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delete BaseMatrixCompare, and add AutoCompare::cmpWithoutArg

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avx_docs
hedaoyuan 9 years ago
parent c410382145
commit ee52c75946
3 changed files with 304 additions and 309 deletions
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154
paddle/math/tests/TestUtils.h
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@ -15,11 +15,28 @@ limitations under the License. */
#pragma once
/**
* TestUtils.h is used to automatically compare CPU and GPU code is consistent.
* This file provides a class(AutoCompare) and a template
* function(BaseMatrixCompare) to simplify the comparison
* This file provides a AutoCompare calss to simplify the comparison
* of CPU and GPU member functions.
* Refer test_Matrix.cpp and test_BaseMatrix.cpp for how to use autotest.
*
* This takes two steps
* 1. Construct an AutoCompare object.
* When constructing an AutoCompare object, you can set the err argument
* to specify the maximum error for CPU and GPU functions.
*
* 2. Use the template functions cmpWithArg or cmpWithoutArg.
* A. [cmpWithArg] Requires the caller construct the cpu arguments.
*
* AutoCompare test;
* Init Argument arg1,arg2...
* test.cmpWithArg(function, arg1, arg2....)
*
* B. [cmpWithoutArg] The caller do not need construct arguments.
* If matrix used in these functions arguments is the same size.
* Such as the element wise function and the aggregate function
* defined in the BaseMatrix.cpp.
*
* AutoCompare test;
* test.cmpWithoutArg<I...>(function, height, width)
*/
#include <gtest/gtest.h>
@ -30,6 +47,8 @@ limitations under the License. */
namespace autotest {
using paddle::BaseMatrix;
using paddle::CpuMatrix;
using paddle::GpuMatrix;
using paddle::CpuIVector;
using paddle::GpuIVector;
using paddle::CpuSparseMatrix;
@ -154,47 +173,6 @@ R call(C& obj, R (FC::*f)(FArgs...), Args&&... args) {
return (obj.*f)(args...);
}
template <bool AsRowVector,
bool AsColVector,
std::size_t... I,
typename C,
typename R,
typename... Args,
typename AssertEq>
void BaseMatrixCompare(R (C::*f)(Args...), AssertEq compare) {
for (auto height : {1, 11, 73, 128, 200, 330}) {
for (auto width : {1, 3, 32, 100, 512, 1000}) {
CpuMatrix obj1(AsRowVector ? 1 : height, AsColVector ? 1 : width);
GpuMatrix obj2(AsRowVector ? 1 : height, AsColVector ? 1 : width);
init(obj1);
copy(obj2, obj1);
auto tuple1 = std::make_tuple(
construct<typename ReplaceType<
typename std::decay<
typename std::tuple_element<I,
std::tuple<Args...>>::type>::type,
CpuMatrix>::type>(height, width)...);
auto tuple2 = std::make_tuple(
construct<typename ReplaceType<
typename std::decay<
typename std::tuple_element<I,
std::tuple<Args...>>::type>::type,
GpuMatrix>::type>(height, width)...);
initTuple(tuple1);
copyTuple(tuple2, tuple1);
call(obj1, f, std::get<I>(tuple1)...);
call(obj2, f, std::get<I>(tuple2)...);
TensorCheck(compare, obj1, obj2);
}
}
}
// AutoCompare
template <typename T>
class ReturnType {
public:
@ -252,64 +230,60 @@ GpuSparseMatrix autoArgs(CpuSparseMatrix& v) {
class AutoCompare {
public:
AutoCompare(size_t height, size_t width)
: cpu(height, width), gpu(height, width) {
/**
* err is the allowed calculation error.
* The smaller the value of err,
* the stricter the comparison is between CPU and GPU calculations.
*/
AutoCompare(size_t height, size_t width, real err = 1e-3)
: cpu(height, width), gpu(height, width), compare(err) {
init(cpu);
copy(gpu, cpu);
}
template <typename C, typename R, typename... FArgs, typename... Args>
void operator()(R (C::*f)(FArgs...), Args&&... args) {
void cmpWithArg(R (C::*f)(FArgs...), Args&&... args) {
static_assert(sizeof...(FArgs) == sizeof...(Args),
"size of parameter packs are not equal");
call(cpu, f, args...);
call(gpu, f, autoArgs(args)...);
TensorCheckErr(cpu, gpu);
TensorCheck(compare, cpu, gpu);
}
template <std::size_t... I, typename C, typename R, typename... Args>
void cmpWithoutArg(R (C::*f)(Args...), size_t height, size_t width) {
static_assert(sizeof...(I) == sizeof...(Args),
"size of parameter packs are not equal");
(void)height;
(void)width;
auto tuple1 = std::make_tuple(
construct<typename ReplaceType<
typename std::decay<
typename std::tuple_element<I,
std::tuple<Args...>>::type>::type,
CpuMatrix>::type>(height, width)...);
auto tuple2 = std::make_tuple(
construct<typename ReplaceType<
typename std::decay<
typename std::tuple_element<I,
std::tuple<Args...>>::type>::type,
GpuMatrix>::type>(height, width)...);
initTuple(tuple1);
copyTuple(tuple2, tuple1);
call(cpu, f, std::get<I>(tuple1)...);
call(gpu, f, std::get<I>(tuple2)...);
TensorCheck(compare, cpu, gpu);
}
protected:
CpuMatrix cpu;
GpuMatrix gpu;
AssertEqual compare;
};
} // namespace autotest
template <std::size_t... I, typename C, typename R, typename... Args>
void BaseMatrixCompare(R (C::*f)(Args...)) {
static_assert(sizeof...(I) == sizeof...(Args),
"size of parameter packs are not equal");
#ifndef PADDLE_TYPE_DOUBLE
autotest::AssertEqual compare(1e-5);
#else
autotest::AssertEqual compare(1e-10);
#endif
autotest::BaseMatrixCompare<false, false, I...>(f, compare);
}
template <std::size_t... I, typename C, typename R, typename... Args>
void BaseMatrixAsColVector(R (C::*f)(Args...)) {
static_assert(sizeof...(I) == sizeof...(Args),
"size of parameter packs are not equal");
#ifndef PADDLE_TYPE_DOUBLE
autotest::AssertEqual compare(1e-3);
#else
autotest::AssertEqual compare(1e-8);
#endif
autotest::BaseMatrixCompare<false, true, I...>(f, compare);
}
template <std::size_t... I, typename C, typename R, typename... Args>
void BaseMatrixAsRowVector(R (C::*f)(Args...)) {
static_assert(sizeof...(I) == sizeof...(Args),
"size of parameter packs are not equal");
#ifndef PADDLE_TYPE_DOUBLE
autotest::AssertEqual compare(1e-3);
#else
autotest::AssertEqual compare(1e-8);
#endif
autotest::BaseMatrixCompare<true, false, I...>(f, compare);
}

366
paddle/math/tests/test_BaseMatrix.cpp
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93
paddle/math/tests/test_Matrix.cpp
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@ -14,20 +14,8 @@ limitations under the License. */
#ifndef PADDLE_ONLY_CPU
/**
* This test file use autotest::AutoCompare to compares the implementation
* of CPU and GPU member function in Matrix.cpp.
*
* 1. Constructs an AutoCompare object, a AutoCompare object contains
* a CpuMatrix and a GpuMatrix;
* 2. Initializes the required parameters for the member function.
* Only need to initialize the CPU parameters.
* 3. Use the operator() template for testing. In the operator() will call back
* member functions, and compare the results.
*
* use case:
* AutoCompare test(...);
* Init Argument arg1,arg2...
* test(function, arg1, arg2....)
* This test file use autotest::AutoCompare and cmpWithArg to compares the
* implementation of CPU and GPU member function in Matrix.cpp.
*/
#include <gtest/gtest.h>
@ -38,11 +26,6 @@ using paddle::Matrix;
using paddle::CpuMatrix;
using paddle::CpuIVector;
using paddle::CpuSparseMatrix;
using paddle::SparseValueType;
using paddle::SparseFormat;
using paddle::NO_VALUE;
using paddle::SPARSE_CSR;
using paddle::initMain;
using autotest::AutoCompare;
void testBilinearFwdBwd(int numSamples,
@ -57,28 +40,28 @@ void testBilinearFwdBwd(int numSamples,
AutoCompare forward(numSamples, outWidth);
CpuMatrix arg1(numSamples, inWidth);
arg1.randomizeUniform();
forward(&Matrix::bilinearForward,
arg1,
imgSizeH,
imgSizeW,
2 * imgSizeH,
2 * imgSizeW,
channels,
ratioH,
ratioW);
forward.cmpWithArg(&Matrix::bilinearForward,
arg1,
imgSizeH,
imgSizeW,
2 * imgSizeH,
2 * imgSizeW,
channels,
ratioH,
ratioW);
AutoCompare backward(numSamples, inWidth);
CpuMatrix arg2(numSamples, outWidth);
arg2.randomizeUniform();
backward(&Matrix::bilinearBackward,
arg2,
2 * imgSizeH,
2 * imgSizeW,
imgSizeH,
imgSizeW,
channels,
ratioH,
ratioW);
backward.cmpWithArg(&Matrix::bilinearBackward,
arg2,
2 * imgSizeH,
2 * imgSizeW,
imgSizeH,
imgSizeW,
channels,
ratioH,
ratioW);
}
TEST(Matrix, BilinearFwdBwd) {
@ -99,9 +82,10 @@ void testMatrixAddBias(int height, int width, real scale) {
AutoCompare test(height, width);
CpuMatrix arg1(1, width);
arg1.randomizeUniform();
test(static_cast<void (Matrix::*)(Matrix&, real)>(&Matrix::addBias),
arg1,
scale);
test.cmpWithArg(
static_cast<void (Matrix::*)(Matrix&, real)>(&Matrix::addBias),
arg1,
scale);
}
void testMatrixAddDotMulMMV(int height, int width) {
@ -110,7 +94,7 @@ void testMatrixAddDotMulMMV(int height, int width) {
CpuMatrix arg2(1, width);
arg1.randomizeUniform();
arg2.randomizeUniform();
test(&BaseMatrix::addDotMulMMV, arg1, arg2);
test.cmpWithArg(&BaseMatrix::addDotMulMMV, arg1, arg2);
}
TEST(Matrix, unary) {
@ -128,14 +112,14 @@ void testMatrixAddAtOffset(int height, int width1, int width2, int offset) {
AutoCompare test(height, width2);
CpuMatrix arg1(height, width1);
arg1.randomizeUniform();
test(&Matrix::addAtOffset, arg1, offset);
test.cmpWithArg(&Matrix::addAtOffset, arg1, offset);
}
void testMatrixAssignAtOffset(int height, int width1, int width2, int offset) {
AutoCompare test(height, width2);
CpuMatrix arg1(height, width1);
arg1.randomizeUniform();
test(&Matrix::assignAtOffset, arg1, offset);
test.cmpWithArg(&Matrix::assignAtOffset, arg1, offset);
}
TEST(Matrix, AtOffset) {
@ -162,7 +146,7 @@ void testMatrixSelectRows(int numSamples, int tableSize, int inputDim) {
CpuIVector arg2(numSamples);
arg1.randomizeUniform();
arg2.rand(tableSize);
test(&Matrix::selectRows, arg1, arg2);
test.cmpWithArg(&Matrix::selectRows, arg1, arg2);
}
TEST(Matrix, tableProjection) {
@ -183,7 +167,7 @@ void testMatrixCopyByRowIndex(int outHeight, int inHeight, int width) {
CpuIVector arg2(outHeight);
arg1.randomizeUniform();
arg2.rand(inHeight);
test(&Matrix::copyByRowIndex, arg1, arg2);
test.cmpWithArg(&Matrix::copyByRowIndex, arg1, arg2);
}
TEST(Matrix, copyByRowIndex) {
@ -204,7 +188,7 @@ void testCosSim(int heightX, int heightY, int width, real scale) {
arg1.randomizeUniform();
arg2.randomizeUniform();
arg2.add(-0.5);
test(&Matrix::cosSim, arg1, arg2, scale);
test.cmpWithArg(&Matrix::cosSim, arg1, arg2, scale);
}
TEST(Matrix, cosSim) {
@ -226,7 +210,7 @@ void testParamReluForward(int height, int width, int w_height, int w_width) {
arg1.randomizeUniform();
arg2.randomizeUniform();
arg1.add(-0.5);
test(&Matrix::paramReluForward, arg1, arg2);
test.cmpWithArg(&Matrix::paramReluForward, arg1, arg2);
}
void testParamReluBackwardW(int height, int width, int w_height, int w_width) {
@ -236,7 +220,7 @@ void testParamReluBackwardW(int height, int width, int w_height, int w_width) {
arg1.randomizeUniform();
arg2.randomizeUniform();
arg2.add(-0.5);
test(&Matrix::paramReluBackwardW, arg1, arg2);
test.cmpWithArg(&Matrix::paramReluBackwardW, arg1, arg2);
}
TEST(Matrix, paramRelu) {
@ -256,14 +240,14 @@ void testAddSharedBias(int numSamples, int dim, int channel) {
AutoCompare test(numSamples, dim);
CpuMatrix arg1(1, channel);
arg1.randomizeUniform();
test(&Matrix::addSharedBias, arg1, 1.0);
test.cmpWithArg(&Matrix::addSharedBias, arg1, 1.0);
}
void testCollectSharedBias(int numSamples, int dim, int channel) {
AutoCompare test(1, channel);
CpuMatrix arg1(numSamples, dim);
arg1.randomizeUniform();
test(&Matrix::collectSharedBias, arg1, 1.0);
test.cmpWithArg(&Matrix::collectSharedBias, arg1, 1.0);
}
TEST(Matrix, sharedBias) {
@ -282,7 +266,8 @@ TEST(Matrix, sharedBias) {
void testMultiBinaryLabelCrossEntropy(int numSamples, int dim) {
AutoCompare forward(numSamples, 1);
CpuMatrix arg1(numSamples, dim);
CpuSparseMatrix arg2(numSamples, dim, numSamples, NO_VALUE, SPARSE_CSR);
CpuSparseMatrix arg2(
numSamples, dim, numSamples, paddle::NO_VALUE, paddle::SPARSE_CSR);
CpuMatrix output1(numSamples, dim);
output1.randomizeUniform();
@ -291,10 +276,10 @@ void testMultiBinaryLabelCrossEntropy(int numSamples, int dim) {
const unsigned int id = std::rand() % dim;
arg2.setRow(i, 1, &id, nullptr);
}
forward(&Matrix::multiBinaryLabelCrossEntropy, arg1, arg2);
forward.cmpWithArg(&Matrix::multiBinaryLabelCrossEntropy, arg1, arg2);
AutoCompare backward(numSamples, dim);
backward(&Matrix::multiBinaryLabelCrossEntropyBp, arg1, arg2);
backward.cmpWithArg(&Matrix::multiBinaryLabelCrossEntropyBp, arg1, arg2);
}
TEST(Matrix, multiBinaryCrossEntropy) {
@ -308,7 +293,7 @@ TEST(Matrix, multiBinaryCrossEntropy) {
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
paddle::initMain(argc, argv);
return RUN_ALL_TESTS();
}

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