[Inference/Kernel] Optimize paged attention: Refactor key cache layout (#5643)

* optimize flashdecodingattention: refactor code with different key cache layout(from [num_blocks, num_kv_heads, block_size, head_size] to [num_blocks, num_kv_heads, head_size/x, block_size, x])

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

colossalai/inference/modeling/models/nopadding_llama.py

@@ -593,7 +593,7 @@ class NopadLlamaAttention(ParallelModule, LlamaAttention):
                     high_precision,
                 )
                 # inference_ops.flash_decoding_attention(
-                #     attn_output,
+                #     output_tensor,
                 #     query_states,
                 #     k_cache,
                 #     v_cache,
@@ -605,6 +605,7 @@ class NopadLlamaAttention(ParallelModule, LlamaAttention):
                 #     fd_inter_tensor.mid_output_lse,
                 #     sm_scale,
                 # )
+                # attn_output = output_tensor
             else:
                 if is_verifier:
                     rotary_embedding(query_states, key_states, cos_sin[0], cos_sin[1])

examples/inference/benchmark_ops/benchmark_flash_decoding_attention.py

@@ -5,6 +5,7 @@ from colossalai.kernel.triton import flash_decoding_attention
 from colossalai.utils import get_current_device
 from tests.test_infer.test_ops.triton.kernel_utils import (
     generate_caches_and_block_tables_v2,
+    generate_caches_and_block_tables_v3,
     generate_caches_and_block_tables_vllm,
 )
 
@@ -95,7 +96,11 @@ def benchmark_flash_decoding_attention(
         BATCH_SIZE, HEAD_SIZE, NUM_ATTN_HEADS, NUM_KV_HEADS, MAX_SEQ_LEN, dtype, device
     )
 
-    k_cache, v_cache, block_tables = generate_caches_and_block_tables_v2(
+    triton_k_cache, triton_v_cache, _ = generate_caches_and_block_tables_v2(
+        k_unpad, v_unpad, kv_seq_lengths, BATCH_SIZE, MAX_NUM_BLOCKS_PER_SEQ, BLOCK_SIZE, dtype, device
+    )
+
+    k_cache, v_cache, block_tables = generate_caches_and_block_tables_v3(
         k_unpad, v_unpad, kv_seq_lengths, BATCH_SIZE, MAX_NUM_BLOCKS_PER_SEQ, BLOCK_SIZE, dtype, device
     )
 
@@ -135,8 +140,8 @@ def benchmark_flash_decoding_attention(
     elif provider == "triton_flash_decoding_attention":
         fn = lambda: flash_decoding_attention(
             q.squeeze(2),
-            k_cache,
+            triton_k_cache,
-            v_cache,
+            triton_v_cache,
             kv_seq_lengths,
             block_tables,
             BLOCK_SIZE,

extensions/csrc/kernel/cuda/attention/attention_utils.h

@@ -41,7 +41,8 @@ namespace attention {
 #define SHFL_SYNC(var, src_lane) __shfl_sync(uint32_t(-1), var, src_lane)
 
 // Q*K^T operation.
-template <int NUM_THREADS_PER_TOKEN, typename VecT, int N>
+template <int NUM_THREADS_PER_ROUNDS, int NUM_THREADS_PER_X, typename VecT,
+          int N>
 inline __device__ float qk_dot_(const VecT (&q)[N], const VecT (&k)[N]) {
   using A_vec = typename common::FloatVecTypeTrait<VecT>::Type;
   // Compute the parallel products for Q*K^T (treat vector lanes separately).
@@ -58,21 +59,27 @@ inline __device__ float qk_dot_(const VecT (&q)[N], const VecT (&k)[N]) {
   // Finalize the reduction across lanes.
   float qk = sum_vect(qk_vec);
 #pragma unroll
-  for (int mask = (NUM_THREADS_PER_TOKEN >> 1); mask > 0; mask >>= 1) {
+  for (int mask = (WARP_SIZE >> 1); mask >= NUM_THREADS_PER_ROUNDS;
+       mask >>= 1) {
+    qk += SHFL_XOR_SYNC(qk, mask);
+  }
+
+#pragma unroll
+  for (int mask = (NUM_THREADS_PER_X >> 1); mask > 0; mask >>= 1) {
     qk += SHFL_XOR_SYNC(qk, mask);
   }
   return qk;
 }
 
