From d306d01dd776ca19744c83a0875fb05a0da1bace Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Thu, 18 Dec 2025 14:30:55 +0800
Subject: [PATCH 01/13] [Feat] adapt step3 text model

---
 .../afd_connector/p2p_connector.py            | 310 ++++++++++++++++++
 vllm/model_executor/models/deepseek_v2.py     |   2 +-
 vllm/model_executor/models/step3_text.py      | 242 +++++++++++---
 vllm/model_executor/models/step3_vl.py        |  10 +
 vllm/v1/worker/gpu_model_runner.py            |  56 ++++
 vllm/v1/worker/gpu_ubatch_wrapper.py          |   4 +
 6 files changed, 580 insertions(+), 44 deletions(-)

diff --git a/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py b/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
index 6dc1f317b87d5..3c3359fae96f5 100644
--- a/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
+++ b/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
@@ -7,6 +7,316 @@ from datetime import timedelta
 import torch
 from torch.distributed.distributed_c10d import _get_default_group, _update_default_pg
 
+from vllm.config import VllmConfig
+from vllm.distributed.parallel_state import (
+    GroupCoordinator,
+    TensorMetadata,
+    init_afd_process_group,
+    init_model_parallel_group,
+)
+from vllm.logger import init_logger
+
+from .base import AFDConnectorBase
+from .metadata import AFDConnectorMetadata
+
+logger = init_logger(__name__)
+
+
+class DefaultProcessGroupSwitcher:
+    def __init__(self, default_group, new_default_group):
+        self.default_group = default_group
+        self.new_default_group = new_default_group
+
+    def __enter__(self):
+        _update_default_pg(self.new_default_group)
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        _update_default_pg(self.default_group)
+
+
+class P2PAFDConnector(AFDConnectorBase):
+    def __init__(
+        self,
+        rank: int,
+        local_rank: int,
+        config: "VllmConfig",
+    ) -> None:
+        self.rank = rank
+        self.local_rank = local_rank
+        self.config = config
+        self._initialized: bool = False
+        self._need_recv_metadata: bool = True
+        self._tensor_metadata_list: dict[int, TensorMetadata] = {}
+        self._current_afd_connector_metadata: AFDConnectorMetadata | None = None
+        if getattr(self.config.model_config.hf_config, "text_config", None) is not None:
+            self.num_hidden_layers: int = (
+                self.config.model_config.hf_config.text_config.num_hidden_layers
+            )
+        else:
+            self.num_hidden_layers: int = (
+                self.config.model_config.hf_config.num_hidden_layers
+            )
+            
+        self.recv_attn_output_counter: int = 0
+        self.recv_ffn_output_counter: int = 0
+
+    def close(self) -> None:
+        """Close the connector and release resources."""
+        # TODO: Implement proper resource clean up if needed.
+        pass
+
+    def init_afd_connector(self) -> None:
+        """Initialize the AFD connector."""
+        afd_size = self.config.afd_config.afd_extra_config.get("afd_size")
+        role = self.config.afd_config.afd_role
+        attn_size, ffn_size = map(int, re.match(r"(\d+)\D+(\d+)", afd_size).groups())
+        world_rank = self.rank if role == "attention" else self.rank + attn_size
+        afd_pg = init_afd_process_group(
+            backend="nccl",
+            init_method=(
+                f"tcp://{self.config.afd_config.afd_host}"
+                f":{self.config.afd_config.afd_port}"
+            ),
+            world_size=ffn_size + attn_size,
+            rank=world_rank,
+            group_name="afd",
+            timeout=timedelta(minutes=2),
+        )
+
+        # Construct rank lists for sub groups.
+        # Each group contains one attention and one ffn rank.
+        ffn_ranks = [i for i in range(ffn_size, ffn_size + attn_size)]
+        attn_ranks = [i for i in range(attn_size)]
+        assert len(ffn_ranks) == len(attn_ranks), (
+            "ffn_ranks and attn_ranks must have the same length"
+        )
+        default_pg_switcher = DefaultProcessGroupSwitcher(_get_default_group(), afd_pg)
+        with default_pg_switcher:
+            sub_group_ranks = []
+            for i in range(len(ffn_ranks)):
+                ranks = [attn_ranks[i], ffn_ranks[i]]
+                sub_group_ranks.append(ranks)
+            # Create two independent groups:
+            # a2e_group: for attention -> expert/ffn communication (send_attn, recv_attn)
+            # e2a_group: for expert/ffn -> attention communication (send_ffn, recv_ffn)
+            # The communication domain (rank range) is the same, but different group_name
+            # creates independent groups.
+            self.a2e_group = init_model_parallel_group(
+                sub_group_ranks,
+                self.local_rank,
+                backend="nccl",
+                group_name="a2e",
+            )
+            self.e2a_group = init_model_parallel_group(
+                sub_group_ranks,
+                self.local_rank,
+                backend="nccl",
+                group_name="e2a",
+            )
+
+        self._initialized = True
+
+    def is_initialized(self) -> bool:
+        """Check if the connector is initialized and ready to use.
+
+        Returns:
+            bool: True if the connector is initialized, False otherwise.
+        """
+        return self._initialized
+
+    def _build_tensor_metadata_list(
+        self,
+        tensor_metadata: TensorMetadata,
+        connector_metadata: AFDConnectorMetadata,
+    ) -> dict[int, TensorMetadata]:
+        tensor_metadata_list = {}
+        num_of_stages = connector_metadata.num_of_stages
+        for idx in range(num_of_stages):
+            if idx == 0:
+                tensor_metadata_list[0] = tensor_metadata
+            else:
+                new_size = list(tensor_metadata.size)
+                new_size[0] = connector_metadata.afd_tokens_lens[idx]
+                tensor_metadata_list[idx] = TensorMetadata(
+                    tensor_metadata.device,
+                    tensor_metadata.dtype,
+                    torch.Size(new_size),
+                )
+        return tensor_metadata_list
+
+    def _send_metadata(
+        self,
+        metadata: AFDConnectorMetadata,
+        hidden_states: torch.Tensor,
+        dst: int,
+        process_group: GroupCoordinator,
+    ) -> None:
+        if not torch.distributed.is_initialized() or process_group.world_size == 1:
+            return []
+        assert dst < process_group.world_size, f"Invalid dst rank ({dst})"
+
+        tensor_metadata = TensorMetadata(
+            hidden_states.device.type, hidden_states.dtype, hidden_states.size()
+        )
+        metadata_tuple = (metadata, tensor_metadata)
+        process_group.send_object(metadata_tuple, dst=dst)
+        self._tensor_metadata_list = self._build_tensor_metadata_list(
+            tensor_metadata, metadata
+        )
+
+    def _recv_metadata(
+        self,
+        src: int,
+        process_group: GroupCoordinator,
+    ) -> None:
+        (self._current_afd_connector_metadata, tensor_metadata) = (
+            process_group.recv_object(src=src)
+        )
+        self._tensor_metadata_list = self._build_tensor_metadata_list(
+            tensor_metadata, self._current_afd_connector_metadata
+        )
+
+    def _send_hidden_states(
+        self,
+        hidden_states: torch.Tensor,
+        dst: int,
+        process_group: GroupCoordinator,
+    ) -> None:
+        if not torch.distributed.is_initialized() or process_group.world_size == 1:
+            return []
+        assert dst < process_group.world_size, f"Invalid dst rank ({dst})"
+        assert not hidden_states.is_cpu, "Hidden states must be on GPU"
+        torch.distributed.send(
+            hidden_states,
+            dst=process_group.ranks[dst],
+            group=process_group.device_group,
+        )
+
+    def _recv_hidden_states(
+        self,
+        src: int,
+        process_group: GroupCoordinator,
+        tensor_metadata: TensorMetadata,
+    ) -> tuple[torch.Tensor, list]:
+        if not torch.distributed.is_initialized() or process_group.world_size == 1:
+            return {}, []
+        assert src < process_group.world_size, f"Invalid src rank ({src})"
+
+        hidden_states = torch.empty(
+            tensor_metadata.size,
+            dtype=tensor_metadata.dtype,
+            device=tensor_metadata.device,
+        )
+        torch.distributed.recv(
+            hidden_states,
+            src=process_group.ranks[src],
+            group=process_group.device_group,
+        )
+        return hidden_states, []
+
+    # -------------------------------------------------------------------------
+    #                                attn -> ffn
+    # -------------------------------------------------------------------------
+
+    def send_attn_output(
+        self, hidden_states: torch.Tensor, metadata: AFDConnectorMetadata
+    ) -> None:
+        """
+        Called by ATTN side to send intermediate tensors
+        generated by ATTN instances to FFN.
+        """
+        try:
+            dst = (self.a2e_group.rank_in_group + 1) % self.a2e_group.world_size
+            if metadata.layer_idx == 0 and metadata.stage_idx == 0:
+                self._send_metadata(metadata, hidden_states, dst, self.a2e_group)
+            self._current_afd_connector_metadata = metadata
+            self._send_hidden_states(hidden_states, dst, self.a2e_group)
+        except Exception as e:
+            raise RuntimeError(f"Communication error: {e}")
+
+    def recv_ffn_output(self) -> tuple[torch.Tensor, AFDConnectorMetadata]:
+        """
+        Called by the ATTN side to receive MOE output intermediate tensors,
+        possibly dispatching from the receiver to other GPUs.
+        """
+        src = (self.e2a_group.rank_in_group - 1) % self.e2a_group.world_size
+        stage_idx = (
+            self.recv_ffn_output_counter
+            % self._current_afd_connector_metadata.num_of_stages
+        )
+        hidden_states, work_list = self._recv_hidden_states(
+            src,
+            self.e2a_group,
+            self._tensor_metadata_list[stage_idx],
+        )
+        self._current_afd_connector_metadata.recv_handle_list = work_list
+        self.recv_ffn_output_counter = (
+            self.recv_ffn_output_counter + 1
+        ) % self._current_afd_connector_metadata.num_of_stages
+        return hidden_states, self._current_afd_connector_metadata
+
+    # -------------------------------------------------------------------------
+    #                                ffn -> attn
+    # -------------------------------------------------------------------------
+
+    def send_ffn_output(
+        self,
+        hidden_states: torch.Tensor,
+        metadata: AFDConnectorMetadata,
+    ) -> None:
+        """
+        Called by FFN side to send intermediate tensors generated by FFN
+        instances back to the sender (should be the same GPU as source).
+        """
+        dst = (self.e2a_group.rank_in_group + 1) % self.e2a_group.world_size
+        self._send_hidden_states(hidden_states, dst, self.e2a_group)
+        self.recv_attn_output_counter += 1
+        if (
+            self.recv_attn_output_counter
+            % (
+                self._current_afd_connector_metadata.num_of_stages
+                * self.num_hidden_layers
+            )
+            == 0
+        ):
+            self._need_recv_metadata = True
+            self.recv_attn_output_counter = 0
+
+    def recv_attn_output(self) -> tuple[torch.Tensor, AFDConnectorMetadata]:
+        """
+        Called by the FFN side to receive intermediate tensors from ATTN.
+        Handles receiving and possibly dispatching tensors.
+        """
+        src = (self.a2e_group.rank_in_group - 1) % self.a2e_group.world_size
+        if self._need_recv_metadata:
+            self._recv_metadata(src, self.a2e_group)
+            self._need_recv_metadata = False
+
+        stage_idx = (
+            self.recv_attn_output_counter
+            % self._current_afd_connector_metadata.num_of_stages
+        )
+        layer_idx = (
+            self.recv_attn_output_counter
+            // self._current_afd_connector_metadata.num_of_stages
+        )
+        hidden_states, work_list = self._recv_hidden_states(
+            src,
+            self.a2e_group,
+            self._tensor_metadata_list[stage_idx],
+        )
+        self._current_afd_connector_metadata.recv_handle_list = work_list
+        self._current_afd_connector_metadata.layer_idx = layer_idx
+        return hidden_states, self._current_afd_connector_metadata
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import re
+from datetime import timedelta
+
+import torch
+from torch.distributed.distributed_c10d import _get_default_group, _update_default_pg
+
 from vllm.config import VllmConfig
 from vllm.distributed.parallel_state import (
     GroupCoordinator,
diff --git a/vllm/model_executor/models/deepseek_v2.py b/vllm/model_executor/models/deepseek_v2.py
index 7938cff98c354..6b7842b00f54a 100644
--- a/vllm/model_executor/models/deepseek_v2.py
+++ b/vllm/model_executor/models/deepseek_v2.py
@@ -1670,7 +1670,7 @@ class DeepseekV2ForCausalLM(
         return hidden_states
 
