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Fix py codegen to delete values that don't have any users (#131028)

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Fixes #131025

Pull Request resolved: https://github.com/pytorch/pytorch/pull/131028
Approved by: https://github.com/ezyang
This commit is contained in:
YangQun1
2024-07-25 13:04:23 +00:00
committed by PyTorch MergeBot
parent 2784b3f1b7
commit 91aba7baac
21 changed files with 724 additions and 692 deletions
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20
test/test_proxy_tensor.py
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@ -1008,7 +1008,7 @@ def forward(self, x_1, y_1):
self.assertExpectedInline(r, """\
def forward(self, x_1, y_1):
sym_size_int = torch.ops.aten.sym_size.int(y_1, 0); y_1 = None
resize_ = torch.ops.aten.resize_.default(x_1, [sym_size_int]); x_1 = sym_size_int = None
resize_ = torch.ops.aten.resize_.default(x_1, [sym_size_int]); x_1 = sym_size_int = resize_ = None
return None""")
def test_broadcast_shapes(self):
@ -1303,7 +1303,7 @@ def forward(self, x_1):
sym_size_int = torch.ops.aten.sym_size.int(x_1, 0)
scalar_tensor = torch.ops.aten.scalar_tensor.default(sym_size_int, dtype = torch.float32, layout = torch.strided, device = device(type='cpu')); sym_size_int = None
select = torch.ops.aten.select.int(x_1, 0, 0)
copy_ = torch.ops.aten.copy_.default(select, scalar_tensor); select = scalar_tensor = None
copy_ = torch.ops.aten.copy_.default(select, scalar_tensor); select = scalar_tensor = copy_ = None
return x_1""" # noqa: B950
)
@ -1321,7 +1321,7 @@ def forward(self, gravity_1, mask_1):
index = torch.ops.aten.index.Tensor(select, [mask_1]); select = None
mul = torch.ops.aten.mul.Tensor(index, -1); index = None
select_1 = torch.ops.aten.select.int(gravity_1, 1, 0); gravity_1 = None
index_put_ = torch.ops.aten.index_put_.default(select_1, [mask_1], mul); select_1 = mask_1 = mul = None
index_put_ = torch.ops.aten.index_put_.default(select_1, [mask_1], mul); select_1 = mask_1 = mul = index_put_ = None
return None""")
def test_reflect_r_over_x(self):
@ -1345,7 +1345,7 @@ def forward(self, crop_camera_1, mask_1):
lift_fresh_copy = torch.ops.aten.lift_fresh_copy.default(_tensor_constant0); _tensor_constant0 = None
select = torch.ops.aten.select.int(eye, 0, 0)
select_1 = torch.ops.aten.select.int(select, 0, 0); select = None
copy_ = torch.ops.aten.copy_.default(select_1, lift_fresh_copy); select_1 = lift_fresh_copy = None
copy_ = torch.ops.aten.copy_.default(select_1, lift_fresh_copy); select_1 = lift_fresh_copy = copy_ = None
sym_size_int = torch.ops.aten.sym_size.int(index, 0)
expand = torch.ops.aten.expand.default(eye, [sym_size_int, 3, 3])
view = torch.ops.aten.view.default(expand, [sym_size_int, 3, 3]); expand = None
@ -1359,7 +1359,7 @@ def forward(self, crop_camera_1, mask_1):
view_3 = torch.ops.aten.view.default(view_2, [mul, 3]); view_2 = mul = None
mm = torch.ops.aten.mm.default(view_3, eye); view_3 = eye = None
view_4 = torch.ops.aten.view.default(mm, [sym_size_int, 3, 3]); mm = sym_size_int = None
index_put_ = torch.ops.aten.index_put_.default(crop_camera_1, [mask_1], view_4); crop_camera_1 = mask_1 = view_4 = None
index_put_ = torch.ops.aten.index_put_.default(crop_camera_1, [mask_1], view_4); crop_camera_1 = mask_1 = view_4 = index_put_ = None
return None""") # noqa: B950
def test_unbacked_slice(self):
@ -1412,7 +1412,7 @@ def forward(self, images_1, handedness_1, valid_1):
eq = torch.ops.aten.eq.Scalar(index_1, 1); index_1 = None
index_2 = torch.ops.aten.index.Tensor(index, [eq])
flip = torch.ops.aten.flip.default(index_2, [-1]); index_2 = None
index_put_ = torch.ops.aten.index_put_.default(index, [eq], flip); index = eq = flip = None
index_put_ = torch.ops.aten.index_put_.default(index, [eq], flip); index = eq = flip = index_put_ = None
return None""")
def test_neg_shape(self):
@ -1481,7 +1481,7 @@ def forward(self, x_1, y_1):
self.assertExpectedInline(r, """\
def forward(self, x_1, y_1):
_local_scalar_dense = torch.ops.aten._local_scalar_dense.default(x_1); x_1 = None
zeros = torch.ops.aten.zeros.default([_local_scalar_dense], device = device(type='cpu'), pin_memory = False); _local_scalar_dense = None
zeros = torch.ops.aten.zeros.default([_local_scalar_dense], device = device(type='cpu'), pin_memory = False); _local_scalar_dense = zeros = None
add = torch.ops.aten.add.Tensor(y_1, 2); y_1 = None
return add""") # noqa: B950
@ -1566,9 +1566,9 @@ def forward(self, lengths_1, values_1):
_local_scalar_dense_1 = torch.ops.aten._local_scalar_dense.default(select_1); select_1 = None
select_2 = torch.ops.aten.select.int(lengths_1, 0, 2); lengths_1 = None
_local_scalar_dense_2 = torch.ops.aten._local_scalar_dense.default(select_2); select_2 = None
sym_constrain_range_for_size = torch.ops.aten.sym_constrain_range_for_size.default(_local_scalar_dense)
sym_constrain_range_for_size_1 = torch.ops.aten.sym_constrain_range_for_size.default(_local_scalar_dense_1)
sym_constrain_range_for_size_2 = torch.ops.aten.sym_constrain_range_for_size.default(_local_scalar_dense_2)
sym_constrain_range_for_size = torch.ops.aten.sym_constrain_range_for_size.default(_local_scalar_dense); sym_constrain_range_for_size = None
sym_constrain_range_for_size_1 = torch.ops.aten.sym_constrain_range_for_size.default(_local_scalar_dense_1); sym_constrain_range_for_size_1 = None
sym_constrain_range_for_size_2 = torch.ops.aten.sym_constrain_range_for_size.default(_local_scalar_dense_2); sym_constrain_range_for_size_2 = None
split_with_sizes = torch.ops.aten.split_with_sizes.default(values_1, [_local_scalar_dense, _local_scalar_dense_1, _local_scalar_dense_2]); values_1 = _local_scalar_dense = _local_scalar_dense_1 = _local_scalar_dense_2 = None
getitem = split_with_sizes[0]
getitem_1 = split_with_sizes[1]