implement unary REGLU/GEGLU/SWIGLU cuda ops

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -2246,6 +2246,15 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
                 case GGML_UNARY_OP_EXP:
                     ggml_cuda_op_exp(ctx, dst);
                     break;
+                case GGML_UNARY_OP_REGLU:
+                    ggml_cuda_op_reglu(ctx, dst);
+                    break;
+                case GGML_UNARY_OP_GEGLU:
+                    ggml_cuda_op_geglu(ctx, dst);
+                    break;
+                case GGML_UNARY_OP_SWIGLU:
+                    ggml_cuda_op_swiglu(ctx, dst);
+                    break;
                 default:
                     return false;
             }
@@ -3039,6 +3048,10 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 case GGML_UNARY_OP_TANH:
                 case GGML_UNARY_OP_EXP:
                     return ggml_is_contiguous(op->src[0]);
+                case GGML_UNARY_OP_REGLU:
+                case GGML_UNARY_OP_GEGLU:
+                case GGML_UNARY_OP_SWIGLU:
+                    return ggml_is_contiguous_1(op->src[0]);
                 default:
                     return false;
             }

ggml/src/ggml-cuda/unary.cu

@@ -196,6 +196,62 @@ void ggml_cuda_op_log(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     ggml_cuda_op_unary<op_log>(ctx, dst);
 }
 
+/* gated ops */
+
+template <float (*op)(float), typename T>
+static __global__ void unary_gated_op_kernel(const T * x, T * dst, const int k, const int n, const int o) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    // perform base op on first half of row and multiply with gate in second half
+    const int j = (i / n) * o + (i % n);
+    dst[i] = (T)(op((float)x[j]) * (float)x[j + n]);
+}
+
+template <float (*op)(float), typename T>
+static void unary_gated_cuda(const T * x, T * dst, const int k, const int n, const int o, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_NEG_BLOCK_SIZE - 1) / CUDA_NEG_BLOCK_SIZE;
+    unary_gated_op_kernel<op><<<num_blocks, CUDA_NEG_BLOCK_SIZE, 0, stream>>>(x, dst, k, n, o);
+}
+
+template <float (*op)(float)>
+void ggml_cuda_op_unary_gated(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const void * src0_d = src0->data;
+    void * dst_d = dst->data;
+    const int nc = src0->ne[0] / 2;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous_1(src0));
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(src0->type == dst->type);
+    GGML_ASSERT(dst->ne[0] >= nc);
+    GGML_ASSERT(ggml_nrows(dst) >= ggml_nrows(src0));
+
+    if (src0->type == GGML_TYPE_F16) {
+        unary_gated_cuda<op>((const half *)src0_d, (half *)dst_d, ggml_nelements(dst), nc, src0->nb[1] / sizeof(half), stream);
+    } else {
+        unary_gated_cuda<op>((const float *)src0_d, (float *)dst_d, ggml_nelements(dst), nc, src0->nb[1] / sizeof(float), stream);
+    }
+}
+
+void ggml_cuda_op_reglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_unary_gated<op_relu>(ctx, dst);
+}
+
+void ggml_cuda_op_geglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_unary_gated<op_gelu>(ctx, dst);
+}
+
+void ggml_cuda_op_swiglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_unary_gated<op_silu>(ctx, dst);
+}
+
 /* silu_back */
 
 static __device__ __forceinline__ float op_silu_back(float grad, float x) {

ggml/src/ggml-cuda/unary.cuh

@@ -57,3 +57,9 @@ void ggml_cuda_op_sin(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 void ggml_cuda_op_cos(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_log(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_reglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_geglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_swiglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);