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

#include "cuda_compat.h"
#include "dispatch_utils.h"

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

#include <algorithm>
#include <cassert>
#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);
    }
  }
}

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,     // [num_blocks, block_size, num_heads,
                                         // head_size]
    cache_t* __restrict__ value_cache,   // [num_blocks, block_size, num_heads,
                                         // head_size]
    const int64_t* __restrict__ slot_mapping,  // [num_tokens]
    const int block_stride, const int key_stride, const int 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 = 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 int64_t tgt_key_value_idx = block_idx * block_stride +
                                      block_offset * num_heads * head_size +
                                      head_idx * head_size + head_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_value_idx] = tgt_key;
      value_cache[tgt_key_value_idx] = tgt_value;
    } else {
      key_cache[tgt_key_value_idx] =
          fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_key, *k_scale);
      value_cache[tgt_key_value_idx] =
          fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_value, *v_scale);
    }
  }
}

template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
__global__ void write_cache_multi_layers_kernel(
    scalar_t* __restrict__  keys,     // [num_layers, num_tokens, num_heads, head_size]
    scalar_t* __restrict__  values,   // [num_layers, num_tokens, num_heads, head_size]
    int64_t* key_cache_ptrs,     // [num_blocks, num_heads, head_size/x,
                                         // block_size, x]
    int64_t* value_cache_ptrs,   // [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 int num_tokens) {
  const int layer_idx = blockIdx.x;
  const int token_idx = blockIdx.y;

  const int64_t slot_idx = slot_mapping[token_idx];
  if (slot_idx < 0) {
    // Padding token that should be ignored.
    return;
  }

  cache_t* key_cache = reinterpret_cast<cache_t*>(key_cache_ptrs[layer_idx]);
  cache_t* value_cache =
      reinterpret_cast<cache_t*>(value_cache_ptrs[layer_idx]);

  scalar_t* key = keys + layer_idx * num_tokens * key_stride;
  scalar_t* value = values + layer_idx * num_tokens * value_stride;

  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 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;

    const int64_t src_key_idx = token_idx * key_stride + i;
    const int64_t src_value_idx = token_idx * value_stride + i;

    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, 1.0);
      value_cache[tgt_value_idx] =
          fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_value, 1.0);
    }
  }
}

template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
__global__ void read_cache_kernel(
    scalar_t* __restrict__  keys,     // [num_layers, num_tokens, num_heads, head_size]
    scalar_t* __restrict__  values,   // [num_layers, num_tokens, num_heads, head_size]
    int64_t* key_cache_ptrs,     // [num_blocks, num_heads, head_size/x,
                                         // block_size, x]
    int64_t* value_cache_ptrs,   // [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 int num_tokens) {
  const int layer_idx = blockIdx.x;
  const int token_idx = blockIdx.y;

  const int64_t slot_idx = slot_mapping[token_idx];
  if (slot_idx < 0) {
    // Padding token that should be ignored.
    return;
  }

  cache_t* key_cache = reinterpret_cast<cache_t*>(key_cache_ptrs[layer_idx]);
  cache_t* value_cache =
      reinterpret_cast<cache_t*>(value_cache_ptrs[layer_idx]);

  scalar_t* key = keys + layer_idx * num_tokens * key_stride;
  scalar_t* value = values + layer_idx * num_tokens * value_stride;

  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 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 src_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 src_value_idx =
        block_idx * num_heads * head_size * block_size +
        head_idx * head_size * block_size + head_offset * block_size +
        block_offset;

    const int64_t tgt_key_idx = token_idx * key_stride + i;
    const int64_t tgt_value_idx = token_idx * value_stride + i;
    cache_t tgt_key = key_cache[src_key_idx];
    cache_t tgt_value = value_cache[src_value_idx];

    if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
      key[tgt_key_idx] = tgt_key;
      value[tgt_value_idx] = tgt_value;
    } else {
      key[tgt_key_idx] = fp8::scaled_convert<scalar_t, cache_t, kv_dt>(tgt_key, 1.0);
      value[tgt_value_idx] = fp8::scaled_convert<scalar_t, cache_t, kv_dt>(tgt_value, 1.0);
    }
  }
    }


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);
}

}  // namespace vllm

// KV_T is the stored data type of kv-cache.
// CACHE_T is the data type of key and value tensors.
// 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 = key.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 stored data type of kv-cache.
// CACHE_T is the data type of key and value tensors.
// 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, 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);

  int key_stride = key.stride(0);
  int value_stride = value.stride(0);
  int block_stride = key_cache.stride(0);
  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 stored data type of kv-cache.
// CACHE_T is the data type of key and value tensors.
// KV_DTYPE is the real data type of kv-cache.
#define CALL_READ_CACHE(KV_T, CACHE_T, KV_DTYPE)               \
  vllm::read_cache_kernel<KV_T, CACHE_T, KV_DTYPE>             \
      <<<grid, block, 0, stream>>>(                                   \
          reinterpret_cast<KV_T*>(keys.data_ptr()),                  \
          reinterpret_cast<KV_T*>(values.data_ptr()),                \
          key_cache_ptrs_tensor.data_ptr<int64_t>(),                  \
          value_cache_ptrs_tensor.data_ptr<int64_t>(),                \
          slot_mapping.data_ptr<int64_t>(), \
          key_stride, value_stride,         \
          num_heads, head_size, block_size, x, num_tokens);

void read_cache(
    torch::Tensor& keys, // [num_layers, seq_len, num_heads, head_size]
    torch::Tensor& values, // [num_layers, seq_len, num_heads, head_size]
    std::vector<torch::Tensor> const& key_caches, // [num_blocks, num_heads, head_size/x, block_size, x]
    std::vector<torch::Tensor> const& value_caches, // [num_blocks, num_heads, head_size, block_size]
    torch::Tensor& slot_mapping,  // [num_tokens]
    const std::string& kv_cache_dtype) {
  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 and 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());
  }

  int num_tokens = keys.size(1);
  auto kv_dtype =  keys.dtype();
  torch::Tensor key_cache = key_caches[0];
  torch::Tensor value_cache = value_caches[0];

  int key_stride = keys.stride(1);
  int value_stride = values.stride(1);

  int num_heads = value_cache.size(1);
  int head_size = value_cache.size(2);
  int block_size = key_cache.size(3);
  int x = key_cache.size(4);

  // 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);

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

  DISPATCH_BY_KV_CACHE_DTYPE(kv_dtype, kv_cache_dtype,
                             CALL_READ_CACHE);
}

// KV_T is the stored data type of kv-cache.
// CACHE_T is the data type of key and value tensors.
// KV_DTYPE is the real data type of kv-cache.
#define CALL_WRITE_CACHE_MULTI_LAYERS(KV_T, CACHE_T, KV_DTYPE)               \
  vllm::write_cache_multi_layers_kernel<KV_T, CACHE_T, KV_DTYPE>             \
      <<<grid, block, 0, stream>>>(                                   \
          reinterpret_cast<KV_T*>(keys.data_ptr()),                  \
          reinterpret_cast<KV_T*>(values.data_ptr()),                \
          key_cache_ptrs_tensor.data_ptr<int64_t>(),                  \
          value_cache_ptrs_tensor.data_ptr<int64_t>(),                \
          slot_mapping.data_ptr<int64_t>(), \
          key_stride, value_stride,         \
          num_heads, head_size, block_size, x, num_tokens);

void write_cache_multi_layers(
    torch::Tensor& keys, // [num_layers, seq_len, num_heads, head_size]
    torch::Tensor& values, // [num_layers, seq_len, num_heads, head_size]
    std::vector<torch::Tensor> const& key_caches, // [num_blocks, num_heads, head_size/x, block_size, x]
    std::vector<torch::Tensor> const& value_caches, // [num_blocks, num_heads, head_size, block_size]
    torch::Tensor& slot_mapping,  // [num_tokens]
    const std::string& kv_cache_dtype) {
  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 and 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());
  }

  auto kv_dtype =  keys.dtype();
  int num_tokens = keys.size(1);
  torch::Tensor key_cache = key_caches[0];
  torch::Tensor value_cache = value_caches[0];

  int key_stride = keys.stride(1);
  int value_stride = values.stride(1);

  int num_heads = value_cache.size(1);
  int head_size = value_cache.size(2);
  int block_size = key_cache.size(3);
  int x = key_cache.size(4);

  // 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);

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

  DISPATCH_BY_KV_CACHE_DTYPE(kv_dtype, kv_cache_dtype,
                             CALL_WRITE_CACHE_MULTI_LAYERS);
}

#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()));

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);

  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);

  dim3 grid(num_tokens);
  dim3 block(std::min(kv_lora_rank, 512));
  const at::cuda::OptionalCUDAGuard device_guard(device_of(kv_c));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  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);
  }
}
