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#ifndef CAFFE2_OPERATORS_LENGTHS_REDUCER_FUSED_8BIT_ROWWISE_OPS_H_
#define CAFFE2_OPERATORS_LENGTHS_REDUCER_FUSED_8BIT_ROWWISE_OPS_H_
#include "caffe2/core/context.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/operators/fused_rowwise_8bit_conversion_ops.h"
#include "caffe2/operators/reducer_functors.h"
#include "caffe2/perfkernels/fused_8bit_rowwise_embedding_lookup.h"
#include "caffe2/utils/math.h"
#ifdef USE_FBGEMM
#include "fbgemm/Fbgemm.h"
#endif
namespace caffe2 {
template <class Context, bool with_weights = false, bool is_mean = false>
class SparseLengthsFused8BitRowwiseOp : public Operator<Context> {
public:
static_assert(
!(with_weights && is_mean),
"Cannot have with_weights and is_mean a the same time");
USE_OPERATOR_CONTEXT_FUNCTIONS;
USE_SIMPLE_CTOR_DTOR(SparseLengthsFused8BitRowwiseOp)
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
this, Input(INDICES));
}
template <typename IndexType>
bool DoRunWithType() {
const auto& data = Input(DATA);
const auto& indices = Input(INDICES);
const auto& lengths = Input(LENGTHS);
CAFFE_ENFORCE_EQ(indices.dim(), 1, "INDICES must be a vector");
CAFFE_ENFORCE_EQ(lengths.dim(), 1, "LENGTHS must be a vector");
const float* weights = nullptr;
if (with_weights) {
const auto& weights_input = Input(WEIGHTS);
CAFFE_ENFORCE_EQ(weights_input.dim(), 1, "WEIGHTS must be a vector");
CAFFE_ENFORCE_EQ(
weights_input.numel(),
indices.numel(),
"WEIGHTS should have the same length as INDICES.");
weights = weights_input.template data<float>();
}
CAFFE_ENFORCE_GT(data.size(1), 8, "DATA must have more than 8 columns");
// Subtract 8 from the #columns of data for the 4 bytes for scale and 4
// bytes for bias that we use in the fused representation (per row).
const std::vector<int64_t> shape = {lengths.size(0), data.size(1) - 8};
auto* output = Output(0, shape, at::dtype<float>());
std::int64_t block_size = output->size(1);
auto output_size = output->size(0);
auto index_size = indices.numel();
auto data_size = data.size(0);
const std::uint8_t* input_data = data.template data<std::uint8_t>();
const int* lengths_data = lengths.template data<int>();
float* output_data = output->template mutable_data<float>();
#ifdef USE_FBGEMM
// Calling the JITed kernel from FBGEMM
// Will Remove the call to C2/perfkernels/
// If this is the first call or block size has changed (should never happen
// actually), generate a kernel.
if (block_size != last_block_size) {
last_block_size = block_size;
if (std::is_same<IndexType, std::int32_t>::value) {
kernel32_ = fbgemm::GenerateEmbeddingSpMDM<std::uint8_t, std::int32_t>(
block_size,
with_weights,
is_mean,
/*prefetch distance*/ 16,
/*is_weight_positional*/ false,
/*use_offsets*/ false);
} else {
CAFFE_ENFORCE((std::is_same<IndexType, std::int64_t>::value));
kernel64_ = fbgemm::GenerateEmbeddingSpMDM<std::uint8_t, std::int64_t>(
block_size,
with_weights,
is_mean,
/*prefetch distance*/ 16,
/*is_weight_positional*/ false,
/*use_offsets*/ false);
}
}
bool success;
if (std::is_same<IndexType, std::int32_t>::value) {
success = kernel32_(
output_size,
index_size,
data_size,
input_data,
indices.template data<std::int32_t>(),
lengths_data,
weights,
output_data);
} else {
success = kernel64_(
output_size,
index_size,
data_size,
input_data,
indices.template data<std::int64_t>(),
lengths_data,
weights,
output_data);
}
if (success) {
return true;
}
auto indices_data = indices.template data<IndexType>();
int64_t current = 0;
for (const auto m : c10::irange(output_size)) {
for (int i = 0; i < lengths_data[m]; ++i) {
CAFFE_ENFORCE_LT(current, index_size);
IndexType idx = indices_data[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.");
return false;
#else
Fused8BitRowwiseEmbeddingLookup(
block_size,
output_size,
index_size,
data_size,
input_data,
indices.template data<IndexType>(),
lengths_data,
weights,
is_mean,
output_data);
return true;
#endif
}
enum {
DATA = 0,
WEIGHTS = 1,
INDICES = 1 + with_weights,
LENGTHS = 2 + with_weights,
};
#ifdef USE_FBGEMM
private:
std::int64_t last_block_size{-1};
fbgemm::EmbeddingSpMDMKernelSignature<std::uint8_t, std::int32_t>::Type
kernel32_;
fbgemm::EmbeddingSpMDMKernelSignature<std::uint8_t, std::int64_t>::Type
kernel64_;
#endif
};
} // namespace caffe2
#endif // CAFFE2_OPERATORS_LENGTHS_REDUCER_FUSED_8BIT_ROWWISE_OPS_H_
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