add indexer_k_cache_kernel

csrc/cache.h

@@ -83,6 +83,13 @@ void indexer_k_quant_and_cache(
     int64_t quant_block_size,     // quantization block size
     const std::string& scale_fmt);
 
+void indexer_k_cache(
+    torch::Tensor& k,             // [num_tokens, head_dim]
+    torch::Tensor& kv_cache,      // [num_blocks, block_size, cache_stride]
+    torch::Tensor& slot_mapping,  // [num_tokens]
+    const std::string& scale_fmt);
+
+
 // Extract function to gather quantized K cache
 void cp_gather_indexer_k_quant_cache(
     const torch::Tensor& kv_cache,  // [num_blocks, block_size, cache_stride]

csrc/cache_kernels.cu

@@ -22,6 +22,7 @@
 #include <cfloat>
 #include <map>
 #include <vector>
+#include <ATen/cuda/CUDAContext.h>
 
 #ifdef USE_ROCM
   #include <hip/hip_bf16.h>
@@ -808,6 +809,52 @@ __global__ void indexer_k_quant_and_cache_kernel(
   }
 }
 
+template <typename scalar_t, typename cache_t>
+__global__ void indexer_k_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 cache_block_size,                // cache block size
+    const int cache_stride                     // stride for each token in kv_cache
+) {
+  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);
+
+  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++) {
+    float val = static_cast<float>(k_val_ptr[i]);
+    
+    if constexpr (std::is_same<cache_t, at::Half>::value ||
+                  std::is_same<cache_t, __half>::value) {
+      kv_cache[dst_offset + i] = __float2half(val);
+    } else if constexpr (std::is_same<cache_t, at::BFloat16>::value ||
+                         std::is_same<cache_t, __nv_bfloat16>::value) {
+      kv_cache[dst_offset + i] = __float2bfloat16(val);
+    } else if constexpr (std::is_same<cache_t, float>::value) {
+      kv_cache[dst_offset + i] = val;
+    } else {
+      kv_cache[dst_offset + i] = static_cast<cache_t>(val);
+    }
+  }
+}
+
 template <int BLOCK_Y_SIZE>
 __global__ void cp_gather_indexer_k_quant_cache_kernel(
     const char* __restrict__ kv_cache,  // [num_blocks, block_size,
@@ -1791,6 +1838,78 @@ void indexer_k_quant_and_cache(
                              CALL_INDEXER_K_QUANT_AND_CACHE);
 }
 
+// Macro to dispatch the kernel based on the data type.
+#define CALL_INDEXER_K_CACHE(KV_T, CACHE_T)                            \
+  vllm::indexer_k_cache_kernel<KV_T, CACHE_T>                          \
+      <<<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,                  \
+          cache_block_size, cache_stride);
+
+void indexer_k_cache(
+    torch::Tensor& k,             // [num_tokens, head_dim]
+    torch::Tensor& kv_cache,      // [num_blocks, block_size, cache_stride]
+    torch::Tensor& slot_mapping,  // [num_tokens]
+    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");
+
+  constexpr int vec_size = 4;
+  dim3 grid(num_tokens, (head_dim + vec_size - 1) / vec_size);
+  dim3 block(32, vec_size);
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(k));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+
+  AT_DISPATCH_FLOATING_TYPES_AND2(
+      at::ScalarType::Half,
+      at::ScalarType::BFloat16,
+      k.scalar_type(), "indexer_k_cache", ([&] {
+        using k_t = scalar_t;
+        auto kv_cache_type = kv_cache.scalar_type();
+
+        if (kv_cache_type == at::ScalarType::Float) {
+          vllm::indexer_k_cache_kernel<k_t, float>
+              <<<grid, block, 0, stream>>>(
+                  k.data_ptr<k_t>(),
+                  kv_cache.data_ptr<float>(),
+                  slot_mapping.data_ptr<int64_t>(),
+                  head_dim,
+                  cache_block_size,
+                  cache_stride);
+        } else if (kv_cache_type == at::ScalarType::Half) {
+          vllm::indexer_k_cache_kernel<k_t, at::Half>
+              <<<grid, block, 0, stream>>>(
+                  k.data_ptr<k_t>(),
+                  kv_cache.data_ptr<at::Half>(),
+                  slot_mapping.data_ptr<int64_t>(),
+                  head_dim,
+                  cache_block_size,
+                  cache_stride);
+        } else if (kv_cache_type == at::ScalarType::BFloat16) {
+          vllm::indexer_k_cache_kernel<k_t, at::BFloat16>
+              <<<grid, block, 0, stream>>>(
+                  k.data_ptr<k_t>(),
+                  kv_cache.data_ptr<at::BFloat16>(),
+                  slot_mapping.data_ptr<int64_t>(),
+                  head_dim,
+                  cache_block_size,
+                  cache_stride);
+        } else {
+          TORCH_CHECK(false, "Unsupported kv_cache dtype: ", kv_cache.dtype());
+        }
+      }));
+}
+
+
 // Macro to dispatch the kernel based on the data amount.
 #define CALL_CP_GATHER_INDEXER_K_QUANT_CACHE(BLOCK_Y_SIZE)                  \
   vllm::cp_gather_indexer_k_quant_cache_kernel<BLOCK_Y_SIZE>                \

csrc/torch_bindings.cpp

@@ -822,6 +822,14 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cache_ops), cache_ops) {
   cache_ops.impl("indexer_k_quant_and_cache", torch::kCUDA,
                  &indexer_k_quant_and_cache);
 
+  cache_ops.def(
+      "indexer_k_cache(Tensor k, Tensor! kv_cache, Tensor "
+      "slot_mapping, "
+      "str kv_cache_dtype) -> ()");
+  cache_ops.impl("indexer_k_cache", torch::kCUDA,
+                 &indexer_k_cache);
+
+
   cache_ops.def(
       "cp_gather_indexer_k_quant_cache(Tensor kv_cache, Tensor! dst_k, Tensor! "
       "dst_scale, Tensor block_table, Tensor cu_seq_lens) -> ()");

vllm/_custom_ops.py

@@ -2615,6 +2615,13 @@ def indexer_k_quant_and_cache(
     torch.ops._C_cache_ops.indexer_k_quant_and_cache(
         k, kv_cache, slot_mapping, quant_block_size, kv_cache_dtype
     )
+    
+def indexer_k_cache(k: torch.Tensor, kv_cache: torch.Tensor,
+                    slot_mapping: torch.Tensor,
+                    kv_cache_dtype: str) -> None:
+    torch.ops._C_cache_ops.indexer_k_cache(
+        k, kv_cache, slot_mapping, kv_cache_dtype
+    )
 
 
 def cp_gather_indexer_k_quant_cache(

vllm/model_executor/models/deepseek_v2.py

@@ -682,6 +682,13 @@ def sparse_attn_indexer(
             quant_block_size,
             scale_fmt,
         )
+    else:
+        ops.indexer_k_cache(
+            k,
+            kv_cache,
+            slot_mapping,
+            scale_fmt,
+        )
 
     topk_indices_buffer[: hidden_states.shape[0]] = -1
     if has_prefill: