Support pipeline parallel in V1 Engine (#1700)

### What this PR does / why we need it?
This patch supports pipeline parallel in V1 Engine

### Does this PR introduce _any_ user-facing change?
Yes, users can run PP in V1

### How was this patch tested?
Manully test














- vLLM version: v0.9.2
- vLLM main:
31d5c1797f

Signed-off-by: weiguihua2 <weiguihua2@huawei.com>

tests/e2e/multicard/test_pipeline_parallel.py

@@ -0,0 +1,43 @@
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# 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.
+# This file is a part of the vllm-ascend project.
+#
+import pytest
+
+from tests.conftest import VllmRunner
+
+MODELS = [
+    "Qwen/Qwen3-0.6B",
+]
+
+TENSOR_PARALLELS = [2]
+PIPELINE_PARALLELS = [2]
+
+prompts = [
+    "Hello, my name is",
+    "The future of AI is",
+]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("tp_size", TENSOR_PARALLELS)
+@pytest.mark.parametrize("pp_size", PIPELINE_PARALLELS)
+def test_models(model: str, tp_size: int, pp_size: int) -> None:
+    with VllmRunner(model,
+                    tensor_parallel_size=tp_size,
+                    pipeline_parallel_size=pp_size,
+                    enforce_eager=True,
+                    gpu_memory_utilization=0.7) as vllm_model:
+        vllm_model.generate_greedy(prompts, 64)

vllm_ascend/worker/model_runner_v1.py

@@ -37,7 +37,8 @@ from vllm.attention import AttentionType, get_attn_backend
 from vllm.attention.layer import Attention
 from vllm.config import CompilationLevel, VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
-from vllm.distributed.parallel_state import get_dp_group, get_pp_group
+from vllm.distributed.parallel_state import (get_dp_group, get_pp_group,
+                                             get_tp_group)
 from vllm.forward_context import set_forward_context
 from vllm.inputs import INPUT_REGISTRY
 from vllm.logger import logger
@@ -146,6 +147,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         self.model_config = vllm_config.model_config
         self.cache_config = vllm_config.cache_config
         self.lora_config = vllm_config.lora_config
+        self.parallel_config = vllm_config.parallel_config
         self.scheduler_config = vllm_config.scheduler_config
         self.speculative_config = vllm_config.speculative_config
         self.block_size = vllm_config.cache_config.block_size
@@ -921,8 +923,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         cu_num_tokens = np.cumsum(num_scheduled_tokens)
         cumsums_offsets = np.repeat(cu_num_tokens - num_scheduled_tokens,
                                     num_scheduled_tokens)
-        sample_indices = cu_num_tokens - 1
+        logits_indices = cu_num_tokens - 1
-        sample_indices = torch.from_numpy(sample_indices).to(self.device,
+        logits_indices = torch.from_numpy(logits_indices).to(self.device,
                                                              non_blocking=True)
         arange = self.arange_np[:total_num_scheduled_tokens] - cumsums_offsets
 
@@ -1153,14 +1155,14 @@ class NPUModelRunner(LoRAModelRunnerMixin):
 
             spec_decode_metadata = self._calc_spec_decode_metadata(
                 num_draft_tokens, cu_num_tokens)
-            sample_indices = spec_decode_metadata.logits_indices
+            logits_indices = spec_decode_metadata.logits_indices
 
         aux_hidden_states = None
         if self.use_aux_hidden_state_outputs:
             hidden_states, aux_hidden_states = hidden_states
 
         return (attn_metadata, hidden_states, spec_decode_metadata, positions,
-                total_num_scheduled_tokens, sample_indices, aux_hidden_states,
+                total_num_scheduled_tokens, logits_indices, aux_hidden_states,
                 num_scheduled_tokens)
 
     def _get_cumsum_and_arange(
@@ -1397,16 +1399,42 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                 # Return empty ModelRunnerOuptut if there's no work to do.
                 return EMPTY_MODEL_RUNNER_OUTPUT
             (attn_metadata, hidden_states, spec_decode_metadata, positions,
-             num_scheduled_tokens, sample_indices, aux_hidden_states,
+             num_scheduled_tokens, logits_indices, aux_hidden_states,
              num_scheduled_tokens_np) = (self._process_reqs(
                  scheduler_output, intermediate_tensors))
 
         with ProfileExecuteDuration().capture_async("post process"):
-            if self.input_batch.pooling_params:
+            # Broadcast PP output for external_launcher (torchrun)
-                return self._pool(hidden_states, num_scheduled_tokens,
+            # to make sure we are synced across pp ranks
-                                  num_scheduled_tokens_np)
+            # TODO: Support overlapping mirco-batches
-            logits = self.model.compute_logits(hidden_states[sample_indices],
+            # https://github.com/vllm-project/vllm/issues/18019
-                                               None)
+            broadcast_pp_output = \
+                self.parallel_config.distributed_executor_backend \
+                == "external_launcher" and len(get_pp_group().ranks) > 0
+            if not get_pp_group().is_last_rank:
+                # For mid-pipeline stages, return the hidden states.
+                if not broadcast_pp_output:
+                    return hidden_states
+                assert isinstance(hidden_states, IntermediateTensors)
+                get_pp_group().send_tensor_dict(
+                    hidden_states.tensors, all_gather_group=get_tp_group())
+                logits = None
+            else:
+                if self.input_batch.pooling_params:
+                    return self._pool(hidden_states, num_scheduled_tokens,
+                                      num_scheduled_tokens_np)
+                sample_hidden_states = hidden_states[logits_indices]
+                logits = self.model.compute_logits(sample_hidden_states, None)
+            if broadcast_pp_output:
+                model_output_broadcast_data = {
+                    "logits": logits.contiguous(),
+                } if logits is not None else {}
+                model_output_broadcast_data = get_pp_group(
+                ).broadcast_tensor_dict(model_output_broadcast_data,
+                                        src=len(get_pp_group().ranks) - 1)
+                assert model_output_broadcast_data is not None
+                logits = model_output_broadcast_data["logits"]
+
             # Apply structured output bitmasks if present
             if scheduler_output.grammar_bitmask is not None:
                 logits = self.apply_grammar_bitmask(scheduler_output, logits)
@@ -1423,6 +1451,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                 # creates a new tensor with separate storage from the original
                 # logits tensor. This means any in-place operations on bonus_logits
                 # won't affect the original logits tensor.
+                assert logits is not None
                 bonus_logits = logits[
                     spec_decode_metadata.bonus_logits_indices]
                 sampler_output = self.sampler(

vllm_ascend/worker/worker_v1.py

@@ -28,8 +28,10 @@ from vllm.config import VllmConfig
 from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment)
 from vllm.distributed.kv_transfer import ensure_kv_transfer_initialized
+from vllm.distributed.parallel_state import get_pp_group, get_tp_group
 from vllm.logger import logger
 from vllm.lora.request import LoRARequest
+from vllm.sequence import IntermediateTensors
 from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, GiB_bytes
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
@@ -206,7 +208,22 @@ class NPUWorker(WorkerBase):
         self,
         scheduler_output: "SchedulerOutput",
     ) -> Optional[ModelRunnerOutput]:
-        output = self.model_runner.execute_model(scheduler_output)
+        intermediate_tensors = None
+        if not get_pp_group().is_first_rank:
+            intermediate_tensors = IntermediateTensors(
+                get_pp_group().recv_tensor_dict(
+                    all_gather_group=get_tp_group()))
+
+        output = self.model_runner.execute_model(scheduler_output,
+                                                 intermediate_tensors)
+        parallel_config = self.vllm_config.parallel_config
+        if parallel_config.distributed_executor_backend != "external_launcher" \
+            and not get_pp_group().is_last_rank:
+            assert isinstance(output, IntermediateTensors)
+            get_pp_group().send_tensor_dict(output.tensors,
+                                            all_gather_group=get_tp_group())
+            return None
+        assert isinstance(output, ModelRunnerOutput)
         return output if self.is_driver_worker else None
 
     def load_model(self) -> None: