support qwen2-vl (#32318)
* support-qwen2-vl * tidy * tidy * tidy * tidy * tidy * tidy * tidy * hyphen->underscore * make style * add-flash2-tipd * delete-tokenize=False * remove-image_processor-in-init-file * add-qwen2_vl-in-MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES * format-doct * support-Qwen2VLVisionConfig * remove-standardize_cache_format * fix-letter-varaibles * remove-torch-in-image-processor * remove-useless-docstring * fix-one-letter-varaible-name * change-block-name * default-quick-gelu-in-vision * remove-useless-doc * use-preimplemented-flash-forward * fix-doc * fix-image-processing-doc * fix-apply-rotary-embed * fix-flash-attn-sliding-window * refactor * remove-default_template * remove-reorder_cache * simple-get-rope_deltas * update-prepare_inputs_for_generation * update-attention-mask * update-rotary_seq_len * remove-state * kv_seq_length * remove-warning * _supports_static_cache * remove-legacy-cache * refactor * fix-replace * mrope-section-doc * code-quality * code-quality * polish-doc * fix-image-processing-test * update readme * Update qwen2_vl.md * fix-test * Update qwen2_vl.md * nit * processor-kwargs * hard-code-norm_layer * code-quality * discard-pixel-values-in-gen * fix-inconsistent-error-msg * unify-image-video * hidden_act * add-docstring * vision-encode-as-PreTrainedModel * pixel-to-target-dtype * update doc and low memoryvit * format * format * channel-foramt * fix vit_flashatt * format * inherit-Qwen2VLPreTrainedModel * simplify * format-test * remove-one-line-func-in-image-processing * avoid-one-line-reshape * simplify-rotary_seq_len * avoid-single-letter-variable * no-for-loop-sdpa * avoid-single-letter-variable * remove-one-line-reshape * remove-one-line-reshape * remove-no-rope-in-vit-logic * default-mrope * add-copied-from * more-docs-for-mrope * polish-doc * comment-and-link * polish-doc * single-letter-variables * simplify-image-processing * video->images * kv_seq_len-update * vision-rope-on-the-fly * vision-eager-attention * change-processor-order --------- Co-authored-by: baishuai <baishuai.bs@alibaba-inc.com> Co-authored-by: ShuaiBai623 <43326198+ShuaiBai623@users.noreply.github.com>
This commit is contained in:
Shijie
@@ -514,6 +514,8 @@
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title: Qwen2Audio
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- local: model_doc/qwen2_moe
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title: Qwen2MoE
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- local: model_doc/qwen2_vl
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title: Qwen2VL
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- local: model_doc/rag
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title: RAG
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- local: model_doc/realm
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@@ -260,6 +260,7 @@ Flax), PyTorch, and/or TensorFlow.
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| [Qwen2](model_doc/qwen2) | ✅ | ❌ | ❌ |
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| [Qwen2Audio](model_doc/qwen2_audio) | ✅ | ❌ | ❌ |
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| [Qwen2MoE](model_doc/qwen2_moe) | ✅ | ❌ | ❌ |
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| [Qwen2VL](model_doc/qwen2_vl) | ✅ | ❌ | ❌ |
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| [RAG](model_doc/rag) | ✅ | ✅ | ❌ |
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| [REALM](model_doc/realm) | ✅ | ❌ | ❌ |
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| [RecurrentGemma](model_doc/recurrent_gemma) | ✅ | ❌ | ❌ |
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329
docs/source/en/model_doc/qwen2_vl.md
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329
docs/source/en/model_doc/qwen2_vl.md
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@@ -0,0 +1,329 @@
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<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# Qwen2_VL
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## Overview
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The [Qwen2_VL](https://qwenlm.github.io/blog/qwen2-vl/) is a major update to our [Qwen-VL](https://arxiv.org/pdf/2308.12966) model from the Qwen team.
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The abstract from the blog is the following:
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*This blog introduces Qwen2-VL, an advanced version of the Qwen-VL model that has undergone significant enhancements over the past year. Key improvements include enhanced image comprehension, advanced video understanding, integrated visual agent functionality, and expanded multilingual support. The model architecture has been optimized for handling arbitrary image resolutions through Naive Dynamic Resolution support and utilizes Multimodal Rotary Position Embedding (M-ROPE) to effectively process both 1D textual and multi-dimensional visual data. This updated model demonstrates competitive performance against leading AI systems like GPT-4o and Claude 3.5 Sonnet in vision-related tasks and ranks highly among open-source models in text capabilities. These advancements make Qwen2-VL a versatile tool for various applications requiring robust multimodal processing and reasoning abilities.*
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## Usage example
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### Single Media inference
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The model can accept both images and videos as input. Here's an example code for inference.
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```python
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from PIL import Image
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import requests
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import torch
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from torchvision import io
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from typing import Dict
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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# Load the model in half-precision on the available device(s)
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model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", device_map="auto")
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
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# Image
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url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
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image = Image.open(requests.get(url, stream=True).raw)
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conversation = [
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{
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"role":"user",
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"content":[
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{
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"type":"image",
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},
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{
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"type":"text",
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"text":"Describe this image."
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}
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]
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}
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]
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# Preprocess the inputs
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text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
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# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe this image.<|im_end|>\n<|im_start|>assistant\n'
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inputs = processor(text=[text_prompt], images=[image], padding=True, return_tensors="pt")
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inputs = inputs.to('cuda')
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# Inference: Generation of the output
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output_ids = model.generate(**inputs, max_new_tokens=128)
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generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
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output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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print(output_text)
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# Video
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def fetch_video(ele: Dict, nframe_factor=2):
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if isinstance(ele['video'], str):
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def round_by_factor(number: int, factor: int) -> int:
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return round(number / factor) * factor
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video = ele["video"]
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if video.startswith("file://"):
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video = video[7:]
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video, _, info = io.read_video(
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video,
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start_pts=ele.get("video_start", 0.0),
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end_pts=ele.get("video_end", None),
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pts_unit="sec",
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output_format="TCHW",
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)
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assert not ("fps" in ele and "nframes" in ele), "Only accept either `fps` or `nframes`"
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if "nframes" in ele:
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nframes = round_by_factor(ele["nframes"], nframe_factor)
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else:
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fps = ele.get("fps", 1.0)
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nframes = round_by_factor(video.size(0) / info["video_fps"] * fps, nframe_factor)
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idx = torch.linspace(0, video.size(0) - 1, nframes, dtype=torch.int64)
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return video[idx]
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video_info = {"type": "video", "video": "/path/to/video.mp4", "fps": 1.0}
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video = fetch_video(video_info)
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conversation = [
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{
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"role": "user",
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"content": [
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{"type": "video"},
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{"type": "text", "text": "What happened in the video?"},
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],
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}
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]
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# Preprocess the inputs
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text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
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# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|video_pad|><|vision_end|>What happened in the video?<|im_end|>\n<|im_start|>assistant\n'
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inputs = processor(text=[text_prompt], videos=[video], padding=True, return_tensors="pt")
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inputs = inputs.to('cuda')
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# Inference: Generation of the output
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output_ids = model.generate(**inputs, max_new_tokens=128)
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generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
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output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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print(output_text)
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```
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### Batch Mixed Media Inference
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The model can batch inputs composed of mixed samples of various types such as images, videos, and text. Here is an example.
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```python
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image1 = Image.open("/path/to/image1.jpg")
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image2 = Image.open("/path/to/image2.jpg")
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image3 = Image.open("/path/to/image3.jpg")
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image4 = Image.open("/path/to/image4.jpg")
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image5 = Image.open("/path/to/image5.jpg")
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video = fetch_video({
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"type": "video",
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"video": "/path/to/video.mp4",
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"fps": 1.0
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})
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# Conversation for the first image
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conversation1 = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": "Describe this image."}
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]
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}
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]
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# Conversation with two images
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conversation2 = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "image"},
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{"type": "text", "text": "What is written in the pictures?"}
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]
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}
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]
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# Conversation with pure text
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conversation3 = [
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{
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"role": "user",
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"content": "who are you?"
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}
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]
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# Conversation with mixed midia
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conversation4 = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "image"},
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{"type": "video"},
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{"type": "text", "text": "What are the common elements in these medias?"},
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],
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}
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]
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conversations = [conversation1, conversation2, conversation3, conversation4]
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# Preparation for batch inference
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texts = [processor.apply_chat_template(msg, add_generation_prompt=True) for msg in conversations]
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inputs = processor(
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text=texts,
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images=[image1, image2, image3, image4, image5],
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videos=[video],
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to('cuda')
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# Batch Inference
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output_ids = model.generate(**inputs, max_new_tokens=128)
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generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
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output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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print(output_text)
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```
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### Usage Tips
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#### Image Resolution for performance boost
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The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs.
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```python
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min_pixels = 224*224
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max_pixels = 2048*2048
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
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```
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#### Multiple Image Inputs
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By default, images and video content are directly included in the conversation. When handling multiple images, it's helpful to add labels to the images and videos for better reference. Users can control this behavior with the following settings:
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```python
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conversation = [
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": "Hello, how are you?"}
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]
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},
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{
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"role": "assistant",
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"content": "I'm doing well, thank you for asking. How can I assist you today?"
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},
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Can you describe these images and video?"},
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{"type": "image"},
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{"type": "image"},
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{"type": "video"},
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{"type": "text", "text": "These are from my vacation."}
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]
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},
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{
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"role": "assistant",
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"content": "I'd be happy to describe the images and video for you. Could you please provide more context about your vacation?"
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},
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{
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"role": "user",
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"content": "It was a trip to the mountains. Can you see the details in the images and video?"
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}
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]
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# default:
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prompt_without_id = processor.apply_chat_template(conversation, add_generation_prompt=True)
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# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Hello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing well, thank you for asking. How can I assist you today?<|im_end|>\n<|im_start|>user\nCan you describe these images and video?<|vision_start|><|image_pad|><|vision_end|><|vision_start|><|image_pad|><|vision_end|><|vision_start|><|video_pad|><|vision_end|>These are from my vacation.<|im_end|>\n<|im_start|>assistant\nI'd be happy to describe the images and video for you. Could you please provide more context about your vacation?<|im_end|>\n<|im_start|>user\nIt was a trip to the mountains. Can you see the details in the images and video?<|im_end|>\n<|im_start|>assistant\n'
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# add ids
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prompt_with_id = processor.apply_chat_template(conversation, add_generation_prompt=True, add_vision_id=True)
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# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nPicture 1: <|vision_start|><|image_pad|><|vision_end|>Hello, how are you?<|im_end|>\n<|im_start|>assistant\nI'm doing well, thank you for asking. How can I assist you today?<|im_end|>\n<|im_start|>user\nCan you describe these images and video?Picture 2: <|vision_start|><|image_pad|><|vision_end|>Picture 3: <|vision_start|><|image_pad|><|vision_end|>Video 1: <|vision_start|><|video_pad|><|vision_end|>These are from my vacation.<|im_end|>\n<|im_start|>assistant\nI'd be happy to describe the images and video for you. Could you please provide more context about your vacation?<|im_end|>\n<|im_start|>user\nIt was a trip to the mountains. Can you see the details in the images and video?<|im_end|>\n<|im_start|>assistant\n'
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```
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#### Flash-Attention 2 to speed up generation
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First, make sure to install the latest version of Flash Attention 2:
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```bash
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pip install -U flash-attn --no-build-isolation
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```
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Also, you should have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILab/flash-attention). FlashAttention-2 can only be used when a model is loaded in `torch.float16` or `torch.bfloat16`.
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To load and run a model using Flash Attention-2, simply add `attn_implementation="flash_attention_2"` when loading the model as follows:
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```python
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from transformers import Qwen2VLForConditionalGeneration
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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"Qwen/Qwen2-VL-7B-Instruct",
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torch_dtype=torch.bfloat16,
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attn_implementation="flash_attention_2",
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)
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```
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## Qwen2VLConfig
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[[autodoc]] Qwen2VLConfig
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## Qwen2VLImageProcessor
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[[autodoc]] Qwen2VLImageProcessor
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- preprocess
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## Qwen2VLProcessor
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[[autodoc]] Qwen2VLProcessor
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## Qwen2VLModel
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[[autodoc]] Qwen2VLModel
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- forward
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## Qwen2VLForConditionalGeneration
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[[autodoc]] Qwen2VLForConditionalGeneration
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- forward
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@@ -79,6 +79,7 @@ FlashAttention-2 is currently supported for the following architectures:
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* [Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2#transformers.Qwen2Model)
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* [Qwen2Audio](https://huggingface.co/docs/transformers/model_doc/qwen2_audio#transformers.Qwen2AudioEncoder)
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* [Qwen2MoE](https://huggingface.co/docs/transformers/model_doc/qwen2_moe#transformers.Qwen2MoeModel)
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* [Qwen2VL](https://huggingface.co/docs/transformers/model_doc/qwen2_vl#transformers.Qwen2VLModel)
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* [Whisper](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperModel)
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* [Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2Model)
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* [Hubert](https://huggingface.co/docs/transformers/model_doc/hubert#transformers.HubertModel)
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@@ -230,6 +231,7 @@ For now, Transformers supports SDPA inference and training for the following arc
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* [Qwen2](https://huggingface.co/docs/transformers/model_doc/qwen2#transformers.Qwen2Model)
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* [Qwen2Audio](https://huggingface.co/docs/transformers/model_doc/qwen2_audio#transformers.Qwen2AudioEncoder)
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* [Qwen2MoE](https://huggingface.co/docs/transformers/model_doc/qwen2_moe#transformers.Qwen2MoeModel)
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* [Qwen2VL](https://huggingface.co/docs/transformers/model_doc/qwen2_vl#transformers.Qwen2VLModel)
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* [Musicgen](https://huggingface.co/docs/transformers/model_doc/musicgen#transformers.MusicgenModel)
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* [MusicGen Melody](https://huggingface.co/docs/transformers/model_doc/musicgen_melody#transformers.MusicgenMelodyModel)
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* [Nemotron](https://huggingface.co/docs/transformers/model_doc/nemotron)
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@@ -663,6 +663,10 @@ _import_structure = {
|
||||
"Qwen2AudioProcessor",
|
||||
],
|
||||
"models.qwen2_moe": ["Qwen2MoeConfig"],
|
||||
"models.qwen2_vl": [
|
||||
"Qwen2VLConfig",
|
||||
"Qwen2VLProcessor",
|
||||
],
|
||||
"models.rag": ["RagConfig", "RagRetriever", "RagTokenizer"],
|
||||
"models.recurrent_gemma": ["RecurrentGemmaConfig"],
|
||||
"models.reformer": ["ReformerConfig"],
