Skip to content

fix torchair execute issue on padding data, and mtp padding logic #1160

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
wangxiyuan merged 1 commit into from
Jun 10, 2025
Merged

fix torchair execute issue on padding data, and mtp padding logic #1160

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 4 additions & 1 deletion vllm_ascend/attention/mla_v1.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -376,7 +376,10 @@ def build(
seq_lens = seq_lens[:self._num_decode_tokens]
input_positions = input_positions[:self._num_decode_tokens]
block_table = block_table[:self._num_decode_tokens, ...]
if use_torchair_graph and self.runner.attn_state == AscendAttentionState.DecodeOnly:
if use_torchair_graph and self.runner.attn_state in [
AscendAttentionState.DecodeOnly,
AscendAttentionState.SpecDecoding
]:
num_seqs = len(seq_lens)
if graph_pad_size != 0:
pad_value = 1
Expand Down
10 changes: 5 additions & 5 deletions vllm_ascend/worker/model_runner_v1.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -943,11 +943,6 @@ def _process_reqs(
self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True)
input_ids = self.input_ids[:num_input_tokens]

if (envs_ascend.VLLM_ENABLE_MC2
or self.torchair_graph_enabled) and not with_prefill:
input_ids = self.input_ids[:padded_batch_size]
positions = self.positions[:padded_batch_size]

# prepare the MRoPE for mllm if using multimodal
num_input_tokens = total_num_scheduled_tokens
# _prepare_inputs may reorder the batch, so we must gather multi
Expand Down Expand Up @@ -985,6 +980,11 @@ def _process_reqs(
else:
positions = self.positions[:num_input_tokens]

if (envs_ascend.VLLM_ENABLE_MC2
or self.torchair_graph_enabled) and not with_prefill:
input_ids = self.input_ids[:padded_batch_size]
positions = self.positions[:padded_batch_size]

# Run forward pass
with set_forward_context(attn_metadata,
self.vllm_config,
Expand Down