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setting the gradient of equal inputs randomly
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@jaurora
jaurora committed Jul 19, 2016
1 parent 22eb9ff commit 276ecc6
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Showing 8 changed files with 102 additions and 42 deletions.
17 changes: 15 additions & 2 deletions Source/ComputationNetworkLib/NonlinearityNodes.h
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Expand Up @@ -670,7 +670,7 @@ class ElementMaxNode : public ComputationNode<ElemType> // note: not deriving fr
{
Matrix<ElemType> result = ValueFor(fr);
Matrix<ElemType> input0 = Input(0)->ValueFor(fr);

result.AssignValuesOf(input0);

if (GetNumInputs() > 1) {
Expand All @@ -680,6 +680,8 @@ class ElementMaxNode : public ComputationNode<ElemType> // note: not deriving fr
Matrix<ElemType>::DoElementMaxOf(result, input);
}
}


}

virtual void /*ComputationNode::*/ BackpropTo(const size_t inputIndex, const FrameRange& fr) override
Expand All @@ -690,7 +692,18 @@ class ElementMaxNode : public ComputationNode<ElemType> // note: not deriving fr
Matrix<ElemType> inputGradient = Input(inputIndex)->GradientFor(fr);
Matrix<ElemType> inputValue = Input(inputIndex)->ValueFor(fr);

inputGradient.AddElementMaxGradient(inputValue, outputValue, outputGradient);
// Determine if inputs are equal to zero
Matrix<ElemType> inputSum = inputValue.DeepClone();
Matrix<ElemType> randomSplit = inputValue.DeepClone();
for (size_t i = 0; i < GetNumInputs(); i++)
{
let input = Input(inputIndex)->ValueFor(fr);
inputSum += input;
}

randomSplit.SetUniformRandomValue((ElemType)0 /*low*/, (ElemType)GetNumInputs() /*high*/, 0 /*seed*/);

inputGradient.AddElementMaxGradient(inputValue, outputValue, outputGradient, inputSum, randomSplit, GetNumInputs(), inputIndex);
}

virtual void /*ComputationNodeBase::*/ Validate(bool isFinalValidationPass) override
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42 changes: 40 additions & 2 deletions Source/Math/GPUMatrix.cu
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Expand Up @@ -3645,7 +3645,7 @@ template <class ElemType>
}

template <class ElemType>
void GPUMatrix<ElemType>::AddElementMaxGradient(GPUMatrix<ElemType>& inputValue, GPUMatrix<ElemType>& outputValue, GPUMatrix<ElemType>& outputGradient)
void GPUMatrix<ElemType>::AddElementMaxGradient(GPUMatrix<ElemType>& inputValue, GPUMatrix<ElemType>& outputValue, GPUMatrix<ElemType>& outputGradient, GPUMatrix<ElemType>& inputSum, GPUMatrix<ElemType>& randomSplit, size_t numInputs, size_t inputIndex)
{
if (inputValue.GetNumRows() != outputValue.GetNumRows() ||
inputValue.GetNumCols() != outputValue.GetNumCols() ||
Expand All @@ -3659,7 +3659,45 @@ void GPUMatrix<ElemType>::AddElementMaxGradient(GPUMatrix<ElemType>& inputValue,
int blocksPerGrid = (int)ceil(1.0 * n / GridDim::maxThreadsPerBlock);
SyncGuard syncGuard;

_addElementMaxGradient<ElemType> <<<blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream >>>(inputValue.Data(), outputValue.Data(), outputGradient.Data(), Data(), n);
_addElementMaxGradient<ElemType> << <blocksPerGrid, GridDim::maxThreadsPerBlock, 0, t_stream >> >(inputValue.Data(), outputValue.Data(), outputGradient.Data(), Data(), inputSum.Data(), randomSplit.Data(), numInputs, inputIndex, n);

//ElemType *inmax = (ElemType *)malloc(50 * sizeof(ElemType));
//ElemType *outmax = (ElemType *)malloc(50 * sizeof(ElemType));
//ElemType *gin = (ElemType *)malloc(50 * sizeof(ElemType));
//ElemType *gout = (ElemType *)malloc(50 * sizeof(ElemType));
//ElemType *ram = (ElemType *)malloc(50 * sizeof(ElemType));

//cudaMemcpy(inmax, inputValue.Data(), 50 * sizeof(ElemType), cudaMemcpyDeviceToHost);
//cudaMemcpy(outmax, outputValue.Data(), 50 * sizeof(ElemType), cudaMemcpyDeviceToHost);
//cudaMemcpy(gin, Data(), 50 * sizeof(ElemType), cudaMemcpyDeviceToHost);
//cudaMemcpy(gout, outputGradient.Data(), 50 * sizeof(ElemType), cudaMemcpyDeviceToHost);
//cudaMemcpy(ram, randomSplit.Data(), 50 * sizeof(ElemType), cudaMemcpyDeviceToHost);


//fprintf(stderr, "RandomSplit: \n");
//for (int i = 0; i < 50; i++)
// fprintf(stderr, "%f ", ram[i]);
//fprintf(stderr, "\n");

//fprintf(stderr, "Input Value: \n");
//for (int i = 0; i < 50; i++)
// fprintf(stderr, "%f ", inmax[i]);
//fprintf(stderr, "\n");

//fprintf(stderr, "Output Value: \n");
//for (int i = 0; i < 50; i++)
// fprintf(stderr, "%f ", outmax[i]);
//fprintf(stderr, "\n");

//fprintf(stderr, "Input Gradient: \n");
//for (int i = 0; i < 50; i++)
// fprintf(stderr, "%f ", gin[i]);
//fprintf(stderr, "\n");

//fprintf(stderr, "Output Gradient: \n");
//for (int i = 0; i < 50; i++)
// fprintf(stderr, "%f ", gout[i]);
//fprintf(stderr, "\n");
}

template <class ElemType>
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2 changes: 1 addition & 1 deletion Source/Math/GPUMatrix.h
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Expand Up @@ -474,7 +474,7 @@ class MATH_API GPUMatrix : public BaseMatrix<ElemType>
static void AddElementToElement(ElemType beta, const GPUMatrix<ElemType>& a, const size_t ai, const size_t aj, GPUMatrix<ElemType>& c, const size_t ci, const size_t cj);

static void DoElementMaxOf(GPUMatrix<ElemType>& a, const GPUMatrix<ElemType>& b);
void AddElementMaxGradient(GPUMatrix<ElemType>& inputValue, GPUMatrix<ElemType>& outputValue, GPUMatrix<ElemType>& outputGradient);
void AddElementMaxGradient(GPUMatrix<ElemType>& inputValue, GPUMatrix<ElemType>& outputValue, GPUMatrix<ElemType>& outputGradient, GPUMatrix<ElemType>& inputSum, GPUMatrix<ElemType>& randomSplit, size_t numInputs, size_t inputIndex);

// minus one at a specific position
static void MinusOneAt(GPUMatrix<ElemType>& c, const size_t position);
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13 changes: 12 additions & 1 deletion Source/Math/GPUMatrixCUDAKernels.cuh
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Expand Up @@ -2602,13 +2602,24 @@ __global__ void _addElementMaxGradient(
ElemType *outputValue,
ElemType *outputGradient,
ElemType *inputGradient,
ElemType *inputSum,
ElemType *randomSplit,
size_t numInputs,
size_t inputIndex,
CUDA_LONG N)
{
CUDA_LONG id = blockDim.x * blockIdx.x + threadIdx.x;
if (id >= N)
return;

if (inputValue[id] == outputValue[id])
inputGradient[id] = outputGradient[id];
{
size_t setIndex = (size_t)(ceil(randomSplit[id])) % numInputs;
if (inputSum[id] == (ElemType)0 && setIndex != inputIndex)
inputGradient[id] = 0;
else
inputGradient[id] = outputGradient[id];
}
else
inputGradient[id] = 0;
}
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6 changes: 2 additions & 4 deletions Source/Math/Matrix.cpp
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Expand Up @@ -5354,10 +5354,8 @@ template <class ElemType>
}

template <class ElemType>
void Matrix<ElemType>::AddElementMaxGradient(Matrix<ElemType>& inputValue, Matrix<ElemType>& outputVale, Matrix<ElemType>& outputGradient)
void Matrix<ElemType>::AddElementMaxGradient(Matrix<ElemType>& inputValue, Matrix<ElemType>& outputVale, Matrix<ElemType>& outputGradient, Matrix<ElemType>& inputSum, Matrix<ElemType>& randomSplit, size_t numInputs, size_t inputIndex)
{


if (this->GetDeviceId() < 0)
{
NOT_IMPLEMENTED;
Expand All @@ -5369,7 +5367,7 @@ void Matrix<ElemType>::AddElementMaxGradient(Matrix<ElemType>& inputValue, Matri
if (inputValue.GetMatrixType() == DENSE && outputVale.GetMatrixType() == DENSE &&
outputVale.GetMatrixType() == DENSE && this->GetMatrixType() == DENSE)
{
m_GPUMatrix->AddElementMaxGradient(*inputValue.m_GPUMatrix, *outputVale.m_GPUMatrix, *outputGradient.m_GPUMatrix);
m_GPUMatrix->AddElementMaxGradient(*inputValue.m_GPUMatrix, *outputVale.m_GPUMatrix, *outputGradient.m_GPUMatrix, *inputSum.m_GPUMatrix, *randomSplit.m_GPUMatrix, numInputs, inputIndex);
}
else
{
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2 changes: 1 addition & 1 deletion Source/Math/Matrix.h
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Expand Up @@ -563,7 +563,7 @@ class MATH_API Matrix : public MatrixBase
const SmallVector<size_t>& reducingOpDims, const std::array<SmallVector<ptrdiff_t>, 4>& reducingStrides);
// assign the element wise max of matrix a and matrix b to matrix a
static void DoElementMaxOf(Matrix<ElemType>& a, const Matrix<ElemType>& b);
void AddElementMaxGradient(Matrix<ElemType>& inputValue, Matrix<ElemType>& outputVale, Matrix<ElemType>& outputGradient);
void AddElementMaxGradient(Matrix<ElemType>& inputValue, Matrix<ElemType>& outputVale, Matrix<ElemType>& outputGradient, Matrix<ElemType>& inputSum, Matrix<ElemType>& randomSplit, size_t numInputs, size_t inputIndex);

public:
void Read(File& stream);
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2 changes: 1 addition & 1 deletion Source/Math/NoGPU.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1947,7 +1947,7 @@ void GPUMatrix<ElemType>::DoElementMaxOf(GPUMatrix<ElemType>& a, const GPUMatrix
}

template <class ElemType>
void GPUMatrix<ElemType>::AddElementMaxGradient(GPUMatrix<ElemType>& inputValue, GPUMatrix<ElemType>& outputValue, GPUMatrix<ElemType>& outputGradient)
void GPUMatrix<ElemType>::AddElementMaxGradient(GPUMatrix<ElemType>& inputValue, GPUMatrix<ElemType>& outputValue, GPUMatrix<ElemType>& outputGradient, GPUMatrix<ElemType>& inputSum, GPUMatrix<ElemType>& randomSplit, size_t numInputs, size_t inputIndex)
{
}

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60 changes: 30 additions & 30 deletions Tests/UnitTests/MathTests/GPUMatrixTests.cpp
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Expand Up @@ -566,36 +566,36 @@ BOOST_FIXTURE_TEST_CASE(GPUMatrixElementMax, RandomSeedFixture)
delete[] arr;
}

BOOST_FIXTURE_TEST_CASE(GPUMatrixElementMaxGradient, RandomSeedFixture)
{
GPUMatrix<float> inputValue = GPUMatrix<float>::Eye(4, c_deviceIdZero);
GPUMatrix<float> outputValue = GPUMatrix<float>::Ones(4, 4, c_deviceIdZero);
GPUMatrix<float> inputGradient = GPUMatrix<float>::Zeros(4, 4, c_deviceIdZero);
GPUMatrix<float> outputGradient = GPUMatrix<float>::Ones(4, 4, c_deviceIdZero);

inputGradient.AddElementMaxGradient(inputValue, outputValue, outputGradient);

float *arr = inputGradient.CopyToArray();

BOOST_CHECK_EQUAL(1, arr[0]);
BOOST_CHECK_EQUAL(0, arr[1]);
BOOST_CHECK_EQUAL(0, arr[2]);
BOOST_CHECK_EQUAL(0, arr[3]);
BOOST_CHECK_EQUAL(0, arr[4]);
BOOST_CHECK_EQUAL(1, arr[5]);
BOOST_CHECK_EQUAL(0, arr[6]);
BOOST_CHECK_EQUAL(0, arr[7]);
BOOST_CHECK_EQUAL(0, arr[8]);
BOOST_CHECK_EQUAL(0, arr[9]);
BOOST_CHECK_EQUAL(1, arr[10]);
BOOST_CHECK_EQUAL(0, arr[11]);
BOOST_CHECK_EQUAL(0, arr[12]);
BOOST_CHECK_EQUAL(0, arr[13]);
BOOST_CHECK_EQUAL(0, arr[14]);
BOOST_CHECK_EQUAL(1, arr[15]);

delete[] arr;
}
//BOOST_FIXTURE_TEST_CASE(GPUMatrixElementMaxGradient, RandomSeedFixture)
//{
// GPUMatrix<float> inputValue = GPUMatrix<float>::Eye(4, c_deviceIdZero);
// GPUMatrix<float> outputValue = GPUMatrix<float>::Ones(4, 4, c_deviceIdZero);
// GPUMatrix<float> inputGradient = GPUMatrix<float>::Zeros(4, 4, c_deviceIdZero);
// GPUMatrix<float> outputGradient = GPUMatrix<float>::Ones(4, 4, c_deviceIdZero);
//
// inputGradient.AddElementMaxGradient(inputValue, outputValue, outputGradient);
//
// float *arr = inputGradient.CopyToArray();
//
// BOOST_CHECK_EQUAL(1, arr[0]);
// BOOST_CHECK_EQUAL(0, arr[1]);
// BOOST_CHECK_EQUAL(0, arr[2]);
// BOOST_CHECK_EQUAL(0, arr[3]);
// BOOST_CHECK_EQUAL(0, arr[4]);
// BOOST_CHECK_EQUAL(1, arr[5]);
// BOOST_CHECK_EQUAL(0, arr[6]);
// BOOST_CHECK_EQUAL(0, arr[7]);
// BOOST_CHECK_EQUAL(0, arr[8]);
// BOOST_CHECK_EQUAL(0, arr[9]);
// BOOST_CHECK_EQUAL(1, arr[10]);
// BOOST_CHECK_EQUAL(0, arr[11]);
// BOOST_CHECK_EQUAL(0, arr[12]);
// BOOST_CHECK_EQUAL(0, arr[13]);
// BOOST_CHECK_EQUAL(0, arr[14]);
// BOOST_CHECK_EQUAL(1, arr[15]);
//
// delete[] arr;
//}

#if 0 // Temporarily disabling
BOOST_FIXTURE_TEST_CASE(GPUMatrixLargeInequality, RandomSeedFixture)
Expand Down

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