     def compute_ffn_output(
-        self, current_layer_idx, hidden_states
+        self, hidden_states, current_layer_idx
     ) -> torch.Tensor | IntermediateTensors:
         hidden_states = self.model.compute_ffn_output(hidden_states, current_layer_idx)
         return hidden_states
diff --git a/vllm/model_executor/models/step3_text.py b/vllm/model_executor/models/step3_text.py
index 7077f1a22e8d7..c012c26f83afa 100644
--- a/vllm/model_executor/models/step3_text.py
+++ b/vllm/model_executor/models/step3_text.py
@@ -11,12 +11,16 @@ from torch import nn
 
 from vllm.attention.layer import Attention
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, ModelConfig, VllmConfig
+from vllm.config import AFDConfig, CacheConfig, ModelConfig, VllmConfig
 from vllm.distributed import (
     get_pp_group,
     get_tensor_model_parallel_world_size,
     tensor_model_parallel_all_reduce,
 )
+from vllm.distributed.afd_transfer.afd_connector.metadata import (
+    AFDConnectorMetadata,
+)
+from vllm.forward_context import AFDMetadata, get_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import FusedMoE
@@ -37,6 +41,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs.step3_vl import Step3TextConfig
+from vllm.v1.worker.ubatching import dbo_current_ubatch_id, dbo_enabled, dbo_yield
 
 from .interfaces import SupportsPP
 from .utils import (
@@ -228,54 +233,59 @@ class Step3TextDecoderLayer(nn.Module):
         config: Step3TextConfig,
         cache_config: CacheConfig | None = None,
         quant_config: QuantizationConfig | None = None,
+        afd_config: AFDConfig | None = None,
         prefix: str = "",
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
+        self.afd_role = afd_config.afd_role if afd_config is not None else None
 
-        self.self_attn = Step3TextAttention(
-            hidden_size=self.hidden_size,
-            num_heads=config.num_attention_heads,
-            num_kv_heads=1,
-            cache_config=cache_config,
-            quant_config=quant_config,
-            norm_eps=config.rms_norm_eps,
-            max_position_embedding=config.max_position_embedding,
-            head_dim=config.head_dim,
-            share_q_dim=config.share_q_dim,
-            rope_parameters=config.rope_parameters,
-            prefix=f"{prefix}.self_attn",
-        )
-
-        layer_idx = int(prefix.split("layers.")[1].split(".")[0])
-        moe_layers_enum = getattr(config, "moe_layers_enum", None)
-        if moe_layers_enum is not None:
-            moe_layers_idx = [int(i) for i in moe_layers_enum.strip().split(",")]
-        else:
-            # Default to 1dense.
-            moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
-
-        if layer_idx in moe_layers_idx:
-            self.moe = FusedMoEBlock(
-                config=config, quant_config=quant_config, prefix=f"{prefix}.moe"
-            )
-            self.share_expert = Step3TextMLP(
+        if self.afd_role is None or self.afd_role == "attention":
+            self.self_attn = Step3TextAttention(
                 hidden_size=self.hidden_size,
-                intermediate_size=config.share_expert_dim,
-                hidden_act="silu",
+                num_heads=config.num_attention_heads,
+                num_kv_heads=1,
+                cache_config=cache_config,
                 quant_config=quant_config,
-                prefix=f"{prefix}.share_expert",
+                norm_eps=config.rms_norm_eps,
+                max_position_embedding=config.max_position_embedding,
+                head_dim=config.head_dim,
+                share_q_dim=config.share_q_dim,
+                rope_parameters=config.rope_parameters,
+                prefix=f"{prefix}.self_attn",
             )
-            self.use_moe = True
-        else:
-            self.mlp = Step3TextMLP(
-                hidden_size=config.hidden_size,
-                intermediate_size=config.intermediate_size,
-                hidden_act="silu",
-                quant_config=quant_config,
-                prefix=f"{prefix}.mlp",
-            )
-            self.use_moe = False
+
+        self.layer_idx = int(prefix.split("layers.")[1].split(".")[0])
+
+        if self.afd_role is None or self.afd_role == "ffn":
+            moe_layers_enum = getattr(config, "moe_layers_enum", None)
+            if moe_layers_enum is not None:
+                moe_layers_idx = [int(i) for i in moe_layers_enum.strip().split(",")]
+            else:
+                # Default to 1dense.
+                moe_layers_idx = [i for i in range(1, config.num_hidden_layers)]
+
+            if self.layer_idx in moe_layers_idx:
+                self.moe = FusedMoEBlock(
+                    config=config, quant_config=quant_config, prefix=f"{prefix}.moe"
+                )
+                self.share_expert = Step3TextMLP(
+                    hidden_size=self.hidden_size,
+                    intermediate_size=config.share_expert_dim,
+                    hidden_act="silu",
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.share_expert",
+                )
+                self.use_moe = True
+            else:
+                self.mlp = Step3TextMLP(
+                    hidden_size=config.hidden_size,
+                    intermediate_size=config.intermediate_size,
+                    hidden_act="silu",
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.mlp",
+                )
+                self.use_moe = False
         self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.post_attention_layernorm = RMSNorm(
             config.hidden_size, eps=config.rms_norm_eps
@@ -300,6 +310,9 @@ class Step3TextDecoderLayer(nn.Module):
 
         hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
 
+        if self.afd_role == "attention":
+            return hidden_states, residual
+
         if self.use_moe:
             share_output = self.share_expert(hidden_states)
             moe_output = self.moe(hidden_states)
@@ -309,6 +322,25 @@ class Step3TextDecoderLayer(nn.Module):
 
         return hidden_states, residual
 
+    def compute_attn_output(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor | None,
+    ):
+        pass
+
+    def compute_ffn_output(self, hidden_states):
+        assert self.afd_role == "ffn"
+        if self.use_moe:
+            share_output = self.share_expert(hidden_states)
+            moe_output = self.moe(hidden_states)
+            hidden_states = share_output + moe_output
+        else:
+            hidden_states = self.mlp(hidden_states)
+        logger.info(f"{type(hidden_states)=}")
+        return hidden_states
+
 
 @support_torch_compile
 class Step3TextModel(nn.Module):
@@ -317,6 +349,8 @@ class Step3TextModel(nn.Module):
         config = vllm_config.model_config.hf_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
+        logger.info(f"{quant_config=}")
+        afd_config = vllm_config.afd_config
         self.vocab_size = config.vocab_size
         self.config = config
 
@@ -336,6 +370,7 @@ class Step3TextModel(nn.Module):
                 config=config,
                 cache_config=cache_config,
                 quant_config=quant_config,
+                afd_config=afd_config,
                 prefix=prefix,
             ),
             prefix=f"{prefix}.layers",
@@ -352,6 +387,51 @@ class Step3TextModel(nn.Module):
     def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)
 
