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vllm.model_executor.models.llama

Inference-only LLaMA model compatible with HuggingFace weights.

LlamaAttention

Bases: Module

Source code in vllm/model_executor/models/llama.py 
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class LlamaAttention(nn.Module):

    def __init__(
        self,
        config: LlamaConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        rope_theta: float = 10000,
        rope_scaling: Optional[dict[str, Any]] = None,
        max_position_embeddings: int = 8192,
        quant_config: Optional[QuantizationConfig] = None,
        bias: bool = False,
        bias_o_proj: bool = False,
        cache_config: Optional[CacheConfig] = None,
        prefix: str = "",
        attn_type: str = AttentionType.DECODER,
    ) -> None:
        super().__init__()
        layer_idx = extract_layer_index(prefix)
        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)
        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
        head_dim = getattr(config, "head_dim", None)
        if head_dim is None:
            head_dim = self.hidden_size // self.total_num_heads
        self.head_dim = head_dim
        # Phi models introduced a partial_rotary_factor parameter in the config
        self.partial_rotary_factor = getattr(config, "partial_rotary_factor",
                                             1)
        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=hidden_size,
            head_size=self.head_dim,
            total_num_heads=self.total_num_heads,
            total_num_kv_heads=self.total_num_kv_heads,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

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

        self._init_rotary_emb(config,
                              rope_scaling=rope_scaling,
                              quant_config=quant_config)

        if hasattr(config, "interleaved_sliding_window"):
            interleaved_sliding_window = config.interleaved_sliding_window
            if isinstance(interleaved_sliding_window, int):
                sliding_window = interleaved_sliding_window
            elif isinstance(interleaved_sliding_window, list):
                sw_idx = layer_idx % len(interleaved_sliding_window)
                sliding_window = interleaved_sliding_window[sw_idx]
            else:
                raise ValueError(
                    f"{type(interleaved_sliding_window)} is not supported.")
        else:
            sliding_window = None

        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            cache_config=cache_config,
            quant_config=quant_config,
            per_layer_sliding_window=sliding_window,
            attn_type=attn_type,
            prefix=f"{prefix}.attn",
        )

    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, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output

    def _init_rotary_emb(self, config: LlamaConfig,
                         rope_scaling: Optional[dict[str, Any]],
                         quant_config: Optional[QuantizationConfig]) -> None:
        is_neox_style = True
        is_gguf = quant_config and quant_config.get_name() == "gguf"
        if is_gguf and config.model_type == "llama":
            is_neox_style = False

        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=self.max_position_embeddings,
            base=self.rope_theta,
            rope_scaling=rope_scaling,
            is_neox_style=is_neox_style,
            partial_rotary_factor=self.partial_rotary_factor,
        )

attn instance-attribute 

attn = Attention(
    num_heads,
    head_dim,
    scaling,
    num_kv_heads=num_kv_heads,
    cache_config=cache_config,
    quant_config=quant_config,
    per_layer_sliding_window=sliding_window,
    attn_type=attn_type,
    prefix=f"{prefix}.attn",
)

head_dim instance-attribute 

head_dim = head_dim

hidden_size instance-attribute 

hidden_size = hidden_size

kv_size instance-attribute 

kv_size = num_kv_heads * head_dim

max_position_embeddings instance-attribute 

max_position_embeddings = max_position_embeddings

num_heads instance-attribute 

num_heads = total_num_heads // tp_size

num_kv_heads instance-attribute 

num_kv_heads = max(1, total_num_kv_heads // tp_size)

o_proj instance-attribute 

o_proj = RowParallelLinear(
    input_size=total_num_heads * head_dim,
    output_size=hidden_size,
    bias=bias_o_proj,
    quant_config=quant_config,
    prefix=f"{prefix}.o_proj",
)

partial_rotary_factor instance-attribute 

partial_rotary_factor = getattr(
    config, "partial_rotary_factor", 1
)

q_size instance-attribute 

q_size = num_heads * head_dim

qkv_proj instance-attribute 

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

rope_theta instance-attribute 

rope_theta = rope_theta

scaling instance-attribute 

scaling = head_dim ** -0.5

total_num_heads instance-attribute 

total_num_heads = num_heads

total_num_kv_heads instance-attribute 

total_num_kv_heads = num_kv_heads

__init__ 

__init__(
    config: LlamaConfig,
    hidden_size: int,
    num_heads: int,
    num_kv_heads: int,
    rope_theta: float = 10000,
    rope_scaling: Optional[dict[str, Any]] = None,
    max_position_embeddings: int = 8192,
    quant_config: Optional[QuantizationConfig] = None,
    bias: bool = False,
    bias_o_proj: bool = False,
    cache_config: Optional[CacheConfig] = None,
    prefix: str = "",
    attn_type: str = DECODER,
) -> None
Source code in vllm/model_executor/models/llama.py 
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def __init__(
    self,
    config: LlamaConfig,
    hidden_size: int,
    num_heads: int,
    num_kv_heads: int,
    rope_theta: float = 10000,
    rope_scaling: Optional[dict[str, Any]] = None,
    max_position_embeddings: int = 8192,
    quant_config: Optional[QuantizationConfig] = None,
    bias: bool = False,
    bias_o_proj: bool = False,
    cache_config: Optional[CacheConfig] = None,
    prefix: str = "",
    attn_type: str = AttentionType.DECODER,
) -> None:
    super().__init__()
    layer_idx = extract_layer_index(prefix)
    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)
    # MistralConfig has an optional head_dim introduced by Mistral-Nemo
    head_dim = getattr(config, "head_dim", None)
    if head_dim is None:
        head_dim = self.hidden_size // self.total_num_heads
    self.head_dim = head_dim
    # Phi models introduced a partial_rotary_factor parameter in the config
    self.partial_rotary_factor = getattr(config, "partial_rotary_factor",
                                         1)
    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=hidden_size,
        head_size=self.head_dim,
        total_num_heads=self.total_num_heads,
        total_num_kv_heads=self.total_num_kv_heads,
        bias=bias,
        quant_config=quant_config,
        prefix=f"{prefix}.qkv_proj",
    )

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

    self._init_rotary_emb(config,
                          rope_scaling=rope_scaling,
                          quant_config=quant_config)

    if hasattr(config, "interleaved_sliding_window"):
        interleaved_sliding_window = config.interleaved_sliding_window
        if isinstance(interleaved_sliding_window, int):
            sliding_window = interleaved_sliding_window
        elif isinstance(interleaved_sliding_window, list):
            sw_idx = layer_idx % len(interleaved_sliding_window)
            sliding_window = interleaved_sliding_window[sw_idx]
        else:
            raise ValueError(
                f"{type(interleaved_sliding_window)} is not supported.")
    else:
        sliding_window = None

    self.attn = Attention(
        self.num_heads,
        self.head_dim,
        self.scaling,
        num_kv_heads=self.num_kv_heads,
        cache_config=cache_config,
        quant_config=quant_config,
        per_layer_sliding_window=sliding_window,
        attn_type=attn_type,
        prefix=f"{prefix}.attn",
    )

_init_rotary_emb 

_init_rotary_emb(
    config: LlamaConfig,
    rope_scaling: Optional[dict[str, Any]],
    quant_config: Optional[QuantizationConfig],
) -> None
Source code in vllm/model_executor/models/llama.py 
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def _init_rotary_emb(self, config: LlamaConfig,
                     rope_scaling: Optional[dict[str, Any]],
                     quant_config: Optional[QuantizationConfig]) -> None:
    is_neox_style = True
    is_gguf = quant_config and quant_config.get_name() == "gguf"
    if is_gguf and config.model_type == "llama":
        is_neox_style = False

    self.rotary_emb = get_rope(
        self.head_dim,
        rotary_dim=self.head_dim,
        max_position=self.max_position_embeddings,
        base=self.rope_theta,
        rope_scaling=rope_scaling,
        is_neox_style=is_neox_style,
        partial_rotary_factor=self.partial_rotary_factor,
    )

forward 

forward(positions: Tensor, hidden_states: Tensor) -> Tensor
Source code in vllm/model_executor/models/llama.py 
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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, k = self.rotary_emb(positions, q, k)
    attn_output = self.attn(q, k, v)
    output, _ = self.o_proj(attn_output)
    return output

LlamaDecoderLayer

Bases: Module

Source code in vllm/model_executor/models/llama.py 
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class LlamaDecoderLayer(nn.Module):

    def __init__(
        self,
        config: LlamaConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        rope_theta = getattr(config, "rope_theta", 10000)
        rope_scaling = getattr(config, "rope_scaling", None)
        if rope_scaling is not None and getattr(
                config, "original_max_position_embeddings", None):
            rope_scaling["original_max_position_embeddings"] = (
                config.original_max_position_embeddings)
        max_position_embeddings = getattr(config, "max_position_embeddings",
                                          8192)
        # Support abacusai/Smaug-72B-v0.1 with attention_bias
        # Support internlm/internlm-7b with bias
        attention_bias = getattr(config, "attention_bias", False) or getattr(
            config, "bias", False)
        bias_o_proj = attention_bias
        # support internlm/internlm3-8b with qkv_bias
        if hasattr(config, 'qkv_bias'):
            attention_bias = config.qkv_bias

        # By default, Llama uses causal attention as it is a decoder-only model.
        # You can override the HF config with `is_causal=False` to enable
        # bidirectional attention, which is used in some embedding models
        # (e.g. parasail-ai/GritLM-7B-vllm)
        if getattr(config, "is_causal", True):
            attn_type = AttentionType.DECODER
        else:
            attn_type = AttentionType.ENCODER_ONLY

        self.self_attn = LlamaAttention(
            config=config,
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=getattr(config, "num_key_value_heads",
                                 config.num_attention_heads),
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            max_position_embeddings=max_position_embeddings,
            quant_config=quant_config,
            bias=attention_bias,
            bias_o_proj=bias_o_proj,
            cache_config=cache_config,
            prefix=f"{prefix}.self_attn",
            attn_type=attn_type,
        )
        self.mlp = LlamaMLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            quant_config=quant_config,
            bias=getattr(config, "mlp_bias", False),
            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: Optional[torch.Tensor],
    ) -> 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)
        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.mlp(hidden_states)
        return hidden_states, residual

hidden_size instance-attribute 

hidden_size = hidden_size

input_layernorm instance-attribute 

input_layernorm = RMSNorm(hidden_size, eps=rms_norm_eps)

mlp instance-attribute 

mlp = LlamaMLP(
    hidden_size=hidden_size,
    intermediate_size=intermediate_size,
    hidden_act=hidden_act,
    quant_config=quant_config,
    bias=getattr(config, "mlp_bias", False),
    prefix=f"{prefix}.mlp",
)

post_attention_layernorm instance-attribute 

post_attention_layernorm = RMSNorm(
    hidden_size, eps=rms_norm_eps
)

self_attn instance-attribute 

self_attn = LlamaAttention(
    config=config,
    hidden_size=hidden_size,
    num_heads=num_attention_heads,
    num_kv_heads=getattr(
        config, "num_key_value_heads", num_attention_heads
    ),
    rope_theta=rope_theta,
    rope_scaling=rope_scaling,
    max_position_embeddings=max_position_embeddings,
    quant_config=quant_config,
    bias=attention_bias,
    bias_o_proj=bias_o_proj,
    cache_config=cache_config,
    prefix=f"{prefix}.self_attn",
    attn_type=attn_type,
)

__init__ 

__init__(
    config: LlamaConfig,
    cache_config: Optional[CacheConfig] = None,
    quant_config: Optional[QuantizationConfig] = None,
    prefix: str = "",
) -> None
Source code in vllm/model_executor/models/llama.py 
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def __init__(
    self,
    config: LlamaConfig,
    cache_config: Optional[CacheConfig] = None,
    quant_config: Optional[QuantizationConfig] = None,
    prefix: str = "",
) -> None:
    super().__init__()
    self.hidden_size = config.hidden_size
    rope_theta = getattr(config, "rope_theta", 10000)
    rope_scaling = getattr(config, "rope_scaling", None)
    if rope_scaling is not None and getattr(
            config, "original_max_position_embeddings", None):
        rope_scaling["original_max_position_embeddings"] = (
            config.original_max_position_embeddings)
    max_position_embeddings = getattr(config, "max_position_embeddings",
                                      8192)
    # Support abacusai/Smaug-72B-v0.1 with attention_bias
    # Support internlm/internlm-7b with bias
    attention_bias = getattr(config, "attention_bias", False) or getattr(
        config, "bias", False)
    bias_o_proj = attention_bias
    # support internlm/internlm3-8b with qkv_bias
    if hasattr(config, 'qkv_bias'):
        attention_bias = config.qkv_bias

    # By default, Llama uses causal attention as it is a decoder-only model.
    # You can override the HF config with `is_causal=False` to enable
    # bidirectional attention, which is used in some embedding models
    # (e.g. parasail-ai/GritLM-7B-vllm)
    if getattr(config, "is_causal", True):
        attn_type = AttentionType.DECODER
    else:
        attn_type = AttentionType.ENCODER_ONLY

    self.self_attn = LlamaAttention(
        config=config,
        hidden_size=self.hidden_size,
        num_heads=config.num_attention_heads,
        num_kv_heads=getattr(config, "num_key_value_heads",
                             config.num_attention_heads),
        rope_theta=rope_theta,
        rope_scaling=rope_scaling,
        max_position_embeddings=max_position_embeddings,
        quant_config=quant_config,
        bias=attention_bias,
        bias_o_proj=bias_o_proj,
        cache_config=cache_config,
        prefix=f"{prefix}.self_attn",
        attn_type=attn_type,
    )
    self.mlp = LlamaMLP(
        hidden_size=self.hidden_size,
        intermediate_size=config.intermediate_size,
        hidden_act=config.hidden_act,
        quant_config=quant_config,
        bias=getattr(config, "mlp_bias", False),
        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)

forward 

forward(
    positions: Tensor,
    hidden_states: Tensor,
    residual: Optional[Tensor],
) -> tuple[Tensor, Tensor]
Source code in vllm/model_executor/models/llama.py 
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def forward(
    self,
    positions: torch.Tensor,
    hidden_states: torch.Tensor,
    residual: Optional[torch.Tensor],
) -> 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)
    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.mlp(hidden_states)
    return hidden_states, residual

LlamaForCausalLM

Bases: Module, SupportsLoRA, SupportsPP

Source code in vllm/model_executor/models/llama.py 
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class LlamaForCausalLM(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"]

    # Mistral/Llama models can also be loaded with --load-format mistral
    # from consolidated.safetensors checkpoints
    mistral_mapping = {
        "layers": "model.layers",
        "attention": "self_attn",
        "qscale_act": "input_scale",
        "qscale_weight": "weight_scale",
        "kv_fake_quantizer.qscale_act": "kv_scale",
        "wq": "q_proj",
        "wk": "k_proj",
        "wv": "v_proj",
        "wo": "o_proj",
        "attention_norm": "input_layernorm",
        "feed_forward": "mlp",
        "w1": "gate_proj",
        "w2": "down_proj",
        "w3": "up_proj",
        "ffn_norm": "post_attention_layernorm",
        "tok_embeddings": "model.embed_tokens",
        "output": "lm_head",
        "norm": "model.norm",
    }

    def __init__(self,
                 *,
                 vllm_config: VllmConfig,
                 prefix: str = "",
                 layer_type: type[nn.Module] = LlamaDecoderLayer):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config
        self.config = config
        self.lora_config = lora_config

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

        if get_pp_group().is_last_rank:
            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),
                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)

            logit_scale = getattr(config, "logit_scale", 1.0)
            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                    config.vocab_size,
                                                    logit_scale)
        else:
            self.lm_head = PPMissingLayer()

        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)

    def set_aux_hidden_state_layers(self, layers: tuple[int]) -> None:
        self.model.aux_hidden_state_layers = layers

    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int]:
        num_layers = len(self.model.layers)
        return (2, num_layers // 2, num_layers - 3)

    def _init_model(self,
                    vllm_config: VllmConfig,
                    prefix: str = "",
                    layer_type: type[nn.Module] = LlamaDecoderLayer):
        return LlamaModel(vllm_config=vllm_config,
                          prefix=prefix,
                          layer_type=layer_type)

    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: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        model_output = self.model(input_ids, positions, intermediate_tensors,
                                  inputs_embeds)
        return model_output

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[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,
            skip_prefixes=(["lm_head."]
                           if self.config.tie_word_embeddings else None),
        )
        return loader.load_weights(
            self.maybe_remap_mistral(name, loaded_weight)
            for name, loaded_weight in weights)

    # This function is used to remap the mistral format as
    # used by Mistral and Llama <=2
    def maybe_remap_mistral(
        self,
        name: str,
        loaded_weight: torch.Tensor,
    ) -> tuple[str, torch.Tensor]:

        def permute(w: torch.Tensor, n_heads: int):
            attn_in = self.config.head_dim * n_heads
            attn_out = self.config.hidden_size

            return w.view(n_heads, attn_in // n_heads // 2, 2,
                          attn_out).transpose(1, 2).reshape(attn_in, attn_out)

        mapping = self.mistral_mapping
        modules = name.split(".")

        # rotary embeds should be sliced
        if "wk" in modules and modules[-1] == "weight":
            loaded_weight = permute(loaded_weight,
                                    self.config.num_key_value_heads)
        elif "wq" in modules and modules[-1] == "weight":
            loaded_weight = permute(loaded_weight,
                                    self.config.num_attention_heads)

        num_modules = len(modules)
        for i in range(num_modules):
            item = modules[i]
            next_item = modules[i + 1] if i < num_modules - 1 else None

            combined_item = (f"{item}.{next_item}"
                             if next_item is not None else None)

            if combined_item in mapping:
                name = name.replace(combined_item, mapping[combined_item])
            elif item in mapping and mapping[item] not in name:
                name = name.replace(item, mapping[item])

        return name, loaded_weight

config instance-attribute 

config = config

embedding_modules class-attribute instance-attribute 

embedding_modules = {
    "embed_tokens": "input_embeddings",
    "lm_head": "output_embeddings",
}

embedding_padding_modules class-attribute instance-attribute 

embedding_padding_modules = ['lm_head']

lm_head instance-attribute 

lm_head = ParallelLMHead(
    unpadded_vocab_size,
    hidden_size,
    org_num_embeddings=vocab_size,
    padding_size=DEFAULT_VOCAB_PADDING_SIZE
    if not lora_config
    else lora_vocab_padding_size,
    quant_config=quant_config,
    prefix=maybe_prefix(prefix, "lm_head"),
)

logits_processor instance-attribute 

logits_processor = LogitsProcessor(
    unpadded_vocab_size, vocab_size, logit_scale
)

lora_config instance-attribute 

lora_config = lora_config

make_empty_intermediate_tensors instance-attribute 

make_empty_intermediate_tensors = (
    make_empty_intermediate_tensors
)

mistral_mapping class-attribute instance-attribute 

mistral_mapping = {
    "layers": "model.layers",
    "attention": "self_attn",
    "qscale_act": "input_scale",
    "qscale_weight": "weight_scale",
    "kv_fake_quantizer.qscale_act": "kv_scale",
    "wq": "q_proj",
    "wk": "k_proj",
    "wv": "v_proj",
    "wo": "o_proj",
    "attention_norm": "input_layernorm",
    "feed_forward": "mlp",
    "w1": "gate_proj",
    "w2": "down_proj",
    "w3": "up_proj",
    "ffn_norm": "post_attention_layernorm",
    "tok_embeddings": "model.embed_tokens",
    "output": "lm_head",
    "norm": "model.norm",
}

model instance-attribute 

model = _init_model(
    vllm_config=vllm_config,
    prefix=maybe_prefix(prefix, "model"),
    layer_type=layer_type,
)

packed_modules_mapping class-attribute instance-attribute 

packed_modules_mapping = {
    "qkv_proj": ["q_proj", "k_proj", "v_proj"],
    "gate_up_proj": ["gate_proj", "up_proj"],
}

unpadded_vocab_size instance-attribute 

unpadded_vocab_size = vocab_size

__init__ 

__init__(
    *,
    vllm_config: VllmConfig,
    prefix: str = "",
    layer_type: type[Module] = LlamaDecoderLayer,
)
Source code in vllm/model_executor/models/llama.py 
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def __init__(self,
             *,
             vllm_config: VllmConfig,
             prefix: str = "",
             layer_type: type[nn.Module] = LlamaDecoderLayer):
    super().__init__()
    config = vllm_config.model_config.hf_config
    quant_config = vllm_config.quant_config
    lora_config = vllm_config.lora_config
    self.config = config
    self.lora_config = lora_config

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

    if get_pp_group().is_last_rank:
        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),
            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)

        logit_scale = getattr(config, "logit_scale", 1.0)
        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                config.vocab_size,
                                                logit_scale)
    else:
        self.lm_head = PPMissingLayer()

    self.make_empty_intermediate_tensors = (
        self.model.make_empty_intermediate_tensors)

_init_model 

_init_model(
    vllm_config: VllmConfig,
    prefix: str = "",
    layer_type: type[Module] = LlamaDecoderLayer,
)
Source code in vllm/model_executor/models/llama.py 
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def _init_model(self,
                vllm_config: VllmConfig,
                prefix: str = "",
                layer_type: type[nn.Module] = LlamaDecoderLayer):
    return LlamaModel(vllm_config=vllm_config,
                      prefix=prefix,
                      layer_type=layer_type)

compute_logits 

compute_logits(
    hidden_states: Tensor,
    sampling_metadata: SamplingMetadata,
) -> Optional[Tensor]
Source code in vllm/model_executor/models/llama.py 
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def compute_logits(
    self,
    hidden_states: torch.Tensor,
    sampling_metadata: SamplingMetadata,
) -> Optional[torch.Tensor]:
    logits = self.logits_processor(self.lm_head, hidden_states,
                                   sampling_metadata)
    return logits

forward 

forward(
    input_ids: Tensor,
    positions: Tensor,
    intermediate_tensors: Optional[
        IntermediateTensors
    ] = None,
    inputs_embeds: Optional[Tensor] = None,
) -> Union[Tensor, IntermediateTensors]
Source code in vllm/model_executor/models/llama.py 
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def forward(
    self,
    input_ids: torch.Tensor,
    positions: torch.Tensor,
    intermediate_tensors: Optional[IntermediateTensors] = None,
    inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
    model_output = self.model(input_ids, positions, intermediate_tensors,
                              inputs_embeds)
    return model_output

get_eagle3_aux_hidden_state_layers 

get_eagle3_aux_hidden_state_layers() -> tuple[int]
Source code in vllm/model_executor/models/llama.py 
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def get_eagle3_aux_hidden_state_layers(self) -> tuple[int]:
    num_layers = len(self.model.layers)
    return (2, num_layers // 2, num_layers - 3)

get_input_embeddings 

get_input_embeddings(input_ids: Tensor) -> Tensor
Source code in vllm/model_executor/models/llama.py 
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
    return self.model.get_input_embeddings(input_ids)

load_weights 

load_weights(
    weights: Iterable[tuple[str, Tensor]],
) -> set[str]
Source code in vllm/model_executor/models/llama.py 
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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(
        self.maybe_remap_mistral(name, loaded_weight)
        for name, loaded_weight in weights)

maybe_remap_mistral 

maybe_remap_mistral(
    name: str, loaded_weight: Tensor
) -> tuple[str, Tensor]
Source code in vllm/model_executor/models/llama.py 
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def maybe_remap_mistral(
    self,
    name: str,
    loaded_weight: torch.Tensor,
) -> tuple[str, torch.Tensor]:

    def permute(w: torch.Tensor, n_heads: int):
        attn_in = self.config.head_dim * n_heads
        attn_out = self.config.hidden_size

        return w.view(n_heads, attn_in // n_heads // 2, 2,
                      attn_out).transpose(1, 2).reshape(attn_in, attn_out)

    mapping = self.mistral_mapping
    modules = name.split(".")

    # rotary embeds should be sliced
    if "wk" in modules and modules[-1] == "weight":
        loaded_weight = permute(loaded_weight,
                                self.config.num_key_value_heads)
    elif "wq" in modules and modules[-1] == "weight":
        loaded_weight = permute(loaded_weight,
                                self.config.num_attention_heads)

    num_modules = len(modules)
    for i in range(num_modules):
        item = modules[i]
        next_item = modules[i + 1] if i < num_modules - 1 else None

        combined_item = (f"{item}.{next_item}"
                         if next_item is not None else None)

        if combined_item in mapping:
            name = name.replace(combined_item, mapping[combined_item])
        elif item in mapping and mapping[item] not in name:
            name = name.replace(item, mapping[item])

    return name, loaded_weight

set_aux_hidden_state_layers 

set_aux_hidden_state_layers(layers: tuple[int]) -> None
Source code in vllm/model_executor/models/llama.py 
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def set_aux_hidden_state_layers(self, layers: tuple[int]) -> None:
    self.model.aux_hidden_state_layers = layers

LlamaMLP

Bases: Module

Source code in vllm/model_executor/models/llama.py 
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class LlamaMLP(nn.Module):

    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: Optional[QuantizationConfig] = None,
        bias: bool = False,
        prefix: str = "",
        reduce_results: bool = True,
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            input_size=hidden_size,
            output_sizes=[intermediate_size] * 2,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            input_size=intermediate_size,
            output_size=hidden_size,
            bias=bias,
            quant_config=quant_config,
            reduce_results=reduce_results,
            prefix=f"{prefix}.down_proj",
        )
        if hidden_act != "silu":
            raise ValueError(f"Unsupported activation: {hidden_act}. "
                             "Only silu is supported for now.")
        self.act_fn = SiluAndMul()

    def forward(self, x):
        x, _ = self.gate_up_proj(x)
        x = self.act_fn(x)
        x, _ = self.down_proj(x)
        return x

act_fn instance-attribute 

act_fn = SiluAndMul()

down_proj instance-attribute 

down_proj = RowParallelLinear(
    input_size=intermediate_size,
    output_size=hidden_size,
    bias=bias,
    quant_config=quant_config,
    reduce_results=reduce_results,
    prefix=f"{prefix}.down_proj",
)

gate_up_proj instance-attribute 

gate_up_proj = MergedColumnParallelLinear(
    input_size=hidden_size,
    output_sizes=[intermediate_size] * 2,
    bias=bias,
    quant_config=quant_config,
    prefix=f"{prefix}.gate_up_proj",
)

__init__ 

__init__(
    hidden_size: int,
    intermediate_size: int,
    hidden_act: str,
    quant_config: Optional[QuantizationConfig] = None,
    bias: bool = False,
    prefix: str = "",
    reduce_results: bool = True,
) -> None
Source code in vllm/model_executor/models/llama.py 
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def __init__(
    self,
    hidden_size: int,
    intermediate_size: int,
    hidden_act: str,
    quant_config: Optional[QuantizationConfig] = None,
    bias: bool = False,
    prefix: str = "",
    reduce_results: bool = True,
) -> None:
    super().__init__()
    self.gate_up_proj = MergedColumnParallelLinear(
        input_size=hidden_size,
        output_sizes=[intermediate_size] * 2,
        bias=bias,
        quant_config=quant_config,
        prefix=f"{prefix}.gate_up_proj",
    )
    self.down_proj = RowParallelLinear(
        input_size=intermediate_size,
        output_size=hidden_size,
        bias=bias,
        quant_config=quant_config,
        reduce_results=reduce_results,
        prefix=f"{prefix}.down_proj",
    )
    if hidden_act != "silu":
        raise ValueError(f"Unsupported activation: {hidden_act}. "
                         "Only silu is supported for now.")
    self.act_fn = SiluAndMul()

forward 

forward(x)
Source code in vllm/model_executor/models/llama.py 
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def forward(self, x):
    x, _ = self.gate_up_proj(x)
    x = self.act_fn(x)
    x, _ = self.down_proj(x)
    return x

LlamaModel

Bases: Module

Source code in vllm/model_executor/models/llama.py 
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@support_torch_compile
class LlamaModel(nn.Module):

    def __init__(self,
                 *,
                 vllm_config: VllmConfig,
                 prefix: str = "",
                 layer_type: type[nn.Module] = LlamaDecoderLayer):
        super().__init__()

        config = vllm_config.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.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
        if get_pp_group().is_first_rank or (config.tie_word_embeddings
                                            and get_pp_group().is_last_rank):
            self.embed_tokens = VocabParallelEmbedding(
                self.vocab_size,
                config.hidden_size,
                org_num_embeddings=config.vocab_size,
                quant_config=quant_config,
            )
        else:
            self.embed_tokens = PPMissingLayer()
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: layer_type(config=config,
                                      cache_config=cache_config,
                                      quant_config=quant_config,
                                      prefix=prefix),
            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()

        self.aux_hidden_state_layers: tuple[int] = tuple()

        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.embed_tokens(input_ids)

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

        aux_hidden_states = []
        for idx, layer in enumerate(
                self.layers[self.start_layer:self.end_layer]):
            if idx in self.aux_hidden_state_layers:
                aux_hidden_states.append(hidden_states + residual)
            hidden_states, residual = layer(positions, hidden_states, residual)

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

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

        if len(aux_hidden_states) > 0:
            return hidden_states, aux_hidden_states
        return 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),
        ]
        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
            if (self.quant_config is not None and
                (scale_name := self.quant_config.get_cache_scale(name))):
                # Loading kv cache quantization scales
                param = params_dict[scale_name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
                                 loaded_weight[0])
                weight_loader(param, loaded_weight)
                loaded_params.add(scale_name)
                continue
            if "scale" in name:
                # Remapping the name of FP8 kv-scale.
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    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:
                # 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

aux_hidden_state_layers instance-attribute 

aux_hidden_state_layers: tuple[int] = tuple()

config instance-attribute 

config = config

embed_tokens instance-attribute 

embed_tokens = VocabParallelEmbedding(
    vocab_size,
    hidden_size,
    org_num_embeddings=vocab_size,
    quant_config=quant_config,
)

make_empty_intermediate_tensors instance-attribute 

make_empty_intermediate_tensors = (
    make_empty_intermediate_tensors_factory(
        ["hidden_states", "residual"], hidden_size
    )
)

norm instance-attribute 

norm = RMSNorm(hidden_size, eps=rms_norm_eps)

org_vocab_size instance-attribute 

org_vocab_size = vocab_size

quant_config instance-attribute 

quant_config = quant_config

vocab_size instance-attribute 

vocab_size = vocab_size + lora_vocab

__init__ 

__init__(
    *,
    vllm_config: VllmConfig,
    prefix: str = "",
    layer_type: type[Module] = LlamaDecoderLayer,
)
Source code in vllm/model_executor/models/llama.py 
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def __init__(self,
             *,
             vllm_config: VllmConfig,
             prefix: str = "",
             layer_type: type[nn.Module] = LlamaDecoderLayer):
    super().__init__()

    config = vllm_config.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.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
    if get_pp_group().is_first_rank or (config.tie_word_embeddings
                                        and get_pp_group().is_last_rank):
        self.embed_tokens = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
            quant_config=quant_config,
        )
    else:
        self.embed_tokens = PPMissingLayer()
    self.start_layer, self.end_layer, self.layers = make_layers(
        config.num_hidden_layers,
        lambda prefix: layer_type(config=config,
                                  cache_config=cache_config,
                                  quant_config=quant_config,
                                  prefix=prefix),
        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()

    self.aux_hidden_state_layers: tuple[int] = tuple()

    self.make_empty_intermediate_tensors = (
        make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size))

forward 

forward(
    input_ids: Optional[Tensor],
    positions: Tensor,
    intermediate_tensors: Optional[IntermediateTensors],
    inputs_embeds: Optional[Tensor] = None,
) -> Union[
    Tensor, IntermediateTensors, tuple[Tensor, list[Tensor]]
]
Source code in vllm/model_executor/models/llama.py 
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def forward(
    self,
    input_ids: Optional[torch.Tensor],
    positions: torch.Tensor,
    intermediate_tensors: Optional[IntermediateTensors],
    inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors, tuple[torch.Tensor,
                                                    list[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"]

    aux_hidden_states = []
    for idx, layer in enumerate(
            self.layers[self.start_layer:self.end_layer]):
        if idx in self.aux_hidden_state_layers:
            aux_hidden_states.append(hidden_states + residual)
        hidden_states, residual = layer(positions, hidden_states, residual)

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

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

    if len(aux_hidden_states) > 0:
        return hidden_states, aux_hidden_states
    return hidden_states

get_input_embeddings 

get_input_embeddings(input_ids: Tensor) -> Tensor
Source code in vllm/model_executor/models/llama.py 
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
    return self.embed_tokens(input_ids)

load_weights 

load_weights(
    weights: Iterable[tuple[str, Tensor]],
) -> set[str]
Source code in vllm/model_executor/models/llama.py 
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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),
    ]
    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
        if (self.quant_config is not None and
            (scale_name := self.quant_config.get_cache_scale(name))):
            # Loading kv cache quantization scales
            param = params_dict[scale_name]
            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
                             loaded_weight[0])
            weight_loader(param, loaded_weight)
            loaded_params.add(scale_name)
            continue
        if "scale" in name:
            # Remapping the name of FP8 kv-scale.
            name = maybe_remap_kv_scale_name(name, params_dict)
            if name is None:
                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:
            # 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