[Kernel] Use flashinfer for decoding (#4353)

Co-authored-by: LiuXiaoxuanPKU <llilyliupku@gmail.com>

csrc/cache.h

@@ -24,6 +24,14 @@ void reshape_and_cache(
   const std::string& kv_cache_dtype,
   const float kv_scale);
 
+void reshape_and_cache_flash(
+  torch::Tensor& key,
+  torch::Tensor& value,
+  torch::Tensor& key_cache,
+  torch::Tensor& value_cache,
+  torch::Tensor& slot_mapping,
+  const std::string& kv_cache_dtype);
+
 // Just for unittest
 void convert_fp8(
   torch::Tensor& src_cache,

csrc/cache_kernels.cu

@@ -215,6 +215,41 @@ __global__ void reshape_and_cache_kernel(
   }
 }
 
+template<typename scalar_t>
+__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]
+  scalar_t* __restrict__ k_cache,             // [num_blocks, block_size, num_heads, head_size]
+  scalar_t* __restrict__ v_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 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_value_idx = block_idx * block_stride
+                              + block_offset * num_heads * head_size
+                              + head_idx * head_size
+                              + head_offset;
+    k_cache[tgt_value_idx] = key[src_key_idx];
+    v_cache[tgt_value_idx] = value[src_value_idx];
+  }
+}
 } // namespace vllm
 
 #define CALL_RESHAPE_AND_CACHE(KV_T, CACHE_T, IS_FP8_KV_CACHE)                                     \
@@ -275,6 +310,51 @@ void reshape_and_cache(
   }
 }
 
+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& k_cache,       // [num_blocks, block_size, num_heads, head_size]
+  torch::Tensor& v_cache,       // [num_blocks, block_size, num_heads, head_size]
+  torch::Tensor& slot_mapping,  // [num_tokens]
+  const std::string& kv_cache_dtype)
+{
+  // FIXME: only support auto datatype, does not support fp8
+  if (kv_cache_dtype != "auto") {
+    TORCH_CHECK(false, "Unsupported data type of kv cache: ", kv_cache_dtype);
+  }
+  int num_tokens = key.size(0);
+  int num_heads = key.size(1);
+  int head_size = key.size(2);
+  int block_size = k_cache.size(1);
+
+  int key_stride = key.stride(0);
+  int value_stride = value.stride(0);
+  int block_stride = k_cache.stride(0);
+  TORCH_CHECK(k_cache.stride(0) == v_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();
+  VLLM_DISPATCH_FLOATING_TYPES(
+    key.scalar_type(),
+    "reshape_and_cache_flash",
+    [&] {
+      vllm::reshape_and_cache_flash_kernel<scalar_t><<<grid, block, 0, stream>>>(
+        key.data_ptr<scalar_t>(),
+        value.data_ptr<scalar_t>(),
+        k_cache.data_ptr<scalar_t>(),
+        v_cache.data_ptr<scalar_t>(),
+        slot_mapping.data_ptr<int64_t>(),
+        block_stride,
+        key_stride,
+        value_stride,
+        num_heads,
+        head_size,
+        block_size);
+    });
+}
+
 namespace vllm {
 
 template<typename Tout, typename Tin>

csrc/pybind.cpp

@@ -96,6 +96,10 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     "reshape_and_cache",
     &reshape_and_cache,
     "Reshape the key and value tensors and cache them");
+  cache_ops.def(
+    "reshape_and_cache_flash",
+    &reshape_and_cache_flash,
+    "Reshape the key and value tensors and cache them");
   cache_ops.def(
     "convert_fp8",
     &convert_fp8,

tests/basic_correctness/test_basic_correctness.py

@@ -2,12 +2,15 @@
 
 Run `pytest tests/basic_correctness/test_basic_correctness.py`.
 """
+import os
+
 import pytest
 
 MODELS = [
     "facebook/opt-125m",
     "meta-llama/Llama-2-7b-hf",
 ]
+VLLM_ATTENTION_BACKEND = "VLLM_ATTENTION_BACKEND"
 
 
 @pytest.mark.parametrize("model", MODELS)
@@ -23,11 +26,18 @@ def test_models(
     max_tokens: int,
     enforce_eager: bool,
 ) -> None:
+    backend_by_env_var = os.getenv(VLLM_ATTENTION_BACKEND)
+    if backend_by_env_var == "FLASHINFER" and enforce_eager is False:
+        pytest.skip("Skipping non-eager test for FlashInferBackend.")
+
     hf_model = hf_runner(model, dtype=dtype)
     hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
     del hf_model
 
-    vllm_model = vllm_runner(model, dtype=dtype, enforce_eager=enforce_eager)
+    vllm_model = vllm_runner(model,
+                             dtype=dtype,
+                             enforce_eager=enforce_eager,
+                             gpu_memory_utilization=0.7)
     vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
     del vllm_model
 

tests/distributed/test_basic_distributed_correctness.py

@@ -18,6 +18,7 @@ import torch
 MODELS = [
     os.environ["TEST_DIST_MODEL"],
 ]
+VLLM_ATTENTION_BACKEND = "VLLM_ATTENTION_BACKEND"
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2,
@@ -33,16 +34,19 @@ def test_models(
     dtype: str,
     max_tokens: int,
 ) -> None:
+    enforce_eager = False
+    backend_by_env_var = os.getenv(VLLM_ATTENTION_BACKEND)
+    if backend_by_env_var == "FLASHINFER":
+        enforce_eager = True
 
     hf_model = hf_runner(model, dtype=dtype)
     hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
     del hf_model
 
-    vllm_model = vllm_runner(
-        model,
-        dtype=dtype,
-        tensor_parallel_size=2,
-    )
+    vllm_model = vllm_runner(model,
+                             dtype=dtype,
+                             tensor_parallel_size=2,
+                             enforce_eager=enforce_eager)
     vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
     del vllm_model
 

tests/kernels/conftest.py

@@ -1,8 +1,14 @@
 import pytest
 
-from vllm.utils import create_kv_caches_with_random
+from vllm.utils import (create_kv_caches_with_random,
+                        create_kv_caches_with_random_flash)
 
 
 @pytest.fixture()
 def kv_cache_factory():
     return create_kv_caches_with_random
+
+
+@pytest.fixture()
+def kv_cache_factory_flashinfer():
+    return create_kv_caches_with_random_flash

tests/kernels/test_cache.py

@@ -5,6 +5,7 @@ import pytest
 import torch
 
 from vllm import _custom_ops as ops
+from vllm._C import cache_ops
 from vllm.utils import is_hip
 
 COPYING_DIRECTION = [('cuda', 'cpu'), ('cuda', 'cuda'), ('cpu', 'cuda')]
@@ -191,6 +192,82 @@ def test_reshape_and_cache(
         assert torch.allclose(value_cache, cloned_value_cache)
 
 
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("block_size", BLOCK_SIZES)
+@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
+@torch.inference_mode()
+def test_reshape_and_cache_flash(
+    kv_cache_factory_flashinfer,
+    num_tokens: int,
+    num_heads: int,
+    head_size: int,
+    block_size: int,
+    num_blocks: int,
+    dtype: torch.dtype,
+    seed: int,
+    device: str,
+    kv_cache_dtype: str,
+) -> None:
+    if kv_cache_dtype == "fp8":
+        pytest.skip()
+    random.seed(seed)
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+
+    # Create a random slot mapping.
+    num_slots = block_size * num_blocks
+    slot_mapping = random.sample(range(num_slots), num_tokens)
+    slot_mapping = torch.tensor(slot_mapping, dtype=torch.long, device='cuda')
+
+    qkv = torch.randn(num_tokens,
+                      3,
+                      num_heads,
+                      head_size,
+                      dtype=dtype,
+                      device=device)
+    _, key, value = qkv.unbind(dim=1)
+
+    # Create the KV caches.
+    key_caches, value_caches = kv_cache_factory_flashinfer(
+        num_blocks,
+        block_size,
+        1,
+        num_heads,
+        head_size,
+        kv_cache_dtype,
+        dtype,
+    )
+    key_cache, value_cache = key_caches[0], value_caches[0]
+
+    # Clone the KV caches.
+    cloned_key_cache = key_cache.clone()
+    cloned_value_cache = value_cache.clone()
+
+    # Call the reshape_and_cache kernel.
+    cache_ops.reshape_and_cache_flash(key, value, key_cache, value_cache,
+                                      slot_mapping, kv_cache_dtype)
+
+    # Run the reference implementation.
+    block_indicies = torch.div(slot_mapping, block_size, rounding_mode='floor')
+    block_indicies = block_indicies.cpu().tolist()
+    block_offsets = slot_mapping % block_size
+    block_offsets = block_offsets.cpu().tolist()
+    for i in range(num_tokens):
+        block_idx = block_indicies[i]
+        block_offset = block_offsets[i]
+        cloned_key_cache[block_idx, block_offset, :, :] = key[i]
+        cloned_value_cache[block_idx, block_offset, :, :] = value[i]
+
+    assert torch.allclose(key_cache, cloned_key_cache)
+    assert torch.allclose(value_cache, cloned_value_cache)
+
+
 @pytest.mark.parametrize("direction", COPYING_DIRECTION)
 @pytest.mark.parametrize("num_mappings", NUM_MAPPINGS)
 @pytest.mark.parametrize("num_heads", NUM_HEADS)

vllm/_custom_ops.py

@@ -222,6 +222,18 @@ def reshape_and_cache(
                                      slot_mapping, kv_cache_dtype, kv_scale)
 
 
+def reshape_and_cache_flash(
+    key: torch.Tensor,
+    value: torch.Tensor,
+    key_cache: torch.Tensor,
+    value_cache: torch.Tensor,
+    slot_mapping: torch.Tensor,
+    kv_cache_dtype: str,
+) -> None:
+    vllm_cache_ops.reshape_and_cache_flash(key, value, key_cache, value_cache,
+                                           slot_mapping, kv_cache_dtype)
+
+
 def copy_blocks(key_caches: torch.Tensor, value_caches: torch.Tensor,
                 block_mapping: torch.Tensor) -> None:
     vllm_cache_ops.copy_blocks(key_caches, value_caches, block_mapping)

vllm/attention/backends/abstract.py

@@ -1,6 +1,7 @@
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, fields
-from typing import Any, Dict, Generic, List, Optional, Tuple, Type, TypeVar
+from typing import (Any, Dict, Generic, List, Optional, Set, Tuple, Type,
+                    TypeVar)
 
 import torch
 
@@ -15,7 +16,7 @@ class AttentionBackend(ABC):
 
     @staticmethod
     @abstractmethod
-    def make_metadata(*args, **kwargs) -> "AttentionMetadata":
+    def make_metadata(*args, **kwargs) -> "AttentionMetadataPerStage":
         raise NotImplementedError
 
     @staticmethod
@@ -50,13 +51,17 @@ class AttentionBackend(ABC):
 class AttentionMetadataPerStage:
     """Attention metadata for a specific stage. I.e., prefill or decode."""
 
-    def asdict_zerocopy(self) -> Dict[str, Any]:
+    def asdict_zerocopy(self,
+                        skip_fields: Optional[Set[str]] = None
+                        ) -> Dict[str, Any]:
         """Similar to dataclasses.asdict, but avoids deepcopying."""
+        if skip_fields is None:
+            skip_fields = set()
         # Note that if we add dataclasses as fields, they will need
         # similar handling.
         return {
             field.name: getattr(self, field.name)
-            for field in fields(self)
+            for field in fields(self) if field.name not in skip_fields
         }
 
 

vllm/attention/backends/flashinfer.py

@@ -0,0 +1,220 @@
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Set, Tuple, Type
+
+try:
+    import flashinfer
+    from flash_attn import flash_attn_varlen_func
+    from flashinfer import BatchDecodeWithPagedKVCacheWrapper
+except ImportError:
+    flashinfer = None
+    flash_attn_varlen_func = None
+    BatchDecodeWithPagedKVCacheWrapper = None
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
+                                              AttentionMetadata,
+                                              AttentionMetadataPerStage)
+
+
+class FlashInferBackend(AttentionBackend):
+
+    @staticmethod
+    def get_impl_cls() -> Type["FlashInferImpl"]:
+        return FlashInferImpl
+
+    @staticmethod
+    def make_metadata(*args, **kwargs) -> "FlashInferMetadata":
+        return FlashInferMetadata(*args, **kwargs)
+
+    @staticmethod
+    def get_kv_cache_shape(
+        num_blocks: int,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+    ) -> Tuple[int, ...]:
+        return (num_blocks, 2, block_size, num_kv_heads, head_size)
+
+    @staticmethod
+    def swap_blocks(
+        src_kv_cache: torch.Tensor,
+        dst_kv_cache: torch.Tensor,
+        src_to_dst: Dict[int, int],
+    ) -> None:
+        raise NotImplementedError
+
+    @staticmethod
+    def copy_blocks(
+        kv_caches: List[torch.Tensor],
+        src_to_dists: Dict[int, List[int]],
+    ) -> None:
+        raise NotImplementedError
+
+    @staticmethod
+    def get_supported_head_sizes() -> List[int]:
+        return [64, 128, 256]
+
+
+@dataclass
+class FlashInferMetadata(AttentionMetadataPerStage):
+
+    is_prompt: bool
+
+    use_cuda_graph: bool = False
+
+    decode_wrapper: Optional[BatchDecodeWithPagedKVCacheWrapper] = None
+
+    # Metadata for the prefill stage since we still
+    # use flash attention for prefill.
+    seq_start_loc: Optional[torch.Tensor] = None
+    max_seq_len: Optional[int] = None
+    block_tables: Optional[torch.Tensor] = None
+
+    # Metadata for the decode stage
+    # Workspace buffer required by the kernel, the buffer should not
+    # be allocated/deacollated by the FalshInfermetadata object.
+    workspace_buffer: Optional[torch.Tensor] = None
+    # An example for paged_kv_indices, paged_kv_indptr:
+    # request 1, page indices [0, 5, 8]
+    # request 2, page indices [1, 6, 7]
+    # request 3, page indices [3, 4]
+    # paged_kv_indices is a concatenation of page indices of all requests:
+    # [0, 5, 8, 1, 6, 7, 3, 4]
+    # paged_kv_indptr is used to index into paged_kv_indices:
+    # [0, 3, 6, 8]
+    # The indptr of the paged kv cache, shape: [batch_size + 1]
+    paged_kv_indptr: Optional[torch.Tensor] = None
+    # The page indices of the paged kv cache
+    paged_kv_indices: Optional[torch.Tensor] = None
+    # The number of entries in the last page of each request in
+    # the paged kv cache, shape: [batch_size]
+    paged_kv_last_page_len: Optional[torch.Tensor] = None
+    # The number of query/output heads
+    num_qo_heads: Optional[int] = None
+    # The number of key/value heads
+    num_kv_heads: Optional[int] = None
+    # The dimension of the attention heads
+    head_dim: Optional[int] = None
+    # Block size of vllm
+    page_size: Optional[int] = None
+    # The data type of the paged kv cache
+    data_type: torch.dtype = None
+
+    def __post_init__(self):
+        # Refer to
+        # https://github.com/flashinfer-ai/flashinfer/blob/3d55c71a62052c590c130897d3a3db49b14fcc34/include/flashinfer/utils.cuh#L157
+        supported_head_sizes = FlashInferBackend.get_supported_head_sizes()
+        if self.head_dim is not None and self.head_dim \
+                not in supported_head_sizes:
+            raise ValueError(
+                f"Only {supported_head_sizes} are supported for head_dim,",
+                f"received {self.head_dim}.")
+
+        # When using flashinfer, we are also creating the FlashInferMetadata,
+        # which will also call post_init by default, here we want to skip the
+        # post_init if it's the prefill phase.
+        if not self.is_prompt:
+            self.decode_wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
+                self.workspace_buffer, "NHD")
+            self.decode_wrapper.begin_forward(
+                self.paged_kv_indptr,
+                self.paged_kv_indices,
+                self.paged_kv_last_page_len,
+                self.num_qo_heads,
+                self.num_kv_heads,
+                self.head_dim,
+                self.page_size,
+                # Disable flashinfer's pos encoding and use vllm's rope.
+                pos_encoding_mode="NONE",
+                data_type=self.data_type)
+
+    def asdict_zerocopy(self,
+                        skip_fields: Optional[Set[str]] = None
+                        ) -> Dict[str, Any]:
+        if skip_fields is None:
+            skip_fields = set()
+        # We need to skip the decode_wrapper field since it cannot be
+        # broadcasted with nccl when TP is enabled.
+        skip_fields.add('decode_wrapper')
+        return super().asdict_zerocopy(skip_fields)
+
+
+class FlashInferImpl(AttentionImpl):
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        num_kv_heads: Optional[int] = None,
+        alibi_slopes: Optional[List[float]] = None,
+        sliding_window: Optional[int] = None,
+    ) -> None:
+        if sliding_window is not None:
+            raise ValueError("Sliding window is not supported in FlashInfer.")
+        self.sliding_window = (-1, -1)
+        self.alibi_slopes = alibi_slopes
+        self.scale = scale
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
+
+    def forward(self, query: torch.Tensor, key: torch.Tensor,
+                value: torch.Tensor, kv_cache: Optional[torch.Tensor],
+                attn_metadata: AttentionMetadata[FlashInferMetadata],
+                kv_scale: float):
+        num_tokens, hidden_size = query.shape
+        query = query.view(-1, self.num_heads, self.head_size)
+        key = key.view(-1, self.num_kv_heads, self.head_size)
+        value = value.view(-1, self.num_kv_heads, self.head_size)
+
+        if attn_metadata.num_prefill_tokens > 0:
+            assert attn_metadata.num_decode_tokens == 0, (
+                "Chunked prefill is not supported with flashinfer yet.")
+        if attn_metadata.num_decode_tokens > 0:
+            assert attn_metadata.num_prefill_tokens == 0, (
+                "Chunked prefill is not supported with flashinfer yet.")
+
+        if kv_cache is not None:
+            # Use the same reshape and cache kernel as flash attention.
+            ops.reshape_and_cache_flash(
+                key,
+                value,
+                kv_cache[:, 0],
+                kv_cache[:, 1],
+                attn_metadata.slot_mapping.flatten(),
+                attn_metadata.kv_cache_dtype,
+            )
+
+        if prefill_meta := attn_metadata.prefill_metadata:
+            assert prefill_meta.block_tables is not None
+            if kv_cache is None or prefill_meta.block_tables.numel() == 0:
+                output = flash_attn_varlen_func(
+                    q=query,
+                    k=key,
+                    v=value,
+                    cu_seqlens_q=prefill_meta.seq_start_loc,
+                    cu_seqlens_k=prefill_meta.seq_start_loc,
+                    max_seqlen_q=prefill_meta.max_seq_len,
+                    max_seqlen_k=prefill_meta.max_seq_len,
+                    softmax_scale=self.scale,
+                    causal=True,
+                    window_size=self.sliding_window,
+                    alibi_slopes=self.alibi_slopes,
+                )
+            else:
+                raise NotImplementedError(
+                    "Prefix caching is not supported with flashinfer yet.")
+        else:
+            assert attn_metadata.decode_metadata is not None
+            assert attn_metadata.decode_metadata.decode_wrapper is not None
+            query = query.contiguous(
+            )  # Flashinfer requires query to be contiguous
+            output = attn_metadata.decode_metadata.decode_wrapper.forward(
+                query,
+                kv_cache,
+                sm_scale=self.scale,
+            )
+        return output.view(num_tokens, hidden_size)

vllm/attention/selector.py

@@ -17,6 +17,7 @@ class _Backend(enum.Enum):
     XFORMERS = enum.auto()
     ROCM_FLASH = enum.auto()
     TORCH_SDPA = enum.auto()
+    FLASHINFER = enum.auto()
 
 
 @lru_cache(maxsize=None)
@@ -41,6 +42,11 @@ def get_attn_backend(dtype: torch.dtype) -> Type[AttentionBackend]:
         logger.info("Using Torch SDPA backend.")
         from vllm.attention.backends.torch_sdpa import TorchSDPABackend
         return TorchSDPABackend
+    elif backend == _Backend.FLASHINFER:
+        logger.info("Using Flashinfer backend.")
+        logger.warning("Eager mode is enforced for the Flashinfer backend. ")
+        from vllm.attention.backends.flashinfer import FlashInferBackend
+        return FlashInferBackend
     else:
         raise ValueError("Invalid attention backend.")
 

vllm/config.py

@@ -298,6 +298,11 @@ class ModelConfig:
         return max(1,
                    total_num_kv_heads // parallel_config.tensor_parallel_size)
 
+    def get_num_attention_heads(self,
+                                parallel_config: "ParallelConfig") -> int:
+        return self.hf_text_config.num_attention_heads // \
+                    parallel_config.tensor_parallel_size
+
     def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
         total_num_hidden_layers = self.hf_text_config.num_hidden_layers
         return total_num_hidden_layers // parallel_config.pipeline_parallel_size

vllm/sequence.py

@@ -579,8 +579,10 @@ class SequenceGroupMetadata:
             query tokens for prefill, we don't need sampling.
         token_chunk_size: The number of tokens to be processed (per sequence).
             None if chunking is not required.
-        state: Internal state tied to this sequence group.
         lora_request: LoRA request.
+        computed_block_nums: The block numbers that are already computed,
+            used in prefix caching.
+        state: Internal state tied to this sequence group.
         multi_modal_data: Multi modal data.
     """
 

vllm/utils.py

@@ -355,21 +355,9 @@ def _generate_random_fp8(
     del tensor_tmp
 
 
-def create_kv_caches_with_random(
-    num_blocks: int,
-    block_size: int,
-    num_layers: int,
-    num_heads: int,
-    head_size: int,
-    cache_dtype: Optional[Union[str, torch.dtype]],
-    model_dtype: Optional[Union[str, torch.dtype]] = None,
-    seed: int = 0,
-    device: Optional[str] = "cuda",
-) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
-    torch.random.manual_seed(seed)
-    if torch.cuda.is_available():
-        torch.cuda.manual_seed(seed)
-
+def get_kv_cache_torch_dtype(
+        cache_dtype: Optional[Union[str, torch.dtype]],
+        model_dtype: Optional[Union[str, torch.dtype]] = None) -> torch.dtype:
     if isinstance(cache_dtype, str):
         if cache_dtype == "auto":
             if isinstance(model_dtype, str):
@@ -388,6 +376,55 @@ def create_kv_caches_with_random(
         torch_dtype = cache_dtype
     else:
         raise ValueError(f"Invalid kv cache dtype: {cache_dtype}")
+    return torch_dtype
+
+
+def create_kv_caches_with_random_flash(
+    num_blocks: int,
+    block_size: int,
+    num_layers: int,
+    num_heads: int,
+    head_size: int,
+    cache_dtype: Optional[Union[str, torch.dtype]],
+    model_dtype: Optional[Union[str, torch.dtype]] = None,
+    seed: int = 0,
+    device: Optional[str] = "cuda",
+) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
+    assert cache_dtype != "fp8"
+    torch.random.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+
+    torch_dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)
+    key_value_cache_shape = (num_blocks, 2, block_size, num_heads, head_size)
+    scale = head_size**-0.5
+    key_caches, value_caches = [], []
+    for _ in range(num_layers):
+        key_value_cache = torch.empty(size=key_value_cache_shape,
+                                      dtype=torch_dtype,
+                                      device=device)
+        key_value_cache.uniform_(-scale, scale)
+        key_caches.append(key_value_cache[:, 0])
+        value_caches.append(key_value_cache[:, 1])
+    return key_caches, value_caches
+
+
+def create_kv_caches_with_random(
+    num_blocks: int,
+    block_size: int,
+    num_layers: int,
+    num_heads: int,
+    head_size: int,
+    cache_dtype: Optional[Union[str, torch.dtype]],
+    model_dtype: Optional[Union[str, torch.dtype]] = None,
+    seed: int = 0,
+    device: Optional[str] = "cuda",
+) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
+    torch.random.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+
+    torch_dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)
 
     scale = head_size**-0.5
     x = 16 // torch.tensor([], dtype=torch_dtype).element_size()

vllm/worker/model_runner.py

@@ -9,6 +9,7 @@ import torch.nn as nn
 
 from vllm.attention import (AttentionMetadata, AttentionMetadataPerStage,
                             get_attn_backend)
+from vllm.attention.backends.flashinfer import FlashInferBackend
 from vllm.config import (DeviceConfig, LoadConfig, LoRAConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig, VisionLanguageConfig)
 from vllm.distributed import broadcast_tensor_dict, with_pynccl_for_all_reduce
@@ -23,8 +24,8 @@ from vllm.model_executor.model_loader import get_model
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import (MultiModalData, SamplerOutput, SequenceData,
                            SequenceGroupMetadata)
-from vllm.utils import (CudaMemoryProfiler, is_hip, is_pin_memory_available,
-                        make_tensor_with_pad)
+from vllm.utils import (CudaMemoryProfiler, get_kv_cache_torch_dtype, is_hip,
+                        is_pin_memory_available, make_tensor_with_pad)
 
 logger = init_logger(__name__)
 
@@ -155,6 +156,9 @@ class ModelRunner:
         # (max batch size to capture, max context len to capture / block size).
         self.graph_block_tables: torch.Tensor  # Set after initial profiling.
 
+        # Set if the backend is flashinfer.
+        self.flashinfer_workspace_buffer: torch.Tensor
+
     def load_model(self) -> None:
         with CudaMemoryProfiler() as m:
             self.model = get_model(
@@ -315,6 +319,7 @@ class ModelRunner:
 
             # Compute the slot mapping.
             block_table = seq_group_metadata.block_tables[seq_id]
+
             # Mask the [0, start_idx) tokens of the prompt with _PAD_SLOT_ID,
             # where start_idx is max(0, seq_len - sliding_window).
             # For example, if the prompt len is 10, sliding window is 8, and
@@ -390,18 +395,26 @@ class ModelRunner:
                      dtype=seq_start_loc.dtype,
                      out=seq_start_loc[1:])
 
-        attn_metadata = self.attn_backend.make_metadata(
-            is_prompt=True,
-            seq_lens=seq_lens,
-            seq_lens_tensor=seq_lens_tensor,
-            max_query_len=max_query_len,
-            max_seq_len=max_seq_len,
-            subquery_start_loc=subquery_start_loc,
-            seq_start_loc=seq_start_loc,
-            context_lens_tensor=context_lens_tensor,
-            block_tables=block_tables,
-            use_cuda_graph=False,
-        )
+        if self.attn_backend is FlashInferBackend:
+            attn_metadata = self.attn_backend.make_metadata(
+                is_prompt=True,
+                use_cuda_graph=False,
+                seq_start_loc=seq_start_loc,
+                max_seq_len=max_seq_len,
+                block_tables=block_tables)
+        else:
+            attn_metadata = self.attn_backend.make_metadata(
+                is_prompt=True,
+                seq_lens=seq_lens,
+                seq_lens_tensor=seq_lens_tensor,
+                max_query_len=max_query_len,
+                max_seq_len=max_seq_len,
+                subquery_start_loc=subquery_start_loc,
+                seq_start_loc=seq_start_loc,
+                context_lens_tensor=context_lens_tensor,
+                block_tables=block_tables,
+                use_cuda_graph=False,
+            )
 
         return PreparePromptMetadata(
             input_tokens=input_tokens,
@@ -429,6 +442,24 @@ class ModelRunner:
         lora_prompt_mapping: List[int] = []
         lora_requests: Set[LoRARequest] = set()
 
+        # The following fields are only for flashinfer
+        # Please follow https://docs.flashinfer.ai/tutorials/kv_layout.html#page-layout
+        # for the precise definition of the following fields.
+        # An example:
+        # request 1, page indices [0, 5, 8]
+        # request 2, page indices [1, 6, 7]
+        # request 3, page indices [3, 4]
+        # paged_kv_indices is a concatenation of page indices of all requests:
+        # [0, 5, 8, 1, 6, 7, 3, 4]
+        # paged_kv_indptr is used to index into paged_kv_indices:
+        # [0, 3, 6, 8]
+        paged_kv_indices: List[int] = []
+        # 0 at the beginning of paged_kv_indptr indicates the start of the
+        # first request’s page indices in the paged_kv_indices list.
+        paged_kv_indptr: List[int] = [0]
+        # paged_kv_last_page_len is the length of the last page of each request
+        paged_kv_last_page_len: List[int] = []
+
         if len(seq_group_metadata_list) == 0:
             return PrepareDecodeMetadata.empty()
 
@@ -469,6 +500,13 @@ class ModelRunner:
                     block_table = block_table[-sliding_window_blocks:]
                 block_tables.append(block_table)
 
+                paged_kv_indices.extend(block_table)
+                paged_kv_indptr.append(paged_kv_indptr[-1] + len(block_table))
+                last_page_len = seq_data.get_len() % self.block_size
+                if last_page_len == 0:
+                    last_page_len = self.block_size
+                paged_kv_last_page_len.append(last_page_len)
+
         # vLLM uses cuda graph only for decoding requests.
         # See `capture_model` API for more details.
         # For decoding requests, batch_size == input_tokens.
@@ -518,18 +556,51 @@ class ModelRunner:
                 device=self.device,
             )
 
-        attn_metadata = self.attn_backend.make_metadata(
-            is_prompt=False,
-            seq_lens=None,
-            seq_lens_tensor=seq_lens_tensor,
-            max_query_len=None,
-            max_seq_len=max_seq_len,
-            subquery_start_loc=None,
-            seq_start_loc=None,
-            context_lens_tensor=None,
-            block_tables=block_tables,
-            use_cuda_graph=use_captured_graph,
-        )
+        if self.attn_backend is FlashInferBackend:
+            if not hasattr(self, "flashinfer_workspace_buffer"):
+                # Allocate 16MB workspace buffer
+                # Follow the example of flashinfer: https://docs.flashinfer.ai/api/python/decode.html
+                self.flashinfer_workspace_buffer = torch.empty(
+                    16 * 1024 * 1024, dtype=torch.uint8, device=self.device)
+            paged_kv_indptr = torch.tensor(paged_kv_indptr,
+                                           dtype=torch.int,
+                                           device=self.device)
+            paged_kv_indices = torch.tensor(paged_kv_indices,
+                                            dtype=torch.int,
+                                            device=self.device)
+            paged_kv_last_page_len = torch.tensor(paged_kv_last_page_len,
+                                                  dtype=torch.int,
+                                                  device=self.device)
+            kv_cache_dtype = get_kv_cache_torch_dtype(self.kv_cache_dtype,
+                                                      self.model_config.dtype)
+
+            attn_metadata = self.attn_backend.make_metadata(
+                is_prompt=False,
+                use_cuda_graph=False,
+                workspace_buffer=self.flashinfer_workspace_buffer,
+                paged_kv_indptr=paged_kv_indptr,
+                paged_kv_indices=paged_kv_indices,
+                paged_kv_last_page_len=paged_kv_last_page_len,
+                num_qo_heads=self.model_config.get_num_attention_heads(
+                    self.parallel_config),
+                num_kv_heads=self.model_config.get_num_kv_heads(
+                    self.parallel_config),
+                head_dim=self.model_config.get_head_size(),
+                page_size=self.block_size,
+                data_type=kv_cache_dtype)
+        else:
+            attn_metadata = self.attn_backend.make_metadata(
+                is_prompt=False,
+                seq_lens=None,
+                seq_lens_tensor=seq_lens_tensor,
+                max_query_len=None,
+                max_seq_len=max_seq_len,
+                subquery_start_loc=None,
+                seq_start_loc=None,
+                context_lens_tensor=None,
+                block_tables=block_tables,
+                use_cuda_graph=use_captured_graph,
+            )
         return PrepareDecodeMetadata(
             input_tokens=input_tokens,
             input_positions=input_positions,