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https://github.com/ml-explore/mlx.git
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556cdf0e06
* resolved extension build issues and added test to ci * missing gguflib * rebased * force mlx install from fix branch * linux build issue * point to git install and comment out ci tests
89 lines
2.7 KiB
C++
89 lines
2.7 KiB
C++
// Copyright © 2023 Apple Inc.
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#pragma once
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#include "mlx/ops.h"
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#include "mlx/primitives.h"
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namespace mlx::core {
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///////////////////////////////////////////////////////////////////////////////
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// Operation
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///////////////////////////////////////////////////////////////////////////////
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/**
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* Scale and sum two vectors element-wise
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* z = alpha * x + beta * y
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*
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* Follow numpy style broadcasting between x and y
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* Inputs are upcasted to floats if needed
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**/
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array axpby(
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const array& x, // Input array x
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const array& y, // Input array y
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const float alpha, // Scaling factor for x
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const float beta, // Scaling factor for y
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StreamOrDevice s = {} // Stream on which to schedule the operation
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);
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///////////////////////////////////////////////////////////////////////////////
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// Primitive
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///////////////////////////////////////////////////////////////////////////////
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class Axpby : public Primitive {
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public:
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explicit Axpby(Stream stream, float alpha, float beta)
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: Primitive(stream), alpha_(alpha), beta_(beta){};
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/**
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* A primitive must know how to evaluate itself on the CPU/GPU
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* for the given inputs and populate the output array.
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*
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* To avoid unnecessary allocations, the evaluation function
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* is responsible for allocating space for the array.
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*/
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void eval_cpu(const std::vector<array>& inputs, std::vector<array>& out)
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override;
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void eval_gpu(const std::vector<array>& inputs, std::vector<array>& out)
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override;
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/** The Jacobian-vector product. */
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std::vector<array> jvp(
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const std::vector<array>& primals,
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const std::vector<array>& tangents,
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const std::vector<int>& argnums) override;
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/** The vector-Jacobian product. */
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std::vector<array> vjp(
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const std::vector<array>& primals,
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const std::vector<array>& cotangents,
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const std::vector<int>& argnums,
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const std::vector<array>& outputs) override;
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/**
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* The primitive must know how to vectorize itself across
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* the given axes. The output is a pair containing the array
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* representing the vectorized computation and the axis which
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* corresponds to the output vectorized dimension.
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*/
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std::pair<std::vector<array>, std::vector<int>> vmap(
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const std::vector<array>& inputs,
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const std::vector<int>& axes) override;
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/** Print the primitive. */
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void print(std::ostream& os) override {
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os << "Axpby";
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}
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/** Equivalence check **/
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bool is_equivalent(const Primitive& other) const override;
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private:
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float alpha_;
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float beta_;
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/** Fall back implementation for evaluation on CPU */
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void eval(const std::vector<array>& inputs, std::vector<array>& out);
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};
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} // namespace mlx::core
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