1110 lines
47 KiB
Python
1110 lines
47 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import dataclasses
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from collections import defaultdict
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from contextlib import contextmanager
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple, Type
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from vllm.multimodal import MultiModalPlaceholderMap
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try:
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from flashinfer import BatchDecodeWithPagedKVCacheWrapper
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from flashinfer.decode import CUDAGraphBatchDecodeWithPagedKVCacheWrapper
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from flashinfer.prefill import BatchPrefillWithPagedKVCacheWrapper
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from vllm.vllm_flash_attn import flash_attn_varlen_func
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FLASHINFER_WORKSPACE_BUFFER_SIZE = 256 * 1024 * 1024
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except ImportError:
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# Avoid turning these types into variables during type checking
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if not TYPE_CHECKING:
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BatchDecodeWithPagedKVCacheWrapper = None
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CUDAGraphBatchDecodeWithPagedKVCacheWrapper = None
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BatchPrefillWithPagedKVCacheWrapper = None
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FLASHINFER_WORKSPACE_BUFFER_SIZE = 0
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import torch
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import vllm.envs as envs
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from vllm import _custom_ops as ops
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionLayer,
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AttentionMetadata,
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AttentionMetadataBuilder,
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AttentionState, AttentionType)
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from vllm.attention.backends.utils import (PAD_SLOT_ID, compute_slot_mapping,
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compute_slot_mapping_start_idx,
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is_block_tables_empty)
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from vllm.attention.layer import Attention
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from vllm.attention.ops.paged_attn import PagedAttention
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from vllm.config import VllmConfig, get_layers_from_vllm_config
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from vllm.logger import init_logger
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from vllm.utils import (async_tensor_h2d, get_kv_cache_torch_dtype,
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make_tensor_with_pad)
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logger = init_logger(__name__)
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if TYPE_CHECKING:
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from vllm.worker.model_runner import (ModelInputForGPUBuilder,
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ModelInputForGPUWithSamplingMetadata)
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FLASHINFER_KV_CACHE_LAYOUT: str = envs.VLLM_KV_CACHE_LAYOUT or "NHD"
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class FlashInferBackend(AttentionBackend):
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@staticmethod
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def get_name() -> str:
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return "FLASHINFER"
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@staticmethod
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def get_impl_cls() -> Type["FlashInferImpl"]:
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return FlashInferImpl
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@staticmethod
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def get_metadata_cls() -> Type["AttentionMetadata"]:
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return FlashInferMetadata
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@staticmethod
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def get_builder_cls() -> Type["FlashInferMetadataBuilder"]:
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return FlashInferMetadataBuilder
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@staticmethod
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def get_state_cls() -> Type["FlashInferState"]:
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return FlashInferState
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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) -> Tuple[int, ...]:
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return (num_blocks, 2, block_size, num_kv_heads, head_size)
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@staticmethod
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def get_kv_cache_stride_order() -> Tuple[int, ...]:
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cache_layout = FLASHINFER_KV_CACHE_LAYOUT
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assert (cache_layout in ("NHD", "HND"))
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stride_order = (0, 1, 2, 3, 4) if cache_layout == "NHD" else (0, 1, 3,
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2, 4)
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return stride_order
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@staticmethod
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def swap_blocks(
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src_kv_cache: torch.Tensor,
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dst_kv_cache: torch.Tensor,
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src_to_dst: torch.Tensor,
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) -> None:
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PagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
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@staticmethod
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def copy_blocks(
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kv_caches: List[torch.Tensor],
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src_to_dists: torch.Tensor,
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) -> None:
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PagedAttention.copy_blocks(kv_caches, src_to_dists)
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@staticmethod
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def get_supported_head_sizes() -> List[int]:
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return [64, 128, 256]
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@staticmethod
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def get_fp8_dtype_for_flashinfer(kv_cache_dtype: str) -> torch.dtype:
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if kv_cache_dtype in ("fp8", "fp8_e4m3"):
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return torch.float8_e4m3fn
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elif kv_cache_dtype == "fp8_e5m2":
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return torch.float8_e5m2
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else:
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raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}")
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@dataclass
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class PerLayerParameters:
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"""
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Currently, FlashInfer backend only support models in which all layers share
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the same values for the following hyperparameters.
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"""
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window_left: int
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logits_soft_cap: Optional[float]
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sm_scale: float
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def get_per_layer_parameters(
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vllm_config: VllmConfig) -> Dict[str, PerLayerParameters]:
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"""
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Scan all attention layers and determine some hyperparameters
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to use during `plan`.
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"""
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layers = get_layers_from_vllm_config(vllm_config, Attention)
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per_layer_params: Dict[str, PerLayerParameters] = {}
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for key, layer in layers.items():
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impl = layer.impl
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assert isinstance(impl, FlashInferImpl)
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# Infer hyperparameters from the attention layer
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window_size = impl.sliding_window
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window_left = window_size[0] if window_size is not None else -1
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logits_soft_cap = impl.logits_soft_cap
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sm_scale = impl.scale
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per_layer_params[key] = PerLayerParameters(window_left,
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logits_soft_cap, sm_scale)
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return per_layer_params
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def infer_global_hyperparameters(
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per_layer_params: Dict[str, PerLayerParameters]) -> PerLayerParameters:
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"""
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Currently, FlashInfer backend only support models in which all layers share
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the same values for the following hyperparameters:
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- `window_left`
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- `logits_soft_cap`
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- `sm_scale`
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So this function asserts that all layers share the same values for these
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hyperparameters and returns the global values.
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"""
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assert len(per_layer_params) > 0, "No attention layers found in the model."
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param_sets = list(per_layer_params.values())
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global_params = param_sets[0]
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for params in param_sets:
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assert params == global_params, (
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"FlashInfer backend currently only supports models in which all "
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"layers share the same values for the following hyperparameters: "
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"`window_left`, `logits_soft_cap`, `sm_scale`.")
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return global_params
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class FlashInferState(AttentionState):
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def __init__(self, runner):
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self.runner = runner
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self._is_graph_capturing = False
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self._workspace_buffer = None
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self._decode_wrapper = None
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self._prefill_wrapper = None
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# Global hyperparameters shared by all attention layers
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self.global_hyperparameters: Optional[PerLayerParameters] = None
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self.vllm_config = self.runner.vllm_config
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self._kv_cache_layout = None
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def _get_workspace_buffer(self):
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if self._workspace_buffer is None:
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self._workspace_buffer = torch.empty(
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FLASHINFER_WORKSPACE_BUFFER_SIZE,
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dtype=torch.uint8,
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device=self.runner.device)
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return self._workspace_buffer
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def get_kv_cache_layout(self):
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if self._kv_cache_layout is None:
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self._kv_cache_layout = FLASHINFER_KV_CACHE_LAYOUT
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return self._kv_cache_layout
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def _get_prefill_wrapper(self):
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if self._prefill_wrapper is None:
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self._prefill_wrapper = BatchPrefillWithPagedKVCacheWrapper(
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self._get_workspace_buffer(), self.get_kv_cache_layout())
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return self._prefill_wrapper
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def _get_decode_wrapper(self):
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if self._decode_wrapper is None:
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num_qo_heads = (self.runner.model_config.get_num_attention_heads(
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self.runner.parallel_config))
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num_kv_heads = self.runner.model_config.get_num_kv_heads(
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self.runner.parallel_config)
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use_tensor_cores = envs.VLLM_FLASHINFER_FORCE_TENSOR_CORES or (
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num_qo_heads // num_kv_heads > 4)
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self._decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
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self._get_workspace_buffer(),
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self.get_kv_cache_layout(),
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use_tensor_cores=use_tensor_cores)
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return self._decode_wrapper
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@contextmanager
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def graph_capture(self, max_batch_size: int):
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self._is_graph_capturing = True
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self._graph_decode_wrapper = None
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self._graph_slot_mapping = torch.full((max_batch_size, ),
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PAD_SLOT_ID,
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dtype=torch.long,
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device=self.runner.device)
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self._graph_seq_lens = torch.ones(max_batch_size,
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dtype=torch.int32,
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device=self.runner.device)
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self._graph_block_tables = torch.from_numpy(
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self.runner.graph_block_tables).to(device=self.runner.device)
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self._graph_decode_workspace_buffer = self._get_workspace_buffer()
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self._graph_indices_buffer = torch.empty(
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max_batch_size * self.runner.cache_config.num_gpu_blocks,
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dtype=torch.int32,
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device=self.runner.device)
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self._graph_indptr_buffer = torch.empty(max_batch_size + 1,
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dtype=torch.int32,
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device=self.runner.device)
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self._graph_last_page_len_buffer = torch.empty(
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max_batch_size, dtype=torch.int32, device=self.runner.device)
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yield
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self._is_graph_capturing = False
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del self._graph_slot_mapping
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del self._graph_seq_lens
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del self._graph_block_tables
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del self._graph_decode_workspace_buffer
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del self._graph_indices_buffer
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del self._graph_indptr_buffer
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del self._graph_last_page_len_buffer
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del self._graph_decode_wrapper
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def graph_clone(self, batch_size: int):
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assert self._is_graph_capturing
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state = self.__class__(self.runner)
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state._workspace_buffer = self._graph_decode_workspace_buffer
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state._decode_wrapper = self._graph_decode_wrapper
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state._prefill_wrapper = self._get_prefill_wrapper()
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return state
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def graph_capture_get_metadata_for_batch(
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self, batch_size: int, is_encoder_decoder_model: bool = False):
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assert self._is_graph_capturing
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_indptr_buffer = self._graph_indptr_buffer[:batch_size + 1]
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_last_page_len_buffer = self._graph_last_page_len_buffer[:batch_size]
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num_qo_heads = (self.runner.model_config.get_num_attention_heads(
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self.runner.parallel_config))
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num_kv_heads = self.runner.model_config.get_num_kv_heads(
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self.runner.parallel_config)
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use_tensor_cores = envs.VLLM_FLASHINFER_FORCE_TENSOR_CORES or (
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num_qo_heads // num_kv_heads > 4)
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self._graph_decode_wrapper = \
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CUDAGraphBatchDecodeWithPagedKVCacheWrapper(
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self._graph_decode_workspace_buffer, _indptr_buffer,
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self._graph_indices_buffer, _last_page_len_buffer,
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self.get_kv_cache_layout(),
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use_tensor_cores)
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if self.runner.kv_cache_dtype.startswith("fp8"):
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kv_cache_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
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self.runner.kv_cache_dtype)
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else:
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kv_cache_dtype = get_kv_cache_torch_dtype(
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self.runner.kv_cache_dtype, self.runner.model_config.dtype)
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paged_kv_indptr_tensor_host = torch.arange(0,
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batch_size + 1,
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dtype=torch.int32)
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paged_kv_indices_tensor_host = torch.arange(0,
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batch_size,
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dtype=torch.int32)
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paged_kv_last_page_len_tensor_host = torch.full((batch_size, ),
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self.runner.block_size,
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dtype=torch.int32)
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query_start_loc_host = torch.arange(0,
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batch_size + 1,
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dtype=torch.int32)
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global_params = infer_global_hyperparameters(
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get_per_layer_parameters(self.vllm_config))
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attn_metadata = self.runner.attn_backend.make_metadata(
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num_prefills=0,
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slot_mapping=self._graph_slot_mapping[:batch_size],
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multi_modal_placeholder_index_maps=None,
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enable_kv_scales_calculation=False,
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num_prefill_tokens=0,
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num_decode_tokens=batch_size,
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max_prefill_seq_len=0,
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block_tables=self._graph_block_tables,
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paged_kv_indptr=paged_kv_indptr_tensor_host,
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paged_kv_indices=paged_kv_indices_tensor_host,
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paged_kv_last_page_len=paged_kv_last_page_len_tensor_host,
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num_qo_heads=num_qo_heads,
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num_kv_heads=num_kv_heads,
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head_dim=self.runner.model_config.get_head_size(),
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page_size=self.runner.block_size,
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seq_start_loc=None,
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query_start_loc=query_start_loc_host,
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device=self.runner.device,
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data_type=kv_cache_dtype,
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q_data_type=self.runner.model_config.dtype,
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use_cuda_graph=True,
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decode_wrapper=self._graph_decode_wrapper,
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prefill_wrapper=None,
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**dataclasses.asdict(global_params),
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)
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attn_metadata.begin_forward()
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return attn_metadata
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def get_graph_input_buffers(self,
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attn_metadata,
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is_encoder_decoder_model: bool = False):
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return {
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"slot_mapping": attn_metadata.slot_mapping,
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}
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def prepare_graph_input_buffers(self,
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input_buffers,
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attn_metadata,
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is_encoder_decoder_model: bool = False):
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return
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def begin_forward(self, model_input):
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assert not self._is_graph_capturing
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state = self
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use_cuda_graph = model_input.attn_metadata.use_cuda_graph
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is_decode = model_input.attn_metadata.num_prefills == 0
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# In case of multistep chunked-prefill, there might be prefill requests
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# scheduled while CUDA graph mode is enabled. We don't run graph in that
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# case.
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if use_cuda_graph and is_decode:
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if model_input.inputs_embeds is None:
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batch_size = model_input.input_tokens.shape[0]
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state = (
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self.runner.graph_runners[model_input.virtual_engine][(
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batch_size, False)].attn_state)
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else:
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batch_size = model_input.inputs_embeds.shape[0]
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state = (
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self.runner.graph_runners[model_input.virtual_engine][(
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batch_size, True)].attn_state)
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model_input.attn_metadata.prefill_wrapper = state._get_prefill_wrapper(
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)
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model_input.attn_metadata.decode_wrapper = state._get_decode_wrapper()
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model_input.attn_metadata.begin_forward()
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@dataclass
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class FlashInferMetadata(AttentionMetadata):
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# Maximum sequence length among prefill batch. 0 if there are decoding
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# requests only.
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max_prefill_seq_len: int
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# Number of query tokens for each request in the batch.
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# Currently, we require that all requests have the same number of query
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# tokens during the decoding phase. When speculavie decoding is enabled,
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# decode_query_len might be greater than 1. In all other cases, it is 1.
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decode_query_len: Optional[int] = 1
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use_cuda_graph: bool = True
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prefill_wrapper: Optional[BatchPrefillWithPagedKVCacheWrapper] = None
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decode_wrapper: Optional[BatchDecodeWithPagedKVCacheWrapper] = None
|
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# Metadata for the prefill stage
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seq_start_loc: Optional[torch.Tensor] = None
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query_start_loc: Optional[torch.Tensor] = None
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block_tables: Optional[torch.Tensor] = None
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||
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# used for GPU in-place advance_step
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seq_lens_tensor: Optional[torch.Tensor] = None
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||
block_table_bound: Optional[torch.Tensor] = None
|
||
|
||
# An example for paged_kv_indices, paged_kv_indptr:
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# request 1, page indices [0, 5, 8]
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# request 2, page indices [1, 6, 7]
|
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# request 3, page indices [3, 4]
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# paged_kv_indices is a concatenation of page indices of all requests:
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# [0, 5, 8, 1, 6, 7, 3, 4]
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# paged_kv_indptr is used to index into paged_kv_indices:
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# [0, 3, 6, 8]
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# The indptr of the paged kv cache, shape: [batch_size + 1]
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||
paged_kv_indptr: Optional[torch.Tensor] = None
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||
# The page indices of the paged kv cache
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paged_kv_indices: Optional[torch.Tensor] = None
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||
# The number of entries in the last page of each request in
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# the paged kv cache, shape: [batch_size]
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||
paged_kv_last_page_len: Optional[torch.Tensor] = None
|
||
# The number of query/output heads
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||
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
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||
page_size: Optional[int] = None
|
||
# The data type of the paged kv cache
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data_type: torch.dtype = None
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# The data type of the query
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q_data_type: torch.dtype = None
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||
# FlashInfer 0.2 encourages passing host tensors
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||
device: torch.device = torch.device("cpu")
|
||
is_profile_run: bool = False
|
||
|
||
# The FlashInfer backend currently supports only models in which all layers
|
||
# share the same following hyperparameters:
|
||
|
||
# The left (inclusive) window size for the attention window, when
|
||
# set to `-1`, the window size will be set to the full length of
|
||
# the sequence. Defaults to `-1`.
|
||
window_left: int = -1
|
||
# The attention logits soft capping value (used in Gemini, Grok and
|
||
# Gemma-2, etc.), if not provided, will be set to `0`. If greater
|
||
# than 0, the logits will be capped according to formula:
|
||
# $$\texttt{logits\_soft\_cap} \times
|
||
# \mathrm{tanh}(x / \texttt{logits\_soft\_cap})$$,
|
||
# where $x$ is the input logits.
|
||
logits_soft_cap: Optional[float] = None
|
||
# The scale used in softmax, if not provided, will be set to
|
||
# `1.0 / sqrt(head_dim)`.
|
||
sm_scale: Optional[float] = 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}.")
|
||
|
||
def begin_forward(self):
|
||
if self.num_prefill_tokens > 0:
|
||
if self.paged_kv_indices is None:
|
||
return
|
||
|
||
assert self.prefill_wrapper is not None
|
||
assert self.query_start_loc is not None
|
||
assert self.paged_kv_indices is not None
|
||
assert self.paged_kv_indptr is not None
|
||
assert self.paged_kv_last_page_len is not None
|
||
assert self.block_table_bound is not None
|
||
assert self.seq_lens_tensor is not None
|
||
self.query_start_loc = self.query_start_loc[:self.num_prefills + 1]
|
||
batch_size = self.query_start_loc.shape[0] - 1
|
||
assert batch_size >= 0
|
||
# We will use flash attention for profiling to
|
||
# determine the number of blocks. Therefore,
|
||
# we don't need to prepare the input for flashinfer for profile run.
|
||
if not self.is_profile_run:
|
||
self.paged_kv_indptr = self.paged_kv_indptr.to(self.device)
|
||
self.paged_kv_last_page_len = self.paged_kv_last_page_len.to(
|
||
self.device)
|
||
self.block_table_bound = self.block_table_bound.to(self.device)
|
||
self.seq_lens_tensor = self.seq_lens_tensor.to(self.device)
|
||
self.paged_kv_indices = self.paged_kv_indices.to(self.device)
|
||
self.prefill_wrapper.plan(
|
||
self.query_start_loc,
|
||
self.paged_kv_indptr[:self.num_prefills + 1],
|
||
self.paged_kv_indices,
|
||
self.paged_kv_last_page_len[:self.num_prefills],
|
||
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.data_type)
|
||
if self.num_decode_tokens > 0:
|
||
assert self.paged_kv_indices is not None
|
||
assert self.paged_kv_indptr is not None
|
||
assert self.paged_kv_last_page_len is not None
|
||
self.paged_kv_indices = self.paged_kv_indices.to(self.device)
|
||
self.paged_kv_indptr = self.paged_kv_indptr.to(self.device)
|
||
self.paged_kv_last_page_len = self.paged_kv_last_page_len.to(
|
||
self.device)
|
||
# handle model warmup path
|
||
if self.block_table_bound is not None:
|
||
self.block_table_bound = self.block_table_bound.to(self.device)
|
||
if self.seq_lens_tensor is not None:
|
||
self.seq_lens_tensor = self.seq_lens_tensor.to(self.device)
|
||
|
||
assert self.decode_wrapper is not None
|
||
self.decode_wrapper.plan(
|
||
self.paged_kv_indptr[self.num_prefills:],
|
||
self.paged_kv_indices,
|
||
self.paged_kv_last_page_len[self.num_prefills:],
|
||
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",
|
||
window_left=self.window_left,
|
||
logits_soft_cap=self.logits_soft_cap,
|
||
sm_scale=self.sm_scale,
|
||
# kv-cache data type.
|
||
kv_data_type=self.data_type,
|
||
# query data type.
|
||
q_data_type=self.q_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 prefill/decode_wrapper field since it cannot be
|
||
# broadcasted with nccl when TP is enabled.
|
||
skip_fields.add('prefill_wrapper')
|
||
skip_fields.add('decode_wrapper')
|
||
return super().asdict_zerocopy(skip_fields)
|
||
|
||
@property
|
||
def prefill_metadata(self) -> Optional["FlashInferMetadata"]:
|
||
if self.num_prefills == 0:
|
||
return None
|
||
return self
|
||
|
||
@property
|
||
def decode_metadata(self) -> Optional["FlashInferMetadata"]:
|
||
if self.num_decode_tokens == 0:
|
||
return None
|
||
return self
|
||
|
||
def advance_step(self,
|
||
model_input: "ModelInputForGPUWithSamplingMetadata",
|
||
sampled_token_ids: Optional[torch.Tensor],
|
||
block_size: int,
|
||
num_seqs: int,
|
||
num_queries: int,
|
||
turn_prefills_into_decodes: bool = False):
|
||
"""
|
||
Update metadata in-place to advance one decode step.
|
||
"""
|
||
|
||
if turn_prefills_into_decodes:
|
||
# When Multi-Step is enabled with Chunked-Prefill, prefills and
|
||
# decodes are scheduled together. In the first step, all the
|
||
# prefills turn into decodes. This update reflects that
|
||
# conversion.
|
||
assert self.num_decode_tokens + self.num_prefills == num_seqs
|
||
# Flashinfer doesn't support speculative decoding + chunked-prefill
|
||
# + multi-step scheduling yet.
|
||
assert self.decode_query_len == 1
|
||
self.num_decode_tokens += self.num_prefills
|
||
self.num_prefills = 0
|
||
self.num_prefill_tokens = 0
|
||
self.max_prefill_seq_len = 0
|
||
self.max_query_len = 1
|
||
|
||
self.slot_mapping = self.slot_mapping[:num_seqs]
|
||
else:
|
||
assert self.seq_lens_tensor is not None
|
||
|
||
assert num_seqs > 0
|
||
assert num_queries > 0
|
||
assert model_input.attn_metadata is not None
|
||
assert sampled_token_ids is not None
|
||
|
||
# When using cudagraph, the num_seqs is padded to the next captured
|
||
# batch sized, but num_queries tracks the actual number of requests in
|
||
# the batch. For --enforce-eager mode, num_seqs == num_queries
|
||
if num_seqs != num_queries:
|
||
assert num_seqs > num_queries
|
||
assert self.use_cuda_graph
|
||
|
||
model_input.input_tokens[:num_queries] = sampled_token_ids.flatten()
|
||
|
||
# Update GPU tensors
|
||
ops.advance_step_flashinfer(
|
||
num_seqs=num_seqs,
|
||
num_queries=num_queries,
|
||
block_size=block_size,
|
||
input_tokens=model_input.input_tokens,
|
||
sampled_token_ids=model_input.input_tokens,
|
||
input_positions=model_input.input_positions,
|
||
seq_lens=self.seq_lens_tensor,
|
||
slot_mapping=self.slot_mapping,
|
||
block_tables=self.block_tables,
|
||
paged_kv_indices=self.paged_kv_indices,
|
||
paged_kv_indptr=self.paged_kv_indptr,
|
||
paged_kv_last_page_len=self.paged_kv_last_page_len,
|
||
block_table_bound=self.block_table_bound)
|
||
|
||
|
||
class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
|
||
|
||
def __init__(self, input_builder: "ModelInputForGPUBuilder"):
|
||
|
||
self.input_builder = input_builder
|
||
self.runner = input_builder.runner
|
||
|
||
self.sliding_window = input_builder.sliding_window
|
||
self.block_size = input_builder.block_size
|
||
|
||
# Global hyperparameters shared by all attention layers
|
||
self.global_hyperparameters: Optional[PerLayerParameters] = None
|
||
|
||
self.vllm_config = self.runner.vllm_config
|
||
|
||
def prepare(self):
|
||
self.slot_mapping: List[int] = []
|
||
self.prefill_seq_lens: List[int] = []
|
||
self.context_lens: List[int] = []
|
||
self.block_tables: List[List[int]] = []
|
||
self.curr_seq_lens: List[int] = []
|
||
self.multimodal_placeholder_maps: Dict[
|
||
str,
|
||
MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
|
||
self.num_prefills = 0
|
||
self.num_prefill_tokens = 0
|
||
self.num_decode_tokens = 0
|
||
|
||
# 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]
|
||
self.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.
|
||
self.paged_kv_indptr: List[int] = [0]
|
||
# paged_kv_last_page_len is the length of the last page of each request
|
||
self.paged_kv_last_page_len: List[int] = []
|
||
self.total_blocks = 0
|
||
self.is_profile_run: bool = False
|
||
|
||
if self.global_hyperparameters is None:
|
||
# Infer global hyperparameters, since currently we only support
|
||
# models in which all layers share the same values for the
|
||
# following hyperparameters:
|
||
# - `window_left`
|
||
# - `logits_soft_cap`
|
||
# - `sm_scale`
|
||
inferred_params = infer_global_hyperparameters(
|
||
get_per_layer_parameters(self.vllm_config))
|
||
self.global_hyperparameters = inferred_params
|
||
self.window_left = inferred_params.window_left
|
||
self.logits_soft_cap = inferred_params.logits_soft_cap
|
||
self.sm_scale = inferred_params.sm_scale
|
||
|
||
def _add_seq_group(
|
||
self, inter_data: "ModelInputForGPUBuilder.InterDataForSeqGroup",
|
||
chunked_prefill_enabled: bool):
|
||
"""Add a sequence group to the metadata. Specifically update/append
|
||
1. context length.
|
||
2. block table.
|
||
3. slot mapping.
|
||
"""
|
||
is_prompt = inter_data.is_prompt
|
||
block_tables = inter_data.block_tables
|
||
computed_block_nums = inter_data.computed_block_nums
|
||
|
||
for (seq_id, token_len, seq_len, curr_seq_len, query_len, context_len,
|
||
curr_sliding_window_block) in zip(
|
||
inter_data.seq_ids, [len(t) for t in inter_data.input_tokens],
|
||
inter_data.orig_seq_lens, inter_data.seq_lens,
|
||
inter_data.query_lens, inter_data.context_lens,
|
||
inter_data.curr_sliding_window_blocks):
|
||
self.context_lens.append(context_len)
|
||
if is_prompt:
|
||
mm_maps = inter_data.multi_modal_placeholder_maps
|
||
if mm_maps:
|
||
for modality, placeholders in mm_maps.items():
|
||
self.multimodal_placeholder_maps[modality].extend(
|
||
placeholders)
|
||
self.num_prefills += 1
|
||
self.num_prefill_tokens += token_len
|
||
self.prefill_seq_lens.append(seq_len)
|
||
else:
|
||
assert query_len == 1, (
|
||
"seq_len: {}, context_len: {}, query_len: {}".format(
|
||
seq_len, context_len, query_len))
|
||
self.num_decode_tokens += query_len
|
||
self.curr_seq_lens.append(curr_seq_len)
|
||
|
||
# Compute block table.
|
||
# TODO(sang): Combine chunked prefill and prefix caching by
|
||
# only allowing multiple of block_size chunk size.
|
||
# NOTE: This only works for oooooooxxx style attention.
|
||
block_table = []
|
||
if inter_data.prefix_cache_hit:
|
||
block_table = computed_block_nums
|
||
elif ((chunked_prefill_enabled or not is_prompt)
|
||
and block_tables is not None):
|
||
block_table = block_tables[seq_id][-curr_sliding_window_block:]
|
||
self.block_tables.append(block_table)
|
||
|
||
is_profile_run = is_block_tables_empty(block_tables)
|
||
|
||
# Compute slot mapping.
|
||
start_idx = compute_slot_mapping_start_idx(is_prompt, query_len,
|
||
context_len,
|
||
self.sliding_window)
|
||
compute_slot_mapping(is_profile_run, self.slot_mapping, seq_id,
|
||
seq_len, context_len, start_idx,
|
||
self.block_size, inter_data.block_tables)
|
||
|
||
# It is not necessary to add paged_kv_indices, paged_kv_indptr,
|
||
# and paged_kv_last_page_len for profile run because we will
|
||
# create dummy inputs.
|
||
if is_profile_run:
|
||
self.is_profile_run = is_profile_run
|
||
return
|
||
|
||
block_table = block_tables[seq_id]
|
||
self._update_paged_kv_tensors(block_table, seq_len)
|
||
|
||
def _update_paged_kv_tensors(self, block_table: List[int], seq_len: int):
|
||
# Get the number of valid blocks based on sequence length.
|
||
# If seq_len = 16, block_size = 16,
|
||
# block_table_bound is 1 with 1 valid block.
|
||
# If seq_len = 15, block_size = 16,
|
||
# block_table_bound is 0 + 1 with 1 valid block.
|
||
self.total_blocks += len(block_table)
|
||
block_table_bound = seq_len // self.block_size + 1 \
|
||
if seq_len % self.block_size != 0 \
|
||
else seq_len // self.block_size
|
||
self.paged_kv_indices.extend(block_table[:block_table_bound])
|
||
self.paged_kv_indptr.append(self.paged_kv_indptr[-1] +
|
||
block_table_bound)
|
||
|
||
last_page_len = seq_len % self.block_size
|
||
if last_page_len == 0:
|
||
last_page_len = self.block_size
|
||
self.paged_kv_last_page_len.append(last_page_len)
|
||
|
||
def build(self, seq_lens: List[int], query_lens: List[int],
|
||
cuda_graph_pad_size: int, batch_size: int,
|
||
tree_attention_masks_tensor: Optional[torch.Tensor] = None):
|
||
"""Build attention metadata with on-device tensors.
|
||
|
||
Args:
|
||
seq_lens: The maybe padded sequence lengths of the input sequences.
|
||
query_lens: The query lengths of the input sequences.
|
||
cuda_graph_pad_size: The padding size for cuda graph.
|
||
-1 if cuda graph is not used.
|
||
batch_size: The maybe padded batch size.
|
||
tree_attention_masks_tensor: attention mask used in tree style attention.
|
||
"""
|
||
for inter_data in self.input_builder.inter_data_list:
|
||
self._add_seq_group(inter_data,
|
||
self.input_builder.chunked_prefill_enabled)
|
||
|
||
device = self.runner.device
|
||
use_captured_graph = cuda_graph_pad_size != -1
|
||
|
||
max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
|
||
num_decode_tokens = self.num_decode_tokens
|
||
decode_query_len = max(query_lens[self.num_prefills:], default=1)
|
||
|
||
if use_captured_graph:
|
||
self.slot_mapping.extend([PAD_SLOT_ID] * cuda_graph_pad_size)
|
||
self.block_tables.extend([] * cuda_graph_pad_size)
|
||
num_decode_tokens = batch_size - self.num_prefill_tokens
|
||
|
||
# The shape of graph_block_tables is
|
||
# [max batch size, max context len // block size].
|
||
input_block_tables = self.runner.graph_block_tables[:batch_size]
|
||
max_blocks = input_block_tables.shape[1]
|
||
for i, block_table in enumerate(self.block_tables):
|
||
if block_table:
|
||
num_blocks = len(block_table)
|
||
if num_blocks <= max_blocks:
|
||
input_block_tables[i, :num_blocks] = block_table
|
||
else:
