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279 lines
9.4 KiB
279 lines
9.4 KiB
#include "gtest/gtest.h"
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#include "paddle/operators/math/math_function.h"
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TEST(math_function, notrans_mul_trans) {
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paddle::framework::Tensor input1;
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paddle::framework::Tensor input1_gpu;
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paddle::framework::Tensor input2_gpu;
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paddle::framework::Tensor out_gpu;
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paddle::framework::Tensor out;
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auto* cpu_place = new paddle::platform::CPUPlace();
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float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
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float arr[6] = {0, 1, 2, 3, 4, 5};
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memcpy(input1_ptr, arr, 6 * sizeof(float));
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auto* gpu_place = new paddle::platform::GPUPlace(0);
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paddle::platform::CUDADeviceContext context(*gpu_place);
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input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
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input2_gpu.CopyFrom<float>(input1, *gpu_place, context);
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out_gpu.mutable_data<float>({2, 2}, *gpu_place);
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paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
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context, input1_gpu, false, input2_gpu, true, 1, &out_gpu, 0);
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out.CopyFrom<float>(out_gpu, *cpu_place, context);
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float* out_ptr = out.data<float>();
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context.Wait();
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EXPECT_EQ(out_ptr[0], 5);
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EXPECT_EQ(out_ptr[1], 14);
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EXPECT_EQ(out_ptr[2], 14);
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EXPECT_EQ(out_ptr[3], 50);
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delete gpu_place;
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}
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TEST(math_function, trans_mul_notrans) {
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paddle::framework::Tensor input1;
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paddle::framework::Tensor input1_gpu;
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paddle::framework::Tensor input2_gpu;
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paddle::framework::Tensor out_gpu;
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paddle::framework::Tensor out;
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auto* cpu_place = new paddle::platform::CPUPlace();
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float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
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float arr[6] = {0, 1, 2, 3, 4, 5};
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memcpy(input1_ptr, arr, 6 * sizeof(float));
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auto* gpu_place = new paddle::platform::GPUPlace(0);
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paddle::platform::CUDADeviceContext context(*gpu_place);
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input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
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input2_gpu.CopyFrom<float>(input1, *gpu_place, context);
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out_gpu.mutable_data<float>({3, 3}, *gpu_place);
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paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
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context, input1_gpu, true, input2_gpu, false, 1, &out_gpu, 0);
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out.CopyFrom<float>(out_gpu, *cpu_place, context);
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float* out_ptr = out.data<float>();
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context.Wait();
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EXPECT_EQ(out_ptr[0], 9);
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EXPECT_EQ(out_ptr[1], 12);
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EXPECT_EQ(out_ptr[2], 15);
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EXPECT_EQ(out_ptr[3], 12);
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EXPECT_EQ(out_ptr[4], 17);
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EXPECT_EQ(out_ptr[5], 22);
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EXPECT_EQ(out_ptr[6], 15);
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EXPECT_EQ(out_ptr[7], 22);
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EXPECT_EQ(out_ptr[8], 29);
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delete gpu_place;
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}
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TEST(math_function, gemm_notrans_cublas) {
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paddle::framework::Tensor input1;
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paddle::framework::Tensor input2;
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paddle::framework::Tensor input3;
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paddle::framework::Tensor input1_gpu;
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paddle::framework::Tensor input2_gpu;
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paddle::framework::Tensor input3_gpu;
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int m = 2;
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int n = 3;
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int k = 3;
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auto* cpu_place = new paddle::platform::CPUPlace();
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float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
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float arr1[6] = {0, 1, 2, 3, 4, 5};
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memcpy(input1_ptr, arr1, 6 * sizeof(float));
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float* input2_ptr = input2.mutable_data<float>({3, 4}, *cpu_place);
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float arr2[12] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
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memcpy(input2_ptr, arr2, 12 * sizeof(float));
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float* input3_ptr = input3.mutable_data<float>({2, 4}, *cpu_place);
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float arr3[8] = {0, 1, 2, 3, 4, 5, 6, 7};
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memcpy(input3_ptr, arr3, 8 * sizeof(float));
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auto* gpu_place = new paddle::platform::GPUPlace(0);
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paddle::platform::CUDADeviceContext context(*gpu_place);
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input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
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input2_gpu.CopyFrom<float>(input2, *gpu_place, context);
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input3_gpu.CopyFrom<float>(input3, *gpu_place, context);
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float* a = input1_gpu.data<float>();
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float* b = input2_gpu.data<float>();
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float* c = input3_gpu.mutable_data<float>(*gpu_place);
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paddle::operators::math::gemm<paddle::platform::GPUPlace, float>(
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context, false, false, m, n, k, 1, a, 3, b + 1, 4, 1, c + 1, 4);
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input3.CopyFrom<float>(input3_gpu, *cpu_place, context);
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// numpy code:
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// a = np.arange(6).reshape(2, 3)
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// b = np.arange(12).reshape(3, 4)[:, 1:]
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// c = np.arange(8).reshape(2, 4)[:, 1:]
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// out = np.arange(8).reshape(2, 4)
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// out[:, 1:] = np.dot(a, b) + c
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context.Wait();
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EXPECT_EQ(input3_ptr[0], 0);
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EXPECT_EQ(input3_ptr[1], 24);
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EXPECT_EQ(input3_ptr[2], 28);
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EXPECT_EQ(input3_ptr[3], 32);
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EXPECT_EQ(input3_ptr[4], 4);
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EXPECT_EQ(input3_ptr[5], 73);
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EXPECT_EQ(input3_ptr[6], 86);
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EXPECT_EQ(input3_ptr[7], 99);
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delete gpu_place;
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}
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TEST(math_function, gemm_trans_cublas) {
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paddle::framework::Tensor input1;
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paddle::framework::Tensor input2;
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paddle::framework::Tensor input3;
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paddle::framework::Tensor input1_gpu;
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paddle::framework::Tensor input2_gpu;
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paddle::framework::Tensor input3_gpu;
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int m = 2;
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int n = 3;
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int k = 3;
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auto* cpu_place = new paddle::platform::CPUPlace();
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float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
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float arr1[6] = {0, 1, 2, 3, 4, 5};
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memcpy(input1_ptr, arr1, 6 * sizeof(float));
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float* input2_ptr = input2.mutable_data<float>({4, 3}, *cpu_place);
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float arr2[12] = {0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11};
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memcpy(input2_ptr, arr2, 12 * sizeof(float));
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float* input3_ptr = input3.mutable_data<float>({2, 4}, *cpu_place);
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float arr3[8] = {0, 1, 2, 3, 4, 5, 6, 7};
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memcpy(input3_ptr, arr3, 8 * sizeof(float));
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auto* gpu_place = new paddle::platform::GPUPlace(0);
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paddle::platform::CUDADeviceContext context(*gpu_place);
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input1_gpu.CopyFrom<float>(input1, *gpu_place, context);
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input2_gpu.CopyFrom<float>(input2, *gpu_place, context);
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input3_gpu.CopyFrom<float>(input3, *gpu_place, context);
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float* a = input1_gpu.data<float>();
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float* b = input2_gpu.data<float>();
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float* c = input3_gpu.mutable_data<float>(*gpu_place);
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paddle::operators::math::gemm<paddle::platform::GPUPlace, float>(
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context, false, true, m, n, k, 1, a, 3, b + 3, 3, 1, c + 1, 4);
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input3.CopyFrom<float>(input3_gpu, *cpu_place, context);
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context.Wait();
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EXPECT_EQ(input3_ptr[0], 0);
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EXPECT_EQ(input3_ptr[1], 24);
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EXPECT_EQ(input3_ptr[2], 28);
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EXPECT_EQ(input3_ptr[3], 32);
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EXPECT_EQ(input3_ptr[4], 4);
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EXPECT_EQ(input3_ptr[5], 73);
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EXPECT_EQ(input3_ptr[6], 86);
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EXPECT_EQ(input3_ptr[7], 99);
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delete gpu_place;
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}
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TEST(math_function, selected_rows_add) {
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using namespace paddle::framework;
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using namespace paddle::platform;
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using namespace paddle::operators::math;
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GPUPlace gpu_place(0);
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CPUPlace cpu_place;
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CUDADeviceContext ctx(gpu_place);
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SetConstant<GPUPlace, float> functor;
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int64_t height = 10;
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int64_t row_numel = 10;
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std::vector<int64_t> rows1{0, 4, 7};
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std::unique_ptr<SelectedRows> selected_rows1{new SelectedRows(rows1, height)};
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auto* in1_value = selected_rows1->mutable_value();
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in1_value->mutable_data<float>(
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make_ddim({static_cast<int64_t>(rows1.size()), row_numel}), gpu_place);
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functor(ctx, in1_value, 1.0);
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std::vector<int64_t> rows2{0, 5, 7, 9};
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std::unique_ptr<SelectedRows> selected_rows2{new SelectedRows(rows2, height)};
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auto* in2_value = selected_rows2->mutable_value();
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in2_value->mutable_data<float>(
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make_ddim({static_cast<int64_t>(rows2.size()), row_numel}), gpu_place);
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functor(ctx, in2_value, 2.0);
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std::unique_ptr<SelectedRows> output{new SelectedRows()};
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auto* out_value = output->mutable_value();
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// simplely concat two SelectedRows
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out_value->mutable_data<float>(make_ddim({7, 10}), gpu_place);
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SelectedRowsAdd<GPUPlace, float> add_functor;
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add_functor(ctx, *selected_rows1, *selected_rows2, output.get());
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auto out_height = output->height();
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EXPECT_EQ(out_height, height);
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auto& out_rows = output->rows();
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// input1 rows
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EXPECT_EQ(out_rows[0], 0);
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EXPECT_EQ(out_rows[1], 4);
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EXPECT_EQ(out_rows[2], 7);
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// input2 rows
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EXPECT_EQ(out_rows[3], 0);
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EXPECT_EQ(out_rows[4], 5);
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EXPECT_EQ(out_rows[5], 7);
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EXPECT_EQ(out_rows[6], 9);
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Tensor out_cpu;
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out_cpu.CopyFrom<float>(*out_value, cpu_place, ctx);
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ctx.Wait();
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auto* out_cpu_data = out_cpu.data<float>();
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// input1 value
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EXPECT_EQ(out_cpu_data[0 * row_numel + 0], 1.0);
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EXPECT_EQ(out_cpu_data[0 * row_numel + 8], 1.0);
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EXPECT_EQ(out_cpu_data[1 * row_numel + 1], 1.0);
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EXPECT_EQ(out_cpu_data[2 * row_numel + 6], 1.0);
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// input2 value
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EXPECT_EQ(out_cpu_data[3 * row_numel + 3], 2.0);
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EXPECT_EQ(out_cpu_data[3 * row_numel + 8], 2.0);
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EXPECT_EQ(out_cpu_data[4 * row_numel + 4], 2.0);
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EXPECT_EQ(out_cpu_data[5 * row_numel + 7], 2.0);
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EXPECT_EQ(out_cpu_data[6 * row_numel + 9], 2.0);
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std::unique_ptr<Tensor> tensor1{new Tensor()};
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tensor1->mutable_data<float>(make_ddim({height, row_numel}), gpu_place);
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SetConstant<GPUPlace, float> constant_functor;
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constant_functor(ctx, tensor1.get(), 3.0);
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std::unique_ptr<Tensor> tensor2{new Tensor()};
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tensor2->mutable_data<float>(make_ddim({height, row_numel}), gpu_place);
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SelectedRowsAddTensor<GPUPlace, float> add_tensor_functor;
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add_tensor_functor(ctx, *output, *tensor1, tensor2.get());
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Tensor tensor2_cpu;
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tensor2_cpu.CopyFrom<float>(*tensor2, cpu_place, ctx);
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ctx.Wait();
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auto* tensor2_cpu_data = tensor2_cpu.data<float>();
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// row0: 1.0 + 2.0 + 3.0
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EXPECT_EQ(tensor2_cpu_data[0 * row_numel + 0], 6.0);
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// row1: 3.0
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EXPECT_EQ(tensor2_cpu_data[1 * row_numel + 1], 3.0);
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// row4 : 1.0 + 3.0
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EXPECT_EQ(tensor2_cpu_data[4 * row_numel + 6], 4.0);
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// row5: 2.0 + 3.0
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EXPECT_EQ(tensor2_cpu_data[5 * row_numel + 7], 5.0);
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// row6: 3.0
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EXPECT_EQ(tensor2_cpu_data[6 * row_numel + 1], 3.0);
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// row7: 1.0 + 2.0 + 3.0
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EXPECT_EQ(tensor2_cpu_data[7 * row_numel + 3], 6.0);
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// row9: 2.0 + 3.0
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EXPECT_EQ(tensor2_cpu_data[9 * row_numel + 6], 5.0);
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}
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