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LLavacheckpoints/files_for_uform_gen2_qwen/.gitattributes ADDED
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LLavacheckpoints/files_for_uform_gen2_qwen/README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags:
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+ - image-captioning
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+ - visual-question-answering
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+ license: apache-2.0
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+ datasets:
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+ - X2FD/LVIS-Instruct4V
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+ - BAAI/SVIT
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+ - HuggingFaceH4/ultrachat_200k
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+ language:
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+ - en
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+ pipeline_tag: image-to-text
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+ widget:
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+ - src: interior.jpg
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+ example_title: Detailed caption
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+ output:
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+ text: "The image showcases a serene and well-lit bedroom. Dominating the scene is a bed, neatly made with a white blanket and a black headboard. Adjacent to the bed, a dresser stands tall, hosting a mirror, a vase, and a flower arrangement. A chair is positioned near the dresser, offering a comfortable spot to sit and relax. The room is adorned with a large window that offers a picturesque view of trees outside. The walls are painted in a soothing shade of white, enhancing the overall ambiance of the space."
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+ - src: cat.jpg
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+ example_title: Short caption
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+ output:
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+ text: "A white and orange cat stands on its hind legs, reaching towards a wooden table with a white teapot and a basket of red berries. The table is set on a wooden bench, surrounded by orange flowers. The cat's position and actions suggest curiosity and playfulness."
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+ ---
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+
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+ <h1 align="center">UForm</h1>
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+ <h3 align="center">
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+ Pocket-Sized Multimodal AI<br/>
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+ For Content Understanding and Generation<br/>
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+ </h3>
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+
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+ ## Description
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+
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+ UForm-Gen is a small generative vision-language model primarily designed for Image Captioning and Visual Question Answering. The model consists of two parts:
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+
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+ 1. CLIP-like ViT-H/14
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+ 2. [Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat)
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+
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+ The model was pre-trained on the internal image captioning dataset and fine-tuned on public instructions datasets: SVIT, LVIS, VQAs datasets.
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+ The model took one day to train on a DGX-H100 with 8x H100 GPUs.
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+ Thanks to [Nebius.ai](https://nebius.ai) for providing the compute 🤗
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+
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+ ### Usage
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+
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+
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+ The generative model can be used to caption images, answer questions about them. Also it is suitable for a multimodal chat.
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+
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+ ```python
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+ from transformers import AutoModel, AutoProcessor
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+
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+ model = AutoModel.from_pretrained("unum-cloud/uform-gen2-qwen-500m", trust_remote_code=True)
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+ processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-qwen-500m", trust_remote_code=True)
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+
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+ prompt = "Question or Instruction"
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+ image = Image.open("image.jpg")
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+
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+ inputs = processor(text=[prompt], images=[image], return_tensors="pt")
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+ with torch.inference_mode():
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+ output = model.generate(
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+ **inputs,
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+ do_sample=False,
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+ use_cache=True,
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+ max_new_tokens=256,
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+ eos_token_id=151645,
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+ pad_token_id=processor.tokenizer.pad_token_id
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+ )
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+
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+ prompt_len = inputs["input_ids"].shape[1]
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+ decoded_text = processor.batch_decode(output[:, prompt_len:])[0]
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+ ```
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+
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+ You can check examples of different prompts in our demo space.
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+
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+ ## Evaluation
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+
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+
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+ | Model | LLM Size | SQA | MME | MMBench | Average¹ |
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+ | :---------------------------------- | -------: | -----:| ------:| --------:| --------:|
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+ | UForm-Gen2-Qwen-500m | 0.5B | 45.5 | 880.1 | 42.0 | 29.31 |
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+ | MobileVLM v2 | 1.4B | 52.1 | 1302.8 | 57.7 | 36.81 |
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+ | LLaVA-Phi | 2.7B | 68.4 | 1335.1 | 59.8 | 42.95 |
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+
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+ ¹MME scores were divided by 2000 before averaging.
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+ {
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+ "_name_or_path": "../weights/vlm-qwen-big-uform",
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+ "architectures": [
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+ "VLMForCausalLM"
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+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_uform_gen.VLMConfig",
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+ "AutoModel": "modeling_uform_gen.VLMForCausalLM",
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+ "AutoProcessor": "processing_uform_gen.VLMProcessor"
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+ },
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+ "image_encoder_hidden_size": 1280,
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+ "image_encoder_name_or_path": "unum-cloud/uform-vl-english-big",
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+ "image_encoder_num_heads": 16,
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+ "image_encoder_num_layers": 32,
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+ "model_type": "vlm",
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+ "num_image_latents": 256,
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+ "text_decoder_name_or_path": "Qwen/Qwen1.5-0.5B-Chat",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.37.2",
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+ "use_cache": true
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+ }
LLavacheckpoints/files_for_uform_gen2_qwen/configuration_uform_gen.py ADDED
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+ from transformers.configuration_utils import PretrainedConfig
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+ from typing import List
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+
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+
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+ class VLMConfig(PretrainedConfig):
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+ model_type = "vlm"
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+
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+ def __init__(
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+ self,
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+ text_decoder_name_or_path: str = "",
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+ image_encoder_name_or_path: str = "",
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+ image_size: int = 336,
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+ image_pooler_num_attn_heads: int = 16,
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+ image_token_id: int = 151646,
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+ image_encoder_patch_size: int = 14,
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+ image_encoder_num_layers: int = 32,
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+ image_encoder_num_heads: int = 16,
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+ image_encoder_pooling: str = "cls",
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+ num_image_latents: int = 256,
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+ initializer_range: float = 0.02,
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+ use_cache: bool = True,
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+ **kwargs,
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+ ):
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+ self.text_decoder_name_or_path = text_decoder_name_or_path
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+ self.image_encoder_name_or_path = image_encoder_name_or_path
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+
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+ self.image_pooler_num_attn_heads = image_pooler_num_attn_heads
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+ self.image_pooler_intermediate_size = image_pooler_intermediate_size
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+ self.image_token_id = image_token_id
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+ self.image_size = image_size
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+ self.image_encoder_hidden_size = image_encoder_hidden_size
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+ self.image_encoder_patch_size = image_encoder_patch_size
