48 lines
1.8 KiB
Plaintext
48 lines
1.8 KiB
Plaintext
#include "tsembd.cuh"
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static __global__ void timestep_embedding_f32(const float * timesteps, float * dst, const int nb1, const int dim, const int max_period) {
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// blockIDx.y: idx of timesteps->ne[0]
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// blockIDx.x: idx of ((dim + 1) / 2) / BLOCK_SIZE
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int i = blockIdx.y;
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int j = threadIdx.x + blockIdx.x * blockDim.x;
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float * embed_data = (float *)((char *)dst + i*nb1);
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if (dim % 2 != 0 && j == ((dim + 1) / 2)) {
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embed_data[dim] = 0.f;
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}
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int half = dim / 2;
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if (j >= half) {
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return;
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}
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float timestep = timesteps[i];
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float freq = (float)expf(-logf(max_period) * j / half);
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float arg = timestep * freq;
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embed_data[j] = cosf(arg);
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embed_data[j + half] = sinf(arg);
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}
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static void timestep_embedding_f32_cuda(const float * x, float * dst, const int ne00, const int nb1,
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const int dim, const int max_period, cudaStream_t stream) {
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int half_ceil = (dim + 1) / 2;
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int num_blocks = (half_ceil + CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE;
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dim3 gridDim(num_blocks, ne00, 1);
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timestep_embedding_f32<<<gridDim, CUDA_TIMESTEP_EMBEDDING_BLOCK_SIZE, 0, stream>>>(x, dst, nb1, dim, max_period);
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}
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void ggml_cuda_op_timestep_embedding(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * src0 = dst->src[0];
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const float * src0_d = (const float *)src0->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(dst->type == GGML_TYPE_F32);
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const int dim = dst->op_params[0];
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const int max_period = dst->op_params[1];
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timestep_embedding_f32_cuda(src0_d, dst_d, src0->ne[0], dst->nb[1], dim, max_period, stream);
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}
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