vllm/csrc/cache_kernels.cu

#include <torch/all.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>

#include "cuda_utils.h"
#include "cuda_compat.h"
#include "dispatch_utils.h"
#include "quantization/vectorization_utils.cuh"

#ifdef USE_ROCM
  #include "quantization/fp8/amd/quant_utils.cuh"
#else
  #include "quantization/fp8/nvidia/quant_utils.cuh"
#endif

#include <algorithm>
#include <cassert>
#include <cfloat>  // FLT_MIN
#include <map>
#include <vector>

#ifdef USE_ROCM
  #include <hip/hip_bf16.h>
typedef __hip_bfloat16 __nv_bfloat16;
#endif

void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
                 const torch::Tensor& block_mapping) {
  torch::Device src_device = src.device();
  torch::Device dst_device = dst.device();
  cudaMemcpyKind memcpy_type;
  if (src_device.is_cuda() && dst_device.is_cuda()) {
    TORCH_CHECK(src_device.index() == dst_device.index(),
                "src and dst must be on the same GPU");
    memcpy_type = cudaMemcpyDeviceToDevice;
  } else if (src_device.is_cuda() && dst_device.is_cpu()) {
    memcpy_type = cudaMemcpyDeviceToHost;
  } else if (src_device.is_cpu() && dst_device.is_cuda()) {
    memcpy_type = cudaMemcpyHostToDevice;
  } else {
    TORCH_CHECK(false, "Invalid device combination");
  }

  // NOTE(youkaichao): keep in mind that `block_mapping` should be
  // a cpu tensor, otherwise every `item` call will require a gpu-cpu
  // synchronization.
  TORCH_CHECK(block_mapping.device().is_cpu(), "block_mapping must be on CPU");

  char* src_ptr = static_cast<char*>(src.data_ptr());
  char* dst_ptr = static_cast<char*>(dst.data_ptr());

  // We use the stride instead of numel in case the cache is padded for memory
  // alignment reasons, we assume the blocks data (inclusive of any padding)
  // is contiguous in memory
  const int64_t block_size_in_bytes = src.element_size() * src.stride(0);
  const at::cuda::OptionalCUDAGuard device_guard(
      src_device.is_cuda() ? src_device : dst_device);
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  // NOTE(woosuk): This can be slow if the number of blocks is large.
  const int64_t num_blocks = block_mapping.size(0);
  for (size_t i = 0; i < num_blocks; i++) {
    int64_t src_block_number = block_mapping[i][0].item<int64_t>();
    int64_t dst_block_number = block_mapping[i][1].item<int64_t>();
    int64_t src_offset = src_block_number * block_size_in_bytes;
    int64_t dst_offset = dst_block_number * block_size_in_bytes;
    cudaMemcpyAsync(dst_ptr + dst_offset, src_ptr + src_offset,
                    block_size_in_bytes, memcpy_type, stream);
  }
}

namespace vllm {

// Grid: (num_layers, num_pairs)
template <typename scalar_t>
__global__ void copy_blocks_kernel(int64_t* key_cache_ptrs,
                                   int64_t* value_cache_ptrs,
                                   const int64_t* __restrict__ block_mapping,
                                   const int numel_per_block) {
  const int layer_idx = blockIdx.x;
  const int pair_idx = blockIdx.y;

  scalar_t* key_cache = reinterpret_cast<scalar_t*>(key_cache_ptrs[layer_idx]);
  scalar_t* value_cache =
      reinterpret_cast<scalar_t*>(value_cache_ptrs[layer_idx]);
  int64_t src_block_number = block_mapping[2 * pair_idx];
  int64_t dst_block_number = block_mapping[2 * pair_idx + 1];

  const int64_t src_block_offset = src_block_number * numel_per_block;
  const int64_t dst_block_offset = dst_block_number * numel_per_block;
  for (int i = threadIdx.x; i < numel_per_block; i += blockDim.x) {
    int64_t src_offset = src_block_offset + i;
    int64_t dst_offset = dst_block_offset + i;
    key_cache[dst_offset] = key_cache[src_offset];
  }
  for (int i = threadIdx.x; i < numel_per_block; i += blockDim.x) {
    int64_t src_offset = src_block_offset + i;
    int64_t dst_offset = dst_block_offset + i;
    value_cache[dst_offset] = value_cache[src_offset];
  }
}

// Kernel for MLA, which works on a single joint kv_cache
// Grid: (num_layers, num_pairs)
template <typename scalar_t>
__global__ void copy_blocks_mla_kernel(
    int64_t* cache_ptrs, const int64_t* __restrict__ block_mapping,
    const int mem_footprint_per_block) {
  const int layer_idx = blockIdx.x;
  const int pair_idx = blockIdx.y;
  scalar_t* cache = reinterpret_cast<scalar_t*>(cache_ptrs[layer_idx]);
  int64_t src_block = block_mapping[2 * pair_idx];
  int64_t dst_block = block_mapping[2 * pair_idx + 1];
  int64_t src_offset = src_block * mem_footprint_per_block;
  int64_t dst_offset = dst_block * mem_footprint_per_block;
  for (int i = threadIdx.x; i < mem_footprint_per_block; i += blockDim.x) {
    cache[dst_offset + i] = cache[src_offset + i];
  }
}

}  // namespace vllm

// Note: the key_caches and value_caches vectors are constant but
// not the Tensors they contain. The vectors need to be const refs
// in order to satisfy pytorch's C++ operator registration code.
void copy_blocks(std::vector<torch::Tensor> const& key_caches,
                 std::vector<torch::Tensor> const& value_caches,
                 const torch::Tensor& block_mapping) {
  int num_layers = key_caches.size();
  TORCH_CHECK(num_layers == value_caches.size());
  if (num_layers == 0) {
    return;
  }
  torch::Device cache_device = key_caches[0].device();
  TORCH_CHECK(cache_device.is_cuda());

  // Create data structures for the kernel.
  // Create an array of pointers to the key and value caches.
  int64_t key_cache_ptrs[num_layers];
  int64_t value_cache_ptrs[num_layers];
  for (int layer_idx = 0; layer_idx < num_layers; ++layer_idx) {
    key_cache_ptrs[layer_idx] =
        reinterpret_cast<int64_t>(key_caches[layer_idx].data_ptr());
    value_cache_ptrs[layer_idx] =
        reinterpret_cast<int64_t>(value_caches[layer_idx].data_ptr());
  }

  // block_mapping is a 2D tensor with shape (num_pairs, 2).
  int num_pairs = block_mapping.size(0);

  // Move the data structures to the GPU.
  // NOTE: This synchronizes the CPU and GPU.
  torch::Tensor key_cache_ptrs_tensor =
      torch::from_blob(key_cache_ptrs, {num_layers}, torch::kInt64)
          .to(cache_device);
  torch::Tensor value_cache_ptrs_tensor =
      torch::from_blob(value_cache_ptrs, {num_layers}, torch::kInt64)
          .to(cache_device);

  // Launch the kernel.
  const int numel_per_block = key_caches[0][0].numel();
  dim3 grid(num_layers, num_pairs);
  dim3 block(std::min(1024, numel_per_block));
  const at::cuda::OptionalCUDAGuard device_guard(cache_device);
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(
      key_caches[0].scalar_type(), "copy_blocks_kernel", ([&] {
        vllm::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
            key_cache_ptrs_tensor.data_ptr<int64_t>(),
            value_cache_ptrs_tensor.data_ptr<int64_t>(),
            block_mapping.data_ptr<int64_t>(), numel_per_block);
      }));
}

// copy blocks kernel for MLA (assumes a joint KV-cache)
void copy_blocks_mla(std::vector<torch::Tensor> const& kv_caches,
                     const torch::Tensor& block_mapping) {
  int num_layers = kv_caches.size();
  if (num_layers == 0) {
    return;
  }
  torch::Device cache_device = kv_caches[0].device();
  TORCH_CHECK(cache_device.is_cuda(), "kv_cache must be on CUDA");

  std::vector<int64_t> cache_ptrs(num_layers);
  for (int layer_idx = 0; layer_idx < num_layers; ++layer_idx) {
    cache_ptrs[layer_idx] =
        reinterpret_cast<int64_t>(kv_caches[layer_idx].data_ptr());
  }
  torch::Tensor cache_ptrs_tensor =
      torch::from_blob(cache_ptrs.data(), {num_layers}, torch::kInt64)
          .to(cache_device);

  int num_pairs = block_mapping.size(0);
  // We use the stride instead of numel in case the cache is padded for memory
  // alignment reasons, we assume the blocks data (inclusive of any padding)
  // is contiguous in memory
  int mem_footprint_per_block = kv_caches[0].stride(0);
  dim3 grid(num_layers, num_pairs);
  dim3 block(std::min(1024, mem_footprint_per_block));
  const at::cuda::OptionalCUDAGuard device_guard(cache_device);
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(
      kv_caches[0].scalar_type(), "copy_blocks_mla_kernel", ([&] {
        vllm::copy_blocks_mla_kernel<scalar_t><<<grid, block, 0, stream>>>(
            cache_ptrs_tensor.data_ptr<int64_t>(),
            block_mapping.data_ptr<int64_t>(), mem_footprint_per_block);
      }));
}

namespace vllm {

template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
__global__ void reshape_and_cache_kernel(
    const scalar_t* __restrict__ key,    // [num_tokens, num_heads, head_size]
    const scalar_t* __restrict__ value,  // [num_tokens, num_heads, head_size]
    cache_t* __restrict__ key_cache,     // [num_blocks, num_heads, head_size/x,
                                         // block_size, x]
    cache_t* __restrict__ value_cache,   // [num_blocks, num_heads, head_size,
                                         // block_size]
    const int64_t* __restrict__ slot_mapping,  // [num_tokens]
    const int key_stride, const int value_stride, const int num_heads,
    const int head_size, const int block_size, const int x,
    const float* k_scale, const float* v_scale) {
  const int64_t token_idx = blockIdx.x;
  const int64_t slot_idx = slot_mapping[token_idx];
  if (slot_idx < 0) {
    // Padding token that should be ignored.
    return;
  }

  const int64_t block_idx = slot_idx / block_size;
  const int64_t block_offset = slot_idx % block_size;

  const int n = num_heads * head_size;
  for (int i = threadIdx.x; i < n; i += blockDim.x) {
    const int64_t src_key_idx = token_idx * key_stride + i;
    const int64_t src_value_idx = token_idx * value_stride + i;

    const int head_idx = i / head_size;
    const int head_offset = i % head_size;
    const int x_idx = head_offset / x;
    const int x_offset = head_offset % x;

    const int64_t tgt_key_idx =
        block_idx * num_heads * (head_size / x) * block_size * x +
        head_idx * (head_size / x) * block_size * x + x_idx * block_size * x +
        block_offset * x + x_offset;
    const int64_t tgt_value_idx =
        block_idx * num_heads * head_size * block_size +
        head_idx * head_size * block_size + head_offset * block_size +
        block_offset;
    scalar_t tgt_key = key[src_key_idx];
    scalar_t tgt_value = value[src_value_idx];
    if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
      key_cache[tgt_key_idx] = tgt_key;
      value_cache[tgt_value_idx] = tgt_value;
    } else {
      key_cache[tgt_key_idx] =
          fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_key, *k_scale);
      value_cache[tgt_value_idx] =
          fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_value, *v_scale);
    }
  }
}

// Used by vectorization_utils to copy/convert one element
template <typename OutT, typename InT, Fp8KVCacheDataType kv_dt>
struct CopyWithScaleOp {
  float scale;

  __device__ __forceinline__ void operator()(OutT& dst, const InT src) const {
    if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
      dst = static_cast<OutT>(src);
    } else {
      dst = fp8::scaled_convert<OutT, InT, kv_dt>(src, scale);
    }
  }
};

template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
__global__ void reshape_and_cache_flash_kernel(
    const scalar_t* __restrict__ key,    // [num_tokens, num_heads, head_size]
    const scalar_t* __restrict__ value,  // [num_tokens, num_heads, head_size]
    cache_t* __restrict__ key_cache,     // NHD or HND, shape see comments below
    cache_t* __restrict__ value_cache,   // same above
    const int64_t* __restrict__ slot_mapping,  // [num_tokens]
    const int64_t block_stride, const int64_t page_stride,
    const int64_t head_stride, const int64_t key_stride,
    const int64_t value_stride, const int num_heads, const int head_size,
    const int block_size, const float* k_scale, const float* v_scale) {
  const int64_t token_idx = blockIdx.x;
  const int64_t slot_idx = slot_mapping[token_idx];
  // NOTE: slot_idx can be -1 if the token is padded
  if (slot_idx < 0) {
    return;
  }
  const int64_t block_idx = slot_idx / block_size;
  const int64_t block_offset = slot_idx % block_size;
  const int n_elems = num_heads * head_size;

  // pointers to the beginning of the source row for this token.
  const scalar_t* __restrict__ key_src = key + token_idx * key_stride;
  const scalar_t* __restrict__ value_src = value + token_idx * value_stride;

  // find the start position inside the kv-cache for this token.
  cache_t* __restrict__ key_dst =
      key_cache + block_idx * block_stride + block_offset * page_stride;
  cache_t* __restrict__ value_dst =
      value_cache + block_idx * block_stride + block_offset * page_stride;

  // this is true for the NHD layout where `head_stride == head_size`
  const bool is_contiguous_heads = (head_stride == head_size);

  float k_scale_val = (kv_dt == Fp8KVCacheDataType::kAuto) ? 0.f : *k_scale;
  float v_scale_val = (kv_dt == Fp8KVCacheDataType::kAuto) ? 0.f : *v_scale;
  constexpr int VEC_SIZE = (sizeof(scalar_t) == 2) ? 8 : 4;
  CopyWithScaleOp<cache_t, scalar_t, kv_dt> k_op{k_scale_val};
  CopyWithScaleOp<cache_t, scalar_t, kv_dt> v_op{v_scale_val};
  if (is_contiguous_heads) {
    // NHD layout
    // kv cache: [num_blocks, block_size, num_heads, head_size]
    vectorize_with_alignment<VEC_SIZE>(key_src, key_dst, n_elems, threadIdx.x,
                                       blockDim.x, k_op);

    vectorize_with_alignment<VEC_SIZE>(value_src, value_dst, n_elems,
                                       threadIdx.x, blockDim.x, v_op);

  } else {
    // HND layout: heads are strided, but each head_size segment is contiguous
    // kv cache: [num_blocks, num_heads, block_size, head_size]
    const int lane = threadIdx.x & 31;     // 0..31 within warp
    const int warp_id = threadIdx.x >> 5;  // warp index within block
    const int warps_per_block = blockDim.x >> 5;

    for (int head = warp_id; head < num_heads; head += warps_per_block) {
      const scalar_t* __restrict__ k_src_h = key_src + head * head_size;
      const scalar_t* __restrict__ v_src_h = value_src + head * head_size;

      cache_t* __restrict__ k_dst_h =
          key_dst + static_cast<int64_t>(head) * head_stride;
      cache_t* __restrict__ v_dst_h =
          value_dst + static_cast<int64_t>(head) * head_stride;

      // within each head, let the 32 threads of the warp perform the vector
      // copy
      vectorize_with_alignment<VEC_SIZE>(k_src_h, k_dst_h, head_size, lane, 32,
                                         k_op);

      vectorize_with_alignment<VEC_SIZE>(v_src_h, v_dst_h, head_size, lane, 32,
                                         v_op);
    }
  }
}

template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
__global__ void concat_and_cache_mla_kernel(
    const scalar_t* __restrict__ kv_c,  // [num_tokens, kv_lora_rank]
    const scalar_t* __restrict__ k_pe,  // [num_tokens, pe_dim]
    cache_t* __restrict__ kv_cache,  // [num_blocks, block_size, (kv_lora_rank
                                     // + pe_dim)]
    const int64_t* __restrict__ slot_mapping,  // [num_tokens]
    const int block_stride,                    //
    const int entry_stride,                    //
    const int kv_c_stride,                     //
    const int k_pe_stride,                     //
    const int kv_lora_rank,                    //
    const int pe_dim,                          //
    const int block_size,                      //
    const float* scale                         //
) {
  const int64_t token_idx = blockIdx.x;
  const int64_t slot_idx = slot_mapping[token_idx];
  // NOTE: slot_idx can be -1 if the token is padded
  if (slot_idx < 0) {
    return;
  }
  const int64_t block_idx = slot_idx / block_size;
  const int64_t block_offset = slot_idx % block_size;

  auto copy = [&](const scalar_t* __restrict__ src, cache_t* __restrict__ dst,
                  int src_stride, int dst_stride, int size, int offset) {
    for (int i = threadIdx.x; i < size; i += blockDim.x) {
      const int64_t src_idx = token_idx * src_stride + i;
      const int64_t dst_idx =
          block_idx * block_stride + block_offset * entry_stride + i + offset;
      if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
        dst[dst_idx] = src[src_idx];
      } else {
        dst[dst_idx] =
            fp8::scaled_convert<cache_t, scalar_t, kv_dt>(src[src_idx], *scale);
      }
    }
  };

  copy(kv_c, kv_cache, kv_c_stride, block_stride, kv_lora_rank, 0);
  copy(k_pe, kv_cache, k_pe_stride, block_stride, pe_dim, kv_lora_rank);
}

template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
__global__ void concat_and_cache_ds_mla_kernel(
    const scalar_t* __restrict__ kv_c,  // [num_tokens, kv_lora_rank]
    const scalar_t* __restrict__ k_pe,  // [num_tokens, pe_dim]
    cache_t* __restrict__ kv_cache,  // [num_blocks, block_size, (kv_lora_rank
                                     // + pe_dim)]
    const int64_t* __restrict__ slot_mapping,  // [num_tokens]
    const int block_stride,                    //
    const int entry_stride,                    //
    const int kv_c_stride,                     //
    const int k_pe_stride,                     //
    const int kv_lora_rank,                    //
    const int pe_dim,                          //
    const int block_size,                      //
    const float* scale                         //
) {
  const int64_t token_idx = blockIdx.x;
  const int64_t slot_idx = slot_mapping[token_idx];
  // NOTE: slot_idx can be -1 if the token is padded
  if (slot_idx < 0) {
    return;
  }
  const int64_t block_idx = slot_idx / block_size;
  const int64_t block_offset = slot_idx % block_size;
  const int64_t dst_idx_start =
      block_idx * block_stride + block_offset * entry_stride;

  // Create 4 tile scales in shared memory
  __shared__ float smem[20];
  float* shard_abs_max = smem;
  float* tile_scales = smem + 16;

  // For the NoPE part, each tile of 128 elements is handled by 4 warps
  // (128 threads). There are 4 total tiles, so 16 warps (512 threads).
  // The first thread of the first warp in each tile writes the scale
  // value for the tile. The RoPE part (last 64 elements) is handled
  // by another 2 warps (64 threads).
  // So in total, we use 18 warps (576 threads) per block.

  // Cast kv_cache to 16_bit for RoPE values
  scalar_t* kv_cache_16bit =
      reinterpret_cast<scalar_t*>(&kv_cache[dst_idx_start]);

  // The last 64 threads handle the RoPE part
  if (threadIdx.x >= kv_lora_rank) {
    const int8_t pe_idx = threadIdx.x - kv_lora_rank;
    const int64_t src_idx = token_idx * k_pe_stride + pe_idx;
    // RoPE values start after the packed 8-bit NoPE values and the
    // 32-bit scales
    const int64_t dst_idx = kv_lora_rank / 2 + 8 + pe_idx;
    kv_cache_16bit[dst_idx] = k_pe[src_idx];
    return;
  }

  // Determine the scale for each chunk of NoPE
  const int16_t tile_idx = threadIdx.x >> 7;
  const int16_t warp_idx = (threadIdx.x & 127) >> 5;
  const int16_t lane_idx = threadIdx.x & 31;

  // Load the NoPE element for this thread into registers
  const int64_t src_idx = token_idx * kv_c_stride + threadIdx.x;
  const scalar_t src_val = kv_c[src_idx];

  // Warp-level reduction to find the max absolute value in the warp
  float max_abs = fabsf(src_val);
#pragma unroll
  for (int offset = 16; offset > 0; offset /= 2) {
#ifdef USE_ROCM
    max_abs = fmaxf(max_abs, __shfl_down_sync(UINT64_MAX, max_abs, offset));
#else
    max_abs = fmaxf(max_abs, __shfl_down_sync(0xFFFFFFFF, max_abs, offset));
#endif
  }

  // The first lane of each warp in each tile writes the max_abs of this part
  // of the tile to shared memory
  if (lane_idx == 0) {
    shard_abs_max[tile_idx * 4 + warp_idx] = max_abs;
  }
  __syncthreads();

