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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/LogSoftMax.c"
#else
void THNN_(LogSoftMax_updateOutput)(
THNNState *state,
THTensor *input,
THTensor *output)
{
real *input_data, *output_data;
ptrdiff_t nframe = 0, dim = 0, stride = 0;
ptrdiff_t t, d;
if (input->nDimension == 1)
{
nframe = 1;
dim = input->size[0];
stride = 1;
}
else if (input->nDimension == 2)
{
nframe = input->size[0];
dim = input->size[1];
stride = 1;
}
else if (input->nDimension == 3)
{
nframe = 1;
dim = input->size[0];
stride = input->size[1]*input->size[2];
}
else if (input->nDimension == 4)
{
nframe = input->size[0];
dim = input->size[1];
stride = input->size[2]*input->size[3];
}
else
THArgCheck(0, 2, "1D, 2D, 3D or 4D tensor expected");
input = THTensor_(newContiguous)(input);
THTensor_(resizeAs)(output, input);
real *input_data0 = THTensor_(data)(input);
real *output_data0 = THTensor_(data)(output);
accreal logsum;
real maxInput;
#pragma omp parallel for private(t, d, maxInput, logsum, input_data, output_data)
for (t = 0; t < stride*nframe; t++)
{
logsum = 0;
maxInput = -THInf;
input_data = input_data0 + (t/stride)*dim*stride + t % stride;
output_data = output_data0 + (t/stride)*dim*stride + t % stride;
for (d = 0; d < dim; d++)
maxInput = THMax(maxInput, input_data[d*stride]);
for (d = 0; d < dim; d++)
logsum += exp(input_data[d*stride] - maxInput);
logsum = maxInput + log(logsum);
for (d = 0; d < dim; d++)
output_data[d*stride] = input_data[d*stride] - logsum;
}
THTensor_(free)(input);
}
void THNN_(LogSoftMax_updateGradInput)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
THTensor *gradInput,
THTensor *output)
{
THNN_CHECK_SHAPE(input, gradOutput);
real *gradInput_data, *gradOutput_data, *output_data;
ptrdiff_t nframe = 0, dim = 0, stride = 0;
ptrdiff_t t, d;
if (output->nDimension == 1)
{
nframe = 1;
dim = output->size[0];
stride = 1;
}
else if (output->nDimension == 2)
{
nframe = output->size[0];
dim = output->size[1];
stride = 1;
}
else if (output->nDimension == 3)
{
nframe = 1;
dim = output->size[0];
stride = output->size[1]*output->size[2];
}
else if (output->nDimension == 4)
{
nframe = output->size[0];
dim = output->size[1];
stride = output->size[2]*output->size[3];
}
else
THError("1D, 2D, 3D or 4D tensor expected");
output = THTensor_(newContiguous)(output);
gradOutput = THTensor_(newContiguous)(gradOutput);
THTensor_(resizeAs)(gradInput, output);
real *gradInput_data0 = THTensor_(data)(gradInput);
real *output_data0 = THTensor_(data)(output);
real *gradOutput_data0 = THTensor_(data)(gradOutput);
accreal sum;
#pragma omp parallel for private(t, sum, d, gradInput_data, output_data, gradOutput_data)
for (t = 0; t < stride*nframe; t++)
{
sum = 0;
gradInput_data = gradInput_data0 + (t/stride)*dim*stride + t % stride;
output_data = output_data0 + (t/stride)*dim*stride + t % stride;
gradOutput_data = gradOutput_data0 + (t/stride)*dim*stride + t % stride;
for (d = 0; d < dim; d++)
sum += gradOutput_data[d*stride];
for (d = 0; d < dim; d++)
gradInput_data[d*stride] = gradOutput_data[d*stride] - exp(output_data[d*stride])*sum;
}
THTensor_(free)(gradOutput);
THTensor_(free)(output);
}
#endif
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