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Commit 6180818c authored by Houssem ROUIS's avatar Houssem ROUIS
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fix BatchNorm for cases where second inputs are 1d tensors

parent 0f6fc784
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2 merge requests!32version 0.2.1,!14MobileNet operators
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src/operator/BatchNormImpl.cpp
+ 64
− 32
View file @ 6180818c
......@@ -10,19 +10,15 @@
********************************************************************************/
#include <cassert>
#include <chrono> // std::chrono::milliseconds
#include <numeric> // std::accumulate
#include <thread> // std::this_thread::sleep_for
#include <vector>
#include <iostream>
#include "aidge/utils/Types.h"
#include "aidge/operator/BatchNorm.hpp"
#include <cuda_runtime.h>
#include <cudnn.h>
#include <vector>
#include "aidge/backend/cuda/data/TensorImpl.hpp"
#include "aidge/backend/cuda/operator/BatchNormImpl.hpp"
#include "aidge/backend/cuda/utils/CudaContext.hpp"
#include "aidge/operator/BatchNorm.hpp"
#include "aidge/utils/Types.h"
template <Aidge::DimIdx_t DIM>
void Aidge::BatchNormImpl_cuda<DIM>::forward() {
......@@ -53,7 +49,6 @@ void Aidge::BatchNormImpl_cuda<DIM>::forward() {
}
CHECK_CUDNN_STATUS(cudnnCreateTensorDescriptor(&mBNDesc));
// auto tst = dynamic_cast<TensorImpl_cuda_*>(input0.getImpl().get())->getCudnnTensorDesc();
CHECK_CUDNN_STATUS(cudnnDeriveBNTensorDescriptor(
mBNDesc, std::dynamic_pointer_cast<TensorImpl_cuda_>(input0.getImpl())->getCudnnTensorDesc(input0), mMode));
......@@ -73,11 +68,18 @@ void Aidge::BatchNormImpl_cuda<DIM>::forward() {
strides.resize(nbDims);
}
if (std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType() == DataType::Float64) {
forward_<double>(input0, input1, input2, input3, input4);
}
else {
forward_<float>(input0, input1, input2, input3, input4);
switch(std::static_pointer_cast<Tensor>(mOp.getRawOutput(0))->dataType()) {
case DataType::Float64:
forward_<double>(input0, input1, input2, input3, input4);
break;
case DataType::Float32:
forward_<float>(input0, input1, input2, input3, input4);
break;
case DataType::Float16:
forward_<half>(input0, input1, input2, input3, input4);
break;
default:
AIDGE_THROW_OR_ABORT(std::runtime_error, "Data type is not supported by Backend Cuda");
}
}
......@@ -88,24 +90,55 @@ void Aidge::BatchNormImpl_cuda<DIM>::forward_(const Tensor& input0, const Tensor
const typename Cuda::cudnn_scaling_type<T>::type alpha = 1.0f;
const typename Cuda::cudnn_scaling_type<T>::type beta = 0.0f;
CHECK_CUDNN_STATUS(
cudnnBatchNormalizationForwardInference(
CudaContext::cudnnHandle(),
mMode,
&alpha,
&beta,
std::dynamic_pointer_cast<TensorImpl_cuda_>(input0.getImpl())->getCudnnTensorDesc(input0),
input0.getImpl()->rawPtr(),
std::dynamic_pointer_cast<TensorImpl_cuda_>(op.getOutput(0)->getImpl())->getCudnnTensorDesc(*op.getOutput(0)),
std::static_pointer_cast<Tensor>(op.getRawOutput(0))->getImpl()->rawPtr(),
std::dynamic_pointer_cast<TensorImpl_cuda_>(input1.getImpl())->getCudnnTensorDesc(input1),//scaleBiasMeanVarDesc,
input1.getImpl()->rawPtr(),
input2.getImpl()->rawPtr(),
input3.getImpl()->rawPtr(),
input4.getImpl()->rawPtr(),
mEpsilon)
);
// For scale, bias, var and mean, the input if we have a 1D tensor, the dim should not go on the channels
if (input1.nbDims() == 1)
{
cudnnTensorDescriptor_t tensorDesc;
CHECK_CUDNN_STATUS(cudnnCreateTensorDescriptor(&tensorDesc));
const std::vector<int> dims = {1, static_cast<int>(input1.size()),1, 1};
const std::vector<int> strides = {static_cast<int>(input1.size()), 1, 1, 1};
CHECK_CUDNN_STATUS(cudnnSetTensorNdDescriptor(tensorDesc, CudaContext::data_type<T>::value, dims.size(), dims.data(), strides.data()));
CHECK_CUDNN_STATUS(
cudnnBatchNormalizationForwardInference(
CudaContext::cudnnHandle(),
mMode,
&alpha,
&beta,
std::dynamic_pointer_cast<TensorImpl_cuda_>(input0.getImpl())->getCudnnTensorDesc(input0),
input0.getImpl()->rawPtr(),
std::dynamic_pointer_cast<TensorImpl_cuda_>(op.getOutput(0)->getImpl())->getCudnnTensorDesc(*op.getOutput(0)),
std::static_pointer_cast<Tensor>(op.getRawOutput(0))->getImpl()->rawPtr(),
tensorDesc,
input1.getImpl()->rawPtr(),
input2.getImpl()->rawPtr(),
input3.getImpl()->rawPtr(),
input4.getImpl()->rawPtr(),
mEpsilon)
);
CHECK_CUDNN_STATUS(cudnnDestroyTensorDescriptor(tensorDesc));
}
else
{
CHECK_CUDNN_STATUS(
cudnnBatchNormalizationForwardInference(
CudaContext::cudnnHandle(),
mMode,
&alpha,
&beta,
std::dynamic_pointer_cast<TensorImpl_cuda_>(input0.getImpl())->getCudnnTensorDesc(input0),
input0.getImpl()->rawPtr(),
std::dynamic_pointer_cast<TensorImpl_cuda_>(op.getOutput(0)->getImpl())->getCudnnTensorDesc(*op.getOutput(0)),
std::static_pointer_cast<Tensor>(op.getRawOutput(0))->getImpl()->rawPtr(),
std::dynamic_pointer_cast<TensorImpl_cuda_>(input1.getImpl())->getCudnnTensorDesc(input1),
input1.getImpl()->rawPtr(),
input2.getImpl()->rawPtr(),
input3.getImpl()->rawPtr(),
input4.getImpl()->rawPtr(),
mEpsilon)
);
}
}
template <Aidge::DimIdx_t DIM>
Aidge::BatchNormImpl_cuda<DIM>::~BatchNormImpl_cuda() {
if(mBNDesc != nullptr)
......@@ -114,6 +147,5 @@ Aidge::BatchNormImpl_cuda<DIM>::~BatchNormImpl_cuda() {
}
}
// Template declarations
template class Aidge::BatchNormImpl_cuda<2>;
unit_tests/Test_BatchNormImpl.cpp
+ 4
− 6
View file @ 6180818c
......@@ -15,7 +15,6 @@
#include "Test_cuda.hpp"
#include <iostream>
#include "aidge/data/Tensor.hpp"
#include "aidge/backend/cpu.hpp"
......@@ -28,10 +27,10 @@ TEST_CASE("[gpu/operator] BatchNorm(forward)") {
auto op = std::static_pointer_cast<OperatorTensor>(myBatchNorm -> getOperator());
op->setDataType(DataType::Float32);
op->setBackend("cuda");
std::shared_ptr<Tensor> myWeights= std::make_shared<Tensor>(Array2D<float,1,3> {{{0.9159252643585205, 0.18772238492965698, 0.4479946792125702}}});
std::shared_ptr<Tensor> myBias = std::make_shared<Tensor>(Array2D<float,1,3> {{{0.33898890018463135, 0.3167555630207062, 0.7047033309936523}}});
std::shared_ptr<Tensor> myMean = std::make_shared<Tensor>(Array2D<float,1,3> {{{0.45547693967819214, 0.22650663554668427, 0.6612948179244995}}});
std::shared_ptr<Tensor> myVar = std::make_shared<Tensor>(Array2D<float,1,3> {{{0.02570258639752865, 0.026536229997873306, 0.15111008286476135}}});
std::shared_ptr<Tensor> myWeights= std::make_shared<Tensor>(Array1D<float,3> {{0.9159252643585205, 0.18772238492965698, 0.4479946792125702}});
std::shared_ptr<Tensor> myBias = std::make_shared<Tensor>(Array1D<float,3> {{0.33898890018463135, 0.3167555630207062, 0.7047033309936523}});
std::shared_ptr<Tensor> myMean = std::make_shared<Tensor>(Array1D<float,3> {{0.45547693967819214, 0.22650663554668427, 0.6612948179244995}});
std::shared_ptr<Tensor> myVar = std::make_shared<Tensor>(Array1D<float,3> {{0.02570258639752865, 0.026536229997873306, 0.15111008286476135}});
std::shared_ptr<Tensor> myInput = std::make_shared<Tensor>(Array4D<float,2,3,3,3> { //NCHW
{
{
......@@ -113,7 +112,6 @@ TEST_CASE("[gpu/operator] BatchNorm(forward)") {
for(int i = 0; i < myOutput->size(); i++){
const float targetOutput = *(static_cast<float*>(myOutput->getImpl()->rawPtr()) + i);
std::cout << "Computed : " << computedOutput[i] << " , target: " << targetOutput << std::endl;
REQUIRE(fabs(computedOutput[i] - targetOutput) < 1e-5);
}
......
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