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