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CBAInferFusion_driver.hpp
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/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2017 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*******************************************************************************/
#ifndef GUARD_MIOPEN_CONV_BN_ACTIV_INFER_DRIVER_HPP
#define GUARD_MIOPEN_CONV_BN_ACTIV_INFER_DRIVER_HPP
#include "../test/verify.hpp"
#include "InputFlags.hpp"
#include "driver.hpp"
#include "miopen_ConvBatchNormActivHost.hpp"
#include "tensor_driver.hpp"
#include "timer.hpp"
#include <algorithm>
#include <cmath>
#include <cstdlib>
#include <float.h>
#include <memory>
#include <miopen/miopen.h>
#include <miopen/handle.hpp>
#include <miopen/tensor.hpp>
#include <miopen/md_graph.hpp>
#include <numeric>
#include <vector>
#include <cassert>
#include "random.hpp"
#include "mloNeuronHost.hpp"
#define MIO_BN_DEBUG 0
#define MIO_BN_MAX_DEBUGLOOP 65536
#undef EPSILON
#define EPSILON 1e-6
//#define MIO_CONV_ALGO_COUNT 4
#define ERRTOL 1e-4
#define RMSTOL_FP32 1e-4
#define RMSTOL_FP16 0.5e-3
#ifdef MIOPEN_BACKEND_HIP
#ifndef CL_SUCCESS
#define CL_SUCCESS 0
#endif
#endif
#define CBA_DEBUG_VALUES 0
//"Fusion mode (cbna = 0, cna = 1, na = 2, cn = 3, cba = 4, ca = 5, cb = 6) (Default=cbna)",
typedef enum {
miopen_fusion_cbna = 0,
miopen_fusion_cna = 1,
miopen_fusion_na = 2,
miopen_fusion_cn = 3,
miopen_fusion_cba = 4,
miopen_fusion_ca = 5,
miopen_fusion_cb = 6,
} fusionMode_t;
template <typename Tgpu, typename Tref>
class CBAInferFusionDriver : public Driver
{
public:
CBAInferFusionDriver() : Driver()
{
miopenCreateTensorDescriptor(&inputTensor);
miopenCreateTensorDescriptor(&weightTensor);
miopenCreateTensorDescriptor(&outputTensor);
miopenCreateTensorDescriptor(&biasTensor);
miopenCreateTensorDescriptor(&biasScaleTensor);
miopenCreateConvolutionDescriptor(&convDesc);
miopenCreateActivationDescriptor(&activDesc);
miopenCreateOperatorArgs(&fusionArgs);
workspace_fwd_dev = nullptr;
data_type = (sizeof(Tgpu) == 4) ? miopenFloat : miopenHalf;
initTiming();
iters = 0;
}
int AddCmdLineArgs();
int ParseCmdLineArgs(int argc, char* argv[]);
InputFlags& GetInputFlags() { return inflags; }
int GetandSetData();
std::vector<int> GetInputTensorLengthsFromCmdLine();
std::vector<int> GetOutputTensorLengths();
std::vector<int> GetWeightTensorLengthsFromCmdLine();
std::vector<int> GetModeFromCmdLine();
int SetActivationDescriptorFromCmdLineArgs();
int SetConvDescriptorFromCmdLineArgs();
int SetBNParametersFromCmdLineArgs();
int AllocateBuffersAndCopy();
int RunForwardGPU();
int RunForwardCPU();
int RunBackwardGPU() { return 0; };
int RunBackwardCPU() { return 0; };
void runGPUConvBatchNormActivInference();
void runGPUConvActivInference();
void runGPUBatchNormActivInference();
void runGPUBNFwdInference();
void runCPUBNFwdInference();
void runGPUActivFwdInference();
void runCPUActivFwdInference();
void runCPUConvFwdInference();
void runGPUConvBiasInference();
void runGPUFusedConvBiasInference();
void runCPUConvBiasInference();
int VerifyBackward() { return 0; };
int VerifyForward();
Timer t;
double fulltime;
float lowtime;
float avgtime;
float time;
int iters;
void initTiming()
{
fulltime = 0.;
lowtime = 100000000.0;
avgtime = 0.;
