# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """Attention layer with FlashInfer.""" from __future__ import annotations from dataclasses import dataclass from typing import ClassVar, Optional, Union import numpy as np import torch from flashinfer import (BatchDecodeWithPagedKVCacheWrapper, BatchPrefillWithPagedKVCacheWrapper, MultiLevelCascadeAttentionWrapper) from flashinfer.decode import _get_range_buf, trtllm_batch_decode_with_kv_cache from flashinfer.prefill import trtllm_batch_context_with_kv_cache from flashinfer.utils import FP4Tensor from vllm import _custom_ops as ops from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl, AttentionType) from vllm.config import CUDAGraphMode, VllmConfig from vllm.logger import init_logger from vllm.model_executor.layers.quantization.utils.quant_utils import ( QuantKey, kFp8StaticTensorSym, kNvfp4Quant) from vllm.platforms import current_platform from vllm.triton_utils import tl, triton from vllm.utils import cdiv, is_pin_memory_available from vllm.utils.flashinfer import (can_use_trtllm_attention, flashinfer_disable_q_quantization, supports_trtllm_attention, use_trtllm_attention) # yapf conflicts with isort for this block # yapf: disable from vllm.v1.attention.backends.utils import (AttentionCGSupport, AttentionMetadataBuilder, CommonAttentionMetadata, get_kv_cache_layout, get_per_layer_parameters, infer_global_hyperparameters, split_decodes_and_prefills) # yapf: enable from vllm.v1.kv_cache_interface import AttentionSpec FLASHINFER_WORKSPACE_BUFFER_SIZE = 256 * 1024 * 1024 FP8_DTYPE = current_platform.fp8_dtype() FP4_DTYPE = torch.uint8 logger = init_logger(__name__) trtllm_gen_workspace_buffer = None def _get_trtllm_gen_workspace_buffer(): global trtllm_gen_workspace_buffer if trtllm_gen_workspace_buffer is None: trtllm_gen_workspace_buffer = torch.zeros( FLASHINFER_WORKSPACE_BUFFER_SIZE, dtype=torch.uint8, device='cuda') return trtllm_gen_workspace_buffer @triton.jit def _trtllm_prefill_attn_kvfp8_dequant( kv_cache_ptr, block_tables_prefill_ptr, block_table_stride, mock_kv_cache_ptr, k_scale_ptr, v_scale_ptr, K_CACHE_STRIDE: tl.constexpr, KV_CACHE_STRIDE: tl.constexpr, ): batch_idx = tl.program_id(0).to(tl.int64) mock_block_table_idx = tl.program_id(1).to(tl.int64) orig_page_num = tl.load(block_tables_prefill_ptr + batch_idx * block_table_stride + mock_block_table_idx).to(tl.int64) if orig_page_num <= 0: return dequant_dtype = mock_kv_cache_ptr.dtype.element_ty # Dequantize K k_scale_val = tl.load(k_scale_ptr) offset = orig_page_num * KV_CACHE_STRIDE + tl.arange(0, K_CACHE_STRIDE) fp8_vals = tl.load(kv_cache_ptr + offset) dequantized_vals = fp8_vals.to(tl.float32) * k_scale_val mock_cache_offset = (batch_idx * block_table_stride + mock_block_table_idx + 1) * KV_CACHE_STRIDE + tl.arange(0, K_CACHE_STRIDE) dequantized_vals = dequantized_vals.to(dequant_dtype) tl.store(mock_kv_cache_ptr + mock_cache_offset, dequantized_vals) # Dequantize V v_scale_val = tl.load(v_scale_ptr) offset = (orig_page_num * KV_CACHE_STRIDE + K_CACHE_STRIDE + tl.arange(0, K_CACHE_STRIDE)) fp8_vals = tl.load(kv_cache_ptr + offset) dequantized_vals = fp8_vals.to(tl.float32) * v_scale_val mock_cache_offset = ( (batch_idx * block_table_stride + mock_block_table_idx + 1) * KV_CACHE_STRIDE + K_CACHE_STRIDE + tl.arange(0, K_CACHE_STRIDE)) dequantized_vals = dequantized_vals.to(dequant_dtype) tl.store(mock_kv_cache_ptr + mock_cache_offset, dequantized_vals) def trtllm_prefill_attn_kvfp8_dequant( kv_cache: torch.Tensor, block_tables_prefill: torch.Tensor, k_scale: torch.Tensor, v_scale: torch.Tensor, dequant_dtype: torch.dtype, ) -> tuple[torch.Tensor, torch.Tensor]: batch_size, num_of_page_per_token = block_tables_prefill.shape s = kv_cache.shape assert s[1] == 2 assert dequant_dtype in (torch.bfloat16, torch.float16) k_cache_stride = s[2] * s[3] * s[4] kv_cache_stride = k_cache_stride * s[1] new_s = (batch_size * num_of_page_per_token + 1, s[1], s[2], s[3], s[4]) # mock kv cache contains just the pages needed by this prefill mock_kv_cache = torch.empty(new_s, dtype=dequant_dtype, device=kv_cache.device) # we simply sequentially index the pages needed by this prefill mock_block_table = torch.arange( start=1, end=batch_size * num_of_page_per_token + 1, dtype=torch.int32, device=block_tables_prefill.device, ).reshape(batch_size, num_of_page_per_token) grid = (batch_size, num_of_page_per_token) _trtllm_prefill_attn_kvfp8_dequant[grid]( kv_cache, block_tables_prefill, num_of_page_per_token, mock_kv_cache, k_scale, v_scale, k_cache_stride, kv_cache_stride, ) return mock_kv_cache, mock_block_table class FlashInferBackend(AttentionBackend): accept_output_buffer: bool = True @classmethod def get_supported_dtypes(cls) -> list[torch.dtype]: return [torch.float16, torch.bfloat16] @classmethod def get_supported_head_sizes(cls) -> list[int]: # https://github.com/flashinfer-ai/flashinfer/blob/3d55c71a62052c590c130897d3a3db49b14fcc34/include/flashinfer/utils.cuh#L157 return [64, 128, 256] @classmethod def validate_head_size(cls, head_size: int) -> None: supported_head_sizes = cls.get_supported_head_sizes() if head_size not in supported_head_sizes: attn_type = cls.__name__.removesuffix("Backend") raise ValueError( f"Head size {head_size} is not supported by {attn_type}. " f"Supported head sizes are: {supported_head_sizes}. " "Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use " "FlexAttention backend which supports all head sizes.") @staticmethod def get_name() -> str: return "FLASHINFER" @staticmethod def get_impl_cls() -> type[FlashInferImpl]: return FlashInferImpl @staticmethod def get_metadata_cls() -> type[FlashInferMetadata]: return FlashInferMetadata @staticmethod def get_builder_cls() -> type[FlashInferMetadataBuilder]: return FlashInferMetadataBuilder @staticmethod def get_kv_cache_shape( num_blocks: int, block_size: int, num_kv_heads: int, head_size: int, cache_dtype_str: str = "auto", ) -> tuple[int, ...]: return (num_blocks, 2, block_size, num_kv_heads, head_size) @staticmethod def get_kv_cache_stride_order() -> tuple[int, ...]: # `stride_order` indicates the permutation that gets us from # `get_kv_cache_shape` to the actual memory layout we want. cache_layout = get_kv_cache_layout() if cache_layout == "NHD": stride_order = (0, 1, 2, 3, 4) elif cache_layout == "HND": stride_order = (0, 1, 3, 2, 4) else: raise ValueError(f"Unknown cache layout format {cache_layout}.") return stride_order @staticmethod def get_fp8_dtype_for_flashinfer(kv_cache_dtype: str) -> torch.dtype: if kv_cache_dtype in ("fp8", "fp8_e4m3"): return torch.float8_e4m3fn elif kv_cache_dtype == "fp8_e5m2": return torch.float8_e5m2 else: raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}") @dataclass class FlashInferMetadata: num_actual_tokens: int # Number of tokens excluding padding. # The data type of the query q_data_type: torch.dtype slot_mapping: torch.Tensor # For flashinfer trtllm batch decode max_q_len: int max_q_len_prefill: int max_seq_len: int seq_lens: torch.Tensor block_table_tensor: torch.Tensor prefill_use_trtllm: bool decode_use_trtllm: bool # For handling prefill decode split num_decodes: int num_decode_tokens: int num_prefills: int num_prefill_tokens: int # For cascade attention (CPU for planning). use_cascade: bool prefill_wrapper: Optional[BatchPrefillWithPagedKVCacheWrapper] = None decode_wrapper: Optional[BatchDecodeWithPagedKVCacheWrapper] = None cascade_wrapper: Optional[MultiLevelCascadeAttentionWrapper] = None qo_indptr_gpu: Optional[torch.Tensor] = None paged_kv_indptr_gpu: Optional[torch.Tensor] = None class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]): cudagraph_support: ClassVar[AttentionCGSupport] = \ AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE reorder_batch_threshold: int = 1 def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str], vllm_config: VllmConfig, device: torch.device): super().__init__(kv_cache_spec, layer_names, vllm_config, device) self.cache_config = vllm_config.cache_config self.model_config = vllm_config.model_config self._workspace_buffer = None self._prefill_wrapper = None # Wrapper for prefill/append self._decode_wrapper = None # Wrapper for decode (general shape) self.compilation_config = vllm_config.compilation_config max_num_pages_per_req = cdiv(self.model_config.max_model_len, self.kv_cache_spec.block_size) max_num_reqs = vllm_config.scheduler_config.max_num_seqs max_num_pages = max_num_reqs * max_num_pages_per_req self.enable_cuda_graph = (self.compilation_config.cudagraph_mode.\ decode_mode() == CUDAGraphMode.FULL) if self.enable_cuda_graph: # For full cudagraph capture, one `decode_wrapper` for each batch # size is needed for FlashInfer. self._decode_wrappers_cudagraph: dict[ int, BatchDecodeWithPagedKVCacheWrapper] = {} self._decode_cudagraph_max_bs = min( max_num_reqs, self.compilation_config.max_capture_size) self.num_qo_heads = self.model_config.get_num_attention_heads( self.vllm_config.parallel_config) self.num_kv_heads = self.kv_cache_spec.num_kv_heads self.head_dim = self.kv_cache_spec.head_size FlashInferBackend.validate_head_size(self.head_dim) self.page_size = self.kv_cache_spec.block_size self.cache_dtype = self.cache_config.cache_dtype if self.cache_dtype.startswith("fp8"): self.kv_cache_dtype = ( FlashInferBackend.get_fp8_dtype_for_flashinfer( self.cache_dtype)) else: assert self.kv_cache_spec.dtype == self.model_config.dtype self.kv_cache_dtype = self.kv_cache_spec.dtype # Use model dtype as q dtype when TRTLLM attn is not supported, or # VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION is set to 1. Otherwise, try to # use fp8 q if kv cache is fp8, and will fall back to model dtype # if TRTLLM attention kernel is not used when building attn metadata if supports_trtllm_attention() and \ not flashinfer_disable_q_quantization(): self.q_data_type = self.kv_cache_dtype else: self.q_data_type = self.model_config.dtype supports_spec_as_decode = \ can_use_trtllm_attention(self.num_qo_heads, self.num_kv_heads) self._init_reorder_batch_threshold(1, supports_spec_as_decode) self._cascade_wrapper = None # Wrapper for cascade attention # Global hyperparameters shared by all attention layers # TODO: discard this for trtllm-gen backend self.global_hyperparameters = infer_global_hyperparameters( get_per_layer_parameters(vllm_config, layer_names, FlashInferImpl)) self.sm_scale = self.global_hyperparameters.sm_scale self.window_left = self.global_hyperparameters.window_left self.logits_soft_cap = self.global_hyperparameters.logits_soft_cap self.has_sinks = self.global_hyperparameters.has_sinks if self.has_sinks and not supports_trtllm_attention(): raise NotImplementedError( "FlashInfer backend currently does not support attention " "sinks, please use trtllm on blackwell or flash attention on " "earlier GPUs.") # Preparing persistent buffers (device-side) self.paged_kv_indptr = torch.zeros(max_num_reqs + 1, dtype=torch.int32, device=self.device) self.paged_kv_indices = torch.zeros( max_num_pages, # max num pages possible dtype=torch.int32, device=self.device) self.paged_kv_last_page_len = torch.zeros(max_num_reqs, dtype=torch.int32, device=self.device) # host-side buffer pin_memory = is_pin_memory_available() self.paged_kv_indptr_cpu = torch.zeros(max_num_reqs + 1, dtype=torch.int32, device="cpu", pin_memory=pin_memory) self.paged_kv_indptr_np = self.paged_kv_indptr_cpu.numpy() self.paged_kv_indptr_buffer = torch.zeros_like( self.paged_kv_indptr_cpu, pin_memory=pin_memory) self.paged_kv_indices_cpu = torch.zeros(max_num_pages, dtype=torch.int32, device="cpu", pin_memory=pin_memory) self.paged_kv_last_page_len_cpu = torch.zeros(max_num_reqs, dtype=torch.int32, device="cpu", pin_memory=pin_memory) self.paged_kv_last_page_len_np = ( self.paged_kv_last_page_len_cpu.numpy()) def _get_workspace_buffer(self): if self._workspace_buffer is None: self._workspace_buffer = torch.zeros( FLASHINFER_WORKSPACE_BUFFER_SIZE, dtype=torch.uint8, device=self.device) return self._workspace_buffer def _get_prefill_wrapper(self): if self._prefill_wrapper is None: self._prefill_wrapper = BatchPrefillWithPagedKVCacheWrapper( self._get_workspace_buffer(), get_kv_cache_layout()) return self._prefill_wrapper def _get_decode_wrapper(self, batch_size: int, use_cudagraph: bool = False): if use_cudagraph: decode_wrapper = self._decode_wrappers_cudagraph.get( batch_size, None) else: decode_wrapper = self._decode_wrapper if decode_wrapper is None: if use_cudagraph: paged_kv_indptr = self.paged_kv_indptr[:batch_size + 1] paged_kv_indices = self.paged_kv_indices paged_kv_last_page_len = self.paged_kv_last_page_len[: batch_size] else: paged_kv_indptr = None paged_kv_indices = None paged_kv_last_page_len = None decode_wrapper = BatchDecodeWithPagedKVCacheWrapper( self._get_workspace_buffer(), get_kv_cache_layout(), use_cuda_graph=use_cudagraph, paged_kv_indptr_buffer=paged_kv_indptr, paged_kv_indices_buffer=paged_kv_indices, paged_kv_last_page_len_buffer=paged_kv_last_page_len, # Tensor cores are enabled by default because the perf would be # at least as good as cuda cores for all attention ops in latest # gpus. use_tensor_cores=True, ) # save the decode wrapper if use_cudagraph: self._decode_wrappers_cudagraph[batch_size] = decode_wrapper else: self._decode_wrapper = decode_wrapper return decode_wrapper def _get_cascade_wrapper(self): if self._cascade_wrapper is None: self._cascade_wrapper = MultiLevelCascadeAttentionWrapper( 2, self._get_workspace_buffer(), get_kv_cache_layout()) return self._cascade_wrapper def build(self, common_prefix_len: int, common_attn_metadata: CommonAttentionMetadata, fast_build: bool = False) -> FlashInferMetadata: num_reqs = common_attn_metadata.num_reqs num_actual_tokens = common_attn_metadata.num_actual_tokens num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens =\ split_decodes_and_prefills(common_attn_metadata, decode_threshold=self.reorder_batch_threshold, require_uniform=True) page_size = self.page_size max_q_len = common_attn_metadata.max_query_len max_seq_len = common_attn_metadata.max_seq_len seq_lens = common_attn_metadata.seq_lens seq_lens_cpu = common_attn_metadata.seq_lens_cpu seq_lens_np = seq_lens_cpu.numpy() block_table_tensor = common_attn_metadata.block_table_tensor num_blocks_np = (seq_lens_np + (page_size - 1)) // page_size use_cascade = common_prefix_len > 0 if use_cascade: # Grab the blocks of the shared prefix from the first request. assert common_prefix_len % page_size == 0 num_common_kv_blocks = common_prefix_len // page_size # Create CPU versions directly for cascade (no GPU versions needed) shared_qo_indptr_cpu = torch.tensor([0, num_actual_tokens], dtype=torch.int32, device='cpu') shared_kv_page_indptr_cpu = torch.tensor([0, num_common_kv_blocks], dtype=torch.int32, device='cpu') shared_kv_page_indices_cpu = block_table_tensor[ 0, :num_common_kv_blocks] shared_kv_last_page_len_cpu = torch.tensor([page_size], dtype=torch.int32, device='cpu') # Remove the blocks of the shared prefix from all requests. block_table_tensor = block_table_tensor[:, num_common_kv_blocks:] num_blocks_np -= num_common_kv_blocks else: shared_qo_indptr_cpu = None shared_kv_page_indptr_cpu = None shared_kv_page_indices_cpu = None shared_kv_last_page_len_cpu = None # write self.paged_kv_indptr_cpu inplace (0-index is always 0) np.cumsum( num_blocks_np, dtype=np.int32, out=self.paged_kv_indptr_np[1:num_reqs + 1], ) # NOTE(woosuk): Because self.paged_kv_indptr_cpu can be modified # after this line (e.g., for cuda graphs), we need to copy the data to # self.paged_kv_indptr_buffer to avoid race condition. self.paged_kv_indptr_buffer[:num_reqs + 1] = (self.paged_kv_indptr_cpu[:num_reqs + 1]) paged_kv_indptr = self.paged_kv_indptr[:num_reqs + 1] paged_kv_indptr.copy_(self.paged_kv_indptr_buffer[:num_reqs + 1], non_blocking=True) # write self.paged_kv_indices inplace num_actual_pages = self.paged_kv_indptr_np[num_reqs] paged_kv_indices = self.paged_kv_indices[:num_actual_pages] _copy_page_indices_kernel[(num_reqs, )]( paged_kv_indices, block_table_tensor, block_table_tensor.stride(0), paged_kv_indptr, BLOCK_SIZE=1024, ) # write self.paged_kv_last_page_len_cpu inplace paged_kv_last_page_len_np = seq_lens_np % page_size self.paged_kv_last_page_len_np[:num_reqs] = np.where( paged_kv_last_page_len_np == 0, page_size, paged_kv_last_page_len_np, ) uses_spec_reorder = self.reorder_batch_threshold > 1 prefill_use_trtllm = use_trtllm_attention(self.num_qo_heads, self.num_kv_heads, num_prefill_tokens, max_seq_len, self.cache_dtype, self.q_data_type, is_prefill=True, has_sinks=self.has_sinks, has_spec=uses_spec_reorder) decode_use_trtllm = use_trtllm_attention(self.num_qo_heads, self.num_kv_heads, num_decode_tokens, max_seq_len, self.cache_dtype, self.q_data_type, is_prefill=False, has_sinks=self.has_sinks, has_spec=uses_spec_reorder) if self.has_sinks and not (prefill_use_trtllm and decode_use_trtllm): raise NotImplementedError( "FlashInfer backend currently does not support attention " "sinks, please use trtllm on blackwell or flash attention on " "earlier GPUs.") # If TRTLLM attention is not used, the q quantization is not supported. # Fall back to use model dtype. if not (prefill_use_trtllm and decode_use_trtllm): self.q_data_type = self.model_config.dtype attn_metadata = FlashInferMetadata( num_actual_tokens=num_actual_tokens, q_data_type=self.q_data_type, slot_mapping=common_attn_metadata.slot_mapping, max_q_len=max_q_len, max_q_len_prefill=max_q_len, max_seq_len=max_seq_len, seq_lens=seq_lens, block_table_tensor=block_table_tensor, prefill_use_trtllm=prefill_use_trtllm, decode_use_trtllm=decode_use_trtllm, num_decodes=num_decodes, num_decode_tokens=num_decode_tokens, num_prefills=num_prefills, num_prefill_tokens=num_prefill_tokens, use_cascade=use_cascade, ) qo_indptr_cpu = common_attn_metadata.query_start_loc_cpu paged_kv_indptr_cpu = self.paged_kv_indptr_cpu[:1 + num_reqs] paged_kv_last_page_len_cpu = self.paged_kv_last_page_len_cpu[:num_reqs] if attn_metadata.use_cascade: attn_metadata.cascade_wrapper = self._get_cascade_wrapper() attn_metadata.cascade_wrapper.plan( [shared_qo_indptr_cpu, qo_indptr_cpu], [shared_kv_page_indptr_cpu, paged_kv_indptr_cpu], [shared_kv_page_indices_cpu, paged_kv_indices], [shared_kv_last_page_len_cpu, paged_kv_last_page_len_cpu], self.num_qo_heads, self.num_kv_heads, self.head_dim, self.page_size, causal=True, sm_scale=self.sm_scale, window_left=self.window_left, logits_soft_cap=self.logits_soft_cap, q_data_type=self.q_data_type, kv_data_type=self.kv_cache_dtype, ) else: # Regular attention (common case). # Decodes are at the front and prefills are at the back, # according to reorder_batch() num_prefills = attn_metadata.num_prefills num_decodes = attn_metadata.num_decodes if num_prefills > 0: # Decodes are first so prefills start after the last decode prefill_start = num_decodes attn_metadata.prefill_wrapper = self._get_prefill_wrapper() assert qo_indptr_cpu[prefill_start:].shape[ 0] == num_prefills + 1 assert paged_kv_indptr_cpu[prefill_start:].shape[ 0] == num_prefills + 1 assert paged_kv_last_page_len_cpu[prefill_start:].shape[ 0] == num_prefills # Since prefill_wrapper.run() will be called with # query[num_decode_tokens:] we need to adjust the qo_indptr # to be relative to the start of the prefill queries. qo_indptr_cpu = qo_indptr_cpu[prefill_start:] - qo_indptr_cpu[ prefill_start] paged_kv_indptr_cpu = paged_kv_indptr_cpu[prefill_start:] # Recompute max_q_len for the slice of requests we are using # for prefills. This can be different from max_q_len when # we have a non-uniform batch with some short decodes offloaded # to the prefill pathway query_lens_prefill = qo_indptr_cpu[1:] - qo_indptr_cpu[:-1] attn_metadata.max_q_len_prefill = \ int(query_lens_prefill.max().item()) if not attn_metadata.prefill_use_trtllm: attn_metadata.prefill_wrapper.plan( qo_indptr_cpu, paged_kv_indptr_cpu, paged_kv_indices, paged_kv_last_page_len_cpu[prefill_start:], self.num_qo_heads, self.num_kv_heads, self.head_dim, self.page_size, causal=True, sm_scale=self.sm_scale, window_left=self.window_left, logits_soft_cap=self.logits_soft_cap, q_data_type=self.q_data_type, kv_data_type=self.kv_cache_dtype, ) else: attn_metadata.qo_indptr_gpu = qo_indptr_cpu.to( self.device, non_blocking=True) attn_metadata.paged_kv_indptr_gpu = paged_kv_indptr_cpu.to( self.device, non_blocking=True) if num_decodes > 0: pure_decode = num_prefills == 0 # possible required padding for cudagraph replay use_cudagraph = (self.enable_cuda_graph and pure_decode and num_decodes <= self._decode_cudagraph_max_bs) if use_cudagraph: num_input_tokens = ( self.vllm_config.pad_for_cudagraph(num_decode_tokens)) # Carefully fulfill the padding region with reasonable value # on cpu. # Make sure paged_kv_indptr_cpu is not decreasing self.paged_kv_indptr_cpu[1 + num_decodes:1 + num_input_tokens].fill_( paged_kv_indptr_cpu[-1]) # Fill the remaining paged_kv_last_page_len_cpu with 1. # This is because flashinfer treats 0 as a full page # instead of empty. self.paged_kv_last_page_len_cpu[ num_decodes:num_input_tokens].fill_(1) else: num_input_tokens = num_decode_tokens attn_metadata.decode_wrapper = self._get_decode_wrapper( num_input_tokens, use_cudagraph) if not attn_metadata.decode_use_trtllm: # Use the persistent buffer with padding length, # instead of the same address but chunked version # in atten_metadata when using cudagraph. fast_plan_decode( attn_metadata.decode_wrapper, self.paged_kv_indptr_cpu[:num_input_tokens + 1], paged_kv_indices, self.paged_kv_last_page_len_cpu[:num_input_tokens], seq_lens_cpu[:num_input_tokens], 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", sm_scale=self.sm_scale, window_left=self.window_left, logits_soft_cap=self.logits_soft_cap, q_data_type=self.q_data_type, kv_data_type=self.kv_cache_dtype, ) return attn_metadata def use_cascade_attention(self, *args, **kwargs) -> bool: if self.kv_cache_spec.dtype != self.vllm_config.model_config.dtype: # TODO: The cascade wrapper currently does not support setting # kv cache dtype to something different from query dtype. return False # TODO: Cascade attention doesn't work, disable it for now # return use_cascade_attention(*args, **kwargs) return False class FlashInferImpl(AttentionImpl): def __init__( self, num_heads: int, head_size: int, scale: float, num_kv_heads: int, alibi_slopes: Optional[list[float]], sliding_window: Optional[int], kv_cache_dtype: str, logits_soft_cap: Optional[float] = None, attn_type: AttentionType = AttentionType.DECODER, kv_sharing_target_layer_name: Optional[int] = None, sinks: Optional[torch.Tensor] = None, ) -> None: self.num_heads = num_heads self.head_size = head_size self.scale = float(scale) self.num_kv_heads = num_kv_heads if alibi_slopes is not None: alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32) self.alibi_slopes = alibi_slopes if sliding_window is None: self.sliding_window = (-1, -1) else: self.sliding_window = (sliding_window - 1, 0) self.window_left = (self.sliding_window[0] if self.sliding_window is not None else -1) self.kv_cache_dtype = kv_cache_dtype self.logits_soft_cap = logits_soft_cap self.kv_sharing_target_layer_name = kv_sharing_target_layer_name self.num_queries_per_kv = self.num_heads // self.num_kv_heads if attn_type != AttentionType.DECODER: raise NotImplementedError("Encoder self-attention and " "encoder/decoder cross-attention " "are not implemented for " "FlashInferImpl") self.sinks: Optional[torch.Tensor] = None if sinks is not None: if sinks.shape[0] != num_heads: raise ValueError( "Sinks must have the same number of heads as the number of " f"heads in the layer. Expected {num_heads}, but got " f"{sinks.shape[0]}.") self.sinks = sinks self.support_trtllm_attn = (supports_trtllm_attention() and num_heads % num_kv_heads == 0) self.bmm1_scale: Optional[float] = None self.bmm2_scale: Optional[float] = None self.o_sf_scale: Optional[float] = None def fused_output_quant_supported(self, quant_key: QuantKey): return (self.support_trtllm_attn and self.kv_cache_dtype.startswith("fp8") and quant_key in (kFp8StaticTensorSym, kNvfp4Quant)) def forward( self, layer: torch.nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: FlashInferMetadata, output: Optional[torch.Tensor] = None, output_scale: Optional[torch.Tensor] = None, output_block_scale: Optional[torch.Tensor] = None, ) -> torch.Tensor: """Forward pass with FlashInfer. Args: query: shape = [num_tokens, num_heads, head_size] key: shape = [num_tokens, num_kv_heads, head_size] value: shape = [num_tokens, num_kv_heads, head_size] kv_cache: KV cache tensor with different possible shapes: - NHD: [num_blocks, 2, block_size, num_kv_heads, head_size] - HND: [num_blocks, 2, num_kv_heads, block_size, head_size] attn_metadata: Metadata for attention. Returns: shape = [num_tokens, num_heads * head_size] """ assert output is not None, "Output tensor must be provided." if attn_metadata is None: # Profiling run. return output if self.bmm1_scale is None: self.bmm1_scale = (layer._q_scale_float * layer._k_scale_float * self.scale) if self.bmm2_scale is None: self.bmm2_scale = layer._v_scale_float # The attn+quant fusion happens when output_scale is provided. if output_scale is None: assert output_block_scale is None, "output_block_scale "\ "is not supported when fusion has not happened" else: assert attn_metadata.q_data_type == FP8_DTYPE, \ "Query must be FP8 when attn+quant fusion happened." assert (attn_metadata.prefill_use_trtllm and attn_metadata.decode_use_trtllm), "Must use TRT-LLM attn" if output.dtype == FP8_DTYPE: assert output_block_scale is None, \ "output_block_scale should not be provided for fp8 output" elif output.dtype == FP4_DTYPE: assert output_block_scale is not None, \ "output_block_scale is required for nvfp4 output" else: raise ValueError(f"Unsupported output dtype: {output.dtype}") # TRTLLM attn kernel requires to scale to pass as a host scalar, # store the o scale as a host scalar in warmup run with cuda graph # not enabled if layer._o_scale_float is None: layer._o_scale_float = output_scale.cpu().item() if output.dtype == FP8_DTYPE: self.bmm2_scale = self.bmm2_scale / layer._o_scale_float elif output.dtype == FP4_DTYPE: self.o_sf_scale = layer._o_scale_float # Insert FP8 quant for query if attn_metadata.q_data_type == FP8_DTYPE: num_tokens, num_heads, head_size = query.shape query, _ = ops.scaled_fp8_quant( query.reshape( (num_tokens, num_heads * head_size)).contiguous(), layer._q_scale) query = query.reshape((num_tokens, num_heads, head_size)) # IMPORTANT! # NOTE(woosuk): With piece-wise CUDA graphs, this method is executed in # eager-mode PyTorch. Thus, we need to be careful about any CPU overhead # in this method. For example, `view` and `slice` (or `[:n]`) operations # are surprisingly slow even in the case they do not invoke any GPU ops. # Minimize the PyTorch ops in this method as much as possible. # Whenever making a change in this method, please benchmark the # performance to make sure it does not introduce any overhead. num_actual_tokens = attn_metadata.num_actual_tokens if self.kv_sharing_target_layer_name is None: # Reshape the input keys and values and store them in the cache. # Skip this if sharing KV cache with an earlier attention layer. # NOTE(woosuk): Here, key and value are padded while slot_mapping is # not padded. However, we don't need to do key[:num_actual_tokens] # and value[:num_actual_tokens] because the reshape_and_cache_flash # op uses the slot_mapping's shape to determine the number of # actual tokens. torch.ops._C_cache_ops.reshape_and_cache_flash( key, value, kv_cache[:, 0], kv_cache[:, 1], attn_metadata.slot_mapping, self.kv_cache_dtype, layer._k_scale, layer._v_scale, ) # The FlashInfer api requires data to be in fp8_e4m3 or fp8_e5m2 # to process the cache when the kv_cache_dtype is fp8 if self.kv_cache_dtype.startswith("fp8"): torch_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer( self.kv_cache_dtype) kv_cache = kv_cache.view(torch_dtype) # Inputs and outputs may be padded for CUDA graphs query = query[:num_actual_tokens] output_padded = output output = output[:num_actual_tokens] if attn_metadata.use_cascade: # Cascade attention (rare case). assert attn_metadata.cascade_wrapper is not None output.copy_(attn_metadata.cascade_wrapper.run(query, kv_cache)) return output # When using spec decoding, num_decodes can be < num_decode_tokens # because some decode requests may have more than one query token. num_decodes = attn_metadata.num_decodes num_decode_tokens = attn_metadata.num_decode_tokens num_prefill_tokens = attn_metadata.num_prefill_tokens stride_order = FlashInferBackend.get_kv_cache_stride_order() kv_cache_permute = kv_cache.permute(*stride_order) # Regular attention (common case). # Decodes are at the front and prefills are at the back, # according to reorder_batch() if num_prefill_tokens > 0: prefill_wrapper = attn_metadata.prefill_wrapper prefill_query = query[num_decode_tokens:] assert prefill_query.shape[0] == num_prefill_tokens assert prefill_wrapper is not None if not attn_metadata.prefill_use_trtllm: assert prefill_wrapper._causal assert prefill_wrapper._window_left == self.window_left assert prefill_wrapper._logits_soft_cap == ( self.logits_soft_cap or 0.0) assert prefill_wrapper._sm_scale == self.scale prefill_wrapper.run( prefill_query, kv_cache_permute, k_scale=layer._k_scale_float, v_scale=layer._v_scale_float, out=output[num_decode_tokens:], ) else: # prefill_query may be non-contiguous prefill_query = prefill_query.contiguous() workspace_buffer = _get_trtllm_gen_workspace_buffer() block_tables_prefill = attn_metadata.block_table_tensor[ num_decodes:] seq_lens_prefill = attn_metadata.seq_lens[num_decodes:] # This path needs to be enabled with VLLM_KV_CACHE_LAYOUT = HND assert get_kv_cache_layout() == "HND" assert prefill_query.is_contiguous() assert kv_cache_permute.is_contiguous() assert workspace_buffer.is_contiguous() assert block_tables_prefill.is_contiguous() assert seq_lens_prefill.is_contiguous() if output.dtype == FP4_DTYPE: assert self.o_sf_scale is not None out = FP4Tensor(data=output[num_decode_tokens:], scale=output_block_scale, scale_start_index=num_decode_tokens, original_shape=prefill_query.shape) else: assert self.o_sf_scale is None out = output[num_decode_tokens:] if attn_metadata.q_data_type != FP8_DTYPE \ and self.kv_cache_dtype.startswith("fp8"): # TRTLLM prefill attention does not support BF16 Q # and fp8 kv cache. So to enable prefill attention # with fp8 kv cache, we can construct a mock block # and mock kv cache with BF16 KV involved in the prefill mock_kv_cache, mock_block_table = ( trtllm_prefill_attn_kvfp8_dequant( kv_cache_permute, block_tables_prefill, layer._k_scale, layer._v_scale, attn_metadata.q_data_type, )) else: mock_kv_cache = kv_cache_permute mock_block_table = block_tables_prefill trtllm_batch_context_with_kv_cache( query=prefill_query, kv_cache=mock_kv_cache, workspace_buffer=workspace_buffer, block_tables=mock_block_table, seq_lens=seq_lens_prefill, max_q_len=attn_metadata.max_q_len_prefill, max_kv_len=attn_metadata.max_seq_len, bmm1_scale=self.bmm1_scale, bmm2_scale=self.bmm2_scale, batch_size=attn_metadata.num_prefills, cum_seq_lens_q=attn_metadata.qo_indptr_gpu, cum_seq_lens_kv=attn_metadata.paged_kv_indptr_gpu, window_left=self.window_left, sinks=self.sinks, o_sf_scale=self.o_sf_scale, out=out, ) if num_decode_tokens > 0: decode_wrapper = attn_metadata.decode_wrapper decode_query = query[:num_decode_tokens] assert decode_query.shape[0] == num_decode_tokens assert decode_wrapper is not None if not attn_metadata.decode_use_trtllm: assert decode_wrapper._window_left == self.window_left assert decode_wrapper._logits_soft_cap == (self.logits_soft_cap or 0.0) assert decode_wrapper._sm_scale == self.scale decode_wrapper.run( decode_query, kv_cache_permute, k_scale=layer._k_scale_float, v_scale=layer._v_scale_float, out=output[:num_decode_tokens], ) else: # decode_query may be non-contiguous decode_query = decode_query.contiguous() workspace_buffer = _get_trtllm_gen_workspace_buffer() block_tables_decode = attn_metadata.\ block_table_tensor[:num_decode_tokens] seq_lens_decode = attn_metadata.seq_lens[:num_decode_tokens] # This path needs to be enabled with VLLM_KV_CACHE_LAYOUT = HND assert get_kv_cache_layout() == "HND" assert decode_query.is_contiguous() assert kv_cache_permute.is_contiguous() assert workspace_buffer.is_contiguous() assert block_tables_decode.is_contiguous() assert seq_lens_decode.is_contiguous() if output.dtype == FP4_DTYPE: assert self.o_sf_scale is not None out = FP4Tensor(data=output[:num_decode_tokens], scale=output_block_scale, scale_start_index=0, original_shape=decode_query.shape) else: assert self.o_sf_scale is None out = output[:num_decode_tokens] if num_decode_tokens % attn_metadata.num_decodes != 0: # This gets triggered when the dummy_run forces # attention to be initialized with q_len = 0 q_len_per_req = 1 else: q_len_per_req = \ num_decode_tokens // attn_metadata.num_decodes trtllm_batch_decode_with_kv_cache( query=decode_query, kv_cache=kv_cache_permute, workspace_buffer=workspace_buffer, block_tables=block_tables_decode, seq_lens=seq_lens_decode, max_seq_len=attn_metadata.max_seq_len, bmm1_scale=self.bmm1_scale, bmm2_scale=self.bmm2_scale, window_left=self.window_left, sinks=self.sinks, o_sf_scale=self.o_sf_scale, out=out, q_len_per_req=q_len_per_req) return output_padded def fast_plan_decode( self, # decode wrapper indptr_cpu: torch.Tensor, indices: torch.Tensor, last_page_len_cpu: torch.Tensor, seq_lens_cpu: torch.Tensor, num_qo_heads: int, num_kv_heads: int, head_dim: int, page_size: int, pos_encoding_mode: str = "NONE", window_left: int = -1, logits_soft_cap: Optional[float] = None, q_data_type: Optional[Union[str, torch.dtype]] = "float16", kv_data_type: Optional[Union[str, torch.dtype]] = None, data_type: Optional[Union[str, torch.dtype]] = None, sm_scale: Optional[float] = None, rope_scale: Optional[float] = None, rope_theta: Optional[float] = None, non_blocking: bool = True, ) -> None: """ A faster version of BatchDecodeWithPagedKVCacheWrapper::plan used for cudagraph capture/replay, while the no cudagraph version turns back to the original plan. using original plan after passing host-side buffers: - only host-to-device copy of indptr and last_page_len buffers Modifications for cudagraph: - only host-to-device copy of indptr and last_page_len buffers. - avoid device-to-device copy of indices buffer. Part of the code get inspiration from the original plan from FlashInfer repo and the implementation of fast_decode_plan for FlashInfer in SGlang repo. """ # Warm up with the original plan if it is first call, and always run the # original plan if we run for dynamic shape. For fixed shape (cudagraph), # this warm up is to generate the _cached_module for the decode wrapper. if not self.is_cuda_graph_enabled or \ getattr(self, "vllm_first_call", True): self.plan( indptr_cpu, indices, last_page_len_cpu, num_qo_heads, num_kv_heads, head_dim, page_size, pos_encoding_mode, window_left, logits_soft_cap, q_data_type, kv_data_type, data_type, sm_scale, rope_scale, rope_theta, non_blocking, ) self.vllm_first_call = False return assert self.is_cuda_graph_enabled, "Should be cudagraph only here" batch_size = len(last_page_len_cpu) if logits_soft_cap is None: logits_soft_cap = 0.0 # Handle data types consistently if data_type is not None: if q_data_type is None: q_data_type = data_type if kv_data_type is None: kv_data_type = data_type elif q_data_type is None: q_data_type = "float16" if kv_data_type is None: kv_data_type = q_data_type q_data_type = getattr(torch, q_data_type) if isinstance( q_data_type, str) else q_data_type kv_data_type = getattr(torch, kv_data_type) if isinstance( kv_data_type, str) else kv_data_type if batch_size != self._fixed_batch_size: raise ValueError( "The batch size should be fixed in cudagraph mode, the runtime " "batch size {} mismatches the batch size set during " "initialization {}".format(batch_size, self._fixed_batch_size)) if len(indices) > len(self._paged_kv_indices_buf): raise ValueError( "The size of indices should be less than or equal to the " "allocated buffer") # host-to-device copy for the indptr buffer self._paged_kv_indptr_buf.copy_(indptr_cpu, non_blocking=True) # host-to-device copy for the last_page_len buffer self._paged_kv_last_page_len_buf.copy_(last_page_len_cpu, non_blocking=True) qo_indptr_host = _get_range_buf(batch_size + 1, "cpu") try: # Make sure we pass exactly 15 arguments for tensor core version self._plan_info = self._cached_module.plan( self._float_workspace_buffer, self._int_workspace_buffer, self._pin_memory_int_workspace_buffer, qo_indptr_host, indptr_cpu, seq_lens_cpu, batch_size, # total_num_rows batch_size, num_qo_heads, num_kv_heads, page_size, self.is_cuda_graph_enabled, head_dim, head_dim, False, # causal ) except Exception as e: raise RuntimeError(f"Error in tensor core plan: {e}") from e self._pos_encoding_mode = pos_encoding_mode self._window_left = window_left self._logits_soft_cap = logits_soft_cap self._sm_scale = sm_scale self._rope_scale = rope_scale self._rope_theta = rope_theta @triton.jit def _copy_page_indices_kernel( page_indices, block_table, block_table_stride, cu_num_blocks, BLOCK_SIZE: tl.constexpr, ): req_idx = tl.program_id(0) row_ptr = block_table + req_idx * block_table_stride start_idx = tl.load(cu_num_blocks + req_idx) end_idx = tl.load(cu_num_blocks + req_idx + 1) num_blocks = end_idx - start_idx offset = tl.arange(0, BLOCK_SIZE) for i in tl.range(0, num_blocks, BLOCK_SIZE): block_ids = tl.load(row_ptr + i + offset, mask=i + offset < num_blocks) tl.store(page_indices + start_idx + i + offset, block_ids, mask=i + offset < num_blocks)