#!/usr/bin/env python import argparse import gc import os import sys from pathlib import Path from typing import List # noqa: F401 import pytorch_lightning as pl import torch from finetune import SummarizationModule, TranslationModule from finetune import main as ft_main from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise from torch import nn from transformers import AutoModelForSeq2SeqLM, MBartTokenizer, T5ForConditionalGeneration from transformers.models.bart.modeling_bart import shift_tokens_right from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params # need the parent dir module sys.path.insert(2, str(Path(__file__).resolve().parents[1])) from lightning_base import generic_train # noqa class SummarizationDistiller(SummarizationModule): """Supports T5, Bart, Pegasus and other models that inherit from Bart.""" loss_names = ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"] def __init__(self, hparams): assert Path(hparams.data_dir).exists() self.output_dir = Path(hparams.output_dir) self.output_dir.mkdir(exist_ok=True) save_dir = self.output_dir.joinpath("student") hparams.model_name_or_path = str(save_dir) # Tell lightning we are training the student teacher = AutoModelForSeq2SeqLM.from_pretrained(hparams.teacher).eval() use_task_specific_params(teacher, hparams.task) # We copy good generation parameters to student by default if hparams.student is not None: student = AutoModelForSeq2SeqLM.from_pretrained(hparams.student) use_task_specific_params(student, hparams.task) e_layer_ids, d_layer_ids = None, None else: student, e_layer_ids, d_layer_ids = create_student_by_copying_alternating_layers( teacher, e=hparams.student_encoder_layers, d=hparams.student_decoder_layers, save_path=save_dir ) if hparams.length_penalty != -1: student.config.length_penalty = hparams.length_penalty hparams.tokenizer_name = hparams.teacher # Use teacher's tokenizer super().__init__(hparams, model=student, config=student.config) assert student.config.model_type == teacher.config.model_type, ( f"teacher, student model types should be the same, got {student.config.model_type} !=" f" {teacher.config.model_type}" ) if student.config.model_type == "t5": student_encoder_layers = len(student.get_encoder().block) student_decoder_layers = len(student.get_decoder().block) teacher_encoder_layers = len(teacher.get_encoder().block) teacher_decoder_layers = len(teacher.get_decoder().block) else: student_encoder_layers = student.config.encoder_layers student_decoder_layers = student.config.decoder_layers teacher_encoder_layers = teacher.config.encoder_layers teacher_decoder_layers = teacher.config.decoder_layers self.different_base_models = not (hparams.student is None or hparams.teacher == hparams.student) self.do_calc_hidden_loss = (not self.different_base_models) and hparams.alpha_hid > 0 self.different_encoder = self.different_base_models or (student_encoder_layers != teacher_encoder_layers) # self.different_encoder determines whether we need to run the teacher encoder self.teacher = teacher freeze_params(self.teacher) if not self.different_encoder: # To save RAM, delete teacher encoder and freeze student encoder. try: del self.teacher.model.encoder except AttributeError: # T5 del self.teacher.encoder if e_layer_ids is None: e_layer_ids = list(range(student_encoder_layers)) if d_layer_ids is None: d_layer_ids = list(range(student_decoder_layers)) self.e_layer_ids, self.d_layer_ids = e_layer_ids, d_layer_ids # type: List[int], List[int] if self.do_calc_hidden_loss: # Intermediate supervision: Decide which layers to supervise if hparams.supervise_forward: self.e_matches = get_layers_to_supervise( n_student=len(self.e_layer_ids), n_teacher=teacher_encoder_layers ) self.d_matches = get_layers_to_supervise( n_student=len(self.d_layer_ids), n_teacher=teacher_decoder_layers ) else: # student layer should emulate hidden states of the teacher layer it was copied from self.e_matches = self.e_layer_ids self.d_matches = self.d_layer_ids else: self.e_matches = None self.d_matches = None self.ce_loss_fct = nn.KLDivLoss(reduction="batchmean") self.temperature = 2.0 self.alpha_mlm = hparams.alpha_mlm self.alpha_ce = hparams.alpha_ce self.alpha_hid = hparams.alpha_hid gc.collect() torch.cuda.empty_cache() def calc_ce_loss(self, mask, s_logits, t_logits): """Copy pasted from distillbert (transformers/examples/distillation/)""" # mask has False at padding_idx sel_mask = mask[:, :, None].expand_as(s_logits) vocab_size = s_logits.size(-1) s_logits_slct = torch.masked_select(s_logits, sel_mask) # (bs * seq_length * voc_size) modulo the 1s in mask t_logits_slct = torch.masked_select(t_logits, sel_mask) # (bs * seq_length * voc_size) modulo the 1s in mask s_logits_slct = s_logits_slct.view(-1, vocab_size) # (bs * seq_length, voc_size) modulo the 1s in mask t_logits_slct = t_logits_slct.view(-1, vocab_size) # (bs * seq_length, voc_size) modulo the 1s in mask assert t_logits_slct.size() == s_logits_slct.size() loss_ce = ( self.ce_loss_fct( nn.functional.log_softmax(s_logits_slct / self.temperature, dim=-1), nn.functional.softmax(t_logits_slct / self.temperature, dim=-1), ) * (self.temperature) ** 2 ) return loss_ce @staticmethod def add_model_specific_args(parser, root_dir): SummarizationModule.add_model_specific_args(parser, root_dir) add_distill_args(parser) return parser def _step(self, batch: dict) -> tuple: """Compute the loss for a batch""" pad_token_id = self.tokenizer.pad_token_id input_ids, src_mask, labels = batch["input_ids"], batch["attention_mask"], batch["labels"] if isinstance(self.model, T5ForConditionalGeneration): decoder_input_ids = self.model._shift_right(labels) else: decoder_input_ids = shift_tokens_right(labels, pad_token_id) # noinspection PyCallingNonCallable student_outputs = self( input_ids, attention_mask=src_mask, decoder_input_ids=decoder_input_ids, output_hidden_states=self.do_calc_hidden_loss, output_attentions=False, use_cache=False, ) lm_logits = student_outputs["logits"] # Same cross entropy vs. label smoothing logic as finetune.py assert lm_logits.shape[-1] == self.model.config.vocab_size if self.hparams.label_smoothing == 0: # Same behavior as modeling_bart.py, besides ignoring pad_token_id loss_fct = nn.CrossEntropyLoss(ignore_index=pad_token_id) student_lm_loss = loss_fct(lm_logits.view(-1, lm_logits.shape[-1]), labels.view(-1)) else: lprobs = nn.functional.log_softmax(lm_logits, dim=-1) student_lm_loss, _ = label_smoothed_nll_loss( lprobs, labels, self.hparams.label_smoothing, ignore_index=pad_token_id ) def zero_tensor(): return torch.tensor(0.0).type_as(student_lm_loss) teacher_enc_outputs = student_outputs[ "encoder_last_hidden_state" ] # use this unless self.different_base_models hid_loss_enc, hid_loss_dec = zero_tensor(), zero_tensor() if self.different_encoder: # compute encoder hidden state loss all_teacher_encoder_outputs = self.teacher.get_encoder()( input_ids, attention_mask=src_mask, output_hidden_states=self.do_calc_hidden_loss, ) if self.different_base_models: teacher_enc_outputs = all_teacher_encoder_outputs["last_hidden_state"] elif self.do_calc_hidden_loss: hid_loss_enc = self.calc_hidden_loss( src_mask, student_outputs["encoder_hidden_states"], all_teacher_encoder_outputs["hidden_states"], self.e_matches, normalize_hidden=self.hparams.normalize_hidden, ) teacher_outputs = self.teacher( input_ids, attention_mask=src_mask, encoder_outputs=(teacher_enc_outputs,), decoder_input_ids=decoder_input_ids, output_hidden_states=self.do_calc_hidden_loss, use_cache=False, # since we are not passing labels, never let this default to True ) dec_mask = decoder_input_ids.ne(pad_token_id) loss_ce = self.calc_ce_loss(dec_mask, lm_logits, teacher_outputs["logits"]) if self.do_calc_hidden_loss: # Intermediate supervision of decoder hidden states hid_loss_dec = self.calc_hidden_loss( dec_mask, student_outputs["decoder_hidden_states"], teacher_outputs["decoder_hidden_states"], self.d_matches, normalize_hidden=self.hparams.normalize_hidden, ) blended_loss = ( self.alpha_ce * loss_ce + self.alpha_mlm * student_lm_loss + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec) ) return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec @staticmethod def calc_hidden_loss(attention_mask, hidden_states, hidden_states_T, matches, normalize_hidden): """MSE(student_hid, teacher_hid[matches]). Called "Intermediate supervision" in paper. Inspired by TinyBERT.""" msg = "expected list or tuple for hidden_states, got tensor of shape: " assert not isinstance(hidden_states, torch.Tensor), f"{msg}{hidden_states.shape}" assert not isinstance(hidden_states_T, torch.Tensor), f"{msg}{hidden_states_T.shape}" mask = attention_mask.to(hidden_states[0]) valid_count = mask.sum() * hidden_states[0].size(-1) student_states = torch.stack([hidden_states[i] for i in range(len(matches))]) teacher_states = torch.stack([hidden_states_T[j] for j in matches]) assert student_states.shape == teacher_states.shape, f"{student_states.shape} != {teacher_states.shape}" if normalize_hidden: student_states = nn.functional.layer_norm(student_states, student_states.shape[1:]) teacher_states = nn.functional.layer_norm(teacher_states, teacher_states.shape[1:]) mse = nn.functional.mse_loss(student_states, teacher_states, reduction="none") masked_mse = (mse * mask.unsqueeze(0).unsqueeze(-1)).sum() / valid_count return masked_mse def add_distill_args(parser): # NOTE: if --student argument was specified and the teacher and student base models # are different, the models still have to have the same tokenizer, specified by # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not # from bart to t5. This s because if the tokenizers are different, the output space # for the two models is also different and their logits are not comparable. parser.add_argument("--teacher", type=str) parser.add_argument("--alpha_ce", default=0.8, type=float) parser.add_argument("--alpha_mlm", default=0.2, type=float) parser.add_argument("--alpha_hid", default=0.0, type=float, required=False) parser.add_argument("--student", type=str, required=False) parser.add_argument("--student_decoder_layers", default=12, type=int, required=False) parser.add_argument("--student_encoder_layers", default=12, type=int, required=False) parser.add_argument("--no_teacher", action="store_true", default=False) parser.add_argument("--length_penalty", type=float, default=-1) parser.add_argument("--supervise_forward", action="store_true", default=False) parser.add_argument("--normalize_hidden", action="store_true", default=False) class TranslationDistiller(SummarizationDistiller): """Supports T5, mBART, Marian, other models that inherit from Bart.""" mode = "translation" metric_names = ["bleu"] default_val_metric = "bleu" def __init__(self, hparams, **kwargs): super().__init__(hparams, **kwargs) assert hparams.src_lang is not None assert hparams.tgt_lang is not None self.dataset_kwargs["src_lang"] = hparams.src_lang self.dataset_kwargs["tgt_lang"] = hparams.tgt_lang if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, MBartTokenizer): self.decoder_start_token_id = self.tokenizer.lang_code_to_id[hparams.tgt_lang] def calc_generative_metrics(self, preds, target) -> dict: return calculate_bleu(preds, target) @staticmethod def add_model_specific_args(parser, root_dir): TranslationModule.add_model_specific_args(parser, root_dir) add_distill_args(parser) return parser def create_module(args): if args.no_teacher: module_cls = TranslationModule if "translation" in args.task else SummarizationModule else: # DISTILL WITH TEACHER module_cls = TranslationDistiller if "translation" in args.task else SummarizationDistiller args.setup_cls: str = module_cls.__name__ print(f"using module {args.setup_cls}") model = module_cls(args) return model def distill_main(args): Path(args.output_dir).mkdir(exist_ok=True) check_output_dir(args, expected_items=3) model = create_module(args) return ft_main(args, model=model) if __name__ == "__main__": parser = argparse.ArgumentParser() parser = pl.Trainer.add_argparse_args(parser) parser = SummarizationDistiller.add_model_specific_args(parser, os.getcwd()) args = parser.parse_args() distill_main(args)