937 lines
33 KiB
Python
937 lines
33 KiB
Python
from __future__ import annotations
|
|
|
|
"""
|
|
Support different attention backends.
|
|
Now there are two backends: FlashInfer and Triton.
|
|
FlashInfer is faster and Triton is easier to customize.
|
|
Each backend supports two operators: extend (i.e. prefill with cached prefix) and decode.
|
|
"""
|
|
|
|
import os
|
|
from dataclasses import dataclass
|
|
from enum import Enum, auto
|
|
from typing import TYPE_CHECKING, List, Optional, Union
|
|
|
|
import torch
|
|
import triton
|
|
import triton.language as tl
|
|
|
|
from sglang.global_config import global_config
|
|
from sglang.srt.layers.attention import AttentionBackend
|
|
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
|
|
from sglang.srt.utils import is_flashinfer_available
|
|
|
|
if TYPE_CHECKING:
|
|
from sglang.srt.layers.radix_attention import RadixAttention
|
|
from sglang.srt.model_executor.model_runner import ModelRunner
|
|
from sglang.srt.speculative.spec_info import SpecInfo
|
|
|
|
if is_flashinfer_available():
|
|
from flashinfer import (
|
|
BatchDecodeWithPagedKVCacheWrapper,
|
|
BatchPrefillWithPagedKVCacheWrapper,
|
|
BatchPrefillWithRaggedKVCacheWrapper,
|
|
)
|
|
from flashinfer.cascade import merge_state
|
|
|
|
|
|
class WrapperDispatch(Enum):
|
|
SLIDING_WINDOW = auto()
|
|
CROSS_ATTENTION = auto()
|
|
|
|
|
|
@dataclass
|
|
class DecodeMetadata:
|
|
decode_wrappers: List[BatchDecodeWithPagedKVCacheWrapper]
|
|
|
|
|
|
@dataclass
|
|
class PrefillMetadata:
|
|
prefill_wrappers: List[BatchPrefillWithPagedKVCacheWrapper]
|
|
use_ragged: bool
|
|
extend_no_prefix: bool
|
|
|
|
|
|
class FlashInferAttnBackend(AttentionBackend):
|
|
"""Flashinfer attention kernels."""
|
|
|
|
def __init__(self, model_runner: ModelRunner):
|
|
super().__init__()
|
|
|
|
# Parse constants
|
|
self.decode_use_tensor_cores = should_use_tensor_core(
|
|
kv_cache_dtype=model_runner.kv_cache_dtype,
|
|
num_attention_heads=model_runner.model_config.num_attention_heads
|
|
// model_runner.tp_size,
|
|
num_kv_heads=model_runner.model_config.get_num_kv_heads(
|
|
model_runner.tp_size
|
|
),
|
|
)
|
|
self.max_context_len = model_runner.model_config.context_len
|
|
|
|
assert not (
|
|
model_runner.sliding_window_size is not None
|
|
and model_runner.model_config.is_encoder_decoder
|
|
), "Sliding window and cross attention are not supported together"
|
|
|
|
if model_runner.sliding_window_size is not None:
|
|
self.num_wrappers = 2
|
|
self.dispatch_reason = WrapperDispatch.SLIDING_WINDOW
|
|
elif model_runner.model_config.is_encoder_decoder:
|
|
self.num_wrappers = 2
|
|
self.dispatch_reason = WrapperDispatch.CROSS_ATTENTION
|
|
else:
|
|
self.num_wrappers = 1
|
|
self.dispatch_reason = None
|
|
|
|
# Qwen2 models require higher flashinfer workspace size
|
|
if "Qwen2ForCausalLM" in model_runner.model_config.hf_config.architectures:
|
|
global_config.flashinfer_workspace_size = 512 * 1024 * 1024
|
|
|
|
# Allocate buffers
|
|
self.workspace_buffer = torch.empty(
|
|
global_config.flashinfer_workspace_size,
|
|
dtype=torch.uint8,
|
|
device=model_runner.device,
|
|
)
|
|
max_bs = model_runner.req_to_token_pool.size
|
|
self.kv_indptr = [
|
|
torch.zeros((max_bs + 1,), dtype=torch.int32, device=model_runner.device)
|
|
for _ in range(self.num_wrappers)
|
|
]
|
|
self.kv_last_page_len = torch.ones(
|
|
(max_bs,), dtype=torch.int32, device=model_runner.device
|
|
)
|
|
self.qo_indptr = [
|
|
torch.zeros((max_bs + 1,), dtype=torch.int32, device=model_runner.device)
|
|
for _ in range(self.num_wrappers)
|
|
]
|
|
|
|
# Create wrappers
|
|
# NOTE: we do not use ragged attention when there are multiple wrappers
|
|
self.prefill_wrapper_ragged = (
|
|
BatchPrefillWithRaggedKVCacheWrapper(self.workspace_buffer, "NHD")
|
|
if self.num_wrappers == 1
|
|
else None
|
|
)
|
|
|
|
# Two wrappers: one for sliding window attention and one for full attention.
|
|
# Using two wrappers is unnecessary in the current PR, but are prepared for future PRs
|
|
self.prefill_wrappers_paged = []
|
|
self.prefill_wrappers_verify = []
|
|
self.decode_wrappers = []
|
|
for _ in range(self.num_wrappers):
|
|
self.prefill_wrappers_paged.append(
|
|
BatchPrefillWithPagedKVCacheWrapper(self.workspace_buffer, "NHD")
|
|
)
|
|
self.prefill_wrappers_verify.append(
|
|
BatchPrefillWithPagedKVCacheWrapper(self.workspace_buffer, "NHD")
|
|
)
|
|
self.decode_wrappers.append(
|
|
BatchDecodeWithPagedKVCacheWrapper(
|
|
self.workspace_buffer,
|
|
"NHD",
|
|
use_tensor_cores=self.decode_use_tensor_cores,
|
|
)
|
|
)
|
|
|
|
# Create indices updater
|
|
self.indices_updater_decode = FlashInferIndicesUpdaterDecode(model_runner, self)
|
|
self.indices_updater_prefill = FlashInferIndicesUpdaterPrefill(
|
|
model_runner, self
|
|
)
|
|
|
|
# Other metadata
|
|
self.forward_metadata: Union[PrefillMetadata, DecodeMetadata] = None
|
|
self.decode_cuda_graph_metadata = {}
|
|
self.prefill_cuda_graph_metadata = {}
|
|
|
|
def init_forward_metadata(self, forward_batch: ForwardBatch):
|
|
if forward_batch.forward_mode.is_decode():
|
|
self.indices_updater_decode.update(
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
forward_batch.seq_lens_sum,
|
|
decode_wrappers=self.decode_wrappers,
|
|
encoder_lens=forward_batch.encoder_lens,
|
|
spec_info=forward_batch.spec_info,
|
|
)
|
|
self.forward_metadata = DecodeMetadata(self.decode_wrappers)
|
|
elif forward_batch.forward_mode.is_draft_extend():
|
|
self.indices_updater_prefill.update(
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
forward_batch.seq_lens_sum,
|
|
prefix_lens=None,
|
|
prefill_wrappers=self.prefill_wrappers_paged,
|
|
use_ragged=False,
|
|
encoder_lens=forward_batch.encoder_lens,
|
|
spec_info=forward_batch.spec_info,
|
|
)
|
|
self.forward_metadata = PrefillMetadata(
|
|
self.prefill_wrappers_paged, False, False
|
|
)
|
|
elif forward_batch.forward_mode.is_target_verify():
|
|
self.indices_updater_prefill.update(
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
forward_batch.seq_lens_sum,
|
|
prefix_lens=None,
|
|
prefill_wrappers=self.prefill_wrappers_verify,
|
|
use_ragged=False,
|
|
encoder_lens=forward_batch.encoder_lens,
|
|
spec_info=forward_batch.spec_info,
|
|
)
|
|
self.forward_metadata = PrefillMetadata(
|
|
self.prefill_wrappers_verify, False, False
|
|
)
|
|
else:
|
|
prefix_lens = forward_batch.extend_prefix_lens
|
|
|
|
# Some heuristics to check whether to use ragged forward
|
|
if forward_batch.extend_num_tokens >= 4096 and self.num_wrappers == 1:
|
|
use_ragged = True
|
|
extend_no_prefix = not any(forward_batch.extend_prefix_lens_cpu)
|
|
else:
|
|
use_ragged = False
|
|
extend_no_prefix = False
|
|
|
|
self.indices_updater_prefill.update(
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
forward_batch.seq_lens_sum,
|
|
prefix_lens,
|
|
prefill_wrappers=self.prefill_wrappers_paged,
|
|
use_ragged=use_ragged,
|
|
encoder_lens=forward_batch.encoder_lens,
|
|
spec_info=None,
|
|
)
|
|
self.forward_metadata = PrefillMetadata(
|
|
self.prefill_wrappers_paged, use_ragged, extend_no_prefix
|
|
)
|
|
|
|
def init_cuda_graph_state(self, max_bs: int):
|
|
cuda_graph_kv_indices = torch.zeros(
|
|
(max_bs * self.max_context_len,),
|
|
dtype=torch.int32,
|
|
device="cuda",
|
|
)
|
|
self.cuda_graph_kv_indices = [cuda_graph_kv_indices] + [
|
|
cuda_graph_kv_indices.clone() for _ in range(self.num_wrappers - 1)
|
|
]
|
|
|
|
self.cuda_graph_custom_mask = torch.zeros(
|
|
(max_bs * self.max_context_len),
|
|
dtype=torch.uint8,
|
|
device="cuda",
|
|
)
|
|
self.cuda_graph_qk_indptr = [x.clone() for x in self.kv_indptr]
|
|
self.cuda_graph_qo_indptr = [x.clone() for x in self.kv_indptr]
|
|
|
|
def init_forward_metadata_capture_cuda_graph(
|
|
self,
|
|
bs: int,
|
|
num_tokens: int,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
forward_mode: ForwardMode,
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
if forward_mode.is_decode():
|
|
decode_wrappers = []
|
|
for i in range(self.num_wrappers):
|
|
decode_wrappers.append(
|
|
BatchDecodeWithPagedKVCacheWrapper(
|
|
self.workspace_buffer,
|
|
"NHD",
|
|
use_cuda_graph=True,
|
|
use_tensor_cores=self.decode_use_tensor_cores,
|
|
paged_kv_indptr_buffer=self.kv_indptr[i][: num_tokens + 1],
|
|
paged_kv_indices_buffer=self.cuda_graph_kv_indices[i],
|
|
paged_kv_last_page_len_buffer=self.kv_last_page_len[
|
|
:num_tokens
|
|
],
|
|
)
|
|
)
|
|
seq_lens_sum = seq_lens.sum().item()
|
|
self.indices_updater_decode.update(
|
|
req_pool_indices,
|
|
seq_lens,
|
|
seq_lens_sum,
|
|
decode_wrappers=decode_wrappers,
|
|
encoder_lens=encoder_lens,
|
|
spec_info=spec_info,
|
|
)
|
|
self.decode_cuda_graph_metadata[bs] = decode_wrappers
|
|
self.forward_metadata = DecodeMetadata(decode_wrappers)
|
|
elif forward_mode.is_target_verify():
|
|
prefill_wrappers = []
|
|
for i in range(self.num_wrappers):
|
|
prefill_wrappers.append(
|
|
BatchPrefillWithPagedKVCacheWrapper(
|
|
self.workspace_buffer,
|
|
"NHD",
|
|
use_cuda_graph=True,
|
|
qo_indptr_buf=self.cuda_graph_qo_indptr[i][: bs + 1],
|
|
paged_kv_indptr_buf=self.kv_indptr[i][: bs + 1],
|
|
paged_kv_indices_buf=self.cuda_graph_kv_indices[i],
|
|
paged_kv_last_page_len_buf=self.kv_last_page_len[:bs],
|
|
custom_mask_buf=self.cuda_graph_custom_mask,
|
|
qk_indptr_buf=self.cuda_graph_qk_indptr[i][: bs + 1],
|
|
)
|
|
)
|
|
seq_lens_sum = seq_lens.sum().item()
|
|
self.indices_updater_prefill.update(
|
|
req_pool_indices,
|
|
seq_lens,
|
|
seq_lens_sum,
|
|
prefix_lens=None,
|
|
prefill_wrappers=prefill_wrappers,
|
|
use_ragged=False,
|
|
encoder_lens=encoder_lens,
|
|
spec_info=spec_info,
|
|
)
|
|
self.prefill_cuda_graph_metadata[bs] = prefill_wrappers
|
|
self.forward_metadata = PrefillMetadata(prefill_wrappers, False, False)
|
|
else:
|
|
raise ValueError(f"Invalid mode: {forward_mode=}")
|
|
|
|
def init_forward_metadata_replay_cuda_graph(
|
|
self,
|
|
bs: int,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
forward_mode: ForwardMode,
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
if forward_mode.is_decode():
|
|
self.indices_updater_decode.update(
|
|
req_pool_indices[:bs],
|
|
seq_lens[:bs],
|
|
seq_lens_sum,
|
|
decode_wrappers=self.decode_cuda_graph_metadata[bs],
|
|
encoder_lens=encoder_lens[:bs] if encoder_lens is not None else None,
|
|
spec_info=spec_info,
|
|
)
|
|
elif forward_mode.is_target_verify():
|
|
self.indices_updater_prefill.update(
|
|
