/********************************************************************************
* Copyright (c) 2023 CEA-List
*
* This program and the accompanying materials are made available under the
* terms of the Eclipse Public License 2.0 which is available at
* http://www.eclipse.org/legal/epl-2.0.
*
* SPDX-License-Identifier: EPL-2.0
*
********************************************************************************/
#include <cassert>
#include <chrono> // std::chrono::milliseconds
#include <numeric> // std::accumulate
#include <thread> // std::this_thread::sleep_for
#include <vector>
#include "aidge/utils/Types.h"
#include "aidge/operator/Conv.hpp"
#include "aidge/backend/cuda/data/TensorImpl.hpp"
#include "aidge/backend/cuda/operator/ConvImpl.hpp"
#include "aidge/backend/cuda/utils/CudaContext.hpp"
template <Aidge::DimIdx_t DIM>
Aidge::NbElts_t Aidge::ConvImpl_cuda<DIM>::getNbRequiredData(const Aidge::IOIndex_t inputIdx) const {
assert(mOp.getInput(inputIdx) && "requires valid input");
// Requires the whole tensors
const auto &inputDims = std::static_pointer_cast<Tensor>(mOp.getInput(inputIdx))->dims();
return std::accumulate(inputDims.begin(), inputDims.end(), Aidge::NbElts_t(1), std::multiplies<NbElts_t>());
}
template <Aidge::DimIdx_t DIM>
Aidge::NbElts_t Aidge::ConvImpl_cuda<DIM>::getNbRequiredProtected(IOIndex_t /*inputIdx*/) const {
// for the direct convolution algorithm, convolutions can be in-place, if
// there is no padding!
return 0;
}
template <Aidge::DimIdx_t DIM>
Aidge::NbElts_t Aidge::ConvImpl_cuda<DIM>::getRequiredMemory(const Aidge::IOIndex_t outputIdx,
const std::vector<Aidge::DimSize_t> &/*inputsSize*/) const {
// Requires the whole tensors, regardless of available data on inputs
assert(outputIdx == 0 && "operator has only one output");
(void) outputIdx;
const auto &outputDims = std::static_pointer_cast<Tensor>(mOp.getOutput(0))->dims();
return std::accumulate(outputDims.begin(), outputDims.end(), NbElts_t(1), std::multiplies<NbElts_t>());
}
template <Aidge::DimIdx_t DIM>
Aidge::NbElts_t Aidge::ConvImpl_cuda<DIM>::getNbConsumedData(Aidge::IOIndex_t inputIdx) const {
assert(static_cast<std::size_t>(inputIdx) < mNbConsumedData.size());
return mNbConsumedData[static_cast<std::size_t>(inputIdx)];
}
template <Aidge::DimIdx_t DIM>
Aidge::NbElts_t Aidge::ConvImpl_cuda<DIM>::getNbProducedData(Aidge::IOIndex_t outputIdx) const {
assert((outputIdx == 0) && (static_cast<std::size_t>(outputIdx) < mNbProducedData.size()));
return mNbProducedData[static_cast<std::size_t>(outputIdx)];
}
template <Aidge::DimIdx_t DIM>
void Aidge::ConvImpl_cuda<DIM>::updateConsummerProducer(){
// Update producer-consumer data
for (std::size_t inputIdx = 0; inputIdx < mNbConsumedData.size(); ++inputIdx)
mNbConsumedData[inputIdx] += getNbRequiredData(static_cast<IOIndex_t>(inputIdx)); // each input is consumed by the minimum
// amount for a forward pass
mNbProducedData[0] += getRequiredMemory(0, {});
}
template <Aidge::DimIdx_t DIM>
void Aidge::ConvImpl_cuda<DIM>::forward() {
// FIXME: uncomment the following code once memory handling will work
assert(mOp.getInput(0) && "missing input #0");
assert(mOp.getInput(1) && "missing input #1");
assert(mOp.getInput(2) && "missing input #2");
const std::vector<int> strides(mOp.template get<ConvParam::StrideDims>().rbegin(), mOp.template get<ConvParam::StrideDims>().rend());
const std::vector<int> paddings(DIM, 0);
const std::vector<int> upscales(mOp.template get<ConvParam::DilationDims>().rbegin(), mOp.template get<ConvParam::DilationDims>().rend());
CHECK_CUDNN_STATUS(
cudnnSetConvolutionNdDescriptor(mConvDesc,
DIM,
&paddings[0],
&strides[0],
&upscales[0],
CUDNN_CROSS_CORRELATION,
DataTypeToCudnn(mOp.getInput(2)->dataType())));
const std::vector<int> cudaKernelDims(mOp.getInput(1)->dims().rbegin(),
mOp.getInput(1)->dims().rend());
CHECK_CUDNN_STATUS(cudnnCreateFilterDescriptor(&mFilterDesc));
CHECK_CUDNN_STATUS(cudnnSetFilterNdDescriptor(mFilterDesc,
DataTypeToCudnn(mOp.getInput(1)->dataType()),
CUDNN_TENSOR_NCHW,
cudaKernelDims.size(),
&cudaKernelDims[0]));
int maxAlgoIterations = 0;
cudnnGetConvolutionForwardAlgorithmMaxCount(CudaContext::cudnnHandle(),
&maxAlgoIterations);
assert(maxAlgoIterations > 0 && "No available CUDNN ConvolutionForwardAlgorithm");
int returnAlgoCounts = 0;
std::vector<cudnnConvolutionFwdAlgoPerf_t> returnFwdAlgo(maxAlgoIterations);
/**************************************************************************************************************
https://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnFindConvolutionForwardAlgorithm
This function attempts all cuDNN algorithms (including CUDNN_TENSOR_OP_MATH and CUDNN_DEFAULT_MATH
versions of algorithms where CUDNN_TENSOR_OP_MATH may be available) for cudnnConvolutionForward(),
using memory allocated via cudaMalloc(), and outputs performance metrics to a user-allocated array
of cudnnConvolutionFwdAlgoPerf_t. These metrics are written in sorted fashion where the first element
has the lowest compute time. The total number of resulting algorithms can be queried through
the API cudnnGetConvolutionForwardMaxCount().
***************************************************************************************************************/
CHECK_CUDNN_STATUS(cudnnFindConvolutionForwardAlgorithm(
CudaContext::cudnnHandle(),
static_cast<TensorImpl_cuda<float>*>(mOp.getInput(0)->getImpl().get())->getCudnnTensorDesc(), // FIXME: PLAIN WRONG
mFilterDesc,
mConvDesc,
static_cast<TensorImpl_cuda<float>*>(mOp.getOutput(0)->getImpl().get())->getCudnnTensorDesc(), // FIXME: PLAIN WRONG
maxAlgoIterations,
&returnAlgoCounts,
&returnFwdAlgo[0]));
// std::cout << "Layer " << mName << "(" << k << ")"
// << " cuDNN forward algorithm heuristic results: " << std::endl;
for(int fwdAlgo = 0; fwdAlgo < maxAlgoIterations; ++fwdAlgo)
{
std::string algoName
= (returnFwdAlgo[fwdAlgo].algo
== CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM)
? "CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM"
: (returnFwdAlgo[fwdAlgo].algo
== CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM)
? "CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM"
: (returnFwdAlgo[fwdAlgo].algo
== CUDNN_CONVOLUTION_FWD_ALGO_GEMM)
? "CUDNN_CONVOLUTION_FWD_ALGO_GEMM"
: (returnFwdAlgo[fwdAlgo].algo
== CUDNN_CONVOLUTION_FWD_ALGO_DIRECT)
? "CUDNN_CONVOLUTION_FWD_ALGO_DIRECT"
: (returnFwdAlgo[fwdAlgo].algo
== CUDNN_CONVOLUTION_FWD_ALGO_FFT)
? "CUDNN_CONVOLUTION_FWD_ALGO_FFT"
: (returnFwdAlgo[fwdAlgo].algo
== CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING)
? "CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING"
: (returnFwdAlgo[fwdAlgo].algo
== CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD)
? "CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD"
: (returnFwdAlgo[fwdAlgo].algo
== CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED)
? "CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED"
: (returnFwdAlgo[fwdAlgo].algo
== CUDNN_CONVOLUTION_FWD_ALGO_COUNT)
? "CUDNN_CONVOLUTION_FWD_ALGO_COUNT"
: "Undetermined Algorithm";
// std::cout << "----> Forward convolution algorithm: " << algoName
// << " [" << returnFwdAlgo[fwdAlgo].time << " ms][" << returnFwdAlgo[fwdAlgo].memory / 1.0e6 << " MB]"
// << std::endl;
}
mFwdAlgo = returnFwdAlgo[0].algo;
}
template <Aidge::DimIdx_t DIM>
Aidge::ConvImpl_cuda<DIM>::~ConvImpl_cuda() {
}
template <Aidge::DimIdx_t DIM>
void Aidge::ConvImpl_cuda<DIM>::backward() { printf("Not implemented yet.\n"); }
// Template declarations
void ConvImpl_cuda_template_declaration ()
{
Aidge::ConvImpl_cuda<2> ConvImpl_cuda2(Aidge::Conv_Op<2>());
}