From 1cbf561466e957b25f0e8163c2386683f8674369 Mon Sep 17 00:00:00 2001 From: Shouzheng Liu <61452103+lshzh-ww@users.noreply.github.com> Date: Wed, 12 Jul 2023 16:10:55 -0400 Subject: [PATCH] metal : new q4_0 matrix-vector kernel (#2188) Prefetch data to improve GPU utilization. ~48% faster for 33B model. --- ggml-metal.m | 5 ++- ggml-metal.metal | 105 +++++++++++++++++++++++++---------------------- 2 files changed, 61 insertions(+), 49 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index d7a16936c..02dc9beb9 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -739,7 +739,10 @@ void ggml_metal_graph_compute( [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:13]; [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:14]; - if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1) { + if (src0t == GGML_TYPE_Q4_0) { + [encoder dispatchThreadgroups:MTLSizeMake(ne01 / 8+((ne01 % 8) & 0x01), ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } + else if (src0t == GGML_TYPE_Q4_1) { [encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } diff --git a/ggml-metal.metal b/ggml-metal.metal index e62fe6842..30d60fa58 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -365,6 +365,10 @@ kernel void kernel_rms_norm( } } +// putting them in the kernel cause a significant performance penalty +#define N_DST 4 // each SIMD group works on 4 rows +#define N_SIMDGROUP 2 // number of SIMD groups in a thread group +#define N_SIMDWIDTH 32 // assuming SIMD group size is 32 kernel void kernel_mul_mat_q4_0_f32( device const void * src0, device const float * src1, @@ -372,64 +376,69 @@ kernel void kernel_mul_mat_q4_0_f32( constant int64_t & ne00, constant int64_t & ne10, constant int64_t & ne0, - threadgroup float * sum [[threadgroup(0)]], + constant int64_t & ne01[[buffer(4)]], uint2 tgpig[[threadgroup_position_in_grid]], - uint2 tpitg[[thread_position_in_threadgroup]], - uint2 tptg[[threads_per_threadgroup]]) { + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { const int nb = ne00/QK4_0; - - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - - device const block_q4_0 * x = (device const block_q4_0 *) src0 + r0*nb; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + device const block_q4_0 * x = (device const block_q4_0 *) src0 + (r0 * N_SIMDGROUP + sgitg) * N_DST * nb; device const float * y = (device const float *) src1 + r1*ne10; + block_q4_0 qb_curr, qb_next; + float4 y_curr[8]; // src1 vector cache + float sumf[N_DST]={0.f}, all_sum; + thread float * yl=(thread float *)y_curr; - const int nth = tptg.x*tptg.y; - const int ith = tptg.y*tpitg.x + tpitg.y; - - const int ix = tpitg.y/4; // 0 or 1 - const int iy = tpitg.y - 4*ix; // 0...3 - - const int first = 4 * iy; - - float sumf = 0; - - for (int i = 2*tpitg.x + ix; i < nb; i += 2*tptg.x) { - - const float d = (float)x[i].d; - - device const uint8_t * xl = x[i].qs + first; - device const float * yl = y + i * QK4_0 + first; - - float2 acc = {0.0f, 0.0f}; - - for (int j = 0; j < 4; ++j) { - - acc[0] += yl[j] * (xl[j] & 0xF) + yl[j+16] * (xl[j] >> 4); - acc[1] += yl[j] + yl[j+16]; + // bootstrap + qb_curr = x[tiisg]; + // each thread in a SIMD group deals with 1 block. + for (int column = 0; column < nb / N_SIMDWIDTH; column++) { + for (int i = 0; i < QK4_0 / 4; i++) { + y_curr[i] = *((device float4 *)(y + N_SIMDWIDTH * (tiisg + column * QK4_0) + 4 * i)); } - sumf += d * (acc[0] - 8.f*acc[1]); + for (int row = 0; row < N_DST; row++) { + // prefetch next x block + qb_next = x[tiisg + ((row + 1) % N_DST) * nb + (column + ((row + 1) / N_DST)) * N_SIMDWIDTH]; + + // calculate + float d = qb_curr.d; + float2 acc = {0.0f, 0.0f}; + for (int i = 0; i < 16; i++) { + acc[0] += yl[i] * (qb_curr.qs[i] & 0xF) + yl[i+16] * (qb_curr.qs[i] >> 4); + acc[1] += yl[i] + yl[i+16]; + } + sumf[row] += d * (acc[0] - 8.f*acc[1]); + qb_curr = qb_next; + } } - sum[ith] = sumf; + for (int i = 0; i < QK4_0 / 4; i++) { + y_curr[i] = *((device float4 *)(y + N_SIMDWIDTH * (tiisg + (nb / N_SIMDWIDTH) * QK4_0) + 4 * i)); + } - // - // Accumulate the sum from all threads in the threadgroup - // - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith%4 == 0) { - sum[ith] += sum[ith+1] + sum[ith+2] + sum[ith+3]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith%16 == 0) { - sum[ith] += sum[ith+4] + sum[ith+8] + sum[ith+12]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith == 0) { - for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; - dst[r1*ne0 + r0] = sum[0]; + for (int row = 0; row < N_DST; row++) { + // prefetch next x block + qb_next = x[tiisg + ((row + 1) % N_DST) * nb + (nb / N_SIMDWIDTH + ((row + 1) / N_DST)) * N_SIMDWIDTH]; + + // calculate + float d = qb_curr.d; + float2 acc = {0.0f, 0.0f}; + for (int i = 0; i < 16; i++) { + acc[0] += yl[i] * (qb_curr.qs[i] & 0xF) + yl[i+16] * (qb_curr.qs[i] >> 4); + acc[1] += yl[i] + yl[i+16]; + } + if (tiisg < nb % N_SIMDWIDTH) { + sumf[row] += d * (acc[0] - 8.f*acc[1]); + } + qb_curr = qb_next; + + all_sum = simd_sum(sumf[row]); + if (tiisg == 0 && ((r0 * N_SIMDGROUP + sgitg) * N_DST + row) < ne01) { + dst[r1*ne0 + (r0 * N_SIMDGROUP + sgitg) * N_DST + row] = all_sum; + } } }