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