  // The first lane of the first warp in each tile computes the scale for the
  // tile and writes it to shared memory and to kv_cache
  if (warp_idx == 0 && lane_idx == 0) {
    float4 shard_abs_max_vec =
        reinterpret_cast<float4*>(shard_abs_max)[tile_idx];
    float tile_scale = fmaxf(fmaxf(shard_abs_max_vec.x, shard_abs_max_vec.y),
                             fmaxf(shard_abs_max_vec.z, shard_abs_max_vec.w)) /
                       448.f;

    // Avoid division by zero in `scaled_convert`
    tile_scales[tile_idx] = fmaxf(tile_scale, FLT_MIN);
    float* kv_cache_32bit = reinterpret_cast<float*>(&kv_cache[dst_idx_start]);
    const uint64_t dst_idx = kv_lora_rank / 4 + tile_idx;
    kv_cache_32bit[dst_idx] = tile_scales[tile_idx];
  }

  __syncthreads();

  // Now all threads in the block scale and write their element
  const float scale_val = tile_scales[tile_idx];
  const int64_t dst_idx = dst_idx_start + threadIdx.x;
  kv_cache[dst_idx] =
      fp8::scaled_convert<uint8_t, scalar_t, Fp8KVCacheDataType::kFp8E4M3>(
          src_val, scale_val);
}

template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
__global__ void indexer_k_quant_and_cache_kernel(
    const scalar_t* __restrict__ k,  // [num_tokens, head_dim]
    cache_t* __restrict__ kv_cache,  // [num_blocks, block_size, cache_stride]
    const int64_t* __restrict__ slot_mapping,  // [num_tokens]
    const int head_dim,                        // dimension of each head
    const int quant_block_size,                // quantization block size
    const int cache_block_size,                // cache block size
    const int cache_stride,  // stride for each token in kv_cache
    const bool use_ue8m0     // use ue8m0 scale format
) {
  constexpr int VEC_SIZE = 4;
  const int64_t token_idx = blockIdx.x;
  const int64_t head_dim_idx = (blockIdx.y * blockDim.y * blockDim.x +
                                threadIdx.y * blockDim.x + threadIdx.x) *
                               VEC_SIZE;
  const int64_t slot_idx = slot_mapping[token_idx];
  const int64_t block_idx = slot_idx / cache_block_size;
  const int64_t block_offset = slot_idx % cache_block_size;

  // NOTE: slot_idx can be -1 if the token is padded
  if (slot_idx < 0 || (head_dim_idx >= head_dim)) {
    return;
  }

  float2 k_val = (reinterpret_cast<const float2*>(
      k))[(token_idx * head_dim + head_dim_idx) / VEC_SIZE];
  scalar_t* k_val_ptr = reinterpret_cast<scalar_t*>(&k_val);
  float amax = 0.0f;
  for (int i = 0; i < VEC_SIZE; i++) {
    amax = fmaxf(amax, fabsf(float(k_val_ptr[i])));
  }
#ifndef USE_ROCM
  __syncwarp();
#endif

  // Reduced amax
  for (int mask = 16; mask > 0; mask /= 2) {
#ifdef USE_ROCM
    amax = fmaxf(amax, __shfl_xor_sync(uint64_t(-1), amax, mask));
#else
    amax = fmaxf(amax, __shfl_xor_sync(unsigned(-1), amax, mask));
#endif
  }
#ifndef USE_ROCM
  __syncwarp();
#endif
  float scale = fmaxf(amax, 1e-4) / 448.0f;
  if (use_ue8m0) {
    scale = exp2f(ceilf(log2f(scale)));
  }

  const int64_t dst_offset = block_idx * cache_block_size * cache_stride +
                             block_offset * head_dim + head_dim_idx;
  for (int i = 0; i < VEC_SIZE; i++) {
    kv_cache[dst_offset + i] =
        fp8::scaled_convert<cache_t, scalar_t, kv_dt>(k_val_ptr[i], scale);
  }
  if (threadIdx.x == 0) {
    const int64_t dst_scale_idx =
        block_idx * cache_block_size * cache_stride +
        cache_block_size * head_dim +
        (block_offset * head_dim + head_dim_idx) * 4 / quant_block_size;
    reinterpret_cast<float*>(kv_cache)[dst_scale_idx / 4] = scale;
  }
}

}  // namespace vllm

// KV_T is the data type of key and value tensors.
// CACHE_T is the stored data type of kv-cache.
// KV_DTYPE is the real data type of kv-cache.
#define CALL_RESHAPE_AND_CACHE(KV_T, CACHE_T, KV_DTYPE)               \
  vllm::reshape_and_cache_kernel<KV_T, CACHE_T, KV_DTYPE>             \
      <<<grid, block, 0, stream>>>(                                   \
          reinterpret_cast<KV_T*>(key.data_ptr()),                    \
          reinterpret_cast<KV_T*>(value.data_ptr()),                  \
          reinterpret_cast<CACHE_T*>(key_cache.data_ptr()),           \
          reinterpret_cast<CACHE_T*>(value_cache.data_ptr()),         \
          slot_mapping.data_ptr<int64_t>(), key_stride, value_stride, \
          num_heads, head_size, block_size, x,                        \
          reinterpret_cast<const float*>(k_scale.data_ptr()),         \
          reinterpret_cast<const float*>(v_scale.data_ptr()));

void reshape_and_cache(
    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, head_size/x, block_size, x]
    torch::Tensor&
        value_cache,  // [num_blocks, num_heads, head_size, block_size]
    torch::Tensor& slot_mapping,  // [num_tokens]
    const std::string& kv_cache_dtype, torch::Tensor& k_scale,
    torch::Tensor& v_scale) {
  int num_tokens = slot_mapping.size(0);
  int num_heads = key.size(1);
  int head_size = key.size(2);
  int block_size = key_cache.size(3);
  int x = key_cache.size(4);

  int key_stride = key.stride(0);
  int value_stride = value.stride(0);

  dim3 grid(num_tokens);
  dim3 block(std::min(num_heads * head_size, 512));
  const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  DISPATCH_BY_KV_CACHE_DTYPE(key.dtype(), kv_cache_dtype,
                             CALL_RESHAPE_AND_CACHE);
}

// KV_T is the data type of key and value tensors.
// CACHE_T is the stored data type of kv-cache.
// KV_DTYPE is the real data type of kv-cache.
#define CALL_RESHAPE_AND_CACHE_FLASH(KV_T, CACHE_T, KV_DTYPE)             \
  vllm::reshape_and_cache_flash_kernel<KV_T, CACHE_T, KV_DTYPE>           \
      <<<grid, block, 0, stream>>>(                                       \
          reinterpret_cast<KV_T*>(key.data_ptr()),                        \
          reinterpret_cast<KV_T*>(value.data_ptr()),                      \
          reinterpret_cast<CACHE_T*>(key_cache.data_ptr()),               \
          reinterpret_cast<CACHE_T*>(value_cache.data_ptr()),             \
          slot_mapping.data_ptr<int64_t>(), block_stride, page_stride,    \
          head_stride, key_stride, value_stride, num_heads, head_size,    \
          block_size, reinterpret_cast<const float*>(k_scale.data_ptr()), \
          reinterpret_cast<const float*>(v_scale.data_ptr()));

void reshape_and_cache_flash(
    torch::Tensor& key,        // [num_tokens, num_heads, head_size]
    torch::Tensor& value,      // [num_tokens, num_heads, head_size]
    torch::Tensor& key_cache,  // [num_blocks, block_size, num_heads, head_size]
    torch::Tensor&
        value_cache,  // [num_blocks, block_size, num_heads, head_size]
    torch::Tensor& slot_mapping,  // [num_tokens] or [num_actual_tokens]
    const std::string& kv_cache_dtype, torch::Tensor& k_scale,
    torch::Tensor& v_scale) {
  // NOTE(woosuk): In vLLM V1, key.size(0) can be different from
  // slot_mapping.size(0) because of padding for CUDA graphs.
  // In vLLM V0, key.size(0) is always equal to slot_mapping.size(0) because
  // both include padding.
  // In vLLM V1, however, key.size(0) can be larger than slot_mapping.size(0)
  // since key includes padding for CUDA graphs, while slot_mapping does not.
  // In this case, slot_mapping.size(0) represents the actual number of tokens
  // before padding.
  // For compatibility with both cases, we use slot_mapping.size(0) as the
  // number of tokens.
  int num_tokens = slot_mapping.size(0);
  int num_heads = key.size(1);
  int head_size = key.size(2);
  int block_size = key_cache.size(1);

  int64_t key_stride = key.stride(0);
  int64_t value_stride = value.stride(0);
  int64_t block_stride = key_cache.stride(0);
  int64_t page_stride = key_cache.stride(1);
  int64_t head_stride = key_cache.stride(2);
  TORCH_CHECK(key_cache.stride(0) == value_cache.stride(0));

  dim3 grid(num_tokens);
  dim3 block(std::min(num_heads * head_size, 512));
  const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  DISPATCH_BY_KV_CACHE_DTYPE(key.dtype(), kv_cache_dtype,
                             CALL_RESHAPE_AND_CACHE_FLASH);
}

// KV_T is the data type of key and value tensors.
// CACHE_T is the stored data type of kv-cache.
// KV_DTYPE is the real data type of kv-cache.
#define CALL_CONCAT_AND_CACHE_MLA(KV_T, CACHE_T, KV_DTYPE)              \
  vllm::concat_and_cache_mla_kernel<KV_T, CACHE_T, KV_DTYPE>            \
      <<<grid, block, 0, stream>>>(                                     \
          reinterpret_cast<KV_T*>(kv_c.data_ptr()),                     \
          reinterpret_cast<KV_T*>(k_pe.data_ptr()),                     \
          reinterpret_cast<CACHE_T*>(kv_cache.data_ptr()),              \
          slot_mapping.data_ptr<int64_t>(), block_stride, entry_stride, \
          kv_c_stride, k_pe_stride, kv_lora_rank, pe_dim, block_size,   \
          reinterpret_cast<const float*>(scale.data_ptr()));

// KV_T is the data type of key and value tensors.
// CACHE_T is the stored data type of kv-cache.
#define CALL_CONCAT_AND_CACHE_DS_MLA(KV_T, CACHE_T, KV_DTYPE)           \
  vllm::concat_and_cache_ds_mla_kernel<KV_T, CACHE_T, KV_DTYPE>         \
      <<<grid, block, 0, stream>>>(                                     \
          reinterpret_cast<KV_T*>(kv_c.data_ptr()),                     \
          reinterpret_cast<KV_T*>(k_pe.data_ptr()),                     \
          reinterpret_cast<CACHE_T*>(kv_cache.data_ptr()),              \
          slot_mapping.data_ptr<int64_t>(), block_stride, entry_stride, \
          kv_c_stride, k_pe_stride, kv_lora_rank, pe_dim, block_size,   \
          reinterpret_cast<const float*>(scale.data_ptr()));

void concat_and_cache_mla(
    torch::Tensor& kv_c,          // [num_tokens, kv_lora_rank]
    torch::Tensor& k_pe,          // [num_tokens, pe_dim]
    torch::Tensor& kv_cache,      // [num_blocks, block_size, (kv_lora_rank +
                                  // pe_dim)]
    torch::Tensor& slot_mapping,  // [num_tokens] or [num_actual_tokens]
    const std::string& kv_cache_dtype, torch::Tensor& scale) {
  // NOTE(woosuk): In vLLM V1, key.size(0) can be different from
  // slot_mapping.size(0) because of padding for CUDA graphs.
  // In vLLM V0, key.size(0) is always equal to slot_mapping.size(0) because
  // both include padding.
  // In vLLM V1, however, key.size(0) can be larger than slot_mapping.size(0)
  // since key includes padding for CUDA graphs, while slot_mapping does not.
  // In this case, slot_mapping.size(0) represents the actual number of tokens
  // before padding.
  // For compatibility with both cases, we use slot_mapping.size(0) as the
  // number of tokens.
  int num_tokens = slot_mapping.size(0);
  int kv_lora_rank = kv_c.size(1);
  int pe_dim = k_pe.size(1);
  int block_size = kv_cache.size(1);

  if (kv_cache_dtype == "fp8_ds_mla") {
    TORCH_CHECK(kv_lora_rank == 512, "kv_lora_rank must be 512 for fp8_ds_mla");
    TORCH_CHECK(pe_dim == 64, "pe_dim must be 64 for fp8_ds_mla");
    TORCH_CHECK(kv_cache.size(2) == 656 / kv_cache.itemsize(),
                "kv_cache.size(2) must be 656 bytes for fp8_ds_mla");
    TORCH_CHECK(kv_c.itemsize() == 2,
                "kv_c.itemsize() must be 2 for fp8_ds_mla");
    TORCH_CHECK(k_pe.itemsize() == 2,
                "k_pe.itemsize() must be 2 for fp8_ds_mla");
  } else {
    TORCH_CHECK(kv_cache.size(2) == kv_lora_rank + pe_dim);
  }

  int kv_c_stride = kv_c.stride(0);
  int k_pe_stride = k_pe.stride(0);
  int block_stride = kv_cache.stride(0);
  int entry_stride = kv_cache.stride(1);

  const at::cuda::OptionalCUDAGuard device_guard(device_of(kv_c));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  if (kv_cache_dtype == "fp8_ds_mla") {
    dim3 grid(num_tokens);
    // For the NoPE part, each tile of 128 elements is handled by 4 warps
    // (128 threads). There are 4 total tiles, so 16 warps (512 threads).
    // The first thread of the first warp in each tile writes the scale
    // value for the tile. The RoPE part (last 64 elements) is handled
    // by another 2 warps (64 threads).
    // So in total, we use 18 warps (576 threads) per block.
    dim3 block(576);
    DISPATCH_BY_KV_CACHE_DTYPE(kv_c.dtype(), kv_cache_dtype,
                               CALL_CONCAT_AND_CACHE_DS_MLA);
  } else {
    dim3 grid(num_tokens);
    dim3 block(std::min(kv_lora_rank, 512));
    DISPATCH_BY_KV_CACHE_DTYPE(kv_c.dtype(), kv_cache_dtype,
                               CALL_CONCAT_AND_CACHE_MLA);
  }
}

namespace vllm {

template <typename Tout, typename Tin, Fp8KVCacheDataType kv_dt>
__global__ void convert_fp8_kernel(const Tin* __restrict__ src_cache,
                                   Tout* __restrict__ dst_cache,
                                   const float scale,
                                   const int64_t block_stride) {
  const int64_t block_idx = blockIdx.x;
  for (int i = threadIdx.x; i < block_stride; i += blockDim.x) {
    int64_t idx = block_idx * block_stride + i;
    dst_cache[idx] =
        fp8::scaled_convert<Tout, Tin, kv_dt>(src_cache[idx], scale);
  }
}

}  // namespace vllm

#define CALL_CONVERT_FP8(Tout, Tin, KV_DTYPE)                                \
  vllm::convert_fp8_kernel<Tout, Tin, KV_DTYPE><<<grid, block, 0, stream>>>( \
      reinterpret_cast<Tin*>(src_cache.data_ptr()),                          \
      reinterpret_cast<Tout*>(dst_cache.data_ptr()), scale, block_stride);

// Only for testing.
void convert_fp8(torch::Tensor& dst_cache, torch::Tensor& src_cache,
                 const double scale, const std::string& kv_cache_dtype) {
  torch::Device src_device = src_cache.device();
  torch::Device dst_device = dst_cache.device();
  TORCH_CHECK(src_device.is_cuda(), "src must be on a GPU")
  TORCH_CHECK(dst_device.is_cuda(), "dst must be on a GPU")
  TORCH_CHECK(src_device.index() == dst_device.index(),
              "src and dst must be on the same GPU");
  at::cuda::OptionalCUDAGuard device_guard(src_device);

  int64_t num_blocks = src_cache.size(0);
  int64_t block_stride = src_cache.stride(0);

  dim3 grid(num_blocks);
  dim3 block(std::min(block_stride, int64_t(512)));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  if (kv_cache_dtype == "auto") {
    if (src_cache.dtype() == at::ScalarType::Float) {
      CALL_CONVERT_FP8(uint8_t, float, vllm::Fp8KVCacheDataType::kAuto);
    } else if (src_cache.dtype() == at::ScalarType::Half) {
      CALL_CONVERT_FP8(uint8_t, uint16_t, vllm::Fp8KVCacheDataType::kAuto);
    } else if (src_cache.dtype() == at::ScalarType::BFloat16) {
      CALL_CONVERT_FP8(uint8_t, __nv_bfloat16, vllm::Fp8KVCacheDataType::kAuto);
    } else if (dst_cache.dtype() == at::ScalarType::Float) {
      CALL_CONVERT_FP8(float, uint8_t, vllm::Fp8KVCacheDataType::kAuto);
    } else if (dst_cache.dtype() == at::ScalarType::Half) {
      CALL_CONVERT_FP8(uint16_t, uint8_t, vllm::Fp8KVCacheDataType::kAuto);
    } else if (dst_cache.dtype() == at::ScalarType::BFloat16) {
      CALL_CONVERT_FP8(__nv_bfloat16, uint8_t, vllm::Fp8KVCacheDataType::kAuto);
    }
  } else if (kv_cache_dtype == "fp8" || kv_cache_dtype == "fp8_e4m3") {
    if (src_cache.dtype() == at::ScalarType::Float) {
      CALL_CONVERT_FP8(uint8_t, float, vllm::Fp8KVCacheDataType::kFp8E4M3);
    } else if (src_cache.dtype() == at::ScalarType::Half) {
      CALL_CONVERT_FP8(uint8_t, uint16_t, vllm::Fp8KVCacheDataType::kFp8E4M3);
    } else if (src_cache.dtype() == at::ScalarType::BFloat16) {
      CALL_CONVERT_FP8(uint8_t, __nv_bfloat16,
                       vllm::Fp8KVCacheDataType::kFp8E4M3);
    } else if (dst_cache.dtype() == at::ScalarType::Float) {
      CALL_CONVERT_FP8(float, uint8_t, vllm::Fp8KVCacheDataType::kFp8E4M3);
    } else if (dst_cache.dtype() == at::ScalarType::Half) {
      CALL_CONVERT_FP8(uint16_t, uint8_t, vllm::Fp8KVCacheDataType::kFp8E4M3);
    } else if (dst_cache.dtype() == at::ScalarType::BFloat16) {
      CALL_CONVERT_FP8(__nv_bfloat16, uint8_t,
                       vllm::Fp8KVCacheDataType::kFp8E4M3);
    }
  } else {
    TORCH_CHECK(false, "Unsupported data type: ", kv_cache_dtype);
  }
}

namespace vllm {

// grid is launched with dimensions (batch, num_splits)
template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
__global__ void gather_and_maybe_dequant_cache(
    const cache_t* __restrict__ src_cache,    // [NUM_BLOCKS, BLOCK_SIZE,
                                              // ENTRIES...]
    scalar_t* __restrict__ dst,               // [TOT_TOKENS, ENTRIES...]
    const int32_t* __restrict__ block_table,  // [BATCH, BLOCK_INDICES]
    const int32_t* __restrict__ cu_seq_lens,  // [BATCH+1]
    const int32_t block_size, const int32_t entry_size,
    const int64_t block_table_stride, const int64_t cache_block_stride,
    const int64_t cache_entry_stride, const int64_t dst_entry_stride,
    const float* __restrict__ scale,
    const int32_t* __restrict__ seq_starts) {  // Optional: starting offsets per
                                               // batch

  const int64_t bid = blockIdx.x;  // Batch ID
  const int32_t num_splits = gridDim.y;
  const int32_t split = blockIdx.y;
  const int32_t seq_start = cu_seq_lens[bid];
  const int32_t seq_end = cu_seq_lens[bid + 1];
  const int32_t seq_len = seq_end - seq_start;
  const int32_t tot_blocks = cuda_utils::ceil_div(seq_len, block_size);
  const int32_t split_blocks = cuda_utils::ceil_div(tot_blocks, num_splits);

  const int32_t split_start = split * split_blocks;
  const int32_t split_end = min((split + 1) * split_blocks, tot_blocks);

  const bool is_active_split = (split_start < tot_blocks);
  const bool is_last_split = (split_end == tot_blocks);

  if (!is_active_split) return;

  int32_t full_blocks_end = split_end;
  int32_t partial_block_size = 0;

  // Adjust the pointer for the block_table for this batch.
  // If seq_starts is provided, compute an offset based on (seq_starts[bid] /
  // page_size)
  const int32_t batch_offset = bid * block_table_stride;
  int32_t offset = 0;
  if (seq_starts != nullptr) {
    offset = seq_starts[bid] / block_size;
  }
  const int32_t* batch_block_table = block_table + batch_offset + offset;

  // Adjust dst pointer based on the cumulative sequence lengths.
  dst += seq_start * dst_entry_stride;

  if (is_last_split) {
    partial_block_size = seq_len % block_size;
    if (partial_block_size) full_blocks_end -= 1;
  }

  auto copy_entry = [&](const cache_t* __restrict__ _src,
                        scalar_t* __restrict__ _dst) {
    for (int i = threadIdx.x; i < entry_size; i += blockDim.x) {
      if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
        _dst[i] = static_cast<scalar_t>(_src[i]);
      } else {
        _dst[i] =
            fp8::scaled_convert<scalar_t, cache_t, kv_dt>(_src[i], *scale);
      }
    }
  };

  for (int pid = split_start; pid < full_blocks_end; ++pid) {
    auto block_id = batch_block_table[pid];
    auto block_start_ptr = src_cache + block_id * cache_block_stride;
    auto block_dst_ptr = dst + pid * block_size * dst_entry_stride;
    for (int eid = 0; eid < block_size; ++eid) {
      copy_entry(block_start_ptr + eid * cache_entry_stride,
                 block_dst_ptr + eid * dst_entry_stride);
    }
  }

  if (partial_block_size) {
    auto block_id = batch_block_table[full_blocks_end];
    auto block_start_ptr = src_cache + block_id * cache_block_stride;
    auto block_dst_ptr = dst + full_blocks_end * block_size * dst_entry_stride;
    for (int eid = 0; eid < partial_block_size; ++eid) {
      copy_entry(block_start_ptr + eid * cache_entry_stride,
                 block_dst_ptr + eid * dst_entry_stride);
    }
  }
}

}  // namespace vllm

// Macro to dispatch the kernel based on the data type.
// SCALAR_T is the data type of the destination tensor.
// CACHE_T is the stored data type of kv-cache.
// KV_DTYPE is the real data type of kv-cache.
#define CALL_GATHER_CACHE(SCALAR_T, CACHE_T, KV_DTYPE)                      \
  vllm::gather_and_maybe_dequant_cache<SCALAR_T, CACHE_T, KV_DTYPE>         \
      <<<grid, block, 0, stream>>>(                                         \
          reinterpret_cast<CACHE_T*>(src_cache.data_ptr()),                 \
          reinterpret_cast<SCALAR_T*>(dst.data_ptr()),                      \
          block_table.data_ptr<int32_t>(), cu_seq_lens.data_ptr<int32_t>(), \
          block_size, entry_size, block_table_stride, cache_block_stride,   \
          cache_entry_stride, dst_entry_stride,                             \
          reinterpret_cast<const float*>(scale.data_ptr()), seq_starts_ptr);

// Gather sequences from the cache into the destination tensor.
//  - cu_seq_lens contains the cumulative sequence lengths for each batch
//  - block_table contains the cache block indices for each sequence
//  - Optionally, seq_starts (if provided) offsets the starting block index by
//  (seq_starts[bid] / page_size)
void gather_and_maybe_dequant_cache(
    torch::Tensor const& src_cache,    // [NUM_BLOCKS, BLOCK_SIZE, ENTRIES...]
    torch::Tensor const& dst,          // [TOT_TOKENS, ENTRIES...]
    torch::Tensor const& block_table,  // [BATCH, BLOCK_INDICES]
    torch::Tensor const& cu_seq_lens,  // [BATCH+1]
    int64_t batch_size, const std::string& kv_cache_dtype,
    torch::Tensor const& scale,
    std::optional<torch::Tensor> seq_starts = std::nullopt) {
  at::cuda::OptionalCUDAGuard device_guard(src_cache.device());
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  int32_t block_size = src_cache.size(1);
  int32_t entry_size = src_cache.flatten(2, -1).size(2);

  TORCH_CHECK(block_table.dtype() == torch::kInt32,
              "block_table must be int32");
  TORCH_CHECK(cu_seq_lens.dtype() == torch::kInt32,
              "cu_seq_lens must be int32");
  if (seq_starts.has_value()) {
    TORCH_CHECK(seq_starts.value().dtype() == torch::kInt32,
                "seq_starts must be int32");
  }

  TORCH_CHECK(src_cache.device() == dst.device(),
              "src_cache and dst must be on the same device");
  TORCH_CHECK(src_cache.device() == block_table.device(),
              "src_cache and block_table must be on the same device");
  TORCH_CHECK(src_cache.device() == cu_seq_lens.device(),
              "src_cache and cu_seq_lens must be on the same device");
  if (seq_starts.has_value()) {
    TORCH_CHECK(src_cache.device() == seq_starts.value().device(),
                "src_cache and seq_starts must be on the same device");
  }

  int64_t block_table_stride = block_table.stride(0);
  int64_t cache_block_stride = src_cache.stride(0);
  int64_t cache_entry_stride = src_cache.stride(1);
  int64_t dst_entry_stride = dst.stride(0);

  // Decide on the number of splits based on the batch size.
  int num_splits = batch_size > 128 ? 2 : batch_size > 64 ? 4 : 16;
  dim3 grid(batch_size, num_splits);
  dim3 block(1024);

  const int32_t* seq_starts_ptr =
      seq_starts.has_value() ? seq_starts.value().data_ptr<int32_t>() : nullptr;

  DISPATCH_BY_KV_CACHE_DTYPE(dst.dtype(), kv_cache_dtype, CALL_GATHER_CACHE);
}

namespace vllm {
template <typename scalar_t>
// Note(hc): The cp_gather_cache allows seq_starts to no longer be divisible by
// block_size.
__global__ void cp_gather_cache(
    const scalar_t* __restrict__ src_cache,   // [NUM_BLOCKS, BLOCK_SIZE,
                                              // ENTRY_SIZE]
    scalar_t* __restrict__ dst,               // [TOT_TOKENS, ENTRY_SIZE]
    const int32_t* __restrict__ block_table,  // [BATCH, BLOCK_INDICES]
    const int32_t* __restrict__ cu_seq_lens,  // [BATCH+1]
    const int32_t block_size, const int32_t entry_size,
    const int64_t block_table_stride, const int64_t cache_block_stride,
    const int64_t cache_entry_stride, const int64_t dst_entry_stride,
    const int32_t* __restrict__ seq_starts  // Optional: starting offsets per
                                            // batch
) {
  const int64_t bid = blockIdx.x;  // Batch ID
  const int32_t num_splits = gridDim.y;
  const int32_t split = blockIdx.y;
  const int32_t seq_start = cu_seq_lens[bid];
  const int32_t seq_end = cu_seq_lens[bid + 1];
  const int32_t seq_len = seq_end - seq_start;
  const int32_t tot_slots = seq_len;
  const int32_t split_slots = cuda_utils::ceil_div(tot_slots, num_splits);

  const int32_t split_start = split * split_slots;
  const int32_t split_end = min((split + 1) * split_slots, tot_slots);

  const bool is_active_split = (split_start < tot_slots);

  if (!is_active_split) return;

  // Adjust the pointer for the block_table for this batch.
  // If seq_starts is provided, compute an offset based on it
  const int32_t batch_offset = bid * block_table_stride;
  int32_t offset = split_start;
  if (seq_starts != nullptr) {
    offset += seq_starts[bid];
  }
  int32_t offset_div = offset / block_size;
  offset = offset % block_size;
  const int32_t* batch_block_table = block_table + batch_offset;

  // Adjust dst pointer based on the cumulative sequence lengths.
  dst += seq_start * dst_entry_stride;

  auto copy_entry = [&](const scalar_t* __restrict__ _src,
                        scalar_t* __restrict__ _dst) {
    for (int i = threadIdx.x; i < entry_size; i += blockDim.x)
      _dst[i] = _src[i];
  };

  for (int pid = split_start; pid < split_end; ++pid) {
    auto block_id = batch_block_table[offset_div];
    auto block_start_ptr = src_cache + block_id * cache_block_stride;
    auto block_dst_ptr = dst + pid * dst_entry_stride;
    copy_entry(block_start_ptr + offset * cache_entry_stride, block_dst_ptr);
    offset += 1;
    // bump to next block
    if (offset == block_size) {
      offset_div += 1;
      offset = 0;
    }
  }
}
}  // namespace vllm

// Macro to dispatch the kernel based on the data type.
#define CALL_CP_GATHER_CACHE(CPY_DTYPE)                                 \
  vllm::cp_gather_cache<CPY_DTYPE><<<grid, block, 0, stream>>>(         \
      reinterpret_cast<CPY_DTYPE*>(src_cache.data_ptr()),               \
      reinterpret_cast<CPY_DTYPE*>(dst.data_ptr()),                     \
      block_table.data_ptr<int32_t>(), cu_seq_lens.data_ptr<int32_t>(), \
      block_size, entry_size, block_table_stride, cache_block_stride,   \
      cache_entry_stride, dst_entry_stride, seq_starts_ptr);

// Gather sequences from the cache into the destination tensor.
//  - cu_seq_lens contains the cumulative sequence lengths for each batch
//  - block_table contains the cache block indices for each sequence
//  - Optionally, seq_starts (if provided) offsets the starting slot index by
//  seq_starts[bid]
void cp_gather_cache(
    torch::Tensor const& src_cache,    // [NUM_BLOCKS, BLOCK_SIZE, ENTRIES...]
    torch::Tensor const& dst,          // [TOT_TOKENS, ENTRIES...]
    torch::Tensor const& block_table,  // [BATCH, BLOCK_INDICES]
    torch::Tensor const& cu_seq_lens,  // [BATCH+1]
    int64_t batch_size,
    std::optional<torch::Tensor> seq_starts = std::nullopt) {
  at::cuda::OptionalCUDAGuard device_guard(src_cache.device());
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  int32_t block_size = src_cache.size(1);
  int32_t entry_size = src_cache.flatten(2, -1).size(2);

  TORCH_CHECK(block_table.dtype() == torch::kInt32,
              "block_table must be int32");
  TORCH_CHECK(cu_seq_lens.dtype() == torch::kInt32,
              "cu_seq_lens must be int32");
  if (seq_starts.has_value()) {
    TORCH_CHECK(seq_starts.value().dtype() == torch::kInt32,
                "seq_starts must be int32");
  }

  TORCH_CHECK(src_cache.device() == dst.device(),
              "src_cache and dst must be on the same device");
  TORCH_CHECK(src_cache.device() == block_table.device(),
              "src_cache and block_table must be on the same device");
  TORCH_CHECK(src_cache.device() == cu_seq_lens.device(),
              "src_cache and cu_seq_lens must be on the same device");
  if (seq_starts.has_value()) {
    TORCH_CHECK(src_cache.device() == seq_starts.value().device(),
                "src_cache and seq_starts must be on the same device");
  }

  int64_t block_table_stride = block_table.stride(0);
  int64_t cache_block_stride = src_cache.stride(0);
  int64_t cache_entry_stride = src_cache.stride(1);
  int64_t dst_entry_stride = dst.stride(0);

  // Decide on the number of splits based on the batch size.
  int num_splits = batch_size > 128 ? 2 : batch_size > 64 ? 4 : 16;
  dim3 grid(batch_size, num_splits);
  dim3 block(1024);

  TORCH_CHECK(src_cache.dtype() == dst.dtype(),
              "src_cache and dst must have the same dtype");

  const int dtype_bits = src_cache.element_size() * 8;
  const int32_t* seq_starts_ptr =
      seq_starts.has_value() ? seq_starts.value().data_ptr<int32_t>() : nullptr;

  if (dtype_bits == 32) {
    CALL_CP_GATHER_CACHE(uint32_t);
  } else if (dtype_bits == 16) {
    CALL_CP_GATHER_CACHE(uint16_t);
  } else if (dtype_bits == 8) {
    CALL_CP_GATHER_CACHE(uint8_t);
  } else {
    TORCH_CHECK(false, "Unsupported data type width: ", dtype_bits);
  }
}

// Macro to dispatch the kernel based on the data type.
#define CALL_INDEXER_K_QUANT_AND_CACHE(KV_T, CACHE_T, KV_DTYPE)         \
  vllm::indexer_k_quant_and_cache_kernel<KV_T, CACHE_T, KV_DTYPE>       \
      <<<grid, block, 0, stream>>>(                                     \
          reinterpret_cast<KV_T*>(k.data_ptr()),                        \
          reinterpret_cast<CACHE_T*>(kv_cache.data_ptr()),              \
          slot_mapping.data_ptr<int64_t>(), head_dim, quant_block_size, \
          cache_block_size, cache_stride, use_ue8m0);

void indexer_k_quant_and_cache(
    torch::Tensor& k,             // [num_tokens, head_dim]
    torch::Tensor& kv_cache,      // [num_blocks, block_size, cache_stride]
    torch::Tensor& slot_mapping,  // [num_tokens]
    int64_t quant_block_size,     // quantization block size
    const std::string& scale_fmt) {
  int num_tokens = k.size(0);
  int head_dim = k.size(1);
  int cache_block_size = kv_cache.size(1);
  int cache_stride = kv_cache.size(2);
  bool use_ue8m0 = scale_fmt == "ue8m0";

  TORCH_CHECK(k.device() == kv_cache.device(),
              "k and kv_cache must be on the same device");
  TORCH_CHECK(k.device() == slot_mapping.device(),
              "k and slot_mapping must be on the same device");
  TORCH_CHECK(head_dim % quant_block_size == 0,
              "head_dim must be divisible by quant_block_size");

  constexpr int vec_size = 4;
  dim3 grid(num_tokens, (head_dim + quant_block_size * vec_size - 1) /
                            (quant_block_size * vec_size));
  dim3 block(32, vec_size);
  const at::cuda::OptionalCUDAGuard device_guard(device_of(k));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  DISPATCH_BY_KV_CACHE_DTYPE(k.dtype(), "fp8_e4m3",
                             CALL_INDEXER_K_QUANT_AND_CACHE);
}