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+ self.image_encoder_num_layers = image_encoder_num_layers
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+ self.image_encoder_num_heads = image_encoder_num_heads
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+ self.image_encoder_pooling = image_encoder_pooling
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+ self.num_image_latents = num_image_latents
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+
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+ self.initializer_range = initializer_range
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+ self.use_cache = use_cache
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+
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+ super().__init__(**kwargs)
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+ }
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+ }
LLavacheckpoints/files_for_uform_gen2_qwen/modeling_uform_gen.py ADDED
@@ -0,0 +1,245 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List, Optional, Tuple, Union
2
+
3
+ from .configuration_uform_gen import VLMConfig
4
+
5
+ import torch
6
+ import torch.nn.functional as F
7
+ from torch.utils.checkpoint import checkpoint
8
+ from torch import nn
9
+
10
+ from transformers.modeling_outputs import CausalLMOutputWithPast
11
+ from transformers.modeling_utils import PreTrainedModel
12
+ from transformers.models.auto.modeling_auto import AutoModelForCausalLM, AutoModel
13
+ from transformers import AutoConfig
14
+ from transformers.utils import logging
15
+
16
+ from .vision_encoder import VisionEncoder
17
+
18
+
19
+ class ImageFeaturesPooler(nn.Module):
20
+ def __init__(self, config, text_config):
21
+ super().__init__()
22
+ self.pooler = nn.TransformerDecoderLayer(
23
+ config.image_encoder_hidden_size,
24
+ config.image_pooler_num_attn_heads,
25
+ config.image_pooler_intermediate_size,
26
+ activation=nn.functional.silu,
27
+ batch_first=True,
28
+ norm_first=True,
29
+ )
30
+ self.image_latents = nn.Parameter(
31
+ torch.randn(1, config.num_image_latents, config.image_encoder_hidden_size)
32
+ * config.initializer_range**0.5
33
+ )
34
+ self.projection = nn.Linear(config.image_encoder_hidden_size, text_config.hidden_size)
35
+
36
+ def forward(self, features):
37
+ features = self.pooler(
38
+ self.image_latents.expand(features.size(0), -1, -1), features
39
+ )
40
+
41
+ return self.projection(features)
42
+
43
+
44
+ class VLMPreTrainedModel(PreTrainedModel):
45
+ config_class = VLMConfig
46
+ base_model_prefix = "vlm"
47
+ supports_gradient_checkpointing = True
48
+ _no_split_modules = []
49
+ _skip_keys_device_placement = "past_key_values"
50
+
51
+ def _init_weights(self, module):
52
+ pass
53
+
54
+ def _initialize_weights(self, module):
55
+ pass
56
+
57
+
58
+ class VLMForCausalLM(VLMPreTrainedModel):
59
+ def __init__(self, config: VLMConfig):
60
+ super().__init__(config)
61
+
62
+ self.config = config
63
+ self.text_config = AutoConfig.from_pretrained(
64
+ config.text_decoder_name_or_path,
65
+ trust_remote_code=True
66
+ )
67
+
68
+ self.text_decoder = AutoModelForCausalLM.from_config(
69
+ self.text_config,
70
+ trust_remote_code=True
71
+ )
72
+
73
+ self.image_encoder = VisionEncoder(
74
+ config.image_encoder_hidden_size,
75
+ config.image_encoder_patch_size,
76
+ config.image_encoder_num_layers,
77
+ config.image_encoder_num_heads,
78
+ )
79
+
80
+ self.image_pooler = ImageFeaturesPooler(config, self.text_config)
81
+
82
+ def get_input_embeddings(self):
83
+ return self.text_decoder.get_input_embeddings()
84
+
85
+ def set_input_embeddings(self, value):
86
+ self.text_decoder.set_input_embeddings(value)
87
+
88
+ def get_images_embeddings(self, images):
89
+ features = self.image_encoder(images)
90
+ return self.image_pooler(features)
91
+
92
+ def gather_continuous_embeddings(
93
+ self,
94
+ input_ids: torch.Tensor,
95
+ word_embeddings: torch.Tensor,
96
+ image_embeddings: torch.Tensor
97
+ ) -> torch.Tensor:
98
+
99
+ start_indices = (input_ids == self.config.image_token_id).nonzero()[:, 1]
100
+ embeddings = []
101
+ for sample_idx, start_idx in enumerate(start_indices.tolist()):
102
+ embeddings.append(
103
+ torch.cat(
104
+ (
105
+ word_embeddings[sample_idx, :start_idx],
106
+ image_embeddings[sample_idx],
107
+ word_embeddings[sample_idx, start_idx + 1 :],
108
+ ),
109
+ dim=0,
110
+ )
111
+ )
112
+
113
+ return torch.stack(embeddings, dim=0)
114
+
115
+ def forward(
116
+ self,
117
+ input_ids: torch.LongTensor = None,
118
+ images: torch.Tensor = None,
119
+ attention_mask: Optional[torch.Tensor] = None,
120
+ position_ids: Optional[torch.LongTensor] = None,
121
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
122
+ inputs_embeds: Optional[torch.FloatTensor] = None,
123
+ use_cache: Optional[bool] = None,
124
+ labels: Optional[torch.Tensor] = None,
125
+ output_attentions: Optional[bool] = None,
126
+ output_hidden_states: Optional[bool] = None,
127
+ return_dict: Optional[bool] = None
128
+ ) -> Union[dict, Tuple, CausalLMOutputWithPast]:
129
+ output_attentions = (
130
+ output_attentions
131
+ if output_attentions is not None
132
+ else self.config.output_attentions
133
+ )
134
+ output_hidden_states = (
135
+ output_hidden_states
136
+ if output_hidden_states is not None
137
+ else self.config.output_hidden_states
138
+ )
139
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
140
+
141
+ return_dict = (
142
+ return_dict if return_dict is not None else self.config.use_return_dict
143
+ )
144
+
145
+ if input_ids is not None and inputs_embeds is not None:
146
+ raise ValueError(
147
+ "You cannot specify both input_ids and inputs_embeds at the same time"
148
+ )
149
+ elif input_ids is None and inputs_embeds is None:
150
+ raise ValueError("You have to specify either input_is or inputs_embeds")
151
+
152
+ if inputs_embeds is None and past_key_values is None:
153
+ inputs_embeds = self.get_input_embeddings()(input_ids)
154
+
155
+ if images is not None:
156
+ image_embeds = self.get_images_embeddings(images)
157
+ inputs_embeds = self.gather_continuous_embeddings(
158
+ input_ids,
159
+ inputs_embeds,
160
+ image_embeds
161
+ )
162
+
163
+ if position_ids is None:
164
+ seq_length = (
165
+ inputs_embeds.shape[1]
166
+ if inputs_embeds is not None
167
+ else input_ids.shape[1]
168
+ )
169
+ past_key_values_length = 0
170
+
171
+ if past_key_values is not None:
172
+ past_key_values_length = past_key_values[0][0].shape[2]
173
+
174
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
175
+ position_ids = torch.arange(
176
+ past_key_values_length,
177
+ seq_length + past_key_values_length,
178
+ dtype=torch.long,
179
+ device=device,
180
+ )
181
+ position_ids = position_ids.unsqueeze(0)
182
+
183
+ outputs = self.text_decoder(
184
+ inputs_embeds=inputs_embeds,
185
+ input_ids=input_ids if past_key_values is not None else None,
186
+ attention_mask=attention_mask,
187
+ position_ids=position_ids,
188
+ past_key_values=past_key_values,
189
+ output_attentions=output_attentions,
190
+ output_hidden_states=output_hidden_states,
191
+ use_cache=use_cache,
192
+ return_dict=return_dict,
193
+ )
194
+
195
+ return outputs
196
+
197
+ def prepare_inputs_for_generation(
198
+ self,
199
+ input_ids,
200
+ images=None,
201
+ past_key_values=None,
202
+ attention_mask=None,
203
+ inputs_embeds=None,
204
+ **kwargs,
205
+ ):
206
+ if past_key_values:
207
+ input_ids = input_ids[:, -1:]
208
+
209
+ position_ids = kwargs.get("position_ids", None)
210
+ if attention_mask is not None and position_ids is None:
211
+ # create position_ids on the fly for batch generation
212
+ position_ids = attention_mask.long().cumsum(-1) - 1
213
+ position_ids.masked_fill_(attention_mask == 0, 1)
214
+ if past_key_values:
215
+ position_ids = position_ids[:, -1].unsqueeze(-1)
216
+
217
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
218
+ if inputs_embeds is not None and past_key_values is None:
219
+ model_inputs = {"inputs_embeds": inputs_embeds}
220
+ n_samples = inputs_embeds.shape[0]
221
+ else:
222
+ model_inputs = {"input_ids": input_ids}
223
+ n_samples = input_ids.shape[0]
224
+
225
+ if images is not None:
226
+ model_inputs["images"] = images
227
+
228
+ model_inputs.update(
229
+ {
230
+ "position_ids": position_ids,
231
+ "past_key_values": past_key_values,
232
+ "use_cache": kwargs.get("use_cache"),
233
+ "attention_mask": attention_mask,
234
+ "images": images if past_key_values is None else None,
235
+ }
236
+ )
237
+ return model_inputs
238
+
239
+ @classmethod
240
+ def from_config(cls, config, **kwargs):
241
+ return cls._from_config(config, **kwargs)
242
+
243
+
244
+ VLMConfig.register_for_auto_class()
245
+ VLMForCausalLM.register_for_auto_class("AutoModel")
LLavacheckpoints/files_for_uform_gen2_qwen/processing_uform_gen.py ADDED
@@ -0,0 +1,181 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from functools import partial
2
+
3
+ import torch
4
+ import torch.nn.functional as F
5
+ from transformers.processing_utils import ProcessorMixin
6
+ from transformers.image_processing_utils import BaseImageProcessor
7
+ from transformers import AutoTokenizer, AutoConfig
8
+ from transformers import BatchFeature
9
+
10
+ from PIL import Image
11
+ from torchvision.transforms import (
12
+ Compose,
13
+ Normalize,
14
+ Resize,
15
+ ToTensor
16
+ )
17
+
18
+
19
+ IMAGENET_MEAN = (0.48145466, 0.4578275, 0.40821073)
20
+ IMAGENET_STD = (0.26862954, 0.26130258, 0.27577711)
21
+
22
+
23
+ def convert_to_rgb(x):
24
+ return x.convert("RGB")
25
+
26
+
27
+ def expand2square(image, background_color):
28
+ width, height = image.size
29
+ if width == height:
30
+ return image
31
+ elif width > height:
32
+ result = Image.new(image.mode, (width, width), background_color)
33
+ result.paste(image, (0, (width - height) // 2))
34
+ return result
35
+ else:
36
+ result = Image.new(image.mode, (height, height), background_color)
37
+ result.paste(image, ((height - width) // 2, 0))
38
+ return result
39
+
40
+
41
+ class ImageProcessor(BaseImageProcessor):
42
+ def __init__(
43
+ self,
44
+ image_size: int,
45
+ **kwargs
46
+ ):
47
+ super().__init__(**kwargs)
48
+ self.transform = Compose(
49
+ [
50
+ convert_to_rgb,
51
+ partial(
52
+ expand2square,
53
+ background_color=tuple(int(255 * v) for v in IMAGENET_MEAN)
54
+ ),
55
+ Resize(image_size),
56
+ ToTensor(),
57
+ Normalize(
58
+ mean=IMAGENET_MEAN,
59
+ std=IMAGENET_STD,
60
+ ),
61
+ ]
62
+ )
63
+
64
+ def preprocess(
65
+ self,
66
+ image: Image
67
+ ):
68
+ return self.transform(image)
69
+
70
+ def __repr__(self):
71
+ return repr(self.transform)
72
+
73
+
74
+ class VLMProcessor(ProcessorMixin):
75
+ def __init__(self, config):
76
+ self.config = config
77
+ self.image_size = config.image_size
78
+
79
+ self.feature_extractor = ImageProcessor(self.image_size)
80
+ self.tokenizer = AutoTokenizer.from_pretrained(
81
+ config.text_decoder_name_or_path, additional_special_tokens=["<image>"]
82
+ )
83
+ self.tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
84
+ self.num_image_latents = config.num_image_latents
85
+ # super().__init__(self.image_processor, self.tokenizer)
86
+
87
+ def __call__(
88
+ self, text=None, images=None, **kwargs
89
+ ):
90
+ if text is not None:
91
+ if isinstance(text, str):
92
+ text = [text]
93
+
94
+ tokenized_texts = []
95
+ for t in text:
96
+ messages = [
97
+ {"role": "system", "content": "You are a helpful assistant."},
98
+ {"role": "user", "content": f" <image> {t}"},
99
+ ]
100
+ tokenized_prompt = self.tokenizer.apply_chat_template(
101
+ messages, add_generation_prompt=True, return_tensors="pt"
102
+ )
103
+
104
+ tokenized_texts.append(tokenized_prompt)
105
+
106
+ max_len = max(len(t[0]) for t in tokenized_texts)
107
+ input_ids = torch.full(
108
+ (len(tokenized_texts), max_len),
109
+ fill_value=self.tokenizer.pad_token_id,
110
+ dtype=torch.int64,
111
+ )
112
+ attention_mask = torch.full(
113
+ (len(tokenized_texts), max_len), fill_value=0, dtype=torch.int64
114
+ )
115
+
116
+ for i, tokens in enumerate(tokenized_texts):
117
+ input_ids[i, -len(tokens[0]) :] = tokens[0]
118
+ attention_mask[i, -len(tokens[0]) :] = 1
119
+
120
+ attention_mask = F.pad(
121
+ attention_mask, pad=(0, self.num_image_latents - 1), value=1
122
+ )
123
+
124
+ encoding = BatchFeature(
125
+ data={"input_ids": input_ids, "attention_mask": attention_mask}
126
+ )
127
+
128
+ if images is not None:
129
+ if isinstance(images, (list, tuple)):
130
+ image_features = torch.empty(
131
+ (len(images), 3, self.image_size , self.image_size),
132
+ dtype=torch.float32,
133
+ )
134
+
135
+ for i, image in enumerate(images):
136
+ image_features[i] = self.feature_extractor(image)
137
+
138
+ else:
139
+ image_features = self.feature_extractor(images).unsqueeze(0)
140
+
141
+ if text is not None and images is not None:
142
+ encoding["images"] = image_features
143
+ return encoding
144
+
145
+ elif text is not None:
146
+ return encoding
147
+
148
+ else:
149
+ return BatchFeature(
150
+ data={
151
+ "images": image_features,
152
+ },
153
+ tensor_type=return_tensors,
154
+ )
155
+
156
+ def batch_decode(self, *args, **kwargs):
157
+ """
158
+ This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
159
+ refer to the docstring of this method for more information.
160
+ """
161
+ return self.tokenizer.batch_decode(*args, **kwargs)
162
+
163
+ def decode(self, *args, **kwargs):
164
+ """
165
+ This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
166
+ the docstring of this method for more information.
167
+ """
168
+ return self.tokenizer.decode(*args, **kwargs)
169
+
170
+ @classmethod
171
+ def from_pretrained(
172
+ cls,
173
+ pretrained_model_name_or_path,
174
+ trust_remote_code=False,
175
+ **kwargs
176
+ ):
177
+ config = AutoConfig.from_pretrained(
178
+ pretrained_model_name_or_path,
179
+ trust_remote_code=trust_remote_code
180
+ )
181
+ return cls(config)
LLavacheckpoints/files_for_uform_gen2_qwen/vision_encoder.py ADDED
@@ -0,0 +1,182 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch.nn as nn
2
+ import torch.nn.functional as F
3
+ import torch
4
+ from torch import Tensor
5
+ from typing import Optional
6
+
7
+ class Attention(nn.Module):
8
+
9
+ def __init__(
10
+ self,
11
+ dim: int,
12
+ num_heads: int,
13
+ dropout_prob: float = 0
14
+ ):
15
+ super().__init__()
16
+
17
+ self.use_sdp = int(torch.__version__[0]) > 1
18
+
19
+ self.query = nn.Linear(dim, dim)
20
+ self.key = nn.Linear(dim, dim)
21
+ self.value = nn.Linear(dim, dim)
22
+ self.out = nn.Linear(dim, dim)
23
+
24
+ self.dropout_prob = dropout_prob
25
+ self.num_heads = num_heads
26
+ self.head_dim = dim // num_heads
27
+ self.scale = self.head_dim**-0.5
28
+
29
+ def forward(
30
+ self,
31
+ x: Tensor,
32
+ attn_mask: Optional[Tensor] = None,
33
+ context: Optional[Tensor] = None,
34
+ is_causal: bool = False,
35
+ ) -> Tensor:
36
+
37
+ query = self.reshape(self.query(x))
38
+ key = self.reshape(self.key(x if context is None else context))
39
+ value = self.reshape(self.value(x if context is None else context))
40
+
41
+ if self.use_sdp:
42
+ x = F.scaled_dot_product_attention(
43
+ query,
44
+ key,
45
+ value,
46
+ attn_mask,
47
+ dropout_p=self.dropout_prob if self.training else 0,
48
+ is_causal=is_causal,
49
+ )
50
+ else:
51
+ attn = query @ key.transpose(-2, -1) * self.scale
52
+ if attn_mask is not None:
53
+ attn += attn_mask
54
+
55
+ attn = attn.softmax(dim=-1)
56
+ x = attn @ value
57
+
58
+ return self.out(x.transpose(2, 1).flatten(2))
59
+
60
+ def reshape(self, x: Tensor) -> Tensor:
61
+ batch_size, seq_len, _ = x.shape
62
+ x = x.view(batch_size, seq_len, self.num_heads, self.head_dim)
63
+ return x.transpose(2, 1)
64
+
65
+
66
+ class MLP(nn.Module):
67
+
68
+ def __init__(
69
+ self,
70
+ dim: int,
71
+ dim_expand_factor: int = 4
72
+ ):
73
+ super().__init__()
74
+
75
+ self.hidden_layer = nn.Linear(dim, dim * dim_expand_factor)
76
+ self.output_layer = nn.Linear(dim * dim_expand_factor, dim)
77
+
78
+ def forward(self, x: Tensor) -> Tensor:
79
+ x = F.gelu(self.hidden_layer(x))
80
+ return self.output_layer(x)
81
+
82
+
83
+ class LayerScale(nn.Module):
84
+
85
+ def __init__(
86
+ self,
87
+ dim: int,
88
+ init_values: float = 1e-5,
89
+ inplace: bool = False
90
+ ):
91
+ super().__init__()
92
+ self.weight = nn.Parameter(init_values * torch.ones(dim))
93
+ self.inplace = inplace
94
+
95
+ def forward(self, x: Tensor) -> Tensor:
96
+ return x.mul_(self.weight) if self.inplace else x * self.weight
97
+
98
+
99
+ class VisionEncoderBlock(nn.Module):
100
+
101
+ def __init__(
102
+ self,
103
+ dim: int,
104
+ num_heads: int
105
+ ):
106
+ super().__init__()
107
+ self.norm1 = nn.LayerNorm(dim, eps=1e-6)
108
+ self.attn = Attention(dim, num_heads)
109
+ self.ls1 = LayerScale(dim)
110
+
111
+ self.norm2 = nn.LayerNorm(dim, eps=1e-6)
112
+ self.mlp = MLP(dim)
113
+ self.ls2 = LayerScale(dim)
114
+
115
+ def forward(self, x: Tensor) -> Tensor:
116
+ x = x + self.ls1(self.attn(self.norm1(x)))
117
+ x = x + self.ls2(self.mlp(self.norm2(x)))
118
+ return x
119
+
120
+
121
+ class VisionEncoder(nn.Module):
122
+
123
+ def __init__(
124
+ self,
125
+ dim: int,
126
+ patch_size: int,
127
+ num_layers: int,
128
+ num_heads: int,
129
+ ):
130
+ super().__init__()
131
+
132
+ self.n_patch = 224 // patch_size
133
+ self.seq_len = self.n_patch ** 2
134
+ self.patch_size = patch_size
135
+
136
+ self.patch_embed = nn.Conv2d(3, dim, patch_size, patch_size)
137
+ self.pos_embed = nn.Parameter(torch.randn(1, self.seq_len, dim) * 0.02)
138
+ self.cls_token = nn.Parameter(torch.zeros(1, 1, dim))
139
+ self.interpolate_offset = 0.1
140
+ self.interpolate_antialias = False
141
+
142
+ self.blocks = nn.Sequential(
143
+ *[
144
+ VisionEncoderBlock(dim, num_heads)
145
+ for _ in range(num_layers)
146
+ ]
147
+ )
148
+
149
+ self.norm = nn.LayerNorm(dim, eps=1e-6)
150
+
151
+ def interpolate_pos_encoding(self, x, h, w):
152
+ previous_dtype = x.dtype
153
+
154
+ if x.shape[1] == self.seq_len and w == h:
155
+ return self.pos_embed
156
+
157
+ pos_embed = self.pos_embed.float()
158
+
159
+ dim = x.shape[-1]
160
+ w0 = w // self.patch_size
161
+ h0 = h // self.patch_size
162
+ # we add a small number to avoid floating point error in the interpolation
163
+ # see discussion at https://github.com/facebookresearch/dino/issues/8
164
+ w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset
165
+ sx, sy = float(w0) / self.n_patch, float(h0) / self.n_patch
166
+
167
+ pos_embed = nn.functional.interpolate(
168
+ pos_embed.reshape(1, self.n_patch, self.n_patch, dim).permute(0, 3, 1, 2),
169
+ scale_factor=(sy, sx),
170
+ mode="bicubic",
171
+ antialias=self.interpolate_antialias,
172
+ )
173
+
174
+ return pos_embed.to(previous_dtype).flatten(start_dim=2).transpose(2, 1)
175
+
176
+ def forward(self, x: Tensor) -> Tensor:
177
+ h, w = x.shape[2:]
178
+ x = self.patch_embed(x).flatten(start_dim=2).transpose(2, 1)
179
+ x = x + self.interpolate_pos_encoding(x, h, w)
180
+ x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
181
+ x = self.blocks(x)
182
+ return self.norm(x)
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