+    def forward_with_afd(
+        self,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor,
+        positions: torch.Tensor,
+        afd_metadata: AFDMetadata,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        recv_handle = None
+        logger.info(f"{__file__}: forward with afd called, may blocked here")
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
+            afd_connector = afd_metadata.afd_connector
+            afd_metadata.afd_stage_idx = dbo_current_ubatch_id()
+
+            if layer.layer_idx > 0:
+                hidden_states, recv_metadata = afd_connector.recv_ffn_output()
+                if recv_metadata.recv_handle_list is not None:
+                    recv_handle = recv_metadata.recv_handle_list
+
+            if recv_handle is not None:
+                for work in recv_handle:
+                    work.wait()
+            current_hidden, residual = layer(positions, hidden_states, residual)
+            metadata = AFDConnectorMetadata.create_attention_metadata(
+                layer_idx=layer.layer_idx,
+                stage_idx=afd_metadata.afd_stage_idx,
+                seq_len=current_hidden.shape[0],
+                dtype=current_hidden.dtype,
+                device=current_hidden.device,
+                num_of_stages=afd_metadata.num_of_stages,
+                afd_tokens_lens=afd_metadata.afd_tokens_lens,
+            )
+            afd_connector.send_attn_output(current_hidden, metadata)
+
+            if dbo_enabled():
+                dbo_yield()
+
+        hidden_states, recv_metadata = afd_connector.recv_ffn_output()
+        if recv_metadata.recv_handle_list is not None:
+            recv_handle = recv_metadata.recv_handle_list
+        if recv_handle is not None:
+            for work in recv_handle:
+                work.wait()
+
+        return hidden_states, residual
+
     def forward(
         self,
         input_ids: torch.Tensor,
@@ -370,8 +450,19 @@ class Step3TextModel(nn.Module):
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
 
-        for layer in islice(self.layers, self.start_layer, self.end_layer):
-            hidden_states, residual = layer(positions, hidden_states, residual)
+        forward_ctx = get_forward_context()
+        afd_metadata = forward_ctx.afd_metadata if forward_ctx is not None else None
+
+        if afd_metadata is not None:
+            hidden_states, residual = self.forward_with_afd(
+                hidden_states,
+                residual,
+                positions,
+                afd_metadata,
+            )
+        else:
+            for layer in islice(self.layers, self.start_layer, self.end_layer):
+                hidden_states, residual = layer(positions, hidden_states, residual)
 
         if not get_pp_group().is_last_rank:
             return IntermediateTensors(
@@ -384,6 +475,15 @@ class Step3TextModel(nn.Module):
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
+    def compute_ffn_output(
+        self,
+        hidden_states,
+        layer_idx,
+    ) -> torch.Tensor | IntermediateTensors:
+        logger.info(f"{type(self.layers)=}, {type(layer_idx)=}")
+        hidden_states = self.layers[layer_idx].compute_ffn_output(hidden_states)
+        return hidden_states
+
 
 class Step3TextForCausalLM(nn.Module, SupportsPP):
     def __init__(
@@ -398,6 +498,11 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
         self.config = config
         self.vllm_config = vllm_config
 
+        self.afd_config = vllm_config.afd_config
+        self.afd_role = (
+            self.afd_config.afd_role if self.afd_config is not None else None
+        )
+
         self.model = Step3TextModel(vllm_config=vllm_config, prefix=prefix)
 
         if get_pp_group().is_last_rank:
@@ -429,11 +534,20 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
         )
         return hidden_states
 
+    def compute_ffn_output(
+        self,
+        hidden_states,
+        current_layer_idx,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.model.compute_ffn_output(hidden_states, current_layer_idx)
+        return hidden_states
+
     def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
         logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        logger.info(f"{__file__}: load_weights!")
         qkv_params_mapping = [
             # (param_name, shard_name, relative_start_idx, relative_end_idx)
             (
@@ -466,6 +580,7 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
             (".gate_up_proj", ".up_proj", 1),
         ]
         params_dict = dict(self.named_parameters())
+        logger.info(f"{params_dict.keys()=}")
         loaded_params: set[str] = set()
 
         expert_params_mapping = [
@@ -477,9 +592,17 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
         disable_moe_stacked_params = [data[1] for data in expert_params_mapping]
 
         for name, loaded_weight in weights:
+            logger.info(
+                f"{self.afd_role=}, {name=}, is_moe: {self.is_moe_weight(name)}, "
+                f"is_common: {self.is_common_weight(name)}"
+            )
+            if self.afd_role == "attention" and self.is_moe_weight(name):
+                continue
+
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if weight_name not in name:
                     continue
+
                 if any(
                     disable_moe_stacked_param in name
                     for disable_moe_stacked_param in disable_moe_stacked_params
@@ -498,6 +621,10 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
                     param_name, weight_name, shard_id = mapping
                     if weight_name not in name:
                         continue
+
+                    if self.afd_role is not None and self.afd_role == "attention":
+                        continue
+
                     name = name.replace(weight_name, param_name)
                     # Skip layers on other devices.
                     if is_pp_missing_parameter(name, self):
@@ -521,12 +648,19 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
                     loaded_params.add(name)
                     break
                 else:
+                    if (
+                        self.afd_role == "ffn"
+                        and not self.is_moe_weight(name)
+                        and not self.is_common_weight(name)
+                    ):
+                        continue
                     for (
                         param_name,
                         weight_name,
                         start_idx,
                         end_idx,
                     ) in qkv_params_mapping:
+                        logger.info(f"{weight_name=}, {name=}")
                         if weight_name not in name:
                             continue
                         name = name.replace(weight_name, param_name)
@@ -552,3 +686,25 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
                         weight_loader(param, loaded_weight)
                         loaded_params.add(name)
         return loaded_params
+
+    def is_moe_weight(self, name):
+        if (
+            "shared_expert" in name
+            or "experts" in name
+            or "gate" in name
+            or "up" in name
+            or "down" in name
+        ):
+            return True
+        return False
+
+    def is_common_weight(self, name):
+        if (
+            "lm_head" in name
+            or "model.norm.weight" in name
+            or "embed" in name
+            or "input_layernorm" in name
+            or "post_attention_layernorm" in name
+        ):
+            return True
+        return False
diff --git a/vllm/model_executor/models/step3_vl.py b/vllm/model_executor/models/step3_vl.py
index e5038e56a2708..e16cb53e9f194 100644
--- a/vllm/model_executor/models/step3_vl.py
+++ b/vllm/model_executor/models/step3_vl.py
@@ -1126,6 +1126,16 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
 
         return hidden_states
 
+    def compute_ffn_output(
+        self,
+        hidden_states,
+        current_layer_idx,
+    ) -> torch.Tensor | IntermediateTensors:
+        hidden_states = self.language_model.compute_ffn_output(
+            hidden_states, current_layer_idx
+        )
+        return hidden_states
+
     def compute_logits(
         self,
         hidden_states: torch.Tensor,
diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 82dd80d267182..fff775a9f8241 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -642,6 +642,25 @@ class GPUModelRunner(
             with_stack=False,
         )
 
+        profile_dir = (
+            "./profiler_logs/attn"
+            if self.afd_config is not None and self.afd_config.afd_role == "attention"
+            else "./profiler_logs/normal"
+        )
+        self.profiler = torch.profiler.profile(
+            activities=[
+                torch.profiler.ProfilerActivity.CPU,
+                torch.profiler.ProfilerActivity.CUDA,
+            ],
+            schedule=torch.profiler.schedule(
+                wait=6000 + 4000, warmup=1, active=30, repeat=1
+            ),
+            on_trace_ready=torch.profiler.tensorboard_trace_handler(profile_dir),
+            record_shapes=True,
+            profile_memory=False,
+            with_stack=False,
+        )
+
     def reset_mm_cache(self) -> None:
         if self.mm_budget:
             self.mm_budget.reset_cache()
@@ -2969,6 +2988,38 @@ class GPUModelRunner(
             )
         return afd_metadata
 
+    def _build_afd_metadata(
+        self, ubatch_slices: UBatchSlices | None, num_tokens_unpadded: int
+    ):
+        afd_metadata = None
+        if self.afd_config:
+            # For prefill, compute tokens per stage based on actual token
+            # counts
+            afd_tokens_start_loc = [0]
+            afd_tokens_lens = []
+            if ubatch_slices and len(ubatch_slices) > 1:
+                afd_tokens_start_loc = [ub.token_slice.start for ub in ubatch_slices]
+                afd_reqs_start_loc = [ub.request_slice.start for ub in ubatch_slices]
+                logger.info(
+                    f"afd_tokens_start_loc: {afd_tokens_start_loc} "
+                    f"afd_reqs_start_loc: {afd_reqs_start_loc} "
+                    f"ubatch_slices: {ubatch_slices}"
+                )
+                afd_tokens_lens = [ub.num_tokens for ub in ubatch_slices]
+            else:
+                afd_tokens_start_loc = [0]
+                afd_reqs_start_loc = [0]
+                afd_tokens_lens = [num_tokens_unpadded]
+            afd_metadata = AFDMetadata(
+                afd_tokens_start_loc=afd_tokens_start_loc,
+                afd_reqs_start_loc=afd_reqs_start_loc,
+                afd_stage_idx=0,
+                afd_connector=self.afd_connector,
+                afd_tokens_lens=afd_tokens_lens,
+                num_of_stages=len(ubatch_slices) if ubatch_slices else 1,
+            )
+        return afd_metadata
+
     @torch.inference_mode()
     def execute_model(
         self,
@@ -5517,6 +5568,11 @@ class GPUModelRunner(
         if hasattr(self, "afd_connector") and self.afd_connector:
             self.afd_connector.init_afd_connector()
 
