279 lines
12 KiB
Plaintext
279 lines
12 KiB
Plaintext
#include <torch/all.h>
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#include <ATen/cuda/CUDAContext.h>
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#include <c10/cuda/CUDAGuard.h>
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#include "cuda_compat.h"
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#include "dispatch_utils.h"
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namespace vllm {
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template <typename scalar_t, bool IS_NEOX>
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inline __device__ void apply_token_rotary_embedding(
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scalar_t* __restrict__ arr, const scalar_t* __restrict__ cos_ptr,
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const scalar_t* __restrict__ sin_ptr, int rot_offset, int embed_dim) {
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int x_index, y_index;
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scalar_t cos, sin;
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if (IS_NEOX) {
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// GPT-NeoX style rotary embedding.
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x_index = rot_offset;
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y_index = embed_dim + rot_offset;
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cos = VLLM_LDG(cos_ptr + x_index);
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sin = VLLM_LDG(sin_ptr + x_index);
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} else {
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// GPT-J style rotary embedding.
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x_index = 2 * rot_offset;
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y_index = 2 * rot_offset + 1;
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cos = VLLM_LDG(cos_ptr + x_index / 2);
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sin = VLLM_LDG(sin_ptr + x_index / 2);
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}
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const scalar_t x = arr[x_index];
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const scalar_t y = arr[y_index];
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arr[x_index] = x * cos - y * sin;
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arr[y_index] = y * cos + x * sin;
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}
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template <typename scalar_t, bool IS_NEOX>
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inline __device__ void apply_rotary_embedding(
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scalar_t* __restrict__ query, // [batch_size, seq_len, num_heads,
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// head_size] or [num_tokens, num_heads,
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// head_size]
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scalar_t* __restrict__ key, // [batch_size, seq_len, num_kv_heads,
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// head_size] or [num_tokens, num_kv_heads,
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// head_size]
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const scalar_t* cache_ptr, const int head_size, const int num_heads,
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const int num_kv_heads, const int rot_dim, const int token_idx,
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const int64_t query_stride, const int64_t key_stride) {
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const int embed_dim = rot_dim / 2;
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const scalar_t* cos_ptr = cache_ptr;
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const scalar_t* sin_ptr = cache_ptr + embed_dim;
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const int nq = num_heads * embed_dim;
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for (int i = threadIdx.x; i < nq; i += blockDim.x) {
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const int head_idx = i / embed_dim;
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const int64_t token_head = token_idx * query_stride + head_idx * head_size;
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const int rot_offset = i % embed_dim;
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apply_token_rotary_embedding<scalar_t, IS_NEOX>(
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query + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
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}
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const int nk = num_kv_heads * embed_dim;
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for (int i = threadIdx.x; i < nk; i += blockDim.x) {
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const int head_idx = i / embed_dim;
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const int64_t token_head = token_idx * key_stride + head_idx * head_size;
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const int rot_offset = i % embed_dim;
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apply_token_rotary_embedding<scalar_t, IS_NEOX>(
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key + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
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}
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}
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template <typename scalar_t, bool IS_NEOX>
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__global__ void rotary_embedding_kernel(
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const int64_t* __restrict__ positions, // [batch_size, seq_len] or
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// [num_tokens]
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scalar_t* __restrict__ query, // [batch_size, seq_len, num_heads,
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// head_size] or [num_tokens, num_heads,
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// head_size]
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scalar_t* __restrict__ key, // [batch_size, seq_len, num_kv_heads,
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// head_size] or [num_tokens, num_kv_heads,
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// head_size]
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const scalar_t* __restrict__ cos_sin_cache, // [max_position, 2, rot_dim //
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// 2]
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const int rot_dim, const int64_t query_stride, const int64_t key_stride,
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const int num_heads, const int num_kv_heads, 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 * rot_dim;
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apply_rotary_embedding<scalar_t, IS_NEOX>(
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query, key, cache_ptr, head_size, num_heads, num_kv_heads, rot_dim,
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token_idx, query_stride, key_stride);
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}
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template <typename scalar_t, bool IS_NEOX>
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__global__ void batched_rotary_embedding_kernel(
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const int64_t* __restrict__ positions, // [batch_size, seq_len] or
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// [num_tokens]
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scalar_t* __restrict__ query, // [batch_size, seq_len, num_heads,
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// head_size] or [num_tokens, num_heads,
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// head_size]
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scalar_t* __restrict__ key, // [batch_size, seq_len, num_kv_heads,
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// head_size] or [num_tokens, num_kv_heads,
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// head_size]
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const scalar_t* __restrict__ cos_sin_cache, // [max_position, 2, rot_dim //
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// 2]
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const int64_t* __restrict__ cos_sin_cache_offsets, // [batch_size, seq_len]
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// or [num_tokens]
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const int rot_dim, const int64_t query_stride, const int64_t key_stride,
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const int num_heads, const int num_kv_heads, 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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int64_t cos_sin_cache_offset = cos_sin_cache_offsets[token_idx];
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const scalar_t* cache_ptr =
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cos_sin_cache + (cos_sin_cache_offset + pos) * rot_dim;
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apply_rotary_embedding<scalar_t, IS_NEOX>(
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query, key, cache_ptr, head_size, num_heads, num_kv_heads, rot_dim,
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token_idx, query_stride, key_stride);
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}
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} // namespace vllm
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void rotary_embedding(
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torch::Tensor& positions, // [batch_size, seq_len] or [num_tokens]
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torch::Tensor& query, // [batch_size, seq_len, num_heads * head_size] or
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// [num_tokens, num_heads * head_size] or
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// [batch_size, seq_len, num_heads, head_size] or
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// [num_tokens, num_heads, head_size]
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torch::Tensor& key, // [batch_size, seq_len, num_kv_heads * head_size] or
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// [num_tokens, num_kv_heads * head_size] or
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// [batch_size, seq_len, num_heads, head_size] or
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// [num_tokens, num_heads, head_size]
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int64_t head_size,
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torch::Tensor& cos_sin_cache, // [max_position, rot_dim]
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bool is_neox) {
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// num_tokens = batch_size * seq_len
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int64_t num_tokens = positions.numel();
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int positions_ndim = positions.dim();
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// Make sure num_tokens dim is consistent across positions, query, and key.
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TORCH_CHECK(
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positions_ndim == 1 || positions_ndim == 2,
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"positions must have shape [num_tokens] or [batch_size, seq_len]");
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if (positions_ndim == 1) {
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TORCH_CHECK(
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query.size(0) == positions.size(0) && key.size(0) == positions.size(0),
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"query, key and positions must have the same number of tokens");
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}
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if (positions_ndim == 2) {
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TORCH_CHECK(
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query.size(0) == positions.size(0) &&
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key.size(0) == positions.size(0) &&
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query.size(1) == positions.size(1) &&
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key.size(1) == positions.size(1),
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"query, key and positions must have the same batch_size and seq_len");
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}
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// Make sure head_size is valid for query and key
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// hidden_size = num_heads * head_size
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int query_hidden_size = query.numel() / num_tokens;
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int key_hidden_size = key.numel() / num_tokens;
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TORCH_CHECK(query_hidden_size % head_size == 0);
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TORCH_CHECK(key_hidden_size % head_size == 0);
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// Make sure query and key have consistent number of heads
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int num_heads = query_hidden_size / head_size;
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int num_kv_heads = key_hidden_size / head_size;
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TORCH_CHECK(num_heads % num_kv_heads == 0);
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int rot_dim = cos_sin_cache.size(1);
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int seq_dim_idx = positions_ndim - 1;
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int64_t query_stride = query.stride(seq_dim_idx);
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int64_t key_stride = key.stride(seq_dim_idx);
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dim3 grid(num_tokens);
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dim3 block(std::min<int64_t>(num_heads * rot_dim / 2, 512));
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const at::cuda::OptionalCUDAGuard device_guard(device_of(query));
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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VLLM_DISPATCH_FLOATING_TYPES(query.scalar_type(), "rotary_embedding", [&] {
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if (is_neox) {
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vllm::rotary_embedding_kernel<scalar_t, true><<<grid, block, 0, stream>>>(
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positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
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key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(), rot_dim,
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query_stride, key_stride, num_heads, num_kv_heads, head_size);
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} else {
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vllm::rotary_embedding_kernel<scalar_t, false>
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<<<grid, block, 0, stream>>>(
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positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
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key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(),
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rot_dim, query_stride, key_stride, num_heads, num_kv_heads,
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head_size);
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}
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});
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}
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/*
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Batched version of rotary embedding, pack multiple LoRAs together
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and process in batched manner.
