#pragma once
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#include "caffe2/core/operator.h"
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#include "caffe2/utils/eigen_utils.h"
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#include "caffe2/utils/math.h"
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namespace caffe2 {
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template <class Context>
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class GatherFused8BitRowwiseOp : public Operator<Context> {
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public:
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USE_OPERATOR_CONTEXT_FUNCTIONS;
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USE_SIMPLE_CTOR_DTOR(GatherFused8BitRowwiseOp);
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bool RunOnDevice() override {
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return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
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this, this->template Input<Tensor>(INDICES, CPU));
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}
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template <typename Index>
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bool DoRunWithType() {
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const auto& data = Input(DATA);
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const auto& indices = Input(INDICES);
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CAFFE_ENFORCE_EQ(data.dim(), 2, "DATA must be a matrix");
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CAFFE_ENFORCE_EQ(indices.dim(), 1, "INDICES must be a vector");
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CAFFE_ENFORCE_GT(data.size(1), 8, "DATA must have more than 8 columns");
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// Subtract 8 from the #columns of data for the 4 bytes for scale and 4
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// bytes for bias that we use in the fused representation (per row).
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const std::vector<int64_t> shape = {indices.size(0), data.size(1) - 8};
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auto* output = Output(0, shape, at::dtype<float>());
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int block_size = shape[1];
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auto block_bytesize = data.size_from_dim(1) * data.dtype().itemsize();
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int N = indices.numel();
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const uint8_t* src_base = data.template data<uint8_t>();
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const Index* idxs = indices.template data<Index>();
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auto out = output->template mutable_data<float>();
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for (int i = 0; i < N; ++i) {
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auto idx = idxs[i];
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CAFFE_ENFORCE(
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0 <= idx && idx < data.size(0),
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"INDICES element is out of DATA bounds, id=",
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idx,
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" data_dim=",
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data.size(0));
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const uint8_t* src = src_base + idx * block_bytesize;
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ConstEigenVectorArrayMap<uint8_t> input_row_values(src, shape[1]);
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ConstEigenVectorArrayMap<float> input_row_scale_bias(
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reinterpret_cast<const float*>(src + shape[1]), 2);
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EigenVectorArrayMap<float> output_row(out + i * shape[1], shape[1]);
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output_row = input_row_values.cast<float>() * input_row_scale_bias(0) +
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input_row_scale_bias(1);
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}
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return true;
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}
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INPUT_TAGS(DATA, INDICES);
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};
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} // namespace caffe2
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