vllm.model_executor.models.mamba ¶

class MambaDecoderLayer(nn.Module):
    def __init__(
        self,
        config: MambaConfig,
        model_config: Optional[ModelConfig] = None,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        is_lora_enabled: Optional[bool] = False,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.is_falcon_mamba = config.model_type == "falcon_mamba"
        self.is_lora_enabled = is_lora_enabled
        mixer_rms_eps = config.mixer_rms_eps if self.is_falcon_mamba else None
        self.mixer = MambaMixer(
            hidden_size=config.hidden_size,
            ssm_state_size=config.state_size,
            conv_kernel_size=config.conv_kernel,
            intermediate_size=config.intermediate_size,
            time_step_rank=config.time_step_rank,
            use_conv_bias=config.use_conv_bias,
            use_bias=config.use_bias,
            use_rms_norm=self.is_falcon_mamba,
            rms_norm_has_weight=not self.is_falcon_mamba,
            rms_norm_eps=mixer_rms_eps,
            activation=config.hidden_act,
            is_lora_enabled=self.is_lora_enabled,
            model_config=model_config,
            cache_config=cache_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],
        **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)
        return output, residual

class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree, SupportsPP):
    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
        self.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.model_config = vllm_config.model_config
        self.backbone = MambaModel(
            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
        if config.tie_word_embeddings:
            self.lm_head = self.backbone.embeddings
        else:
            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,
                prefix=maybe_prefix(prefix, "lm_head"),
            )

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

        return hidden_states

    @classmethod
    def get_mamba_state_dtype_from_config(
        cls,
        vllm_config: "VllmConfig",
    ) -> tuple[torch.dtype, torch.dtype]:
        return MambaStateDtypeCalculator.mamba1_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",
    ) -> tuple[tuple[int, int], tuple[int, int]]:
        parallel_config = vllm_config.parallel_config
        hf_config = vllm_config.model_config.hf_config

        return MambaStateShapeCalculator.mamba1_state_shape(
            tp_world_size=parallel_config.tensor_parallel_size,
            intermediate_size=hf_config.intermediate_size,
            state_size=hf_config.state_size,
            conv_kernel=hf_config.conv_kernel,
        )

    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) -> 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]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)

@support_torch_compile
class MambaModel(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)

        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: MambaDecoderLayer(
                config,
                model_config=model_config,
                cache_config=cache_config,
                quant_config=quant_config,
                is_lora_enabled=is_lora_enabled,
                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,
        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"]

        for i in range(self.start_layer, self.end_layer):
            layer = self.layers[i]
            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_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