vllm/vllm/model_executor/models/qwen3_next_mtp.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Inference-only Qwen3Next MTP model."""
from collections.abc import Iterable
from typing import Optional

import torch
from torch import nn

from vllm.compilation.decorators import support_torch_compile
from vllm.config import VllmConfig
from vllm.distributed.parallel_state import get_pp_group
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.linear import ColumnParallelLinear
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.vocab_parallel_embedding import (
    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.qwen3_next import (Qwen3NextDecoderLayer,
                                                   Qwen3NextRMSNorm)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs import Qwen3NextConfig

from .interfaces import SupportsPP
from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
                    make_empty_intermediate_tensors_factory, maybe_prefix)

logger = init_logger(__name__)

KVCache = tuple[torch.Tensor, torch.Tensor]


@support_torch_compile
class Qwen3NextMultiTokenPredictor(nn.Module):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config
        config: Qwen3NextConfig = model_config.hf_config

        self.config = config
        lora_vocab = ((lora_config.lora_extra_vocab_size *
                       (lora_config.max_loras or 1)) if lora_config else 0)
        self.vocab_size = config.vocab_size + lora_vocab
        self.org_vocab_size = config.vocab_size

        self.mtp_start_layer_idx = config.num_hidden_layers
        self.num_mtp_layers = getattr(config, "num_nextn_predict_layers", 1)

        self.embed_tokens = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
        )

        self.fc = ColumnParallelLinear(self.config.hidden_size * 2,
                                       self.config.hidden_size,
                                       gather_output=True,
                                       bias=False,
                                       return_bias=False)

        self.layers = torch.nn.ModuleList(
            Qwen3NextDecoderLayer(
                config,
                layer_type="full_attention",
                model_config=model_config,
                cache_config=cache_config,
                quant_config=quant_config,
                prefix=f'{prefix}.layers.{self.mtp_start_layer_idx + idx}',
            ) for idx in range(self.num_mtp_layers))

        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(
                ["hidden_states", "residual"], config.hidden_size))

        self.norm = Qwen3NextRMSNorm(config.hidden_size,
                                     eps=config.rms_norm_eps)
        self.pre_fc_norm_hidden = Qwen3NextRMSNorm(config.hidden_size,
                                                   eps=config.rms_norm_eps)
        self.pre_fc_norm_embedding = Qwen3NextRMSNorm(config.hidden_size,
                                                      eps=config.rms_norm_eps)

    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_tokens(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        spec_step_idx: int = 0,
    ) -> torch.Tensor:
        if get_pp_group().is_first_rank:
            if inputs_embeds is None:
                inputs_embeds = self.get_input_embeddings(input_ids)
            assert hidden_states.shape[-1] == inputs_embeds.shape[-1]
            inputs_embeds = self.pre_fc_norm_embedding(inputs_embeds)
            hidden_states = self.pre_fc_norm_hidden(hidden_states)
            hidden_states = torch.cat([inputs_embeds, hidden_states], dim=-1)
            hidden_states = self.fc(hidden_states)
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        current_step_idx = (spec_step_idx % self.num_mtp_layers)
        hidden_states, residual = self.layers[current_step_idx](
            positions=positions,
            hidden_states=hidden_states,
            residual=residual,
        )

        if not get_pp_group().is_last_rank:
            return IntermediateTensors({
                "hidden_states": hidden_states,
                "residual": residual
            })

        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]

        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        expert_params_mapping = FusedMoE.make_expert_params_mapping(
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            num_experts=self.config.num_experts)

        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue

                if "mlp.experts" in name:
                    continue

                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                # Skip layers on other devices.
                if is_pp_missing_parameter(name, self):
                    continue
                if name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in name:
                        continue
                    name = name.replace(weight_name, param_name)
                    # Skip layers on other devices.
                    if is_pp_missing_parameter(name, self):
                        continue
                    # Skip loading extra bias for GPTQ models.
                    if ((name.endswith(".bias") or name.endswith("_bias"))
                            and name not in params_dict):
                        continue
                    param = params_dict[name]
                    weight_loader = param.weight_loader
                    weight_loader(param,
                                  loaded_weight,
                                  name,
                                  shard_id=shard_id,
                                  expert_id=expert_id)
                    break
                else:
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    if is_pp_missing_parameter(name, self):
                        continue

                    param = params_dict[name]
                    weight_loader = getattr(param, "weight_loader",
                                            default_weight_loader)
                    weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


@support_torch_compile
class Qwen3NextMTP(nn.Module, SupportsPP):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": ["up_proj", "down_proj"]
    }

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        config = vllm_config.model_config.hf_config
        self.vllm_config = vllm_config
        cache_config = vllm_config.cache_config
        assert not cache_config.enable_prefix_caching, \
            "Qwen3NextMTP currently does not support prefix caching"

        self.quant_config = vllm_config.quant_config

        super().__init__()
        self.config = config
        self.model = Qwen3NextMultiTokenPredictor(vllm_config=vllm_config,
                                                  prefix=maybe_prefix(
                                                      prefix, "model"))
        self.unpadded_vocab_size = config.vocab_size
        self.lm_head = ParallelLMHead(self.unpadded_vocab_size,
                                      config.hidden_size,
                                      org_num_embeddings=config.vocab_size,
                                      padding_size=DEFAULT_VOCAB_PADDING_SIZE,
                                      prefix=maybe_prefix(prefix, "lm_head"))
        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                config.vocab_size)
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)

    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.get_input_embeddings(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ):
        hidden_states = self.model(input_ids, positions, hidden_states,
                                   intermediate_tensors, inputs_embeds)
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
        spec_step_idx: int = 0,
    ) -> Optional[torch.Tensor]:
        return self.logits_processor(self.lm_head, hidden_states,
                                     sampling_metadata)

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        shared_weight_names = ["embed_tokens", "lm_head"]

        def remap_weight_names(weights):
            for name, weight in weights:
                if name.startswith("mtp."):
                    name = name.replace("mtp.", "model.")
                elif not any(key in name for key in shared_weight_names):
                    continue
                yield name, weight

        loader = AutoWeightsLoader(self)
        return loader.load_weights(remap_weight_names(weights))