vllm.model_executor.models.kimi_linear ¶

class KimiDecoderLayer(nn.Module):
    def __init__(
        self,
        config: KimiLinearConfig,
        layer_idx: int,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        parallel_config: ParallelConfig | None = None,
        model_config: ModelConfig | None = None,
        prefix: str = "",
        **kwargs,
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size

        self.is_moe = config.is_moe

        if config.is_kda_layer(layer_idx):
            self.self_attn = KimiDeltaAttention(
                layer_idx=layer_idx,
                hidden_size=config.hidden_size,
                quant_config=quant_config,
                cache_config=cache_config,
                model_config=config,
                prefix=f"{prefix}.self_attn",
            )
        else:
            self.self_attn = KimiMLAAttention(
                layer_idx=layer_idx,
                hidden_size=self.hidden_size,
                num_heads=config.num_attention_heads,
                quant_config=quant_config,
                cache_config=cache_config,
                model_config=model_config,
                prefix=f"{prefix}.self_attn",
                config=config,
                qk_nope_head_dim=config.qk_nope_head_dim,
                qk_rope_head_dim=config.qk_rope_head_dim,
                v_head_dim=config.v_head_dim,
                q_lora_rank=config.q_lora_rank,
                kv_lora_rank=config.kv_lora_rank,
                use_nope=config.mla_use_nope,
            )

        if (
            self.is_moe
            and config.num_experts is not None
            and layer_idx >= config.first_k_dense_replace
            and layer_idx % config.moe_layer_freq == 0
        ):
            self.block_sparse_moe = KimiMoE(
                config=config,
                quant_config=quant_config,
                prefix=f"{prefix}.block_sparse_moe",
            )
            self.mlp = self.block_sparse_moe
        else:
            self.mlp = KimiMLP(
                hidden_size=self.hidden_size,
                intermediate_size=config.intermediate_size,
                hidden_act=config.hidden_act,
                quant_config=quant_config,
                prefix=f"{prefix}.mlp",
            )
        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
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: torch.Tensor | None,
        **kwargs,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # Self Attention
        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(hidden_states, residual)

        attn_output = torch.empty_like(hidden_states)
        self.self_attn(
            hidden_states=hidden_states,
            positions=positions,
            output=attn_output,
        )
        hidden_states = attn_output

        # Fully Connected
        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
        hidden_states = self.mlp(hidden_states)
        return hidden_states, residual

class KimiLinearForCausalLM(
    nn.Module, HasInnerState, SupportsPP, MixtureOfExperts, IsHybrid
):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        self.model_config = vllm_config.model_config
        self.vllm_config = vllm_config
        self.config = self.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.quant_config = quant_config
        self.model = KimiLinearModel(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
        if get_pp_group().is_last_rank:
            self.lm_head = ParallelLMHead(
                self.config.vocab_size,
                self.config.hidden_size,
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "lm_head"),
            )
        else:
            self.lm_head = PPMissingLayer()
        logit_scale = getattr(self.config, "logit_scale", 1.0)
        self.logits_processor = LogitsProcessor(
            self.config.vocab_size, scale=logit_scale
        )

    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,
        intermediate_tensors: IntermediateTensors | None = None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs,
    ) -> torch.Tensor | IntermediateTensors:
        hidden_states = self.model(
            input_ids, positions, intermediate_tensors, inputs_embeds, **kwargs
        )
        return hidden_states

    @classmethod
    def get_mamba_state_dtype_from_config(
        cls,
        vllm_config: "VllmConfig",
    ) -> tuple[torch.dtype, torch.dtype, torch.dtype, torch.dtype]:
        return MambaStateDtypeCalculator.kda_state_dtype(
            vllm_config.model_config.dtype, vllm_config.cache_config.mamba_cache_dtype
        )

    @classmethod
    def get_mamba_state_shape_from_config(
        cls, vllm_config: "VllmConfig"
    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
        parallel_config = vllm_config.parallel_config
        hf_config = vllm_config.model_config.hf_config
        tp_size = parallel_config.tensor_parallel_size
        num_spec = (
            vllm_config.speculative_config.num_speculative_tokens
            if vllm_config.speculative_config
            else 0
        )
        return MambaStateShapeCalculator.kda_state_shape(
            tp_size,
            hf_config.linear_attn_config["num_heads"],
            hf_config.linear_attn_config["head_dim"],
            conv_kernel_size=hf_config.linear_attn_config["short_conv_kernel_size"],
            num_spec=num_spec,
        )

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor | None:
        return self.logits_processor(self.lm_head, hidden_states)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]
        if self.config.is_moe:
            # 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="w1",
                ckpt_down_proj_name="w2",
                ckpt_up_proj_name="w3",
                num_experts=self.config.num_experts,
            )
        else:
            expert_params_mapping = []
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        for args in weights:
            name, loaded_weight = args[:2]
            kwargs = args[2] if len(args) > 2 else {}
            if "rotary_emb.inv_freq" in name:
                continue

            spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
            if spec_layer is not None:
                continue  # skip spec decode layers for main model
            if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
                # Models trained using ColossalAI may include these tensors in
                # the checkpoint. Skip them.
                continue
            for param_name, weight_name, shard_id in stacked_params_mapping:
                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)
                # 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 = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                for idx, (param_name, weight_name, expert_id, shard_id) in enumerate(
                    expert_params_mapping
                ):
                    if weight_name not in name:
                        continue
                    name = name.replace(weight_name, param_name)
                    if is_pp_missing_parameter(name, self):
                        continue
                    param = params_dict[name]
                    weight_loader = param.weight_loader
                    weight_loader(
                        param,
                        loaded_weight,
                        name,
                        expert_id=expert_id,
                        shard_id=shard_id,
                    )
                    break
                else:
                    # Skip loading extra bias for GPTQ models.
                    if (
                        name.endswith(".bias")
                        and name not in params_dict
                        and not self.config.is_linear_attn
                    ):  # noqa: E501
                        continue
                    # Remapping the name of FP8 kv-scale.
                    name = maybe_remap_kv_scale_name(name, params_dict)
                    if name is None:
                        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, **kwargs)
            loaded_params.add(name)

@support_torch_compile
class KimiLinearModel(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_text_config
        model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        parallel_config = vllm_config.parallel_config
        self.config = config

        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        if get_pp_group().is_first_rank:
            self.embed_tokens = VocabParallelEmbedding(
                config.vocab_size,
                config.hidden_size,
                prefix=f"{prefix}.embed_tokens",
            )
        else:
            self.embed_tokens = PPMissingLayer()

        extra_kwargs = {}

        def get_layer(prefix: str):
            layer_idx = int(prefix.rsplit(".", 1)[1])
            return KimiDecoderLayer(
                config,
                layer_idx,
                cache_config,
                quant_config,
                parallel_config,
                model_config,
                prefix,
                **extra_kwargs,
            )

        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            get_layer,
            prefix=f"{prefix}.layers",
        )

        if get_pp_group().is_last_rank:
            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer()

        world_size = get_tensor_model_parallel_world_size()
        assert config.num_attention_heads % world_size == 0, (
            "num_attention_heads must be divisible by world_size"
        )

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

    def forward(
        self,
        input_ids: torch.Tensor | None,
        positions: torch.Tensor,
        intermediate_tensors: IntermediateTensors | None,
        inputs_embeds: torch.Tensor | None = None,
        **kwargs,
    ) -> torch.Tensor:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.get_input_embeddings(input_ids)
            residual = None
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        for _, layer in enumerate(self.layers[self.start_layer : self.end_layer]):
            hidden_states, residual = layer(
                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

class KimiMLAAttention(nn.Module):
    """
    Main reference: DeepseekV2 vllm Implementation
    """

    def __init__(
        self,
        config: KimiLinearConfig,
        hidden_size: int,
        num_heads: int,
        qk_nope_head_dim: int,
        qk_rope_head_dim: int,
        v_head_dim: int,
        q_lora_rank: int | None,
        kv_lora_rank: int,
        rope_theta: float = 10000,
        use_nope: bool = False,
        rope_scaling: dict[str, Any] | None = None,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
        **kwargs,
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        self.qk_nope_head_dim = qk_nope_head_dim
        self.qk_rope_head_dim = qk_rope_head_dim
        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
        self.v_head_dim = v_head_dim
        self.q_lora_rank = q_lora_rank
        self.kv_lora_rank = kv_lora_rank
        self.num_heads = num_heads
        tp_size = get_tensor_model_parallel_world_size()
        self.num_local_heads = num_heads // tp_size
        self.scaling = self.qk_head_dim**-0.5
        self.rope_theta = rope_theta
        self.use_nope = use_nope
        assert self.use_nope is True
        assert self.q_lora_rank is None
        assert rope_scaling is None
        assert num_heads % tp_size == 0
        self.kv_a_proj_with_mqa = ReplicatedLinear(
            self.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.kv_a_proj_with_mqa",
        )
        self.q_proj = ColumnParallelLinear(
            self.hidden_size,
            self.num_heads * self.qk_head_dim,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.q_proj",
        )
        self.kv_a_layernorm = RMSNorm(
            self.kv_lora_rank,
            eps=config.rms_norm_eps,
        )
        self.kv_b_proj = ColumnParallelLinear(
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.kv_b_proj",
        )
        self.o_proj = RowParallelLinear(
            self.num_heads * self.v_head_dim,
            self.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        mla_modules = MLAModules(
            kv_a_layernorm=self.kv_a_layernorm,
            kv_b_proj=self.kv_b_proj,
            rotary_emb=None,
            o_proj=self.o_proj,
            fused_qkv_a_proj=None,
            kv_a_proj_with_mqa=self.kv_a_proj_with_mqa,
            q_a_layernorm=None,
            q_b_proj=None,
            q_proj=self.q_proj,
            indexer=None,
            is_sparse=False,
            topk_indices_buffer=None,
        )
        self.mla_attn = MultiHeadLatentAttentionWrapper(
            self.hidden_size,
            self.num_local_heads,
            self.scaling,
            self.qk_nope_head_dim,
            self.qk_rope_head_dim,
            self.v_head_dim,
            self.q_lora_rank,
            self.kv_lora_rank,
            mla_modules,
            cache_config,
            quant_config,
            prefix,
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        output: torch.Tensor,
    ) -> None:
        output[:] = self.mla_attn(positions, hidden_states)