time = 0.0;
return;
}
void startTiming()
{
START_TIME
return;
}
void finishTiming(int i)
{
if(inflags.GetValueStr("time") == "1")
{
time = 0.0;
miopenGetKernelTime(GetHandle(), &time);
lowtime = (time < lowtime) ? time : lowtime;
if(iters > 1 && i > 0)
avgtime += time;
}
miopen::deref(GetHandle()).Finish();
STOP_TIME
if(WALL_CLOCK)
{
if(iters > 1 && i > 0)
fulltime += t.gettime_ms();
else if(iters == 1)
fulltime = t.gettime_ms();
// else do nothing, drop the first iteration
}
return;
}
~CBAInferFusionDriver()
{
miopenDestroyTensorDescriptor(outputTensor);
miopenDestroyTensorDescriptor(inputTensor);
miopenDestroyTensorDescriptor(weightTensor);
miopenDestroyTensorDescriptor(biasTensor);
miopenDestroyTensorDescriptor(biasScaleTensor);
miopenDestroyActivationDescriptor(activDesc);
miopenDestroyConvolutionDescriptor(convDesc);
miopenDestroyFusionPlan(fusePlanDesc);
miopenDestroyOperatorArgs(fusionArgs);
}
private:
miopenBatchNormMode_t bn_mode;
int bias_mode = 0;
int fusion_mode = 0;
bool estimatedMeanVar;
bool useBatchNorm = false;
unsigned char back;
InputFlags inflags;
miopenTensorDescriptor_t inputTensor;
miopenTensorDescriptor_t weightTensor;
miopenTensorDescriptor_t biasScaleTensor;
miopenTensorDescriptor_t biasTensor;
miopenTensorDescriptor_t outputTensor;
miopenActivationDescriptor_t activDesc;
miopenConvolutionDescriptor_t convDesc;
std::unique_ptr<GPUMem> in_dev;
std::unique_ptr<GPUMem> conv_res_dev;
std::unique_ptr<GPUMem> bn_res_dev;
std::unique_ptr<GPUMem> wei_dev;
std::unique_ptr<GPUMem> out_dev;
std::unique_ptr<GPUMem> scale_dev;
std::unique_ptr<GPUMem> bias_dev;
std::unique_ptr<GPUMem> workspace_fwd_dev;
std::unique_ptr<GPUMem> runningMean_dev;
std::unique_ptr<GPUMem> runningVariance_dev;
std::unique_ptr<GPUMem> saveMean_dev;
std::unique_ptr<GPUMem> saveInvVariance_dev;
std::unique_ptr<GPUMem> b_dev;
std::vector<Tgpu> b;
std::vector<Tgpu> in;
std::vector<Tgpu> out;
std::vector<Tgpu> wei;
std::vector<Tgpu> conv_res;
std::vector<Tgpu> bn_res;
std::vector<Tref> in_host;
std::vector<Tref> conv_res_host;
std::vector<Tref> bn_res_host;
std::vector<Tref> out_host;
std::vector<Tgpu> scale;
std::vector<Tgpu> bias;
std::vector<Tgpu> runningMean;
std::vector<Tgpu> runningVariance;
int createSaveBuffers();
int createRunningBuffers();
miopenStatus_t miopenError;
miopenFusionPlanDescriptor_t fusePlanDesc;
miopenFusionOpDescriptor_t bNormOp;
miopenFusionOpDescriptor_t convoOp;
miopenFusionOpDescriptor_t biasOp;
miopenFusionOpDescriptor_t activOp;
miopenOperatorArgs_t fusionArgs;
};
template <typename Tgpu, typename Tref>
int CBAInferFusionDriver<Tgpu, Tref>::ParseCmdLineArgs(int argc, char* argv[])
{
inflags.Parse(argc, argv);
if(inflags.GetValueStr("dot_graph") != "")
{
std::string op = inflags.GetValueStr("dot_graph");
miopen::FusionMDGraph mdg;
if(op == "ConvForward")
{
miopen::FusionMDGraph::InitConv(mdg);
}
else if(op == "BatchNormInference")
{
miopen::FusionMDGraph::InitBN(mdg);
}
else
{
std::cerr
<< "Invalid Graph specified, valid values are ConvForward or BatchNormInference"
<< std::endl;
}
mdg.WriteToFile("/tmp/mdgraph.dot");
std::cerr << "Graph written to /tmp/mdgraph.dot" << std::endl;
exit(EXIT_SUCCESS);
}
if(inflags.GetValueInt("time") == 1)
{
miopenEnableProfiling(GetHandle(), true);
}
fusion_mode = inflags.GetValueInt("fusion_mode");
if(fusion_mode > 6 || fusion_mode < 0)
{
std::cout << "Fusion mode out of range.\n Exiting..." << std::endl;
exit(EXIT_FAILURE);
}
if(fusion_mode != miopen_fusion_cba && fusion_mode != miopen_fusion_ca &&
fusion_mode != miopen_fusion_cb)
useBatchNorm = true;
else
useBatchNorm = false;
if(fusion_mode == miopen_fusion_cbna || fusion_mode == miopen_fusion_cba ||
fusion_mode == miopen_fusion_cb)
bias_mode = 1;
else
bias_mode = 0;
return miopenStatusSuccess;
}
template <typename Tgpu, typename Tref>
int CBAInferFusionDriver<Tgpu, Tref>::SetActivationDescriptorFromCmdLineArgs()
{
miopenActivationMode_t mode;
double Alpha = inflags.GetValueDouble("alpha");
double Beta = inflags.GetValueDouble("beta");
double Gamma = inflags.GetValueDouble("gamma");
mode = static_cast<miopenActivationMode_t>(inflags.GetValueInt("activMode"));
return (miopenSetActivationDescriptor(activDesc, mode, Alpha, Beta, Gamma));
}
template <typename Tgpu, typename Tref>
std::vector<int> CBAInferFusionDriver<Tgpu, Tref>::GetWeightTensorLengthsFromCmdLine()
{
int wei_n = inflags.GetValueInt("out_channels");
int wei_c = inflags.GetValueInt("in_channels");
int wei_h = inflags.GetValueInt("fil_h");
int wei_w = inflags.GetValueInt("fil_w");
return std::vector<int>({wei_n, wei_c, wei_h, wei_w});
}
template <typename Tgpu, typename Tref>
int CBAInferFusionDriver<Tgpu, Tref>::GetandSetData()
{
SetBNParametersFromCmdLineArgs();
SetConvDescriptorFromCmdLineArgs();
SetActivationDescriptorFromCmdLineArgs();
std::vector<int> in_len = GetInputTensorLengthsFromCmdLine();
std::vector<int> wei_len = GetWeightTensorLengthsFromCmdLine();
SetTensor4d(inputTensor, in_len, data_type);
miopenCreateFusionPlan(&fusePlanDesc, miopenVerticalFusion, inputTensor);
SetTensor4d(weightTensor, wei_len, data_type);
std::vector<int> out_len{};
if(fusion_mode != miopen_fusion_na)
{
out_len = GetOutputTensorLengths();
}
else
{
out_len = in_len;
}
SetTensor4d(outputTensor, out_len, data_type);
if(bias_mode)
{
std::vector<int> b_len{1, out_len[1], 1, 1};
SetTensor4d(biasTensor, b_len, data_type);
}
return miopenStatusSuccess;
}
template <typename Tgpu, typename Tref>
int CBAInferFusionDriver<Tgpu, Tref>::AddCmdLineArgs()
{
inflags.AddInputFlag("batchsize", 'n', "32", "Mini-batch size (Default=32)", "int");
inflags.AddInputFlag("in_channels", 'c', "3", "Number of Input Channels (Default=3)", "int");
inflags.AddInputFlag("in_h", 'H', "32", "Input Height (Default=32)", "int");
inflags.AddInputFlag("in_w", 'W', "32", "Input Width (Default=32)", "int");
inflags.AddInputFlag(
"out_channels", 'k', "32", "Number of Output Channels (Default=32)", "int");
inflags.AddInputFlag(
"group_count", 'g', "1", "Number of groups in convolution (Default=1)", "int");
inflags.AddInputFlag("fil_h", 'y', "3", "Filter Height (Default=3)", "int");
inflags.AddInputFlag("fil_w", 'x', "3", "Filter Width (Default=3)", "int");
inflags.AddInputFlag(
"conv_stride_h", 'u', "1", "Convolution Stride Vertical (Default=1)", "int");
inflags.AddInputFlag(
"conv_stride_w", 'v', "1", "Convolution Stride Horizontal (Default=1)", "int");
inflags.AddInputFlag("pad_h", 'p', "0", "Zero Padding Height (Default=0)", "int");
inflags.AddInputFlag("pad_w", 'q', "0", "Zero Padding Width (Default=0)", "int");
inflags.AddInputFlag("pad_val", 'r', "0", "Padding Value (Default=0)", "int");
inflags.AddInputFlag("alpha", 'A', "1.0", "Alpha (Default=1.0)", "float");
inflags.AddInputFlag("beta", 'B', "0.", "Beta (Default=0.)", "float");
inflags.AddInputFlag("gamma", 'G', "1", "Activation gamma (Default=1)", "double");
inflags.AddInputFlag("iter", 'i', "1", "Number of Iterations (Default=1)", "int");
inflags.AddInputFlag("verify", 'V', "1", "Verify Each Layer (Default=1)", "int");
inflags.AddInputFlag("time", 't', "0", "Time Each Layer (Default=0)", "int");
/*inflags.AddInputFlag("printconv", 'P', "1", "Print Convolution Dimensions (Default=1)",
* "int");*/
inflags.AddInputFlag(
"activMode", 'm', "3", "Activation Mode (relu,..., see spec) (Default=3(relu))", "int");
inflags.AddInputFlag("bnMode",
'M',
"0",
"Normalization Mode (per-activation (0) or spatial (1)) (Default=0)",
"int");
inflags.AddInputFlag(
"wall", 'w', "0", "Wall-clock Time Each Layer, Requires time == 1 (Default=0)", "int");
inflags.AddInputFlag("dilation_h", 'l', "1", "Dilation of Filter Height (Default=1)", "int");
inflags.AddInputFlag("dilation_w", 'j', "1", "Dilation of Filter Width (Default=1)", "int");
/*inflags.AddInputFlag("search", 's', "0", "Search Kernel Config (Default=0)", "int");*/
inflags.AddInputFlag(
"pad_mode", 'z', "conv", "Padding Mode (same, valid, default) (Default=default)", "str");
inflags.AddInputFlag(
"fusion_mode",
'F',
"0",
"Fusion mode (cbna = 0, cna = 1, na = 2, cn = 3, cba = 4, ca = 5, cb = 6) (Default=cbna)",
"int");
inflags.AddInputFlag("dot_graph",
'D',
"",
"Write out the fusion metadata graph for the specified operator",
"str");
return miopenStatusSuccess;
}
template <typename Tgpu, typename Tref>
std::vector<int> CBAInferFusionDriver<Tgpu, Tref>::GetInputTensorLengthsFromCmdLine()
{
int in_n = inflags.GetValueInt("batchsize");
int in_c = inflags.GetValueInt("in_channels");
int in_h = inflags.GetValueInt("in_h");
int in_w = inflags.GetValueInt("in_w");
return std::vector<int>({in_n, in_c, in_h, in_w});
}
template <typename Tgpu, typename Tref>
int CBAInferFusionDriver<Tgpu, Tref>::SetBNParametersFromCmdLineArgs()
{
// batch norm mode type
if(inflags.GetValueInt("bnMode") == 0)
{
bn_mode = miopenBNPerActivation;
}
else if(inflags.GetValueInt("bnMode") == 1)
{
bn_mode = miopenBNSpatial;
}
else
{
printf("Incorrect Batch Normalization Mode\n");
exit(EXIT_FAILURE);
}
return miopenStatusSuccess;
}
template <typename Tgpu, typename Tref>
int CBAInferFusionDriver<Tgpu, Tref>::SetConvDescriptorFromCmdLineArgs()
{
miopenConvolutionMode_t mode;
miopenPaddingMode_t pmode = miopenPaddingDefault;
int in_h = inflags.GetValueInt("in_h");
int in_w = inflags.GetValueInt("in_w");
int wei_h = inflags.GetValueInt("fil_h");
int wei_w = inflags.GetValueInt("fil_w");
int pad_h = inflags.GetValueInt("pad_h");
int pad_w = inflags.GetValueInt("pad_w");
int stride_h = inflags.GetValueInt("conv_stride_h");
int stride_w = inflags.GetValueInt("conv_stride_w");
int dilation_h = inflags.GetValueInt("dilation_h");
int dilation_w = inflags.GetValueInt("dilation_w");
int group_count = inflags.GetValueInt("group_count");
pmode = miopenPaddingDefault;
mode = miopenConvolution;
if((inflags.GetValueStr("pad_mode")) == "same")
{
mode = miopenConvolution;
