import warnings from pathlib import Path from typing import List, Tuple, Union import fire from torch import nn from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel from transformers.utils import logging logger = logging.get_logger(__name__) def copy_layers(src_layers: nn.ModuleList, dest_layers: nn.ModuleList, layers_to_copy: List[int]) -> None: layers_to_copy = nn.ModuleList([src_layers[i] for i in layers_to_copy]) assert len(dest_layers) == len(layers_to_copy), f"{len(dest_layers)} != {len(layers_to_copy)}" dest_layers.load_state_dict(layers_to_copy.state_dict()) LAYERS_TO_COPY = { # maps num layers in teacher -> num_layers in student -> which teacher layers to copy. # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP 12: { 1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher 2: [0, 6], 3: [0, 6, 11], 4: [0, 4, 8, 11], 6: [0, 2, 4, 7, 9, 11], 9: [0, 1, 2, 4, 5, 7, 9, 10, 11], 12: list(range(12)), }, 16: { # maps num layers in student -> which teacher layers to copy 1: [0], 2: [0, 15], 3: [0, 8, 15], 4: [0, 5, 10, 15], 6: [0, 3, 6, 9, 12, 15], 8: [0, 2, 4, 6, 8, 10, 12, 15], 9: [0, 1, 3, 5, 7, 9, 11, 13, 15], 12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15], 16: list(range(16)), }, 6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))}, } LAYERS_TO_SUPERVISE = { # maps num layers in student -> which teacher layers to copy. 6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]}, 12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]}, 16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]}, } def pick_layers_to_copy(n_student, n_teacher): try: val = LAYERS_TO_COPY[n_teacher][n_student] return val except KeyError: if n_student != n_teacher: warnings.warn( f"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first" f" {n_student}" ) return list(range(n_student)) def get_layers_to_supervise(n_student, n_teacher) -> List[int]: """Used or the --supervise_forward kwarg""" if n_student > n_teacher: raise ValueError(f"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}") elif n_teacher == n_student: return list(range(n_teacher)) elif n_student == 1: return [n_teacher - 1] else: return LAYERS_TO_SUPERVISE[n_teacher][n_student] def create_student_by_copying_alternating_layers( teacher: Union[str, PreTrainedModel], save_path: Union[str, Path] = "student", e: Union[int, None] = None, d: Union[int, None] = None, copy_first_teacher_layers=False, e_layers_to_copy=None, d_layers_to_copy=None, **extra_config_kwargs, ) -> Tuple[PreTrainedModel, List[int], List[int]]: """Make a student by copying alternating layers from a teacher, save it to save_path. Args: teacher: str or PreTrainedModel if str, this will call AutoModelForSeq2SeqLM.from_pretrained(teacher) before copying layers save_path: where to save the student, defaults to student directory. e: how many Encoder layers should the student have, default is fully copy of teacher d: how many Decoder layers should the student have, default is fully copy of teacher copy_first_teacher_layers: [bool] dont copy alternating layers, just the first e/d. **extra_config_kwargs: extra kwargs to pass to the student, by default the teacher config is used. Returns: student: new, smaller model. (Also saves it to save_path) e_layers_to_copy: list of which teacher encoder layers were used d_layers_to_copy: list of which teacher decoder layers were used """ _msg = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher." assert (e is not None) or (d is not None), _msg if isinstance(teacher, str): AutoTokenizer.from_pretrained(teacher).save_pretrained(save_path) # purely for convenience teacher = AutoModelForSeq2SeqLM.from_pretrained(teacher).eval() else: assert isinstance(teacher, PreTrainedModel), f"teacher must be a model or string got type {type(teacher)}" init_kwargs = teacher.config.to_diff_dict() try: teacher_e, teacher_d = teacher.config.encoder_layers, teacher.config.decoder_layers if e is None: e = teacher_e if d is None: d = teacher_d init_kwargs.update({"encoder_layers": e, "decoder_layers": d}) except AttributeError: # T5 if hasattr(teacher.config, "num_encoder_layers"): teacher_e, teacher_d = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers else: teacher_e, teacher_d = teacher.config.num_layers, teacher.config.num_decoder_layers if e is None: e = teacher_e if d is None: d = teacher_d if hasattr(teacher.config, "num_encoder_layers"): init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d}) else: init_kwargs.update({"num_layers": e, "num_decoder_layers": d}) # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs init_kwargs.update(extra_config_kwargs) # Copy weights student_cfg = teacher.config_class(**init_kwargs) student = AutoModelForSeq2SeqLM.from_config(student_cfg) # Start by copying the full teacher state dict this will copy the first N teacher layers to the student. info = student.load_state_dict(teacher.state_dict(), strict=False) assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys. if copy_first_teacher_layers: # Our copying is done. We just log and save e_layers_to_copy, d_layers_to_copy = list(range(e)), list(range(d)) logger.info( f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to" f" {save_path}" ) student.save_pretrained(save_path) return student, e_layers_to_copy, d_layers_to_copy # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer. if e_layers_to_copy is None: e_layers_to_copy: List[int] = pick_layers_to_copy(e, teacher_e) if d_layers_to_copy is None: d_layers_to_copy: List[int] = pick_layers_to_copy(d, teacher_d) try: if hasattr( teacher, "prophetnet" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers copy_layers(teacher.prophetnet.encoder.layers, student.prophetnet.encoder.layers, e_layers_to_copy) copy_layers(teacher.prophetnet.decoder.layers, student.prophetnet.decoder.layers, d_layers_to_copy) else: copy_layers(teacher.model.encoder.layers, student.model.encoder.layers, e_layers_to_copy) copy_layers(teacher.model.decoder.layers, student.model.decoder.layers, d_layers_to_copy) except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block copy_layers(teacher.encoder.block, student.encoder.block, e_layers_to_copy) copy_layers(teacher.decoder.block, student.decoder.block, d_layers_to_copy) logger.info( f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}" ) student.config.init_metadata = { "teacher_type": teacher.config.model_type, "copied_encoder_layers": e_layers_to_copy, "copied_decoder_layers": d_layers_to_copy, } student.save_pretrained(save_path) # Save information about copying for easier reproducibility return student, e_layers_to_copy, d_layers_to_copy if __name__ == "__main__": fire.Fire(create_student_by_copying_alternating_layers)