vllm.model_executor.models.mamba2

class Mamba2DecoderLayer(nn.Module):

    def __init__(self,
                 config: MambaConfig,
                 model_config: Optional[ModelConfig] = None,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = "") -> None:
        super().__init__()
        self.config = config
        self.mixer = MambaMixer2(hidden_size=config.hidden_size,
                                 ssm_state_size=config.state_size,
                                 conv_kernel_size=config.conv_kernel,
                                 intermediate_size=getattr(
                                     config, "intermediate_size",
                                     config.expand * config.hidden_size),
                                 use_conv_bias=config.use_conv_bias,
                                 use_bias=config.use_bias,
                                 n_groups=config.n_groups,
                                 num_heads=config.num_heads,
                                 head_dim=config.head_dim,
                                 rms_norm_eps=config.layer_norm_epsilon,
                                 activation=config.hidden_act,
                                 model_config=model_config,
                                 cache_config=cache_config,
                                 quant_config=quant_config,
                                 prefix=f"{prefix}.mixer")

        self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)

    def forward(
        self,
        hidden_states: torch.Tensor,
        residual: Optional[torch.Tensor],
        mamba_cache_params: MambaCacheParams,
        mamba2_metadata: Mamba2Metadata,
        **kwargs,
    ):
        if residual is None:
            residual = hidden_states
            hidden_states = self.norm(hidden_states)
        else:
            hidden_states, residual = self.norm(hidden_states, residual)

        output = torch.empty_like(hidden_states)
        self.mixer(hidden_states, output, mamba_cache_params, mamba2_metadata)
        return output, residual

class Mamba2ForCausalLM(nn.Module, HasInnerState, IsAttentionFree):

    @classmethod
    def get_mamba_state_dtype_from_config(
        cls,
        vllm_config: "VllmConfig",
    ) -> tuple[torch.dtype, torch.dtype]:

        return MambaStateDtypeCalculator.mamba2_state_dtype(
            vllm_config.model_config.dtype,
            vllm_config.cache_config.mamba_cache_dtype,
            vllm_config.cache_config.mamba_ssm_cache_dtype,
        )

    @classmethod
    def get_mamba_state_shape_from_config(
        cls,
        vllm_config: "VllmConfig",
        use_v1: bool = True,
    ) -> tuple[tuple[int, int], tuple[int, int, int]]:
        """Calculate shapes for Mamba's convolutional and state caches.

        Args:
            vllm_config: vLLM config
            use_v1: Get shapes for V1 (or V0)

        Returns:
            Tuple containing:
            - conv_state_shape: Shape for convolutional state cache
            - temporal_state_shape: Shape for state space model cache
        """
        parallel_config = vllm_config.parallel_config
        hf_config = vllm_config.model_config.hf_config
        intermediate_size = hf_config.expand * hf_config.hidden_size

        return MambaStateShapeCalculator.mamba2_state_shape(
            intermediate_size=intermediate_size,
            tp_world_size=parallel_config.tensor_parallel_size,
            n_groups=hf_config.n_groups,
            num_heads=hf_config.num_heads,
            head_dim=hf_config.head_dim,
            state_size=hf_config.state_size,
            conv_kernel=hf_config.conv_kernel,
            use_v1=use_v1,
        )

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

        super().__init__()
        self.config = config
        self.vllm_config = vllm_config
        self.scheduler_config = scheduler_config
        self.model_config = vllm_config.model_config
        self.backbone = Mamba2Model(vllm_config=vllm_config,
                                    prefix=maybe_prefix(prefix, "backbone"))
        self.unpadded_vocab_size = config.vocab_size
        if lora_config:
            self.unpadded_vocab_size += lora_config.lora_extra_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
            # We need bigger padding if using lora for kernel
            # compatibility
            if not lora_config else lora_config.lora_vocab_padding_size,
        )
        if config.tie_word_embeddings:
            self.lm_head = self.lm_head.tie_weights(self.backbone.embeddings)

        # Used to track and store by the Mamba cache between steps.
        self.mamba_cache: Optional[MambaCacheManager] = None

        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                config.vocab_size)

        self.make_empty_intermediate_tensors = (
            self.backbone.make_empty_intermediate_tensors)

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

    def forward(self,
                input_ids: torch.Tensor,
                positions: torch.Tensor,
                intermediate_tensors: Optional[IntermediateTensors] = None,
                inputs_embeds: Optional[torch.Tensor] = None,
                **kwargs):
        if not envs.VLLM_USE_V1:
            if self.mamba_cache is None:
                num_mamba_layers = (
                    self.model_config.get_num_layers_by_block_type(
                        self.vllm_config.parallel_config,
                        LayerBlockType.mamba))
                mamba_state_shape = \
                    self.get_mamba_state_shape_from_config(
                        self.vllm_config, use_v1=False)
                mamba_state_dtype = \
                    self.get_mamba_state_dtype_from_config(
                    self.vllm_config)
                self.mamba_cache = MambaCacheManager(self.vllm_config,
                                                     num_mamba_layers,
                                                     *mamba_state_shape,
                                                     *mamba_state_dtype)

            mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)
        else:
            # NOTE: mamba_cache_params is not needed for v1
            mamba_cache_params = None

        hidden_states = self.backbone(input_ids, positions, mamba_cache_params,
                                      intermediate_tensors, inputs_embeds)

        return hidden_states

    def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
        return self.mamba_cache.copy_inputs_before_cuda_graphs(
            input_buffers, **kwargs)

    def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
        return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)

    def compute_logits(self, hidden_states: torch.Tensor,
                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
        logits = self.logits_processor(self.lm_head, hidden_states,
                                       sampling_metadata)
        return logits

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)

@support_torch_compile
class Mamba2Model(nn.Module):

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

        config = vllm_config.model_config.hf_config
        model_config = vllm_config.model_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config
        is_lora_enabled = bool(lora_config)
        assert not is_lora_enabled

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

        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: Mamba2DecoderLayer(config,
                                              model_config=model_config,
                                              cache_config=cache_config,
                                              quant_config=quant_config,
                                              prefix=prefix),
            prefix=f"{prefix}.layers")

        self.norm_f = RMSNorm(config.hidden_size,
                              eps=config.layer_norm_epsilon)
        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(
                ["hidden_states", "residual"], config.hidden_size))

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

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        mamba_cache_params: MambaCacheParams,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> 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"]

        attn_metadata: AttentionMetadata = get_forward_context().attn_metadata

        if not envs.VLLM_USE_V1:
            mamba2_metadata = prepare_mamba2_metadata(
                chunk_size=self.config.chunk_size,
                attn_metadata=attn_metadata,
            )
        else:
            # v1 get mamba2_metadata from forward_context
            mamba2_metadata = None

        for i in range(len(self.layers)):
            layer = self.layers[i]

            hidden_states, residual = layer(
                positions=positions,
                hidden_states=hidden_states,
                residual=residual,
                mamba_cache_params=mamba_cache_params.at_layer_idx(
                    i - self.start_layer) if mamba_cache_params else None,
                mamba2_metadata=mamba2_metadata)

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

        hidden_states, _ = self.norm_f(hidden_states, residual)

        return hidden_states

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

            # 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