Commit
•
9cf3498
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Parent(s):
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Upload folder using huggingface_hub
Browse files- README.md +253 -0
- added_tokens.json +40 -0
- config.json +48 -0
- configuration_phi.py +193 -0
- generation_config.json +4 -0
- merges.txt +0 -0
- modeling_phi.py +1366 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer_config.json +324 -0
- vocab.json +0 -0
README.md
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---
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tags:
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- generated_from_trainer
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model-index:
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- name: phi-600M-mix
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.3.0`
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```yaml
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base_model: phi-600M-cont/checkpoint-5000
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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trust_remote_code: true
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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# max_steps: 8000
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#pretraining_dataset: nampdn-ai/tiny-strange-textbooks
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datasets:
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- path: math-ai/StackMathQA
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name: stackmathqa100k
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type:
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system_prompt: ""
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field_system: system
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field_instruction: Q
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field_output: A
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format: "[INST] {instruction} [/INST]"
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no_input_format: "[INST] {instruction} [/INST]"
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train_on_split: train[:10%]
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- path: SciPhi/textbooks-are-all-you-need-lite
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type: completion
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field: completion
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train_on_split: train[:10%]
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dataset_prepared_path:
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val_set_size: 0.001
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output_dir: ./phi-600M-mix
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sequence_len: 2048
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sample_packing: true # currently unsupported
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pad_to_sequence_len:
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adapter:
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+
lora_model_dir:
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lora_r:
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lora_alpha:
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lora_dropout:
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lora_target_linear:
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lora_fan_in_fan_out:
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lora_modules_to_save:
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wandb_project: phine
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wandb_entity: willfulbytes
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 4
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micro_batch_size: 1
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num_epochs: 1
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optimizer: paged_adamw_8bit
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adam_beta2: 0.98
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adam_epsilon: 0.0000001
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max_grad_norm: 1.0
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lr_scheduler: cosine
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learning_rate: 1e-4
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cosine_min_lr_ratio: 0.2
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train_on_inputs: false
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group_by_length: false
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bf16: true
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fp16: false
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tf32: true
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gradient_checkpointing: true
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early_stopping_patience: false
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_steps: 0
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evals_per_epoch: 100
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saves_per_epoch: 10
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save_steps:
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debug:
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deepspeed:
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weight_decay: 0.1
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fsdp:
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fsdp_config:
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resize_token_embeddings_to_32x: true
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special_tokens:
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pad_token: "<|endoftext|>"
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```
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</details><br>
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# phi-600M-mix
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This model was trained from scratch on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.6549
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
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- lr_scheduler_type: cosine
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- num_epochs: 1
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+
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 3.366 | 0.0 | 1 | 3.3037 |
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| 2.5809 | 0.01 | 84 | 2.5172 |
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| 2.5684 | 0.02 | 168 | 2.3902 |
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| 2.6054 | 0.03 | 252 | 2.3144 |
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| 2.2944 | 0.04 | 336 | 2.2658 |
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| 2.2836 | 0.05 | 420 | 2.2178 |
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| 2.4438 | 0.06 | 504 | 2.1837 |
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| 2.1093 | 0.07 | 588 | 2.1460 |
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| 2.1831 | 0.08 | 672 | 2.1220 |
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| 2.3081 | 0.09 | 756 | 2.0990 |
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| 1.9909 | 0.1 | 840 | 2.0850 |
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| 2.114 | 0.11 | 924 | 2.0550 |
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| 1.8529 | 0.12 | 1008 | 2.0410 |
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| 2.1594 | 0.13 | 1092 | 2.0215 |
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| 2.0632 | 0.14 | 1176 | 2.0035 |
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| 1.9221 | 0.15 | 1260 | 1.9906 |
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| 2.0664 | 0.16 | 1344 | 1.9861 |
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| 1.931 | 0.17 | 1428 | 1.9708 |
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| 1.9948 | 0.18 | 1512 | 1.9533 |
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| 1.9229 | 0.19 | 1596 | 1.9464 |
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| 2.0231 | 0.2 | 1680 | 1.9332 |
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| 2.2535 | 0.21 | 1764 | 1.9232 |
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| 1.8994 | 0.22 | 1848 | 1.9140 |
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| 1.9913 | 0.23 | 1932 | 1.8935 |
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| 1.8613 | 0.24 | 2016 | 1.8916 |
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| 1.9724 | 0.25 | 2100 | 1.8790 |
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| 1.9965 | 0.26 | 2184 | 1.8653 |
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| 2.0012 | 0.27 | 2268 | 1.8648 |
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| 1.9752 | 0.28 | 2352 | 1.8572 |
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| 1.9709 | 0.29 | 2436 | 1.8504 |
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| 1.7314 | 0.3 | 2520 | 1.8432 |
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| 1.7373 | 0.31 | 2604 | 1.8470 |
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| 1.93 | 0.32 | 2688 | 1.8353 |
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| 1.7185 | 0.33 | 2772 | 1.8210 |
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| 1.8435 | 0.34 | 2856 | 1.8201 |
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| 1.8117 | 0.35 | 2940 | 1.8118 |
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| 2.1292 | 0.36 | 3024 | 1.8095 |
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| 1.7536 | 0.37 | 3108 | 1.8023 |
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| 1.7596 | 0.38 | 3192 | 1.7956 |
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| 1.9481 | 0.39 | 3276 | 1.7890 |
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| 1.7915 | 0.4 | 3360 | 1.7872 |
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| 1.8639 | 0.41 | 3444 | 1.7782 |
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| 1.6688 | 0.42 | 3528 | 1.7754 |
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| 1.6312 | 0.43 | 3612 | 1.7669 |
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| 1.8053 | 0.45 | 3696 | 1.7602 |
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| 1.8867 | 0.46 | 3780 | 1.7544 |
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| 1.9305 | 0.47 | 3864 | 1.7546 |
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| 1.7926 | 0.48 | 3948 | 1.7496 |
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| 1.8326 | 0.49 | 4032 | 1.7436 |
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| 1.7334 | 0.5 | 4116 | 1.7437 |
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| 1.6552 | 0.51 | 4200 | 1.7348 |
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| 1.6622 | 0.52 | 4284 | 1.7330 |
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| 1.9858 | 0.53 | 4368 | 1.7303 |
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| 1.7784 | 0.54 | 4452 | 1.7271 |
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| 1.8752 | 0.55 | 4536 | 1.7222 |
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| 1.5931 | 0.56 | 4620 | 1.7186 |
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| 1.6785 | 0.57 | 4704 | 1.7131 |
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| 1.8382 | 0.58 | 4788 | 1.7101 |
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| 1.5888 | 0.59 | 4872 | 1.7081 |
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| 1.8055 | 0.6 | 4956 | 1.7062 |
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| 1.6869 | 0.61 | 5040 | 1.7021 |
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| 1.8096 | 0.62 | 5124 | 1.6999 |
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| 1.9318 | 0.63 | 5208 | 1.6980 |
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| 1.6153 | 0.64 | 5292 | 1.6963 |
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| 1.6556 | 0.65 | 5376 | 1.6924 |
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| 1.4087 | 0.66 | 5460 | 1.6908 |
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| 1.7946 | 0.67 | 5544 | 1.6881 |
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| 1.6097 | 0.68 | 5628 | 1.6867 |
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| 1.6397 | 0.69 | 5712 | 1.6847 |
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| 1.7799 | 0.7 | 5796 | 1.6828 |
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| 1.6216 | 0.71 | 5880 | 1.6809 |
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| 1.5052 | 0.72 | 5964 | 1.6790 |
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| 1.6931 | 0.73 | 6048 | 1.6773 |
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| 1.5936 | 0.74 | 6132 | 1.6762 |
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| 1.803 | 0.75 | 6216 | 1.6737 |
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| 1.5175 | 0.76 | 6300 | 1.6719 |
|
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| 1.6305 | 0.77 | 6384 | 1.6711 |
|
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| 1.715 | 0.78 | 6468 | 1.6698 |
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| 1.8779 | 0.79 | 6552 | 1.6686 |
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| 1.6844 | 0.8 | 6636 | 1.6669 |
|
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| 1.3624 | 0.81 | 6720 | 1.6658 |
|
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| 1.5534 | 0.82 | 6804 | 1.6650 |
|
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| 1.8579 | 0.83 | 6888 | 1.6648 |
|
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| 1.6093 | 0.84 | 6972 | 1.6632 |
|
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| 1.5325 | 0.85 | 7056 | 1.6618 |
|
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| 1.6753 | 0.86 | 7140 | 1.6619 |
|
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| 1.3612 | 0.87 | 7224 | 1.6611 |
|
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| 1.4817 | 0.88 | 7308 | 1.6606 |
|
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| 1.7252 | 0.89 | 7392 | 1.6599 |
|
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| 1.7463 | 0.9 | 7476 | 1.6586 |
|
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| 1.8894 | 0.91 | 7560 | 1.6581 |
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| 1.545 | 0.92 | 7644 | 1.6575 |
|
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| 1.7251 | 0.93 | 7728 | 1.6572 |
|
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| 1.7265 | 0.94 | 7812 | 1.6572 |
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| 1.7813 | 0.95 | 7896 | 1.6564 |
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| 1.7005 | 0.96 | 7980 | 1.6560 |
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| 1.6444 | 0.97 | 8064 | 1.6555 |
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| 1.5202 | 0.98 | 8148 | 1.6552 |
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| 1.8648 | 0.99 | 8232 | 1.6549 |
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### Framework versions
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- Transformers 4.37.0.dev0
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- Pytorch 2.0.1
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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added_tokens.json
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{
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"\t\t": 50294,
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"\t\t\t": 50293,
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"\t\t\t\t": 50292,
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"\t\t\t\t\t": 50291,
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"\t\t\t\t\t\t": 50290,
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"\t\t\t\t\t\t\t": 50289,
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"\t\t\t\t\t\t\t\t": 50288,
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"\t\t\t\t\t\t\t\t\t": 50287,
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" ": 50286,
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" ": 50284,
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" ": 50283,
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" ": 50282,
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" ": 50281,
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" ": 50276,
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" ": 50275,
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23 |
+
" ": 50273,
|
24 |
+
" ": 50272,
|
25 |
+
" ": 50271,
|
26 |
+
" ": 50270,
|
27 |
+
" ": 50269,
|
28 |
+
" ": 50268,
|
29 |
+
" ": 50267,
|
30 |
+
" ": 50266,
|
31 |
+
" ": 50265,
|
32 |
+
" ": 50264,
|
33 |
+
" ": 50263,
|
34 |
+
" ": 50262,
|
35 |
+
" ": 50261,
|
36 |
+
" ": 50260,
|
37 |
+
" ": 50259,
|
38 |
+
" ": 50258,
|
39 |
+
" ": 50257
|
40 |
+
}
|
config.json
ADDED
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "phi-600M-cont/checkpoint-5000",
|
3 |
+
"activation_function": "gelu_new",
|
4 |
+
"architectures": [
|
5 |
+
"PhiForCausalLM"
|
6 |
+
],
|
7 |
+
"attention_dropout": 0.0,
|
8 |
+
"attn_pdrop": 0.0,
|
9 |
+
"auto_map": {
|
10 |
+
"AutoConfig": "configuration_phi.PhiConfig",
|
11 |
+
"AutoModelForCausalLM": "modeling_phi.PhiForCausalLM"
|
12 |
+
},
|
13 |
+
"bos_token_id": 50256,
|
14 |
+
"embd_pdrop": 0.0,
|
15 |
+
"eos_token_id": 50256,
|
16 |
+
"flash_attn": true,
|
17 |
+
"flash_rotary": true,
|
18 |
+
"fused_dense": true,
|
19 |
+
"hidden_act": "gelu_new",
|
20 |
+
"hidden_size": 2048,
|
21 |
+
"img_processor": null,
|
22 |
+
"initializer_range": 0.02,
|
23 |
+
"intermediate_size": 8192,
|
24 |
+
"layer_norm_eps": 1e-05,
|
25 |
+
"layer_norm_epsilon": 1e-05,
|
26 |
+
"max_position_embeddings": 2048,
|
27 |
+
"model_type": "phi",
|
28 |
+
"n_embd": 1536,
|
29 |
+
"n_head": 16,
|
30 |
+
"n_head_kv": null,
|
31 |
+
"n_inner": null,
|
32 |
+
"n_layer": 16,
|
33 |
+
"n_positions": 2048,
|
34 |
+
"num_attention_heads": 32,
|
35 |
+
"num_hidden_layers": 24,
|
36 |
+
"num_key_value_heads": 32,
|
37 |
+
"partial_rotary_factor": 0.5,
|
38 |
+
"qk_layernorm": false,
|
39 |
+
"resid_pdrop": 0.1,
|
40 |
+
"rope_scaling": null,
|
41 |
+
"rope_theta": 10000.0,
|
42 |
+
"rotary_dim": 32,
|
43 |
+
"tie_word_embeddings": false,
|
44 |
+
"torch_dtype": "bfloat16",
|
45 |
+
"transformers_version": "4.37.0.dev0",
|
46 |
+
"use_cache": false,
|
47 |
+
"vocab_size": 51200
|
48 |
+
}
|
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 |
+
}
|
merges.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
modeling_phi.py
ADDED
@@ -0,0 +1,1366 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
# 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 |
+
from transformers.cache_utils import Cache, DynamicCache
|
30 |
+
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
|
31 |
+
from transformers.modeling_outputs import (
|
32 |
+
BaseModelOutputWithPast,
|
33 |
+
CausalLMOutputWithPast,
|
34 |
+
SequenceClassifierOutputWithPast,
|
35 |
+
TokenClassifierOutput,
|
36 |
+
)
|
37 |
+
from transformers.modeling_utils import PreTrainedModel
|
38 |
+
from transformers.utils import (
|
39 |
+
add_code_sample_docstrings,
|
40 |
+
add_start_docstrings,
|
41 |
+
add_start_docstrings_to_model_forward,
|
42 |
+
is_flash_attn_2_available,
|
43 |
+
is_flash_attn_greater_or_equal_2_10,
|
44 |
+
logging,
|
45 |
+
replace_return_docstrings,
|
46 |
+
)
|
47 |
+
from .configuration_phi import PhiConfig
|
48 |
+
|
49 |
+
|
50 |
+
try:
|
51 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
52 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
53 |
+
except:
|
54 |
+
pass
|
55 |
+
|
56 |
+
|
57 |
+
logger = logging.get_logger(__name__)
|
58 |
+
|
59 |
+
_CHECKPOINT_FOR_DOC = "microsoft/phi-2"
|
60 |
+
_CONFIG_FOR_DOC = "PhiConfig"
|
61 |
+
|
62 |
+
PHI_PRETRAINED_MODEL_ARCHIVE_LIST = [
|
63 |
+
"microsoft/phi-2",
|
64 |
+
# See all Phi models at https://huggingface.co/models?filter=phi
|
65 |
+
]
|
66 |
+
|
67 |
+
|
68 |
+
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
|
69 |
+
def _get_unpad_data(attention_mask):
|
70 |
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
71 |
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
72 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
73 |
+
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
|
74 |
+
return (
|
75 |
+
indices,
|
76 |
+
cu_seqlens,
|
77 |
+
max_seqlen_in_batch,
|
78 |
+
)
|
79 |
+
|
80 |
+
|
81 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Phi
|
82 |
+
class PhiRotaryEmbedding(nn.Module):
|
83 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
84 |
+
super().__init__()
|
85 |
+
|
86 |
+
self.dim = dim
|
87 |
+
self.max_position_embeddings = max_position_embeddings
|
88 |
+
self.base = base
|
89 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
90 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
91 |
+
|
92 |
+
# Build here to make `torch.jit.trace` work.
|
93 |
+
self._set_cos_sin_cache(
|
94 |
+
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
|
95 |
+
)
|
96 |
+
|
97 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
98 |
+
self.max_seq_len_cached = seq_len
|
99 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
100 |
+
|
101 |
+
freqs = torch.outer(t, self.inv_freq)
|
102 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
103 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
104 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
105 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
106 |
+
|
107 |
+
def forward(self, x, seq_len=None):
|
108 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
109 |
+
if seq_len > self.max_seq_len_cached:
|
110 |
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
111 |
+
|
112 |
+
return (
|
113 |
+
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
114 |
+
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
115 |
+
)
|
116 |
+
|
117 |
+
|
118 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Phi
|
119 |
+
class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
|
120 |
+
"""PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
121 |
+
|
122 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
123 |
+
self.scaling_factor = scaling_factor
|
124 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
125 |
+
|
126 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
127 |
+
self.max_seq_len_cached = seq_len
|
128 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
129 |
+
t = t / self.scaling_factor
|
130 |
+
|
131 |
+
freqs = torch.outer(t, self.inv_freq)
|
132 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
133 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
134 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
135 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
136 |
+
|
137 |
+
|
138 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Phi
|
139 |
+
class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
|
140 |
+
"""PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
141 |
+
|
142 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
143 |
+
self.scaling_factor = scaling_factor
|
144 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
145 |
+
|
146 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
147 |
+
self.max_seq_len_cached = seq_len
|
148 |
+
|
149 |
+
if seq_len > self.max_position_embeddings:
|
150 |
+
base = self.base * (
|
151 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
152 |
+
) ** (self.dim / (self.dim - 2))
|
153 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
154 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
155 |
+
|
156 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
157 |
+
|
158 |
+
freqs = torch.outer(t, self.inv_freq)
|
159 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
160 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
161 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
162 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
163 |
+
|
164 |
+
|
165 |
+
# Copied from transformers.models.llama.modeling_llama.rotate_half
|
166 |
+
def rotate_half(x):
|
167 |
+
"""Rotates half the hidden dims of the input."""
|
168 |
+
x1 = x[..., : x.shape[-1] // 2]
|
169 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
170 |
+
return torch.cat((-x2, x1), dim=-1)
|
171 |
+
|
172 |
+
|
173 |
+
# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
|
174 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
|
175 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
176 |
+
|
177 |
+
Args:
|
178 |
+
q (`torch.Tensor`): The query tensor.
|
179 |
+
k (`torch.Tensor`): The key tensor.
|
180 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
181 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
182 |
+
position_ids (`torch.Tensor`):
|
183 |
+
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
|
184 |
+
used to pass offsetted position ids when working with a KV-cache.
|
185 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
186 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
187 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
188 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
189 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
190 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
191 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
192 |
+
Returns:
|
193 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
194 |
+
"""
|
195 |
+
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
|
196 |
+
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
|
197 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
198 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
199 |
+
return q_embed, k_embed
|
200 |
+
|
201 |
+
|
202 |
+
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi
|
203 |
+
class PhiMLP(nn.Module):
|
204 |
+
def __init__(self, config):
|
205 |
+
super().__init__()
|
206 |
+
self.config = config
|
207 |
+
self.activation_fn = ACT2FN[config.hidden_act]
|
208 |
+
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
|
209 |
+
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
|
210 |
+
|
211 |
+
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
212 |
+
hidden_states = self.fc1(hidden_states)
|
213 |
+
hidden_states = self.activation_fn(hidden_states)
|
214 |
+
hidden_states = self.fc2(hidden_states)
|
215 |
+
return hidden_states
|
216 |
+
|
217 |
+
|
218 |
+
# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
|
219 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
220 |
+
"""
|
221 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
222 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
223 |
+
"""
|
224 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
225 |
+
if n_rep == 1:
|
226 |
+
return hidden_states
|
227 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
228 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
229 |
+
|
230 |
+
|
231 |
+
class PhiAttention(nn.Module):
|
232 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
233 |
+
|
234 |
+
def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None):
|
235 |
+
super().__init__()
|
236 |
+
self.config = config
|
237 |
+
self.layer_idx = layer_idx
|
238 |
+
if layer_idx is None:
|
239 |
+
logger.warning_once(
|
240 |
+
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
|
241 |
+
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
|
242 |
+
"when creating this class."
|
243 |
+
)
|
244 |
+
|
245 |
+
self.attention_dropout = config.attention_dropout
|
246 |
+
self.hidden_size = config.hidden_size
|
247 |
+
self.num_heads = config.num_attention_heads
|
248 |
+
self.head_dim = self.hidden_size // self.num_heads
|
249 |
+
self.num_key_value_heads = config.num_key_value_heads
|
250 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
251 |
+
self.max_position_embeddings = config.max_position_embeddings
|
252 |
+
self.rope_theta = config.rope_theta
|
253 |
+
self.partial_rotary_factor = config.partial_rotary_factor
|
254 |
+
self.is_causal = True
|
255 |
+
|
256 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
257 |
+
raise ValueError(
|
258 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
259 |
+
f" and `num_heads`: {self.num_heads})."
|
260 |
+
)
|
261 |
+
|
262 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
|
263 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
|
264 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
|
265 |
+
self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True)
|
266 |
+
|
267 |
+
self.qk_layernorm = config.qk_layernorm
|
268 |
+
if self.qk_layernorm:
|
269 |
+
self.q_layernorm = nn.LayerNorm(
|
270 |
+
config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
|
271 |
+
)
|
272 |
+
self.k_layernorm = nn.LayerNorm(
|
273 |
+
config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
|
274 |
+
)
|
275 |
+
|
276 |
+
self._init_rope()
|
277 |
+
|
278 |
+
def _init_rope(self):
|
279 |
+
if self.config.rope_scaling is None:
|
280 |
+
self.rotary_emb = PhiRotaryEmbedding(
|
281 |
+
int(self.partial_rotary_factor * self.head_dim),
|
282 |
+
max_position_embeddings=self.max_position_embeddings,
|
283 |
+
base=self.rope_theta,
|
284 |
+
)
|
285 |
+
else:
|
286 |
+
scaling_type = self.config.rope_scaling["type"]
|
287 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
288 |
+
if scaling_type == "linear":
|
289 |
+
self.rotary_emb = PhiLinearScalingRotaryEmbedding(
|
290 |
+
int(self.partial_rotary_factor * self.head_dim),
|
291 |
+
max_position_embeddings=self.max_position_embeddings,
|
292 |
+
scaling_factor=scaling_factor,
|
293 |
+
base=self.rope_theta,
|
294 |
+
)
|
295 |
+
elif scaling_type == "dynamic":
|
296 |
+
self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding(
|
297 |
+
int(self.partial_rotary_factor * self.head_dim),
|
298 |
+
max_position_embeddings=self.max_position_embeddings,
|
299 |
+
scaling_factor=scaling_factor,
|
300 |
+
base=self.rope_theta,
|
301 |
+
)
|
302 |
+
else:
|
303 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
304 |
+
|
305 |
+
def forward(
|
306 |
+
self,
|
307 |
+
hidden_states: torch.Tensor,
|
308 |
+
attention_mask: Optional[torch.Tensor] = None,
|
309 |
+
position_ids: Optional[torch.LongTensor] = None,
|
310 |
+
past_key_value: Optional[Cache] = None,
|
311 |
+
output_attentions: bool = False,
|
312 |
+
use_cache: bool = False,
|
313 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
314 |
+
bsz, q_len, _ = hidden_states.size()
|
315 |
+
|
316 |
+
query_states = self.q_proj(hidden_states)
|
317 |
+
key_states = self.k_proj(hidden_states)
|
318 |
+
value_states = self.v_proj(hidden_states)
|
319 |
+
|
320 |
+
if self.qk_layernorm:
|
321 |
+
query_states = self.q_layernorm(query_states)
|
322 |
+
key_states = self.k_layernorm(key_states)
|
323 |
+
|
324 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
325 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
326 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
327 |
+
|
328 |
+
kv_seq_len = key_states.shape[-2]
|
329 |
+
if past_key_value is not None:
|
330 |
+
if self.layer_idx is None:
|
331 |
+
raise ValueError(
|
332 |
+
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
333 |
+
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
334 |
+
"with a layer index."
|
335 |
+
)
|
336 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
337 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
338 |
+
|
339 |
+
# Partial rotary embedding
|
340 |
+
query_rot, query_pass = (
|
341 |
+
query_states[..., : self.rotary_emb.dim],
|
342 |
+
query_states[..., self.rotary_emb.dim :],
|
343 |
+
)
|
344 |
+
key_rot, key_pass = (
|
345 |
+
key_states[..., : self.rotary_emb.dim],
|
346 |
+
key_states[..., self.rotary_emb.dim :],
|
347 |
+
)
|
348 |
+
# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
|
349 |
+
query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
|
350 |
+
|
351 |
+
# [batch_size, seq_length, num_heads, head_dim]
|
352 |
+
query_states = torch.cat((query_rot, query_pass), dim=-1)
|
353 |
+
key_states = torch.cat((key_rot, key_pass), dim=-1)
|
354 |
+
|
355 |
+
if past_key_value is not None:
|
356 |
+
cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
|
357 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
358 |
+
|
359 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
360 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
361 |
+
|
362 |
+
# Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
|
363 |
+
attn_weights = torch.matmul(
|
364 |
+
query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
|
365 |
+
) / math.sqrt(self.head_dim)
|
366 |
+
|
367 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
368 |
+
raise ValueError(
|
369 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
370 |
+
f" {attn_weights.size()}"
|
371 |
+
)
|
372 |
+
|
373 |
+
if attention_mask is not None:
|
374 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
375 |
+
raise ValueError(
|
376 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
377 |
+
)
|
378 |
+
attn_weights = attn_weights + attention_mask
|
379 |
+
|
380 |
+
# upcast attention to fp32
|
381 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
|
382 |
+
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
383 |
+
|
384 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
385 |
+
|
386 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
387 |
+
raise ValueError(
|
388 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
389 |
+
f" {attn_output.size()}"
|
390 |
+
)
|
391 |
+
|
392 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
393 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
394 |
+
|
395 |
+
attn_output = self.dense(attn_output)
|
396 |
+
|
397 |
+
if not output_attentions:
|
398 |
+
attn_weights = None
|
399 |
+
|
400 |
+
return attn_output, attn_weights, past_key_value
|
401 |
+
|
402 |
+
|
403 |
+
class PhiFlashAttention2(PhiAttention):
|
404 |
+
"""
|
405 |
+
Phi flash attention module. This module inherits from `PhiAttention` as the weights of the module stays
|
406 |
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
407 |
+
flash attention and deal with padding tokens in case the input contains any of them.
