vllm.model_executor.models.longcat_flash_mtp ¶

class LongCatFlashMTP(nn.Module, SupportsPP):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        # LongCat MTP without MoE layers
        vllm_config.model_config.hf_config.n_routed_experts = None
        self.config = FlashConfig(
            **vllm_config.model_config.hf_config.__dict__)
        self.quant_config = None if "mtp" in getattr(
            self.config, "disable_quant_module",
            []) else vllm_config.quant_config

        self.model = LongCatMultiTokenPredictor(vllm_config=vllm_config,
                                                quant_config=self.quant_config,
                                                prefix=maybe_prefix(
                                                    prefix, "model"))
        self.lm_head = ParallelLMHead(
            self.config.vocab_size,
            self.config.hidden_size,
            quant_config=self.quant_config,
        )
        self.logits_processor = LogitsProcessor(self.config.vocab_size)

    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:
        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,
        spec_step_idx: int = 0,
    ) -> Optional[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]:
        stacked_params_mapping = [
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
            ("fused_qkv_a_proj", "q_a_proj", 0),
            ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
        ]

        new_to_old_names_mapping = {
            "model.mtp.embed_tokens.weight":
            "model.layers.0.embed_tokens.weight",
            "model.mtp.layers.0.eh_proj.weight": "eh_proj.weight",
            "model.mtp.layers.0.eh_proj.weight_scale_inv":
            "eh_proj.weight_scale_inv",
            "model.mtp.layers.0.enorm.m.weight": "enorm.weight",
            "model.mtp.layers.0.hnorm.m.weight": "hnorm.weight",
            "model.mtp.layers.0.input_layernorm.weight":
            "model.layers.0.input_layernorm.weight",
            "model.mtp.layers.0.post_attention_layernorm.weight":
            "model.layers.0.post_attention_layernorm.weight",
            "model.mtp.layers.0.self_attn.kv_a_layernorm.weight":
            "model.layers.0.self_attn.kv_a_layernorm.weight",
            "model.mtp.layers.0.self_attn.kv_a_proj_with_mqa.weight":
            "model.layers.0.self_attn.kv_a_proj_with_mqa.weight",
            "model.mtp.layers.0.self_attn.kv_a_proj_with_mqa.weight_scale_inv":
            "model.layers.0.self_attn.kv_a_proj_with_mqa.weight_scale_inv",
            "model.mtp.layers.0.self_attn.kv_b_proj.weight":
            "model.layers.0.self_attn.kv_b_proj.weight",
            "model.mtp.layers.0.self_attn.kv_b_proj.weight_scale_inv":
            "model.layers.0.self_attn.kv_b_proj.weight_scale_inv",
            "model.mtp.layers.0.self_attn.o_proj.weight":
            "model.layers.0.self_attn.o_proj.weight",
            "model.mtp.layers.0.self_attn.o_proj.weight_scale_inv":
            "model.layers.0.self_attn.o_proj.weight_scale_inv",
            "model.mtp.layers.0.self_attn.q_a_layernorm.weight":
            "model.layers.0.self_attn.q_a_layernorm.weight",
            "model.mtp.layers.0.self_attn.q_a_proj.weight":
            "model.layers.0.self_attn.q_a_proj.weight",
            "model.mtp.layers.0.self_attn.q_a_proj.weight_scale_inv":
            "model.layers.0.self_attn.q_a_proj.weight_scale_inv",
            "model.mtp.layers.0.self_attn.q_b_proj.weight":
            "model.layers.0.self_attn.q_b_proj.weight",
            "model.mtp.layers.0.self_attn.q_b_proj.weight_scale_inv":
            "model.layers.0.self_attn.q_b_proj.weight_scale_inv",
            "model.mtp.layers.0.transformer_layer.mlp.down_proj.weight":
            "model.layers.0.mlp.down_proj.weight",
            "model.mtp.layers.0.transformer_layer.mlp.down_proj.weight_scale_inv":
            "model.layers.0.mlp.down_proj.weight_scale_inv",
            "model.mtp.layers.0.transformer_layer.mlp.gate_proj.weight":
            "model.layers.0.mlp.gate_proj.weight",
            "model.mtp.layers.0.transformer_layer.mlp.gate_proj.weight_scale_inv":
            "model.layers.0.mlp.gate_proj.weight_scale_inv",
            "model.mtp.layers.0.transformer_layer.mlp.up_proj.weight":
            "model.layers.0.mlp.up_proj.weight",
            "model.mtp.layers.0.transformer_layer.mlp.up_proj.weight_scale_inv":
            "model.layers.0.mlp.up_proj.weight_scale_inv",
            "model.mtp.norm.weight": "final_layernorm.weight",
        }

        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
            spec_layer = self.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,
                                                 new_to_old_names_mapping)
            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
                # 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)

                # QKV fusion is optional, fall back to normal
                # weight loading if it's not enabled
                if ((param_name == "fused_qkv_a_proj")
                        and name not in params_dict):
                    continue

                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    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") and name not in params_dict:
                    continue

                # According to DeepSeek-V3 Technical Report, MTP modules
                # shares embedding layer. We only load the first weights.
                if (spec_layer != self.model.mtp_start_layer_idx
                        and ".layers" 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)
        spec_layer_id = self.config.num_hidden_layers * 2
        self_attn = self.model.layers[str(spec_layer_id)].mtp_block.self_attn
        if hasattr(
                self.quant_config,
                "weight_block_size") and self_attn.kv_b_proj.weight.dtype in (
                    torch.float8_e4m3fn,
                    torch.float8_e4m3fnuz,
                ):
            weight_block_size = self.quant_config.weight_block_size
            if weight_block_size is not None:
                dtype = torch.get_default_dtype()
                w = block_dequant(self_attn.kv_b_proj.weight,
                                  self_attn.kv_b_proj.weight_scale_inv,
                                  weight_block_size).to(dtype)
            else:
                w = self_attn.kv_b_proj.weight
        else:
            w = self_attn.kv_b_proj.weight
        w_kc, w_vc = w.unflatten(
            0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)).split(
                [self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
        self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
        self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
        if self.config.mla_scale_q_lora:
            self_attn.q_a_layernorm.weight.data *= (
                self.config.hidden_size / self.config.q_lora_rank)**0.5
        if self.config.mla_scale_kv_lora:
            self_attn.kv_a_layernorm.weight.data *= (
                self.config.hidden_size / self.config.kv_lora_rank)**0.5
        return loaded_params

    def _rewrite_spec_layer_name(self, spec_layer: int, name: str,
                                 new_to_old_names_mapping: dict) -> str:
        """
        Rewrite the weight name to match the format of the original model.
        Add .mtp_block for modules in transformer layer block for spec layer
        and rename shared layer weights to be top level.
        """
        if name in new_to_old_names_mapping:
            name = new_to_old_names_mapping[name]
        spec_layer_weight_names = [
            "embed_tokens", "enorm", "hnorm", "eh_proj", "shared_head"
        ]
        if name.startswith("enorm") or name.startswith(
                "hnorm") or name.startswith("eh_proj") or name.startswith(
                    "final_layernorm"):
            name = "model.layers." + str(spec_layer) + "." + name
        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:
            # treat rest weights as weights for transformer layer block
            name = name.replace("model.layers.0.",
                                f"model.layers.{spec_layer}.mtp_block.")
        elif shared_weight:
            # treat shared weights as top level weights
            name = name.replace("model.layers.0.", "model.")
        return name

    def get_spec_layer_idx_from_weight_name(self, config: PretrainedConfig,
                                            weight_name: str) -> Optional[int]:
        if "model.mtp" in weight_name:
            return config.num_hidden_layers * 2
        return None

class LongCatMultiTokenPredictor(nn.Module):

    def __init__(self,
                 *,
                 vllm_config: VllmConfig,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = ""):
        super().__init__()
        config = FlashConfig(**vllm_config.model_config.hf_config.__dict__)
        vllm_config.model_config.hf_config.intermediate_size \
                                                = config.intermediate_size
        self.mtp_start_layer_idx = config.num_hidden_layers * 2
        self.num_mtp_layers = 1
        self.layers = torch.nn.ModuleDict({
            str(idx):
            LongCatMultiTokenPredictorLayer(
                config,
                prefix=f"{prefix}.layers.{idx}",
                vllm_config=vllm_config,
                quant_config=quant_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,
        )

    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)
        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,
        )

class LongCatMultiTokenPredictorLayer(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        prefix: str,
        vllm_config: VllmConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        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 = ReplicatedLinear(2 * config.hidden_size,
                                        config.hidden_size,
                                        bias=False,
                                        quant_config=quant_config,
                                        prefix="eh_proj")
        self.mtp_block = DeepseekV2DecoderLayer(vllm_config, prefix)
        self.final_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        previous_hidden_states: torch.Tensor,
        inputs_embeds: Optional[torch.Tensor] = None,
        spec_step_index: int = 0,
    ) -> torch.Tensor:
        assert inputs_embeds is not None
        inputs_embeds = self.enorm(inputs_embeds)
        previous_hidden_states = self.hnorm(previous_hidden_states)

        hidden_states, _ = self.eh_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, _ = self.final_layernorm(hidden_states, residual)
        return hidden_states