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https://github.com/ml-explore/mlx.git
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537 lines
14 KiB
C++
537 lines
14 KiB
C++
// Copyright © 2023 Apple Inc.
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#pragma once
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#include "mlx/backend/common/binary.h"
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#include "mlx/backend/common/utils.h"
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namespace mlx::core {
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namespace {
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template <typename T, typename U, typename Op>
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void binary_op_dims1(
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const array& a,
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const array& b,
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array& out_a,
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array& out_b,
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Op op) {
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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* dst_a = out_a.data<U>();
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U* dst_b = out_b.data<U>();
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size_t a_idx = 0;
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size_t b_idx = 0;
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for (size_t i = 0; i < out_a.size(); ++i) {
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auto dst = op(a_ptr[a_idx], b_ptr[b_idx]);
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dst_a[i] = dst.first;
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dst_b[i] = dst.second;
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a_idx += a.strides()[0];
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b_idx += b.strides()[0];
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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_dims1(
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const array& a,
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const array& b,
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array& out_a,
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array& out_b,
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Op op,
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int stride) {
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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* dst_a = out_a.data<U>();
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U* dst_b = out_b.data<U>();
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size_t a_idx = 0;
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size_t b_idx = 0;
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for (size_t i = 0; i < a.shape()[0]; i++) {
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op(a_ptr + a_idx, b_ptr + b_idx, dst_a, dst_b, stride);
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a_idx += a.strides()[0];
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b_idx += b.strides()[0];
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dst_a += stride;
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dst_b += stride;
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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_dims2(
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const array& a,
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const array& b,
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array& out_a,
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array& out_b,
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Op op) {
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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* dst_a = out_a.data<U>();
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U* dst_b = out_b.data<U>();
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size_t a_idx = 0;
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size_t b_idx = 0;
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size_t out_idx = 0;
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for (size_t i = 0; i < a.shape()[0]; ++i) {
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for (size_t j = 0; j < a.shape()[1]; ++j) {
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auto dst = op(a_ptr[a_idx], b_ptr[b_idx]);
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dst_a[out_idx] = dst.first;
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dst_b[out_idx++] = dst.second;
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a_idx += a.strides()[1];
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b_idx += b.strides()[1];
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}
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a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
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b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
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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_dims2(
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const array& a,
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const array& b,
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array& out_a,
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array& out_b,
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Op op,
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int stride) {
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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* dst_a = out_a.data<U>();
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U* dst_b = out_b.data<U>();
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size_t a_idx = 0;
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size_t b_idx = 0;
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for (size_t i = 0; i < a.shape()[0]; ++i) {
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for (size_t j = 0; j < a.shape()[1]; ++j) {
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op(a_ptr + a_idx, b_ptr + b_idx, dst_a, dst_b, stride);
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a_idx += a.strides()[1];
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b_idx += b.strides()[1];
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dst_a += stride;
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dst_b += stride;
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}
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a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
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b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
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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_dims3(
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const array& a,
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const array& b,
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array& out_a,
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array& out_b,
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Op op) {
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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* dst_a = out_a.data<U>();
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U* dst_b = out_b.data<U>();
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size_t a_idx = 0;
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size_t b_idx = 0;
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size_t out_idx = 0;
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for (size_t i = 0; i < a.shape()[0]; ++i) {
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for (size_t j = 0; j < a.shape()[1]; ++j) {
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for (size_t k = 0; k < a.shape()[2]; ++k) {
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auto dst = op(a_ptr[a_idx], b_ptr[b_idx]);
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dst_a[out_idx] = dst.first;
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dst_b[out_idx++] = dst.second;
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a_idx += a.strides()[2];
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b_idx += b.strides()[2];
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}
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a_idx += a.strides()[1] - a.strides()[2] * a.shape()[2];
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b_idx += b.strides()[1] - b.strides()[2] * b.shape()[2];
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}
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a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
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b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
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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_dims4(
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const array& a,
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const array& b,
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array& out_a,
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array& out_b,
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Op op) {
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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* dst_a = out_a.data<U>();
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U* dst_b = out_b.data<U>();
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size_t a_idx = 0;
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size_t b_idx = 0;
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size_t out_idx = 0;
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for (size_t i = 0; i < a.shape()[0]; ++i) {
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for (size_t j = 0; j < a.shape()[1]; ++j) {
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for (size_t k = 0; k < a.shape()[2]; ++k) {
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for (size_t ii = 0; ii < a.shape()[3]; ++ii) {
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auto dst = op(a_ptr[a_idx], b_ptr[b_idx]);
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dst_a[out_idx] = dst.first;
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dst_b[out_idx++] = dst.second;
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a_idx += a.strides()[3];
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b_idx += b.strides()[3];
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}
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a_idx += a.strides()[2] - a.strides()[3] * a.shape()[3];
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b_idx += b.strides()[2] - b.strides()[3] * b.shape()[3];
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}
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a_idx += a.strides()[1] - a.strides()[2] * a.shape()[2];
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b_idx += b.strides()[1] - b.strides()[2] * b.shape()[2];
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}
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a_idx += a.strides()[0] - a.strides()[1] * a.shape()[1];
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b_idx += b.strides()[0] - b.strides()[1] * b.shape()[1];
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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_dispatch_dims(
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const array& a,
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const array& b,
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array& out_a,
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array& out_b,
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Op op) {
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switch (out_a.ndim()) {
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case 1:
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binary_op_dims1<T, U, Op>(a, b, out_a, out_b, op);
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return;
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case 2:
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binary_op_dims2<T, U, Op>(a, b, out_a, out_b, op);
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return;
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case 3:
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binary_op_dims3<T, U, Op>(a, b, out_a, out_b, op);
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return;
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case 4:
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binary_op_dims4<T, U, Op>(a, b, out_a, out_b, op);
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return;
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}
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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* dst_a = out_a.data<U>();
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U* dst_b = out_b.data<U>();
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for (size_t i = 0; i < out_a.size(); i++) {
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int a_idx = elem_to_loc(i, a.shape(), a.strides());
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int b_idx = elem_to_loc(i, b.shape(), b.strides());
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std::tie(dst_a[i], dst_b[i]) = op(a_ptr[a_idx], b_ptr[b_idx]);
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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_dispatch_dims(
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const array& a,
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const array& b,
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array& out_a,
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array& out_b,
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Op op,
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int dim,
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int stride) {
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// Number of dimensions to loop over for vectorized ops
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switch (dim) {
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case 1:
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binary_op_dims1<T, U, Op>(a, b, out_a, out_b, op, stride);
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return;
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case 2:
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binary_op_dims2<T, U, Op>(a, b, out_a, out_b, op, stride);
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return;
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}
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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* dst_a = out_a.data<U>();
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U* dst_b = out_b.data<U>();
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for (size_t i = 0; i < out_a.size(); i += stride) {
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int a_idx = elem_to_loc(i, a.shape(), a.strides());
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int b_idx = elem_to_loc(i, b.shape(), b.strides());
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op(a_ptr + a_idx, b_ptr + b_idx, dst_a, dst_b, stride);
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dst_a += stride;
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dst_b += stride;
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}
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}
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template <
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typename T,
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typename U,
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typename Op,
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typename OpSV,
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typename OpVS,
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typename OpVV>
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void binary_op(
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const array& a,
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const array& b,
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array& out_a,
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array& out_b,
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Op op,
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OpSV opsv,
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OpVS opvs,
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OpVV opvv) {
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auto bopt = get_binary_op_type(a, b);
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set_binary_op_output_data(a, b, out_a, bopt);
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set_binary_op_output_data(a, b, out_b, 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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std::tie(*(out_a.data<U>()), *(out_b.data<U>())) =
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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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opsv(
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a.data<T>(),
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b.data<T>(),
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out_a.data<U>(),
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out_b.data<U>(),
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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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opvs(
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a.data<T>(),
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b.data<T>(),
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out_a.data<U>(),
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out_b.data<U>(),
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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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opvv(
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a.data<T>(),
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b.data<T>(),
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out_a.data<U>(),
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out_b.data<U>(),
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out_a.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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// Get the left-most dim such that the array is row contiguous after
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auto& strides = out_a.strides();
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auto leftmost_rc_dim = [&strides](const array& arr) {
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int d = arr.ndim() - 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);
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auto b_rc_dim = leftmost_rc_dim(b);
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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 array& arr) {
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int d = arr.ndim() - 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);
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auto b_s_dim = leftmost_s_dim(b);
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auto ndim = out_a.ndim();
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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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size_t stride;
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if (dim == 0 || strides[dim - 1] < 16) {
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stride = 1;
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bopt = BinaryOpType::General;
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dim = ndim;
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} else {
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stride = strides[dim - 1];
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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>(a, b, out_a, out_b, opvv, dim, stride);
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break;
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case BinaryOpType::VectorScalar:
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binary_op_dispatch_dims<T, U>(a, b, out_a, out_b, opvs, dim, stride);
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break;
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case BinaryOpType::ScalarVector:
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binary_op_dispatch_dims<T, U>(a, b, out_a, out_b, opsv, dim, stride);
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break;
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default:
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binary_op_dispatch_dims<T, U>(a, b, out_a, out_b, op);
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break;
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}
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}
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template <typename T, typename Op, typename OpSV, typename OpVS, typename OpVV>
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void binary_op(
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const array& a,
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const array& b,
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std::vector<array>& outputs,
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Op op,
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OpSV opsv,
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OpVS opvs,
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OpVV opvv) {
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// TODO: The following mess of constexpr evaluations can probably be achieved
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// with template specializations and overloading. Would it be simpler?
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if (std::is_same<decltype(opsv), UseDefaultBinaryOp>::value) {
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if (std::is_same<decltype(opvs), UseDefaultBinaryOp>::value) {
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if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
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// All ops are UseDefaultBinaryOp (why oh why would someone call that?)
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binary_op<T, T>(
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a,
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b,
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outputs[0],
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outputs[1],
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op,
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DefaultScalarVector<T, T, Op>(op),
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DefaultVectorScalar<T, T, Op>(op),
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DefaultVectorVector<T, T, Op>(op));
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} else {
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// opsv and opvs were UseDefaultBinaryOp
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binary_op<T, T>(
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a,
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b,
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outputs[0],
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outputs[1],
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op,
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DefaultScalarVector<T, T, Op>(op),
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DefaultVectorScalar<T, T, Op>(op),
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opvv);
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}
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} else if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
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// opsv and opvv were UseDefaultBinaryOp
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binary_op<T, T>(
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a,
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b,
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outputs[0],
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outputs[1],
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op,
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DefaultScalarVector<T, T, Op>(op),
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opvs,
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DefaultVectorVector<T, T, Op>(op));
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} else {
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// opsv was UseDefaultBinaryOp
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binary_op<T, T>(
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a,
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b,
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outputs[0],
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outputs[1],
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op,
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DefaultScalarVector<T, T, Op>(op),
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opvs,
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opvv);
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}
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} else if (std::is_same<decltype(opvs), UseDefaultBinaryOp>::value) {
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if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
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// opvs and opvv were UseDefaultBinaryOp
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binary_op<T, T>(
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a,
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b,
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outputs[0],
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outputs[1],
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op,
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opsv,
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DefaultVectorScalar<T, T, Op>(op),
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DefaultVectorVector<T, T, Op>(op));
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} else {
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// opvs was UseDefaultBinaryOp
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binary_op<T, T>(
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a,
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b,
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outputs[0],
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outputs[1],
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op,
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opsv,
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DefaultVectorScalar<T, T, Op>(op),
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opvv);
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}
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} else if (std::is_same<decltype(opvv), UseDefaultBinaryOp>::value) {
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// opvv was UseDefaultBinaryOp
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binary_op<T, T>(
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a,
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b,
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outputs[0],
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outputs[1],
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op,
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opsv,
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opvs,
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DefaultVectorVector<T, T, Op>(op));
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} else {
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// All ops provided
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binary_op<T, T>(a, b, outputs[0], outputs[1], op, opsv, opvs, opvv);
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}
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}
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template <typename T, typename Op>
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void binary_op(
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const array& a,
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const array& b,
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std::vector<array>& outputs,
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Op op) {
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DefaultScalarVector<T, T, Op> opsv(op);
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DefaultVectorScalar<T, T, Op> opvs(op);
|
|
DefaultVectorVector<T, T, Op> opvv(op);
|
|
binary_op<T, T>(a, b, outputs[0], outputs[1], op, opsv, opvs, opvv);
|
|
}
|
|
|
|
template <typename... Ops>
|
|
void binary(
|
|
const array& a,
|
|
const array& b,
|
|
std::vector<array>& outputs,
|
|
Ops... ops) {
|
|
switch (outputs[0].dtype()) {
|
|
case bool_:
|
|
binary_op<bool>(a, b, outputs, ops...);
|
|
break;
|
|
case uint8:
|
|
binary_op<uint8_t>(a, b, outputs, ops...);
|
|
break;
|
|
case uint16:
|
|
binary_op<uint16_t>(a, b, outputs, ops...);
|
|
break;
|
|
case uint32:
|
|
binary_op<uint32_t>(a, b, outputs, ops...);
|
|
break;
|
|
case uint64:
|
|
binary_op<uint64_t>(a, b, outputs, ops...);
|
|
break;
|
|
case int8:
|
|
binary_op<int8_t>(a, b, outputs, ops...);
|
|
break;
|
|
case int16:
|
|
binary_op<int16_t>(a, b, outputs, ops...);
|
|
break;
|
|
case int32:
|
|
binary_op<int32_t>(a, b, outputs, ops...);
|
|
break;
|
|
case int64:
|
|
binary_op<int64_t>(a, b, outputs, ops...);
|
|
break;
|
|
case float16:
|
|
binary_op<float16_t>(a, b, outputs, ops...);
|
|
break;
|
|
case float32:
|
|
binary_op<float>(a, b, outputs, ops...);
|
|
break;
|
|
case bfloat16:
|
|
binary_op<bfloat16_t>(a, b, outputs, ops...);
|
|
break;
|
|
case complex64:
|
|
binary_op<complex64_t>(a, b, outputs, ops...);
|
|
break;
|
|
}
|
|
}
|
|
|
|
} // namespace
|
|
|
|
} // namespace mlx::core
|