[NPU] Upgrade to v0.27.0 (#6096)

Signed-off-by: Weiming Liao <liaowm5@gmail.com>

docker/Dockerfile.npu

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.26.0
+ARG VLLM_ASCEND_TAG=v0.27.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 # WORKDIR /vllm-workspace/vllm

docker/Dockerfile.npu.a3

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.26.0-a3
+ARG VLLM_ASCEND_TAG=v0.27.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 # WORKDIR /vllm-workspace/vllm

docker/Dockerfile.npu.ci

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.26.0
+ARG VLLM_ASCEND_TAG=v0.27.0
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 ARG APP_DIR=/vllm-workspace/vllm-omni

docker/Dockerfile.npu.ci.a3

@@ -1,5 +1,5 @@
 ARG VLLM_ASCEND_IMAGE=quay.io/atlas-ci/vllm-ascend
-ARG VLLM_ASCEND_TAG=v0.26.0-a3
+ARG VLLM_ASCEND_TAG=v0.27.0-a3
 FROM ${VLLM_ASCEND_IMAGE}:${VLLM_ASCEND_TAG}
 
 ARG APP_DIR=/vllm-workspace/vllm-omni

vllm_omni/platforms/npu/worker/npu_ar_model_runner.py

@@ -730,7 +730,9 @@ class NPUARModelRunner(OmniNPUModelRunner, OmniConnectorModelRunnerMixin):
         has_encoder_input = self.model_config.is_encoder_decoder and num_encoder_reqs > 0
 
         # Run forward pass
-        clear_kv_metadata = self.speculative_config is None
+        defer_kv_connector_finalize = self.speculative_config is not None and (
+            get_pp_group().is_last_rank or self.broadcast_pp_output
+        )
         with (
             record_function_or_nullcontext("forward"),
             set_ascend_forward_context(
@@ -744,13 +746,12 @@ class NPUARModelRunner(OmniNPUModelRunner, OmniConnectorModelRunnerMixin):
                 model_instance=self.model,
                 skip_compiled=has_encoder_input,
                 has_sinks=self._has_sinks,
-                input_ids=input_ids,
                 eplb_heat_collection_status=self.eplb_heat_collection_status if self.dynamic_eplb else False,
             ),
             self.maybe_get_kv_connector_output(
                 scheduler_output,
                 **(
-                    {"defer_finalize": not clear_kv_metadata}
+                    {"defer_finalize": defer_kv_connector_finalize}
                 ),
             ) as kv_connector_output,
         ):

vllm_omni/platforms/npu/worker/npu_generation_model_runner.py

@@ -381,7 +381,9 @@ class NPUGenerationModelRunner(OmniNPUModelRunner, OmniConnectorModelRunnerMixin
         has_encoder_input = self.model_config.is_encoder_decoder and num_encoder_reqs > 0
 
         # Run forward pass
-        clear_kv_metadata = self.speculative_config is None
+        defer_kv_connector_finalize = self.speculative_config is not None and (
+            get_pp_group().is_last_rank or self.broadcast_pp_output
+        )
         with (
             record_function_or_nullcontext("forward"),
             set_ascend_forward_context(
@@ -395,12 +397,11 @@ class NPUGenerationModelRunner(OmniNPUModelRunner, OmniConnectorModelRunnerMixin
                 model_instance=self.model,
                 skip_compiled=has_encoder_input,
                 has_sinks=self._has_sinks,
-                input_ids=input_ids,
                 eplb_heat_collection_status=self.eplb_heat_collection_status if self.dynamic_eplb else False,
             ),
             self.maybe_get_kv_connector_output(
                 scheduler_output,
-                **({"defer_finalize": not clear_kv_metadata}),
+                **({"defer_finalize": defer_kv_connector_finalize}),
             ) as kv_connector_output,
         ):
             if self.cache_config.mamba_cache_mode == "align":
@@ -654,9 +655,10 @@ class NPUGenerationModelRunner(OmniNPUModelRunner, OmniConnectorModelRunnerMixin
         num_active_loras: int = 0,
         profile_seq_lens: int | None = None,
         profile_cpp: bool = False,
+        randomize_inputs: bool = False,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         # only support eager mode and piecewise graph now
-        assert cudagraph_runtime_mode is None or cudagraph_runtime_mode.valid_runtime_modes()
+        assert cudagraph_runtime_mode is None or cudagraph_runtime_mode.is_valid_runtime_mode()
         # If cudagraph_mode.decode_mode() == FULL and
         # cudagraph_mode.separate_routine(). This means that we are using
         # different graphs and/or modes for mixed prefill-decode batches vs.
@@ -917,7 +919,6 @@ class NPUGenerationModelRunner(OmniNPUModelRunner, OmniConnectorModelRunnerMixin
                 batch_descriptor=batch_desc,
                 model_instance=self.model,
                 has_sinks=self._has_sinks,
-                input_ids=input_ids,
                 eplb_heat_collection_status=self.eplb_heat_collection_status if self.dynamic_eplb else False,
             ):
                 outputs = self.model(

vllm_omni/platforms/npu/worker/npu_model_runner.py

@@ -125,9 +125,10 @@ class OmniNPUModelRunner(OmniGPUModelRunner, NPUModelRunner):
         num_active_loras: int = 0,
         profile_seq_lens: int | None = None,
         profile_cpp: bool = False,
+        randomize_inputs: bool = False,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         # only support eager mode and piecewise graph now
-        assert cudagraph_runtime_mode is None or cudagraph_runtime_mode.valid_runtime_modes()
+        assert cudagraph_runtime_mode is None or cudagraph_runtime_mode.is_valid_runtime_mode()
         # If cudagraph_mode.decode_mode() == FULL and
         # cudagraph_mode.separate_routine(). This means that we are using
         # different graphs and/or modes for mixed prefill-decode batches vs.
@@ -367,19 +368,21 @@ class OmniNPUModelRunner(OmniGPUModelRunner, NPUModelRunner):
                 if hasattr(self.drafter, "model") and hasattr(self.drafter.model, "compute_logits"):
                     return self.drafter.model.compute_logits(hidden_states[dummy_indices])
 
-            with set_ascend_forward_context(
-                attn_metadata,
-                self.vllm_config,
-                num_tokens=num_tokens_padded,
-                num_tokens_across_dp=num_tokens_across_dp,
-                in_profile_run=is_profile,
-                num_actual_tokens=num_tokens_padded,
-                aclgraph_runtime_mode=cudagraph_runtime_mode,
-                batch_descriptor=batch_desc,
-                model_instance=self.model,
-                has_sinks=self._has_sinks,
-                input_ids=input_ids,
-                eplb_heat_collection_status=self.eplb_heat_collection_status if self.dynamic_eplb else False,
+            with (
+                self.maybe_randomize_inputs(input_ids, inputs_embeds, randomize_inputs=randomize_inputs),
+                set_ascend_forward_context(
+                    attn_metadata,
+                    self.vllm_config,
+                    num_tokens=num_tokens_padded,
+                    num_tokens_across_dp=num_tokens_across_dp,
+                    in_profile_run=is_profile,
+                    num_actual_tokens=num_tokens_padded,
+                    aclgraph_runtime_mode=cudagraph_runtime_mode,
+                    batch_descriptor=batch_desc,
+                    model_instance=self.model,
+                    has_sinks=self._has_sinks,
+                    eplb_heat_collection_status=self.eplb_heat_collection_status if self.dynamic_eplb else False,
+                ),
             ):
                 # ---------------------------------------Omni-new----------------------------------------------
                 if getattr(self.model, "talker", None) is not None and self.has_talker_mtp: