chatglm.py

# SPDX-License-Identifier: Apache-2.0

# Adapted from
# https://github.com/THUDM/CogAgent
"""Inference-only CogAgent model compatible with THUDM weights."""
from argparse import Namespace
from typing import (Iterable, List, Mapping, Optional, Sequence, Set, Tuple,
                    TypedDict, Union)

import torch
from torch import nn
from torch.nn import LayerNorm
from torchvision import transforms
from torchvision.transforms import InterpolationMode
from transformers import PreTrainedTokenizer, TensorType
from transformers.image_utils import ImageInput
from transformers.tokenization_utils_base import TextInput

from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.logger import init_logger
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.models.glm4_vision_encoder import EVA2CLIPModel
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.inputs import MultiModalKwargs, NestedTensors
from vllm.multimodal.parse import ImageSize, MultiModalDataItems
from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                        BaseProcessingInfo, BatchFeature,
                                        BoundPromptReplacement,
                                        MultiModalFieldConfig,
                                        PlaceholderFeaturesInfo,
                                        PromptReplacement)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.configs import ChatGLMConfig

from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
                    make_empty_intermediate_tensors_factory, make_layers,
                    maybe_prefix, merge_multimodal_embeddings)

logger = init_logger(__name__)

IMAGE_TOKEN_ID = 151329


def build_normalization_transform(image_size: int) -> transforms.Compose:
    """
    Build a normalization transform which can be applied to one or
    more input images from which we want to extract visual features.

    Args:
        image_size: size of the image to be processed for visual embeddings.
    
    Returns:
        Callable transform for normalizing and resizing one RGB image.
    """

    return transforms.Compose([
        transforms.Resize(
            (image_size, image_size),
            interpolation=InterpolationMode.BICUBIC,
        ),
        transforms.ToTensor(),
        transforms.Normalize(
            (0.48145466, 0.4578275, 0.40821073),
            (0.26862954, 0.26130258, 0.27577711),
        ),
    ])


def calculate_image_placeholder(vision_config):
    return (vision_config["image_size"] // vision_config["patch_size"] // 2)**2


class GLMImagePixelInputs(TypedDict):
    pixel_values: torch.Tensor
    """Shape: `(batch_size, num_channels, height, width)`"""


class GLM4VProcessor:
    """
    This model doesn't define its own HF processor,
    so we implement our own one here.

    """

    def __init__(
        self,
        config: ChatGLMConfig,
        tokenizer: PreTrainedTokenizer,
    ) -> None:
        super().__init__()

        self.config = config
        self.tokenizer = tokenizer

        if hasattr(self.config, "vision_config"):
            self.image_transform = build_normalization_transform(
                config.vision_config["image_size"])
        else:
            self.image_transform = None

    def __call__(
        self,
        text: Optional[Union[TextInput, list[TextInput]]] = None,
        images: Optional[Union[ImageInput, list[ImageInput]]] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
    ) -> BatchFeature:
        if text is None:
            text = []
        if not isinstance(text, list):
            text = [text]
        if images is None:
            images = []
        if not isinstance(images, list):
            images = [images]
        text_inputs = self.tokenizer(text)
        if len(images) == 0:
            image_inputs = {}
        else:
            if self.image_transform is None:
                raise ValueError("This model does not support image inputs")

            pixel_values = [self.image_transform(image) for image in images]
            image_inputs = {"pixel_values": torch.stack(pixel_values)}

        return BatchFeature(
            {
                **text_inputs,
                **image_inputs,
            },
            tensor_type=return_tensors,
        )


class GLM4VProcessingInfo(BaseProcessingInfo):

    def __init__(self, ctx):
        super().__init__(ctx)
        self._pre_calculate()

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"image": 1}

    def get_mm_max_tokens_per_item(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> Mapping[str, int]:

        return {"image": self.image_token_num + 2}

    def _pre_calculate(self):
        hf_config = self.get_hf_config()
        vision_config = hf_config.vision_config
        self.image_token_num = calculate_image_placeholder(vision_config)
        self.image_size = vision_config["image_size"]

    def get_num_image_tokens(self) -> int:
        return self.image_token_num + 2

    def get_image_size(self) -> ImageSize:

        return ImageSize(height=self.image_size, width=self.image_size)

    def get_hf_processor(self) -> GLM4VProcessor:
        return GLM4VProcessor(
            self.get_hf_config(),
            self.get_tokenizer(),
        )


class GLM4VDummyInputsBuilder(BaseDummyInputsBuilder[GLM4VProcessingInfo]):

    def get_dummy_processor_inputs(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> ProcessorInputs:
        num_images = mm_counts.get("image", 0)
        target_width, target_height = self.info.get_image_size()

        mm_data = {
            "image":
            self._get_dummy_images(width=target_width,
                                   height=target_height,
                                   num_images=num_images)
        }
        text = "<|begin_of_image|><|endoftext|><|end_of_image|>"
        return ProcessorInputs(
            prompt_text=text,
            mm_data=mm_data,
        )


class GLM4VMultiModalProcessor(BaseMultiModalProcessor[GLM4VProcessingInfo]):

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return dict(pixel_values=MultiModalFieldConfig.batched("image"))

    def _get_prompt_replacements(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargs,
    ) -> list[PromptReplacement]:

        def get_replacement(item_idx: int):
            image_tokens = self.info.image_token_num
            return [IMAGE_TOKEN_ID] * image_tokens

        return [
            PromptReplacement(
                modality="image",
                target=[IMAGE_TOKEN_ID],
                replacement=get_replacement,
            ),
        ]

    def _apply_prompt_replacements(
        self,
        token_ids: list[int],
        mm_prompt_repls: Mapping[str, Sequence[BoundPromptReplacement]],
        mm_item_counts: Mapping[str, int],
    ) -> tuple[list[int], str, Mapping[str, list[PlaceholderFeaturesInfo]]]:
        token_ids, text, placeholders = super()._apply_prompt_replacements(
            token_ids=token_ids,
            mm_prompt_repls=mm_prompt_repls,
            mm_item_counts=mm_item_counts,
        )
        hf_config = self.info.get_hf_config()
        boi_token_id = hf_config.boi_token_id
        eoi_token_id = hf_config.eoi_token_id
        placeholders = {
            modality: [
                PlaceholderFeaturesInfo(
                    modality=p.modality,
                    item_idx=p.item_idx,
                    start_idx=p.start_idx - 1,
                    tokens=[boi_token_id] + p.tokens + [eoi_token_id],
                ) for p in ps
            ]
            for modality, ps in placeholders.items()
        }

        return token_ids, text, placeholders


class GLMAttention(nn.Module):

    def __init__(
        self,
        config: ChatGLMConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = config.num_attention_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.multi_query_attention = config.multi_query_attention
        self.total_num_kv_heads = (config.multi_query_group_num
                                   if config.multi_query_attention else
                                   config.num_attention_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 = config.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.query_key_value = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=config.add_bias_linear or config.add_qkv_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.query_key_value",
        )
        self.dense = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            config.hidden_size,
            bias=config.add_bias_linear,
            quant_config=quant_config,
            prefix=f"{prefix}.dense",
        )

        # https://huggingface.co/THUDM/chatglm3-6b-32k/blob/e210410255278dd9d74463cf396ba559c0ef801c/modeling_chatglm.py#L141
        rope_ratio = getattr(config, "rope_ratio", 1.0)
        max_positions = getattr(config, "seq_length", 8192)
        # NOTE: THUDM/cogagent-9b-20241220 uses original_rope=False,
        # which is equivalent to is_neox_style=True
        is_neox_style = not config.original_rope
        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim // 2,
            max_position=max_positions,
            base=10000 * rope_ratio,
            is_neox_style=is_neox_style,
        )
        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,
                              prefix=f"{prefix}.attn")

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        qkv, _ = self.query_key_value(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q, k = self.rotary_emb(position_ids, q, k)
        context_layer = self.attn(
            q,
            k,
            v,
            kv_cache,
            attn_metadata,
        )
        attn_output, _ = self.dense(context_layer)
        return attn_output


class GLMMLP(nn.Module):
    """MLP.

    MLP will take the input with h hidden state, project it to 4*h
    hidden dimension, perform nonlinear transformation, and project the
    state back into h hidden dimension.
    """

    def __init__(
        self,
        config: ChatGLMConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()

        self.add_bias = config.add_bias_linear

        # Project to 4h.
        self.dense_h_to_4h = MergedColumnParallelLinear(
            config.hidden_size,
            [config.ffn_hidden_size] * 2,
            bias=config.add_bias_linear,
            quant_config=quant_config,
            prefix=f"{prefix}.dense_h_to_4h",
        )

        self.activation_func = SiluAndMul()

        # Project back to h.
        self.dense_4h_to_h = RowParallelLinear(
            config.ffn_hidden_size,
            config.hidden_size,
            bias=config.add_bias_linear,
            quant_config=quant_config,
            prefix=f"{prefix}.dense_4h_to_h",
        )

    def forward(self, hidden_states):
        # [s, b, 4hp]
        intermediate_parallel, _ = self.dense_h_to_4h(hidden_states)
        intermediate_parallel = self.activation_func(intermediate_parallel)
        # [s, b, h]
        output, _ = self.dense_4h_to_h(intermediate_parallel)
        return output


class GLMBlock(nn.Module):
    """A single transformer layer.

    Transformer layer takes input with size [s, b, h] and returns an
    output of the same size.
    """

    def __init__(
        self,
        config: ChatGLMConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.apply_residual_connection_post_layernorm = (
            config.apply_residual_connection_post_layernorm)

        self.fp32_residual_connection = config.fp32_residual_connection

        layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
        # Layernorm on the input data.
        self.input_layernorm = layer_norm_func(config.hidden_size,
                                               eps=config.layernorm_epsilon)

        # Self attention.
        self.self_attention = GLMAttention(config,
                                           cache_config,
                                           quant_config,
                                           prefix=f"{prefix}.self_attention")
        self.hidden_dropout = config.hidden_dropout

        # Layernorm on the attention output
        self.post_attention_layernorm = layer_norm_func(
            config.hidden_size, eps=config.layernorm_epsilon)

        # MLP
        self.mlp = GLMMLP(config, quant_config, prefix=f"{prefix}.mlp")

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        # hidden_states: [num_tokens, h]
        # Layer norm at the beginning of the transformer layer.
        layernorm_output = self.input_layernorm(hidden_states)
        # Self attention.
        attention_output = self.self_attention(
            hidden_states=layernorm_output,
            position_ids=position_ids,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )

        # Residual connection.
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = hidden_states

        layernorm_input = residual + attention_output

        # Layer norm post the self attention.
        layernorm_output = self.post_attention_layernorm(layernorm_input)

        # Second residual connection.
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = layernorm_input

        output = self.mlp(layernorm_output) + residual

        return output


class GLMTransformer(nn.Module):
    """Transformer class."""

    def __init__(
        self,
        config: ChatGLMConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.post_layer_norm = config.post_layer_norm

        # Number of layers.
        self.num_layers = config.num_layers

        # Transformer layers.
        self.start_layer, self.end_layer, self.layers = make_layers(
            self.num_layers,
            lambda prefix: GLMBlock(
                config, cache_config, quant_config, prefix=prefix),
            prefix=f"{prefix}.layers",
        )

        if self.post_layer_norm:
            layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
            # Final layer norm before output.
            self.final_layernorm = layer_norm_func(
                config.hidden_size, eps=config.layernorm_epsilon)

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

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_ids: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        for i in range(self.start_layer, self.end_layer):
            layer = self.layers[i]
            hidden_states = layer(
                hidden_states=hidden_states,
                position_ids=position_ids,
                kv_cache=kv_caches[i - self.start_layer],
                attn_metadata=attn_metadata,
            )
        # Final layer norm.
        if get_pp_group().is_last_rank and self.post_layer_norm:
            hidden_states = self.final_layernorm(hidden_states)

        return hidden_states


class ChatGLMModel(nn.Module):

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

        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config

        self.config = config

        self.embedding = VocabParallelEmbedding(config.padded_vocab_size,
                                                config.hidden_size,
                                                quant_config=quant_config,
                                                prefix=f"{prefix}.embedding")

        self.num_layers = config.num_layers
        self.multi_query_group_num = config.multi_query_group_num
        self.kv_channels = config.kv_channels
        self.encoder = GLMTransformer(config,
                                      cache_config,
                                      quant_config,
                                      prefix=f"{prefix}.encoder")

        self.output_layer = ParallelLMHead(config.padded_vocab_size,
                                           config.hidden_size,
                                           quant_config=quant_config,
                                           prefix=f"{prefix}.output_layer")

        vision_config_flag = getattr(config, 'vision_config', None)
        if vision_config_flag is not None:
            self.vision_config = Namespace(**config.vision_config)
            self.vision = EVA2CLIPModel(self.config,
                                        quant_config,
                                        prefix=f"{prefix}.vision")
        else:
            self.vision = None

        self.make_empty_intermediate_tensors = (
            self.encoder.make_empty_intermediate_tensors)

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> GLMImagePixelInputs:

        pixel_values = kwargs.pop("pixel_values", None)
        if pixel_values is not None and self.vision is not None:
            if isinstance(pixel_values, torch.Tensor):
                if pixel_values.ndim > 2:
                    pixel_values = torch.concat(list(pixel_values))
            elif isinstance(pixel_values, list):
                return torch.concat(pixel_values)
            else:
                raise TypeError("""pixel_values must be a torch.Tensor
                    or a list of torch.Tensor
                    """)
        return GLMImagePixelInputs(pixel_values=pixel_values)

    def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
        image_input = self._parse_and_validate_image_input(**kwargs)
        if image_input["pixel_values"] is None:
            return None
        pixel_values = image_input["pixel_values"].to(
            dtype=self.config.torch_dtype)
        vision_embeddings = self.vision(pixel_values)
        return vision_embeddings

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[NestedTensors] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.embedding(input_ids)
        if multimodal_embeddings is not None:
            inputs_embeds = merge_multimodal_embeddings(
                input_ids=input_ids,
                inputs_embeds=inputs_embeds,
                multimodal_embeddings=multimodal_embeddings,
                placeholder_token_id=[
                    self.config.boi_token_id,
                    IMAGE_TOKEN_ID,
                    self.config.eoi_token_id,
                ],
            )
        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ) -> torch.Tensor:

        # NOTE: In v1, inputs_embeds is always generated at model runner, this
        # condition is for v0 compatibility.
        if intermediate_tensors is not None:
            inputs_embeds = intermediate_tensors["hidden_states"]
        elif inputs_embeds is None:
            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
            inputs_embeds = self.get_input_embeddings(input_ids,
                                                      vision_embeddings)
        # Run encoder.
        hidden_states = self.encoder(
            hidden_states=inputs_embeds,
            position_ids=positions,
            kv_caches=kv_caches,
            attn_metadata=attn_metadata,
        )

        if not get_pp_group().is_last_rank:
            return IntermediateTensors({"hidden_states": 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)
            ("linear_proj.merged_proj", "linear_proj.gate_proj", 0),
            ("linear_proj.merged_proj", "linear_proj.dense_h_to_4h", 1),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()

        for name, loaded_weight in weights:
            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:
                if "rotary_pos_emb.inv_freq" in name:
                    continue
                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


class ChatGLMBaseModel(nn.Module, SupportsLoRA, SupportsPP):

    hf_to_vllm_mapper = WeightsMapper(
        orig_to_new_substr={".word_embeddings": ""}, )

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

        self.quant_config = quant_config
        self.max_position_embeddings = getattr(config, "max_sequence_length",
                                               8192)
        self.transformer = ChatGLMModel(vllm_config=vllm_config,
                                        prefix=maybe_prefix(
                                            prefix, "transformer"))
        if self.config.tie_word_embeddings:
            self.transformer.output_layer.weight = (
                self.transformer.embedding.weight)
        self.lm_head = self.transformer.output_layer
        self.logits_processor = LogitsProcessor(config.padded_vocab_size)
        self.sampler = get_sampler()

    def forward(self,
                input_ids: torch.Tensor,
                positions: torch.Tensor,
                kv_caches: List[torch.Tensor],
                attn_metadata: AttentionMetadata,
                intermediate_tensors: Optional[IntermediateTensors] = None,
                **kwargs) -> torch.Tensor:
        hidden_states = self.transformer(input_ids, positions, kv_caches,
                                         attn_metadata, intermediate_tensors,
                                         **kwargs)
        return hidden_states

    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 sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)


class ChatGLM(ChatGLMBaseModel):
    packed_modules_mapping = {
        "query_key_value": ["query_key_value"],
        "dense_h_to_4h": ["dense_h_to_4h"]
    }
    # LoRA specific attributes
    supported_lora_modules = [
        "query_key_value",
        "dense",
        "dense_h_to_4h",
        "dense_4h_to_h",
    ]

    embedding_modules = {}
    embedding_padding_modules = []


class ChatGLMV(ChatGLMBaseModel, SupportsMultiModal):

    packed_modules_mapping = {
        "query_key_value": ["query_key_value"],
        "dense_h_to_4h": ["dense_h_to_4h"],
        "merged_proj": ["gate_proj", "dense_h_to_4h"]
    }
    # LoRA specific attributes
    supported_lora_modules = [
        "query_key_value",
        "dense",
        "dense_h_to_4h",
        "dense_4h_to_h",
        # vision
        "fc1",
        "fc2",
        "merged_proj",
        "linear_proj"
    ]

    embedding_modules = {}
    embedding_padding_modules = []

    def get_mm_mapping(self) -> MultiModelKeys:
        """
        Get the module prefix in multimodal models
        """
        return MultiModelKeys.from_string_field(
            language_model="transformer.encoder",
            connector="transformer.vision.linear_proj",
            tower_model="transformer.vision.transformer")

    def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
        return self.transformer.get_multimodal_embeddings(**kwargs)

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[NestedTensors] = None,
    ) -> torch.Tensor:
        return self.transformer.get_input_embeddings(input_ids,
                                                     multimodal_embeddings)


@MULTIMODAL_REGISTRY.register_processor(GLM4VMultiModalProcessor,
                                        info=GLM4VProcessingInfo,
                                        dummy_inputs=GLM4VDummyInputsBuilder)
class ChatGLMForCausalLM(ChatGLMBaseModel, SupportsLoRA, SupportsPP,
                         SupportsMultiModal):
    # Ensure that the LoRA support check passes when the class is not
    # initialized, but set all these attributes to empty.
    # These will be updated when an instance class is selected
    packed_modules_mapping = {}
    supported_lora_modules = []
    embedding_modules = {}
    embedding_padding_modules = []

    def __new__(
        cls,
        vllm_config: VllmConfig,
        prefix: str = "",
    ) -> None:
        config = vllm_config.model_config.hf_config

        # Initialize VL
        if hasattr(config, "vision_config"):  # noqa: SIM108
            instance_cls = ChatGLMV
        # Initialize LLM
        else:
            instance_cls = ChatGLM

        # quant_config references base class members,
        # so update values before init is called
        cls.packed_modules_mapping.update(instance_cls.packed_modules_mapping)
        cls.supported_lora_modules += instance_cls.supported_lora_modules
        cls.embedding_modules.update(instance_cls.embedding_modules)
        cls.embedding_padding_modules += instance_cls.embedding_padding_modules
        return instance_cls(vllm_config=vllm_config, prefix=prefix)