-template <typename T, int NUM_THREADS_PER_TOKEN>
+template <typename T, int NUM_THREADS_PER_ROUNDS, int NUM_THREADS_PER_X>
 struct Qk_dot {
   template <typename VecT, int N>
   static inline __device__ float dot(const VecT (&q)[N], const VecT (&k)[N]) {
-    return qk_dot_<NUM_THREADS_PER_TOKEN>(q, k);
+    return qk_dot_<NUM_THREADS_PER_ROUNDS, NUM_THREADS_PER_X>(q, k);
   }
 };
 
-template <int NUM_WARPS, int NUM_THREADS_PER_TOKEN>
+template <int NUM_WARPS, int NUM_THREADS_PER_ROUNDS, int NUM_THREADS_PER_X>
 inline __device__ float block_max(float* red_smem, float max) {
   int warp = threadIdx.x >> 5;
   int lane = threadIdx.x & 0x1f;
@@ -81,7 +88,8 @@ inline __device__ float block_max(float* red_smem, float max) {
 // for each warp, the 1st out of NUM_THREADS_PER_TOKEN thread already has the
 // max value among every NUM_THREADS_PER_TOKEN threads.
 #pragma unroll
-  for (int mask = (WARP_SIZE >> 1); mask >= NUM_THREADS_PER_TOKEN; mask >>= 1) {
+  for (int mask = (NUM_THREADS_PER_ROUNDS >> 1); mask >= NUM_THREADS_PER_X;
+       mask >>= 1) {
     max = fmaxf(max, SHFL_XOR_SYNC(max, mask));
   }
 
@@ -155,10 +163,12 @@ inline __device__ void block_sum(float* red_smem, VecT& acc) {
       if (lane < NUM_THREADS_PER_GROUP) {
         if constexpr (N == VEC_SIZE_8) {
           VecT* vdst = &((reinterpret_cast<VecT*>(dst))[lane]);
-          (reinterpret_cast<float4*>(vdst))[0] =
+          const int idx0 = (lane >> 2) & 0x1;
-              (reinterpret_cast<float4*>(acc_ptr))[0];
+          const int idx1 = idx0 ^ 0x1;
-          (reinterpret_cast<float4*>(vdst))[1] =
+          (reinterpret_cast<float4*>(vdst))[idx0] =
-              (reinterpret_cast<float4*>(acc_ptr))[1];
+              (reinterpret_cast<float4*>(acc_ptr))[idx0];
+          (reinterpret_cast<float4*>(vdst))[idx1] =
+              (reinterpret_cast<float4*>(acc_ptr))[idx1];
         } else {
           (reinterpret_cast<VecT*>(dst))[lane] = acc;
         }
@@ -173,10 +183,12 @@ inline __device__ void block_sum(float* red_smem, VecT& acc) {
         float* src_ptr = reinterpret_cast<float*>(&src_reg);
         if constexpr (N == VEC_SIZE_8) {
           VecT* vsrc = &((reinterpret_cast<VecT*>(src))[lane]);
-          (reinterpret_cast<float4*>(src_ptr))[0] =
+          const int idx0 = (lane >> 2) & 0x1;
-              (reinterpret_cast<float4*>(vsrc))[0];
+          const int idx1 = idx0 ^ 0x1;
-          (reinterpret_cast<float4*>(src_ptr))[1] =
+          (reinterpret_cast<float4*>(src_ptr))[idx0] =
-              (reinterpret_cast<float4*>(vsrc))[1];
+              (reinterpret_cast<float4*>(vsrc))[idx0];
+          (reinterpret_cast<float4*>(src_ptr))[idx1] =
+              (reinterpret_cast<float4*>(vsrc))[idx1];
         } else {
           src_reg = (reinterpret_cast<VecT*>(src))[lane];
         }

extensions/csrc/kernel/cuda/flash_decoding_attention_kernel.cu

@@ -1,6 +1,6 @@
 /*This code adapted from vllm:
  *     https://github.com/vllm-project/vllm/blob/main/csrc/attention/attention_kernels.cu
- *     with different kvcache layout. */
+ */
 
 #include <ATen/cuda/CUDAContext.h>
 #include <torch/extension.h>
@@ -50,7 +50,7 @@ template<typename scalar_t, typename cache_t, int HEAD_SIZE, int BLOCK_SIZE, int
 __global__ void flash_decoding_attention_kernel(
   scalar_t* __restrict__ out,                 // [num_tokens, num_heads, head_size]
   const scalar_t* __restrict__ q,             // [num_tokens, num_heads, head_size]
-  const cache_t* __restrict__ k_cache,        // [num_blocks, num_kv_heads, block_size, head_size]
+  const cache_t* __restrict__ k_cache,        // [num_blocks, num_kv_heads, head_size/x, block_size, x]
   const cache_t* __restrict__ v_cache,        // [num_blocks, num_kv_heads, block_size, head_size]
   const int* __restrict__ context_lens,       // [num_tokens]
   const int* __restrict__ block_tables,       // [num_tokens, max_num_blocks_per_seq]
@@ -70,15 +70,19 @@ __global__ void flash_decoding_attention_kernel(
   const int num_queries_per_kv = num_heads / num_kv_heads;
   const int kv_head_idx = head_idx / num_queries_per_kv;
   constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
-  constexpr int Q_SHARED_SIZE = (HEAD_SIZE * sizeof(scalar_t)) / sizeof(float4);
+  constexpr int x = sizeof(float4) / sizeof(scalar_t);
+  constexpr int Q_SHARED_SIZE = HEAD_SIZE / x;
   // here thread_group does not determine the number of threads responsible for a key
   // but only the VEC_SIZE of each thread
   constexpr int THREAD_GROUP_SIZE = MAX(WARP_SIZE / BLOCK_SIZE, 1);
-  constexpr int VEC_SIZE = MIN(ROUND_DOWN_HIGHEST_POWER_OF_TWO((HEAD_SIZE / THREAD_GROUP_SIZE)), sizeof(float4) / sizeof(scalar_t));
+  constexpr int VEC_SIZE = MIN(ROUND_DOWN_HIGHEST_POWER_OF_TWO((HEAD_SIZE / THREAD_GROUP_SIZE)), x);
   constexpr int NUM_VECS_PER_TOKEN = HEAD_SIZE / VEC_SIZE;
   constexpr int NUM_THREADS_PER_TOKEN = MIN(NUM_VECS_PER_TOKEN, WARP_SIZE);
   constexpr int NUM_ROUNDS_PER_TOKEN = NUM_VECS_PER_TOKEN / NUM_THREADS_PER_TOKEN;
   constexpr int WARP_STRIDE = WARP_SIZE * NUM_ROUNDS_PER_TOKEN;
+  constexpr int NUM_THREADS_PER_X = x / VEC_SIZE;
+  constexpr int NUM_ROWS_PER_ROUNDS = MIN(WARP_SIZE / NUM_THREADS_PER_X, BLOCK_SIZE);
+  constexpr int NUM_VECS_PER_THREAD = NUM_ROWS_PER_ROUNDS * NUM_VECS_PER_TOKEN / WARP_SIZE;
 
   using K_vec = typename VecTypeTrait<scalar_t, VEC_SIZE>::Type;
   using V_vec = typename VecTypeTrait<scalar_t, VEC_SIZE>::Type;
@@ -86,15 +90,17 @@ __global__ void flash_decoding_attention_kernel(
   using Float_vec = typename FloatVecTypeTrait<L_vec>::Type;
 
   const int context_len = context_lens[seq_idx];
-  const int thread_group_offset = thread_idx % NUM_THREADS_PER_TOKEN;
+  const int thread_group_offset = lane % NUM_THREADS_PER_X;
   const int num_context_blocks = DIVIDE_ROUND_UP(context_len, BLOCK_SIZE);
   const int* block_table = block_tables + seq_idx * max_num_blocks_per_seq;
+  const int shared_memory_offset = DIVIDE_ROUND_UP(max_num_blocks_per_seq * sizeof(int), sizeof(float4)) * sizeof(float4);
 
   __shared__ float4 q_shared[Q_SHARED_SIZE];
   __shared__ float red_shared_mem[2 * NUM_WARPS];
   extern __shared__ char shared_mem[];
-  float* logits = reinterpret_cast<float*>(shared_mem);
+  int* block_table_shared = reinterpret_cast<int*>(shared_mem);
-  float* out_shared_mem = reinterpret_cast<float*>(shared_mem);
+  float* logits = reinterpret_cast<float*>(shared_mem + shared_memory_offset);
+  float* out_shared_mem = reinterpret_cast<float*>(shared_mem + shared_memory_offset);
   float qk_max = -FLT_MAX;
 
   const float4* q_ptr = reinterpret_cast<const float4*>(q + seq_idx * q_stride + head_idx * HEAD_SIZE);
@@ -102,32 +108,47 @@ __global__ void flash_decoding_attention_kernel(
   for (int idx = thread_idx; idx < Q_SHARED_SIZE; idx += blockDim.x) {
     q_shared[idx] = q_ptr[idx];
   }
+
+  #pragma unroll
+  for (int idx = thread_idx; idx < max_num_blocks_per_seq; idx += blockDim.x) {
+    block_table_shared[idx] = block_table[idx];
+  }
+
   __syncthreads();
 
   scalar_t* q_shared_ptr = reinterpret_cast<scalar_t*>(q_shared);
   // each warp access a whole block
+
+  K_vec q_vecs[NUM_VECS_PER_THREAD];
+  #pragma unroll
+  for (int idx = lane, i = 0; idx < NUM_ROWS_PER_ROUNDS * NUM_VECS_PER_TOKEN; idx += WARP_SIZE, i += 1) {
+    const int offset0 = idx / NUM_THREADS_PER_X / NUM_ROWS_PER_ROUNDS;
+    const int offset1 = idx % NUM_THREADS_PER_X;
+    q_vecs[i] = *reinterpret_cast<K_vec*>(q_shared_ptr + offset0 * x + offset1 * VEC_SIZE);
+  }
+
   for (int block_idx = warp_idx; block_idx < num_context_blocks; block_idx += NUM_WARPS) {
-    const int64_t physical_block_number = static_cast<int64_t>(block_table[block_idx]);
+    const int64_t physical_block_number = static_cast<int64_t>(block_table_shared[block_idx]);
+
+    K_vec k_vecs[NUM_VECS_PER_THREAD];
+
     #pragma unroll
-    for (int idx = lane; idx < BLOCK_SIZE * NUM_VECS_PER_TOKEN; idx += WARP_STRIDE) {
+    for (int i = 0; i < BLOCK_SIZE; i += NUM_ROWS_PER_ROUNDS) {
-      const int token_idx = block_idx * BLOCK_SIZE + idx / NUM_VECS_PER_TOKEN;
       const cache_t* k_ptr = k_cache + physical_block_number * kv_block_stride
                                      + kv_head_idx * kv_head_stride
-                                     + idx * VEC_SIZE;
+                                     + i * x;
-
-      K_vec k_vecs[NUM_ROUNDS_PER_TOKEN];
-      K_vec q_vecs[NUM_ROUNDS_PER_TOKEN];
-
-      // we must calculate at least one row of hidden vectors
       #pragma unroll
-      for (int i = 0; i < NUM_ROUNDS_PER_TOKEN; i++) {
+      for (int idx = lane, j = 0; idx < NUM_ROWS_PER_ROUNDS * NUM_VECS_PER_TOKEN; idx += WARP_SIZE, j += 1) {
-        k_vecs[i] = (reinterpret_cast<const K_vec*>(k_ptr))[i * WARP_SIZE];
+        const int offset0 = idx / NUM_THREADS_PER_X / NUM_ROWS_PER_ROUNDS;
-        q_vecs[i] = (reinterpret_cast<K_vec*>(q_shared_ptr))[(idx + i * WARP_SIZE) % NUM_VECS_PER_TOKEN];
+        const int offset1 = (idx / NUM_THREADS_PER_X) % NUM_ROWS_PER_ROUNDS;
+        const int offset2 = idx % NUM_THREADS_PER_X;
+        k_vecs[j] = *reinterpret_cast<const K_vec*>(k_ptr + offset0 * BLOCK_SIZE * x + offset1 * x + offset2 * VEC_SIZE);
       }
 
-      float qk = scale * Qk_dot<scalar_t, NUM_THREADS_PER_TOKEN>::dot(q_vecs, k_vecs);
+      float qk = scale * Qk_dot<scalar_t, NUM_ROWS_PER_ROUNDS * NUM_THREADS_PER_X, NUM_THREADS_PER_X>::dot(q_vecs, k_vecs);
 
-      if (thread_group_offset == 0) {
+      if (thread_group_offset == 0 && lane < NUM_ROWS_PER_ROUNDS * NUM_THREADS_PER_X) {
+        const int token_idx = block_idx * BLOCK_SIZE + i * NUM_ROWS_PER_ROUNDS + lane / NUM_THREADS_PER_X;
         const bool mask = token_idx >= context_len;
         logits[token_idx] = mask ? 0.f : qk;
         qk_max = mask ? qk_max : fmaxf(qk_max, qk);
@@ -136,7 +157,7 @@ __global__ void flash_decoding_attention_kernel(
   }
 
   // there exists a __syncthreads within this function
-  qk_max = block_max<NUM_WARPS, NUM_THREADS_PER_TOKEN>(red_shared_mem, qk_max);
+  qk_max = block_max<NUM_WARPS, NUM_ROWS_PER_ROUNDS * NUM_THREADS_PER_X, NUM_THREADS_PER_X>(red_shared_mem, qk_max);
 
   // Get the sum of the exp values.
   float exp_sum = 0.f;
@@ -162,7 +183,7 @@ __global__ void flash_decoding_attention_kernel(
   V_vec zero_value;
   zero(zero_value);
   for (int block_idx = warp_idx; block_idx < num_context_blocks; block_idx += NUM_WARPS) {
-    const int64_t physical_block_number = static_cast<int64_t>(block_table[block_idx]);
+    const int64_t physical_block_number = static_cast<int64_t>(block_table_shared[block_idx]);
     scalar_t logit;
 
     #pragma unroll
@@ -241,7 +262,7 @@ template<
 void flash_decoding_attention_v1_launcher(
   torch::Tensor& out,              // [num_tokens, num_heads, head_size]
   torch::Tensor& query,            // [num_tokens, num_heads, head_size]
-  torch::Tensor& key_cache,        // [num_blocks, num_kv_heads, block_size, head_size]
+  torch::Tensor& key_cache,        // [num_blocks, num_kv_heads, head_size/x, block_size, x]
   torch::Tensor& value_cache,      // [num_blocks, num_kv_heads, block_size, head_size]
   torch::Tensor& context_lens,     // [num_tokens]
   torch::Tensor& block_tables,     // [num_tokens, max_num_blocks_per_seq]
@@ -266,7 +287,7 @@ void flash_decoding_attention_v1_launcher(
   int logits_size = padded_max_context_len * sizeof(float);
   int outputs_size = (NUM_WARPS / 2) * NUM_THREADS_PER_TOKEN * VEC_SIZE * sizeof(float);
   // Keep that in sync with the logic here!
-  int shared_mem_size = std::max(logits_size, outputs_size);
+  int shared_mem_size = std::max(logits_size, outputs_size) + DIVIDE_ROUND_UP(max_num_blocks_per_seq * sizeof(int), sizeof(float4)) * sizeof(float4);
 
