1 files changed, 137 insertions, 0 deletions
diff --git a/contrib/lua-torch/nn/lib/THNN/generic/LogSoftMax.c b/contrib/lua-torch/nn/lib/THNN/generic/LogSoftMax.c
new file mode 100644
index 000000000..a7280422b
--- /dev/null
+++ b/contrib/lua-torch/nn/lib/THNN/generic/LogSoftMax.c
@@ -0,0 +1,137 @@
+#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