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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/LookupTable.c"
#else
static void THNN_(LookupTable_resetCount)(
THInteger_t *count_data,
THIndexTensor *input)
{
ptrdiff_t i;
THIndex_t *input_data = THIndexTensor_(data)(input);
ptrdiff_t numel = THIndexTensor_(nElement)(input);
for (i = 0; i<numel; i++)
{
long k = input_data[i] - TH_INDEX_BASE;
count_data[k] = 0;
}
for (i = 0; i<numel; i++)
{
long k = input_data[i] - TH_INDEX_BASE;
count_data[k]++;
}
}
void THNN_(LookupTable_accGradParameters)(
THNNState *state,
THIndexTensor *input,
THTensor *gradOutput,
THTensor *gradWeight,
THIntegerTensor *count,
THTensor *sorted,
THIndexTensor *indices,
bool scaleGradByFreq,
int paddingValue,
accreal ascale)
{
real scale = TH_CONVERT_ACCREAL_TO_REAL(ascale);
ptrdiff_t i;
THInteger_t *count_data = NULL;
if (scaleGradByFreq)
{
THIntegerTensor_(resize1d)(count, gradWeight->size[0]);
count_data = THIntegerTensor_(data)(count);
}
if (!THTensor_(isContiguous)(gradWeight))
THError("gradWeight must be contiguous");
if (!THIndexTensor_(isContiguous)(input))
THError("input must be contiguous");
if (THIndexTensor_(nDimension)(input) != 1 && THIndexTensor_(nDimension)(input) != 2) {
THDescBuff s1 = THIndexTensor_(sizeDesc)(input);
THError("input must be a vector or matrix, but is of shape: %s", s1.str);
}
THIndex_t *input_data = THIndexTensor_(data)(input);
ptrdiff_t numel = THIndexTensor_(nElement)(input);
long numw = THTensor_(size)(gradWeight, 0);
// check that inputs are all within range
for (i=0; i<numel; i++)
if (input_data[i] < TH_INDEX_BASE || input_data[i] >= numw + TH_INDEX_BASE) {
THError("inputs need to be in the range %ld <= input < %ld, "
"but got input of value: %ld", TH_INDEX_BASE, (numw + TH_INDEX_BASE),
input_data[i]);
}
gradOutput = THTensor_(newContiguous)(gradOutput);
real *gw = THTensor_(data)(gradWeight);
real *go = THTensor_(data)(gradOutput);
long stride = THTensor_(stride)(gradWeight, 0);
if (count_data)
THNN_(LookupTable_resetCount)(count_data, input);
#ifdef _OPENMP
if (numel > 1000)
{
// The strategy is to parallelize over sections of the vocabulary, so that
// thread 1 handles updates to gradWeight[0..nVocab/nThreads]. Every thread
// has to traverse the entire input, but the dominating factor is the axpy
// BLAS call.
#pragma omp parallel private(i)
{
int tid = omp_get_thread_num();
int nthreads = omp_get_num_threads();
long start = tid * (numw/nthreads + 1);
long end = start + (numw/nthreads + 1);
for (i=0; i<numel; i++)
{
if (input_data[i] != paddingValue)
{
long k = input_data[i] - TH_INDEX_BASE;
if (k >= start && k < end)
{
real scale_ = scale;
if (count_data) scale_ /= count_data[k];
THBlas_(axpy)(stride, scale_, go + i*stride, 1, gw + k*stride, 1);
}
}
}
}
THTensor_(free)(gradOutput);
return;
}
#endif
for (i=0; i<numel; i++)
{
if (input_data[i] != paddingValue)
{
long k = input_data[i] - TH_INDEX_BASE;
real scale_ = scale;
if (count_data) scale_ /= count_data[k];
THBlas_(axpy)(stride, scale_, go + i*stride, 1, gw + k*stride, 1);
}
}
THTensor_(free)(gradOutput);
}
/*
* Keep the norm of weight smaller than maxNorm
*/
static void THNN_(LookupTable_renormRow)(
real *row_data,
long stride,
real maxNorm,
real normType)
{
real norm = 0;
real new_norm;
long j;
for (j=0; j<stride; j++)
{
if (normType == 1) {
norm += fabs(row_data[j]);
} else if (normType == 2) {
norm += row_data[j] * row_data[j];
} else {
norm += pow(fabs(row_data[j]), normType);
}
}
norm = pow(norm, 1.0 / normType);
if (norm > maxNorm)
{
new_norm = maxNorm / (norm + 1e-7);
for (j=0; j<stride; j++) {
row_data[j] *= new_norm;
}
}
}
static int THNN_(compare_THIndex)(const void* a, const void* b)
{
return *(const THIndex_t*)a < *(const THIndex_t*)b ? -1 : 1;
}
void THNN_(LookupTable_renorm)(
THNNState *state,
THIndexTensor *idx,
THTensor *weight,
accreal maxNorm_,
accreal normType_)
{
real maxNorm = TH_CONVERT_ACCREAL_TO_REAL(maxNorm_);
real normType = TH_CONVERT_ACCREAL_TO_REAL(normType_);
if (!THTensor_(isContiguous)(weight))
THError("weight must be contiguous");
if (!THIndexTensor_(isContiguous)(idx))
THError("input must be contiguous");
if (THIndexTensor_(nDimension)(idx) != 1)
THError("idx must be a vector");
if (normType <= 0)
THError("non-positive-norm not supported");
ptrdiff_t i;
THIndex_t *row_idx = THIndexTensor_(data)(idx);
ptrdiff_t numel = THIndexTensor_(nElement)(idx);
long numw = THTensor_(size)(weight, 0);
long stride = THTensor_(stride)(weight, 0);
real *gw = THTensor_(data)(weight);
for (i=0; i<numel; i++) {
if (row_idx[i] < TH_INDEX_BASE || row_idx[i] >= numw + TH_INDEX_BASE) {
THError("input need to be in the range %ld <= input < %ld, "
"but got input of value: %ld", TH_INDEX_BASE, (numw + TH_INDEX_BASE),
row_idx[i]);
}
}
// get unique indices
qsort(row_idx, numel, sizeof(THIndex_t), THNN_(compare_THIndex));
ptrdiff_t ptr = 0;
for (i=0; i<numel; i++)
if (i == 0 || row_idx[i] != row_idx[i-1])
row_idx[ptr++] = row_idx[i];
numel = ptr;
#ifdef _OPENMP
if (numel > 1000)
{
// The strategy is to parallelize over the rows that appear in
// row_idx, so that thread 1 handles the rows in row_idx[0..numel/nThreads].
// This distributes the work evenly to each thread.
#pragma omp parallel for private(i)
for (i=0; i<numel; i++)
{
long k = row_idx[i] - TH_INDEX_BASE;
THNN_(LookupTable_renormRow)(gw + k*stride, stride, maxNorm, normType);
}
return;
}
#endif
for (i=0; i<numel; i++)
{
long k = row_idx[i] - TH_INDEX_BASE;
THNN_(LookupTable_renormRow)(gw + k*stride, stride, maxNorm, normType);
}
}
#endif
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