Source code for textattack.models.wrappers.pytorch_model_wrapper

PyTorch Model Wrapper

import torch
from torch.nn import CrossEntropyLoss

import textattack

from .model_wrapper import ModelWrapper


[docs]class PyTorchModelWrapper(ModelWrapper): """Loads a PyTorch model (`nn.Module`) and tokenizer. Args: model (torch.nn.Module): PyTorch model tokenizer: tokenizer whose output can be packed as a tensor and passed to the model. No type requirement, but most have `tokenizer` method that accepts list of strings. """ def __init__(self, model, tokenizer): if not isinstance(model, torch.nn.Module): raise TypeError( f"PyTorch model must be torch.nn.Module, got type {type(model)}" ) self.model = model self.tokenizer = tokenizer
[docs] def to(self, device):
def __call__(self, text_input_list, batch_size=32): model_device = next(self.model.parameters()).device ids = self.tokenizer(text_input_list) ids = torch.tensor(ids).to(model_device) with torch.no_grad(): outputs = textattack.shared.utils.batch_model_predict( self.model, ids, batch_size=batch_size ) return outputs
[docs] def get_grad(self, text_input, loss_fn=CrossEntropyLoss()): """Get gradient of loss with respect to input tokens. Args: text_input (str): input string loss_fn (torch.nn.Module): loss function. Default is `torch.nn.CrossEntropyLoss` Returns: Dict of ids, tokens, and gradient as numpy array. """ if not hasattr(self.model, "get_input_embeddings"): raise AttributeError( f"{type(self.model)} must have method `get_input_embeddings` that returns `torch.nn.Embedding` object that represents input embedding layer" ) if not isinstance(loss_fn, torch.nn.Module): raise ValueError("Loss function must be of type `torch.nn.Module`.") self.model.train() embedding_layer = self.model.get_input_embeddings() original_state = embedding_layer.weight.requires_grad embedding_layer.weight.requires_grad = True emb_grads = [] def grad_hook(module, grad_in, grad_out): emb_grads.append(grad_out[0]) emb_hook = embedding_layer.register_backward_hook(grad_hook) self.model.zero_grad() model_device = next(self.model.parameters()).device ids = self.tokenizer([text_input]) ids = torch.tensor(ids).to(model_device) predictions = self.model(ids) output = predictions.argmax(dim=1) loss = loss_fn(predictions, output) loss.backward() # grad w.r.t to word embeddings # Fix for Issue #601 # Check if gradient has shape [max_sequence,1,_] ( when model input in transpose of input sequence) if emb_grads[0].shape[1] == 1: grad = torch.transpose(emb_grads[0], 0, 1)[0].cpu().numpy() else: # gradient has shape [1,max_sequence,_] grad = emb_grads[0][0].cpu().numpy() embedding_layer.weight.requires_grad = original_state emb_hook.remove() self.model.eval() output = {"ids": ids[0].tolist(), "gradient": grad} return output
def _tokenize(self, inputs): """Helper method that for `tokenize` Args: inputs (list[str]): list of input strings Returns: tokens (list[list[str]]): List of list of tokens as strings """ return [self.tokenizer.convert_ids_to_tokens(self.tokenizer(x)) for x in inputs]