Fix quantized k-cache without FA (#105)

* Added Johannes' changes, still getting NaNs with quantized k-cache.

Also getting NaN's on Johannes's mainline branch.

* This fixes it

---------

Co-authored-by: Iwan Kawrakow <[email protected]>

Author: ikawrakow

ggml/src/ggml-cuda.cu

@@ -1170,8 +1170,8 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
     void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) {
 
     GGML_ASSERT(ggml_backend_buffer_is_cuda(src->buffer));
-    char * src_ptr = (char *) src->data;
-    char * dst_ptr = (char *) dst;
+    const char * src_ptr = (const char *) src->data;
+    char       * dst_ptr = (char       *) dst;
 
     const int64_t ne0 = src->ne[0];
     const int64_t nb0 = src->nb[0];
@@ -1182,7 +1182,7 @@ static cudaError_t ggml_cuda_cpy_tensor_2d(
     const int64_t ts = ggml_type_size(type);
     const int64_t rs = ggml_row_size(type, ne0);
     const int64_t bs = ggml_blck_size(type);
-    int64_t i1_diff = i1_high - i1_low;
+    const int64_t i1_diff = i1_high - i1_low;
 
     const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3;
     if (nb0 == ts && nb1 == rs) {
@@ -1532,10 +1532,14 @@ static void ggml_cuda_op_mul_mat(
         if (src0_is_contiguous) {
             dev[id].src0_dd = split ? (char *) src0_extra->data_device[id] : (char *) src0->data;
         } else {
-            dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), ggml_nbytes(src0));
+            // If src0 is not contiguous it will be copied to a temporary buffer, it may then be necessary to clear padding.
+            const size_t nbytes_data    = ggml_nbytes(src0);
+            const size_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING);
+            dev[id].src0_dd = dev[id].src0_dd_alloc.alloc(ctx.pool(id), nbytes_data + nbytes_padding);
+            CUDA_CHECK(cudaMemsetAsync(dev[id].src0_dd, 0, nbytes_data + nbytes_padding, stream));
         }
 
-        // If src0 is on a temporary compute buffers (partial offloading) there may be some padding that needs to be cleared:
+        // If src0 is on a temporary compute buffer (partial offloading) there may be some padding that needs to be cleared:
         if (ne00 % MATRIX_ROW_PADDING != 0 && ggml_is_quantized(src0->type) && ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE && src0->view_src == nullptr) {
             const int64_t nbytes_data    = ggml_row_size(src0->type, (dev[id].row_high - dev[id].row_low)*ne00);
             const int64_t nbytes_padding = ggml_row_size(src0->type, MATRIX_ROW_PADDING - ne00 % MATRIX_ROW_PADDING);

ggml/src/ggml-cuda/mmq.cu

@@ -8,16 +8,14 @@ void ggml_cuda_op_mul_mat_q(
 
     const int64_t ne00 = src0->ne[0];
 
-    const int64_t nb01 = src0->nb[1];
-
     const int64_t ne10 = src1->ne[0];
     const int64_t ne11 = src1->ne[1];
     GGML_ASSERT(ne10 % QK8_1 == 0);
 
     const int64_t ne0 = dst->ne[0];
 
     const int64_t row_diff = row_high - row_low;
-    const int64_t stride00 = nb01 / ggml_type_size(src0->type);
+    const int64_t stride00 = ne00 / ggml_blck_size(src0->type);
 
     int id = ggml_cuda_get_device();
     const int compute_capability = ggml_cuda_info().devices[id].cc;

ggml/src/ggml-cuda/quantize.cu

@@ -84,7 +84,8 @@ static __global__ void quantize_mmq_q8_1(
         }
     }
 
-    const float d_inv = 127.0f / amax;
+    const float d = amax/127.f;
+    const float d_inv = d > 0 ? 1/d : 0.f;
     char4 q;
     q.x = roundf(xi.x*d_inv);
     q.y = roundf(xi.y*d_inv);
@@ -106,8 +107,6 @@ static __global__ void quantize_mmq_q8_1(
             return;
         }
 
-        const float d = 1.0f / d_inv;
-
         y[ib].d2s6[iqs/64] = d;
 
         return;
@@ -117,8 +116,6 @@ static __global__ void quantize_mmq_q8_1(
         return;
     }
 
-    const float d = 1.0f / d_inv;
-
     if (ds_layout == MMQ_Q8_1_DS_LAYOUT_DS4) {
         y[ib].ds4[iqs/32] = make_half2(d, sum);
     } else {