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conv_sample.cpp
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conv_sample.cpp
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/*
* Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
*
* 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.
*/
#include "conv_sample.h"
#include <cudnn_frontend_find_plan.h>
#include <cudnn_frontend_get_plan.h>
namespace {
bool
isNonDeterministic(cudnnBackendDescriptor_t engine_config) {
return cudnn_frontend::hasNumericalNote<CUDNN_NUMERICAL_NOTE_NONDETERMINISTIC>(engine_config);
}
bool
isReducedPrecisionReduction(cudnnBackendDescriptor_t engine_config) {
return cudnn_frontend::hasNumericalNote<CUDNN_NUMERICAL_NOTE_REDUCED_PRECISION_REDUCTION>(engine_config);
}
bool
isDownConvertingInputs(cudnnBackendDescriptor_t engine_config) {
return cudnn_frontend::hasNumericalNote<CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS>(engine_config);
}
bool
isNonDeterministicOrisDownConverting(cudnnBackendDescriptor_t engine_config) {
return isNonDeterministic(engine_config) || isDownConvertingInputs(engine_config);
}
bool
allowAll(cudnnBackendDescriptor_t engine_config) {
(void)engine_config;
return false;
}
bool allowErrata(int64_t *padA) {
return std::all_of(padA,padA + 2, [](int64_t pad) {
return pad == 0;});
}
bool isInt8Errata(cudnnDataType_t type) {
return type == CUDNN_DATA_INT8;
}
}
enum {
X_TENSOR,
Y_TENSOR,
W_TENSOR,
Z_TENSOR,
B_TENSOR,
AFTERADD_TENSOR,
AFTERBIAS_TENSOR,
AFTERCONV_TENSOR,
};
using common_conv_descriptors =
std::tuple<cudnn_frontend::Tensor, cudnn_frontend::Tensor, cudnn_frontend::Tensor, cudnn_frontend::ConvDesc>;
using common_convbias_descriptors = std::tuple<cudnn_frontend::Tensor,
cudnn_frontend::Tensor,
cudnn_frontend::Tensor,
cudnn_frontend::Tensor,
cudnn_frontend::Tensor,
cudnn_frontend::Tensor,
cudnn_frontend::Tensor,
cudnn_frontend::Tensor>;
common_convbias_descriptors
create_conv_add_bias_act_descriptors(int64_t* x_dim,
int64_t* padA,
int64_t* convstrideA,
int64_t* dilationA,
int64_t* w_dim,
int64_t* y_dim,
cudnnDataType_t dataType) {
(void)padA;
(void)convstrideA;
(void)dilationA;
int64_t b_dim[4];
b_dim[0] = 1;
b_dim[1] = y_dim[1];
b_dim[2] = 1;
b_dim[3] = 1;
int64_t x_stride[4];
int64_t y_stride[4];
int64_t w_stride[4];
int64_t b_stride[4];
generateStrides(w_dim, w_stride, 4, CUDNN_TENSOR_NCHW);
generateStrides(x_dim, x_stride, 4, CUDNN_TENSOR_NCHW);
generateStrides(y_dim, y_stride, 4, CUDNN_TENSOR_NCHW);
generateStrides(b_dim, b_stride, 4, CUDNN_TENSOR_NCHW);
return common_convbias_descriptors(cudnn_frontend::TensorBuilder()
.setDim(4, x_dim)
.setStrides(4, x_stride)
.setId('x')
.setAlignment(4)
.setDataType(dataType)
.build(),
cudnn_frontend::TensorBuilder()
.setDim(4, y_dim)
.setStrides(4, y_stride)
.setId('y')
.setAlignment(4)
.setDataType(dataType)
.build(),
cudnn_frontend::TensorBuilder()
.setDim(4, w_dim)
.setStrides(4, w_stride)
.setId('w')
.setAlignment(4)
.setDataType(dataType)
.build(),
cudnn_frontend::TensorBuilder()
.setDim(4, y_dim)
.setStrides(4, y_stride)
.setId('z')
.setAlignment(4)
.setDataType(dataType)
.build(),
cudnn_frontend::TensorBuilder()
.setDim(4, b_dim)
.setStrides(4, b_stride)
.setId('b')
.setAlignment(4)
.setDataType(dataType)
.build(),
cudnn_frontend::TensorBuilder()
.setDim(4, y_dim)
.setStrides(4, y_stride)
.setVirtual()
.setId('A') // after add
.setAlignment(4)
.setDataType(dataType)
.build(),
cudnn_frontend::TensorBuilder()
.setDim(4, y_dim)
.setStrides(4, y_stride)
.setVirtual()
.setId('B') // after bias
.setAlignment(4)
.setDataType(dataType)
.build(),
cudnn_frontend::TensorBuilder()
.setDim(4, y_dim)
.setStrides(4, y_stride)
.setId('C') // after conv
.setAlignment(4)
.setVirtual()
.setDataType(dataType)
.build());
}
common_conv_descriptors
create_common_descriptors(int64_t* x_dim,
int64_t* padA,
int64_t* convstrideA,
int64_t* dilationA,
int64_t* w_dim,
int64_t* y_dim,
cudnnDataType_t dataType,
cudnnConvolutionMode_t mode) {
const int convDim = 2;
int64_t strideA[4];
int64_t outstrideA[4];
int64_t filterstrideA[4];
generateStrides(w_dim, filterstrideA, 4, CUDNN_TENSOR_NCHW);
generateStrides(x_dim, strideA, 4, CUDNN_TENSOR_NCHW);
generateStrides(y_dim, outstrideA, 4, CUDNN_TENSOR_NCHW);
return common_conv_descriptors(cudnn_frontend::TensorBuilder()
.setDim(4, x_dim)
.setStrides(4, strideA)
.setId('x')
.setAlignment(4)
.setDataType(dataType)
.build(),
cudnn_frontend::TensorBuilder()
.setDim(4, y_dim)
.setStrides(4, outstrideA)
.setId('y')
.setAlignment(4)
.setDataType(dataType)
.build(),
cudnn_frontend::TensorBuilder()
.setDim(4, w_dim)
.setStrides(4, filterstrideA)
.setId('w')
.setAlignment(4)
.setDataType(dataType)
.build(),
cudnn_frontend::ConvDescBuilder()
.setDataType(dataType)
.setMathMode(mode)
.setNDims(convDim)
.setStrides(convDim, convstrideA)
.setPrePadding(convDim, padA)
.setPostPadding(convDim, padA)
.setDilation(convDim, dilationA)
.build());
}
cudnn_frontend::OperationGraph
create_operation_graph(common_conv_descriptors& descriptors, cudnnBackendDescriptorType_t mode, cudnnHandle_t handle_) {
float alpha = 1.0f;
float beta = 0.0;
auto op = cudnn_frontend::OperationBuilder(mode)
.setxDesc(std::get<X_TENSOR>(descriptors))
.setyDesc(std::get<Y_TENSOR>(descriptors))
.setwDesc(std::get<W_TENSOR>(descriptors))
.setcDesc(std::get<3>(descriptors))
.setAlpha(alpha)
.setBeta(beta)
.build();
std::cout << "Operation is " << op.describe() << std::endl;
std::array<cudnn_frontend::Operation const*, 1> ops = {&op};
return cudnn_frontend::OperationGraphBuilder().setHandle(handle_).setOperationGraph(ops.size(), ops.data()).build();
}
// Method for engine config generator based on heuristics
auto heurgen_method = [](cudnn_frontend::OperationGraph &opGraph) -> cudnn_frontend::EngineConfigList {
auto heuristics = cudnn_frontend::EngineHeuristicsBuilder()
.setOperationGraph(opGraph)
.setHeurMode(CUDNN_HEUR_MODE_INSTANT)
.build();
std::cout << "Heuristic has " << heuristics.getEngineConfigCount() << " configurations " << std::endl;
auto &engine_configs = heuristics.getEngineConfig(heuristics.getEngineConfigCount());
cudnn_frontend::EngineConfigList filtered_configs;
cudnn_frontend::filter(engine_configs, filtered_configs, ::allowAll);
return filtered_configs;
};
// Method for engine config generator based on fallback list
auto fallback_method = [](cudnn_frontend::OperationGraph &opGraph) -> cudnn_frontend::EngineConfigList {
auto fallback = cudnn_frontend::EngineFallbackListBuilder()
.setOperationGraph(opGraph)
.setOperation(CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR)
.build();
auto &fallback_list = fallback.getFallbackList();
cudnn_frontend::EngineConfigList filtered_configs;
// We create this filter to pre-remove configs being passed to cudnnFind.
// This is just a sample and is not necessary
cudnn_frontend::filter(fallback_list, filtered_configs, ::isNonDeterministic);
return filtered_configs;
};
void
run_from_heuristics(int64_t* x_dim,
int64_t* padA,
int64_t* convstrideA,
int64_t* dilationA,
int64_t* w_dim,
int64_t* y_dim,
cudnnDataType_t dataType,
cudnnConvolutionMode_t mode,
float* devPtrX,
float* devPtrW,
float* devPtrY,
cudnnBackendHeurMode_t heur_mode) {
cudnnHandle_t handle_;
try {
checkCudnnErr(cudnnCreate(&handle_));
common_conv_descriptors descriptors = create_common_descriptors(
x_dim, padA, convstrideA, dilationA, w_dim, y_dim, dataType, mode);
std::cout << std::get<X_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<Y_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<W_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<3>(descriptors).describe() << std::endl;
auto opGraph =
create_operation_graph(descriptors, CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR, handle_);
std::cout << opGraph.describe() << std::endl;
auto heuristics = cudnn_frontend::EngineHeuristicsBuilder()
.setOperationGraph(opGraph)
.setHeurMode(heur_mode)
.build();
std::cout << "Heuristic has " << heuristics.getEngineConfigCount() << " configurations " << std::endl;
auto& engine_config = heuristics.getEngineConfig(heuristics.getEngineConfigCount());
auto plan_builder = [&]() -> cudnn_frontend::ExecutionPlan {
for (auto &ecfg : engine_config) {
try {
auto plan = cudnn_frontend::ExecutionPlanBuilder().setHandle(handle_).setEngineConfig(ecfg, opGraph.getTag()).build();
return plan;
} catch (cudnn_frontend::cudnnException &e) {
continue;
}
}
return cudnn_frontend::ExecutionPlanBuilder().setHandle(handle_).setEngineConfig(engine_config[0], opGraph.getTag()).build();
};
auto plan = plan_builder();
std::cout << "Plan tag: " << plan.getTag() << std::endl;
auto workspace_size = plan.getWorkspaceSize();
std::cout << plan.describe() << " requires workspace " << workspace_size << std::endl;
void* workspace_ptr = nullptr;
if (workspace_size > 0) {
checkCudaErr(cudaMalloc(&workspace_ptr, workspace_size));
}
void* data_ptrs[] = {devPtrX, devPtrY, devPtrW};
int64_t uids[] = {'x', 'y', 'w'};
auto variantPack = cudnn_frontend::VariantPackBuilder()
.setWorkspacePointer(workspace_ptr)
.setDataPointers(3, data_ptrs)
.setUids(3, uids)
.build();
std::cout << "variantPack " << variantPack.describe() << std::endl;
cudnnStatus_t status = cudnnBackendExecute(handle_, plan.get_raw_desc(), variantPack.get_raw_desc());
if (workspace_size > 0) {
checkCudaErr(cudaFree(workspace_ptr));
}
cudnn_frontend::throw_if([status]() { return (status != CUDNN_STATUS_SUCCESS); }, "Plan execute error", status);
} catch (cudnn_frontend::cudnnException &e) {
std::cout << "[ERROR] Exception " << e.what() << std::endl;
CHECK(false);
}
if (handle_) cudnnDestroy(handle_);
return;
}
void
run_from_global_index(int64_t* x_dim,
int64_t* padA,
int64_t* convstrideA,
int64_t* dilationA,
int64_t* w_dim,
int64_t* y_dim,
cudnnDataType_t dataType,
cudnnConvolutionMode_t mode,
float* devPtrX,
float* devPtrW,
float* devPtrY) {
cudnnHandle_t handle_;
try {
checkCudnnErr(cudnnCreate(&handle_));
common_conv_descriptors descriptors = create_common_descriptors(
x_dim, padA, convstrideA, dilationA, w_dim, y_dim, dataType, mode);
std::cout << std::get<X_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<Y_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<W_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<3>(descriptors).describe() << std::endl;
auto opGraph = create_operation_graph(
descriptors, CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR, handle_);
std::cout << opGraph.describe() << std::endl;
// We have to randomly pick one engine from [0, total_engines)
// Selecting "0" by default
auto engine = cudnn_frontend::EngineBuilder().setGlobalEngineIdx(0).setOperationGraph(opGraph).build();
std::cout << engine.describe() << std::endl;
auto& knobs = engine.getSupportedKnobs();
for (auto it = std::begin(knobs); it != std::end(knobs); ++it) {
std::cout << it->describe() << std::endl;
}
if (knobs.begin() != knobs.end()) {
std::cout << "Updated knob choice" << std::endl;
knobs.begin()->setChoice(knobs.begin()->getMinValue() + 1);
std::cout << knobs.begin()->describe() << std::endl;
}
auto engine_config = cudnn_frontend::EngineConfigBuilder().setEngine(engine).build();
std::cout << engine_config.describe() << std::endl;
auto plan = cudnn_frontend::ExecutionPlanBuilder().setHandle(handle_).setEngineConfig(engine_config).build();
std::cout << "Plan tag: " << plan.getTag() << std::endl;
auto workspace_size = plan.getWorkspaceSize();
std::cout << plan.describe() << " requires workspace " << workspace_size << std::endl;
void* data_ptrs[] = {devPtrX, devPtrY, devPtrW};
int64_t uids[] = {'x', 'y', 'w'};
auto variantPack = cudnn_frontend::VariantPackBuilder()
.setWorkspacePointer(nullptr)
.setDataPointers(3, data_ptrs)
.setUids(3, uids)
.build();
std::cout << "variantPack " << variantPack.describe() << std::endl;
cudnnStatus_t status = cudnnBackendExecute(handle_, plan.get_raw_desc(), variantPack.get_raw_desc());
cudnn_frontend::throw_if([status]() { return (status != CUDNN_STATUS_SUCCESS); }, "Plan execute error", status);
} catch (cudnn_frontend::cudnnException &e) {
std::cout << "[ERROR] Exception " << e.what() << std::endl;
CHECK(false);
}
if (handle_) cudnnDestroy(handle_);
}
void
run_with_external_config(int64_t* x_dim,
int64_t* padA,
int64_t* convstrideA,
int64_t* dilationA,
int64_t* w_dim,
int64_t* y_dim,
cudnnDataType_t dataType,
cudnnConvolutionMode_t mode,
float* devPtrX,
float* devPtrW,
float* devPtrY) {
cudnnHandle_t handle_;
try {
checkCudnnErr(cudnnCreate(&handle_));
common_conv_descriptors descriptors = create_common_descriptors(
x_dim, padA, convstrideA, dilationA, w_dim, y_dim, dataType, mode);
std::cout << std::get<X_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<Y_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<W_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<3>(descriptors).describe() << std::endl;
auto opGraph =
create_operation_graph(descriptors, CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR, handle_);
std::cout << opGraph.describe() << std::endl;
#if (CUDNN_VERSION >= 8300)
cudnn_frontend::EngineConfigList filtered_configs =
cudnn_frontend::get_heuristics_list<2>({CUDNN_HEUR_MODE_INSTANT, CUDNN_HEUR_MODE_FALLBACK}, opGraph,::isNonDeterministic);
#else
auto heuristics = cudnn_frontend::EngineHeuristicsBuilder()
.setOperationGraph(opGraph)
.setHeurMode(CUDNN_HEUR_MODE_INSTANT)
.build();
std::cout << "Heuristic has " << heuristics.getEngineConfigCount() << " configurations " << std::endl;
auto& engine_config = heuristics.getEngineConfig(heuristics.getEngineConfigCount());
auto fallback = cudnn_frontend::EngineFallbackListBuilder()
.setOperationGraph(opGraph)
.setOperation(CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR)
.build();
auto& fallback_list = fallback.getFallbackList();
std::cout << "Fallback List has " << fallback_list.size() << " configurations " << std::endl;
cudnn_frontend::EngineConfigList filtered_configs;
cudnn_frontend::filter(engine_config, filtered_configs, ::isNonDeterministicOrisDownConverting);
cudnn_frontend::filter(fallback_list, filtered_configs, ::isNonDeterministic);
std::cout << "Heuristic has " << heuristics.getEngineConfigCount() << " configurations " << std::endl;
std::cout << "Fallback List has " << fallback_list.size() << " configurations " << std::endl;
#endif
std::cout << "Filter config list has " << filtered_configs.size() << " configurations " << std::endl;
auto plan =
cudnn_frontend::ExecutionPlanBuilder().setHandle(handle_).setEngineConfig(filtered_configs[0], opGraph.getTag()).build();
std::cout << "Plan tag: " << plan.getTag() << std::endl;
std::cout << plan.describe() << std::endl;
void* data_ptrs[] = {devPtrX, devPtrY, devPtrW};
int64_t uids[] = {'x', 'y', 'w'};
auto variantPack = cudnn_frontend::VariantPackBuilder()
.setWorkspacePointer(nullptr)
.setDataPointers(3, data_ptrs)
.setUids(3, uids)
.build();
std::cout << "variantPack " << variantPack.describe() << std::endl;
cudnnStatus_t status = cudnnBackendExecute(handle_, plan.get_raw_desc(), variantPack.get_raw_desc());
cudnn_frontend::throw_if([status]() { return (status != CUDNN_STATUS_SUCCESS); }, "Plan execute error", status);
} catch (cudnn_frontend::cudnnException &e) {
std::cout << "[ERROR] Exception " << e.what() << std::endl;
CHECK(false);
}
if (handle_) cudnnDestroy(handle_);
return;
}
// create_plan(std::vector<cudnnBackendDescriptor_t> &)
void
run_conv_add_bias_activation(int64_t* x_dim,
int64_t* pad,
int64_t* convstride,
int64_t* dilation,
int64_t* w_dim,
int64_t* y_dim,
cudnnDataType_t dataType,
float* devPtrX,
float* devPtrW,
float* devPtrY,
float* devPtrZ,
float* devPtrB) {
cudnnHandle_t handle_;
try {
int convDim = 2;
// Create cudnn handle
checkCudnnErr(cudnnCreate(&handle_));
// Creates the necessary tensor descriptors
common_convbias_descriptors tensors = create_conv_add_bias_act_descriptors(
x_dim, pad, convstride, dilation, w_dim, y_dim, dataType);
std::cout << std::get<X_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<Y_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<W_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<Z_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<B_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<AFTERADD_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<AFTERBIAS_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<AFTERCONV_TENSOR>(tensors).describe() << std::endl;
// Define the add operation
auto addDesc = cudnn_frontend::PointWiseDescBuilder()
.setMode(CUDNN_POINTWISE_ADD)
.setMathPrecision(CUDNN_DATA_FLOAT)
.build();
std::cout << addDesc.describe() << std::endl;
// Define the bias operation
auto addDesc2 = cudnn_frontend::PointWiseDescBuilder()
.setMode(CUDNN_POINTWISE_ADD)
.setMathPrecision(CUDNN_DATA_FLOAT)
.build();
std::cout << addDesc2.describe() << std::endl;
// Define the activation operation
auto actDesc = cudnn_frontend::PointWiseDescBuilder()
.setMode(CUDNN_POINTWISE_RELU_FWD)
.setMathPrecision(CUDNN_DATA_FLOAT)
.build();
std::cout << actDesc.describe() << std::endl;
// Define the convolution problem
auto convDesc = cudnn_frontend::ConvDescBuilder()
.setDataType(dataType)
.setMathMode(CUDNN_CONVOLUTION)
.setNDims(convDim)
.setStrides(convDim, convstride)
.setPrePadding(convDim, pad)
.setPostPadding(convDim, pad)
.setDilation(convDim, dilation)
.build();
std::cout << convDesc.describe() << std::endl;
float alpha = 1.0f;
float alpha2 = 0.5f;
float beta = 0.0f;
// Create a convolution Node
auto conv_op = cudnn_frontend::OperationBuilder(CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR)
.setxDesc(std::get<X_TENSOR>(tensors))
.setwDesc(std::get<W_TENSOR>(tensors))
.setyDesc(std::get<AFTERCONV_TENSOR>(tensors))
.setcDesc(convDesc)
.setAlpha(alpha)
.setBeta(beta)
.build();
std::cout << conv_op.describe() << std::endl;
// Create a Add Node with scaling parameters.
auto add_op1 = cudnn_frontend::OperationBuilder(CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR)
.setxDesc(conv_op.getOutputTensor())
.setbDesc(std::get<Z_TENSOR>(tensors))
.setyDesc(std::get<AFTERADD_TENSOR>(tensors))
.setpwDesc(addDesc)
.setAlpha(alpha)
.setAlpha2(alpha2)
.build();
std::cout << add_op1.describe() << std::endl;
// Create a Bias Node.
auto add_op2 = cudnn_frontend::OperationBuilder(CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR)
.setxDesc(add_op1.getOutputTensor())
.setbDesc(std::get<B_TENSOR>(tensors))
.setyDesc(std::get<AFTERBIAS_TENSOR>(tensors))
.setpwDesc(addDesc2)
.build();
std::cout << add_op2.describe() << std::endl;
// Create an Activation Node.
auto act_op = cudnn_frontend::OperationBuilder(CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR)
.setxDesc(add_op2.getOutputTensor())
.setyDesc(std::get<Y_TENSOR>(tensors))
.setpwDesc(actDesc)
.build();
std::cout << act_op.describe() << std::endl;
// Create an Operation Graph. In this case it is convolution add bias activation
std::array<cudnn_frontend::Operation const*, 4> ops = {&conv_op, &add_op1, &add_op2, &act_op};
auto opGraph = cudnn_frontend::OperationGraphBuilder()
.setHandle(handle_)
.setOperationGraph(ops.size(), ops.data())
.build();
// How many engines support this operation graph ?
auto total_engines = opGraph.getEngineCount();
std::cout << "conv_add_bias_activation " << opGraph.describe() << " has " << total_engines << " engines." << std::endl;
// We have to randomly pick one engine from [0, total_engines)
// Selecting "0" by default
auto engine = cudnn_frontend::EngineBuilder().setGlobalEngineIdx(0).setOperationGraph(opGraph).build();
std::cout << engine.describe() << std::endl;
auto& knobs = engine.getSupportedKnobs();
for (auto it = std::begin(knobs); it != std::end(knobs); ++it) {
std::cout << it->describe() << std::endl;
}
if (knobs.begin() != knobs.end()) {
std::cout << "Updated knob choice" << std::endl;
knobs.begin()->setChoice(knobs.begin()->getMinValue() + 1);
std::cout << knobs.begin()->describe() << std::endl;
}
// Create the requisite engine config
auto engine_config = cudnn_frontend::EngineConfigBuilder().setEngine(engine).build();
std::cout << engine_config.describe() << std::endl;
auto plan = cudnn_frontend::ExecutionPlanBuilder().setHandle(handle_).setEngineConfig(engine_config).build();
std::cout << "Plan tag: " << plan.getTag() << std::endl;
auto workspace_size = plan.getWorkspaceSize();
std::cout << plan.describe() << " requires workspace " << workspace_size << std::endl;
void* workspace_ptr = nullptr;
if (workspace_size > 0) {
checkCudaErr(cudaMalloc(&workspace_ptr, workspace_size));
}
void* data_ptrs[] = {devPtrX, devPtrY, devPtrW, devPtrZ, devPtrB};
int64_t uids[] = {'x', 'y', 'w', 'z', 'b'};
auto variantPack = cudnn_frontend::VariantPackBuilder()
.setWorkspacePointer(workspace_ptr)
.setDataPointers(5, data_ptrs)
.setUids(5, uids)
.build();
std::cout << "variantPack " << variantPack.describe() << std::endl;
cudnnStatus_t status = cudnnBackendExecute(handle_, plan.get_raw_desc(), variantPack.get_raw_desc());
if (workspace_size > 0) {
checkCudaErr(cudaFree(workspace_ptr));
}
cudnn_frontend::throw_if([status]() { return (status != CUDNN_STATUS_SUCCESS); }, "Plan execute error", status);
} catch (cudnn_frontend::cudnnException &e) {
std::cout << "[ERROR] Exception " << e.what() << std::endl;
CHECK(false);
}
}
void
run_from_cudnn_find(int64_t* x_dim,
int64_t* padA,
int64_t* convstrideA,
int64_t* dilationA,
int64_t* w_dim,
int64_t* y_dim,
cudnnDataType_t dataType,
cudnnConvolutionMode_t mode,
void* devPtrX,
void* devPtrW,
void* devPtrY) {
cudnnHandle_t handle_;
try {
checkCudnnErr(cudnnCreate(&handle_));
common_conv_descriptors descriptors = create_common_descriptors(
x_dim, padA, convstrideA, dilationA, w_dim, y_dim, dataType, mode);
std::cout << std::get<X_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<Y_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<W_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<3>(descriptors).describe() << std::endl;
auto opGraph =
create_operation_graph(descriptors, CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR, handle_);
std::cout << opGraph.describe() << std::endl;
void* data_ptrs[] = {devPtrX, devPtrY, devPtrW};
int64_t uids[] = {'x', 'y', 'w'};
auto variantPack = cudnn_frontend::VariantPackBuilder()
.setDataPointers(3, data_ptrs)
.setUids(3, uids)
.build();
std::cout << "variantPack " << variantPack.describe() << std::endl;
auto sample_predicate_function = [](cudnn_frontend::ExecutionPlan const& plan) -> bool {
return plan.getWorkspaceSize() != 0;
};
std::array<cudnn_frontend::GeneratorSource const, 2> sources = {heurgen_method, fallback_method};
cudnn_frontend::EngineConfigGenerator generator(sources.size(), sources.data());
auto options = generator.cudnnFindPlan<cudnn_frontend::CudnnFindSamplingTechnique::CUDNN_FIND_SAMPLE_MEDIAN_OF_THREE>(
handle_, opGraph, variantPack, sample_predicate_function);
std::for_each(options.begin(), options.end(), [](cudnn_frontend::ExecutionPlan& opt) {
std::cout << "Plan tag: " << opt.getTag() << " finished in " << opt.getExecutionTime() << " ms,"
<< " workspace: " << opt.getWorkspaceSize() << " bytes" << std::endl;
});
cudnnStatus_t status =
cudnnBackendExecute(handle_, options.front().get_raw_desc(), variantPack.get_raw_desc());
cudnn_frontend::throw_if([status]() { return (status != CUDNN_STATUS_SUCCESS); }, "Plan execute error", status);
} catch (cudnn_frontend::cudnnException &e) {
std::cout << "[ERROR] Exception " << e.what() << std::endl;
CHECK(false);
}
if (handle_) cudnnDestroy(handle_);
return;
}
void
run_conv_add_bias_activation_with_cudnn_find(int64_t* x_dim,
int64_t* pad,
int64_t* convstride,
int64_t* dilation,
int64_t* w_dim,
int64_t* y_dim,
cudnnDataType_t dataType,
float* devPtrX,
float* devPtrW,
float* devPtrY,
float* devPtrZ,
float* devPtrB) {
cudnnHandle_t handle_;
try {
int convDim = 2;
// Create cudnn handle
checkCudnnErr(cudnnCreate(&handle_));
// Creates the necessary tensor descriptors
common_convbias_descriptors tensors = create_conv_add_bias_act_descriptors(
x_dim, pad, convstride, dilation, w_dim, y_dim, dataType);
std::cout << std::get<X_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<Y_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<W_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<Z_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<B_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<AFTERADD_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<AFTERBIAS_TENSOR>(tensors).describe() << std::endl;
std::cout << std::get<AFTERCONV_TENSOR>(tensors).describe() << std::endl;
// Define the add operation
auto addDesc = cudnn_frontend::PointWiseDescBuilder()
.setMode(CUDNN_POINTWISE_ADD)
.setMathPrecision(CUDNN_DATA_FLOAT)
.build();
std::cout << addDesc.describe() << std::endl;
// Define the bias operation
auto addDesc2 = cudnn_frontend::PointWiseDescBuilder()
.setMode(CUDNN_POINTWISE_ADD)
.setMathPrecision(CUDNN_DATA_FLOAT)
.build();
std::cout << addDesc2.describe() << std::endl;
// Define the activation operation
auto actDesc = cudnn_frontend::PointWiseDescBuilder()
.setMode(CUDNN_POINTWISE_RELU_FWD)
.setMathPrecision(CUDNN_DATA_FLOAT)
.build();
std::cout << actDesc.describe() << std::endl;
// Define the convolution problem
auto convDesc = cudnn_frontend::ConvDescBuilder()
.setDataType(dataType)
.setMathMode(CUDNN_CONVOLUTION)
.setNDims(convDim)
.setStrides(convDim, convstride)
.setPrePadding(convDim, pad)
.setPostPadding(convDim, pad)
.setDilation(convDim, dilation)
.build();
std::cout << convDesc.describe() << std::endl;
float alpha = 1.0f;
float alpha2 = 0.5f;
float beta = 0.0f;
// Create a convolution Node
auto conv_op = cudnn_frontend::OperationBuilder(CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR)
.setxDesc(std::get<X_TENSOR>(tensors))
.setwDesc(std::get<W_TENSOR>(tensors))
.setyDesc(std::get<AFTERCONV_TENSOR>(tensors))
.setcDesc(convDesc)
.setAlpha(alpha)
.setBeta(beta)
.build();
std::cout << conv_op.describe() << std::endl;
// Create a Add Node with scaling parameters.
auto add_op1 = cudnn_frontend::OperationBuilder(CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR)
.setxDesc(conv_op.getOutputTensor())
.setbDesc(std::get<Z_TENSOR>(tensors))
.setyDesc(std::get<AFTERADD_TENSOR>(tensors))
.setpwDesc(addDesc)
.setAlpha(alpha)
.setAlpha2(alpha2)
.build();
std::cout << add_op1.describe() << std::endl;
// Create a Bias Node.
auto add_op2 = cudnn_frontend::OperationBuilder(CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR)
.setxDesc(add_op1.getOutputTensor())
.setbDesc(std::get<B_TENSOR>(tensors))
.setyDesc(std::get<AFTERBIAS_TENSOR>(tensors))
.setpwDesc(addDesc2)
.build();
std::cout << add_op2.describe() << std::endl;
// Create an Activation Node.
auto act_op = cudnn_frontend::OperationBuilder(CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR)
.setxDesc(add_op2.getOutputTensor())
.setyDesc(std::get<Y_TENSOR>(tensors))
.setpwDesc(actDesc)
.build();
std::cout << act_op.describe() << std::endl;
// Create an Operation Graph. In this case it is convolution add bias activation
std::array<cudnn_frontend::Operation const*, 4> ops = {&conv_op, &add_op1, &add_op2, &act_op};
auto opGraph = cudnn_frontend::OperationGraphBuilder()
.setHandle(handle_)
.setOperationGraph(ops.size(), ops.data())
.build();
auto max_workspace_size = 10 * 1024 * 1024; // 10 MiB
void* workspace_ptr = nullptr;
checkCudaErr(cudaMalloc(&workspace_ptr, max_workspace_size));
void* data_ptrs[] = {devPtrX, devPtrY, devPtrW, devPtrZ, devPtrB};
int64_t uids[] = {'x', 'y', 'w', 'z', 'b'};
auto variantPack = cudnn_frontend::VariantPackBuilder()
.setWorkspacePointer(workspace_ptr)
.setDataPointers(5, data_ptrs)
.setUids(5, uids)
.build();
std::cout << "variantPack " << variantPack.describe() << std::endl;
auto sample_predicate_function = [=](cudnn_frontend::ExecutionPlan const& plan) -> bool {
return plan.getWorkspaceSize() > max_workspace_size;
};
std::array<cudnn_frontend::GeneratorSource const, 2> sources = {heurgen_method, fallback_method};
cudnn_frontend::EngineConfigGenerator generator(sources.size(), sources.data());
auto options = generator.cudnnFindPlan<cudnn_frontend::CudnnFindSamplingTechnique::CUDNN_FIND_SAMPLE_MEDIAN_OF_THREE>(
handle_, opGraph, variantPack, sample_predicate_function);
std::for_each(options.begin(), options.end(), [](cudnn_frontend::ExecutionPlan& opt) {
std::cout << "Plan tag: " << opt.getTag() << " finished in " << opt.getExecutionTime() << " ms,"
<< " workspace: " << opt.getWorkspaceSize() << " bytes" << std::endl;
});
cudnnStatus_t status =
cudnnBackendExecute(handle_, options.front().get_raw_desc(), variantPack.get_raw_desc());
checkCudaErr(cudaFree(workspace_ptr));
cudnn_frontend::throw_if([status]() { return (status != CUDNN_STATUS_SUCCESS); }, "Plan execute error", status);
} catch (cudnn_frontend::cudnnException &e) {
std::cout << "[ERROR] Exception " << e.what() << std::endl;
CHECK(false);
}
}
void
run_from_cudnn_get(int64_t* x_dim,
int64_t* padA,
int64_t* convstrideA,
int64_t* dilationA,
int64_t* w_dim,
int64_t* y_dim,
cudnnDataType_t dataType,
cudnnConvolutionMode_t mode,
float* devPtrX,
float* devPtrW,
float* devPtrY) {
cudnnHandle_t handle_;
try {
checkCudnnErr(cudnnCreate(&handle_));
common_conv_descriptors descriptors = create_common_descriptors(
x_dim, padA, convstrideA, dilationA, w_dim, y_dim, dataType, mode);
std::cout << std::get<X_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<Y_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<W_TENSOR>(descriptors).describe() << std::endl;
std::cout << std::get<3>(descriptors).describe() << std::endl;
auto opGraph =
create_operation_graph(descriptors, CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR, handle_);
std::cout << opGraph.describe() << std::endl;
auto sample_predicate_function = [](cudnn_frontend::ExecutionPlan const& plan) -> bool {
(void) plan;
return false;
};
std::array<cudnn_frontend::GeneratorSource const, 1> sources = {heurgen_method};
cudnn_frontend::EngineConfigGenerator generator(sources.size(), sources.data());
auto plans = generator.cudnnGetPlan(handle_, opGraph, sample_predicate_function);
int64_t max_workspace_size = 0u;
std::for_each(plans.begin(), plans.end(), [&max_workspace_size](cudnn_frontend::ExecutionPlan& plan) {
std::cout << "Plan tag: " << plan.getTag() << " workspace: " << plan.getWorkspaceSize() << " bytes"
<< std::endl;
if (plan.getWorkspaceSize() > max_workspace_size) {
max_workspace_size = plan.getWorkspaceSize();
}
});
std::cout << "Max workspace size required " << max_workspace_size << std::endl;
void* workspace_ptr = nullptr;
checkCudaErr(cudaMalloc(&workspace_ptr, max_workspace_size));
void* data_ptrs[] = {devPtrX, devPtrY, devPtrW};
int64_t uids[] = {'x', 'y', 'w'};
auto variantPack = cudnn_frontend::VariantPackBuilder()
.setWorkspacePointer(workspace_ptr)
.setDataPointers(3, data_ptrs)
.setUids(3, uids)
.build();
std::cout << "variantPack " << variantPack.describe() << std::endl;
// This is an optional step in this test.
// time_sorted_plan makes this equivalent to using find for autotuning, and this step is not necessary if the intent is to just use the heuristics.
auto options = cudnn_frontend::time_sorted_plan<cudnn_frontend::CudnnFindSamplingTechnique::CUDNN_FIND_SAMPLE_MEDIAN_OF_THREE>(handle_, std::move(plans), variantPack);
std::for_each(options.begin(), options.end(), [](cudnn_frontend::ExecutionPlan& opt) {
std::cout << "Plan tag: " << opt.getTag() << " finished in " << opt.getExecutionTime() << " ms,"
<< " workspace: " << opt.getWorkspaceSize() << " bytes" << std::endl;
});
cudnnStatus_t status = cudnnBackendExecute(handle_, options.front().get_raw_desc(), variantPack.get_raw_desc());
cudnn_frontend::throw_if([status]() { return (status != CUDNN_STATUS_SUCCESS); }, "Plan execute error", status);
} catch (cudnn_frontend::cudnnException &e) {
std::cout << "[ERROR] Exception " << e.what() << std::endl;
CHECK(false);
}
if (handle_) cudnnDestroy(handle_);
return;
}
void
block_using_errata(int64_t* x_dim,
int64_t* padA,
int64_t* convstrideA,
int64_t* dilationA,
int64_t* w_dim,
int64_t* y_dim,
cudnnDataType_t dataType,
cudnnConvolutionMode_t mode,
float* devPtrX,
float* devPtrW,
float* devPtrY) {
cudnnHandle_t handle_;
try {
checkCudnnErr(cudnnCreate(&handle_));
common_conv_descriptors descriptors = create_common_descriptors(
x_dim, padA, convstrideA, dilationA, w_dim, y_dim, dataType, mode);