2022-09-08 21:31:35 +08:00
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#pragma once
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#include "core/operator.h"
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#include <assert.h>
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namespace infini {
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2023-02-13 22:48:20 +08:00
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/**
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* @brief General band matrix multiplication. See
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* https://cscproxy.mpi-magdeburg.mpg.de/mpcsc/benner/pub/brdeq-cle2014.pdf for
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* detail.
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*
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*/
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2022-09-08 21:31:35 +08:00
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class GBMMObj : public OperatorObj {
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private:
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int dilation;
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ActType act;
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int b, m, w, n;
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public:
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/**
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2023-02-13 22:48:20 +08:00
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* @brief Construct a new GBMM object.
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2022-09-08 21:31:35 +08:00
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*
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2023-02-13 22:48:20 +08:00
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* @param graph The computation graph that this operator belongs to.
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* @param A The input tensor.
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* @param B The input tensor.
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* @param C C is the output of G2BMM. If outputs are going to be created in
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2022-09-08 21:31:35 +08:00
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* the constructor, C should be an empty Ref.
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2023-02-13 22:48:20 +08:00
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* @param dilation The dilation of the attention window.
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* @param bias The bias tensor.
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* @param act The activation.
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2022-09-08 21:31:35 +08:00
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*/
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GBMMObj(GraphObj *graph, Tensor A, Tensor B, Tensor C, const int dilation,
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Tensor bias = nullptr, ActType act = ActType::None);
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2023-02-12 18:27:52 +08:00
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OP_CLONE(GBMMObj);
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2022-09-08 21:31:35 +08:00
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std::string toString() const override;
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2023-11-23 13:11:50 +08:00
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optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
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2022-09-08 21:31:35 +08:00
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int numInputs() const override { return 2; }
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int numOutputs() const override { return 1; }
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int getDilation() const { return dilation; }
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Tensor getBias() const { return inputs[2]; }
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ActType getAct() const { return act; }
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int getB() const { return b; }
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int getM() const { return m; }
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int getW() const { return w; }
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int getN() const { return n; }
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auto getBMWND() const { return tuple{b, m, w, n, dilation}; }
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private:
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vector<int> getWorkloadVector() const override;
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vector<int> getOpAttrVector() const override;
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};
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2022-10-15 16:29:28 +08:00
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} // namespace infini
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