/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #pragma once /** * This file provides a AutoCompare calss to simplify the comparison * of CPU and GPU member functions. * * 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(function, height, width) */ #include #include "paddle/math/Matrix.h" #include "paddle/math/SparseMatrix.h" #include "TensorCheck.h" namespace autotest { using paddle::BaseMatrix; using paddle::CpuMatrix; using paddle::GpuMatrix; using paddle::CpuIVector; using paddle::GpuIVector; using paddle::CpuSparseMatrix; using paddle::GpuSparseMatrix; template class ReplaceType { public: typedef T1 type; }; template <> class ReplaceType { public: typedef CpuMatrix type; }; template <> class ReplaceType { public: typedef GpuMatrix type; }; template <> class ReplaceType { public: typedef CpuMatrix type; }; template <> class ReplaceType { public: typedef GpuMatrix type; }; // construct a argument template T construct(int height, int width); template <> float construct(int height, int width) { return 0.5; } template <> double construct(int height, int width) { return 0.5; } template <> size_t construct(int height, int width) { size_t offset = std::rand() % (height < width ? height : width); return offset; } template <> CpuMatrix construct(int height, int width) { CpuMatrix a(height, width); return a; } template <> GpuMatrix construct(int height, int width) { GpuMatrix a(height, width); return a; } // init a argument template void init(T& v) { return; } template <> void init(CpuMatrix& v) { v.randomizeUniform(); } template <> void init(GpuMatrix& v) { v.randomizeUniform(); } // init a tuple which contains a set of arguments. template inline typename std::enable_if::type initTuple( std::tuple& t) {} template inline typename std::enable_if < I::type initTuple(std::tuple& t) { init(std::get(t)); initTuple(t); } // copy a argument, copy src to dest template void copy(T1& dest, T2& src) { dest = src; } template <> void copy(GpuMatrix& dest, CpuMatrix& src) { dest.copyFrom(src); } // copy a tuple, copy src to dest template inline typename std::enable_if::type copyTuple( std::tuple& dest, std::tuple& src) {} template inline typename std::enable_if < I::type copyTuple(std::tuple& dest, std::tuple& src) { copy(std::get(dest), std::get(src)); copyTuple(dest, src); } // call member function template R call(C& obj, R (FC::*f)(FArgs...), Args&&... args) { return (obj.*f)(args...); } template class ReturnType { public: typedef T type; }; template <> class ReturnType { public: typedef GpuMatrix type; }; template <> class ReturnType { public: typedef GpuIVector type; }; template <> class ReturnType { public: typedef GpuSparseMatrix type; }; template typename ReturnType::type autoArgs(T& v) { return v; } template <> GpuMatrix autoArgs(CpuMatrix& v) { GpuMatrix a(v.getHeight(), v.getWidth()); a.copyFrom(v); return a; } template <> GpuIVector autoArgs(CpuIVector& v) { GpuIVector a(v.getSize()); a.copyFrom(v); return a; } template <> GpuSparseMatrix autoArgs(CpuSparseMatrix& v) { GpuSparseMatrix a(v.getHeight(), v.getWidth(), v.getElementCnt(), v.getValueType(), v.getFormat()); a.copyFrom(v, HPPL_STREAM_DEFAULT); hl_stream_synchronize(HPPL_STREAM_DEFAULT); return a; } class AutoCompare { public: /** * 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 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)...); TensorCheck(compare, cpu, gpu); } template 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>::type>::type, CpuMatrix>::type>(height, width)...); auto tuple2 = std::make_tuple( construct>::type>::type, GpuMatrix>::type>(height, width)...); initTuple(tuple1); copyTuple(tuple2, tuple1); call(cpu, f, std::get(tuple1)...); call(gpu, f, std::get(tuple2)...); TensorCheck(compare, cpu, gpu); } protected: CpuMatrix cpu; GpuMatrix gpu; AssertEqual compare; }; } // namespace autotest