250 lines
7.5 KiB
Python
250 lines
7.5 KiB
Python
import os
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os.environ["TOKENIZERS_PARALLELISM"] = "true"
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from collections import defaultdict
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import numpy as np
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import torch
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import transformers
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from sklearn.model_selection import train_test_split
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from torch.optim import AdamW
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from torch.utils.data import DataLoader, Dataset
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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from vgfs.learners.svms import FeatureSet2Posteriors
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from vgfs.viewGen import ViewGen
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from vgfs.transformerGen import TransformerGen
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from vgfs.commons import Trainer, predict
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transformers.logging.set_verbosity_error()
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# TODO: add support to loggers
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# TODO: multiple inheritance - maybe define a superclass for TransformerGenerator, whether it is a Textual or a Visual one, implementing dataset creation functions
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class TextualTransformerGen(ViewGen, TransformerGen):
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def __init__(
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self,
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model_name,
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dataset_name,
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epochs=10,
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lr=1e-5,
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batch_size=4,
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batch_size_eval=32,
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max_length=512,
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print_steps=50,
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device="cpu",
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probabilistic=False,
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n_jobs=-1,
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evaluate_step=10,
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verbose=False,
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patience=5,
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):
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super().__init__(
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model_name,
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dataset_name,
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epochs,
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lr,
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batch_size,
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batch_size_eval,
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max_length,
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print_steps,
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device,
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probabilistic,
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n_jobs,
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evaluate_step,
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verbose,
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patience,
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)
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self.fitted = False
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self._init()
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def _init(self):
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if self.probabilistic:
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self.feature2posterior_projector = FeatureSet2Posteriors(
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n_jobs=self.n_jobs, verbose=False
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)
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self.model_name = self._get_model_name(self.model_name)
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print(
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f"- init TransformerModel model_name: {self.model_name}, device: {self.device}]"
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)
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def _get_model_name(self, name):
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if "bert" == name:
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name_model = "bert-base-uncased"
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elif "mbert" == name:
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name_model = "bert-base-multilingual-uncased"
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elif "xlm" == name:
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name_model = "xlm-roberta-base"
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else:
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raise NotImplementedError
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return name_model
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def load_pretrained_model(self, model_name, num_labels):
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return AutoModelForSequenceClassification.from_pretrained(
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model_name, num_labels=num_labels, output_hidden_states=True
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)
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def load_tokenizer(self, model_name):
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return AutoTokenizer.from_pretrained(model_name)
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def init_model(self, model_name, num_labels):
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return self.load_pretrained_model(model_name, num_labels), self.load_tokenizer(
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model_name
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)
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def _tokenize(self, X):
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return self.tokenizer(
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X,
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return_tensors="pt",
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padding="max_length",
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truncation=True,
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max_length=self.max_length,
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)
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def fit(self, lX, lY):
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if self.fitted:
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return self
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print("- fitting Textual Transformer View Generating Function")
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_l = list(lX.keys())[0]
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self.num_labels = lY[_l].shape[-1]
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self.model, self.tokenizer = self.init_model(
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self.model_name, num_labels=self.num_labels
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)
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tr_lX, tr_lY, val_lX, val_lY = self.get_train_val_data(
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lX, lY, split=0.2, seed=42
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)
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tra_dataloader = self.build_dataloader(
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tr_lX,
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tr_lY,
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processor_fn=self._tokenize,
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torchDataset=MultilingualDatasetTorch,
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batch_size=self.batch_size,
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split="train",
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shuffle=True,
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)
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val_dataloader = self.build_dataloader(
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val_lX,
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val_lY,
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processor_fn=self._tokenize,
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torchDataset=MultilingualDatasetTorch,
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batch_size=self.batch_size_eval,
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split="val",
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shuffle=False,
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)
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experiment_name = (
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f"{self.model_name}-{self.epochs}-{self.batch_size}-{self.dataset_name}"
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)
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trainer = Trainer(
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model=self.model,
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optimizer_name="adamW",
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lr=self.lr,
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device=self.device,
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loss_fn=torch.nn.CrossEntropyLoss(),
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print_steps=self.print_steps,
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evaluate_step=self.evaluate_step,
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patience=self.patience,
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experiment_name=experiment_name,
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)
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trainer.train(
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train_dataloader=tra_dataloader,
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eval_dataloader=val_dataloader,
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epochs=self.epochs,
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)
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if self.probabilistic:
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self.feature2posterior_projector.fit(self.transform(lX), lY)
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self.fitted = True
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return self
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def transform(self, lX):
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_embeds = []
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l_embeds = defaultdict(list)
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dataloader = self.build_dataloader(
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lX,
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lY=None,
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processor_fn=self._tokenize,
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torchDataset=MultilingualDatasetTorch,
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batch_size=self.batch_size_eval,
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split="whole",
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shuffle=False,
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)
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self.model.eval()
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with torch.no_grad():
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for input_ids, lang in dataloader:
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input_ids = input_ids.to(self.device)
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out = self.model(input_ids).hidden_states[-1]
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batch_embeddings = out[:, 0, :].cpu().numpy()
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_embeds.append((batch_embeddings, lang))
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for embed, lang in _embeds:
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for sample_embed, sample_lang in zip(embed, lang):
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l_embeds[sample_lang].append(sample_embed)
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if self.probabilistic and self.fitted:
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l_embeds = self.feature2posterior_projector.transform(l_embeds)
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return l_embeds
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def fit_transform(self, lX, lY):
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return self.fit(lX, lY).transform(lX)
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def save_vgf(self, model_id):
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import pickle
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from os import makedirs
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from os.path import join
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vgf_name = "transformerGen"
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_basedir = join("models", "vgfs", "transformer")
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makedirs(_basedir, exist_ok=True)
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_path = join(_basedir, f"{vgf_name}_{model_id}.pkl")
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with open(_path, "wb") as f:
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pickle.dump(self, f)
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return self
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def __str__(self):
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str = f"[Transformer VGF (t)]\n- model_name: {self.model_name}\n- max_length: {self.max_length}\n- batch_size: {self.batch_size}\n- batch_size_eval: {self.batch_size_eval}\n- lr: {self.lr}\n- epochs: {self.epochs}\n- device: {self.device}\n- print_steps: {self.print_steps}\n- evaluate_step: {self.evaluate_step}\n- patience: {self.patience}\n- probabilistic: {self.probabilistic}\n"
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return str
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class MultilingualDatasetTorch(Dataset):
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def __init__(self, lX, lY, split="train"):
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self.lX = lX
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self.lY = lY
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self.split = split
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self.langs = []
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self.init()
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def init(self):
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self.X = torch.vstack([data.input_ids for data in self.lX.values()])
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if self.split != "whole":
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self.Y = torch.vstack([torch.Tensor(data) for data in self.lY.values()])
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self.langs = sum(
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[
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v
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for v in {
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lang: [lang] * len(data.input_ids) for lang, data in self.lX.items()
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}.values()
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],
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[],
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)
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return self
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def __len__(self):
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return len(self.X)
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def __getitem__(self, index):
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if self.split == "whole":
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return self.X[index], self.langs[index]
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return self.X[index], self.Y[index], self.langs[index]
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