model.py

import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn import init
from transformers import AutoModel
from torch.optim import Optimizer



class ClassModel(nn.Module):
    def __init__(self, encoder_name, enc_dim, class_embeddings):
        super(ClassModel, self).__init__()

        self.doc_encoder = AutoModel.from_pretrained(encoder_name)
        self.doc_dim = enc_dim

        self.num_classes, self.label_dim = class_embeddings.size()
        self.label_embedding_weights=nn.Parameter(class_embeddings, requires_grad=True)

        self.interaction = LBM(self.doc_dim, self.label_dim, n_classes=self.num_classes, bias=False)

    def forward(self, input_ids, attention_mask):
        doc_tensor = self.doc_encoder(input_ids, attention_mask=attention_mask)[1]
        scores = self.interaction(doc_tensor, self.label_embedding_weights)
        return scores

    def __str__(self):
        """
        Model prints with number of trainable parameters
        """
        model_parameters = filter(lambda p: p.requires_grad, self.parameters())
        params = sum([np.prod(p.size()) for p in model_parameters])
        return super().__str__() + '\nTrainable parameters: {}'.format(params)


""" Interaction Layers
"""
class LBM(nn.Module):
    def __init__(self, l_dim, r_dim, n_classes=None, bias=True):
        super(LBM, self).__init__()
        self.weight = Parameter(torch.Tensor(l_dim, r_dim))
        self.use_bias = bias
        if self.use_bias:
            self.bias = Parameter(torch.Tensor(n_classes))

        bound = 1.0 / math.sqrt(l_dim)
        init.uniform_(self.weight, -bound, bound)
        if self.use_bias:
            init.uniform_(self.bias, -bound, bound)

    def forward(self, e1, e2):
        """
        e1: tensor of size (batch_size, l_dim)
        e2: tensor of size (n_classes, r_dim)
        return: tensor of size (batch_size, n_classes)
        """
        scores = torch.matmul(torch.matmul(e1, self.weight), e2.T)
        if self.use_bias:
            scores = scores + self.bias
        return scores

def multilabel_bce_loss_w(output, target, weight=None):
    """
    output: a (batch_size, num_classes) logit tensor
    target: a (batch_size, num_classes) multi-hot target tensor
    weight: a (batch_size, num_classes) weights tensor
    """
    loss = F.binary_cross_entropy_with_logits(output, target.type_as(output), weight.type_as(output), reduction="sum")
    return loss


class AdamW(Optimizer):
    """ Implements Adam algorithm with weight decay fix.

    Parameters:
        lr (float): learning rate. Default 1e-3.
        betas (tuple of 2 floats): Adams beta parameters (b1, b2). Default: (0.9, 0.999)
        eps (float): Adams epsilon. Default: 1e-6
        weight_decay (float): Weight decay. Default: 0.0
        correct_bias (bool): can be set to False to avoid correcting bias in Adam (e.g. like in Bert TF repository). Default True.
    """

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6, weight_decay=0.0, correct_bias=True):
        if lr < 0.0:
            raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[0]))
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[1]))
        if not 0.0 <= eps:
            raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(eps))
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, correct_bias=correct_bias)
        super().__init__(params, defaults)

    def step(self, closure=None):
        """Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group["params"]:
                if p.grad is None:
                    continue
                grad = p.grad.data
                if grad.is_sparse:
                    raise RuntimeError("Adam does not support sparse gradients, please consider SparseAdam instead")

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state["step"] = 0
                    # Exponential moving average of gradient values
                    state["exp_avg"] = torch.zeros_like(p.data)
                    # Exponential moving average of squared gradient values
                    state["exp_avg_sq"] = torch.zeros_like(p.data)

                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
                beta1, beta2 = group["betas"]

                state["step"] += 1

                # Decay the first and second moment running average coefficient
                # In-place operations to update the averages at the same time
                exp_avg.mul_(beta1).add_(1.0 - beta1, grad)
                exp_avg_sq.mul_(beta2).addcmul_(1.0 - beta2, grad, grad)
                denom = exp_avg_sq.sqrt().add_(group["eps"])

                step_size = group["lr"]
                if group["correct_bias"]:  # No bias correction for Bert
                    bias_correction1 = 1.0 - beta1 ** state["step"]
                    bias_correction2 = 1.0 - beta2 ** state["step"]
                    step_size = step_size * math.sqrt(bias_correction2) / bias_correction1

                p.data.addcdiv_(-step_size, exp_avg, denom)

                # Just adding the square of the weights to the loss function is *not*
                # the correct way of using L2 regularization/weight decay with Adam,
                # since that will interact with the m and v parameters in strange ways.
                #
                # Instead we want to decay the weights in a manner that doesn't interact
                # with the m/v parameters. This is equivalent to adding the square
                # of the weights to the loss with plain (non-momentum) SGD.
                # Add weight decay at the end (fixed version)
                if group["weight_decay"] > 0.0:
                    p.data.add_(-group["lr"] * group["weight_decay"], p.data)

        return loss