Optimize q4_matmul: optimize non-128 group sizes

exllama_ext/cuda_func/q4_matmul.cu

@@ -11,6 +11,8 @@
 const int THREADS_X = 32;       // Block size and thread count along columns in w and out
 const int THREADS_Y = 1;        // Block size and thread count along rows in x and out
 
+const int GROUP_STEP = 32;      // Assumed group size when block_size_z % groupsize != 0
+
 typedef void (*fp_q4_matmul_kernel)
 (
     const half*,
@@ -52,7 +54,7 @@ __global__ void q4_matmul_kernel
     int x_column = block_size_z * blockIdx.z;
     int x_column_end = min(dim, block_size_z * (blockIdx.z + 1));
 
-    int w_column = THREADS_X * blockIdx.x + threadIdx.x;  // assume width of weight matrix divisible by THREADS_X (32)
+    int w_column = THREADS_X * blockIdx.x + threadIdx.x;  // assume width of weight matrix divisible by THREADS_X
     int x_row = THREADS_Y * blockIdx.y + threadIdx.y;
 
     int iterations = (x_column_end - x_column) / 8;
@@ -109,11 +111,11 @@ __global__ void q4_matmul_kernel
     }
     else
     {
-        // Otherwise assume groupsize is a multiple of 8, do 8 columns per iteration and trust the cache
+        // Otherwise assume groupsize is a multiple of GROUP_STEP, do GROUP_STEP columns per iteration and trust the cache
 
-        for (int k = x_column; k < x_column + iterations * 8; k += 8)
+        for (int k = x_column; k < x_column + iterations * 8; k += GROUP_STEP)
         {
-            for (int i = threadIdx.x; i < 8; i += THREADS_X)
+            for (int i = threadIdx.x; i < GROUP_STEP; i += THREADS_X)
             {
                 if constexpr (use_x_map) x_cache_h[i] = *x_.item_ptr(x_row, x_map[k + i]);
                 else                     x_cache_h[i] = *x_.item_ptr(x_row, k + i);
@@ -125,14 +127,14 @@ __global__ void q4_matmul_kernel
                 int group = k / groupsize;
                 half2 w_scale = w_scales_.item_half2half2(group, w_column);
                 uint32_t w_zero = w_zeros_.item(group, w_column) + 1;
-                acc = dot_product_8(acc, x_cache, w_, k, w_column, w_scale, w_zero, 1);
+                acc = dot_product_8(acc, x_cache, w_, k, w_column, w_scale, w_zero, GROUP_STEP / 8);
             }
             else
             {
                 int group = k / groupsize;
                 half w_scale = w_scales_.item(group, w_column);
                 uint32_t w_zero = w_zeros_.item(group, w_column) + 1;
-                acc_h = dot_product_8_h(acc_h, x_cache_h, w_, k, w_column, w_scale, w_zero, 1);
+                acc_h = dot_product_8_h(acc_h, x_cache_h, w_, k, w_column, w_scale, w_zero, GROUP_STEP / 8);
             }
             __syncthreads();
         }
@@ -224,7 +226,8 @@ void q4_matmul_cuda
     );
 
     fp_q4_matmul_kernel kernel = q4_matmul_kernel_pick(tuningParams, block_size_z, w->groupsize, x_map);
-    kernel<<<blocks, threads, w->groupsize * sizeof(half), alt_stream>>>(x_mapped, w->cuda_qweight, out, w->cuda_scales, w->cuda_qzeros, height, dim, width, w->groupsize, block_size_z, x_map, no_zero);
+    int shared_mem = (block_size_z % w->groupsize == 0 ? w->groupsize : GROUP_STEP) * sizeof(half);
+    kernel<<<blocks, threads, shared_mem, alt_stream>>>(x_mapped, w->cuda_qweight, out, w->cuda_scales, w->cuda_qzeros, height, dim, width, w->groupsize, block_size_z, x_map, no_zero);
 }
 
 void q4_matmul_recons_cuda