672 lines
24 KiB
Python
672 lines
24 KiB
Python
# coding=utf-8
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# Adapted from
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# https://github.com/THUDM/GLM-4
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"""Inference-only ChatGLM model compatible with THUDM weights."""
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from argparse import Namespace
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from array import array
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from typing import Dict, Iterable, List, Mapping, Optional, Tuple, TypedDict
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import torch
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from PIL import Image
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from torch import nn
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from torch.nn import LayerNorm
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig, LoRAConfig, MultiModalConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
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from vllm.logger import init_logger
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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.base_config import (
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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 Sampler, SamplerOutput
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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.sampling_metadata import SamplingMetadata
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from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalDataDict,
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MultiModalInputs)
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from vllm.multimodal.base import MultiModalData
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from vllm.multimodal.utils import cached_get_tokenizer
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from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
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SequenceData)
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from vllm.transformers_utils.configs import ChatGLMConfig
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from .interfaces import SupportsLoRA, SupportsMultiModal
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logger = init_logger(__name__)
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def calculate_image_placeholder(vision_config):
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return (vision_config["image_size"] // vision_config["patch_size"] // 2)**2
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def mm_input_mapper_for_glmv(
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ctx: InputContext,
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data: MultiModalData[object],
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) -> Dict:
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model_config = ctx.model_config
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tokenizer = cached_get_tokenizer(model_config.tokenizer,
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trust_remote_code=True)
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if tokenizer is None:
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raise RuntimeError("No HuggingFace processor is available "
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"to process the image object")
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try:
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raw_batch_data = tokenizer.apply_chat_template(
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conversation=[{
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"role": "user",
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"image": data
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}],
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add_generation_prompt=True,
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tokenize=True,
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return_tensors="pt",
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return_dict=True).data
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except Exception:
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logger.error("Failed to process image (%s)", data)
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raise
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pixel_values = raw_batch_data['images']
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return MultiModalInputs({'pixel_values': pixel_values})
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def merge_glm_vision_embeddings(
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input_ids: torch.Tensor,
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inputs_embeds: torch.Tensor,
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vision_embeddings: torch.Tensor,
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boi_token_id: int,
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eoi_token_id: int,
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) -> torch.Tensor:
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boi_positions = (input_ids == boi_token_id).nonzero(as_tuple=True)[0]
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eoi_positions = (input_ids == eoi_token_id).nonzero(as_tuple=True)[0]
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mask = torch.zeros_like(input_ids, dtype=torch.bool)
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for boi_pos, eoi_pos in zip(boi_positions, eoi_positions):
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assert boi_pos < eoi_pos
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mask[boi_pos:eoi_pos + 1] = True
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inputs_embeds[mask] = vision_embeddings.view(-1,
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vision_embeddings.shape[-1])
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return inputs_embeds
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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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def get_max_glmv_image_tokens(ctx: InputContext):
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hf_config = ctx.get_hf_config(ChatGLMConfig)
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vision_config = getattr(hf_config, 'vision_config', None)
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if vision_config is None:
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return 1
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elif isinstance(vision_config, dict):
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return calculate_image_placeholder(vision_config)
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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def dummy_data_for_glmv(
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ctx: InputContext, seq_len: int, mm_counts: Mapping[str, int]
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) -> Tuple[SequenceData, Optional[MultiModalDataDict]]:
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hf_config = ctx.get_hf_config(ChatGLMConfig)
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vision_config = getattr(hf_config, 'vision_config', None)
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if vision_config is None:
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token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE, [0] * seq_len)
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seq_data = SequenceData(token_ids)
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return seq_data, None
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elif isinstance(vision_config, dict):
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image_size = vision_config["image_size"]
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image_placeholder_length = calculate_image_placeholder(vision_config)
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token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE, [hf_config.boi_token_id] +
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[0] * image_placeholder_length +
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[hf_config.eoi_token_id])
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token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
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[0] * (seq_len - image_placeholder_length - 2))
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seq_data = SequenceData(token_ids)
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mm_data = {
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"image": Image.new("RGB", (image_size, image_size), color=0)
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}
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return seq_data, mm_data
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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def find_all_positions(input_ids: List[int], target: int) -> List[int]:
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return [index for index, value in enumerate(input_ids) if value == target]
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def input_processor_for_glmv(ctx: InputContext, llm_inputs: LLMInputs):
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hf_config = ctx.get_hf_config(ChatGLMConfig)
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vision_config = getattr(hf_config, 'vision_config', None)
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if vision_config is None:
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return llm_inputs
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elif isinstance(vision_config, dict):
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image_placeholder_length = calculate_image_placeholder(vision_config)
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else:
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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input_ids = llm_inputs.get("prompt_token_ids")
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position_ids = llm_inputs.get("position_ids")
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tokenizer = cached_get_tokenizer(
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ctx.model_config.model,
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trust_remote_code=ctx.model_config.trust_remote_code)
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try:
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raw_batch_data = tokenizer.apply_chat_template(
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conversation=[{
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"role": "user",
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"image": llm_inputs['multi_modal_data']["image"],
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"content": llm_inputs['prompt']
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}],
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add_generation_prompt=True,
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tokenize=True,
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return_tensors="pt",
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return_dict=True).data
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except Exception:
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logger.error("Failed to process content (%s)", llm_inputs['prompt'])
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raise
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input_ids = raw_batch_data['input_ids'][0].tolist()
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if position_ids is None:
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position_ids = list(range(len(input_ids)))
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boi_token_id = hf_config.boi_token_id
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eoi_token_id = hf_config.eoi_token_id
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boi_positions = find_all_positions(input_ids, boi_token_id)
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eoi_positions = find_all_positions(input_ids, eoi_token_id)
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assert len(boi_positions) == len(eoi_positions)
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new_input_ids = []
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new_position_ids = []
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final_processed_position = 0
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final_processed_position = 0
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for boi_position, eoi_position in zip(boi_positions, eoi_positions):
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assert boi_position < eoi_position
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new_input_ids.extend(input_ids[final_processed_position:boi_position +
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1])
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new_position_ids.extend(
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list(range(final_processed_position, boi_position + 1)))
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new_input_ids.extend([input_ids[boi_position + 1]] *
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image_placeholder_length)
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new_position_ids.extend([boi_position + 1] * image_placeholder_length)
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final_processed_position = eoi_position
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new_input_ids.extend(input_ids[final_processed_position:])
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new_position_ids.extend(
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list(range(final_processed_position, len(input_ids))))
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assert len(new_input_ids) == len(new_position_ids)
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llm_inputs["prompt_token_ids"] = new_input_ids
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llm_inputs["position_ids"] = new_position_ids
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return llm_inputs
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class GLMAttention(nn.Module):
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def __init__(
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self,
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config,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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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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)
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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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)
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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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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=False,
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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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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,
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quant_config: Optional[QuantizationConfig] = None,
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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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)
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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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)
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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,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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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, cache_config, quant_config)
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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)
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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,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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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.layers = nn.ModuleList([
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GLMBlock(config, cache_config, quant_config)
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for i in range(self.num_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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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.num_layers):
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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],
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attn_metadata=attn_metadata,
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)
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# Final layer norm.
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if self.post_layer_norm:
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states
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class ChatGLMModel(nn.Module):
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def __init__(
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self,
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config,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.config = config
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self.embedding = VocabParallelEmbedding(config.padded_vocab_size,
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config.hidden_size,
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quant_config=quant_config)
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self.num_layers = config.num_layers
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self.multi_query_group_num = config.multi_query_group_num
|
|
self.kv_channels = config.kv_channels
|
|
self.encoder = GLMTransformer(config, cache_config, quant_config)
|
|
|
|
self.output_layer = ParallelLMHead(config.padded_vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config)
|
|
|
|
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)
|
|
else:
|
|
self.vision = None
|
|
|
|
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 forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
**kwargs: object,
|
|
) -> torch.Tensor:
|
|
|
|
inputs_embeds = self.embedding(input_ids)
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
|
|
if image_input["pixel_values"] is not None:
|
|
pixel_values = image_input["pixel_values"].to(
|
|
dtype=inputs_embeds.dtype)
|
|
image_embeds = self.vision(pixel_values)
|
|
|
|
boi_token_id = self.config.boi_token_id
|
|
eoi_token_id = self.config.eoi_token_id
|
|
|
|
inputs_embeds = merge_glm_vision_embeddings(
|
|
input_ids=input_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
vision_embeddings=image_embeds,
|
|
boi_token_id=boi_token_id,
|
|
eoi_token_id=eoi_token_id)
|
|
|
|
# Run encoder.
|
|
hidden_states = self.encoder(
|
|
hidden_states=inputs_embeds,
|
|
position_ids=positions,
|
|
kv_caches=kv_caches,
|
|
attn_metadata=attn_metadata,
|
|
)
|
|
return hidden_states
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_image_input_mapper(mm_input_mapper_for_glmv)
|
|
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_glmv_image_tokens)
|
|
@INPUT_REGISTRY.register_dummy_data(dummy_data_for_glmv)
|
|
@INPUT_REGISTRY.register_input_processor(input_processor_for_glmv)
|
|
class ChatGLMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
|
|
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 = []
|
|
|
|
def __init__(
|
|
self,
|
|
config: ChatGLMConfig,
|
|
multimodal_config: MultiModalConfig,
|
|
cache_config: Optional[CacheConfig] = None,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
lora_config: Optional[LoRAConfig] = None,
|
|
):
|
|
super().__init__()
|
|
|
|
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(config, cache_config, quant_config)
|
|
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 = 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, **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]]):
|
|
# Merge two ColumnParallelLinear into one MergedColumnParallelLinear
|
|
merged_weights_dict: Dict[str, Dict[str, Optional[torch.Tensor]]] = {
|
|
"transformer.vision.linear_proj.merged_proj.weight": {
|
|
"transformer.vision.linear_proj.gate_proj.weight": None,
|
|
"transformer.vision.linear_proj.dense_h_to_4h.weight": None,
|
|
}
|
|
}
|
|
|
|
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
|
for name, loaded_weight in weights:
|
|
is_weight_to_be_merge = False
|
|
for _, merged_weight_dict in merged_weights_dict.items():
|
|
if name in merged_weight_dict:
|
|
assert merged_weight_dict[name] is None
|
|
merged_weight_dict[name] = loaded_weight
|
|
is_weight_to_be_merge = True
|
|
if is_weight_to_be_merge:
|
|
continue
|
|
if "rotary_pos_emb.inv_freq" in name:
|
|
continue
|
|
if "word_embeddings" in name:
|
|
name = name.replace(".word_embeddings", "")
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
|
|
for combined_name, merged_weight_dict in merged_weights_dict.items():
|
|
if combined_name in params_dict:
|
|
param = params_dict[combined_name]
|
|
combined_weight = torch.cat(list(merged_weight_dict.values()),
|
|
dim=0)
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, combined_weight)
|