diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..cb4a193c8fd50c399688aec65eb363270ab08cdd 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,24 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +PP-DocLayoutV2/inference.pdiparams filter=lfs diff=lfs merge=lfs -text +PP-DocLayoutV2/inference.pdmodel filter=lfs diff=lfs merge=lfs -text +PaddleOCR-VL-0.9B/tokenizer.json filter=lfs diff=lfs merge=lfs -text +imgs/allmetric.png filter=lfs diff=lfs merge=lfs -text +imgs/chart_01.jpg filter=lfs diff=lfs merge=lfs -text +imgs/chart_02.jpg filter=lfs diff=lfs merge=lfs -text +imgs/formula_EN.jpg filter=lfs diff=lfs merge=lfs -text +imgs/inhouseocr.png filter=lfs diff=lfs merge=lfs -text +imgs/inhousetable.png filter=lfs diff=lfs merge=lfs -text +imgs/omni10.png filter=lfs diff=lfs merge=lfs -text +imgs/omni15.png filter=lfs diff=lfs merge=lfs -text +imgs/omnibenchocr.png filter=lfs diff=lfs merge=lfs -text +imgs/overview1.jpg filter=lfs diff=lfs merge=lfs -text +imgs/overview2.jpg filter=lfs diff=lfs merge=lfs -text +imgs/overview3.jpg filter=lfs diff=lfs merge=lfs -text +imgs/overview4.jpg filter=lfs diff=lfs merge=lfs -text +imgs/paddleocrvl.png filter=lfs diff=lfs merge=lfs -text +imgs/table_01.jpg filter=lfs diff=lfs merge=lfs -text +imgs/table_02.jpg filter=lfs diff=lfs merge=lfs -text +imgs/text_english_arabic.jpg filter=lfs diff=lfs merge=lfs -text +imgs/text_handwriting_02.jpg filter=lfs diff=lfs merge=lfs -text diff --git a/PP-DocLayoutV2/config.json b/PP-DocLayoutV2/config.json new file mode 100644 index 0000000000000000000000000000000000000000..16a3e88b81ec3ad30dbaba930d3707ad86a2ec16 --- /dev/null +++ b/PP-DocLayoutV2/config.json @@ -0,0 +1,176 @@ +{ + "mode": "paddle", + "draw_threshold": 0.5, + "metric": "COCO", + "use_dynamic_shape": false, + "Global": { + "model_name": "PP-DocLayoutV2" + }, + "arch": "DETR", + "min_subgraph_size": 3, + "Preprocess": [ + { + "interp": 2, + "keep_ratio": false, + "target_size": [ + 800, + 800 + ], + "type": "Resize" + }, + { + "mean": [ + 0.0, + 0.0, + 0.0 + ], + "norm_type": "none", + "std": [ + 1.0, + 1.0, + 1.0 + ], + "type": "NormalizeImage" + }, + { + "type": "Permute" + } + ], + "label_list": [ + "abstract", + "algorithm", + "aside_text", + "chart", + "content", + "display_formula", + "doc_title", + "figure_title", + "footer", + "footer_image", + "footnote", + "formula_number", + "header", + "header_image", + "image", + "inline_formula", + "number", + "paragraph_title", + "reference", + "reference_content", + "seal", + "table", + "text", + "vertical_text", + "vision_footnote" + ], + "Hpi": { + "backend_configs": { + "paddle_infer": { + "trt_dynamic_shapes": { + "image": [ + [ + 1, + 3, + 800, + 800 + ], + [ + 1, + 3, + 800, + 800 + ], + [ + 8, + 3, + 800, + 800 + ] + ], + "scale_factor": [ + [ + 1, + 2 + ], + [ + 1, + 2 + ], + [ + 8, + 2 + ] + ] + }, + "trt_dynamic_shape_input_data": { + "scale_factor": [ + [ + 2, + 2 + ], + [ + 1, + 1 + ], + [ + 0.67, + 0.67, + 0.67, + 0.67, + 0.67, + 0.67, + 0.67, + 0.67, + 0.67, + 0.67, + 0.67, + 0.67, + 0.67, + 0.67, + 0.67, + 0.67 + ] + ] + } + }, + "tensorrt": { + "dynamic_shapes": { + "image": [ + [ + 1, + 3, + 800, + 800 + ], + [ + 1, + 3, + 800, + 800 + ], + [ + 8, + 3, + 800, + 800 + ] + ], + "scale_factor": [ + [ + 1, + 2 + ], + [ + 1, + 2 + ], + [ + 8, + 2 + ] + ] + } + } + } + } +} \ No newline at end of file diff --git a/PP-DocLayoutV2/inference.pdiparams b/PP-DocLayoutV2/inference.pdiparams new file mode 100644 index 0000000000000000000000000000000000000000..278c0ec2696c53c84932672a85a95272a6fa6d69 --- /dev/null +++ b/PP-DocLayoutV2/inference.pdiparams @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:45404a84c9fdf91d7bbc94bd47ac4c03649bda84167de04c62bff4726657869a +size 212170944 diff --git a/PP-DocLayoutV2/inference.pdmodel b/PP-DocLayoutV2/inference.pdmodel new file mode 100644 index 0000000000000000000000000000000000000000..87a33b0019d2d47873e52164efe6f0aa7e1ee85f --- /dev/null +++ b/PP-DocLayoutV2/inference.pdmodel @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fddd4b4359b95e6f1dd86c86f05e9516517bead9089287681838cdcbf003563b +size 1515181 diff --git a/PP-DocLayoutV2/inference.yml b/PP-DocLayoutV2/inference.yml new file mode 100644 index 0000000000000000000000000000000000000000..5cc9ec8970c00fc1b0ca7a37824d9e09acbb451e --- /dev/null +++ b/PP-DocLayoutV2/inference.yml @@ -0,0 +1,100 @@ +mode: paddle +draw_threshold: 0.5 +metric: COCO +use_dynamic_shape: false +Global: + model_name: PP-DocLayoutV2 +arch: DETR +min_subgraph_size: 3 +Preprocess: +- interp: 2 + keep_ratio: false + target_size: + - 800 + - 800 + type: Resize +- mean: + - 0.0 + - 0.0 + - 0.0 + norm_type: none + std: + - 1.0 + - 1.0 + - 1.0 + type: NormalizeImage +- type: Permute +label_list: +- abstract +- algorithm +- aside_text +- chart +- content +- display_formula +- doc_title +- figure_title +- footer +- footer_image +- footnote +- formula_number +- header +- header_image +- image +- inline_formula +- number +- paragraph_title +- reference +- reference_content +- seal +- table +- text +- vertical_text +- vision_footnote +Hpi: + backend_configs: + paddle_infer: + trt_dynamic_shapes: &id001 + image: + - - 1 + - 3 + - 800 + - 800 + - - 1 + - 3 + - 800 + - 800 + - - 8 + - 3 + - 800 + - 800 + scale_factor: + - - 1 + - 2 + - - 1 + - 2 + - - 8 + - 2 + trt_dynamic_shape_input_data: + scale_factor: + - - 2 + - 2 + - - 1 + - 1 + - - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + - 0.67 + tensorrt: + dynamic_shapes: *id001 diff --git a/PaddleOCR-VL-0.9B/.gitattributes b/PaddleOCR-VL-0.9B/.gitattributes new file mode 100644 index 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"<|LOC_END|>": 101299, + "<|LOC_SEP|>": 101300, + "<|image_pad|>": 101304, + "<|video_pad|>": 101307 +} diff --git a/PaddleOCR-VL-0.9B/chat_template.jinja b/PaddleOCR-VL-0.9B/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..f92b0663a6945668c9b6d3cdab2de756816e2dc1 --- /dev/null +++ b/PaddleOCR-VL-0.9B/chat_template.jinja @@ -0,0 +1,22 @@ +{%- if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = true -%} +{%- endif -%} +{%- if not cls_token is defined -%} + {%- set cls_token = "<|begin_of_sentence|>" -%} +{%- endif -%} +{%- if not sep_token is defined -%} + {%- set sep_token = "<|end_of_sentence|>" -%} +{%- endif -%} +{{- cls_token -}} +{%- for message in messages -%} + {%- if message["role"] == "user" -%} + {{- "User: <|IMAGE_START|><|IMAGE_PLACEHOLDER|><|IMAGE_END|>" + message["content"] + "\n" -}} + {%- elif message["role"] == "assistant" -%} + {{- "Assistant: " + message["content"] + sep_token -}} + {%- elif message["role"] == "system" -%} + {{- message["content"] -}} + {%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt -%} + {{- "Assistant: " -}} +{%- endif -%} diff --git a/PaddleOCR-VL-0.9B/config.json b/PaddleOCR-VL-0.9B/config.json new file mode 100644 index 0000000000000000000000000000000000000000..2617b7e8fe4579d0695125002ed18e4132e106e5 --- /dev/null +++ b/PaddleOCR-VL-0.9B/config.json @@ -0,0 +1,75 @@ +{ + "architectures": [ + "PaddleOCRVLForConditionalGeneration" + ], + "attention_probs_dropout_prob": 0.0, + "auto_map": { + "AutoConfig": "configuration_paddleocr_vl.PaddleOCRVLConfig", + "AutoModel": "modeling_paddleocr_vl.PaddleOCRVLForConditionalGeneration", + "AutoModelForCausalLM": "modeling_paddleocr_vl.PaddleOCRVLForConditionalGeneration" + }, + "compression_ratio": 1.0, + "head_dim": 128, + "hidden_act": "silu", + "hidden_dropout_prob": 0.0, + "hidden_size": 1024, + "ignored_index": -100, + "image_token_id": 100295, + "intermediate_size": 3072, + "max_position_embeddings": 131072, + "max_sequence_length": null, + "model_type": "paddleocr_vl", + "num_attention_heads": 16, + "num_hidden_layers": 18, + "num_key_value_heads": 2, + "pad_token_id": 0, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "mrope_section": [ + 16, + 24, + 24 + ], + "rope_type": "default", + "type": "default" + }, + "rope_theta": 500000, + "sliding_window": null, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.55.0", + "use_bias": false, + "use_cache": false, + "use_flash_attention": false, + "video_token_id": 101307, + "vision_config": { + "architectures": [ + "SiglipVisionModel" + ], + "attention_dropout": 0.0, + "auto_map": { + "AutoConfig": "configuration_paddleocr_vl.PaddleOCRVLConfig", + "AutoModel": "modeling_paddleocr_vl.SiglipVisionModel" + }, + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "image_size": 384, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_type": "paddleocr_vl", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 27, + "pad_token_id": 0, + "patch_size": 14, + "spatial_merge_size": 2, + "temporal_patch_size": 2, + "tokens_per_second": 2, + "torch_dtype": "bfloat16" + }, + "vision_start_token_id": 101305, + "vocab_size": 103424, + "weight_share_add_bias": true, + "use_3d_rope": true, + "rope_is_neox_style": true +} diff --git a/PaddleOCR-VL-0.9B/configuration_paddleocr_vl.py b/PaddleOCR-VL-0.9B/configuration_paddleocr_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..a8fd139287293301b287db6dfdaac21d7ad1a236 --- /dev/null +++ b/PaddleOCR-VL-0.9B/configuration_paddleocr_vl.py @@ -0,0 +1,191 @@ +# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. +# +# 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. + +from transformers.configuration_utils import PretrainedConfig +from transformers.modeling_rope_utils import rope_config_validation + +class PaddleOCRVisionConfig(PretrainedConfig): + model_type = "paddleocr_vl" + base_config_key = "vision_config" + + def __init__( + self, + hidden_size=768, + intermediate_size=3072, + num_hidden_layers=12, + num_attention_heads=12, + num_channels=3, + image_size=224, + patch_size=14, + hidden_act="gelu_pytorch_tanh", + layer_norm_eps=1e-6, + attention_dropout=0.0, + spatial_merge_size=2, + temporal_patch_size=2, + tokens_per_second=2, + **kwargs, + ): + super().__init__(**kwargs) + + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.num_channels = num_channels + self.patch_size = patch_size + self.image_size = image_size + self.attention_dropout = attention_dropout + self.layer_norm_eps = layer_norm_eps + self.hidden_act = hidden_act + self.spatial_merge_size = spatial_merge_size + self.temporal_patch_size = temporal_patch_size + self.tokens_per_second = tokens_per_second + + + +class PaddleOCRVLConfig(PretrainedConfig): + """ + Configuration class. + + This class stores the configuration of an Ernie model, defining the model architecture. + It inherits from PretrainedConfig and can be used to control model outputs. + """ + + model_type = "paddleocr_vl" + keys_to_ignore_at_inference = ["past_key_values"] + sub_configs = {"vision_config": PaddleOCRVisionConfig} + + # Default tensor parallel plan for base model `Qwen3` + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + def __init__( + self, + vocab_size=32000, + hidden_size=768, + intermediate_size=11008, + max_position_embeddings=32768, + num_hidden_layers=2, + num_attention_heads=2, + image_token_id=101304, + video_token_id=101305, + vision_start_token_id=101306, + rms_norm_eps=1e-6, + use_cache=False, + use_flash_attention=False, + pad_token_id=0, + bos_token_id=1, + eos_token_id=2, + head_dim=128, + hidden_act="silu", + use_bias=False, + rope_theta=10000, + weight_share_add_bias=True, + ignored_index=-100, + attention_probs_dropout_prob=0.0, + hidden_dropout_prob=0.0, + compression_ratio: float = 1.0, + num_key_value_heads=None, + max_sequence_length=None, + tie_word_embeddings=False, + vision_config=None, + rope_scaling=None, + **kwargs, + ): + """ + Initialize configuration with default or specified parameters. + + Args: + vocab_size (int): Size of the vocabulary (number of unique tokens) + hidden_size (int): Dimensionality of the encoder layers and the pooler layer + intermediate_size (int): Dimensionality of the "intermediate" (feed-forward) layer + max_position_embeddings (int): Maximum sequence length the model can handle + num_hidden_layers (int): Number of hidden layers in the Transformer encoder + num_attention_heads (int): Number of attention heads for each attention layer + rms_norm_eps (float): The epsilon used by the RMS normalization layers + use_cache (bool): Whether to use caching for faster generation (decoding) + use_flash_attention (bool): Whether to use FlashAttention for optimized attention computation + pad_token_id (int): Token ID used for padding sequences + bos_token_id (int): Token ID used for beginning-of-sequence + eos_token_id (int): Token ID used for end-of-sequence + use_bias (bool): Whether to use bias terms in linear layers + rope_theta (float): The base period of the RoPE embeddings + weight_share_add_bias (bool): Whether to share bias weights in certain layers + ignored_index (int): Target value that is ignored during loss computation + attention_probs_dropout_prob (float): Dropout probability for attention weights + hidden_dropout_prob (float): Dropout probability for hidden layers + compression_ratio (float): Ratio for KV cache compression (1.0 = no compression) + num_key_value_heads (int): Number of key/value heads (for Grouped Query Attention) + max_sequence_length (int): Maximum sequence length for positional embeddings + **kwargs: Additional keyword arguments passed to parent class + """ + + # Set default for tied embeddings if not specified. + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + **kwargs, + ) + if isinstance(vision_config, dict): + self.vision_config = self.sub_configs["vision_config"](**vision_config) + elif vision_config is None: + self.vision_config = self.sub_configs["vision_config"]() + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.max_position_embeddings = max_position_embeddings + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.use_flash_attention = use_flash_attention + self.pad_token_id = pad_token_id + self.bos_token_id = bos_token_id + self.eos_token_id = eos_token_id + self.image_token_id = image_token_id + self.video_token_id = video_token_id + self.vision_start_token_id = vision_start_token_id + self.head_dim = head_dim + self.hidden_act=hidden_act + self.sliding_window = None + self.hidden_size = hidden_size + self.use_bias = use_bias + self.weight_share_add_bias = weight_share_add_bias + self.rope_theta = rope_theta + self.ignored_index = ignored_index + self.attention_probs_dropout_prob = attention_probs_dropout_prob + self.hidden_dropout_prob = hidden_dropout_prob + self.compression_ratio = compression_ratio + self.num_key_value_heads = num_key_value_heads + self.max_sequence_length = max_sequence_length + self.rope_scaling = rope_scaling + if self.rope_scaling is not None and "type" in self.rope_scaling: + if self.rope_scaling["type"] == "mrope": + self.rope_scaling["type"] = "default" + self.rope_scaling["rope_type"] = self.rope_scaling["type"] + rope_config_validation(self, ignore_keys={"mrope_section"}) + super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) \ No newline at end of file diff --git a/PaddleOCR-VL-0.9B/generation_config.json b/PaddleOCR-VL-0.9B/generation_config.json new file mode 100644 index 0000000000000000000000000000000000000000..85c86e67de84e9d4bec3f387c95a759957f6f3ca --- /dev/null +++ b/PaddleOCR-VL-0.9B/generation_config.json @@ -0,0 +1,6 @@ +{ + "_from_model_config": true, + "eos_token_id": 2, + "transformers_version": "4.55.0", + "use_cache": false +} diff --git a/PaddleOCR-VL-0.9B/image_processing.py b/PaddleOCR-VL-0.9B/image_processing.py new file mode 100644 index 0000000000000000000000000000000000000000..4bec8978d9bafd055bf4c3412fe5b4ebdc318cd1 --- /dev/null +++ b/PaddleOCR-VL-0.9B/image_processing.py @@ -0,0 +1,569 @@ +# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. +# +# 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. + +"""Image processor class for PaddleOCR-VL.""" + +import math +from typing import Dict, List, Optional, Union + +import numpy as np +import torch +from transformers.image_processing_utils import BaseImageProcessor, BatchFeature +from torchvision.transforms import functional as TF +from transformers.image_transforms import ( + convert_to_rgb, + resize, + to_channel_dimension_format, +) +from transformers.image_utils import ( + OPENAI_CLIP_MEAN, + OPENAI_CLIP_STD, + ChannelDimension, + PILImageResampling, + get_image_size, + infer_channel_dimension_format, + is_scaled_image, + is_valid_image, + make_list_of_images, + to_numpy_array, + valid_images, + validate_preprocess_arguments, +) +from transformers.utils import TensorType, is_vision_available, logging + + +logger = logging.get_logger(__name__) + + +if is_vision_available(): + from PIL import Image + +ImageInput = Union[ + "PIL.Image.Image", + np.ndarray, + "torch.Tensor", + List["PIL.Image.Image"], + List[np.ndarray], + List["torch.Tensor"], +] # noqa + + +VideoInput = Union[ + List["PIL.Image.Image"], + "np.ndarray", + "torch.Tensor", + List["np.ndarray"], + List["torch.Tensor"], + List[List["PIL.Image.Image"]], + List[List["np.ndarrray"]], + List[List["torch.Tensor"]], +] # noqa + + +def make_batched_images(images) -> List[List[ImageInput]]: + """ + Accepts images in list or nested list format, and makes a list of images for preprocessing. + + Args: + images (`Union[List[List[ImageInput]], List[ImageInput], ImageInput]`): + The input image. + + Returns: + list: A list of images. + """ + if ( + isinstance(images, (list, tuple)) + and isinstance(images[0], (list, tuple)) + and is_valid_image(images[0][0]) + ): + return [img for img_list in images for img in img_list] + + elif isinstance(images, (list, tuple)) and is_valid_image(images[0]): + return images + + elif is_valid_image(images): + return [images] + + raise ValueError(f"Could not make batched images from {images}") + + +def adjust_size(size, patch_size): + num_patches = size // patch_size + if num_patches % 2 != 0: # 如果是奇数，减1 + num_patches -= 1 + return num_patches * patch_size + + +def make_batched_videos(videos) -> List[VideoInput]: + if ( + isinstance(videos, (list, tuple)) + and isinstance(videos[0], (list, tuple)) + and is_valid_image(videos[0][0]) + ): + return videos + + elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]): + if isinstance(videos[0], Image.Image): + return [videos] + elif len(videos[0].shape) == 4: + return [list(video) for video in videos] + + elif is_valid_image(videos) and len(videos.shape) == 4: + return [list(videos)] + + raise ValueError(f"Could not make batched video from {videos}") + + +def smart_resize( + height: int, + width: int, + factor: int = 28, + min_pixels: int = 28 * 28 * 130, + max_pixels: int = 28 * 28 * 1280, +): + """Rescales the image so that the following conditions are met: + + 1. Both dimensions (height and width) are divisible by 'factor'. + + 2. The total number of pixels is within the range ['min_pixels', 'max_pixels']. + + 3. The aspect ratio of the image is maintained as closely as possible. + + """ + # if height < factor or width < factor: + # raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor}") + # if int(height < factor//4) + int(width < factor//4): + # raise ValueError(f"height:{height} or width:{width} must be larger than factor:{factor//4}") + + if height < factor: + print(f"smart_resize: height={height} < factor={factor}, reset height=factor") + width = round((width * factor) / height) + height = factor + + if width < factor: + print(f"smart_resize: width={width} < factor={factor}, reset width=factor") + height = round((height * factor) / width) + width = factor + + if max(height, width) / min(height, width) > 200: + raise ValueError( + f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}" + ) + h_bar = round(height / factor) * factor + w_bar = round(width / factor) * factor + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = math.floor(height / beta / factor) * factor + w_bar = math.floor(width / beta / factor) * factor + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = math.ceil(height * beta / factor) * factor + w_bar = math.ceil(width * beta / factor) * factor + return h_bar, w_bar + + +class SiglipImageProcessor(BaseImageProcessor): + r""" + Constructs a Siglip image processor that dynamically resizes images based on the original images. + + Args: + do_resize (`bool`, *optional*, defaults to `True`): + Whether to resize the image's (height, width) dimensions. + resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`): + Resampling filter to use when resizing the image. + do_rescale (`bool`, *optional*, defaults to `True`): + Whether to rescale the image by the specified scale `rescale_factor`. + rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): + Scale factor to use if rescaling the image. + do_normalize (`bool`, *optional*, defaults to `True`): + Whether to normalize the image. + image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`): + Mean to use if normalizing the image. This is a float or list of floats for each channel in the image. + image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`): + Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image. + do_convert_rgb (`bool`, *optional*, defaults to `True`): + Whether to convert the image to RGB. + min_pixels (`int`, *optional*, defaults to `28 * 28 * 130`): + The min pixels of the image to resize the image. + max_pixels (`int`, *optional*, defaults to `28 * 28 * 1670`): + The max pixels of the image to resize the image. + patch_size (`int`, *optional*, defaults to 14): + The spacial patch size of the vision encoder. + temporal_patch_size (`int`, *optional*, defaults to 2): + The temporal patch size of the vision encoder. + merge_size (`int`, *optional*, defaults to 2): + The merge size of the vision encoder to llm encoder. + """ + + model_input_names = [ + "pixel_values", + "image_grid_thw", + "pixel_values_videos", + "video_grid_thw", + ] + + def __init__( + self, + do_resize: bool = True, + resample: PILImageResampling = PILImageResampling.BICUBIC, + do_rescale: bool = True, + rescale_factor: Union[int, float] = 1 / 255, + do_normalize: bool = True, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_convert_rgb: bool = True, + min_pixels: int = 28 * 28 * 130, + max_pixels: int = 28 * 28 * 1280, + patch_size: int = 14, + temporal_patch_size: int = 1, + merge_size: int = 2, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self.do_resize = do_resize + self.resample = resample + self.do_rescale = do_rescale + self.rescale_factor = rescale_factor + self.do_normalize = do_normalize + self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN + self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD + self.min_pixels = min_pixels + self.max_pixels = max_pixels + self.patch_size = patch_size + self.temporal_patch_size = temporal_patch_size + self.merge_size = merge_size + self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels} # not used + self.do_convert_rgb = do_convert_rgb + + def mvit_rescale(self, image: Image.Image, merge_size: int = 2) -> Image.Image: + try: + w, h = image.size + except: + raise ValueError(str((type(image), image))) + patch_size = self.patch_size + + if (w // patch_size) * (h // patch_size) > self.in_token_limit: + scale = math.sqrt( + self.in_token_limit / ((w // patch_size) * (h // patch_size)) + ) + new_w, new_h = int(w * scale), int(h * scale) + + image = image.resize((new_w, new_h), Image.Resampling.BICUBIC) + if self.pad_input: + new_w, new_h = image.size + pad_size_h = merge_size * patch_size + pad_size_w = merge_size * patch_size + + pad_h = (pad_size_h - new_h % pad_size_h) % pad_size_h + pad_w = (pad_size_w - new_w % pad_size_w) % pad_size_w + + image = TF.pad(image, (0, 0, pad_w, pad_h)) + else: + new_w, new_h = image.size + new_w = new_w - new_w % patch_size + new_h = new_h - new_h % patch_size + + new_w = adjust_size(new_w, patch_size) + new_h = adjust_size(new_h, patch_size) + + image = TF.center_crop(image, (new_h, new_w)) + + w, h = image.size + if w // patch_size >= 512 or h // patch_size >= 512: + new_h = min(patch_size * 510, h) + new_w = min(patch_size * 510, w) + image = TF.center_crop(image, (new_h, new_w)) + # raise ValueError("Exceed pos emb") + return image + + def _preprocess( + self, + images: Union[ImageInput, VideoInput], + do_resize: bool = None, + resample: PILImageResampling = None, + do_rescale: bool = None, + rescale_factor: float = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_convert_rgb: bool = None, + data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ): + """ + Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`. + + Args: + images (`ImageInput`): + Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`. + vision_info (`List[Dict]`, *optional*): + Optional list of dictionaries containing additional information about vision inputs. + do_resize (`bool`, *optional*, defaults to `self.do_resize`): + Whether to resize the image. + resample (`PILImageResampling`, *optional*, defaults to `self.resample`): + Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums. + do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image. + rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): + Scale factor to use if rescaling the image. + do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): + Whether to normalize the image. + image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): + Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image. + image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): + Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image. + do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): + Whether to convert the image to RGB. + data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: Use the channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + images = make_list_of_images(images) + + if do_convert_rgb: + images = [convert_to_rgb(image) for image in images] + + # All transformations expect numpy arrays. + images = [to_numpy_array(image) for image in images] + + if is_scaled_image(images[0]) and do_rescale: + logger.warning_once( + "It looks like you are trying to rescale already rescaled images. If the input" + " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." + ) + if input_data_format is None: + # We assume that all images have the same channel dimension format. + input_data_format = infer_channel_dimension_format(images[0]) + + height, width = get_image_size(images[0], channel_dim=input_data_format) + resized_height, resized_width = height, width + processed_images = [] + + for image in images: + if do_resize: + resized_height, resized_width = smart_resize( + height, + width, + factor=self.patch_size * self.merge_size, + min_pixels=self.min_pixels, + max_pixels=self.max_pixels, + ) + image = resize( + image, + size=(resized_height, resized_width), + resample=resample, + input_data_format=input_data_format, + ) + + if do_rescale: + image = self.rescale( + image, scale=rescale_factor, input_data_format=input_data_format + ) + + if do_normalize: + image = self.normalize( + image=image, + mean=image_mean, + std=image_std, + input_data_format=input_data_format, + ) + image = to_channel_dimension_format( + image, data_format, input_channel_dim=input_data_format + ) + processed_images.append(image) + + patches = np.array(processed_images) + if data_format == ChannelDimension.LAST: + patches = patches.transpose(0, 3, 1, 2) + if patches.shape[0] == 1: + patches = np.tile(patches, (self.temporal_patch_size, 1, 1, 1)) + init_patches = patches + channel = patches.shape[1] + grid_t = patches.shape[0] // self.temporal_patch_size + grid_h, grid_w = ( + resized_height // self.patch_size, + resized_width // self.patch_size, + ) + patches = patches.reshape( + grid_t, + self.temporal_patch_size, + channel, + grid_h, + self.patch_size, + grid_w, + self.patch_size, + ) + patches = patches.transpose(0, 3, 5, 2, 1, 4, 6) + assert self.temporal_patch_size == 1 + flatten_patches = patches.reshape( + grid_t * grid_h * grid_w, channel, self.patch_size, self.patch_size + ) + return flatten_patches, (grid_t, grid_h, grid_w) + + def preprocess( + self, + images: ImageInput, + videos: VideoInput = None, + do_resize: bool = None, + size: Dict[str, int] = None, + resample: PILImageResampling = None, + do_rescale: bool = None, + rescale_factor: float = None, + do_normalize: bool = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_convert_rgb: bool = None, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: Optional[ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ): + """ + Args: + images (`ImageInput`): + Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If + passing in images with pixel values between 0 and 1, set `do_rescale=False`. + videos (`VideoInput`): + Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If + passing in videos with pixel values between 0 and 1, set `do_rescale=False`. + do_resize (`bool`, *optional*, defaults to `self.do_resize`): + Whether to resize the image. + size (`Dict[str, int]`, *optional*, defaults to `self.size`): + Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with + the longest edge resized to keep the input aspect ratio. + resample (`int`, *optional*, defaults to `self.resample`): + Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only + has an effect if `do_resize` is set to `True`. + do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): + Whether to rescale the image. + rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): + Rescale factor to rescale the image by if `do_rescale` is set to `True`. + do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): + Whether to normalize the image. + image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): + Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`. + image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): + Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to + `True`. + do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`): + Whether to convert the image to RGB. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: Use the channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + + """ + do_resize = do_resize if do_resize is not None else self.do_resize + size = size if size is not None else self.size + resample = resample if resample is not None else self.resample + do_rescale = do_rescale if do_rescale is not None else self.do_rescale + rescale_factor = ( + rescale_factor if rescale_factor is not None else self.rescale_factor + ) + do_normalize = do_normalize if do_normalize is not None else self.do_normalize + image_mean = image_mean if image_mean is not None else self.image_mean + image_std = image_std if image_std is not None else self.image_std + do_convert_rgb = ( + do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb + ) + + if images is not None: + images = make_batched_images(images) + if videos is not None: + videos = make_batched_videos(videos) + + if images is not None and not valid_images(images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + + validate_preprocess_arguments( + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + do_resize=do_resize, + size=size, + resample=resample, + ) + + if images is not None: + pixel_values, vision_grid_thws = [], [] + for image in images: + patches, image_grid_thw = self._preprocess( + image, + do_resize=do_resize, + resample=resample, + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + data_format=data_format, + do_convert_rgb=do_convert_rgb, + input_data_format=input_data_format, + ) + pixel_values.extend(patches) + vision_grid_thws.append(image_grid_thw) + pixel_values = np.array(pixel_values) + vision_grid_thws = np.array(vision_grid_thws) + data = {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws} + + if videos is not None: + pixel_values, vision_grid_thws = [], [] + for images in videos: + patches, video_grid_thw = self._preprocess( + images, + do_resize=do_resize, + resample=resample, + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + data_format=data_format, + do_convert_rgb=do_convert_rgb, + input_data_format=input_data_format, + ) + pixel_values.extend(patches) + vision_grid_thws.append(video_grid_thw) + pixel_values = np.array(pixel_values) + vision_grid_thws = np.array(vision_grid_thws) + data = { + "pixel_values_videos": pixel_values, + "video_grid_thw": vision_grid_thws, + } + + return BatchFeature(data=data, tensor_type=return_tensors) diff --git a/PaddleOCR-VL-0.9B/inference.yml b/PaddleOCR-VL-0.9B/inference.yml new file mode 100644 index 0000000000000000000000000000000000000000..65349b5c7425e7cda4d54b636b8f74b833b2dccc --- /dev/null +++ b/PaddleOCR-VL-0.9B/inference.yml @@ -0,0 +1,2 @@ +Global: + model_name: PaddleOCR-VL-0.9B \ No newline at end of file diff --git a/PaddleOCR-VL-0.9B/model.safetensors b/PaddleOCR-VL-0.9B/model.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..f3dd703d2af3f0a7f70ca531a018625c8d7f2410 --- /dev/null +++ b/PaddleOCR-VL-0.9B/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3085f1042e184f68f8a412aa0f64f2c4b8562989598bbfba326aaa11fc685de8 +size 1917255968 diff --git a/PaddleOCR-VL-0.9B/modeling_paddleocr_vl.py b/PaddleOCR-VL-0.9B/modeling_paddleocr_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..92fedc57a2435b8bc01c96640de3d66be26cba47 --- /dev/null +++ b/PaddleOCR-VL-0.9B/modeling_paddleocr_vl.py @@ -0,0 +1,2674 @@ +# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. +# +# 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. + +import math +import warnings +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import CrossEntropyLoss +from torch.nn.init import _calculate_fan_in_and_fan_out +from transformers.activations import ACT2FN, GELUActivation +from transformers.cache_utils import ( + Cache, + DynamicCache, + SlidingWindowCache, + StaticCache, +) +from transformers.generation import GenerationMixin +from transformers.integrations import use_kernel_forward_from_hub +from transformers.modeling_attn_mask_utils import AttentionMaskConverter +from transformers.modeling_layers import GradientCheckpointingLayer +from transformers.modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPast, + BaseModelOutputWithPooling, + CausalLMOutputWithPast, +) +from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from transformers.modeling_utils import ( + ALL_ATTENTION_FUNCTIONS, + PreTrainedModel, + sdpa_attention_forward, +) +from transformers.processing_utils import Unpack +from transformers.utils import ( + ModelOutput, + TransformersKwargs, + auto_docstring, + can_return_tuple, + is_flash_attn_2_available, + torch_int, +) +from transformers.utils.generic import check_model_inputs + +if is_flash_attn_2_available(): + from flash_attn import flash_attn_varlen_func + from flash_attn.layers.rotary import apply_rotary_emb +else: + flash_attn_varlen_func = None + apply_rotary_emb = None + +from .configuration_paddleocr_vl import PaddleOCRVisionConfig, PaddleOCRVLConfig + + +class RotaryEmbedding(nn.Module): + def __init__(self, config: PaddleOCRVLConfig, device=None): + super().__init__() + self.rope_kwargs = {} + + # BC: "rope_type" was originally "type" + if config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get( + "rope_type", config.rope_scaling.get("type") + ) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get( + "rope_type", config.rope_scaling.get("type") + ) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + def _dynamic_frequency_update(self, position_ids, device): + """ + dynamic RoPE layers should recompute `inv_freq` in the following situations: + 1 - growing beyond the cached sequence length (allow scaling) + 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) + """ + seq_len = torch.max(position_ids) + 1 + if seq_len > self.max_seq_len_cached: + inv_freq, self.attention_scaling = self.rope_init_fn( + self.config, device, seq_len=seq_len, **self.rope_kwargs + ) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.max_seq_len_cached = seq_len + + if ( + seq_len < self.original_max_seq_len + and self.max_seq_len_cached > self.original_max_seq_len + ): + self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) + self.max_seq_len_cached = self.original_max_seq_len + + @torch.no_grad() + def forward(self, x, position_ids): + if "dynamic" in self.rope_type: + self._dynamic_frequency_update(position_ids, device=x.device) + + inv_freq_expanded = ( + self.inv_freq[None, None, :, None] + .float() + .expand(3, position_ids.shape[1], -1, 1) + ) + position_ids_expanded = position_ids[ + :, :, None, : + ].float() # shape (3, bs, 1, positions) + # Force float32 (see https://github.com/huggingface/transformers/pull/29285) + device_type = x.device.type + device_type = ( + device_type + if isinstance(device_type, str) and device_type != "mps" + else "cpu" + ) + with torch.autocast(device_type=device_type, enabled=False): + freqs = ( + inv_freq_expanded.float() @ position_ids_expanded.float() + ).transpose(2, 3) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + + # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention + cos = cos * self.attention_scaling + sin = sin * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + def rope_init(self): + inv_freq, self.attention_scaling = self.rope_init_fn( + self.config, device=None, **self.rope_kwargs + ) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + +class Ernie4_5RotaryEmbedding(nn.Module): + def __init__(self, config: PaddleOCRVLConfig, device=None): + super().__init__() + # BC: "rope_type" was originally "type" + if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict): + self.rope_type = config.rope_scaling.get( + "rope_type", config.rope_scaling.get("type") + ) + else: + self.rope_type = "default" + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = ( + self.inv_freq[None, :, None] + .float() + .expand(position_ids.shape[0], -1, 1) + .to(x.device) + ) + position_ids_expanded = position_ids[:, None, :].float() + device_type = ( + x.device.type + if isinstance(x.device.type, str) and x.device.type != "mps" + else "cpu" + ) + with torch.autocast(device_type=device_type, enabled=False): # Force float32 + freqs = ( + inv_freq_expanded.float() @ position_ids_expanded.float() + ).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + # keeping it in full precision + return cos, sin + + +class Ernie4_5MLP(nn.Module): + def __init__(self, config: PaddleOCRVLConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + + self.gate_proj = nn.Linear( + self.hidden_size, self.intermediate_size, bias=config.use_bias + ) + self.up_proj = nn.Linear( + self.hidden_size, self.intermediate_size, bias=config.use_bias + ) + self.down_proj = nn.Linear( + self.intermediate_size, self.hidden_size, bias=config.use_bias + ) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand( + batch, num_key_value_heads, n_rep, slen, head_dim + ) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward_ernie( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: Optional[torch.Tensor], + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights = attn_weights + causal_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to( + query.dtype + ) + attn_weights = nn.functional.dropout( + attn_weights, p=dropout, training=module.training + ) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`, *optional*): + Deprecated and unused. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + # glm rope style (with full dim) and full precision + original_dtype = q.dtype + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + + # Interleave them instead of usual shape + cos = cos[..., : cos.shape[-1] // 2].repeat_interleave(2, dim=-1) + sin = sin[..., : sin.shape[-1] // 2].repeat_interleave(2, dim=-1) + + q_embed = (q.float() * cos) + (rotate_half(q).float() * sin) + k_embed = (k.float() * cos) + (rotate_half(k).float() * sin) + + return q_embed.to(original_dtype), k_embed.to(original_dtype) + + +def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1): + """Applies Rotary Position Embedding with Multimodal Sections to the query and key tensors (https://qwenlm.github.io/blog/qwen2-vl/). + + Explanation: + Multimodal 3D rotary position embedding is an extension to 1D rotary position embedding. The input embedding + sequence contains vision (images / videos) embedding and text embedding or just contains text embedding. For + vision embedding part, we apply rotary position embedding on temporal, height and width dimension separately. + Here we split the channel dimension to 3 chunks for the temporal, height and width rotary position embedding. + For text embedding part, we just apply 1D rotary position embedding. The three rotary position index (temporal, + height and width) of text embedding is always the same, so the text embedding rotary position embedding has no + difference with modern LLMs. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + mrope_section(`List(int)`): + Multimodal rope section is for channel dimension of temporal, height and width in rope calculation. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + mrope_section = mrope_section * 2 + cos = torch.cat( + [m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1 + ).unsqueeze(unsqueeze_dim) + sin = torch.cat( + [m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1 + ).unsqueeze(unsqueeze_dim) + + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class Ernie4_5Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: PaddleOCRVLConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr( + config, "head_dim", config.hidden_size // config.num_attention_heads + ) + self.num_key_value_groups = ( + config.num_attention_heads // config.num_key_value_heads + ) + self.scaling = self.head_dim**-0.5 + self.rope_scaling = config.rope_scaling + self.attention_dropout = 0.0 + self.is_causal = True + self.q_proj = nn.Linear( + config.hidden_size, + config.num_attention_heads * self.head_dim, + bias=config.use_bias, + ) + self.k_proj = nn.Linear( + config.hidden_size, + config.num_key_value_heads * self.head_dim, + bias=config.use_bias, + ) + self.v_proj = nn.Linear( + config.hidden_size, + config.num_key_value_heads * self.head_dim, + bias=config.use_bias, + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, + config.hidden_size, + bias=config.use_bias, + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: Optional[torch.Tensor], + past_key_value: Optional[Cache] = None, + cache_position: Optional[torch.LongTensor] = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + if "position_ids" in kwargs and kwargs["position_ids"] is not None: + position_ids = kwargs["position_ids"] + if position_ids.dim() == 3 and position_ids.shape[0] > 1: + kwargs["position_ids"] = position_ids[0:1] + + cos, sin = position_embeddings + query_states, key_states = apply_multimodal_rotary_pos_emb( + query_states, key_states, cos, sin, self.rope_scaling["mrope_section"] + ) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update( + key_states, value_states, self.layer_idx, cache_kwargs + ) + + attention_interface: Callable = eager_attention_forward_ernie + if self.config._attn_implementation != "eager": + attention_interface = ALL_ATTENTION_FUNCTIONS[ + self.config._attn_implementation + ] + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +@use_kernel_forward_from_hub("RMSNorm") +class Ernie4_5RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + Ernie4_5RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class Ernie4_5DecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PaddleOCRVLConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = Ernie4_5Attention(config=config, layer_idx=layer_idx) + + self.mlp = Ernie4_5MLP(config) + self.input_layernorm = Ernie4_5RMSNorm( + config.hidden_size, eps=config.rms_norm_eps + ) + self.post_attention_layernorm = Ernie4_5RMSNorm( + config.hidden_size, eps=config.rms_norm_eps + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[ + tuple[torch.Tensor, torch.Tensor] + ] = None, # necessary, but kept here for BC + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor]: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class Ernie4_5PreTrainedModel(PreTrainedModel): + config: PaddleOCRVLConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Ernie4_5DecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": Ernie4_5DecoderLayer, + "attentions": Ernie4_5Attention, + } + + +@auto_docstring +class Ernie4_5Model(Ernie4_5PreTrainedModel): + def __init__(self, config: PaddleOCRVLConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding( + config.vocab_size, config.hidden_size, self.padding_idx + ) + self.layers = nn.ModuleList( + [ + Ernie4_5DecoderLayer(config, layer_idx) + for layer_idx in range(config.num_hidden_layers) + ] + ) + self.norm = Ernie4_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = RotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @check_model_inputs + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + cache_position: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError( + "You must specify exactly one of input_ids or inputs_embeds" + ) + + if inputs_embeds is None: + inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache() + + if cache_position is None: + past_seen_tokens = ( + past_key_values.get_seq_length() if past_key_values is not None else 0 + ) + cache_position: torch.Tensor = torch.arange( + past_seen_tokens, + past_seen_tokens + inputs_embeds.shape[1], + device=inputs_embeds.device, + ) + + if position_ids is None: + position_ids = cache_position.view(1, 1, -1).expand( + 3, inputs_embeds.shape[0], -1 + ) + elif position_ids.dim() == 2: + position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) + + causal_mask = self._update_causal_mask( + attention_mask, + inputs_embeds, + cache_position, + past_key_values, + output_attentions, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_value=past_key_values, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool = False, + ): + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and past_key_values is not None: + is_padding_right = ( + attention_mask[:, -1].sum().item() != input_tensor.size()[0] + ) + if is_padding_right: + raise ValueError + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = ( + past_key_values.get_seq_length() if past_key_values is not None else 0 + ) + using_static_cache = isinstance(past_key_values, StaticCache) + using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if ( + self.config._attn_implementation == "sdpa" + and not (using_static_cache or using_sliding_window_cache) + and not output_attentions + ): + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + sliding_window=self.config.sliding_window, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + sequence_length = input_tensor.shape[1] + # SlidingWindowCache or StaticCache + if using_sliding_window_cache or using_static_cache: + target_length = past_key_values.get_max_cache_shape() + # DynamicCache or no cache + else: + target_length = ( + attention_mask.shape[-1] + if isinstance(attention_mask, torch.Tensor) + else past_seen_tokens + sequence_length + 1 + ) + + # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). + causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + cache_position=cache_position, + batch_size=input_tensor.shape[0], + config=self.config, + past_key_values=past_key_values, + ) + + if ( + self.config._attn_implementation == "sdpa" + and attention_mask is not None + and attention_mask.device.type in ["cuda", "xpu"] + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended( + causal_mask, min_dtype + ) + + return causal_mask + + @staticmethod + def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + cache_position: torch.Tensor, + batch_size: int, + config: PaddleOCRVLConfig, + past_key_values: Cache, + ): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to place the 4D attention mask on. + cache_position (`torch.Tensor`): + Indices depicting the position of the input sequence tokens in the sequence. + batch_size (`torch.Tensor`): + Batch size. + config (`PaddleOCRVLConfig`): + The model's configuration class + past_key_values (`Cache`): + The cache class that is being used currently to generate + """ + if attention_mask is not None and attention_mask.dim() == 4: + # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. + causal_mask = attention_mask + else: + min_dtype = torch.finfo(dtype).min + causal_mask = torch.full( + (sequence_length, target_length), + fill_value=min_dtype, + dtype=dtype, + device=device, + ) + diagonal_attend_mask = torch.arange( + target_length, device=device + ) > cache_position.reshape(-1, 1) + if config.sliding_window is not None: + # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also + # the check is needed to verify is current checkpoint was trained with sliding window or not + if ( + not isinstance(past_key_values, SlidingWindowCache) + or sequence_length > target_length + ): + sliding_attend_mask = torch.arange( + target_length, device=device + ) <= (cache_position.reshape(-1, 1) - config.sliding_window) + diagonal_attend_mask.bitwise_or_(sliding_attend_mask) + causal_mask *= diagonal_attend_mask + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = ( + causal_mask.clone() + ) # copy to contiguous memory for in-place edit + if attention_mask.shape[-1] > target_length: + attention_mask = attention_mask[:, :target_length] + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[ + :, None, None, : + ].to(causal_mask.device) + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[ + :, :, :, :mask_length + ].masked_fill(padding_mask, min_dtype) + return causal_mask + + +class Ernie4_5ForCausalLM(Ernie4_5PreTrainedModel, GenerationMixin): + _tied_weights_keys = ["lm_head.weight"] + _tp_plan = {"lm_head": "colwise_rep"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = Ernie4_5Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @can_return_tuple + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + logits_to_keep: Union[int, torch.Tensor] = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + """ + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + cache_position=cache_position, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = ( + slice(-logits_to_keep, None) + if isinstance(logits_to_keep, int) + else logits_to_keep + ) + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, + labels=labels, + vocab_size=self.config.vocab_size, + **kwargs, + ) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +def _trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2, + ) + + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + + +def trunc_normal_tf_( + tensor: torch.Tensor, + mean: float = 0.0, + std: float = 1.0, + a: float = -2.0, + b: float = 2.0, +) -> torch.Tensor: + """Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \\leq \text{mean} \\leq b`. + + NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the + bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 + and the result is subsequently scaled and shifted by the mean and std args. + + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + """ + with torch.no_grad(): + _trunc_normal_(tensor, 0, 1.0, a, b) + tensor.mul_(std).add_(mean) + + +def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"): + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + if mode == "fan_in": + denom = fan_in + elif mode == "fan_out": + denom = fan_out + elif mode == "fan_avg": + denom = (fan_in + fan_out) / 2 + + variance = scale / denom + + if distribution == "truncated_normal": + # constant is stddev of standard normal truncated to (-2, 2) + trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978) + elif distribution == "normal": + with torch.no_grad(): + tensor.normal_(std=math.sqrt(variance)) + elif distribution == "uniform": + bound = math.sqrt(3 * variance) + with torch.no_grad(): + tensor.uniform_(-bound, bound) + else: + raise ValueError(f"invalid distribution {distribution}") + + +def lecun_normal_(tensor): + variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal") + + +def default_flax_embed_init(tensor): + variance_scaling_(tensor, mode="fan_in", distribution="normal") + + +class Projector(nn.Module): + + def __init__(self, text_config: PaddleOCRVLConfig, vision_config: PaddleOCRVisionConfig): + super().__init__() + self.text_config = text_config + self.vision_config = vision_config + self.merge_kernel_size = (2, 2) + + self.hidden_size = ( + self.vision_config.hidden_size + * self.merge_kernel_size[0] + * self.merge_kernel_size[1] + ) + + self.pre_norm = torch.nn.LayerNorm(self.vision_config.hidden_size, eps=1e-05) + self.linear_1 = nn.Linear(self.hidden_size, self.hidden_size, bias=True) + self.act = GELUActivation() + self.linear_2 = nn.Linear( + self.hidden_size, self.text_config.hidden_size, bias=True + ) + + def forward( + self, image_features: torch.Tensor, image_grid_thw: List[Tuple[int, int, int]] + ) -> torch.Tensor: + m1, m2 = self.merge_kernel_size + if isinstance(image_features, (list, tuple)): + processed_features = list() + for image_feature, image_grid in zip(image_features, image_grid_thw): + image_feature = self.pre_norm(image_feature) + t, h, w = image_grid + from einops import rearrange + + image_feature = rearrange( + image_feature, + "(t h p1 w p2) d -> (t h w) (p1 p2 d)", + t=t, + h=h // m1, + p1=m1, + w=w // m2, + p2=m2, + ) + hidden_states = self.linear_1(image_feature) + hidden_states = self.act(hidden_states) + hidden_states = self.linear_2(hidden_states) + processed_features.append(hidden_states) + + return processed_features + + dims = image_features.shape[:-1] + dim = image_features.shape[-1] + image_features = image_features.view(np.prod(dims), dim) + hidden_states = self.pre_norm(image_features).view(-1, self.hidden_size) + hidden_states = self.linear_1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.linear_2(hidden_states) + + return hidden_states.view(*dims, -1) + + +class SiglipVisionEmbeddings(nn.Module): + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + + self.num_patches = (self.image_size // self.patch_size) ** 2 + self.num_positions = self.num_patches + self.cache_position_embedding = dict() + self.cache_position_count = dict() + self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) + self.packing_position_embedding = nn.Embedding(32768, self.embed_dim) + + self.register_buffer( + "position_ids", + torch.arange(self.num_positions).expand((1, -1)), + persistent=False, + ) + + def interpolate_pos_encoding( + self, + embeddings: torch.Tensor, + height: int, + width: int, + is_after_patchify: bool = False, + ) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution + images. This method is also adapted to support torch.jit tracing and no class embeddings. + + Adapted from: + - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and + - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 + """ + num_positions = self.position_embedding.weight.shape[0] + + patch_pos_embed = self.position_embedding.weight.unsqueeze(0) + + dim = embeddings.shape[-1] + + if is_after_patchify: + new_height = height + new_width = width + else: + new_height = height // self.patch_size + new_width = width // self.patch_size + + sqrt_num_positions = torch_int(num_positions**0.5) + patch_pos_embed = patch_pos_embed.reshape( + 1, sqrt_num_positions, sqrt_num_positions, dim + ) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + size=(new_height, new_width), + mode="bilinear", + align_corners=False, + ) + + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return patch_pos_embed + + @staticmethod + def flatten_list(image_grid_thw): + tmp_image_grid_thw = list() + for image_grid in image_grid_thw: + if isinstance(image_grid, list): + tmp_image_grid_thw.extend(image_grid) + else: + tmp_image_grid_thw.append(image_grid) + return tmp_image_grid_thw + + def fetch_position_embedding_lfu_cache(self, embeddings, h, w, max_cache=20): + grid = (h, w) + if grid in self.cache_position_embedding: + self.cache_position_count[grid] += 1 + return self.cache_position_embedding[grid] + + if len(self.cache_position_embedding) >= max_cache: + min_hit_grid = min( + self.cache_position_count, key=self.cache_position_count.get + ) + self.cache_position_count.pop(min_hit_grid) + self.cache_position_embedding.pop(min_hit_grid) + + position_embedding = self.interpolate_pos_encoding(embeddings, h, w, True) + self.cache_position_count[grid] = 1 + self.cache_position_embedding[grid] = position_embedding + return position_embedding + + def forward( + self, + pixel_values: torch.FloatTensor, + position_ids: Optional[torch.Tensor] = None, + image_grid_thw: Optional[ + List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]] + ] = None, + interpolate_pos_encoding=False, + ) -> torch.Tensor: + if pixel_values.dim() == 5: + assert position_ids is not None + from einops import rearrange + + batch_size, squence_len, channel, height, width = pixel_values.shape + target_dtype = self.patch_embedding.weight.dtype + pixel_values = rearrange(pixel_values, "b l c h w -> (b l) c h w") + patch_embeds = self.patch_embedding( + pixel_values.to(dtype=target_dtype) + ) # shape = [*, width, grid, grid] + embeddings = patch_embeds.flatten(-2).squeeze(-1) + embeddings = rearrange( + embeddings, "(b l) d -> b l d", b=batch_size, l=squence_len + ) + + # todo: not dubug + if interpolate_pos_encoding and image_grid_thw is not None: + flatten_image_grid_thw = self.flatten_list(image_grid_thw) + assert batch_size == 1 + start = 0 + image_embedding_list = list() + assert ( + sum([np.prod(x) for x in flatten_image_grid_thw]) + == embeddings.shape[1] + ), (flatten_image_grid_thw, embeddings.shape) + embeddings = embeddings.squeeze(0) + tmp_embeddings = list() + for image_grid in image_grid_thw: + t, h, w = image_grid + end = start + t * h * w + image_embeddings = embeddings[start:end, :] + position_embedding = ( + self.interpolate_pos_encoding(image_embeddings, h, w, True) + .squeeze(0) + .repeat(t, 1) + ) + image_embeddings = image_embeddings + position_embedding + tmp_embeddings.append(image_embeddings) + start = end + embeddings = torch.concat(tmp_embeddings, dim=0).unsqueeze(0) + else: + embeddings = embeddings + self.packing_position_embedding(position_ids) + return embeddings + else: + raise NotImplementedError(str(pixel_values.shape)) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: Optional[torch.Tensor], + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to( + query.dtype + ) + attn_weights = nn.functional.dropout( + attn_weights, p=dropout, training=module.training + ) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class SiglipAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + self.is_causal = False + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = False, + cu_seqlens: Optional[List[torch.Tensor]] = None, + rope_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Input shape: Batch x Time x Channel""" + + use_flash_attn = ( + cu_seqlens is not None + ) and self.config._attn_implementation == "flash_attention_2" + + batch_size, seq_length, embed_dim = hidden_states.shape + + queries = self.q_proj(hidden_states) + keys = self.k_proj(hidden_states) + values = self.v_proj(hidden_states) + + if rope_emb is None: + queries = queries.view( + batch_size, seq_length, self.num_heads, self.head_dim + ).transpose(1, 2) + keys = keys.view( + batch_size, seq_length, self.num_heads, self.head_dim + ).transpose(1, 2) + values = values.view( + batch_size, seq_length, self.num_heads, self.head_dim + ).transpose(1, 2) + else: + assert cu_seqlens is not None, "Rope support flash attn only." + cos, sin = rope_emb + queries = queries.view( + batch_size, seq_length, self.num_heads, self.head_dim + ) + keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim) + if use_flash_attn: + queries, keys = apply_rotary_pos_emb_flashatt(queries, keys, cos, sin) + else: + queries, keys = apply_rotary_pos_emb_vision(queries, keys, cos, sin) + queries = queries.transpose(1, 2) + keys = keys.transpose(1, 2) + values = values.view( + batch_size, seq_length, self.num_heads, self.head_dim + ).transpose(1, 2) + + if not use_flash_attn: + attention_interface: Callable = eager_attention_forward + if self.config._attn_implementation != "eager": + if self.config._attn_implementation == "sdpa" and output_attentions: + warnings.warn( + "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to " + 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + elif self.config._attn_implementation == "sdpa": + attention_interface = sdpa_attention_forward + + attn_output, attn_weights = attention_interface( + self, + queries, + keys, + values, + attention_mask, + is_causal=self.is_causal, + scaling=self.scale, + dropout=0.0 if not self.training else self.dropout, + ) + attn_output = attn_output.reshape( + batch_size, seq_length, embed_dim + ).contiguous() + else: + assert batch_size == 1, hidden_states.shape + queries = queries.transpose(1, 2).squeeze(0) + keys = keys.transpose(1, 2).squeeze(0) + values = values.transpose(1, 2).squeeze(0) + + max_seqlen_q = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item() + max_seqlen_k = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item() + assert ( + cu_seqlens[-1].item() + == queries.shape[0] + == keys.shape[0] + == values.shape[0] + ), (cu_seqlens, queries.shape, keys.shape, values.shape) + + attn_output = flash_attn_varlen_func( + queries, + keys, + values, + cu_seqlens, + cu_seqlens, + max_seqlen_q, + max_seqlen_k, + causal=False, + softmax_scale=self.scale, + ) + attn_output = attn_output.flatten(-2).unsqueeze(0) + attn_weights = None + + attn_output = self.out_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights + + +# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip +class SiglipMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class SiglipEncoderLayer(nn.Module): + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.self_attn = SiglipAttention(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = SiglipMLP(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + output_attentions: Optional[bool] = False, + cu_seqlens: Optional[List[torch.Tensor]] = None, + rope_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + ) -> Tuple[torch.FloatTensor]: + """ + Args: + hidden_states (`torch.FloatTensor`): + Input to the layer of shape `(batch, seq_len, embed_dim)`. + attention_mask (`torch.FloatTensor`): + Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*, defaults to `False`): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + cu_seqlens=cu_seqlens, + rope_emb=rope_emb, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class SiglipPreTrainedModel(PreTrainedModel): + config_class = PaddleOCRVLConfig + base_model_prefix = "siglip" + supports_gradient_checkpointing = True + + _no_split_modules = [ + "SiglipTextEmbeddings", + "SiglipEncoderLayer", + "SiglipVisionEmbeddings", + "SiglipMultiheadAttentionPoolingHead", + ] + _supports_flash_attn_2 = True + _supports_sdpa = True + + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, SiglipVisionEmbeddings): + width = ( + self.config.vision_config.hidden_size + if isinstance(self.config, PaddleOCRVLConfig) + else self.config.hidden_size + ) + nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width)) + elif isinstance(module, nn.Embedding): + default_flax_embed_init(module.weight) + elif isinstance(module, SiglipAttention): + nn.init.xavier_uniform_(module.q_proj.weight) + nn.init.xavier_uniform_(module.k_proj.weight) + nn.init.xavier_uniform_(module.v_proj.weight) + nn.init.xavier_uniform_(module.out_proj.weight) + nn.init.zeros_(module.q_proj.bias) + nn.init.zeros_(module.k_proj.bias) + nn.init.zeros_(module.v_proj.bias) + nn.init.zeros_(module.out_proj.bias) + elif isinstance(module, SiglipMLP): + nn.init.xavier_uniform_(module.fc1.weight) + nn.init.xavier_uniform_(module.fc2.weight) + nn.init.normal_(module.fc1.bias, std=1e-6) + nn.init.normal_(module.fc2.bias, std=1e-6) + elif isinstance(module, SiglipMultiheadAttentionPoolingHead): + nn.init.xavier_uniform_(module.probe.data) + nn.init.xavier_uniform_(module.attention.in_proj_weight.data) + nn.init.zeros_(module.attention.in_proj_bias.data) + elif isinstance(module, (nn.Linear, nn.Conv2d)): + lecun_normal_(module.weight) + if module.bias is not None: + nn.init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + +# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoder with AltCLIP->Siglip +class SiglipEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`SiglipEncoderLayer`]. + + Args: + config: PaddleOCRVLConfig + """ + + def __init__(self, config: PaddleOCRVLConfig): + super().__init__() + self.config = config + embed_dim = config.hidden_size + num_heads = config.num_attention_heads + head_dim = embed_dim // num_heads + self.layers = nn.ModuleList( + [SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.rotary_pos_emb = SigLIPRotaryEmbedding(head_dim // 2) + self.gradient_checkpointing = False + + @staticmethod + def flatten_list(image_grid_thw): + tmp_image_grid_thw = list() + for image_grid in image_grid_thw: + if isinstance(image_grid, list): + tmp_image_grid_thw.extend(image_grid) + else: + tmp_image_grid_thw.append(image_grid) + return tmp_image_grid_thw + + def build_window_index(self, image_grid, window_size, device): + from einops import rearrange + + window_indices = list() + pad_values = -100 + start_window_index = 0 + cu_seqlens_within_windows = list() + + for t, h, w in image_grid: + window_index = torch.arange(t * h * w, device=device).reshape(t, h, w) + pad_h = (-h) % window_size + pad_w = (-w) % window_size + assert pad_h >= 0 and pad_w >= 0, (pad_h, pad_w) + window_index = F.pad(window_index, (0, pad_w, 0, pad_h), value=pad_values) + window_index = rearrange( + window_index, + "t (h p1) (w p2) -> t (h w) (p1 p2)", + p1=window_size, + p2=window_size, + ) + window_seqlens = (window_index != pad_values).long().sum(-1).reshape(-1) + window_index = window_index.reshape(-1) + window_index = window_index[window_index != pad_values] + window_indices.append(window_index + start_window_index) + cu_seqlens_within_windows.append( + window_seqlens.cumsum(0) + start_window_index + ) + start_window_index += t * h * w + window_indices = torch.concat(window_indices, dim=0) + cu_seqlens_within_windows = torch.concat(cu_seqlens_within_windows, dim=0) + cu_seqlens_within_windows = F.pad( + cu_seqlens_within_windows, (1, 0), value=0 + ).to(torch.int32) + return window_indices, cu_seqlens_within_windows + + # Ignore copy + # @can_return_tuple + def forward( + self, + inputs_embeds, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + cu_seqlens: Optional[List[torch.Tensor]] = None, + image_grid_thw: Optional[ + List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]] + ] = None, + height_position_ids: Optional[torch.Tensor] = None, + width_position_ids: Optional[torch.Tensor] = None, + use_rope: Optional[bool] = False, + window_size: Optional[bool] = -1, + vision_or_text: str = "vision", + ) -> BaseModelOutput: + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + + vision_or_text = "vision" + assert vision_or_text in ["vision", "text"] + use_window_attn = window_size > 0 and vision_or_text == "vision" + use_rope = (use_rope is True) and (vision_or_text == "vision") + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + device = inputs_embeds.device + hidden_states = inputs_embeds + attention_mask = ( + attention_mask.to(inputs_embeds.dtype) + if attention_mask is not None + else None + ) + if use_rope is True: + flatten_image_grid_thw = self.flatten_list(image_grid_thw) + assert ( + sum([np.prod(x) for x in flatten_image_grid_thw]) + == hidden_states.shape[1] + ), (flatten_image_grid_thw, hidden_states.shape) + + if width_position_ids is None or height_position_ids is None: + split_hids = list() + split_wids = list() + for t, h, w in flatten_image_grid_thw: + image_pids = torch.arange(t * h * w, device=device) % (h * w) + sample_hids = image_pids // w + sample_wids = image_pids % w + split_hids.append(sample_hids) + split_wids.append(sample_wids) + width_position_ids = torch.concat(split_wids, dim=0) + height_position_ids = torch.concat(split_hids, dim=0) + + window_indices, cu_seqlens_within_windows = None, None + + if use_window_attn: + window_indices, cu_seqlens_within_windows = self.build_window_index( + flatten_image_grid_thw, window_size, device + ) + reversed_window_indices = window_indices.argsort() + height_position_ids = height_position_ids[window_indices] + width_position_ids = width_position_ids[window_indices] + + pids = torch.stack([height_position_ids, width_position_ids], dim=-1) + max_grid_size = pids.max() + 1 + rope_emb_max_grid = self.rotary_pos_emb(max_grid_size) + rope_emb = rope_emb_max_grid[pids].flatten(1) + rope_emb = rope_emb.repeat(1, 2) + rope_emb = (rope_emb.cos(), rope_emb.sin()) + else: + + rope_emb = None + window_indices, cu_seqlens_within_windows = None, None + + if use_window_attn: + flatten_image_grid_thw = self.flatten_list(image_grid_thw) + assert ( + sum([np.prod(x) for x in flatten_image_grid_thw]) + == hidden_states.shape[1] + ), (flatten_image_grid_thw, hidden_states.shape) + + window_indices, cu_seqlens_within_windows = self.build_window_index( + flatten_image_grid_thw, window_size, device + ) + reversed_window_indices = window_indices.argsort() + + if use_window_attn: + assert cu_seqlens_within_windows is not None + attn_cu_seqlens = cu_seqlens_within_windows + hidden_states = hidden_states[:, window_indices, :] + else: + attn_cu_seqlens = cu_seqlens + + for encoder_layer in self.layers: + if output_hidden_states: + encoder_states = encoder_states + ( + (hidden_states[:, reversed_window_indices, :],) + if use_window_attn + else (hidden_states,) + ) + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + encoder_layer.__call__, + hidden_states, + attention_mask, + output_attentions, + attn_cu_seqlens, + rope_emb, + ) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + output_attentions=output_attentions, + cu_seqlens=attn_cu_seqlens, + rope_emb=rope_emb, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if use_window_attn: + hidden_states = hidden_states[:, reversed_window_indices, :] + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_states, + attentions=all_attentions, + ) + + +class SiglipVisionTransformer(nn.Module): + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + self.config = config + embed_dim = config.hidden_size + + self.embeddings = SiglipVisionEmbeddings(config) + self.encoder = SiglipEncoder(config) + self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + self.use_head = ( + True if not hasattr(config, "vision_use_head") else config.vision_use_head + ) + if self.use_head: + self.head = SiglipMultiheadAttentionPoolingHead(config) + + # @can_return_tuple + def forward( + self, + pixel_values, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: Optional[bool] = False, + attention_mask: Optional[torch.Tensor] = None, + sample_indices: Optional[torch.Tensor] = None, + image_indices: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + height_position_ids: Optional[torch.Tensor] = None, + width_position_ids: Optional[torch.Tensor] = None, + cu_seqlens: Optional[List[torch.Tensor]] = None, + padding_mask: Optional[torch.Tensor] = None, + vision_return_embed_list: Optional[bool] = False, + image_grid_thw: Optional[ + List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]] + ] = None, + return_pooler_output: Optional[bool] = True, + use_rope: Optional[bool] = False, + window_size: Optional[bool] = -1, + ) -> BaseModelOutputWithPooling: + r""" + Returns: + + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + hidden_states = self.embeddings( + pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + position_ids=position_ids, + image_grid_thw=image_grid_thw, + ) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + attention_mask=attention_mask, + cu_seqlens=cu_seqlens, + image_grid_thw=image_grid_thw, + use_rope=use_rope, + height_position_ids=height_position_ids, + width_position_ids=width_position_ids, + window_size=window_size, + vision_or_text="vision", + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.post_layernorm(last_hidden_state) + + if return_pooler_output is True: + if sample_indices is not None: + assert self.use_head is True + dim = last_hidden_state.shape[-1] + sample_hidden_state_list = list() + + hidden_state = last_hidden_state.squeeze(0) + sample_index = sample_indices + unique_sample_index = torch.unique(sample_index).sort().values.unbind(0) + unique_sample_index = list(unique_sample_index) + if len(unique_sample_index) > 0 and unique_sample_index[0] == -1: + unique_sample_index = unique_sample_index[1:] + for sample_idx in unique_sample_index: + token_indices = (sample_index == sample_idx).nonzero().flatten() + sample_hidden_state = hidden_state[token_indices] + sample_hidden_state_list.append(sample_hidden_state) + + if not vision_return_embed_list: + max_length = max( + [_state.shape[0] for _state in sample_hidden_state_list] + ) + tmp_sample_hidden_state_list = list() + padding_mask = list() + for idx, _state in enumerate(sample_hidden_state_list): + padding_length = max_length - _state.shape[0] + mask = _state.new_zeros(size=(max_length,), dtype=torch.int64) + mask[-padding_length:] = 1 + padding_mask.append(mask) + padding = _state.new_zeros(size=(padding_length, dim)) + new_state = torch.concat([_state, padding], dim=0) + tmp_sample_hidden_state_list.append(new_state) + sample_hidden_state = torch.stack( + tmp_sample_hidden_state_list, dim=0 + ) + padding_mask = ( + torch.stack(padding_mask, dim=0) + .float() + .to(last_hidden_state.dtype) + ) + pooler_output = self.head( + sample_hidden_state, key_padding_mask=padding_mask + ) + else: + pooler_output = list() + for state in sample_hidden_state_list: + sample_pooler_output = self.head(state.unsqueeze(0)) + pooler_output.append(sample_pooler_output) + pooler_output = torch.concat(pooler_output, dim=0) + sample_hidden_state = sample_hidden_state_list + + return BaseModelOutputWithPooling( + last_hidden_state=sample_hidden_state, + pooler_output=pooler_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + else: + pooler_output = self.head(last_hidden_state) if self.use_head else None + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooler_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + sample_hidden_state = list() + assert cu_seqlens is not None + for i in range(cu_seqlens.shape[0] - 1): + start = cu_seqlens[i] + end = cu_seqlens[i + 1] + tensor = last_hidden_state[:, start:end, :].squeeze(0) + sample_hidden_state.append(tensor) + + return BaseModelOutputWithPooling( + last_hidden_state=sample_hidden_state, + pooler_output=None, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +class SiglipMultiheadAttentionPoolingHead(nn.Module): + """Multihead Attention Pooling.""" + + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + + self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + self.attention = torch.nn.MultiheadAttention( + config.hidden_size, config.num_attention_heads, batch_first=True + ) + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.mlp = SiglipMLP(config) + + def forward(self, hidden_state, key_padding_mask=None): + batch_size = hidden_state.shape[0] + probe = self.probe.repeat(batch_size, 1, 1) + + hidden_state = self.attention( + probe, hidden_state, hidden_state, key_padding_mask=key_padding_mask + )[0] + + residual = hidden_state + hidden_state = self.layernorm(hidden_state) + hidden_state = residual + self.mlp(hidden_state) + + return hidden_state[:, 0] + + +class SiglipVisionModel(SiglipPreTrainedModel): + config_class = PaddleOCRVisionConfig + main_input_name = "pixel_values" + + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__(config) + + self.vision_model = SiglipVisionTransformer(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.vision_model.embeddings.patch_embedding + + # @can_return_tuple + def forward( + self, + pixel_values, + sample_indices: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + interpolate_pos_encoding: bool = False, + position_ids: Optional[torch.Tensor] = None, + vision_return_embed_list: Optional[bool] = False, + image_grid_thw: Optional[ + List[Union[Tuple[int, int, int], List[Tuple[int, int, int]]]] + ] = None, + cu_seqlens: Optional[List[torch.Tensor]] = None, + return_pooler_output: Optional[bool] = True, + use_rope: Optional[bool] = False, + window_size: Optional[bool] = -1, + ) -> BaseModelOutputWithPooling: + r""" + Returns: + + Examples: + + ```python + >>> from PIL import Image + >>> import requests + >>> from transformers import AutoProcessor, SiglipVisionModel + + >>> model = SiglipVisionModel.from_pretrained("google/siglip-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled features + ```""" + + return self.vision_model( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + interpolate_pos_encoding=interpolate_pos_encoding, + position_ids=position_ids, + vision_return_embed_list=vision_return_embed_list, + image_grid_thw=image_grid_thw, + sample_indices=sample_indices, + cu_seqlens=cu_seqlens, + return_pooler_output=return_pooler_output, + use_rope=use_rope, + window_size=window_size, + ) + + +def apply_rotary_pos_emb_flashatt( + q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor +) -> Tuple[torch.Tensor, torch.Tensor]: + cos = cos.chunk(2, dim=-1)[0].contiguous() + sin = sin.chunk(2, dim=-1)[0].contiguous() + q_embed = apply_rotary_emb(q.float(), cos.float(), sin.float()).type_as(q) + k_embed = apply_rotary_emb(k.float(), cos.float(), sin.float()).type_as(k) + return q_embed, k_embed + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb_vision( + q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor +) -> Tuple[torch.Tensor, torch.Tensor]: + orig_q_dtype = q.dtype + orig_k_dtype = k.dtype + q, k = q.float(), k.float() + cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float() + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + q_embed = q_embed.to(orig_q_dtype) + k_embed = k_embed.to(orig_k_dtype) + return q_embed, k_embed + + +class SigLIPRotaryEmbedding(nn.Module): + def __init__(self, dim: int, theta: float = 10000.0) -> None: + super().__init__() + self.dim = dim + self.theta = theta + self.rope_init() + + def rope_init(self): + inv_freq = 1.0 / ( + self.theta ** (torch.arange(0, self.dim, 2, dtype=torch.float) / self.dim) + ) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + def forward(self, seqlen: int) -> torch.Tensor: + seq = torch.arange( + seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype + ) + freqs = torch.outer(seq, self.inv_freq) + return freqs + + +@dataclass +class PaddleOCRVLCausalLMOutputWithPast(ModelOutput): + """ + Base class for PaddleOCRVL causal language model (or autoregressive) outputs. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape + `(batch_size, num_heads, sequence_length, embed_size_per_head)`) + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + """ + + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + past_key_values: Optional[List[torch.FloatTensor]] = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + rope_deltas: Optional[torch.LongTensor] = None + + +class PaddleOCRVLForConditionalGeneration(Ernie4_5PreTrainedModel, GenerationMixin): + _tied_weights_keys = ["lm_head.weight"] + config_class = PaddleOCRVLConfig + _no_split_modules = ["Ernie4_5_DecoderLayer", "SiglipEncoderLayer"] + + def __init__(self, config): + super().__init__(config) + self.mlp_AR = Projector(config, config.vision_config) + self.visual = SiglipVisionModel(config.vision_config) + self.model = Ernie4_5Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.rope_deltas = None + + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + def get_rope_index( + self, + input_ids: Optional[torch.LongTensor] = None, + image_grid_thw: Optional[torch.LongTensor] = None, + video_grid_thw: Optional[torch.LongTensor] = None, + second_per_grid_ts: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Calculate the 3D rope index based on image and video's temporal, height and width in LLM. + + Explanation: + Each embedding sequence contains vision embedding and text embedding or just contains text embedding. + + For pure text embedding sequence, the rotary position embedding has no difference with modern LLMs. + Examples: + input_ids: [T T T T T], here T is for text. + temporal position_ids: [0, 1, 2, 3, 4] + height position_ids: [0, 1, 2, 3, 4] + width position_ids: [0, 1, 2, 3, 4] + + For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part + and 1D rotary position embedding for text part. + Examples: + Temporal (Time): 3 patches, representing different segments of the video in time. + Height: 2 patches, dividing each frame vertically. + Width: 2 patches, dividing each frame horizontally. + We also have some important parameters: + fps (Frames Per Second): The video's frame rate, set to 1. This means one frame is processed each second. + tokens_per_second: This is a crucial parameter. It dictates how many "time-steps" or "temporal tokens" are conceptually packed into a one-second interval of the video. In this case, we have 25 tokens per second. So each second of the video will be represented with 25 separate time points. It essentially defines the temporal granularity. + temporal_patch_size: The number of frames that compose one temporal patch. Here, it's 2 frames. + interval: The step size for the temporal position IDs, calculated as tokens_per_second * temporal_patch_size / fps. In this case, 25 * 2 / 1 = 50. This means that each temporal patch will be have a difference of 50 in the temporal position IDs. + input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision. + vision temporal position_ids: [0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100] + vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1] + vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] + text temporal position_ids: [101, 102, 103, 104, 105] + text height position_ids: [101, 102, 103, 104, 105] + text width position_ids: [101, 102, 103, 104, 105] + Here we calculate the text start position_ids as the max vision position_ids plus 1. + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*): + The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + Returns: + position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`) + mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`) + """ + spatial_merge_size = self.config.vision_config.spatial_merge_size + image_token_id = self.config.image_token_id + video_token_id = self.config.video_token_id + vision_start_token_id = self.config.vision_start_token_id + mrope_position_deltas = [] + if input_ids is not None and ( + image_grid_thw is not None or video_grid_thw is not None + ): + total_input_ids = input_ids + if attention_mask is None: + attention_mask = torch.ones_like(total_input_ids) + position_ids = torch.ones( + 3, + input_ids.shape[0], + input_ids.shape[1], + dtype=input_ids.dtype, + device=input_ids.device, + ) + image_index, video_index = 0, 0 + attention_mask = attention_mask.to(total_input_ids.device) + for i, input_ids in enumerate(total_input_ids): + input_ids = input_ids[attention_mask[i] == 1] + image_nums, video_nums = 0, 0 + vision_start_indices = torch.argwhere( + input_ids == vision_start_token_id + ).squeeze(1) + vision_tokens = input_ids[vision_start_indices + 1] + image_nums = (vision_tokens == image_token_id).sum() + video_nums = (vision_tokens == video_token_id).sum() + input_tokens = input_ids.tolist() + llm_pos_ids_list: list = [] + st = 0 + remain_images, remain_videos = image_nums, video_nums + for _ in range(image_nums + video_nums): + if image_token_id in input_tokens and remain_images > 0: + ed_image = input_tokens.index(image_token_id, st) + else: + ed_image = len(input_tokens) + 1 + if video_token_id in input_tokens and remain_videos > 0: + ed_video = input_tokens.index(video_token_id, st) + else: + ed_video = len(input_tokens) + 1 + if ed_image < ed_video: + t, h, w = ( + image_grid_thw[image_index][0], + image_grid_thw[image_index][1], + image_grid_thw[image_index][2], + ) + second_per_grid_t = 0 + image_index += 1 + remain_images -= 1 + ed = ed_image + + else: + t, h, w = ( + video_grid_thw[video_index][0], + video_grid_thw[video_index][1], + video_grid_thw[video_index][2], + ) + if second_per_grid_ts is not None: + second_per_grid_t = second_per_grid_ts[video_index] + else: + second_per_grid_t = 1.0 + video_index += 1 + remain_videos -= 1 + ed = ed_video + llm_grid_t, llm_grid_h, llm_grid_w = ( + t.item(), + h.item() // spatial_merge_size, + w.item() // spatial_merge_size, + ) + text_len = ed - st + + st_idx = ( + llm_pos_ids_list[-1].max() + 1 + if len(llm_pos_ids_list) > 0 + else 0 + ) + llm_pos_ids_list.append( + torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx + ) + + if torch.is_tensor(second_per_grid_t): + second_per_grid_t = second_per_grid_t.detach().item() + range_tensor = torch.arange(llm_grid_t).view(-1, 1) + expanded_range = range_tensor.expand(-1, llm_grid_h * llm_grid_w) + + time_tensor = ( + expanded_range + * second_per_grid_t + * self.config.vision_config.tokens_per_second + ) + + time_tensor_long = time_tensor.long() + t_index = time_tensor_long.flatten() + + h_index = ( + torch.arange(llm_grid_h) + .view(1, -1, 1) + .expand(llm_grid_t, -1, llm_grid_w) + .flatten() + ) + w_index = ( + torch.arange(llm_grid_w) + .view(1, 1, -1) + .expand(llm_grid_t, llm_grid_h, -1) + .flatten() + ) + llm_pos_ids_list.append( + torch.stack([t_index, h_index, w_index]) + text_len + st_idx + ) + st = ed + llm_grid_t * llm_grid_h * llm_grid_w + + if st < len(input_tokens): + st_idx = ( + llm_pos_ids_list[-1].max() + 1 + if len(llm_pos_ids_list) > 0 + else 0 + ) + text_len = len(input_tokens) - st + llm_pos_ids_list.append( + torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx + ) + + llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) + position_ids[..., i, attention_mask[i] == 1] = llm_positions.to( + position_ids.device + ) + mrope_position_deltas.append( + llm_positions.max() + 1 - len(total_input_ids[i]) + ) + mrope_position_deltas = torch.tensor( + mrope_position_deltas, device=input_ids.device + ).unsqueeze(1) + return position_ids, mrope_position_deltas + else: + if attention_mask is not None: + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + position_ids = ( + position_ids.unsqueeze(0) + .expand(3, -1, -1) + .to(attention_mask.device) + ) + max_position_ids = position_ids.max(0, keepdim=False)[0].max( + -1, keepdim=True + )[0] + mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1] + else: + position_ids = ( + torch.arange(input_ids.shape[1], device=input_ids.device) + .view(1, 1, -1) + .expand(3, input_ids.shape[0], -1) + ) + mrope_position_deltas = torch.zeros( + [input_ids.shape[0], 1], + device=input_ids.device, + dtype=input_ids.dtype, + ) + + return position_ids, mrope_position_deltas + + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + pixel_values: Optional[torch.Tensor] = None, + pixel_values_videos: Optional[torch.FloatTensor] = None, + image_grid_thw: Optional[torch.LongTensor] = None, + video_grid_thw: Optional[torch.LongTensor] = None, + rope_deltas: Optional[torch.LongTensor] = None, + cache_position: Optional[torch.LongTensor] = None, + second_per_grid_ts: Optional[torch.Tensor] = None, + **kwargs, + ) -> Union[Tuple, PaddleOCRVLCausalLMOutputWithPast]: + r""" + Returns: + """ + output_attentions = ( + output_attentions + if output_attentions is not None + else self.config.output_attentions + ) + output_hidden_states = ( + output_hidden_states + if output_hidden_states is not None + else self.config.output_hidden_states + ) + return_dict = ( + return_dict if return_dict is not None else self.config.use_return_dict + ) + + if inputs_embeds is None: + inputs_embeds = self.model.embed_tokens(input_ids) + if pixel_values is not None: + pixel_values = pixel_values.type(self.visual.dtype) + pixel_values = pixel_values.unsqueeze(0) + siglip_position_ids = list() + image_grid_hws = list() + sample_indices = list() + cu_seqlens = [0] + + pro = 0 + for idx, thw in enumerate(image_grid_thw): + thw_tuple = tuple(thw.detach().cpu().numpy().tolist()) + numel = np.prod(thw_tuple) + image_grid_hws.append(thw_tuple) + image_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:]) + siglip_position_ids.append(image_position_ids) + sample_indices.append(torch.full((numel,), idx, dtype=torch.int64)) + cu_seqlens.append(cu_seqlens[-1] + numel) + + siglip_position_ids = torch.concat(siglip_position_ids, dim=0).to( + pixel_values.device + ) + cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to( + pixel_values.device + ) + sample_indices = torch.concat(sample_indices, dim=0).to( + pixel_values.device + ) + + vision_outputs = self.visual( + pixel_values=pixel_values, + image_grid_thw=image_grid_hws, + position_ids=siglip_position_ids, + vision_return_embed_list=True, + interpolate_pos_encoding=True, + sample_indices=sample_indices, + cu_seqlens=cu_seqlens, + return_pooler_output=False, + use_rope=True, + window_size=-1, + ) + image_embeds = vision_outputs.last_hidden_state + + image_embeds = self.mlp_AR(image_embeds, image_grid_thw) + + n_image_tokens = (input_ids == self.config.image_token_id).sum().item() + # image_embeds is a list of tensor, each tensor is a image feature,I want to concat them all into a tensor + image_embeds = torch.cat(image_embeds, dim=0) + n_image_features = image_embeds.shape[0] + if n_image_tokens != n_image_features: + raise ValueError( + f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}" + ) + + mask = input_ids == self.config.image_token_id + mask_unsqueezed = mask.unsqueeze(-1) + mask_expanded = mask_unsqueezed.expand_as(inputs_embeds) + image_mask = mask_expanded.to(inputs_embeds.device) + + image_embeds = image_embeds.to( + inputs_embeds.device, inputs_embeds.dtype + ) + + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) + + if attention_mask is not None: + attention_mask = attention_mask.to(inputs_embeds.device) + # position_ids = None + # if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme + if position_ids is None and ( + attention_mask is None or attention_mask.ndim == 2 + ): + # calculate RoPE index once per generation in the pre-fill stage only + if ( + (cache_position is not None and cache_position[0] == 0) + or self.rope_deltas is None + or (past_key_values is None or past_key_values.get_seq_length() == 0) + ): + position_ids, rope_deltas = self.get_rope_index( + input_ids, + image_grid_thw, + video_grid_thw, + second_per_grid_ts, + attention_mask, + ) + self.rope_deltas = rope_deltas + # then use the prev pre-calculated rope-deltas to get the correct position ids + else: + batch_size, seq_length, _ = inputs_embeds.shape + delta = ( + (cache_position[0] + self.rope_deltas).to(inputs_embeds.device) + if cache_position is not None + else 0 + ) + position_ids = torch.arange(seq_length, device=inputs_embeds.device) + position_ids = position_ids.view(1, -1).expand(batch_size, -1) + if cache_position is not None: # otherwise `deltas` is an int `0` + delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0) + position_ids = position_ids.add(delta) + position_ids = position_ids.unsqueeze(0).expand(3, -1, -1) + + outputs = self.model( + input_ids=None, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + **kwargs, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # Upcast to float if we need to compute the loss to avoid potential precision issues + logits = logits.float() + # Shift so that tokens < n predict n + shift_logits = logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + shift_logits = shift_logits.view(-1, self.config.vocab_size) + shift_labels = shift_labels.view(-1) + # Enable model parallelism + shift_labels = shift_labels.to(shift_logits.device) + loss = loss_fct(shift_logits, shift_labels) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return PaddleOCRVLCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=self.rope_deltas, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + cache_position=None, + position_ids=None, + use_cache=True, + pixel_values=None, + pixel_values_videos=None, + image_grid_thw=None, + video_grid_thw=None, + second_per_grid_ts=None, + **kwargs, + ): + # Overwritten -- in specific circumstances we don't want to forward image inputs to the model + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + cache_position=cache_position, + position_ids=position_ids, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + second_per_grid_ts=second_per_grid_ts, + use_cache=use_cache, + **kwargs, + ) + + model_inputs["position_ids"] = None + + if cache_position[0] != 0: + model_inputs["pixel_values"] = None + model_inputs["pixel_values_videos"] = None + + return model_inputs + + def _get_image_nums_and_video_nums( + self, + input_ids: Optional[torch.LongTensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Get the number of images and videos for each sample to calculate the separation length of the sample tensor. + These parameters are not passed through the processor to avoid unpredictable impacts from interface modifications. + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Returns: + image_nums (`torch.LongTensor` of shape `(batch_size, num_images_sample)`) + video_nums (`torch.LongTensor` of shape `(batch_size, num_videos_sample)`) + """ + image_token_id = self.config.image_token_id + video_token_id = self.config.video_token_id + vision_start_token_id = self.config.vision_start_token_id + + vision_start_mask = input_ids == vision_start_token_id + vision_first_mask = torch.roll(vision_start_mask, shifts=1, dims=1) + image_mask = input_ids == image_token_id + video_mask = input_ids == video_token_id + image_nums = torch.sum(vision_first_mask & image_mask, dim=1) + video_nums = torch.sum(vision_first_mask & video_mask, dim=1) + + return image_nums, video_nums + + def _expand_inputs_for_generation( + self, + expand_size: int = 1, + is_encoder_decoder: bool = False, + input_ids: Optional[torch.LongTensor] = None, + **model_kwargs, + ) -> Tuple[torch.LongTensor, Dict[str, Any]]: + # Overwritten -- Support for expanding tensors without a batch size dimension + # e.g., pixel_values, image_grid_thw, pixel_values_videos, video_grid_thw, second_per_grid_t + # pixel_values.shape[0] is sum(seqlen_images for samples) + # image_grid_thw.shape[0] is sum(num_images for samples) + + if expand_size == 1: + return input_ids, model_kwargs + + visual_keys = [ + "pixel_values", + "image_grid_thw", + "pixel_values_videos", + "video_grid_thw", + "second_per_grid_ts", + ] + + def _expand_dict_for_generation_visual(dict_to_expand): + image_grid_thw = model_kwargs.get("image_grid_thw", None) + video_grid_thw = model_kwargs.get("video_grid_thw", None) + image_nums, video_nums = self._get_image_nums_and_video_nums(input_ids) + + def _repeat_interleave_samples(x, lengths, repeat_times): + samples = torch.split(x, lengths) + repeat_args = [repeat_times] + [1] * (x.dim() - 1) + result = torch.cat( + [sample.repeat(*repeat_args) for sample in samples], dim=0 + ) + return result + + for key in dict_to_expand: + if key == "pixel_values": + # split images into samples + samples = torch.split(image_grid_thw, list(image_nums)) + # compute the sequence length of images for each sample + lengths = [torch.prod(sample, dim=1).sum() for sample in samples] + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "image_grid_thw": + # get the num of images for each sample + lengths = list(image_nums) + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "pixel_values_videos": + samples = torch.split(video_grid_thw, list(video_nums)) + lengths = [torch.prod(sample, dim=1).sum() for sample in samples] + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "video_grid_thw": + lengths = list(video_nums) + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "second_per_grid_ts": + if not isinstance(dict_to_expand[key], list): + raise TypeError( + f"Expected value for key '{key}' to be a list, but got {type(dict_to_expand[key])} instead." + ) + tensor = torch.tensor(dict_to_expand[key]) + lengths = list(video_nums) + tensor = _repeat_interleave_samples( + tensor, lengths=lengths, repeat_times=expand_size + ) + dict_to_expand[key] = tensor.tolist() + return dict_to_expand + + def _expand_dict_for_generation(dict_to_expand): + for key in dict_to_expand: + if ( + key != "cache_position" + and dict_to_expand[key] is not None + and isinstance(dict_to_expand[key], torch.Tensor) + and key not in visual_keys + ): + dict_to_expand[key] = dict_to_expand[key].repeat_interleave( + expand_size, dim=0 + ) + return dict_to_expand + + # input_ids is required for expanding visual inputs + # If input_ids is unavailable, visual inputs will not be used; therefore, there is no need to expand visual inputs. + if input_ids is not None and input_ids.numel() != 0: + model_kwargs = _expand_dict_for_generation_visual(model_kwargs) + + if input_ids is not None: + input_ids = input_ids.repeat_interleave(expand_size, dim=0) + + model_kwargs = _expand_dict_for_generation(model_kwargs) + + if is_encoder_decoder: + if model_kwargs.get("encoder_outputs") is None: + raise ValueError( + "If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined." + ) + model_kwargs["encoder_outputs"] = _expand_dict_for_generation( + model_kwargs["encoder_outputs"] + ) + + return input_ids, model_kwargs diff --git a/PaddleOCR-VL-0.9B/preprocessor_config.json b/PaddleOCR-VL-0.9B/preprocessor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..1f0535ab954e18bfc28be5d34377e6433184a8f2 --- /dev/null +++ b/PaddleOCR-VL-0.9B/preprocessor_config.json @@ -0,0 +1,33 @@ +{ + "auto_map": { + "AutoImageProcessor": "image_processing.SiglipImageProcessor", + "AutoProcessor": "processing_paddleocr_vl.PaddleOCRVLProcessor" + }, + "do_convert_rgb": true, + "do_normalize": true, + "do_rescale": true, + "do_resize": true, + "image_mean": [ + 0.5, + 0.5, + 0.5 + ], + "image_processor_type": "SiglipImageProcessor", + "image_std": [ + 0.5, + 0.5, + 0.5 + ], + "max_pixels": 2822400, + "merge_size": 2, + "min_pixels": 147384, + "patch_size": 14, + "processor_class": "PaddleOCRVLProcessor", + "resample": 3, + "rescale_factor": 0.00392156862745098, + "size": { + "max_pixels": 2822400, + "min_pixels": 147384 + }, + "temporal_patch_size": 1 +} diff --git a/PaddleOCR-VL-0.9B/processing_paddleocr_vl.py b/PaddleOCR-VL-0.9B/processing_paddleocr_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..73c3faeff201555fc7b52709848e3c669419dbb1 --- /dev/null +++ b/PaddleOCR-VL-0.9B/processing_paddleocr_vl.py @@ -0,0 +1,293 @@ +# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. +# +# 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. + +from typing import List, Union +import numpy as np +import torch +from transformers.feature_extraction_utils import BatchFeature +from transformers.processing_utils import ( + ProcessingKwargs, + ProcessorMixin, + Unpack, + VideosKwargs, +) +from transformers.tokenization_utils_base import PreTokenizedInput, TextInput + + +ImageInput = Union[ + "PIL.Image.Image", + np.ndarray, + "torch.Tensor", + List["PIL.Image.Image"], + List[np.ndarray], + List["torch.Tensor"], +] # noqa + + +VideoInput = Union[ + List["PIL.Image.Image"], + "np.ndarray", + "torch.Tensor", + List["np.ndarray"], + List["torch.Tensor"], + List[List["PIL.Image.Image"]], + List[List["np.ndarrray"]], + List[List["torch.Tensor"]], +] # noqa + + +class PaddleOCRVLVideosProcessorKwargs(VideosKwargs, total=False): + fps: Union[List[float], float] + + +class PaddleOCRVLProcessorKwargs(ProcessingKwargs, total=False): + videos_kwargs: PaddleOCRVLVideosProcessorKwargs + _defaults = { + "text_kwargs": { + "padding": False, + }, + "videos_kwargs": {"fps": 2.0}, + } + + +class PaddleOCRVLProcessor(ProcessorMixin): + r""" + [`PaddleOCRVLProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`Qwen2TokenizerFast`]. See the + [`~PaddleOCRVLProcessor.__call__`] and [`~PaddleOCRVLProcessor.decode`] for more information. + Args: + image_processor ([`SiglipImageProcessor`], *optional*): + The image processor is a required input. + tokenizer ([`Qwen2TokenizerFast`], *optional*): + The tokenizer is a required input. + chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages + in a chat into a tokenizable string. + """ + + attributes = ["image_processor", "tokenizer"] + valid_kwargs = [ + "chat_template", + "image_std", + "min_pixels", + "image_mean", + "merge_size", + "image_processor_type", + "temporal_patch_size", + "patch_size", + "max_pixels", + ] + + image_processor_class = "AutoImageProcessor" + tokenizer_class = "AutoTokenizer" + + def __init__( + self, image_processor=None, tokenizer=None, chat_template=None, **kwargs + ): + self.image_token = ( + "<|IMAGE_PLACEHOLDER|>" + if not hasattr(tokenizer, "image_token") + else tokenizer.image_token + ) + self.video_token = ( + "<|video_pad|>" + if not hasattr(tokenizer, "video_token") + else tokenizer.video_token + ) + super().__init__(image_processor, tokenizer, chat_template=chat_template) + + def __call__( + self, + images: ImageInput = None, + text: Union[ + TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput] + ] = None, + videos: VideoInput = None, + **kwargs: Unpack[PaddleOCRVLProcessorKwargs], + ) -> BatchFeature: + """ + Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` + and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode + the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to + SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `vision_infos` is not `None`. + + Args: + images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): + The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch + tensor. Both channels-first and channels-last formats are supported. + text (`str`, `List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings + (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set + `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`): + The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch + tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported. + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors of a particular framework. Acceptable values are: + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return NumPy `np.ndarray` objects. + - `'jax'`: Return JAX `jnp.ndarray` objects. + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`. + - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. + - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`. + - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`. + """ + output_kwargs = self._merge_kwargs( + PaddleOCRVLProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + if images is not None: + image_inputs = self.image_processor(images=images, return_tensors="pt") + image_inputs["pixel_values"] = image_inputs["pixel_values"] + image_grid_thw = image_inputs["image_grid_thw"] + + else: + image_inputs = {} + image_grid_thw = None + + if videos is not None: + # TODO: add video processing + videos_inputs = self.image_processor( + images=None, videos=videos, **output_kwargs["images_kwargs"] + ) + video_grid_thw = videos_inputs["video_grid_thw"] + + fps = output_kwargs["videos_kwargs"].pop("fps", 2.0) + if isinstance(fps, (int, float)): + second_per_grid_ts = [ + self.image_processor.temporal_patch_size / fps + ] * len(video_grid_thw) + elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw): + second_per_grid_ts = [ + self.image_processor.temporal_patch_size / tmp for tmp in fps + ] + else: + raise ValueError( + f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number." + ) + videos_inputs.update( + {"second_per_grid_ts": torch.tensor(second_per_grid_ts)} + ) + + else: + videos_inputs = {} + video_grid_thw = None + + if not isinstance(text, list): + text = [text] + + if image_grid_thw is not None: + index = 0 + for i in range(len(text)): + while self.image_token in text[i]: + text[i] = text[i].replace( + self.image_token, + "<|placeholder|>" + * ( + image_grid_thw[index].prod() + // self.image_processor.merge_size + // self.image_processor.merge_size + ), + 1, + ) + index += 1 + text[i] = text[i].replace("<|placeholder|>", self.image_token) + + if video_grid_thw is not None: + index = 0 + for i in range(len(text)): + while self.video_token in text[i]: + text[i] = text[i].replace( + self.video_token, + "<|placeholder|>" + * ( + video_grid_thw[index].prod() + // self.image_processor.merge_size + // self.image_processor.merge_size + ), + 1, + ) + index += 1 + text[i] = text[i].replace("<|placeholder|>", self.video_token) + + text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) + + return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}) + + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + def post_process_image_text_to_text( + self, + generated_outputs, + skip_special_tokens=True, + clean_up_tokenization_spaces=False, + **kwargs, + ): + """ + Post-process the output of the model to decode the text. + + Args: + generated_outputs (`torch.Tensor` or `np.ndarray`): + The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` + or `(sequence_length,)`. + skip_special_tokens (`bool`, *optional*, defaults to `True`): + Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method. + Clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): + Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method. + **kwargs: + Additional arguments to be passed to the tokenizer's `batch_decode method`. + + Returns: + `List[str]`: The decoded text. + """ + return self.tokenizer.batch_decode( + generated_outputs, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + @property + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + names_from_processor = list( + dict.fromkeys(tokenizer_input_names + image_processor_input_names) + ) + return names_from_processor + ["second_per_grid_ts"] + + +__all__ = ["PaddleOCRVLProcessor", "PaddleOCRVLProcessor"] diff --git a/PaddleOCR-VL-0.9B/processor_config.json b/PaddleOCR-VL-0.9B/processor_config.json new file mode 100644 index 0000000000000000000000000000000000000000..033053ac4d8b5de2e47884ce85a6b4939cc58e87 --- /dev/null +++ b/PaddleOCR-VL-0.9B/processor_config.json @@ -0,0 +1,6 @@ +{ + "auto_map": { + "AutoProcessor": "processing_paddleocr_vl.PaddleOCRVLProcessor" + }, + "processor_class": "PaddleOCRVLProcessor" +} diff --git a/PaddleOCR-VL-0.9B/special_tokens_map.json b/PaddleOCR-VL-0.9B/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..3c4aec2f502327373e3524be6ac37ff9070dec8b --- /dev/null +++ b/PaddleOCR-VL-0.9B/special_tokens_map.json @@ -0,0 +1,58 @@ +{ + "additional_special_tokens": [ + "<|IMAGE_PLACEHOLDER|>", + "<|image_pad|>", + "<|IMAGE_START|>", + "<|IMAGE_END|>", + "<|video_pad|>" + ], + "bos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "cls_token": { + "content": "<|begin_of_sentence|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "", + "lstrip": false, + "normalized": false, + "rstrip": false, + 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"use_default_system_prompt": false +} diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a2ab3678e538e8e4ef9c89850a9fd4a31f83f2cf --- /dev/null +++ b/README.md @@ -0,0 +1,294 @@ +--- +license: apache-2.0 +pipeline_tag: image-text-to-text +tags: +- ERNIE4.5 +- PaddleOCR +- PaddlePaddle +- image-to-text +- ocr +- document-parse +- layout +- table +- formula +- chart +base_model: baidu/ERNIE-4.5-0.3B-Paddle +language: +- en +- zh +- multilingual +library_name: PaddleOCR +--- + +
+ + +

+ +PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model + +

+ +[![repo](https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf)](https://github.com/PaddlePaddle/PaddleOCR) +[![HuggingFace](https://img.shields.io/badge/HuggingFace-black.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/PaddlePaddle/PaddleOCR-VL) +[![ModelScope](https://img.shields.io/badge/ModelScope-black?logo=data:image/svg+xml;base64,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&labelColor=white)](https://modelscope.cn/models/PaddlePaddle/PaddleOCR-VL) +[![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-black.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/spaces/PaddlePaddle/PaddleOCR-VL_Online_Demo) +[![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-black?logo=data:image/svg+xml;base64,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&labelColor=white)](https://modelscope.cn/studios/PaddlePaddle/PaddleOCR-VL_Online_Demo/summary) +[![Discord](https://img.shields.io/badge/Discord-ERNIE-5865F2?logo=discord&logoColor=white)](https://discord.gg/JPmZXDsEEK) +[![X](https://img.shields.io/badge/X-PaddlePaddle-6080F0)](https://x.com/PaddlePaddle) +[![License](https://img.shields.io/badge/license-Apache_2.0-green)](./LICENSE) + +**🔥 Official Demo**: [Baidu AI Studio](https://aistudio.baidu.com/application/detail/98365) | +**📝 Blog**: [Technical Report](https://ernie.baidu.com/blog/publication/PaddleOCR-VL_Technical_Report.pdf) + +
+ +
+ +
+ +## Introduction + +**PaddleOCR-VL** is a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios. + +### **Core Features** + +1. **Compact yet Powerful VLM Architecture:** We present a novel vision-language model that is specifically designed for resource-efficient inference, achieving outstanding performance in element recognition. By integrating a NaViT-style dynamic high-resolution visual encoder with the lightweight ERNIE-4.5-0.3B language model, we significantly enhance the model’s recognition capabilities and decoding efficiency. This integration maintains high accuracy while reducing computational demands, making it well-suited for efficient and practical document processing applications. + + +2. **SOTA Performance on Document Parsing:** PaddleOCR-VL achieves state-of-the-art performance in both page-level document parsing and element-level recognition. It significantly outperforms existing pipeline-based solutions and exhibiting strong competitiveness against leading vision-language models (VLMs) in document parsing. Moreover, it excels in recognizing complex document elements, such as text, tables, formulas, and charts, making it suitable for a wide range of challenging content types, including handwritten text and historical documents. This makes it highly versatile and suitable for a wide range of document types and scenarios. + + +3. **Multilingual Support:** PaddleOCR-VL Supports 109 languages, covering major global languages, including but not limited to Chinese, English, Japanese, Latin, and Korean, as well as languages with different scripts and structures, such as Russian (Cyrillic script), Arabic, Hindi (Devanagari script), and Thai. This broad language coverage substantially enhances the applicability of our system to multilingual and globalized document processing scenarios. + + +### **Model Architecture** + + + +
+ +
+ + +## News +* ```2025.10.16``` 🚀 We release [PaddleOCR-VL](https://github.com/PaddlePaddle/PaddleOCR), — a multilingual documents parsing via a 0.9B Ultra-Compact Vision-Language Model with SOTA performance. + + +## Usage + +### Install Dependencies + +Install [PaddlePaddle](https://www.paddlepaddle.org.cn/install/quick) and [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR): + +```bash +python -m pip install paddlepaddle-gpu==3.2.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ +python -m pip install -U "paddleocr[doc-parser]" +``` + +### Basic Usage + +CLI usage: + +```bash +paddleocr doc_parser -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pp_ocr_vl_demo.png +``` + +Python API usage: + +```python +from paddleocr import PaddleOCRVL + +pipeline = PaddleOCRVL() +output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pp_ocr_vl_demo.png") +for res in output: + res.print() + res.save_to_json(save_path="output") + res.save_to_markdown(save_path="output") +``` + +### Accelerate VLM Inference via Optimized Inference Servers + +1. Start the VLM inference server (the default port is `8080`): + + ```bash + docker run \ + --rm \ + --gpus all \ + --network host \ + ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddlex-genai-vllm-server + # You can also use ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddlex-genai-vllm-server for the SGLang server + ``` + +2. Call the PaddleOCR CLI or Python API: + + ```bash + paddleocr doc_parser \ + -i https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pp_ocr_vl_demo.png \ + --vl_rec_backend vllm-server \ + --vl_rec_server_url http://127.0.0.1:8080 + ``` + + ```python + from paddleocr import PaddleOCRVL + + pipeline = PaddleOCRVL(vl_rec_backend="vllm-server", vl_rec_server_url="http://127.0.0.1:8080") + output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/pp_ocr_vl_demo.png") + for res in output: + res.print() + res.save_to_json(save_path="output") + res.save_to_markdown(save_path="output") + ``` + +## Performance + +### Page-Level Document Parsing + + +#### 1. OmniDocBench v1.5 + +##### PaddleOCR-VL achieves SOTA performance for overall, text, formula, tables and reading order on OmniDocBench v1.5 + +
+ +
+ + + + +#### 2. OmniDocBench v1.0 + +##### PaddleOCR-VL achieves SOTA performance for almost all metrics of overall, text, formula, tables and reading order on OmniDocBench v1.0 + + +
+ +
+ + +> **Notes:** +> - The metrics are from [MinerU](https://github.com/opendatalab/MinerU), [OmniDocBench](https://github.com/opendatalab/OmniDocBench), and our own internal evaluations. + + +### Element-level Recognition + +#### 1. Text + +**Comparison of OmniDocBench-OCR-block Performance** + +PaddleOCR-VL’s robust and versatile capability in handling diverse document types, establishing it as the leading method in the OmniDocBench-OCR-block performance evaluation. + +
+ +
+ + +**Comparison of In-house-OCR Performance** + +In-house-OCR provides a evaluation of performance across multiple languages and text types. Our model demonstrates outstanding accuracy with the lowest edit distances in all evaluated scripts. + +
+ +
+ + + +#### 2. Table + +**Comparison of In-house-Table Performance** + +Our self-built evaluation set contains diverse types of table images, such as Chinese, English, mixed Chinese-English, and tables with various characteristics like full, partial, or no borders, book/manual formats, lists, academic papers, merged cells, as well as low-quality, watermarked, etc. PaddleOCR-VL achieves remarkable performance across all categories. + +
+ +
+ +#### 3. Formula + +**Comparison of In-house-Formula Performance** + +In-house-Formula evaluation set contains simple prints, complex prints, camera scans, and handwritten formulas. PaddleOCR-VL demonstrates the best performance in every category. + +
+ +
+ + +#### 4. Chart + +**Comparison of In-house-Chart Performance** + +The evaluation set is broadly categorized into 11 chart categories, including bar-line hybrid, pie, 100% stacked bar, area, bar, bubble, histogram, line, scatterplot, stacked area, and stacked bar. PaddleOCR-VL not only outperforms expert OCR VLMs but also surpasses some 72B-level multimodal language models. + +
+ +
+ + + + + + + +## Visualization + + +### Comprehensive Document Parsing + +
+ + + + +
+ + +### Text + +
+ + +
+ + +### Table + +
+ + +
+ + +### Formula + +
+ + +
+ +### Chart + +
+ + +
+ + +## Acknowledgments + +We would like to thank [ERNIE](https://github.com/PaddlePaddle/ERNIE), [Keye](https://github.com/Kwai-Keye/Keye), [MinerU](https://github.com/opendatalab/MinerU), [OmniDocBench](https://github.com/opendatalab/OmniDocBench) for providing valuable code, model weights and benchmarks. We also appreciate everyone's contribution to this open-source project! + +## Citation + +If you find PaddleOCR-VL helpful, feel free to give us a star and citation. + +```bibtex +@misc{paddleocrvl2025technicalreport, + title={PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model}, + author={Cui, C. et al.}, + year={2025}, + primaryClass={cs.CL}, + howpublished={\url{https://ernie.baidu.com/blog/publication/PaddleOCR-VL_Technical_Report.pdf}} +} +``` + diff --git a/imgs/allmetric.png b/imgs/allmetric.png new file mode 100644 index 0000000000000000000000000000000000000000..45168b175bd20175a73462f8db7bf60bae1d4eee --- /dev/null +++ b/imgs/allmetric.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49f44053be7a3078cfa63393fe6ea4b1468b3800a5c8981a7af20f694cc69950 +size 477053 diff --git a/imgs/chart_01.jpg b/imgs/chart_01.jpg new file mode 100644 index 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