vllm.model_executor.models.minimax_m2 ¶

class MiniMaxM2Attention(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        rotary_dim: int,
        rope_theta: float = 10000,
        rope_scaling: dict[str, Any] | None = None,
        attn_window_size: int | None = None,
        max_position_embeddings: int = 8192,
        head_dim: int | None = None,
        rms_norm_eps: float = 1e-06,
        qkv_bias: bool = False,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = head_dim or (hidden_size // self.total_num_heads)
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.rope_theta = rope_theta
        self.max_position_embeddings = max_position_embeddings

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=qkv_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=rotary_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
            rope_scaling=rope_scaling,
        )
        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            per_layer_sliding_window=attn_window_size,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
        )

        self.q_norm = MiniMaxText01RMSNormTP(
            self.head_dim * self.total_num_heads, eps=rms_norm_eps
        )
        self.k_norm = MiniMaxText01RMSNormTP(
            self.head_dim * self.total_num_kv_heads, eps=rms_norm_eps
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q = self.q_norm(q)
        k = self.k_norm(k)
        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output

class MiniMaxM2DecoderLayer(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        prefix: str,
        model_config: ModelConfig,
        cache_config: CacheConfig | None = None,
        quant_config: QuantizationConfig | None = None,
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        rope_theta = getattr(config, "rope_theta", 10000)
        rope_scaling = getattr(config, "rope_scaling", None)
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
        if hasattr(config, "max_model_len") and isinstance(config.max_model_len, int):
            max_position_embeddings = max(
                config.max_position_embeddings, config.max_model_len
            )
        # DecoderLayers are created with `make_layers` which passes the prefix
        # with the layer's index.
        layer_idx = int(prefix.split(sep=".")[-1])

        # TODO: support MTP
        attn_window_size = getattr(config, "attn_window_size", None)
        if attn_window_size is not None:
            if isinstance(attn_window_size, list):
                attn_window_size = attn_window_size[layer_idx]
            elif isinstance(attn_window_size, int):
                attn_window_size = attn_window_size
            else:
                raise ValueError(f"Invalid attn_window_size: {attn_window_size}")
            attn_window_size = None if attn_window_size <= 0 else attn_window_size

        # different rope theta for full layer and swa layer
        swa_rope_theta = getattr(config, "swa_rope_theta", -1)
        # default to full rope theta
        swa_rope_theta = rope_theta if swa_rope_theta <= 0 else swa_rope_theta
        rope_theta = swa_rope_theta if attn_window_size is not None else rope_theta

        self.layer_idx = layer_idx
        self.self_attn = MiniMaxM2Attention(
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=config.num_key_value_heads,
            rotary_dim=config.rotary_dim,
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            attn_window_size=attn_window_size,
            max_position_embeddings=max_position_embeddings,
            rms_norm_eps=config.rms_norm_eps,
            qkv_bias=getattr(config, "attention_bias", False),
            head_dim=getattr(config, "head_dim", None),
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )

        self.block_sparse_moe = MiniMaxM2MoE(
            config=config,
            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,
    ) -> 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)
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
        )

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

        hidden_states = self.block_sparse_moe(hidden_states)

        return hidden_states, residual

class MiniMaxM2ForCausalLM(nn.Module, SupportsPP):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config
        self.quant_config = quant_config
        if hasattr(vllm_config.model_config, "max_model_len"):
            self.config.max_model_len = vllm_config.model_config.max_model_len
        self.model = MiniMaxM2Model(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
        )
        if get_pp_group().is_last_rank:
            self.lm_head = ParallelLMHead(
                config.vocab_size, config.hidden_size, quant_config=None
            )
        else:
            self.lm_head = PPMissingLayer()
        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors
        )

    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
        )
        return hidden_states

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

    def make_empty_intermediate_tensors(
        self, batch_size: int, dtype: torch.dtype, device: torch.device
    ) -> IntermediateTensors:
        return IntermediateTensors(
            {
                "hidden_states": torch.zeros(
                    (batch_size, self.config.hidden_size), dtype=dtype, device=device
                ),
                "residual": torch.zeros(
                    (batch_size, self.config.hidden_size), dtype=dtype, device=device
                ),
            }
        )

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

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        return self.model.get_expert_mapping()

class MiniMaxM2MoE(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: QuantizationConfig | None = None,
        prefix: str = "",
    ):
        super().__init__()
        self.tp_size = get_tensor_model_parallel_world_size()

        if self.tp_size > config.num_local_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
                f"the number of experts {config.num_local_experts}."
            )
        self.use_routing_bias = getattr(config, "use_routing_bias", False)
        if self.use_routing_bias:
            self.e_score_correction_bias = nn.Parameter(
                torch.empty(config.num_local_experts, dtype=torch.float32)
            )
            self.e_score_correction_bias.weight_loader = (
                MiniMaxM2MoE.ebias_weight_loader
            )
        else:
            self.e_score_correction_bias = None

        self.experts = FusedMoE(
            num_experts=config.num_local_experts,
            top_k=config.num_experts_per_tok,
            scoring_func=config.scoring_func,
            use_grouped_topk=True,
            num_expert_group=1,
            topk_group=1,
            e_score_correction_bias=self.e_score_correction_bias,
            hidden_size=config.hidden_size,
            intermediate_size=config.intermediate_size,
            reduce_results=False,
            renormalize=True,
            quant_config=quant_config,
            prefix=f"{prefix}.experts",
        )

        self.gate = ReplicatedLinear(
            config.hidden_size,
            config.num_local_experts,
            bias=False,
            params_dtype=torch.float32,
            quant_config=None,
            prefix=f"{prefix}.gate",
        )

    @staticmethod
    def ebias_weight_loader(param: nn.Parameter, loaded_weight: torch.Tensor) -> None:
        assert param.size() == loaded_weight.size()
        param.data.copy_(loaded_weight.to(torch.float32))

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        num_tokens, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)

        # router_logits: (num_tokens, n_experts)
        router_logits, _ = self.gate(hidden_states.to(torch.float32))
        final_hidden_states = self.experts(
            hidden_states=hidden_states, router_logits=router_logits
        )
        final_hidden_states = final_hidden_states
        if self.tp_size > 1:
            final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)

        return final_hidden_states.view(num_tokens, hidden_dim)