1 files changed, 77 insertions, 10 deletions

diff --git a/ggml-cuda/quantize.cu b/ggml-cuda/quantize.cu
index 7578c4b6..b4678682 100644
--- a/ggml-cuda/quantize.cu
+++ b/ggml-cuda/quantize.cu
@@ -1,22 +1,23 @@
 #include "quantize.cuh"
+#include <cstdint>
 
-static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int64_t kx, const int64_t kx_padded) {
-    const int64_t ix = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
+static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int64_t kx, const int64_t kx0_padded) {
+    const int64_t ix0 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
 
-    if (ix >= kx_padded) {
+    if (ix0 >= kx0_padded) {
         return;
     }
 
-    const int64_t iy = (int64_t)blockDim.y*blockIdx.y + threadIdx.y;
+    const int64_t ix1 = blockIdx.y;
 
-    const int64_t i_padded = (int64_t)iy*kx_padded + ix;
+    const int64_t i_padded = ix1*kx0_padded + ix0;
 
     block_q8_1 * y = (block_q8_1 *) vy;
 
     const int64_t ib = i_padded / QK8_1; // block index
     const int64_t iqs = i_padded % QK8_1; // quant index
 
-    const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
+    const float xi = ix0 < kx ? x[ix1*kx + ix0] : 0.0f;
     float amax = fabsf(xi);
     float sum = xi;
 
@@ -36,10 +37,76 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest
     reinterpret_cast<half&>(y[ib].ds.y) = sum;
 }
 
-void quantize_row_q8_1_cuda(const float * x, void * vy, const int64_t kx, const int64_t ky, const int64_t kx_padded, cudaStream_t stream) {
-    const int64_t block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
-    const dim3 num_blocks(block_num_x, ky, 1);
+template <bool need_sum>
+static __global__ void quantize_mmq_q8_1(
+    const float * __restrict__ x, void * __restrict__ vy, const int64_t kx0, const int64_t kx1, const int64_t kx0_padded) {
+
+    const int64_t ix0 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (ix0 >= kx0_padded) {
+        return;
+    }
+
+    const int64_t ix1 = kx1*blockIdx.z + blockIdx.y;
+
+    block_q8_1_mmq * y = (block_q8_1_mmq *) vy;
+
+    const int64_t ib0 = blockIdx.z*(gridDim.y*gridDim.x*blockDim.x/(4*QK8_1)); // first block of channel
+    const int64_t ib  = ib0 + (ix0 / (4*QK8_1))*kx1 + blockIdx.y;              // block index in channel
+    const int64_t iqs = ix0 % (4*QK8_1);                                       // quant index in block
+
+    const float xi = ix0 < kx0 ? x[ix1*kx0 + ix0] : 0.0f;
+    float amax = fabsf(xi);
+
+    amax = warp_reduce_max(amax);
+
+    float sum;
+    if (need_sum) {
+        sum = warp_reduce_sum(xi);
+    }
+
+    const float d = amax / 127;
+    const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
+
+    y[ib].qs[iqs] = q;
+
+    if (iqs % QK8_1 != 0) {
+        return;
+    }
+
+    if (need_sum) {
+        y[ib].ds[iqs/QK8_1] = make_half2(d, sum);
+    } else {
+        ((float *) y[ib].ds)[iqs/QK8_1] = d;
+    }
+}
+
+void quantize_row_q8_1_cuda(
+    const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels,
+    const int64_t kx0_padded, const ggml_type type_x, cudaStream_t stream) {
+
+    GGML_ASSERT(kx0_padded % QK8_1 == 0);
+
+    const int64_t block_num_x = (kx0_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    const dim3 num_blocks(block_num_x, kx1*channels, 1);
     const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
-    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
+    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx0_padded);
+
+    GGML_UNUSED(type_x);
 }
 
+void quantize_mmq_q8_1_cuda(
+    const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels,
+    const int64_t kx0_padded, const ggml_type type_x, cudaStream_t stream) {
+
+    GGML_ASSERT(kx0_padded % (4*QK8_1) == 0);
+
+    const int64_t block_num_x = (kx0_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
+    const dim3 num_blocks(block_num_x, kx1, channels);
+    const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1);
+    if (mmq_need_sum(type_x)) {
+        quantize_mmq_q8_1<true><<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
+    } else {
+        quantize_mmq_q8_1<false><<<num_blocks, block_size, 0, stream>>>(x, vy, kx0, kx1, kx0_padded);
+    }
+}