[Inference/kernel]Add Fused Rotary Embedding and KVCache Memcopy CUDA Kernel (#5418)

* add rotary embedding kernel

* add rotary_embedding_kernel

* add fused rotary_emb and kvcache memcopy

* add fused_rotary_emb_and_cache_kernel.cu

* add fused_rotary_emb_and_memcopy

* fix bugs in fused_rotary_emb_and_cache_kernel.cu

* fix ci bugs

* use vec memcopy and opt the  gloabl memory access

* fix code style

* fix test_rotary_embdding_unpad.py

* codes revised based on the review comments

* fix bugs about include path

* rm inline

extensions/csrc/common/vector_copy_utils.h

@@ -0,0 +1,98 @@
+
+#include <c10/macros/Macros.h>
+#include <cuda_fp16.h>
+
+#include <cfloat>
+
+#include "string"
+
+template <typename Datatype, int ELEMENTS_PER_LDG>
+__device__ __inline__ void copy_vector(Datatype *dst, const Datatype *src);
+
+template <>
+__device__ __inline__ void copy_vector<c10::BFloat16, 1>(
+    c10::BFloat16 *dst, const c10::BFloat16 *src) {
+  *dst = *src;
+}
+
+template <>
+__device__ __inline__ void copy_vector<c10::BFloat16, 2>(
+    c10::BFloat16 *dst, const c10::BFloat16 *src) {
+  *((float *)dst) = *((float *)src);
+}
+
+template <>
+__device__ __inline__ void copy_vector<c10::BFloat16, 4>(
+    c10::BFloat16 *dst, const c10::BFloat16 *src) {
+  *((float2 *)dst) = *((float2 *)src);
+}
+
+template <>
+__device__ __inline__ void copy_vector<c10::BFloat16, 8>(
+    c10::BFloat16 *dst, const c10::BFloat16 *src) {
+  *((float4 *)dst) = *((float4 *)src);
+}
+
+template <>
+__device__ __inline__ void copy_vector<c10::Half, 1>(c10::Half *dst,
+                                                     const c10::Half *src) {
+  *dst = *src;
+}
+
+template <>
+__device__ __inline__ void copy_vector<c10::Half, 2>(c10::Half *dst,
+                                                     const c10::Half *src) {
+  *((float *)dst) = *((float *)src);
+}
+
+template <>
+__device__ __inline__ void copy_vector<c10::Half, 4>(c10::Half *dst,
+                                                     const c10::Half *src) {
+  *((float2 *)dst) = *((float2 *)src);
+}
+
+template <>
+__device__ __inline__ void copy_vector<c10::Half, 8>(c10::Half *dst,
+                                                     const c10::Half *src) {
+  *((float4 *)dst) = *((float4 *)src);
+}
+
+template <>
+__device__ __inline__ void copy_vector<float, 1>(float *dst, const float *src) {
+  *dst = *src;
+}
+
+template <>
+__device__ __inline__ void copy_vector<float, 2>(float *dst, const float *src) {
+  *((float2 *)dst) = *((float2 *)src);
+}
+
+template <>
+__device__ __inline__ void copy_vector<float, 4>(float *dst, const float *src) {
+  *((float4 *)dst) = *((float4 *)src);
+}
+
+template <>
+__device__ __inline__ void copy_vector<float, 8>(float *dst, const float *src) {
+  // Since the maximum memory alignment length is 128 bits, we choose float4
+  // here.
+  *((float4 *)dst) = *((float4 *)src);
+  *((float4 *)(dst + 4)) = *((float4 *)(src + 4));
+}
+
+template <typename T>
+int get_vec_size(const torch::Tensor &tensor) {
+  uint64_t address = reinterpret_cast<uint64_t>(tensor.data_ptr<T>());
+  const int max_aligned_size = 128;
+  const int dtype_size = sizeof(T) * 8;
+
+  const int vec_size = max_aligned_size / sizeof(T) / 8;
+
+  if (address % (dtype_size * 4) == 0) {
+    return std::min(4, vec_size);
+  } else if (address % (dtype_size * 2) == 0) {
+    return std::min(2, vec_size);
+  } else {
+    return 1;
+  }
+}

extensions/csrc/cuda/activation_kernel.cu

@@ -39,6 +39,9 @@ torch::Tensor silu_and_mul(const torch::Tensor& ins)
     auto ins_shape = ins.sizes().vec();
 
     ins_shape[0] = ins_shape[0]/2;
+    if (ins_shape[0] == 1) {
+      ins_shape.erase(ins_shape.begin());
+    }
     auto outs = torch::zeros(ins_shape,ins.options());
     auto outs_shape = ins.sizes().vec();
 

extensions/csrc/cuda/decode_kv_cache_memcpy_kernel.cu

@@ -1,10 +1,10 @@
 #include <ATen/cuda/CUDAContext.h>
 #include <torch/extension.h>
-#include <stdio.h>
 
