vLLM/vllm/models/kimi_k3/amd/mtp.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Inference-only Kimi-K3 Multi-Token-Prediction (MTP) draft model."""

import copy
from collections.abc import Iterable

import torch
import torch.nn as nn

from vllm.config import VllmConfig
from vllm.logger import init_logger
from vllm.model_executor.layers.fused_moe import (
    fused_moe_make_expert_params_mapping,
)
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from vllm.model_executor.model_loader.weight_utils import (
    default_weight_loader,
    maybe_remap_kv_scale_name,
)
from vllm.model_executor.models.utils import get_pp_missing_layer_names, maybe_prefix
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs.kimi_linear import KimiLinearConfig

from ..common.mtp import fused_mtp_input
from .linear import KimiDecoderLayer, get_spec_layer_idx_from_weight_name

logger = init_logger(__name__)


class SharedHead(nn.Module):
    def __init__(
        self,
        config: KimiLinearConfig,
        prefix: str,
        quant_config: QuantizationConfig | None = None,
    ) -> None:
        super().__init__()
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.head = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
            quant_config=quant_config,
            prefix=maybe_prefix(prefix, "head"),
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        return self.norm(hidden_states)


class KimiK3MultiTokenPredictorLayer(nn.Module):
    def __init__(
        self,
        config: KimiLinearConfig,
        vllm_config: VllmConfig,
        prefix: str,
    ) -> None:
        super().__init__()
        self.config = config
        quant_config = vllm_config.quant_config

        self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)

        self.shared_head = SharedHead(
            config=config, prefix=prefix, quant_config=quant_config
        )
        # The MTP block is a standard KimiDecoderLayer, but it must NOT use the
        # attn-residual (block-residual) scheme even when the base model does:
        # the draft starts from the target's hidden state, without the main
        # model's accumulated per-block residual tensor. We disable it by
        # shallow-copying the config with ``attn_res_block_size=None``.
        block_config = copy.copy(config)
        block_config.attn_res_block_size = None
        # NOTE: the prefix must end in the numeric spec-layer index so that
        # KimiDecoderLayer can parse ``layer_idx`` and pick MLA (full attn).
        self.mtp_block = KimiDecoderLayer(block_config, vllm_config, prefix=prefix)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        previous_hidden_states: torch.Tensor,
        inputs_embeds: torch.Tensor | None = None,
        spec_step_index: int = 0,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        assert inputs_embeds is not None
        hidden_states = self.eh_proj(
            fused_mtp_input(
                positions,
                inputs_embeds,
                previous_hidden_states,
                self.enorm.weight,
                self.hnorm.weight,
                self.enorm.variance_epsilon,
            )
        )

        hidden_states, residual = self.mtp_block(
            positions=positions,
            hidden_states=hidden_states,
            residual=None,
        )
        # Produce the normalized logits input and the pre-norm recurrent state
        # in one fused add-RMSNorm launch.
        logits_hidden_states, hidden_states = self.shared_head.norm(
            hidden_states, residual
        )
        return logits_hidden_states, hidden_states


class KimiK3MultiTokenPredictor(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config: KimiLinearConfig = vllm_config.model_config.hf_text_config
        self.config = config
        self.mtp_start_layer_idx = config.num_hidden_layers
        self.num_mtp_layers = config.num_nextn_predict_layers

        self.layers = torch.nn.ModuleDict(
            {
                str(idx): KimiK3MultiTokenPredictorLayer(
                    config, vllm_config, f"{prefix}.layers.{idx}"
                )
                for idx in range(
                    self.mtp_start_layer_idx,
                    self.mtp_start_layer_idx + self.num_mtp_layers,
                )
            }
        )
        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
            prefix=maybe_prefix(prefix, "embed_tokens"),
        )
        self.logits_processor = LogitsProcessor(config.vocab_size)

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

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        previous_hidden_states: torch.Tensor,
        inputs_embeds: torch.Tensor | None = None,
        spec_step_idx: int = 0,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
        current_step_idx = spec_step_idx % self.num_mtp_layers
        return self.layers[str(self.mtp_start_layer_idx + current_step_idx)](
            input_ids,
            positions,
            previous_hidden_states,
            inputs_embeds,
            current_step_idx,
        )

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        spec_step_idx: int = 0,
    ) -> torch.Tensor:
        current_step_idx = spec_step_idx % self.num_mtp_layers
        mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)]
        logits = self.logits_processor(mtp_layer.shared_head.head, hidden_states)
        return logits


class KimiK3MTP(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        self.config = vllm_config.model_config.hf_text_config
        self.quant_config = vllm_config.quant_config
        self.model = KimiK3MultiTokenPredictor(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )

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

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        spec_step_idx: int = 0,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        return self.model(
            input_ids,
            positions,
            hidden_states,
            inputs_embeds,
            spec_step_idx,
        )

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        spec_step_idx: int = 0,
    ) -> torch.Tensor | None:
        return self.model.compute_logits(hidden_states, spec_step_idx)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        # Mirror KimiLinearForCausalLM.load_weights naming: leading-dot shard
        # names, q_lora-conditional fused QKV, and w1/w2/w3 expert weights.
        kda_config = self.config.linear_attn_config
        use_full_rank_gate = bool(
            kda_config and kda_config.get("use_full_rank_gate", False)
        )
        beta_shard_id = 5 if use_full_rank_gate else 3
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".in_proj_qkvgfab", ".q_proj", 0),
            (".in_proj_qkvgfab", ".k_proj", 1),
            (".in_proj_qkvgfab", ".v_proj", 2),
            (".in_proj_qkvgfab", ".b_proj", beta_shard_id),
            (".in_proj_qkvgfab", ".f_a_proj", 4),
            (".conv1d", ".q_conv1d", 0),
            (".conv1d", ".k_conv1d", 1),
            (".conv1d", ".v_conv1d", 2),
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]
        if use_full_rank_gate:
            stacked_params_mapping.append((".in_proj_qkvgfab", ".g_proj", 3))
        if getattr(self.config, "q_lora_rank", None) is not None:
            stacked_params_mapping += [
                (".fused_qkv_a_proj", ".q_a_proj", 0),
                (".fused_qkv_a_proj", ".kv_a_proj_with_mqa", 1),
            ]

        expert_params_mapping = (
            fused_moe_make_expert_params_mapping(
                self,
                ckpt_gate_proj_name="w1",
                ckpt_down_proj_name="w2",
                ckpt_up_proj_name="w3",
                num_experts=self.config.num_experts,
            )
            if self.config.is_moe
            else []
        )

        pp_missing_layer_names = get_pp_missing_layer_names(self)
        params_dict = dict(self.named_parameters())
        # Under the MXFP4 quant interface the routed experts register unpacked
        # params (``w13_weight``), while the compressed-tensors checkpoint names
        # them ``.weight_packed``. Rebind so the expert mapping resolves; scales
        # already share the ``.weight_scale`` suffix.
        experts_unpacked = not any(n.endswith("w13_weight_packed") for n in params_dict)
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            # The multimodal checkpoint prefixes text weights with
            # ``language_model.``; strip it so names match this draft model's
            # parameter paths (``model.layers.{i}.``). Non-text weights
            # (vision_tower, mm_projector, ...) never match a spec layer below.
            if name.startswith("language_model."):
                name = name[len("language_model.") :]
            if experts_unpacked and name.endswith(".weight_packed"):
                name = name.replace(".weight_packed", ".weight")
            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
            if spec_layer is None:
                continue
            name = self._rewrite_spec_layer_name(spec_layer, name)

            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in name:
                    continue
                # Routed experts (``.experts.{i}.w1/w2/w3``) are handled by the
                # expert mapping below; skip them here. Shared experts
                # (``.shared_experts.``) use gate/up_proj and fall through.
                if ".experts." in name:
                    continue
                name_mapped = name.replace(weight_name, param_name)
                # Only take this mapping if the fused destination actually
                # exists (e.g. QKV fusion is only present when q_lora is used).
                if name_mapped not in params_dict:
                    continue
                if name_mapped in pp_missing_layer_names:
                    continue
                name = name_mapped
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                for (
                    expert_param_name,
                    expert_weight_name,
                    expert_id,
                    expert_shard_id,
                ) in expert_params_mapping:
                    if expert_weight_name not in name:
                        continue
                    name_mapped = name.replace(expert_weight_name, expert_param_name)
                    if name_mapped in pp_missing_layer_names:
                        continue
                    param = params_dict[name_mapped]
                    weight_loader = param.weight_loader
                    weight_loader(
                        param,
                        loaded_weight,
                        name_mapped,
                        shard_id=expert_shard_id,
                        expert_id=expert_id,
                    )
                    name = name_mapped
                    break
                else:
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    remapped_name = maybe_remap_kv_scale_name(name, params_dict)
                    if remapped_name is None:
                        continue
                    name = remapped_name

                    # The embedding is shared across MTP layers; only the first
                    # spec layer carries the hoisted (non-".layers") copy.
                    if spec_layer != self.model.mtp_start_layer_idx and (
                        ".layers" not in name
                    ):
                        continue
                    if name in pp_missing_layer_names:
                        continue
                    # The base model uses an attn-residual scheme whose per-layer
                    # weights (self_attention_res_*, mlp_res_*) are not used by
                    # the draft block; such names have no matching parameter and
                    # are safely skipped.
                    if name not in params_dict:
                        continue

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

        # Validate that weights were loaded for each expected MTP layer.
        loaded_layers: set[int] = set()
        for param_name in loaded_params:
            spec_layer = get_spec_layer_idx_from_weight_name(self.config, param_name)
            if spec_layer is not None:
                loaded_layers.add(spec_layer)
        for layer_idx in range(
            self.model.mtp_start_layer_idx,
            self.model.mtp_start_layer_idx + self.model.num_mtp_layers,
        ):
            if layer_idx not in loaded_layers:
                raise ValueError(
                    f"MTP speculative decoding layer {layer_idx} weights "
                    f"missing from checkpoint. The checkpoint may not include "
                    f"the MTP layer weights. Use a checkpoint that includes "
                    f"MTP layer weights, or disable speculative decoding."
                )

        return loaded_params

    def _rewrite_spec_layer_name(self, spec_layer: int, name: str) -> str:
        """Rewrite a checkpoint weight name to this module's parameter path.

        Top-level MTP submodules (enorm/hnorm/eh_proj/shared_head) stay under
        ``model.layers.{spec_layer}.*``; the shared ``embed_tokens`` is hoisted
        to ``model.*``; everything else is a transformer-block weight and gets
        ``.mtp_block`` inserted.
        """
        spec_layer_weight_names = [
            "embed_tokens",
            "enorm",
            "hnorm",
            "eh_proj",
            "shared_head",
        ]
        shared_weight_names = ["embed_tokens"]
        spec_layer_weight = False
        shared_weight = False
        for weight_name in spec_layer_weight_names:
            if weight_name in name:
                spec_layer_weight = True
                if weight_name in shared_weight_names:
                    shared_weight = True
                break
        if not spec_layer_weight:
            name = name.replace(
                f"model.layers.{spec_layer}.",
                f"model.layers.{spec_layer}.mtp_block.",
            )
        elif shared_weight:
            name = name.replace(f"model.layers.{spec_layer}.", "model.")
        return name