vllm.model_executor.models.minicpm_eagle ¶

class EagleMiniCPMDecoderLayer(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.config = config
        self.cache_config = cache_config
        self.quant_config = quant_config
        self.hidden_size = config.hidden_size
        self.rope_theta = getattr(config, "rope_theta", 10000)
        self.rope_scaling = getattr(config, "rope_scaling", None)
        self.max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
        self.prefix = prefix
        self._init_attn_block()
        self._init_ffn_block()

    def _init_attn_block(self):
        self.input_layernorm = RMSNorm(
            self.config.hidden_size, eps=self.config.rms_norm_eps
        )
        self.self_attn = EagleMiniCPMAttention(
            hidden_size=self.hidden_size,
            num_heads=self.config.num_attention_heads,
            num_kv_heads=self.config.num_key_value_heads,
            rope_theta=self.rope_theta,
            rope_scaling=self.rope_scaling,
            max_position_embeddings=self.max_position_embeddings,
            cache_config=self.cache_config,
            quant_config=self.quant_config,
            prefix=f"{self.prefix}.self_attn",
        )

    def _init_ffn_block(self):
        self.post_attention_layernorm = RMSNorm(
            self.config.hidden_size, eps=self.config.rms_norm_eps
        )
        self.num_experts = getattr(self.config, "num_experts", 0)
        if self.num_experts == 0:
            self.mlp = EagleMiniCPMMLP(
                hidden_size=self.hidden_size,
                intermediate_size=self.config.intermediate_size,
                hidden_act=self.config.hidden_act,
                hidden_act_param=getattr(self.config, "hidden_act_param", 0.0),
                quant_config=self.quant_config,
            )
        else:
            self.mlp = EagleMiniCPMMoE(
                num_experts=self.config.num_experts,
                top_k=self.config.num_experts_per_tok,
                hidden_size=self.config.hidden_size,
                intermediate_size=self.config.intermediate_size,
            )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: Optional[torch.Tensor],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # Self Attention
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
        )
        hidden_states = residual + hidden_states * (
            self.config.scale_depth / math.sqrt(self.config.mup_denominator)
        )

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states * (
            self.config.scale_depth / math.sqrt(self.config.mup_denominator)
        )

        return hidden_states, None

class EagleMiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "gate_up_proj": [
            "gate_proj",
            "up_proj",
        ],
    }

    # LoRA specific attributes
    embedding_modules = {
        "embed_tokens": "input_embeddings",
        "lm_head": "output_embeddings",
    }
    embedding_padding_modules = ["lm_head"]

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.speculative_config.draft_model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config

        self.prefix = prefix
        self.vllm_config = vllm_config
        self.config = config
        self.lora_config = lora_config
        self.cache_config = cache_config
        self.quant_config = quant_config

        target_layer_num = vllm_config.model_config.get_num_layers(
            vllm_config.parallel_config
        )

        self.model = self._init_model(
            vllm_config=vllm_config,
            prefix=maybe_prefix(prefix, "model"),
            start_layer=target_layer_num,
        )

        unpadded_vocab_size = config.vocab_size
        if lora_config:
            unpadded_vocab_size += lora_config.lora_extra_vocab_size
        self.lm_head = ParallelLMHead(
            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,
            quant_config=quant_config,
            prefix=maybe_prefix(prefix, "lm_head"),
        )
        if config.tie_word_embeddings:
            self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
        self.scale_width = self.config.hidden_size / self.config.dim_model_base

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

    def _init_model(
        self, *, vllm_config: VllmConfig, prefix: str = "", start_layer: int = 0
    ):
        return EagleMiniCPMModel(
            vllm_config=vllm_config, prefix=prefix, start_layer=start_layer
        )

    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,
        hidden_states: torch.Tensor,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        hidden_states, hidden_states2 = self.model(input_ids, positions, hidden_states)
        hidden_states = hidden_states / self.scale_width
        hidden_states2 = hidden_states2 / self.scale_width
        return hidden_states, hidden_states2

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> 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]:
        loader = AutoWeightsLoader(
            self,
            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
        )
        return loader.load_weights(weights)

@support_torch_compile
class EagleMiniCPMModel(nn.Module):
    def __init__(
        self, *, vllm_config: VllmConfig, prefix: str = "", start_layer: int = 0
    ):
        super().__init__()

        config = vllm_config.speculative_config.draft_model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config

        self.config = config
        self.cache_config = cache_config
        self.quant_config = quant_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.fc = torch.nn.Linear(
            self.config.hidden_size * 2, self.config.hidden_size, bias=False
        )
        self.input_norm1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.input_norm2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.embed_tokens = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
        )
        self.num_experts = getattr(self.config, "num_experts", 0)
        self._init_layers(prefix, config, cache_config, quant_config, start_layer)
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], self.config.hidden_size
        )

    def _init_layers(
        self,
        prefix: str,
        config: PretrainedConfig,
        cache_config: Optional[CacheConfig],
        quant_config: Optional[QuantizationConfig],
        start_layer: int,
    ):
        self.eagle_layers = nn.ModuleList(
            [
                EagleMiniCPMDecoderLayer(
                    config,
                    cache_config,
                    quant_config,
                    f"{prefix}.eagle_layers.{i + start_layer}",
                )
                for i in range(self.config.num_hidden_layers)
            ]
        )

    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        embedding = self.embed_tokens(input_ids)
        return embedding * self.config.scale_emb

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        input_embeds = self.get_input_embeddings(input_ids)
        input_embeds = self.input_norm1(input_embeds)
        hidden_states = self.input_norm2(hidden_states)

        hidden_states = self.fc(torch.cat((input_embeds, hidden_states), dim=-1))
        residual = None
        for layer in self.eagle_layers:
            hidden_states, residual = layer(
                positions,
                hidden_states,
                residual,
            )

        return hidden_states, hidden_states

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        expert_params_mapping = [
            # (param_name, weight_name, expert_id)
            (
                "ws" if weight_name in ["w1", "w3"] else "w2s",
                f"experts.{expert_id}.{weight_name}.weight",
                expert_id,
            )
            for expert_id in range(self.num_experts)
            for weight_name in ["w1", "w2", "w3"]
        ]
        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
            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
                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 param_name, weight_name, expert_id in 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, weight_name, expert_id=expert_id
                    )
                    break
                else:
                    # 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