|
408 |
+
"""
|
409 |
+
|
410 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
|
411 |
+
def __init__(self, *args, **kwargs):
|
412 |
+
super().__init__(*args, **kwargs)
|
413 |
+
|
414 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
415 |
+
# 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.
|
416 |
+
# 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).
|
417 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
418 |
+
|
419 |
+
def forward(
|
420 |
+
self,
|
421 |
+
hidden_states: torch.Tensor,
|
422 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
423 |
+
position_ids: Optional[torch.LongTensor] = None,
|
424 |
+
past_key_value: Optional[Cache] = None,
|
425 |
+
output_attentions: bool = False,
|
426 |
+
use_cache: bool = False,
|
427 |
+
**kwargs,
|
428 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
429 |
+
# PhiFlashAttention2 attention does not support output_attentions
|
430 |
+
|
431 |
+
output_attentions = False
|
432 |
+
|
433 |
+
bsz, q_len, _ = hidden_states.size()
|
434 |
+
|
435 |
+
query_states = self.q_proj(hidden_states)
|
436 |
+
key_states = self.k_proj(hidden_states)
|
437 |
+
value_states = self.v_proj(hidden_states)
|
438 |
+
|
439 |
+
if self.qk_layernorm:
|
440 |
+
query_states = self.q_layernorm(query_states)
|
441 |
+
key_states = self.k_layernorm(key_states)
|
442 |
+
|
443 |
+
# Flash attention requires the input to have the shape
|
444 |
+
# batch_size x seq_length x head_dim x hidden_dim
|
445 |
+
# therefore we just need to keep the original shape
|
446 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
447 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
448 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
449 |
+
|
450 |
+
kv_seq_len = key_states.shape[-2]
|
451 |
+
if past_key_value is not None:
|
452 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
453 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
454 |
+
|
455 |
+
# Partial rotary embedding
|
456 |
+
query_rot, query_pass = (
|
457 |
+
query_states[..., : self.rotary_emb.dim],
|
458 |
+
query_states[..., self.rotary_emb.dim :],
|
459 |
+
)
|
460 |
+
key_rot, key_pass = (
|
461 |
+
key_states[..., : self.rotary_emb.dim],
|
462 |
+
key_states[..., self.rotary_emb.dim :],
|
463 |
+
)
|
464 |
+
# [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
|
465 |
+
query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
|
466 |
+
|
467 |
+
# [batch_size, seq_length, num_heads, head_dim]
|
468 |
+
query_states = torch.cat((query_rot, query_pass), dim=-1)
|
469 |
+
key_states = torch.cat((key_rot, key_pass), dim=-1)
|
470 |
+
|
471 |
+
if past_key_value is not None:
|
472 |
+
cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
|
473 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
474 |
+
|
475 |
+
# 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
|
476 |
+
# to be able to avoid many of these transpose/reshape/view.
|
477 |
+
query_states = query_states.transpose(1, 2)
|
478 |
+
key_states = key_states.transpose(1, 2)
|
479 |
+
value_states = value_states.transpose(1, 2)
|
480 |
+
|
481 |
+
attn_dropout = self.attention_dropout if self.training else 0.0
|
482 |
+
|
483 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
484 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
485 |
+
# cast them back in the correct dtype just to be sure everything works as expected.
|
486 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
487 |
+
# in fp32.
|
488 |
+
|
489 |
+
if query_states.dtype == torch.float32:
|
490 |
+
if torch.is_autocast_enabled():
|
491 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
492 |
+
# Handle the case where the model is quantized
|
493 |
+
elif hasattr(self.config, "_pre_quantization_dtype"):
|
494 |
+
target_dtype = self.config._pre_quantization_dtype
|
495 |
+
else:
|
496 |
+
target_dtype = self.q_proj.weight.dtype
|
497 |
+
|
498 |
+
logger.warning_once(
|
499 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
500 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
501 |
+
f" {target_dtype}."
|
502 |
+
)
|
503 |
+
|
504 |
+
query_states = query_states.to(target_dtype)
|
505 |
+
key_states = key_states.to(target_dtype)
|
506 |
+
value_states = value_states.to(target_dtype)
|
507 |
+
|
508 |
+
attn_output = self._flash_attention_forward(
|
509 |
+
query_states, key_states, value_states, attention_mask, q_len, dropout=attn_dropout, softmax_scale=None
|
510 |
+
)
|
511 |
+
|
512 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
513 |
+
attn_output = self.dense(attn_output)
|
514 |
+
|
515 |
+
if not output_attentions:
|
516 |
+
attn_weights = None
|
517 |
+
|
518 |
+
return attn_output, attn_weights, past_key_value
|
519 |
+
|
520 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
|
521 |
+
def _flash_attention_forward(
|
522 |
+
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
523 |
+
):
|
524 |
+
"""
|
525 |
+
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
526 |
+
first unpad the input, then computes the attention scores and pad the final attention scores.
|
527 |
+
|
528 |
+
Args:
|
529 |
+
query_states (`torch.Tensor`):
|
530 |
+
Input query states to be passed to Flash Attention API
|
531 |
+
key_states (`torch.Tensor`):
|
532 |
+
Input key states to be passed to Flash Attention API
|
533 |
+
value_states (`torch.Tensor`):
|
534 |
+
Input value states to be passed to Flash Attention API
|
535 |
+
attention_mask (`torch.Tensor`):
|
536 |
+
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
537 |
+
position of padding tokens and 1 for the position of non-padding tokens.
|
538 |
+
dropout (`int`, *optional*):
|
539 |
+
Attention dropout
|
540 |
+
softmax_scale (`float`, *optional*):
|
541 |
+
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
542 |
+
"""
|
543 |
+
if not self._flash_attn_uses_top_left_mask:
|
544 |
+
causal = self.is_causal
|
545 |
+
else:
|
546 |
+
# 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__.
|
547 |
+
causal = self.is_causal and query_length != 1
|
548 |
+
|
549 |
+
# Contains at least one padding token in the sequence
|
550 |
+
if attention_mask is not None:
|
551 |
+
batch_size = query_states.shape[0]
|
552 |
+
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
553 |
+
query_states, key_states, value_states, attention_mask, query_length
|
554 |
+
)
|
555 |
+
|
556 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
557 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
558 |
+
|
559 |
+
attn_output_unpad = flash_attn_varlen_func(
|
560 |
+
query_states,
|
561 |
+
key_states,
|
562 |
+
value_states,
|
563 |
+
cu_seqlens_q=cu_seqlens_q,
|
564 |
+
cu_seqlens_k=cu_seqlens_k,
|
565 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
566 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
567 |
+
dropout_p=dropout,
|
568 |
+
softmax_scale=softmax_scale,
|
569 |
+
causal=causal,
|
570 |
+
)
|
571 |
+
|
572 |
+
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
573 |
+
else:
|
574 |
+
attn_output = flash_attn_func(
|
575 |
+
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
576 |
+
)
|
577 |
+
|
578 |
+
return attn_output
|
579 |
+
|
580 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
|
581 |
+
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
582 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
583 |
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
584 |
+
|
585 |
+
key_layer = index_first_axis(
|
586 |
+
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
587 |
+
)
|
588 |
+
value_layer = index_first_axis(
|
589 |
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
590 |
+
)
|
591 |
+
if query_length == kv_seq_len:
|
592 |
+
query_layer = index_first_axis(
|
593 |
+
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
594 |
+
)
|
595 |
+
cu_seqlens_q = cu_seqlens_k
|
596 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
597 |
+
indices_q = indices_k
|
598 |
+
elif query_length == 1:
|
599 |
+
max_seqlen_in_batch_q = 1
|
600 |
+
cu_seqlens_q = torch.arange(
|
601 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
602 |
+
) # There is a memcpy here, that is very bad.
|
603 |
+
indices_q = cu_seqlens_q[:-1]
|
604 |
+
query_layer = query_layer.squeeze(1)
|
605 |
+
else:
|
606 |
+
# The -q_len: slice assumes left padding.
|
607 |
+
attention_mask = attention_mask[:, -query_length:]
|
608 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
609 |
+
|
610 |
+
return (
|
611 |
+
query_layer,
|
612 |
+
key_layer,
|
613 |
+
value_layer,
|
614 |
+
indices_q,
|
615 |
+
(cu_seqlens_q, cu_seqlens_k),
|
616 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
617 |
+
)
|
618 |
+
|
619 |
+
|
620 |
+
PHI_ATTENTION_CLASSES = {
|
621 |
+
"eager": PhiAttention,
|
622 |
+
"flash_attention_2": PhiFlashAttention2,
|
623 |
+
}
|
624 |
+
|
625 |
+
|
626 |
+
class PhiDecoderLayer(nn.Module):
|
627 |
+
def __init__(self, config: PhiConfig, layer_idx: int):
|
628 |
+
super().__init__()
|
629 |
+
self.self_attn = PHI_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
|
630 |
+
self.mlp = PhiMLP(config)
|
631 |
+
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
632 |
+
self.resid_dropout = nn.Dropout(config.resid_pdrop)
|
633 |
+
|
634 |
+
def forward(
|
635 |
+
self,
|
636 |
+
hidden_states: torch.Tensor,
|
637 |
+
attention_mask: Optional[torch.Tensor] = None,
|
638 |
+
position_ids: Optional[torch.LongTensor] = None,
|
639 |
+
output_attentions: Optional[bool] = False,
|
640 |
+
use_cache: Optional[bool] = False,
|
641 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
642 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
643 |
+
"""
|
644 |
+
Args:
|
645 |
+
hidden_states (`torch.FloatTensor`):
|
646 |
+
input to the layer of shape `(batch, seq_len, embed_dim)`
|
647 |
+
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
648 |
+
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
649 |
+
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
|
650 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
|
651 |
+
`[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
|
652 |
+
output_attentions (`bool`, *optional*):
|
653 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
654 |
+
returned tensors for more detail.
|
655 |
+
use_cache (`bool`, *optional*):
|
656 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
657 |
+
(see `past_key_values`).
|
658 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
659 |
+
"""
|
660 |
+
|
661 |
+
residual = hidden_states
|
662 |
+
|
663 |
+
hidden_states = self.input_layernorm(hidden_states)
|
664 |
+
|
665 |
+
# Self Attention
|
666 |
+
attn_outputs, self_attn_weights, present_key_value = self.self_attn(
|
667 |
+
hidden_states=hidden_states,
|
668 |
+
attention_mask=attention_mask,
|
669 |
+
position_ids=position_ids,
|
670 |
+
past_key_value=past_key_value,
|
671 |
+
output_attentions=output_attentions,
|
672 |
+
use_cache=use_cache,
|
673 |
+
)
|
674 |
+
attn_outputs = self.resid_dropout(attn_outputs)
|
675 |
+
|
676 |
+
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
|
677 |
+
hidden_states = attn_outputs + feed_forward_hidden_states + residual
|
678 |
+
outputs = (hidden_states,)
|
679 |
+
|
680 |
+
if output_attentions:
|
681 |
+
outputs += (self_attn_weights,)
|
682 |
+
|
683 |
+
if use_cache:
|
684 |
+
outputs += (present_key_value,)
|
685 |
+
|
686 |
+
return outputs
|
687 |
+
|
688 |
+
|
689 |
+
PHI_START_DOCSTRING = r"""
|
690 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
691 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
692 |
+
etc.)
|
693 |
+
|
694 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
695 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
696 |
+
and behavior.
|
697 |
+
|
698 |
+
Parameters:
|
699 |
+
config ([`PhiConfig`]):
|
700 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
701 |
+
load the weights associated with the model, only the configuration. Check out the
|
702 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
703 |
+
"""
|
704 |
+
|
705 |
+
|
706 |
+
@add_start_docstrings(
|
707 |
+
"The bare Phi Model outputting raw hidden-states without any specific head on top.",
|
708 |
+
PHI_START_DOCSTRING,
|
709 |
+
)
|
710 |
+
class PhiPreTrainedModel(PreTrainedModel):
|
711 |
+
config_class = PhiConfig
|
712 |
+
base_model_prefix = "model"
|
713 |
+
supports_gradient_checkpointing = True
|
714 |
+
_no_split_modules = ["PhiDecoderLayer"]
|
715 |
+
_skip_keys_device_placement = "past_key_values"
|
716 |
+
_supports_flash_attn_2 = True
|
717 |
+
_supports_cache_class = True
|
718 |
+
|
719 |
+
def _init_weights(self, module):
|
720 |
+
std = self.config.initializer_range
|
721 |
+
if isinstance(module, nn.Linear):
|
722 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
723 |
+
if module.bias is not None:
|
724 |
+
module.bias.data.zero_()
|
725 |
+
elif isinstance(module, nn.Embedding):
|
726 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
727 |
+
if module.padding_idx is not None:
|
728 |
+
module.weight.data[module.padding_idx].zero_()
|
729 |
+
|
730 |
+
|
731 |
+
PHI_INPUTS_DOCSTRING = r"""
|
732 |
+
Args:
|
733 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
734 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
735 |
+
it.
|
736 |
+
|
737 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
738 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
739 |
+
|
740 |
+
[What are input IDs?](../glossary#input-ids)
|
741 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
742 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
743 |
+
|
744 |
+
- 1 for tokens that are **not masked**,
|
745 |
+
- 0 for tokens that are **masked**.
|
746 |
+
|
747 |
+
[What are attention masks?](../glossary#attention-mask)
|
748 |
+
|
749 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
750 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
751 |
+
|
752 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
753 |
+
`past_key_values`).
|
754 |
+
|
755 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
756 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
757 |
+
information on the default strategy.
|
758 |
+
|
759 |
+
- 1 indicates the head is **not masked**,
|
760 |
+
- 0 indicates the head is **masked**.
|
761 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
762 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
763 |
+
config.n_positions - 1]`.
|
764 |
+
|
765 |
+
[What are position IDs?](../glossary#position-ids)
|
766 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
767 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
768 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
769 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
770 |
+
|
771 |
+
Two formats are allowed:
|
772 |
+
- a [`~cache_utils.Cache`] instance;
|
773 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
774 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
775 |
+
cache format.
|
776 |
+
|
777 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
778 |
+
legacy cache format will be returned.
|
779 |
+
|
780 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
781 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
782 |
+
of shape `(batch_size, sequence_length)`.
|
783 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
784 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
785 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
786 |
+
model's internal embedding lookup matrix.
|
787 |
+
use_cache (`bool`, *optional*):
|
788 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
789 |
+
`past_key_values`).
|
790 |
+
output_attentions (`bool`, *optional*):
|
791 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
792 |
+
tensors for more detail.
|
793 |
+
output_hidden_states (`bool`, *optional*):
|
794 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
795 |
+
more detail.
|
796 |
+
return_dict (`bool`, *optional*):
|
797 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
798 |
+
"""
|
799 |
+
|
800 |
+
|
801 |
+
@add_start_docstrings(
|
802 |
+
"The bare Phi Model outputting raw hidden-states without any specific head on top.",
|
803 |
+
PHI_START_DOCSTRING,
|
804 |
+
)
|
805 |
+
class PhiModel(PhiPreTrainedModel):
|
806 |
+
"""
|
807 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
|
808 |
+
|
809 |
+
Args:
|
810 |
+
config: PhiConfig
|
811 |
+
"""
|
812 |
+
|
813 |
+
def __init__(self, config: PhiConfig):
|
814 |
+
super().__init__(config)
|
815 |
+
self.padding_idx = config.pad_token_id
|
816 |
+
self.vocab_size = config.vocab_size
|
817 |
+
|
818 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
819 |
+
self.embed_dropout = nn.Dropout(config.embd_pdrop)
|
820 |
+
self.layers = nn.ModuleList(
|
821 |
+
[PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
822 |
+
)
|
823 |
+
self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
824 |
+
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
825 |
+
|
826 |
+
self.gradient_checkpointing = False
|
827 |
+
# Initialize weights and apply final processing
|
828 |
+
self.post_init()
|
829 |
+
|
830 |
+
def get_input_embeddings(self):
|
831 |
+
return self.embed_tokens
|
832 |
+
|
833 |
+
def set_input_embeddings(self, value):
|
834 |
+
self.embed_tokens = value
|
835 |
+
|
836 |
+
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
|
837 |
+
def forward(
|
838 |
+
self,
|
839 |
+
input_ids: torch.LongTensor = None,
|
840 |
+
attention_mask: Optional[torch.Tensor] = None,
|
841 |
+
position_ids: Optional[torch.LongTensor] = None,
|
842 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
843 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
844 |
+
use_cache: Optional[bool] = None,
|
845 |
+
output_attentions: Optional[bool] = None,
|
846 |
+
output_hidden_states: Optional[bool] = None,
|
847 |
+
return_dict: Optional[bool] = None,
|
848 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
849 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
850 |
+
output_hidden_states = (
|
851 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
852 |
+
)
|
853 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
854 |
+
|
855 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
856 |
+
|
857 |
+
# retrieve input_ids and inputs_embeds
|
858 |
+
if input_ids is not None and inputs_embeds is not None:
|
859 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
860 |
+
elif input_ids is not None:
|
861 |
+
batch_size, seq_length = input_ids.shape[:2]
|
862 |
+
elif inputs_embeds is not None:
|
863 |
+
batch_size, seq_length = inputs_embeds.shape[:2]
|
864 |
+
else:
|
865 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
866 |
+
|
867 |
+
past_key_values_length = 0
|
868 |
+
|
869 |
+
if self.gradient_checkpointing and self.training:
|
870 |
+
if use_cache:
|
871 |
+
logger.warning_once(
|
872 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
873 |
+
)
|
874 |
+
use_cache = False
|
875 |
+
|
876 |
+
if use_cache:
|
877 |
+
use_legacy_cache = not isinstance(past_key_values, Cache)
|
878 |
+
if use_legacy_cache:
|
879 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
880 |
+
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
881 |
+
|
882 |
+
if position_ids is None:
|
883 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
884 |
+
position_ids = torch.arange(
|
885 |
+
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
886 |
+
)
|
887 |
+
position_ids = position_ids.unsqueeze(0)
|
888 |
+
|
889 |
+
if inputs_embeds is None:
|
890 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
891 |
+
|
892 |
+
inputs_embeds = self.embed_dropout(inputs_embeds)
|
893 |
+
|
894 |
+
# Attention mask.
|
895 |
+
if self._use_flash_attention_2:
|
896 |
+
# 2d mask is passed through the layers
|
897 |
+
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
898 |
+
else:
|
899 |
+
# 4d mask is passed through the layers
|
900 |
+
attention_mask = _prepare_4d_causal_attention_mask(
|
901 |
+
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
902 |
+
)
|
903 |
+
|
904 |
+
hidden_states = inputs_embeds
|
905 |
+
|
906 |
+
# decoder layers
|
907 |
+
all_hidden_states = () if output_hidden_states else None
|
908 |
+
all_self_attns = () if output_attentions else None
|
909 |
+
next_decoder_cache = None
|
910 |
+
|
911 |
+
for decoder_layer in self.layers:
|
912 |
+
if output_hidden_states:
|
913 |
+
all_hidden_states += (hidden_states,)
|
914 |
+
|
915 |
+
if self.gradient_checkpointing and self.training:
|
916 |
+
layer_outputs = self._gradient_checkpointing_func(
|
917 |
+
decoder_layer.__call__,
|
918 |
+
hidden_states,
|
919 |
+
attention_mask,
|
920 |
+
position_ids,
|
921 |
+
past_key_values,
|
922 |
+
output_attentions,
|
923 |
+
)
|
924 |
+
else:
|
925 |
+
layer_outputs = decoder_layer(
|
926 |
+
hidden_states,
|
927 |
+
attention_mask=attention_mask,
|
928 |
+
position_ids=position_ids,
|
929 |
+
past_key_value=past_key_values,
|
930 |
+
output_attentions=output_attentions,
|
931 |
+
use_cache=use_cache,
|
932 |
+
)
|
933 |
+
|
934 |
+
hidden_states = layer_outputs[0]
|
935 |
+
|
936 |
+
if use_cache:
|
937 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
938 |
+
|
939 |
+
if output_attentions:
|
940 |
+
all_self_attns += (layer_outputs[1],)
|
941 |
+
|
942 |
+
hidden_states = self.final_layernorm(hidden_states)
|
943 |
+
|
944 |
+
# add hidden states from the last decoder layer
|
945 |
+
if output_hidden_states:
|
946 |
+
all_hidden_states += (hidden_states,)
|
947 |
+
|
948 |
+
next_cache = None
|
949 |
+
if use_cache:
|
950 |
+
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
|
951 |
+
if not return_dict:
|
952 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
953 |
+
return BaseModelOutputWithPast(
|
954 |
+
last_hidden_state=hidden_states,
|
955 |
+
past_key_values=next_cache,
|
956 |
+
hidden_states=all_hidden_states,
|
957 |
+
attentions=all_self_attns,
|
958 |
+
)
|
959 |
+
|
960 |
+
|
961 |
+
class PhiForCausalLM(PhiPreTrainedModel):
|
962 |
+
_tied_weights_keys = ["lm_head.weight"]
|
963 |
+
|
964 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
|
965 |
+
def __init__(self, config):
|
966 |
+
super().__init__(config)
|
967 |
+
self.model = PhiModel(config)
|
968 |
+
self.vocab_size = config.vocab_size
|
969 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)
|
970 |
+
|
971 |
+
# Initialize weights and apply final processing
|
972 |
+
self.post_init()
|
973 |
+
|
974 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
|
975 |
+
def get_input_embeddings(self):
|
976 |
+
return self.model.embed_tokens
|
977 |
+
|
978 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
|
979 |
+
def set_input_embeddings(self, value):
|
980 |
+
self.model.embed_tokens = value
|
981 |
+
|
982 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
|
983 |
+
def get_output_embeddings(self):
|
984 |
+
return self.lm_head
|
985 |
+
|
986 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
|
987 |
+
def set_output_embeddings(self, new_embeddings):
|
988 |
+
self.lm_head = new_embeddings
|
989 |
+
|
990 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
|
991 |
+
def set_decoder(self, decoder):
|
992 |
+
self.model = decoder
|
993 |
+
|
994 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
|
995 |
+
def get_decoder(self):
|
996 |
+
return self.model
|
997 |
+
|
998 |
+
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
|
999 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
1000 |
+
def forward(
|
1001 |
+
self,
|
1002 |
+
input_ids: torch.LongTensor = None,
|
1003 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1004 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1005 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1006 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1007 |
+
labels: Optional[torch.LongTensor] = None,
|
1008 |
+
use_cache: Optional[bool] = None,
|
1009 |
+
output_attentions: Optional[bool] = None,
|
1010 |
+
output_hidden_states: Optional[bool] = None,
|
1011 |
+
return_dict: Optional[bool] = None,
|
1012 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
1013 |
+
r"""
|
1014 |
+
Args:
|
1015 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1016 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
1017 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
1018 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
1019 |
+
|
1020 |
+
Returns:
|
1021 |
+
|
1022 |
+
Example:
|
1023 |
+
|
1024 |
+
```python
|
1025 |
+
>>> from transformers import AutoTokenizer, PhiForCausalLM
|
1026 |
+
|
1027 |
+
>>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1")
|
1028 |
+
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
|
1029 |
+
|
1030 |
+
>>> prompt = "This is an example script ."
|
1031 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
1032 |
+
|
1033 |
+
>>> # Generate
|
1034 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
1035 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
1036 |
+
'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str'
|
1037 |
+
```"""
|
1038 |
+
|
1039 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
1040 |
+
output_hidden_states = (
|
1041 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
1042 |
+
)
|
1043 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1044 |
+
|
1045 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
1046 |
+
outputs = self.model(
|
1047 |
+
input_ids=input_ids,
|
1048 |
+
attention_mask=attention_mask,
|
1049 |
+
position_ids=position_ids,
|
1050 |
+
past_key_values=past_key_values,
|
1051 |
+
inputs_embeds=inputs_embeds,
|
1052 |
+
use_cache=use_cache,
|
1053 |
+
output_attentions=output_attentions,
|
1054 |
+
output_hidden_states=output_hidden_states,
|
1055 |
+
return_dict=return_dict,
|
1056 |
+
)
|
1057 |
+
|
1058 |
+
hidden_states = outputs[0]
|
1059 |
+
logits = self.lm_head(hidden_states)
|
1060 |
+
logits = logits.float()
|
1061 |
+
|
1062 |
+
loss = None
|
1063 |
+
if labels is not None:
|
1064 |
+
# Shift so that tokens < n predict n
|
1065 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
1066 |
+
shift_labels = labels[..., 1:].contiguous()
|
1067 |
+
# Flatten the tokens
|
1068 |
+
loss_fct = CrossEntropyLoss()
|
1069 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
1070 |
+
shift_labels = shift_labels.view(-1)
|
1071 |
+
# Enable model parallelism
|
1072 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
1073 |
+
loss = loss_fct(shift_logits, shift_labels)
|
1074 |
+
|
1075 |
+
if not return_dict:
|
1076 |
+
output = (logits,) + outputs[1:]
|
1077 |
+
return (loss,) + output if loss is not None else output
|
1078 |
+
|
1079 |
+
return CausalLMOutputWithPast(
|
1080 |
+
loss=loss,
|
1081 |
+
logits=logits,
|
1082 |
+
past_key_values=outputs.past_key_values,
|
1083 |
+
hidden_states=outputs.hidden_states,
|
1084 |
+
attentions=outputs.attentions,
|
1085 |
+
)
|
1086 |
+
|
1087 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation
|
1088 |
+
def prepare_inputs_for_generation(
|
1089 |
+
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
1090 |
+
):
|
1091 |
+
if past_key_values is not None:
|
1092 |
+
if isinstance(past_key_values, Cache):
|
1093 |
+
cache_length = past_key_values.get_seq_length()
|
1094 |
+
past_length = past_key_values.seen_tokens
|
1095 |
+
max_cache_length = past_key_values.get_max_length()
|
1096 |
+
else:
|
1097 |
+
cache_length = past_length = past_key_values[0][0].shape[2]
|
1098 |
+
max_cache_length = None
|
1099 |
+
|
1100 |
+
# Keep only the unprocessed tokens:
|
1101 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
1102 |
+
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
1103 |
+
# input)
|
1104 |
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
1105 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
1106 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
1107 |
+
# input_ids based on the past_length.
|
1108 |
+
elif past_length < input_ids.shape[1]:
|
1109 |
+
input_ids = input_ids[:, past_length:]
|
1110 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
1111 |
+
|
1112 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
1113 |
+
if (
|
1114 |
+
max_cache_length is not None
|
1115 |
+
and attention_mask is not None
|
1116 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
1117 |
+
):
|
1118 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
1119 |
+
|
1120 |
+
position_ids = kwargs.get("position_ids", None)
|
1121 |
+
if attention_mask is not None and position_ids is None:
|
1122 |
+
# create position_ids on the fly for batch generation
|
1123 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
1124 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
1125 |
+
if past_key_values:
|
1126 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
1127 |
+
|
1128 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1129 |
+
if inputs_embeds is not None and past_key_values is None:
|
1130 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
1131 |
+
else:
|
1132 |
+
model_inputs = {"input_ids": input_ids}
|
1133 |
+
|
1134 |
+
model_inputs.update(
|
1135 |
+
{
|
1136 |
+
"position_ids": position_ids,
|
1137 |
+
"past_key_values": past_key_values,
|
1138 |
+
"use_cache": kwargs.get("use_cache"),
|
1139 |
+
"attention_mask": attention_mask,
|
1140 |
+
}
|
1141 |
+
)
|
1142 |
+
return model_inputs
|
1143 |
+
|
1144 |
+
@staticmethod
|
1145 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
|
1146 |
+
def _reorder_cache(past_key_values, beam_idx):
|
1147 |
+
reordered_past = ()
|
1148 |
+
for layer_past in past_key_values:
|
1149 |
+
reordered_past += (
|
1150 |
+
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
1151 |
+
)
|
1152 |
+
return reordered_past
|
1153 |
+
|
1154 |
+
|
1155 |
+
@add_start_docstrings(
|
1156 |
+
"""
|
1157 |
+
The PhiModel with a sequence classification head on top (linear layer).
|
1158 |
+
|
1159 |
+
[`PhiForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
1160 |
+
(e.g. GPT-2) do.
|
1161 |
+
|
1162 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
1163 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
1164 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
1165 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
1166 |
+
each row of the batch).
|
1167 |
+
""",
|
1168 |
+
PHI_START_DOCSTRING,
|
1169 |
+
)
|
1170 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->PHI,Llama->Phi with self.transformer->self.model, transformer_outputs->model_outputs
|
1171 |
+
class PhiForSequenceClassification(PhiPreTrainedModel):
|
1172 |
+
def __init__(self, config):
|
1173 |
+
super().__init__(config)
|
1174 |
+
self.num_labels = config.num_labels
|
1175 |
+
self.model = PhiModel(config)
|
1176 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
1177 |
+
|
1178 |
+
# Initialize weights and apply final processing
|
1179 |
+
self.post_init()
|
1180 |
+
|
1181 |
+
def get_input_embeddings(self):
|
1182 |
+
return self.model.embed_tokens
|
1183 |
+
|
1184 |
+
def set_input_embeddings(self, value):
|
1185 |
+
self.model.embed_tokens = value
|
1186 |
+
|
1187 |
+
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
|
1188 |
+
def forward(
|
1189 |
+
self,
|
1190 |
+
input_ids: torch.LongTensor = None,
|
1191 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1192 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1193 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1194 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1195 |
+
labels: Optional[torch.LongTensor] = None,
|
1196 |
+
use_cache: Optional[bool] = None,
|
1197 |
+
output_attentions: Optional[bool] = None,
|
1198 |
+
output_hidden_states: Optional[bool] = None,
|
1199 |
+
return_dict: Optional[bool] = None,
|
1200 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
1201 |
+
r"""
|
1202 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1203 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
1204 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1205 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1206 |
+
"""
|
1207 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1208 |
+
|
1209 |
+
model_outputs = self.model(
|
1210 |
+
input_ids,
|
1211 |
+
attention_mask=attention_mask,
|
1212 |
+
position_ids=position_ids,
|
1213 |
+
past_key_values=past_key_values,
|
1214 |
+
inputs_embeds=inputs_embeds,
|
1215 |
+
use_cache=use_cache,
|
1216 |
+
output_attentions=output_attentions,
|
1217 |
+
output_hidden_states=output_hidden_states,
|
1218 |
+
return_dict=return_dict,
|
1219 |
+
)
|
1220 |
+
hidden_states = model_outputs[0]
|
1221 |
+
logits = self.score(hidden_states)
|
1222 |
+
|
1223 |
+
if input_ids is not None:
|
1224 |
+
batch_size = input_ids.shape[0]
|
1225 |
+
else:
|
1226 |
+
batch_size = inputs_embeds.shape[0]
|
1227 |
+
|
1228 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
1229 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
1230 |
+
if self.config.pad_token_id is None:
|
1231 |
+
sequence_lengths = -1
|
1232 |
+
else:
|
1233 |
+
if input_ids is not None:
|
1234 |
+
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
1235 |
+
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
1236 |
+
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
1237 |
+
sequence_lengths = sequence_lengths.to(logits.device)
|
1238 |
+
else:
|
1239 |
+
sequence_lengths = -1
|
1240 |
+
|
1241 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
1242 |
+
|
1243 |
+
loss = None
|
1244 |
+
if labels is not None:
|
1245 |
+
labels = labels.to(logits.device)
|
1246 |
+
if self.config.problem_type is None:
|
1247 |
+
if self.num_labels == 1:
|
1248 |
+
self.config.problem_type = "regression"
|
1249 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
1250 |
+
self.config.problem_type = "single_label_classification"
|
1251 |
+
else:
|
1252 |
+
self.config.problem_type = "multi_label_classification"
|
1253 |
+
|
1254 |
+
if self.config.problem_type == "regression":
|
1255 |
+
loss_fct = MSELoss()
|
1256 |
+
if self.num_labels == 1:
|
1257 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
1258 |
+
else:
|
1259 |
+
loss = loss_fct(pooled_logits, labels)
|
1260 |
+
elif self.config.problem_type == "single_label_classification":
|
1261 |
+
loss_fct = CrossEntropyLoss()
|
1262 |
+
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
1263 |
+
elif self.config.problem_type == "multi_label_classification":
|
1264 |
+
loss_fct = BCEWithLogitsLoss()
|
1265 |
+
loss = loss_fct(pooled_logits, labels)
|
1266 |
+
if not return_dict:
|
1267 |
+
output = (pooled_logits,) + model_outputs[1:]
|
1268 |
+
return ((loss,) + output) if loss is not None else output
|
1269 |
+
|
1270 |
+
return SequenceClassifierOutputWithPast(
|
1271 |
+
loss=loss,
|
1272 |
+
logits=pooled_logits,
|
1273 |
+
past_key_values=model_outputs.past_key_values,
|
1274 |
+
hidden_states=model_outputs.hidden_states,
|
1275 |
+
attentions=model_outputs.attentions,
|
1276 |
+
)
|
1277 |
+
|
1278 |
+
|
1279 |
+
@add_start_docstrings(
|
1280 |
+
"""
|
1281 |
+
PhiModel with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
|
1282 |
+
Named-Entity-Recognition (NER) tasks.
|
1283 |
+
""",
|
1284 |
+
PHI_START_DOCSTRING,
|
1285 |
+
)
|
1286 |
+
# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with MPT->PHI,Mpt->Phi,self.transformer->self.model,transformer_outputs->model_outputs
|
1287 |
+
class PhiForTokenClassification(PhiPreTrainedModel):
|
1288 |
+
def __init__(self, config: PhiConfig):
|
1289 |
+
super().__init__(config)
|
1290 |
+
self.num_labels = config.num_labels
|
1291 |
+
|
1292 |
+
self.model = PhiModel(config)
|
1293 |
+
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
|
1294 |
+
classifier_dropout = config.classifier_dropout
|
1295 |
+
elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
|
1296 |
+
classifier_dropout = config.hidden_dropout
|
1297 |
+
else:
|
1298 |
+
classifier_dropout = 0.1
|
1299 |
+
self.dropout = nn.Dropout(classifier_dropout)
|
1300 |
+
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
1301 |
+
|
1302 |
+
# Initialize weights and apply final processing
|
1303 |
+
self.post_init()
|
1304 |
+
|
1305 |
+
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING)
|
1306 |
+
@add_code_sample_docstrings(
|
1307 |
+
checkpoint=_CHECKPOINT_FOR_DOC,
|
1308 |
+
output_type=TokenClassifierOutput,
|
1309 |
+
config_class=_CONFIG_FOR_DOC,
|
1310 |
+
)
|
1311 |
+
def forward(
|
1312 |
+
self,
|
1313 |
+
input_ids: Optional[torch.LongTensor] = None,
|
1314 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
1315 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1316 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
1317 |
+
labels: Optional[torch.Tensor] = None,
|
1318 |
+
use_cache: Optional[bool] = None,
|
1319 |
+
output_attentions: Optional[bool] = None,
|
1320 |
+
output_hidden_states: Optional[bool] = None,
|
1321 |
+
return_dict: Optional[bool] = None,
|
1322 |
+
**deprecated_arguments,
|
1323 |
+
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
|
1324 |
+
r"""
|
1325 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1326 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
1327 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1328 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1329 |
+
"""
|
1330 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1331 |
+
|
1332 |
+
model_outputs = self.model(
|
1333 |
+
input_ids,
|
1334 |
+
past_key_values=past_key_values,
|
1335 |
+
attention_mask=attention_mask,
|
1336 |
+
inputs_embeds=inputs_embeds,
|
1337 |
+
use_cache=use_cache,
|
1338 |
+
output_attentions=output_attentions,
|
1339 |
+
output_hidden_states=output_hidden_states,
|
1340 |
+
return_dict=return_dict,
|
1341 |
+
)
|
1342 |
+
|
1343 |
+
hidden_states = model_outputs[0]
|
1344 |
+
hidden_states = self.dropout(hidden_states)
|
1345 |
+
logits = self.classifier(hidden_states)
|
1346 |
+
|
1347 |
+
loss = None
|
1348 |
+
if labels is not None:
|
1349 |
+
# move labels to correct device to enable model parallelism
|
1350 |
+
labels = labels.to(logits.device)
|
1351 |
+
batch_size, seq_length = labels.shape
|
1352 |
+
loss_fct = CrossEntropyLoss()
|
1353 |
+
loss = loss_fct(
|
1354 |
+
logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
|
1355 |
+
)
|
1356 |
+
|
1357 |
+
if not return_dict:
|
1358 |
+
output = (logits,) + model_outputs[2:]
|
1359 |
+
return ((loss,) + output) if loss is not None else output
|
1360 |
+
|
1361 |
+
return TokenClassifierOutput(
|
1362 |
+
loss=loss,
|
1363 |
+
logits=logits,
|
1364 |
+
hidden_states=model_outputs.hidden_states,
|
1365 |
+
attentions=model_outputs.attentions,
|
1366 |
+
)
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8d5e1b28a3cdce1ce4cb0738b4f848dbedaac901aed61a1efc25f780f81bcfbc
|
3 |
+
size 2836654177
|
special_tokens_map.json
ADDED
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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": {
|
17 |
+
"content": "<|endoftext|>",
|
18 |
+
"lstrip": false,
|
19 |
+
"normalized": false,
|
20 |
+
"rstrip": false,
|
21 |
+
"single_word": false
|
22 |
+
},
|
23 |
+
"unk_token": {
|
24 |
+
"content": "<|endoftext|>",
|
25 |
+
"lstrip": false,
|
26 |
+
"normalized": false,
|
27 |
+
"rstrip": false,
|
28 |
+
"single_word": false
|
29 |
+
}
|
30 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,324 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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1 |
+
{
|
2 |
+
"add_prefix_space": false,
|
3 |
+
"added_tokens_decoder": {
|
4 |
+
"50256": {
|
5 |
+
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|
6 |
+
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|
7 |
+
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|
8 |
+
"rstrip": false,
|
9 |
+
"single_word": false,
|
10 |
+
"special": true
|
11 |
+
},
|
12 |
+
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|
13 |
+
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|
14 |
+
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|
15 |
+
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|
16 |
+
"rstrip": false,
|
17 |
+
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|
18 |
+
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|
19 |
+
},
|
20 |
+
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|
21 |
+
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|
22 |
+
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|
23 |
+
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|
24 |
+
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|
25 |
+
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|
26 |
+
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|
27 |
+
},
|
28 |
+
"50259": {
|
29 |
+
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|
30 |
+
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|
31 |
+
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|
32 |
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|
33 |
+
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|
34 |
+
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|
35 |
+
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|
36 |
+
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|
37 |
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|
38 |
+
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|
39 |
+
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|
40 |
+
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|
41 |
+
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|
42 |
+
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|
43 |
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},
|
44 |
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|
45 |
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|
46 |
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|
47 |
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|
48 |
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49 |
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50 |
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|
51 |
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|
52 |
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|
53 |
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54 |
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|
55 |
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|
56 |
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|
57 |
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|
58 |
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|
59 |
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|
60 |
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|
61 |
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62 |
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|
63 |
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|
64 |
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65 |
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66 |
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67 |
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|
68 |
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|
69 |
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|
70 |
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|
71 |
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|
72 |
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|
73 |
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|
74 |
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|
75 |
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|
76 |
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|
77 |
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78 |
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|
79 |
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|
80 |
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|
81 |
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|
82 |
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|
83 |
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|
84 |
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|
85 |
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|
86 |
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|
87 |
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|
88 |
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|
89 |
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|
90 |
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|
91 |
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},
|
92 |
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|
93 |
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94 |
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|
95 |
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|
96 |
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97 |
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98 |
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|
99 |
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|
100 |
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|
101 |
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|
102 |
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|
103 |
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|
104 |
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|
105 |
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|
106 |
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|
107 |
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108 |
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|
109 |
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|
110 |
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|
111 |
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|
112 |
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|
113 |
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|
114 |
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|
115 |
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|
116 |
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|
117 |
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118 |
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119 |
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120 |
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121 |
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122 |
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123 |
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|
124 |
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|
125 |
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126 |
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127 |
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128 |
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129 |
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130 |
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131 |
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132 |
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133 |
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134 |
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135 |
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137 |
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138 |
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139 |
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140 |
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|
141 |
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142 |
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143 |
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144 |
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145 |
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146 |
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147 |
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148 |
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|
149 |
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150 |
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151 |
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|
152 |
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153 |
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154 |
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155 |
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156 |
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|
157 |
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158 |
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|
159 |
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|
160 |
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161 |
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162 |
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163 |
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164 |
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|
165 |
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166 |
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167 |
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168 |
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169 |
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170 |
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171 |
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172 |
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173 |
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174 |
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|
175 |
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176 |
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|
177 |
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178 |
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179 |
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180 |
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|
181 |
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|
182 |
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183 |
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184 |
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185 |
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186 |
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|
187 |
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|
188 |
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|
189 |
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|
190 |
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191 |
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192 |
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193 |
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194 |
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195 |
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196 |
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|
197 |
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|
198 |
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|
199 |
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|
200 |
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|
201 |
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|
202 |
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|
203 |
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|
204 |
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|
205 |
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|
206 |
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|
207 |
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|
208 |
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|
209 |
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|
210 |
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|
211 |
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|
212 |
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|
213 |
+
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|
214 |
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|
215 |
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|
216 |
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|
217 |
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|
218 |
+
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|
219 |
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|
220 |
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|
221 |
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|
222 |
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|
223 |
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|
224 |
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|
225 |
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|
226 |
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|
227 |
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|
228 |
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|
229 |
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|
230 |
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|
231 |
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232 |
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233 |
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234 |
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|
235 |
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|
236 |
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|
237 |
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238 |
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|
239 |
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|
240 |
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243 |
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244 |
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|
245 |
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|
246 |
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|
247 |
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|
248 |
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249 |
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|
250 |
+
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|
251 |
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|
252 |
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"50287": {
|
253 |
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|
254 |
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|
255 |
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|
256 |
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257 |
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|
258 |
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259 |
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|
260 |
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|
261 |
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|
262 |
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263 |
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264 |
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265 |
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266 |
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267 |
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268 |
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|
269 |
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|
270 |
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|
271 |
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|
272 |
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|
273 |
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|
274 |
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|
275 |
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|
276 |
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|
277 |
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|
278 |
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|
279 |
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|
280 |
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|
281 |
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|
282 |
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|
283 |
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|
284 |
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|
285 |
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|
286 |
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|
287 |
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|
288 |
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|
289 |
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|
290 |
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|
291 |
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|
292 |
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|
293 |
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|
294 |
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|
295 |
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|
296 |
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|
297 |
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|
298 |
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|
299 |
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|
300 |
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|
301 |
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|
302 |
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|
303 |
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|
304 |
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|
305 |
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|
306 |
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|
307 |
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|
308 |
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|
309 |
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|
310 |
+
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|
311 |
+
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|
312 |
+
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|
313 |
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|
314 |
+
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|
315 |
+
}
|
316 |
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},
|
317 |
+
"bos_token": "<|endoftext|>",
|
318 |
+
"clean_up_tokenization_spaces": true,
|
319 |
+
"eos_token": "<|endoftext|>",
|
320 |
+
"model_max_length": 2048,
|
321 |
+
"pad_token": "<|endoftext|>",
|
322 |
+
"tokenizer_class": "CodeGenTokenizer",
|
323 |
+
"unk_token": "<|endoftext|>"
|
324 |
+
}
|
vocab.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|