from contextlib import contextmanager
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple, Type
try:
from flashinfer import BatchDecodeWithPagedKVCacheWrapper
from flashinfer.decode import CUDAGraphBatchDecodeWithPagedKVCacheWrapper
from flashinfer.prefill import BatchPrefillWithPagedKVCacheWrapper
import aphrodite.attention.backends.flash_attn # noqa
FLASHINFER_WORKSPACE_BUFFER_SIZE = 256 * 1024 * 1024
except ImportError:
BatchDecodeWithPagedKVCacheWrapper = None
CUDAGraphBatchDecodeWithPagedKVCacheWrapper = None
BatchPrefillWithPagedKVCacheWrapper = None
FLASHINFER_WORKSPACE_BUFFER_SIZE = 0
import torch
from aphrodite import _custom_ops as ops
from aphrodite.attention.backends.abstract import (AttentionBackend,
AttentionImpl,
AttentionMetadata,
AttentionMetadataBuilder,
AttentionState,
AttentionType)
from aphrodite.attention.backends.utils import (PAD_SLOT_ID,
compute_slot_mapping,
compute_slot_mapping_start_idx,
is_block_tables_empty)
from aphrodite.attention.ops.paged_attn import PagedAttention
from aphrodite.common.utils import (async_tensor_h2d, get_kv_cache_torch_dtype,
make_tensor_with_pad)
if TYPE_CHECKING:
from aphrodite.worker.model_runner import (
ModelInputForGPUBuilder, ModelInputForGPUWithSamplingMetadata)
class FlashInferBackend(AttentionBackend):
@staticmethod
def get_name() -> str:
return "flashinfer"
@staticmethod
def get_impl_cls() -> Type["FlashInferImpl"]:
return FlashInferImpl
@staticmethod
def get_metadata_cls() -> Type["AttentionMetadata"]:
return FlashInferMetadata
@staticmethod
def get_builder_cls() -> Type["FlashInferMetadataBuilder"]:
return FlashInferMetadataBuilder
@staticmethod
def get_state_cls() -> Type["FlashInferState"]:
return FlashInferState
@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: torch.Tensor,
) -> None:
PagedAttention.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst)
@staticmethod
def copy_blocks(
kv_caches: List[torch.Tensor],
src_to_dists: torch.Tensor,
) -> None:
PagedAttention.copy_blocks(kv_caches, src_to_dists)
@staticmethod
def get_supported_head_sizes() -> List[int]:
return [64, 128, 256]
@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}")
class FlashInferState(AttentionState):
def __init__(self, runner):
self.runner = runner
self._is_graph_capturing = False
self._workspace_buffer = None
self._decode_wrapper = None
self._prefill_wrapper = None
def _get_workspace_buffer(self):
if self._workspace_buffer is None:
self._workspace_buffer = torch.empty(
FLASHINFER_WORKSPACE_BUFFER_SIZE,
dtype=torch.uint8,
device=self.runner.device)
return self._workspace_buffer
def _get_prefill_wrapper(self):
if self._prefill_wrapper is None:
self._prefill_wrapper = BatchPrefillWithPagedKVCacheWrapper(
self._get_workspace_buffer(), "NHD")
return self._prefill_wrapper
def _get_decode_wrapper(self):
if self._decode_wrapper is None:
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)
use_tensor_cores = num_qo_heads // num_kv_heads > 4
self._decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
self._get_workspace_buffer(),
"NHD",
use_tensor_cores=use_tensor_cores)
return self._decode_wrapper
@contextmanager
def graph_capture(self, max_batch_size: int):
self._is_graph_capturing = True
self._graph_decode_wrapper = None
self._graph_slot_mapping = torch.full((max_batch_size, ),
PAD_SLOT_ID,
dtype=torch.long,
device=self.runner.device)
self._graph_seq_lens = torch.ones(max_batch_size,
dtype=torch.int32,
device=self.runner.device)
self._graph_block_tables = torch.from_numpy(
self.runner.graph_block_tables).to(device=self.runner.device)
self._graph_decode_workspace_buffer = self._get_workspace_buffer()
self._graph_indices_buffer = torch.empty(
max_batch_size * self.runner.cache_config.num_gpu_blocks,
dtype=torch.int32,
device=self.runner.device)
self._graph_indptr_buffer = torch.empty(max_batch_size + 1,
dtype=torch.int32,
device=self.runner.device)
self._graph_last_page_len_buffer = torch.empty(
max_batch_size, dtype=torch.int32, device=self.runner.device)
yield
self._is_graph_capturing = False
del self._graph_slot_mapping
del self._graph_seq_lens
del self._graph_block_tables
del self._graph_decode_workspace_buffer
del self._graph_indices_buffer
del self._graph_indptr_buffer
del self._graph_last_page_len_buffer
del self._graph_decode_wrapper
def graph_clone(self, batch_size: int):
assert self._is_graph_capturing
state = self.__class__(self.runner)
state._workspace_buffer = self._graph_decode_workspace_buffer
state._decode_wrapper = self._graph_decode_wrapper
state._prefill_wrapper = self._get_prefill_wrapper()
return state
def graph_capture_get_metadata_for_batch(self, batch_size: int):
assert self._is_graph_capturing
_indptr_buffer = self._graph_indptr_buffer[:batch_size + 1]
_last_page_len_buffer = self._graph_last_page_len_buffer[:batch_size]
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)
use_tensor_cores = num_qo_heads // num_kv_heads > 4
self._graph_decode_wrapper = \
CUDAGraphBatchDecodeWithPagedKVCacheWrapper(
self._graph_decode_workspace_buffer, _indptr_buffer,
self._graph_indices_buffer, _last_page_len_buffer, "NHD",
use_tensor_cores)
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)
paged_kv_indptr_tensor_host = torch.arange(0,
batch_size + 1,
dtype=torch.int32)
paged_kv_indices_tensor_host = torch.arange(0,
batch_size,
dtype=torch.int32)
paged_kv_last_page_len_tensor_host = torch.full((batch_size, ),
self.runner.block_size,
dtype=torch.int32)
query_start_loc_host = torch.arange(0,
batch_size + 1,
dtype=torch.int32)
attn_metadata = self.runner.attn_backend.make_metadata(
num_prefills=0,
slot_mapping=self._graph_slot_mapping[:batch_size],
num_prefill_tokens=0,
num_decode_tokens=batch_size,
max_prefill_seq_len=0,
block_tables=self._graph_block_tables,
paged_kv_indptr=paged_kv_indptr_tensor_host,
paged_kv_indices=paged_kv_indices_tensor_host,
paged_kv_last_page_len=paged_kv_last_page_len_tensor_host,
num_qo_heads=num_qo_heads,
num_kv_heads=num_kv_heads,
head_dim=self.runner.model_config.get_head_size(),
page_size=self.runner.block_size,
seq_start_loc=None,
query_start_loc=query_start_loc_host,
device=self.runner.device,
data_type=kv_cache_dtype,
q_data_type=self.runner.model_config.dtype,
use_cuda_graph=True,
decode_wrapper=self._graph_decode_wrapper,
prefill_wrapper=None)
attn_metadata.begin_forward()
return attn_metadata
def get_graph_input_buffers(self, attn_metadata):
return {
"slot_mapping": attn_metadata.slot_mapping,
}
def prepare_graph_input_buffers(self, input_buffers, attn_metadata):
return
def begin_forward(self, model_input):
assert not self._is_graph_capturing
state = self
if model_input.attn_metadata.use_cuda_graph:
batch_size = model_input.input_tokens.shape[0]
state = (self.runner.graph_runners[model_input.virtual_engine]
[batch_size].attn_state)
model_input.attn_metadata.prefill_wrapper = state._get_prefill_wrapper(
)
model_input.attn_metadata.decode_wrapper = state._get_decode_wrapper()
model_input.attn_metadata.begin_forward()
@dataclass
class FlashInferMetadata(AttentionMetadata):
# Maximum sequence length among prefill batch. 0 if there are decoding
# requests only.
max_prefill_seq_len: int
use_cuda_graph: bool = True
prefill_wrapper: Optional[BatchPrefillWithPagedKVCacheWrapper] = None
decode_wrapper: Optional[BatchDecodeWithPagedKVCacheWrapper] = None
# Metadata for the prefill stage
seq_start_loc: Optional[torch.Tensor] = None
query_start_loc: Optional[torch.Tensor] = None
block_tables: Optional[torch.Tensor] = None
# used for GPU in-place advance_step
seq_lens_tensor: Optional[torch.Tensor] = None
block_table_bound: 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 aphrodite
page_size: Optional[int] = None
# The data type of the paged kv cache
data_type: torch.dtype = None
# The data type of the query
q_data_type: torch.dtype = None
device: torch.device = torch.device("cuda")
is_profile_run: bool = False
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
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.end_forward()
self.prefill_wrapper.begin_forward(
self.query_start_loc, 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)
else:
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.end_forward()
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 aphrodite's rope.
pos_encoding_mode="NONE",
# kv-cache data type.
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"]:
# Currently chunked prefill is not supported
if self.num_decode_tokens == 0:
assert self.num_prefills > 0
return self
return None
@property
def decode_metadata(self) -> Optional["FlashInferMetadata"]:
# Currently chunked prefill is not supported
if self.num_prefills > 0:
assert self.num_decode_tokens == 0, (
"Chunked prefill is not supported with flashinfer yet.")
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,
):
"""
Update metadata in-place to advance one decode step.
"""
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.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.num_prefills = 0
self.num_prefill_tokens = 0
self.num_decode_tokens = 0
self.input_builder = input_builder
self.runner = input_builder.runner
self.sliding_window = input_builder.sliding_window
self.block_size = input_builder.block_size
self.use_v2_block_manager = (
input_builder.scheduler_config.use_v2_block_manager)
# 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
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:
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,
self.use_v2_block_manager)
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):
"""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.
"""
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_query_len = max(query_lens)
max_prefill_seq_len = max(self.prefill_seq_lens, default=0)
num_decode_tokens = self.num_decode_tokens
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
# 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 max_query_len > 0, ("query_lens: {}".format(query_lens))
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)
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(
num_prefills=self.num_prefills,
slot_mapping=slot_mapping_tensor,
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)
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,
) -> 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 not None:
raise ValueError("Sliding window is not supported in FlashInfer.")
self.sliding_window = (-1, -1)
self.kv_cache_dtype = kv_cache_dtype
self.logits_soft_cap = logits_soft_cap
assert self.num_heads % self.num_kv_heads == 0
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
kv_cache: Optional[torch.Tensor],
attn_metadata: FlashInferMetadata,
k_scale: float = 1.0,
v_scale: float = 1.0,
attn_type: AttentionType = AttentionType.DECODER,
) -> torch.Tensor:
assert k_scale == 1.0 and v_scale == 1.0, (
"key/v_scale is not supported in FlashInfer.")
if attn_type != AttentionType.DECODER:
raise NotImplementedError("Encoder self-attention and "
"encoder/decoder cross-attention "
"are not implemented for "
"FlashInferImpl")
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(),
self.kv_cache_dtype,
k_scale,
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)
query = query.contiguous(
) # Flashinfer requires query to be contiguous
if prefill_meta := attn_metadata.prefill_metadata:
# We will use flash attention for prefill
# when kv_cache is not provided.
# This happens when aphrodite runs the profiling to
# determine the number of blocks.
if kv_cache is None:
output = torch.ops.aphrodite.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=self.scale,
causal=True,
window_size=self.sliding_window,
alibi_slopes=self.alibi_slopes,
)
else:
assert prefill_meta is not None
assert prefill_meta.prefill_wrapper is not None
output = prefill_meta.prefill_wrapper.forward(
query,
kv_cache,
logits_soft_cap=self.logits_soft_cap,
causal=True)
else:
assert attn_metadata.decode_metadata is not None
assert attn_metadata.decode_metadata.decode_wrapper is not None
output = attn_metadata.decode_metadata.decode_wrapper.forward(
query,
kv_cache,
sm_scale=self.scale,
logits_soft_cap=self.logits_soft_cap,
k_scale=k_scale,
v_scale=v_scale)
return output.view(num_tokens, hidden_size)