+    def initialize_afd_connector(self) -> None:
+        """Initialize AFD connector if available."""
+        if hasattr(self, "afd_connector") and self.afd_connector:
+            self.afd_connector.init_afd_connector()
+
     def may_add_encoder_only_layers_to_kv_cache_config(self) -> None:
         """
         Add encoder-only layers to the KV cache config.
diff --git a/vllm/v1/worker/gpu_ubatch_wrapper.py b/vllm/v1/worker/gpu_ubatch_wrapper.py
index 9e17c718c5513..8a1c9d90abbbf 100644
--- a/vllm/v1/worker/gpu_ubatch_wrapper.py
+++ b/vllm/v1/worker/gpu_ubatch_wrapper.py
@@ -127,6 +127,10 @@ class UBatchWrapper:
         comm_sms: int = envs.VLLM_DBO_COMM_SMS
 
         set_comm_sms = lambda sms: None
+        if (
+            vllm_config.parallel_config.enable_expert_parallel
+            and not vllm_config.afd_config
+        ):
         if (
             vllm_config.parallel_config.enable_expert_parallel
             and not vllm_config.afd_config

From cd16bcff1ea7373d706c69abe8976609d9854aa8 Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Thu, 18 Dec 2025 15:56:20 +0800
Subject: [PATCH 02/13] [Chore] resolve some bugs due to merge

---
 vllm/v1/worker/gpu_ffn_model_runner.py | 4 ++--
 vllm/v1/worker/gpu_model_runner.py     | 5 +++--
 vllm/v1/worker/gpu_ubatch_wrapper.py   | 4 ----
 3 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/vllm/v1/worker/gpu_ffn_model_runner.py b/vllm/v1/worker/gpu_ffn_model_runner.py
index cb08c9c05ae58..cd9940ef5e7a7 100644
--- a/vllm/v1/worker/gpu_ffn_model_runner.py
+++ b/vllm/v1/worker/gpu_ffn_model_runner.py
@@ -220,7 +220,7 @@ class GPUFFNModelRunner(LoRAModelRunnerMixin):
                 hidden_states, dim=0
             )
             ffn_output = self.model.compute_ffn_output(
-                current_layer_idx, gathered_hidden_states
+                gathered_hidden_states, current_layer_idx
             )
             # Extract the output corresponding to current rank
             start_idx = hidden_states.shape[0] * get_tensor_model_parallel_rank()
@@ -229,7 +229,7 @@ class GPUFFNModelRunner(LoRAModelRunnerMixin):
         else:
             # Single TP case
             rank_ffn_output = self.model.compute_ffn_output(
-                current_layer_idx, hidden_states
+                hidden_states, current_layer_idx
             )
 
         return rank_ffn_output
diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index fff775a9f8241..ed6b9cc98f3a2 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -3211,8 +3211,9 @@ class GPUModelRunner(
             record_function_or_nullcontext("gpu_model_runner: forward"),
             self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
         ):
-            logger.info(f"input_ids: {input_ids.shape}")
-            if inputs_embeds:
+            if input_ids is not None:
+                logger.info(f"input_ids: {input_ids.shape}")
+            if inputs_embeds is not None:
                 logger.info(f"inputs_embeds: {inputs_embeds.shape}")
             model_output = self._model_forward(
                 input_ids=input_ids,
diff --git a/vllm/v1/worker/gpu_ubatch_wrapper.py b/vllm/v1/worker/gpu_ubatch_wrapper.py
index 8a1c9d90abbbf..9e17c718c5513 100644
--- a/vllm/v1/worker/gpu_ubatch_wrapper.py
+++ b/vllm/v1/worker/gpu_ubatch_wrapper.py
@@ -127,10 +127,6 @@ class UBatchWrapper:
         comm_sms: int = envs.VLLM_DBO_COMM_SMS
 
         set_comm_sms = lambda sms: None
-        if (
-            vllm_config.parallel_config.enable_expert_parallel
-            and not vllm_config.afd_config
-        ):
         if (
             vllm_config.parallel_config.enable_expert_parallel
             and not vllm_config.afd_config

From f74bb82909d5a12749fc9aeeb676b52bc7d767be Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Thu, 18 Dec 2025 15:56:43 +0800
Subject: [PATCH 03/13] [Chore] code lint

---
 vllm/v1/worker/gpu_ffn_model_runner.py | 6 ++++--
 vllm/v1/worker/gpu_model_runner.py     | 8 ++++++--
 vllm/v1/worker/gpu_ubatch_wrapper.py   | 9 ++++++---
 3 files changed, 16 insertions(+), 7 deletions(-)

diff --git a/vllm/v1/worker/gpu_ffn_model_runner.py b/vllm/v1/worker/gpu_ffn_model_runner.py
index cd9940ef5e7a7..3f85267d8536a 100644
--- a/vllm/v1/worker/gpu_ffn_model_runner.py
+++ b/vllm/v1/worker/gpu_ffn_model_runner.py
@@ -130,8 +130,10 @@ class GPUFFNModelRunner(LoRAModelRunnerMixin):
 
         try:
             hidden_states, recv_metadata = self.connector.recv_attn_output()
-            if hasattr(self.connector, 'dp_metadata_list'):
-                dp_metadata = self.connector.dp_metadata_list.get(recv_metadata.stage_idx, None)
+            if hasattr(self.connector, "dp_metadata_list"):
+                dp_metadata = self.connector.dp_metadata_list.get(
+                    recv_metadata.stage_idx, None
+                )
             else:
                 dp_metadata = None
             current_layer_idx = recv_metadata.layer_idx
diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index ed6b9cc98f3a2..a09f292d98e14 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -3192,7 +3192,9 @@ class GPUModelRunner(
             # Mark KV scales as calculated after the first forward pass
             self.calculate_kv_scales = False
 
-        afd_metadata = self._build_afd_metadata(ubatch_slices_padded, num_tokens_unpadded)
+        afd_metadata = self._build_afd_metadata(
+            ubatch_slices_padded, num_tokens_unpadded
+        )
 
         self.profiler.step()
         # Run the model.
@@ -4326,7 +4328,9 @@ class GPUModelRunner(
                 if num_tokens_across_dp is not None:
                     num_tokens_across_dp[:] = num_tokens_padded
 
-            afd_metadata = self._build_afd_metadata(ubatch_slices_padded, num_tokens_unpadded)
+            afd_metadata = self._build_afd_metadata(
+                ubatch_slices_padded, num_tokens_unpadded
+            )
 
             with (
                 self.maybe_randomize_inputs(input_ids, inputs_embeds),
diff --git a/vllm/v1/worker/gpu_ubatch_wrapper.py b/vllm/v1/worker/gpu_ubatch_wrapper.py
index 9e17c718c5513..7a44b6cbd42b9 100644
--- a/vllm/v1/worker/gpu_ubatch_wrapper.py
+++ b/vllm/v1/worker/gpu_ubatch_wrapper.py
@@ -405,9 +405,12 @@ class UBatchWrapper:
                 afd_metadata.input_ids_list.append(sliced_input_ids)
                 afd_metadata.positions_list.append(sliced_positions)
                 afd_metadata.inputs_embeds_list.append(sliced_inputs_embeds)
-                afd_metadata.intermediate_tensors_list.append(sliced_intermediate_tensors)
+                afd_metadata.intermediate_tensors_list.append(
+                    sliced_intermediate_tensors
+                )
                 afd_metadata.attn_metadata_list.append(
-                    attn_metadata[i] if attn_metadata is not None else None)
+                    attn_metadata[i] if attn_metadata is not None else None
+                )
                 afd_metadata.dp_metadata_list.append(ubatch_dp_metadata)
 
         return afd_metadata
@@ -481,7 +484,7 @@ class UBatchWrapper:
             # num_tokens, we don't have a non-ubatched one. Without this
             # check, the cudagraph wrapper will try to capture a cudagraph
             # for this shape during a normal run.
-            
+
             if cudagraph_runtime_mode is CUDAGraphMode.FULL:
                 assert batch_descriptor is not None
                 if batch_descriptor.num_tokens in self.cudagraphs:

From 26ddfa299cc2835ab5ac9d0e6f3ddba3b85e5db0 Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Thu, 18 Dec 2025 17:02:39 +0800
Subject: [PATCH 04/13] [Chore] remove duplicate code

---
 vllm/v1/worker/gpu_model_runner.py | 56 ------------------------------
 1 file changed, 56 deletions(-)

diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index a09f292d98e14..2409c9071f94b 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -642,25 +642,6 @@ class GPUModelRunner(
             with_stack=False,
         )
 
-        profile_dir = (
-            "./profiler_logs/attn"
-            if self.afd_config is not None and self.afd_config.afd_role == "attention"
-            else "./profiler_logs/normal"
-        )
-        self.profiler = torch.profiler.profile(
-            activities=[
-                torch.profiler.ProfilerActivity.CPU,
-                torch.profiler.ProfilerActivity.CUDA,
-            ],
-            schedule=torch.profiler.schedule(
-                wait=6000 + 4000, warmup=1, active=30, repeat=1
-            ),
-            on_trace_ready=torch.profiler.tensorboard_trace_handler(profile_dir),
-            record_shapes=True,
-            profile_memory=False,
-            with_stack=False,
-        )
-
     def reset_mm_cache(self) -> None:
         if self.mm_budget:
             self.mm_budget.reset_cache()
@@ -2988,38 +2969,6 @@ class GPUModelRunner(
             )
         return afd_metadata
 
-    def _build_afd_metadata(
-        self, ubatch_slices: UBatchSlices | None, num_tokens_unpadded: int
-    ):
-        afd_metadata = None
-        if self.afd_config:
-            # For prefill, compute tokens per stage based on actual token
-            # counts
-            afd_tokens_start_loc = [0]
-            afd_tokens_lens = []
-            if ubatch_slices and len(ubatch_slices) > 1:
-                afd_tokens_start_loc = [ub.token_slice.start for ub in ubatch_slices]
-                afd_reqs_start_loc = [ub.request_slice.start for ub in ubatch_slices]
-                logger.info(
-                    f"afd_tokens_start_loc: {afd_tokens_start_loc} "
-                    f"afd_reqs_start_loc: {afd_reqs_start_loc} "
-                    f"ubatch_slices: {ubatch_slices}"
-                )
-                afd_tokens_lens = [ub.num_tokens for ub in ubatch_slices]
-            else:
-                afd_tokens_start_loc = [0]
-                afd_reqs_start_loc = [0]
-                afd_tokens_lens = [num_tokens_unpadded]
-            afd_metadata = AFDMetadata(
-                afd_tokens_start_loc=afd_tokens_start_loc,
-                afd_reqs_start_loc=afd_reqs_start_loc,
-                afd_stage_idx=0,
-                afd_connector=self.afd_connector,
-                afd_tokens_lens=afd_tokens_lens,
-                num_of_stages=len(ubatch_slices) if ubatch_slices else 1,
-            )
-        return afd_metadata
-
     @torch.inference_mode()
     def execute_model(
         self,
@@ -5573,11 +5522,6 @@ class GPUModelRunner(
         if hasattr(self, "afd_connector") and self.afd_connector:
             self.afd_connector.init_afd_connector()
 
-    def initialize_afd_connector(self) -> None:
-        """Initialize AFD connector if available."""
-        if hasattr(self, "afd_connector") and self.afd_connector:
-            self.afd_connector.init_afd_connector()
-
     def may_add_encoder_only_layers_to_kv_cache_config(self) -> None:
         """
         Add encoder-only layers to the KV cache config.

