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14
ggml-metal.m
14
ggml-metal.m
@ -994,7 +994,7 @@ void ggml_metal_graph_compute(
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GGML_ASSERT(ne03 == ne13);
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// find the break-even point where the matrix-matrix kernel becomes more efficient compared
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// to the matrix-vector kernel. the numbers below are measure on M2 Ultra
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// to the matrix-vector kernel. the numbers below are measured on M2 Ultra
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// not sure if this translates across all chips
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int ne11_mm_min = 1;
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@ -1015,12 +1015,13 @@ void ggml_metal_graph_compute(
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// for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
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// AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
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if (!ggml_is_transposed(src0) &&
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if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
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!ggml_is_transposed(src0) &&
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!ggml_is_transposed(src1) &&
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src1t == GGML_TYPE_F32 &&
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[ctx->device supportsFamily:MTLGPUFamilyApple7] &&
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ne00%32 == 0 &&
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ne00 % 32 == 0 &&
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ne11 > ne11_mm_min) {
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//printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
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switch (src0->type) {
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case GGML_TYPE_F32: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f32_f32]; break;
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case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32]; break;
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@ -1049,11 +1050,12 @@ void ggml_metal_graph_compute(
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[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:12];
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[encoder setBytes:&gqa length:sizeof(gqa) atIndex:13];
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[encoder setThreadgroupMemoryLength:8192 atIndex:0];
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[encoder dispatchThreadgroups:MTLSizeMake( (ne11+31)/32, (ne01+63) / 64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
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[encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
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} else {
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int nth0 = 32;
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int nth1 = 1;
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int nrows = 1;
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//printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
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// use custom matrix x vector kernel
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switch (src0t) {
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@ -1175,7 +1177,7 @@ void ggml_metal_graph_compute(
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[encoder setBytes:&gqa length:sizeof(gqa) atIndex:17];
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if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q8_0 ||
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src0t == GGML_TYPE_Q2_K) {// || src0t == GGML_TYPE_Q4_K) {
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src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) {
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[encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
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}
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else if (src0t == GGML_TYPE_Q4_K) {
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@ -13,8 +13,8 @@ typedef struct {
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#define QK4_1 32
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typedef struct {
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half d; // delta
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half m; // min
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half d; // delta
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half m; // min
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uint8_t qs[QK4_1 / 2]; // nibbles / quants
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} block_q4_1;
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@ -2397,7 +2397,7 @@ kernel void kernel_mul_mm(device const uchar * src0,
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+ nb10 * (BLOCK_SIZE_K / THREAD_PER_COL * (tiitg % THREAD_PER_COL)));
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for (int loop_k = 0; loop_k < ne00; loop_k += BLOCK_SIZE_K) {
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//load data and store to threadgroup memory
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// load data and store to threadgroup memory
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half4x4 temp_a;
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dequantize_func(x, il, temp_a);
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threadgroup_barrier(mem_flags::mem_threadgroup);
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@ -2417,7 +2417,7 @@ kernel void kernel_mul_mm(device const uchar * src0,
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threadgroup_barrier(mem_flags::mem_threadgroup);
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//load matrices from threadgroup memory and conduct outer products
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// load matrices from threadgroup memory and conduct outer products
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threadgroup half * lsma = (sa + THREAD_MAT_M * SG_MAT_SIZE * (sgitg % 2));
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threadgroup float * lsmb = (sb + THREAD_MAT_N * SG_MAT_SIZE * (sgitg / 2));
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@ -2444,25 +2444,25 @@ kernel void kernel_mul_mm(device const uchar * src0,
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}
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if ((r0 + 1) * BLOCK_SIZE_M <= ne0 && (r1 + 1) * BLOCK_SIZE_N <= ne1) {
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device float *C = dst + (BLOCK_SIZE_M * r0 + 32 * (sgitg & 1)) \
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+ (BLOCK_SIZE_N * r1 + 16 * (sgitg >> 1)) * ne0 + im*ne1*ne0;
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device float * C = dst + (BLOCK_SIZE_M * r0 + 32 * (sgitg & 1)) \
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+ (BLOCK_SIZE_N * r1 + 16 * (sgitg >> 1)) * ne0 + im*ne1*ne0;
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for (int i = 0; i < 8; i++) {
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simdgroup_store(c_res[i], C + 8 * (i%4) + 8 * ne0 * (i/4), ne0);
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}
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} else {
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// block is smaller than 64x32, we should avoid writing data outside of the matrix
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threadgroup_barrier(mem_flags::mem_threadgroup);
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threadgroup float *temp_str = ((threadgroup float *)shared_memory) \
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threadgroup float * temp_str = ((threadgroup float *)shared_memory) \
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+ 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M;
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for (int i = 0; i < 8; i++) {
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simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M);
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}
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threadgroup_barrier(mem_flags::mem_threadgroup);
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device float *C = dst + BLOCK_SIZE_M * r0 + (BLOCK_SIZE_N * r1) * ne0 + im*ne1*ne0;
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if (sgitg==0) {
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device float * C = dst + BLOCK_SIZE_M * r0 + (BLOCK_SIZE_N * r1) * ne0 + im*ne1*ne0;
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if (sgitg == 0) {
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for (int i = 0; i < n_rows; i++) {
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for (int j = tiitg; j< n_cols; j += BLOCK_SIZE_N) {
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for (int j = tiitg; j < n_cols; j += BLOCK_SIZE_N) {
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*(C + i + j * ne0) = *(temp_str + i + j * BLOCK_SIZE_M);
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}
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}
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