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