metal : switch to parallel reduce
This commit is contained in:
Georgi Gerganov
parent
5733b00e53
commit
e51778de5e
2 changed files with 119 additions and 93 deletions
16
ggml-metal.m
16
ggml-metal.m
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@ -2615,13 +2615,23 @@ static enum ggml_status ggml_metal_graph_compute(
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// simdgroups per threadgroup (a.k.a. warps)
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// for small batches use more simdgroups (needs more tests, to confirm if it's worth it)
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const int64_t nsg = MAX(4, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32));
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//const int64_t nsg = MAX(4, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32));
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const int64_t nsg = 8;
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const size_t smem = nqptg*(ne00 + nsg*(ncpsg + nqptg))*(sizeof(float)/2);
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// require power of 2
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//{
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// int64_t nsgm = 1;
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// while (nsgm < nsg) {
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// nsgm *= 2;
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// }
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// GGML_ASSERT(nsg == nsgm);
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//}
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const size_t smem = (nqptg*(ne00 + nsg*(ncpsg + nqptg)) + nsg*ne00)*(sizeof(float)/2);
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//printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength);
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GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength);
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[encoder setThreadgroupMemoryLength:smem atIndex:0];
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[encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0];
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[encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
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}
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196
ggml-metal.metal
196
ggml-metal.metal
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@ -2457,6 +2457,8 @@ template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f
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template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128, 8, 32>;
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template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256, 8, 32>;
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#define HALF_MAX_HALF half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction.
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template<int64_t D, int64_t Q, int64_t C> // head size, queries per threadgroup, cache items per threadgroup
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kernel void kernel_flash_attn_ext_vec_f16(
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device const char * q,
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@ -2500,6 +2502,7 @@ kernel void kernel_flash_attn_ext_vec_f16(
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const short iq1 = tgpig[0]*Q;
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const short D4 = D/4;
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const short D8 = D/8;
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const short NW = N_SIMDWIDTH;
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const short SH = (C + Q); // shared memory per simdgroup in (half)
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@ -2510,6 +2513,7 @@ kernel void kernel_flash_attn_ext_vec_f16(
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threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4
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threadgroup half * ss = (threadgroup half *) (shared + sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix
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threadgroup half4 * ss4 = (threadgroup half4 *) (shared + sgitg*SH + 1*D); // same as above but in half4
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threadgroup half4 * sr4 = (threadgroup half4 *) (shared + sgitg*D + Q*T); // scratch buffer for the results
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// store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper)
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half4 lo[Q][D4];
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@ -2545,7 +2549,7 @@ kernel void kernel_flash_attn_ext_vec_f16(
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{
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half S[Q] = { [0 ... Q-1] = 0.0h };
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half M[Q] = { [0 ... Q-1] = -INFINITY };
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half M[Q] = { [0 ... Q-1] = -HALF_MAX_HALF };
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// assume K and V are same shape
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const short ne22 = ne12;
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@ -2571,21 +2575,21 @@ kernel void kernel_flash_attn_ext_vec_f16(
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const short iv3 = iq3 / rv3;
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// load the queries from shared memory into local memory
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half4 mq[Q][D4];
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simdgroup_half8x8 mq[Q][D8];
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for (short j = 0; j < Q; ++j) {
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for (short i = tiisg; i < D4; i += NW) {
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//simdgroup_load(mq[j][i], sq + 8*j*T + i*8, T);
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mq[j][i] = sq4[j*T4 + i];
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for (short i = 0; i < D8; ++i) {
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simdgroup_load(mq[j][i], sq + 8*j*T + i*8, T);
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}
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}
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// pointer to the mask
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device const half4 * mp4 = (device const half4 *) (mask + iq1*nb31);
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//device const half4 * mp4 = (device const half4 *) (mask + iq1*nb31);
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device const half * mp = (device const half *) (mask + iq1*nb31);
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// prepare diagonal scale matrix
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//simdgroup_half8x8 mscale(scale);
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half mscale(scale);
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simdgroup_half8x8 mscale(scale);
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//half mscale(scale);
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// loop over the KV cache
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// each simdgroup handles blocks of Q rows and C columns
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@ -2595,55 +2599,83 @@ kernel void kernel_flash_attn_ext_vec_f16(
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break;
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}
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// Q*K^T
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//{
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// for (short cc = 0; cc < C/4; ++cc) {
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// half4 mqk[Q];
