[Feat] adapt step3 text model

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,

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

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(
+        if self.afd_role is None or self.afd_role == "attention":
-            hidden_size=self.hidden_size,
+            self.self_attn = Step3TextAttention(
-            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(
                 hidden_size=self.hidden_size,
-                intermediate_size=config.share_expert_dim,
+                num_heads=config.num_attention_heads,
-                hidden_act="silu",
+                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.layer_idx = int(prefix.split("layers.")[1].split(".")[0])
-            self.mlp = Step3TextMLP(
+
-                hidden_size=config.hidden_size,
+        if self.afd_role is None or self.afd_role == "ffn":
-                intermediate_size=config.intermediate_size,
+            moe_layers_enum = getattr(config, "moe_layers_enum", None)
-                hidden_act="silu",
+            if moe_layers_enum is not None:
-                quant_config=quant_config,
+                moe_layers_idx = [int(i) for i in moe_layers_enum.strip().split(",")]
-                prefix=f"{prefix}.mlp",
+            else:
-            )
+                # Default to 1dense.
-            self.use_moe = False
+                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):
+        forward_ctx = get_forward_context()
-            hidden_states, residual = layer(positions, hidden_states, residual)
+        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

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,

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.

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