req_pool_indices[:bs],
|
|
seq_lens[:bs],
|
|
seq_lens_sum,
|
|
prefix_lens=None,
|
|
prefill_wrappers=self.prefill_cuda_graph_metadata[bs],
|
|
use_ragged=False,
|
|
encoder_lens=encoder_lens[:bs] if encoder_lens is not None else None,
|
|
spec_info=spec_info,
|
|
)
|
|
else:
|
|
raise ValueError("Invalid forward mode")
|
|
|
|
def get_cuda_graph_seq_len_fill_value(self):
|
|
return 0
|
|
|
|
def forward_extend(
|
|
self,
|
|
q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
layer: RadixAttention,
|
|
forward_batch: ForwardBatch,
|
|
save_kv_cache=True,
|
|
):
|
|
prefill_wrapper_paged = self.forward_metadata.prefill_wrappers[
|
|
self._get_wrapper_idx(layer)
|
|
]
|
|
cache_loc = (
|
|
forward_batch.out_cache_loc
|
|
if not layer.is_cross_attention
|
|
else forward_batch.encoder_out_cache_loc
|
|
)
|
|
|
|
logits_soft_cap = layer.logit_cap
|
|
|
|
if not self.forward_metadata.use_ragged:
|
|
if k is not None:
|
|
assert v is not None
|
|
if save_kv_cache:
|
|
forward_batch.token_to_kv_pool.set_kv_buffer(
|
|
layer, cache_loc, k, v, layer.k_scale, layer.v_scale
|
|
)
|
|
|
|
o = prefill_wrapper_paged.forward(
|
|
q.contiguous().view(-1, layer.tp_q_head_num, layer.head_dim),
|
|
forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id),
|
|
causal=not layer.is_cross_attention,
|
|
sm_scale=layer.scaling,
|
|
window_left=layer.sliding_window_size,
|
|
logits_soft_cap=logits_soft_cap,
|
|
k_scale=layer.k_scale,
|
|
v_scale=layer.v_scale,
|
|
)
|
|
else:
|
|
o1, s1 = self.prefill_wrapper_ragged.forward_return_lse(
|
|
q.contiguous().view(-1, layer.tp_q_head_num, layer.head_dim),
|
|
k.contiguous().view(-1, layer.tp_k_head_num, layer.head_dim),
|
|
v.contiguous().view(-1, layer.tp_v_head_num, layer.head_dim),
|
|
causal=True,
|
|
sm_scale=layer.scaling,
|
|
logits_soft_cap=logits_soft_cap,
|
|
)
|
|
|
|
if self.forward_metadata.extend_no_prefix:
|
|
o = o1
|
|
else:
|
|
o2, s2 = prefill_wrapper_paged.forward_return_lse(
|
|
q.contiguous().view(-1, layer.tp_q_head_num, layer.head_dim),
|
|
forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id),
|
|
causal=False,
|
|
sm_scale=layer.scaling,
|
|
logits_soft_cap=layer.logit_cap,
|
|
)
|
|
|
|
o, _ = merge_state(o1, s1, o2, s2)
|
|
|
|
if save_kv_cache:
|
|
forward_batch.token_to_kv_pool.set_kv_buffer(
|
|
layer, cache_loc, k, v, layer.k_scale, layer.v_scale
|
|
)
|
|
|
|
return o.view(-1, layer.tp_q_head_num * layer.head_dim)
|
|
|
|
def forward_decode(
|
|
self,
|
|
q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
layer: RadixAttention,
|
|
forward_batch: ForwardBatch,
|
|
save_kv_cache=True,
|
|
):
|
|
decode_wrapper = self.forward_metadata.decode_wrappers[
|
|
self._get_wrapper_idx(layer)
|
|
]
|
|
cache_loc = (
|
|
forward_batch.out_cache_loc
|
|
if not layer.is_cross_attention
|
|
else forward_batch.encoder_out_cache_loc
|
|
)
|
|
|
|
if k is not None:
|
|
assert v is not None
|
|
if save_kv_cache:
|
|
forward_batch.token_to_kv_pool.set_kv_buffer(
|
|
layer, cache_loc, k, v, layer.k_scale, layer.v_scale
|
|
)
|
|
|
|
o = decode_wrapper.forward(
|
|
q.contiguous().view(-1, layer.tp_q_head_num, layer.head_dim),
|
|
forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id),
|
|
sm_scale=layer.scaling,
|
|
logits_soft_cap=layer.logit_cap,
|
|
k_scale=layer.k_scale,
|
|
v_scale=layer.v_scale,
|
|
)
|
|
|
|
return o.view(-1, layer.tp_q_head_num * layer.head_dim)
|
|
|
|
def _get_wrapper_idx(self, layer: RadixAttention):
|
|
if self.num_wrappers == 1:
|
|
return 0
|
|
|
|
if self.dispatch_reason == WrapperDispatch.SLIDING_WINDOW:
|
|
return layer.sliding_window_size == -1
|
|
if self.dispatch_reason == WrapperDispatch.CROSS_ATTENTION:
|
|
return layer.is_cross_attention
|
|
|
|
raise ValueError(f"Unknown dispatch reason: {self.dispatch_reason}")
|
|
|
|
|
|
class FlashInferIndicesUpdaterDecode:
|
|
def __init__(self, model_runner: ModelRunner, attn_backend: AttentionBackend):
|
|
# Parse Constants
|
|
self.num_qo_heads = (
|
|
model_runner.model_config.num_attention_heads // model_runner.tp_size
|
|
)
|
|
self.num_kv_heads = model_runner.model_config.get_num_kv_heads(
|
|
model_runner.tp_size
|
|
)
|
|
self.head_dim = model_runner.model_config.head_dim
|
|
self.data_type = model_runner.kv_cache_dtype
|
|
self.q_data_type = model_runner.dtype
|
|
self.sliding_window_size = model_runner.sliding_window_size
|
|
self.attn_backend = attn_backend
|
|
|
|
# Buffers and wrappers
|
|
self.kv_indptr = attn_backend.kv_indptr
|
|
self.kv_last_page_len = attn_backend.kv_last_page_len
|
|
self.req_to_token = model_runner.req_to_token_pool.req_to_token
|
|
|
|
# Dispatch the update function
|
|
if self.attn_backend.dispatch_reason == WrapperDispatch.SLIDING_WINDOW:
|
|
self.update = self.update_sliding_window
|
|
elif self.attn_backend.dispatch_reason == WrapperDispatch.CROSS_ATTENTION:
|
|
self.update = self.update_cross_attention
|
|
else:
|
|
assert self.attn_backend.num_wrappers == 1
|
|
self.update = self.update_single_wrapper
|
|
|
|
def update(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
decode_wrappers: List[BatchDecodeWithPagedKVCacheWrapper],
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
# Keep the signature for type checking. It will be assigned during runtime.
|
|
raise NotImplementedError()
|
|
|
|
def update_single_wrapper(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
decode_wrappers: List[BatchDecodeWithPagedKVCacheWrapper],
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
decode_wrappers = decode_wrappers or self.decode_wrappers
|
|
self.call_begin_forward(
|
|
decode_wrappers[0],
|
|
req_pool_indices,
|
|
seq_lens,
|
|
seq_lens_sum,
|
|
self.kv_indptr[0],
|
|
None,
|
|
spec_info,
|
|
)
|
|
|
|
def update_sliding_window(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
decode_wrappers: List[BatchDecodeWithPagedKVCacheWrapper],
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
for wrapper_id in range(2):
|
|
if wrapper_id == 0:
|
|
# Sliding window attention
|
|
paged_kernel_lens_tmp = torch.minimum( # TODO: replace this with clamp
|
|
seq_lens,
|
|
torch.tensor(self.sliding_window_size + 1),
|
|
)
|
|
paged_kernel_lens_sum_tmp = paged_kernel_lens_tmp.sum().item()
|
|
kv_start_idx_tmp = seq_lens - paged_kernel_lens_tmp
|
|
else:
|
|
# Full attention
|
|
paged_kernel_lens_tmp = seq_lens
|
|
paged_kernel_lens_sum_tmp = seq_lens_sum
|
|
kv_start_idx_tmp = None
|
|
|
|
self.call_begin_forward(
|
|
decode_wrappers[wrapper_id],
|
|
req_pool_indices,
|
|
paged_kernel_lens_tmp,
|
|
paged_kernel_lens_sum_tmp,
|
|
self.kv_indptr[wrapper_id],
|
|
kv_start_idx_tmp,
|
|
spec_info,
|
|
)
|
|
|
|
def update_cross_attention(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
decode_wrappers: List[BatchDecodeWithPagedKVCacheWrapper],
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
for wrapper_id in range(2):
|
|
if wrapper_id == 0:
|
|
# Normal attention
|
|
paged_kernel_lens = seq_lens
|
|
kv_start_idx = encoder_lens
|
|
else:
|
|
# Cross attention
|
|
paged_kernel_lens = encoder_lens
|
|
kv_start_idx = torch.zeros_like(encoder_lens)
|
|
seq_lens_sum = encoder_lens.sum().item()
|
|
|
|
self.call_begin_forward(
|
|
decode_wrappers[wrapper_id],
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
seq_lens_sum,
|
|
self.kv_indptr[wrapper_id],
|
|
kv_start_idx,
|
|
spec_info,
|
|
)
|
|
|
|
def call_begin_forward(
|
|
self,
|
|
wrapper: BatchDecodeWithPagedKVCacheWrapper,
|
|
req_pool_indices: torch.Tensor,
|
|
paged_kernel_lens: torch.Tensor,
|
|
paged_kernel_lens_sum: int,
|
|
kv_indptr: torch.Tensor,
|
|
kv_start_idx: torch.Tensor,
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
if spec_info is None:
|
|
bs = len(req_pool_indices)
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(paged_kernel_lens, dim=0)
|
|
kv_indptr = kv_indptr[: bs + 1]
|
|
kv_indices = torch.empty(
|
|
paged_kernel_lens_sum, dtype=torch.int32, device="cuda"
|
|
)
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
kv_indptr,
|
|
kv_start_idx,
|
|
kv_indices,
|
|
self.req_to_token.shape[1],
|
|
)
|
|
else:
|
|
bs, kv_indices, kv_indptr = spec_info.generate_attn_arg_decode(
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
self.req_to_token,
|
|
)
|
|
|
|
wrapper.end_forward()
|
|
wrapper.begin_forward(
|
|
kv_indptr,
|
|
kv_indices,
|
|
self.kv_last_page_len[:bs],
|
|
self.num_qo_heads,
|
|
self.num_kv_heads,
|
|
self.head_dim,
|
|
1,
|
|
data_type=self.data_type,
|
|
q_data_type=self.q_data_type,
|
|
)
|
|
|
|
|
|
class FlashInferIndicesUpdaterPrefill:
|
|
def __init__(self, model_runner: ModelRunner, attn_backend: AttentionBackend):
|
|
# Parse Constants
|
|
self.num_qo_heads = (
|
|
model_runner.model_config.num_attention_heads // model_runner.tp_size
|
|
)
|
|
self.num_kv_heads = model_runner.model_config.get_num_kv_heads(
|
|
model_runner.tp_size
|
|
)
|
|
self.head_dim = model_runner.model_config.head_dim
|
|
self.data_type = model_runner.kv_cache_dtype
|
|
self.q_data_type = model_runner.dtype
|
|
self.sliding_window_size = model_runner.sliding_window_size
|
|
self.attn_backend = attn_backend
|
|
|
|
# Buffers and wrappers
|
|
self.kv_indptr = attn_backend.kv_indptr
|
|
self.kv_last_page_len = attn_backend.kv_last_page_len
|
|
self.qo_indptr = attn_backend.qo_indptr
|
|
self.req_to_token = model_runner.req_to_token_pool.req_to_token
|
|
self.prefill_wrapper_ragged = attn_backend.prefill_wrapper_ragged
|
|
|
|
# Dispatch the update function
|
|
if self.attn_backend.dispatch_reason == WrapperDispatch.SLIDING_WINDOW:
|
|
self.update = self.update_sliding_window
|
|
elif self.attn_backend.dispatch_reason == WrapperDispatch.CROSS_ATTENTION:
|
|
self.update = self.update_cross_attention
|
|
else:
|
|
assert self.attn_backend.num_wrappers == 1
|
|
self.update = self.update_single_wrapper
|
|
|
|
def update(
|
|
self,
|
|
req_pool_indices: torch.Tnesor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
prefix_lens: torch.Tensor,
|
|
prefill_wrappers: List[BatchPrefillWithPagedKVCacheWrapper],
|
|
use_ragged: bool,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
# Keep the signature for type checking. It will be assigned during runtime.
|
|
raise NotImplementedError()
|
|
|
|
def update_single_wrapper(
|
|
self,
|
|
req_pool_indices: torch.Tnesor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
prefix_lens: torch.Tensor,
|
|
prefill_wrappers: List[BatchPrefillWithPagedKVCacheWrapper],
|
|
use_ragged: bool,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
if use_ragged:
|
|
paged_kernel_lens = prefix_lens
|
|
paged_kernel_lens_sum = paged_kernel_lens.sum().item()
|
|
else:
|
|
paged_kernel_lens = seq_lens
|
|
paged_kernel_lens_sum = seq_lens_sum
|
|
|
|
self.call_begin_forward(
|
|
self.prefill_wrapper_ragged,
|
|
prefill_wrappers[0],
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
paged_kernel_lens_sum,
|
|
seq_lens,
|
|
prefix_lens,
|
|
None,
|
|
self.kv_indptr[0],
|
|
self.qo_indptr[0],
|
|
use_ragged,
|
|
spec_info,
|
|
)
|
|
|
|
def update_sliding_window(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
prefix_lens: torch.Tensor,
|
|
prefill_wrappers: List[BatchPrefillWithPagedKVCacheWrapper],
|
|
use_ragged: bool,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
for wrapper_id in range(2):
|
|
if wrapper_id == 0:
|
|
# window attention use paged only
|
|
paged_kernel_lens = torch.minimum(
|
|
seq_lens,
|
|
torch.tensor(self.sliding_window_size) + seq_lens - prefix_lens,
|
|
)
|
|
paged_kernel_lens_sum = paged_kernel_lens.sum().item()
|
|
else:
|
|
# full attention
|
|
paged_kernel_lens = seq_lens
|
|
paged_kernel_lens_sum = seq_lens_sum
|
|
|
|
kv_start_idx = seq_lens - paged_kernel_lens
|
|
|
|
self.call_begin_forward(
|
|
self.prefill_wrapper_ragged,
|
|
prefill_wrappers[wrapper_id],
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
paged_kernel_lens_sum,
|
|
seq_lens,
|
|
prefix_lens,
|
|
kv_start_idx,
|
|
self.kv_indptr[wrapper_id],
|
|
self.qo_indptr[wrapper_id],
|
|
use_ragged,
|
|
spec_info,
|
|
)
|
|
|
|
def update_cross_attention(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
prefix_lens: torch.Tensor,
|
|
prefill_wrappers: List[BatchPrefillWithPagedKVCacheWrapper],
|
|
use_ragged: bool,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
for wrapper_id in range(2):
|
|
if wrapper_id == 0:
|
|
# normal attention
|
|
paged_kernel_lens = seq_lens
|
|
kv_start_idx = encoder_lens
|
|
paged_kernel_lens_sum = seq_lens_sum
|
|
else:
|
|
# cross attention
|
|
paged_kernel_lens = encoder_lens
|
|
kv_start_idx = torch.zeros_like(encoder_lens)
|
|
paged_kernel_lens_sum = paged_kernel_lens.sum().item()
|
|
|
|
self.call_begin_forward(
|
|
self.prefill_wrapper_ragged,
|
|
prefill_wrappers[wrapper_id],
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
paged_kernel_lens_sum,
|
|
seq_lens,
|
|
prefix_lens,
|
|
kv_start_idx,
|
|
self.kv_indptr[wrapper_id],
|
|
self.qo_indptr[wrapper_id],
|
|
use_ragged,
|
|
spec_info,
|
|
)
|
|
|
|
def call_begin_forward(
|
|
self,
|
|
wrapper_ragged: BatchPrefillWithRaggedKVCacheWrapper,
|
|
wrapper_paged: BatchPrefillWithPagedKVCacheWrapper,
|
|
req_pool_indices: torch.Tensor,
|
|
paged_kernel_lens: torch.Tensor,
|
|
paged_kernel_lens_sum: int,
|
|
seq_lens: torch.Tensor,
|
|
prefix_lens: torch.Tensor,
|
|
kv_start_idx: torch.Tensor,
|
|
kv_indptr: torch.Tensor,
|
|
qo_indptr: torch.Tensor,
|
|
use_ragged: bool,
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
bs = len(req_pool_indices)
|
|
if spec_info is None:
|
|
# Normal extend
|
|
kv_indptr[1 : bs + 1] = torch.cumsum(paged_kernel_lens, dim=0)
|
|
kv_indptr = kv_indptr[: bs + 1]
|
|
kv_indices = torch.empty(
|
|
paged_kernel_lens_sum, dtype=torch.int32, device="cuda"
|
|
)
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
kv_indptr,
|
|
kv_start_idx,
|
|
kv_indices,
|
|
self.req_to_token.shape[1],
|
|
)
|
|
|
|
qo_indptr[1 : bs + 1] = torch.cumsum(seq_lens - prefix_lens, dim=0)
|
|
qo_indptr = qo_indptr[: bs + 1]
|
|
custom_mask = None
|
|
else:
|
|
kv_indices, kv_indptr, qo_indptr, custom_mask = (
|
|
spec_info.generate_attn_arg_prefill(
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
self.req_to_token,
|
|
)
|
|
)
|
|
|
|
# extend part
|
|
if use_ragged:
|
|
wrapper_ragged.end_forward()
|
|
wrapper_ragged.begin_forward(
|
|
qo_indptr,
|
|
qo_indptr,
|
|
self.num_qo_heads,
|
|
self.num_kv_heads,
|
|
self.head_dim,
|
|
q_data_type=self.q_data_type,
|
|
)
|
|
|
|
# cached part
|
|
wrapper_paged.end_forward()
|
|
wrapper_paged.begin_forward(
|
|
qo_indptr,
|
|
kv_indptr,
|
|
kv_indices,
|
|
self.kv_last_page_len[:bs],
|
|
self.num_qo_heads,
|
|
self.num_kv_heads,
|
|
self.head_dim,
|
|
1,
|
|
q_data_type=self.q_data_type,
|
|
custom_mask=custom_mask,
|
|
)
|
|
|
|
|
|
@triton.jit
|
|
def create_flashinfer_kv_indices_triton(
|
|
req_to_token_ptr, # [max_batch, max_context_len]
|
|
req_pool_indices_ptr,
|
|
page_kernel_lens_ptr,
|
|
kv_indptr,
|
|
kv_start_idx,
|
|
kv_indices_ptr,
|
|
req_to_token_ptr_stride: tl.constexpr,
|
|
):
|
|
BLOCK_SIZE: tl.constexpr = 512
|
|
pid = tl.program_id(axis=0)
|
|
|
|
req_pool_index = tl.load(req_pool_indices_ptr + pid)
|
|
kv_indices_offset = tl.load(kv_indptr + pid)
|
|
|
|
kv_start = 0
|
|
kv_end = 0
|
|
if kv_start_idx:
|
|
kv_start = tl.load(kv_start_idx + pid).to(tl.int32)
|
|
kv_end = kv_start
|
|
kv_end += tl.load(page_kernel_lens_ptr + pid).to(tl.int32)
|
|
|
|
num_loop = tl.cdiv(kv_end - kv_start, BLOCK_SIZE)
|
|
for i in range(num_loop):
|
|
offset = tl.arange(0, BLOCK_SIZE) + i * BLOCK_SIZE
|
|
mask = offset < kv_end - kv_start
|
|
data = tl.load(
|
|
req_to_token_ptr
|
|
+ req_pool_index * req_to_token_ptr_stride
|
|
+ kv_start
|
|
+ offset,
|
|
mask=mask,
|
|
)
|
|
tl.store(kv_indices_ptr + kv_indices_offset + offset, data, mask=mask)
|
|
|
|
|
|
def should_use_tensor_core(
|
|
kv_cache_dtype: torch.dtype,
|
|
num_attention_heads: int,
|
|
num_kv_heads: int,
|
|
) -> bool:
|
|
"""
|
|
Determine whether to use tensor cores for attention computation.
|
|
|
|
Args:
|
|
kv_cache_dtype: Data type of the KV cache
|
|
num_attention_heads: Number of attention heads
|
|
num_kv_heads: Number of key/value heads
|
|
|
|
Returns:
|
|
bool: Whether to use tensor cores
|
|
"""
|
|
# Try to use environment variable first
|
|
env_override = os.environ.get("SGLANG_FLASHINFER_USE_TENSOR_CORE")
|
|
if env_override is not None:
|
|
return env_override.lower() == "true"
|
|
|
|
# Try to use _grouped_size_compiled_for_decode_kernels if available
|
|
# This is for flashinfer <=0.1.6. Otherwise, there is an accuracy bug
|
|
try:
|
|
from flashinfer.decode import _grouped_size_compiled_for_decode_kernels
|
|
|
|
if not _grouped_size_compiled_for_decode_kernels(
|
|
num_attention_heads,
|
|
num_kv_heads,
|
|
):
|
|
return True
|
|
else:
|
|
return False
|
|
except (ImportError, AttributeError):
|
|
pass
|
|
|
|
# Calculate GQA group size
|
|
gqa_group_size = num_attention_heads // num_kv_heads
|
|
|
|
# Determine based on dtype and GQA group size
|
|
if kv_cache_dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
|
|
return True
|
|
elif kv_cache_dtype in (torch.float16, torch.half, torch.bfloat16):
|
|
return gqa_group_size > 4
|
|
else:
|
|
return False
|