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// for (short j = 0; j < Q; ++j) {
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// mqk[j] = 0.0h;
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// }
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// device const half4 * pk4 = (device const half4 *) ((device const char *) k + ((ic + 4*cc)*nb11 + ik2*nb12 + ik3*nb13));
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// for (short i = tiisg; i < D4; i += NW) {
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// half4x4 mk;
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// mk[0] = pk4[i + 0*(nb11/8)];
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// mk[1] = pk4[i + 1*(nb11/8)];
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// mk[2] = pk4[i + 2*(nb11/8)];
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// mk[3] = pk4[i + 3*(nb11/8)];
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// for (short j = 0; j < Q; ++j) {
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// mqk[j] += mq[j][i] * mk;
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// }
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// }
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// // reduce the results from the threads in the simdgroup
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// simdgroup_barrier(mem_flags::mem_none);
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// for (short i = NW/2; i > 0; i /= 2) {
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// if (tiisg < i) {
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// for (short j = 0; j < Q; ++j) {
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// mqk[j] += simd_shuffle_down(mqk[j], i);
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// }
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// }
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// simdgroup_barrier(mem_flags::mem_none);
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// }
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// // mqk = mqk*scale + mask
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// if (tiisg == 0) {
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// for (short j = 0; j < Q; ++j) {
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// half4 mm = mp4[(j*(nb31/sizeof(half)) + ic)/4 + cc];
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// mqk[j] = mqk[j]*mscale + mm;
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// ss4[j*T4 + cc] = mqk[j];
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// }
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// }
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// }
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//}
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// Q*K^T
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{
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for (short cc = 0; cc < C/4; ++cc) {
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half4 mqk[Q];
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for (short cc = 0; cc < C/8; ++cc) {
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simdgroup_half8x8 mqk[Q];
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for (short j = 0; j < Q; ++j) {
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mqk[j] = 0.0h;
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mqk[j] = make_filled_simdgroup_matrix<half, 8>(0.h);
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}
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device const half4 * pk4 = (device const half4 *) ((device const char *) k + ((ic + 4*cc)*nb11 + ik2*nb12 + ik3*nb13));
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device const half * pk = (device const half *) ((device const char *) k + ((ic + 8*cc)*nb11 + ik2*nb12 + ik3*nb13));
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for (short i = tiisg; i < D4; i += NW) {
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half4x4 mk;
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mk[0] = pk4[i + 0*(nb11/8)];
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mk[1] = pk4[i + 1*(nb11/8)];
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mk[2] = pk4[i + 2*(nb11/8)];
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mk[3] = pk4[i + 3*(nb11/8)];
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for (short i = 0; i < D8; ++i) {
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simdgroup_half8x8 mk;
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simdgroup_load(mk, pk + i*8, nb11/sizeof(half), 0, true); // transpose
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for (short j = 0; j < Q; ++j) {
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mqk[j] += mq[j][i] * mk;
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simdgroup_multiply_accumulate(mqk[j], mq[j][i], mk, mqk[j]);
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}
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}
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// reduce the results from the threads in the simdgroup
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simdgroup_barrier(mem_flags::mem_none);
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for (short i = NW/2; i > 0; i /= 2) {
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if (tiisg < i) {
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for (short j = 0; j < Q; ++j) {
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mqk[j] += simd_shuffle_down(mqk[j], i);
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}
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}
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simdgroup_barrier(mem_flags::mem_none);
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}
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// mqk = mqk*scale + mask
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if (tiisg == 0) {
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for (short j = 0; j < Q; ++j) {
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half4 mm = mp4[(j*(nb31/sizeof(half)) + ic)/4 + cc];
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mqk[j] = mqk[j]*mscale + mm;
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for (short j = 0; j < Q; ++j) {
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simdgroup_half8x8 mm;
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simdgroup_load(mm, mp + 8*j*(nb31/sizeof(half)) + ic + 8*cc, nb31/sizeof(half), 0, false);
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simdgroup_multiply_accumulate(mqk[j], mqk[j], mscale, mm);
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ss4[j*T4 + cc] = mqk[j];
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}
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simdgroup_store(mqk[j], ss + 8*j*T + 8*cc, T, 0, false);
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}
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}
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}
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simdgroup_barrier(mem_flags::mem_threadgroup);
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// online softmax
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half ms[Q];
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@ -2655,8 +2687,8 @@ kernel void kernel_flash_attn_ext_vec_f16(
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M[j] = simd_max(max(M[j], s));
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ms[j] = m == -INFINITY ? 0.0h : exp(m - M[j]);
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const half vs = s == -INFINITY ? 0.0h : exp(s - M[j]);
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ms[j] = exp(m - M[j]);
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const half vs = exp(s - M[j]);
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S[j] = S[j]*ms[j] + simd_sum(vs);
