util_funct.py

import os, numpy as np, torch, math

def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
    """ Load tf checkpoints in a pytorch model.
    """
    import re
    import tensorflow as tf
    tf_path = os.path.abspath(tf_checkpoint_path)
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []
    for name, shape in init_vars:
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array)

    for name, array in zip(names, arrays):
        name = name.split('/')
        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
        # which are not required for using pretrained model
        if any(n in ["adam_v", "adam_m", "global_step"] for n in name):
            continue
        pointer = model
        for m_name in name:
            if re.fullmatch(r'[A-Za-z]+_\d+', m_name):
                l = re.split(r'_(\d+)', m_name)
            else:
                l = [m_name]
            if l[0] == 'kernel' or l[0] == 'gamma':
                pointer = getattr(pointer, 'weight')
            elif l[0] == 'output_bias' or l[0] == 'beta':
                pointer = getattr(pointer, 'bias')
            elif l[0] == 'output_weights':
                pointer = getattr(pointer, 'weight')
            elif l[0] == 'squad':
                pointer = getattr(pointer, 'classifier')
            else:
                try:
                    pointer = getattr(pointer, l[0])
                except AttributeError:
                    continue
            if len(l) >= 2:
                num = int(l[1])
                pointer = pointer[num]
        if m_name[-11:] == '_embeddings':
            pointer = getattr(pointer, 'weight')
        elif m_name == 'kernel':
            array = np.transpose(array)
        try:
            assert pointer.shape == array.shape
        except AssertionError as e:
            e.args += (pointer.shape, array.shape)
            raise
        pointer.data = torch.from_numpy(array)
    return model

def load_tf_weights_in_xlnet(model, config, tf_path):
    """ Load tf checkpoints in a pytorch model
    """
    import numpy as np
    import tensorflow as tf
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    tf_weights = {}
    for name, shape in init_vars:
        array = tf.train.load_variable(tf_path, name)
        tf_weights[name] = array

    # Build TF to PyTorch weights loading map
    tf_to_pt_map = build_tf_xlnet_to_pytorch_map(model, config, tf_weights)

    for name, pointer in tf_to_pt_map.items():
        if name not in tf_weights:
            continue
        array = tf_weights[name]
        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
        # which are not required for using pretrained model
        if 'kernel' in name and ('ff' in name or 'summary' in name or 'logit' in name):
            array = np.transpose(array)
        if isinstance(pointer, list):
            # Here we will split the TF weigths
            assert len(pointer) == array.shape[0]
            for i, p_i in enumerate(pointer):
                arr_i = array[i, ...]
                try:
                    assert p_i.shape == arr_i.shape
                except AssertionError as e:
                    e.args += (p_i.shape, arr_i.shape)
                    raise
                p_i.data = torch.from_numpy(arr_i)
        else:
            try:
                assert pointer.shape == array.shape
            except AssertionError as e:
                e.args += (pointer.shape, array.shape)
                raise
            pointer.data = torch.from_numpy(array)
        tf_weights.pop(name, None)
        tf_weights.pop(name + '/Adam', None)
        tf_weights.pop(name + '/Adam_1', None)

    return model

def gelu(x):
    """ Original Implementation of the gelu activation function in Google Bert repo when initially created.
        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
        Also see https://arxiv.org/abs/1606.08415
    """
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))

def gelu_new(x):
    """ Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT).
        Also see https://arxiv.org/abs/1606.08415
    """
    return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))

def swish(x):
    return x * torch.sigmoid(x)

ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new}