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Fix einsum operator for empty inputs #23379

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Fix einsum operator for empty inputs #23379

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fb93fb6
Fix einsum
neNasko1 Jan 14, 2025
e206e2e
QOL fixes
neNasko1 Jan 15, 2025
de1bd91
More general fix
neNasko1 Jan 15, 2025
0b8e6a1
Abstract over device
neNasko1 Jan 18, 2025
71421c7
Provide cpu allocator
neNasko1 Jan 21, 2025
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21 changes: 21 additions & 0 deletions onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.cc
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Original file line number Diff line number Diff line change
@@ -1,6 +1,8 @@
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

#include <algorithm>

#include "einsum_typed_compute_processor.h"
#include "core/common/narrow.h"
#include "core/common/span_utils.h"
Expand Down Expand Up @@ -357,6 +359,25 @@ Status EinsumTypedComputeProcessor<T>::Run() {

auto num_inputs = context_->InputCount();

{
bool has_empty_input = std::any_of(raw_inputs.begin(), raw_inputs.end(), [](const auto& input) {
return input->Shape().Size() == 0;
});
// Skip all the work, fill with zeros if needed
if (has_empty_input) {
const auto output_dims = einsum_compute_preprocessor_.GetOutputDims();
Tensor& output = *context_->Output(0, output_dims);

if constexpr (std::is_integral<T>::value) {
std::fill_n(reinterpret_cast<T*>(output.MutableDataRaw()), output.Shape().Size(), T(0));
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This logic needs a device abstraction. The output data could on CUDA for example and we need to populate it appropriately.

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Thanks for the quick review!

Can you look over the changes again and tell me whether they make sense to you, and if so run the test suite again?

} else {
std::fill_n(reinterpret_cast<T*>(output.MutableDataRaw()), output.Shape().Size(), T(0.f));
}

return Status::OK();
}
}

// Pre-process the first input so as to reduce any dims that only it has
std::unique_ptr<const Tensor> result;

Expand Down
46 changes: 46 additions & 0 deletions onnxruntime/test/providers/cpu/math/einsum_test.cc
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Expand Up @@ -755,6 +755,52 @@ TEST(Einsum, ExplicitEinsumAsTensorContraction_Half) {
test.Run();
}

// Theme: Empty inputs
TEST(Einsum, EinsumEmptyInputOuterProduct) {
OpTester test("Einsum", 12, onnxruntime::kOnnxDomain);
test.AddAttribute<std::string>("equation", "i,j->ij");
test.AddInput<float>("x", {0}, {});
test.AddInput<float>("y", {0}, {});
test.AddOutput<float>("o", {0, 0}, {});
test.Run();
}

TEST(Einsum, EinsumEmptyInputTranspose) {
OpTester test("Einsum", 12, onnxruntime::kOnnxDomain);
test.AddAttribute<std::string>("equation", "ab,ba->ab");
test.AddInput<float>("x", {0, 1}, {});
test.AddInput<float>("y", {1, 0}, {});
test.AddOutput<float>("o", {0, 1}, {});
test.Run();
}

TEST(Einsum, EinsumEmptyInputVanish) {
OpTester test("Einsum", 12, onnxruntime::kOnnxDomain);
test.AddAttribute<std::string>("equation", "ab,ba->a");
test.AddInput<float>("x", {1, 0}, {});
test.AddInput<float>("y", {0, 1}, {});
test.AddOutput<float>("o", {1}, {0.f});
test.Run();
}

TEST(Einsum, EinsumEmptyInputVanish3d) {
OpTester test("Einsum", 12, onnxruntime::kOnnxDomain);
test.AddAttribute<std::string>("equation", "abc,bad->ad");
test.AddInput<float>("x", {10, 0, 10}, {});
test.AddInput<float>("y", {0, 10, 1}, {});
test.AddOutput<float>("o", {10, 1}, std::vector<float>(10, 0.f));
test.Run();
}

TEST(Einsum, EinsumEmptyInputVanish3d2empty) {
OpTester test("Einsum", 12, onnxruntime::kOnnxDomain);
test.AddAttribute<std::string>("equation", "abc,bcd->ad");
test.AddInput<float>("x", {0, 0, 0}, {});
test.AddInput<float>("y", {0, 0, 1}, {});
test.AddOutput<float>("o", {0, 1}, {});
test.Run();
}

// Theme: Tests involving MatMul(s) interleaved with Transpose(s)
// for two and three inputs (most common use-case of Einsum operator)

Expand Down
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