48 lines
1.9 KiB
Plaintext
48 lines
1.9 KiB
Plaintext
#include "acc.cuh"
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static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne,
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const int ne10, const int ne11, const int ne12,
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const int nb1, const int nb2, int offset) {
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const int i = blockDim.x * blockIdx.x + threadIdx.x;
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if (i >= ne) {
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return;
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}
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int src1_idx = i - offset;
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int oz = src1_idx / nb2;
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int oy = (src1_idx - (oz * nb2)) / nb1;
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int ox = src1_idx % nb1;
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if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
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dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
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} else {
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dst[i] = x[i];
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}
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}
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static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements,
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const int ne10, const int ne11, const int ne12,
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const int nb1, const int nb2, const int offset, cudaStream_t stream) {
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int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
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acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset);
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}
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void ggml_cuda_op_acc(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * src0 = dst->src[0];
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const ggml_tensor * src1 = dst->src[1];
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const float * src0_d = (const float *)src0->data;
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const float * src1_d = (const float *)src1->data;
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float * dst_d = (float *)dst->data;
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cudaStream_t stream = ctx.stream();
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GGML_ASSERT(src0->type == GGML_TYPE_F32);
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GGML_ASSERT(src1->type == GGML_TYPE_F32);
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GGML_ASSERT( dst->type == GGML_TYPE_F32);
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GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
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int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
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int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
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// int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
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int offset = dst->op_params[3] / 4; // offset in bytes
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acc_f32_cuda(src0_d, src1_d, dst_d, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, stream);
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}
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