vllm.model_executor.models.ernie_mtp

class ErnieMTP(nn.Module, SupportsPP):

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

        self.config = vllm_config.model_config.hf_config
        self.model = ErnieMultiTokenPredictor(vllm_config=vllm_config,
                                              prefix=maybe_prefix(
                                                  prefix, "model"))
        self.lm_head = ParallelLMHead(self.config.vocab_size,
                                      self.config.hidden_size)
        self.sampler = get_sampler()

        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.model.embed_tokens.weight

    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:
        assert spec_step_idx == 0, "ernie_mtp only support predict one token"
        hidden_states = self.model(input_ids, positions, hidden_states,
                                   inputs_embeds, spec_step_idx)
        return hidden_states

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

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            ("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_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:

            if self.config.tie_word_embeddings and name.endswith(
                    "lm_head.weight"):
                continue
            if "rotary_emb.inv_freq" in name:
                continue
            if "mtp" in name:
                name = self._rewrite_spec_layer_name(self.config, name)

            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                # Skip non-stacked layers and experts (experts handled below).
                if weight_name not in name:
                    continue
                if "mtp" not in name:
                    continue
                # We have mlp.experts[0].gate_proj in the checkpoint.
                # Since we handle the experts below in expert_params_mapping,
                # we need to skip here BEFORE we update the name, otherwise
                # name will be updated to mlp.experts[0].gate_up_proj, which
                # will then be updated below in expert_params_mapping
                # for mlp.experts[0].gate_gate_up_proj, which breaks load.
                if (("mlp.experts." in name) and name not in params_dict):
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if ((name.endswith(".bias") or name.endswith("_bias"))
                        and name not in params_dict):
                    continue
                # Skip layers on other devices.
                if is_pp_missing_parameter(name, self):
                    continue

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

                # According to DeepSeek-V3 Technical Report, MTP modules
                # shares embedding layer. We only load the first weights.
                if "mtp_" not in name and ("embed_tokens" not in name
                                           and "lm_head" not in name):
                    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

    def _rewrite_spec_layer_name(self, config: PretrainedConfig,
                                 name: str) -> str:
        """
        Rewrite the weight name to match the format of the original model.
        """
        spec_layer_weight_names = [
            "embed_tokens", "mtp_emb_norm", "mtp_hidden_norm",
            "mtp_linear_proj"
        ]
        layer_idx = config.num_hidden_layers
        for weight_name in spec_layer_weight_names:
            if weight_name in name:
                name = name.replace(
                    f"model.{weight_name}.0.",
                    f"model.layers.{layer_idx}.{weight_name}.")
                return name
        name = name.replace("model.mtp_block.0.",
                            f"model.layers.{layer_idx}.mtp_block.")
        return name

class ErnieMultiTokenPredictor(nn.Module):

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

        config = vllm_config.model_config.hf_config
        self.mtp_start_layer_idx = config.num_hidden_layers
        self.num_mtp_layers = config.num_nextn_predict_layers
        # to map the exact layer index from weights
        self.layers = torch.nn.ModuleDict({
            str(idx):
            ErnieMultiTokenPredictorLayer(
                config,
                f"{prefix}.layers.{idx}",
                model_config=vllm_config.model_config,
                cache_config=vllm_config.cache_config,
            )
            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,
        )
        self.logits_processor = LogitsProcessor(config.vocab_size)

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

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        lm_head: ParallelLMHead,
        sampling_metadata: SamplingMetadata,
        spec_step_idx: int = 0,
    ) -> torch.Tensor:
        self.layers[str(self.mtp_start_layer_idx + spec_step_idx)]
        logits = self.logits_processor(lm_head, hidden_states,
                                       sampling_metadata)
        return logits

class ErnieMultiTokenPredictorLayer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        prefix: str,
        model_config: ModelConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()

        self.mtp_emb_norm = RMSNorm(config.hidden_size,
                                    eps=config.rms_norm_eps)
        self.mtp_hidden_norm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.mtp_linear_proj = nn.Linear(config.hidden_size * 2,
                                         config.hidden_size,
                                         bias=False)
        self.mtp_block = LlamaDecoderLayer(config, cache_config, quant_config,
                                           prefix)

    def forward(
        self,
        inputs_embeds: torch.Tensor,
        positions: torch.Tensor,
        previous_hidden_states: torch.Tensor,
        spec_step_index: int = 0,
    ) -> torch.Tensor:
        assert inputs_embeds is not None
        # masking inputs at position 0, as not needed by MTP
        inputs_embeds[positions == 0] = 0

        inputs_embeds = self.mtp_emb_norm(inputs_embeds)
        previous_hidden_states = self.mtp_hidden_norm(previous_hidden_states)

        hidden_states = self.mtp_linear_proj(
            torch.cat([inputs_embeds, previous_hidden_states], dim=-1))

        hidden_states, residual = self.mtp_block(positions=positions,
                                                 hidden_states=hidden_states,
                                                 residual=None)
        hidden_states = residual + hidden_states

        return hidden_states