|
||
# It may be possible to have more blocks allocated due
|
||
# to lookahead slots of multi-step, however, they are
|
||
# not used anyway, so can be safely ignored.
|
||
input_block_tables[
|
||
i, :max_blocks] = block_table[:max_blocks]
|
||
|
||
block_tables = torch.from_numpy(input_block_tables).to(
|
||
device, non_blocking=True)
|
||
|
||
last_paged_kv_indptr = self.paged_kv_indptr[-1]
|
||
self.paged_kv_indptr.extend([last_paged_kv_indptr] *
|
||
cuda_graph_pad_size)
|
||
self.paged_kv_last_page_len.extend([0] * cuda_graph_pad_size)
|
||
else:
|
||
block_tables = make_tensor_with_pad(
|
||
self.block_tables,
|
||
pad=0,
|
||
dtype=torch.int,
|
||
device=device,
|
||
)
|
||
|
||
assert device is not None
|
||
seq_lens_tensor = async_tensor_h2d(seq_lens, torch.int, device,
|
||
self.runner.pin_memory)
|
||
query_lens_tensor = async_tensor_h2d(query_lens, torch.long, device,
|
||
self.runner.pin_memory)
|
||
slot_mapping_tensor = async_tensor_h2d(self.slot_mapping, torch.long,
|
||
device, self.runner.pin_memory)
|
||
query_start_loc = torch.zeros(query_lens_tensor.shape[0] + 1,
|
||
dtype=torch.int32,
|
||
device=device)
|
||
seq_start_loc = torch.zeros(seq_lens_tensor.shape[0] + 1,
|
||
dtype=torch.int32,
|
||
device=device)
|
||
placeholder_index_maps = {
|
||
modality: placeholder_map.index_map()
|
||
for modality, placeholder_map in
|
||
self.multimodal_placeholder_maps.items()
|
||
}
|
||
torch.cumsum(seq_lens_tensor,
|
||
dim=0,
|
||
dtype=seq_start_loc.dtype,
|
||
out=seq_start_loc[1:])
|
||
torch.cumsum(query_lens_tensor,
|
||
dim=0,
|
||
dtype=query_start_loc.dtype,
|
||
out=query_start_loc[1:])
|
||
|
||
if len(self.paged_kv_indptr) > 0:
|
||
# extend to the maximum number of blocks as returned by the
|
||
# scheduler
|
||
self.paged_kv_indices.extend(
|
||
[0] * (self.total_blocks - len(self.paged_kv_indices)))
|
||
paged_kv_indices_tensor = torch.tensor(self.paged_kv_indices,
|
||
device="cpu",
|
||
dtype=torch.int)
|
||
paged_kv_indptr_tensor = torch.tensor(self.paged_kv_indptr,
|
||
device="cpu",
|
||
dtype=torch.int)
|
||
paged_kv_last_page_len_tensor = torch.tensor(
|
||
self.paged_kv_last_page_len, device="cpu", dtype=torch.int)
|
||
block_table_bound_tensor = torch.zeros(len(self.paged_kv_indptr) -
|
||
1,
|
||
device="cpu",
|
||
dtype=torch.int)
|
||
else:
|
||
paged_kv_indices_tensor = None
|
||
paged_kv_indptr_tensor = None
|
||
paged_kv_last_page_len_tensor = None
|
||
block_table_bound_tensor = None
|
||
|
||
if self.runner.kv_cache_dtype.startswith("fp8"):
|
||
kv_cache_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
|
||
self.runner.kv_cache_dtype)
|
||
else:
|
||
kv_cache_dtype = get_kv_cache_torch_dtype(
|
||
self.runner.kv_cache_dtype, self.runner.model_config.dtype)
|
||
|
||
return FlashInferMetadata(
|
||
decode_query_len=decode_query_len,
|
||
num_prefills=self.num_prefills,
|
||
slot_mapping=slot_mapping_tensor,
|
||
multi_modal_placeholder_index_maps=placeholder_index_maps,
|
||
enable_kv_scales_calculation=False,
|
||
num_prefill_tokens=self.num_prefill_tokens,
|
||
num_decode_tokens=num_decode_tokens,
|
||
max_prefill_seq_len=max_prefill_seq_len,
|
||
block_tables=block_tables,
|
||
paged_kv_indptr=paged_kv_indptr_tensor,
|
||
paged_kv_indices=paged_kv_indices_tensor,
|
||
paged_kv_last_page_len=paged_kv_last_page_len_tensor,
|
||
block_table_bound=block_table_bound_tensor,
|
||
seq_lens_tensor=seq_lens_tensor,
|
||
num_qo_heads=self.runner.model_config.get_num_attention_heads(
|
||
self.runner.parallel_config),
|
||
num_kv_heads=self.runner.model_config.get_num_kv_heads(
|
||
self.runner.parallel_config),
|
||
head_dim=self.runner.model_config.get_head_size(),
|
||
page_size=self.block_size,
|
||
seq_start_loc=seq_start_loc,
|
||
query_start_loc=query_start_loc,
|
||
device=device,
|
||
data_type=kv_cache_dtype,
|
||
q_data_type=self.runner.model_config.dtype,
|
||
use_cuda_graph=use_captured_graph,
|
||
is_profile_run=self.is_profile_run,
|
||
window_left=self.window_left,
|
||
logits_soft_cap=self.logits_soft_cap,
|
||
sm_scale=self.sm_scale,
|
||
)
|
||
|
||
|
||
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,
|
||
blocksparse_params: Optional[Dict[str, Any]] = None,
|
||
logits_soft_cap: Optional[float] = None,
|
||
attn_type: str = AttentionType.DECODER,
|
||
kv_sharing_target_layer_name: Optional[str] = None,
|
||
use_irope: bool = False,
|
||
) -> None:
|
||
if kv_sharing_target_layer_name is not None:
|
||
raise NotImplementedError("KV sharing is not supported in V0.")
|
||
if use_irope:
|
||
logger.warning_once(
|
||
"Using irope in FlashInfer is not supported yet, it will fall"
|
||
" back to global attention for long context.")
|
||
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
|
||
self.sliding_window = ((sliding_window - 1,
|
||
0) if sliding_window is not None else (-1, -1))
|
||
self.kv_cache_dtype = kv_cache_dtype
|
||
self.logits_soft_cap = logits_soft_cap
|
||
|
||
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")
|
||
|
||
def forward(
|
||
self,
|
||
layer: AttentionLayer,
|
||
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,
|
||
) -> torch.Tensor:
|
||
|
||
if output_scale is not None:
|
||
raise NotImplementedError(
|
||
"fused output quantization is not yet supported"
|
||
" for FlashInferImpl")
|
||
|
||
# TODO: directly write to output tensor
|
||
num_heads: int = self.num_heads
|
||
head_size: int = self.head_size
|
||
num_kv_heads: int = self.num_kv_heads
|
||
kv_cache_dtype: str = self.kv_cache_dtype
|
||
softmax_scale: float = self.scale
|
||
window_size = self.sliding_window
|
||
alibi_slopes = self.alibi_slopes
|
||
logits_soft_cap = self.logits_soft_cap
|
||
|
||
num_tokens, hidden_size = query.shape
|
||
query = query.view(-1, num_heads, head_size)
|
||
key = key.view(-1, num_kv_heads, head_size)
|
||
value = value.view(-1, num_kv_heads, head_size)
|
||
|
||
if kv_cache.numel() > 0:
|
||
# 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(),
|
||
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 kv_cache_dtype.startswith("fp8"):
|
||
torch_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
|
||
kv_cache_dtype)
|
||
kv_cache = kv_cache.view(torch_dtype)
|
||
|
||
num_prefill_tokens = attn_metadata.num_prefill_tokens
|
||
num_decode_tokens = attn_metadata.num_decode_tokens
|
||
assert key.shape[0] == num_prefill_tokens + num_decode_tokens, \
|
||
f"key : {key.shape} : #prefill tokens {num_prefill_tokens} : #decode tokens {num_decode_tokens}" # noqa
|
||
assert value.shape[0] == num_prefill_tokens + num_decode_tokens, \
|
||
f"value : {value.shape} : #prefill toks {num_prefill_tokens} : #decode toks {num_decode_tokens}" # noqa
|
||
query = query.contiguous(
|
||
) # Flashinfer requires query to be contiguous
|
||
# Query for decode. KV is not needed because it is already cached.
|
||
# QKV for prefill.
|
||
decode_query = query[num_prefill_tokens:]
|
||
query = query[:num_prefill_tokens]
|
||
|
||
key = key[:num_prefill_tokens]
|
||
value = value[:num_prefill_tokens]
|
||
|
||
assert query.shape[0] == num_prefill_tokens
|
||
assert decode_query.shape[0] == num_decode_tokens
|
||
|
||
window_left = window_size[0] if window_size is not None else -1
|
||
|
||
prefill_output: Optional[torch.Tensor] = None
|
||
decode_output: Optional[torch.Tensor] = None
|
||
stride_order = FlashInferBackend.get_kv_cache_stride_order()
|
||
if prefill_meta := attn_metadata.prefill_metadata:
|
||
# We will use flash attention for prefill
|
||
# when kv_cache is not provided.
|
||
# This happens when vllm runs the profiling to
|
||
# determine the number of blocks.
|
||
if kv_cache.numel() == 0:
|
||
prefill_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_prefill_seq_len,
|
||
max_seqlen_k=prefill_meta.max_prefill_seq_len,
|
||
softmax_scale=softmax_scale,
|
||
causal=True,
|
||
window_size=window_size,
|
||
alibi_slopes=alibi_slopes,
|
||
)
|
||
else:
|
||
assert prefill_meta is not None
|
||
assert prefill_meta.prefill_wrapper is not None
|
||
|
||
assert prefill_meta.prefill_wrapper._causal
|
||
assert prefill_meta.prefill_wrapper._window_left == window_left
|
||
assert prefill_meta.prefill_wrapper._logits_soft_cap == (
|
||
logits_soft_cap or 0.0)
|
||
assert prefill_meta.prefill_wrapper._sm_scale == softmax_scale
|
||
|
||
prefill_output = prefill_meta.prefill_wrapper.run(
|
||
query,
|
||
kv_cache.permute(*stride_order),
|
||
k_scale=layer._k_scale_float,
|
||
v_scale=layer._v_scale_float,
|
||
)
|
||
if decode_meta := attn_metadata.decode_metadata:
|
||
assert decode_meta is not None
|
||
assert decode_meta.decode_wrapper is not None
|
||
|
||
assert decode_meta.decode_wrapper._window_left == window_left
|
||
assert decode_meta.decode_wrapper._logits_soft_cap == (
|
||
logits_soft_cap or 0.0)
|
||
assert decode_meta.decode_wrapper._sm_scale == softmax_scale
|
||
|
||
decode_output = decode_meta.decode_wrapper.run(
|
||
decode_query,
|
||
kv_cache.permute(*stride_order),
|
||
k_scale=layer._k_scale_float,
|
||
v_scale=layer._v_scale_float,
|
||
)
|
||
|
||
if prefill_output is None and decode_output is not None:
|
||
# Decode only batch.
|
||
output, num_tokens = decode_output, num_decode_tokens
|
||
elif decode_output is None and prefill_output is not None:
|
||
# Prefill only batch.
|
||
output, num_tokens = prefill_output, num_prefill_tokens
|
||
else:
|
||
# Chunked prefill batch does not work with speculative decoding in
|
||
# FlashInfer backend, so the query length for decode should be 1.
|
||
assert prefill_output is not None
|
||
assert decode_output is not None
|
||
assert decode_meta is not None
|
||
assert decode_meta.decode_query_len == 1
|
||
decode_output = decode_output.squeeze(1)
|
||
output = torch.cat([prefill_output, decode_output], dim=0)
|
||
return output.view(num_tokens, hidden_size)
|