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[Bugfix] Follow vLLM Qwen-Moe/VL and KV Connector change to fix broken CI (#2181)

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### What this PR does / why we need it?
This pr fix broken CI:
1. Fix the
ee2eb6ecd8
changes, in this commit, they fused the gate and up projections in the
vision MLP, This can improve performance by reducing one matrix
multiplication. so, this pr do the following things:
- Specify that the two linear layers are fused as `mlp.gate_up_proj`
when loading the weights.
    - Use a SiluAndMul activation function.
2. Fix
aefeea0fde,
Update ModelRunnerOutput parameters to adapt to its changes
3. Fix
[vllm-commit](https://github.com/vllm-project/vllm/pull/20815/files#diff-3ffb829a39ab2b3e4706aa28f5e476815f36c3a87b98d6a66514ebedc8f3ffb4R354-R356),
fix qwen moe
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?


- vLLM version: v0.10.0
- vLLM main:
fed5849d3f

---------

Signed-off-by: wangli <wangli858794774@gmail.com>
This commit is contained in:
Li Wang
2025-08-04 21:37:50 +08:00
committed by GitHub
parent e38fab011d
commit ad366bf908
8 changed files with 137 additions and 56 deletions
Download Patch File Download Diff File Expand all files Collapse all files

61
vllm_ascend/worker/model_runner_v1.py
View File

@@ -94,6 +94,8 @@ from vllm_ascend.worker.npu_input_batch import CachedRequestState, InputBatch
if not vllm_version_is("0.10.0"):
from vllm.tasks import GenerationTask, SupportedTask
from vllm.v1.worker.kv_connector_model_runner_mixin import \
KVConnectorOutput
if TYPE_CHECKING:
import xgrammar as xgr # type: ignore[import-untyped]
@@ -1472,8 +1474,9 @@ class NPUModelRunner(LoRAModelRunnerMixin):
hidden_states: torch.Tensor,
num_scheduled_tokens: int,
num_scheduled_tokens_np: np.ndarray,
finished_sending: Optional[set[str]],
finished_receiving: Optional[set[str]],
finished_sending: Optional[set[str]] = None,
finished_recving: Optional[set[str]] = None,
kv_connector_output: Optional["KVConnectorOutput"] = None,
) -> ModelRunnerOutput:
assert self.input_batch.num_reqs ==\
len(self.input_batch.pooling_params), \
@@ -1499,6 +1502,12 @@ class NPUModelRunner(LoRAModelRunnerMixin):
pooler_output.append(raw_output.data.cpu())
else:
pooler_output.append(None)
extra_args = ({
"finished_sending": finished_sending,
"finished_recving": finished_recving
} if vllm_version_is("0.10.0") else {
"kv_connector_output": kv_connector_output
})
return ModelRunnerOutput(
req_ids=self.input_batch.req_ids,
@@ -1508,8 +1517,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
logprobs=None,
prompt_logprobs_dict={},
pooler_output=pooler_output,
finished_sending=finished_sending,
finished_recving=finished_receiving)
**extra_args,
)
@torch.inference_mode()
def execute_model(
@@ -1533,7 +1542,13 @@ class NPUModelRunner(LoRAModelRunnerMixin):
num_scheduled_tokens_np, finished_sending,
finished_recving) = (self._process_reqs(scheduler_output,
intermediate_tensors))
kv_connector_output = None
if not vllm_version_is("0.10.0"):
kv_connector_output = KVConnectorOutput(
finished_sending=finished_sending,
finished_recving=finished_recving)
finished_sending = None
finished_recving = None
with ProfileExecuteDuration().capture_async("post process"):
# Broadcast PP output for external_launcher (torchrun)
# to make sure we are synced across pp ranks
@@ -1545,7 +1560,10 @@ class NPUModelRunner(LoRAModelRunnerMixin):
if not get_pp_group().is_last_rank:
# For mid-pipeline stages, return the hidden states.
if not broadcast_pp_output:
if finished_sending or finished_recving:
if kv_connector_output is not None:
hidden_states.kv_connector_output = kv_connector_output
else:
#TODO: Remove this after we drop vllm v0.10.0
hidden_states.finished_sending = finished_sending
hidden_states.finished_recving = finished_recving
return hidden_states
@@ -1557,7 +1575,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
if self.input_batch.pooling_params:
return self._pool(hidden_states, num_scheduled_tokens,
num_scheduled_tokens_np,
finished_sending, finished_recving)
finished_sending, finished_recving,
kv_connector_output)
sample_hidden_states = hidden_states[logits_indices]
logits = self.model.compute_logits(sample_hidden_states, None)
if broadcast_pp_output:
@@ -1691,17 +1710,23 @@ class NPUModelRunner(LoRAModelRunnerMixin):
if has_kv_transfer_group():
get_kv_transfer_group().clear_connector_metadata()
model_runner_output = ModelRunnerOutput(
req_ids=self.input_batch.req_ids,
req_id_to_index=self.input_batch.req_id_to_index,
sampled_token_ids=valid_sampled_token_ids,
spec_token_ids=spec_token_ids,
logprobs=logprobs_lists,
prompt_logprobs_dict=prompt_logprobs_dict,
pooler_output=[],
finished_sending=finished_sending,
finished_recving=finished_recving,
)
extra_args = ({
"finished_sending": finished_sending,
"finished_recving": finished_recving
} if vllm_version_is("0.10.0") else {
"kv_connector_output": kv_connector_output
})
model_runner_output = ModelRunnerOutput(
req_ids=self.input_batch.req_ids,
req_id_to_index=self.input_batch.req_id_to_index,
sampled_token_ids=valid_sampled_token_ids,
spec_token_ids=spec_token_ids,
logprobs=logprobs_lists,
prompt_logprobs_dict=prompt_logprobs_dict,
pooler_output=[],
**extra_args,
)
durations = ProfileExecuteDuration().pop_captured_sync()
if durations:

27
vllm_ascend/worker/worker_v1.py
View File

@@ -209,12 +209,27 @@ class NPUWorker(WorkerBase):
if not has_kv_transfer_group():
return None
new_output = EMPTY_MODEL_RUNNER_OUTPUT
if output.finished_sending or output.finished_recving:
new_output = copy.copy(new_output)
new_output.finished_sending = output.finished_sending
new_output.finished_recving = output.finished_recving
output = new_output
is_legacy = vllm_version_is("0.10.0")
if is_legacy:
finished_sending = output.finished_sending
finished_recving = output.finished_recving
else:
kv_connector_output = output.kv_connector_output
finished_sending = kv_connector_output.finished_sending
finished_recving = kv_connector_output.finished_recving
if not finished_sending and not finished_recving:
return EMPTY_MODEL_RUNNER_OUTPUT
new_output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT)
if is_legacy:
new_output.finished_sending = finished_sending
new_output.finished_recving = finished_recving
else:
new_output.kv_connector_output = kv_connector_output
return new_output
assert isinstance(output, ModelRunnerOutput)
return output