mirror of
https://github.com/huggingface/transformers.git
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282 lines
12 KiB
Python
Executable File
282 lines
12 KiB
Python
Executable File
#!/usr/bin/env python
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import argparse
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import gc
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import os
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import sys
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from pathlib import Path
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from typing import List
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import pytorch_lightning as pl
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import torch
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from torch import nn
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from torch.nn import functional as F
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from finetune import SummarizationModule, TranslationModule
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from finetune import main as ft_main
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from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
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from transformers import AutoModelForSeq2SeqLM, MBartTokenizer, T5ForConditionalGeneration
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from transformers.modeling_bart import shift_tokens_right
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from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params
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# need the parent dir module
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sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
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from lightning_base import generic_train # noqa
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class BartSummarizationDistiller(SummarizationModule):
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"""Supports Bart, Pegasus and other models that inherit from Bart."""
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loss_names = ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]
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def __init__(self, hparams):
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assert Path(hparams.data_dir).exists()
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self.output_dir = Path(hparams.output_dir)
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self.output_dir.mkdir(exist_ok=True)
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save_dir = self.output_dir.joinpath("student")
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hparams.model_name_or_path = str(save_dir) # Tell lightning we are training the student
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teacher = AutoModelForSeq2SeqLM.from_pretrained(hparams.teacher).eval()
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use_task_specific_params(teacher, hparams.task) # We copy good generation parameters to student by default
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student, e_layer_ids, d_layer_ids = create_student_by_copying_alternating_layers(
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teacher, e=hparams.student_encoder_layers, d=hparams.student_decoder_layers, save_path=save_dir
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)
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if hparams.length_penalty != -1:
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student.config.length_penalty = hparams.length_penalty
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super().__init__(hparams, model=student, config=student.config)
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model_type = student.config.model_type
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self.e_layer_ids, self.d_layer_ids = e_layer_ids, d_layer_ids # type: List[int], List[int]
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if model_type == "t5":
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teacher_encoder_layers = len(teacher.get_encoder().block)
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teacher_decoder_layers = len(teacher.get_decoder().block)
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else:
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teacher_encoder_layers = teacher.config.encoder_layers
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teacher_decoder_layers = teacher.config.decoder_layers
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self.different_encoder = hparams.student_encoder_layers != teacher_encoder_layers
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self.different_decoder = hparams.student_decoder_layers != teacher_decoder_layers
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self.teacher = teacher
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freeze_params(self.teacher)
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if not self.different_encoder: # To save RAM, delete teacher encoder and freeze student encoder.
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try:
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del self.teacher.model.encoder
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except AttributeError: # T5
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del self.teacher.encoder
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# Intermediate supervision: Decide which layers to supervise
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if hparams.supervise_forward:
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self.e_matches = get_layers_to_supervise(n_student=len(self.e_layer_ids), n_teacher=teacher_encoder_layers)
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self.d_matches = get_layers_to_supervise(n_student=len(self.d_layer_ids), n_teacher=teacher_decoder_layers)
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else: # student layer should emulate hidden states of the teacher layer it was copied from
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self.e_matches = self.e_layer_ids
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self.d_matches = self.d_layer_ids
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self.ce_loss_fct = nn.KLDivLoss(reduction="batchmean")
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self.temperature = 2.0
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self.alpha_mlm = hparams.alpha_mlm
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self.alpha_ce = hparams.alpha_ce
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self.alpha_hid = hparams.alpha_hid
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gc.collect()
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torch.cuda.empty_cache()
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def calc_mse_loss(self, teacher_outputs: torch.Tensor, student_outputs: torch.Tensor, mask) -> torch.FloatTensor:
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"""Supervise MSE(teacher.encoder_outputs, student.encoder_outputs)."""
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# raise NotImplementedError()
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if mask is not None:
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# mask has False at padding_idx
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sel_mask = mask[:, :, None].expand_as(student_outputs).bool()
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s_logits_slct = torch.masked_select(student_outputs, sel_mask)
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t_logits_slct = torch.masked_select(teacher_outputs, sel_mask)
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else:
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t_logits_slct = teacher_outputs
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s_logits_slct = student_outputs
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return F.mse_loss(s_logits_slct, t_logits_slct)
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def calc_ce_loss(self, mask, s_logits, t_logits):
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"""Copy pasted from distillbert (transformers/examples/distillation/)"""
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# mask has False at padding_idx
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sel_mask = mask[:, :, None].expand_as(s_logits)
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vocab_size = s_logits.size(-1)
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s_logits_slct = torch.masked_select(s_logits, sel_mask) # (bs * seq_length * voc_size) modulo the 1s in mask
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t_logits_slct = torch.masked_select(t_logits, sel_mask) # (bs * seq_length * voc_size) modulo the 1s in mask
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s_logits_slct = s_logits_slct.view(-1, vocab_size) # (bs * seq_length, voc_size) modulo the 1s in mask
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t_logits_slct = t_logits_slct.view(-1, vocab_size) # (bs * seq_length, voc_size) modulo the 1s in mask
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assert t_logits_slct.size() == s_logits_slct.size()
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loss_ce = (
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self.ce_loss_fct(
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F.log_softmax(s_logits_slct / self.temperature, dim=-1),
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F.softmax(t_logits_slct / self.temperature, dim=-1),
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)
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* (self.temperature) ** 2
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)
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return loss_ce
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@staticmethod
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def add_model_specific_args(parser, root_dir):
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SummarizationModule.add_model_specific_args(parser, root_dir)
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add_distill_args(parser)
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return parser
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def _step(self, batch):
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# assert is_frozen(self.teacher) copied_decoder_layers
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pad_token_id = self.tokenizer.pad_token_id
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input_ids, src_mask, labels = batch["input_ids"], batch["attention_mask"], batch["labels"]
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if isinstance(self.model, T5ForConditionalGeneration):
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decoder_input_ids = self.model._shift_right(labels)
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else:
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decoder_input_ids = shift_tokens_right(labels, pad_token_id)
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# noinspection PyCallingNonCallable
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lm_logits, dec_hidden, enc_outputs, enc_hidden_state = self(
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input_ids,
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attention_mask=src_mask,
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decoder_input_ids=decoder_input_ids,
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output_hidden_states=True,
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output_attentions=False,
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use_cache=False,
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)
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# Same cross entropy vs. label smoothing logic as finetune.py
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assert lm_logits.shape[-1] == self.model.config.vocab_size
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if self.hparams.label_smoothing == 0:
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# Same behavior as modeling_bart.py, besides ignoring pad_token_id
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loss_fct = torch.nn.CrossEntropyLoss(ignore_index=pad_token_id)
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student_lm_loss = loss_fct(lm_logits.view(-1, lm_logits.shape[-1]), labels.view(-1))
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else:
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lprobs = torch.nn.functional.log_softmax(lm_logits, dim=-1)
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student_lm_loss, _ = label_smoothed_nll_loss(
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lprobs, labels, self.hparams.label_smoothing, ignore_index=pad_token_id
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)
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def zero_tensor():
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return torch.tensor(0.0).type_as(student_lm_loss)
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hid_loss_enc, hid_loss_dec = zero_tensor(), zero_tensor()
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if self.different_encoder: # compute encoder hidden state loss
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with torch.no_grad():
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teacher_enc_hid = self.teacher.get_encoder()(
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input_ids, attention_mask=src_mask, output_hidden_states=True, return_dict=True
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).hidden_states
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hid_loss_enc = self.calc_hidden_loss(
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src_mask,
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enc_hidden_state,
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teacher_enc_hid,
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self.e_matches,
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normalize_hidden=self.hparams.normalize_hidden,
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)
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with torch.no_grad():
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outputs = self.teacher(
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input_ids,
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attention_mask=src_mask,
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encoder_outputs=(enc_outputs,),
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decoder_input_ids=decoder_input_ids,
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lm_labels=labels,
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output_hidden_states=True,
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return_dict=True,
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)
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tlogits, tdec_hidden = outputs.logits, outputs.decoder_hidden_states
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dec_mask = decoder_input_ids.ne(pad_token_id)
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loss_ce = self.calc_ce_loss(dec_mask, lm_logits, tlogits)
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if self.alpha_hid > 0: # Intermediate supervision of decoder hidden states
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hid_loss_dec = self.calc_hidden_loss(
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dec_mask, dec_hidden, tdec_hidden, self.d_matches, normalize_hidden=self.hparams.normalize_hidden
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)
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blended_loss = (
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self.alpha_ce * loss_ce
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+ self.alpha_mlm * student_lm_loss
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+ self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
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)
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return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec
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@staticmethod
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def calc_hidden_loss(attention_mask, hidden_states, hidden_states_T, matches, normalize_hidden):
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"""MSE(student_hid, teacher_hid[matches]). Called "Intermediate supervision" in paper. Inspired by TinyBERT."""
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msg = "expected list or tuple for hidden_states, got tensor of shape: "
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assert not isinstance(hidden_states, torch.Tensor), f"{msg}{hidden_states.shape}"
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assert not isinstance(hidden_states_T, torch.Tensor), f"{msg}{hidden_states_T.shape}"
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mask = attention_mask.to(hidden_states[0])
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valid_count = mask.sum() * hidden_states[0].size(-1)
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student_states = torch.stack([hidden_states[i] for i in range(len(matches))])
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teacher_states = torch.stack([hidden_states_T[j] for j in matches])
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if normalize_hidden:
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student_states = F.layer_norm(student_states, student_states.shape[1:])
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teacher_states = F.layer_norm(teacher_states, teacher_states.shape[1:])
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mse = F.mse_loss(student_states, teacher_states, reduction="none")
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masked_mse = (mse * mask.unsqueeze(0).unsqueeze(-1)).sum() / valid_count
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return masked_mse
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def add_distill_args(parser):
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parser.add_argument("--teacher", type=str)
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parser.add_argument("--alpha_ce", default=0.8, type=float)
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parser.add_argument("--alpha_mlm", default=0.2, type=float)
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parser.add_argument("--alpha_hid", default=0.0, type=float, required=False)
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parser.add_argument("--student_decoder_layers", default=12, type=int, required=False)
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parser.add_argument("--student_encoder_layers", default=12, type=int, required=False)
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parser.add_argument("--no_teacher", action="store_true", default=False)
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parser.add_argument("--length_penalty", type=float, default=-1)
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parser.add_argument("--supervise_forward", action="store_true", default=False)
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parser.add_argument("--normalize_hidden", action="store_true", default=False)
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class BartTranslationDistiller(BartSummarizationDistiller):
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"""Supports Mbart, Marian, other models that inherit from Bart."""
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mode = "translation"
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metric_names = ["bleu"]
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default_val_metric = "bleu"
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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assert hparams.src_lang is not None
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assert hparams.tgt_lang is not None
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self.dataset_kwargs["src_lang"] = hparams.src_lang
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self.dataset_kwargs["tgt_lang"] = hparams.tgt_lang
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if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, MBartTokenizer):
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self.decoder_start_token_id = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
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def calc_generative_metrics(self, preds, target) -> dict:
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return calculate_bleu(preds, target)
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@staticmethod
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def add_model_specific_args(parser, root_dir):
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TranslationModule.add_model_specific_args(parser, root_dir)
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add_distill_args(parser)
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return parser
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def create_module(args):
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if args.no_teacher:
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module_cls = TranslationModule if "translation" in args.task else SummarizationModule
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else: # DISTILL WITH TEACHER
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module_cls = BartTranslationDistiller if "translation" in args.task else BartSummarizationDistiller
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args.setup_cls: str = module_cls.__name__
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print(f"using module {args.setup_cls}")
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model = module_cls(args)
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return model
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def distill_main(args):
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Path(args.output_dir).mkdir(exist_ok=True)
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check_output_dir(args, expected_items=3)
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model = create_module(args)
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return ft_main(args, model=model)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser = pl.Trainer.add_argparse_args(parser)
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parser = BartSummarizationDistiller.add_model_specific_args(parser, os.getcwd())
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args = parser.parse_args()
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distill_main(args)
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