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README.md
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README.md
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# mPLUG-Owl3-2B-241014_a14065968674238464724418
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: visual-question-answering
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tags:
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- chat
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---
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# mPLUG-Owl3
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## Introduction
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mPLUG-Owl3 is a state-of-the-art multi-modal large language model designed to tackle the challenges of long image sequence understanding. We propose Hyper Attention, which boosts the speed of long visual sequence understanding in multimodal large language models by sixfold, allowing for processing of visual sequences that are eight times longer. Meanwhile, we maintain excellent performance on single-image, multi-image, and video tasks.
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Github: [mPLUG-Owl](https://github.com/X-PLUG/mPLUG-Owl)
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## Quickstart
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Load the mPLUG-Owl3. We now only support attn_implementation in ```['sdpa', 'flash_attention_2']```.
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```Python
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import torch
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from modelscope import AutoConfig, AutoModel
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model_path = 'iic/mPLUG-Owl3-2B-241014'
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config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
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print(config)
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# model = mPLUGOwl3Model(config).cuda().half()
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model = AutoModel.from_pretrained(model_path, attn_implementation='sdpa', torch_dtype=torch.half, trust_remote_code=True)
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model.eval().cuda()
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```
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Chat with images.
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```Python
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from PIL import Image
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from modelscope import AutoTokenizer
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from decord import VideoReader, cpu
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model_path = 'iic/mPLUG-Owl3-2B-241014'
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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processor = model.init_processor(tokenizer)
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image = Image.new('RGB', (500, 500), color='red')
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messages = [
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{"role": "user", "content": """<|image|>
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Describe this image."""},
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{"role": "assistant", "content": ""}
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]
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inputs = processor(messages, images=[image], videos=None)
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inputs.to('cuda')
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inputs.update({
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'tokenizer': tokenizer,
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'max_new_tokens':100,
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'decode_text':True,
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})
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g = model.generate(**inputs)
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print(g)
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```
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Chat with a video.
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```Python
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from PIL import Image
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from modelscope import AutoTokenizer
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from decord import VideoReader, cpu # pip install decord
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model_path = 'iic/mPLUG-Owl3-2B-241014'
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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processor = model.init_processor(tokenizer)
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messages = [
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{"role": "user", "content": """<|video|>
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Describe this video."""},
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{"role": "assistant", "content": ""}
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]
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videos = ['/nas-mmu-data/examples/car_room.mp4']
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MAX_NUM_FRAMES=16
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def encode_video(video_path):
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def uniform_sample(l, n):
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gap = len(l) / n
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idxs = [int(i * gap + gap / 2) for i in range(n)]
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return [l[i] for i in idxs]
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vr = VideoReader(video_path, ctx=cpu(0))
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sample_fps = round(vr.get_avg_fps() / 1) # FPS
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frame_idx = [i for i in range(0, len(vr), sample_fps)]
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if len(frame_idx) > MAX_NUM_FRAMES:
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frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES)
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frames = vr.get_batch(frame_idx).asnumpy()
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frames = [Image.fromarray(v.astype('uint8')) for v in frames]
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print('num frames:', len(frames))
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return frames
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video_frames = [encode_video(_) for _ in videos]
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inputs = processor(messages, images=None, videos=video_frames)
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inputs.to('cuda')
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inputs.update({
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'tokenizer': tokenizer,
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'max_new_tokens':100,
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'decode_text':True,
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})
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g = model.generate(**inputs)
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print(g)
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```
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## Citation
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If you find our work helpful, feel free to give us a cite.
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```
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@misc{ye2024mplugowl3longimagesequenceunderstanding,
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title={mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models},
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author={Jiabo Ye and Haiyang Xu and Haowei Liu and Anwen Hu and Ming Yan and Qi Qian and Ji Zhang and Fei Huang and Jingren Zhou},
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year={2024},
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eprint={2408.04840},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2408.04840},
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}
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```
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mPLUG-Owl3-2B-241014
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{
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"architectures": [
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"mPLUGOwl3Model"
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],
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"auto_map": {
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"AutoConfig": "configuration_mplugowl3.mPLUGOwl3Config",
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"AutoModel": "modeling_mplugowl3.mPLUGOwl3Model",
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"AutoModelForCausalLM": "modeling_mplugowl3.mPLUGOwl3Model"
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},
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "mplugowl3",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_theta": 1000000.0,
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"sliding_window": 32768,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.41.2",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151851,
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"hyper_layers": [
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7,
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15,
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23,
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26
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],
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"vision_config": {
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"hidden_size": 1152,
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"image_size": 384,
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"intermediate_size": 4304,
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"model_type": "siglip_vision_model",
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"num_attention_heads": 16,
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"num_hidden_layers": 27,
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"patch_size": 14
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}
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}
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{"framework":"Pytorch","task":"image-text-to-text"}
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from transformers.configuration_utils import PretrainedConfig
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class HyperQwen2Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Qwen2Model`]. It is used to instantiate a
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Qwen2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of
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Qwen2-7B-beta [Qwen/Qwen2-7B-beta](https://huggingface.co/Qwen/Qwen2-7B-beta).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 151936):
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Vocabulary size of the Qwen2 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Qwen2Model`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 22016):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 32):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 32768):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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use_sliding_window (`bool`, *optional*, defaults to `False`):
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Whether to use sliding window attention.
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sliding_window (`int`, *optional*, defaults to 4096):
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Sliding window attention (SWA) window size. If not specified, will default to `4096`.
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max_window_layers (`int`, *optional*, defaults to 28):
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The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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```python
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>>> from transformers import Qwen2Model, Qwen2Config
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>>> # Initializing a Qwen2 style configuration
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>>> configuration = Qwen2Config()
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>>> # Initializing a model from the Qwen2-7B style configuration
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>>> model = Qwen2Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "qwen2"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=151936,
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hidden_size=4096,
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intermediate_size=22016,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=32,
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hidden_act="silu",
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max_position_embeddings=32768,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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use_sliding_window=False,
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sliding_window=4096,
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max_window_layers=28,
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attention_dropout=0.0,
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hyper_layers=[1,9,17,25],
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.use_sliding_window = use_sliding_window
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self.sliding_window = sliding_window if use_sliding_window else None
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self.max_window_layers = max_window_layers
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.attention_dropout = attention_dropout
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self.hyper_layers = hyper_layers
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super().__init__(
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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# coding=utf-8
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""" mPLUGOwl3 model configuration"""
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import os
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from typing import Union
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from transformers.utils import logging
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from .configuration_hyper_qwen2 import HyperQwen2Config
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from transformers.models.siglip.configuration_siglip import SiglipVisionConfig
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logger = logging.get_logger(__name__)
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class mPLUGOwl3Config(HyperQwen2Config):
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model_type = "mplugowl3"
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keys_to_ignore_at_inference = ["past_key_values"]
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default_vision_config = {
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"hidden_size": 1152,
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"image_size": 384,
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"intermediate_size": 4304,
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"model_type": "siglip_vision_model",
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"num_attention_heads": 16,
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"num_hidden_layers": 27,
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"patch_size": 14
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}
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def __init__(
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self,
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use_cache=True,
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vision_config=None,
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**kwargs,
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):
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self.use_cache = use_cache
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# same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit add tgt_sizes
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if vision_config is None:
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self.vision_config = SiglipVisionConfig(**self.default_vision_config)
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logger.info("vision_config is None, using default vision config")
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elif isinstance(vision_config, dict):
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self.vision_config = SiglipVisionConfig(**vision_config)
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elif isinstance(vision_config, SiglipVisionConfig):
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self.vision_config = vision_config
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self.image_size = self.vision_config.image_size
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self.patch_size = self.vision_config.patch_size
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super().__init__(**kwargs)
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{
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"bos_token_id": 151643,
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"pad_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_p": 0.8,
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"top_k": 20,
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"transformers_version": "4.37.0"
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}
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@ -0,0 +1,416 @@
|
||||||
|
import random
|
||||||
|
from typing import Optional, Union, Dict, Any, List
|
||||||
|
|
||||||
|
from einops import rearrange, repeat
|
||||||
|
import torch
|
||||||
|
import math
|
||||||
|
import PIL.Image
|
||||||
|
import PIL.ImageSequence
|
||||||
|
import numpy as np
|
||||||
|
import PIL
|
||||||
|
from PIL import Image
|
||||||
|
|
||||||
|
from transformers.utils import TensorType, requires_backends, is_torch_dtype, is_torch_device
|
||||||
|
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
|
||||||
|
from transformers import AutoImageProcessor
|
||||||
|
from transformers.image_transforms import to_channel_dimension_format
|
||||||
|
from transformers.image_utils import (
|
||||||
|
ImageInput,
|
||||||
|
make_list_of_images,
|
||||||
|
valid_images,
|
||||||
|
is_torch_tensor,
|
||||||
|
is_batched,
|
||||||
|
to_numpy_array,
|
||||||
|
infer_channel_dimension_format,
|
||||||
|
ChannelDimension
|
||||||
|
)
|
||||||
|
from torchvision.ops.boxes import box_area
|
||||||
|
from torchvision.transforms import functional as F
|
||||||
|
from torchvision.transforms.transforms import InterpolationMode
|
||||||
|
from torchvision import transforms
|
||||||
|
|
||||||
|
def recursive_converter(converter, value):
|
||||||
|
if isinstance(value, list):
|
||||||
|
new_value = []
|
||||||
|
for v in value:
|
||||||
|
new_value += [recursive_converter(converter, v)]
|
||||||
|
return new_value
|
||||||
|
else:
|
||||||
|
return converter(value)
|
||||||
|
|
||||||
|
def box_iou(boxes1, area1, boxes2, eps=1e-5):
|
||||||
|
area2 = box_area(boxes2)
|
||||||
|
|
||||||
|
lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
|
||||||
|
rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
|
||||||
|
|
||||||
|
wh = (rb - lt).clamp(min=0) # [N,M,2]
|
||||||
|
inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
|
||||||
|
|
||||||
|
union = area1[:, None] + area2 - inter
|
||||||
|
|
||||||
|
iou = inter / (union+eps)
|
||||||
|
return iou, union
|
||||||
|
|
||||||
|
available_anchor_strategy = ['docowl', 'random', 'highest', 'last', 'llava']
|
||||||
|
|
||||||
|
grid_dict = {
|
||||||
|
'grid_33':[
|
||||||
|
(1,1),
|
||||||
|
(1,2),(2,1),
|
||||||
|
(1,3),(3,1),
|
||||||
|
(2,2),(1,4),(4,1),
|
||||||
|
(1,5),(5,1),
|
||||||
|
(1,6),(6,1),(2,3),(3,2),
|
||||||
|
(1,7),(7,1),
|
||||||
|
(4,2),(2,4),(1,8),(8,1),
|
||||||
|
(3,3),(1,9),(9,1)],
|
||||||
|
'grid_squ_3x3':[
|
||||||
|
(1,1),(2,2),(3,3)
|
||||||
|
],
|
||||||
|
'grid_squ_4':[
|
||||||
|
(2,2),(1,3),(1,4),(3,1),(4,1)
|
||||||
|
],
|
||||||
|
'grid_squ_6':[
|
||||||
|
(2,2),(1,3),(1,4),(3,1),(4,1), (2,3),(3,2)
|
||||||
|
],
|
||||||
|
'grid_squ_2':[
|
||||||
|
(2,1)
|
||||||
|
],
|
||||||
|
'grid_squ_9':[
|
||||||
|
(1,1),
|
||||||
|
(1,2),(2,1),
|
||||||
|
(1,3),(3,1),
|
||||||
|
(2,2),(1,4),(4,1),
|
||||||
|
(1,5),(5,1),
|
||||||
|
(1,6),(6,1),(2,3),(3,2),
|
||||||
|
(1,7),(7,1),
|
||||||
|
(4,2),(2,4),(1,8),(8,1),
|
||||||
|
(3,3),(1,9),(9,1)],
|
||||||
|
}
|
||||||
|
|
||||||
|
cut_prompt_template_dict = {
|
||||||
|
'v0': lambda img_token, h, w: f''.join([f"{img_token}" for i in range(h) for j in range(w)]),
|
||||||
|
'v1': lambda img_token, h, w: f'Cut to {h} rows {w} columns, '+ ' '.join([f"subimg({i},{j}){img_token}"for i in range(h) for j in range(w)]),
|
||||||
|
'v1_global': lambda img_token, h, w: f'Cut to {h} rows {w} columns with a global view, '+ ' '.join([f"subimg({i},{j}){img_token}"for i in range(h) for j in range(w)]+[f"global_view{img_token}"]),
|
||||||
|
'v2_global': lambda img_token, h, w: f'Cut to {h} rows {w} columns with a global view\n'+ '\n'.join([' '.join([f"subimg({i},{j}){img_token}" for j in range(w)]) for i in range(h)])+f"\nglobal_view{img_token}",
|
||||||
|
'v3': lambda img_token, h, w: f'<|start_cut|>{h}*{w}'+ ' '.join([f"{img_token}"for i in range(h) for j in range(w)])+'<|end_cut|>',
|
||||||
|
'v3_global': lambda img_token, h, w: f'<|start_cut|>{h}*{w}\n'+ '\n'.join([' '.join([f"{img_token}" for j in range(w)]) for i in range(h)])+f'\n{img_token}<|end_cut|>',
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
def anchor_rank(anchors, anchors_areas, input_image_size, eps=1e-5):
|
||||||
|
# anchors x1 y1 x2 y2
|
||||||
|
|
||||||
|
# image_size: (h, w)
|
||||||
|
# xyxy
|
||||||
|
input_image_bbox = torch.tensor([0, 0, input_image_size[1], input_image_size[0]]).unsqueeze(0)
|
||||||
|
|
||||||
|
boxes1 = anchors
|
||||||
|
boxes2 = input_image_bbox
|
||||||
|
boxes3 = anchors.clone()
|
||||||
|
# y2
|
||||||
|
boxes3[:,3] = input_image_size[0]/input_image_size[1]*anchors[:,2] # 用于算分辨率无关的iou
|
||||||
|
|
||||||
|
area1 = anchors_areas
|
||||||
|
|
||||||
|
iou, _ = box_iou(boxes1, area1, boxes2)
|
||||||
|
iou = iou.squeeze(1)
|
||||||
|
shape_iou, _ = box_iou(boxes1, area1, boxes3)
|
||||||
|
shape_iou = shape_iou.diag()
|
||||||
|
# 优先匹配形状接近 再匹配分辨率接近
|
||||||
|
index = torch.argmax(shape_iou*100+iou,dim=0)
|
||||||
|
return index
|
||||||
|
|
||||||
|
def select_best_resolution(anchors, anchors_areas, input_image_size): # TODO For a futher check
|
||||||
|
"""
|
||||||
|
Selects the best resolution from a list of possible resolutions based on the original size.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
original_size (tuple): The original size of the image in the format (width, height).
|
||||||
|
possible_resolutions (list): A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
tuple: The best fit resolution in the format (width, height).
|
||||||
|
"""
|
||||||
|
original_size = (input_image_size[1], input_image_size[0])
|
||||||
|
possible_resolutions = [(_[2], _[3]) for _ in anchors] # xyxy -> w,h
|
||||||
|
|
||||||
|
original_width, original_height = original_size
|
||||||
|
best_fit = None
|
||||||
|
max_effective_resolution = 0
|
||||||
|
min_wasted_resolution = float('inf')
|
||||||
|
|
||||||
|
index = 0
|
||||||
|
for i, (width, height) in enumerate(possible_resolutions):
|
||||||
|
scale = min(width / original_width, height / original_height)
|
||||||
|
downscaled_width, downscaled_height = int(original_width * scale), int(original_height * scale)
|
||||||
|
effective_resolution = min(downscaled_width * downscaled_height, original_width * original_height)
|
||||||
|
wasted_resolution = (width * height) - effective_resolution
|
||||||
|
|
||||||
|
if effective_resolution > max_effective_resolution or (effective_resolution == max_effective_resolution and wasted_resolution < min_wasted_resolution):
|
||||||
|
max_effective_resolution = effective_resolution
|
||||||
|
min_wasted_resolution = wasted_resolution
|
||||||
|
best_fit = (width, height)
|
||||||
|
index = i
|
||||||
|
|
||||||
|
return index
|
||||||
|
|
||||||
|
def build_cut_shape_indices(cut_shape):
|
||||||
|
# cut_shape: a list of (nh,nw)
|
||||||
|
cut_shape_indices = []
|
||||||
|
for shape in cut_shape:
|
||||||
|
n=shape[0]*shape[1]
|
||||||
|
indices = torch.cat([
|
||||||
|
repeat(torch.tensor(shape),'l -> n l',n=n),
|
||||||
|
torch.arange(n).unsqueeze(1)
|
||||||
|
], dim=1)
|
||||||
|
assert indices.shape[0] == n
|
||||||
|
assert indices.shape[1] == 3 # nh,nw,idx
|
||||||
|
|
||||||
|
cut_shape_indices.append(indices)
|
||||||
|
cut_shape_indices = torch.cat(cut_shape_indices,dim=0).long()
|
||||||
|
return cut_shape_indices
|
||||||
|
|
||||||
|
class AnchorResize(torch.nn.Module):
|
||||||
|
|
||||||
|
def __init__(self, image_size, anchors, interpolation=InterpolationMode.BILINEAR, antialias=None, anchor_strategy='docowl'):
|
||||||
|
super().__init__()
|
||||||
|
self.image_size = image_size
|
||||||
|
# xyxy
|
||||||
|
self.anchors = torch.tensor(
|
||||||
|
[[0, 0, _[1]*image_size[1], _[0]*image_size[0]]
|
||||||
|
for _ in anchors], requires_grad=False
|
||||||
|
)
|
||||||
|
|
||||||
|
self.anchor_areas = box_area(self.anchors)
|
||||||
|
|
||||||
|
self.interpolation = interpolation
|
||||||
|
self.antialias = antialias
|
||||||
|
self.anchor_strategy = anchor_strategy
|
||||||
|
assert self.anchor_strategy in available_anchor_strategy
|
||||||
|
|
||||||
|
def resize_global(self, img):
|
||||||
|
return F.resize(img, self.image_size, self.interpolation, max_size=None, antialias=self.antialias)
|
||||||
|
|
||||||
|
def forward(self, img, skip_resize=False):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
img (PIL Image or Tensor): Image to be scaled.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
PIL Image or Tensor: Rescaled image.
|
||||||
|
"""
|
||||||
|
if self.anchor_strategy == 'docowl':
|
||||||
|
selected_anchor = anchor_rank(self.anchors, self.anchor_areas, (img.size[1], img.size[0]))
|
||||||
|
elif self.anchor_strategy == 'random':
|
||||||
|
selected_anchor = random.randint(0,len(self.anchors)-1)
|
||||||
|
elif self.anchor_strategy == 'highest':
|
||||||
|
# 选面积最大的 在这个基础上 尽可能选最方正的
|
||||||
|
selected_anchor = torch.argmax(self.anchors[:,2]*self.anchors[:,3]*100-torch.abs(self.anchors[:,2]-self.anchors[:,3]))
|
||||||
|
elif self.anchor_strategy == 'last':
|
||||||
|
selected_anchor = len(self.anchors)-1
|
||||||
|
elif self.anchor_strategy == 'llava':
|
||||||
|
selected_anchor = select_best_resolution(self.anchors, self.anchor_areas, (img.size[1], img.size[0]))
|
||||||
|
else:
|
||||||
|
selected_anchor = None
|
||||||
|
assert selected_anchor is not None
|
||||||
|
|
||||||
|
target_size = self.anchors[selected_anchor][2:].tolist() # w,h
|
||||||
|
if skip_resize:
|
||||||
|
# for debug
|
||||||
|
return selected_anchor
|
||||||
|
return F.resize(img, [target_size[1],target_size[0]], self.interpolation, max_size=None, antialias=self.antialias), selected_anchor
|
||||||
|
|
||||||
|
def __repr__(self) -> str:
|
||||||
|
detail = f"(size={self.image_size}, anchor={self.anchors}, interpolation={self.interpolation.value}, antialias={self.antialias})"
|
||||||
|
return f"{self.__class__.__name__}{detail}"
|
||||||
|
|
||||||
|
class CutMixin:
|
||||||
|
def __init__(self, cut_cfg={"anchors": "grid_squ_6", "anchor_strategy": "docowl", "cut_prompt": "v3", "add_global": True, "cut_prob": 1.0}) -> None:
|
||||||
|
if cut_cfg is None:
|
||||||
|
self.cut_enable = False
|
||||||
|
return
|
||||||
|
else:
|
||||||
|
self.cut_enable = True
|
||||||
|
image_size = self.image_size
|
||||||
|
anchors = cut_cfg.get('anchors','grid_33')
|
||||||
|
anchor_strategy = cut_cfg.get('anchor_strategy','docowl')
|
||||||
|
cut_prompt = cut_cfg.get('cut_prompt','v0')
|
||||||
|
self.cut_prob = cut_cfg.get('cut_prob', 1.0)
|
||||||
|
|
||||||
|
self.force_shape_cut = cut_cfg.get('force_shape_cut', False)
|
||||||
|
force_shape_cut_anchors = cut_cfg.get('force_shape_cut_anchors', 'force_shape_cut_anchors')
|
||||||
|
|
||||||
|
|
||||||
|
self.add_global = cut_cfg.get('add_global', False)
|
||||||
|
|
||||||
|
# h,w
|
||||||
|
if isinstance(image_size, int):
|
||||||
|
image_size = (image_size, image_size)
|
||||||
|
self.image_size = image_size
|
||||||
|
|
||||||
|
if anchors in grid_dict:
|
||||||
|
anchors = grid_dict[anchors]
|
||||||
|
else:
|
||||||
|
anchors = eval(anchors)
|
||||||
|
self.anchors = [tuple(_) for _ in anchors]
|
||||||
|
self.anchor_max = max([max(_) for _ in self.anchors])
|
||||||
|
self.resizer = AnchorResize(image_size=image_size, anchors=anchors, interpolation=InterpolationMode.BICUBIC, anchor_strategy=anchor_strategy)
|
||||||
|
|
||||||
|
if force_shape_cut_anchors in grid_dict:
|
||||||
|
force_shape_cut_anchors = grid_dict[force_shape_cut_anchors]
|
||||||
|
else:
|
||||||
|
force_shape_cut_anchors = eval(force_shape_cut_anchors)
|
||||||
|
self.force_shape_cut_anchors = [tuple(_) for _ in force_shape_cut_anchors]
|
||||||
|
self.force_shape_cut_anchors_max = max([max(_) for _ in self.force_shape_cut_anchors])
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
self.old_resizer = transforms.Resize(image_size,interpolation=InterpolationMode.BICUBIC)
|
||||||
|
|
||||||
|
# 把image processor的缩放去掉 只保留后面的变换
|
||||||
|
self.image_transform = transforms.Compose(self.image_transform.transforms[1:])
|
||||||
|
if self.add_global:
|
||||||
|
self.cut_prompt_template = cut_prompt_template_dict[cut_prompt+'_global']
|
||||||
|
else:
|
||||||
|
self.cut_prompt_template = cut_prompt_template_dict[cut_prompt]
|
||||||
|
|
||||||
|
self.media_tokens = ["<|image|>", "<|video|>"]
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def _process_image(self, images):
|
||||||
|
new_images = []
|
||||||
|
cut_shape = []
|
||||||
|
for image in images:
|
||||||
|
raw_image = image
|
||||||
|
|
||||||
|
image, selected_anchor = self.resizer(image)
|
||||||
|
image_input = self.image_transform(image) # h,w,3 -> 3,h,w
|
||||||
|
cut_shape.append((image_input.shape[1]//self.image_size[0], image_input.shape[2]//self.image_size[1])) # cut_h, cut_w
|
||||||
|
image_input = rearrange(image_input, 'C (num_h h) (num_w w) -> (num_h num_w) C h w', h=self.image_size[0], w=self.image_size[1])
|
||||||
|
|
||||||
|
new_images.append(image_input)
|
||||||
|
|
||||||
|
if self.add_global:
|
||||||
|
new_images.append(self.image_transform(self.resizer.resize_global(raw_image)).unsqueeze(0))
|
||||||
|
cut_shape.append((1,1))
|
||||||
|
|
||||||
|
new_images = torch.cat(new_images,dim=0)
|
||||||
|
cut_shape_indices = build_cut_shape_indices(cut_shape)
|
||||||
|
return new_images, cut_shape, cut_shape_indices
|
||||||
|
|
||||||
|
class mPLUGOwl3BatchFeature(BatchFeature):
|
||||||
|
r"""
|
||||||
|
Extend from BatchFeature for supporting various image size
|
||||||
|
"""
|
||||||
|
def __init__(self, data: Optional[Dict[str, Any]] = None, tensor_type: Union[None, str, TensorType] = None):
|
||||||
|
super().__init__(data)
|
||||||
|
self.convert_to_tensors(tensor_type=tensor_type)
|
||||||
|
|
||||||
|
def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None):
|
||||||
|
if tensor_type is None:
|
||||||
|
return self
|
||||||
|
|
||||||
|
is_tensor, as_tensor = self._get_is_as_tensor_fns(tensor_type)
|
||||||
|
|
||||||
|
def converter(value):
|
||||||
|
try:
|
||||||
|
if not is_tensor(value):
|
||||||
|
tensor = as_tensor(value)
|
||||||
|
return tensor
|
||||||
|
except: # noqa E722
|
||||||
|
if key == "overflowing_values":
|
||||||
|
raise ValueError("Unable to create tensor returning overflowing values of different lengths. ")
|
||||||
|
raise ValueError(
|
||||||
|
"Unable to create tensor, you should probably activate padding "
|
||||||
|
"with 'padding=True' to have batched tensors with the same length."
