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132 lines
4.4 KiB
132 lines
4.4 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <gtest/gtest.h>
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#include "FunctionTest.h"
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#include "paddle/math/Matrix.h"
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using namespace paddle; // NOLINT
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void testCosSimForward(size_t height_x,
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size_t height_y,
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size_t width,
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real scale) {
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FunctionCompare compare("CosSimForward", FuncConfig().set("scale", scale));
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CpuMatrix cpu_arg1(height_x, width);
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CpuMatrix gpu_arg1(height_x, width);
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CpuMatrix cpu_arg2(height_y, width);
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CpuMatrix gpu_arg2(height_y, width);
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cpu_arg1.randomizeUniform();
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gpu_arg1.copyFrom(cpu_arg1);
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cpu_arg2.randomizeUniform();
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cpu_arg2.add(-0.5);
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gpu_arg2.copyFrom(cpu_arg2);
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CpuMatrix cpu_out(height_x, 1);
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GpuMatrix gpu_out(height_x, 1);
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compare.getCpuFunction()->calc(
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{Tensor(cpu_arg1.getData(), Dims{height_x, width}),
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Tensor(cpu_arg2.getData(), Dims{height_y, width})},
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{Tensor(cpu_out.getData(), Dims{height_x, 1})},
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{});
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compare.getGpuFunction()->calc(
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{Tensor(gpu_arg1.getData(), Dims{height_x, width}),
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Tensor(gpu_arg2.getData(), Dims{height_y, width})},
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{Tensor(gpu_out.getData(), Dims{height_x, 1})},
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{});
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autotest::TensorCheckErr(cpu_out, gpu_out);
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}
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TEST(Matrix, cosSimForward) {
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for (auto height_x : {10, 100, 1000}) {
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for (auto height_y : {1, height_x}) {
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for (auto width : {10, 100, 1000}) {
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for (auto scale : {1.0, 2.0}) {
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testCosSimForward(height_x, height_y, width, scale);
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}
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}
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}
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}
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}
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void testCosSimBackward(size_t height_x,
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size_t height_y,
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size_t width,
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real scale) {
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FunctionCompare compare("CosSimBackward", FuncConfig().set("scale", scale));
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CpuMatrix cpu_out_grad(height_x, 1);
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CpuMatrix cpu_out_val(height_x, 1);
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CpuMatrix cpu_in1_val(height_x, width);
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CpuMatrix cpu_in2_val(height_x, width);
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CpuMatrix cpu_in1_grad(height_x, width);
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CpuMatrix cpu_in2_grad(height_x, width);
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cpu_out_grad.randomizeUniform();
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cpu_out_val.randomizeUniform();
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cpu_in1_val.randomizeUniform();
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cpu_in2_val.randomizeUniform();
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cpu_in1_grad.randomizeUniform();
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cpu_in2_grad.randomizeUniform();
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GpuMatrix gpu_out_grad(height_x, 1);
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GpuMatrix gpu_out_val(height_x, 1);
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GpuMatrix gpu_in1_val(height_x, width);
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GpuMatrix gpu_in2_val(height_x, width);
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GpuMatrix gpu_in1_grad(height_x, width);
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GpuMatrix gpu_in2_grad(height_x, width);
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gpu_out_grad.copyFrom(cpu_out_grad);
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gpu_out_val.copyFrom(cpu_out_val);
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gpu_in1_val.copyFrom(cpu_in1_val);
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gpu_in2_val.copyFrom(cpu_in2_val);
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gpu_in1_grad.copyFrom(cpu_in1_grad);
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gpu_in2_grad.copyFrom(cpu_in2_grad);
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compare.getCpuFunction()->calc(
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{Tensor(cpu_out_grad.getData(), Dims{height_x, 1}),
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Tensor(cpu_out_val.getData(), Dims{height_x, 1}),
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Tensor(cpu_in1_val.getData(), Dims{height_x, width}),
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Tensor(cpu_in2_val.getData(), Dims{height_x, width})},
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{},
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{Tensor(cpu_in1_grad.getData(), Dims{height_x, width}),
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Tensor(cpu_in2_grad.getData(), Dims{height_x, width})});
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compare.getGpuFunction()->calc(
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{Tensor(gpu_out_grad.getData(), Dims{height_x, 1}),
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Tensor(gpu_out_val.getData(), Dims{height_x, 1}),
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Tensor(gpu_in1_val.getData(), Dims{height_x, width}),
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Tensor(gpu_in2_val.getData(), Dims{height_x, width})},
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{},
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{Tensor(gpu_in1_grad.getData(), Dims{height_x, width}),
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Tensor(gpu_in2_grad.getData(), Dims{height_x, width})});
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autotest::TensorCheckErr(cpu_in1_grad, gpu_in1_grad);
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autotest::TensorCheckErr(cpu_in2_grad, gpu_in2_grad);
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}
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TEST(Matrix, cosSimBackward) {
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for (auto height_x : {1, 10, 100}) {
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for (auto height_y : {1, height_x}) {
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for (auto width : {1, 10, 100}) {
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for (auto scale : {1.0, 2.0}) {
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testCosSimBackward(height_x, height_y, width, scale);
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}
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}
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}
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}
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}
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