py
View File

@ -102,9 +102,10 @@ class TestFunctionalization(TestCase):
def forward(self, a_1):
ones = torch.ops.aten.ones.default([4, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
empty = torch.ops.aten.empty.memory_format([4, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
fill_scalar = torch.ops.aten.fill.Scalar(empty, 1.0); empty = None
view_copy_default = torch.ops.aten.view_copy.default(a_1, [4, 2]); a_1 = None
add_tensor = torch.ops.aten.add.Tensor(view_copy_default, ones); view_copy_default = ones = None
add_tensor = torch.ops.aten.add.Tensor(view_copy_default, fill_scalar); view_copy_default = fill_scalar = None
view_copy_default_1 = torch.ops.aten.view_copy.default(add_tensor, [4, 2])
mul_tensor = torch.ops.aten.mul.Tensor(view_copy_default_1, view_copy_default_1); view_copy_default_1 = None
return add_tensor
@ -126,10 +127,11 @@ def forward(self, a_1):
def forward(self, a_1):
ones = torch.ops.aten.ones.default([4, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
empty = torch.ops.aten.empty.memory_format([4, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
fill_scalar = torch.ops.aten.fill.Scalar(empty, 1.0); empty = None
view_copy_default = torch.ops.aten.view_copy.default(a_1, [4, 2]); a_1 = None
empty = torch.ops.aten.empty.memory_format([], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
add_tensor = torch.ops.aten.add.Tensor(view_copy_default, ones); view_copy_default = ones = None
empty_1 = torch.ops.aten.empty.memory_format([], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
add_tensor = torch.ops.aten.add.Tensor(view_copy_default, fill_scalar); view_copy_default = fill_scalar = None
mul_tensor = torch.ops.aten.mul.Tensor(add_tensor, add_tensor); add_tensor = None
return mul_tensor
""")
@ -180,9 +182,10 @@ def forward(self, a_1):
def forward(self, a_1):
ones = torch.ops.aten.ones.default([4, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
empty = torch.ops.aten.empty.memory_format([4, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
fill_scalar = torch.ops.aten.fill.Scalar(empty, 1.0); empty = None
view_copy_default = torch.ops.aten.view_copy.default(a_1, [4, 2])
add_tensor = torch.ops.aten.add.Tensor(a_1, ones); a_1 = ones = None
add_tensor = torch.ops.aten.add.Tensor(a_1, fill_scalar); a_1 = fill_scalar = None
view_copy_default_1 = torch.ops.aten.view_copy.default(add_tensor, [4, 2]); add_tensor = None
return view_copy_default_1
""")
@ -275,9 +278,10 @@ def forward(self, a_1):
def forward(self, a_1):
ones = torch.ops.aten.ones.default([2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
empty = torch.ops.aten.empty.memory_format([2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
fill_scalar = torch.ops.aten.fill.Scalar(empty, 1.0); empty = None
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(a_1)
add_tensor = torch.ops.aten.add.Tensor(diagonal_copy_default, ones); diagonal_copy_default = ones = None
add_tensor = torch.ops.aten.add.Tensor(diagonal_copy_default, fill_scalar); diagonal_copy_default = fill_scalar = None
diagonal_scatter_default = torch.ops.aten.diagonal_scatter.default(a_1, add_tensor); a_1 = add_tensor = None
mul_tensor = torch.ops.aten.mul.Tensor(diagonal_scatter_default, diagonal_scatter_default); diagonal_scatter_default = None
return mul_tensor
@ -309,12 +313,13 @@ def forward(self, a_1):
def forward(self, a_1):
ones = torch.ops.aten.ones.default([2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
empty = torch.ops.aten.empty.memory_format([2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
fill_scalar = torch.ops.aten.fill.Scalar(empty, 1.0); empty = None
split_copy_tensor = torch.ops.aten.split_copy.Tensor(a_1, 2)
getitem = split_copy_tensor[0]
getitem_1 = split_copy_tensor[1]; split_copy_tensor = None
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(getitem_1); getitem_1 = None
add_tensor = torch.ops.aten.add.Tensor(diagonal_copy_default, ones); diagonal_copy_default = ones = None
add_tensor = torch.ops.aten.add.Tensor(diagonal_copy_default, fill_scalar); diagonal_copy_default = fill_scalar = None
split_copy_tensor_1 = torch.ops.aten.split_copy.Tensor(a_1, 2)
getitem_2 = split_copy_tensor_1[0]
getitem_3 = split_copy_tensor_1[1]; split_copy_tensor_1 = None
@ -339,10 +344,11 @@ def forward(self, a_1):
def forward(self, a_1):
ones = torch.ops.aten.ones.default([4], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
empty = torch.ops.aten.empty.memory_format([4], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
fill_scalar = torch.ops.aten.fill.Scalar(empty, 1.0); empty = None
transpose_copy_int = torch.ops.aten.transpose_copy.int(a_1, 1, 0)
select_copy_int = torch.ops.aten.select_copy.int(transpose_copy_int, 0, 0); transpose_copy_int = None
add_tensor = torch.ops.aten.add.Tensor(select_copy_int, ones); select_copy_int = ones = None
add_tensor = torch.ops.aten.add.Tensor(select_copy_int, fill_scalar); select_copy_int = fill_scalar = None
transpose_copy_int_1 = torch.ops.aten.transpose_copy.int(a_1, 1, 0); a_1 = None
select_scatter_default = torch.ops.aten.select_scatter.default(transpose_copy_int_1, add_tensor, 0, 0); transpose_copy_int_1 = add_tensor = None
transpose_copy_int_2 = torch.ops.aten.transpose_copy.int(select_scatter_default, 1, 0); select_scatter_default = None
@ -367,8 +373,10 @@ def forward(self, a_1):
def forward(self, a_1):
view_copy_default = torch.ops.aten.view_copy.default(a_1, [8]); a_1 = None
arange = torch.ops.aten.arange.default(4, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
arange_1 = torch.ops.aten.arange.default(4, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
empty = torch.ops.aten.empty.memory_format([0], dtype = torch.int64, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
arange = torch.ops.aten.arange.start_step(0, 4, 1, dtype = torch.int64, layout = torch.strided, device = device(type='cpu'))
empty_1 = torch.ops.aten.empty.memory_format([0], dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
arange_1 = torch.ops.aten.arange.start_step(0, 4, 1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'))
index_put_default = torch.ops.aten.index_put.default(view_copy_default, [arange], arange_1); view_copy_default = arange = arange_1 = None
view_copy_default_1 = torch.ops.aten.view_copy.default(index_put_default, [4, 2])
return index_put_default
@ -390,7 +398,8 @@ def forward(self, a_1):
def forward(self, a_1):
ones = torch.ops.aten.ones.default([4, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
empty = torch.ops.aten.empty.memory_format([4, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
fill_scalar = torch.ops.aten.fill.Scalar(empty, 1.0); empty = None
view_copy_default = torch.ops.aten.view_copy.default(a_1, [4, 2]); a_1 = None
add_tensor = torch.ops.aten.add.Tensor(view_copy_default, 1); view_copy_default = None
mul_tensor = torch.ops.aten.mul.Tensor(add_tensor, 2)
@ -413,8 +422,8 @@ def forward(self, a_1):
def forward(self, a_1):
ge_scalar = torch.ops.aten.ge.Scalar(a_1, 0); a_1 = None
_to_copy_default = torch.ops.aten._to_copy.default(ge_scalar, dtype = torch.float32, layout = torch.strided); ge_scalar = None
return _to_copy_default
to_dtype_layout = torch.ops.aten.to.dtype_layout(ge_scalar, dtype = torch.float32, layout = torch.strided); ge_scalar = None
return to_dtype_layout
""")
@skipIfTorchDynamo("Test does not work with TorchDynamo")
@ -472,7 +481,8 @@ def forward(self, a_1):
def forward(self, a_1):
ones = torch.ops.aten.ones.default([2, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
empty = torch.ops.aten.empty.memory_format([2, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
fill_scalar = torch.ops.aten.fill.Scalar(empty, 1.0); empty = None
add_tensor = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
view_copy_default = torch.ops.aten.view_copy.default(add_tensor, [8])
_reshape_alias_copy_default = torch.ops.aten._reshape_alias_copy.default(view_copy_default, [2, 4], [4, 1]); view_copy_default = None
@ -482,7 +492,7 @@ def forward(self, a_1):
split_copy_tensor = torch.ops.aten.split_copy.Tensor(squeeze_copy_default, 2); squeeze_copy_default = None
getitem = split_copy_tensor[0]
getitem_1 = split_copy_tensor[1]; split_copy_tensor = None
add_tensor_1 = torch.ops.aten.add.Tensor(getitem, ones); getitem = ones = None
add_tensor_1 = torch.ops.aten.add.Tensor(getitem, fill_scalar); getitem = fill_scalar = None
select_copy_int = torch.ops.aten.select_copy.int(_reshape_alias_copy_default, 0, 0); _reshape_alias_copy_default = None
clone_default = torch.ops.aten.clone.default(add_tensor_1, memory_format = torch.contiguous_format)
_unsafe_view_default = torch.ops.aten._unsafe_view.default(clone_default, [4]); clone_default = None
@ -518,9 +528,10 @@ def forward(self, a_1):
def forward(self, a_1):
ones = torch.ops.aten.ones.default([4, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
empty = torch.ops.aten.empty.memory_format([4, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
fill_scalar = torch.ops.aten.fill.Scalar(empty, 1.0); empty = None
view_default = torch.ops.aten.view.default(a_1, [4, 2]); a_1 = None
add_tensor = torch.ops.aten.add.Tensor(view_default, ones); view_default = ones = None
add_tensor = torch.ops.aten.add.Tensor(view_default, fill_scalar); view_default = fill_scalar = None
view_default_1 = torch.ops.aten.view.default(add_tensor, [4, 2])
mul_tensor = torch.ops.aten.mul.Tensor(view_default_1, view_default_1); view_default_1 = None
return add_tensor
@ -569,9 +580,12 @@ def forward(self, a_1):
def forward(self, a_1):
zeros = torch.ops.aten.zeros.default([2, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
add_tensor = torch.ops.aten.add.Tensor(a_1, a_1); a_1 = None
empty = torch.ops.aten.empty.memory_format([2, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
zero_default = torch.ops.aten.zero.default(empty); empty = None
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zero_default)
diagonal_copy_default_1 = torch.ops.aten.diagonal_copy.default(zero_default); zero_default = None
copy_default = torch.ops.aten.copy.default(diagonal_copy_default_1, a_1); diagonal_copy_default_1 = None
add_tensor = torch.ops.aten.add.Tensor(copy_default, a_1); copy_default = a_1 = None
return add_tensor
""")
@ -583,10 +597,12 @@ def forward(self, a_1):
def forward(self, a_1):
zeros = torch.ops.aten.zeros.default([2, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
expand_copy_default = torch.ops.aten.expand_copy.default(a_1, [2])
add_tensor = torch.ops.aten.add.Tensor(expand_copy_default, a_1); expand_copy_default = a_1 = None
empty = torch.ops.aten.empty.memory_format([2, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
zero_default = torch.ops.aten.zero.default(empty); empty = None
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zero_default)
diagonal_copy_default_1 = torch.ops.aten.diagonal_copy.default(zero_default); zero_default = None
copy_default = torch.ops.aten.copy.default(diagonal_copy_default_1, a_1); diagonal_copy_default_1 = None
add_tensor = torch.ops.aten.add.Tensor(copy_default, a_1); copy_default = a_1 = None
return add_tensor
""")
@ -598,12 +614,14 @@ def forward(self, a_1):
def forward(self, a_1):
zeros = torch.ops.aten.zeros.default([2, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
_to_copy_default = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
add_tensor = torch.ops.aten.add.Tensor(_to_copy_default, a_1); _to_copy_default = a_1 = None
empty = torch.ops.aten.empty.memory_format([2, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
zero_default = torch.ops.aten.zero.default(empty); empty = None
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zero_default)
diagonal_copy_default_1 = torch.ops.aten.diagonal_copy.default(zero_default); zero_default = None
copy_default = torch.ops.aten.copy.default(diagonal_copy_default_1, a_1); diagonal_copy_default_1 = None
add_tensor = torch.ops.aten.add.Tensor(copy_default, a_1); copy_default = a_1 = None
return add_tensor
""") # noqa: B950
""")
# Test 4: copy_() with different dtype, different shape
self.assert_functionalization(f, torch.ones(1, dtype=torch.long))
@ -613,13 +631,14 @@ def forward(self, a_1):
def forward(self, a_1):
zeros = torch.ops.aten.zeros.default([2, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zeros); zeros = None
_to_copy_default = torch.ops.aten._to_copy.default(a_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False)
expand_copy_default = torch.ops.aten.expand_copy.default(_to_copy_default, [2]); _to_copy_default = None
add_tensor = torch.ops.aten.add.Tensor(expand_copy_default, a_1); expand_copy_default = a_1 = None
empty = torch.ops.aten.empty.memory_format([2, 2], dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
zero_default = torch.ops.aten.zero.default(empty); empty = None
diagonal_copy_default = torch.ops.aten.diagonal_copy.default(zero_default)
diagonal_copy_default_1 = torch.ops.aten.diagonal_copy.default(zero_default); zero_default = None
copy_default = torch.ops.aten.copy.default(diagonal_copy_default_1, a_1); diagonal_copy_default_1 = None
add_tensor = torch.ops.aten.add.Tensor(copy_default, a_1); copy_default = a_1 = None
return add_tensor
""") # noqa: B950
""")
def test_expand_symint(self):
# Once some existing SymInt bugs are ironed out, we should update