softcap: CUDA

ggml/src/ggml-cuda.cu

@@ -24,6 +24,7 @@
 #include "ggml-cuda/quantize.cuh"
 #include "ggml-cuda/rope.cuh"
 #include "ggml-cuda/scale.cuh"
+#include "ggml-cuda/softcap.cuh"
 #include "ggml-cuda/softmax.cuh"
 #include "ggml-cuda/sumrows.cuh"
 #include "ggml-cuda/tsembd.cuh"
@@ -2261,6 +2262,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_SCALE:
             ggml_cuda_op_scale(ctx, dst);
             break;
+        case GGML_OP_SOFTCAP:
+            ggml_cuda_op_softcap(ctx, dst);
+            break;
         case GGML_OP_SQR:
             ggml_cuda_op_sqr(ctx, dst);
             break;

ggml/src/ggml-cuda/softcap.cu

@@ -0,0 +1,31 @@
+#include "softcap.cuh"
+
+static __global__ void softcap_f32(const float * x, float * dst, const float * scales, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = scales[1] * tanh(scales[0]*x[i]);
+}
+
+static void softcap_f32_cuda(const float * x, float * dst, const float * scales, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_SOFTCAP_BLOCK_SIZE - 1) / CUDA_SOFTCAP_BLOCK_SIZE;
+    softcap_f32<<<num_blocks, CUDA_SOFTCAP_BLOCK_SIZE, 0, stream>>>(x, dst, scales, k);
+}
+
+void ggml_cuda_op_softcap(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const float * src0_d = (const float *)src0->data;
+    float * dst_d = (float *)dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    float scales[2];
+    memcpy(scales, dst->op_params, sizeof(scales));
+
+    softcap_f32_cuda(src0_d, dst_d, scales, ggml_nelements(src0), stream);
+}

ggml/src/ggml-cuda/softcap.cuh

@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_SOFTCAP_BLOCK_SIZE 256
+
+void ggml_cuda_op_softcap(ggml_backend_cuda_context & ctx, ggml_tensor * dst);