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
for key, value in self.items():
|
||||||
|
self[key] = recursive_converter(converter, value)
|
||||||
|
return self
|
||||||
|
|
||||||
|
def to(self, *args, **kwargs) -> "mPLUGOwl3BatchFeature":
|
||||||
|
requires_backends(self, ["torch"])
|
||||||
|
import torch
|
||||||
|
|
||||||
|
def cast_tensor(v):
|
||||||
|
# check if v is a floating point
|
||||||
|
if torch.is_floating_point(v):
|
||||||
|
# cast and send to device
|
||||||
|
return v.to(*args, **kwargs)
|
||||||
|
elif device is not None:
|
||||||
|
return v.to(device=device)
|
||||||
|
else:
|
||||||
|
return v
|
||||||
|
|
||||||
|
new_data = {}
|
||||||
|
device = kwargs.get("device")
|
||||||
|
# Check if the args are a device or a dtype
|
||||||
|
if device is None and len(args) > 0:
|
||||||
|
# device should be always the first argument
|
||||||
|
arg = args[0]
|
||||||
|
if is_torch_dtype(arg):
|
||||||
|
# The first argument is a dtype
|
||||||
|
pass
|
||||||
|
elif isinstance(arg, str) or is_torch_device(arg) or isinstance(arg, int):
|
||||||
|
device = arg
|
||||||
|
else:
|
||||||
|
# it's something else
|
||||||
|
raise ValueError(f"Attempting to cast a BatchFeature to type {str(arg)}. This is not supported.")
|
||||||
|
# We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor`
|
||||||
|
for k, v in self.items():
|
||||||
|
new_data[k] = recursive_converter(cast_tensor, v)
|
||||||
|
self.data = new_data
|
||||||
|
return self
|
||||||
|
|
||||||
|
|
||||||
|
class mPLUGOwl3ImageProcessor(BaseImageProcessor, CutMixin):
|
||||||
|
model_input_names = ["pixel_values"]
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
image_size,
|
||||||
|
mean=[0.5, 0.5, 0.5],
|
||||||
|
std=[0.5, 0.5, 0.5],
|
||||||
|
**kwargs):
|
||||||
|
super().__init__(**kwargs)
|
||||||
|
self.image_size = image_size
|
||||||
|
self.image_transform = transforms.Compose([
|
||||||
|
transforms.Resize((image_size, image_size), interpolation=Image.BICUBIC),
|
||||||
|
transforms.ToTensor(),
|
||||||
|
transforms.Normalize(mean, std),
|
||||||
|
])
|
||||||
|
CutMixin.__init__(self)
|
||||||
|
|
||||||
|
def preprocess(
|
||||||
|
self,
|
||||||
|
images: Union[Image.Image, List[Image.Image]],
|
||||||
|
cut_enable=True,
|
||||||
|
**kwargs
|
||||||
|
) -> mPLUGOwl3BatchFeature:
|
||||||
|
if isinstance(images, Image.Image):
|
||||||
|
images_list = [images]
|
||||||
|
else:
|
||||||
|
images_list = images
|
||||||
|
|
||||||
|
if self.cut_enable and cut_enable:
|
||||||
|
image_data, cut_shape, cut_shape_indices = self._process_image(images_list)
|
||||||
|
else:
|
||||||
|
image_data = [self.image_transform(self.resizer.resize_global(image)) for image in images_list]
|
||||||
|
image_data = torch.stack(image_data, dim=0)
|
||||||
|
cut_shape = cut_shape_indices = None
|
||||||
|
|
||||||
|
return mPLUGOwl3BatchFeature(data={'pixel_values': image_data, 'cut_shape':cut_shape, 'cut_shape_indices':cut_shape_indices})
|
||||||
|
|
||||||
|
def to_dict(self):
|
||||||
|
encoder_dict = super().to_dict()
|
||||||
|
pop_keys = ['image_transform', 'resizer', 'old_resizer', 'cut_prompt_template']
|
||||||
|
for pk in pop_keys:
|
||||||
|
encoder_dict.pop(pk, None)
|
||||||
|
return encoder_dict
|
||||||
|
|
||||||
|
AutoImageProcessor.register("mPLUGOwl3ImageProcessor", mPLUGOwl3ImageProcessor)
|
File diff suppressed because it is too large
Load Diff
Binary file not shown.
File diff suppressed because it is too large
Load Diff
|
@ -0,0 +1,231 @@
|
||||||
|
import math
|
||||||
|
from typing import List, Optional
|
||||||
|
import json
|
||||||
|
import torch
|
||||||
|
import torchvision
|
||||||
|
|
||||||
|
from threading import Thread
|
||||||
|
from copy import deepcopy
|
||||||
|
from PIL import Image
|
||||||
|
from transformers import AutoProcessor, Qwen2PreTrainedModel, Qwen2ForCausalLM, TextIteratorStreamer
|
||||||
|
from .processing_mplugowl3 import mPLUGOwl3Processor
|
||||||
|
from .image_processing_mplugowl3 import mPLUGOwl3ImageProcessor
|
||||||
|
from .configuration_mplugowl3 import mPLUGOwl3Config
|
||||||
|
# from .modeling_navit_siglip import SiglipVisionTransformer
|
||||||
|
from transformers.models.siglip.modeling_siglip import SiglipVisionTransformer
|
||||||
|
from .x_sdpa import ScaleDotProductAttention
|
||||||
|
from .modeling_hyper_qwen2 import HyperQwen2ForCausalLM
|
||||||
|
from torch import nn
|
||||||
|
|
||||||
|
|
||||||
|
class mPLUGOwl3PreTrainedModel(Qwen2PreTrainedModel):
|
||||||
|
config_class = mPLUGOwl3Config
|
||||||
|
|
||||||
|
|
||||||
|
class mPLUGOwl3Model(mPLUGOwl3PreTrainedModel):
|
||||||
|
def __init__(self, config):
|
||||||
|
super().__init__(config)
|
||||||
|
self.language_model = HyperQwen2ForCausalLM(config)
|
||||||
|
self.vision_model = self.init_vision_module()
|
||||||
|
self.vision_dim = self.vision_model.embed_dim
|
||||||
|
self.embed_dim = self.language_model.config.hidden_size
|
||||||
|
self.vision2text_model = nn.Linear(self.vision_dim, self.embed_dim)
|
||||||
|
self.processor = None
|
||||||
|
|
||||||
|
self.terminators = ['<|im_end|>', '<|endoftext|>']
|
||||||
|
|
||||||
|
def init_vision_module(self):
|
||||||
|
|
||||||
|
self.config.vision_config._attn_implementation = self.config.vision_config._attn_implementation
|
||||||
|
model = SiglipVisionTransformer(self.config.vision_config)
|
||||||
|
|
||||||
|
setattr(model, 'embed_dim', model.embeddings.embed_dim)
|
||||||
|
setattr(model, 'patch_size', model.embeddings.patch_size)
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def get_input_embeddings(self):
|
||||||
|
return self.language_model.get_input_embeddings()
|
||||||
|
|
||||||
|
def set_input_embeddings(self, value):
|
||||||
|
self.language_model.embed_tokens = value
|
||||||
|
|
||||||
|
def get_output_embeddings(self):
|
||||||
|
return self.language_model.lm_head
|
||||||
|
|
||||||
|
def set_output_embeddings(self, new_embeddings):
|
||||||
|
self.language_model.lm_head = new_embeddings
|
||||||
|
|
||||||
|
def set_decoder(self, decoder):
|
||||||
|
self.language_model = decoder
|
||||||
|
|
||||||
|
def get_decoder(self):
|
||||||
|
return self.language_model
|
||||||
|
|
||||||
|
def forward_image(self, pixel_values):
|
||||||
|
if pixel_values is None:
|
||||||
|
return None
|
||||||
|
dtype = self.language_model.model.embed_tokens.weight.dtype
|
||||||
|
with torch.inference_mode():
|
||||||
|
image_embeds = self.vision_model(pixel_values.to(dtype), output_hidden_states=True).hidden_states[-2]
|
||||||
|
|
||||||
|
if self.vision2text_model is not None:
|
||||||
|
image_embeds = self.vision2text_model(image_embeds)
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
|
||||||
|
return image_embeds
|
||||||
|
|
||||||
|
def forward(self, pixel_values=None, **kwargs):
|
||||||
|
image_embeds = self.forward_image(pixel_values)
|
||||||
|
|
||||||
|
return self.language_model(
|
||||||
|
image_embeds=image_embeds,
|
||||||
|
**kwargs
|
||||||
|
)
|
||||||
|
|
||||||
|
def _decode(self, input_ids, image_embeds, media_offset, tokenizer, attention_mask, decode_text=False, **kwargs):
|
||||||
|
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
|
||||||
|
output = self.language_model.generate(
|
||||||
|
input_ids=input_ids,
|
||||||
|
image_embeds=image_embeds,
|
||||||
|
media_offset=media_offset,
|
||||||
|
pad_token_id=0,
|
||||||
|
eos_token_id=terminators,
|
||||||
|
attention_mask=attention_mask,
|
||||||
|
**kwargs
|
||||||
|
)
|
||||||
|
|
||||||
|
output = output[:,input_ids.shape[1]:]
|
||||||
|
if decode_text:
|
||||||
|
return self._decode_text(output, tokenizer)
|
||||||
|
return output
|
||||||
|
|
||||||
|
def _decode_stream(self, input_ids, image_embeds, media_offset, tokenizer, **kwargs):
|
||||||
|
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
|
||||||
|
streamer = TextIteratorStreamer(tokenizer=tokenizer)
|
||||||
|
generation_kwargs = {
|
||||||
|
'input_ids': input_ids,
|
||||||
|
'image_embeds': image_embeds,
|
||||||
|
'media_offset': media_offset,
|
||||||
|
'pad_token_id': 0,
|
||||||
|
'eos_token_id': terminators,
|
||||||
|
'streamer': streamer
|
||||||
|
}
|
||||||
|
generation_kwargs.update(kwargs)
|
||||||
|
|
||||||
|
thread = Thread(target=self.language_model.generate, kwargs=generation_kwargs)
|
||||||
|
thread.start()
|
||||||
|
|
||||||
|
return streamer
|
||||||
|
|
||||||
|
def _decode_text(self, result_ids, tokenizer):
|
||||||
|
terminators = [tokenizer.convert_tokens_to_ids(i) for i in self.terminators]
|
||||||
|
result_text = []
|
||||||
|
for result in result_ids:
|
||||||
|
result = result[result != 0]
|
||||||
|
if result[-1] in terminators:
|
||||||
|
result = result[:-1]
|
||||||
|
result_text.append(tokenizer.decode(result).strip())
|
||||||
|
return result_text
|
||||||
|
|
||||||
|
def init_processor(self, tokenizer):
|
||||||
|
ip = mPLUGOwl3ImageProcessor(image_size=384)
|
||||||
|
self.processor = mPLUGOwl3Processor(image_processor=ip, tokenizer=tokenizer)
|
||||||
|
processor = self.processor
|
||||||
|
return processor
|
||||||
|
|
||||||
|
def generate(
|
||||||
|
self,
|
||||||
|
input_ids=None,
|
||||||
|
pixel_values=None,
|
||||||
|
media_offset=None,
|
||||||
|
attention_mask=None,
|
||||||
|
tokenizer=None,
|
||||||
|
stream=False,
|
||||||
|
decode_text=False,
|
||||||
|
**kwargs
|
||||||
|
):
|
||||||
|
assert input_ids is not None
|
||||||
|
|
||||||
|
with torch.inference_mode():
|
||||||
|
image_embeds = self.forward_image(pixel_values)
|
||||||
|
|
||||||
|
if stream:
|
||||||
|
result = self._decode_stream(input_ids=input_ids, image_embeds=image_embeds, media_offset=media_offset, tokenizer=tokenizer, **kwargs)
|
||||||
|
else:
|
||||||
|
result = self._decode(input_ids=input_ids, image_embeds=image_embeds, media_offset=media_offset, tokenizer=tokenizer, attention_mask=attention_mask, decode_text=decode_text, **kwargs)
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
def chat(
|
||||||
|
self,
|
||||||
|
images,
|
||||||
|
videos,
|
||||||
|
messages,
|
||||||
|
tokenizer,
|
||||||
|
processor=None,
|
||||||
|
max_new_tokens=2048,
|
||||||
|
min_new_tokens=0,
|
||||||
|
sampling=True,
|
||||||
|
max_inp_length=8192,
|
||||||
|
system_prompt='',
|
||||||
|
stream=False,
|
||||||
|
max_slice_nums=None,
|
||||||
|
use_image_id=None,
|
||||||
|
**kwargs
|
||||||
|
):
|
||||||
|
cut_flag = kwargs.get('kwargs', True)
|
||||||
|
if processor is None:
|
||||||
|
if self.processor is None:
|
||||||
|
processor = self.init_processor(tokenizer)
|
||||||
|
else:
|
||||||
|
processor = self.processor
|
||||||
|
inputs = processor(messages, images=images, videos=videos, cut_enable=cut_flag)
|
||||||
|
inputs.to('cuda')
|
||||||
|
inputs.update({
|
||||||
|
'tokenizer': tokenizer,
|
||||||
|
'max_new_tokens': max_new_tokens,
|
||||||
|
# 'stream':True,
|
||||||
|
})
|
||||||
|
|
||||||
|
if sampling:
|
||||||
|
generation_config = {
|
||||||
|
"top_p": 0.8,
|
||||||
|
"top_k": 100,
|
||||||
|
"temperature": 0.7,
|
||||||
|
"do_sample": True,
|
||||||
|
# "repetition_penalty": 1.05
|
||||||
|
}
|
||||||
|
else:
|
||||||
|
generation_config = {
|
||||||
|
"num_beams": 3,
|
||||||
|
# "repetition_penalty": 1.2,
|
||||||
|
}
|
||||||
|
|
||||||
|
if min_new_tokens > 0:
|
||||||
|
generation_config['min_new_tokens'] = min_new_tokens
|
||||||
|
|
||||||
|
generation_config.update(
|
||||||
|
(k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()
|
||||||
|
)
|
||||||
|
with torch.inference_mode():
|
||||||
|
res = self.generate(
|
||||||
|
**inputs,
|
||||||
|
stream=stream,
|
||||||
|
decode_text=True,
|
||||||
|
**generation_config
|
||||||
|
)
|
||||||
|
|
||||||
|
if stream:
|
||||||
|
def stream_gen():
|
||||||
|
for text in res:
|
||||||
|
for term in self.terminators:
|
||||||
|
text = text.replace(term, '')
|
||||||
|
yield text
|
||||||
|
return stream_gen()
|
||||||
|
|
||||||
|
else:
|
||||||
|
answer = res[0]
|
||||||
|
return answer
|
||||||
|
|
|
@ -0,0 +1,396 @@
|
||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2024 The HuggingFace Inc. team.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
"""
|
||||||
|
Processor class for mPLUGOwl3.
|
||||||
|
"""
|
||||||
|
|
||||||
|
from typing import List, Optional, Union, Dict, Any
|
||||||
|
import warnings
|
||||||
|
import torch
|
||||||
|
import re
|
||||||
|
|
||||||
|
from transformers.image_processing_utils import BatchFeature
|
||||||
|
from transformers.image_utils import ImageInput
|
||||||
|
from transformers.processing_utils import ProcessorMixin
|
||||||
|
from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
|
||||||
|
from transformers.utils import TensorType, requires_backends, is_torch_dtype, is_torch_device
|
||||||
|
|
||||||
|
from .image_processing_mplugowl3 import mPLUGOwl3BatchFeature, mPLUGOwl3ImageProcessor
|
||||||
|
|
||||||
|
OWL_MEDIA_TOKEN=['<|image|>']
|
||||||
|
|
||||||
|
class MediaIndicesHelper():
|
||||||
|
def __init__(self, tokenizer) -> None:
|
||||||
|
self.media_position = []
|
||||||
|
self.tokenizer = tokenizer
|
||||||
|
|
||||||
|
|
||||||
|
def has_media(self, text, media_tokens=None):
|
||||||
|
if media_tokens is None:
|
||||||
|
media_tokens = OWL_MEDIA_TOKEN
|
||||||
|
has_media_flag = any([media_token == text for media_token in media_tokens])
|
||||||
|
if any([media_token in text for media_token in media_tokens]):
|
||||||
|
# 不允许出现text中包含media token但是不仅仅是media token。 media token必须单独为一个chunk
|
||||||
|
assert has_media_flag, text
|
||||||
|
return has_media_flag
|
||||||
|
|
||||||
|
def add_media(self, text_chunk, text=None, tokenize_fn=None):
|
||||||
|
|
||||||
|
# cross
|
||||||
|
assert tokenize_fn is not None
|
||||||
|
assert text is not None
|
||||||
|
assert text in OWL_MEDIA_TOKEN
|
||||||
|
media_token_ids = tokenize_fn(text)
|
||||||
|
start = len(text_chunk)
|
||||||
|
end = start + len(media_token_ids)
|
||||||
|
self.media_position.append([start, end])
|
||||||
|
text_chunk.extend(media_token_ids)
|
||||||
|
return len(media_token_ids)
|
||||||
|
|
||||||
|
def cal_media_offset(self, input_ids):
|
||||||
|
if len(self.media_position) == 0:
|
||||||
|
return torch.ones_like(input_ids)*(-1000000)
|
||||||
|
|
||||||
|
media_starts = torch.tensor([_[0] for _ in self.media_position]).reshape(1,-1)
|
||||||
|
rng = torch.arange(input_ids.shape[0]).reshape(-1,1)
|
||||||
|
matrix = (rng > media_starts).sum(dim=1)
|
||||||
|
|
||||||
|
return matrix
|
||||||
|
|
||||||
|
def len_images(self,):
|
||||||
|
return len(self.media_position)
|
||||||
|
|
||||||
|
class mPLUGOwl3Processor(ProcessorMixin):
|
||||||
|
r"""
|
||||||
|
Args:
|
||||||
|
image_processor ([`mPLUGOwl3ImageProcessor`], *optional*):
|
||||||
|
The image processor is a required input.
|
||||||
|
tokenizer ([`LlamaTokenizerWrapper`], *optional*):
|
||||||
|
The tokenizer is a required input.
|
||||||
|
"""
|
||||||
|
attributes = ["image_processor", "tokenizer"]
|
||||||
|
image_processor_class = "AutoImageProcessor"
|
||||||
|
tokenizer_class = "AutoTokenizer"
|
||||||
|
|
||||||
|
def __init__(self, image_processor: mPLUGOwl3ImageProcessor = None, tokenizer=None, prompt_style='chatml', inference_mode=True, addition_eod="<|endoftext|>"):
|
||||||
|
super().__init__(image_processor, tokenizer)
|
||||||
|
self.image_processor: mPLUGOwl3ImageProcessor
|
||||||
|
self.prompt_style = prompt_style
|
||||||
|
self.inference_mode = inference_mode
|
||||||
|
self.media_tokens = ["<|image|>"]
|
||||||
|
self.addition_eod = addition_eod
|
||||||
|
|
||||||
|
def build_text_qwen(self, messages):
|
||||||
|
# role should be within ['system', 'user', 'assistant']
|
||||||
|
im_start, im_end = '<|im_start|>', '<|im_end|>'
|
||||||
|
|
||||||
|
text = []
|
||||||
|
for num_turn, message in enumerate(messages):
|
||||||
|
if num_turn == 0 and message['role'] != 'system':
|
||||||
|
if self.prompt_style != 'plain':
|
||||||
|
text.append({
|
||||||
|
"text": f"{im_start}system\n{im_end}",
|
||||||
|
"label": 0
|
||||||
|
})
|
||||||
|
if message['role'] == 'system':
|
||||||
|
if self.prompt_style != 'plain':
|
||||||
|
text.append({
|
||||||
|
"text": f"{im_start}system\n{message['content']}{im_end}",
|
||||||
|
"label": 0
|
||||||
|
})
|
||||||
|
elif message['role'] == 'user':
|
||||||
|
if self.prompt_style != 'plain':
|
||||||
|
content = f"\n{im_start}user\n{message['content']}{im_end}"
|
||||||
|
else:
|
||||||
|
content = message['content']
|
||||||
|
pattern = '|'.join(map(re.escape, self.media_tokens))
|
||||||
|
chunk_strs = re.split(f'({pattern})', content)
|
||||||
|
for chunk_str in chunk_strs:
|
||||||
|
text.append({
|
||||||
|
"text": chunk_str,
|
||||||
|
"label": 0
|
||||||
|
})
|
||||||
|
|
||||||
|
elif message['role'] == 'assistant':
|
||||||
|
if self.prompt_style != 'plain':
|
||||||
|
text.append({"text": f"\n{im_start}assistant\n", "label": 0})
|
||||||
|
text.append({"text": f"{message['content']}{im_end}", "label": 1})
|
||||||
|
else:
|
||||||
|
text.append({"text": f"{message['content']}", "label": 1})
|
||||||
|
text.append({"text": self.addition_eod, "label": 1})
|
||||||
|
else:
|
||||||
|
raise NotImplementedError
|
||||||
|
if self.inference_mode:
|
||||||
|
while text and text[-1]['label']==1: # 只要列表非空且最后一个元素满足条件
|
||||||
|
text.pop() # 就移除最后一个元素
|
||||||
|
return text
|
||||||
|
|
||||||
|
def wrapped_tokenize(self, text):
|
||||||
|
return self.tokenizer(text).input_ids
|
||||||
|
|
||||||
|
def encode_text_sft(self, texts):
|
||||||
|
# output enc_chunk
|
||||||
|
|
||||||
|
enc_chunk = []
|
||||||
|
label_chunk = []
|
||||||
|
enc_length = 0
|
||||||
|
|
||||||
|
num_images = 0
|
||||||
|
|
||||||
|
media_helper = MediaIndicesHelper(tokenizer=self.tokenizer)
|
||||||
|
for current_ti, text_chunk in enumerate(texts):
|
||||||
|
|
||||||
|
text = text_chunk["text"]
|
||||||
|
label = text_chunk["label"]
|
||||||
|
|
||||||
|
if not media_helper.has_media(text):
|
||||||
|
curr_chunk=self.wrapped_tokenize(text)
|
||||||
|
if label == 1:
|
||||||
|
enc_length += len(curr_chunk)
|
||||||
|
enc_chunk += curr_chunk
|
||||||
|
label_chunk += [label] * len(curr_chunk)
|
||||||
|
else:
|
||||||
|
|
||||||
|
enc_length += len(curr_chunk)
|
||||||
|
enc_chunk += curr_chunk
|
||||||
|
label_chunk += [label] * len(curr_chunk)
|
||||||
|
# For media tokens
|
||||||
|
else:
|
||||||
|
|
||||||
|
add_length = media_helper.add_media(
|
||||||
|
enc_chunk,
|
||||||
|
text=text,
|
||||||
|
tokenize_fn=self.wrapped_tokenize)
|
||||||
|
enc_length += add_length
|
||||||
|
label_chunk += [label] * add_length
|
||||||
|
# enc_chunk.extend([self.media_tokens[text]] * self.media_lengths[text])
|
||||||
|
# enc_length += self.media_lengths[text]
|
||||||
|
# label_chunk += [label] * self.media_lengths[text]
|
||||||
|
num_images += 1
|
||||||
|
|
||||||
|
enc_chunk = torch.tensor(enc_chunk).long()
|
||||||
|
media_offset = []
|
||||||
|
media_before = 0
|
||||||
|
for i,_ in enumerate([media_helper]):
|
||||||
|
mo = _.cal_media_offset(enc_chunk)
|
||||||
|
media_offset.append(torch.cat([(torch.ones(mo.shape[0],1)*media_before).long().to(mo.device), (mo+media_before).unsqueeze(1)], dim=1)) # L 2
|
||||||
|
|
||||||
|
media_before += _.len_images()
|
||||||
|
media_offset = torch.stack(media_offset, dim=0)
|
||||||
|
return {
|
||||||
|
'input_ids': enc_chunk.unsqueeze(0),
|
||||||
|
'media_offset': media_offset,
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
def __call__(
|
||||||
|
self,
|
||||||
|
messages,
|
||||||
|
images = None,
|
||||||
|
videos = None,
|
||||||
|
max_length: Optional[int] = None,
|
||||||
|
cut_enable=True,
|
||||||
|
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
|
||||||
|
**kwargs
|
||||||
|
) -> mPLUGOwl3BatchFeature:
|
||||||
|
medias = []
|
||||||
|
if videos is not None:
|
||||||
|
medias.extend([{'type': 'video', 'content': video, 'use_video_span': True} for video in videos])
|
||||||
|
if images is not None:
|
||||||
|
medias.extend([{'type':'image', 'content': image} for image in images])
|
||||||
|
|
||||||
|
if len(medias):
|
||||||
|
image_tensor_list = []
|
||||||
|
pattern = r"(<\|image\|>|<\|video\|>)"
|
||||||
|
# 存在媒体
|
||||||
|
image_token_ptr = 0
|
||||||
|
media_layout = []
|
||||||
|
for message in messages:
|
||||||
|
text_list = re.split(pattern, message['content'])
|
||||||
|
text = ''
|
||||||
|
for text_content in text_list:
|
||||||
|
if text_content in ['<|image|>', '<|video|>']:
|
||||||
|
media_item = medias[image_token_ptr]
|
||||||
|
image_token_ptr += 1
|
||||||
|
if text_content == '<|image|>':
|
||||||
|
assert media_item['type'] == 'image'
|
||||||
|
image = media_item['content']
|
||||||
|
|
||||||
|
image_inputs = self.image_processor([image], cut_enable=cut_enable, return_tensors=return_tensors)
|
||||||
|
if image_inputs.get('cut_shape',None) is not None:
|
||||||
|
cut_shape = image_inputs['cut_shape']
|
||||||
|
cut_text = self.image_processor.cut_prompt_template(img_token='<|image|>', h=cut_shape[0][0], w=cut_shape[0][1])
|
||||||
|
text += cut_text
|
||||||
|
image_tensor_list.append(image_inputs['pixel_values'])
|
||||||
|
else:
|
||||||
|
text += text_content
|
||||||
|
elif text_content == '<|video|>':
|
||||||
|
assert media_item['type'] == 'video'
|
||||||
|
video = media_item['content']
|
||||||
|
use_video_span = media_item['use_video_span']
|
||||||
|
image_tensor = self.image_processor(video, cut_enable=False)['pixel_values']
|
||||||
|
image_tensor_list.append(image_tensor)
|
||||||
|
num_video_frame = image_tensor.shape[0]
|
||||||
|
if use_video_span:
|
||||||
|
text_content = '<|start_video_frame|>'+'<|image|>'*num_video_frame+'<|end_video_frame|>'
|
||||||
|
else:
|
||||||
|
text_content = '<|image|>'*num_video_frame
|
||||||
|
text += text_content
|
||||||
|
else:
|
||||||
|
text += text_content
|
||||||
|
message['content'] = text
|
||||||
|
assert image_token_ptr == len(medias), (image_token_ptr,len(medias)) # 保证图和token数目一致
|
||||||
|
assert all(len(_.shape) == 4 for _ in image_tensor_list), [_.shape for _ in image_tensor_list]
|
||||||
|
num_image_tokens = sum([_['content'].count('<|image|>')for _ in messages])
|
||||||
|
num_image_shapes = sum([_.shape[0] for _ in image_tensor_list])
|
||||||
|
assert num_image_tokens == num_image_shapes, (messages, [_.shape for _ in image_tensor_list])
|
||||||
|
|
||||||
|
image_tensor_list = torch.cat(image_tensor_list, dim=0)
|
||||||
|
|
||||||
|
# text = ''.join([_['text'] for _ in text])
|
||||||
|
text = self.build_text_qwen(messages)
|
||||||
|
model_inputs = self.encode_text_sft(text)
|
||||||
|
|
||||||
|
if len(medias) is not None:
|
||||||
|
model_inputs.update({'pixel_values': image_tensor_list})
|
||||||
|
# if 'cut_shape' in model_inputs:
|
||||||
|
# model_inputs.pop('cut_shape')
|
||||||
|
# if 'cut_shape_indices' in model_inputs:
|
||||||
|
# model_inputs.pop('cut_shape_indices')
|
||||||
|
return mPLUGOwl3BatchFeature(model_inputs)
|
||||||
|
|
||||||
|
def check_media(self, images, messages):
|
||||||
|
media_num = 0 if images is None else len(images)
|
||||||
|
media_count = sum([message['content'].count('<|image|>') for message in messages])
|
||||||
|
assert media_num == media_count
|
||||||
|
|
||||||
|
|
||||||
|
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
|
||||||
|
def batch_decode(self, *args, **kwargs):
|
||||||
|
"""
|
||||||
|
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
||||||
|
refer to the docstring of this method for more information.
|
||||||
|
"""
|
||||||
|
output_ids = args[0]
|
||||||
|
result_text = []
|
||||||
|
for result in output_ids:
|
||||||
|
result = result[result != 0]
|
||||||
|
if result[0] == self.tokenizer.bos_id:
|
||||||
|
result = result[1:]
|
||||||
|
if result[-1] == self.tokenizer.eos_id:
|
||||||
|
result = result[:-1]
|
||||||
|
result_text.append(self.tokenizer.decode(result, *args[1:], **kwargs).strip())
|
||||||
|
return result_text
|
||||||
|
# return self.tokenizer.batch_decode(*args, **kwargs)
|
||||||
|
|
||||||
|
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
|
||||||
|
def decode(self, *args, **kwargs):
|
||||||
|
"""
|
||||||
|
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
||||||
|
the docstring of this method for more information.
|
||||||
|
"""
|
||||||
|
result = args[0]
|
||||||
|
result = result[result != 0]
|
||||||
|
if result[0] == self.tokenizer.bos_id:
|
||||||
|
result = result[1:]
|
||||||
|
if result[-1] == self.tokenizer.eos_id or (hasattr(self.tokenizer, "eot_id") and result[-1] == self.tokenizer.eot_id):
|
||||||
|
result = result[:-1]
|
||||||
|
return self.tokenizer.decode(result, *args[1:], **kwargs).strip()
|
||||||
|
|
||||||
|
def _convert(
|
||||||
|
self, input_str, max_inp_length: Optional[int] = None
|
||||||
|
):
|
||||||
|
if self.version > 2.5 or not getattr(self.tokenizer, "add_bos_token", False):
|
||||||
|
input_ids = self.tokenizer.encode(input_str)
|
||||||
|
else:
|
||||||
|
input_ids = [self.tokenizer.bos_id] + self.tokenizer.encode(input_str)
|
||||||
|
if max_inp_length is not None:
|
||||||
|
input_ids = input_ids[:max_inp_length]
|
||||||
|
input_ids = torch.tensor(input_ids, dtype=torch.int32)
|
||||||
|
|
||||||
|
start_cond = (input_ids == self.tokenizer.im_start_id) | (input_ids == self.tokenizer.slice_start_id)
|
||||||
|
end_cond = (input_ids == self.tokenizer.im_end_id) | (input_ids == self.tokenizer.slice_end_id)
|
||||||
|
|
||||||
|
image_start_tokens = torch.where(start_cond)[0]
|
||||||
|
image_start_tokens += 1
|
||||||
|
image_end_tokens = torch.where(end_cond)[0]
|
||||||
|
|
||||||
|
valid_image_nums = max(len(image_start_tokens), len(image_end_tokens))
|
||||||
|
|
||||||
|
image_bounds = torch.hstack(
|
||||||
|
[
|
||||||
|
image_start_tokens[:valid_image_nums].unsqueeze(-1),
|
||||||
|
image_end_tokens[:valid_image_nums].unsqueeze(-1),
|
||||||
|
]
|
||||||
|
)
|
||||||
|
return input_ids, image_bounds
|
||||||
|
|
||||||
|
|
||||||
|
@property
|
||||||
|
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
|
||||||
|
def model_input_names(self):
|
||||||
|
tokenizer_input_names = self.tokenizer.model_input_names
|
||||||
|
image_processor_input_names = self.image_processor.model_input_names
|
||||||
|
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
||||||
|
|
||||||
|
|
||||||
|
def pad(self, inputs, max_length=None, padding_value=0, padding_side="left"):
|
||||||
|
items = []
|
||||||
|
if isinstance(inputs[0], list):
|
||||||
|
assert isinstance(inputs[0][0], torch.Tensor)
|
||||||
|
for it in inputs:
|
||||||
|
for tr in it:
|
||||||
|
items.append(tr)
|
||||||
|
else:
|
||||||
|
assert isinstance(inputs[0], torch.Tensor)
|
||||||
|
items = inputs
|
||||||
|
|
||||||
|
batch_size = len(items)
|
||||||
|
shape = items[0].shape
|
||||||
|
dim = len(shape)
|
||||||
|
assert dim <= 2
|
||||||
|
if max_length is None:
|
||||||
|
max_length = 0
|
||||||
|
max_length = max(max_length, max(item.shape[-1] for item in items))
|
||||||
|
min_length = min(item.shape[-1] for item in items)
|
||||||
|
dtype = items[0].dtype
|
||||||
|
|
||||||
|
if dim == 0:
|
||||||
|
return torch.stack([item for item in items], dim=0), [0]
|
||||||
|
elif dim == 1:
|
||||||
|
if max_length == min_length:
|
||||||
|
return torch.stack([item for item in items], dim=0), [0] * batch_size
|
||||||
|
tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
|
||||||
|
else:
|
||||||
|
tensor = (
|
||||||
|
torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype)
|
||||||
|
+ padding_value
|
||||||
|
)
|
||||||
|
|
||||||
|
padding_length = []
|
||||||
|
for i, item in enumerate(items):
|
||||||
|
if dim == 1:
|
||||||
|
if padding_side == "left":
|
||||||
|
tensor[i, -len(item) :] = item.clone()
|
||||||
|
else:
|
||||||
|
tensor[i, : len(item)] = item.clone()
|
||||||
|
elif dim == 2:
|
||||||
|
if padding_side == "left":
|
||||||
|
tensor[i, -len(item) :, :] = item.clone()
|
||||||
|
else:
|
||||||
|
tensor[i, : len(item), :] = item.clone()
|
||||||
|
padding_length.append(tensor.shape[-1] - len(item))
|
||||||
|
|
||||||
|
return tensor, padding_length
|
File diff suppressed because it is too large
Load Diff
|
@ -0,0 +1,40 @@
|
||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"151643": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151644": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151645": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": ["<|im_start|>", "<|im_end|>"],
|
||||||
|
"bos_token": null,
|
||||||
|
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"model_max_length": 32768,
|
||||||
|
"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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@ -0,0 +1,60 @@
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from torch import nn
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from einops import rearrange
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|
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|
class ScaleDotProductAttention(nn.Module):
|
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|
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|
def __init__(self, layer_number, causal=False, softmax_scale=None, attention_dropout=0.0):
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|
super().__init__()
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|
self.layer_number = layer_number
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|
self.causal = causal
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|
self.softmax_scale = softmax_scale
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|
self.dropout_p = attention_dropout
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|
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|
# Qwen 不需要scale
|
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|
|
||||||
|
def forward(self, q, k, v, attn_mask=None, order='sbhd'):
|
||||||
|
"""Implements the multihead softmax attention.
|
||||||
|
Arguments
|
||||||
|
---------
|
||||||
|
q, k, v: The tensor containing the query, key, and value. (B, S, H, D)
|
||||||
|
"""
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|
# (N,...,L,E)
|
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|
import torch
|
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|
import torch.nn as nn
|
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|
import torch.nn.functional as F
|
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|
if order == 'sbhd':
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|
q, k, v = [rearrange(x, 's b h d -> b h s d').contiguous()
|
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|
for x in (q, k, v)]
|
||||||
|
elif order == 'bhsd':
|
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|
pass
|
||||||
|
|
||||||
|
if attn_mask is not None:
|
||||||
|
attn_mask = (~attn_mask.clone().bool()).contiguous()
|
||||||
|
else:
|
||||||
|
attn_mask = None
|
||||||
|
# attention mask, True means it will take part in attention B H s_q s_k
|
||||||
|
if self.training:
|
||||||
|
# during training q,k,v always have same seqlen
|
||||||
|
if self.causal:
|
||||||
|
assert q.shape[-2] == k.shape[-2]
|
||||||
|
is_causal = self.causal
|
||||||
|
dropout_p = self.dropout_p
|
||||||
|
else:
|
||||||
|
# turn off FA causal mask after first inference autoregressive iteration
|
||||||
|
# only on first autoregressive step q,k,v have same seqlen
|
||||||
|
if self.causal:
|
||||||
|
is_causal = q.shape[-2] == k.shape[-2]
|
||||||
|
else:
|
||||||
|
is_causal = self.causal
|
||||||
|
dropout_p = 0.0
|
||||||
|
|
||||||
|
# 如果is_causal则无视输入的mask 反之会使用输入的mask
|
||||||
|
o = F.scaled_dot_product_attention(q, k, v,
|
||||||
|
attn_mask=attn_mask,
|
||||||
|
dropout_p=dropout_p,
|
||||||
|
is_causal=is_causal,
|
||||||
|
scale=self.softmax_scale
|
||||||
|
)
|
||||||
|
# B Head L D -> L B (Head D)
|
||||||
|
o = rearrange(o, 'B Head L D -> L B (Head D)').contiguous()
|
||||||
|
return o
|
Loading…
Reference in New Issue