   dim3 grid(num_heads, num_tokens, 1);
   dim3 block(NUM_THREADS);
@@ -323,7 +344,7 @@ void flash_decoding_attention_v1_launcher(
 void flash_decoding_attention(
   torch::Tensor& out,             // [num_tokens, num_heads, head_size]
   torch::Tensor& query,           // [num_tokens, num_heads, head_size]
-  torch::Tensor& key_cache,       // [num_blocks, num_kv_heads, block_size, head_size]
+  torch::Tensor& key_cache,       // [num_blocks, num_kv_heads, head_size/x, block_size, x]
   torch::Tensor& value_cache,     // [num_blocks, num_kv_heads, block_size, head_size]
   torch::Tensor& context_lens,    // [num_tokens]
   torch::Tensor& block_tables,    // [num_tokens, max_num_blocks_per_seq]

extensions/csrc/kernel/cuda/rms_layernorm_kernel.cu

@@ -287,7 +287,7 @@ void rms_layernorm(
         RMSNORM_LAUNCHER(8, block);
         break;
       default:
-        AT_ERROR("unroll_factor must be 1, 2, 4 or 8");
+        AT_ERROR("unroll_factor must be 1, 2, 3, 4 or 8");
     }
   }
 }
@@ -334,7 +334,7 @@ void fused_add_rms_layernorm(
         FUSED_ADD_RMSNORM_LAUNCHER(8, block);
         break;
       default:
-        AT_ERROR("unroll_factor must be 1, 2, 4 or 8");
+        AT_ERROR("unroll_factor must be 1, 2, 3, 4 or 8");
     }
   }
 }

extensions/pybind/inference/inference.cpp

@@ -62,7 +62,7 @@ void flash_decoding_attention(
     torch::Tensor& out,    // [num_tokens, num_heads, head_size]
     torch::Tensor& query,  // [num_tokens, num_heads, head_size]
     torch::Tensor&
-        key_cache,  // [num_blocks, num_kv_heads, block_size, head_size]
+        key_cache,  // [num_blocks, num_kv_heads, head_size/x, block_size, x]
     torch::Tensor&
         value_cache,  // [num_blocks, num_kv_heads, block_size, head_size]
     torch::Tensor& context_lens,  // [num_tokens]

tests/test_infer/test_ops/cuda/test_flash_decoding_attention.py

@@ -12,7 +12,7 @@ inference_ops = InferenceOpsLoader().load()
 from tests.test_infer.test_ops.triton.kernel_utils import (
     convert_kv_unpad_to_padded,
     create_attention_mask,
-    generate_caches_and_block_tables_v2,
+    generate_caches_and_block_tables_v3,
     generate_caches_and_block_tables_vllm,
     torch_attn_ref,
 )
@@ -77,7 +77,7 @@ def test_flash_decoding_attention(
         BATCH_SIZE, HEAD_SIZE, NUM_ATTN_HEADS, NUM_KV_HEADS, MAX_SEQ_LEN, dtype, device
     )
 
-    k_cache, v_cache, block_tables = generate_caches_and_block_tables_v2(
+    k_cache, v_cache, block_tables = generate_caches_and_block_tables_v3(
         k_unpad, v_unpad, kv_seq_lengths, BATCH_SIZE, MAX_NUM_BLOCKS_PER_SEQ, BLOCK_SIZE, dtype, device
     )
 

tests/test_infer/test_ops/triton/kernel_utils.py

@@ -150,6 +150,50 @@ def mock_alloc_block_table_and_kvcache_v2(
     return block_tables
 
 
+def mock_alloc_block_table_and_kvcache_v3(
+    k: torch.Tensor,
+    v: torch.Tensor,
+    k_cache: torch.Tensor,
+    v_cache: torch.Tensor,
+    context_lengths: torch.Tensor,
+    num_seqs: int,
+    max_num_blocks_per_seq: int,
+    block_size: int,
+) -> torch.Tensor:
+    """Allocate block tables based on provided context lengths; and copy KV to blocked KV Cache."""
+    block_id = 0
+    block_tables = torch.full(size=(num_seqs, max_num_blocks_per_seq), fill_value=-1, dtype=torch.int32)
+    num_tokens_processed = 0
+
+    _, num_kv_heads, head_dim = k.shape
+
+    x = 16 // torch.tensor([], dtype=k.dtype).element_size()
+
+    for i, seq_len in enumerate(context_lengths.tolist()):
+        right_bound = (seq_len + block_size - 1) // block_size  # open bound
+        block_tables[i, :right_bound] = torch.arange(block_id, block_id + right_bound, dtype=torch.int32)
+        # Manually fill kv caches by copying from k and v
+        for i in range(right_bound):
+            if i == right_bound - 1:
+                allocated_locs = seq_len % block_size or block_size
+            else:
+                allocated_locs = block_size
+            # [block_size, num_kv_heads, head_dim/x, x]->[num_kv_heads, head_dim/x, block_size,x]
+            k_block = (
+                k[num_tokens_processed : num_tokens_processed + allocated_locs, :, :]
+                .reshape(allocated_locs, num_kv_heads, head_dim // x, x)
+                .permute(1, 2, 0, 3)
+            )
+            v_block = v[num_tokens_processed : num_tokens_processed + allocated_locs, :, :].permute(1, 0, 2)
+            k_cache[block_id, :, :, :allocated_locs, :] = k_block
+            v_cache[block_id, :, :allocated_locs, :] = v_block
+
+            num_tokens_processed += allocated_locs
+            block_id += 1
+
+    return block_tables
+
+
 def mock_alloc_block_table_and_kvcache_vllm(
     k: torch.Tensor,
     v: torch.Tensor,
@@ -251,6 +295,26 @@ def generate_caches_and_block_tables_v2(
     return k_cache, v_cache, block_tables
 
 
+def generate_caches_and_block_tables_v3(
+    k_unpad, v_unpad, kv_lengths, bsz, max_num_blocks_per_seq, block_size, dtype=torch.float16, device="cuda"
+) -> Tuple[torch.Tensor, ...]:
+    # Mock generation of k/v blocked caches and block tables from providied kv unpad and seq lengths
+    # k_unpad/v_unpad [num_total_tokens, num_kv_heads, head_dim]
+    _, num_kv_heads, head_dim = k_unpad.shape
+
+    x = 16 // torch.tensor([], dtype=dtype).element_size()
+
+    k_cache_shape = (bsz * max_num_blocks_per_seq, num_kv_heads, head_dim // x, block_size, x)
+    v_cache_shape = (bsz * max_num_blocks_per_seq, num_kv_heads, block_size, head_dim)
+    k_cache = torch.zeros(size=k_cache_shape, dtype=dtype, device=device)
+    v_cache = torch.zeros(size=v_cache_shape, dtype=dtype, device=device)
+    # Mock allocation on block tables as well as blocked kv caches
+    block_tables = mock_alloc_block_table_and_kvcache_v3(
+        k_unpad, v_unpad, k_cache, v_cache, kv_lengths, bsz, max_num_blocks_per_seq, block_size
+    )
+    return k_cache, v_cache, block_tables
+
+
 def generate_caches_and_block_tables_vllm(
     k_unpad, v_unpad, kv_lengths, bsz, max_num_blocks_per_seq, block_size, dtype=torch.float16, device="cuda"
 ) -> Tuple[torch.Tensor, ...]: