distinguish intermediate layer & prediction layer distillation phases…

… with different parameters (#2001)

* add parameters to allow for different hyperparameters in stage 1 and 2 of tinybert distillation

* Add latest docstring and tutorial changes

* improve default parameters

* Add latest docstring and tutorial changes

* split up distillation method

* Add latest docstring and tutorial changes

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

docs/_src/api/api/reader.md

@@ -157,23 +157,26 @@ If any checkpoints are stored, a subsequent run of train() will resume training
 
 None
 
-<a name="farm.FARMReader.distil_from"></a>
-#### distil\_from
+<a name="farm.FARMReader.distil_prediction_layer_from"></a>
+#### distil\_prediction\_layer\_from
 
 ```python
- | distil_from(teacher_model: "FARMReader", data_dir: str, train_filename: str, dev_filename: Optional[str] = None, test_filename: Optional[str] = None, use_gpu: Optional[bool] = None, student_batch_size: int = 10, teacher_batch_size: Optional[int] = None, n_epochs: int = 2, learning_rate: float = 1e-5, max_seq_len: Optional[int] = None, warmup_proportion: float = 0.2, dev_split: float = 0, evaluate_every: int = 300, save_dir: Optional[str] = None, num_processes: Optional[int] = None, use_amp: str = None, checkpoint_root_dir: Path = Path("model_checkpoints"), checkpoint_every: Optional[int] = None, checkpoints_to_keep: int = 3, caching: bool = False, cache_path: Path = Path("cache/data_silo"), distillation_loss_weight: float = 0.5, distillation_loss: Union[str, Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = "kl_div", temperature: float = 1.0, tinybert_loss: bool = False, tinybert_epochs: int = 1)
+ | distil_prediction_layer_from(teacher_model: "FARMReader", data_dir: str, train_filename: str, dev_filename: Optional[str] = None, test_filename: Optional[str] = None, use_gpu: Optional[bool] = None, student_batch_size: int = 10, teacher_batch_size: Optional[int] = None, n_epochs: int = 2, learning_rate: float = 3e-5, max_seq_len: Optional[int] = None, warmup_proportion: float = 0.2, dev_split: float = 0, evaluate_every: int = 300, save_dir: Optional[str] = None, num_processes: Optional[int] = None, use_amp: str = None, checkpoint_root_dir: Path = Path("model_checkpoints"), checkpoint_every: Optional[int] = None, checkpoints_to_keep: int = 3, caching: bool = False, cache_path: Path = Path("cache/data_silo"), distillation_loss_weight: float = 0.5, distillation_loss: Union[str, Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = "kl_div", temperature: float = 1.0)
 ```
 
-Fine-tune a model on a QA dataset using distillation. You need to provide a teacher model that is already finetuned on the dataset
-and a student model that will be trained using the teacher's logits. The idea of this is to increase the accuracy of a lightweight student model
+Fine-tune a model on a QA dataset using logit-based distillation. You need to provide a teacher model that is already finetuned on the dataset
+and a student model that will be trained using the teacher's logits. The idea of this is to increase the accuracy of a lightweight student model.
 using a more complex teacher.
+Originally proposed in: https://arxiv.org/pdf/1503.02531.pdf
+This can also be considered as the second stage of distillation finetuning as described in the TinyBERT paper:
+https://arxiv.org/pdf/1909.10351.pdf
 
 **Example**
 ```python
 student = FARMReader(model_name_or_path="prajjwal1/bert-medium")
 teacher = FARMReader(model_name_or_path="deepset/bert-large-uncased-whole-word-masking-squad2")
 
-student.distil_from(teacher, data_dir="squad2", train_filename="train.json", test_filename="dev.json",
+student.distil_prediction_layer_from(teacher, data_dir="squad2", train_filename="train.json", test_filename="dev.json",
                     learning_rate=3e-5, distillation_loss_weight=1.0, temperature=5)
 ```
 
@@ -222,6 +225,75 @@ If any checkpoints are stored, a subsequent run of train() will resume training
 - `temperature`: The temperature for distillation. A higher temperature will result in less certainty of teacher outputs. A lower temperature means more certainty. A temperature of 1.0 does not change the certainty of the model.
 - `tinybert_loss`: Whether to use the TinyBERT loss function for distillation. This requires the student to be a TinyBERT model and the teacher to be a finetuned version of bert-base-uncased.
 - `tinybert_epochs`: Number of epochs to train the student model with the TinyBERT loss function. After this many epochs, the student model is trained with the regular distillation loss function.
+- `tinybert_learning_rate`: Learning rate to use when training the student model with the TinyBERT loss function.
+- `tinybert_train_filename`: Filename of training data to use when training the student model with the TinyBERT loss function. To best follow the original paper, this should be an augmented version of the training data created using the augment_squad.py script. If not specified, the training data from the original training is used.
+
+**Returns**:
+
+None
+
+<a name="farm.FARMReader.distil_intermediate_layers_from"></a>
+#### distil\_intermediate\_layers\_from
+
+```python
+ | distil_intermediate_layers_from(teacher_model: "FARMReader", data_dir: str, train_filename: str, dev_filename: Optional[str] = None, test_filename: Optional[str] = None, use_gpu: Optional[bool] = None, student_batch_size: int = 10, teacher_batch_size: Optional[int] = None, n_epochs: int = 5, learning_rate: float = 5e-5, max_seq_len: Optional[int] = None, warmup_proportion: float = 0.2, dev_split: float = 0, evaluate_every: int = 300, save_dir: Optional[str] = None, num_processes: Optional[int] = None, use_amp: str = None, checkpoint_root_dir: Path = Path("model_checkpoints"), checkpoint_every: Optional[int] = None, checkpoints_to_keep: int = 3, caching: bool = False, cache_path: Path = Path("cache/data_silo"), distillation_loss: Union[str, Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = "mse", temperature: float = 1.0)
+```
+
+The first stage of distillation finetuning as described in the TinyBERT paper:
+https://arxiv.org/pdf/1909.10351.pdf
+
+**Example**
+```python
+student = FARMReader(model_name_or_path="prajjwal1/bert-medium")
+teacher = FARMReader(model_name_or_path="huawei-noah/TinyBERT_General_6L_768D")
+
+student.distil_intermediate_layers_from(teacher, data_dir="squad2", train_filename="train.json", test_filename="dev.json",
+                    learning_rate=3e-5, distillation_loss_weight=1.0, temperature=5)
+```
+
+Checkpoints can be stored via setting `checkpoint_every` to a custom number of steps.
+If any checkpoints are stored, a subsequent run of train() will resume training from the latest available checkpoint.
+
+**Arguments**:
+
+- `teacher_model`: Model whose logits will be used to improve accuracy
+- `data_dir`: Path to directory containing your training data in SQuAD style
+- `train_filename`: Filename of training data. To best follow the original paper, this should be an augmented version of the training data created using the augment_squad.py script
+- `dev_filename`: Filename of dev / eval data
+- `test_filename`: Filename of test data
+- `dev_split`: Instead of specifying a dev_filename, you can also specify a ratio (e.g. 0.1) here
+                  that gets split off from training data for eval.
+- `use_gpu`: Whether to use GPU (if available)
+- `student_batch_size`: Number of samples the student model receives in one batch for training
+- `student_batch_size`: Number of samples the teacher model receives in one batch for distillation
+- `n_epochs`: Number of iterations on the whole training data set
+- `learning_rate`: Learning rate of the optimizer
+- `max_seq_len`: Maximum text length (in tokens). Everything longer gets cut down.
+- `warmup_proportion`: Proportion of training steps until maximum learning rate is reached.
+                          Until that point LR is increasing linearly. After that it's decreasing again linearly.
+                          Options for different schedules are available in FARM.
+- `evaluate_every`: Evaluate the model every X steps on the hold-out eval dataset
+- `save_dir`: Path to store the final model
+- `num_processes`: The number of processes for `multiprocessing.Pool` during preprocessing.
+                      Set to value of 1 to disable multiprocessing. When set to 1, you cannot split away a dev set from train set.
+                      Set to None to use all CPU cores minus one.
+- `use_amp`: Optimization level of NVIDIA's automatic mixed precision (AMP). The higher the level, the faster the model.
+                Available options:
+                None (Don't use AMP)
+                "O0" (Normal FP32 training)
+                "O1" (Mixed Precision => Recommended)
+                "O2" (Almost FP16)
+                "O3" (Pure FP16).
+                See details on: https://nvidia.github.io/apex/amp.html
+- `checkpoint_root_dir`: the Path of directory where all train checkpoints are saved. For each individual
+       checkpoint, a subdirectory with the name epoch_{epoch_num}_step_{step_num} is created.
+- `checkpoint_every`: save a train checkpoint after this many steps of training.
+- `checkpoints_to_keep`: maximum number of train checkpoints to save.
+:param caching whether or not to use caching for preprocessed dataset and teacher logits
+- `cache_path`: Path to cache the preprocessed dataset and teacher logits
+- `distillation_loss_weight`: The weight of the distillation loss. A higher weight means the teacher outputs are more important.
+- `distillation_loss`: Specifies how teacher and model logits should be compared. Can either be a string ("mse" for mean squared error or "kl_div" for kl divergence loss) or a callable loss function (needs to have named parameters student_logits and teacher_logits)
+- `temperature`: The temperature for distillation. A higher temperature will result in less certainty of teacher outputs. A lower temperature means more certainty. A temperature of 1.0 does not change the certainty of the model.
 
 **Returns**: