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authorKawrakow <48489457+ikawrakow@users.noreply.github.com>2023-06-08 19:46:22 +0300
committerGitHub <noreply@github.com>2023-06-08 19:46:22 +0300
commit0f291e1f65c1d68201e71ce99c89562a36686b6d (patch)
tree5325c9bd1f8954db8862d7021331c8b60840b631 /ggml-metal.metal
parent8fc8179919a11738910db07a800f2b176f8adf09 (diff)
metal : Q6_K implementation (#1752)
* Metal implementation for Q4_K Very slow for now: 42 ms / token, Q4_0 runs in 28 ms/token on my 30-core M2 Max GPU. * Optimizing Q4_K on metal The first token always takes longer, I guess because the metal kernel is being jit-compiled. So, using n = 128 to measure time. At this point Q4_K takes 29.5 ms / token compared to 27.2 ms / token for Q4_0. Quite a bit better than the initial attempt, but still not good enough. * Optimizing q4_K metal dot some more For n = 256 it is now 28.1 ms/token compared to 27 ms/token for q4_0. * Fix after merge with master * Metal implementation for Q6_K Similar to the CUDA implementation. No idea if this is the optimum for Metal, but the few alternative variants I tried all had a lower performance. We get 36.5 ms / token on M2 Max with 30 GPU cores. This corresponds to ~200 GB/second throughput. * clang-tidy : add config back * Much better Q6_K implementation for metal 28.3 ms / token for 7B. Subtracting ~9 ms that is spent in other compute graph operations, we are left with ~19 ms for the matrix multiplications. The model is ~5.5 GB, so we are getting 1000 / 19 * 5.5 = 290 GB/s! --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Diffstat (limited to 'ggml-metal.metal')
-rw-r--r--ggml-metal.metal177
1 files changed, 170 insertions, 7 deletions
diff --git a/ggml-metal.metal b/ggml-metal.metal
index cbcd59a..e851cbd 100644
--- a/ggml-metal.metal
+++ b/ggml-metal.metal
@@ -303,18 +303,37 @@ kernel void kernel_mul_mat_q4_0_f32(
sum[ith] += acc*d;
}
- // accumulate the sum from all threads in the threadgroup
+ //
+ // Accumulate the sum from all threads in the threadgroup
+ // This version is slightly faster than the commented out one below,
+ // which I copy-pasted from ggerganov's q4_0 dot product for metal.
+ //
threadgroup_barrier(mem_flags::mem_threadgroup);
- for (uint i = nth/2; i > 0; i /= 2) {
- if (ith < i) {
- sum[ith] += sum[ith + i];
- }
- threadgroup_barrier(mem_flags::mem_threadgroup);
+ if (ith%4 == 0) {
+ for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i];
}
-
+ threadgroup_barrier(mem_flags::mem_threadgroup);
+ if (ith%16 == 0) {
+ for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i];
+ }
+ 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];
}
+
+ //// accumulate the sum from all threads in the threadgroup
+ //threadgroup_barrier(mem_flags::mem_threadgroup);
+ //for (uint i = nth/2; i > 0; i /= 2) {
+ // if (ith < i) {
+ // sum[ith] += sum[ith + i];
+ // }
+ // threadgroup_barrier(mem_flags::mem_threadgroup);
+ //}
+
+ //if (ith == 0) {
+ // dst[r1*ne0 + r0] = sum[0];
+ //}
}
kernel void kernel_mul_mat_f16_f32(
@@ -515,6 +534,13 @@ typedef struct {
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_k;
+typedef struct {
+ uint8_t ql[QK_K/2]; // quants, lower 4 bits
+ uint8_t qh[QK_K/4]; // quants, upper 2 bits
+ int8_t scales[QK_K/16]; // scales, quantized with 8 bits
+ half d; // super-block scale
+} block_q6_k;
+
static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) {
uchar4 r;
if (j < 4) {
@@ -554,6 +580,38 @@ static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, i
}
}
+static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, int k) {
+ assert(k % QK_K == 0);
+ const int nb = k / QK_K;
+
+ for (int i = 0; i < nb; i++) {
+
+ const float d = x[i].d;
+
+ device const uint8_t * ql = x[i].ql;
+ device const uint8_t * qh = x[i].qh;
+ device const int8_t * sc = x[i].scales;
+
+ for (int n = 0; n < QK_K; n += 128) {
+ for (int l = 0; l < 32; ++l) {
+ int is = l/16;
+ const int8_t q1 = (int8_t)((ql[l + 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+ const int8_t q2 = (int8_t)((ql[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+ const int8_t q3 = (int8_t)((ql[l + 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+ const int8_t q4 = (int8_t)((ql[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+ y[l + 0] = d * sc[is + 0] * q1;
+ y[l + 32] = d * sc[is + 2] * q2;
+ y[l + 64] = d * sc[is + 4] * q3;
+ y[l + 96] = d * sc[is + 6] * q4;
+ }
+ y += 128;
+ ql += 64;
+ qh += 32;
+ sc += 8;
+ }
+ }
+}
+
kernel void kernel_get_rows_q4_k(
device const void * src0,
device const int * src1,
@@ -665,3 +723,108 @@ kernel void kernel_mul_mat_q4_k_f32(
// dst[r1*ne0 + r0] = sum[0];
//}
}
+
+kernel void kernel_get_rows_q6_k(
+ device const void * src0,
+ device const int * src1,
+ device float * dst,
+ constant int64_t & ne00,
+ constant uint64_t & nb01,
+ constant uint64_t & nb1,
+ uint tpig[[thread_position_in_grid]]) {
+ const int i = tpig;
+ const int r = ((device int32_t *) src1)[i];
+
+ dequantize_row_q6_k(
+ (device const block_q6_k *) ((device char *) src0 + r*nb01),
+ (device float *) ((device char *) dst + i*nb1), ne00);
+}
+
+kernel void kernel_mul_mat_q6_k_f32(
+ device const void * src0,
+ device const float * src1,
+ device float * dst,
+ constant int64_t & ne00,
+ constant int64_t & ne01,
+ constant uint64_t & nb00,
+ constant uint64_t & nb01,
+ constant uint64_t & nb02,
+ constant int64_t & ne10,
+ constant int64_t & ne11,
+ constant uint64_t & nb10,
+ constant uint64_t & nb11,
+ constant uint64_t & nb12,
+ constant int64_t & ne0,
+ constant int64_t & ne1,
+ threadgroup float * sum [[threadgroup(0)]],
+ uint2 tgpig[[threadgroup_position_in_grid]],
+ uint2 tpig[[thread_position_in_grid]], // we don't use this for now
+ uint2 tpitg[[thread_position_in_threadgroup]],
+ uint2 tptg[[threads_per_threadgroup]]) {
+
+ const uint8_t kmask1 = 0x03;
+ const uint8_t kmask2 = 0x0C;
+ const uint8_t kmask3 = 0x30;
+ const uint8_t kmask4 = 0xC0;
+
+ const int nb = ne00/QK_K;
+
+ const int64_t r0 = tgpig.x;
+ const int64_t r1 = tgpig.y;
+
+ device const block_q6_k * x = (device const block_q6_k *) src0 + r0*nb;
+ device const float * yy = (device const float *) src1 + r1*ne10;
+
+ const uint nth = tptg.x*tptg.y;
+ const uint ith = tptg.y*tpitg.x + tpitg.y;
+
+ const int step = QK_K / tptg.y; // we expect this to be 16
+ const int iqs = step * tpitg.y; // 0...240 in steps of 16
+ const int ip = iqs / 128; // 0 or 1
+ const int il = (iqs - 128*ip)/16; // 0...7
+ const int n = 4;
+ const int is = 8*ip + (n*il)/16;
+
+ float sumf = 0;
+ for (int i = tpitg.x; i < nb; i += tptg.x) {
+
+ device const uint8_t * ql = x[i].ql + 64*ip + n*il;
+ device const uint8_t * qh = x[i].qh + 32*ip + n*il;
+ device const int8_t * sc = x[i].scales + is;
+
+ device const float * y = yy + i * QK_K + 128*ip + n*il;
+
+ const float dall = x[i].d;
+
+ float4 sums = {0.f, 0.f, 0.f, 0.f};
+ for (int l = 0; l < n; ++l) {
+ sums[0] += y[l+ 0] * ((int8_t)((ql[l+ 0] & 0xF) | ((qh[l] & kmask1) << 4)) - 32);
+ sums[1] += y[l+32] * ((int8_t)((ql[l+32] & 0xF) | ((qh[l] & kmask2) << 2)) - 32);
+ sums[2] += y[l+64] * ((int8_t)((ql[l+ 0] >> 4) | ((qh[l] & kmask3) << 0)) - 32);
+ sums[3] += y[l+96] * ((int8_t)((ql[l+32] >> 4) | ((qh[l] & kmask4) >> 2)) - 32);
+ }
+
+ sumf += dall * (sums[0] * sc[0] + sums[1] * sc[2] + sums[2] * sc[4] + sums[3] * sc[6]);
+
+ }
+
+ sum[ith] = sumf;
+
+ //
+ // Accumulate the sum from all threads in the threadgroup
+ //
+ threadgroup_barrier(mem_flags::mem_threadgroup);
+ if (ith%4 == 0) {
+ for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i];
+ }
+ threadgroup_barrier(mem_flags::mem_threadgroup);
+ if (ith%16 == 0) {
+ for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i];
+ }
+ 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];
+ }
+
+}