pmode = miopenPaddingSame;
pad_h = (in_h % stride_h == 0) ? (std::max((wei_h - stride_h), 0))
: (std::max((wei_h - (in_h % stride_h)), 0));
pad_w = (in_w % stride_w == 0) ? (std::max((wei_w - stride_w), 0))
: (std::max((wei_w - (in_w % stride_w)), 0));
pad_h /= 2;
pad_w /= 2;
}
else if((inflags.GetValueStr("pad_mode")) == "valid")
{
pmode = miopenPaddingValid;
mode = miopenConvolution;
pad_h = 0;
pad_w = 0;
}
miopen::deref(convDesc) = miopen::ConvolutionDescriptor(2,
mode,
pmode,
{pad_h, pad_w},
{stride_h, stride_w},
{dilation_h, dilation_w},
{0, 0},
group_count);
return miopenStatusSuccess;
}
template <typename Tgpu, typename Tref>
std::vector<int> CBAInferFusionDriver<Tgpu, Tref>::GetOutputTensorLengths()
{
int n, c, h, w;
miopenGetConvolutionForwardOutputDim(convDesc, inputTensor, weightTensor, &n, &c, &h, &w);
return std::vector<int>({n, c, h, w});
}
template <typename Tgpu, typename Tref>
int CBAInferFusionDriver<Tgpu, Tref>::createSaveBuffers()
{
#if MIOPEN_BACKEND_OPENCL
cl_int status = CL_SUCCESS;
cl_context ctx;
clGetCommandQueueInfo(q, CL_QUEUE_CONTEXT, sizeof(cl_context), &ctx, nullptr);
#elif MIOPEN_BACKEND_HIP
int status = 0;
#endif
if(status != CL_SUCCESS)
printf("Error copying data to GPU\n");
return miopenStatusSuccess;
}
template <typename Tgpu, typename Tref>
int CBAInferFusionDriver<Tgpu, Tref>::createRunningBuffers()
{
#if MIOPEN_BACKEND_OPENCL
cl_int status = CL_SUCCESS;
cl_context ctx;
clGetCommandQueueInfo(q, CL_QUEUE_CONTEXT, sizeof(cl_context), &ctx, nullptr);
#elif MIOPEN_BACKEND_HIP
int status = 0;
uint32_t ctx = 0;
#endif
if(useBatchNorm)
{
size_t sb_sz = GetTensorSize(biasScaleTensor);
// GPU allocation
runningMean_dev = std::make_unique<GPUMem>(ctx, sb_sz, sizeof(Tgpu));
runningVariance_dev = std::make_unique<GPUMem>(ctx, sb_sz, sizeof(Tgpu));
// GPU host allocation
runningMean = std::vector<Tgpu>(sb_sz, static_cast<Tgpu>(0));
runningVariance = std::vector<Tgpu>(sb_sz, static_cast<Tgpu>(0));
// Populate
for(int i = 0; i < sb_sz; i++)
{
#if(CBA_DEBUG_VALUES == 1)
runningMean[i] = 0.;
runningVariance[i] = 1.;
#else
runningMean[i] = RAN_GEN<Tgpu>(static_cast<Tgpu>(0.0), static_cast<Tgpu>(1.0));
runningVariance[i] = RAN_GEN<Tgpu>(static_cast<Tgpu>(0.0), static_cast<Tgpu>(1.0));
#endif
}
// GPU data transfer
status |= runningMean_dev->ToGPU(q, runningMean.data());
status |= runningVariance_dev->ToGPU(q, runningVariance.data());
if(status != CL_SUCCESS)
{
printf("Error copying data to GPU\n");
exit(EXIT_FAILURE);
}
}
else
{
runningMean_dev = nullptr;
runningVariance_dev = nullptr;
}
return miopenStatusSuccess;
}
template <typename Tgpu, typename Tref>
int CBAInferFusionDriver<Tgpu, Tref>::AllocateBuffersAndCopy()
{
#if MIOPEN_BACKEND_OPENCL
cl_int status = CL_SUCCESS;
cl_context ctx;
clGetCommandQueueInfo(q, CL_QUEUE_CONTEXT, sizeof(cl_context), &ctx, nullptr);
#elif MIOPEN_BACKEND_HIP
int status = 0;
uint32_t ctx = 0;
#endif
size_t in_sz = GetTensorSize(inputTensor);
size_t wei_sz = GetTensorSize(weightTensor);
size_t sb_sz = 0;
if(useBatchNorm)
{
miopenDeriveBNTensorDescriptor(biasScaleTensor, inputTensor, bn_mode);
sb_sz = GetTensorSize(biasScaleTensor);
}
size_t out_sz = 0;
if(fusion_mode != miopen_fusion_na)
out_sz = GetTensorSize(outputTensor);
else
out_sz = in_sz; // This is for N+A so the output is the same as the input size
if(miopen::IsEnabled(MIOPEN_DRIVER_PAD_BUFFERS_2M{}))
{
PadBufferSize(wei_sz, sizeof(Tgpu));
}
if(bias_mode)
{
size_t b_sz = GetTensorSize(biasTensor);
b_dev = std::make_unique<GPUMem>(ctx, b_sz, sizeof(Tgpu));
b = std::vector<Tgpu>(b_sz, static_cast<Tgpu>(0));
for(int i = 0; i < b_sz; i++)
{
b[i] = std::fabs(RAN_GEN<float>(static_cast<float>(0.0), static_cast<float>(1.0)));
}
status |= b_dev->ToGPU(q, b.data());
}
// GPU allocation
in_dev = std::make_unique<GPUMem>(ctx, in_sz, sizeof(Tgpu));
conv_res_dev = std::make_unique<GPUMem>(ctx, out_sz, sizeof(Tgpu));
wei_dev = std::make_unique<GPUMem>(ctx, wei_sz, sizeof(Tgpu));
out_dev = std::make_unique<GPUMem>(ctx, out_sz, sizeof(Tgpu));
if(useBatchNorm)
{
scale = std::vector<Tgpu>(sb_sz, static_cast<Tgpu>(0));
bias = std::vector<Tgpu>(sb_sz, static_cast<Tgpu>(0));
bn_res = std::vector<Tgpu>(out_sz, static_cast<Tgpu>(0));
bn_res_host = std::vector<Tref>(out_sz, static_cast<Tref>(0));
bn_res_dev = std::make_unique<GPUMem>(ctx, out_sz, sizeof(Tgpu));
scale_dev = std::make_unique<GPUMem>(ctx, sb_sz, sizeof(Tgpu));
bias_dev = std::make_unique<GPUMem>(ctx, sb_sz, sizeof(Tgpu));
// Using random beta and gamma
for(int i = 0; i < sb_sz; i++)
{
#if(CBA_DEBUG_VALUES == 1)
scale[i] = 1.; // std::fabs(RAN_GEN<Tgpu>(static_cast<Tgpu>(0.0),
// static_cast<Tgpu>(1.0))); // 1.0;
bias[i] = 10.;
#else
scale[i] = RAN_GEN<Tgpu>(static_cast<Tgpu>(0.0), static_cast<Tgpu>(1.0));
bias[i] = RAN_GEN<Tgpu>(static_cast<Tgpu>(0.0), static_cast<Tgpu>(1.0));
#endif
}
status |= scale_dev->ToGPU(q, scale.data());
status |= bias_dev->ToGPU(q, bias.data());
}
// GPU host allocation
in = std::vector<Tgpu>(in_sz, static_cast<Tgpu>(0));
out = std::vector<Tgpu>(out_sz, static_cast<Tgpu>(0));
conv_res = std::vector<Tgpu>(out_sz, static_cast<Tgpu>(0));
// CPU allocation
in_host = std::vector<Tref>(in_sz, static_cast<Tref>(0));
conv_res_host = std::vector<Tref>(out_sz, static_cast<Tref>(0));
out_host = std::vector<Tref>(out_sz, static_cast<Tref>(0));
// Data initialization
for(int i = 0; i < in_sz; i++)
{
#if(CBA_DEBUG_VALUES == 1)
auto rval =
1.; // std::fabs(RAN_GEN<Tgpu>(static_cast<Tgpu>(0.0), static_cast<Tgpu>(1.0))); // 1.0;
in_host[i] = static_cast<double>(rval);
in[i] = rval;
#else
auto rval = std::fabs(RAN_GEN<float>(static_cast<float>(0.0), static_cast<float>(1.0)));
in_host[i] = static_cast<double>(rval);
in[i] = rval;
#endif
}
if(fusion_mode != miopen_fusion_na)
{
wei = std::vector<Tgpu>(wei_sz, static_cast<Tgpu>(0));
for(int i = 0; i < wei_sz; i++)
{
#if(CBA_DEBUG_VALUES == 1)
wei[i] = 1.; // std::fabs(RAN_GEN<Tgpu>(static_cast<Tgpu>(0.0),
// static_cast<Tgpu>(1.0))); // 1.;
#else
wei[i] = std::fabs(RAN_GEN<Tgpu>(static_cast<Tgpu>(0.0), static_cast<Tgpu>(1.0)));
#endif
}
status |= wei_dev->ToGPU(q, wei.data());
}
status |= in_dev->ToGPU(q, in.data());
status |= createRunningBuffers();
if(status != CL_SUCCESS)
printf("Fatal: Error copying data to GPU\nExiting...\n\n");
return miopenStatusSuccess;
}
template <typename Tgpu, typename Tref>
void CBAInferFusionDriver<Tgpu, Tref>::runGPUBatchNormActivInference()
{
miopenError = miopenStatusSuccess;
double activ_alpha, activ_beta, activ_gamma;
miopenActivationMode_t activ_mode;
miopenGetActivationDescriptor(activDesc, &activ_mode, &activ_alpha, &activ_beta, &activ_gamma);
double epsilon = static_cast<double>(EPSILON);
float alpha = static_cast<float>(1), beta = static_cast<float>(0);
miopenCreateOpBatchNormInference(fusePlanDesc, &bNormOp, bn_mode, biasScaleTensor);
miopenCreateOpActivationForward(fusePlanDesc, &activOp, activ_mode);
miopenSetOpArgsBatchNormInference(fusionArgs,
bNormOp,
&alpha,
&beta,
scale_dev->GetMem(),
bias_dev->GetMem(),
runningMean_dev->GetMem(),
runningVariance_dev->GetMem(),
epsilon);
miopenSetOpArgsActivForward(
fusionArgs, activOp, &alpha, &beta, activ_alpha, activ_beta, activ_gamma);
miopenError = miopenCompileFusionPlan(GetHandle(), fusePlanDesc);
if(miopenError != miopenStatusSuccess)
{
std::cerr << "BatchNormActivInference plan not supported." << std::endl;
exit(EXIT_FAILURE);
}
for(int it = 0; it < iters; it++)
{
startTiming();
miopenExecuteFusionPlan(GetHandle(),
fusePlanDesc,
inputTensor,
in_dev->GetMem(),
outputTensor,
out_dev->GetMem(),
fusionArgs);
finishTiming(it);
}
}
template <typename Tgpu, typename Tref>
void CBAInferFusionDriver<Tgpu, Tref>::runGPUConvBatchNormActivInference()
{
miopenError = miopenStatusSuccess;
double activ_alpha, activ_beta, activ_gamma;
miopenActivationMode_t activ_mode;
miopenGetActivationDescriptor(activDesc, &activ_mode, &activ_alpha, &activ_beta, &activ_gamma);
double epsilon = static_cast<double>(EPSILON);
float alpha = static_cast<float>(1), beta = static_cast<float>(0);
int stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w;
std::string plan_error_str;
miopenConvolutionMode_t mode;
miopenGetConvolutionDescriptor(
convDesc, &mode, &pad_h, &pad_w, &stride_h, &stride_w, &dilation_h, &dilation_w);
miopenCreateOpConvForward(fusePlanDesc, &convoOp, convDesc, weightTensor);
plan_error_str += "Convolution";
if(bias_mode)
{
miopenCreateOpBiasForward(fusePlanDesc, &biasOp, biasTensor);
plan_error_str += "+Bias";
}
if(fusion_mode != miopen_fusion_cba)
{
miopenCreateOpBatchNormInference(fusePlanDesc, &bNormOp, bn_mode, biasScaleTensor);
plan_error_str += "+BatchNorm";
}
if(fusion_mode != miopen_fusion_cn)
{
miopenCreateOpActivationForward(fusePlanDesc, &activOp, activ_mode);
plan_error_str += "+Activation";
}
miopenSetOpArgsConvForward(fusionArgs, convoOp, &alpha, &beta, wei_dev->GetMem());
if(bias_mode)
{
miopenSetOpArgsBiasForward(fusionArgs, biasOp, &alpha, &beta, b_dev->GetMem());
}
if(fusion_mode != miopen_fusion_cn)
{
miopenSetOpArgsActivForward(
fusionArgs, activOp, &alpha, &beta, activ_alpha, activ_beta, activ_gamma);
}
if(fusion_mode != miopen_fusion_cba)
miopenSetOpArgsBatchNormInference(fusionArgs,
bNormOp,
&alpha,
&beta,
scale_dev->GetMem(),
bias_dev->GetMem(),
runningMean_dev->GetMem(),
runningVariance_dev->GetMem(),
epsilon);
miopenError = miopenCompileFusionPlan(GetHandle(), fusePlanDesc);
if(miopenError != miopenStatusSuccess)
{
std::cerr << plan_error_str << " plan not supported." << std::endl;
exit(EXIT_FAILURE);
}
for(int it = 0; it < iters; it++)
{
startTiming();
miopenExecuteFusionPlan(GetHandle(),
fusePlanDesc,
inputTensor,
in_dev->GetMem(),
outputTensor,
out_dev->GetMem(),
fusionArgs);
finishTiming(it);
}
}
template <typename Tgpu, typename Tref>
void CBAInferFusionDriver<Tgpu, Tref>::runGPUConvActivInference()
{
miopenError = miopenStatusSuccess;
double activ_alpha, activ_beta, activ_gamma;
miopenActivationMode_t activ_mode;
miopenGetActivationDescriptor(activDesc, &activ_mode, &activ_alpha, &activ_beta, &activ_gamma);
float alpha = static_cast<float>(1), beta = static_cast<float>(0);
int stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w;
miopenConvolutionMode_t mode;
miopenGetConvolutionDescriptor(
convDesc, &mode, &pad_h, &pad_w, &stride_h, &stride_w, &dilation_h, &dilation_w);
miopenCreateOpConvForward(fusePlanDesc, &convoOp, convDesc, weightTensor);
if(bias_mode)
{
miopenCreateOpBiasForward(fusePlanDesc, &biasOp, biasTensor);
}
miopenCreateOpActivationForward(fusePlanDesc, &activOp, activ_mode);
miopenSetOpArgsConvForward(fusionArgs, convoOp, &alpha, &beta, wei_dev->GetMem());
miopenSetOpArgsActivForward(
fusionArgs, activOp, &alpha, &beta, activ_alpha, activ_beta, activ_gamma);
if(bias_mode)
{
miopenSetOpArgsBiasForward(fusionArgs, biasOp, &alpha, &beta, b_dev->GetMem());
}
miopenError = miopenCompileFusionPlan(GetHandle(), fusePlanDesc);
if(miopenError != miopenStatusSuccess)
{
if(bias_mode)
std::cerr << "ConvBiasActivInference plan not supported." << std::endl;
else
std::cerr << "ConvActivInference plan not supported." << std::endl;
exit(EXIT_FAILURE);
}
for(int it = 0; it < iters; it++)
{
startTiming();
miopenExecuteFusionPlan(GetHandle(),
fusePlanDesc,
inputTensor,
in_dev->GetMem(),
outputTensor,
out_dev->GetMem(),
fusionArgs);
finishTiming(it);
}
}
template <typename Tgpu, typename Tref>
void CBAInferFusionDriver<Tgpu, Tref>::runGPUBNFwdInference()
{
double epsilon = static_cast<double>(EPSILON);
float alpha = static_cast<float>(1), beta = static_cast<float>(0);
miopenBatchNormalizationForwardInference(GetHandle(),
bn_mode,
&alpha,
&beta,
outputTensor,
conv_res_dev->GetMem(),
outputTensor,
bn_res_dev->GetMem(),
biasScaleTensor,
scale_dev->GetMem(),
bias_dev->GetMem(),
runningMean_dev->GetMem(),
runningVariance_dev->GetMem(),
epsilon);
// bn_res_dev->FromGPU(GetStream(), bn_res.data());
return;
}
template <typename Tgpu, typename Tref>
void CBAInferFusionDriver<Tgpu, Tref>::runCPUActivFwdInference()
{
double activ_alpha, activ_beta, activ_gamma;
miopenActivationMode_t activ_mode;
miopenGetActivationDescriptor(activDesc, &activ_mode, &activ_alpha, &activ_beta, &activ_gamma);
miopenActivationFwdHost<Tgpu, Tref>(activ_mode,
activ_gamma,
activ_beta,
activ_alpha,
out.size(),
(!useBatchNorm) ? conv_res_host.data() : bn_res_host.data(),
out_host.data());
return;
}
template <typename Tgpu, typename Tref>
void CBAInferFusionDriver<Tgpu, Tref>::runGPUActivFwdInference()
{
float alpha = static_cast<float>(1), beta = static_cast<float>(0);
miopenActivationForward(GetHandle(),
activDesc,
&alpha,
outputTensor,
bn_res_dev->GetMem(), // DLOWELL this might be a bug if not using BN
&beta,
outputTensor,
out_dev->GetMem());
return;
}
template <typename Tgpu, typename Tref>
void CBAInferFusionDriver<Tgpu, Tref>::runGPUConvBiasInference()
{
float alpha = static_cast<float>(1), beta = static_cast<float>(0);
if(bias_mode)
{
miopenConvolutionForwardBias(GetHandle(),
&alpha,
biasTensor,
b_dev->GetMem(),
&beta,
outputTensor,
conv_res_dev->GetMem());
}
}
template <typename Tgpu, typename Tref>
void CBAInferFusionDriver<Tgpu, Tref>::runGPUFusedConvBiasInference()