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xc-llm-ascend/vllm_ascend/attention/mla_cp.py
wujinyuan1 545e856971 [Refactor]3/N Refactor mla_v1.py & extract mla_cp (#4933) 
RFC: https://github.com/vllm-project/vllm-ascend/issues/4629
Reason：
The functions related to Cp differ significantly from those of normal
MLA-Attention, but the coupling is quite severe.

Steps：
Isolate PCP and DCP
(1) create a new python file: mla_cp.py
(2) add classes AscendMlaCPImpl and
AscendMlaCPMetadataBuilder，Inheritance AscendMLAImpl and
AscendMLAMetadataBuilder
(3) Remove PCP and DCP-related methods from mla_v1.py to mla_cp.py

vLLM version: v0.12.0

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: wujinyuan1 <wjy9595@qq.com>
Co-authored-by: wujinyuan1 <wjy9595@qq.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
2025-12-15 12:59:18 +08:00

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from typing import ClassVar, List, Optional, Tuple, TypeVar
import numpy as np
import torch
import torch.distributed as dist
import torch_npu
from torch import nn
from vllm.attention.backends.utils import PAD_SLOT_ID
from vllm.config import VllmConfig
from vllm.distributed import (get_dcp_group,
get_decode_context_model_parallel_rank,
get_decode_context_model_parallel_world_size,
get_pcp_group)
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.utils.math_utils import cdiv, round_down
from vllm.v1.attention.backends.utils import AttentionCGSupport
from vllm.v1.kv_cache_interface import MLAAttentionSpec
from vllm_ascend.ascend_forward_context import get_cos_and_sin
from vllm_ascend.attention.mla_v1 import (AscendMLADecodeMetadata,
AscendMLAImpl, AscendMLAMetadata,
AscendMLAMetadataBuilder,
AscendMLAPrefillMetadata,
DecodeMLAPreprocessResult,
PrefillMLAPreprocessResult)
from vllm_ascend.attention.utils import (AscendCommonAttentionMetadata,
maybe_save_kv_layer_to_connector,
split_decodes_and_prefills,
wait_for_kv_layer_from_connector)
from vllm_ascend.compilation.acl_graph import (get_graph_params,
update_graph_params_workspaces)
from vllm_ascend.ops.shared_weight_layer import (
is_hidden_layer, reach_layer_for_shared_weight_series)
from vllm_ascend.ops.weight_prefetch import maybe_npu_prefetch
from vllm_ascend.utils import weak_ref_tensors
MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024
M = TypeVar("M", bound=AscendMLAMetadata)
class AscendMlaCPMetadataBuilder(AscendMLAMetadataBuilder):
# Does this backend/builder support ACL Graphs for attention (default: no).
aclgraph_support: ClassVar[AttentionCGSupport] = \
AttentionCGSupport.UNIFORM_BATCH
"""
NOTE: Please read the comment at the top of the file before trying to
understand this class
"""
def __init__(self,
kv_cache_spec: MLAAttentionSpec,
layer_names: list[str],
vllm_config: VllmConfig,
device: torch.device,
metadata_cls: Optional[AscendMLAMetadata] = None):
super().__init__(kv_cache_spec, layer_names, vllm_config, device,
metadata_cls)
self.pcp_size = get_pcp_group().world_size
self.pcp_rank = get_pcp_group(
).rank_in_group if self.pcp_size > 1 else 0
self.dcp_size = get_decode_context_model_parallel_world_size()
self.dcp_rank = get_decode_context_model_parallel_rank(
) if self.dcp_size > 1 else 0
self.cp_local_block_size = vllm_config.parallel_config.cp_kv_cache_interleave_size
self.cp_virtual_block_size = self.cp_local_block_size * self.dcp_size * self.pcp_size
scheduler_config = vllm_config.scheduler_config
decode_max_num_seqs = getattr(scheduler_config, 'decode_max_num_seqs',
0)
max_num_seqs = max(scheduler_config.max_num_seqs, decode_max_num_seqs)
self.batch_seq_mask_buf = torch.empty(max_num_seqs *
self.decode_threshold,
dtype=torch.uint8,
device=device)
def build(
self,
common_prefix_len: int,
common_attn_metadata: AscendCommonAttentionMetadata,
model: nn.Module,
) -> AscendMLAMetadata:
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
long_seq_metadata = common_attn_metadata.prefill_context_parallel_metadata
if long_seq_metadata is None:
raise AssertionError("long_seq_metadata should not be None.")
num_actual_tokens_pcp_padded = long_seq_metadata.num_actual_tokens_pcp_padded
if num_actual_tokens_pcp_padded is None:
num_actual_tokens_pcp_padded = num_actual_tokens
num_computed_tokens_of_pcp_dcp = long_seq_metadata.num_computed_tokens_of_pcp_dcp
assert num_computed_tokens_of_pcp_dcp is not None
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
# If graph_pad_size > -1, mean is running in fullgraph mode.
graph_pad_size = common_attn_metadata.graph_pad_size
# NOTE: Maybe this block_table change can be removed when graph_pad_size > 1.
if graph_pad_size > num_reqs and self.speculative_config.disable_padded_drafter_batch:
block_table = (
common_attn_metadata.block_table_tensor[:graph_pad_size])
else:
block_table = (common_attn_metadata.block_table_tensor[:num_reqs])
# NOTE: Currently, MTP-fullgraph is incompatibility pcp
if self.pcp_size > 1:
num_decodes_flatten = num_decodes * self.decode_threshold
block_table = common_attn_metadata.block_table_tensor[:
num_decodes_flatten
+
num_prefills]
# NOTE: Currently, MTP-fullgraph is incompatibility pcp
slot_mapping = common_attn_metadata.slot_mapping[:
num_actual_tokens_pcp_padded]
input_positions = common_attn_metadata.positions[:
num_actual_tokens_pcp_padded].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:
pcp_metadata = AscendMLAPrefillMetadata.AscendPCPMetadata(
q_head_idx=long_seq_metadata.q_head_idx_tensor,
q_tail_idx=long_seq_metadata.q_tail_idx_tensor,
kv_with_q_head_nomask_idx=long_seq_metadata.
kv_with_q_head_nomask_idx_tensor,
kv_with_q_head_mask_idx=long_seq_metadata.
kv_with_q_head_mask_idx_tensor,
kv_with_q_tail_nomask_idx=long_seq_metadata.
kv_with_q_tail_nomask_idx_tensor,
kv_with_q_tail_mask_idx=long_seq_metadata.
kv_with_q_tail_mask_idx_tensor,
attn_mask_seqlens=long_seq_metadata.attn_mask_seqlens,
head_attn_nomask_seqlens=long_seq_metadata.
head_attn_nomask_seqlens,
tail_attn_nomask_seqlens=long_seq_metadata.
tail_attn_nomask_seqlens,
q_full_idx=long_seq_metadata.q_full_idx,
pcp_prefill_mask=long_seq_metadata.pcp_prefill_mask,
pcp_allgather_restore_idx=long_seq_metadata.
pcp_allgather_restore_idx)
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)
local_context_lens_allranks = torch.tensor(
num_computed_tokens_of_pcp_dcp[reqs_start:num_reqs]
).reshape(-1, self.dcp_size * self.pcp_size)
# Note(qcs): The max local context lengths
# padded to `cp_local_block_size`.
padded_local_context_lens_cpu = (cdiv(
context_lens_cpu,
self.cp_virtual_block_size,
) * self.cp_local_block_size)
padded_local_max_context_chunk_across_ranks = (cdiv(
max_context_chunk,
self.cp_virtual_block_size,
) * self.cp_local_block_size)
local_chunk_starts = (
torch.arange(num_chunks,
dtype=torch.int32).unsqueeze(1).expand(
-1, num_prefills) *
padded_local_max_context_chunk_across_ranks)
local_chunk_ends = torch.min(
padded_local_context_lens_cpu.unsqueeze(0),
local_chunk_starts +
padded_local_max_context_chunk_across_ranks,
)
padded_local_chunk_seq_lens = (local_chunk_ends -
local_chunk_starts).clamp(min=0)
padded_local_cu_chunk_seq_lens_cpu = torch.zeros(
num_chunks,
num_prefills + 1,
dtype=torch.int32,
pin_memory=True)
torch.cumsum(
padded_local_chunk_seq_lens,
dim=1,
out=padded_local_cu_chunk_seq_lens_cpu[:, 1:],
dtype=torch.int32,
)
chunked_context_metadata = AscendMLAPrefillMetadata.ChunkedContextMetadata(
cu_seq_lens=cu_seq_lens_cpu.pin_memory().to(
device, non_blocking=True),
starts=local_chunk_starts.pin_memory().to(
device, non_blocking=True),
seq_tot=padded_local_chunk_seq_lens.sum(dim=1).tolist(),
max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
chunk_seq_lens=chunk_seq_lens,
chunk_seq_lens_npu=chunk_seq_lens.npu(),
workspace=self.chunked_prefill_workspace,
padded_chunk_seq_lens_npu=padded_local_chunk_seq_lens.npu(
),
padded_local_chunk_seq_lens=padded_local_chunk_seq_lens.
tolist(),
local_context_lens_allranks=local_context_lens_allranks.
tolist(),
padded_local_cu_seq_lens=padded_local_cu_chunk_seq_lens_cpu
.pin_memory().to(device, non_blocking=True),
cu_seq_lens_lst=cu_seq_lens_cpu.tolist(),
chunk_size=padded_local_max_context_chunk_across_ranks,
)
prefill_input_positions = input_positions[tokens_start:]
assert self.cos_cache is not None
assert self.sin_cache is not None
cos = self.cos_cache[prefill_input_positions].unsqueeze(
1).unsqueeze(2)
sin = self.sin_cache[prefill_input_positions].unsqueeze(
1).unsqueeze(2)
prefill_metadata = AscendMLAPrefillMetadata(
attn_mask=common_attn_metadata.attn_mask,
query_lens=query_lens[reqs_start:].to(torch.int32),
seq_lens=seq_lens,
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,
pcp_metadata=pcp_metadata,
)
if self.pcp_size > 1:
prefill_metadata.block_table = block_table[
num_decodes_flatten:, ...]
decode_metadata = None
if num_decodes > 0:
cos, sin = get_cos_and_sin()
# Notice that num_decodes != num_decode_tokens in SpecDecoding Scenario
actual_seq_lengths_q = query_start_loc_cpu[1:num_decodes +
1].tolist()
max_seq_lens = seq_lens[:num_decodes].max().item()
seq_lens = seq_lens[:num_decodes]
input_positions = input_positions[:num_decode_tokens]
if self.pcp_size > 1:
# For pcp + spec decode, we flatten seq_lens and block_table
# to avoid irregular spec_attn_mask shape
block_table = block_table[:num_decodes_flatten, ...]
else:
block_table = block_table[:num_decodes, ...]
# NOTE: Currently, MTP-fullgraph is incompatibility pcp
# NOTE: Maybe this block_table change can be removed when graph_pad_size > 1.
if graph_pad_size > num_decodes and \
self.speculative_config.disable_padded_drafter_batch:
block_table = block_table[:graph_pad_size, ...]
seq_lens_list = seq_lens.tolist()
# [bs, pcp_size, dcp_size]
num_computed_tokens_of_cp_dcp_array = np.array(
num_computed_tokens_of_pcp_dcp)[:num_decodes *
self.decode_threshold]
cp_seq_len = num_computed_tokens_of_cp_dcp_array[:, self.pcp_rank,
self.dcp_rank]
cp_seq_len = torch.tensor(cp_seq_len, dtype=torch.int32)
batch_seq_mask = (cp_seq_len == 0)
self.batch_seq_mask_buf[:batch_seq_mask.shape[0]].copy_(
batch_seq_mask, non_blocking=True)
batch_seq_mask = self.batch_seq_mask_buf[:batch_seq_mask.shape[0]]
cp_seq_len = torch.where(cp_seq_len == 0, 1, cp_seq_len)
if graph_pad_size > num_reqs:
if self.speculative_config.disable_padded_drafter_batch:
num_reqs_pad_size = graph_pad_size - num_reqs
actual_seq_lengths_q = self.pad_actual_seq_len_q_mtp_disable_pad(
num_reqs_pad_size, num_reqs, actual_seq_lengths_q)
seq_lens_list = seq_lens_list + [0] * (graph_pad_size -
num_decodes)
num_block_pad_size = graph_pad_size - block_table.shape[0]
if num_block_pad_size > 0:
block_table_padding = torch.zeros(
(num_block_pad_size, ) + block_table.shape[1:],
dtype=block_table.dtype,
device=block_table.device)
block_table = torch.cat(
[block_table, block_table_padding], dim=0)
else:
num_token_pad_size = graph_pad_size - num_decode_tokens
num_reqs_pad_size = (
graph_pad_size //
common_attn_metadata.decode_token_per_req - num_reqs)
num_block_table_pad_size = (
graph_pad_size //
common_attn_metadata.decode_token_per_req -
num_decodes)
seq_lens_list = seq_lens.tolist() + [0] * num_reqs_pad_size
slot_padding = torch.full((num_token_pad_size, ),
PAD_SLOT_ID,
dtype=slot_mapping.dtype,
device=slot_mapping.device)
slot_mapping = torch.cat([slot_mapping, slot_padding])
block_table_padding = torch.zeros(
(num_block_table_pad_size, ) + block_table.shape[1:],
dtype=block_table.dtype,
device=block_table.device)
block_table = torch.cat([block_table, block_table_padding],
dim=0)
position_padding = torch.zeros(
num_token_pad_size,
dtype=input_positions.dtype,
device=input_positions.device)
input_positions = torch.cat(
[input_positions, position_padding])
actual_seq_lengths_q = self.pad_actual_seq_len_q_mtp_enable_pad(
num_reqs_pad_size, num_reqs, actual_seq_lengths_q,
common_attn_metadata)
# TODO: After the fullgraph supports MTP, the if branch needs to deleted
assert self.cos_cache is not None
assert self.sin_cache is not None
if cos is None and sin is None:
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 = AscendMLADecodeMetadata(
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,
cp_seq_len=cp_seq_len,
batch_seq_mask=batch_seq_mask)
else:
cos[:num_decode_tokens,
...] = self.cos_cache[input_positions].unsqueeze(
1).unsqueeze(2)
sin[:num_decode_tokens,
...] = self.sin_cache[input_positions].unsqueeze(
1).unsqueeze(2)
decode_metadata = AscendMLADecodeMetadata(
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[:num_decode_tokens, ...],
cos=cos[:num_decode_tokens, ...],
cp_seq_len=cp_seq_len,
batch_seq_mask=batch_seq_mask)
return self.metadata_cls( # type: ignore
num_actual_tokens_pcp_padded=num_actual_tokens_pcp_padded,
num_input_tokens=common_attn_metadata.num_input_tokens,
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,
)
class AscendMlaCPImpl(AscendMLAImpl):
"""
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,
):
super().__init__(num_heads, head_size, scale, num_kv_heads,
alibi_slopes, sliding_window, kv_cache_dtype,
logits_soft_cap, attn_type,
kv_sharing_target_layer_name, **kwargs)
self.pcp_size = get_pcp_group().world_size
self.pcp_rank = get_pcp_group(
).rank_in_group if self.pcp_size > 1 else 0
self.pcp_group = get_pcp_group(
).device_group if self.pcp_size > 1 else None
self.dcp_size = get_decode_context_model_parallel_world_size()
self.dcp_rank = get_decode_context_model_parallel_rank(
) if self.dcp_size > 1 else 0
self.dcp_group = get_dcp_group(
).device_group if self.dcp_size > 1 else None
def _v_up_proj(self, x):
# Convert from (B, N, L) to (N, B, L)
x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
# # Multiply (N, B, L) x (N, L, V) -> (N, B, V)
x = torch.bmm(x, self.W_UV)
# # Convert from (N, B, V) to (B, N * V)
x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
return x
def _compute_prefill_context(
self,
q_nope: torch.Tensor,
q_pe: torch.Tensor,
kv_c_and_k_pe_cache: Tuple[torch.Tensor],
rope_dim: int,
attn_metadata: AscendMLAMetadata,
prefix_output: torch.Tensor,
prefix_lse: torch.Tensor,
):
assert len(kv_c_and_k_pe_cache) > 1
prefill_metadata = attn_metadata.prefill
if prefill_metadata is None or prefill_metadata.chunked_context is None:
return prefix_output, prefix_lse
iters = len(prefill_metadata.chunked_context.seq_tot)
current_seq_len = torch.tensor(prefill_metadata.query_lens,
dtype=torch.int32)
cache_kv_c = kv_c_and_k_pe_cache[0]
cache_k_pe = kv_c_and_k_pe_cache[1]
num_heads = cache_k_pe.size(2)
latent_kv_dim = kv_c_and_k_pe_cache[0].size(-1)
for i in range(iters):
toks = prefill_metadata.chunked_context.seq_tot[i]
# chunk_seq_lens will be padded when pcp&dcp
context_seq_len = prefill_metadata.chunked_context.chunk_seq_lens[
i]
context_seq_len_npu = prefill_metadata.chunked_context.padded_chunk_seq_lens_npu[
i]
seq_len = torch.stack([current_seq_len, context_seq_len])
kv_c_normed = torch.empty(toks,
num_heads,
latent_kv_dim,
dtype=q_nope.dtype,
device=q_nope.device)
k_pe = torch.empty(toks,
num_heads,
rope_dim,
dtype=q_nope.dtype,
device=q_nope.device)
torch_npu.atb.npu_paged_cache_load(
cache_kv_c,
cache_k_pe,
prefill_metadata.block_table,
context_seq_len_npu,
seq_starts=prefill_metadata.chunked_context.starts[i],
key=kv_c_normed,
value=k_pe,
)
cache_kv_c_k_pe = torch.cat([kv_c_normed, k_pe], dim=-1)
if self.dcp_size > 1:
cache_kv_c_k_pe = get_dcp_group().all_gather(
cache_kv_c_k_pe, 0)
if self.pcp_size > 1:
cache_kv_c_k_pe = get_pcp_group().all_gather(
cache_kv_c_k_pe, 0)
allgatered_kv_c_normed, allgatered_k_pe = cache_kv_c_k_pe.split(
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
kv_c_normed, k_pe = self._reorg_kvcache(
allgatered_kv_c_normed,
allgatered_k_pe,
padded_local_chunk_seq_lens_lst=prefill_metadata.
chunked_context.padded_local_chunk_seq_lens[i],
local_context_lens_allranks=prefill_metadata.chunked_context.
local_context_lens_allranks,
sum_seq_len=prefill_metadata.chunked_context.cu_seq_lens_lst[i]
[-1],
max_seq_len=prefill_metadata.chunked_context.max_seq_lens[i],
chunk_size=prefill_metadata.chunked_context.chunk_size,
chunk_idx=i,
toks=toks,
)
kv_c_normed = kv_c_normed.squeeze()
kv_nope = self.kv_b_proj(kv_c_normed)[0].view(
-1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
k_nope, v = kv_nope \
.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
k_pe = k_pe.expand((*k_nope.shape[:-1], -1))
mask = attn_metadata.attn_mask
torch_npu.atb.npu_ring_mla(
q_nope=q_nope,
q_rope=q_pe,
k_nope=k_nope,
k_rope=k_pe,
value=v,
mask=mask,
seqlen=seq_len,
head_num=self.num_heads,
kv_head_num=self.num_heads,
pre_out=prefix_output,
prev_lse=prefix_lse,
qk_scale=self.scale,
kernel_type="kernel_type_high_precision",
mask_type="no_mask",
input_layout="type_bsnd",
calc_type="calc_type_default",
output=prefix_output,
softmax_lse=prefix_lse)
return prefix_output, prefix_lse
def forward(
self,
layer_name,
hidden_states: torch.Tensor, # query in unified attn
kv_cache: Tuple[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.
if self.fc2_o_shared_enable and is_hidden_layer(
self.vllm_config, self.o_proj):
reach_layer_for_shared_weight_series(self.o_proj)
return output.fill_(0)
if self.pcp_size > 1:
num_actual_tokens = attn_metadata.num_actual_tokens_pcp_padded // self.pcp_size
else:
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_padded = output
o_proj_input_shape = (get_forward_context().num_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)
# MLA Preprocess
forward_context = get_forward_context()
if (self.enable_mlapo and
(attn_metadata is None or not forward_context.with_prefill)):
hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
hidden_states.contiguous(), need_gather_q_kv)
decode_preprocess_res, prefill_preprocess_res = self._mla_decode_preprocess(
hidden_states, kv_cache, attn_metadata)
else:
decode_preprocess_res, prefill_preprocess_res = self._mla_preprocess(
layer_name, hidden_states, kv_cache, attn_metadata,
need_gather_q_kv)
if decode_preprocess_res is not None:
# MLA Preprocess for decoding
if self.pcp_size * self.dcp_size > 1:
output_decode = self._forward_decode_pcp_dcp(
decode_preprocess_res.ql_nope,
decode_preprocess_res.q_pe,
decode_preprocess_res.k_nope,
decode_preprocess_res.k_pe,
kv_cache[0].shape[1],
attn_metadata,
)
else:
output_decode = self._forward_decode(
decode_preprocess_res.ql_nope, decode_preprocess_res.q_pe,
decode_preprocess_res.k_nope, decode_preprocess_res.k_pe,
kv_cache[0].shape[1], attn_metadata)
o_proj_input[:num_decode_tokens] = output_decode
if prefill_preprocess_res is not None:
# FIX: aicore move should be also placed on the comm stream in dbo,
# otherwise it may affect the accuracy
# TODO: use an elegant way to overlap
if self.pcp_size > 1:
output_prefill = self._forward_prefill_cp(
prefill_preprocess_res.q_nope, prefill_preprocess_res.q_pe,
prefill_preprocess_res.k_nope, prefill_preprocess_res.k_pe,
prefill_preprocess_res.value, kv_cache, attn_metadata)
else:
output_prefill = self._forward_prefill(
prefill_preprocess_res.q_nope, prefill_preprocess_res.q_pe,
prefill_preprocess_res.k_nope, prefill_preprocess_res.k_pe,
prefill_preprocess_res.value, kv_cache, attn_metadata)
o_proj_input[num_decode_tokens:num_actual_tokens] = output_prefill
# O proj
MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024
maybe_npu_prefetch(inputs=self.o_proj.weight,
dependency=o_proj_input,
max_size=MAX_O_PROJ_PREFETCH_SIZE,
enabled=self.enable_prefetch)
output[...] = self.o_proj(o_proj_input,
is_prefill=(prefill_preprocess_res
is not None))[0]
del o_proj_input
has_prefill = attn_metadata.num_prefills > 0
if has_prefill:
maybe_save_kv_layer_to_connector(layer_name, list(kv_cache))
return output_padded
def _mla_preprocess(self, layer_name, hidden_states, kv_cache,
attn_metadata, need_gather_q_kv):
# MLA Preprocess:
# 1. Perform fused_qkv_a_proj and q_a_layernorm to obtain q_c and kv_no_split
# or
# Perform kv_a_proj_with_mqa to obtain kv_no_split
# 2. If need_gather_q_kv, perform all_gather.
# 3. Preprocess decode tokens, write kv cache and get:
# decode_ql_nope, decode_q_pe, decode_k_pe, decode_k_nope
# 4. 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 self.fused_qkv_a_proj is not None:
maybe_npu_prefetch(inputs=self.fused_qkv_a_proj.weight,
dependency=hidden_states,
enabled=self.enable_prefetch)
qkv_lora = self.fused_qkv_a_proj(hidden_states)[0]
q_c, kv_no_split = qkv_lora.split(
[self.q_lora_rank, self.kv_lora_rank + self.qk_rope_head_dim],
dim=-1,
)
q_c = self.q_a_layernorm(q_c)
# allgather need contiguous data
kv_no_split = kv_no_split.contiguous()
else:
q_c = hidden_states
kv_no_split = self.kv_a_proj_with_mqa(hidden_states)[0]
# Process for Flash Comm V1
q_c = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
q_c.contiguous(), need_gather_q_kv)
kv_no_split = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
kv_no_split.contiguous(), need_gather_q_kv)
if self.fc2_o_shared_enable and is_hidden_layer(
self.vllm_config, self.o_proj):
reach_layer_for_shared_weight_series(self.o_proj)
decode_preprocess_res = None
prefill_preprocess_res = None
if has_prefill:
wait_for_kv_layer_from_connector(layer_name)
# Preprocess for decode tokens
if has_decode:
decode_q_c = q_c[:num_decode_tokens]
cos = attn_metadata.decode.cos
sin = attn_metadata.decode.sin
decode_ql_nope, decode_q_pe = \
self._q_proj_and_k_up_proj(decode_q_c)
if self.dcp_size > 1:
decode_q_no_split = torch.cat([decode_ql_nope, decode_q_pe],
dim=-1)
decode_q_no_split = get_dcp_group().all_gather(
decode_q_no_split, 1)
decode_ql_nope, decode_q_pe = decode_q_no_split.split(
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
decode_q_pe = self.rope_single(decode_q_pe, cos, sin)
decode_slots = attn_metadata.slot_mapping[:num_decode_tokens *
self.pcp_size:self.
pcp_size]
decode_kv_no_split = kv_no_split[:num_decode_tokens]
decode_k_pe, decode_k_nope = self.exec_kv_decode(
decode_kv_no_split, cos, sin, kv_cache, decode_slots)
decode_preprocess_res = DecodeMLAPreprocessResult(
decode_ql_nope, decode_q_pe, decode_k_nope, decode_k_pe)
# Preprocess for prefill tokens
if has_prefill:
if self.pcp_size > 1:
num_actual_tokens = (attn_metadata.num_actual_tokens_pcp_padded
- self.pcp_size * num_decode_tokens
) // self.pcp_size + num_decode_tokens
prefill_kv_no_split = kv_no_split[
num_decode_tokens:num_actual_tokens]
prefill_q_c = q_c[num_decode_tokens:num_actual_tokens]
prefill_q = self.q_proj(prefill_q_c)[0] \
.view(-1, self.num_heads, self.qk_head_dim)
prefill_q_pe = prefill_q[..., self.qk_nope_head_dim:]
prefill_q_nope = prefill_q[..., :self.qk_nope_head_dim]
if self.pcp_size > 1:
cos = attn_metadata.prefill.cos[:num_actual_tokens -
num_decode_tokens]
sin = attn_metadata.prefill.sin[:num_actual_tokens -
num_decode_tokens]
else:
cos = attn_metadata.prefill.cos
sin = attn_metadata.prefill.sin
prefill_slots = attn_metadata.slot_mapping[
num_decode_tokens:num_actual_tokens]
prefill_q_pe = self.rope_single(prefill_q_pe, cos, sin)
if self.pcp_size > 1:
prefill_kv_no_split = kv_no_split[:num_actual_tokens]
kv_c, k_pe = prefill_kv_no_split.split(
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
kv_c_normed = self.kv_a_layernorm(kv_c.contiguous())
assert len(
kv_cache
) > 1, "the number of kv cache should be greater than 1, namely (nope_cache and rope_cache)"
kv_c_normed = kv_c_normed.view(
[num_actual_tokens, self.num_kv_heads, -1])
k_pe = k_pe.unsqueeze(1)
prefill_k_pe = k_pe
prefill_k_pe[
num_decode_tokens:num_actual_tokens] = self.rope_single(
prefill_k_pe[num_decode_tokens:num_actual_tokens], cos,
sin)
prefill_k_c_normed = kv_c_normed[:num_actual_tokens]
prefill_kv_c_k_pe = torch.cat(
[prefill_k_c_normed, prefill_k_pe], dim=-1)
prefill_kv_c_k_pe = get_pcp_group().all_gather(
prefill_kv_c_k_pe, 0)
prefill_kv_c_k_pe = torch.index_select(
prefill_kv_c_k_pe, 0, attn_metadata.prefill.pcp_metadata.
pcp_allgather_restore_idx)
prefill_kv_c_k_pe = prefill_kv_c_k_pe[num_decode_tokens *
self.pcp_size:]
prefill_k_c_normed, prefill_k_pe = prefill_kv_c_k_pe.split(
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
kv_c_normed, k_pe = prefill_k_c_normed, prefill_k_pe
prefill_k_c_normed = prefill_k_c_normed.squeeze()
slot_mapping = attn_metadata.slot_mapping[self.pcp_size *
num_decode_tokens:]
torch_npu._npu_reshape_and_cache(key=kv_c_normed,
value=k_pe,
key_cache=kv_cache[0],
value_cache=kv_cache[1],
slot_indices=slot_mapping)
else:
prefill_k_pe, prefill_k_c_normed = self.exec_kv_prefill(
prefill_kv_no_split, cos, sin, kv_cache, prefill_slots)
prefill_k_nope, prefill_value = self.kv_b_proj(
prefill_k_c_normed)[0].view(
-1, self.num_heads,
self.qk_nope_head_dim + self.v_head_dim).split(
[self.qk_nope_head_dim, self.v_head_dim], dim=-1)
if not self.pcp_size > 1:
prefill_k_pe = prefill_k_pe.view(prefill_q_c.shape[0],
self.num_kv_heads, -1)
prefill_k_pe = prefill_k_pe.expand(
(*prefill_k_nope.shape[:-1], -1))
prefill_preprocess_res = PrefillMLAPreprocessResult(
prefill_q_nope, prefill_q_pe, prefill_k_nope, prefill_k_pe,
prefill_value)
return decode_preprocess_res, prefill_preprocess_res
def _mla_decode_preprocess(self, hidden_states, kv_cache, attn_metadata):
bsz = attn_metadata.num_decode_tokens
hidden_states = hidden_states[:bsz]
cos_shape = attn_metadata.decode.cos.shape
cos = attn_metadata.decode.cos.view(cos_shape[0], cos_shape[-1])
sin = attn_metadata.decode.sin.view(cos_shape[0], cos_shape[-1])
decode_k_nope, decode_k_pe = kv_cache[0], kv_cache[1]
decode_q_nope = torch.empty(
(hidden_states.shape[0], self.W_UK_T.shape[0],
decode_k_nope.shape[-1]),
dtype=hidden_states.dtype,
device=hidden_states.device,
)
decode_q_pe = torch.empty(
(hidden_states.shape[0], self.W_UK_T.shape[0],
decode_k_pe.shape[-1]),
dtype=hidden_states.dtype,
device=hidden_states.device,
)
torch.ops._C_ascend.mla_preprocess(
hidden_states,
self.wd_qkv,
self.deq_scale_qkv,
self.gamma1,
self.beta1,
self.wu_q,
self.qb_deq_scl,
self.gamma2,
cos,
sin,
self.W_UK_T,
decode_k_nope,
decode_k_pe,
attn_metadata.slot_mapping[:bsz].flatten(),
quant_scale0=self.quant_scale0,
quant_offset0=self.quant_offset0,
bias0=self.quant_bias_qkv,
quant_scale1=self.quant_scale1,
quant_offset1=self.quant_offset1,
bias1=self.qb_qt_bias,
ctkv_scale=self.ctkv_scale,
q_nope_scale=self.q_nope_scale,
cache_mode="krope_ctkv",
quant_mode="per_tensor_quant_asymm",
q_out0=decode_q_nope,
kv_cache_out0=decode_k_nope,
q_out1=decode_q_pe,
kv_cache_out1=decode_k_pe,
enable_inner_out=False,
inner_out=torch.tensor([], device=hidden_states.device))
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)
if self.dcp_size > 1:
decode_q_no_split = torch.cat([decode_q_nope, decode_q_pe], dim=-1)
decode_q_no_split = get_dcp_group().all_gather(
decode_q_no_split, 1)
decode_q_nope, decode_q_pe = decode_q_no_split.split(
[self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
decode_preprocess_res = DecodeMLAPreprocessResult(
decode_q_nope, decode_q_pe, decode_k_nope, decode_k_pe)
return decode_preprocess_res, None
def _forward_prefill_cp(
self,
q_nope: torch.Tensor,
q_pe: torch.Tensor,
k_nope: torch.Tensor,
k_pe: torch.Tensor,
value: torch.Tensor,
kv_c_and_k_pe_cache: Tuple[torch.Tensor],
attn_metadata: AscendMLAMetadata,
) -> torch.Tensor:
assert attn_metadata.prefill is not None
assert attn_metadata.prefill.pcp_metadata is not None
num_tokens = q_nope.size(0)
# Use precomputed indices from the metadata (already converted to tensors and on device)
q_head_idx = attn_metadata.prefill.pcp_metadata.q_head_idx
q_tail_idx = attn_metadata.prefill.pcp_metadata.q_tail_idx
kv_with_q_head_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_nomask_idx
kv_with_q_head_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_head_mask_idx
kv_with_q_tail_nomask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_nomask_idx
kv_with_q_tail_mask_idx = attn_metadata.prefill.pcp_metadata.kv_with_q_tail_mask_idx
attn_mask_seqlens = attn_metadata.prefill.pcp_metadata.attn_mask_seqlens
head_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.head_attn_nomask_seqlens
tail_attn_nomask_seqlens = attn_metadata.prefill.pcp_metadata.tail_attn_nomask_seqlens
mask = attn_metadata.prefill.pcp_metadata.pcp_prefill_mask
output_head, lse_head = self._attention_with_mask_and_nomask(
q_nope=torch.index_select(q_nope, 0, q_head_idx),
q_pe=torch.index_select(q_pe, 0, q_head_idx),
k_nope=k_nope,
k_pe=k_pe,
value=value,
kv_mask_idx=kv_with_q_head_mask_idx,
kv_nomask_idx=kv_with_q_head_nomask_idx,
attn_mask_seqlens=attn_mask_seqlens,
attn_nomask_seqlens=head_attn_nomask_seqlens,
mask=mask)
output_tail, lse_tail = self._attention_with_mask_and_nomask(
q_nope=torch.index_select(q_nope, 0, q_tail_idx),
q_pe=torch.index_select(q_pe, 0, q_tail_idx),
k_nope=k_nope,
k_pe=k_pe,
value=value,
kv_mask_idx=kv_with_q_tail_mask_idx,
kv_nomask_idx=kv_with_q_tail_nomask_idx,
attn_mask_seqlens=attn_mask_seqlens,
attn_nomask_seqlens=tail_attn_nomask_seqlens,
mask=mask)
q_full_idx = attn_metadata.prefill.pcp_metadata.q_full_idx
attn_output = torch.index_select(
torch.cat([output_head, output_tail], dim=0), 0, q_full_idx)
attn_lse = torch.index_select(torch.cat([lse_head, lse_tail], dim=1),
1, q_full_idx)
output, _ = self._compute_prefill_context(q_nope, q_pe,
kv_c_and_k_pe_cache,
self.qk_rope_head_dim,
attn_metadata, attn_output,
attn_lse)
output = output.reshape([num_tokens, self.num_heads * self.v_head_dim])
return output
def _attention_with_mask_and_nomask(
self, q_nope: torch.Tensor, q_pe: torch.Tensor,
k_nope: torch.Tensor, k_pe: torch.Tensor, value: torch.Tensor,
kv_mask_idx: torch.Tensor, kv_nomask_idx: torch.Tensor,
attn_mask_seqlens: torch.Tensor, attn_nomask_seqlens: torch.Tensor,
mask: torch.Tensor):
attn_output = torch.empty(q_nope.shape[0],
self.num_heads,
self.v_head_dim,
dtype=k_pe.dtype,
device=k_pe.device)
attn_lse = torch.empty(self.num_heads,
q_pe.shape[0],
dtype=torch.float32,
device=k_pe.device)
# mask
k_nope_mask = torch.index_select(k_nope, 0, kv_mask_idx)
value_mask = torch.index_select(value, 0, kv_mask_idx)
k_pe_mask = torch.index_select(k_pe, 0, kv_mask_idx)
torch_npu.atb.npu_ring_mla(q_nope=q_nope,
q_rope=q_pe,
k_nope=k_nope_mask,
k_rope=k_pe_mask,
value=value_mask,
mask=mask,
seqlen=attn_mask_seqlens,
head_num=self.num_heads,
kv_head_num=self.num_heads,
pre_out=None,
prev_lse=None,
qk_scale=self.scale,
kernel_type="kernel_type_high_precision",
mask_type="mask_type_triu",
input_layout="type_bsnd",
calc_type="calc_type_first_ring",
output=attn_output,
softmax_lse=attn_lse)
# nomask
if kv_nomask_idx.shape[0] == 0:
return attn_output, attn_lse
k_nope_nomask = torch.index_select(k_nope, 0, kv_nomask_idx)
value_nomask = torch.index_select(value, 0, kv_nomask_idx)
k_pe_nomask = torch.index_select(k_pe, 0, kv_nomask_idx)
torch_npu.atb.npu_ring_mla(q_nope=q_nope,
q_rope=q_pe,
k_nope=k_nope_nomask,
k_rope=k_pe_nomask,
value=value_nomask,
mask=mask,
seqlen=attn_nomask_seqlens,
head_num=self.num_heads,
kv_head_num=self.num_heads,
pre_out=attn_output,
prev_lse=attn_lse,
qk_scale=self.scale,
kernel_type="kernel_type_high_precision",
mask_type="no_mask",
input_layout="type_bsnd",
calc_type="calc_type_default",
output=attn_output,
softmax_lse=attn_lse)
return attn_output, attn_lse
def _forward_decode_pcp_dcp(
self,
q_nope: torch.Tensor,
q_pe: torch.Tensor,
k_nope: torch.Tensor,
k_pe: torch.Tensor,
block_size: int,
attn_metadata: AscendMLAMetadata,
) -> torch.Tensor:
decode_meta = attn_metadata.decode
assert decode_meta is not None
num_tokens = q_nope.size(0)
# shape of knope/k_pe for npu graph mode should be:
# [num_blocks, num_kv_heads, block_size, self.kv_lora_rank/self.qk_rope_head_dim]
if self.dcp_size > 1:
num_heads = self.num_heads * self.dcp_size
else:
num_heads = self.num_heads
k_nope = k_nope.view(-1, block_size, self.num_kv_heads,
self.kv_lora_rank)
k_pe = k_pe.view(-1, block_size, self.num_kv_heads,
self.qk_rope_head_dim)
q_nope = q_nope.view(num_tokens, num_heads, -1)
q_pe = q_pe.view(num_tokens, num_heads, -1)
# use pcp & dcp split computed token nums from scheduler to compute actual seq_len and seq_mask
seq_len = decode_meta.cp_seq_len
common_kwargs = {
"return_lse": True,
"calc_type": "calc_type_ring",
}
graph_params = get_graph_params()
forward_context: ForwardContext = get_forward_context()
if forward_context.capturing:
stream = torch_npu.npu.current_stream()
event = torch.npu.ExternalEvent()
event.wait(stream)
event.reset(stream)
graph_params.events[num_tokens].append(event)
workspace = graph_params.workspaces.get(num_tokens)
if workspace is None:
workspace = torch_npu.atb._npu_multi_head_latent_attention_get_workspace(
q_nope, q_pe, k_nope, k_pe, decode_meta.block_table,
seq_len, num_heads, self.scale, self.num_kv_heads,
**common_kwargs)
update_graph_params_workspaces(num_tokens, workspace)
attn_output = torch.empty_like(q_nope)
softmax_lse = torch.empty((num_tokens, num_heads, 1),
dtype=q_nope.dtype,
device=q_nope.device)
graph_params.attn_params[num_tokens].append(
(weak_ref_tensors(q_nope), weak_ref_tensors(q_pe),
weak_ref_tensors(k_nope), weak_ref_tensors(k_pe),
decode_meta.block_table, seq_len, num_heads, self.scale,
self.num_kv_heads, weak_ref_tensors(attn_output),
weak_ref_tensors(softmax_lse)))
torch.npu.graph_task_group_begin(stream)
torch_npu.atb.npu_multi_head_latent_attention(
q_nope,
q_pe,
k_nope,
k_pe,
decode_meta.block_table,
seq_len,
num_heads,
self.scale,
self.num_kv_heads,
**common_kwargs,
workspace=workspace,
output=attn_output,
lse=softmax_lse)
handle = torch.npu.graph_task_group_end(stream)
graph_params.handles[num_tokens].append(handle)
else:
attn_output = torch.empty_like(q_nope)
softmax_lse = torch.empty((num_tokens, num_heads, 1),
dtype=q_nope.dtype,
device=q_nope.device)
torch_npu.atb.npu_multi_head_latent_attention(
q_nope,
q_pe,
k_nope,
k_pe,
decode_meta.block_table,
seq_len,
num_heads,
self.scale,
self.num_kv_heads,
return_lse=True,
calc_type="calc_type_ring",
output=attn_output,
lse=softmax_lse)
# Update out&lse
attn_out_lse_list = self._process_attn_out_lse(attn_output,
softmax_lse,
decode_meta)
attn_output = self._npu_attention_update(attn_out_lse_list)
return self._v_up_proj(attn_output)
def _npu_attention_update(
self, attn_out_lse_list: List[torch.Tensor]) -> torch.Tensor:
attn_out_split_cp = []
attn_lse_split_cp = []
for attn_out_lse in attn_out_lse_list:
attn_out_allgather, attn_lse_allgather = self._out_lse_reshape(
*torch.split(attn_out_lse, [self.kv_lora_rank, 1], dim=-1))
attn_out_split_cp.append(attn_out_allgather)
attn_lse_split_cp.append(attn_lse_allgather)
attn_out, _ = torch_npu.npu_attention_update(attn_lse_split_cp,
attn_out_split_cp, 0)
attn_out = attn_out.view(-1, attn_out_lse_list[0].shape[1],
self.kv_lora_rank)
return attn_out
def _out_lse_reshape(self, attn_out: torch.Tensor,
attn_lse: torch.Tensor) -> torch.Tensor:
attn_out = attn_out.contiguous().view(
attn_out.shape[0] * attn_out.shape[1], attn_out.shape[2])
attn_lse = attn_lse.contiguous().view(
attn_lse.shape[0] * attn_lse.shape[1] * attn_lse.shape[2])
return attn_out, attn_lse
def _process_attn_out_lse(
self,
attn_output: torch.Tensor,
softmax_lse: torch.Tensor,
decode_meta: AscendMLADecodeMetadata,
) -> List[torch.Tensor]:
attn_out_lse_list = []
out_mask = decode_meta.batch_seq_mask[:, None,
None].expand_as(attn_output)
attn_output = torch.where(out_mask, 0, attn_output)
lse_mask = decode_meta.batch_seq_mask[:, None,
None].expand_as(softmax_lse)
softmax_lse = torch.where(lse_mask, -torch.inf, softmax_lse)
softmax_lse = softmax_lse.to(torch.float32)
attn_output = attn_output.to(torch.float32)
# Concat out&lse: [bs,num_heads,v_head_dim] + [bs,num_heads,1] -> [bs,num_heads,v_head_dim+1]
attn_out_lse = torch.cat([attn_output, softmax_lse], dim=-1)
if self.dcp_size > 1:
# permute: [bs, num_heads, v_head_dim+1] -> [num_heads, v_head_dim+1, bs]
attn_out_lse = attn_out_lse.permute([1, 2, 0]).contiguous()
attn_out_lse_all2all = torch.empty_like(attn_out_lse)
dist.all_to_all_single(attn_out_lse_all2all,
attn_out_lse,
group=self.dcp_group)
# permute: [num_heads, v_head_dim+1, bs] -> [bs, num_heads, v_head_dim+1]
attn_out_lse_all2all = attn_out_lse_all2all.permute([2, 0, 1])
if self.pcp_size > 1:
attn_out_lse = attn_out_lse_all2all.contiguous()
attn_out_lse_list = list(
torch.chunk(attn_out_lse_all2all, self.dcp_size, dim=1))
if self.pcp_size > 1:
# AllGather out&lse within PCP group
attn_out_lse_list = [
torch.empty_like(attn_out_lse) for _ in range(self.pcp_size)
]
dist.all_gather(attn_out_lse_list,
attn_out_lse,
group=self.pcp_group)
if self.dcp_size > 1 and self.pcp_size > 1:
attn_out_lse_list_pcp_dcp = []
for s in attn_out_lse_list:
attn_out_lse_list_split = list(
torch.chunk(s, self.dcp_size, dim=1))
attn_out_lse_list_pcp_dcp += attn_out_lse_list_split
attn_out_lse_list = attn_out_lse_list_pcp_dcp
return attn_out_lse_list
def _reorg_kvcache(
self,
allgatered_kv_c_normed: torch.Tensor,
allgatered_k_pe: torch.Tensor,
padded_local_chunk_seq_lens_lst: list[int],
local_context_lens_allranks: list[list[int]],
sum_seq_len: int,
max_seq_len: int,
chunk_size: int,
chunk_idx: int,
toks: int,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
reorg and unpad kvcache after cp local gather to tp layout for attn kernel.
e.g.
kv_c_normed in rank0 = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...]
kv_c_normed in rank1 = [T0_4, T0_5, pad, pad, T1_2, pad, ...]
allgatered_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...,
T0_4, T0_5, pad, pad, T1_2, pad, ...]
-> reorganized_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T0_4, T0_5,
T1_0, T1_1, T1_2, ...]
Args:
padded_local_chunk_seq_lens_lst: local chunk context lengths
under current CP rank.
local_context_lens_allranks: local context lengths on each CP rank.
sum_seq_len: the sum of cp_chunk_seq_lens_lst.
max_seq_len: the max value of cp_chunk_seq_lens_lst.
chunk_size: the local padded max context chunk from
chunked_context_metadata building.
chunk_idx: chunk idx of chunked_prefill.
toks: the number of tokens for local gather cache.
"""
kv_c_segments = []
k_pe_segments = []
src_token_idx = 0
max_seq_len_check = 0
for padded_local_chunk_seq_len, local_context_lens in zip(
padded_local_chunk_seq_lens_lst, local_context_lens_allranks):
cur_seq_len = 0
for rank, local_context_len in enumerate(local_context_lens):
# Note(qcs): We split the context into multiple chunks,
# depending on the size of the workspace.
# local_context in dcp0: |-----------------|
# local_context in dcp1: |--------------|
# n*padded_local_chunk: |-----|-----|-----|
# local_chunk_len in dcp1: |-----|-----|--|
# so we need update the last chunk length in dcp1.
local_chunk_len = min(
max(0, local_context_len - chunk_idx * chunk_size),
padded_local_chunk_seq_len,
)
if local_chunk_len != 0:
kv_c_segment = allgatered_kv_c_normed[rank * toks +
src_token_idx:rank *
toks +
src_token_idx +
local_chunk_len]
k_pe_segment = allgatered_k_pe[rank * toks +
src_token_idx:rank * toks +
src_token_idx +
local_chunk_len]
kv_c_segments.append(kv_c_segment)
k_pe_segments.append(k_pe_segment)
cur_seq_len += local_chunk_len
max_seq_len_check = max(max_seq_len_check, cur_seq_len)
src_token_idx += padded_local_chunk_seq_len
reorganized_kv_c_normed = torch.cat(kv_c_segments, dim=0)
reorganized_k_pe = torch.cat(k_pe_segments, dim=0)
assert reorganized_kv_c_normed.shape[0] == sum_seq_len
assert reorganized_k_pe.shape[0] == sum_seq_len
assert max_seq_len_check == max_seq_len
return reorganized_kv_c_normed, reorganized_k_pe
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