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introduce definitely_contiguous and use it for reshape and tensor meta data computation. (#153432)

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when a tensor has unbacked symbols it can be general enough to represent both contiguous and non contiguous tensors.
in that case we cant really evaluate is_contiguous. In many places in the code base, we check for is_contiguous to take a fast path. but the general path usually works for both contiguous and not contiguous in that case we probably want
to use definitely _contiguous API.

This is appleid for reshape in this PR and also to  tensor meta data computation, the meta data now will have an attribute that says that its contiguous when its always contiguous. We would store that only if definitely _contiguous is true  now.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/153432
Approved by: https://github.com/bobrenjc93
This commit is contained in:
Laith Sakka
2025-05-27 13:24:57 -07:00
committed by PyTorch MergeBot
parent 54f1f29fed
commit 39df901b2a
10 changed files with 178 additions and 53 deletions
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4
test/test_proxy_tensor.py
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@ -1370,8 +1370,8 @@ def forward(self, crop_camera_1, mask_1):
view_1 = torch.ops.aten.view.default(expand_1, [sym_size_int, sym_size_int_1, sym_size_int_2]); expand_1 = sym_size_int_1 = sym_size_int_2 = None
bmm = torch.ops.aten.bmm.default(view, view_1); view = view_1 = None
view_2 = torch.ops.aten.view.default(bmm, [sym_size_int, 3, 3]); bmm = None
mul_4 = sym_size_int * 3
view_3 = torch.ops.aten.view.default(view_2, [mul_4, 3]); view_2 = mul_4 = None
mul_6 = sym_size_int * 3
view_3 = torch.ops.aten.view.default(view_2, [mul_6, 3]); view_2 = mul_6 = None
mm = torch.ops.aten.mm.default(view_3, eye); view_3 = eye = None
_unsafe_view = torch.ops.aten._unsafe_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], _unsafe_view); crop_camera_1 = mask_1 = _unsafe_view = index_put_ = None