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[Cpp API Compatibility] Align resize api #78554

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Merged
SigureMo merged 3 commits into from
Apr 2, 2026
+166 −6
Merged

[Cpp API Compatibility] Align resize api #78554

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902aa11
fix resize_
youge325 Mar 30, 2026
f027442
fix resize_ again
youge325 Mar 30, 2026
cf8a6da
fix
youge325 Apr 2, 2026
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26 changes: 22 additions & 4 deletions paddle/phi/api/include/compat/ATen/ops/resize.h
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Original file line number Diff line number Diff line change
Expand Up @@ -23,13 +23,31 @@

namespace at {

// resize_ - in-place resize using reshape
// resize_ - use reshape for same-numel cases and set_ for storage-changing
// cases so repeated resize_ calls stay stable.
inline const at::Tensor& Tensor::resize_(
at::IntArrayRef size,
::std::optional<at::MemoryFormat> memory_format) const {
auto result =
paddle::experimental::reshape(tensor_, size._PD_ToPaddleIntArray());
const_cast<Tensor*>(this)->tensor_ = result;
if (memory_format.has_value()) {
TORCH_CHECK(*memory_format == at::MemoryFormat::Contiguous,
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@SigureMo SigureMo Apr 2, 2026

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嗯？相比于之前不是 breaking 吧？之前应该 Paddle 本身也没支持，会有更不友好的报错，这里报错是合理的

"resize_ only supports contiguous memory format, but got ",
static_cast<int>(*memory_format));
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Copilot AI Apr 1, 2026

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The memory_format rejection message reports static_cast<int>(*memory_format), which is hard to interpret and inconsistent with other compat ops that report unsupported MemoryFormat values more directly. Prefer emitting a clearer value (e.g., the enum name) and/or reusing the same phrasing used in other ATen compat ops (like empty/empty_like) so downstream users get consistent diagnostics.

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static_cast<int>(*memory_format));
*memory_format);

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}

std::vector<int64_t> dims(size.begin(), size.end());
int64_t new_numel = 1;
for (auto dim : dims) {
new_numel *= dim;
}
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new_numel is computed by multiplying dims without any checks for negative dimensions or int64 overflow. If a caller passes an invalid/very large size, new_numel can wrap and the code may take the reshape path or pass sizes into set_ in a surprising way. Consider validating dim >= 0 and using overflow-safe multiplication (or an existing checked helper) before comparing against tensor_.numel().

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if (tensor_.numel() == new_numel) {
const_cast<Tensor*>(this)->tensor_ =
paddle::experimental::reshape(tensor_, phi::IntArray(dims));
return *this;
}

auto source = tensor_.copy_to(tensor_.place(), /*blocking=*/true);
paddle::experimental::set_(const_cast<Tensor*>(this)->tensor_, source, dims);
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@SigureMo SigureMo Apr 2, 2026

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@ShigureNyako 给点详细的 suggestion 吧，通过 code suggestion 提出来，这里具体在兼容性的风险是什么？是否会增加很多复杂性？如果在可控范围内可以按照你说的改

return *this;
}

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32 changes: 30 additions & 2 deletions test/cpp/compat/ATen_resize_test.cc
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Expand Up @@ -24,8 +24,8 @@
#include "torch/all.h"

// ======================== resize_ tests ========================
// Note: Paddle's resize_ is implemented via reshape, which requires
// total element count to remain unchanged.
// Note: compat resize_ uses reshape when numel is unchanged, and falls back to
// set_ for storage-changing cases so repeated resize_ calls remain stable.

TEST(TensorResizeTest, ResizeBasic) {
// Create a 2x3 tensor
Expand Down Expand Up @@ -109,6 +109,34 @@ TEST(TensorResizeTest, ResizePreservesData) {
ASSERT_FLOAT_EQ(data[5], 5.0f);
}

TEST(TensorResizeTest, ResizeShrinkDifferentNumel) {
at::Tensor t = at::arange(24, at::kFloat).reshape({2, 3, 4});

t.resize_({4, 5});

Comment on lines +116 to +120
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New behavior was added for the memory_format argument (only contiguous is accepted), but the test suite here doesn't cover that path. Adding a small gtest that passes a non-contiguous memory format and asserts the expected failure would lock in the intended compatibility behavior.

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ASSERT_EQ(t.sizes()[0], 4);
ASSERT_EQ(t.sizes()[1], 5);

float* data = t.data_ptr<float>();
for (int i = 0; i < 20; ++i) {
ASSERT_FLOAT_EQ(data[i], static_cast<float>(i));
}
}

TEST(TensorResizeTest, ResizeGrowDifferentNumelPreservesPrefix) {
at::Tensor t = at::arange(6, at::kFloat).reshape({2, 3});

t.resize_({2, 5});

ASSERT_EQ(t.sizes()[0], 2);
ASSERT_EQ(t.sizes()[1], 5);

float* data = t.data_ptr<float>();
for (int i = 0; i < 6; ++i) {
ASSERT_FLOAT_EQ(data[i], static_cast<float>(i));
}
}

TEST(TensorResizeTest, ResizeReturnReference) {
// Create a tensor
at::Tensor t = at::zeros({2, 3});
Expand Down
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