820 lines
30 KiB
Python
820 lines
30 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Adapted from
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# https://github.com/THUDM/CogAgent
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"""Inference-only CogAgent model compatible with THUDM weights."""
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from argparse import Namespace
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from typing import (Iterable, List, Mapping, Optional, Set, Tuple, TypedDict,
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Union)
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import torch
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from torch import nn
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from torch.nn import LayerNorm
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from torchvision import transforms
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from torchvision.transforms import InterpolationMode
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from transformers import PreTrainedTokenizer, TensorType
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from transformers.image_utils import ImageInput
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from transformers.tokenization_utils_base import TextInput
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.glm4_vision_encoder import EVA2CLIPModel
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import MultiModalKwargs, NestedTensors
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from vllm.multimodal.parse import MultiModalDataItems
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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BaseProcessingInfo, BatchFeature,
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MultiModalFieldConfig,
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PromptReplacement)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs import ChatGLMConfig
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from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
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from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix, merge_multimodal_embeddings)
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class GLMImagePixelInputs(TypedDict):
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pixel_values: torch.Tensor
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"""Shape: `(batch_size, num_channels, height, width)`"""
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class GLM4VProcessor:
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"""
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This model doesn't define its own HF processor,
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so we implement our own one here.
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"""
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def __init__(
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self,
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config: ChatGLMConfig,
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tokenizer: PreTrainedTokenizer,
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) -> None:
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super().__init__()
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self.config = config
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self.tokenizer = tokenizer
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if vision_config := getattr(config, "vision_config", None):
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image_size = vision_config["image_size"]
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self.image_transform = transforms.Compose([
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transforms.Resize(
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(image_size, image_size),
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interpolation=InterpolationMode.BICUBIC,
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),
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transforms.ToTensor(),
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transforms.Normalize(
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mean=(0.48145466, 0.4578275, 0.40821073),
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std=(0.26862954, 0.26130258, 0.27577711),
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),
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])
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else:
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self.image_transform = None
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def __call__(
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self,
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text: Optional[Union[TextInput, list[TextInput]]] = None,
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images: Optional[Union[ImageInput, list[ImageInput]]] = None,
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return_tensors: Optional[Union[str, TensorType]] = None,
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) -> BatchFeature:
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if text is None:
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text = []
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if not isinstance(text, list):
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text = [text]
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if images is None:
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images = []
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if not isinstance(images, list):
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images = [images]
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text_inputs = self.tokenizer(text)
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if len(images) == 0:
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image_inputs = {}
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else:
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if self.image_transform is None:
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raise ValueError("This model does not support image inputs")
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pixel_values = [self.image_transform(image) for image in images]
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image_inputs = {"pixel_values": torch.stack(pixel_values)}
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return BatchFeature(
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{
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**text_inputs,
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**image_inputs,
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},
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tensor_type=return_tensors,
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)
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class GLM4VProcessingInfo(BaseProcessingInfo):
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def get_tokenizer(self):
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tokenizer = self.ctx.tokenizer
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assert isinstance(tokenizer, PreTrainedTokenizer)
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return tokenizer
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def get_hf_config(self):
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return self.ctx.get_hf_config(ChatGLMConfig)
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def get_hf_processor(self) -> GLM4VProcessor:
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return GLM4VProcessor(
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self.get_hf_config(),
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self.get_tokenizer(),
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)
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def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
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return {"image": 1}
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def get_mm_max_tokens_per_item(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> Mapping[str, int]:
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return {"image": self.get_num_image_feature_tokens()}
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def get_num_image_tokens(self) -> int:
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hf_config = self.get_hf_config()
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if not (vision_config := getattr(hf_config, "vision_config", None)):
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return 0
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image_size = vision_config["image_size"]
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patch_size = vision_config["patch_size"]
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grid_length = image_size // patch_size // 2
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return grid_length * grid_length
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def get_num_image_feature_tokens(self) -> int:
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# EVA2CLIPModel has embeddings for boi and eoi tokens as well
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return self.get_num_image_tokens() + 2
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class GLM4VDummyInputsBuilder(BaseDummyInputsBuilder[GLM4VProcessingInfo]):
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def get_dummy_processor_inputs(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> ProcessorInputs:
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hf_config = self.info.get_hf_config()
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if not (vision_config := getattr(hf_config, "vision_config", None)):
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return ProcessorInputs(prompt_text="", mm_data={})
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target_width = target_height = vision_config["image_size"]
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num_images = mm_counts.get("image", 0)
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mm_data = {
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"image":
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self._get_dummy_images(width=target_width,
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height=target_height,
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num_images=num_images)
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}
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base_text = "<|begin_of_image|><|endoftext|><|end_of_image|>"
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return ProcessorInputs(
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prompt_text=base_text * num_images,
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mm_data=mm_data,
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)
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class GLM4VMultiModalProcessor(BaseMultiModalProcessor[GLM4VProcessingInfo]):
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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return dict(pixel_values=MultiModalFieldConfig.batched("image"))
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def _get_prompt_replacements(
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self,
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mm_items: MultiModalDataItems,
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hf_processor_mm_kwargs: Mapping[str, object],
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out_mm_kwargs: MultiModalKwargs,
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) -> list[PromptReplacement]:
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hf_config = self.info.get_hf_config()
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if not hasattr(hf_config, "vision_config"):
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return []
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boi_token_id = hf_config.boi_token_id
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image_token_id = hf_config.pad_token_id
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eoi_token_id = hf_config.eoi_token_id
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def get_replacement(item_idx: int):
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num_image_tokens = self.info.get_num_image_tokens()
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image_tokens = [image_token_id] * num_image_tokens
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return [boi_token_id] + image_tokens + [eoi_token_id]
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return [
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PromptReplacement(
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modality="image",
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target=[boi_token_id, image_token_id, eoi_token_id],
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replacement=get_replacement,
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),
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]
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class GLMAttention(nn.Module):
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def __init__(
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self,
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config: ChatGLMConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = config.hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.multi_query_attention = config.multi_query_attention
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self.total_num_kv_heads = (config.multi_query_group_num
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if config.multi_query_attention else
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config.num_attention_heads)
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = config.hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.query_key_value = QKVParallelLinear(
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self.hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=config.add_bias_linear or config.add_qkv_bias,
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quant_config=quant_config,
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prefix=f"{prefix}.query_key_value",
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)
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self.dense = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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config.hidden_size,
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bias=config.add_bias_linear,
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quant_config=quant_config,
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prefix=f"{prefix}.dense",
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)
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# https://huggingface.co/THUDM/chatglm3-6b-32k/blob/e210410255278dd9d74463cf396ba559c0ef801c/modeling_chatglm.py#L141
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rope_ratio = getattr(config, "rope_ratio", 1.0)
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max_positions = getattr(config, "seq_length", 8192)
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# NOTE: THUDM/cogagent-9b-20241220 uses original_rope=False,
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# which is equivalent to is_neox_style=True
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is_neox_style = not config.original_rope
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim // 2,
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max_position=max_positions,
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base=10000 * rope_ratio,
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is_neox_style=is_neox_style,
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn")
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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qkv, _ = self.query_key_value(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(position_ids, q, k)
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context_layer = self.attn(
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q,
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k,
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v,
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kv_cache,
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attn_metadata,
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)
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attn_output, _ = self.dense(context_layer)
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return attn_output
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class GLMMLP(nn.Module):
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"""MLP.
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MLP will take the input with h hidden state, project it to 4*h
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hidden dimension, perform nonlinear transformation, and project the
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state back into h hidden dimension.
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"""
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def __init__(
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self,
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config: ChatGLMConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.add_bias = config.add_bias_linear
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# Project to 4h.
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self.dense_h_to_4h = MergedColumnParallelLinear(
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config.hidden_size,
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[config.ffn_hidden_size] * 2,
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bias=config.add_bias_linear,
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quant_config=quant_config,
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prefix=f"{prefix}.dense_h_to_4h",
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)
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self.activation_func = SiluAndMul()
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# Project back to h.
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self.dense_4h_to_h = RowParallelLinear(
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config.ffn_hidden_size,
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config.hidden_size,
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bias=config.add_bias_linear,
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quant_config=quant_config,
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prefix=f"{prefix}.dense_4h_to_h",
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)
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def forward(self, hidden_states):
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# [s, b, 4hp]
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intermediate_parallel, _ = self.dense_h_to_4h(hidden_states)
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intermediate_parallel = self.activation_func(intermediate_parallel)
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# [s, b, h]
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output, _ = self.dense_4h_to_h(intermediate_parallel)
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return output
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class GLMBlock(nn.Module):
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"""A single transformer layer.
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Transformer layer takes input with size [s, b, h] and returns an
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output of the same size.
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"""
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def __init__(
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self,
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config: ChatGLMConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.apply_residual_connection_post_layernorm = (
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config.apply_residual_connection_post_layernorm)
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self.fp32_residual_connection = config.fp32_residual_connection
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layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
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# Layernorm on the input data.
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self.input_layernorm = layer_norm_func(config.hidden_size,
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eps=config.layernorm_epsilon)
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# Self attention.
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self.self_attention = GLMAttention(config,
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cache_config,
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quant_config,
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prefix=f"{prefix}.self_attention")
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self.hidden_dropout = config.hidden_dropout
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# Layernorm on the attention output
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self.post_attention_layernorm = layer_norm_func(
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config.hidden_size, eps=config.layernorm_epsilon)
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# MLP
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self.mlp = GLMMLP(config, quant_config, prefix=f"{prefix}.mlp")
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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# hidden_states: [num_tokens, h]
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# Layer norm at the beginning of the transformer layer.
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layernorm_output = self.input_layernorm(hidden_states)
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# Self attention.
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attention_output = self.self_attention(
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hidden_states=layernorm_output,
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position_ids=position_ids,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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# Residual connection.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = hidden_states
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layernorm_input = residual + attention_output
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# Layer norm post the self attention.
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layernorm_output = self.post_attention_layernorm(layernorm_input)
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# Second residual connection.
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if self.apply_residual_connection_post_layernorm:
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residual = layernorm_output
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else:
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residual = layernorm_input
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output = self.mlp(layernorm_output) + residual
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return output
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class GLMTransformer(nn.Module):
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"""Transformer class."""
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def __init__(
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self,
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config: ChatGLMConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.post_layer_norm = config.post_layer_norm
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# Number of layers.
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self.num_layers = config.num_layers
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# Transformer layers.
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self.start_layer, self.end_layer, self.layers = make_layers(
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self.num_layers,
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lambda prefix: GLMBlock(
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config, cache_config, quant_config, prefix=prefix),
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prefix=f"{prefix}.layers",
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)
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if self.post_layer_norm:
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layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
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# Final layer norm before output.
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self.final_layernorm = layer_norm_func(
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config.hidden_size, eps=config.layernorm_epsilon)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(["hidden_states"],
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config.hidden_size))
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_ids: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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hidden_states = layer(
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hidden_states=hidden_states,
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position_ids=position_ids,
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kv_cache=kv_caches[i - self.start_layer],
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attn_metadata=attn_metadata,
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)
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# 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,
|
|
self.config.pad_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)
|