|
||||
@@ -1189,6 +1193,7 @@ else:
|
||||
_import_structure["models.pix2struct"].extend(["Pix2StructImageProcessor"])
|
||||
_import_structure["models.poolformer"].extend(["PoolFormerFeatureExtractor", "PoolFormerImageProcessor"])
|
||||
_import_structure["models.pvt"].extend(["PvtImageProcessor"])
|
||||
_import_structure["models.qwen2_vl"].extend(["Qwen2VLImageProcessor"])
|
||||
_import_structure["models.rt_detr"].extend(["RTDetrImageProcessor"])
|
||||
_import_structure["models.sam"].extend(["SamImageProcessor"])
|
||||
_import_structure["models.segformer"].extend(["SegformerFeatureExtractor", "SegformerImageProcessor"])
|
||||
@@ -3017,6 +3022,13 @@ else:
|
||||
"Qwen2MoePreTrainedModel",
|
||||
]
|
||||
)
|
||||
_import_structure["models.qwen2_vl"].extend(
|
||||
[
|
||||
"Qwen2VLForConditionalGeneration",
|
||||
"Qwen2VLModel",
|
||||
"Qwen2VLPreTrainedModel",
|
||||
]
|
||||
)
|
||||
_import_structure["models.rag"].extend(
|
||||
[
|
||||
"RagModel",
|
||||
@@ -5424,6 +5436,10 @@ if TYPE_CHECKING:
|
||||
Qwen2AudioProcessor,
|
||||
)
|
||||
from .models.qwen2_moe import Qwen2MoeConfig
|
||||
from .models.qwen2_vl import (
|
||||
Qwen2VLConfig,
|
||||
Qwen2VLProcessor,
|
||||
)
|
||||
from .models.rag import RagConfig, RagRetriever, RagTokenizer
|
||||
from .models.recurrent_gemma import RecurrentGemmaConfig
|
||||
from .models.reformer import ReformerConfig
|
||||
@@ -5978,6 +5994,7 @@ if TYPE_CHECKING:
|
||||
PoolFormerImageProcessor,
|
||||
)
|
||||
from .models.pvt import PvtImageProcessor
|
||||
from .models.qwen2_vl import Qwen2VLImageProcessor
|
||||
from .models.rt_detr import RTDetrImageProcessor
|
||||
from .models.sam import SamImageProcessor
|
||||
from .models.segformer import SegformerFeatureExtractor, SegformerImageProcessor
|
||||
@@ -7457,6 +7474,11 @@ if TYPE_CHECKING:
|
||||
Qwen2MoeModel,
|
||||
Qwen2MoePreTrainedModel,
|
||||
)
|
||||
from .models.qwen2_vl import (
|
||||
Qwen2VLForConditionalGeneration,
|
||||
Qwen2VLModel,
|
||||
Qwen2VLPreTrainedModel,
|
||||
)
|
||||
from .models.rag import (
|
||||
RagModel,
|
||||
RagPreTrainedModel,
|
||||
|
||||
@@ -193,6 +193,7 @@ from . import (
|
||||
qwen2,
|
||||
qwen2_audio,
|
||||
qwen2_moe,
|
||||
qwen2_vl,
|
||||
rag,
|
||||
recurrent_gemma,
|
||||
reformer,
|
||||
|
||||
@@ -213,6 +213,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
|
||||
("qwen2_audio", "Qwen2AudioConfig"),
|
||||
("qwen2_audio_encoder", "Qwen2AudioEncoderConfig"),
|
||||
("qwen2_moe", "Qwen2MoeConfig"),
|
||||
("qwen2_vl", "Qwen2VLConfig"),
|
||||
("rag", "RagConfig"),
|
||||
("realm", "RealmConfig"),
|
||||
("recurrent_gemma", "RecurrentGemmaConfig"),
|
||||
@@ -513,6 +514,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
|
||||
("qwen2_audio", "Qwen2Audio"),
|
||||
("qwen2_audio_encoder", "Qwen2AudioEncoder"),
|
||||
("qwen2_moe", "Qwen2MoE"),
|
||||
("qwen2_vl", "Qwen2VL"),
|
||||
("rag", "RAG"),
|
||||
("realm", "REALM"),
|
||||
("recurrent_gemma", "RecurrentGemma"),
|
||||
|
||||
@@ -116,6 +116,7 @@ else:
|
||||
("poolformer", ("PoolFormerImageProcessor",)),
|
||||
("pvt", ("PvtImageProcessor",)),
|
||||
("pvt_v2", ("PvtImageProcessor",)),
|
||||
("qwen2_vl", ("Qwen2VLImageProcessor",)),
|
||||
("regnet", ("ConvNextImageProcessor",)),
|
||||
("resnet", ("ConvNextImageProcessor",)),
|
||||
("rt_detr", "RTDetrImageProcessor"),
|
||||
|
||||
@@ -200,6 +200,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
|
||||
("qwen2", "Qwen2Model"),
|
||||
("qwen2_audio_encoder", "Qwen2AudioEncoder"),
|
||||
("qwen2_moe", "Qwen2MoeModel"),
|
||||
("qwen2_vl", "Qwen2VLModel"),
|
||||
("recurrent_gemma", "RecurrentGemmaModel"),
|
||||
("reformer", "ReformerModel"),
|
||||
("regnet", "RegNetModel"),
|
||||
@@ -726,6 +727,7 @@ MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict(
|
||||
("llava_next_video", "LlavaNextVideoForConditionalGeneration"),
|
||||
("paligemma", "PaliGemmaForConditionalGeneration"),
|
||||
("pix2struct", "Pix2StructForConditionalGeneration"),
|
||||
("qwen2_vl", "Qwen2VLForConditionalGeneration"),
|
||||
("video_llava", "VideoLlavaForConditionalGeneration"),
|
||||
("vipllava", "VipLlavaForConditionalGeneration"),
|
||||
("vision-encoder-decoder", "VisionEncoderDecoderModel"),
|
||||
|
||||
@@ -83,6 +83,7 @@ PROCESSOR_MAPPING_NAMES = OrderedDict(
|
||||
("pix2struct", "Pix2StructProcessor"),
|
||||
("pop2piano", "Pop2PianoProcessor"),
|
||||
("qwen2_audio", "Qwen2AudioProcessor"),
|
||||
("qwen2_vl", "Qwen2VLProcessor"),
|
||||
("sam", "SamProcessor"),
|
||||
("seamless_m4t", "SeamlessM4TProcessor"),
|
||||
("sew", "Wav2Vec2Processor"),
|
||||
|
||||
74
src/transformers/models/qwen2_vl/__init__.py
Normal file
74
src/transformers/models/qwen2_vl/__init__.py
Normal file
@@ -0,0 +1,74 @@
|
||||
# Copyright 2024 The Qwen Team and The HuggingFace Inc. team. 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 TYPE_CHECKING
|
||||
|
||||
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
|
||||
|
||||
|
||||
_import_structure = {
|
||||
"configuration_qwen2_vl": ["Qwen2VLConfig"],
|
||||
"processing_qwen2_vl": ["Qwen2VLProcessor"],
|
||||
}
|
||||
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["modeling_qwen2_vl"] = [
|
||||
"Qwen2VLForConditionalGeneration",
|
||||
"Qwen2VLModel",
|
||||
"Qwen2VLPreTrainedModel",
|
||||
]
|
||||
|
||||
try:
|
||||
if not is_vision_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
_import_structure["image_processing_qwen2_vl"] = ["Qwen2VLImageProcessor"]
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .configuration_qwen2_vl import Qwen2VLConfig
|
||||
from .processing_qwen2_vl import Qwen2VLProcessor
|
||||
|
||||
try:
|
||||
if not is_torch_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
from .modeling_qwen2_vl import (
|
||||
Qwen2VLForConditionalGeneration,
|
||||
Qwen2VLModel,
|
||||
Qwen2VLPreTrainedModel,
|
||||
)
|
||||
|
||||
try:
|
||||
if not is_vision_available():
|
||||
raise OptionalDependencyNotAvailable()
|
||||
except OptionalDependencyNotAvailable:
|
||||
pass
|
||||
else:
|
||||
from .image_processing_qwen2_vl import Qwen2VLImageProcessor
|
||||
|
||||
|
||||
else:
|
||||
import sys
|
||||
|
||||
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
|
||||
206
src/transformers/models/qwen2_vl/configuration_qwen2_vl.py
Normal file
206
src/transformers/models/qwen2_vl/configuration_qwen2_vl.py
Normal file
@@ -0,0 +1,206 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. 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.
|
||||
"""Qwen2VL model configuration"""
|
||||
|
||||
import os
|
||||
from typing import Union
|
||||
|
||||
from ...configuration_utils import PretrainedConfig
|
||||
from ...utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class Qwen2VLVisionConfig(PretrainedConfig):
|
||||
model_type = "qwen2_vl"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
depth=32,
|
||||
embed_dim=1280,
|
||||
hidden_size=3584,
|
||||
hidden_act="quick_gelu",
|
||||
mlp_ratio=4,
|
||||
num_heads=16,
|
||||
in_channels=3,
|
||||
patch_size=14,
|
||||
spatial_merge_size=2,
|
||||
temporal_patch_size=2,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.depth = depth
|
||||
self.embed_dim = embed_dim
|
||||
self.hidden_size = hidden_size
|
||||
self.hidden_act = hidden_act
|
||||
self.mlp_ratio = mlp_ratio
|
||||
self.num_heads = num_heads
|
||||
self.in_channels = in_channels
|
||||
self.patch_size = patch_size
|
||||
self.spatial_merge_size = spatial_merge_size
|
||||
self.temporal_patch_size = temporal_patch_size
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
|
||||
cls._set_token_in_kwargs(kwargs)
|
||||
|
||||
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
||||
|
||||
if config_dict.get("model_type") == "qwen2_vl":
|
||||
config_dict = config_dict["vision_config"]
|
||||
|
||||
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
|
||||
logger.warning(
|
||||
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
|
||||
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
|
||||
)
|
||||
|
||||
return cls.from_dict(config_dict, **kwargs)
|
||||
|
||||
|
||||
class Qwen2VLConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`Qwen2VLModel`]. It is used to instantiate a
|
||||
Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
|
||||
with the defaults will yield a similar configuration to that of
|
||||
Qwen2-VL-7B-Instruct [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
|
||||
Args:
|
||||
vocab_size (`int`, *optional*, defaults to 152064):
|
||||
Vocabulary size of the Qwen2VL model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`Qwen2VLModel`]
|
||||
hidden_size (`int`, *optional*, defaults to 8192):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 29568):
|
||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 80):
|
||||
Number of hidden layers in the Transformer encoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 64):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
num_key_value_heads (`int`, *optional*, defaults to 8):
|
||||
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||||
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||||
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||||
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||||
by meanpooling all the original heads within that group. For more details checkout [this
|
||||
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 32768):
|
||||
The maximum sequence length that this model might ever be used with.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
|
||||
The epsilon used by the rms normalization layers.
|
||||
use_cache (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||
relevant if `config.is_decoder=True`.
|
||||
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||
Whether the model's input and output word embeddings should be tied.
|
||||
rope_theta (`float`, *optional*, defaults to 1000000.0):
|
||||
The base period of the RoPE embeddings.
