84 lines
3.2 KiB
Plaintext
84 lines
3.2 KiB
Plaintext
#include <torch/extension.h>
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#include <ATen/cuda/CUDAContext.h>
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namespace cacheflow {
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template<typename scalar_t>
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__global__ void rotary_embedding_neox_kernel(
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scalar_t* __restrict__ out_query, // [num_tokens, num_heads, head_size]
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scalar_t* __restrict__ out_key, // [num_tokens, num_heads, head_size]
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const int64_t* __restrict__ positions, // [num_tokens]
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const scalar_t* __restrict__ query, // [num_tokens, num_heads, head_size]
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const scalar_t* __restrict__ key, // [num_tokens, num_heads, head_size]
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const scalar_t* __restrict__ cos_sin_cache, // [max_position, 2, head_size // 2]
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const int num_heads,
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const int head_size) {
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// Each thread block is responsible for one token.
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const int token_idx = blockIdx.x;
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int64_t pos = positions[token_idx];
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const scalar_t* cache_ptr = cos_sin_cache + pos * head_size;
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const int embed_dim = head_size / 2;
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const int n = num_heads * head_size;
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for (int i = threadIdx.x; i < n; i += blockDim.x) {
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const int idx = token_idx * n + i;
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const int head_idx = i / head_size;
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const int head_offset = i % head_size;
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const int token_head = token_idx * n + head_idx * head_size;
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const bool is_first_half = head_offset < embed_dim;
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const int rot_offset = head_offset % embed_dim;
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const int x_index = rot_offset;
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const int y_index = embed_dim + rot_offset;
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const scalar_t cos = __ldg(cache_ptr + x_index);
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const scalar_t sin = __ldg(cache_ptr + y_index);
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const scalar_t q_x = __ldg(query + token_head + x_index);
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const scalar_t q_y = __ldg(query + token_head + y_index);
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const scalar_t q_cos = is_first_half ? q_x : q_y;
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const scalar_t q_sin = is_first_half ? -q_y : q_x;
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out_query[idx] = q_cos * cos + q_sin * sin;
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const scalar_t k_x = __ldg(key + token_head + x_index);
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const scalar_t k_y = __ldg(key + token_head + y_index);
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const scalar_t k_cos = is_first_half ? k_x : k_y;
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const scalar_t k_sin = is_first_half ? -k_y : k_x;
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out_key[idx] = k_cos * cos + k_sin * sin;
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}
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}
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} // namespace cacheflow
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void rotary_embedding_neox(
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torch::Tensor& out_query, // [num_tokens, num_heads * head_size]
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torch::Tensor& out_key, // [num_tokens, num_heads * head_size]
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torch::Tensor& positions, // [num_tokens]
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torch::Tensor& query, // [num_tokens, num_heads * head_size]
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torch::Tensor& key, // [num_tokens, num_heads * head_size]
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torch::Tensor& cos_sin_cache) // [max_position, head_size]
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{
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int num_tokens = query.size(0);
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int head_size = cos_sin_cache.size(1);
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int num_heads = query.size(1) / head_size;
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dim3 grid(num_tokens);
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dim3 block(std::min(num_heads * head_size, 512));
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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AT_DISPATCH_FLOATING_TYPES_AND_HALF(
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query.scalar_type(),
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"rotary_embedding_neox",
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[&] {
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cacheflow::rotary_embedding_neox_kernel<scalar_t><<<grid, block, 0, stream>>>(
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out_query.data_ptr<scalar_t>(),
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out_key.data_ptr<scalar_t>(),
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positions.data_ptr<int64_t>(),
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query.data_ptr<scalar_t>(),
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key.data_ptr<scalar_t>(),
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cos_sin_cache.data_ptr<scalar_t>(),
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num_heads,
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head_size);
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});
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}
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