Source code for transformers.modeling_mobilebert

# MIT License
#
# Copyright (c) 2020  The Google AI Language Team Authors, The HuggingFace Inc. team and github/lonePatient
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

import math
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple

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

from .activations import gelu, gelu_new, swish
from .configuration_mobilebert import MobileBertConfig
from .file_utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_callable,
    replace_return_docstrings,
)
from .modeling_bert import BertIntermediate
from .modeling_outputs import (
    BaseModelOutput,
    BaseModelOutputWithPooling,
    MaskedLMOutput,
    MultipleChoiceModelOutput,
    NextSentencePredictorOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from .modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
from .utils import logging


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "MobileBertConfig"
_TOKENIZER_FOR_DOC = "MobileBertTokenizer"

MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = ["google/mobilebert-uncased"]


def load_tf_weights_in_mobilebert(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):
        name = name.replace("ffn_layer", "ffn")
        name = name.replace("FakeLayerNorm", "LayerNorm")
        name = name.replace("extra_output_weights", "dense/kernel")
        name = name.replace("bert", "mobilebert")
        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", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
            for n 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
            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
        except AssertionError as e:
            e.args += (pointer.shape, array.shape)
            raise
        logger.info("Initialize PyTorch weight {}".format(name))
        pointer.data = torch.from_numpy(array)
    return model


def mish(x):
    return x * torch.tanh(nn.functional.softplus(x))


class NoNorm(nn.Module):
    def __init__(self, feat_size, eps=None):
        super().__init__()
        self.bias = nn.Parameter(torch.zeros(feat_size))
        self.weight = nn.Parameter(torch.ones(feat_size))

    def forward(self, input_tensor):
        return input_tensor * self.weight + self.bias


ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_new": gelu_new, "mish": mish}
NORM2FN = {"layer_norm": torch.nn.LayerNorm, "no_norm": NoNorm}


class MobileBertEmbeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, config):
        super().__init__()
        self.trigram_input = config.trigram_input
        self.embedding_size = config.embedding_size
        self.hidden_size = config.hidden_size

        self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)

        embed_dim_multiplier = 3 if self.trigram_input else 1
        embedded_input_size = self.embedding_size * embed_dim_multiplier
        self.embedding_transformation = nn.Linear(embedded_input_size, config.hidden_size)

        self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))

    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = self.position_ids[:, :seq_length]

        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        if self.trigram_input:
            # From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited
            # Devices (https://arxiv.org/abs/2004.02984)
            #
            # The embedding table in BERT models accounts for a substantial proportion of model size. To compress
            # the embedding layer, we reduce the embedding dimension to 128 in MobileBERT.
            # Then, we apply a 1D convolution with kernel size 3 on the raw token embedding to produce a 512
            # dimensional output.
            inputs_embeds = torch.cat(
                [
                    F.pad(inputs_embeds[:, 1:], [0, 0, 0, 1, 0, 0], value=0),
                    inputs_embeds,
                    F.pad(inputs_embeds[:, :-1], [0, 0, 1, 0, 0, 0], value=0),
                ],
                dim=2,
            )
        if self.trigram_input or self.embedding_size != self.hidden_size:
            inputs_embeds = self.embedding_transformation(inputs_embeds)

        # Add positional embeddings and token type embeddings, then layer
        # normalize and perform dropout.
        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class MobileBertSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.true_hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.true_hidden_size, self.all_head_size)
        self.key = nn.Linear(config.true_hidden_size, self.all_head_size)
        self.value = nn.Linear(
            config.true_hidden_size if config.use_bottleneck_attention else config.hidden_size, self.all_head_size
        )
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)

    def forward(
        self,
        query_tensor,
        key_tensor,
        value_tensor,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        output_attentions=None,
    ):
        mixed_query_layer = self.query(query_tensor)
        mixed_key_layer = self.key(key_tensor)
        mixed_value_layer = self.value(value_tensor)

        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()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs


class MobileBertSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.use_bottleneck = config.use_bottleneck
        self.dense = nn.Linear(config.true_hidden_size, config.true_hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)
        if not self.use_bottleneck:
            self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, residual_tensor):
        layer_outputs = self.dense(hidden_states)
        if not self.use_bottleneck:
            layer_outputs = self.dropout(layer_outputs)
        layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
        return layer_outputs


class MobileBertAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.self = MobileBertSelfAttention(config)
        self.output = MobileBertSelfOutput(config)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        if len(heads) == 0:
            return
        heads, index = find_pruneable_heads_and_indices(
            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
        )

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

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

    def forward(
        self,
        query_tensor,
        key_tensor,
        value_tensor,
        layer_input,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        output_attentions=None,
    ):
        self_outputs = self.self(
            query_tensor,
            key_tensor,
            value_tensor,
            attention_mask,
            head_mask,
            encoder_hidden_states,
            encoder_attention_mask,
            output_attentions,
        )
        # Run a linear projection of `hidden_size` then add a residual
        # with `layer_input`.
        attention_output = self.output(self_outputs[0], layer_input)
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


class MobileBertIntermediate(BertIntermediate):
    def __init__(self, config):
        super().__init__(config)
        self.dense = nn.Linear(config.true_hidden_size, config.intermediate_size)


class OutputBottleneck(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.true_hidden_size, config.hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, residual_tensor):
        layer_outputs = self.dense(hidden_states)
        layer_outputs = self.dropout(layer_outputs)
        layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
        return layer_outputs


class MobileBertOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.use_bottleneck = config.use_bottleneck
        self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size)
        if not self.use_bottleneck:
            self.dropout = nn.Dropout(config.hidden_dropout_prob)
        else:
            self.bottleneck = OutputBottleneck(config)

    def forward(self, intermediate_states, residual_tensor_1, residual_tensor_2):
        layer_output = self.dense(intermediate_states)
        if not self.use_bottleneck:
            layer_output = self.dropout(layer_output)
            layer_output = self.LayerNorm(layer_output + residual_tensor_1)
        else:
            layer_output = self.LayerNorm(layer_output + residual_tensor_1)
            layer_output = self.bottleneck(layer_output, residual_tensor_2)
        return layer_output


class BottleneckLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intra_bottleneck_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.intra_bottleneck_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states):
        layer_input = self.dense(hidden_states)
        layer_input = self.LayerNorm(layer_input)
        return layer_input


class Bottleneck(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.key_query_shared_bottleneck = config.key_query_shared_bottleneck
        self.use_bottleneck_attention = config.use_bottleneck_attention
        self.input = BottleneckLayer(config)
        if self.key_query_shared_bottleneck:
            self.attention = BottleneckLayer(config)

    def forward(self, hidden_states):
        # This method can return three different tuples of values. These different values make use of bottlenecks,
        # which are linear layers used to project the hidden states to a lower-dimensional vector, reducing memory
        # usage. These linear layer have weights that are learned during training.
        #
        # If `config.use_bottleneck_attention`, it will return the result of the bottleneck layer four times for the
        # key, query, value, and "layer input" to be used by the attention layer.
        # This bottleneck is used to project the hidden. This last layer input will be used as a residual tensor
        # in the attention self output, after the attention scores have been computed.
        #
        # If not `config.use_bottleneck_attention` and `config.key_query_shared_bottleneck`, this will return
        # four values, three of which have been passed through a bottleneck: the query and key, passed through the same
        # bottleneck, and the residual layer to be applied in the attention self output, through another bottleneck.
        #
        # Finally, in the last case, the values for the query, key and values are the hidden states without bottleneck,
        # and the residual layer will be this value passed through a bottleneck.

        bottlenecked_hidden_states = self.input(hidden_states)
        if self.use_bottleneck_attention:
            return (bottlenecked_hidden_states,) * 4
        elif self.key_query_shared_bottleneck:
            shared_attention_input = self.attention(hidden_states)
            return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states)
        else:
            return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states)


class FFNOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.true_hidden_size)
        self.LayerNorm = NORM2FN[config.normalization_type](config.true_hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states, residual_tensor):
        layer_outputs = self.dense(hidden_states)
        layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
        return layer_outputs


class FFNLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.intermediate = MobileBertIntermediate(config)
        self.output = FFNOutput(config)

    def forward(self, hidden_states):
        intermediate_output = self.intermediate(hidden_states)
        layer_outputs = self.output(intermediate_output, hidden_states)
        return layer_outputs


class MobileBertLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.use_bottleneck = config.use_bottleneck
        self.num_feedforward_networks = config.num_feedforward_networks

        self.attention = MobileBertAttention(config)
        self.intermediate = MobileBertIntermediate(config)
        self.output = MobileBertOutput(config)
        if self.use_bottleneck:
            self.bottleneck = Bottleneck(config)
        if config.num_feedforward_networks > 1:
            self.ffn = nn.ModuleList([FFNLayer(config) for _ in range(config.num_feedforward_networks - 1)])

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        output_attentions=None,
    ):
        if self.use_bottleneck:
            query_tensor, key_tensor, value_tensor, layer_input = self.bottleneck(hidden_states)
        else:
            query_tensor, key_tensor, value_tensor, layer_input = [hidden_states] * 4

        self_attention_outputs = self.attention(
            query_tensor,
            key_tensor,
            value_tensor,
            layer_input,
            attention_mask,
            head_mask,
            output_attentions=output_attentions,
        )
        attention_output = self_attention_outputs[0]
        s = (attention_output,)
        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights

        if self.num_feedforward_networks != 1:
            for i, ffn_module in enumerate(self.ffn):
                attention_output = ffn_module(attention_output)
                s += (attention_output,)

        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output, hidden_states)
        outputs = (
            (layer_output,)
            + outputs
            + (
                torch.tensor(1000),
                query_tensor,
                key_tensor,
                value_tensor,
                layer_input,
                attention_output,
                intermediate_output,
            )
            + s
        )
        return outputs


class MobileBertEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.layer = nn.ModuleList([MobileBertLayer(config) for _ in range(config.num_hidden_layers)])

    def forward(
        self,
        hidden_states,
        attention_mask=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        output_attentions=False,
        output_hidden_states=False,
        return_dict=False,
    ):
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_outputs = layer_module(
                hidden_states,
                attention_mask,
                head_mask[i],
                encoder_hidden_states,
                encoder_attention_mask,
                output_attentions,
            )
            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
        return BaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )


class MobileBertPooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.do_activate = config.classifier_activation
        if self.do_activate:
            self.dense = nn.Linear(config.hidden_size, config.hidden_size)

    def forward(self, hidden_states):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        if not self.do_activate:
            return first_token_tensor
        else:
            pooled_output = self.dense(first_token_tensor)
            pooled_output = torch.tanh(pooled_output)
            return pooled_output


class MobileBertPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str):
            self.transform_act_fn = ACT2FN[config.hidden_act]
        else:
            self.transform_act_fn = config.hidden_act
        self.LayerNorm = NORM2FN["layer_norm"](config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


class MobileBertLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = MobileBertPredictionHeadTransform(config)
        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.dense = nn.Linear(config.vocab_size, config.hidden_size - config.embedding_size, bias=False)
        self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=False)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        # 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.transform(hidden_states)
        hidden_states = hidden_states.matmul(torch.cat([self.decoder.weight.t(), self.dense.weight], dim=0))
        hidden_states += self.bias
        return hidden_states


class MobileBertOnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = MobileBertLMPredictionHead(config)

    def forward(self, sequence_output):
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores


class MobileBertPreTrainingHeads(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = MobileBertLMPredictionHead(config)
        self.seq_relationship = nn.Linear(config.hidden_size, 2)

    def forward(self, sequence_output, pooled_output):
        prediction_scores = self.predictions(sequence_output)
        seq_relationship_score = self.seq_relationship(pooled_output)
        return prediction_scores, seq_relationship_score


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

    config_class = MobileBertConfig
    pretrained_model_archive_map = MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST
    load_tf_weights = load_tf_weights_in_mobilebert
    base_model_prefix = "mobilebert"

    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)
        elif isinstance(module, (nn.LayerNorm, NoNorm)):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        if isinstance(module, nn.Linear) and module.bias is not None:
            module.bias.data.zero_()


[docs]@dataclass class MobileBertForPreTrainingOutput(ModelOutput): """ Output type of :class:`~transformers.MobileBertForPreTrainingModel`. Args: loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`): Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss. prediction_logits (: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). seq_relationship_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or 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 ``output_attentions=True`` is passed or 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. """ loss: Optional[torch.FloatTensor] = None prediction_logits: torch.FloatTensor = None seq_relationship_logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None
MOBILEBERT_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. Parameters: config (:class:`~transformers.MobileBertConfig`): 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. """ MOBILEBERT_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.MobileBertTokenizer`. See :func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.__call__` for details. `What are input IDs? <../glossary.html#input-ids>`__ attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): 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`): 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`): 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`): 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**. inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): 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. encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens. output_attentions (:obj:`bool`, `optional`): If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail. output_hidden_states (:obj:`bool`, `optional`): If set to ``True``, the hidden states of all layers are returned. See ``hidden_states`` under returned tensors for more detail. return_dict (:obj:`bool`, `optional`): If set to ``True``, the model will return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple. """
[docs]@add_start_docstrings( "The bare MobileBert Model transformer outputting raw hidden-states without any specific head on top.", MOBILEBERT_START_DOCSTRING, ) class MobileBertModel(MobileBertPreTrainedModel): """ https://arxiv.org/pdf/2004.02984.pdf """ authorized_missing_keys = [r"position_ids"] def __init__(self, config): super().__init__(config) self.config = config self.embeddings = MobileBertEmbeddings(config) self.encoder = MobileBertEncoder(config) self.pooler = MobileBertPooler(config) 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 _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} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads)
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="google/mobilebert-uncased", output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, output_hidden_states=None, output_attentions=None, return_dict=None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict 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) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( attention_mask, input_shape, self.device ) # If a 2D ou 3D attention mask is provided for the cross-attention # we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, )
[docs]@add_start_docstrings( """MobileBert Model with two heads on top as done during the pre-training: a `masked language modeling` head and a `next sentence prediction (classification)` head. """, MOBILEBERT_START_DOCSTRING, ) class MobileBertForPreTraining(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config) self.cls = MobileBertPreTrainingHeads(config) self.init_weights()
[docs] def get_output_embeddings(self): return self.cls.predictions.decoder
[docs] def tie_weights(self): """ Tie the weights between the input embeddings and the output embeddings. If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning the weights instead. """ output_embeddings = self.get_output_embeddings() input_embeddings = self.get_input_embeddings() resized_dense = nn.Linear( input_embeddings.num_embeddings, self.config.hidden_size - self.config.embedding_size, bias=False ) kept_data = self.cls.predictions.dense.weight.data[ ..., : min(self.cls.predictions.dense.weight.data.shape[1], resized_dense.weight.data.shape[1]) ] resized_dense.weight.data[..., : self.cls.predictions.dense.weight.data.shape[1]] = kept_data self.cls.predictions.dense = resized_dense self.cls.predictions.dense.to(self.device) if output_embeddings is not None and self.config.tie_word_embeddings: self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=MobileBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, next_sentence_label=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (``torch.LongTensor`` of shape ``(batch_size, sequence_length)``, `optional`): 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]`` next_sentence_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see :obj:`input_ids` docstring) Indices should be in ``[0, 1]``. ``0`` indicates sequence B is a continuation of sequence A, ``1`` indicates sequence B is a random sequence. Returns: Examples:: >>> from transformers import MobileBertTokenizer, MobileBertForPreTraining >>> import torch >>> tokenizer = MobileBertTokenizer.from_pretrained("google/mobilebert-uncased") >>> model = MobileBertForPreTraining.from_pretrained("google/mobilebert-uncased", return_dict=True) >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 >>> outputs = model(input_ids) >>> prediction_logits = outptus.prediction_logits >>> seq_relationship_logits = outputs.seq_relationship_logits """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output, pooled_output = outputs[:2] prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) total_loss = None if labels is not None and next_sentence_label is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) total_loss = masked_lm_loss + next_sentence_loss if not return_dict: output = (prediction_scores, seq_relationship_score) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return MobileBertForPreTrainingOutput( loss=total_loss, prediction_logits=prediction_scores, seq_relationship_logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[docs]@add_start_docstrings("""MobileBert Model with a `language modeling` head on top. """, MOBILEBERT_START_DOCSTRING) class MobileBertForMaskedLM(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config) self.cls = MobileBertOnlyMLMHead(config) self.config = config self.init_weights()
[docs] def get_output_embeddings(self): return self.cls.predictions.decoder
[docs] def tie_weights(self): """ Tie the weights between the input embeddings and the output embeddings. If the `torchscript` flag is set in the configuration, can't handle parameter sharing so we are cloning the weights instead. """ output_embeddings = self.get_output_embeddings() input_embeddings = self.get_input_embeddings() resized_dense = nn.Linear( input_embeddings.num_embeddings, self.config.hidden_size - self.config.embedding_size, bias=False ) kept_data = self.cls.predictions.dense.weight.data[ ..., : min(self.cls.predictions.dense.weight.data.shape[1], resized_dense.weight.data.shape[1]) ] resized_dense.weight.data[..., : self.cls.predictions.dense.weight.data.shape[1]] = kept_data self.cls.predictions.dense = resized_dense self.cls.predictions.dense.to(self.device) if output_embeddings is not None and self.config.tie_word_embeddings: self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="google/mobilebert-uncased", output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, encoder_hidden_states=None, encoder_attention_mask=None, output_attentions=None, output_hidden_states=None, return_dict=None, **kwargs ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): 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]`` kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`): Used to hide legacy arguments that have been deprecated. """ if "masked_lm_labels" in kwargs: warnings.warn( "The `masked_lm_labels` argument is deprecated and will be removed in a future version, use `labels` instead.", FutureWarning, ) labels = kwargs.pop("masked_lm_labels") return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
class MobileBertOnlyNSPHead(nn.Module): def __init__(self, config): super().__init__() self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, pooled_output): seq_relationship_score = self.seq_relationship(pooled_output) return seq_relationship_score
[docs]@add_start_docstrings( """MobileBert Model with a `next sentence prediction (classification)` head on top. """, MOBILEBERT_START_DOCSTRING, ) class MobileBertForNextSentencePrediction(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config) self.cls = MobileBertOnlyNSPHead(config) self.init_weights()
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")) @replace_return_docstrings(output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, next_sentence_label=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" next_sentence_label (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see ``input_ids`` docstring) Indices should be in ``[0, 1]``. ``0`` indicates sequence B is a continuation of sequence A, ``1`` indicates sequence B is a random sequence. Returns: Examples:: >>> from transformers import MobileBertTokenizer, MobileBertForNextSentencePrediction >>> import torch >>> tokenizer = MobileBertTokenizer.from_pretrained('google/mobilebert-uncased') >>> model = MobileBertForNextSentencePrediction.from_pretrained('google/mobilebert-uncased', return_dict=True) >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." >>> encoding = tokenizer(prompt, next_sentence, return_tensors='pt') >>> outputs = model(**encoding, next_sentence_label=torch.LongTensor([1])) >>> loss = outputs.loss >>> logits = outputs.logits """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] seq_relationship_score = self.cls(pooled_output) next_sentence_loss = None if next_sentence_label is not None: loss_fct = CrossEntropyLoss() next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) if not return_dict: output = (seq_relationship_score,) + outputs[2:] return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output return NextSentencePredictorOutput( loss=next_sentence_loss, logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[docs]@add_start_docstrings( """MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, MOBILEBERT_START_DOCSTRING, ) class MobileBertForSequenceClassification(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.mobilebert = MobileBertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, self.num_labels) self.init_weights()
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="google/mobilebert-uncased", output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ..., config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss), If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None 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)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[docs]@add_start_docstrings( """MobileBert 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`). """, MOBILEBERT_START_DOCSTRING, ) class MobileBertForQuestionAnswering(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.mobilebert = MobileBertModel(config) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) self.init_weights()
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="google/mobilebert-uncased", output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) 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, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): 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`): 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. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) 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) total_loss = None 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 if not return_dict: output = (start_logits, end_logits) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[docs]@add_start_docstrings( """MobileBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, MOBILEBERT_START_DOCSTRING, ) class MobileBertForMultipleChoice(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.mobilebert = MobileBertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, 1) self.init_weights()
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("(batch_size, num_choices, sequence_length)")) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="google/mobilebert-uncased", output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): Labels for computing the multiple choice classification loss. Indices should be in ``[0, ..., num_choices-1]`` where `num_choices` is the size of the second dimension of the input tensors. (see `input_ids` above) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[docs]@add_start_docstrings( """MoibleBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, MOBILEBERT_START_DOCSTRING, ) class MobileBertForTokenClassification(MobileBertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.mobilebert = MobileBertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.classifier = nn.Linear(config.hidden_size, config.num_labels) self.init_weights()
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("(batch_size, sequence_length)")) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="google/mobilebert-uncased", output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels - 1]``. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.mobilebert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() # Only keep active parts of the loss if attention_mask is not None: active_loss = attention_mask.view(-1) == 1 active_logits = logits.view(-1, self.num_labels) active_labels = torch.where( active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels) ) loss = loss_fct(active_logits, active_labels) else: loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )