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xc-llm-ascend/vllm_ascend/attention/sfa_v1.py
Mengqing Cao 8abe517870 [Refactor] Adapt deepseek-v3.2 to vllm 0.11.0 (#3432) 
### What this PR does / why we need it?
Adapt deepseek-v3.2 to vllm 0.11.0, removing the useless patch.

The final goal is to remove all the patches and align the code arch to
vllm, thus we need to do the following work in next prs.
TODO:
- [x] remove patch on attention spec
- [ ] refactor the kvcache creation logic

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
1. CI passed with existing test.
2. Test pass with deepseek-v3.2-exp


- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

Signed-off-by: MengqingCao <cmq0113@163.com>
2025-10-15 17:48:58 +08:00

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from dataclasses import dataclass
from typing import (TYPE_CHECKING, ClassVar, NamedTuple, Optional, Tuple, Type,
TypeVar)
import torch
import torch_npu
from torch import nn
from vllm.attention.backends.abstract import (AttentionBackend,
AttentionMetadata,
MLAAttentionImpl)
from vllm.config import VllmConfig, get_current_vllm_config
from vllm.distributed import get_tensor_model_parallel_world_size, get_tp_group
from vllm.model_executor.layers.linear import (LinearBase,
UnquantizedLinearMethod)
from vllm.utils import cdiv, round_down
from vllm.v1.attention.backends.utils import AttentionCGSupport
from vllm_ascend.ascend_config import get_ascend_config
from vllm_ascend.attention.attention_v1 import AscendAttentionState
from vllm_ascend.attention.mla_v1 import AscendMLAMetadata
from vllm_ascend.attention.utils import (AscendCommonAttentionMetadata,
split_decodes_and_prefills)
from vllm_ascend.multistream.base import MSAttentionMetadataSplitConfig
from vllm_ascend.multistream.ms_split import model_input_split_v1_mla_attn
from vllm_ascend.worker.npu_input_batch import InputBatch
if TYPE_CHECKING:
from vllm.v1.core.sched.output import SchedulerOutput
class AscendSFABackend(AttentionBackend):
accept_output_buffer: bool = True
@staticmethod
def get_name() -> str:
return "ASCEND_SFA"
@staticmethod
def get_metadata_cls() -> type["AttentionMetadata"]:
return AscendSFAMetadata
@staticmethod
def get_builder_cls():
return AscendSFAMetadataBuilder
@staticmethod
def get_kv_cache_shape(num_blocks: int, block_size: int, num_kv_heads: int,
head_size: int) -> tuple[int, ...]:
return (num_blocks, block_size, num_kv_heads, head_size)
@staticmethod
def get_impl_cls() -> Type["AscendSFAImpl"]:
return AscendSFAImpl
@dataclass
class AscendSFAPrefillMetadata:
""" Prefill Specific Metadata for Ascend"""
@dataclass
class ChunkedContextMetadata:
# New for MLA (compared to FlashAttention)
# For handling chunked prefill
cu_seq_lens: torch.Tensor
starts: torch.Tensor
seq_tot: list[int]
max_seq_lens: list[int]
workspace: torch.Tensor
chunk_seq_lens: torch.Tensor
attn_mask: torch.Tensor
query_lens: list[int]
seq_lens: list[int]
context_lens: torch.Tensor
input_positions: torch.Tensor
query_start_loc: torch.Tensor
block_table: torch.Tensor
max_query_len: int
max_seq_lens: int
sin: torch.Tensor
cos: torch.Tensor
chunked_context: Optional[ChunkedContextMetadata] = None
@dataclass
class AscendSFADecodeMetadata:
# Input positions for rotrary embeddings since for MLA the rotary
# position embeddings are applied inside the attention backend
input_positions: torch.Tensor
block_table: torch.Tensor
seq_lens: torch.Tensor
max_seq_lens: int
seq_lens_list: list[int]
actual_seq_lengths_q: torch.Tensor
sin: torch.Tensor
cos: torch.Tensor
attn_mask: Optional[torch.Tensor] = None
@dataclass
class AscendSFAMetadata:
"""Metadata for MLACommon.
NOTE: Please read the comment at the top of the file before trying to
understand this class
"""
# NOTE(sang): Definition of context_len, query_len, and seq_len.
# |---------- N-1 iteration --------|
# |---------------- N iteration ---------------------|
# |- tokenA -|......................|-- newTokens ---|
# |---------- context_len ----------|
# |-------------------- seq_len ---------------------|
# |-- query_len ---|
num_actual_tokens: int # Number of tokens excluding padding.
slot_mapping: torch.Tensor
query_start_loc: torch.Tensor
seq_lens: torch.Tensor
block_tables: torch.Tensor
# New for MLA (compared to FlashAttention)
# For handling prefill decode split
num_decodes: int
num_decode_tokens: int
num_prefills: int
# For logging.
num_input_tokens: int = 0 # Number of tokens including padding.
query_lens: Optional[list[int]] = None
# The dimension of the attention heads
head_dim: Optional[int] = None
attn_mask: torch.Tensor = None
# chunked prefill by default if no attn_states passed
attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
decode: Optional[AscendSFADecodeMetadata] = None
prefill: Optional[AscendSFAPrefillMetadata] = None
enable_dbo_across_dp: bool = False
def __post_init__(self):
pass
# supported_head_sizes = AscendMLABackend.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 split_metadata_for_multistream(
self,
ms_split_config: MSAttentionMetadataSplitConfig,
) -> list["AscendSFAMetadata"]:
"""Split metadata for multi-stream with AscendSFAMetadata"""
return model_input_split_v1_mla_attn(
ms_split_config=ms_split_config,
attn_metadata=self,
_metadata_cls=AscendMLAMetadata,
)
M = TypeVar("M", bound=AscendSFAMetadata)
class AscendSFAMetadataBuilder:
# Does this backend/builder support ACL Graphs for attention (default: no).
aclgraph_support: ClassVar[AttentionCGSupport] = \
AttentionCGSupport.NEVER
"""
NOTE: Please read the comment at the top of the file before trying to
understand this class
"""
# _attn_mask_builder = None
def __init__(self,
kv_cache_spec,
layer_names,
vllm_config: VllmConfig,
device: torch.device,
metadata_cls: Optional[AscendSFAMetadata] = None):
self.metadata_cls: Optional[AscendSFAMetadata] = metadata_cls \
if metadata_cls is not None else AscendSFAMetadata # type: ignore
self.vllm_config = vllm_config
self.model_config = vllm_config.model_config
self.device = device
scheduler_config = vllm_config.scheduler_config
self.block_size = vllm_config.cache_config.block_size
self.max_blocks = (vllm_config.model_config.max_model_len +
self.block_size - 1) // self.block_size
self.chunked_prefill_enabled = scheduler_config.chunked_prefill_enabled
self.speculative_config = vllm_config.speculative_config
self.decode_threshold = 1
if self.speculative_config:
spec_token_num = self.speculative_config.num_speculative_tokens
self.decode_threshold += spec_token_num
assert self.decode_threshold <= 16, f"decode_threshold exceeded \
npu_fused_infer_attention_score TND layout's limit of 16, \
got {self.decode_threshold}"
if self.chunked_prefill_enabled:
self.chunked_prefill_workspace_size = min(
# Max sure there is enough for 8 full length request or at least
# 4 pages of cache per request
max(8 * self.model_config.max_model_len,
4 * scheduler_config.max_num_seqs * self.block_size),
# For long-context models try not to over-allocate limiting
# kv-cache space, limiting it to 64k tokens,
# which would result in the workspace being:
# 2*(576)*(64*1024) = 144mb
# (assuming 576 MLA head dim, and fp16)
# which would result in up-projected context being
# 2*(192*128)*(64*1024) = 3gb
# (assuming 192 QK head dim, 128 heads, and fp16)
128 * 1024)
assert self.chunked_prefill_workspace_size >= \
scheduler_config.max_num_seqs * self.block_size
self.chunked_prefill_workspace = torch.empty(
(self.chunked_prefill_workspace_size,
self.model_config.get_head_size()),
dtype=self.model_config.dtype,
device=device,
)
self.rope_dim = self.model_config.hf_text_config.qk_rope_head_dim
self.cos_cache = None
self.sin_cache = None
def reorder_batch(self, input_batch: "InputBatch",
scheduler_output: "SchedulerOutput") -> bool:
# We now want to reorder the batch so that the "decode" requests are at
# the front and the "prefill" requests are at the using the least amount
# swaps possible. (NOTE for now we loosely use "decode" to mean requests
# where attention is likely memory-bound and "prefill" to mean requests
# where attention is likely compute-bound, TODO(lucas): figure out a
# better naming here)
decodes = []
prefills = []
for i, req_id in enumerate(input_batch.req_ids):
num_tokens = scheduler_output.num_scheduled_tokens[req_id]
if num_tokens <= self.decode_threshold:
decodes.append(i)
else:
prefills.append(i)
# We hope that this is fairly minimal since decodes
# should be around for a number of iterations so hopefully they are
# relatively stationary (and new request are generally appended to the
# persistent batch so already should be at the back)
# To achieve this we loop over the decodes in descending order and
# the prefills in ascending order. We swap decodes from the "back"
# i.e. past where the last decode should be in the reodorered with
# prefills from the front of the batch.
# `decodes` and `prefills` are already in ascending order just based on
# the above loop
num_decodes = len(decodes)
num_prefills = len(prefills)
first_prefill = 0
modified_batch = False
for i in range(1, min(num_decodes, num_prefills) + 1):
# If the decode is at the "back" of the batch, i, we can swap it
# with the prefill closest to the front of the batch
if decodes[num_decodes - i] >= num_decodes:
input_batch.swap_states(prefills[first_prefill],
decodes[num_decodes - i])
first_prefill += 1
modified_batch = True
else:
break
# Save for next `build` call
# TODO(lucas): this is a bit of a hack, we should probably have a
# better way of doing this
return modified_batch
def build(
self,
common_prefix_len: int,
common_attn_metadata: AscendCommonAttentionMetadata,
model: nn.Module,
) -> AscendSFAMetadata:
num_reqs = common_attn_metadata.num_reqs
num_actual_tokens = common_attn_metadata.num_actual_tokens
query_start_loc = common_attn_metadata.query_start_loc
query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = \
split_decodes_and_prefills(common_attn_metadata, decode_threshold=self.decode_threshold)
assert num_decodes + num_prefills == num_reqs
assert num_decode_tokens + num_prefill_tokens == num_actual_tokens
# Note(simon): be careful about the CPU <> GPU memory movement in this
# function. We should avoid GPU -> CPU sync as much as possible because
# it blocks on all previous kernels.
device = self.device
block_table = (common_attn_metadata.block_table_tensor[:num_reqs])
slot_mapping = common_attn_metadata.slot_mapping[:
num_actual_tokens].to(
device,
non_blocking=True)
input_positions = common_attn_metadata.positions[:
num_actual_tokens].long(
)
if self.cos_cache is None:
self.cos_cache = model.model.layers[
model.model.start_layer].self_attn.rotary_emb.cos_cached
self.sin_cache = model.model.layers[
model.model.start_layer].self_attn.rotary_emb.sin_cached
if self.cos_cache.dtype != self.model_config.dtype: # type: ignore
self.cos_cache = self.cos_cache.to( # type: ignore
self.model_config.dtype) # type: ignore
self.sin_cache = self.sin_cache.to( # type: ignore
self.model_config.dtype) # type: ignore
query_seq_lens_cpu = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
query_lens = query_seq_lens_cpu[:num_reqs]
seq_lens = common_attn_metadata.seq_lens_cpu[:num_reqs]
num_computed_tokens_cpu = (seq_lens - query_lens)
prefill_metadata = None
chunked_context_metadata = None
if num_prefills > 0:
reqs_start = num_decodes # prefill_start
tokens_start = num_decode_tokens
max_query_len = query_lens[reqs_start:].max().item()
max_seq_lens = seq_lens[reqs_start:].max().item()
prefill_query_start_loc = query_start_loc[
reqs_start:] - query_start_loc[reqs_start]
context_lens_cpu = num_computed_tokens_cpu[reqs_start:num_reqs]
max_context_len_cpu = context_lens_cpu.max().item()
num_prefills_with_context_cpu = (context_lens_cpu > 0).sum().item()
if self.chunked_prefill_enabled and max_context_len_cpu > 0:
max_context_chunk = (self.chunked_prefill_workspace_size //
num_prefills_with_context_cpu)
max_context_chunk = round_down(max_context_chunk,
self.block_size)
assert max_context_chunk > 0
num_chunks = cdiv(max_context_len_cpu, max_context_chunk)
chunk_starts = torch.arange(num_chunks, dtype=torch.int32) \
.unsqueeze(1).expand(-1, num_prefills) * max_context_chunk
chunk_ends = torch.min(context_lens_cpu.unsqueeze(0),
chunk_starts + max_context_chunk)
chunk_seq_lens = (chunk_ends - chunk_starts).clamp(min=0)
cu_seq_lens_cpu = torch.zeros(num_chunks,
num_prefills + 1,
dtype=torch.int32,
pin_memory=True)
torch.cumsum(chunk_seq_lens,
dim=1,
out=cu_seq_lens_cpu[:, 1:],
dtype=torch.int32)
chunked_context_metadata = \
AscendSFAPrefillMetadata.ChunkedContextMetadata(
cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
starts=chunk_starts.to(device, non_blocking=True),
seq_tot=chunk_seq_lens.sum(dim=1).tolist(),
max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
chunk_seq_lens=chunk_seq_lens,
workspace=self.chunked_prefill_workspace,
)
prefill_input_positions = input_positions[tokens_start:]
cos = self.cos_cache[
prefill_input_positions].unsqueeze( # type: ignore
1).unsqueeze(2)
sin = self.sin_cache[
prefill_input_positions].unsqueeze( # type: ignore
1).unsqueeze(2)
actual_query_lens = torch.tensor(query_lens[reqs_start:],
dtype=torch.int32).npu()
query_lens_prefill_sfa = torch.cumsum(actual_query_lens,
dim=0).to(torch.int32)
seq_lens_prefill_sfa = seq_lens[reqs_start:].to(torch.int32).npu()
prefill_metadata = AscendSFAPrefillMetadata(
attn_mask=common_attn_metadata.attn_mask,
query_lens=query_lens_prefill_sfa,
seq_lens=seq_lens_prefill_sfa,
context_lens=seq_lens[reqs_start:],
input_positions=prefill_input_positions,
block_table=block_table[reqs_start:, ...],
max_query_len=max_query_len,
max_seq_lens=max_seq_lens,
query_start_loc=prefill_query_start_loc,
chunked_context=chunked_context_metadata,
sin=sin,
cos=cos,
)
decode_metadata = None
if num_decodes > 0:
# Notice that num_decodes != num_decode_tokens in SpecDecoding Scenario
actual_seq_lengths_q = query_start_loc[1:num_decodes + 1].to(
torch.int32).npu()
max_seq_lens = seq_lens[:num_decodes].max().item()
seq_lens = seq_lens[:num_decodes].to(torch.int32).npu()
input_positions = input_positions[:num_decode_tokens]
block_table = block_table[:num_decodes, ...]
seq_lens_list = seq_lens.tolist()
cos = self.cos_cache[input_positions].unsqueeze( # type: ignore
1).unsqueeze(2)
sin = self.sin_cache[input_positions].unsqueeze( # type: ignore
1).unsqueeze(2)
decode_metadata = AscendSFADecodeMetadata(
input_positions=input_positions,
block_table=block_table,
seq_lens=seq_lens,
seq_lens_list=seq_lens_list,
max_seq_lens=max_seq_lens,
attn_mask=common_attn_metadata.spec_attn_mask,
actual_seq_lengths_q=actual_seq_lengths_q,
sin=sin,
cos=cos)
return self.metadata_cls( # type: ignore
num_actual_tokens=num_actual_tokens,
query_lens=query_lens.tolist(),
slot_mapping=slot_mapping,
head_dim=self.model_config.get_head_size(),
num_decodes=num_decodes,
num_decode_tokens=num_decode_tokens,
num_prefills=num_prefills,
attn_mask=common_attn_metadata.attn_mask,
attn_state=common_attn_metadata.attn_state,
prefill=prefill_metadata,
decode=decode_metadata,
query_start_loc=query_start_loc,
block_tables=block_table,
seq_lens=seq_lens,
enable_dbo_across_dp=common_attn_metadata.enable_dbo_across_dp,
)
class PrefillSFAPreprocessResult(NamedTuple):
q_nope: Optional[torch.Tensor] = None
q_pe: Optional[torch.Tensor] = None
k_nope: Optional[torch.Tensor] = None
k_pe: Optional[torch.Tensor] = None
topk_indices: Optional[torch.Tensor] = None
query_states: Optional[torch.Tensor] = None
key_states: Optional[torch.Tensor] = None
class DecodeSFAPreprocessResult(NamedTuple):
q_nope: Optional[torch.Tensor] = None
q_pe: Optional[torch.Tensor] = None
# nope_cache: Optional[torch.Tensor] = None
# rope_cache: Optional[torch.Tensor] = None
topk_indices: Optional[torch.Tensor] = None
query_states: Optional[torch.Tensor] = None
key_states: Optional[torch.Tensor] = None
bsz: Optional[int] = None
class AscendSFAImpl(MLAAttentionImpl):
"""
NOTE: Please read the comment at the top of the file before trying to
understand this class
"""
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,
logits_soft_cap: Optional[float],
attn_type: str,
kv_sharing_target_layer_name: Optional[str],
**kwargs,
) -> None:
self.num_heads = num_heads
self.head_size = head_size
self.scale = float(scale)
self.num_kv_heads = num_kv_heads
self.kv_cache_dtype = kv_cache_dtype
# MLA Args
self.q_lora_rank = kwargs['q_lora_rank']
self.kv_lora_rank = kwargs['kv_lora_rank']
self.qk_nope_head_dim = kwargs['qk_nope_head_dim']
self.qk_rope_head_dim = kwargs['qk_rope_head_dim']
self.qk_head_dim = kwargs['qk_head_dim']
self.v_head_dim = kwargs['v_head_dim']
self.rotary_emb = kwargs['rotary_emb']
self.q_proj = kwargs['q_proj']
self.kv_b_proj = kwargs['kv_b_proj']
self.o_proj = kwargs['o_proj']
self.indexer = kwargs['indexer']
self.kv_a_proj_with_mqa = kwargs.get('kv_a_proj_with_mqa', None)
self.kv_a_layernorm = kwargs.get('kv_a_layernorm', None)
self.q_a_proj = kwargs.get('q_a_proj', None)
self.q_a_layernorm = kwargs.get('q_a_layernorm', None)
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
self.tp_size = get_tensor_model_parallel_world_size()
self.num_heads_per_rank = self.num_heads // self.tp_size
if self.q_a_proj is not None:
self.q_b_proj = self.q_proj
else:
self.q_b_proj = None
ascend_config = get_ascend_config()
self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
self.enable_kv_nz = ascend_config.torchair_graph_config.enable_kv_nz
vllm_config = get_current_vllm_config()
self.ring_mla_mask_size = 512
self.prefill_mask = None
# indexer param
self.dim = self.indexer.dim
self.n_heads: int = self.indexer.n_heads # 64
self.head_dim: int = self.indexer.head_dim # 128
self.index_topk: int = self.indexer.index_topk # 2048
self.wq_b = self.indexer.wq_b
self.wk = self.indexer.wk
self.weights_proj = self.indexer.weights_proj
self.k_norm = self.indexer.k_norm
self.softmax_scale = self.indexer.softmax_scale
# Adapt torch air graph mode with spec decoding.
speculative_config = vllm_config.speculative_config
if speculative_config is not None:
self.spec_token_num = speculative_config.num_speculative_tokens
assert self.spec_token_num > 0
self.cp_size = 1
def process_weights_after_loading(self, act_dtype: torch.dtype):
def get_layer_weight(layer):
WEIGHT_NAMES = ("weight", "qweight", "weight_packed")
for attr in WEIGHT_NAMES:
if hasattr(layer, attr):
return getattr(layer, attr)
raise AttributeError(
f"Layer '{layer}' has no recognized weight attribute:"
f" {WEIGHT_NAMES}.")
def get_and_maybe_dequant_weights(layer: LinearBase):
if not isinstance(layer.quant_method, UnquantizedLinearMethod):
# NOTE: This should only be used offline, since it's O(N^3)
eye = torch.eye(layer.input_size_per_partition,
dtype=act_dtype,
device=get_layer_weight(layer).device)
dequant_weights = layer.quant_method.apply(layer,
eye,
bias=None)
del eye
# standardize to (output, input)
return dequant_weights.T
return layer.weight
# we currently do not have quantized bmm's which are needed for
# `W_UV` and `W_UK_T`, we we just store fp16/bf16 copies and perform
# the bmm's in 16-bit, the extra memory overhead of this is fairly low
kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
assert kv_b_proj_weight.shape == (
self.kv_lora_rank,
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim)), (
f"{kv_b_proj_weight.shape=}, "
f"{self.kv_lora_rank=}, "
f"{self.num_heads=}, "
f"{self.qk_nope_head_dim=}, "
f"{self.v_head_dim=}")
kv_b_proj_weight = kv_b_proj_weight.view(
self.kv_lora_rank,
self.num_heads,
self.qk_nope_head_dim + self.v_head_dim,
)
self.kv_b_proj_w_k, self.kv_b_proj_w_v = kv_b_proj_weight.split(
[self.qk_nope_head_dim, self.v_head_dim], dim=-1)
# Convert from (L, N, V) to (N, L, V)
self.kv_b_proj_w_v = self.kv_b_proj_w_v.transpose(0, 1).contiguous()
# Convert from (L, N, P) to (N, P, L)
self.kv_b_proj_w_k = self.kv_b_proj_w_k.permute(1, 2, 0).contiguous()
# Waiting for BMM NZ support
# self.W_UV.data = torch_npu.npu_format_cast(self.W_UV.data, 29)
# self.W_UK_T.data = torch_npu.npu_format_cast(self.W_UK_T.data, 29)
def _sfa_preprocess(self, hidden_states, kv_cache, attn_metadata,
need_gather_q_kv):
# SFA Preprocess:
# 1. Perform q_a_proj and q_a_layernorm to obtain q_c
# 2. Perform kv_a_proj_with_mqa to obtain kv_no_split
# 3. If need_gather_q_kv, perform all_gather.
# 4. Preprocess decode tokens, write kv cache and get:
# decode_ql_nope, decode_q_pe, decode_k_pe, decode_k_nope
# 5. Preprocess prefill tokens, write kv cache and get:
# prefill_q_nope, prefill_q_pe, prefill_k_nope, prefill_k_pe, prefill_value
has_decode = attn_metadata.num_decodes > 0
has_prefill = attn_metadata.num_prefills > 0
num_decode_tokens = attn_metadata.num_decode_tokens
num_actual_tokens = attn_metadata.num_actual_tokens
if need_gather_q_kv:
# q_c = get_tp_group().all_gather(q_c, 0)
# kv_no_split = get_tp_group().all_gather(kv_no_split, 0)
hidden_states = get_tp_group().all_gather(hidden_states, 0)
# hidden_states_decode = hidden_states[:num_decode_tokens]
# if self.q_a_proj is not None:
# npu_prefetch(self.q_a_proj.weight,
# hidden_states,
# enabled=self.enable_prefetch)
# ckq = self.q_a_proj(hidden_states) # q down
# q_c = self.q_a_layernorm(ckq) # q down layernorm
# else:
# q_c = hidden_states
# kv_no_split = self.kv_a_proj_with_mqa(hidden_states) # c_kv
# Process for shared_expert_dp
decode_preprocess_res = None
prefill_preprocess_res = None
# Preprocess for decode tokens
if has_decode:
q_len = 1
hidden_states_decode = hidden_states[:num_decode_tokens]
decode_kq = self.q_a_proj(hidden_states_decode) # q down
decode_q_c = self.q_a_layernorm(decode_kq) # q down layernorm
decode_kv_no_split = self.kv_a_proj_with_mqa(
hidden_states_decode) # c_kv
# decode_q_c = q_c[:num_decode_tokens]
decode_slot_mapping = attn_metadata.slot_mapping[:
num_decode_tokens]
# decode_kv_no_split = decode_kv_no_split[:num_decode_tokens]
decode_q = self.q_b_proj(decode_q_c)
bsz, _ = decode_q.shape
decode_q = decode_q.view(bsz, self.num_heads, 1, self.qk_head_dim)
decode_q_nope, decode_q_pe = torch.split(
decode_q, [self.qk_nope_head_dim, self.qk_rope_head_dim],
dim=-1)
decode_q_nope = decode_q_nope.view(
-1, self.num_heads, self.qk_nope_head_dim).transpose(0, 1)
decode_q_nope = (torch.matmul(decode_q_nope,
self.kv_b_proj_w_k).transpose(
1,
0).view(bsz, q_len,
self.num_heads,
self.kv_lora_rank))
# stream2 kv
key_cache = kv_cache[0]
value_cache = kv_cache[1]
cos = attn_metadata.decode.cos
sin = attn_metadata.decode.sin
cos_q, sin_q = cos, sin
cos = cos.view(-1, 1, 1, self.qk_rope_head_dim)
sin = sin.view(-1, 1, 1, self.qk_rope_head_dim)
decode_kv_no_split = decode_kv_no_split.unsqueeze(1).unsqueeze(1)
decode_k_rope, decode_k_nope, _, _ = torch_npu.npu_kv_rmsnorm_rope_cache(
decode_kv_no_split,
self.kv_a_layernorm.weight,
cos,
sin,
decode_slot_mapping.to(torch.int64),
value_cache,
key_cache,
c_kv_scale=None,
epsilon=self.kv_a_layernorm.variance_epsilon,
cache_mode='PA') # adapter NZ
# nz_block_size = 16
# KVCACHE_NZ_DIM = 16
# decode_k_nope = decode_k_nope.view(block_num, 1, self.kv_lora_rank // nz_block_size, block_size, nz_block_size)
# decode_k_rope = decode_k_rope.view(block_num, 1, self.qk_rope_head_dim // KVCACHE_NZ_DIM, block_size, KVCACHE_NZ_DIM)
decode_q_pe = torch_npu.npu_interleave_rope(decode_q_pe, cos,
sin) # BNSD
decode_q_nope = decode_q_nope.view(bsz, self.num_heads,
self.kv_lora_rank)
decode_q_pe = decode_q_pe.view(bsz, self.num_heads, -1)
topk_indices = self.indexer_select(hidden_states_decode,
decode_q_c,
attn_metadata=attn_metadata,
cos=cos,
sin=sin,
kv_cache=kv_cache)
query_states = (decode_q_nope, decode_q_pe)
key_states = (decode_k_nope, decode_k_rope)
decode_preprocess_res = DecodeSFAPreprocessResult(
q_nope=decode_q_nope,
q_pe=decode_q_pe,
# nope_cache = nope_cache,
# rope_cache = rope_cache,
topk_indices=topk_indices,
query_states=query_states,
key_states=key_states,
bsz=bsz,
)
# Preprocess for prefill tokens
if has_prefill:
bsz = 1
hidden_states_prefill = hidden_states[
num_decode_tokens:num_actual_tokens]
prefill_kq = self.q_a_proj(hidden_states_prefill) # q down
prefill_q_c = self.q_a_layernorm(prefill_kq) # q down layernorm
prefill_kv_no_split = self.kv_a_proj_with_mqa(
hidden_states_prefill) # c_kv
# prefill_q_c = q_c[
# num_decode_tokens:num_actual_tokens]
prefill_slot_mapping = attn_metadata.slot_mapping[
num_decode_tokens:num_actual_tokens]
# decode_kv_no_split = decode_kv_no_split[:num_decode_tokens]
prefill_slot_mapping = attn_metadata.slot_mapping[
num_decode_tokens:num_actual_tokens]
# prefill_kv_no_split = kv_no_split[
# num_decode_tokens:num_actual_tokens]
# prefill_qr = prefill_q_c[num_decode_tokens:num_actual_tokens]
prefill_qr = prefill_q_c
prefill_q = self.q_b_proj(prefill_qr)
prefill_q = prefill_q.view(-1, self.num_heads, self.qk_head_dim)
prefill_q_nope, prefill_q_pe = torch.split(
prefill_q, [self.qk_nope_head_dim, self.qk_rope_head_dim],
dim=-1)
prefill_q_nope = prefill_q_nope.view(
-1, self.num_heads, self.qk_nope_head_dim).transpose(0, 1)
prefill_q_nope = (torch.matmul(prefill_q_nope,
self.kv_b_proj_w_k).transpose(
1,
0).view(-1, self.num_heads,
self.kv_lora_rank))
prefill_q_pe = prefill_q_pe.unsqueeze(2)
# stream2 kv
nope_cache = kv_cache[0]
rope_cache = kv_cache[1]
cos = attn_metadata.prefill.cos
sin = attn_metadata.prefill.sin
cos_q, sin_q = cos, sin
# cos = cos.view(-1, 1, 1, self.qk_rope_head_dim)
# sin = sin.view(-1, 1, 1, self.qk_rope_head_dim)
prefill_q_pe = torch_npu.npu_interleave_rope(
prefill_q_pe, cos_q, sin_q) # BNSD
prefill_q_pe = prefill_q_pe.squeeze(2) #BSH
# q[..., self.qk_nope_head_dim:] = prefill_q_pe # TODO:????
prefill_latent_cache = prefill_kv_no_split # (B,S,N,D)
prefill_k_pe, prefill_k_nope, _, _ = torch_npu.npu_kv_rmsnorm_rope_cache(
prefill_latent_cache.view(
-1, 1, 1, self.kv_lora_rank + self.qk_rope_head_dim),
self.kv_a_layernorm.weight,
cos.view(-1, 1, 1, self.qk_rope_head_dim),
sin.view(-1, 1, 1, self.qk_rope_head_dim),
prefill_slot_mapping.to(torch.int64),
rope_cache,
nope_cache,
k_rope_scale=None,
c_kv_scale=None,
k_rope_offset=None,
c_kv_offset=None,
epsilon=self.kv_a_layernorm.variance_epsilon,
cache_mode="PA")
topk_indices = self.indexer_select(x=hidden_states_prefill,
qr=prefill_qr,
kv_cache=kv_cache,
cos=cos,
sin=sin,
attn_metadata=attn_metadata)
query_states = (prefill_q_nope, prefill_q_pe)
key_states = (prefill_k_nope, prefill_k_pe)
prefill_preprocess_res = PrefillSFAPreprocessResult(
q_nope=prefill_q_nope,
q_pe=prefill_q_pe,
topk_indices=topk_indices,
k_nope=prefill_k_nope,
k_pe=prefill_k_pe,
query_states=query_states,
key_states=key_states,
)
return decode_preprocess_res, prefill_preprocess_res
def forward(
self,
hidden_states: torch.Tensor, # query in unified attn
kv_cache: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
attn_metadata: M,
need_gather_q_kv: bool = False,
output: Optional[torch.Tensor] = None,
) -> torch.Tensor:
assert output is not None, "Output tensor must be provided."
if attn_metadata is None:
# Profiling run.
return output
num_actual_tokens = attn_metadata.num_actual_tokens
assert attn_metadata.num_decodes is not None and \
attn_metadata.num_prefills is not None and \
attn_metadata.num_decode_tokens is not None
num_decode_tokens = attn_metadata.num_decode_tokens
# Inputs and outputs may be padded for CUDA graphs
output = output[:num_actual_tokens, ...]
o_proj_input_shape = (num_actual_tokens,
self.num_heads * self.v_head_dim)
o_proj_input = torch.empty(o_proj_input_shape,
dtype=hidden_states.dtype,
device=hidden_states.device)
# SFA Preprocess
decode_preprocess_res, prefill_preprocess_res = self._sfa_preprocess(
hidden_states, kv_cache, attn_metadata, need_gather_q_kv)
if decode_preprocess_res is not None:
# bsz, q_len, _, _ = query_states[0].shape
decode_attn_output = self.apply_attention_fusion(
query_states=decode_preprocess_res.query_states,
key_states=decode_preprocess_res.key_states,
attn_metadata=attn_metadata,
topk_indices=decode_preprocess_res.topk_indices)
o_proj_input[:num_decode_tokens] = decode_attn_output
if prefill_preprocess_res is not None:
prefill_attn_output = self.apply_attention_fusion(
query_states=prefill_preprocess_res.query_states,
key_states=prefill_preprocess_res.key_states,
attn_metadata=attn_metadata,
topk_indices=prefill_preprocess_res.topk_indices)
o_proj_input[num_decode_tokens:] = prefill_attn_output
output[...] = self.mla_epilog(o_proj_input, absorb=True)
return output
def apply_attention_fusion(self, query_states, key_states, topk_indices,
attn_metadata: M):
# repeat k/v heads if n_kv_heads < n_heads
q_nope, q_pe = query_states
k_nope, k_rope = key_states
if attn_metadata.prefill is not None:
prefill_metadata = attn_metadata.prefill
slc_fa_fusion = torch.ops.custom.npu_sparse_flash_attention(
query=q_nope,
key=k_nope,
value=k_nope,
sparse_indices=topk_indices,
scale_value=self.scale,
sparse_block_size=1,
block_table=prefill_metadata.block_table,
actual_seq_lengths_query=prefill_metadata.query_lens,
actual_seq_lengths_kv=prefill_metadata.seq_lens,
query_rope=q_pe,
key_rope=k_rope,
layout_query="TND",
layout_kv="PA_BSND",
sparse_mode=3,
)
elif attn_metadata.decode is not None:
decode_metadata = attn_metadata.decode
slc_fa_fusion = torch.ops.custom.npu_sparse_flash_attention(
query=q_nope,
key=k_nope,
value=k_nope,
sparse_indices=topk_indices,
scale_value=self.scale,
sparse_block_size=1,
block_table=attn_metadata.decode.block_table,
actual_seq_lengths_query=decode_metadata.actual_seq_lengths_q,
actual_seq_lengths_kv=decode_metadata.seq_lens,
query_rope=q_pe,
key_rope=k_rope,
layout_query="TND",
layout_kv="PA_BSND",
sparse_mode=3,
)
slc_fa_fusion = slc_fa_fusion.squeeze(1)
slc_fa_fusion = slc_fa_fusion.transpose(0, 1)
# input shape [N//attn_tp_size, T(bs*q_len), D]
# output shape [T(bs*q_len), N//attn_tp_size, D]
attn_output = torch.matmul(slc_fa_fusion,
self.kv_b_proj_w_v).transpose(1, 0).reshape(
-1, self.num_heads * self.v_head_dim)
# Note: Considering the fusion rules of TBMM, attn_output shape requires a 3-dim shape, and
# with appropriate tensor stride for the later 'view' operation if oproj_tp_size > 1.
# after reshape: [T(bs*q_len), 1, N//attn_tp_size*D]
# attn_output = attn_output.reshape(-1, self.num_heads * self.v_head_dim)
return attn_output
def mla_epilog(self,
attn_output: torch.Tensor = None,
absorb: bool = False):
# TODO: need to check
attn_output = self.o_proj(attn_output.reshape(attn_output.shape[0],
-1),
is_prefill=True,
is_force_scatter=False)
return attn_output
def indexer_select(
self,
x: torch.Tensor,
qr: torch.Tensor,
kv_cache: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
cos,
sin,
attn_metadata: M,
):
if attn_metadata.prefill is not None:
actual_seq_lengths_query = attn_metadata.prefill.query_lens
actual_seq_lengths_key = attn_metadata.prefill.seq_lens
block_table = attn_metadata.prefill.block_table
elif attn_metadata.decode is not None:
actual_seq_lengths_query = attn_metadata.decode.actual_seq_lengths_q
actual_seq_lengths_key = attn_metadata.decode.seq_lens
block_table = attn_metadata.decode.block_table
cos_q, sin_q = cos, sin
cos = cos.view(-1, 1, 1, self.qk_rope_head_dim)
sin = sin.view(-1, 1, 1, self.qk_rope_head_dim)
# q process in new stream
q = self.wq_b(qr) # [b,s,1536] @ [1536,64*128] = [b,s,64*128]
q = q.view(-1, self.n_heads, self.head_dim) # [b,s,64,128]
q_pe, q_nope = torch.split(
q, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim],
dim=-1) # [b,s,64,64+64]
q_pe = q_pe.unsqueeze(2)
q_pe = torch_npu.npu_interleave_rope(q_pe, cos_q, sin_q)
q_pe = q_pe.squeeze(2)
q = torch.cat([q_pe, q_nope], dim=-1) # [b*s,64,128]
k_proj = self.wk(x) # [b,s,7168] @ [7168,128] = [b,s,128]
k = self.k_norm(k_proj).unsqueeze(1)
k_pe, k_nope = torch.split(
k, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim],
dim=-1) # [b,s,64+64]
k_pe = k_pe.unsqueeze(2)
k_pe = torch_npu.npu_interleave_rope(k_pe, cos, sin)
k_pe = k_pe.squeeze(2)
k = torch.cat([k_pe, k_nope], dim=-1) # [b*s,128]
if kv_cache is not None:
torch_npu.npu_scatter_nd_update_(kv_cache[2].view(-1, k.shape[-1]),
attn_metadata.slot_mapping.view(
-1, 1),
k.view(-1,
k.shape[-1])) # b, s, n, d
weights = self.weights_proj(x)
topk_indices = torch.ops.custom.npu_lightning_indexer(
query=q,
key=kv_cache[2],
weights=weights,
actual_seq_lengths_query=actual_seq_lengths_query,
actual_seq_lengths_key=actual_seq_lengths_key,
block_table=block_table,
layout_query="TND",
layout_key="PA_BSND",
sparse_count=2048,
sparse_mode=3)
return topk_indices
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