Source code for transformers.modeling_albert

# coding=utf-8
# Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""PyTorch ALBERT model. """

import logging
import math
import os

import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.configuration_albert import AlbertConfig
from transformers.modeling_bert import ACT2FN, BertEmbeddings, BertSelfAttention, prune_linear_layer
from transformers.modeling_utils import PreTrainedModel

from .file_utils import add_start_docstrings, add_start_docstrings_to_callable


logger = logging.getLogger(__name__)


ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
    "albert-base-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-pytorch_model.bin",
    "albert-large-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-pytorch_model.bin",
    "albert-xlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-pytorch_model.bin",
    "albert-xxlarge-v1": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-pytorch_model.bin",
    "albert-base-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-base-v2-pytorch_model.bin",
    "albert-large-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-large-v2-pytorch_model.bin",
    "albert-xlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xlarge-v2-pytorch_model.bin",
    "albert-xxlarge-v2": "https://s3.amazonaws.com/models.huggingface.co/bert/albert-xxlarge-v2-pytorch_model.bin",
}


def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
    """ Load tf checkpoints in a pytorch model."""
    try:
        import re
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error(
            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions."
        )
        raise
    tf_path = os.path.abspath(tf_checkpoint_path)
    logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []
    for name, shape in init_vars:
        logger.info("Loading TF weight {} with shape {}".format(name, shape))
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array)

    for name, array in zip(names, arrays):
        print(name)

    for name, array in zip(names, arrays):
        original_name = name

        # If saved from the TF HUB module
        name = name.replace("module/", "")

        # Renaming and simplifying
        name = name.replace("ffn_1", "ffn")
        name = name.replace("bert/", "albert/")
        name = name.replace("attention_1", "attention")
        name = name.replace("transform/", "")
        name = name.replace("LayerNorm_1", "full_layer_layer_norm")
        name = name.replace("LayerNorm", "attention/LayerNorm")
        name = name.replace("transformer/", "")

        # The feed forward layer had an 'intermediate' step which has been abstracted away
        name = name.replace("intermediate/dense/", "")
        name = name.replace("ffn/intermediate/output/dense/", "ffn_output/")

        # ALBERT attention was split between self and output which have been abstracted away
        name = name.replace("/output/", "/")
        name = name.replace("/self/", "/")

        # The pooler is a linear layer
        name = name.replace("pooler/dense", "pooler")

        # The classifier was simplified to predictions from cls/predictions
        name = name.replace("cls/predictions", "predictions")
        name = name.replace("predictions/attention", "predictions")

        # Naming was changed to be more explicit
        name = name.replace("embeddings/attention", "embeddings")
        name = name.replace("inner_group_", "albert_layers/")
        name = name.replace("group_", "albert_layer_groups/")

        # Classifier
        if len(name.split("/")) == 1 and ("output_bias" in name or "output_weights" in name):
            name = "classifier/" + name

        # No ALBERT model currently handles the next sentence prediction task
        if "seq_relationship" in name:
            continue

        name = name.split("/")

        # Ignore the gradients applied by the LAMB/ADAM optimizers.
        if (
            "adam_m" in name
            or "adam_v" in name
            or "AdamWeightDecayOptimizer" in name
            or "AdamWeightDecayOptimizer_1" in name
            or "global_step" in name
        ):
            logger.info("Skipping {}".format("/".join(name)))
            continue

        pointer = model
        for m_name in name:
            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
                scope_names = re.split(r"_(\d+)", m_name)
            else:
                scope_names = [m_name]

            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
                pointer = getattr(pointer, "bias")
            elif scope_names[0] == "output_weights":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "squad":
                pointer = getattr(pointer, "classifier")
            else:
                try:
                    pointer = getattr(pointer, scope_names[0])
                except AttributeError:
                    logger.info("Skipping {}".format("/".join(name)))
                    continue
            if len(scope_names) >= 2:
                num = int(scope_names[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
        print("Initialize PyTorch weight {} from {}".format(name, original_name))
        pointer.data = torch.from_numpy(array)

    return model


class AlbertEmbeddings(BertEmbeddings):
    """
    Construct the embeddings from word, position and token_type embeddings.
    """

    def __init__(self, config):
        super().__init__(config)

        self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=0)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
        self.LayerNorm = torch.nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps)


class AlbertAttention(BertSelfAttention):
    def __init__(self, config):
        super().__init__(config)

        self.output_attentions = config.output_attentions
        self.num_attention_heads = config.num_attention_heads
        self.hidden_size = config.hidden_size
        self.attention_head_size = config.hidden_size // config.num_attention_heads
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        mask = torch.ones(self.num_attention_heads, self.attention_head_size)
        heads = set(heads) - self.pruned_heads  # Convert to set and emove already pruned heads
        for head in heads:
            # Compute how many pruned heads are before the head and move the index accordingly
            head = head - sum(1 if h < head else 0 for h in self.pruned_heads)
            mask[head] = 0
        mask = mask.view(-1).contiguous().eq(1)
        index = torch.arange(len(mask))[mask].long()

        # Prune linear layers
        self.query = prune_linear_layer(self.query, index)
        self.key = prune_linear_layer(self.key, index)
        self.value = prune_linear_layer(self.value, index)
        self.dense = prune_linear_layer(self.dense, index, dim=1)

        # Update hyper params and store pruned heads
        self.num_attention_heads = self.num_attention_heads - len(heads)
        self.all_head_size = self.attention_head_size * self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

    def forward(self, input_ids, attention_mask=None, head_mask=None):
        mixed_query_layer = self.query(input_ids)
        mixed_key_layer = self.key(input_ids)
        mixed_value_layer = self.value(input_ids)

        query_layer = self.transpose_for_scores(mixed_query_layer)
        key_layer = self.transpose_for_scores(mixed_key_layer)
        value_layer = self.transpose_for_scores(mixed_value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
            attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.Softmax(dim=-1)(attention_scores)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(attention_probs)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = attention_probs * head_mask

        context_layer = torch.matmul(attention_probs, value_layer)

        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()

        # Should find a better way to do this
        w = (
            self.dense.weight.t()
            .view(self.num_attention_heads, self.attention_head_size, self.hidden_size)
            .to(context_layer.dtype)
        )
        b = self.dense.bias.to(context_layer.dtype)

        projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b
        projected_context_layer_dropout = self.dropout(projected_context_layer)
        layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer_dropout)
        return (layernormed_context_layer, attention_probs) if self.output_attentions else (layernormed_context_layer,)


class AlbertLayer(nn.Module):
    def __init__(self, config):
        super().__init__()

        self.config = config
        self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attention = AlbertAttention(config)
        self.ffn = nn.Linear(config.hidden_size, config.intermediate_size)
        self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size)
        self.activation = ACT2FN[config.hidden_act]

    def forward(self, hidden_states, attention_mask=None, head_mask=None):
        attention_output = self.attention(hidden_states, attention_mask, head_mask)
        ffn_output = self.ffn(attention_output[0])
        ffn_output = self.activation(ffn_output)
        ffn_output = self.ffn_output(ffn_output)
        hidden_states = self.full_layer_layer_norm(ffn_output + attention_output[0])

        return (hidden_states,) + attention_output[1:]  # add attentions if we output them


class AlbertLayerGroup(nn.Module):
    def __init__(self, config):
        super().__init__()

        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.albert_layers = nn.ModuleList([AlbertLayer(config) for _ in range(config.inner_group_num)])

    def forward(self, hidden_states, attention_mask=None, head_mask=None):
        layer_hidden_states = ()
        layer_attentions = ()

        for layer_index, albert_layer in enumerate(self.albert_layers):
            layer_output = albert_layer(hidden_states, attention_mask, head_mask[layer_index])
            hidden_states = layer_output[0]

            if self.output_attentions:
                layer_attentions = layer_attentions + (layer_output[1],)

            if self.output_hidden_states:
                layer_hidden_states = layer_hidden_states + (hidden_states,)

        outputs = (hidden_states,)
        if self.output_hidden_states:
            outputs = outputs + (layer_hidden_states,)
        if self.output_attentions:
            outputs = outputs + (layer_attentions,)
        return outputs  # last-layer hidden state, (layer hidden states), (layer attentions)


class AlbertTransformer(nn.Module):
    def __init__(self, config):
        super().__init__()

        self.config = config
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)
        self.albert_layer_groups = nn.ModuleList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)])

    def forward(self, hidden_states, attention_mask=None, head_mask=None):
        hidden_states = self.embedding_hidden_mapping_in(hidden_states)

        all_attentions = ()

        if self.output_hidden_states:
            all_hidden_states = (hidden_states,)

        for i in range(self.config.num_hidden_layers):
            # Number of layers in a hidden group
            layers_per_group = int(self.config.num_hidden_layers / self.config.num_hidden_groups)

            # Index of the hidden group
            group_idx = int(i / (self.config.num_hidden_layers / self.config.num_hidden_groups))

            layer_group_output = self.albert_layer_groups[group_idx](
                hidden_states,
                attention_mask,
                head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group],
            )
            hidden_states = layer_group_output[0]

            if self.output_attentions:
                all_attentions = all_attentions + layer_group_output[-1]

            if self.output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

        outputs = (hidden_states,)
        if self.output_hidden_states:
            outputs = outputs + (all_hidden_states,)
        if self.output_attentions:
            outputs = outputs + (all_attentions,)
        return outputs  # last-layer hidden state, (all hidden states), (all attentions)


class AlbertPreTrainedModel(PreTrainedModel):
    """ An abstract class to handle weights initialization and
        a simple interface for downloading and loading pretrained models.
    """

    config_class = AlbertConfig
    pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
    base_model_prefix = "albert"

    def _init_weights(self, module):
        """ Initialize the weights.
        """
        if isinstance(module, (nn.Linear, nn.Embedding)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if isinstance(module, (nn.Linear)) and module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


ALBERT_START_DOCSTRING = r"""

    This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
    usage and behavior.

    Args:
        config (:class:`~transformers.AlbertConfig`): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the configuration.
            Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""

ALBERT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`transformers.AlbertTokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.encode_plus` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.

            `What are attention masks? <../glossary.html#attention-mask>`__
        token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Segment token indices to indicate first and second portions of the inputs.
            Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
            corresponds to a `sentence B` token

            `What are token type IDs? <../glossary.html#token-type-ids>`_
        position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
            Indices of positions of each input sequence tokens in the position embeddings.
            Selected in the range ``[0, config.max_position_embeddings - 1]``.

            `What are position IDs? <../glossary.html#position-ids>`_
        head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            :obj:`1` indicates the head is **not masked**, :obj:`0` indicates the head is **masked**.
        input_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
            Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
            This is useful if you want more control over how to convert `input_ids` indices into associated vectors
            than the model's internal embedding lookup matrix.
"""


[docs]@add_start_docstrings( "The bare ALBERT Model transformer outputting raw hidden-states without any specific head on top.", ALBERT_START_DOCSTRING, ) class AlbertModel(AlbertPreTrainedModel): config_class = AlbertConfig pretrained_model_archive_map = ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP load_tf_weights = load_tf_weights_in_albert base_model_prefix = "albert" def __init__(self, config): super().__init__(config) self.config = config self.embeddings = AlbertEmbeddings(config) self.encoder = AlbertTransformer(config) self.pooler = nn.Linear(config.hidden_size, config.hidden_size) self.pooler_activation = nn.Tanh() self.init_weights()
[docs] def get_input_embeddings(self): return self.embeddings.word_embeddings
[docs] def set_input_embeddings(self, value): self.embeddings.word_embeddings = value
def _resize_token_embeddings(self, new_num_tokens): old_embeddings = self.embeddings.word_embeddings new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens) self.embeddings.word_embeddings = new_embeddings return self.embeddings.word_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} ALBERT has a different architecture in that its layers are shared across groups, which then has inner groups. If an ALBERT model has 12 hidden layers and 2 hidden groups, with two inner groups, there is a total of 4 different layers. These layers are flattened: the indices [0,1] correspond to the two inner groups of the first hidden layer, while [2,3] correspond to the two inner groups of the second hidden layer. Any layer with in index other than [0,1,2,3] will result in an error. See base class PreTrainedModel for more information about head pruning """ for layer, heads in heads_to_prune.items(): group_idx = int(layer / self.config.inner_group_num) inner_group_idx = int(layer - group_idx * self.config.inner_group_num) self.encoder.albert_layer_groups[group_idx].albert_layers[inner_group_idx].attention.prune_heads(heads)
[docs] @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, ): r""" Return: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs: last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pre-training. This output is usually *not* a good summary of the semantic content of the input, you're often better with averaging or pooling the sequence of hidden-states for the whole input sequence. hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Example:: from transformers import AlbertModel, AlbertTokenizer import torch tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') model = AlbertModel.from_pretrained('albert-base-v2') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 outputs = model(input_ids) last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple """ if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") device = input_ids.device if input_ids is not None else inputs_embeds.device if attention_mask is None: attention_mask = torch.ones(input_shape, device=device) if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) extended_attention_mask = extended_attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 if head_mask is not None: if head_mask.dim() == 1: head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1) elif head_mask.dim() == 2: head_mask = ( head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) ) # We can specify head_mask for each layer head_mask = head_mask.to( dtype=next(self.parameters()).dtype ) # switch to fload if need + fp16 compatibility else: head_mask = [None] * self.config.num_hidden_layers embedding_output = self.embeddings( input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds ) encoder_outputs = self.encoder(embedding_output, extended_attention_mask, head_mask=head_mask) sequence_output = encoder_outputs[0] pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0])) outputs = (sequence_output, pooled_output) + encoder_outputs[ 1: ] # add hidden_states and attentions if they are here return outputs
class AlbertMLMHead(nn.Module): def __init__(self, config): super().__init__() self.LayerNorm = nn.LayerNorm(config.embedding_size) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) self.dense = nn.Linear(config.hidden_size, config.embedding_size) self.decoder = nn.Linear(config.embedding_size, config.vocab_size) self.activation = ACT2FN[config.hidden_act] # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.activation(hidden_states) hidden_states = self.LayerNorm(hidden_states) hidden_states = self.decoder(hidden_states) prediction_scores = hidden_states + self.bias return prediction_scores
[docs]@add_start_docstrings( "Albert Model with a `language modeling` head on top.", ALBERT_START_DOCSTRING, ) class AlbertForMaskedLM(AlbertPreTrainedModel): def __init__(self, config): super().__init__(config) self.albert = AlbertModel(config) self.predictions = AlbertMLMHead(config) self.init_weights() self.tie_weights()
[docs] def tie_weights(self): self._tie_or_clone_weights(self.predictions.decoder, self.albert.embeddings.word_embeddings)
[docs] def get_output_embeddings(self): return self.predictions.decoder
[docs] @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, masked_lm_labels=None, ): r""" masked_lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]`` Returns: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs: loss (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Masked language modeling loss. prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`) Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Example:: from transformers import AlbertTokenizer, AlbertForMaskedLM import torch tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') model = AlbertForMaskedLM.from_pretrained('albert-base-v2') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 outputs = model(input_ids, masked_lm_labels=input_ids) loss, prediction_scores = outputs[:2] """ outputs = self.albert( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, ) sequence_outputs = outputs[0] prediction_scores = self.predictions(sequence_outputs) outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here if masked_lm_labels is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), masked_lm_labels.view(-1)) outputs = (masked_lm_loss,) + outputs return outputs
[docs]@add_start_docstrings( """Albert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, ALBERT_START_DOCSTRING, ) class AlbertForSequenceClassification(AlbertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.albert = AlbertModel(config) self.dropout = nn.Dropout(config.classifier_dropout_prob) self.classifier = nn.Linear(config.hidden_size, self.config.num_labels) self.init_weights()
[docs] @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): Labels for computing the sequence classification/regression loss. Indices should be in ``[0, ..., config.num_labels - 1]``. If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss), If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy). Returns: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs: loss: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Classification (or regression if config.num_labels==1) loss. logits ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)`` Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: from transformers import AlbertTokenizer, AlbertForSequenceClassification import torch tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') model = AlbertForSequenceClassification.from_pretrained('albert-base-v2') input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1 labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=labels) loss, logits = outputs[:2] """ outputs = self.albert( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here if labels is not None: if self.num_labels == 1: # We are doing regression loss_fct = MSELoss() loss = loss_fct(logits.view(-1), labels.view(-1)) else: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) outputs = (loss,) + outputs return outputs # (loss), logits, (hidden_states), (attentions)
[docs]@add_start_docstrings( """Albert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). """, ALBERT_START_DOCSTRING, ) class AlbertForQuestionAnswering(AlbertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.albert = AlbertModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) self.init_weights()
[docs] @add_start_docstrings_to_callable(ALBERT_INPUTS_DOCSTRING) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, start_positions=None, end_positions=None, ): r""" start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`, defaults to :obj:`None`): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. Returns: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.AlbertConfig`) and inputs: loss: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``: Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_scores ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)`` Span-start scores (before SoftMax). end_scores: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)`` Span-end scores (before SoftMax). hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: # The checkpoint albert-base-v2 is not fine-tuned for question answering. Please see the # examples/run_squad.py example to see how to fine-tune a model to a question answering task. from transformers import AlbertTokenizer, AlbertForQuestionAnswering import torch tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2') model = AlbertForQuestionAnswering.from_pretrained('albert-base-v2') question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" input_dict = tokenizer.encode_plus(question, text, return_tensors='pt') start_scores, end_scores = model(**input_dict) """ outputs = self.albert( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = logits.split(1, dim=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) outputs = (start_logits, end_logits,) + outputs[2:] if start_positions is not None and end_positions is not None: # If we are on multi-GPU, split add a dimension if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1) if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1) # sometimes the start/end positions are outside our model inputs, we ignore these terms ignored_index = start_logits.size(1) start_positions.clamp_(0, ignored_index) end_positions.clamp_(0, ignored_index) loss_fct = CrossEntropyLoss(ignore_index=ignored_index) start_loss = loss_fct(start_logits, start_positions) end_loss = loss_fct(end_logits, end_positions) total_loss = (start_loss + end_loss) / 2 outputs = (total_loss,) + outputs return outputs # (loss), start_logits, end_logits, (hidden_states), (attentions)