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#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);
}
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