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Include tensor shapes in get_broadcast_target_size error message #7944

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Merged
merged 12 commits into from
Feb 7, 2025
Merged

Include tensor shapes in get_broadcast_target_size error message #7944

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20 changes: 16 additions & 4 deletions kernels/portable/cpu/util/broadcast_util.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -213,10 +213,22 @@ ET_NODISCARD Error get_broadcast_target_size(
Tensor::SizesType* out_sizes,
const size_t out_sizes_len,
size_t* out_dim) {
ET_CHECK_OR_RETURN_ERROR(
tensors_are_broadcastable_between(a_size, b_size),
InvalidArgument,
"Two input tensors should be broadcastable.\n");
if ET_UNLIKELY (!tensors_are_broadcastable_between(a_size, b_size)) {
#ifdef ET_LOG_ENABLED
const auto a_shape_str = tensor_shape_to_c_string(
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This new API is great, keeping the caller totally ignorant of how big a buffer to allocate.

executorch::runtime::Span<const Tensor::SizesType>(
a_size.data(), a_size.size()));
const auto b_shape_str = tensor_shape_to_c_string(
executorch::runtime::Span<const Tensor::SizesType>(
b_size.data(), b_size.size()));
#endif
ET_LOG(
Error,
"Two input tensors should be broadcastable but got shapes %s and %s.",
a_shape_str.data(),
b_shape_str.data());
Comment on lines +224 to +229
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I think it'd be less surprising for readers if the if/endif completely covered the scopes of the locals that are defined in it, so they don't need to do the extra mental jump of "I guess ET_LOG evaluates to nothing when ET_LOG_ENABLED is false"

Suggested change
#endif
ET_LOG(
Error,
"Two input tensors should be broadcastable but got shapes %s and %s.",
a_shape_str.data(),
b_shape_str.data());
ET_LOG(
Error,
"Two input tensors should be broadcastable but got shapes %s and %s.",
a_shape_str.data(),
b_shape_str.data());
#endif

return executorch::runtime::Error::InvalidArgument;
}

auto a_dim = a_size.size();
auto b_dim = b_size.size();
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9 changes: 9 additions & 0 deletions kernels/portable/cpu/util/test/broadcast_test.cpp
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Expand Up @@ -129,6 +129,15 @@ TEST(BroadcastUtilTest, GetBroadcastTargetSize) {
EXPECT_TRUE(
ArrayRef<Tensor::SizesType>(expected_output_size, expected_output_dim)
.equals(ArrayRef<Tensor::SizesType>({5, 2, 2})));

Tensor c = tf.zeros({4, 5});
err = get_broadcast_target_size(
a,
c,
expected_output_size,
torch::executor::kTensorDimensionLimit,
&expected_output_dim);
EXPECT_EQ(err, torch::executor::Error::InvalidArgument);
}

size_t linearize_indexes(size_t* indexes, size_t indexes_len, const Tensor& t) {
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