+#include "../common/vector_copy_utils.h"
 #include "../common/micros.h"
 
-template<typename scalar_t>
+template<typename scalar_t, int VecSize>
 __global__ void decode_kv_cache_memcpy_kernel(
     const scalar_t* __restrict__ key,
     const scalar_t* __restrict__ value,
@@ -12,79 +12,146 @@ __global__ void decode_kv_cache_memcpy_kernel(
     scalar_t* __restrict__ value_cache,
     const int* __restrict__ sequence_lengths,
     const int* __restrict__ block_tables,
-    const int num_heads,
-    const int head_size,
+    const int head_num,
+    const int head_dim,
     const int block_size,
-    const int key_stride,
-    const int value_stride,
+    const int64_t key_stride,
+    const int64_t value_stride,
     const int block_table_stride
 )
 {
     const int seq_id = blockIdx.x;
     const int seq_len = sequence_lengths[seq_id] - 1;
-    const int seq_id_in_block_table = seq_len / block_size;
     const int block_offset = seq_len % block_size;
-    const int block_id = block_tables[seq_id * block_table_stride + seq_id_in_block_table];
-    const int hidden_size = num_heads * head_size;
+    const int block_id = block_tables[seq_id * block_table_stride + seq_len / block_size];
+    const int hidden_size = head_num * head_dim;
 
     if ( block_id < 0 ) {
         return ;
     }
 
-    for (int i = threadIdx.x; i < hidden_size; i += blockDim.x) {
-        const int head_id = i / head_size;
-        const int head_offset = i % head_size;
-        const int key_src_id = seq_id * key_stride + i;
-        const int value_src_id = seq_id * value_stride + i;
-        const int target_src_id = block_id * hidden_size * block_size
-                                      + head_id * block_size * head_size
-                                      + block_offset * head_size + head_offset;
+    for (int i = threadIdx.x * VecSize; i < hidden_size; i += blockDim.x * VecSize) {
+        const int head_id = i / head_dim;
+        const int head_offset = i % head_dim;
+        const int64_t key_src_id = seq_id * key_stride + i;
+        const int64_t value_src_id = seq_id * value_stride + i;
+        const int64_t target_id = block_id * hidden_size * block_size
+                                      + head_id * block_size * head_dim
+                                      + block_offset * head_dim + head_offset;
 
-        key_cache[target_src_id] = key[key_src_id];
-        value_cache[target_src_id] = value[value_src_id];
+        copy_vector<scalar_t, VecSize>(key_cache + target_id, key + key_src_id);
+        copy_vector<scalar_t, VecSize>(value_cache + target_id, value + value_src_id);
     }
 
 }
 
-void decode_kv_cache_memcpy(
-    torch::Tensor& key,                 // [num_tokens, num_heads, head_size]
-    torch::Tensor& value,               // [num_tokens, num_heads, head_size]
-    torch::Tensor& key_cache,           // [num_blocks, num_heads, block_size, head_size]
-    torch::Tensor& value_cache,         // [num_blocks, num_heads, block_size, head_size]
-    torch::Tensor& sequence_lengths,    // [batch_size]
-    torch::Tensor& block_tables)       // [batch_size, max_seq_len]
+template<typename scalar_t>
+void apply_decode_kv_cache_memcpy(
+    at::Tensor& key,                 // [num_tokens, head_num, head_dim]
+    at::Tensor& value,               // [num_tokens, head_num, head_dim]
+    at::Tensor& key_cache,           // [num_blocks, head_num, block_size, head_dim]
+    at::Tensor& value_cache,         // [num_blocks, head_num, block_size, head_dim]
+    at::Tensor& sequence_lengths,    // [batch_size]
+    at::Tensor& block_tables)        // [batch_size, max_seq_len]
 {
     int num_tokens = key.size(0);
-    int num_heads = key.size(1);
-    int head_size = key.size(2);
+    int head_num = key.size(1);
+    int head_dim = key.size(2);
     int block_size = key_cache.size(2);
 
-    int key_stride = key.stride(0);
-    int value_stride = value.stride(0);
+    int64_t key_stride = key.stride(0);
+    int64_t value_stride = value.stride(0);
     int block_table_stride = block_tables.stride(0);
 
+    int vec_size = get_vec_size<scalar_t>(key);
+
+    if (head_dim % vec_size != 0) {
+        // Disable vectorized loading optimization when head_dim is not divisible by VecSize.
+        vec_size = 1;
+    }
+
+    int thread_nums = head_num * head_dim / vec_size;
+
     const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
     dim3 grid(num_tokens);
-    dim3 block(std::min(num_heads * head_size, 512));
-    DISPATCH_FLOAT_HALF_AND_BFLOAT(
-        key.scalar_type(),
-        "decode_kv_cache_memcpy",
-        decode_kv_cache_memcpy_kernel<scalar_t><<<grid, block, 0, stream>>>(
-            key.data_ptr<scalar_t>(),
-            value.data_ptr<scalar_t>(),
-            key_cache.data_ptr<scalar_t>(),
-            value_cache.data_ptr<scalar_t>(),
-            sequence_lengths.data_ptr<int>(),
-            block_tables.data_ptr<int>(),
-            num_heads,
-            head_size,
-            block_size,
-            key_stride,
-            value_stride,
-            block_table_stride
-        );)
+    dim3 block(std::min(thread_nums, 512));
+
+    switch (vec_size) {
+        case 1:
+            decode_kv_cache_memcpy_kernel<scalar_t, 1><<<grid, block, 0, stream>>>(
+                key.data_ptr<scalar_t>(),
+                value.data_ptr<scalar_t>(),
+                key_cache.data_ptr<scalar_t>(),
+                value_cache.data_ptr<scalar_t>(),
+                sequence_lengths.data_ptr<int>(),
+                block_tables.data_ptr<int>(),
+                head_num,
+                head_dim,
+                block_size,
+                key_stride,
+                value_stride,
+                block_table_stride
+            );
+            break;
+        case 2:
+            decode_kv_cache_memcpy_kernel<scalar_t, 2><<<grid, block, 0, stream>>>(
+                key.data_ptr<scalar_t>(),
+                value.data_ptr<scalar_t>(),
+                key_cache.data_ptr<scalar_t>(),
+                value_cache.data_ptr<scalar_t>(),
+                sequence_lengths.data_ptr<int>(),
+                block_tables.data_ptr<int>(),
+                head_num,
+                head_dim,
+                block_size,
+                key_stride,
+                value_stride,
+                block_table_stride
+            );
+            break;
+        case 4:
+            decode_kv_cache_memcpy_kernel<scalar_t, 4><<<grid, block, 0, stream>>>(
+                key.data_ptr<scalar_t>(),
+                value.data_ptr<scalar_t>(),
+                key_cache.data_ptr<scalar_t>(),
+                value_cache.data_ptr<scalar_t>(),
+                sequence_lengths.data_ptr<int>(),
+                block_tables.data_ptr<int>(),
+                head_num,
+                head_dim,
+                block_size,
+                key_stride,
+                value_stride,
+                block_table_stride
+            );
+            break;
+        default:
+            AT_ERROR("Unsupported vectorized size ", vec_size);
+            break;
+    }
 
     AT_CUDA_CHECK(cudaGetLastError());
 
 }
+
+void decode_kv_cache_memcpy(
+    at::Tensor& key,                 // [num_tokens, head_num, head_dim]
+    at::Tensor& value,               // [num_tokens, head_num, head_dim]
+    at::Tensor& key_cache,           // [num_blocks, head_num, block_size, head_dim]
+    at::Tensor& value_cache,         // [num_blocks, head_num, block_size, head_dim]
+    at::Tensor& sequence_lengths,    // [batch_size]
+    at::Tensor& block_tables)        // [batch_size, max_seq_len]
+{
+    DISPATCH_FLOAT_HALF_AND_BFLOAT(
+        key.scalar_type(),
+        "decode_kv_cache_memcpy",
+        apply_decode_kv_cache_memcpy<scalar_t>(
+            key,
+            value,
+            key_cache,
+            value_cache,
+            sequence_lengths,
+            block_tables
+        );)
+}

extensions/csrc/cuda/fused_rotary_emb_and_cache_kernel.cu

@@ -0,0 +1,472 @@
+
+#include <ATen/cuda/CUDAContext.h>
+#include <torch/extension.h>
+
+#include "../common/vector_copy_utils.h"
+#include "../common/micros.h"
+
+template <typename scalar_t, int VecSize>
+__device__ void apply_emb_rotary_compute(
+    scalar_t* __restrict__ src, const scalar_t* __restrict__ cos_ptr,
+    const scalar_t* __restrict__ sin_ptr, const int64_t stride,
+    const int token_id, const int shard_block_size, const int half_head_dim,
+    const int head_num, const int head_dim) {
+  scalar_t x[VecSize];
+  scalar_t y[VecSize];
+  scalar_t out_x[VecSize];
+  scalar_t out_y[VecSize];
+
+  for (int i = threadIdx.x * VecSize; i < head_num * half_head_dim;
+       i += blockDim.x * VecSize) {
+    const int head_offset = i % half_head_dim;
+    const int shard_offset =
+        (head_offset / shard_block_size) * shard_block_size +
+        (head_offset % shard_block_size) / VecSize;
+    const int64_t addr_offset =
+        token_id * stride + (i / half_head_dim) * head_dim + head_offset;
+
+    copy_vector<scalar_t, VecSize>(x, src + addr_offset);
+    copy_vector<scalar_t, VecSize>(y, src + addr_offset + half_head_dim);
+
+#pragma unroll
+    for (int j = 0; j < VecSize; j++) {
+      out_x[j] = x[j] * cos_ptr[j * 32 + shard_offset] -
+                 y[j] * sin_ptr[j * 32 + shard_offset];
+      out_y[j] = y[j] * cos_ptr[j * 32 + shard_offset] +
+                 x[j] * sin_ptr[j * 32 + shard_offset];
+    }
+
+    copy_vector<scalar_t, VecSize>(src + addr_offset, out_x);
+    copy_vector<scalar_t, VecSize>(src + addr_offset + half_head_dim, out_y);
+  }
+}
+
+template <typename scalar_t, int VecSize>
+__device__ void apply_kv_memcopy(
+    scalar_t* __restrict__ src, scalar_t* __restrict__ cache,
+    const int64_t stride, const int token_id, const int block_id,
+    const int hidden_size, const int block_size, const int block_offset,
+    const int head_dim, const int half_head_dim) {
+  for (int i = threadIdx.x * VecSize; i < hidden_size / 2;
+       i += blockDim.x * VecSize) {
+    const int head_id = i / half_head_dim;
+    const int head_offset = i % half_head_dim;
+    const int64_t src_id = token_id * stride + head_id * head_dim + head_offset;
+    const int64_t target_id = block_id * hidden_size * block_size +
+                              head_id * block_size * head_dim +
+                              block_offset * head_dim + head_offset;
+
+    copy_vector<scalar_t, VecSize>(cache + target_id, src + src_id);
+    copy_vector<scalar_t, VecSize>(cache + target_id + half_head_dim,
+                                   src + src_id + half_head_dim);
+  }
+}
+
+template <typename scalar_t, int VecSize>
+__device__ void cos_sin_memory_access(
+    const scalar_t* __restrict__ cos, const scalar_t* __restrict__ sin,
+    scalar_t* cos_ptr, scalar_t* sin_ptr, const int token_id,
+    const int shard_block_size, const int cos_stride, const int sin_stride,
+    const int half_head_dim) {
+  for (int i = threadIdx.x; i < half_head_dim; i += blockDim.x) {
+    // We assume that the value of head_dim is less than 128*128.
+    const int shard_offset = (i % shard_block_size) / VecSize;
+    const int shard_head =
+        (i / shard_block_size) * shard_block_size + i % VecSize * 32;
+    cos_ptr[shard_head + shard_offset] = cos[token_id * cos_stride + i];
+    sin_ptr[shard_head + shard_offset] = sin[token_id * sin_stride + i];
+  }
+}
+
+template <typename scalar_t, int VecSize>
+__device__ void apply_k_rotary_emb_compute(
+    scalar_t* __restrict__ key, scalar_t* __restrict__ value,
+    scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
+    const scalar_t* __restrict__ cos_ptr, const scalar_t* __restrict__ sin_ptr,
+    const int* __restrict__ sequence_lengths,
+    const int* __restrict__ block_tables, const int64_t key_stride,
+    const int64_t value_stride, const int token_id,
+    const int block_table_stride, const int head_num, const int head_dim,
+    const int kv_head_num, const int block_size, const int half_head_dim,
+    const int shard_block_size) {
+  const int seq_len = sequence_lengths[token_id] - 1;
+  const int block_offset = seq_len % block_size;
+  const int block_id =
+      block_tables[token_id * block_table_stride + seq_len / block_size];
+
+  if (block_id < 0) {
+    return;
+  }
+
+  scalar_t x[VecSize];
+  scalar_t y[VecSize];
+  scalar_t out_x[VecSize];
+  scalar_t out_y[VecSize];
+
+  for (int i = threadIdx.x * VecSize; i < kv_head_num * half_head_dim;
+       i += blockDim.x * VecSize) {
+    const int head_offset = i % half_head_dim;
+    const int shard_offset =
+        (head_offset / shard_block_size) * shard_block_size +
+        (head_offset % shard_block_size) / VecSize;
+    const int64_t addr_offset =
+        token_id * key_stride + (i / half_head_dim) * head_dim + head_offset;
+    const int64_t target_id = block_id * head_num * head_dim * block_size +
+                              (i / half_head_dim) * block_size * head_dim +
+                              block_offset * head_dim + head_offset;
+
+    copy_vector<scalar_t, VecSize>(x, key + addr_offset);
+    copy_vector<scalar_t, VecSize>(y, key + addr_offset + half_head_dim);
+
+#pragma unroll
+    for (int j = 0; j < VecSize; j++) {
+      out_x[j] = x[j] * cos_ptr[j * 32 + shard_offset] -
+                 y[j] * sin_ptr[j * 32 + shard_offset];
+      out_y[j] = y[j] * cos_ptr[j * 32 + shard_offset] +
+                 x[j] * sin_ptr[j * 32 + shard_offset];
+    }
+
+    copy_vector<scalar_t, VecSize>(key_cache + target_id, out_x);
+    copy_vector<scalar_t, VecSize>(key_cache + target_id + half_head_dim,
+                                   out_y);
+  }
+
+  // apply value memcopy
+  apply_kv_memcopy<scalar_t, VecSize>(
+      value, value_cache, value_stride, token_id, block_id, head_num * head_dim,
+      block_size, block_offset, head_dim, half_head_dim);
+}
+
+template<typename scalar_t, int VecSize>
+__global__ void rotary_embedding_and_cache_copy_kernel(
+    scalar_t* __restrict__ query,
+    scalar_t* __restrict__ key,
+    scalar_t* __restrict__ value,
+    const scalar_t* __restrict__ cos,
+    const scalar_t* __restrict__ sin,
+    scalar_t* __restrict__ key_cache,
+    scalar_t* __restrict__ value_cache,
+    const int* __restrict__ sequence_lengths,
+    const int* __restrict__ block_tables,
+    const int64_t query_stride,
+    const int64_t key_stride,
+    const int64_t value_stride,
+    const int64_t half_shard_element_num,
+    const int cos_stride,
+    const int sin_stride,
+    const int block_table_stride,
+    const int head_num,
+    const int head_dim,
+    const int kv_head_num,
+    const int block_size
+) {
+
+    const int token_id = blockIdx.x;
+    const int half_head_dim = head_dim / 2;
+    const int shard_block_size = VecSize * 32;
+
+    extern __shared__ char shard_ptr[];
+
+    scalar_t *cos_ptr = (scalar_t*)shard_ptr;
+    scalar_t *sin_ptr = cos_ptr + half_shard_element_num;
+
+    // apply cos_sin memcopy
+    cos_sin_memory_access<scalar_t, VecSize>(cos, sin, cos_ptr, sin_ptr, token_id, shard_block_size, cos_stride, sin_stride, half_head_dim);
+    __syncthreads();
+
+    //compute query
+    apply_emb_rotary_compute<scalar_t, VecSize>(query, cos_ptr, sin_ptr, query_stride, token_id, shard_block_size, half_head_dim, head_num, head_dim);
+
+    //compute key and copy kv
+    apply_k_rotary_emb_compute<scalar_t, VecSize>(key, value, key_cache, value_cache, cos_ptr, sin_ptr, sequence_lengths, block_tables, key_stride, value_stride, token_id, block_table_stride, head_num, head_dim, kv_head_num, block_size, half_head_dim, shard_block_size);
+}
+
+template<typename scalar_t, int VecSize>
+__global__ void rotary_embedding_kernel(
+    scalar_t* __restrict__ query,
+    scalar_t* __restrict__ key,
+    const scalar_t* __restrict__ cos,
+    const scalar_t* __restrict__ sin,
+    const int64_t query_stride,
+    const int64_t key_stride,
+    const int64_t half_shard_element_num,
+    const int cos_stride,
+    const int sin_stride,
+    const int head_num,
+    const int head_dim,
+    const int kv_head_num
+) {
+    const int token_id = blockIdx.x;
+    const int half_head_dim = head_dim / 2;
+    const int shard_block_size = VecSize * 32;
+
+    extern __shared__ char shard_ptr[];
+
+    scalar_t *cos_ptr = (scalar_t*)shard_ptr;
+    scalar_t *sin_ptr = cos_ptr + half_shard_element_num;
+
+    // apply cos_sin memcopy
+    cos_sin_memory_access<scalar_t, VecSize>(cos, sin, cos_ptr, sin_ptr, token_id, shard_block_size, cos_stride, sin_stride, half_head_dim);
+    __syncthreads();
+
+    //compute query
+    apply_emb_rotary_compute<scalar_t, VecSize>(query, cos_ptr, sin_ptr, query_stride, token_id, shard_block_size, half_head_dim, head_num, head_dim);
+
+    //compute key
+    apply_emb_rotary_compute<scalar_t, VecSize>(key, cos_ptr, sin_ptr, key_stride, token_id, shard_block_size, half_head_dim, kv_head_num, head_dim);
+}
+
+template<typename scalar_t>
+void apply_rotary_embedding_and_cache_copy(
+    at::Tensor& query,               // [num_tokens, head_num, head_dim]
+    at::Tensor& key,                 // [num_tokens, kv_head_num, head_dim]
+    at::Tensor& value,               // [num_tokens, kv_head_num, head_dim]
+    at::Tensor& cos,                 // [num_tokens, head_dim]
+    at::Tensor& sin,                 // [num_tokens, head_dim]
+    at::Tensor& key_cache,           // [num_blocks, head_num, block_size, head_dim]
+    at::Tensor& value_cache,         // [num_blocks, head_num, block_size, head_dim]
+    at::Tensor& sequence_lengths,    // [batch_size]
+    at::Tensor& block_tables)        // [batch_size, max_seq_len]
+{
+    int num_tokens = query.size(0);
+    int head_num = query.size(1);
+    int head_dim = query.size(2);
+    int kv_head_num = key.size(1);
+    int block_size = key_cache.size(2);
+
+    int64_t query_stride = query.stride(0);
+    int64_t key_stride = key.stride(0);
+    int64_t value_stride = value.stride(0);
+    int cos_stride = cos.stride(0);
+    int sin_stride = sin.stride(0);
+    int block_table_stride = block_tables.stride(0);
+
+    int vec_size = get_vec_size<scalar_t>(query);
+
+    if ((head_dim / 2) % vec_size != 0) {
+        // Disable vectorized loading optimization when head_dim is not divisible by VecSize.
+        vec_size = 1;
+    }
+
+    const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+    int thread_nums = head_num * head_dim / vec_size / 2;
+    const int shard_block_size = vec_size * 32 * 2;
+
+    dim3 grid(num_tokens);
+    dim3 block(std::min(thread_nums, 512));
+    int64_t shard_element_num = ((head_dim + shard_block_size - 1) / shard_block_size) * shard_block_size ;
+
+    switch (vec_size) {
+        case 1:
+            rotary_embedding_and_cache_copy_kernel<scalar_t, 1><<<grid, block, shard_element_num * sizeof(scalar_t), stream>>>(
+                query.data_ptr<scalar_t>(),
+                key.data_ptr<scalar_t>(),
+                value.data_ptr<scalar_t>(),
+                cos.data_ptr<scalar_t>(),
+                sin.data_ptr<scalar_t>(),
+                key_cache.data_ptr<scalar_t>(),
+                value_cache.data_ptr<scalar_t>(),
+                sequence_lengths.data_ptr<int>(),
+                block_tables.data_ptr<int>(),
+                query_stride,
+                key_stride,
+                value_stride,
+                shard_element_num / 2,
+                cos_stride,
+                sin_stride,
+                block_table_stride,
+                head_num,
+                head_dim,
+                kv_head_num,
+                block_size
+            );
+            break;
+        case 2:
+            rotary_embedding_and_cache_copy_kernel<scalar_t, 2><<<grid, block, shard_element_num * sizeof(scalar_t), stream>>>(
+                query.data_ptr<scalar_t>(),
+                key.data_ptr<scalar_t>(),
+                value.data_ptr<scalar_t>(),
+                cos.data_ptr<scalar_t>(),
+                sin.data_ptr<scalar_t>(),
+                key_cache.data_ptr<scalar_t>(),
+                value_cache.data_ptr<scalar_t>(),
+                sequence_lengths.data_ptr<int>(),
+                block_tables.data_ptr<int>(),
+                query_stride,
+                key_stride,
+                value_stride,
+                shard_element_num / 2,
+                cos_stride,
+                sin_stride,
+                block_table_stride,
+                head_num,
+                head_dim,
+                kv_head_num,
+                block_size
+            );
+            break;
+        case 4:
+            rotary_embedding_and_cache_copy_kernel<scalar_t, 4><<<grid, block, shard_element_num * sizeof(scalar_t), stream>>>(
+                query.data_ptr<scalar_t>(),
+                key.data_ptr<scalar_t>(),
+                value.data_ptr<scalar_t>(),
+                cos.data_ptr<scalar_t>(),
+                sin.data_ptr<scalar_t>(),
+                key_cache.data_ptr<scalar_t>(),
+                value_cache.data_ptr<scalar_t>(),
+                sequence_lengths.data_ptr<int>(),
+                block_tables.data_ptr<int>(),
+                query_stride,
+                key_stride,
+                value_stride,
+                shard_element_num / 2,
+                cos_stride,
+                sin_stride,
+                block_table_stride,
+                head_num,
+                head_dim,
+                kv_head_num,
+                block_size
+            );
+            break;
+        default:
+            AT_ERROR("Unsupported vectorized size ", vec_size);
+            break;
+    }
+
+    AT_CUDA_CHECK(cudaGetLastError());
+}
+
+template<typename scalar_t>
+void apply_rotary_embedding(
+    at::Tensor& query,   // [total_tokens, head_num, head_dim]
+    at::Tensor& key,     // [total_tokens, kv_head_num, head_dim]