|
||||
use_sliding_window (`bool`, *optional*, defaults to `False`):
|
||||
Whether to use sliding window attention.
|
||||
sliding_window (`int`, *optional*, defaults to 4096):
|
||||
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
|
||||
max_window_layers (`int`, *optional*, defaults to 80):
|
||||
The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
vision_config (`Dict`, *optional*):
|
||||
The config for the visual encoder initialization.
|
||||
rope_scaling (`Dict`, *optional*):
|
||||
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
||||
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
||||
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
||||
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
||||
these scaling strategies behave:
|
||||
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
|
||||
experimental feature, subject to breaking API changes in future versions.
|
||||
|
||||
```python
|
||||
>>> from transformers import Qwen2VLForConditionalGeneration, Qwen2VLConfig
|
||||
|
||||
>>> # Initializing a Qwen2VL style configuration
|
||||
>>> configuration = Qwen2VLConfig()
|
||||
|
||||
>>> # Initializing a model from the Qwen2-VL-7B style configuration
|
||||
>>> model = Qwen2VLForConditionalGeneration(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "qwen2_vl"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=152064,
|
||||
hidden_size=8192,
|
||||
intermediate_size=29568,
|
||||
num_hidden_layers=80,
|
||||
num_attention_heads=64,
|
||||
num_key_value_heads=8,
|
||||
hidden_act="silu",
|
||||
max_position_embeddings=32768,
|
||||
initializer_range=0.02,
|
||||
rms_norm_eps=1e-05,
|
||||
use_cache=True,
|
||||
tie_word_embeddings=False,
|
||||
rope_theta=1000000.0,
|
||||
use_sliding_window=False,
|
||||
sliding_window=4096,
|
||||
max_window_layers=80,
|
||||
attention_dropout=0.0,
|
||||
vision_config=None,
|
||||
rope_scaling=None,
|
||||
**kwargs,
|
||||
):
|
||||
if isinstance(vision_config, dict):
|
||||
self.vision_config = Qwen2VLVisionConfig(**vision_config)
|
||||
elif vision_config is None:
|
||||
self.vision_config = Qwen2VLVisionConfig()
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
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.use_sliding_window = use_sliding_window
|
||||
self.sliding_window = sliding_window
|
||||
self.max_window_layers = max_window_layers
|
||||
|
||||
# for backward compatibility
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.hidden_act = hidden_act
|
||||
self.initializer_range = initializer_range
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.attention_dropout = attention_dropout
|
||||
self.rope_scaling = rope_scaling
|
||||
|
||||
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
|
||||
458
src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py
Normal file
458
src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py
Normal file
@@ -0,0 +1,458 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# 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 Qwen2-VL."""
|
||||
|
||||
import math
|
||||
from typing import Dict, List, Optional, Union
|
||||
|
||||
import numpy as np
|
||||
|
||||
from ...image_processing_utils import BaseImageProcessor, BatchFeature
|
||||
from ...image_transforms import (
|
||||
convert_to_rgb,
|
||||
resize,
|
||||
to_channel_dimension_format,
|
||||
)
|
||||
from ...image_utils import (
|
||||
OPENAI_CLIP_MEAN,
|
||||
OPENAI_CLIP_STD,
|
||||
ChannelDimension,
|
||||
ImageInput,
|
||||
PILImageResampling,
|
||||
VideoInput,
|
||||
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 ...utils import TensorType, is_vision_available, logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
|
||||
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}")
|
||||
|
||||
|
||||
# Copied from transformers.models.llava_next_video.image_processing_llava_next_video.make_batched_videos
|
||||
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 = 56 * 56, max_pixels: int = 14 * 14 * 4 * 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}")
|
||||
elif 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 Qwen2VLImageProcessor(BaseImageProcessor):
|
||||
r"""
|
||||
Constructs a Qwen2-VL 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 `56 * 56`):
|
||||
The min pixels of the image to resize the image.
|
||||
max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
|
||||
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 = 56 * 56,
|
||||
max_pixels: int = 28 * 28 * 1280,
|
||||
patch_size: int = 14,
|
||||
temporal_patch_size: int = 2,
|
||||
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}
|
||||
self.do_convert_rgb = do_convert_rgb
|
||||
|
||||
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))
|
||||
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.merge_size,
|
||||
self.merge_size,
|
||||
self.patch_size,
|
||||
grid_w // self.merge_size,
|
||||
self.merge_size,
|
||||
self.patch_size,
|
||||
)
|
||||
patches = patches.transpose(0, 3, 6, 4, 7, 2, 1, 5, 8)
|
||||
flatten_patches = patches.reshape(
|
||||
grid_t * grid_h * grid_w, channel * self.temporal_patch_size * 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)
|
||||
1725
src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
Normal file
1725
src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
Normal file
File diff suppressed because it is too large
Load Diff
183
src/transformers/models/qwen2_vl/processing_qwen2_vl.py
Normal file
183
src/transformers/models/qwen2_vl/processing_qwen2_vl.py
Normal file
@@ -0,0 +1,183 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Processor class for Qwen2-VL.
|
||||
"""
|
||||
|
||||
from typing import List, Optional, Union
|
||||
|
||||
from ...feature_extraction_utils import BatchFeature
|
||||
from ...image_utils import ImageInput, VideoInput
|
||||
from ...processing_utils import ProcessorMixin
|
||||
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
|
||||
from ...utils import TensorType, logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class Qwen2VLProcessor(ProcessorMixin):
|
||||
r"""
|
||||
Constructs a Qwen2-VL processor which wraps a Qwen2-VL image processor and a Qwen2 tokenizer into a single processor.
|
||||
|
||||
[`Qwen2VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
|
||||
[`~Qwen2VLProcessor.__call__`] and [`~Qwen2VLProcessor.decode`] for more information.
|
||||
|
||||
Args:
|
||||
image_processor ([`Qwen2VLImageProcessor`], *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_processor_class = "Qwen2VLImageProcessor"
|
||||
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
|
||||
|
||||
def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
|
||||
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,
|
||||
padding: Union[bool, str, PaddingStrategy] = False,
|
||||
truncation: Union[bool, str, TruncationStrategy] = None,
|
||||
max_length: int = None,
|
||||
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
|
||||
) -> 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
|
||||
Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__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.
|
||||
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
|
||||
Select a strategy to pad the returned sequences (according to the model's padding side and padding
|
||||
index) among:
|
||||
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
|
||||
sequence if provided).
|
||||
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
|
||||
acceptable input length for the model if that argument is not provided.
|
||||
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
|
||||
lengths).
|
||||
max_length (`int`, *optional*):
|
||||
Maximum length of the returned list and optionally padding length (see above).
|
||||
truncation (`bool`, *optional*):
|
||||
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
|
||||
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`.
|
||||
"""
|
||||
if images is not None:
|
||||
image_inputs = self.image_processor(images=images, videos=None, return_tensors=return_tensors)
|
||||
image_grid_thw = image_inputs["image_grid_thw"]
|
||||
else:
|
||||
image_inputs = {}
|
||||
image_grid_thw = None
|
||||
|
||||
if videos is not None:
|
||||
videos_inputs = self.image_processor(images=None, videos=videos, return_tensors=return_tensors)
|
||||
video_grid_thw = videos_inputs["video_grid_thw"]
|
||||
else:
|
||||
videos_inputs = {}
|
||||
video_grid_thw = None
|
||||
|
||||
if not isinstance(text, list):
|
||||
text = [text]
|
||||
|
||||
if image_grid_thw is not None:
|
||||
merge_length = self.image_processor.merge_size**2
|
||||
index = 0
|
||||
for i in range(len(text)):
|
||||
while "<|image_pad|>" in text[i]:
|
||||
text[i] = text[i].replace(
|
||||
"<|image_pad|>", "<|placeholder|>" * (image_grid_thw[index].prod() // merge_length), 1
|
||||
)
|
||||
index += 1
|
||||
text[i] = text[i].replace("<|placeholder|>", "<|image_pad|>")
|
||||
|
||||
if video_grid_thw is not None:
|
||||
merge_length = self.image_processor.merge_size**2
|
||||
index = 0
|
||||
for i in range(len(text)):
|
||||
while "<|video_pad|>" in text[i]:
|
||||
text[i] = text[i].replace(
|
||||
"<|video_pad|>", "<|placeholder|>" * (video_grid_thw[index].prod() // merge_length), 1
|
||||
)
|
||||
index += 1
|
||||
text[i] = text[i].replace("<|placeholder|>", "<|video_pad|>")
|
||||
|
||||
text_inputs = self.tokenizer(
|
||||
text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
|
||||
)
|
||||
|
||||
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)
|
||||
|
||||
@property
|
||||
def model_input_names(self):
|
||||
tokenizer_input_names = self.tokenizer.model_input_names
|
||||
image_processor_input_names = self.image_processor.model_input_names
|
||||
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
||||
@@ -7315,6 +7315,27 @@ class Qwen2MoePreTrainedModel(metaclass=DummyObject):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Qwen2VLForConditionalGeneration(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Qwen2VLModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class Qwen2VLPreTrainedModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class RagModel(metaclass=DummyObject):
|
||||
_backends = ["torch"]
|
||||
|
||||
|
||||
@@ -513,6 +513,13 @@ class PvtImageProcessor(metaclass=DummyObject):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class Qwen2VLImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["vision"])
|
||||
|
||||
|
||||
class RTDetrImageProcessor(metaclass=DummyObject):
|
||||
_backends = ["vision"]
|
||||
|
||||
|
||||
0
tests/models/qwen2_vl/__init__.py
Normal file
0
tests/models/qwen2_vl/__init__.py
Normal file
249
tests/models/qwen2_vl/test_image_processing_qwen2_vl.py
Normal file
249
tests/models/qwen2_vl/test_image_processing_qwen2_vl.py
Normal file
@@ -0,0 +1,249 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 HuggingFace Inc.
|
||||
#
|
||||
# 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 unittest
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
|
||||
from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
|
||||
from transformers.testing_utils import require_torch, require_vision
|
||||
from transformers.utils import is_torch_available, is_vision_available
|
||||
|
||||
from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
from transformers import Qwen2VLImageProcessor
|
||||
|
||||
|
||||
class Qwen2VLImageProcessingTester(unittest.TestCase):
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=7,
|
||||
num_channels=3,
|
||||
min_resolution=56,
|
||||
max_resolution=1024,
|
||||
min_pixels=56 * 56,
|
||||
max_pixels=28 * 28 * 1280,
|
||||
do_normalize=True,
|
||||
image_mean=OPENAI_CLIP_MEAN,
|
||||
image_std=OPENAI_CLIP_STD,
|
||||
do_resize=True,
|
||||
patch_size=14,
|
||||
temporal_patch_size=2,
|
||||
merge_size=2,
|
||||
do_convert_rgb=True,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.min_resolution = min_resolution
|
||||
self.max_resolution = max_resolution
|
||||
self.num_channels = num_channels
|
||||
self.image_mean = OPENAI_CLIP_MEAN
|
||||
self.image_std = 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.do_resize = do_resize
|
||||
self.do_normalize = do_normalize
|
||||
self.image_mean = image_mean
|
||||
self.image_std = image_std
|
||||
self.do_convert_rgb = do_convert_rgb
|
||||
|
||||
def prepare_image_processor_dict(self):
|
||||
return {
|
||||
"do_resize": self.do_resize,
|
||||
"image_mean": self.image_mean,
|
||||
"image_std": self.image_std,
|
||||
"min_pixels": self.min_pixels,
|
||||
"max_pixels": self.max_pixels,
|
||||
"patch_size": self.patch_size,
|
||||
"temporal_patch_size": self.temporal_patch_size,
|
||||
"merge_size": self.merge_size,
|
||||
}
|
||||
|
||||
def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
|
||||
images = prepare_image_inputs(
|
||||
batch_size=self.batch_size,
|
||||
num_channels=self.num_channels,
|
||||
min_resolution=self.min_resolution,
|
||||
max_resolution=self.max_resolution,
|
||||
equal_resolution=equal_resolution,
|
||||
numpify=numpify,
|
||||
torchify=torchify,
|
||||
)
|
||||
return [[image] for image in images]
|
||||
|
||||
|
||||
@require_torch
|
||||
@require_vision
|
||||
class Qwen2VLImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
|
||||
image_processing_class = Qwen2VLImageProcessor if is_vision_available() else None
|
||||
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
self.image_processor_tester = Qwen2VLImageProcessingTester(self)
|
||||
|
||||
@property
|
||||
def image_processor_dict(self):
|
||||
return self.image_processor_tester.prepare_image_processor_dict()
|
||||
|
||||
def test_image_processor_properties(self):
|
||||
image_processing = self.image_processing_class(**self.image_processor_dict)
|
||||
self.assertTrue(hasattr(image_processing, "do_normalize"))
|
||||
self.assertTrue(hasattr(image_processing, "image_mean"))
|
||||
self.assertTrue(hasattr(image_processing, "image_std"))
|
||||
self.assertTrue(hasattr(image_processing, "do_resize"))
|
||||
self.assertTrue(hasattr(image_processing, "min_pixels"))
|
||||
self.assertTrue(hasattr(image_processing, "max_pixels"))
|
||||
self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
|
||||
self.assertTrue(hasattr(image_processing, "patch_size"))
|
||||
self.assertTrue(hasattr(image_processing, "temporal_patch_size"))
|
||||
self.assertTrue(hasattr(image_processing, "merge_size"))
|
||||
|
||||
def test_image_processor_from_dict_with_kwargs(self):
|
||||
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
|
||||
self.assertEqual(image_processor.min_pixels, 56 * 56)
|
||||
self.assertEqual(image_processor.max_pixels, 28 * 28 * 1280)
|
||||
|
||||
image_processor = self.image_processing_class.from_dict(
|
||||
self.image_processor_dict, min_pixels=256 * 256, max_pixels=640 * 640
|
||||
)
|
||||
self.assertEqual(image_processor.min_pixels, 256 * 256)
|
||||
self.assertEqual(image_processor.max_pixels, 640 * 640)
|
||||
|
||||
def test_select_best_resolution(self):
|
||||
# Test with a final resize resolution
|
||||
best_resolution = smart_resize(561, 278, factor=28)
|
||||
self.assertEqual(best_resolution, (560, 280))
|
||||
|
||||
def test_call_pil(self):
|
||||
# Initialize image_processing
|
||||
image_processing = self.image_processing_class(**self.image_processor_dict)
|
||||
# create random PIL images
|
||||
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
|
||||
for image in image_inputs:
|
||||
self.assertIsInstance(image[0], Image.Image)
|
||||
|
||||
# Test not batched input
|
||||
prcocess_out = image_processing(image_inputs[0], return_tensors="pt")
|
||||
encoded_images = prcocess_out.pixel_values
|
||||
image_grid_thws = prcocess_out.image_grid_thw
|
||||
expected_output_image_shape = (4900, 1176)
|
||||
expected_image_grid_thws = torch.Tensor([[1, 70, 70]])
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
|
||||
|
||||
# Test batched
|
||||
prcocess_out = image_processing(image_inputs, return_tensors="pt")
|
||||
encoded_images = prcocess_out.pixel_values
|
||||
image_grid_thws = prcocess_out.image_grid_thw
|
||||
expected_output_image_shape = (34300, 1176)
|
||||
expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7)
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
|
||||
|
||||
def test_call_numpy(self):
|
||||
# Initialize image_processing
|
||||
image_processing = self.image_processing_class(**self.image_processor_dict)
|
||||
# create random numpy tensors
|
||||
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
|
||||
for image in image_inputs:
|
||||
self.assertIsInstance(image[0], np.ndarray)
|
||||
|
||||
# Test not batched input
|
||||
prcocess_out = image_processing(image_inputs[0], return_tensors="pt")
|
||||
encoded_images = prcocess_out.pixel_values
|
||||
image_grid_thws = prcocess_out.image_grid_thw
|
||||
expected_output_image_shape = (4900, 1176)
|
||||
expected_image_grid_thws = torch.Tensor([[1, 70, 70]])
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
|
||||
|
||||
# Test batched
|
||||
prcocess_out = image_processing(image_inputs, return_tensors="pt")
|
||||
encoded_images = prcocess_out.pixel_values
|
||||
image_grid_thws = prcocess_out.image_grid_thw
|
||||
expected_output_image_shape = (34300, 1176)
|
||||
expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7)
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
|
||||
|
||||
def test_call_pytorch(self):
|
||||
# Initialize image_processing
|
||||
image_processing = self.image_processing_class(**self.image_processor_dict)
|
||||
# create random PyTorch tensors
|
||||
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
|
||||
|
||||
for image in image_inputs:
|
||||
self.assertIsInstance(image[0], torch.Tensor)
|
||||
|
||||
# Test not batched input
|
||||
prcocess_out = image_processing(image_inputs[0], return_tensors="pt")
|
||||
encoded_images = prcocess_out.pixel_values
|
||||
image_grid_thws = prcocess_out.image_grid_thw
|
||||
expected_output_image_shape = (4900, 1176)
|
||||
expected_image_grid_thws = torch.Tensor([[1, 70, 70]])
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
|
||||
|
||||
# Test batched
|
||||
prcocess_out = image_processing(image_inputs, return_tensors="pt")
|
||||
encoded_images = prcocess_out.pixel_values
|
||||
image_grid_thws = prcocess_out.image_grid_thw
|
||||
expected_output_image_shape = (34300, 1176)
|
||||
expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7)
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
|
||||
|
||||
@unittest.skip(reason="Qwen2VLImageProcessor doesn't treat 4 channel PIL and numpy consistently yet")
|
||||
def test_call_numpy_4_channels(self):
|
||||
pass
|
||||
|
||||
def test_nested_input(self):
|
||||
image_processing = self.image_processing_class(**self.image_processor_dict)
|
||||
image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
|
||||
|
||||
# Test batched as a list of images
|
||||
prcocess_out = image_processing(image_inputs, return_tensors="pt")
|
||||
encoded_images = prcocess_out.pixel_values
|
||||
image_grid_thws = prcocess_out.image_grid_thw
|
||||
expected_output_image_shape = (34300, 1176)
|
||||
expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7)
|
||||
self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
|
||||
self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
|
||||
|
||||
# Test batched as a nested list of images, where each sublist is one batch
|
||||
image_inputs_nested = image_inputs[:3] + image_inputs[3:]
|
||||
prcocess_out = image_processing(image_inputs_nested, return_tensors="pt")
|
||||
encoded_images_nested = prcocess_out.pixel_values
|
||||
image_grid_thws_nested = prcocess_out.image_grid_thw
|
||||
expected_output_image_shape = (34300, 1176)
|
||||
expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7)
|
||||
self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)
|
||||
self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
|
||||
|
||||
# Image processor should return same pixel values, independently of ipnut format
|
||||
self.assertTrue((encoded_images_nested == encoded_images).all())
|
||||
self.assertTrue((image_grid_thws_nested == expected_image_grid_thws).all())
|
||||
448
tests/models/qwen2_vl/test_modeling_qwen2_vl.py
Normal file
448
tests/models/qwen2_vl/test_modeling_qwen2_vl.py
Normal file
@@ -0,0 +1,448 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2024 The HuggingFace Inc. team. 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.
|
||||
"""Testing suite for the PyTorch Qwen2-VL model."""
|
||||
|
||||
import gc
|
||||
import unittest
|
||||
|
||||
import requests
|
||||
|
||||
from transformers import (
|
||||
AutoProcessor,
|
||||
Qwen2VLConfig,
|
||||
Qwen2VLForConditionalGeneration,
|
||||
is_torch_available,
|
||||
is_vision_available,
|
||||
)
|
||||
from transformers.testing_utils import (
|
||||
require_bitsandbytes,
|
||||
require_torch,
|
||||
slow,
|
||||
torch_device,
|
||||
)
|
||||
|
||||
from ...generation.test_utils import GenerationTesterMixin
|
||||
from ...test_configuration_common import ConfigTester
|
||||
from ...test_modeling_common import (
|
||||
ModelTesterMixin,
|
||||
_config_zero_init,
|
||||
floats_tensor,
|
||||
ids_tensor,
|
||||
)
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
else:
|
||||
is_torch_greater_or_equal_than_2_0 = False
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
|
||||
class Qwen2VLVisionText2TextModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=8,
|
||||
seq_length=7,
|
||||
num_channels=3,
|
||||
ignore_index=-100,
|
||||
image_size=28,
|
||||
bos_token_id=0,
|
||||
eos_token_id=1,
|
||||
vision_start_token_id=151652,
|
||||
image_token_id=151655,
|
||||
video_token_id=151656,
|
||||
hidden_act="silu",
|
||||
hidden_size=32,
|
||||
vocab_size=152064,
|
||||
intermediate_size=37,
|
||||
max_position_embeddings=512,
|
||||
max_window_layers=3,
|
||||
model_type="qwen2_vl",
|
||||
num_attention_heads=4,
|
||||
num_hidden_layers=3,
|
||||
num_key_value_heads=2,
|
||||
rope_theta=10000,
|
||||
tie_word_embeddings=True,
|
||||
is_training=True,
|
||||
vision_config={
|
||||
"depth": 2,
|
||||
"embed_dim": 32,
|
||||
"hidden_act": "quick_gelu",
|
||||
"hidden_size": 32,
|
||||
"mlp_ratio": 4,
|
||||
"num_heads": 4,
|
||||
"patch_size": 14,
|
||||
"spatial_merge_size": 2,
|
||||
"temporal_patch_size": 2,
|
||||
},
|
||||
rope_scaling={"type": "mrope", "mrope_section": [2, 1, 1]},
|
||||
):
|
||||
self.parent = parent
|
||||
self.ignore_index = ignore_index
|
||||
self.bos_token_id = bos_token_id
|
||||
self.eos_token_id = eos_token_id
|
||||
self.vision_start_token_id = vision_start_token_id
|
||||
self.image_token_id = image_token_id
|
||||
self.video_token_id = video_token_id
|
||||
self.hidden_act = hidden_act
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.max_window_layers = max_window_layers
|
||||
self.model_type = model_type
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.rope_theta = rope_theta
|
||||
self.tie_word_embeddings = tie_word_embeddings
|
||||
self.vision_config = vision_config
|
||||
self.rope_scaling = rope_scaling
|
||||
self.batch_size = batch_size
|
||||
self.num_channels = num_channels
|
||||
self.image_size = image_size
|
||||
self.seq_length = seq_length
|
||||
self.is_training = is_training
|
||||
self.vocab_size = vocab_size
|
||||
|
||||
def get_config(self):
|
||||
return Qwen2VLConfig(
|
||||
hidden_size=self.hidden_size,
|
||||
intermediate_size=self.intermediate_size,
|
||||
num_hidden_layers=self.num_hidden_layers,
|
||||
num_attention_heads=self.num_attention_heads,
|
||||
num_key_value_heads=self.num_key_value_heads,
|
||||
hidden_act=self.hidden_act,
|
||||
max_position_embeddings=self.max_position_embeddings,
|
||||
vision_config=self.vision_config,
|
||||
model_type=self.model_type,
|
||||
max_window_layers=self.max_window_layers,
|
||||
rope_scaling=self.rope_scaling,
|
||||
tie_word_embeddings=self.tie_word_embeddings,
|
||||
bos_token_id=self.bos_token_id,
|
||||
eos_token_id=self.eos_token_id,
|
||||
vision_start_token_id=self.vision_start_token_id,
|
||||
image_token_id=self.image_token_id,
|
||||
video_token_id=self.video_token_id,
|
||||
vocab_size=self.vocab_size,
|
||||
)
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
config = self.get_config()
|
||||
patch_size = config.vision_config.patch_size
|
||||
temporal_patch_size = config.vision_config.temporal_patch_size
|
||||
pixel_values = floats_tensor(
|
||||
[
|
||||
self.batch_size * (self.image_size**2) // (patch_size**2),
|
||||
self.num_channels * (patch_size**2) * temporal_patch_size,
|
||||
]
|
||||
)
|
||||
|
||||
return config, pixel_values
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
config, pixel_values = config_and_inputs
|
||||
vision_seqlen = pixel_values.shape[0] // self.batch_size // (self.vision_config["spatial_merge_size"] ** 2)
|
||||
input_ids = ids_tensor([self.batch_size, self.seq_length - 1 + vision_seqlen], self.vocab_size)
|
||||
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
|
||||
input_ids[:, torch.arange(vision_seqlen, device=torch_device) + 1] = self.image_token_id
|
||||
labels = torch.zeros(
|
||||
(self.batch_size, self.seq_length - 1 + vision_seqlen), dtype=torch.long, device=torch_device
|
||||
)
|
||||
patch_size = self.vision_config["patch_size"]
|
||||
inputs_dict = {
|
||||
"pixel_values": pixel_values,
|
||||
"image_grid_thw": torch.tensor(
|
||||
[[1, self.image_size // patch_size, self.image_size // patch_size]] * self.batch_size
|
||||
),
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"labels": labels,
|
||||
}
|
||||
return config, inputs_dict
|
||||
|
||||
def create_and_check_qwen2_vl_model_fp16_forward(
|
||||
self, config, input_ids, pixel_values, attention_mask, image_grid_thw
|
||||
):
|
||||
model = Qwen2VLForConditionalGeneration(config=config)
|
||||
model.to(torch_device)
|
||||
model.half()
|
||||
model.eval()
|
||||
logits = model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
image_grid_thw=image_grid_thw,
|
||||
pixel_values=pixel_values.to(torch.bfloat16),
|
||||
return_dict=True,
|
||||
)["logits"]
|
||||
self.parent.assertFalse(torch.isnan(logits).any().item())
|
||||
|
||||
def create_and_check_qwen2_vl_model_fp16_autocast_forward(
|
||||
self, config, input_ids, pixel_values, attention_mask, image_grid_thw
|
||||
):
|
||||
config.torch_dtype = torch.float16
|
||||
model = Qwen2VLForConditionalGeneration(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
with torch.autocast(device_type="cuda", dtype=torch.float16):
|
||||
logits = model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
image_grid_thw=image_grid_thw,
|
||||
pixel_values=pixel_values.to(torch.bfloat16),
|
||||
return_dict=True,
|
||||
)["logits"]
|
||||
self.parent.assertFalse(torch.isnan(logits).any().item())
|
||||
|
||||
|
||||
@require_torch
|
||||
class Qwen2VLModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
||||
"""
|
||||
Model tester for `Qwen2VLForConditionalGeneration`.
|
||||
"""
|
||||
|
||||
all_model_classes = (Qwen2VLForConditionalGeneration,) if is_torch_available() else ()
|
||||
test_pruning = False
|
||||
test_head_masking = False
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = Qwen2VLVisionText2TextModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=Qwen2VLConfig, has_text_modality=False)
|
||||
|
||||
def test_initialization(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
configs_no_init = _config_zero_init(config)
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config=configs_no_init)
|
||||
for name, param in model.named_parameters():
|
||||
if param.requires_grad:
|
||||
self.assertIn(
|
||||
((param.data.mean() * 1e9).round() / 1e9).item(),
|
||||
[0.0, 1.0],
|
||||
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
||||
)
|
||||
|
||||
@unittest.skip(
|
||||
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
||||
)
|
||||
def test_training_gradient_checkpointing(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(
|
||||
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
||||
)
|
||||
def test_training_gradient_checkpointing_use_reentrant(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(
|
||||
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
||||
)
|
||||
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Feedforward chunking is not yet supported")
|
||||
def test_feed_forward_chunking(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Generate needs input ids")
|
||||
def test_inputs_embeds_matches_input_ids_with_generate(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="CPU offload is not yet supported")
|
||||
def test_cpu_offload(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
|
||||
def test_disk_offload_bin(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
|
||||
def test_disk_offload_safetensors(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Some undefined behavior encountered with test versions of this model. Skip for now.")
|
||||
def test_model_parallelism(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Compile not yet supported because in Qwen2VL models")
|
||||
def test_sdpa_can_compile_dynamic(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Compile not yet supported because in Qwen2VL models")
|
||||
def test_sdpa_can_dispatch_on_flash(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
|
||||
def test_multi_gpu_data_parallel_forward(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="We cannot configure to output a smaller model.")
|
||||
def test_model_is_small(self):
|
||||
pass
|
||||
|
||||
|
||||
@require_torch
|
||||
class Qwen2VLIntegrationTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
|
||||
self.messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image"},
|
||||
{"type": "text", "text": "What kind of dog is this?"},
|
||||
],
|
||||
}
|
||||
]
|
||||
url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
|
||||
self.image = Image.open(requests.get(url, stream=True).raw)
|
||||
|
||||
def tearDown(self):
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
@slow
|
||||
@require_bitsandbytes
|
||||
def test_small_model_integration_test(self):
|
||||
model = Qwen2VLForConditionalGeneration.from_pretrained(
|
||||
"Qwen/Qwen2-VL-7B-Instruct",
|
||||
load_in_4bit=True,
|
||||
)
|
||||
|
||||
text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
|
||||
inputs = self.processor(text=[text], images=[self.image], return_tensors="pt")
|
||||
|
||||
expected_input_ids = [151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 151652, 151655, 151655] # fmt: skip
|
||||
assert expected_input_ids == inputs.input_ids[0].tolist()[:17]
|
||||
|
||||
expected_pixel_slice = torch.tensor(
|
||||
[
|
||||
[0.8501, 0.8647, 0.8647],
|
||||
[1.0106, 1.0106, 1.0252],
|
||||
[0.9960, 1.0106, 1.0252],
|
||||
[1.0982, 1.1128, 1.1274],
|
||||
[1.0836, 1.0982, 1.0982],
|
||||
[1.1858, 1.1858, 1.1858],
|
||||
],
|
||||
dtype=torch.float32,
|
||||
device="cpu",
|
||||
)
|
||||
assert torch.allclose(expected_pixel_slice, inputs.pixel_values[:6, :3], atol=1e-3)
|
||||
|
||||
# verify generation
|
||||
inputs = inputs.to(torch_device)
|
||||
|
||||
output = model.generate(**inputs, max_new_tokens=30)
|
||||
EXPECTED_DECODED_TEXT = "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?assistant\nThe dog in the picture appears to be a Labrador Retriever or a similar breed. Labradors are known for their friendly and intelligent nature,"
|
||||
|
||||
self.assertEqual(
|
||||
self.processor.decode(output[0], skip_special_tokens=True),
|
||||
EXPECTED_DECODED_TEXT,
|
||||
)
|
||||
|
||||
@slow
|
||||
@require_bitsandbytes
|
||||
def test_small_model_integration_test_batch(self):
|
||||
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", load_in_4bit=True)
|
||||
text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
|
||||
inputs = self.processor(text=[text, text], images=[self.image, self.image], return_tensors="pt").to(
|
||||
torch_device
|
||||
)
|
||||
|
||||
# it should not matter whether two images are the same size or not
|
||||
output = model.generate(**inputs, max_new_tokens=30)
|
||||
|
||||
EXPECTED_DECODED_TEXT = [
|
||||
"system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?assistant\nThe dog in the picture appears to be a Labrador Retriever or a similar breed. Labradors are known for their friendly and intelligent nature,",
|
||||
"system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?assistant\nThe dog in the image appears to be a Labrador Retriever or a similar breed. Labradors are known for their friendly and outgoing nature,",
|
||||
]
|
||||
self.assertEqual(
|
||||
self.processor.batch_decode(output, skip_special_tokens=True),
|
||||
EXPECTED_DECODED_TEXT,
|
||||
)
|
||||
self.assertEqual(
|
||||
self.processor.batch_decode(output, skip_special_tokens=True)[0],
|
||||
self.processor.batch_decode(output, skip_special_tokens=True)[1],
|
||||
)
|
||||
|
||||
@slow
|
||||
@require_bitsandbytes
|
||||
def test_small_model_integration_test_batch_wo_image(self):
|
||||
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", load_in_4bit=True)
|
||||
text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
|
||||
messages2 = [
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": "Who are you?"},
|
||||
]
|
||||
text2 = self.processor.apply_chat_template(messages2, tokenize=False, add_generation_prompt=True)
|
||||
inputs = self.processor(text=[text, text2], images=[self.image], return_tensors="pt").to(torch_device)
|
||||
|
||||
# it should not matter whether two images are the same size or not
|
||||
output = model.generate(**inputs, max_new_tokens=30)
|
||||
|
||||
EXPECTED_DECODED_TEXT = [
|
||||
"system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?assistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and outgoing personalities, as well as their",
|
||||
"system\nYou are a helpful assistant.user\nWho are you?assistant\nI am Qwen, a large language model created by Alibaba Cloud. I am designed to assist with various tasks and answer a wide range of questions to",
|
||||
]
|
||||
|
||||
self.assertEqual(
|
||||
self.processor.batch_decode(output, skip_special_tokens=True),
|
||||
EXPECTED_DECODED_TEXT,
|
||||
)
|
||||
|
||||
@slow
|
||||
@require_bitsandbytes
|
||||
def test_small_model_integration_test_batch_different_resolutions(self):
|
||||
model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-7B-Instruct", load_in_4bit=True)
|
||||
text, vision_infos = self.processor.apply_chat_template(
|
||||
self.messages, tokenize=False, add_generation_prompt=True
|
||||
)
|
||||
messages2 = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "image",
|
||||
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
|
||||
"resized_height": 504,
|
||||
"resized_width": 252,
|
||||
},
|
||||
{"type": "text", "text": "What kind of dog is this?"},
|
||||
],
|
||||
}
|
||||
]
|
||||
text2, vision_infos2 = self.processor.apply_chat_template(
|
||||
messages2, tokenize=False, add_generation_prompt=True
|
||||
)
|
||||
inputs = self.processor(
|
||||
text=[text, text2], vision_infos=[vision_infos, vision_infos2], return_tensors="pt"
|
||||
).to(torch_device)
|
||||
|
||||
# it should not matter whether two images are the same size or not
|
||||
output = model.generate(**inputs, max_new_tokens=30)
|
||||
|
||||
EXPECTED_DECODED_TEXT = [
|
||||
"system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?assistant\nThe dog in the picture appears to be a Labrador Retriever or a similar breed. Labradors are known for their friendly and intelligent nature,",
|
||||
"system\nYou are a helpful assistant.\nuser\nWho are you?assistant\nI am a large language model created by Alibaba Cloud. I am called Qwen.",
|
||||
]
|
||||
self.assertEqual(
|
||||
self.processor.batch_decode(output, skip_special_tokens=True),
|
||||
EXPECTED_DECODED_TEXT,
|
||||
)
|
||||
@@ -43,6 +43,7 @@ SPECIAL_CASES_TO_ALLOW = {
|
||||
],
|
||||
"Qwen2Config": ["use_sliding_window"],
|
||||
"Qwen2MoeConfig": ["use_sliding_window"],
|
||||
"Qwen2VLConfig": ["use_sliding_window"],
|
||||
"Gemma2Config": ["tie_word_embeddings"],
|
||||
# used to compute the property `self.chunk_length`
|
||||
"EncodecConfig": ["overlap"],
|
||||
|
||||
@@ -70,6 +70,7 @@ PRIVATE_MODELS = [
|
||||
"UMT5Stack",
|
||||
"Pop2PianoStack",
|
||||
"Qwen2AudioEncoder",
|
||||
"Qwen2VisionTransformerPretrainedModel",
|
||||
"SwitchTransformersStack",
|
||||
"TFDPRSpanPredictor",
|
||||
"MaskFormerSwinModel",
|
||||
@@ -86,50 +87,54 @@ PRIVATE_MODELS = [
|
||||
|
||||
# Update this list for models that are not tested with a comment explaining the reason it should not be.
|
||||
# Being in this list is an exception and should **not** be the rule.
|
||||
IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [
|
||||
# models to ignore for not tested
|
||||
"RecurrentGemmaModel", # Building part of bigger (tested) model.
|
||||
"FuyuForCausalLM", # Not tested fort now
|
||||
"InstructBlipQFormerModel", # Building part of bigger (tested) model.
|
||||
"InstructBlipVideoQFormerModel", # Building part of bigger (tested) model.
|
||||
"UMT5EncoderModel", # Building part of bigger (tested) model.
|
||||
"Blip2QFormerModel", # Building part of bigger (tested) model.
|
||||
"ErnieMForInformationExtraction",
|
||||
"FastSpeech2ConformerHifiGan", # Already tested by SpeechT5HifiGan (# Copied from)
|
||||
"FastSpeech2ConformerWithHifiGan", # Built with two smaller (tested) models.
|
||||
"GraphormerDecoderHead", # Building part of bigger (tested) model.
|
||||
"JukeboxVQVAE", # Building part of bigger (tested) model.
|
||||
"JukeboxPrior", # Building part of bigger (tested) model.
|
||||
"DecisionTransformerGPT2Model", # Building part of bigger (tested) model.
|
||||
"SegformerDecodeHead", # Building part of bigger (tested) model.
|
||||
"MgpstrModel", # Building part of bigger (tested) model.
|
||||
"BertLMHeadModel", # Needs to be setup as decoder.
|
||||
"MegatronBertLMHeadModel", # Building part of bigger (tested) model.
|
||||
"RealmBertModel", # Building part of bigger (tested) model.
|
||||
"RealmReader", # Not regular model.
|
||||
"RealmScorer", # Not regular model.
|
||||
"RealmForOpenQA", # Not regular model.
|
||||
"ReformerForMaskedLM", # Needs to be setup as decoder.
|
||||
"TFElectraMainLayer", # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?)
|
||||
"TFRobertaForMultipleChoice", # TODO: fix
|
||||
"TFRobertaPreLayerNormForMultipleChoice", # TODO: fix
|
||||
"SeparableConv1D", # Building part of bigger (tested) model.
|
||||
"FlaxBartForCausalLM", # Building part of bigger (tested) model.
|
||||
"FlaxBertForCausalLM", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM.
|
||||
"OPTDecoderWrapper",
|
||||
"TFSegformerDecodeHead", # Not a regular model.
|
||||
"AltRobertaModel", # Building part of bigger (tested) model.
|
||||
"BlipTextLMHeadModel", # No need to test it as it is tested by BlipTextVision models
|
||||
"TFBlipTextLMHeadModel", # No need to test it as it is tested by BlipTextVision models
|
||||
"BridgeTowerTextModel", # No need to test it as it is tested by BridgeTowerModel model.
|
||||
"BridgeTowerVisionModel", # No need to test it as it is tested by BridgeTowerModel model.
|
||||
"BarkCausalModel", # Building part of bigger (tested) model.
|
||||
"BarkModel", # Does not have a forward signature - generation tested with integration tests.
|
||||
"SeamlessM4TTextToUnitModel", # Building part of bigger (tested) model.
|
||||
"SeamlessM4TCodeHifiGan", # Building part of bigger (tested) model.
|
||||
"SeamlessM4TTextToUnitForConditionalGeneration", # Building part of bigger (tested) model.
|
||||
"ChameleonVQVAE", # VQVAE here is used only for encoding (discretizing) and is tested as part of bigger model
|
||||
]
|
||||
IGNORE_NON_TESTED = (
|
||||
PRIVATE_MODELS.copy()
|
||||
+ [
|
||||
# models to ignore for not tested
|
||||
"RecurrentGemmaModel", # Building part of bigger (tested) model.
|
||||
"FuyuForCausalLM", # Not tested fort now
|
||||
"InstructBlipQFormerModel", # Building part of bigger (tested) model.
|
||||
"InstructBlipVideoQFormerModel", # Building part of bigger (tested) model.
|
||||
"UMT5EncoderModel", # Building part of bigger (tested) model.
|
||||
"Blip2QFormerModel", # Building part of bigger (tested) model.
|
||||
"ErnieMForInformationExtraction",
|
||||
"FastSpeech2ConformerHifiGan", # Already tested by SpeechT5HifiGan (# Copied from)
|
||||
"FastSpeech2ConformerWithHifiGan", # Built with two smaller (tested) models.
|
||||
"GraphormerDecoderHead", # Building part of bigger (tested) model.
|
||||
"JukeboxVQVAE", # Building part of bigger (tested) model.
|
||||
"JukeboxPrior", # Building part of bigger (tested) model.
|
||||
"DecisionTransformerGPT2Model", # Building part of bigger (tested) model.
|
||||
"SegformerDecodeHead", # Building part of bigger (tested) model.
|
||||
"MgpstrModel", # Building part of bigger (tested) model.
|
||||
"BertLMHeadModel", # Needs to be setup as decoder.
|
||||
"MegatronBertLMHeadModel", # Building part of bigger (tested) model.
|
||||
"RealmBertModel", # Building part of bigger (tested) model.
|
||||
"RealmReader", # Not regular model.
|
||||
"RealmScorer", # Not regular model.
|
||||
"RealmForOpenQA", # Not regular model.
|
||||
"ReformerForMaskedLM", # Needs to be setup as decoder.
|
||||
"TFElectraMainLayer", # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?)
|
||||
"TFRobertaForMultipleChoice", # TODO: fix
|
||||
"TFRobertaPreLayerNormForMultipleChoice", # TODO: fix
|
||||
"SeparableConv1D", # Building part of bigger (tested) model.
|
||||
"FlaxBartForCausalLM", # Building part of bigger (tested) model.
|
||||
"FlaxBertForCausalLM", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM.
|
||||
"OPTDecoderWrapper",
|
||||
"TFSegformerDecodeHead", # Not a regular model.
|
||||
"AltRobertaModel", # Building part of bigger (tested) model.
|
||||
"BlipTextLMHeadModel", # No need to test it as it is tested by BlipTextVision models
|
||||
"TFBlipTextLMHeadModel", # No need to test it as it is tested by BlipTextVision models
|
||||
"BridgeTowerTextModel", # No need to test it as it is tested by BridgeTowerModel model.
|
||||
"BridgeTowerVisionModel", # No need to test it as it is tested by BridgeTowerModel model.
|
||||
"BarkCausalModel", # Building part of bigger (tested) model.
|
||||
"BarkModel", # Does not have a forward signature - generation tested with integration tests.
|
||||
"SeamlessM4TTextToUnitModel", # Building part of bigger (tested) model.
|
||||
"SeamlessM4TCodeHifiGan", # Building part of bigger (tested) model.
|
||||
"SeamlessM4TTextToUnitForConditionalGeneration", # Building part of bigger (tested) model.
|
||||
"ChameleonVQVAE", # VQVAE here is used only for encoding (discretizing) and is tested as part of bigger model
|
||||
"Qwen2VLModel", # Building part of bigger (tested) model. Tested implicitly through Qwen2VLForConditionalGeneration.
|
||||
]
|
||||
)
|
||||
|
||||
# Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't
|
||||
# trigger the common tests.
|
||||
|
||||
Reference in New Issue
Block a user