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*/
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void batched_rotary_embedding(
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torch::Tensor& positions, // [batch_size, seq_len] or [num_tokens]
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torch::Tensor& query, // [batch_size, seq_len, num_heads * head_size] or
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// [num_tokens, num_heads * head_size] or
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// [batch_size, seq_len, num_heads, head_size] or
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// [num_tokens, num_heads, head_size]
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torch::Tensor& key, // [batch_size, seq_len, num_kv_heads * head_size] or
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// [num_tokens, num_kv_heads * head_size] or
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// [batch_size, seq_len, num_heads, head_size] or
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// [num_tokens, num_heads, head_size]
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int64_t head_size,
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torch::Tensor& cos_sin_cache, // [max_position, rot_dim]
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bool is_neox, int64_t rot_dim,
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torch::Tensor& cos_sin_cache_offsets // [num_tokens] or [batch_size]
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) {
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// num_tokens = batch_size * seq_len
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int64_t num_tokens = cos_sin_cache_offsets.size(0);
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TORCH_CHECK(
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positions.size(0) == num_tokens || positions.numel() == num_tokens,
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"positions must have the same num_tokens or batch_size as "
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"cos_sin_cache_offsets");
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int positions_ndim = positions.dim();
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// Make sure num_tokens dim is consistent across positions, query, and key.
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TORCH_CHECK(
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positions_ndim == 1 || positions_ndim == 2,
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"positions must have shape [num_tokens] or [batch_size, seq_len]");
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if (positions_ndim == 1) {
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TORCH_CHECK(
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query.size(0) == positions.size(0) && key.size(0) == positions.size(0),
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"query, key and positions must have the same number of tokens");
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}
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if (positions_ndim == 2) {
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TORCH_CHECK(
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query.size(0) == positions.size(0) &&
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key.size(0) == positions.size(0) &&
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query.size(1) == positions.size(1) &&
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key.size(1) == positions.size(1),
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"query, key and positions must have the same batch_size and seq_len");
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}
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// Make sure head_size is valid for query and key
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int query_hidden_size = query.numel() / num_tokens;
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int key_hidden_size = key.numel() / num_tokens;
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TORCH_CHECK(query_hidden_size % head_size == 0);
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TORCH_CHECK(key_hidden_size % head_size == 0);
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// Make sure query and key have concistent number of heads
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int num_heads = query_hidden_size / head_size;
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int num_kv_heads = key_hidden_size / head_size;
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TORCH_CHECK(num_heads % num_kv_heads == 0);
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int seq_dim_idx = positions_ndim - 1;
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int64_t query_stride = query.stride(seq_dim_idx);
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int64_t key_stride = key.stride(seq_dim_idx);
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dim3 grid(num_tokens);
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dim3 block(std::min<int64_t>(num_heads * rot_dim / 2, 512));
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const at::cuda::OptionalCUDAGuard device_guard(device_of(query));
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const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
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VLLM_DISPATCH_FLOATING_TYPES(query.scalar_type(), "rotary_embedding", [&] {
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if (is_neox) {
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vllm::batched_rotary_embedding_kernel<scalar_t, true>
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<<<grid, block, 0, stream>>>(
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positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
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key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(),
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cos_sin_cache_offsets.data_ptr<int64_t>(), rot_dim, query_stride,
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key_stride, num_heads, num_kv_heads, head_size);
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} else {
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vllm::batched_rotary_embedding_kernel<scalar_t, false>
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<<<grid, block, 0, stream>>>(
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positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
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key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(),
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cos_sin_cache_offsets.data_ptr<int64_t>(), rot_dim, query_stride,
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key_stride, num_heads, num_kv_heads, head_size);
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}
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});
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}
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