Duplicate from FreedomIntelligence/ALLaVA-3B-Longer
Browse filesCo-authored-by: guiminghardychen <[email protected]>
- .gitattributes +35 -0
- README.md +135 -0
- added_tokens.json +40 -0
- config.json +50 -0
- configuration_phi.py +193 -0
- generation_config.json +4 -0
- generation_utils.py +287 -0
- llava_arch.py +531 -0
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- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +854 -0
- modeling_llava_phi.py +252 -0
- modeling_phi.py +1383 -0
- special_tokens_map.json +24 -0
- tokenizer.json +0 -0
- tokenizer_config.json +324 -0
- trainer_state.json +0 -0
- training_args.bin +3 -0
- training_datasets_by_stage.jpg +0 -0
- vocab.json +0 -0
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README.md
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---
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license: apache-2.0
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datasets:
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- FreedomIntelligence/ALLaVA-4V
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language:
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- en
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pipeline_tag: text-generation
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---
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# ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model
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<p align="center">
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⚡ALLaVA is a project that provides a large-scale GPT4V-synthesized dataset for training LVLMs.⚡
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</p>
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<!-- <p align="center">
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</p> -->
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<p align="center">
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📃 <a href="https://arxiv.org/abs/2402.11684" target="_blank">Paper</a> • 🌐 <a href="https://allava.freedomai.cn/#/" target="_blank">Demo</a> • 👨🏻💻 <a href="https://github.com/FreedomIntelligence/ALLaVA" target="_blank">Github</a>
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</p>
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<p align="center">
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🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V" target="_blank">ALLaVA-4V Dataset</a>
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</p>
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<p align="center">
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🤗 <a href="https://huggingface.co/FreedomIntelligence/ALLaVA-3B-Longer" target="_blank">ALLaVA-3B-Longer</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/ALLaVA-3B" target="_blank">ALLaVA-3B</a>
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</p>
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<!-- <p align="center">
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📃 <a href="https://arxiv.org/abs/2402.11684" target="_blank">Paper</a> • 🌐 <a href="https://allava.freedomai.cn/#/" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V" target="_blank">ALLaVA-4V Dataset</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/ALLaVA-3B-Longer" target="_blank">ALLaVA-3B-Longer</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/ALLaVA-3B" target="_blank">ALLaVA-3B</a>
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<br> <a href="https://github.com/FreedomIntelligence/CMB/blob/main/README_zh.md"> 中文</a> | <a href="https://github.com/FreedomIntelligence/CMB/blob/main/README.md"> English
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</p> -->
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## Benchmark Result
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Our model [**ALLaVA-3B-Longer**](https://huggingface.co/FreedomIntelligence/ALLaVA-3B-Longer) and [**ALLaVA-3B**](https://huggingface.co/FreedomIntelligence/ALLaVA-3B) achieve competitive results on 12 benchmarks. Bold numbers denote the SOTA performance among 3B-scale models.
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| Model | Backbone | Vicuna-80 | MMB | SEEDBench-v1 (img) | MM-Vet | MMMU (val) | MME | TextVQA | GQA | EMT (CIFAR10) | MLLM-Bench | TouchStone | LLaVA (In-the-Wild) |
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|-------|----------|-----------|-----|-------------|--------|----------|-----|------|-----|---------|----|----|--------|
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| Qwen-VL-Chat | Qwen-7B | - | 60.6 | 65.4 | - | 35.9 | 1487.5 | 61.5 | 57.5 | - | 6.2 | 711.6 | - |
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| LLaVA-v1.5-7B | Vicuna-7B | - | 64.3 | - | 31.1 | - | 1510.7 | 58.2 | 62.0 | - | - | | 65.4 |
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| LLaVA-v1.5-13B | Vicuna-13B | 22.50 | 67.7 | 68.2 | 35.4 | 36.4 | 1531.3 | 61.3 | 63.3 | 85.0 | 7.4 | 637.7 | 70.7 |
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| ShareGPT4V-7B | Vicuna-7B | - | 68.8 | 69.7 | 37.6 | - | 1943.8 | 60.4 | 63.3 | - | - | - | 72.6 |
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| TinyGPT-V | Phi2-2.7B | - | - | - | - | - | - | - | 33.6 | - | - | - | - |
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| MobileVLM | MobileLLaMA-2.7B | - | 59.6 | - | - | - | 1288.9 | 47.5 | - | - | - | - | - |
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| LLaVA-Phi | Phi2-2.7B | - | 59.8 | - | 28.9 | - | 1335.1 | 48.6 | - | - | - | - | - |
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| **ALLaVA-3B** | Phi2-2.7B | 48.8 | 64.0 | 65.2 | 32.2 | **35.3** | **1623.2** | 49.5 | 48.8 | **90.2** | 6.7 | 632.0 | 69.4 |
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| **ALLaVA-3B-Longer** | Phi2-2.7B | **52.5** | **64.6** | **65.6** | **35.5** | 33.2 | 1564.6 | **50.3** | **50.0** | 85.9 | **8.8** | **636.5** | **71.7** |
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The detailed information of each benchmark is shown in Table 4 of our [technical report](https://arxiv.org/pdf/2402.11684.pdf).
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## 🏭 Inference
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### Load from 🤗 (Recommended)
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See the [example script](https://github.com/FreedomIntelligence/ALLaVA/blob/main/allava/serve/huggingface_inference.py).
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### CLI
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See [here](https://github.com/FreedomIntelligence/ALLaVA/tree/main?tab=readme-ov-file#cli) for CLI code snippet.
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## 🏋️♂️ Training
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### Data
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<div align=center>
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<img src="training_datasets_by_stage.jpg" width = "640" alt="training_datasets" align=center />
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</div>
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As shown in the table, ALLaVA-3B uses 1M and 1.5M data for PT. and FT., respectively.
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ALLaVA-3B-Longer trains one more epoch (i.e. 3M in total) for the FT. stage.
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### Code
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The training code is largely based on [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA).
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We wholeheartedly express our gratitude for their invaluable contributions to open-sourcing LVLMs.
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### Cost
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We train our models on 8*A800 GPUs.
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[ALLaVA-3B-Longer](https://huggingface.co/FreedomIntelligence/ALLaVA-3B-Longer) takes 8.3h for PT and 21.3h for FT.
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[ALLaVA-3B](https://huggingface.co/FreedomIntelligence/ALLaVA-3B) takes 8.3h for PT and 10.6h for FT.
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These two models share the same PT procedure.
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### Hyperparameters
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| Global Batch Size| ZeRO Stage| Optimizer | Max LR| Min LR | Scheduler | Max length | Weight decay |
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| ---: | ---: |--:| ---: | ---: | ---: | ---: | ---: |
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| 256 (PT) / 128 (FT) | 1| AdamW | 2e-5 | 2e-6 | CosineAnnealingWarmRestarts | 2048 | 0 |
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The LM backbone, projector are trainable, while the vision encoder is kept frozen.
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**The trainabilities of each module are the same for both stages.**
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## 📚 ALLaVA-4V Data
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The majority part of training data is [ALLaVA-4V](https://huggingface.co/datasets/FreedomIntelligence/ALLaVA-4V). See [here](https://github.com/FreedomIntelligence/ALLaVA/tree/main?tab=readme-ov-file#data-preparation) to prepare it for training.
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## 🙌 Contributors
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- Project Leader: [Guiming Hardy Chen](https://g-h-chen.github.io/)
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- Data: Shunian Chen, [Junying Chen](https://jymchen.github.io/), Xiangbo Wu
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- Evaluation: [Ruifei Zhang](https://scholar.google.com/citations?user=W4zOhmEAAAAJ&hl=zh-CN)
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- Deployment: Xiangbo Wu, Zhiyi Zhang
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- Advising: [Zhihong Chen](https://zhjohnchan.github.io/), [Benyou Wang](https://wabyking.github.io/old.html)
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- Others: Jianquan Li, [Xiang Wan](https://scholar.google.com/citations?user=e3_kWigAAAAJ&hl=zh-CN)
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## 📝 Citation
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If you find our data useful, please consider citing our work! We are FreedomIntelligence from [Shenzhen Research Institute of Big Data](http://sribd.cn/en) and [The Chinese University of Hong Kong, Shenzhen](https://sds.cuhk.edu.cn/en)
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```
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@article{chen2024allava,
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title={ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model},
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author={Chen, Guiming Hardy and Chen, Shunian and Zhang, Ruifei and Chen, Junying and Wu, Xiangbo and Zhang, Zhiyi and Chen, Zhihong and Li, Jianquan and Wan, Xiang and Wang, Benyou},
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journal={arXiv preprint arXiv:2402.11684},
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year={2024}
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}
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```
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| 39 |
+
" ": 50257
|
| 40 |
+
}
|
config.json
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "FreedomIntelligence/ALLaVA-3B-Longer",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"LlavaPhiForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"auto_map": {
|
| 8 |
+
"AutoConfig": "configuration_phi.PhiConfig",
|
| 9 |
+
"AutoModelForCausalLM": "modeling_llava_phi.LlavaPhiForCausalLM"
|
| 10 |
+
},
|
| 11 |
+
"bos_token_id": null,
|
| 12 |
+
"embd_pdrop": 0.0,
|
| 13 |
+
"eos_token_id": null,
|
| 14 |
+
"flash_attn": true,
|
| 15 |
+
"flash_rotary": true,
|
| 16 |
+
"fused_dense": true,
|
| 17 |
+
"hidden_act": "gelu_new",
|
| 18 |
+
"hidden_size": 2560,
|
| 19 |
+
"image_aspect_ratio": "pad",
|
| 20 |
+
"initializer_range": 0.02,
|
| 21 |
+
"intermediate_size": 10240,
|
| 22 |
+
"layer_norm_eps": 1e-05,
|
| 23 |
+
"max_position_embeddings": 2048,
|
| 24 |
+
"mm_hidden_size": 1024,
|
| 25 |
+
"mm_projector_lr": null,
|
| 26 |
+
"mm_projector_type": "mlp2x_gelu",
|
| 27 |
+
"mm_use_im_patch_token": false,
|
| 28 |
+
"mm_use_im_start_end": false,
|
| 29 |
+
"mm_vision_select_feature": "patch",
|
| 30 |
+
"mm_vision_select_layer": -2,
|
| 31 |
+
"mm_vision_tower": "openai/clip-vit-large-patch14-336",
|
| 32 |
+
"model_type": "llava_phi",
|
| 33 |
+
"num_attention_heads": 32,
|
| 34 |
+
"num_hidden_layers": 32,
|
| 35 |
+
"num_key_value_heads": 32,
|
| 36 |
+
"partial_rotary_factor": 0.4,
|
| 37 |
+
"qk_layernorm": false,
|
| 38 |
+
"resid_pdrop": 0.1,
|
| 39 |
+
"rope_scaling": null,
|
| 40 |
+
"rope_theta": 10000.0,
|
| 41 |
+
"tie_word_embeddings": false,
|
| 42 |
+
"tokenizer_model_max_length": 2048,
|
| 43 |
+
"tokenizer_padding_side": "right",
|
| 44 |
+
"torch_dtype": "bfloat16",
|
| 45 |
+
"transformers_version": "4.37.0.dev0",
|
| 46 |
+
"tune_mm_mlp_adapter": false,
|
| 47 |
+
"use_cache": true,
|
| 48 |
+
"use_mm_proj": true,
|
| 49 |
+
"vocab_size": 51200
|
| 50 |
+
}
|
configuration_phi.py
ADDED
|
@@ -0,0 +1,193 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
|
| 3 |
+
#
|
| 4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
+
# you may not use this file except in compliance with the License.
|
| 6 |
+
# You may obtain a copy of the License at
|
| 7 |
+
#
|
| 8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
+
#
|
| 10 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
+
# See the License for the specific language governing permissions and
|
| 14 |
+
# limitations under the License.
|
| 15 |
+
|
| 16 |
+
""" Phi model configuration"""
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
from transformers.configuration_utils import PretrainedConfig
|
| 20 |
+
from transformers.utils import logging
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
logger = logging.get_logger(__name__)
|
| 24 |
+
|
| 25 |
+
PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
| 26 |
+
"microsoft/phi-2": "https://huggingface.co/microsoft/phi-2/resolve/main/config.json",
|
| 27 |
+
}
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
class PhiConfig(PretrainedConfig):
|
| 31 |
+
r"""
|
| 32 |
+
This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
|
| 33 |
+
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
| 34 |
+
defaults will yield a similar configuration to that of the Phi
|
| 35 |
+
[microsoft/phi-1](https://huggingface.co/microsoft/phi-1).
|
| 36 |
+
|
| 37 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
| 38 |
+
documentation from [`PretrainedConfig`] for more information.
|
| 39 |
+
|
| 40 |
+
Args:
|
| 41 |
+
vocab_size (`int`, *optional*, defaults to 51200):
|
| 42 |
+
Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
|
| 43 |
+
`inputs_ids` passed when calling [`PhiModel`].
|
| 44 |
+
hidden_size (`int`, *optional*, defaults to 2048):
|
| 45 |
+
Dimension of the hidden representations.
|
| 46 |
+
intermediate_size (`int`, *optional*, defaults to 8192):
|
| 47 |
+
Dimension of the MLP representations.
|
| 48 |
+
num_hidden_layers (`int`, *optional*, defaults to 24):
|
| 49 |
+
Number of hidden layers in the Transformer decoder.
|
| 50 |
+
num_attention_heads (`int`, *optional*, defaults to 32):
|
| 51 |
+
Number of attention heads for each attention layer in the Transformer decoder.
|
| 52 |
+
num_key_value_heads (`int`, *optional*):
|
| 53 |
+
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
| 54 |
+
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
| 55 |
+
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
| 56 |
+
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
| 57 |
+
by meanpooling all the original heads within that group. For more details checkout [this
|
| 58 |
+
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
| 59 |
+
`num_attention_heads`.
|
| 60 |
+
resid_pdrop (`float`, *optional*, defaults to 0.0):
|
| 61 |
+
Dropout probability for mlp outputs.
|
| 62 |
+
embd_pdrop (`int`, *optional*, defaults to 0.0):
|
| 63 |
+
The dropout ratio for the embeddings.
|
| 64 |
+
attention_dropout (`float`, *optional*, defaults to 0.0):
|
| 65 |
+
The dropout ratio after computing the attention scores.
|
| 66 |
+
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
|
| 67 |
+
The non-linear activation function (function or string) in the decoder.
|
| 68 |
+
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
| 69 |
+
The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048
|
| 70 |
+
tokens.
|
| 71 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
| 72 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
| 73 |
+
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
|
| 74 |
+
The epsilon used by the rms normalization layers.
|
| 75 |
+
use_cache (`bool`, *optional*, defaults to `True`):
|
| 76 |
+
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
| 77 |
+
relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
|
| 78 |
+
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
| 79 |
+
Whether to tie weight embeddings
|
| 80 |
+
rope_theta (`float`, *optional*, defaults to 10000.0):
|
| 81 |
+
The base period of the RoPE embeddings.
|
| 82 |
+
rope_scaling (`Dict`, *optional*):
|
| 83 |
+
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
| 84 |
+
strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
|
| 85 |
+
is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
| 86 |
+
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
| 87 |
+
these scaling strategies behave:
|
| 88 |
+
https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
|
| 89 |
+
is an experimental feature, subject to breaking API changes in future versions.
|
| 90 |
+
partial_rotary_factor (`float`, *optional*, defaults to 0.5):
|
| 91 |
+
Percentage of the query and keys which will have rotary embedding.
|
| 92 |
+
qk_layernorm (`bool`, *optional*, defaults to `False`):
|
| 93 |
+
Whether or not to normalize the Queries and Keys after projecting the hidden states.
|
| 94 |
+
bos_token_id (`int`, *optional*, defaults to 1):
|
| 95 |
+
Denotes beginning of sequences token id.
|
| 96 |
+
eos_token_id (`int`, *optional*, defaults to 2):
|
| 97 |
+
Denotes end of sequences token id.
|
| 98 |
+
|
| 99 |
+
Example:
|
| 100 |
+
|
| 101 |
+
```python
|
| 102 |
+
>>> from transformers import PhiModel, PhiConfig
|
| 103 |
+
|
| 104 |
+
>>> # Initializing a Phi-1 style configuration
|
| 105 |
+
>>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")
|
| 106 |
+
|
| 107 |
+
>>> # Initializing a model from the configuration
|
| 108 |
+
>>> model = PhiModel(configuration)
|
| 109 |
+
|
| 110 |
+
>>> # Accessing the model configuration
|
| 111 |
+
>>> configuration = model.config
|
| 112 |
+
```"""
|
| 113 |
+
|
| 114 |
+
model_type = "phi"
|
| 115 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
| 116 |
+
|
| 117 |
+
def __init__(
|
| 118 |
+
self,
|
| 119 |
+
vocab_size=51200,
|
| 120 |
+
hidden_size=2048,
|
| 121 |
+
intermediate_size=8192,
|
| 122 |
+
num_hidden_layers=24,
|
| 123 |
+
num_attention_heads=32,
|
| 124 |
+
num_key_value_heads=None,
|
| 125 |
+
resid_pdrop=0.0,
|
| 126 |
+
embd_pdrop=0.0,
|
| 127 |
+
attention_dropout=0.0,
|
| 128 |
+
hidden_act="gelu_new",
|
| 129 |
+
max_position_embeddings=2048,
|
| 130 |
+
initializer_range=0.02,
|
| 131 |
+
layer_norm_eps=1e-5,
|
| 132 |
+
use_cache=True,
|
| 133 |
+
tie_word_embeddings=False,
|
| 134 |
+
rope_theta=10000.0,
|
| 135 |
+
rope_scaling=None,
|
| 136 |
+
partial_rotary_factor=0.5,
|
| 137 |
+
qk_layernorm=False,
|
| 138 |
+
bos_token_id=1,
|
| 139 |
+
eos_token_id=2,
|
| 140 |
+
**kwargs,
|
| 141 |
+
):
|
| 142 |
+
self.vocab_size = vocab_size
|
| 143 |
+
self.hidden_size = hidden_size
|
| 144 |
+
self.intermediate_size = intermediate_size
|
| 145 |
+
self.num_hidden_layers = num_hidden_layers
|
| 146 |
+
self.num_attention_heads = num_attention_heads
|
| 147 |
+
|
| 148 |
+
if num_key_value_heads is None:
|
| 149 |
+
num_key_value_heads = num_attention_heads
|
| 150 |
+
|
| 151 |
+
self.num_key_value_heads = num_key_value_heads
|
| 152 |
+
self.resid_pdrop = resid_pdrop
|
| 153 |
+
self.embd_pdrop = embd_pdrop
|
| 154 |
+
self.attention_dropout = attention_dropout
|
| 155 |
+
self.hidden_act = hidden_act
|
| 156 |
+
self.max_position_embeddings = max_position_embeddings
|
| 157 |
+
self.initializer_range = initializer_range
|
| 158 |
+
self.layer_norm_eps = layer_norm_eps
|
| 159 |
+
self.use_cache = use_cache
|
| 160 |
+
self.rope_theta = rope_theta
|
| 161 |
+
self.rope_scaling = rope_scaling
|
| 162 |
+
self.partial_rotary_factor = partial_rotary_factor
|
| 163 |
+
self.qk_layernorm = qk_layernorm
|
| 164 |
+
self._rope_scaling_validation()
|
| 165 |
+
|
| 166 |
+
super().__init__(
|
| 167 |
+
bos_token_id=bos_token_id,
|
| 168 |
+
eos_token_id=eos_token_id,
|
| 169 |
+
tie_word_embeddings=tie_word_embeddings,
|
| 170 |
+
**kwargs,
|
| 171 |
+
)
|
| 172 |
+
|
| 173 |
+
# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
|
| 174 |
+
def _rope_scaling_validation(self):
|
| 175 |
+
"""
|
| 176 |
+
Validate the `rope_scaling` configuration.
|
| 177 |
+
"""
|
| 178 |
+
if self.rope_scaling is None:
|
| 179 |
+
return
|
| 180 |
+
|
| 181 |
+
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
| 182 |
+
raise ValueError(
|
| 183 |
+
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
|
| 184 |
+
f"got {self.rope_scaling}"
|
| 185 |
+
)
|
| 186 |
+
rope_scaling_type = self.rope_scaling.get("type", None)
|
| 187 |
+
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
| 188 |
+
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
|
| 189 |
+
raise ValueError(
|
| 190 |
+
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
| 191 |
+
)
|
| 192 |
+
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
| 193 |
+
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
|
generation_config.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_from_model_config": true,
|
| 3 |
+
"transformers_version": "4.37.0.dev0"
|
| 4 |
+
}
|
generation_utils.py
ADDED
|
@@ -0,0 +1,287 @@
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|
|
|
| 1 |
+
from typing import List
|
| 2 |
+
from queue import Queue
|
| 3 |
+
|
| 4 |
+
import torch
|
| 5 |
+
from PIL import Image
|
| 6 |
+
from copy import deepcopy
|
| 7 |
+
import requests, os
|
| 8 |
+
|
| 9 |
+
IMAGE_TOKEN_INDEX=-200
|
| 10 |
+
blacklist = ['<image>', '<s>', '</s>']
|
| 11 |
+
max_num_images = 3 # phi has a context length limit of 2048 and each image occupies 576 tokens.
|
| 12 |
+
|
| 13 |
+
def input_moderation(texts: list[list[str]]):
|
| 14 |
+
# perform input moderation on each message
|
| 15 |
+
for text_pair in texts:
|
| 16 |
+
# in-place operation
|
| 17 |
+
for b in blacklist:
|
| 18 |
+
text_pair[0] = text_pair[0].replace(b, '')
|
| 19 |
+
if text_pair[1] is not None:
|
| 20 |
+
text_pair[1] = text_pair[1].replace(b, '')
|
| 21 |
+
|
| 22 |
+
return texts
|
| 23 |
+
|
| 24 |
+
def insert_image_placeholder(t, num_images, placeholder='<image>', sep='\n'):
|
| 25 |
+
for _ in range(num_images):
|
| 26 |
+
t = f"{placeholder}{sep}" + t
|
| 27 |
+
return t
|
| 28 |
+
|
| 29 |
+
def get_conv(texts):
|
| 30 |
+
ret = []
|
| 31 |
+
|
| 32 |
+
for conv in texts:
|
| 33 |
+
ret.append({'from': 'human', 'value': conv[0]})
|
| 34 |
+
ret.append({'from': 'gpt', 'value': conv[1]}) # this is None for the last one
|
| 35 |
+
|
| 36 |
+
return ret
|
| 37 |
+
|
| 38 |
+
# copied from llava
|
| 39 |
+
def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
|
| 40 |
+
prompt_chunks = [tokenizer(chunk, add_special_tokens=False).input_ids for chunk in prompt.split('<image>')]
|
| 41 |
+
|
| 42 |
+
def insert_separator(X, sep):
|
| 43 |
+
return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
|
| 44 |
+
|
| 45 |
+
input_ids = []
|
| 46 |
+
offset = 0
|
| 47 |
+
if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
|
| 48 |
+
offset = 1
|
| 49 |
+
input_ids.append(prompt_chunks[0][0])
|
| 50 |
+
|
| 51 |
+
for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
|
| 52 |
+
input_ids.extend(x[offset:])
|
| 53 |
+
|
| 54 |
+
if return_tensors is not None:
|
| 55 |
+
if return_tensors == 'pt':
|
| 56 |
+
return torch.tensor(input_ids, dtype=torch.long)
|
| 57 |
+
raise ValueError(f'Unsupported tensor type: {return_tensors}')
|
| 58 |
+
return input_ids
|
| 59 |
+
|
| 60 |
+
def preprocess(tokenizer, data: list, return_tensors='pt'):
|
| 61 |
+
'''
|
| 62 |
+
[
|
| 63 |
+
{
|
| 64 |
+
'from': 'human',
|
| 65 |
+
'value': xxx,
|
| 66 |
+
},
|
| 67 |
+
{
|
| 68 |
+
'from': 'gpt',
|
| 69 |
+
'value': xxx
|
| 70 |
+
}
|
| 71 |
+
]
|
| 72 |
+
'''
|
| 73 |
+
# needs update
|
| 74 |
+
if not isinstance(data, list):
|
| 75 |
+
raise ValueError('must be a list')
|
| 76 |
+
|
| 77 |
+
# this is per model (tokenizer)
|
| 78 |
+
return preprocess_allava(tokenizer, data, return_tensors=return_tensors)
|
| 79 |
+
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
def preprocess_vicuna_v1(self, convs: list, return_tensors) -> list: # tokenize and concat the coversations
|
| 83 |
+
input_ids = None
|
| 84 |
+
for ind, conv in enumerate(convs):
|
| 85 |
+
if ind % 2 == 0: # human
|
| 86 |
+
h = conv['value'].strip()
|
| 87 |
+
h = f"USER: {h} "
|
| 88 |
+
cur_input_ids = self.tokenizer_image_token(prompt=h, return_tensors=return_tensors)
|
| 89 |
+
|
| 90 |
+
if input_ids is None:
|
| 91 |
+
input_ids = cur_input_ids
|
| 92 |
+
else:
|
| 93 |
+
input_ids = torch.cat([input_ids, cur_input_ids])
|
| 94 |
+
|
| 95 |
+
else: # gpt
|
| 96 |
+
g = conv['value']
|
| 97 |
+
if g is not None:
|
| 98 |
+
cur_input_ids = self.tokenizer(f"ASSISTANT: {g}</s>", add_special_tokens= False, max_length=self.maxlen, truncation=True, return_tensors='pt').input_ids[0]
|
| 99 |
+
input_ids = torch.cat([input_ids, cur_input_ids])
|
| 100 |
+
else:
|
| 101 |
+
cur_input_ids = self.tokenizer(f"ASSISTANT:", add_special_tokens= False, max_length=self.maxlen, truncation=True, return_tensors='pt').input_ids[0]
|
| 102 |
+
input_ids = torch.cat([input_ids, cur_input_ids])
|
| 103 |
+
|
| 104 |
+
|
| 105 |
+
return input_ids
|
| 106 |
+
|
| 107 |
+
def preprocess_allava(tokenizer, convs: list, return_tensors) -> list: # tokenize and concat the coversations
|
| 108 |
+
input_ids = None
|
| 109 |
+
|
| 110 |
+
for ind, conv in enumerate(convs):
|
| 111 |
+
if ind % 2 == 0: # human
|
| 112 |
+
h = conv['value'].strip()
|
| 113 |
+
h = f"[INST] {h} [/INST] "
|
| 114 |
+
cur_input_ids = tokenizer_image_token(prompt=h, tokenizer=tokenizer, return_tensors=return_tensors)
|
| 115 |
+
|
| 116 |
+
if input_ids is None:
|
| 117 |
+
input_ids = cur_input_ids
|
| 118 |
+
else:
|
| 119 |
+
input_ids = torch.cat([input_ids, cur_input_ids])
|
| 120 |
+
|
| 121 |
+
else: # gpt
|
| 122 |
+
g = conv['value']
|
| 123 |
+
if g is not None:
|
| 124 |
+
cur_input_ids = tokenizer(f"{g}{tokenizer.eos_token}", add_special_tokens= False, truncation=True, return_tensors='pt').input_ids[0]
|
| 125 |
+
input_ids = torch.cat([input_ids, cur_input_ids])
|
| 126 |
+
|
| 127 |
+
return input_ids
|
| 128 |
+
|
| 129 |
+
|
| 130 |
+
# copied from llava
|
| 131 |
+
def get_image_tensors(processor, images, device):
|
| 132 |
+
list_image_tensors = []
|
| 133 |
+
crop_size = processor.crop_size
|
| 134 |
+
for fp in images:
|
| 135 |
+
if fp is None: # None is used as a placeholder
|
| 136 |
+
list_image_tensors.append(torch.zeros(3, crop_size['height'], crop_size['width']).to(device))
|
| 137 |
+
continue
|
| 138 |
+
elif isinstance(fp, str):
|
| 139 |
+
image = Image.open(fp).convert('RGB')
|
| 140 |
+
elif isinstance(fp, Image.Image):
|
| 141 |
+
image = fp # already an image
|
| 142 |
+
else:
|
| 143 |
+
raise TypeError(f'Unsupported type {type(fp)}')
|
| 144 |
+
|
| 145 |
+
# this is the way of preprocessing images we used in training, so we impose it here
|
| 146 |
+
if True:
|
| 147 |
+
# self.data_args.image_aspect_ratio == 'pad'
|
| 148 |
+
def expand2square(pil_img, background_color):
|
| 149 |
+
width, height = pil_img.size
|
| 150 |
+
if pil_img.mode == 'L':
|
| 151 |
+
pil_img = pil_img.convert('RGB')
|
| 152 |
+
|
| 153 |
+
if width == height:
|
| 154 |
+
return pil_img
|
| 155 |
+
elif width > height:
|
| 156 |
+
result = Image.new(pil_img.mode, (width, width), background_color)
|
| 157 |
+
result.paste(pil_img, (0, (width - height) // 2))
|
| 158 |
+
return result
|
| 159 |
+
else:
|
| 160 |
+
result = Image.new(pil_img.mode, (height, height), background_color)
|
| 161 |
+
result.paste(pil_img, ((height - width) // 2, 0))
|
| 162 |
+
return result
|
| 163 |
+
|
| 164 |
+
image = expand2square(image, tuple(int(x*255) for x in processor.image_mean))
|
| 165 |
+
image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
|
| 166 |
+
else:
|
| 167 |
+
image = processor.preprocess(image, return_tensors='pt')['pixel_values'][0] # a tensor
|
| 168 |
+
list_image_tensors.append(image.to(device))
|
| 169 |
+
# list_image_tensors.append(image)
|
| 170 |
+
return list_image_tensors
|
| 171 |
+
|
| 172 |
+
|
| 173 |
+
|
| 174 |
+
|
| 175 |
+
def build_allava_input(tokenizer, processor, texts, images, history=None, return_history=False, device='cuda'):
|
| 176 |
+
'''
|
| 177 |
+
texts: [[]]
|
| 178 |
+
'''
|
| 179 |
+
|
| 180 |
+
############################
|
| 181 |
+
# 1. preprocess texts
|
| 182 |
+
############################
|
| 183 |
+
if isinstance(texts, str):
|
| 184 |
+
texts = [[texts, None]]
|
| 185 |
+
else:
|
| 186 |
+
assert isinstance(texts, list) and isinstance(texts[0], list) , 'texts must be a list of list'
|
| 187 |
+
|
| 188 |
+
if history is not None:
|
| 189 |
+
texts = history + texts # concat them together
|
| 190 |
+
|
| 191 |
+
texts = input_moderation(texts)
|
| 192 |
+
|
| 193 |
+
|
| 194 |
+
############################
|
| 195 |
+
# 2. preprocess images
|
| 196 |
+
############################
|
| 197 |
+
if isinstance(images, str) or isinstance(images, Image.Image):
|
| 198 |
+
images = [images]
|
| 199 |
+
|
| 200 |
+
valid_images = []
|
| 201 |
+
if images is None:
|
| 202 |
+
images = [None]
|
| 203 |
+
|
| 204 |
+
for img in images:
|
| 205 |
+
try:
|
| 206 |
+
if os.path.exists(img): # make sure that the path exists
|
| 207 |
+
img = Image.open(img).convert('RGB')
|
| 208 |
+
else: # else it must be a URL
|
| 209 |
+
img = Image.open(requests.get(img, stream=True).raw)
|
| 210 |
+
|
| 211 |
+
valid_images.append(img)
|
| 212 |
+
except:
|
| 213 |
+
continue
|
| 214 |
+
|
| 215 |
+
images = valid_images
|
| 216 |
+
|
| 217 |
+
if images == []:
|
| 218 |
+
images = [None]
|
| 219 |
+
|
| 220 |
+
|
| 221 |
+
assert len(images) < max_num_images, f'Currently at most {max_num_images} images are supported'
|
| 222 |
+
|
| 223 |
+
############################
|
| 224 |
+
# 3. collate conv
|
| 225 |
+
############################
|
| 226 |
+
|
| 227 |
+
history = deepcopy(texts) # history is the texts without <image> placeholders
|
| 228 |
+
|
| 229 |
+
# insert <image>
|
| 230 |
+
image_place_holder_inserted = insert_image_placeholder(texts[0][0], len(images) if None not in images else 0) # only insert the placeholders for user input at the 1st round
|
| 231 |
+
texts[0][0] = image_place_holder_inserted
|
| 232 |
+
|
| 233 |
+
# collate strings into conv
|
| 234 |
+
conv = get_conv(texts)
|
| 235 |
+
|
| 236 |
+
# make input ids
|
| 237 |
+
input_ids = preprocess(tokenizer, conv, return_tensors='pt').unsqueeze(0).to(device)
|
| 238 |
+
|
| 239 |
+
list_image_tensors = get_image_tensors(processor, images, device)
|
| 240 |
+
image_tensors = torch.stack(list_image_tensors)
|
| 241 |
+
|
| 242 |
+
try:
|
| 243 |
+
dtype = torch.bfloat16
|
| 244 |
+
# if your hardware does not support bf16, the following line raises an error
|
| 245 |
+
torch.tensor(1, dtype=dtype).cuda()
|
| 246 |
+
except:
|
| 247 |
+
# default using fp16
|
| 248 |
+
dtype = torch.float16
|
| 249 |
+
|
| 250 |
+
if return_history:
|
| 251 |
+
return input_ids, image_tensors, history
|
| 252 |
+
|
| 253 |
+
return input_ids, image_tensors, None
|
| 254 |
+
|
| 255 |
+
|
| 256 |
+
|
| 257 |
+
class TextIterStreamer:
|
| 258 |
+
def __init__(self, tokenizer, skip_prompt=False, skip_special_tokens=False):
|
| 259 |
+
self.tokenizer = tokenizer
|
| 260 |
+
self.skip_prompt = skip_prompt
|
| 261 |
+
self.skip_special_tokens = skip_special_tokens
|
| 262 |
+
self.tokens = []
|
| 263 |
+
self.text_queue = Queue()
|
| 264 |
+
self.next_tokens_are_prompt = True
|
| 265 |
+
|
| 266 |
+
def put(self, value):
|
| 267 |
+
if self.skip_prompt and self.next_tokens_are_prompt:
|
| 268 |
+
self.next_tokens_are_prompt = False
|
| 269 |
+
else:
|
| 270 |
+
if len(value.shape) > 1:
|
| 271 |
+
value = value[0]
|
| 272 |
+
self.tokens.extend(value.tolist())
|
| 273 |
+
self.text_queue.put(
|
| 274 |
+
self.tokenizer.decode(self.tokens, skip_special_tokens=self.skip_special_tokens))
|
| 275 |
+
|
| 276 |
+
def end(self):
|
| 277 |
+
self.text_queue.put(None)
|
| 278 |
+
|
| 279 |
+
def __iter__(self):
|
| 280 |
+
return self
|
| 281 |
+
|
| 282 |
+
def __next__(self):
|
| 283 |
+
value = self.text_queue.get()
|
| 284 |
+
if value is None:
|
| 285 |
+
raise StopIteration()
|
| 286 |
+
else:
|
| 287 |
+
return value
|
llava_arch.py
ADDED
|
@@ -0,0 +1,531 @@
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|
|
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|
|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
|
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|
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|
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|
|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
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|
|
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|
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|
|
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|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Copyright 2023 Haotian Liu
|
| 2 |
+
#
|
| 3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
+
# you may not use this file except in compliance with the License.
|
| 5 |
+
# You may obtain a copy of the License at
|
| 6 |
+
#
|
| 7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
+
#
|
| 9 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
+
# See the License for the specific language governing permissions and
|
| 13 |
+
# limitations under the License.
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
from abc import ABC, abstractmethod
|
| 17 |
+
|
| 18 |
+
import torch
|
| 19 |
+
import torch.nn as nn
|
| 20 |
+
|
| 21 |
+
# from .multimodal_encoder.builder import build_vision_tower
|
| 22 |
+
# from .multimodal_projector.builder import build_vision_projector
|
| 23 |
+
|
| 24 |
+
# from .builders import build_vision_tower, build_vision_projector
|
| 25 |
+
# from .constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
|
| 26 |
+
import pdb
|
| 27 |
+
|
| 28 |
+
|
| 29 |
+
#############################################################################
|
| 30 |
+
# builders
|
| 31 |
+
#############################################################################
|
| 32 |
+
|
| 33 |
+
###################################################################
|
| 34 |
+
|
| 35 |
+
import torch
|
| 36 |
+
import torch.nn as nn
|
| 37 |
+
import re
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
class IdentityMap(nn.Module):
|
| 41 |
+
def __init__(self):
|
| 42 |
+
super().__init__()
|
| 43 |
+
|
| 44 |
+
def forward(self, x, *args, **kwargs):
|
| 45 |
+
return x
|
| 46 |
+
|
| 47 |
+
@property
|
| 48 |
+
def config(self):
|
| 49 |
+
return {"mm_projector_type": 'identity'}
|
| 50 |
+
|
| 51 |
+
|
| 52 |
+
class SimpleResBlock(nn.Module):
|
| 53 |
+
def __init__(self, channels):
|
| 54 |
+
super().__init__()
|
| 55 |
+
self.pre_norm = nn.LayerNorm(channels)
|
| 56 |
+
|
| 57 |
+
self.proj = nn.Sequential(
|
| 58 |
+
nn.Linear(channels, channels),
|
| 59 |
+
nn.GELU(),
|
| 60 |
+
nn.Linear(channels, channels)
|
| 61 |
+
)
|
| 62 |
+
def forward(self, x):
|
| 63 |
+
x = self.pre_norm(x)
|
| 64 |
+
return x + self.proj(x)
|
| 65 |
+
|
| 66 |
+
|
| 67 |
+
def build_vision_projector(config, delay_load=False, **kwargs):
|
| 68 |
+
projector_type = getattr(config, 'mm_projector_type', 'linear')
|
| 69 |
+
|
| 70 |
+
if projector_type == 'linear':
|
| 71 |
+
return nn.Linear(config.mm_hidden_size, config.hidden_size)
|
| 72 |
+
|
| 73 |
+
mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
|
| 74 |
+
if mlp_gelu_match:
|
| 75 |
+
mlp_depth = int(mlp_gelu_match.group(1))
|
| 76 |
+
modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
|
| 77 |
+
for _ in range(1, mlp_depth):
|
| 78 |
+
modules.append(nn.GELU())
|
| 79 |
+
modules.append(nn.Linear(config.hidden_size, config.hidden_size))
|
| 80 |
+
return nn.Sequential(*modules)
|
| 81 |
+
|
| 82 |
+
if projector_type == 'identity':
|
| 83 |
+
return IdentityMap()
|
| 84 |
+
|
| 85 |
+
raise ValueError(f'Unknown projector type: {projector_type}')
|
| 86 |
+
###################################################################
|
| 87 |
+
|
| 88 |
+
|
| 89 |
+
|
| 90 |
+
###################################################################
|
| 91 |
+
|
| 92 |
+
import os
|
| 93 |
+
from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
|
| 94 |
+
from transformers import AutoModel
|
| 95 |
+
|
| 96 |
+
|
| 97 |
+
class CLIPVisionTower(nn.Module):
|
| 98 |
+
def __init__(self, vision_tower, args, delay_load=False):
|
| 99 |
+
super().__init__()
|
| 100 |
+
|
| 101 |
+
self.is_loaded = False
|
| 102 |
+
|
| 103 |
+
self.vision_tower_name = vision_tower
|
| 104 |
+
self.select_layer = args.mm_vision_select_layer
|
| 105 |
+
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
|
| 106 |
+
|
| 107 |
+
if not delay_load:
|
| 108 |
+
self.load_model()
|
| 109 |
+
else:
|
| 110 |
+
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
|
| 111 |
+
|
| 112 |
+
def load_model(self):
|
| 113 |
+
print(f'loading vision model from {self.vision_tower_name}')
|
| 114 |
+
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
|
| 115 |
+
if 'clip' in self.vision_tower_name.lower():
|
| 116 |
+
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
|
| 117 |
+
|
| 118 |
+
elif 'internvit' in self.vision_tower_name.lower():
|
| 119 |
+
self.vision_tower = AutoModel.from_pretrained(self.vision_tower_name, trust_remote_code=True)
|
| 120 |
+
else:
|
| 121 |
+
raise ValueError(f'Please implement the loading of vision encoder here')
|
| 122 |
+
|
| 123 |
+
self.vision_tower.requires_grad_(False)
|
| 124 |
+
|
| 125 |
+
self.is_loaded = True
|
| 126 |
+
|
| 127 |
+
def feature_select(self, image_forward_outs):
|
| 128 |
+
image_features = image_forward_outs.hidden_states[self.select_layer]
|
| 129 |
+
if self.select_feature == 'patch':
|
| 130 |
+
image_features = image_features[:, 1:]
|
| 131 |
+
elif self.select_feature == 'cls_patch':
|
| 132 |
+
image_features = image_features
|
| 133 |
+
else:
|
| 134 |
+
raise ValueError(f'Unexpected select feature: {self.select_feature}')
|
| 135 |
+
return image_features
|
| 136 |
+
|
| 137 |
+
@torch.no_grad()
|
| 138 |
+
def forward(self, images):
|
| 139 |
+
if type(images) is list:
|
| 140 |
+
image_features = []
|
| 141 |
+
for image in images:
|
| 142 |
+
image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
|
| 143 |
+
image_feature = self.feature_select(image_forward_out).to(image.dtype)
|
| 144 |
+
image_features.append(image_feature)
|
| 145 |
+
else:
|
| 146 |
+
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
|
| 147 |
+
image_features = self.feature_select(image_forward_outs).to(images.dtype)
|
| 148 |
+
|
| 149 |
+
return image_features
|
| 150 |
+
|
| 151 |
+
@property
|
| 152 |
+
def dummy_feature(self):
|
| 153 |
+
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
|
| 154 |
+
|
| 155 |
+
@property
|
| 156 |
+
def dtype(self):
|
| 157 |
+
return self.vision_tower.dtype
|
| 158 |
+
|
| 159 |
+
@property
|
| 160 |
+
def device(self):
|
| 161 |
+
return self.vision_tower.device
|
| 162 |
+
|
| 163 |
+
@property
|
| 164 |
+
def config(self):
|
| 165 |
+
if self.is_loaded:
|
| 166 |
+
return self.vision_tower.config
|
| 167 |
+
else:
|
| 168 |
+
return self.cfg_only
|
| 169 |
+
|
| 170 |
+
@property
|
| 171 |
+
def hidden_size(self):
|
| 172 |
+
return self.config.hidden_size
|
| 173 |
+
|
| 174 |
+
@property
|
| 175 |
+
def num_patches(self):
|
| 176 |
+
return (self.config.image_size // self.config.patch_size) ** 2
|
| 177 |
+
|
| 178 |
+
|
| 179 |
+
|
| 180 |
+
def build_vision_tower(vision_tower_cfg, **kwargs):
|
| 181 |
+
vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None))
|
| 182 |
+
is_absolute_path_exists = os.path.exists(vision_tower)
|
| 183 |
+
if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion"):
|
| 184 |
+
return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
|
| 185 |
+
|
| 186 |
+
raise ValueError(f'Unknown vision tower: {vision_tower}')
|
| 187 |
+
|
| 188 |
+
|
| 189 |
+
#############################################################################
|
| 190 |
+
# builders
|
| 191 |
+
#############################################################################
|
| 192 |
+
|
| 193 |
+
|
| 194 |
+
|
| 195 |
+
#############################################################################
|
| 196 |
+
# constants
|
| 197 |
+
#############################################################################
|
| 198 |
+
|
| 199 |
+
CONTROLLER_HEART_BEAT_EXPIRATION = 30
|
| 200 |
+
WORKER_HEART_BEAT_INTERVAL = 15
|
| 201 |
+
|
| 202 |
+
LOGDIR = "."
|
| 203 |
+
|
| 204 |
+
# Model Constants
|
| 205 |
+
IGNORE_INDEX = -100
|
| 206 |
+
IMAGE_TOKEN_INDEX = -200
|
| 207 |
+
DEFAULT_IMAGE_TOKEN = "<image>"
|
| 208 |
+
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
|
| 209 |
+
DEFAULT_IM_START_TOKEN = "<im_start>"
|
| 210 |
+
DEFAULT_IM_END_TOKEN = "<im_end>"
|
| 211 |
+
IMAGE_PLACEHOLDER = "<image-placeholder>"
|
| 212 |
+
|
| 213 |
+
#############################################################################
|
| 214 |
+
# constants
|
| 215 |
+
#############################################################################
|
| 216 |
+
|
| 217 |
+
|
| 218 |
+
class LlavaMetaModel:
|
| 219 |
+
|
| 220 |
+
def __init__(self, config):
|
| 221 |
+
super(LlavaMetaModel, self).__init__(config)
|
| 222 |
+
|
| 223 |
+
if hasattr(config, "mm_vision_tower"):
|
| 224 |
+
self.vision_tower = build_vision_tower(config, delay_load=True)
|
| 225 |
+
self.mm_projector = build_vision_projector(config)
|
| 226 |
+
|
| 227 |
+
def get_vision_tower(self):
|
| 228 |
+
vision_tower = getattr(self, 'vision_tower', None)
|
| 229 |
+
if type(vision_tower) is list:
|
| 230 |
+
vision_tower = vision_tower[0]
|
| 231 |
+
return vision_tower
|
| 232 |
+
|
| 233 |
+
def initialize_vision_modules(self, model_args, fsdp=None):
|
| 234 |
+
vision_tower = model_args.vision_tower
|
| 235 |
+
mm_vision_select_layer = model_args.mm_vision_select_layer
|
| 236 |
+
mm_vision_select_feature = model_args.mm_vision_select_feature
|
| 237 |
+
pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
|
| 238 |
+
|
| 239 |
+
self.config.mm_vision_tower = vision_tower
|
| 240 |
+
|
| 241 |
+
if self.get_vision_tower() is None:
|
| 242 |
+
vision_tower = build_vision_tower(model_args)
|
| 243 |
+
|
| 244 |
+
if fsdp is not None and len(fsdp) > 0:
|
| 245 |
+
self.vision_tower = [vision_tower]
|
| 246 |
+
else:
|
| 247 |
+
self.vision_tower = vision_tower
|
| 248 |
+
else:
|
| 249 |
+
if fsdp is not None and len(fsdp) > 0:
|
| 250 |
+
vision_tower = self.vision_tower[0]
|
| 251 |
+
else:
|
| 252 |
+
vision_tower = self.vision_tower
|
| 253 |
+
vision_tower.load_model()
|
| 254 |
+
|
| 255 |
+
self.config.use_mm_proj = True
|
| 256 |
+
self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
|
| 257 |
+
self.config.mm_hidden_size = vision_tower.hidden_size
|
| 258 |
+
self.config.mm_vision_select_layer = mm_vision_select_layer
|
| 259 |
+
self.config.mm_vision_select_feature = mm_vision_select_feature
|
| 260 |
+
|
| 261 |
+
if getattr(self, 'mm_projector', None) is None:
|
| 262 |
+
self.mm_projector = build_vision_projector(self.config)
|
| 263 |
+
else:
|
| 264 |
+
# In case it is frozen by LoRA
|
| 265 |
+
for p in self.mm_projector.parameters():
|
| 266 |
+
p.requires_grad = True
|
| 267 |
+
|
| 268 |
+
if pretrain_mm_mlp_adapter is not None:
|
| 269 |
+
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
|
| 270 |
+
def get_w(weights, keyword):
|
| 271 |
+
return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
|
| 272 |
+
|
| 273 |
+
self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
|
| 274 |
+
|
| 275 |
+
|
| 276 |
+
class LlavaMetaForCausalLM(ABC):
|
| 277 |
+
|
| 278 |
+
@abstractmethod
|
| 279 |
+
def get_model(self):
|
| 280 |
+
pass
|
| 281 |
+
|
| 282 |
+
@abstractmethod
|
| 283 |
+
def get_tokenizer(self):
|
| 284 |
+
pass
|
| 285 |
+
|
| 286 |
+
def get_vision_tower(self):
|
| 287 |
+
return self.get_model().get_vision_tower()
|
| 288 |
+
|
| 289 |
+
def encode_images(self, images):
|
| 290 |
+
image_features = self.get_model().get_vision_tower()(images)
|
| 291 |
+
image_features = self.get_model().mm_projector(image_features)
|
| 292 |
+
return image_features
|
| 293 |
+
|
| 294 |
+
def prepare_inputs_labels_for_multimodal_new(
|
| 295 |
+
self, input_ids: list[torch.tensor], position_ids, attention_mask: list[torch.tensor], past_key_values, labels, images
|
| 296 |
+
):
|
| 297 |
+
vision_tower = self.get_vision_tower()
|
| 298 |
+
if not self.training: # TODO: check this out!!
|
| 299 |
+
# pdb.set_trace()
|
| 300 |
+
if vision_tower is None or images is None or input_ids.shape[1] == 1:
|
| 301 |
+
if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:
|
| 302 |
+
|
| 303 |
+
if attention_mask is None:
|
| 304 |
+
# only happen for qwen at inference
|
| 305 |
+
# raise ValueError(f'should not be here except for Qwen!')
|
| 306 |
+
return input_ids, None, attention_mask, past_key_values, None, labels
|
| 307 |
+
|
| 308 |
+
target_shape = past_key_values[-1][-1].shape[-2] + 1
|
| 309 |
+
attention_mask = torch.cat((attention_mask, torch.ones(
|
| 310 |
+
(attention_mask.shape[0], target_shape - attention_mask.shape[1]),
|
| 311 |
+
dtype=attention_mask.dtype,
|
| 312 |
+
device=attention_mask.device
|
| 313 |
+
)), dim=1)
|
| 314 |
+
position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
|
| 315 |
+
return input_ids, position_ids, attention_mask, past_key_values, None, labels
|
| 316 |
+
|
| 317 |
+
|
| 318 |
+
# ####################### this block must be optimized! #######################
|
| 319 |
+
# if type(images) is list or images.ndim == 5:
|
| 320 |
+
# concat_images = torch.cat([image for image in images], dim=0)
|
| 321 |
+
# image_features = self.encode_images(concat_images)
|
| 322 |
+
# split_sizes = [image.shape[0] for image in images]
|
| 323 |
+
# image_features = torch.split(image_features, split_sizes, dim=0)
|
| 324 |
+
# image_features = [x.flatten(0, 1).to(self.device) for x in image_features]
|
| 325 |
+
# else:
|
| 326 |
+
# image_features = self.encode_images(images).to(self.device)
|
| 327 |
+
# ####################### this block must be optimized! #######################
|
| 328 |
+
|
| 329 |
+
# ####################### optimized #######################
|
| 330 |
+
if getattr(self, 'cached_image_features', None) is None:
|
| 331 |
+
# this attribute should be cleared in bot.clear_history()
|
| 332 |
+
if type(images) is list or images.ndim == 5:
|
| 333 |
+
concat_images = torch.cat([image for image in images], dim=0)
|
| 334 |
+
image_features = self.encode_images(concat_images)
|
| 335 |
+
split_sizes = [image.shape[0] for image in images]
|
| 336 |
+
image_features = torch.split(image_features, split_sizes, dim=0)
|
| 337 |
+
image_features = [x.flatten(0, 1).to(self.device) for x in image_features]
|
| 338 |
+
else:
|
| 339 |
+
image_features = self.encode_images(images).to(self.device)
|
| 340 |
+
self.cached_image_features = image_features
|
| 341 |
+
image_features = self.cached_image_features
|
| 342 |
+
# ####################### optimized #######################
|
| 343 |
+
|
| 344 |
+
|
| 345 |
+
# TODO: image start / end is not implemented here to support pretraining.
|
| 346 |
+
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
|
| 347 |
+
raise NotImplementedError
|
| 348 |
+
|
| 349 |
+
# Let's just add dummy tensors if they do not exist,
|
| 350 |
+
# it is a headache to deal with None all the time.
|
| 351 |
+
# But it is not ideal, and if you have a better idea,
|
| 352 |
+
# please open an issue / submit a PR, thanks.
|
| 353 |
+
_labels = labels
|
| 354 |
+
_position_ids = position_ids
|
| 355 |
+
_attention_mask = attention_mask
|
| 356 |
+
if attention_mask is None:
|
| 357 |
+
# attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
|
| 358 |
+
attention_mask = [torch.tensor([1]*l).to(input_ids).bool() for l in map(len, [ip for ip in input_ids])]
|
| 359 |
+
else:
|
| 360 |
+
# attention_mask = attention_mask.bool()
|
| 361 |
+
attention_mask = [att.bool() for att in attention_mask]
|
| 362 |
+
|
| 363 |
+
# if position_ids is None:
|
| 364 |
+
# position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
|
| 365 |
+
|
| 366 |
+
if labels is None:
|
| 367 |
+
labels = [torch.tensor([IGNORE_INDEX]*l).to(input_ids) for l in map(len, [ip for ip in input_ids])]
|
| 368 |
+
# labels = torch.full_like(input_ids, IGNORE_INDEX)
|
| 369 |
+
else:
|
| 370 |
+
labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
|
| 371 |
+
# remove the padding using attention_mask -- TODO: double check
|
| 372 |
+
# pdb.set_trace()
|
| 373 |
+
input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
|
| 374 |
+
|
| 375 |
+
new_input_embeds = []
|
| 376 |
+
new_labels = []
|
| 377 |
+
cur_image_idx = 0
|
| 378 |
+
for batch_idx, cur_input_ids in enumerate(input_ids):
|
| 379 |
+
num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
|
| 380 |
+
if num_images == 0:
|
| 381 |
+
|
| 382 |
+
############### FIXME ###############
|
| 383 |
+
if cur_image_idx > len(image_features)-1:
|
| 384 |
+
cur_image_idx = len(image_features)-1
|
| 385 |
+
print(f'warning: {input_ids}')
|
| 386 |
+
############### FIXME ###############
|
| 387 |
+
|
| 388 |
+
cur_image_features = image_features[cur_image_idx]
|
| 389 |
+
cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
|
| 390 |
+
cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
|
| 391 |
+
new_input_embeds.append(cur_input_embeds)
|
| 392 |
+
new_labels.append(labels[batch_idx])
|
| 393 |
+
cur_image_idx += 1
|
| 394 |
+
continue
|
| 395 |
+
|
| 396 |
+
image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
|
| 397 |
+
cur_input_ids_noim = []
|
| 398 |
+
cur_labels = labels[batch_idx]
|
| 399 |
+
cur_labels_noim = []
|
| 400 |
+
for i in range(len(image_token_indices) - 1):
|
| 401 |
+
cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]])
|
| 402 |
+
cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]])
|
| 403 |
+
split_sizes = [x.shape[0] for x in cur_labels_noim]
|
| 404 |
+
cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
|
| 405 |
+
cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
|
| 406 |
+
cur_new_input_embeds = []
|
| 407 |
+
cur_new_labels = []
|
| 408 |
+
|
| 409 |
+
# you have 10 images, but you have 11 placeholders
|
| 410 |
+
for i in range(num_images + 1):
|
| 411 |
+
cur_new_input_embeds.append(cur_input_embeds_no_im[i])
|
| 412 |
+
cur_new_labels.append(cur_labels_noim[i])
|
| 413 |
+
if i < num_images:
|
| 414 |
+
############### FIXME ###############
|
| 415 |
+
if cur_image_idx > len(image_features)-1:
|
| 416 |
+
cur_image_idx = len(image_features)-1
|
| 417 |
+
print(f'warning: {input_ids}')
|
| 418 |
+
############### FIXME ###############
|
| 419 |
+
|
| 420 |
+
cur_image_features = image_features[cur_image_idx]
|
| 421 |
+
cur_image_idx += 1
|
| 422 |
+
cur_new_input_embeds.append(cur_image_features)
|
| 423 |
+
cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
|
| 424 |
+
|
| 425 |
+
cur_new_input_embeds = torch.cat(cur_new_input_embeds)
|
| 426 |
+
cur_new_labels = torch.cat(cur_new_labels)
|
| 427 |
+
|
| 428 |
+
new_input_embeds.append(cur_new_input_embeds)
|
| 429 |
+
new_labels.append(cur_new_labels)
|
| 430 |
+
|
| 431 |
+
# Truncate sequences to max length as image embeddings can make the sequence longer
|
| 432 |
+
tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
|
| 433 |
+
if tokenizer_model_max_length is not None:
|
| 434 |
+
new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
|
| 435 |
+
new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
|
| 436 |
+
|
| 437 |
+
# Combine them
|
| 438 |
+
max_len = max(x.shape[0] for x in new_input_embeds)
|
| 439 |
+
batch_size = len(new_input_embeds)
|
| 440 |
+
|
| 441 |
+
new_input_embeds_padded = []
|
| 442 |
+
new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
|
| 443 |
+
attention_mask = torch.zeros((batch_size, max_len), dtype=torch.bool, device=attention_mask[0].device)
|
| 444 |
+
position_ids = torch.zeros((batch_size, max_len), dtype=torch.long, device=attention_mask[0].device)
|
| 445 |
+
|
| 446 |
+
for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
|
| 447 |
+
cur_len = cur_new_embed.shape[0]
|
| 448 |
+
# print(f'cur_len[{i}]before padding: {cur_len}')
|
| 449 |
+
# if i==0:
|
| 450 |
+
# print(f"{getattr(self.config, 'tokenizer_padding_side', 'right')} {self.get_tokenizer().padding_side}")
|
| 451 |
+
if getattr(self.config, 'tokenizer_padding_side', 'right') == "left": # checked, this is correct
|
| 452 |
+
# if self.get_tokenizer().padding_side == 'left':
|
| 453 |
+
new_input_embeds_padded.append(torch.cat((
|
| 454 |
+
torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
|
| 455 |
+
cur_new_embed
|
| 456 |
+
), dim=0))
|
| 457 |
+
if cur_len > 0:
|
| 458 |
+
new_labels_padded[i, -cur_len:] = cur_new_labels
|
| 459 |
+
attention_mask[i, -cur_len:] = True
|
| 460 |
+
position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
|
| 461 |
+
else:
|
| 462 |
+
new_input_embeds_padded.append(torch.cat((
|
| 463 |
+
cur_new_embed,
|
| 464 |
+
torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
|
| 465 |
+
), dim=0))
|
| 466 |
+
if cur_len > 0:
|
| 467 |
+
new_labels_padded[i, :cur_len] = cur_new_labels
|
| 468 |
+
attention_mask[i, :cur_len] = True
|
| 469 |
+
position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
|
| 470 |
+
|
| 471 |
+
new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
|
| 472 |
+
|
| 473 |
+
if _labels is None:
|
| 474 |
+
new_labels = None
|
| 475 |
+
else:
|
| 476 |
+
new_labels = new_labels_padded
|
| 477 |
+
|
| 478 |
+
if _attention_mask is None:
|
| 479 |
+
attention_mask = None
|
| 480 |
+
else:
|
| 481 |
+
# attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
|
| 482 |
+
attention_mask = attention_mask.to(dtype=torch.bool)
|
| 483 |
+
|
| 484 |
+
if _position_ids is None:
|
| 485 |
+
position_ids = None
|
| 486 |
+
|
| 487 |
+
return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
|
| 488 |
+
|
| 489 |
+
def initialize_vision_tokenizer(self, model_args, tokenizer):
|
| 490 |
+
if model_args.mm_use_im_patch_token:
|
| 491 |
+
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
|
| 492 |
+
self.resize_token_embeddings(len(tokenizer))
|
| 493 |
+
|
| 494 |
+
if model_args.mm_use_im_start_end:
|
| 495 |
+
num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
|
| 496 |
+
self.resize_token_embeddings(len(tokenizer))
|
| 497 |
+
|
| 498 |
+
if num_new_tokens > 0:
|
| 499 |
+
input_embeddings = self.get_input_embeddings().weight.data
|
| 500 |
+
output_embeddings = self.get_output_embeddings().weight.data
|
| 501 |
+
|
| 502 |
+
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
|
| 503 |
+
dim=0, keepdim=True)
|
| 504 |
+
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
|
| 505 |
+
dim=0, keepdim=True)
|
| 506 |
+
|
| 507 |
+
input_embeddings[-num_new_tokens:] = input_embeddings_avg
|
| 508 |
+
output_embeddings[-num_new_tokens:] = output_embeddings_avg
|
| 509 |
+
|
| 510 |
+
if model_args.tune_mm_mlp_adapter:
|
| 511 |
+
for p in self.get_input_embeddings().parameters():
|
| 512 |
+
p.requires_grad = True
|
| 513 |
+
for p in self.get_output_embeddings().parameters():
|
| 514 |
+
p.requires_grad = False
|
| 515 |
+
|
| 516 |
+
if model_args.pretrain_mm_mlp_adapter:
|
| 517 |
+
mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
|
| 518 |
+
embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
|
| 519 |
+
assert num_new_tokens == 2
|
| 520 |
+
if input_embeddings.shape == embed_tokens_weight.shape:
|
| 521 |
+
input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
|
| 522 |
+
elif embed_tokens_weight.shape[0] == num_new_tokens:
|
| 523 |
+
input_embeddings[-num_new_tokens:] = embed_tokens_weight
|
| 524 |
+
else:
|
| 525 |
+
raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
|
| 526 |
+
elif model_args.mm_use_im_patch_token:
|
| 527 |
+
if model_args.tune_mm_mlp_adapter:
|
| 528 |
+
for p in self.get_input_embeddings().parameters():
|
| 529 |
+
p.requires_grad = False
|
| 530 |
+
for p in self.get_output_embeddings().parameters():
|
| 531 |
+
p.requires_grad = False
|
merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
model-00001-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d64556d4c1c702af723fbc8697f1d71ef5218fa64e1d7e6cbfd30725119c09e0
|
| 3 |
+
size 4995584848
|
model-00002-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a1605c57e327cfaf2f22c45a927b62512f8572e7ec9a661f8b30da8240267df6
|
| 3 |
+
size 1189167008
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,854 @@
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|
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| 808 |
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| 812 |
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|
| 820 |
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| 821 |
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| 822 |
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| 823 |
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| 824 |
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| 825 |
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| 826 |
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| 829 |
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| 830 |
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| 832 |
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| 833 |
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| 834 |
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| 835 |
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| 836 |
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| 837 |
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| 838 |
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| 839 |
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| 840 |
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| 841 |
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| 842 |
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| 843 |
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| 844 |
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|
| 845 |
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|
| 846 |
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|
| 847 |
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|
| 848 |
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|
| 849 |
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|
| 850 |
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"model.vision_tower.vision_tower.vision_model.post_layernorm.weight": "model-00002-of-00002.safetensors",
|
| 851 |
+
"model.vision_tower.vision_tower.vision_model.pre_layrnorm.bias": "model-00002-of-00002.safetensors",
|
| 852 |
+
"model.vision_tower.vision_tower.vision_model.pre_layrnorm.weight": "model-00002-of-00002.safetensors"
|
| 853 |
+
}
|
| 854 |
+
}
|
modeling_llava_phi.py
ADDED
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|
| 1 |
+
from typing import List, Optional, Tuple, Union
|
| 2 |
+
|
| 3 |
+
import torch
|
| 4 |
+
import torch.nn as nn
|
| 5 |
+
import math
|
| 6 |
+
import pdb
|
| 7 |
+
from typing import Dict, Any
|
| 8 |
+
from PIL import Image
|
| 9 |
+
|
| 10 |
+
from transformers import AutoConfig, AutoModelForCausalLM, PretrainedConfig, PreTrainedModel
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
from transformers.modeling_outputs import CausalLMOutputWithPast
|
| 14 |
+
|
| 15 |
+
from .llava_arch import LlavaMetaModel, LlavaMetaForCausalLM
|
| 16 |
+
|
| 17 |
+
from transformers.cache_utils import Cache, DynamicCache
|
| 18 |
+
|
| 19 |
+
from transformers.generation.utils import GenerationConfig
|
| 20 |
+
|
| 21 |
+
import sys
|
| 22 |
+
from .modeling_phi import PhiForCausalLM, PhiModel, PhiConfig
|
| 23 |
+
from .generation_utils import build_allava_input
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
################ Phi ###############################
|
| 29 |
+
|
| 30 |
+
class LlavaPhiConfig(PhiConfig):
|
| 31 |
+
model_type = "llava_phi"
|
| 32 |
+
|
| 33 |
+
class LlavaPhiModel(LlavaMetaModel, PhiModel):
|
| 34 |
+
config_class = LlavaPhiConfig
|
| 35 |
+
|
| 36 |
+
def __init__(self, config: PhiConfig):
|
| 37 |
+
super(LlavaPhiModel, self).__init__(config)
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
class LlavaPhiForCausalLM(PhiForCausalLM, LlavaMetaForCausalLM):
|
| 42 |
+
config_class = LlavaPhiConfig
|
| 43 |
+
|
| 44 |
+
def __init__(self, config, init_vision_encoder_from_ckpt=True):
|
| 45 |
+
# note that the default value is set to True for this inference version. In training `init_vision_encoder_from_ckpt` is default to be True.
|
| 46 |
+
config._attn_implementation = "flash_attention_2"
|
| 47 |
+
|
| 48 |
+
super(PhiForCausalLM, self).__init__(config)
|
| 49 |
+
# self.model is used in LlavaMetaForCausalLM.get_model(); self.transformer is used in PhiForCausalLM.forward()
|
| 50 |
+
self.model = LlavaPhiModel(config)
|
| 51 |
+
if hasattr(self.model, '_use_flash_attention_2'):
|
| 52 |
+
assert self.model._use_flash_attention_2, 'flash attn is not enabled. check it out!'
|
| 53 |
+
self.vocab_size = config.vocab_size
|
| 54 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
| 55 |
+
|
| 56 |
+
if init_vision_encoder_from_ckpt:
|
| 57 |
+
vision_tower = self.get_vision_tower()
|
| 58 |
+
print(f'loading from CLIP first. This should only be used at inference!!!')
|
| 59 |
+
vision_tower.load_model() #
|
| 60 |
+
|
| 61 |
+
# Initialize weights and apply final processing
|
| 62 |
+
self.post_init()
|
| 63 |
+
|
| 64 |
+
def get_model(self):
|
| 65 |
+
return self.model
|
| 66 |
+
|
| 67 |
+
def get_tokenizer(self):
|
| 68 |
+
return self.tokenizer
|
| 69 |
+
|
| 70 |
+
def get_processor(self):
|
| 71 |
+
return self.model.vision_tower.image_processor
|
| 72 |
+
|
| 73 |
+
|
| 74 |
+
def forward(
|
| 75 |
+
self,
|
| 76 |
+
input_ids: torch.LongTensor = None,
|
| 77 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 78 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 79 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 80 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 81 |
+
labels: Optional[torch.LongTensor] = None,
|
| 82 |
+
use_cache: Optional[bool] = None,
|
| 83 |
+
output_attentions: Optional[bool] = None,
|
| 84 |
+
output_hidden_states: Optional[bool] = None,
|
| 85 |
+
images: Optional[torch.FloatTensor] = None,
|
| 86 |
+
return_dict: Optional[bool] = None,
|
| 87 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
| 88 |
+
|
| 89 |
+
|
| 90 |
+
if inputs_embeds is None:
|
| 91 |
+
(
|
| 92 |
+
input_ids,
|
| 93 |
+
position_ids,
|
| 94 |
+
attention_mask,
|
| 95 |
+
past_key_values,
|
| 96 |
+
inputs_embeds,
|
| 97 |
+
labels
|
| 98 |
+
# ) = self.prepare_inputs_labels_for_multimodal(
|
| 99 |
+
) = self.prepare_inputs_labels_for_multimodal_new(
|
| 100 |
+
input_ids,
|
| 101 |
+
position_ids,
|
| 102 |
+
attention_mask,
|
| 103 |
+
past_key_values,
|
| 104 |
+
labels,
|
| 105 |
+
images
|
| 106 |
+
)
|
| 107 |
+
|
| 108 |
+
# pdb.set_trace()
|
| 109 |
+
return super().forward(
|
| 110 |
+
input_ids=input_ids,
|
| 111 |
+
attention_mask=attention_mask,
|
| 112 |
+
position_ids=position_ids,
|
| 113 |
+
past_key_values=past_key_values,
|
| 114 |
+
inputs_embeds=inputs_embeds,
|
| 115 |
+
labels=labels,
|
| 116 |
+
use_cache=use_cache,
|
| 117 |
+
output_attentions=output_attentions,
|
| 118 |
+
output_hidden_states=output_hidden_states,
|
| 119 |
+
return_dict=return_dict
|
| 120 |
+
)
|
| 121 |
+
|
| 122 |
+
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs):
|
| 123 |
+
'''
|
| 124 |
+
This function is called for each token at inference
|
| 125 |
+
'''
|
| 126 |
+
# pdb.set_trace()
|
| 127 |
+
images = kwargs.pop("images", None)
|
| 128 |
+
|
| 129 |
+
####################################################
|
| 130 |
+
# lines from modeling_phi.py
|
| 131 |
+
####################################################
|
| 132 |
+
|
| 133 |
+
if past_key_values is not None:
|
| 134 |
+
if isinstance(past_key_values, Cache):
|
| 135 |
+
cache_length = past_key_values.get_seq_length()
|
| 136 |
+
past_length = past_key_values.seen_tokens
|
| 137 |
+
max_cache_length = past_key_values.get_max_length()
|
| 138 |
+
else:
|
| 139 |
+
cache_length = past_length = past_key_values[0][0].shape[2]
|
| 140 |
+
max_cache_length = None
|
| 141 |
+
|
| 142 |
+
# Keep only the unprocessed tokens:
|
| 143 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
| 144 |
+
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
| 145 |
+
# input)
|
| 146 |
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
| 147 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
| 148 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
| 149 |
+
# input_ids based on the past_length.
|
| 150 |
+
elif past_length < input_ids.shape[1]:
|
| 151 |
+
input_ids = input_ids[:, past_length:]
|
| 152 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
| 153 |
+
elif past_length >= input_ids.shape[1]:
|
| 154 |
+
input_ids = input_ids[:, [-1]] # only keep the last one!
|
| 155 |
+
|
| 156 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
| 157 |
+
if (
|
| 158 |
+
max_cache_length is not None
|
| 159 |
+
and attention_mask is not None
|
| 160 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
| 161 |
+
):
|
| 162 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
| 163 |
+
|
| 164 |
+
position_ids = kwargs.get("position_ids", None)
|
| 165 |
+
if attention_mask is not None and position_ids is None:
|
| 166 |
+
# create position_ids on the fly for batch generation
|
| 167 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
| 168 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
| 169 |
+
if past_key_values:
|
| 170 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
| 171 |
+
|
| 172 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
| 173 |
+
if inputs_embeds is not None and past_key_values is None:
|
| 174 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
| 175 |
+
else:
|
| 176 |
+
model_inputs = {"input_ids": input_ids}
|
| 177 |
+
|
| 178 |
+
model_inputs.update(
|
| 179 |
+
{
|
| 180 |
+
"position_ids": position_ids,
|
| 181 |
+
"past_key_values": past_key_values,
|
| 182 |
+
"use_cache": kwargs.get("use_cache"),
|
| 183 |
+
"attention_mask": attention_mask,
|
| 184 |
+
}
|
| 185 |
+
)
|
| 186 |
+
####################################################
|
| 187 |
+
# end of lines from modeling_phi.py
|
| 188 |
+
####################################################
|
| 189 |
+
|
| 190 |
+
|
| 191 |
+
if images is not None:
|
| 192 |
+
model_inputs['images'] = images
|
| 193 |
+
return model_inputs
|
| 194 |
+
|
| 195 |
+
|
| 196 |
+
|
| 197 |
+
|
| 198 |
+
def chat(
|
| 199 |
+
self,
|
| 200 |
+
texts: Optional[str | list[list[str, str]]],
|
| 201 |
+
images: Optional[str | list[str]] = None,
|
| 202 |
+
history: Optional[list[str]] = None,
|
| 203 |
+
stream = False,
|
| 204 |
+
return_history = False,
|
| 205 |
+
**kwargs
|
| 206 |
+
):
|
| 207 |
+
'''
|
| 208 |
+
texts: if `str`, then generate for a single round; if list[dict],
|
| 209 |
+
images: str (optional), local path to an image.
|
| 210 |
+
'''
|
| 211 |
+
use_cache = kwargs.pop('use_cache', True)
|
| 212 |
+
|
| 213 |
+
|
| 214 |
+
############################
|
| 215 |
+
# merge history
|
| 216 |
+
############################
|
| 217 |
+
input_ids, image_tensors, history = build_allava_input(
|
| 218 |
+
tokenizer = self.get_tokenizer(),
|
| 219 |
+
processor = self.get_processor(),
|
| 220 |
+
texts = texts,
|
| 221 |
+
images = images,
|
| 222 |
+
history=history,
|
| 223 |
+
return_history=return_history,
|
| 224 |
+
device = self.device
|
| 225 |
+
)
|
| 226 |
+
|
| 227 |
+
############################
|
| 228 |
+
# generate response
|
| 229 |
+
############################
|
| 230 |
+
# with torch.autocast(device_type='cuda'):
|
| 231 |
+
if 'cuda' in str(self.device):
|
| 232 |
+
device_type = 'cuda'
|
| 233 |
+
else:
|
| 234 |
+
device_type = 'cpu'
|
| 235 |
+
|
| 236 |
+
with torch.autocast(device_type=device_type, dtype=self.dtype):
|
| 237 |
+
output_ids = self.generate(
|
| 238 |
+
inputs=input_ids,
|
| 239 |
+
images=image_tensors,
|
| 240 |
+
use_cache=use_cache,
|
| 241 |
+
**kwargs)
|
| 242 |
+
|
| 243 |
+
answer = self.get_tokenizer().decode(output_ids[0, input_ids.shape[1]:], skip_special_tokens=True).strip()
|
| 244 |
+
|
| 245 |
+
if return_history:
|
| 246 |
+
history[-1][-1] = answer
|
| 247 |
+
return answer, history
|
| 248 |
+
return answer
|
| 249 |
+
|
| 250 |
+
|
| 251 |
+
AutoConfig.register("llava_phi", LlavaPhiConfig)
|
| 252 |
+
AutoModelForCausalLM.register(LlavaPhiConfig, LlavaPhiForCausalLM)
|
modeling_phi.py
ADDED
|
@@ -0,0 +1,1383 @@
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|
| 1 |
+
# coding=utf-8
|
| 2 |
+
# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
|
| 3 |
+
#
|
| 4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
+
# you may not use this file except in compliance with the License.
|
| 6 |
+
# You may obtain a copy of the License at
|
| 7 |
+
#
|
| 8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
+
#
|
| 10 |
+
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
+
# See the License for the specific language governing permissions and
|
| 14 |
+
# limitations under the License.
|
| 15 |
+
|
| 16 |
+
""" PyTorch Phi model."""
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
import math
|
| 20 |
+
from typing import List, Optional, Tuple, Union
|
| 21 |
+
|
| 22 |
+
import torch
|
| 23 |
+
import torch.nn.functional as F
|
| 24 |
+
import torch.utils.checkpoint
|
| 25 |
+
from torch import nn
|
| 26 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
| 27 |
+
|
| 28 |
+
from transformers.activations import ACT2FN
|
| 29 |
+
# try:
|
| 30 |
+
from transformers.cache_utils import Cache, DynamicCache
|
| 31 |
+
# except:
|
| 32 |
+
# Cache, DynamicCache = None, None
|
| 33 |
+
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
|
| 34 |
+
from transformers.modeling_outputs import (
|
| 35 |
+
BaseModelOutputWithPast,
|
| 36 |
+
CausalLMOutputWithPast,
|
| 37 |
+
SequenceClassifierOutputWithPast,
|
| 38 |
+
TokenClassifierOutput,
|
| 39 |
+
)
|
| 40 |
+
from transformers.modeling_utils import PreTrainedModel
|
| 41 |
+
from transformers.utils import (
|
| 42 |
+
add_code_sample_docstrings,
|
| 43 |
+
add_start_docstrings,
|
| 44 |
+
add_start_docstrings_to_model_forward,
|
| 45 |
+
is_flash_attn_2_available,
|
| 46 |
+
is_flash_attn_greater_or_equal_2_10, # dbg
|
| 47 |
+
logging,
|
| 48 |
+
replace_return_docstrings,
|
| 49 |
+
)
|
| 50 |
+
|
| 51 |
+
# is_flash_attn_greater_or_equal_2_10 = lambda:True # dbg
|
| 52 |
+
|
| 53 |
+
try:
|
| 54 |
+
from configuration_phi import PhiConfig
|
| 55 |
+
except:
|
| 56 |
+
from .configuration_phi import PhiConfig
|
| 57 |
+
|
| 58 |
+
try:
|
| 59 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
| 60 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
| 61 |
+
except:
|
| 62 |
+
pass
|
| 63 |
+
|
| 64 |
+
import pdb
|
| 65 |
+
|
| 66 |
+
logger = logging.get_logger(__name__)
|
| 67 |
+
|
| 68 |
+
_CHECKPOINT_FOR_DOC = "microsoft/phi-2"
|
| 69 |
+
_CONFIG_FOR_DOC = "PhiConfig"
|
| 70 |
+
|
| 71 |
+
PHI_PRETRAINED_MODEL_ARCHIVE_LIST = [
|
| 72 |
+
"microsoft/phi-2",
|
| 73 |
+
# See all Phi models at https://huggingface.co/models?filter=phi
|
| 74 |
+
]
|
| 75 |
+
|
| 76 |
+
|
| 77 |
+
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
|
| 78 |
+
def _get_unpad_data(attention_mask):
|
| 79 |
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
| 80 |
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
| 81 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
| 82 |
+
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
|
| 83 |
+
return (
|
| 84 |
+
indices,
|
| 85 |
+
cu_seqlens,
|
| 86 |
+
max_seqlen_in_batch,
|
| 87 |
+
)
|
| 88 |
+
|
| 89 |
+
|
| 90 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Phi
|
| 91 |
+
class PhiRotaryEmbedding(nn.Module):
|
| 92 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
| 93 |
+
super().__init__()
|
| 94 |
+
|
| 95 |
+
self.dim = dim
|
| 96 |
+
self.max_position_embeddings = max_position_embeddings
|
| 97 |
+
self.base = base
|
| 98 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
| 99 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 100 |
+
|
| 101 |
+
# Build here to make `torch.jit.trace` work.
|
| 102 |
+
self._set_cos_sin_cache(
|
| 103 |
+
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
|
| 104 |
+
)
|
| 105 |
+
|
| 106 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 107 |
+
self.max_seq_len_cached = seq_len
|
| 108 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
| 109 |
+
|
| 110 |
+
freqs = torch.outer(t, self.inv_freq)
|
| 111 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 112 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 113 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 114 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 115 |
+
|
| 116 |
+
def forward(self, x, seq_len=None):
|
| 117 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
| 118 |
+
if seq_len > self.max_seq_len_cached:
|
| 119 |
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
| 120 |
+
|
| 121 |
+
return (
|
| 122 |
+
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
| 123 |
+
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
| 124 |
+
)
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Phi
|
| 128 |
+
class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
|
| 129 |
+
"""PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
| 130 |
+
|
| 131 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
| 132 |
+
self.scaling_factor = scaling_factor
|
| 133 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
| 134 |
+
|
| 135 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 136 |
+
self.max_seq_len_cached = seq_len
|
| 137 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
| 138 |
+
t = t / self.scaling_factor
|
| 139 |
+
|
| 140 |
+
freqs = torch.outer(t, self.inv_freq)
|
| 141 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 142 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 143 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 144 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 145 |
+
|
| 146 |
+
|
| 147 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Phi
|
| 148 |
+
class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
|
| 149 |
+
"""PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
| 150 |
+
|
| 151 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
| 152 |
+
self.scaling_factor = scaling_factor
|
| 153 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
| 154 |
+
|
| 155 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
| 156 |
+
self.max_seq_len_cached = seq_len
|
| 157 |
+
|
| 158 |
+
if seq_len > self.max_position_embeddings:
|
| 159 |
+
base = self.base * (
|
| 160 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
| 161 |
+
) ** (self.dim / (self.dim - 2))
|
| 162 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
| 163 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
| 164 |
+
|
| 165 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
| 166 |
+
|
| 167 |
+
freqs = torch.outer(t, self.inv_freq)
|
| 168 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
| 169 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
| 170 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
| 171 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
| 172 |
+
|
| 173 |
+
|
| 174 |
+
# Copied from transformers.models.llama.modeling_llama.rotate_half
|
| 175 |
+
def rotate_half(x):
|
| 176 |
+
"""Rotates half the hidden dims of the input."""
|
| 177 |
+
x1 = x[..., : x.shape[-1] // 2]
|
| 178 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
| 179 |
+
return torch.cat((-x2, x1), dim=-1)
|
| 180 |
+
|
| 181 |
+
|
| 182 |
+
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
|
| 183 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
|
| 184 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
| 185 |
+
|
| 186 |
+
Args:
|
| 187 |
+
q (`torch.Tensor`): The query tensor.
|
| 188 |
+
k (`torch.Tensor`): The key tensor.
|
| 189 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
| 190 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
| 191 |
+
position_ids (`torch.Tensor`):
|
| 192 |
+
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
|
| 193 |
+
used to pass offsetted position ids when working with a KV-cache.
|
| 194 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
| 195 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
| 196 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
| 197 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
| 198 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
| 199 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
| 200 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
| 201 |
+
Returns:
|
| 202 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
| 203 |
+
"""
|
| 204 |
+
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
|
| 205 |
+
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
|
| 206 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
| 207 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
| 208 |
+
return q_embed, k_embed
|
| 209 |
+
|
| 210 |
+
|
| 211 |
+
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi
|
| 212 |
+
class PhiMLP(nn.Module):
|
| 213 |
+
def __init__(self, config):
|
| 214 |
+
super().__init__()
|
| 215 |
+
self.config = config
|
| 216 |
+
self.activation_fn = ACT2FN[config.hidden_act]
|
| 217 |
+
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
|
| 218 |
+
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
|
| 219 |
+
|
| 220 |
+
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
| 221 |
+
hidden_states = self.fc1(hidden_states)
|
| 222 |
+
hidden_states = self.activation_fn(hidden_states)
|
| 223 |
+
hidden_states = self.fc2(hidden_states)
|
| 224 |
+
return hidden_states
|
| 225 |
+
|
| 226 |
+
|
| 227 |
+
# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
|
| 228 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
| 229 |
+
"""
|
| 230 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
| 231 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
| 232 |
+
"""
|
| 233 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
| 234 |
+
if n_rep == 1:
|
| 235 |
+
return hidden_states
|
| 236 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
| 237 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
| 238 |
+
|
| 239 |
+
|
| 240 |
+
class PhiAttention(nn.Module):
|
| 241 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
| 242 |
+
|
| 243 |
+
def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None):
|
| 244 |
+
super().__init__()
|
| 245 |
+
self.config = config
|
| 246 |
+
self.layer_idx = layer_idx
|
| 247 |
+
if layer_idx is None:
|
| 248 |
+
logger.warning_once(
|
| 249 |
+
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
|
| 250 |
+
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
|
| 251 |
+
"when creating this class."
|
| 252 |
+
)
|
| 253 |
+
|
| 254 |
+
self.attention_dropout = config.attention_dropout
|
| 255 |
+
self.hidden_size = config.hidden_size
|
| 256 |
+
self.num_heads = config.num_attention_heads
|
| 257 |
+
self.head_dim = self.hidden_size // self.num_heads
|
| 258 |
+
self.num_key_value_heads = config.num_key_value_heads
|
| 259 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
| 260 |
+
self.max_position_embeddings = config.max_position_embeddings
|
| 261 |
+
self.rope_theta = config.rope_theta
|
| 262 |
+
self.partial_rotary_factor = config.partial_rotary_factor
|
| 263 |
+
self.is_causal = True
|
| 264 |
+
|
| 265 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
| 266 |
+
raise ValueError(
|
| 267 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
| 268 |
+
f" and `num_heads`: {self.num_heads})."
|
| 269 |
+
)
|
| 270 |
+
|
| 271 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
|
| 272 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
|
| 273 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
|
| 274 |
+
self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True)
|
| 275 |
+
|
| 276 |
+
self.qk_layernorm = config.qk_layernorm
|
| 277 |
+
if self.qk_layernorm:
|
| 278 |
+
self.q_layernorm = nn.LayerNorm(
|
| 279 |
+
config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
|
| 280 |
+
)
|
| 281 |
+
self.k_layernorm = nn.LayerNorm(
|
| 282 |
+
config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
|
| 283 |
+
)
|
| 284 |
+
|
| 285 |
+
self._init_rope()
|
| 286 |
+
|
| 287 |
+
def _init_rope(self):
|
| 288 |
+
if self.config.rope_scaling is None:
|
| 289 |
+
self.rotary_emb = PhiRotaryEmbedding(
|
| 290 |
+
int(self.partial_rotary_factor * self.head_dim),
|
| 291 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 292 |
+
base=self.rope_theta,
|
| 293 |
+
)
|
| 294 |
+
else:
|
| 295 |
+
scaling_type = self.config.rope_scaling["type"]
|
| 296 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
| 297 |
+
if scaling_type == "linear":
|
| 298 |
+
self.rotary_emb = PhiLinearScalingRotaryEmbedding(
|
| 299 |
+
int(self.partial_rotary_factor * self.head_dim),
|
| 300 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 301 |
+
scaling_factor=scaling_factor,
|
| 302 |
+
base=self.rope_theta,
|
| 303 |
+
)
|
| 304 |
+
elif scaling_type == "dynamic":
|
| 305 |
+
self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding(
|
| 306 |
+
int(self.partial_rotary_factor * self.head_dim),
|
| 307 |
+
max_position_embeddings=self.max_position_embeddings,
|
| 308 |
+
scaling_factor=scaling_factor,
|
| 309 |
+
base=self.rope_theta,
|
| 310 |
+
)
|
| 311 |
+
else:
|
| 312 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
| 313 |
+
|
| 314 |
+
# Phi-2 has an attention overflow issue (with FP16) and requires autocast to be disabled
|
| 315 |
+
@torch.autocast("cpu", enabled=False)
|
| 316 |
+
@torch.autocast("cuda", enabled=False)
|
| 317 |
+
def forward(
|
| 318 |
+
self,
|
| 319 |
+
hidden_states: torch.Tensor,
|
| 320 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 321 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 322 |
+
past_key_value: Optional[Cache] = None,
|
| 323 |
+
output_attentions: bool = False,
|
| 324 |
+
use_cache: bool = False,
|
| 325 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 326 |
+
bsz, q_len, _ = hidden_states.size()
|
| 327 |
+
|
| 328 |
+
query_states = self.q_proj(hidden_states)
|
| 329 |
+
key_states = self.k_proj(hidden_states)
|
| 330 |
+
value_states = self.v_proj(hidden_states)
|
| 331 |
+
|
| 332 |
+
if self.qk_layernorm:
|
| 333 |
+
query_states = self.q_layernorm(query_states)
|
| 334 |
+
key_states = self.k_layernorm(key_states)
|
| 335 |
+
|
| 336 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
| 337 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 338 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 339 |
+
|
| 340 |
+
kv_seq_len = key_states.shape[-2]
|
| 341 |
+
if past_key_value is not None:
|
| 342 |
+
if self.layer_idx is None:
|
| 343 |
+
raise ValueError(
|
| 344 |
+
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
| 345 |
+
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
| 346 |
+
"with a layer index."
|
| 347 |
+
)
|
| 348 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
| 349 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
| 350 |
+
|
| 351 |
+
# Partial rotary embedding
|
| 352 |
+
query_rot, query_pass = (
|
| 353 |
+
query_states[..., : self.rotary_emb.dim],
|
| 354 |
+
query_states[..., self.rotary_emb.dim :],
|
| 355 |
+
)
|
| 356 |
+
key_rot, key_pass = (
|
| 357 |
+
key_states[..., : self.rotary_emb.dim],
|
| 358 |
+
key_states[..., self.rotary_emb.dim :],
|
| 359 |
+
)
|
| 360 |
+
# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
|
| 361 |
+
query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
|
| 362 |
+
|
| 363 |
+
# [batch_size, seq_length, num_heads, head_dim]
|
| 364 |
+
query_states = torch.cat((query_rot, query_pass), dim=-1)
|
| 365 |
+
key_states = torch.cat((key_rot, key_pass), dim=-1)
|
| 366 |
+
|
| 367 |
+
if past_key_value is not None:
|
| 368 |
+
cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
|
| 369 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
| 370 |
+
|
| 371 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
| 372 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
| 373 |
+
|
| 374 |
+
# Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
|
| 375 |
+
attn_weights = torch.matmul(
|
| 376 |
+
query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
|
| 377 |
+
) / math.sqrt(self.head_dim)
|
| 378 |
+
|
| 379 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
| 380 |
+
raise ValueError(
|
| 381 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
| 382 |
+
f" {attn_weights.size()}"
|
| 383 |
+
)
|
| 384 |
+
|
| 385 |
+
if attention_mask is not None:
|
| 386 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
| 387 |
+
raise ValueError(
|
| 388 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
| 389 |
+
)
|
| 390 |
+
attn_weights = attn_weights + attention_mask
|
| 391 |
+
|
| 392 |
+
# upcast attention to fp32
|
| 393 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
|
| 394 |
+
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
| 395 |
+
|
| 396 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
| 397 |
+
|
| 398 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
| 399 |
+
raise ValueError(
|
| 400 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
| 401 |
+
f" {attn_output.size()}"
|
| 402 |
+
)
|
| 403 |
+
|
| 404 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
| 405 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
| 406 |
+
|
| 407 |
+
attn_output = self.dense(attn_output)
|
| 408 |
+
|
| 409 |
+
if not output_attentions:
|
| 410 |
+
attn_weights = None
|
| 411 |
+
|
| 412 |
+
return attn_output, attn_weights, past_key_value
|
| 413 |
+
|
| 414 |
+
|
| 415 |
+
class PhiFlashAttention2(PhiAttention):
|
| 416 |
+
"""
|
| 417 |
+
Phi flash attention module. This module inherits from `PhiAttention` as the weights of the module stays
|
| 418 |
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
| 419 |
+
flash attention and deal with padding tokens in case the input contains any of them.
|
| 420 |
+
"""
|
| 421 |
+
|
| 422 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
|
| 423 |
+
def __init__(self, *args, **kwargs):
|
| 424 |
+
super().__init__(*args, **kwargs)
|
| 425 |
+
|
| 426 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
| 427 |
+
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
| 428 |
+
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
| 429 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
| 430 |
+
|
| 431 |
+
def forward(
|
| 432 |
+
self,
|
| 433 |
+
hidden_states: torch.Tensor,
|
| 434 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
| 435 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 436 |
+
past_key_value: Optional[Cache] = None,
|
| 437 |
+
output_attentions: bool = False,
|
| 438 |
+
use_cache: bool = False,
|
| 439 |
+
**kwargs,
|
| 440 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 441 |
+
# PhiFlashAttention2 attention does not support output_attentions
|
| 442 |
+
|
| 443 |
+
output_attentions = False
|
| 444 |
+
|
| 445 |
+
bsz, q_len, _ = hidden_states.size()
|
| 446 |
+
|
| 447 |
+
query_states = self.q_proj(hidden_states)
|
| 448 |
+
key_states = self.k_proj(hidden_states)
|
| 449 |
+
value_states = self.v_proj(hidden_states)
|
| 450 |
+
|
| 451 |
+
if self.qk_layernorm:
|
| 452 |
+
query_states = self.q_layernorm(query_states)
|
| 453 |
+
key_states = self.k_layernorm(key_states)
|
| 454 |
+
|
| 455 |
+
# Flash attention requires the input to have the shape
|
| 456 |
+
# batch_size x seq_length x head_dim x hidden_dim
|
| 457 |
+
# therefore we just need to keep the original shape
|
| 458 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
| 459 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 460 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
| 461 |
+
|
| 462 |
+
kv_seq_len = key_states.shape[-2]
|
| 463 |
+
if past_key_value is not None:
|
| 464 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
| 465 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
| 466 |
+
|
| 467 |
+
# Partial rotary embedding
|
| 468 |
+
query_rot, query_pass = (
|
| 469 |
+
query_states[..., : self.rotary_emb.dim],
|
| 470 |
+
query_states[..., self.rotary_emb.dim :],
|
| 471 |
+
)
|
| 472 |
+
key_rot, key_pass = (
|
| 473 |
+
key_states[..., : self.rotary_emb.dim],
|
| 474 |
+
key_states[..., self.rotary_emb.dim :],
|
| 475 |
+
)
|
| 476 |
+
# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
|
| 477 |
+
query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
|
| 478 |
+
|
| 479 |
+
# [batch_size, seq_length, num_heads, head_dim]
|
| 480 |
+
query_states = torch.cat((query_rot, query_pass), dim=-1)
|
| 481 |
+
key_states = torch.cat((key_rot, key_pass), dim=-1)
|
| 482 |
+
|
| 483 |
+
if past_key_value is not None:
|
| 484 |
+
cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
|
| 485 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
| 486 |
+
|
| 487 |
+
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
| 488 |
+
# to be able to avoid many of these transpose/reshape/view.
|
| 489 |
+
query_states = query_states.transpose(1, 2)
|
| 490 |
+
key_states = key_states.transpose(1, 2)
|
| 491 |
+
value_states = value_states.transpose(1, 2)
|
| 492 |
+
|
| 493 |
+
attn_dropout = self.attention_dropout if self.training else 0.0
|
| 494 |
+
|
| 495 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
| 496 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
| 497 |
+
# cast them back in the correct dtype just to be sure everything works as expected.
|
| 498 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
| 499 |
+
# in fp32.
|
| 500 |
+
|
| 501 |
+
if query_states.dtype == torch.float32:
|
| 502 |
+
if torch.is_autocast_enabled():
|
| 503 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
| 504 |
+
# Handle the case where the model is quantized
|
| 505 |
+
elif hasattr(self.config, "_pre_quantization_dtype"):
|
| 506 |
+
target_dtype = self.config._pre_quantization_dtype
|
| 507 |
+
else:
|
| 508 |
+
target_dtype = self.q_proj.weight.dtype
|
| 509 |
+
|
| 510 |
+
logger.warning_once(
|
| 511 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
| 512 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
| 513 |
+
f" {target_dtype}."
|
| 514 |
+
)
|
| 515 |
+
|
| 516 |
+
query_states = query_states.to(target_dtype)
|
| 517 |
+
key_states = key_states.to(target_dtype)
|
| 518 |
+
value_states = value_states.to(target_dtype)
|
| 519 |
+
|
| 520 |
+
attn_output = self._flash_attention_forward(
|
| 521 |
+
query_states, key_states, value_states, attention_mask, q_len, dropout=attn_dropout, softmax_scale=None
|
| 522 |
+
)
|
| 523 |
+
|
| 524 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
| 525 |
+
attn_output = self.dense(attn_output)
|
| 526 |
+
|
| 527 |
+
if not output_attentions:
|
| 528 |
+
attn_weights = None
|
| 529 |
+
|
| 530 |
+
return attn_output, attn_weights, past_key_value
|
| 531 |
+
|
| 532 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
|
| 533 |
+
def _flash_attention_forward(
|
| 534 |
+
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
| 535 |
+
):
|
| 536 |
+
"""
|
| 537 |
+
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
| 538 |
+
first unpad the input, then computes the attention scores and pad the final attention scores.
|
| 539 |
+
|
| 540 |
+
Args:
|
| 541 |
+
query_states (`torch.Tensor`):
|
| 542 |
+
Input query states to be passed to Flash Attention API
|
| 543 |
+
key_states (`torch.Tensor`):
|
| 544 |
+
Input key states to be passed to Flash Attention API
|
| 545 |
+
value_states (`torch.Tensor`):
|
| 546 |
+
Input value states to be passed to Flash Attention API
|
| 547 |
+
attention_mask (`torch.Tensor`):
|
| 548 |
+
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
| 549 |
+
position of padding tokens and 1 for the position of non-padding tokens.
|
| 550 |
+
dropout (`int`, *optional*):
|
| 551 |
+
Attention dropout
|
| 552 |
+
softmax_scale (`float`, *optional*):
|
| 553 |
+
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
| 554 |
+
"""
|
| 555 |
+
if not self._flash_attn_uses_top_left_mask:
|
| 556 |
+
causal = self.is_causal
|
| 557 |
+
else:
|
| 558 |
+
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
|
| 559 |
+
causal = self.is_causal and query_length != 1
|
| 560 |
+
|
| 561 |
+
# Contains at least one padding token in the sequence
|
| 562 |
+
if attention_mask is not None:
|
| 563 |
+
batch_size = query_states.shape[0]
|
| 564 |
+
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
| 565 |
+
query_states, key_states, value_states, attention_mask, query_length
|
| 566 |
+
)
|
| 567 |
+
|
| 568 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
| 569 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
| 570 |
+
|
| 571 |
+
attn_output_unpad = flash_attn_varlen_func(
|
| 572 |
+
query_states,
|
| 573 |
+
key_states,
|
| 574 |
+
value_states,
|
| 575 |
+
cu_seqlens_q=cu_seqlens_q,
|
| 576 |
+
cu_seqlens_k=cu_seqlens_k,
|
| 577 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
| 578 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
| 579 |
+
dropout_p=dropout,
|
| 580 |
+
softmax_scale=softmax_scale,
|
| 581 |
+
causal=causal,
|
| 582 |
+
)
|
| 583 |
+
|
| 584 |
+
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
| 585 |
+
else:
|
| 586 |
+
attn_output = flash_attn_func(
|
| 587 |
+
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
| 588 |
+
)
|
| 589 |
+
|
| 590 |
+
return attn_output
|
| 591 |
+
|
| 592 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
|
| 593 |
+
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
| 594 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
| 595 |
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
| 596 |
+
|
| 597 |
+
key_layer = index_first_axis(
|
| 598 |
+
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
| 599 |
+
)
|
| 600 |
+
value_layer = index_first_axis(
|
| 601 |
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
| 602 |
+
)
|
| 603 |
+
if query_length == kv_seq_len:
|
| 604 |
+
query_layer = index_first_axis(
|
| 605 |
+
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
| 606 |
+
)
|
| 607 |
+
cu_seqlens_q = cu_seqlens_k
|
| 608 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
| 609 |
+
indices_q = indices_k
|
| 610 |
+
elif query_length == 1:
|
| 611 |
+
max_seqlen_in_batch_q = 1
|
| 612 |
+
cu_seqlens_q = torch.arange(
|
| 613 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
| 614 |
+
) # There is a memcpy here, that is very bad.
|
| 615 |
+
indices_q = cu_seqlens_q[:-1]
|
| 616 |
+
query_layer = query_layer.squeeze(1)
|
| 617 |
+
else:
|
| 618 |
+
# The -q_len: slice assumes left padding.
|
| 619 |
+
attention_mask = attention_mask[:, -query_length:]
|
| 620 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
| 621 |
+
|
| 622 |
+
return (
|
| 623 |
+
query_layer,
|
| 624 |
+
key_layer,
|
| 625 |
+
value_layer,
|
| 626 |
+
indices_q,
|
| 627 |
+
(cu_seqlens_q, cu_seqlens_k),
|
| 628 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
| 629 |
+
)
|
| 630 |
+
|
| 631 |
+
|
| 632 |
+
PHI_ATTENTION_CLASSES = {
|
| 633 |
+
"eager": PhiAttention,
|
| 634 |
+
"flash_attention_2": PhiFlashAttention2,
|
| 635 |
+
}
|
| 636 |
+
|
| 637 |
+
|
| 638 |
+
class PhiDecoderLayer(nn.Module):
|
| 639 |
+
def __init__(self, config: PhiConfig, layer_idx: int):
|
| 640 |
+
super().__init__()
|
| 641 |
+
self.self_attn = PHI_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
|
| 642 |
+
self.mlp = PhiMLP(config)
|
| 643 |
+
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
| 644 |
+
self.resid_dropout = nn.Dropout(config.resid_pdrop)
|
| 645 |
+
|
| 646 |
+
def forward(
|
| 647 |
+
self,
|
| 648 |
+
hidden_states: torch.Tensor,
|
| 649 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 650 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 651 |
+
output_attentions: Optional[bool] = False,
|
| 652 |
+
use_cache: Optional[bool] = False,
|
| 653 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
| 654 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
| 655 |
+
"""
|
| 656 |
+
Args:
|
| 657 |
+
hidden_states (`torch.FloatTensor`):
|
| 658 |
+
input to the layer of shape `(batch, seq_len, embed_dim)`
|
| 659 |
+
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
| 660 |
+
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
| 661 |
+
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
| 662 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
|
| 663 |
+
`[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
|
| 664 |
+
output_attentions (`bool`, *optional*):
|
| 665 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
| 666 |
+
returned tensors for more detail.
|
| 667 |
+
use_cache (`bool`, *optional*):
|
| 668 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
| 669 |
+
(see `past_key_values`).
|
| 670 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
| 671 |
+
"""
|
| 672 |
+
|
| 673 |
+
residual = hidden_states
|
| 674 |
+
|
| 675 |
+
hidden_states = self.input_layernorm(hidden_states)
|
| 676 |
+
|
| 677 |
+
# Self Attention
|
| 678 |
+
attn_outputs, self_attn_weights, present_key_value = self.self_attn(
|
| 679 |
+
hidden_states=hidden_states,
|
| 680 |
+
attention_mask=attention_mask,
|
| 681 |
+
position_ids=position_ids,
|
| 682 |
+
past_key_value=past_key_value,
|
| 683 |
+
output_attentions=output_attentions,
|
| 684 |
+
use_cache=use_cache,
|
| 685 |
+
)
|
| 686 |
+
attn_outputs = self.resid_dropout(attn_outputs)
|
| 687 |
+
|
| 688 |
+
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
|
| 689 |
+
hidden_states = attn_outputs + feed_forward_hidden_states + residual
|
| 690 |
+
outputs = (hidden_states,)
|
| 691 |
+
|
| 692 |
+
if output_attentions:
|
| 693 |
+
outputs += (self_attn_weights,)
|
| 694 |
+
|
| 695 |
+
if use_cache:
|
| 696 |
+
outputs += (present_key_value,)
|
| 697 |
+
|
| 698 |
+
return outputs
|
| 699 |
+
|
| 700 |
+
|
| 701 |
+
PHI_START_DOCSTRING = r"""
|
| 702 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
| 703 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
| 704 |
+
etc.)
|
| 705 |
+
|
| 706 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
| 707 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
| 708 |
+
and behavior.
|
| 709 |
+
|
| 710 |
+
Parameters:
|
| 711 |
+
config ([`PhiConfig`]):
|
| 712 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
| 713 |
+
load the weights associated with the model, only the configuration. Check out the
|
| 714 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
| 715 |
+
"""
|
| 716 |
+
|
| 717 |
+
|
| 718 |
+
@add_start_docstrings(
|
| 719 |
+
"The bare Phi Model outputting raw hidden-states without any specific head on top.",
|
| 720 |
+
PHI_START_DOCSTRING,
|
| 721 |
+
)
|
| 722 |
+
class PhiPreTrainedModel(PreTrainedModel):
|
| 723 |
+
config_class = PhiConfig
|
| 724 |
+
base_model_prefix = "model"
|
| 725 |
+
supports_gradient_checkpointing = True
|
| 726 |
+
_no_split_modules = ["PhiDecoderLayer"]
|
| 727 |
+
_skip_keys_device_placement = "past_key_values"
|
| 728 |
+
_supports_flash_attn_2 = True
|
| 729 |
+
_supports_cache_class = True
|
| 730 |
+
|
| 731 |
+
def _init_weights(self, module):
|
| 732 |
+
std = self.config.initializer_range
|
| 733 |
+
if isinstance(module, nn.Linear):
|
| 734 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
| 735 |
+
if module.bias is not None:
|
| 736 |
+
module.bias.data.zero_()
|
| 737 |
+
elif isinstance(module, nn.Embedding):
|
| 738 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
| 739 |
+
if module.padding_idx is not None:
|
| 740 |
+
module.weight.data[module.padding_idx].zero_()
|
| 741 |
+
|
| 742 |
+
|
| 743 |
+
PHI_INPUTS_DOCSTRING = r"""
|
| 744 |
+
Args:
|
| 745 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
| 746 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
| 747 |
+
it.
|
| 748 |
+
|
| 749 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
| 750 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
| 751 |
+
|
| 752 |
+
[What are input IDs?](../glossary#input-ids)
|
| 753 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 754 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
| 755 |
+
|
| 756 |
+
- 1 for tokens that are **not masked**,
|
| 757 |
+
- 0 for tokens that are **masked**.
|
| 758 |
+
|
| 759 |
+
[What are attention masks?](../glossary#attention-mask)
|
| 760 |
+
|
| 761 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
| 762 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
| 763 |
+
|
| 764 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
| 765 |
+
`past_key_values`).
|
| 766 |
+
|
| 767 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
| 768 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
| 769 |
+
information on the default strategy.
|
| 770 |
+
|
| 771 |
+
- 1 indicates the head is **not masked**,
|
| 772 |
+
- 0 indicates the head is **masked**.
|
| 773 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 774 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
| 775 |
+
config.n_positions - 1]`.
|
| 776 |
+
|
| 777 |
+
[What are position IDs?](../glossary#position-ids)
|
| 778 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
| 779 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
| 780 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
| 781 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
| 782 |
+
|
| 783 |
+
Two formats are allowed:
|
| 784 |
+
- a [`~cache_utils.Cache`] instance;
|
| 785 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
| 786 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
| 787 |
+
cache format.
|
| 788 |
+
|
| 789 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
| 790 |
+
legacy cache format will be returned.
|
| 791 |
+
|
| 792 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
| 793 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
| 794 |
+
of shape `(batch_size, sequence_length)`.
|
| 795 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
| 796 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
| 797 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
| 798 |
+
model's internal embedding lookup matrix.
|
| 799 |
+
use_cache (`bool`, *optional*):
|
| 800 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
| 801 |
+
`past_key_values`).
|
| 802 |
+
output_attentions (`bool`, *optional*):
|
| 803 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
| 804 |
+
tensors for more detail.
|
| 805 |
+
output_hidden_states (`bool`, *optional*):
|
| 806 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
| 807 |
+
more detail.
|
| 808 |
+
return_dict (`bool`, *optional*):
|
| 809 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
| 810 |
+
"""
|
| 811 |
+
|
| 812 |
+
|
| 813 |
+
@add_start_docstrings(
|
| 814 |
+
"The bare Phi Model outputting raw hidden-states without any specific head on top.",
|
| 815 |
+
PHI_START_DOCSTRING,
|
| 816 |
+
)
|
| 817 |
+
class PhiModel(PhiPreTrainedModel):
|
| 818 |
+
"""
|
| 819 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
|
| 820 |
+
|
| 821 |
+
Args:
|
| 822 |
+
config: PhiConfig
|
| 823 |
+
"""
|
| 824 |
+
|
| 825 |
+
def __init__(self, config: PhiConfig):
|
| 826 |
+
super().__init__(config)
|
| 827 |
+
self.padding_idx = config.pad_token_id
|
| 828 |
+
self.vocab_size = config.vocab_size
|
| 829 |
+
|
| 830 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
| 831 |
+
self.embed_dropout = nn.Dropout(config.embd_pdrop)
|
| 832 |
+
self.layers = nn.ModuleList(
|
| 833 |
+
[PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
| 834 |
+
)
|
| 835 |
+
self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
| 836 |
+
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
| 837 |
+
|
| 838 |
+
self.gradient_checkpointing = False
|
| 839 |
+
# Initialize weights and apply final processing
|
| 840 |
+
self.post_init()
|
| 841 |
+
|
| 842 |
+
def get_input_embeddings(self):
|
| 843 |
+
return self.embed_tokens
|
| 844 |
+
|
| 845 |
+
def set_input_embeddings(self, value):
|
| 846 |
+
self.embed_tokens = value
|
| 847 |
+
|
| 848 |
+
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
|
| 849 |
+
def forward(
|
| 850 |
+
self,
|
| 851 |
+
input_ids: torch.LongTensor = None,
|
| 852 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 853 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 854 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 855 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 856 |
+
use_cache: Optional[bool] = None,
|
| 857 |
+
output_attentions: Optional[bool] = None,
|
| 858 |
+
output_hidden_states: Optional[bool] = None,
|
| 859 |
+
return_dict: Optional[bool] = None,
|
| 860 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
| 861 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
| 862 |
+
output_hidden_states = (
|
| 863 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 864 |
+
)
|
| 865 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
| 866 |
+
|
| 867 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 868 |
+
|
| 869 |
+
# retrieve input_ids and inputs_embeds
|
| 870 |
+
if input_ids is not None and inputs_embeds is not None:
|
| 871 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
| 872 |
+
elif input_ids is not None:
|
| 873 |
+
batch_size, seq_length = input_ids.shape[:2]
|
| 874 |
+
elif inputs_embeds is not None:
|
| 875 |
+
batch_size, seq_length = inputs_embeds.shape[:2]
|
| 876 |
+
else:
|
| 877 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
| 878 |
+
|
| 879 |
+
past_key_values_length = 0
|
| 880 |
+
|
| 881 |
+
if self.gradient_checkpointing and self.training:
|
| 882 |
+
if use_cache:
|
| 883 |
+
logger.warning_once(
|
| 884 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
| 885 |
+
)
|
| 886 |
+
use_cache = False
|
| 887 |
+
|
| 888 |
+
if use_cache:
|
| 889 |
+
# dbg: uncomment is original
|
| 890 |
+
use_legacy_cache = not isinstance(past_key_values, Cache)
|
| 891 |
+
if use_legacy_cache:
|
| 892 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
| 893 |
+
|
| 894 |
+
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
| 895 |
+
|
| 896 |
+
if position_ids is None:
|
| 897 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
| 898 |
+
position_ids = torch.arange(
|
| 899 |
+
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
| 900 |
+
)
|
| 901 |
+
position_ids = position_ids.unsqueeze(0)
|
| 902 |
+
|
| 903 |
+
if inputs_embeds is None:
|
| 904 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
| 905 |
+
|
| 906 |
+
inputs_embeds = self.embed_dropout(inputs_embeds)
|
| 907 |
+
|
| 908 |
+
# Attention mask.
|
| 909 |
+
if self._use_flash_attention_2:
|
| 910 |
+
# 2d mask is passed through the layers
|
| 911 |
+
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
| 912 |
+
else:
|
| 913 |
+
# 4d mask is passed through the layers
|
| 914 |
+
attention_mask = _prepare_4d_causal_attention_mask(
|
| 915 |
+
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
| 916 |
+
)
|
| 917 |
+
|
| 918 |
+
hidden_states = inputs_embeds
|
| 919 |
+
|
| 920 |
+
# decoder layers
|
| 921 |
+
all_hidden_states = () if output_hidden_states else None
|
| 922 |
+
all_self_attns = () if output_attentions else None
|
| 923 |
+
next_decoder_cache = None
|
| 924 |
+
|
| 925 |
+
for decoder_layer in self.layers:
|
| 926 |
+
if output_hidden_states:
|
| 927 |
+
all_hidden_states += (hidden_states,)
|
| 928 |
+
|
| 929 |
+
if self.gradient_checkpointing and self.training:
|
| 930 |
+
layer_outputs = self._gradient_checkpointing_func(
|
| 931 |
+
decoder_layer.__call__,
|
| 932 |
+
hidden_states,
|
| 933 |
+
attention_mask,
|
| 934 |
+
position_ids,
|
| 935 |
+
past_key_values,
|
| 936 |
+
output_attentions,
|
| 937 |
+
)
|
| 938 |
+
else:
|
| 939 |
+
layer_outputs = decoder_layer(
|
| 940 |
+
hidden_states,
|
| 941 |
+
attention_mask=attention_mask,
|
| 942 |
+
position_ids=position_ids,
|
| 943 |
+
past_key_value=past_key_values,
|
| 944 |
+
output_attentions=output_attentions,
|
| 945 |
+
use_cache=use_cache,
|
| 946 |
+
)
|
| 947 |
+
|
| 948 |
+
hidden_states = layer_outputs[0]
|
| 949 |
+
|
| 950 |
+
if use_cache:
|
| 951 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
| 952 |
+
|
| 953 |
+
if output_attentions:
|
| 954 |
+
all_self_attns += (layer_outputs[1],)
|
| 955 |
+
|
| 956 |
+
hidden_states = self.final_layernorm(hidden_states)
|
| 957 |
+
|
| 958 |
+
# add hidden states from the last decoder layer
|
| 959 |
+
if output_hidden_states:
|
| 960 |
+
all_hidden_states += (hidden_states,)
|
| 961 |
+
|
| 962 |
+
next_cache = None
|
| 963 |
+
if use_cache:
|
| 964 |
+
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
|
| 965 |
+
if not return_dict:
|
| 966 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
| 967 |
+
return BaseModelOutputWithPast(
|
| 968 |
+
last_hidden_state=hidden_states,
|
| 969 |
+
past_key_values=next_cache,
|
| 970 |
+
hidden_states=all_hidden_states,
|
| 971 |
+
attentions=all_self_attns,
|
| 972 |
+
)
|
| 973 |
+
|
| 974 |
+
|
| 975 |
+
class PhiForCausalLM(PhiPreTrainedModel):
|
| 976 |
+
_tied_weights_keys = ["lm_head.weight"]
|
| 977 |
+
|
| 978 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
|
| 979 |
+
def __init__(self, config):
|
| 980 |
+
super().__init__(config)
|
| 981 |
+
self.model = PhiModel(config)
|
| 982 |
+
self.vocab_size = config.vocab_size
|
| 983 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)
|
| 984 |
+
|
| 985 |
+
# Initialize weights and apply final processing
|
| 986 |
+
self.post_init()
|
| 987 |
+
|
| 988 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
|
| 989 |
+
def get_input_embeddings(self):
|
| 990 |
+
return self.model.embed_tokens
|
| 991 |
+
|
| 992 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
|
| 993 |
+
def set_input_embeddings(self, value):
|
| 994 |
+
self.model.embed_tokens = value
|
| 995 |
+
|
| 996 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
|
| 997 |
+
def get_output_embeddings(self):
|
| 998 |
+
return self.lm_head
|
| 999 |
+
|
| 1000 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
|
| 1001 |
+
def set_output_embeddings(self, new_embeddings):
|
| 1002 |
+
self.lm_head = new_embeddings
|
| 1003 |
+
|
| 1004 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
|
| 1005 |
+
def set_decoder(self, decoder):
|
| 1006 |
+
self.model = decoder
|
| 1007 |
+
|
| 1008 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
|
| 1009 |
+
def get_decoder(self):
|
| 1010 |
+
return self.model
|
| 1011 |
+
|
| 1012 |
+
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
|
| 1013 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
| 1014 |
+
def forward(
|
| 1015 |
+
self,
|
| 1016 |
+
input_ids: torch.LongTensor = None,
|
| 1017 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1018 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1019 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 1020 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1021 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1022 |
+
use_cache: Optional[bool] = None,
|
| 1023 |
+
output_attentions: Optional[bool] = None,
|
| 1024 |
+
output_hidden_states: Optional[bool] = None,
|
| 1025 |
+
return_dict: Optional[bool] = None,
|
| 1026 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
| 1027 |
+
r"""
|
| 1028 |
+
Args:
|
| 1029 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 1030 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
| 1031 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
| 1032 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
| 1033 |
+
|
| 1034 |
+
Returns:
|
| 1035 |
+
|
| 1036 |
+
Example:
|
| 1037 |
+
|
| 1038 |
+
```python
|
| 1039 |
+
>>> from transformers import AutoTokenizer, PhiForCausalLM
|
| 1040 |
+
|
| 1041 |
+
>>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1")
|
| 1042 |
+
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
|
| 1043 |
+
|
| 1044 |
+
>>> prompt = "This is an example script ."
|
| 1045 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
| 1046 |
+
|
| 1047 |
+
>>> # Generate
|
| 1048 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
| 1049 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
| 1050 |
+
'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str'
|
| 1051 |
+
```"""
|
| 1052 |
+
|
| 1053 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
| 1054 |
+
output_hidden_states = (
|
| 1055 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 1056 |
+
)
|
| 1057 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1058 |
+
|
| 1059 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
| 1060 |
+
outputs = self.model(
|
| 1061 |
+
input_ids=input_ids,
|
| 1062 |
+
attention_mask=attention_mask,
|
| 1063 |
+
position_ids=position_ids,
|
| 1064 |
+
past_key_values=past_key_values,
|
| 1065 |
+
inputs_embeds=inputs_embeds,
|
| 1066 |
+
use_cache=use_cache,
|
| 1067 |
+
output_attentions=output_attentions,
|
| 1068 |
+
output_hidden_states=output_hidden_states,
|
| 1069 |
+
return_dict=return_dict,
|
| 1070 |
+
)
|
| 1071 |
+
|
| 1072 |
+
|
| 1073 |
+
# concat the feature back?
|
| 1074 |
+
|
| 1075 |
+
hidden_states = outputs[0]
|
| 1076 |
+
logits = self.lm_head(hidden_states)
|
| 1077 |
+
logits = logits.float()
|
| 1078 |
+
|
| 1079 |
+
loss = None
|
| 1080 |
+
if labels is not None:
|
| 1081 |
+
# Shift so that tokens < n predict n
|
| 1082 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
| 1083 |
+
shift_labels = labels[..., 1:].contiguous()
|
| 1084 |
+
# Flatten the tokens
|
| 1085 |
+
loss_fct = CrossEntropyLoss()
|
| 1086 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
| 1087 |
+
shift_labels = shift_labels.view(-1)
|
| 1088 |
+
# Enable model parallelism
|
| 1089 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
| 1090 |
+
loss = loss_fct(shift_logits, shift_labels)
|
| 1091 |
+
|
| 1092 |
+
if not return_dict:
|
| 1093 |
+
output = (logits,) + outputs[1:]
|
| 1094 |
+
return (loss,) + output if loss is not None else output
|
| 1095 |
+
|
| 1096 |
+
return CausalLMOutputWithPast(
|
| 1097 |
+
loss=loss,
|
| 1098 |
+
logits=logits,
|
| 1099 |
+
past_key_values=outputs.past_key_values,
|
| 1100 |
+
hidden_states=outputs.hidden_states,
|
| 1101 |
+
attentions=outputs.attentions,
|
| 1102 |
+
)
|
| 1103 |
+
|
| 1104 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation
|
| 1105 |
+
def prepare_inputs_for_generation(
|
| 1106 |
+
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
| 1107 |
+
):
|
| 1108 |
+
if past_key_values is not None:
|
| 1109 |
+
if isinstance(past_key_values, Cache):
|
| 1110 |
+
cache_length = past_key_values.get_seq_length()
|
| 1111 |
+
past_length = past_key_values.seen_tokens
|
| 1112 |
+
max_cache_length = past_key_values.get_max_length()
|
| 1113 |
+
else:
|
| 1114 |
+
cache_length = past_length = past_key_values[0][0].shape[2]
|
| 1115 |
+
max_cache_length = None
|
| 1116 |
+
|
| 1117 |
+
# Keep only the unprocessed tokens:
|
| 1118 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
| 1119 |
+
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
| 1120 |
+
# input)
|
| 1121 |
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
| 1122 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
| 1123 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
| 1124 |
+
# input_ids based on the past_length.
|
| 1125 |
+
elif past_length < input_ids.shape[1]:
|
| 1126 |
+
input_ids = input_ids[:, past_length:]
|
| 1127 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
| 1128 |
+
|
| 1129 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
| 1130 |
+
if (
|
| 1131 |
+
max_cache_length is not None
|
| 1132 |
+
and attention_mask is not None
|
| 1133 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
| 1134 |
+
):
|
| 1135 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
| 1136 |
+
|
| 1137 |
+
position_ids = kwargs.get("position_ids", None)
|
| 1138 |
+
if attention_mask is not None and position_ids is None:
|
| 1139 |
+
# create position_ids on the fly for batch generation
|
| 1140 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
| 1141 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
| 1142 |
+
if past_key_values:
|
| 1143 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
| 1144 |
+
|
| 1145 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
| 1146 |
+
if inputs_embeds is not None and past_key_values is None:
|
| 1147 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
| 1148 |
+
else:
|
| 1149 |
+
model_inputs = {"input_ids": input_ids}
|
| 1150 |
+
|
| 1151 |
+
model_inputs.update(
|
| 1152 |
+
{
|
| 1153 |
+
"position_ids": position_ids,
|
| 1154 |
+
"past_key_values": past_key_values,
|
| 1155 |
+
"use_cache": kwargs.get("use_cache"),
|
| 1156 |
+
"attention_mask": attention_mask,
|
| 1157 |
+
}
|
| 1158 |
+
)
|
| 1159 |
+
return model_inputs
|
| 1160 |
+
|
| 1161 |
+
@staticmethod
|
| 1162 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
|
| 1163 |
+
def _reorder_cache(past_key_values, beam_idx):
|
| 1164 |
+
reordered_past = ()
|
| 1165 |
+
for layer_past in past_key_values:
|
| 1166 |
+
reordered_past += (
|
| 1167 |
+
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
| 1168 |
+
)
|
| 1169 |
+
return reordered_past
|
| 1170 |
+
|
| 1171 |
+
|
| 1172 |
+
@add_start_docstrings(
|
| 1173 |
+
"""
|
| 1174 |
+
The PhiModel with a sequence classification head on top (linear layer).
|
| 1175 |
+
|
| 1176 |
+
[`PhiForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
| 1177 |
+
(e.g. GPT-2) do.
|
| 1178 |
+
|
| 1179 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
| 1180 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
| 1181 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
| 1182 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
| 1183 |
+
each row of the batch).
|
| 1184 |
+
""",
|
| 1185 |
+
PHI_START_DOCSTRING,
|
| 1186 |
+
)
|
| 1187 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->PHI,Llama->Phi with self.transformer->self.model, transformer_outputs->model_outputs
|
| 1188 |
+
class PhiForSequenceClassification(PhiPreTrainedModel):
|
| 1189 |
+
def __init__(self, config):
|
| 1190 |
+
super().__init__(config)
|
| 1191 |
+
self.num_labels = config.num_labels
|
| 1192 |
+
self.model = PhiModel(config)
|
| 1193 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
| 1194 |
+
|
| 1195 |
+
# Initialize weights and apply final processing
|
| 1196 |
+
self.post_init()
|
| 1197 |
+
|
| 1198 |
+
def get_input_embeddings(self):
|
| 1199 |
+
return self.model.embed_tokens
|
| 1200 |
+
|
| 1201 |
+
def set_input_embeddings(self, value):
|
| 1202 |
+
self.model.embed_tokens = value
|
| 1203 |
+
|
| 1204 |
+
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
|
| 1205 |
+
def forward(
|
| 1206 |
+
self,
|
| 1207 |
+
input_ids: torch.LongTensor = None,
|
| 1208 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1209 |
+
position_ids: Optional[torch.LongTensor] = None,
|
| 1210 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
| 1211 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 1212 |
+
labels: Optional[torch.LongTensor] = None,
|
| 1213 |
+
use_cache: Optional[bool] = None,
|
| 1214 |
+
output_attentions: Optional[bool] = None,
|
| 1215 |
+
output_hidden_states: Optional[bool] = None,
|
| 1216 |
+
return_dict: Optional[bool] = None,
|
| 1217 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
| 1218 |
+
r"""
|
| 1219 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1220 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
| 1221 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
| 1222 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
| 1223 |
+
"""
|
| 1224 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1225 |
+
|
| 1226 |
+
model_outputs = self.model(
|
| 1227 |
+
input_ids,
|
| 1228 |
+
attention_mask=attention_mask,
|
| 1229 |
+
position_ids=position_ids,
|
| 1230 |
+
past_key_values=past_key_values,
|
| 1231 |
+
inputs_embeds=inputs_embeds,
|
| 1232 |
+
use_cache=use_cache,
|
| 1233 |
+
output_attentions=output_attentions,
|
| 1234 |
+
output_hidden_states=output_hidden_states,
|
| 1235 |
+
return_dict=return_dict,
|
| 1236 |
+
)
|
| 1237 |
+
hidden_states = model_outputs[0]
|
| 1238 |
+
logits = self.score(hidden_states)
|
| 1239 |
+
|
| 1240 |
+
if input_ids is not None:
|
| 1241 |
+
batch_size = input_ids.shape[0]
|
| 1242 |
+
else:
|
| 1243 |
+
batch_size = inputs_embeds.shape[0]
|
| 1244 |
+
|
| 1245 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
| 1246 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
| 1247 |
+
if self.config.pad_token_id is None:
|
| 1248 |
+
sequence_lengths = -1
|
| 1249 |
+
else:
|
| 1250 |
+
if input_ids is not None:
|
| 1251 |
+
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
| 1252 |
+
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
| 1253 |
+
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
| 1254 |
+
sequence_lengths = sequence_lengths.to(logits.device)
|
| 1255 |
+
else:
|
| 1256 |
+
sequence_lengths = -1
|
| 1257 |
+
|
| 1258 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
| 1259 |
+
|
| 1260 |
+
loss = None
|
| 1261 |
+
if labels is not None:
|
| 1262 |
+
labels = labels.to(logits.device)
|
| 1263 |
+
if self.config.problem_type is None:
|
| 1264 |
+
if self.num_labels == 1:
|
| 1265 |
+
self.config.problem_type = "regression"
|
| 1266 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
| 1267 |
+
self.config.problem_type = "single_label_classification"
|
| 1268 |
+
else:
|
| 1269 |
+
self.config.problem_type = "multi_label_classification"
|
| 1270 |
+
|
| 1271 |
+
if self.config.problem_type == "regression":
|
| 1272 |
+
loss_fct = MSELoss()
|
| 1273 |
+
if self.num_labels == 1:
|
| 1274 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
| 1275 |
+
else:
|
| 1276 |
+
loss = loss_fct(pooled_logits, labels)
|
| 1277 |
+
elif self.config.problem_type == "single_label_classification":
|
| 1278 |
+
loss_fct = CrossEntropyLoss()
|
| 1279 |
+
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
| 1280 |
+
elif self.config.problem_type == "multi_label_classification":
|
| 1281 |
+
loss_fct = BCEWithLogitsLoss()
|
| 1282 |
+
loss = loss_fct(pooled_logits, labels)
|
| 1283 |
+
if not return_dict:
|
| 1284 |
+
output = (pooled_logits,) + model_outputs[1:]
|
| 1285 |
+
return ((loss,) + output) if loss is not None else output
|
| 1286 |
+
|
| 1287 |
+
return SequenceClassifierOutputWithPast(
|
| 1288 |
+
loss=loss,
|
| 1289 |
+
logits=pooled_logits,
|
| 1290 |
+
past_key_values=model_outputs.past_key_values,
|
| 1291 |
+
hidden_states=model_outputs.hidden_states,
|
| 1292 |
+
attentions=model_outputs.attentions,
|
| 1293 |
+
)
|
| 1294 |
+
|
| 1295 |
+
|
| 1296 |
+
@add_start_docstrings(
|
| 1297 |
+
"""
|
| 1298 |
+
PhiModel with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
|
| 1299 |
+
Named-Entity-Recognition (NER) tasks.
|
| 1300 |
+
""",
|
| 1301 |
+
PHI_START_DOCSTRING,
|
| 1302 |
+
)
|
| 1303 |
+
# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with MPT->PHI,Mpt->Phi,self.transformer->self.model,transformer_outputs->model_outputs
|
| 1304 |
+
class PhiForTokenClassification(PhiPreTrainedModel):
|
| 1305 |
+
def __init__(self, config: PhiConfig):
|
| 1306 |
+
super().__init__(config)
|
| 1307 |
+
self.num_labels = config.num_labels
|
| 1308 |
+
|
| 1309 |
+
self.model = PhiModel(config)
|
| 1310 |
+
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
|
| 1311 |
+
classifier_dropout = config.classifier_dropout
|
| 1312 |
+
elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
|
| 1313 |
+
classifier_dropout = config.hidden_dropout
|
| 1314 |
+
else:
|
| 1315 |
+
classifier_dropout = 0.1
|
| 1316 |
+
self.dropout = nn.Dropout(classifier_dropout)
|
| 1317 |
+
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
| 1318 |
+
|
| 1319 |
+
# Initialize weights and apply final processing
|
| 1320 |
+
self.post_init()
|
| 1321 |
+
|
| 1322 |
+
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
|
| 1323 |
+
@add_code_sample_docstrings(
|
| 1324 |
+
checkpoint=_CHECKPOINT_FOR_DOC,
|
| 1325 |
+
output_type=TokenClassifierOutput,
|
| 1326 |
+
config_class=_CONFIG_FOR_DOC,
|
| 1327 |
+
)
|
| 1328 |
+
def forward(
|
| 1329 |
+
self,
|
| 1330 |
+
input_ids: Optional[torch.LongTensor] = None,
|
| 1331 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
| 1332 |
+
attention_mask: Optional[torch.Tensor] = None,
|
| 1333 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
| 1334 |
+
labels: Optional[torch.Tensor] = None,
|
| 1335 |
+
use_cache: Optional[bool] = None,
|
| 1336 |
+
output_attentions: Optional[bool] = None,
|
| 1337 |
+
output_hidden_states: Optional[bool] = None,
|
| 1338 |
+
return_dict: Optional[bool] = None,
|
| 1339 |
+
**deprecated_arguments,
|
| 1340 |
+
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
|
| 1341 |
+
r"""
|
| 1342 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
| 1343 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
| 1344 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
| 1345 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
| 1346 |
+
"""
|
| 1347 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 1348 |
+
|
| 1349 |
+
model_outputs = self.model(
|
| 1350 |
+
input_ids,
|
| 1351 |
+
past_key_values=past_key_values,
|
| 1352 |
+
attention_mask=attention_mask,
|
| 1353 |
+
inputs_embeds=inputs_embeds,
|
| 1354 |
+
use_cache=use_cache,
|
| 1355 |
+
output_attentions=output_attentions,
|
| 1356 |
+
output_hidden_states=output_hidden_states,
|
| 1357 |
+
return_dict=return_dict,
|
| 1358 |
+
)
|
| 1359 |
+
|
| 1360 |
+
hidden_states = model_outputs[0]
|
| 1361 |
+
hidden_states = self.dropout(hidden_states)
|
| 1362 |
+
logits = self.classifier(hidden_states)
|
| 1363 |
+
|
| 1364 |
+
loss = None
|
| 1365 |
+
if labels is not None:
|
| 1366 |
+
# move labels to correct device to enable model parallelism
|
| 1367 |
+
labels = labels.to(logits.device)
|
| 1368 |
+
batch_size, seq_length = labels.shape
|
| 1369 |
+
loss_fct = CrossEntropyLoss()
|
| 1370 |
+
loss = loss_fct(
|
| 1371 |
+
logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
|
| 1372 |
+
)
|
| 1373 |
+
|
| 1374 |
+
if not return_dict:
|
| 1375 |
+
output = (logits,) + model_outputs[2:]
|
| 1376 |
+
return ((loss,) + output) if loss is not None else output
|
| 1377 |
+
|
| 1378 |
+
return TokenClassifierOutput(
|
| 1379 |
+
loss=loss,
|
| 1380 |
+
logits=logits,
|
| 1381 |
+
hidden_states=model_outputs.hidden_states,
|
| 1382 |
+
attentions=model_outputs.attentions,
|
| 1383 |
+
)
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<|endoftext|>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|endoftext|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": "<|endoftext|>",
|
| 17 |
+
"unk_token": {
|
| 18 |
+
"content": "<|endoftext|>",
|
| 19 |
+
"lstrip": false,
|
| 20 |
+
"normalized": false,
|
| 21 |
+
"rstrip": false,
|
| 22 |
+
"single_word": false
|
| 23 |
+
}
|
| 24 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,324 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"50256": {
|
| 5 |
+
"content": "<|endoftext|>",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"50257": {
|
| 13 |
+
"content": " ",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": true,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": false
|
| 19 |
+
},
|
| 20 |
+
"50258": {
|
| 21 |
+
"content": " ",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": true,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": false
|
| 27 |
+
},
|
| 28 |
+
"50259": {
|
| 29 |
+
"content": " ",
|
| 30 |
+
"lstrip": false,
|
| 31 |
+
"normalized": true,
|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false,
|
| 34 |
+
"special": false
|
| 35 |
+
},
|
| 36 |
+
"50260": {
|
| 37 |
+
"content": " ",
|
| 38 |
+
"lstrip": false,
|
| 39 |
+
"normalized": true,
|
| 40 |
+
"rstrip": false,
|
| 41 |
+
"single_word": false,
|
| 42 |
+
"special": false
|
| 43 |
+
},
|
| 44 |
+
"50261": {
|
| 45 |
+
"content": " ",
|
| 46 |
+
"lstrip": false,
|
| 47 |
+
"normalized": true,
|
| 48 |
+
"rstrip": false,
|
| 49 |
+
"single_word": false,
|
| 50 |
+
"special": false
|
| 51 |
+
},
|
| 52 |
+
"50262": {
|
| 53 |
+
"content": " ",
|
| 54 |
+
"lstrip": false,
|
| 55 |
+
"normalized": true,
|
| 56 |
+
"rstrip": false,
|
| 57 |
+
"single_word": false,
|
| 58 |
+
"special": false
|
| 59 |
+
},
|
| 60 |
+
"50263": {
|
| 61 |
+
"content": " ",
|
| 62 |
+
"lstrip": false,
|
| 63 |
+
"normalized": true,
|
| 64 |
+
"rstrip": false,
|
| 65 |
+
"single_word": false,
|
| 66 |
+
"special": false
|
| 67 |
+
},
|
| 68 |
+
"50264": {
|
| 69 |
+
"content": " ",
|
| 70 |
+
"lstrip": false,
|
| 71 |
+
"normalized": true,
|
| 72 |
+
"rstrip": false,
|
| 73 |
+
"single_word": false,
|
| 74 |
+
"special": false
|
| 75 |
+
},
|
| 76 |
+
"50265": {
|
| 77 |
+
"content": " ",
|
| 78 |
+
"lstrip": false,
|
| 79 |
+
"normalized": true,
|
| 80 |
+
"rstrip": false,
|
| 81 |
+
"single_word": false,
|
| 82 |
+
"special": false
|
| 83 |
+
},
|
| 84 |
+
"50266": {
|
| 85 |
+
"content": " ",
|
| 86 |
+
"lstrip": false,
|
| 87 |
+
"normalized": true,
|
| 88 |
+
"rstrip": false,
|
| 89 |
+
"single_word": false,
|
| 90 |
+
"special": false
|
| 91 |
+
},
|
| 92 |
+
"50267": {
|
| 93 |
+
"content": " ",
|
| 94 |
+
"lstrip": false,
|
| 95 |
+
"normalized": true,
|
| 96 |
+
"rstrip": false,
|
| 97 |
+
"single_word": false,
|
| 98 |
+
"special": false
|
| 99 |
+
},
|
| 100 |
+
"50268": {
|
| 101 |
+
"content": " ",
|
| 102 |
+
"lstrip": false,
|
| 103 |
+
"normalized": true,
|
| 104 |
+
"rstrip": false,
|
| 105 |
+
"single_word": false,
|
| 106 |
+
"special": false
|
| 107 |
+
},
|
| 108 |
+
"50269": {
|
| 109 |
+
"content": " ",
|
| 110 |
+
"lstrip": false,
|
| 111 |
+
"normalized": true,
|
| 112 |
+
"rstrip": false,
|
| 113 |
+
"single_word": false,
|
| 114 |
+
"special": false
|
| 115 |
+
},
|
| 116 |
+
"50270": {
|
| 117 |
+
"content": " ",
|
| 118 |
+
"lstrip": false,
|
| 119 |
+
"normalized": true,
|
| 120 |
+
"rstrip": false,
|
| 121 |
+
"single_word": false,
|
| 122 |
+
"special": false
|
| 123 |
+
},
|
| 124 |
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vocab.json
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