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Georgi Gerganov 2023-10-07 19:20:40 +03:00
parent 8f6ad68427
commit 545b03491c
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2 changed files with 18 additions and 16 deletions
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14
ggml-metal.m
View File

@ -994,7 +994,7 @@ void ggml_metal_graph_compute(
GGML_ASSERT(ne03 == ne13); GGML_ASSERT(ne03 == ne13);
// find the break-even point where the matrix-matrix kernel becomes more efficient compared // find the break-even point where the matrix-matrix kernel becomes more efficient compared
// to the matrix-vector kernel. the numbers below are measure on M2 Ultra // to the matrix-vector kernel. the numbers below are measured on M2 Ultra
// not sure if this translates across all chips // not sure if this translates across all chips
int ne11_mm_min = 1; int ne11_mm_min = 1;
@ -1015,12 +1015,13 @@ void ggml_metal_graph_compute(
// for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
// AMD GPU and older A-chips will reuse matrix-vector multiplication kernel // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
if (!ggml_is_transposed(src0) && if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
!ggml_is_transposed(src0) &&
!ggml_is_transposed(src1) && !ggml_is_transposed(src1) &&
src1t == GGML_TYPE_F32 && src1t == GGML_TYPE_F32 &&
[ctx->device supportsFamily:MTLGPUFamilyApple7] && ne00 % 32 == 0 &&
ne00%32 == 0 &&
ne11 > ne11_mm_min) { ne11 > ne11_mm_min) {
//printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
switch (src0->type) { switch (src0->type) {
case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f32_f32]; break; case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f32_f32]; break;
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32]; break; case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32]; break;
@ -1049,11 +1050,12 @@ void ggml_metal_graph_compute(
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12]; [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12];
[encoder setBytes:&gqa length:sizeof(gqa) atIndex:13]; [encoder setBytes:&gqa length:sizeof(gqa) atIndex:13];
[encoder setThreadgroupMemoryLength:8192 atIndex:0]; [encoder setThreadgroupMemoryLength:8192 atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake( (ne11+31)/32, (ne01+63) / 64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)]; [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
} else { } else {
int nth0 = 32; int nth0 = 32;
int nth1 = 1; int nth1 = 1;
int nrows = 1; int nrows = 1;
//printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
// use custom matrix x vector kernel // use custom matrix x vector kernel
switch (src0t) { switch (src0t) {
@ -1175,7 +1177,7 @@ void ggml_metal_graph_compute(
[encoder setBytes:&gqa length:sizeof(gqa) atIndex:17]; [encoder setBytes:&gqa length:sizeof(gqa) atIndex:17];
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q8_0 || if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q8_0 ||
src0t == GGML_TYPE_Q2_K) {// || src0t == GGML_TYPE_Q4_K) { src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) {
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} }
else if (src0t == GGML_TYPE_Q4_K) { else if (src0t == GGML_TYPE_Q4_K) {

14
ggml-metal.metal
View File

@ -2397,7 +2397,7 @@ kernel void kernel_mul_mm(device const uchar * src0,
+ nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL))); + nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL)));
for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) { for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) {
//load data and store to threadgroup memory // load data and store to threadgroup memory
half4x4 temp_a; half4x4 temp_a;
dequantize_func(x, il, temp_a); dequantize_func(x, il, temp_a);
threadgroup_barrier(mem_flags::mem_threadgroup); threadgroup_barrier(mem_flags::mem_threadgroup);
@ -2417,7 +2417,7 @@ kernel void kernel_mul_mm(device const uchar * src0,
threadgroup_barrier(mem_flags::mem_threadgroup); threadgroup_barrier(mem_flags::mem_threadgroup);
//load matrices from threadgroup memory and conduct outer products // load matrices from threadgroup memory and conduct outer products
threadgroup half * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2)); threadgroup half * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2));
threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2)); threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2));
@ -2444,7 +2444,7 @@ kernel void kernel_mul_mm(device const uchar * src0,
} }
if ((r0 + 1) * BLOCK_SIZE_M <= ne0 && (r1 + 1) * BLOCK_SIZE_N <= ne1) { if ((r0 + 1) * BLOCK_SIZE_M <= ne0 && (r1 + 1) * BLOCK_SIZE_N <= ne1) {
device float *C = dst + (BLOCK_SIZE_M * r0 + 32 * (sgitg & 1)) \ device float * C = dst + (BLOCK_SIZE_M * r0 + 32 * (sgitg & 1)) \
+ (BLOCK_SIZE_N * r1 + 16 * (sgitg >> 1)) * ne0 + im*ne1*ne0; + (BLOCK_SIZE_N * r1 + 16 * (sgitg >> 1)) * ne0 + im*ne1*ne0;
for (int i = 0; i < 8; i++) { for (int i = 0; i < 8; i++) {
simdgroup_store(c_res[i], C + 8 * (i%4) + 8 * ne0 * (i/4), ne0); simdgroup_store(c_res[i], C + 8 * (i%4) + 8 * ne0 * (i/4), ne0);
@ -2452,17 +2452,17 @@ kernel void kernel_mul_mm(device const uchar * src0,
} else { } else {
// block is smaller than 64x32, we should avoid writing data outside of the matrix // block is smaller than 64x32, we should avoid writing data outside of the matrix
threadgroup_barrier(mem_flags::mem_threadgroup); threadgroup_barrier(mem_flags::mem_threadgroup);
threadgroup float *temp_str = ((threadgroup float *)shared_memory) \ threadgroup float * temp_str = ((threadgroup float *)shared_memory) \
+ 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M; + 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M;
for (int i = 0; i < 8; i++) { for (int i = 0; i < 8; i++) {
simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M); simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M);
} }
threadgroup_barrier(mem_flags::mem_threadgroup); threadgroup_barrier(mem_flags::mem_threadgroup);
device float *C = dst + BLOCK_SIZE_M * r0 + (BLOCK_SIZE_N * r1) * ne0 + im*ne1*ne0; device float * C = dst + BLOCK_SIZE_M * r0 + (BLOCK_SIZE_N * r1) * ne0 + im*ne1*ne0;
if (sgitg==0) { if (sgitg == 0) {
for (int i = 0; i < n_rows; i++) { for (int i = 0; i < n_rows; i++) {
for (int j = tiitg; j< n_cols; j += BLOCK_SIZE_N) { for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) {
*(C + i + j * ne0) = *(temp_str + i + j * BLOCK_SIZE_M); *(C + i + j * ne0) = *(temp_str + i + j * BLOCK_SIZE_M);
} }
} }