249
vllm_ascend/_310p/ops/gdn_attn_builder_310.py
Normal file
249
vllm_ascend/_310p/ops/gdn_attn_builder_310.py
Normal file
@@ -0,0 +1,249 @@
|
||||
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
"""310P RC GDN metadata builder.
|
||||
|
||||
This 310P-specific builder keeps the upstream RC-safe prefill metadata path
|
||||
and adds ACL graph replay padding for decode / speculative decode metadata.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import torch
|
||||
from vllm.v1.attention.backend import CommonAttentionMetadata
|
||||
from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadata
|
||||
from vllm.v1.attention.backends.utils import NULL_BLOCK_ID
|
||||
|
||||
from vllm_ascend._310p.ops.fla.cumpute_causal_conv1d_metadata_310 import (
|
||||
compute_causal_conv1d_metadata,
|
||||
)
|
||||
from vllm_ascend.ops.gdn_attn_builder import (
|
||||
AscendGDNAttentionBackend,
|
||||
AscendGDNAttentionMetadataBuilder,
|
||||
)
|
||||
|
||||
|
||||
class GDNAttentionMetadataBuilder310(AscendGDNAttentionMetadataBuilder):
|
||||
"""310P overrides on top of :class:`AscendGDNAttentionMetadataBuilder`.
|
||||
|
||||
310P does not support Triton, so fallback metadata attachment is skipped.
|
||||
For ACL graph replay, decode metadata is padded into fixed graph buffers.
|
||||
"""
|
||||
|
||||
use_full_cuda_graph: bool
|
||||
|
||||
def _build_prefill_has_initial_state_and_causal_conv1d_meta(
|
||||
self,
|
||||
*,
|
||||
common_attn_metadata: CommonAttentionMetadata,
|
||||
context_lens_tensor: torch.Tensor,
|
||||
num_prefills: int,
|
||||
spec_sequence_masks_cpu: torch.Tensor | None,
|
||||
non_spec_sequence_indices: torch.Tensor | None,
|
||||
non_spec_query_start_loc_cpu: torch.Tensor | None,
|
||||
query_start_loc: torch.Tensor,
|
||||
) -> tuple[
|
||||
torch.Tensor | None,
|
||||
dict[int, dict[str, object]] | None,
|
||||
torch.Tensor | None,
|
||||
torch.Tensor | None,
|
||||
]:
|
||||
del common_attn_metadata, num_prefills
|
||||
assert non_spec_query_start_loc_cpu is not None
|
||||
|
||||
has_initial_state = context_lens_tensor > 0
|
||||
if spec_sequence_masks_cpu is not None:
|
||||
assert non_spec_sequence_indices is not None
|
||||
has_initial_state = torch.index_select(
|
||||
has_initial_state,
|
||||
0,
|
||||
non_spec_sequence_indices,
|
||||
)
|
||||
nums_dict, batch_ptr, token_chunk_offset_ptr = compute_causal_conv1d_metadata(
|
||||
non_spec_query_start_loc_cpu,
|
||||
device=query_start_loc.device,
|
||||
)
|
||||
return (
|
||||
has_initial_state,
|
||||
nums_dict,
|
||||
batch_ptr,
|
||||
token_chunk_offset_ptr,
|
||||
)
|
||||
|
||||
def _attach_non_spec_prefill_fallback_meta(
|
||||
self,
|
||||
attn_metadata: GDNAttentionMetadata,
|
||||
common_attn_metadata: CommonAttentionMetadata,
|
||||
non_spec_query_start_loc_cpu: torch.Tensor | None,
|
||||
) -> GDNAttentionMetadata:
|
||||
del common_attn_metadata, non_spec_query_start_loc_cpu
|
||||
return attn_metadata
|
||||
|
||||
def _attach_spec_decode_fallback_meta(
|
||||
self,
|
||||
attn_metadata: GDNAttentionMetadata,
|
||||
common_attn_metadata: CommonAttentionMetadata,
|
||||
num_decode_draft_tokens_cpu: torch.Tensor | None,
|
||||
) -> GDNAttentionMetadata:
|
||||
del common_attn_metadata, num_decode_draft_tokens_cpu
|
||||
return attn_metadata
|
||||
|
||||
def _attach_non_spec_decode_fallback_meta(
|
||||
self,
|
||||
attn_metadata: GDNAttentionMetadata,
|
||||
common_attn_metadata: CommonAttentionMetadata,
|
||||
num_decode_draft_tokens_cpu: torch.Tensor | None,
|
||||
) -> GDNAttentionMetadata:
|
||||
del common_attn_metadata, num_decode_draft_tokens_cpu
|
||||
return attn_metadata
|
||||
|
||||
def _pad_spec_decode_metadata(
|
||||
self,
|
||||
attn_metadata: GDNAttentionMetadata,
|
||||
graph_batch_size: int,
|
||||
) -> None:
|
||||
num_spec_decodes = attn_metadata.num_spec_decodes
|
||||
spec_state_indices = attn_metadata.spec_state_indices_tensor
|
||||
spec_sequence_masks = attn_metadata.spec_sequence_masks
|
||||
spec_query_start_loc = attn_metadata.spec_query_start_loc
|
||||
num_accepted_tokens = attn_metadata.num_accepted_tokens
|
||||
assert spec_state_indices is not None
|
||||
assert spec_sequence_masks is not None
|
||||
assert spec_query_start_loc is not None
|
||||
assert num_accepted_tokens is not None
|
||||
|
||||
self.spec_state_indices_tensor[:num_spec_decodes].copy_(
|
||||
spec_state_indices,
|
||||
non_blocking=True,
|
||||
)
|
||||
attn_metadata.spec_state_indices_tensor = self.spec_state_indices_tensor[:graph_batch_size]
|
||||
attn_metadata.spec_state_indices_tensor[num_spec_decodes:].fill_(NULL_BLOCK_ID)
|
||||
|
||||
self.spec_sequence_masks[:num_spec_decodes].copy_(
|
||||
spec_sequence_masks[:num_spec_decodes],
|
||||
non_blocking=True,
|
||||
)
|
||||
attn_metadata.spec_sequence_masks = self.spec_sequence_masks[:graph_batch_size]
|
||||
attn_metadata.spec_sequence_masks[num_spec_decodes:].fill_(False)
|
||||
|
||||
assert attn_metadata.non_spec_token_indx is not None
|
||||
assert attn_metadata.spec_token_indx is not None
|
||||
non_spec_tokens = attn_metadata.non_spec_token_indx
|
||||
spec_tokens = attn_metadata.spec_token_indx
|
||||
self.non_spec_token_indx[: non_spec_tokens.size(0)].copy_(
|
||||
non_spec_tokens,
|
||||
non_blocking=True,
|
||||
)
|
||||
self.spec_token_indx[: spec_tokens.size(0)].copy_(
|
||||
spec_tokens,
|
||||
non_blocking=True,
|
||||
)
|
||||
attn_metadata.non_spec_token_indx = self.non_spec_token_indx[: non_spec_tokens.size(0)]
|
||||
attn_metadata.spec_token_indx = self.spec_token_indx[: spec_tokens.size(0)]
|
||||
|
||||
self.spec_query_start_loc[: num_spec_decodes + 1].copy_(
|
||||
spec_query_start_loc,
|
||||
non_blocking=True,
|
||||
)
|
||||
attn_metadata.spec_query_start_loc = self.spec_query_start_loc[: graph_batch_size + 1]
|
||||
query_padding = attn_metadata.spec_query_start_loc[num_spec_decodes + 1 :]
|
||||
if query_padding.numel() > 0:
|
||||
query_padding.copy_(
|
||||
spec_query_start_loc[-1].expand_as(query_padding),
|
||||
non_blocking=True,
|
||||
)
|
||||
|
||||
self.num_accepted_tokens[:num_spec_decodes].copy_(
|
||||
num_accepted_tokens,
|
||||
non_blocking=True,
|
||||
)
|
||||
attn_metadata.num_accepted_tokens = self.num_accepted_tokens[:graph_batch_size]
|
||||
attn_metadata.num_accepted_tokens[num_spec_decodes:].fill_(0)
|
||||
self._attach_spec_decode_metadata(attn_metadata)
|
||||
|
||||
def _pad_decode_metadata(
|
||||
self,
|
||||
attn_metadata: GDNAttentionMetadata,
|
||||
graph_batch_size: int,
|
||||
) -> None:
|
||||
state_indices = attn_metadata.non_spec_state_indices_tensor
|
||||
query_start_loc = attn_metadata.non_spec_query_start_loc
|
||||
assert state_indices is not None
|
||||
assert query_start_loc is not None
|
||||
|
||||
(
|
||||
attn_metadata.non_spec_state_indices_tensor,
|
||||
attn_metadata.non_spec_query_start_loc,
|
||||
) = self._pad_non_spec_decode_graph_inputs(
|
||||
state_indices,
|
||||
query_start_loc,
|
||||
num_decode_tokens=attn_metadata.num_decode_tokens,
|
||||
graph_batch_size=graph_batch_size,
|
||||
)
|
||||
self._attach_non_spec_decode_metadata(
|
||||
attn_metadata,
|
||||
attn_metadata.non_spec_state_indices_tensor,
|
||||
)
|
||||
|
||||
def build( # type: ignore[override]
|
||||
self,
|
||||
common_prefix_len: int,
|
||||
common_attn_metadata: CommonAttentionMetadata,
|
||||
num_accepted_tokens: torch.Tensor | None = None,
|
||||
num_decode_draft_tokens_cpu: torch.Tensor | None = None,
|
||||
fast_build: bool = False,
|
||||
) -> GDNAttentionMetadata:
|
||||
use_full_graph = self.use_full_cuda_graph
|
||||
self.use_full_cuda_graph = False
|
||||
try:
|
||||
attn_metadata = super().build(
|
||||
common_prefix_len,
|
||||
common_attn_metadata,
|
||||
num_accepted_tokens,
|
||||
num_decode_draft_tokens_cpu,
|
||||
fast_build,
|
||||
)
|
||||
finally:
|
||||
self.use_full_cuda_graph = use_full_graph
|
||||
|
||||
if not use_full_graph:
|
||||
return attn_metadata
|
||||
|
||||
graph_batch_size = common_attn_metadata.num_reqs
|
||||
if (
|
||||
attn_metadata.num_prefills == 0
|
||||
and attn_metadata.num_decodes == 0
|
||||
and attn_metadata.num_spec_decodes <= self.decode_cudagraph_max_bs
|
||||
and attn_metadata.num_spec_decode_tokens <= self.decode_cudagraph_max_bs
|
||||
):
|
||||
self._pad_spec_decode_metadata(attn_metadata, graph_batch_size)
|
||||
elif (
|
||||
attn_metadata.num_prefills == 0
|
||||
and attn_metadata.num_spec_decodes == 0
|
||||
and attn_metadata.num_decodes <= self.decode_cudagraph_max_bs
|
||||
):
|
||||
self._pad_decode_metadata(attn_metadata, graph_batch_size)
|
||||
return attn_metadata
|
||||
|
||||
|
||||
# Keep the name introduced by the 310P ACL graph padding patch so existing
|
||||
# imports and tests from that patch continue to work after rebasing onto
|
||||
# upstream/main, whose class name is GDNAttentionMetadataBuilder310.
|
||||
AscendGDNAttentionMetadataBuilder310 = GDNAttentionMetadataBuilder310
|
||||
|
||||
|
||||
class AscendGDNAttentionBackend310(AscendGDNAttentionBackend):
|
||||
@staticmethod
|
||||
def get_builder_cls() -> type[AscendGDNAttentionMetadataBuilder310]:
|
||||
return AscendGDNAttentionMetadataBuilder310
|
||||
Reference in New Issue
Block a user