+    at::Tensor& cos,     // [total_tokens, head_dim]
+    at::Tensor& sin      // [total_tokens, head_dim]
+){
+    int num_tokens = query.size(0);
+    int head_num = query.size(1);
+    int head_dim = query.size(2);
+    int kv_head_num = key.size(1);
+
+    int query_stride = query.stride(0);
+    int key_stride = key.stride(0);
+    int cos_stride = cos.stride(0);
+    int sin_stride = sin.stride(0);
+
+    int vec_size = get_vec_size<scalar_t>(query);
+
+    if ((head_dim / 2) % vec_size != 0) {
+        // Disable vectorized loading optimization when head_dim is not divisible by VecSize.
+        vec_size = 1;
+    }
+
+    const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+    int thread_nums = head_num * head_dim / vec_size / 2;
+    const int shard_block_size = vec_size * 32 * 2;
+
+    dim3 grid(num_tokens);
+    dim3 block(std::min(thread_nums, 512));
+    int64_t shard_element_num = ((head_dim + shard_block_size - 1) / shard_block_size) * shard_block_size ;
+
+    switch (vec_size) {
+        case 1:
+            rotary_embedding_kernel<scalar_t, 1><<<grid, block, shard_element_num * sizeof(scalar_t), stream>>>(
+                    query.data_ptr<scalar_t>(),
+                    key.data_ptr<scalar_t>(),
+                    cos.data_ptr<scalar_t>(),
+                    sin.data_ptr<scalar_t>(),
+                    query_stride,
+                    key_stride,
+                    shard_element_num / 2,
+                    cos_stride,
+                    sin_stride,
+                    head_num,
+                    head_dim,
+                    kv_head_num
+                );
+            break;
+        case 2:
+            rotary_embedding_kernel<scalar_t, 2><<<grid, block, shard_element_num * sizeof(scalar_t), stream>>>(
+                    query.data_ptr<scalar_t>(),
+                    key.data_ptr<scalar_t>(),
+                    cos.data_ptr<scalar_t>(),
+                    sin.data_ptr<scalar_t>(),
+                    query_stride,
+                    key_stride,
+                    shard_element_num / 2,
+                    cos_stride,
+                    sin_stride,
+                    head_num,
+                    head_dim,
+                    kv_head_num
+                );
+            break;
+        case 4:
+            rotary_embedding_kernel<scalar_t, 4><<<grid, block, shard_element_num * sizeof(scalar_t), stream>>>(
+                    query.data_ptr<scalar_t>(),
+                    key.data_ptr<scalar_t>(),
+                    cos.data_ptr<scalar_t>(),
+                    sin.data_ptr<scalar_t>(),
+                    query_stride,
+                    key_stride,
+                    shard_element_num / 2,
+                    cos_stride,
+                    sin_stride,
+                    head_num,
+                    head_dim,
+                    kv_head_num
+                );
+            break;
+        default:
+            AT_ERROR("Unsupported vectorized size ", vec_size);
+            break;
+    }
+    AT_CUDA_CHECK(cudaGetLastError());
+}
+
+void rotary_embedding_and_cache_copy(
+    at::Tensor& query,               // [num_tokens, head_num, head_dim]
+    at::Tensor& key,                 // [num_tokens, kv_head_num, head_dim]
+    at::Tensor& value,               // [num_tokens, kv_head_num, head_dim]
+    at::Tensor& cos,                 // [num_tokens, head_dim]
+    at::Tensor& sin,                 // [num_tokens, head_dim]
+    at::Tensor& key_cache,           // [num_blocks, head_num, block_size, head_dim]
+    at::Tensor& value_cache,         // [num_blocks, head_num, block_size, head_dim]
+    at::Tensor& sequence_lengths,    // [batch_size]
+    at::Tensor& block_tables)        // [batch_size, max_seq_len]
+{
+    DISPATCH_FLOAT_HALF_AND_BFLOAT(
+        query.scalar_type(),
+        "rotary_embedding_and_cache_copy",
+        apply_rotary_embedding_and_cache_copy<scalar_t>(
+            query,
+            key,
+            value,
+            cos,
+            sin,
+            key_cache,
+            value_cache,
+            sequence_lengths,
+            block_tables
+        );)
+}
+
+void rotary_embedding(
+    at::Tensor& query,   // [total_tokens, head_num, head_dim]
+    at::Tensor& key,     // [total_tokens, kv_head_num, head_dim]
+    at::Tensor& cos,     // [total_tokens, head_dim]
+    at::Tensor& sin      // [total_tokens, head_dim]
+){
+    DISPATCH_FLOAT_HALF_AND_BFLOAT(
+        query.scalar_type(),
+        "rotary_embedding",
+        apply_rotary_embedding<scalar_t>(
+            query,
+            key,
+            cos,
+            sin
+        );)
+}

extensions/csrc/cuda/pybind/inference.cpp

@@ -9,6 +9,23 @@ void decode_kv_cache_memcpy(
     torch::Tensor& sequence_lengths,  // [batch_size]
     torch::Tensor& block_tables);     // [batch_size, max_seq_len]
 
+void rotary_embedding(
+    torch::Tensor& query,  // [total_tokens, head_num, head_dim]
+    torch::Tensor& key,    // [total_tokens, kv_head_num, head_dim]
+    torch::Tensor& cos,    // [total_tokens, head_dim]
+    torch::Tensor& sin);   // [total_tokens, head_dim]
+
+void rotary_embedding_and_cache_copy(
+    torch::Tensor& query,      // [num_tokens, head_num, head_dim]
+    torch::Tensor& key,        // [num_tokens, kv_head_num, head_dim]
+    torch::Tensor& value,      // [num_tokens, num_heads, head_dim]
+    torch::Tensor& cos,        // [num_tokens, head_dim]
+    torch::Tensor& sin,        // [num_tokens, head_dim]
+    torch::Tensor& key_cache,  // [num_blocks, num_heads, block_size, head_dim]
+    torch::Tensor&
+        value_cache,  // [num_blocks, num_heads, block_size, head_dim]
+    torch::Tensor& sequence_lengths,  // [batch_size]
+    torch::Tensor& block_tables);     // [batch_size, max_seq_len]
 torch::Tensor silu_and_mul(const torch::Tensor& ins);
 
 void rms_layernorm(torch::Tensor& out,     // [..., hidden_size]
@@ -25,6 +42,13 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("decode_kv_cache_memcpy", &decode_kv_cache_memcpy,
         "Copy the GPU memory of kvcache during the decode stage.");
 
+  m.def(
+      "rotary_embedding_and_cache_copy", &rotary_embedding_and_cache_copy,
+      "performing Rotary Embedding-related calculations and KVCache Memcopy.");
+
+  m.def("rotary_embedding", &rotary_embedding,
+        "performing Rotary Embedding-related calculations.");
+
   m.def("silu_and_mul", &silu_and_mul, "Silu with a following multiply");
 
   m.def("rms_layernorm", &rms_layernorm,

extensions/inference/inference_ops_cuda.py

@@ -12,6 +12,7 @@ class InferenceOpsCudaExtension(_CudaExtension):
             for fname in [
                 "cuda/pybind/inference.cpp",
                 "cuda/decode_kv_cache_memcpy_kernel.cu",
+                "cuda/fused_rotary_emb_and_cache_kernel.cu",
                 "cuda/activation_kernel.cu",
                 "cuda/rms_layernorm_kernel.cu",
             ]