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