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@ -2706,75 +2738,59 @@ kernel void kernel_flash_attn_ext_vec_f16(
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}
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}
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// reduce the warps sequentially
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for (short sg = 1; sg < nsg; ++sg) {
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half S = { 0.0h };
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half M = { -INFINITY };
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threadgroup_barrier(mem_flags::mem_threadgroup);
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threadgroup_barrier(mem_flags::mem_threadgroup);
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// each simdgroup stores its output to shared memory, reusing sq
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if (sgitg == sg) {
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for (short j = 0; j < Q; ++j) {
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for (short i = tiisg; i < D4; i += NW) {
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//simdgroup_store(lo[j][i], sq + 8*j*T + i*8, T, 0, false);
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sq4[j*T4 + i] = lo[j][i];
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}
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}
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// store results to shared memory
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for (short j = 0; j < Q; ++j) {
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for (short i = tiisg; i < D4; i += NW) {
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sr4[i] = lo[j][i];
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}
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}
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threadgroup_barrier(mem_flags::mem_threadgroup);
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// parallel reduce
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for (short r = nsg/2; r > 0; r >>= 1) {
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if (sgitg < r) {
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if (tiisg == 0) {
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for (short j = 0; j < Q; ++j) {
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const half S0 = ss[j*T + 0];
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const half S1 = ss[j*T + r*SH + 0];
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// the first simdgroup accumulates the results from the other simdgroups
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if (sgitg == 0) {
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for (short j = 0; j < Q; ++j) {
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const half S0 = ss[j*T + 0];
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const half S1 = ss[j*T + sg*SH + 0];
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const half M0 = ss[j*T + 1];
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const half M1 = ss[j*T + r*SH + 1];
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const half M0 = ss[j*T + 1];
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const half M1 = ss[j*T + sg*SH + 1];
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const half M = max(M0, M1);
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M = max(M0, M1);
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const half ms0 = exp(M0 - M);
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const half ms1 = exp(M1 - M);
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const half ms0 = M0 == -INFINITY ? 0.0h : exp(M0 - M);
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const half ms1 = M1 == -INFINITY ? 0.0h : exp(M1 - M);
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const half S = S0*ms0 + S1*ms1;
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S = S0*ms0 + S1*ms1;
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if (tiisg == 0) {
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ss[j*T + 0] = S;
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ss[j*T + 1] = M;
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ss[j*T + C + j ] = ms0;
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ss[j*T + C + j + sg*SH] = ms1;
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ss[j*T + C + j ] = ms0;
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ss[j*T + C + j + r*SH] = ms1;
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}
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}
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}
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// O_0 = diag(ms0)*O_0 + diag(ms1)*O_1
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threadgroup_barrier(mem_flags::mem_threadgroup);
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if (sgitg < r) {
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for (short j = 0; j < Q; ++j) {
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for (short i = tiisg; i < D4; i += NW) {
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half4 t = sq4[j*T4 + i];
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half ms0 = ss[j*T + C + j];
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half ms1 = ss[j*T + C + j + sg*SH];
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const half ms0 = ss[j*T + C + j];
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const half ms1 = ss[j*T + C + j + r*SH];
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lo[j][i] = lo[j][i]*ms0 + t*ms1;
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// O_0 = diag(ms0)*O_0 + diag(ms1)*O_1
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for (short i = tiisg; i < D4; i += NW) {
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sr4[i] = sr4[i]*ms0 + sr4[i + r*D4]*ms1;
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}
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}
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}
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}
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// store result to shared memory (reuse sq)
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if (sgitg == 0) {
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for (short j = 0; j < Q; ++j) {
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for (short i = tiisg; i < D4; i += NW) {
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//simdgroup_store(lo[j][i], sq + 8*j*T + i*8, T, 0, false);
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sq4[j*T4 + i] = lo[j][i];
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}
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}
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threadgroup_barrier(mem_flags::mem_threadgroup);
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}
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threadgroup_barrier(mem_flags::mem_threadgroup);
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device float4 * dst4 = (device float4 *) dst;
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// final rescale with 1/S and store to global memory
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@ -2783,7 +2799,7 @@ kernel void kernel_flash_attn_ext_vec_f16(
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const half S = ss[j*T + 0];
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for (short i = tiisg; i < D4; i += NW) {
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dst4[(iq3*ne2*ne1 + iq2 + (iq1 + j)*ne1)*D4 + i] = (float4) sq4[j*T4 + i]/S;
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dst4[(iq3*ne2*ne1 + iq2 + (iq1 + j)*ne1)*D4 + i] = (float4) sr4[i]/S;
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}
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}
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}
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