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authorMitja Felicijan <mitja.felicijan@gmail.com>2026-02-12 20:57:17 +0100
committerMitja Felicijan <mitja.felicijan@gmail.com>2026-02-12 20:57:17 +0100
commitb333b06772c89d96aacb5490d6a219fba7c09cc6 (patch)
tree211df60083a5946baa2ed61d33d8121b7e251b06 /llama.cpp/ggml/src/ggml-cuda/softcap.cu
downloadllmnpc-b333b06772c89d96aacb5490d6a219fba7c09cc6.tar.gz
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Diffstat (limited to 'llama.cpp/ggml/src/ggml-cuda/softcap.cu')
-rw-r--r--llama.cpp/ggml/src/ggml-cuda/softcap.cu34
1 files changed, 34 insertions, 0 deletions
diff --git a/llama.cpp/ggml/src/ggml-cuda/softcap.cu b/llama.cpp/ggml/src/ggml-cuda/softcap.cu
new file mode 100644
index 0000000..40dfe45
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-cuda/softcap.cu
@@ -0,0 +1,34 @@
+#include "softcap.cuh"
+
+static __global__ void softcap_f32(const float * x, float * dst, const float scale, const float softcap, const int k) {
+ const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+ if (i >= k) {
+ return;
+ }
+
+ dst[i] = tanhf(scale * x[i]) * softcap;
+}
+
+static void softcap_f32_cuda(const float * x, float * dst, const float scale, const float softcap, 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, scale, softcap, k);
+}
+
+// fused GGML_OP_SCALE + GGML_UNARY_OP_TANH + GGML_OP_SCALE
+void ggml_cuda_op_softcap(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * src) {
+ const ggml_tensor * src0 = src->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 scale;
+ float softcap;
+ memcpy(&scale, (float *) src->op_params + 0, sizeof(float));
+ memcpy(&softcap, (float *) dst->op_params + 0, sizeof(float));
+
+ softcap_f32_cuda(src0_d, dst_d, scale, softcap, ggml_nelements(src0), stream);
+}