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https://github.com/zebrajr/pytorch.git
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dcbca53e58
Summary: Codemod generated with clangr shard mode, 50 files per diff Reviewed By: li-roy Differential Revision: D10866373 fbshipit-source-id: 589194164d4fea93b74d83fa7fc4c59558c41f4a
79 lines
2.2 KiB
C++
79 lines
2.2 KiB
C++
#include <memory>
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#include <vector>
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#include <gtest/gtest.h>
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#include "caffe2/operators/batch_matmul_op.h"
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namespace caffe2 {
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namespace {
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class BatchMatMulOpTest : public testing::Test {
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protected:
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void SetUp() override {
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cpu_context_ = make_unique<CPUContext>(option_);
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def_.set_name("test");
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def_.set_type("BatchMatMul");
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def_.add_input("A");
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def_.add_input("B");
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def_.add_output("Y");
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}
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void AddConstInput(
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const std::vector<int64_t>& dims,
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const float value,
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const string& name) {
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Blob* blob = ws_.CreateBlob(name);
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auto* tensor = BlobGetMutableTensor(blob, CPU);
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tensor->Resize(dims);
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math::Set<float, CPUContext>(
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tensor->numel(),
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value,
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tensor->template mutable_data<float>(),
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cpu_context_.get());
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}
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void VerifyOutput(const std::vector<int64_t>& dims, const float value) const {
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const Blob* Y_blob = ws_.GetBlob("Y");
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ASSERT_NE(nullptr, Y_blob);
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const auto& Y = Y_blob->Get<TensorCPU>();
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const auto Y_dims = Y.sizes();
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ASSERT_EQ(dims.size(), Y_dims.size());
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for (std::size_t i = 0; i < dims.size(); ++i) {
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ASSERT_EQ(dims[i], Y_dims[i]);
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}
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for (int i = 0; i < Y.numel(); ++i) {
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EXPECT_FLOAT_EQ(value, Y.data<float>()[i]);
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}
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}
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DeviceOption option_;
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std::unique_ptr<CPUContext> cpu_context_;
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Workspace ws_;
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OperatorDef def_;
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};
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TEST_F(BatchMatMulOpTest, BatchMatMulOpNormalTest) {
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AddConstInput(std::vector<int64_t>{3, 5, 10}, 1.0f, "A");
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AddConstInput(std::vector<int64_t>{3, 10, 6}, 1.0f, "B");
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std::unique_ptr<OperatorBase> op(CreateOperator(def_, &ws_));
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ASSERT_NE(nullptr, op);
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ASSERT_TRUE(op->Run());
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VerifyOutput(std::vector<int64_t>{3, 5, 6}, 10.0f);
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}
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TEST_F(BatchMatMulOpTest, BatchMatMulOpBroadcastTest) {
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auto* arg = def_.add_arg();
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arg->set_name("broadcast");
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arg->set_i(1);
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AddConstInput(std::vector<int64_t>{3, 5, 10}, 1.0f, "A");
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AddConstInput(std::vector<int64_t>{2, 3, 10, 6}, 1.0f, "B");
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std::unique_ptr<OperatorBase> op(CreateOperator(def_, &ws_));
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ASSERT_NE(nullptr, op);
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ASSERT_TRUE(op->Run());
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VerifyOutput(std::vector<int64_t>{2, 3, 5, 6}, 10.0f);
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}
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} // namespace
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} // namespace caffe2
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