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Refactor common into cpu specific and truly common (#1817)
* refactor * fix extension example * fix no-cpu
This commit is contained in:
Awni Hannun
370
mlx/backend/cpu/binary.h
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370
mlx/backend/cpu/binary.h
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// Copyright © 2023 Apple Inc.
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#pragma once
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#include <cassert>
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#include "mlx/allocator.h"
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#include "mlx/array.h"
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#include "mlx/backend/common/binary.h"
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#include "mlx/backend/common/utils.h"
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#include "mlx/backend/cpu/simd/simd.h"
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namespace mlx::core {
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template <typename Op>
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struct VectorScalar {
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Op op;
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VectorScalar(Op op_) : op(op_) {}
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template <typename T, typename U>
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void operator()(const T* a, const T* b, U* dst, int size) {
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T scalar = *b;
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constexpr int N = simd::max_size<T>;
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while (size >= N) {
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simd::store(dst, op(simd::load<T, N>(a), simd::Simd<T, N>(scalar)));
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dst += N;
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a += N;
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size -= N;
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}
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while (size-- > 0) {
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*dst = op(*a, scalar);
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dst++;
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a++;
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}
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}
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};
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template <typename Op>
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struct ScalarVector {
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Op op;
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ScalarVector(Op op_) : op(op_) {}
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template <typename T, typename U>
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void operator()(const T* a, const T* b, U* dst, int size) {
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T scalar = *a;
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constexpr int N = simd::max_size<T>;
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while (size >= N) {
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simd::store(dst, op(simd::Simd<T, N>(scalar), simd::load<T, N>(b)));
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dst += N;
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b += N;
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size -= N;
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}
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while (size-- > 0) {
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*dst = op(scalar, *b);
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dst++;
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b++;
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}
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}
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};
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template <typename Op>
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struct VectorVector {
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Op op;
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VectorVector(Op op_) : op(op_) {}
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template <typename T, typename U>
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void operator()(const T* a, const T* b, U* dst, int size) {
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constexpr int N = simd::max_size<T>;
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while (size >= N) {
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simd::store(dst, op(simd::load<T, N>(a), simd::load<T, N>(b)));
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dst += N;
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a += N;
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b += N;
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size -= N;
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}
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while (size-- > 0) {
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*dst = op(*a, *b);
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dst++;
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a++;
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b++;
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}
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}
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};
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template <typename T, typename U, typename Op, int D, bool Strided>
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void binary_op_dims(
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const T* a,
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const T* b,
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U* out,
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Op op,
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const Shape& shape,
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const Strides& a_strides,
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const Strides& b_strides,
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const Strides& out_strides,
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int axis) {
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auto stride_a = a_strides[axis];
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auto stride_b = b_strides[axis];
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auto stride_out = out_strides[axis];
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auto N = shape[axis];
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for (int i = 0; i < N; i++) {
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if constexpr (D > 1) {
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binary_op_dims<T, U, Op, D - 1, Strided>(
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a, b, out, op, shape, a_strides, b_strides, out_strides, axis + 1);
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} else {
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if constexpr (Strided) {
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op(a, b, out, stride_out);
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} else {
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*out = op(*a, *b);
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}
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}
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out += stride_out;
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a += stride_a;
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b += stride_b;
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}
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}
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template <typename T, typename U, bool Strided, typename Op>
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void binary_op_dispatch_dims(
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const array& a,
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const array& b,
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array& out,
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Op op,
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int dim,
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const Shape& shape,
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const Strides& a_strides,
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const Strides& b_strides,
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const Strides& out_strides) {
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const T* a_ptr = a.data<T>();
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const T* b_ptr = b.data<T>();
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U* out_ptr = out.data<U>();
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switch (dim) {
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case 1:
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binary_op_dims<T, U, Op, 1, Strided>(
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a_ptr,
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b_ptr,
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out_ptr,
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op,
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shape,
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a_strides,
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b_strides,
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out_strides,
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0);
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return;
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case 2:
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binary_op_dims<T, U, Op, 2, Strided>(
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a_ptr,
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b_ptr,
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out_ptr,
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op,
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shape,
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a_strides,
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b_strides,
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out_strides,
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0);
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return;
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case 3:
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binary_op_dims<T, U, Op, 3, Strided>(
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a_ptr,
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b_ptr,
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out_ptr,
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op,
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shape,
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a_strides,
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b_strides,
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out_strides,
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0);
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return;
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}
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ContiguousIterator a_it(shape, a_strides, dim - 3);
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ContiguousIterator b_it(shape, b_strides, dim - 3);
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auto stride = out_strides[dim - 4];
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for (int64_t elem = 0; elem < a.size(); elem += stride) {
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binary_op_dims<T, U, Op, 3, Strided>(
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a_ptr + a_it.loc,
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b_ptr + b_it.loc,
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out_ptr + elem,
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op,
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shape,
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a_strides,
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b_strides,
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out_strides,
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dim - 3);
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a_it.step();
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b_it.step();
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}
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}
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template <typename T, typename U, typename Op>
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void binary_op(const array& a, const array& b, array& out, Op op) {
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auto bopt = get_binary_op_type(a, b);
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set_binary_op_output_data(a, b, out, bopt);
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// The full computation is scalar scalar so call the base op once
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if (bopt == BinaryOpType::ScalarScalar) {
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*(out.data<U>()) = op(*a.data<T>(), *b.data<T>());
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return;
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}
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// The full computation is scalar vector so delegate to the op
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if (bopt == BinaryOpType::ScalarVector) {
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ScalarVector{op}(a.data<T>(), b.data<T>(), out.data<U>(), b.data_size());
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return;
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}
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// The full computation is vector scalar so delegate to the op
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if (bopt == BinaryOpType::VectorScalar) {
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VectorScalar{op}(a.data<T>(), b.data<T>(), out.data<U>(), a.data_size());
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return;
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}
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// The full computation is vector vector so delegate to the op
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if (bopt == BinaryOpType::VectorVector) {
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VectorVector{op}(a.data<T>(), b.data<T>(), out.data<U>(), out.size());
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return;
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}
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// General computation so let's try to optimize
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auto [new_shape, new_strides] = collapse_contiguous_dims(
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a.shape(), {a.strides(), b.strides(), out.strides()});
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const auto& a_strides = new_strides[0];
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const auto& b_strides = new_strides[1];
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const auto& strides = new_strides[2];
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// Get the left-most dim such that the array is row contiguous after
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auto leftmost_rc_dim = [&strides](const auto& arr_strides) {
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int d = arr_strides.size() - 1;
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for (; d >= 0 && arr_strides[d] == strides[d]; d--) {
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}
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return d + 1;
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};
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auto a_rc_dim = leftmost_rc_dim(a_strides);
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auto b_rc_dim = leftmost_rc_dim(b_strides);
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// Get the left-most dim such that the array is a broadcasted "scalar" after
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auto leftmost_s_dim = [](const auto& arr_strides) {
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int d = arr_strides.size() - 1;
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for (; d >= 0 && arr_strides[d] == 0; d--) {
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}
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return d + 1;
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};
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auto a_s_dim = leftmost_s_dim(a_strides);
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auto b_s_dim = leftmost_s_dim(b_strides);
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auto ndim = new_shape.size();
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// Case 1: LxM and FxM where L and F are broadcastable and M is row contiguous
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int dim = ndim;
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if (int d = std::max(a_rc_dim, b_rc_dim); d < ndim) {
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bopt = BinaryOpType::VectorVector;
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dim = d;
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// Case 2: LxM and Fx1 where L and F are broadcastable and M is row
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// contiguous
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} else if (int d = std::max(a_rc_dim, b_s_dim); d < ndim) {
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bopt = BinaryOpType::VectorScalar;
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dim = d;
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// Case 3: Lx1 and FxM where L and F are broadcastable and M is row
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// contiguous
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} else if (int d = std::max(a_s_dim, b_rc_dim); d < ndim) {
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bopt = BinaryOpType::ScalarVector;
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dim = d;
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}
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// Can be sure dim > 0 since otherwise we would have used one of the fully
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// contiguous methods above. Except for the case that the flags do not
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// correspond to the underlying contiguity.
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if (dim == 0 || strides[dim - 1] < 16) {
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bopt = BinaryOpType::General;
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dim = ndim;
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}
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switch (bopt) {
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case BinaryOpType::VectorVector:
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binary_op_dispatch_dims<T, U, true>(
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a,
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b,
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out,
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VectorVector{op},
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dim,
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new_shape,
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a_strides,
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b_strides,
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strides);
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break;
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case BinaryOpType::VectorScalar:
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binary_op_dispatch_dims<T, U, true>(
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a,
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b,
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out,
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VectorScalar{op},
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dim,
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new_shape,
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a_strides,
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b_strides,
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strides);
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break;
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case BinaryOpType::ScalarVector:
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binary_op_dispatch_dims<T, U, true>(
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a,
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b,
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out,
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ScalarVector{op},
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dim,
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new_shape,
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a_strides,
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b_strides,
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strides);
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break;
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default:
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binary_op_dispatch_dims<T, U, false>(
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a, b, out, op, dim, new_shape, a_strides, b_strides, strides);
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break;
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}
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}
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template <typename T, typename Op>
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void binary_op(const array& a, const array& b, array& out, Op op) {
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binary_op<T, T>(a, b, out, op);
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}
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template <typename Op>
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void binary(const array& a, const array& b, array& out, Op op) {
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switch (out.dtype()) {
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case bool_:
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binary_op<bool>(a, b, out, op);
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break;
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case uint8:
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binary_op<uint8_t>(a, b, out, op);
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break;
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case uint16:
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binary_op<uint16_t>(a, b, out, op);
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break;
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case uint32:
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binary_op<uint32_t>(a, b, out, op);
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break;
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case uint64:
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binary_op<uint64_t>(a, b, out, op);
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break;
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case int8:
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binary_op<int8_t>(a, b, out, op);
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break;
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case int16:
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binary_op<int16_t>(a, b, out, op);
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break;
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case int32:
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binary_op<int32_t>(a, b, out, op);
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break;
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case int64:
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binary_op<int64_t>(a, b, out, op);
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break;
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case float16:
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binary_op<float16_t>(a, b, out, op);
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break;
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case float32:
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binary_op<float>(a, b, out, op);
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break;
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case bfloat16:
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binary_op<bfloat16_t>(a, b, out, op);
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break;
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case complex64:
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binary_op<complex64_t>(a, b, out, op);
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break;
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}
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}
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} // namespace mlx::core
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