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https://github.com/ggml-org/llama.cpp.git
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add CUDA
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@@ -1,18 +1,18 @@
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#include "scale.cuh"
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static __global__ void scale_f32(const float * x, float * dst, const float scale, const int k) {
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static __global__ void scale_f32(const float * x, float * dst, const float scale, const float bias, const int k) {
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const int i = blockDim.x*blockIdx.x + threadIdx.x;
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if (i >= k) {
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return;
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}
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dst[i] = scale * x[i];
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dst[i] = scale * x[i] + bias;
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}
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static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) {
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static void scale_f32_cuda(const float * x, float * dst, const float scale, const float bias, const int k, cudaStream_t stream) {
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const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
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scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
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scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, bias, k);
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}
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void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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@@ -24,8 +24,8 @@ void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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GGML_ASSERT(src0->type == GGML_TYPE_F32);
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GGML_ASSERT( dst->type == GGML_TYPE_F32);
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float scale;
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memcpy(&scale, dst->op_params, sizeof(float));
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float scale = ((const float *)(dst->op_params))[0];
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float bias = ((const float *)(dst->op_params))[1];
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scale_f32_cuda(src0_d, dst_d, scale, ggml_nelements(src0), stream);
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scale_f32_cuda(src0_d, dst_d, scale, bias, ggml_nelements(src0), stream);
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}
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