583 lines
20 KiB
Python
583 lines
20 KiB
Python
from __future__ import annotations
|
|
|
|
"""
|
|
Support attention backend for flashinfer MLA.
|
|
The flashinfer_mla_disable_ragged flag controls whether to use ragged prefill wrapper and defaults to be false.
|
|
When it's set to false, all wrappers are BatchMLAPaged wrapper.
|
|
When it's set to true, the backend uses BatchRagged and BatchMLAPaged wrapper for prefilling,
|
|
and uses BatchMLAPaged wrapper for decoding.
|
|
More details can be found in https://docs.flashinfer.ai/api/mla.html
|
|
"""
|
|
|
|
from dataclasses import dataclass
|
|
from functools import partial
|
|
from typing import TYPE_CHECKING, Optional, Union
|
|
|
|
import torch
|
|
|
|
from sglang.global_config import global_config
|
|
from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
|
|
from sglang.srt.layers.attention.flashinfer_backend import (
|
|
create_flashinfer_kv_indices_triton,
|
|
)
|
|
from sglang.srt.layers.dp_attention import get_attention_tp_size
|
|
from sglang.srt.managers.schedule_batch import global_server_args_dict
|
|
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 (
|
|
BatchMLAPagedAttentionWrapper,
|
|
BatchPrefillWithRaggedKVCacheWrapper,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class DecodeMetadata:
|
|
decode_wrapper: BatchMLAPagedAttentionWrapper
|
|
|
|
|
|
@dataclass
|
|
class PrefillMetadata:
|
|
prefill_wrapper: BatchMLAPagedAttentionWrapper
|
|
use_ragged: bool
|
|
|
|
|
|
# Reuse this workspace buffer across all flashinfer wrappers
|
|
global_workspace_buffer = None
|
|
|
|
|
|
class FlashInferMLAAttnBackend(AttentionBackend):
|
|
"""Flashinfer attention kernels."""
|
|
|
|
def __init__(
|
|
self,
|
|
model_runner: ModelRunner,
|
|
):
|
|
super().__init__()
|
|
|
|
# Parse constants
|
|
self.max_context_len = model_runner.model_config.context_len
|
|
self.device = model_runner.device
|
|
|
|
global_config.enable_flashinfer_mla = True
|
|
|
|
# Allocate buffers
|
|
global global_workspace_buffer
|
|
if global_workspace_buffer is None:
|
|
global_workspace_buffer = torch.empty(
|
|
global_config.flashinfer_workspace_size,
|
|
dtype=torch.uint8,
|
|
device=model_runner.device,
|
|
)
|
|
self.workspace_buffer = global_workspace_buffer
|
|
|
|
max_bs = model_runner.req_to_token_pool.size
|
|
self.kv_indptr = torch.zeros(
|
|
(max_bs + 1,), dtype=torch.int32, device=model_runner.device
|
|
)
|
|
|
|
self.qo_indptr = torch.zeros(
|
|
(max_bs + 1,), dtype=torch.int32, device=model_runner.device
|
|
)
|
|
|
|
self.q_indptr_decode = torch.arange(
|
|
0, max_bs + 1, dtype=torch.int32, device=model_runner.device
|
|
)
|
|
|
|
self.prefill_wrapper_ragged = BatchPrefillWithRaggedKVCacheWrapper(
|
|
self.workspace_buffer, "NHD"
|
|
)
|
|
|
|
self.prefill_wrapper_paged = BatchMLAPagedAttentionWrapper(
|
|
self.workspace_buffer,
|
|
backend="auto",
|
|
)
|
|
|
|
self.decode_wrapper = BatchMLAPagedAttentionWrapper(
|
|
self.workspace_buffer, backend="auto"
|
|
)
|
|
|
|
# Create indices updater
|
|
self.indices_updater_prefill = FlashInferMLAIndicesUpdaterPrefill(
|
|
model_runner, self
|
|
)
|
|
self.indices_updater_decode = FlashInferMLAIndicesUpdaterDecode(
|
|
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_or_idle():
|
|
self.indices_updater_decode.update(
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
forward_batch.seq_lens_sum,
|
|
decode_wrapper=self.decode_wrapper,
|
|
init_metadata_replay=False,
|
|
)
|
|
self.forward_metadata = DecodeMetadata(self.decode_wrapper)
|
|
else:
|
|
prefix_lens = forward_batch.extend_prefix_lens
|
|
extend_no_prefix = not any(forward_batch.extend_prefix_lens_cpu)
|
|
use_ragged = (
|
|
not global_server_args_dict["flashinfer_mla_disable_ragged"]
|
|
and extend_no_prefix
|
|
)
|
|
|
|
self.indices_updater_prefill.update(
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
forward_batch.seq_lens_sum,
|
|
prefix_lens,
|
|
prefill_wrapper_paged=self.prefill_wrapper_paged,
|
|
use_ragged=use_ragged,
|
|
)
|
|
self.forward_metadata = PrefillMetadata(
|
|
self.prefill_wrapper_paged, use_ragged
|
|
)
|
|
|
|
def init_cuda_graph_state(
|
|
self, max_bs: int, kv_indices_buf: Optional[torch.Tensor] = None
|
|
):
|
|
if kv_indices_buf is None:
|
|
cuda_graph_kv_indices = torch.zeros(
|
|
(max_bs * self.max_context_len,),
|
|
dtype=torch.int32,
|
|
device="cuda",
|
|
)
|
|
else:
|
|
cuda_graph_kv_indices = kv_indices_buf
|
|
|
|
self.cuda_graph_kv_indices = cuda_graph_kv_indices
|
|
self.cuda_graph_qo_indptr = self.q_indptr_decode.clone()
|
|
self.cuda_graph_kv_indptr = self.kv_indptr.clone()
|
|
self.cuda_graph_kv_lens = torch.ones(
|
|
(max_bs,), dtype=torch.int32, device=self.device
|
|
)
|
|
|
|
# For fast decode plan in graph replaying
|
|
self.cuda_graph_qo_indptr_cpu = self.cuda_graph_qo_indptr.to("cpu")
|
|
self.cuda_graph_kv_indptr_cpu = self.cuda_graph_kv_indptr.to("cpu")
|
|
self.fast_decode_kwargs = {
|
|
"qo_indptr_cpu": self.cuda_graph_qo_indptr_cpu,
|
|
"kv_indptr_cpu": self.cuda_graph_kv_indptr_cpu,
|
|
"kv_indices": self.cuda_graph_kv_indices,
|
|
}
|
|
|
|
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_or_idle():
|
|
decode_wrapper = BatchMLAPagedAttentionWrapper(
|
|
self.workspace_buffer,
|
|
use_cuda_graph=True,
|
|
qo_indptr=self.cuda_graph_qo_indptr[: num_tokens + 1],
|
|
kv_indptr=self.cuda_graph_kv_indptr[: num_tokens + 1],
|
|
kv_indices=self.cuda_graph_kv_indices,
|
|
kv_len_arr=self.cuda_graph_kv_lens[:num_tokens],
|
|
backend="auto",
|
|
)
|
|
|
|
seq_lens_sum = seq_lens.sum().item()
|
|
self.indices_updater_decode.update(
|
|
req_pool_indices,
|
|
seq_lens,
|
|
seq_lens_sum,
|
|
decode_wrapper=decode_wrapper,
|
|
init_metadata_replay=False,
|
|
)
|
|
self.decode_cuda_graph_metadata[bs] = decode_wrapper
|
|
self.forward_metadata = DecodeMetadata(decode_wrapper)
|
|
decode_wrapper.plan = partial(fast_mla_decode_plan, decode_wrapper)
|
|
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],
|
|
seq_lens_cpu: Optional[torch.Tensor],
|
|
):
|
|
if forward_mode.is_decode_or_idle():
|
|
kv_len_arr_cpu = seq_lens_cpu[:bs]
|
|
self.cuda_graph_kv_indptr_cpu[1 : bs + 1] = torch.cumsum(
|
|
kv_len_arr_cpu, dim=0
|
|
)
|
|
self.fast_decode_kwargs.update(
|
|
{
|
|
"qo_indptr_cpu": self.cuda_graph_qo_indptr_cpu[: bs + 1],
|
|
"kv_indptr_cpu": self.cuda_graph_kv_indptr_cpu[: bs + 1],
|
|
"kv_len_arr_cpu": kv_len_arr_cpu,
|
|
}
|
|
)
|
|
|
|
self.indices_updater_decode.update(
|
|
req_pool_indices[:bs],
|
|
seq_lens[:bs],
|
|
seq_lens_sum,
|
|
decode_wrapper=self.decode_cuda_graph_metadata[bs],
|
|
init_metadata_replay=True,
|
|
**self.fast_decode_kwargs,
|
|
)
|
|
else:
|
|
raise ValueError(f"Invalid forward mode: {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,
|
|
):
|
|
|
|
cache_loc = forward_batch.out_cache_loc
|
|
logits_soft_cap = layer.logit_cap
|
|
prefill_wrapper_paged = self.forward_metadata.prefill_wrapper
|
|
qall = q.view(-1, layer.tp_q_head_num, layer.head_dim)
|
|
k_buf = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
|
|
|
|
# Save kv cache
|
|
if save_kv_cache and 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)
|
|
|
|
if self.forward_metadata.use_ragged:
|
|
# ragged prefill
|
|
o, _ = self.prefill_wrapper_ragged.forward_return_lse(
|
|
qall,
|
|
k.view(-1, layer.tp_k_head_num, layer.head_dim),
|
|
v.view(-1, layer.tp_k_head_num, layer.v_head_dim),
|
|
causal=True,
|
|
sm_scale=layer.scaling,
|
|
logits_soft_cap=logits_soft_cap,
|
|
)
|
|
else:
|
|
# mla paged prefill
|
|
o = prefill_wrapper_paged.run(
|
|
qall[:, :, : layer.v_head_dim],
|
|
qall[:, :, layer.v_head_dim :],
|
|
k_buf[:, :, : layer.v_head_dim],
|
|
k_buf[:, :, layer.v_head_dim :],
|
|
)
|
|
|
|
return o.view(-1, layer.tp_q_head_num * layer.v_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_wrapper
|
|
cache_loc = forward_batch.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,
|
|
)
|
|
reshaped_q = q.view(-1, layer.tp_q_head_num, layer.head_dim)
|
|
k_buffer = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
|
|
reshaped_k = k_buffer.view(-1, 1, layer.head_dim)
|
|
o = decode_wrapper.run(
|
|
reshaped_q[:, :, : layer.v_head_dim],
|
|
reshaped_q[:, :, layer.v_head_dim :],
|
|
reshaped_k[:, :, : layer.v_head_dim],
|
|
reshaped_k[:, :, layer.v_head_dim :],
|
|
)
|
|
|
|
return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
|
|
|
|
|
|
class FlashInferMLAIndicesUpdaterDecode:
|
|
def __init__(self, model_runner: ModelRunner, attn_backend: AttentionBackend):
|
|
# Parse Constants
|
|
self.num_local_heads = (
|
|
model_runner.model_config.num_attention_heads // get_attention_tp_size()
|
|
)
|
|
self.kv_lora_rank = model_runner.model_config.kv_lora_rank
|
|
self.qk_nope_head_dim = model_runner.model_config.qk_nope_head_dim
|
|
self.qk_rope_head_dim = model_runner.model_config.qk_rope_head_dim
|
|
self.scaling = model_runner.model_config.scaling
|
|
self.data_type = model_runner.kv_cache_dtype
|
|
self.attn_backend = attn_backend
|
|
|
|
# Buffers and wrappers
|
|
self.kv_indptr = attn_backend.kv_indptr
|
|
self.req_to_token = model_runner.req_to_token_pool.req_to_token
|
|
self.q_indptr = attn_backend.q_indptr_decode
|
|
|
|
def update(
|
|
self,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
decode_wrapper: BatchMLAPagedAttentionWrapper,
|
|
init_metadata_replay: bool = False,
|
|
**fast_decode_kwargs,
|
|
):
|
|
decode_wrapper = decode_wrapper or self.decode_wrapper
|
|
self.call_begin_forward(
|
|
decode_wrapper,
|
|
req_pool_indices,
|
|
seq_lens,
|
|
seq_lens_sum,
|
|
self.q_indptr,
|
|
self.kv_indptr,
|
|
init_metadata_replay,
|
|
**fast_decode_kwargs,
|
|
)
|
|
|
|
def call_begin_forward(
|
|
self,
|
|
wrapper: BatchMLAPagedAttentionWrapper,
|
|
req_pool_indices: torch.Tensor,
|
|
paged_kernel_lens: torch.Tensor,
|
|
paged_kernel_lens_sum: int,
|
|
q_indptr: torch.Tensor,
|
|
kv_indptr: torch.Tensor,
|
|
init_metadata_replay: bool = False,
|
|
**fast_decode_kwargs,
|
|
):
|
|
bs = len(req_pool_indices)
|
|
q_indptr = q_indptr[: bs + 1]
|
|
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")
|
|
if not init_metadata_replay
|
|
else fast_decode_kwargs["kv_indices"]
|
|
)
|
|
|
|
kv_lens = paged_kernel_lens.to(torch.int32)
|
|
sm_scale = self.scaling
|
|
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
kv_indptr,
|
|
None,
|
|
kv_indices,
|
|
self.req_to_token.shape[1],
|
|
)
|
|
if not init_metadata_replay:
|
|
wrapper.plan(
|
|
q_indptr,
|
|
kv_indptr,
|
|
kv_indices,
|
|
kv_lens,
|
|
self.num_local_heads,
|
|
self.kv_lora_rank,
|
|
self.qk_rope_head_dim,
|
|
1,
|
|
False,
|
|
sm_scale,
|
|
self.data_type,
|
|
self.data_type,
|
|
)
|
|
else:
|
|
wrapper.plan(
|
|
fast_decode_kwargs["qo_indptr_cpu"],
|
|
fast_decode_kwargs["kv_indptr_cpu"],
|
|
kv_indices,
|
|
fast_decode_kwargs["kv_len_arr_cpu"],
|
|
self.num_local_heads,
|
|
self.kv_lora_rank,
|
|
self.qk_rope_head_dim,
|
|
1,
|
|
False,
|
|
sm_scale,
|
|
self.data_type,
|
|
self.data_type,
|
|
)
|
|
|
|
|
|
class FlashInferMLAIndicesUpdaterPrefill:
|
|
def __init__(self, model_runner: ModelRunner, attn_backend: AttentionBackend):
|
|
# Parse Constants
|
|
self.num_local_heads = (
|
|
model_runner.model_config.num_attention_heads // get_attention_tp_size()
|
|
)
|
|
self.kv_lora_rank = model_runner.model_config.kv_lora_rank
|
|
self.qk_nope_head_dim = model_runner.model_config.qk_nope_head_dim
|
|
self.qk_rope_head_dim = model_runner.model_config.qk_rope_head_dim
|
|
self.v_head_dim = model_runner.model_config.v_head_dim
|
|
self.scaling = model_runner.model_config.scaling
|
|
self.data_type = model_runner.kv_cache_dtype
|
|
self.q_data_type = model_runner.dtype
|
|
self.attn_backend = attn_backend
|
|
|
|
# Buffers and wrappers
|
|
self.kv_indptr = attn_backend.kv_indptr
|
|
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
|
|
|
|
def update(
|
|
self,
|
|
req_pool_indices: torch.Tnesor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
prefix_lens: torch.Tensor,
|
|
prefill_wrapper_paged: BatchMLAPagedAttentionWrapper,
|
|
use_ragged: bool,
|
|
):
|
|
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_wrapper_paged,
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
paged_kernel_lens_sum,
|
|
seq_lens,
|
|
prefix_lens,
|
|
self.kv_indptr,
|
|
self.qo_indptr,
|
|
use_ragged,
|
|
)
|
|
|
|
def call_begin_forward(
|
|
self,
|
|
wrapper_ragged: BatchPrefillWithRaggedKVCacheWrapper,
|
|
wrapper_paged: BatchMLAPagedAttentionWrapper,
|
|
req_pool_indices: torch.Tensor,
|
|
paged_kernel_lens: torch.Tensor,
|
|
paged_kernel_lens_sum: int,
|
|
seq_lens: torch.Tensor,
|
|
prefix_lens: torch.Tensor,
|
|
kv_indptr: torch.Tensor,
|
|
qo_indptr: torch.Tensor,
|
|
use_ragged: bool,
|
|
):
|
|
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=req_pool_indices.device,
|
|
)
|
|
create_flashinfer_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
paged_kernel_lens,
|
|
kv_indptr,
|
|
None,
|
|
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]
|
|
sm_scale = self.scaling
|
|
|
|
if use_ragged:
|
|
# ragged prefill
|
|
wrapper_ragged.begin_forward(
|
|
qo_indptr=qo_indptr,
|
|
kv_indptr=qo_indptr,
|
|
num_qo_heads=self.num_local_heads,
|
|
num_kv_heads=self.num_local_heads,
|
|
head_dim_qk=self.qk_nope_head_dim + self.qk_rope_head_dim,
|
|
head_dim_vo=self.v_head_dim,
|
|
q_data_type=self.q_data_type,
|
|
)
|
|
else:
|
|
# mla paged prefill
|
|
kv_len_arr = kv_indptr[1:] - kv_indptr[:-1]
|
|
wrapper_paged.plan(
|
|
qo_indptr,
|
|
kv_indptr,
|
|
kv_indices,
|
|
kv_len_arr,
|
|
self.num_local_heads,
|
|
self.kv_lora_rank,
|
|
self.qk_rope_head_dim,
|
|
1,
|
|
True,
|
|
sm_scale,
|
|
self.q_data_type,
|
|
self.data_type,
|
|
)
|
|
|
|
|
|
def fast_mla_decode_plan(
|
|
self,
|
|
qo_indptr_cpu: torch.Tensor,
|
|
kv_indptr_cpu: torch.Tensor,
|
|
kv_indices: torch.Tensor,
|
|
kv_len_arr_cpu: torch.Tensor,
|
|
num_heads: int,
|
|
head_dim_ckv: int,
|
|
head_dim_kpe: int,
|
|
page_size: int,
|
|
causal: bool,
|
|
sm_scale: float,
|
|
q_data_type: torch.dtype,
|
|
kv_data_type: torch.dtype,
|
|
) -> None:
|
|
"""A faster version of BatchMLAPagedAttentionWrapper::plan,
|
|
for skipping the stream synchronization in original plan function during
|
|
cuda graph replaying.
|
|
"""
|
|
self._causal = causal
|
|
self._page_size = page_size
|
|
self._sm_scale = sm_scale
|
|
|
|
with self.device as device:
|
|
stream = torch.cuda.current_stream(device).cuda_stream
|
|
self._cached_module.plan(
|
|
self._float_workspace_buffer,
|
|
self._int_workspace_buffer,
|
|
self._pin_memory_int_workspace_buffer,
|
|
qo_indptr_cpu,
|
|
kv_indptr_cpu,
|
|
kv_len_arr_cpu,
|
|
num_heads,
|
|
head_dim_ckv,
|
|
causal,
|
|
stream,
|
|
)
|