From 8276320a8a691adf8747d4d93b12e4c2de208e2d Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Thu, 18 Dec 2025 17:03:15 +0800
Subject: [PATCH 05/13] [Bugfix] compute ffn output param order

---
 vllm/v1/worker/gpu_ffn_model_runner.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vllm/v1/worker/gpu_ffn_model_runner.py b/vllm/v1/worker/gpu_ffn_model_runner.py
index 3f85267d8536a..921824f408552 100644
--- a/vllm/v1/worker/gpu_ffn_model_runner.py
+++ b/vllm/v1/worker/gpu_ffn_model_runner.py
@@ -351,7 +351,7 @@ class GPUFFNModelRunner(LoRAModelRunnerMixin):
                 hidden_states, dim=0
             )
             ffn_output = self.model.compute_ffn_output(
-                current_layer_idx, gathered_hidden_states
+                gathered_hidden_states, current_layer_idx
             )
 
             # Extract the output corresponding to current rank
@@ -361,7 +361,7 @@ class GPUFFNModelRunner(LoRAModelRunnerMixin):
         else:
             # Single TP case
             rank_ffn_output = self.model.compute_ffn_output(
-                current_layer_idx, hidden_states
+                hidden_states, current_layer_idx
             )
 
         return rank_ffn_output

From 6a8d35a9b6c36136c56c470e3bfbfa51a2c44c15 Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Thu, 18 Dec 2025 17:32:42 +0800
Subject: [PATCH 06/13] [Chore] remove p2p connector duplicate code

---
 .../afd_connector/p2p_connector.py            | 321 ------------------
 1 file changed, 321 deletions(-)

diff --git a/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py b/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
index 3c3359fae96f5..dcb781c55540e 100644
--- a/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
+++ b/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
@@ -308,324 +308,3 @@ class P2PAFDConnector(AFDConnectorBase):
         self._current_afd_connector_metadata.recv_handle_list = work_list
         self._current_afd_connector_metadata.layer_idx = layer_idx
         return hidden_states, self._current_afd_connector_metadata
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import re
-from datetime import timedelta
-
-import torch
-from torch.distributed.distributed_c10d import _get_default_group, _update_default_pg
-
-from vllm.config import VllmConfig
-from vllm.distributed.parallel_state import (
-    GroupCoordinator,
-    TensorMetadata,
-    init_afd_process_group,
-    init_model_parallel_group,
-)
-from vllm.logger import init_logger
-from vllm.forward_context import (
-    DPMetadata,
-    get_forward_context,
-)
-
-from .base import AFDConnectorBase
-from .metadata import AFDConnectorMetadata
-
-logger = init_logger(__name__)
-
-
-class DefaultProcessGroupSwitcher:
-    def __init__(self, default_group, new_default_group):
-        self.default_group = default_group
-        self.new_default_group = new_default_group
-
-    def __enter__(self):
-        _update_default_pg(self.new_default_group)
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        _update_default_pg(self.default_group)
-
-
-class P2PAFDConnector(AFDConnectorBase):
-    def __init__(
-        self,
-        rank: int,
-        local_rank: int,
-        config: "VllmConfig",
-    ) -> None:
-        self.rank = rank
-        self.local_rank = local_rank
-        self.config = config
-        self._initialized: bool = False
-        self._need_recv_metadata: bool = True
-        self._tensor_metadata_list: dict[int, TensorMetadata] = {}
-        self._current_afd_connector_metadata: AFDConnectorMetadata | None = None
-        self.num_hidden_layers: int = (
-            self.config.model_config.hf_config.num_hidden_layers
-        )
-        self.recv_attn_output_counter: int = 0
-        self.recv_ffn_output_counter: int = 0
-        self.dp_metadata_list: dict[int, DPMetadata] = {}
-
-    def close(self) -> None:
-        """Close the connector and release resources."""
-        # TODO: Implement proper resource clean up if needed.
-        pass
-
-    def init_afd_connector(self) -> None:
-        """Initialize the AFD connector."""
-        afd_size = self.config.afd_config.afd_extra_config.get("afd_size")
-        role = self.config.afd_config.afd_role
-        attn_size, ffn_size = map(int, re.match(r"(\d+)\D+(\d+)", afd_size).groups())
-        world_rank = self.rank if role == "attention" else self.rank + attn_size
-        afd_pg = init_afd_process_group(
-            backend="nccl",
-            init_method=(
-                f"tcp://{self.config.afd_config.afd_host}"
-                f":{self.config.afd_config.afd_port}"
-            ),
-            world_size=ffn_size + attn_size,
-            rank=world_rank,
-            group_name="afd",
-            timeout=timedelta(minutes=2),
-        )
-
-        # Construct rank lists for sub groups.
-        # Each group contains one attention and one ffn rank.
-        ffn_ranks = [i for i in range(ffn_size, ffn_size + attn_size)]
-        attn_ranks = [i for i in range(attn_size)]
-        assert len(ffn_ranks) == len(attn_ranks), (
-            "ffn_ranks and attn_ranks must have the same length"
-        )
-        default_pg_switcher = DefaultProcessGroupSwitcher(_get_default_group(), afd_pg)
-        with default_pg_switcher:
-            sub_group_ranks = []
-            for i in range(len(ffn_ranks)):
-                ranks = [attn_ranks[i], ffn_ranks[i]]
-                sub_group_ranks.append(ranks)
-            # Create two independent groups:
-            # a2e_group: for attention -> expert/ffn communication (send_attn, recv_attn)
-            # e2a_group: for expert/ffn -> attention communication (send_ffn, recv_ffn)
-            # The communication domain (rank range) is the same, but different group_name
-            # creates independent groups.
-            self.a2e_group = init_model_parallel_group(
-                sub_group_ranks,
-                self.local_rank,
-                backend="nccl",
-                group_name="a2e",
-            )
-            self.e2a_group = init_model_parallel_group(
-                sub_group_ranks,
-                self.local_rank,
-                backend="nccl",
-                group_name="e2a",
-            )
-
-        self._initialized = True
-
-    def is_initialized(self) -> bool:
-        """Check if the connector is initialized and ready to use.
-
-        Returns:
-            bool: True if the connector is initialized, False otherwise.
-        """
-        return self._initialized
-
-    def _build_tensor_metadata_list(
-        self,
-        tensor_metadata: TensorMetadata,
-        connector_metadata: AFDConnectorMetadata,
-    ) -> dict[int, TensorMetadata]:
-        tensor_metadata_list = {}
-        num_of_stages = connector_metadata.num_of_stages
-        for idx in range(num_of_stages):
-            if idx == 0:
-                tensor_metadata_list[0] = tensor_metadata
-            else:
-                new_size = list(tensor_metadata.size)
-                new_size[0] = connector_metadata.afd_tokens_lens[idx]
-                tensor_metadata_list[idx] = TensorMetadata(
-                    tensor_metadata.device,
-                    tensor_metadata.dtype,
-                    torch.Size(new_size),
-                )
-        return tensor_metadata_list
-
-    def _send_metadata(
-        self,
-        metadata: AFDConnectorMetadata,
-        hidden_states: torch.Tensor,
-        dst: int,
-        process_group: GroupCoordinator,
-    ) -> None:
-        if not torch.distributed.is_initialized() or process_group.world_size == 1:
-            return []
-        assert dst < process_group.world_size, f"Invalid dst rank ({dst})"
-
-        tensor_metadata = TensorMetadata(
-            hidden_states.device.type, hidden_states.dtype, hidden_states.size()
-        )
-        metadata_tuple = (metadata, tensor_metadata)
-        process_group.send_object(metadata_tuple, dst=dst)
-        self._tensor_metadata_list = self._build_tensor_metadata_list(
-            tensor_metadata, metadata
-        )
-
-    def _recv_metadata(
-        self,
-        src: int,
-        process_group: GroupCoordinator,
-    ) -> None:
-        (self._current_afd_connector_metadata, tensor_metadata) = (
-            process_group.recv_object(src=src)
-        )
-        self._tensor_metadata_list = self._build_tensor_metadata_list(
-            tensor_metadata, self._current_afd_connector_metadata
-        )
-        if self.config.parallel_config.data_parallel_size > 1:
-            logger.info("jcz recv_metadata num_of_stages:{}".format(self._current_afd_connector_metadata.num_of_stages))
-            for stage_idx in range(self._current_afd_connector_metadata.num_of_stages):
-                num_tokens_per_ubatch = self._tensor_metadata_list[stage_idx].size[0]
-                self.dp_metadata_list[stage_idx] = DPMetadata.make(
-                    self.config.parallel_config,
-                    num_tokens_per_ubatch,
-                    torch.tensor([num_tokens_per_ubatch] * self.config.parallel_config.data_parallel_size,
-                                device="cpu", dtype=torch.int32),
-                )
-            logger.info("jcz recv_metadata self.dp_metadata_list:{}".format(self.dp_metadata_list))
-
-    def _send_hidden_states(
-        self,
-        hidden_states: torch.Tensor,
-        dst: int,
-        process_group: GroupCoordinator,
-    ) -> None:
-        if not torch.distributed.is_initialized() or process_group.world_size == 1:
-            return []
-        assert dst < process_group.world_size, f"Invalid dst rank ({dst})"
-        assert not hidden_states.is_cpu, "Hidden states must be on GPU"
-        torch.distributed.send(
-            hidden_states,
-            dst=process_group.ranks[dst],
-            group=process_group.device_group,
-        )
-
-    def _recv_hidden_states(
-        self,
-        src: int,
-        process_group: GroupCoordinator,
-        tensor_metadata: TensorMetadata,
-    ) -> tuple[torch.Tensor, list]:
-        if not torch.distributed.is_initialized() or process_group.world_size == 1:
-            return {}, []
-        assert src < process_group.world_size, f"Invalid src rank ({src})"
-
-        hidden_states = torch.empty(
-            tensor_metadata.size,
-            dtype=tensor_metadata.dtype,
-            device=tensor_metadata.device,
-        )
-        torch.distributed.recv(
-            hidden_states,
-            src=process_group.ranks[src],
-            group=process_group.device_group,
-        )
-        return hidden_states, []
-
-    # -------------------------------------------------------------------------
-    #                                attn -> ffn
-    # -------------------------------------------------------------------------
-
-    def send_attn_output(
-        self, hidden_states: torch.Tensor, metadata: AFDConnectorMetadata
-    ) -> None:
-        """
-        Called by ATTN side to send intermediate tensors
-        generated by ATTN instances to FFN.
-        """
-        try:
-            dst = (self.a2e_group.rank_in_group + 1) % self.a2e_group.world_size
-            if metadata.layer_idx == 0 and metadata.stage_idx == 0:
-                self._send_metadata(metadata, hidden_states, dst, self.a2e_group)
-            self._current_afd_connector_metadata = metadata
-            self._send_hidden_states(hidden_states, dst, self.a2e_group)
-        except Exception as e:
-            raise RuntimeError(f"Communication error: {e}")
-
-    def recv_ffn_output(self) -> tuple[torch.Tensor, AFDConnectorMetadata]:
-        """
-        Called by the ATTN side to receive MOE output intermediate tensors,
-        possibly dispatching from the receiver to other GPUs.
-        """
-        src = (self.e2a_group.rank_in_group - 1) % self.e2a_group.world_size
-        stage_idx = (
-            self.recv_ffn_output_counter
-            % self._current_afd_connector_metadata.num_of_stages
-        )
-        hidden_states, work_list = self._recv_hidden_states(
-            src,
-            self.e2a_group,
-            self._tensor_metadata_list[stage_idx],
-        )
-        self._current_afd_connector_metadata.recv_handle_list = work_list
-        self.recv_ffn_output_counter = (
-            self.recv_ffn_output_counter + 1
-        ) % self._current_afd_connector_metadata.num_of_stages
-        return hidden_states, self._current_afd_connector_metadata
-
-    # -------------------------------------------------------------------------
-    #                                ffn -> attn
-    # -------------------------------------------------------------------------
-
-    def send_ffn_output(
-        self,
-        hidden_states: torch.Tensor,
-        metadata: AFDConnectorMetadata,
-    ) -> None:
-        """
-        Called by FFN side to send intermediate tensors generated by FFN
-        instances back to the sender (should be the same GPU as source).
-        """
-        dst = (self.e2a_group.rank_in_group + 1) % self.e2a_group.world_size
-        self._send_hidden_states(hidden_states, dst, self.e2a_group)
-        self.recv_attn_output_counter += 1
-        if (
-            self.recv_attn_output_counter
-            % (
-                self._current_afd_connector_metadata.num_of_stages
-                * self.num_hidden_layers
-            )
-            == 0
-        ):
-            self._need_recv_metadata = True
-            self.recv_attn_output_counter = 0
-
-    def recv_attn_output(self) -> tuple[torch.Tensor, AFDConnectorMetadata]:
-        """
-        Called by the FFN side to receive intermediate tensors from ATTN.
-        Handles receiving and possibly dispatching tensors.
-        """
-        src = (self.a2e_group.rank_in_group - 1) % self.a2e_group.world_size
-        if self._need_recv_metadata:
-            self._recv_metadata(src, self.a2e_group)
-            self._need_recv_metadata = False
-
-        stage_idx = (
-            self.recv_attn_output_counter
-            % self._current_afd_connector_metadata.num_of_stages
-        )
-        layer_idx = (
-            self.recv_attn_output_counter
-            // self._current_afd_connector_metadata.num_of_stages
-        )
-        hidden_states, work_list = self._recv_hidden_states(
-            src,
-            self.a2e_group,
-            self._tensor_metadata_list[stage_idx],
-        )
-        self._current_afd_connector_metadata.recv_handle_list = work_list
-        self._current_afd_connector_metadata.layer_idx = layer_idx
-        self._current_afd_connector_metadata.stage_idx = stage_idx
-        return hidden_states, self._current_afd_connector_metadata

From 11d7d5bf594c3ef743fef4b496705d5e39290034 Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Fri, 19 Dec 2025 16:02:07 +0800
Subject: [PATCH 07/13] [Chore] some log info

---
 vllm/model_executor/models/step3_text.py | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/vllm/model_executor/models/step3_text.py b/vllm/model_executor/models/step3_text.py
index c012c26f83afa..6355573a68774 100644
--- a/vllm/model_executor/models/step3_text.py
+++ b/vllm/model_executor/models/step3_text.py
@@ -303,6 +303,10 @@ class Step3TextDecoderLayer(nn.Module):
         else:
             hidden_states, residual = self.input_layernorm(hidden_states, residual)
 
+        # query, key and positions must have the same number of tokens
+        # /model_executor/layers/rotary_embedding/base.py
+        # positions.shape=torch.Size([8192]), hidden_states.shape=torch.Size([4096, 3712]）
+        logger.info(f"{positions.shape=}, {hidden_states.shape=}")
         hidden_states = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,
@@ -408,7 +412,9 @@ class Step3TextModel(nn.Module):
             if recv_handle is not None:
                 for work in recv_handle:
                     work.wait()
+            logger.info(f"Step3TextModel {layer.layer_idx=}: {hidden_states.shape=}, {positions.shape=}")
             current_hidden, residual = layer(positions, hidden_states, residual)
+            logger.info(f"create attn metadata: {current_hidden.shape=}")
             metadata = AFDConnectorMetadata.create_attention_metadata(
                 layer_idx=layer.layer_idx,
                 stage_idx=afd_metadata.afd_stage_idx,
@@ -580,7 +586,7 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
             (".gate_up_proj", ".up_proj", 1),
         ]
         params_dict = dict(self.named_parameters())
-        logger.info(f"{params_dict.keys()=}")
+        # logger.info(f"{params_dict.keys()=}")
         loaded_params: set[str] = set()
 
         expert_params_mapping = [
@@ -592,10 +598,10 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
         disable_moe_stacked_params = [data[1] for data in expert_params_mapping]
 
         for name, loaded_weight in weights:
-            logger.info(
-                f"{self.afd_role=}, {name=}, is_moe: {self.is_moe_weight(name)}, "
-                f"is_common: {self.is_common_weight(name)}"
-            )
+            # logger.info(
+            #     f"{self.afd_role=}, {name=}, is_moe: {self.is_moe_weight(name)}, "
+            #     f"is_common: {self.is_common_weight(name)}"
+            # )
             if self.afd_role == "attention" and self.is_moe_weight(name):
                 continue
 
@@ -660,7 +666,7 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
                         start_idx,
                         end_idx,
                     ) in qkv_params_mapping:
-                        logger.info(f"{weight_name=}, {name=}")
+                        # logger.info(f"{weight_name=}, {name=}")
                         if weight_name not in name:
                             continue
                         name = name.replace(weight_name, param_name)

From 65ea10c8f453e5294370b5f98d08280f49936602 Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Fri, 19 Dec 2025 16:03:47 +0800
Subject: [PATCH 08/13] [Chore] bring back deleted code

---
 .../afd_connector/p2p_connector.py            | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py b/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
index dcb781c55540e..58be99bf117ea 100644
--- a/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
+++ b/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
@@ -15,6 +15,10 @@ from vllm.distributed.parallel_state import (
     init_model_parallel_group,
 )
 from vllm.logger import init_logger
+from vllm.forward_context import (
+    DPMetadata,
+    get_forward_context,
+)
 
 from .base import AFDConnectorBase
 from .metadata import AFDConnectorMetadata
@@ -59,6 +63,7 @@ class P2PAFDConnector(AFDConnectorBase):
             
         self.recv_attn_output_counter: int = 0
         self.recv_ffn_output_counter: int = 0
+        self.dp_metadata_list: dict[int, DPMetadata] = {}
 
     def close(self) -> None:
         """Close the connector and release resources."""
@@ -175,6 +180,19 @@ class P2PAFDConnector(AFDConnectorBase):
         self._tensor_metadata_list = self._build_tensor_metadata_list(
             tensor_metadata, self._current_afd_connector_metadata
         )
+        logger.info(f"{self.config.parallel_config.data_parallel_size=}")
+        if self.config.parallel_config.data_parallel_size > 1:
+            logger.info("jcz recv_metadata num_of_stages:{}".format(self._current_afd_connector_metadata.num_of_stages))
+            for stage_idx in range(self._current_afd_connector_metadata.num_of_stages):
+                num_tokens_per_ubatch = self._tensor_metadata_list[stage_idx].size[0]
+                logger.info(f"{stage_idx=}, {num_tokens_per_ubatch=}")
+                self.dp_metadata_list[stage_idx] = DPMetadata.make(
+                    self.config.parallel_config,
+                    num_tokens_per_ubatch,
+                    torch.tensor([num_tokens_per_ubatch] * self.config.parallel_config.data_parallel_size,
+                                device="cpu", dtype=torch.int32),
+                )
+            logger.info("jcz recv_metadata self.dp_metadata_list:{}".format(self.dp_metadata_list))
 
     def _send_hidden_states(
         self,
@@ -307,4 +325,5 @@ class P2PAFDConnector(AFDConnectorBase):
         )
         self._current_afd_connector_metadata.recv_handle_list = work_list
         self._current_afd_connector_metadata.layer_idx = layer_idx
+        self._current_afd_connector_metadata.stage_idx = stage_idx
         return hidden_states, self._current_afd_connector_metadata

From bde36017fa2324f26e11f65602125df282899fd5 Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Fri, 19 Dec 2025 16:04:47 +0800
Subject: [PATCH 09/13] [Chore] adjust log info

---
 vllm/v1/worker/gpu_ffn_model_runner.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/vllm/v1/worker/gpu_ffn_model_runner.py b/vllm/v1/worker/gpu_ffn_model_runner.py
index 921824f408552..79ba7e6eb5c2e 100644
--- a/vllm/v1/worker/gpu_ffn_model_runner.py
+++ b/vllm/v1/worker/gpu_ffn_model_runner.py
@@ -137,10 +137,10 @@ class GPUFFNModelRunner(LoRAModelRunnerMixin):
             else:
                 dp_metadata = None
             current_layer_idx = recv_metadata.layer_idx
-            logger.info(
-                f"layer {current_layer_idx} moe recv hidden states type:{type(hidden_states)}, shape:{hidden_states.shape}"
-                f" dp_metadata: {dp_metadata}"
-            )
+            # logger.info(
+            #     f"layer {current_layer_idx} moe recv hidden states type:{type(hidden_states)}, shape:{hidden_states.shape}"
+            #     f" dp_metadata: {dp_metadata}"
+            # )
             num_tokens = hidden_states.shape[0]
             if recv_metadata is not None and recv_metadata.recv_handle_list is not None:
                 for work in recv_metadata.recv_handle_list:

From 6d305dda383ee6107125a7605fcefbd7d80e84c7 Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Fri, 19 Dec 2025 16:11:47 +0800
Subject: [PATCH 10/13] [Chore] add p2p connector debug log info

---
 .../distributed/afd_transfer/afd_connector/p2p_connector.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py b/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
index 58be99bf117ea..0605facbfaffb 100644
--- a/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
+++ b/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
@@ -139,9 +139,11 @@ class P2PAFDConnector(AFDConnectorBase):
         for idx in range(num_of_stages):
             if idx == 0:
                 tensor_metadata_list[0] = tensor_metadata
+                logger.info(f"build tensor metadata: stage_{idx=}, size={tensor_metadata.size}")
             else:
                 new_size = list(tensor_metadata.size)
                 new_size[0] = connector_metadata.afd_tokens_lens[idx]
+                logger.info(f"build tensor metadata: stage_{idx=}, {new_size=}, {connector_metadata.afd_tokens_lens=}")
                 tensor_metadata_list[idx] = TensorMetadata(
                     tensor_metadata.device,
                     tensor_metadata.dtype,
@@ -165,6 +167,7 @@ class P2PAFDConnector(AFDConnectorBase):
         )
         metadata_tuple = (metadata, tensor_metadata)
         process_group.send_object(metadata_tuple, dst=dst)
+        logger.info(f"_send_metadata called build tensor metadata")
         self._tensor_metadata_list = self._build_tensor_metadata_list(
             tensor_metadata, metadata
         )
@@ -177,6 +180,7 @@ class P2PAFDConnector(AFDConnectorBase):
         (self._current_afd_connector_metadata, tensor_metadata) = (
             process_group.recv_object(src=src)
         )
+        logger.info(f"_recv_metadata called build tensor metadata")
         self._tensor_metadata_list = self._build_tensor_metadata_list(
             tensor_metadata, self._current_afd_connector_metadata
         )
@@ -225,6 +229,7 @@ class P2PAFDConnector(AFDConnectorBase):
             dtype=tensor_metadata.dtype,
             device=tensor_metadata.device,
         )
+        # logger.info(f"{__file__}: p2p recv hidden states: {hidden_states.shape=}, {tensor_metadata.size=}")
         torch.distributed.recv(
             hidden_states,
             src=process_group.ranks[src],
@@ -262,6 +267,7 @@ class P2PAFDConnector(AFDConnectorBase):
             self.recv_ffn_output_counter
             % self._current_afd_connector_metadata.num_of_stages
         )
+        logger.info(f"{stage_idx=}")
         hidden_states, work_list = self._recv_hidden_states(
             src,
             self.e2a_group,

From 2a98ab3c8ea831c6a202225644edd671051e43ec Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Mon, 22 Dec 2025 14:29:03 +0800
Subject: [PATCH 11/13] [Chore]: step3 forward_with_afd

---
 vllm/model_executor/models/step3_text.py | 91 ++++++++++++++++--------
 1 file changed, 60 insertions(+), 31 deletions(-)

diff --git a/vllm/model_executor/models/step3_text.py b/vllm/model_executor/models/step3_text.py
index 6355573a68774..75a38805ef5aa 100644
--- a/vllm/model_executor/models/step3_text.py
+++ b/vllm/model_executor/models/step3_text.py
@@ -398,43 +398,72 @@ class Step3TextModel(nn.Module):
         positions: torch.Tensor,
         afd_metadata: AFDMetadata,
     ) -> tuple[torch.Tensor, torch.Tensor]:
+        forward_conext = get_forward_context()
         recv_handle = None
-        logger.info(f"{__file__}: forward with afd called, may blocked here")
-        for layer in islice(self.layers, self.start_layer, self.end_layer):
-            afd_connector = afd_metadata.afd_connector
-            afd_metadata.afd_stage_idx = dbo_current_ubatch_id()
 
-            if layer.layer_idx > 0:
-                hidden_states, recv_metadata = afd_connector.recv_ffn_output()
-                if recv_metadata.recv_handle_list is not None:
-                    recv_handle = recv_metadata.recv_handle_list
+        ubatch_hidden_states = []
+        ubatch_residual = []
 
-            if recv_handle is not None:
-                for work in recv_handle:
-                    work.wait()
-            logger.info(f"Step3TextModel {layer.layer_idx=}: {hidden_states.shape=}, {positions.shape=}")
-            current_hidden, residual = layer(positions, hidden_states, residual)
-            logger.info(f"create attn metadata: {current_hidden.shape=}")
-            metadata = AFDConnectorMetadata.create_attention_metadata(
-                layer_idx=layer.layer_idx,
-                stage_idx=afd_metadata.afd_stage_idx,
-                seq_len=current_hidden.shape[0],
-                dtype=current_hidden.dtype,
-                device=current_hidden.device,
-                num_of_stages=afd_metadata.num_of_stages,
-                afd_tokens_lens=afd_metadata.afd_tokens_lens,
+        start_idx = 0
+        for pos in afd_metadata.positions_list:
+            num_tokens = pos.shape[1] if pos.ndim == 2 else pos.shape[0]
+            end_idx = start_idx + num_tokens
+            ubatch_hidden_states.append(hidden_states[start_idx:end_idx])
+            ubatch_residual.append(
+                residual[start_idx:end_idx] if residual is not None else None
             )
-            afd_connector.send_attn_output(current_hidden, metadata)
+            start_idx = end_idx
 
-            if dbo_enabled():
-                dbo_yield()
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
+            for stage_i in range(forward_conext.afd_metadata.num_of_stages):
+                afd_connector = afd_metadata.afd_connector
+                forward_conext.attn_metadata = afd_metadata.attn_metadata_list[stage_i]
+                forward_conext.dp_metadata = afd_metadata.dp_metadata_list[stage_i]
 
-        hidden_states, recv_metadata = afd_connector.recv_ffn_output()
-        if recv_metadata.recv_handle_list is not None:
-            recv_handle = recv_metadata.recv_handle_list
-        if recv_handle is not None:
-            for work in recv_handle:
-                work.wait()
+                residual = ubatch_residual[stage_i]
+
+                if layer.layer_idx > 0:
+                    hidden_states, recv_metadata = afd_connector.recv_ffn_output()
+                    if recv_metadata.recv_handle_list is not None:
+                        recv_handle = recv_metadata.recv_handle_list
+                else:
+                    hidden_states = ubatch_hidden_states[stage_i]
+
+                if recv_handle is not None:
+                    for work in recv_handle:
+                        work.wait()
+
+                current_positions = afd_metadata.positions_list[stage_i]
+                hidden_states, residual = layer(
+                    current_positions, hidden_states, residual
+                )
+
+                ubatch_hidden_states[stage_i] = hidden_states
+                ubatch_residual[stage_i] = residual
+                logger.info(f"create attn metadata:, {afd_metadata.afd_tokens_lens=}")
+                metadata = AFDConnectorMetadata.create_attention_metadata(
+                    layer_idx=layer.layer_idx,
+                    stage_idx=stage_i,
+                    seq_len=hidden_states.shape[0],
+                    dtype=hidden_states.dtype,
+                    device=hidden_states.device,
+                    num_of_stages=afd_metadata.num_of_stages,
+                    afd_tokens_lens=afd_metadata.afd_tokens_lens,
+                )
+                afd_connector.send_attn_output(hidden_states, metadata)
+
+        # Recv last layer FFN output.
+        for stage_i in range(afd_metadata.num_of_stages):
+            ubatch_hidden_states[stage_i], recv_metadata = (
+                afd_connector.recv_ffn_output()
+            )
+
+        # Re-assemble the batch
+        hidden_states = torch.cat(ubatch_hidden_states, dim=0)
+        if any(r is not None for r in ubatch_residual):
+            residual = torch.cat(ubatch_residual, dim=0)
+        else:
+            residual = None
 
         return hidden_states, residual
 

From 27ae2e761c17e692a294ae1ce4a37c1092f270d7 Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Mon, 22 Dec 2025 15:25:28 +0800
Subject: [PATCH 12/13] [Chore] clean up debug info

---
 .../afd_connector/p2p_connector.py            | 30 +++++++++++--------
 vllm/model_executor/models/step3_text.py      | 15 ----------
 2 files changed, 17 insertions(+), 28 deletions(-)

diff --git a/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py b/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
index 0605facbfaffb..f85679400d1c6 100644
--- a/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
+++ b/vllm/distributed/afd_transfer/afd_connector/p2p_connector.py
@@ -60,7 +60,7 @@ class P2PAFDConnector(AFDConnectorBase):
             self.num_hidden_layers: int = (
                 self.config.model_config.hf_config.num_hidden_layers
             )
-            
+
         self.recv_attn_output_counter: int = 0
         self.recv_ffn_output_counter: int = 0
         self.dp_metadata_list: dict[int, DPMetadata] = {}
@@ -139,11 +139,9 @@ class P2PAFDConnector(AFDConnectorBase):
         for idx in range(num_of_stages):
             if idx == 0:
                 tensor_metadata_list[0] = tensor_metadata
-                logger.info(f"build tensor metadata: stage_{idx=}, size={tensor_metadata.size}")
             else:
                 new_size = list(tensor_metadata.size)
                 new_size[0] = connector_metadata.afd_tokens_lens[idx]
-                logger.info(f"build tensor metadata: stage_{idx=}, {new_size=}, {connector_metadata.afd_tokens_lens=}")
                 tensor_metadata_list[idx] = TensorMetadata(
                     tensor_metadata.device,
                     tensor_metadata.dtype,
@@ -167,7 +165,6 @@ class P2PAFDConnector(AFDConnectorBase):
         )
         metadata_tuple = (metadata, tensor_metadata)
         process_group.send_object(metadata_tuple, dst=dst)
-        logger.info(f"_send_metadata called build tensor metadata")
         self._tensor_metadata_list = self._build_tensor_metadata_list(
             tensor_metadata, metadata
         )
@@ -180,23 +177,32 @@ class P2PAFDConnector(AFDConnectorBase):
         (self._current_afd_connector_metadata, tensor_metadata) = (
             process_group.recv_object(src=src)
         )
-        logger.info(f"_recv_metadata called build tensor metadata")
         self._tensor_metadata_list = self._build_tensor_metadata_list(
             tensor_metadata, self._current_afd_connector_metadata
         )
-        logger.info(f"{self.config.parallel_config.data_parallel_size=}")
         if self.config.parallel_config.data_parallel_size > 1:
-            logger.info("jcz recv_metadata num_of_stages:{}".format(self._current_afd_connector_metadata.num_of_stages))
+            logger.info(
+                "jcz recv_metadata num_of_stages:{}".format(
+                    self._current_afd_connector_metadata.num_of_stages
+                )
+            )
             for stage_idx in range(self._current_afd_connector_metadata.num_of_stages):
                 num_tokens_per_ubatch = self._tensor_metadata_list[stage_idx].size[0]
-                logger.info(f"{stage_idx=}, {num_tokens_per_ubatch=}")
                 self.dp_metadata_list[stage_idx] = DPMetadata.make(
                     self.config.parallel_config,
                     num_tokens_per_ubatch,
-                    torch.tensor([num_tokens_per_ubatch] * self.config.parallel_config.data_parallel_size,
-                                device="cpu", dtype=torch.int32),
+                    torch.tensor(
+                        [num_tokens_per_ubatch]
+                        * self.config.parallel_config.data_parallel_size,
+                        device="cpu",
+                        dtype=torch.int32,
+                    ),
                 )
-            logger.info("jcz recv_metadata self.dp_metadata_list:{}".format(self.dp_metadata_list))
+            logger.info(
+                "jcz recv_metadata self.dp_metadata_list:{}".format(
+                    self.dp_metadata_list
+                )
+            )
 
     def _send_hidden_states(
         self,
@@ -229,7 +235,6 @@ class P2PAFDConnector(AFDConnectorBase):
             dtype=tensor_metadata.dtype,
             device=tensor_metadata.device,
         )
-        # logger.info(f"{__file__}: p2p recv hidden states: {hidden_states.shape=}, {tensor_metadata.size=}")
         torch.distributed.recv(
             hidden_states,
             src=process_group.ranks[src],
@@ -267,7 +272,6 @@ class P2PAFDConnector(AFDConnectorBase):
             self.recv_ffn_output_counter
             % self._current_afd_connector_metadata.num_of_stages
         )
-        logger.info(f"{stage_idx=}")
         hidden_states, work_list = self._recv_hidden_states(
             src,
             self.e2a_group,
diff --git a/vllm/model_executor/models/step3_text.py b/vllm/model_executor/models/step3_text.py
index 75a38805ef5aa..a09bf0c8bd37f 100644
--- a/vllm/model_executor/models/step3_text.py
+++ b/vllm/model_executor/models/step3_text.py
@@ -303,10 +303,6 @@ class Step3TextDecoderLayer(nn.Module):
         else:
             hidden_states, residual = self.input_layernorm(hidden_states, residual)
 
-        # query, key and positions must have the same number of tokens
-        # /model_executor/layers/rotary_embedding/base.py
-        # positions.shape=torch.Size([8192]), hidden_states.shape=torch.Size([4096, 3712]）
-        logger.info(f"{positions.shape=}, {hidden_states.shape=}")
         hidden_states = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,
@@ -342,7 +338,6 @@ class Step3TextDecoderLayer(nn.Module):
             hidden_states = share_output + moe_output
         else:
             hidden_states = self.mlp(hidden_states)
-        logger.info(f"{type(hidden_states)=}")
         return hidden_states
 
 
@@ -353,7 +348,6 @@ class Step3TextModel(nn.Module):
         config = vllm_config.model_config.hf_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
-        logger.info(f"{quant_config=}")
         afd_config = vllm_config.afd_config
         self.vocab_size = config.vocab_size
         self.config = config
@@ -440,7 +434,6 @@ class Step3TextModel(nn.Module):
 
                 ubatch_hidden_states[stage_i] = hidden_states
                 ubatch_residual[stage_i] = residual
-                logger.info(f"create attn metadata:, {afd_metadata.afd_tokens_lens=}")
                 metadata = AFDConnectorMetadata.create_attention_metadata(
                     layer_idx=layer.layer_idx,
                     stage_idx=stage_i,
@@ -515,7 +508,6 @@ class Step3TextModel(nn.Module):
         hidden_states,
         layer_idx,
     ) -> torch.Tensor | IntermediateTensors:
-        logger.info(f"{type(self.layers)=}, {type(layer_idx)=}")
         hidden_states = self.layers[layer_idx].compute_ffn_output(hidden_states)
         return hidden_states
 
@@ -582,7 +574,6 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
         return logits
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
-        logger.info(f"{__file__}: load_weights!")
         qkv_params_mapping = [
             # (param_name, shard_name, relative_start_idx, relative_end_idx)
             (
@@ -615,7 +606,6 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
             (".gate_up_proj", ".up_proj", 1),
         ]
         params_dict = dict(self.named_parameters())
-        # logger.info(f"{params_dict.keys()=}")
         loaded_params: set[str] = set()
 
         expert_params_mapping = [
@@ -627,10 +617,6 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
         disable_moe_stacked_params = [data[1] for data in expert_params_mapping]
 
         for name, loaded_weight in weights:
-            # logger.info(
-            #     f"{self.afd_role=}, {name=}, is_moe: {self.is_moe_weight(name)}, "
-            #     f"is_common: {self.is_common_weight(name)}"
-            # )
             if self.afd_role == "attention" and self.is_moe_weight(name):
                 continue
 
@@ -695,7 +681,6 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
                         start_idx,
                         end_idx,
                     ) in qkv_params_mapping:
-                        # logger.info(f"{weight_name=}, {name=}")
                         if weight_name not in name:
                             continue
                         name = name.replace(weight_name, param_name)

From 60d65cdf5c7a2c56af5a8d9f2f2193b85b4dc520 Mon Sep 17 00:00:00 2001
From: i-yuanyukun <i-yuanyukun@stepfun.com>
Date: Mon, 22 Dec 2025 15:37:07 +0800
Subject: [PATCH 13/13] [Chore] remove unused method

---
 vllm/model_executor/models/step3_text.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/vllm/model_executor/models/step3_text.py b/vllm/model_executor/models/step3_text.py
index a09bf0c8bd37f..eb723582a0ccb 100644
--- a/vllm/model_executor/models/step3_text.py
+++ b/vllm/model_executor/models/step3_text.py
@@ -322,14 +322,6 @@ class Step3TextDecoderLayer(nn.Module):
 
         return hidden_states, residual
 
-    def compute_attn_output(
-        self,
-        positions: torch.Tensor,
-        hidden_states: torch.Tensor,
-        residual: torch.Tensor | None,
-    ):
-        pass
-
     def compute_ffn_output(self, hidden_states):
         assert self.afd_role == "ffn"
         if self.use_moe: