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fused_8bit_rowwise_embedding_lookup.cc
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fused_8bit_rowwise_embedding_lookup.cc
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#include "caffe2/perfkernels/fused_8bit_rowwise_embedding_lookup.h"
#include "caffe2/core/types.h"
#include "caffe2/perfkernels/common.h"
#include "caffe2/utils/cpuid.h"
namespace caffe2 {
/**
* Base implementation does runtime dispatch for each segment of reduction
* @return false if there is an out-of-bound error
*/
template <
typename IndexType,
typename InType,
typename OutType,
bool IS_WEIGHT_POSITIONAL = false>
static bool Fused8BitRowwiseEmbeddingLookupGenericSlow(
const int64_t block_size,
const int64_t output_size,
const int64_t index_size,
const int64_t data_size,
const InType* input,
const IndexType* indices,
const int* lengths,
const float* weights, // optional, can be null for sum reducer
bool normalize_by_lengths,
OutType* out) {
// block_size is the number of elements and fused_block_size is the size of
// an entire row, including scale and bias.
const auto scale_bias_offset = 8 / sizeof(InType);
const int64_t fused_block_size = block_size + scale_bias_offset;
int64_t current = 0;
for (int m = 0; m < output_size; ++m) {
memset(out, 0, sizeof(OutType) * block_size);
if (current + lengths[m] > index_size) {
return false;
}
for (int i = 0; i < lengths[m]; ++i) {
int64_t idx = indices[current];
if (idx < 0 || idx >= data_size) {
return false;
}
#ifdef __GNUC__
if (current + 1 < index_size) {
__builtin_prefetch(
input + fused_block_size * indices[current + 1], 0, 1);
}
#endif // __GNUC__
const float* scale_bias = reinterpret_cast<const float*>(
input + fused_block_size * indices[current] + block_size);
float weight = 1.0f;
if (weights) {
weight = weights[IS_WEIGHT_POSITIONAL ? i : current];
}
const float scale = weight * scale_bias[0];
const float bias = weight * scale_bias[1];
for (int j = 0; j < block_size; ++j) {
out[j] += scale * input[fused_block_size * indices[current] + j] + bias;
}
++current;
}
if (normalize_by_lengths && lengths[m]) {
float scale = 1.f / lengths[m];
for (int j = 0; j < block_size; ++j) {
out[j] *= scale;
}
}
out += block_size;
}
return current == index_size;
}
// Proxy back to generic implementation
#define FUSED_8BIT_ROWWISE_EMBEDDING_SPECIALIZATION(IndexType, OutType) \
bool \
Fused8BitRowwiseEmbeddingLookup_##IndexType##_uint8_t_##OutType##_false__base( \
const int64_t block_size, \
const int64_t output_size, \
const int64_t index_size, \
const int64_t data_size, \
const uint8_t* input, \
const IndexType* indices, \
const int* lengths, \
const float* weights, \
bool normalize_by_lengths, \
OutType* out) { \
return Fused8BitRowwiseEmbeddingLookupGenericSlow< \
IndexType, \
uint8_t, \
OutType, \
false>( \
block_size, \
output_size, \
index_size, \
data_size, \
input, \
indices, \
lengths, \
weights, \
normalize_by_lengths, \
out); \
} \
decltype( \
Fused8BitRowwiseEmbeddingLookup_##IndexType##_uint8_t_##OutType##_false__base) \
Fused8BitRowwiseEmbeddingLookup_##IndexType##_uint8_t_##OutType##_false__avx2_fma; \
bool Fused8BitRowwiseEmbeddingLookup_##IndexType##_uint8_t_##OutType( \
const int64_t block_size, \
const int64_t output_size, \
const int64_t index_size, \
const int64_t data_size, \
const uint8_t* input, \
const IndexType* indices, \
const int* lengths, \
const float* weights, \
bool normalize_by_lengths, \
OutType* out) { \
const int32_t one = 1; \
CAFFE_ENFORCE_EQ( \
reinterpret_cast<const uint8_t*>(&one)[0], \
1, \
"Fused8BitRowwiseEmbeddingLookup is not supported on this platform"); \
AVX2_FMA_DO( \
Fused8BitRowwiseEmbeddingLookup_##IndexType##_uint8_t_##OutType##_false, \
block_size, \
output_size, \
index_size, \
data_size, \
input, \
indices, \
lengths, \
weights, \
normalize_by_lengths, \
out); \
BASE_DO( \
Fused8BitRowwiseEmbeddingLookup_##IndexType##_uint8_t_##OutType##_false, \
block_size, \
output_size, \
index_size, \
data_size, \
input, \
indices, \
lengths, \
weights, \
normalize_by_lengths, \
out); \
} \
template <> \
void Fused8BitRowwiseEmbeddingLookup<IndexType, uint8_t, OutType, false>( \
const int64_t block_size, \
const int64_t output_size, \
const int64_t index_size, \
const int64_t data_size, \
const uint8_t* input, \
const IndexType* indices, \
const int* lengths, \
const float* weights, \
bool normalize_by_lengths, \
OutType* out) { \
bool success = \
Fused8BitRowwiseEmbeddingLookup_##IndexType##_uint8_t_##OutType( \
block_size, \
output_size, \
index_size, \
data_size, \
input, \
indices, \
lengths, \
weights, \
normalize_by_lengths, \
out); \
if (success) { \
return; \
} \
int64_t current = 0; \
for (int m = 0; m < output_size; ++m) { \
for (int i = 0; i < lengths[m]; ++i) { \
CAFFE_ENFORCE_LT(current, index_size); \
IndexType idx = indices[current]; \
CAFFE_ENFORCE( \
0 <= idx && idx < data_size, \
"Index ", \
current, \
" is out of bounds: ", \
idx, \
", range 0 to ", \
data_size); \
++current; \
} \
} \
CAFFE_ENFORCE_EQ( \
current, \
index_size, \
"Your input seems to be incorrect: the sum of lengths values should be " \
"the size of the indices tensor, but it appears not."); \
}
FUSED_8BIT_ROWWISE_EMBEDDING_SPECIALIZATION(int32_t, float);
FUSED_8BIT_ROWWISE_EMBEDDING_SPECIALIZATION(int64_t, float);
#undef FUSED_8BIT_ROWWISE_EMBEDDING_SPECIALIZATION
} // namespace caffe2