mirror of
https://github.com/ml-explore/mlx.git
synced 2025-07-23 01:51:18 +08:00
5ad133f8bb
* Enable collapsing batch dims in gemm * Update gemm to only make copies when neither of the last 2 axes are contiguous * Update addmm to support gemv shapes * Update addmm to support irregular batch strides * Update tests
557 lines
19 KiB
Metal
557 lines
19 KiB
Metal
// Copyright © 2023-2024 Apple Inc.
|
|
|
|
#include <metal_stdlib>
|
|
#include <metal_simdgroup>
|
|
|
|
#include "mlx/backend/metal/kernels/bf16.h"
|
|
#include "mlx/backend/metal/kernels/defines.h"
|
|
#include "mlx/backend/metal/kernels/utils.h"
|
|
|
|
using namespace metal;
|
|
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
/// Matrix vector multiplication
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
|
|
#define MLX_MTL_CONST static constant constexpr const
|
|
|
|
MLX_MTL_CONST int SIMD_SIZE = 32;
|
|
|
|
template <
|
|
typename T,
|
|
const int BM, /* Threadgroup rows (in threads) */
|
|
const int BN, /* Threadgroup cols (in threads) */
|
|
const int TM, /* Thread rows (in elements) */
|
|
const int TN , /* Thread cols (in elements) */
|
|
const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
|
|
struct GEMVKernel {
|
|
|
|
static_assert(BN == SIMD_SIZE, "gemv block must have a width of SIMD_SIZE");
|
|
|
|
// - The matrix of size (M = out_vec_size, N = in_vec_size) is divided up
|
|
// into blocks of (BM * TM, BN * TN) divided among threadgroups
|
|
// - Every thread works on a block of (TM, TN)
|
|
// - We assume each thead group is launched with (BN, BM, 1) threads
|
|
//
|
|
// 1. A thread loads TN elements each from mat along TM contiguous rows
|
|
// and the corresponding scalar from the vector
|
|
// 2. The thread then multiplies and adds to accumulate its local result for the block
|
|
// 3. At the end, each thread has accumulated results over all blocks across the rows
|
|
// These are then summed up across the threadgroup
|
|
// 4. Each threadgroup writes its accumulated BN * TN outputs
|
|
//
|
|
// Edge case handling:
|
|
// - The threadgroup with the largest tid will have blocks that exceed the matrix
|
|
// * The blocks that start outside the matrix are never read (thread results remain zero)
|
|
// * The last thread that partially overlaps with the matrix is shifted inwards
|
|
// such that the thread block fits exactly in the matrix
|
|
|
|
MLX_MTL_CONST short tgp_mem_size = BN * TN * 2;
|
|
|
|
static METAL_FUNC void run(
|
|
const device T* mat [[buffer(0)]],
|
|
const device T* in_vec [[buffer(1)]],
|
|
const device T* bias [[buffer(2)]],
|
|
device T* out_vec [[buffer(3)]],
|
|
const constant int& in_vec_size [[buffer(4)]],
|
|
const constant int& out_vec_size [[buffer(5)]],
|
|
const constant int& marix_ld [[buffer(6)]],
|
|
const constant float& alpha [[buffer(7)]],
|
|
const constant float& beta [[buffer(8)]],
|
|
const constant int& bias_stride [[buffer(14)]],
|
|
threadgroup T* tgp_memory [[threadgroup(0)]],
|
|
uint3 tid [[threadgroup_position_in_grid]],
|
|
uint3 lid [[thread_position_in_threadgroup]],
|
|
uint simd_gid [[simdgroup_index_in_threadgroup]],
|
|
uint simd_lid [[thread_index_in_simdgroup]]) {
|
|
|
|
// Appease compiler
|
|
(void)lid;
|
|
|
|
// Threadgroup in_vec cache
|
|
threadgroup T* in_vec_block = tgp_memory + simd_lid * TN * 2;
|
|
|
|
// Thread local accumulation results
|
|
thread T result[TM] = {0};
|
|
thread T inter[TN];
|
|
thread T v_coeff[TN];
|
|
|
|
// Block position
|
|
int out_row = (tid.x * BM + simd_gid) * TM;
|
|
|
|
// Exit simdgroup if rows out of bound
|
|
if(out_row >= out_vec_size)
|
|
return;
|
|
|
|
// Adjust tail simdgroup to ensure in bound reads
|
|
out_row = out_row + TM <= out_vec_size ? out_row : out_vec_size - TM;
|
|
|
|
// Advance matrix
|
|
mat += out_row * marix_ld;
|
|
|
|
// Loop over in_vec in blocks of BN * TN
|
|
for(int bn = simd_lid * TN; bn < in_vec_size; bn += BN * TN) {
|
|
|
|
threadgroup_barrier(mem_flags::mem_threadgroup);
|
|
|
|
// Prefetch in_vector for threadgroup use
|
|
if(simd_gid == 0) {
|
|
// Main load loop
|
|
if(bn + TN <= in_vec_size) {
|
|
|
|
#pragma clang loop unroll(full)
|
|
for(int tn = 0; tn < TN; tn++) {
|
|
in_vec_block[tn] = in_vec[bn + tn];
|
|
}
|
|
|
|
} else { // Edgecase
|
|
|
|
#pragma clang loop unroll(full)
|
|
for(int tn = 0; tn < TN; tn++) {
|
|
in_vec_block[tn] = bn + tn < in_vec_size ? in_vec[bn + tn] : 0;
|
|
}
|
|
|
|
}
|
|
}
|
|
|
|
threadgroup_barrier(mem_flags::mem_threadgroup);
|
|
|
|
// Load for all rows
|
|
#pragma clang loop unroll(full)
|
|
for(int tn = 0; tn < TN; tn++) {
|
|
v_coeff[tn] = in_vec_block[tn];
|
|
}
|
|
|
|
// Per thread work loop
|
|
#pragma clang loop unroll(full)
|
|
for(int tm = 0; tm < TM; tm++) {
|
|
|
|
// Load for the row
|
|
if(bn + TN <= in_vec_size) {
|
|
#pragma clang loop unroll(full)
|
|
for(int tn = 0; tn < TN; tn++) {
|
|
inter[tn] = mat[tm * marix_ld + bn + tn];
|
|
}
|
|
|
|
} else { // Edgecase
|
|
#pragma clang loop unroll(full)
|
|
for(int tn = 0; tn < TN; tn++) {
|
|
int col_idx = (bn + tn) < in_vec_size ? (bn + tn) : (in_vec_size - 1);
|
|
inter[tn] = mat[tm * marix_ld + col_idx];
|
|
}
|
|
}
|
|
|
|
// Accumulate results
|
|
for(int tn = 0; tn < TN; tn++) {
|
|
result[tm] += inter[tn] * v_coeff[tn];
|
|
}
|
|
|
|
}
|
|
}
|
|
|
|
// Simdgroup accumulations
|
|
#pragma clang loop unroll(full)
|
|
for(int tm = 0; tm < TM; tm++) {
|
|
result[tm] = simd_sum(result[tm]);
|
|
}
|
|
|
|
// Write outputs
|
|
if(simd_lid == 0) {
|
|
|
|
#pragma clang loop unroll(full)
|
|
for(int tm = 0; tm < TM; tm++) {
|
|
if(kDoAxpby) {
|
|
out_vec[out_row + tm] =
|
|
static_cast<T>(alpha) * result[tm] +
|
|
static_cast<T>(beta) * bias[(out_row + tm) * bias_stride];
|
|
} else {
|
|
out_vec[out_row + tm] = result[tm];
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
};
|
|
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
/// Vector matrix multiplication
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <
|
|
typename T,
|
|
const int BM, /* Threadgroup rows (in threads) */
|
|
const int BN, /* Threadgroup cols (in threads) */
|
|
const int TM, /* Thread rows (in elements) */
|
|
const int TN, /* Thread cols (in elements) */
|
|
const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
|
|
struct GEMVTKernel {
|
|
|
|
// - The matrix of size (M = in_vec_size, N = out_vec_size) is divided up
|
|
// into blocks of (BM * TM, BN * TN) divided among threadgroups
|
|
// - Every thread works on a block of (TM, TN)
|
|
// - We assume each thead group is launched with (BN, BM, 1) threads
|
|
//
|
|
// 1. A thread loads TN elements each from mat along TM contiguous rows
|
|
// and the corresponding scalar from the vector
|
|
// 2. The thread then multiplies and adds to accumulate its local result for the block
|
|
// 3. At the end, each thread has accumulated results over all blocks across the rows
|
|
// These are then summed up across the threadgroup
|
|
// 4. Each threadgroup writes its accumulated BN * TN outputs
|
|
//
|
|
// Edge case handling:
|
|
// - The threadgroup with the largest tid will have blocks that exceed the matrix
|
|
// * The blocks that start outside the matrix are never read (thread results remain zero)
|
|
// * The last thread that partially overlaps with the matrix is shifted inwards
|
|
// such that the thread block fits exactly in the matrix
|
|
|
|
|
|
MLX_MTL_CONST short tgp_mem_size = BN * BM * TN;
|
|
|
|
static METAL_FUNC void run(
|
|
const device T* mat [[buffer(0)]],
|
|
const device T* in_vec [[buffer(1)]],
|
|
const device T* bias [[buffer(2)]],
|
|
device T* out_vec [[buffer(3)]],
|
|
const constant int& in_vec_size [[buffer(4)]],
|
|
const constant int& out_vec_size [[buffer(5)]],
|
|
const constant int& marix_ld [[buffer(6)]],
|
|
const constant float& alpha [[buffer(7)]],
|
|
const constant float& beta [[buffer(8)]],
|
|
const constant int& bias_stride [[buffer(14)]],
|
|
threadgroup T* tgp_memory [[threadgroup(0)]],
|
|
uint3 tid [[threadgroup_position_in_grid]],
|
|
uint3 lid [[thread_position_in_threadgroup]],
|
|
uint simd_gid [[simdgroup_index_in_threadgroup]],
|
|
uint simd_lid [[thread_index_in_simdgroup]]) {
|
|
|
|
// Appease compiler
|
|
(void)simd_gid;
|
|
(void)simd_lid;
|
|
|
|
// Thread local accumulation results
|
|
T result[TN] = {0};
|
|
T inter[TN];
|
|
T v_coeff[TM];
|
|
|
|
// Threadgroup accumulation results
|
|
threadgroup T* tgp_results = tgp_memory + lid.x * BM * TN;
|
|
|
|
int out_col = (tid.x * BN + lid.x) * TN;
|
|
int in_row = lid.y * TM;
|
|
|
|
// Edgecase handling
|
|
if (out_col < out_vec_size) {
|
|
|
|
out_col = out_col + TN < out_vec_size ? out_col : out_vec_size - TN;
|
|
|
|
// Per thread accumulation main loop
|
|
int bm = in_row;
|
|
for(; bm < in_vec_size; bm += BM * TM) {
|
|
// Adding a threadgroup_barrier improves performance slightly
|
|
// This is possibly it may help exploit cache better
|
|
threadgroup_barrier(mem_flags::mem_none);
|
|
|
|
if(bm + TM <= in_vec_size) {
|
|
|
|
#pragma clang loop unroll(full)
|
|
for(int tm = 0; tm < TM; tm++) {
|
|
v_coeff[tm] = in_vec[bm + tm];
|
|
}
|
|
|
|
#pragma clang loop unroll(full)
|
|
for(int tm = 0; tm < TM; tm++) {
|
|
for(int tn = 0; tn < TN; tn++) {
|
|
inter[tn] = mat[(bm + tm) * marix_ld + out_col + tn];
|
|
}
|
|
for(int tn = 0; tn < TN; tn++) {
|
|
result[tn] += v_coeff[tm] * inter[tn];
|
|
}
|
|
}
|
|
|
|
} else { // Edgecase handling
|
|
for(int tm = 0; bm + tm < in_vec_size; tm++) {
|
|
v_coeff[tm] = in_vec[bm + tm];
|
|
|
|
for(int tn = 0; tn < TN; tn++) {
|
|
inter[tn] = mat[(bm + tm) * marix_ld + out_col + tn];
|
|
}
|
|
for(int tn = 0; tn < TN; tn++) {
|
|
result[tn] += v_coeff[tm] * inter[tn];
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
// Threadgroup collection
|
|
|
|
#pragma clang loop unroll(full)
|
|
for(int i = 0; i < TN; i++) {
|
|
tgp_results[lid.y * TN + i] = result[i];
|
|
}
|
|
|
|
threadgroup_barrier(mem_flags::mem_threadgroup);
|
|
|
|
// Threadgroup accumulation and writing out results
|
|
if(lid.y == 0 && out_col < out_vec_size) {
|
|
|
|
#pragma clang loop unroll(full)
|
|
for(int i = 1; i < BM; i++) {
|
|
|
|
#pragma clang loop unroll(full)
|
|
for(int j = 0; j < TN; j++) {
|
|
result[j] += tgp_results[i * TN + j];
|
|
}
|
|
}
|
|
|
|
#pragma clang loop unroll(full)
|
|
for(int j = 0; j < TN; j++) {
|
|
|
|
if(kDoAxpby) {
|
|
out_vec[out_col + j] =
|
|
static_cast<T>(alpha) * result[j] +
|
|
static_cast<T>(beta) * bias[(out_col + j) * bias_stride];
|
|
} else {
|
|
out_vec[out_col + j] = result[j];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
/// Matrix vector multiplication
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <
|
|
typename T,
|
|
const int BM, /* Threadgroup rows (in threads) */
|
|
const int BN, /* Threadgroup cols (in threads) */
|
|
const int TM, /* Thread rows (in elements) */
|
|
const int TN, /* Thread cols (in elements) */
|
|
const bool kDoNCBatch, /* Batch ndim > 1 */
|
|
const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
|
|
[[kernel, max_total_threads_per_threadgroup(BM * BN)]] void gemv(
|
|
const device T* mat [[buffer(0)]],
|
|
const device T* in_vec [[buffer(1)]],
|
|
const device T* bias [[buffer(2)]],
|
|
device T* out_vec [[buffer(3)]],
|
|
const constant int& in_vec_size [[buffer(4)]],
|
|
const constant int& out_vec_size [[buffer(5)]],
|
|
const constant int& marix_ld [[buffer(6)]],
|
|
const constant float& alpha [[buffer(7)]],
|
|
const constant float& beta [[buffer(8)]],
|
|
const constant int& batch_ndim [[buffer(9)]],
|
|
const constant int* batch_shape [[buffer(10)]],
|
|
const constant size_t* vector_batch_stride [[buffer(11)]],
|
|
const constant size_t* matrix_batch_stride [[buffer(12)]],
|
|
const constant size_t* bias_batch_stride [[buffer(13)]],
|
|
const constant int& bias_stride [[buffer(14)]],
|
|
uint3 tid [[threadgroup_position_in_grid]],
|
|
uint3 lid [[thread_position_in_threadgroup]],
|
|
uint simd_gid [[simdgroup_index_in_threadgroup]],
|
|
uint simd_lid [[thread_index_in_simdgroup]]) {
|
|
|
|
using gemv_kernel = GEMVKernel<T, BM, BN, TM, TN, kDoAxpby>;
|
|
threadgroup T tgp_memory[gemv_kernel::tgp_mem_size];
|
|
|
|
// Update batch offsets
|
|
if(kDoNCBatch) {
|
|
in_vec += elem_to_loc(tid.z, batch_shape, vector_batch_stride, batch_ndim);
|
|
mat += elem_to_loc(tid.z, batch_shape, matrix_batch_stride, batch_ndim);
|
|
|
|
if(kDoAxpby) {
|
|
bias += elem_to_loc(tid.z, batch_shape, bias_batch_stride, batch_ndim);
|
|
}
|
|
|
|
} else {
|
|
in_vec += tid.z * vector_batch_stride[0];
|
|
mat += tid.z * matrix_batch_stride[0];
|
|
|
|
if(kDoAxpby) {
|
|
bias += tid.z * bias_batch_stride[0];
|
|
}
|
|
}
|
|
|
|
out_vec += tid.z * out_vec_size;
|
|
|
|
gemv_kernel::run(
|
|
mat,
|
|
in_vec,
|
|
bias,
|
|
out_vec,
|
|
in_vec_size,
|
|
out_vec_size,
|
|
marix_ld,
|
|
alpha,
|
|
beta,
|
|
bias_stride,
|
|
tgp_memory,
|
|
tid,
|
|
lid,
|
|
simd_gid,
|
|
simd_lid
|
|
);
|
|
|
|
}
|
|
|
|
|
|
#define instantiate_gemv_helper(name, itype, bm, bn, tm, tn, nc, axpby) \
|
|
template [[host_name("gemv_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn "_nc" #nc "_axpby" #axpby)]] \
|
|
[[kernel]] void gemv<itype, bm, bn, tm, tn, nc, axpby>( \
|
|
const device itype* mat [[buffer(0)]], \
|
|
const device itype* in_vec [[buffer(1)]], \
|
|
const device itype* bias [[buffer(2)]], \
|
|
device itype* out_vec [[buffer(3)]], \
|
|
const constant int& in_vec_size [[buffer(4)]], \
|
|
const constant int& out_vec_size [[buffer(5)]], \
|
|
const constant int& marix_ld [[buffer(6)]], \
|
|
const constant float& alpha [[buffer(7)]], \
|
|
const constant float& beta [[buffer(8)]], \
|
|
const constant int& batch_ndim [[buffer(9)]], \
|
|
const constant int* batch_shape [[buffer(10)]], \
|
|
const constant size_t* vector_batch_stride [[buffer(11)]], \
|
|
const constant size_t* matrix_batch_stride [[buffer(12)]], \
|
|
const constant size_t* bias_batch_stride [[buffer(13)]], \
|
|
const constant int& bias_stride [[buffer(14)]], \
|
|
uint3 tid [[threadgroup_position_in_grid]], \
|
|
uint3 lid [[thread_position_in_threadgroup]], \
|
|
uint simd_gid [[simdgroup_index_in_threadgroup]], \
|
|
uint simd_lid [[thread_index_in_simdgroup]]);
|
|
|
|
#define instantiate_gemv(name, itype, bm, bn, tm, tn) \
|
|
instantiate_gemv_helper(name, itype, bm, bn, tm, tn, 0, 0) \
|
|
instantiate_gemv_helper(name, itype, bm, bn, tm, tn, 0, 1) \
|
|
instantiate_gemv_helper(name, itype, bm, bn, tm, tn, 1, 0) \
|
|
instantiate_gemv_helper(name, itype, bm, bn, tm, tn, 1, 1)
|
|
|
|
#define instantiate_gemv_blocks(name, itype) \
|
|
instantiate_gemv(name, itype, 4, 32, 1, 4) \
|
|
instantiate_gemv(name, itype, 4, 32, 4, 4) \
|
|
instantiate_gemv(name, itype, 8, 32, 4, 4)
|
|
|
|
instantiate_gemv_blocks(float32, float);
|
|
instantiate_gemv_blocks(float16, half);
|
|
instantiate_gemv_blocks(bfloat16, bfloat16_t);
|
|
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
/// Vector matrix multiplication
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <
|
|
typename T,
|
|
const int BM, /* Threadgroup rows (in threads) */
|
|
const int BN, /* Threadgroup cols (in threads) */
|
|
const int TM, /* Thread rows (in elements) */
|
|
const int TN, /* Thread cols (in elements) */
|
|
const bool kDoNCBatch, /* Batch ndim > 1 */
|
|
const bool kDoAxpby> /* Do out = alpha * out + beta * bias */
|
|
[[kernel, max_total_threads_per_threadgroup(BM * BN)]] void gemv_t(
|
|
const device T* mat [[buffer(0)]],
|
|
const device T* in_vec [[buffer(1)]],
|
|
const device T* bias [[buffer(2)]],
|
|
device T* out_vec [[buffer(3)]],
|
|
const constant int& in_vec_size [[buffer(4)]],
|
|
const constant int& out_vec_size [[buffer(5)]],
|
|
const constant int& marix_ld [[buffer(6)]],
|
|
const constant float& alpha [[buffer(7)]],
|
|
const constant float& beta [[buffer(8)]],
|
|
const constant int& batch_ndim [[buffer(9)]],
|
|
const constant int* batch_shape [[buffer(10)]],
|
|
const constant size_t* vector_batch_stride [[buffer(11)]],
|
|
const constant size_t* matrix_batch_stride [[buffer(12)]],
|
|
const constant size_t* bias_batch_stride [[buffer(13)]],
|
|
const constant int& bias_stride [[buffer(14)]],
|
|
uint3 tid [[threadgroup_position_in_grid]],
|
|
uint3 lid [[thread_position_in_threadgroup]],
|
|
uint simd_gid [[simdgroup_index_in_threadgroup]],
|
|
uint simd_lid [[thread_index_in_simdgroup]]) {
|
|
|
|
using gemv_kernel = GEMVTKernel<T, BM, BN, TM, TN, kDoAxpby>;
|
|
threadgroup T tgp_memory[gemv_kernel::tgp_mem_size];
|
|
|
|
// Update batch offsets
|
|
if(kDoNCBatch) {
|
|
in_vec += elem_to_loc(tid.z, batch_shape, vector_batch_stride, batch_ndim);
|
|
mat += elem_to_loc(tid.z, batch_shape, matrix_batch_stride, batch_ndim);
|
|
|
|
if(kDoAxpby) {
|
|
bias += elem_to_loc(tid.z, batch_shape, bias_batch_stride, batch_ndim);
|
|
}
|
|
|
|
} else {
|
|
in_vec += tid.z * vector_batch_stride[0];
|
|
mat += tid.z * matrix_batch_stride[0];
|
|
|
|
if(kDoAxpby) {
|
|
bias += tid.z * bias_batch_stride[0];
|
|
}
|
|
}
|
|
|
|
out_vec += tid.z * out_vec_size;
|
|
|
|
gemv_kernel::run(
|
|
mat,
|
|
in_vec,
|
|
bias,
|
|
out_vec,
|
|
in_vec_size,
|
|
out_vec_size,
|
|
marix_ld,
|
|
alpha,
|
|
beta,
|
|
bias_stride,
|
|
tgp_memory,
|
|
tid,
|
|
lid,
|
|
simd_gid,
|
|
simd_lid
|
|
);
|
|
|
|
}
|
|
|
|
#define instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, nc, axpby) \
|
|
template [[host_name("gemv_t_" #name "_bm" #bm "_bn" #bn "_tm" #tm "_tn" #tn "_nc" #nc "_axpby" #axpby)]] \
|
|
[[kernel]] void gemv_t<itype, bm, bn, tm, tn, nc, axpby>( \
|
|
const device itype* mat [[buffer(0)]], \
|
|
const device itype* in_vec [[buffer(1)]], \
|
|
const device itype* bias [[buffer(2)]], \
|
|
device itype* out_vec [[buffer(3)]], \
|
|
const constant int& in_vec_size [[buffer(4)]], \
|
|
const constant int& out_vec_size [[buffer(5)]], \
|
|
const constant int& marix_ld [[buffer(6)]], \
|
|
const constant float& alpha [[buffer(7)]], \
|
|
const constant float& beta [[buffer(8)]], \
|
|
const constant int& batch_ndim [[buffer(9)]], \
|
|
const constant int* batch_shape [[buffer(10)]], \
|
|
const constant size_t* vector_batch_stride [[buffer(11)]], \
|
|
const constant size_t* matrix_batch_stride [[buffer(12)]], \
|
|
const constant size_t* bias_batch_stride [[buffer(13)]], \
|
|
const constant int& bias_stride [[buffer(14)]], \
|
|
uint3 tid [[threadgroup_position_in_grid]], \
|
|
uint3 lid [[thread_position_in_threadgroup]], \
|
|
uint simd_gid [[simdgroup_index_in_threadgroup]], \
|
|
uint simd_lid [[thread_index_in_simdgroup]]);
|
|
|
|
#define instantiate_gemv_t(name, itype, bm, bn, tm, tn) \
|
|
instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 0, 0) \
|
|
instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 0, 1) \
|
|
instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 1, 0) \
|
|
instantiate_gemv_t_helper(name, itype, bm, bn, tm, tn, 1, 1)
|
|
|
|
#define instantiate_gemv_t_blocks(name, itype) \
|
|
instantiate_gemv_t(name, itype, 8, 8, 4, 1) \
|
|
instantiate_gemv_t(name, itype, 8, 8, 4, 4) \
|
|
instantiate_gemv_t(name, itype, 8, 16, 4, 4) \
|
|
instantiate_gemv_t(name, itype, 8, 32, 4, 4) \
|
|
instantiate_gemv_t(name, itype, 8, 64, 4, 4) \
|
|
instantiate_gemv_t(name, itype, 8, 128, 4, 4)
|
|
|
|
instantiate_gemv_t_blocks(float32, float);
|
|
instantiate_gemv_t_blocks(float16, half);
|
|
instantiate_gemv_t_blocks(bfloat16, bfloat16_t);
|