models/grit_src/grit/modeling/text/modeling_bert.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch BERT model. """
# Adapted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py

from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import os
import json
import logging
import math
import sys
from io import open
import torch
from torch import nn
import torch.utils.checkpoint as checkpoint
from .file_utils import cached_path


logger = logging.getLogger()


BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json",
    'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-config.json",
    'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-config.json",
    'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-config.json",
    'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-config.json",
    'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-config.json",
    'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-config.json",
    'bert-base-german-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-cased-config.json",
    'bert-large-uncased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-config.json",
    'bert-large-cased-whole-word-masking': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-config.json",
    'bert-large-uncased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-whole-word-masking-finetuned-squad-config.json",
    'bert-large-cased-whole-word-masking-finetuned-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-whole-word-masking-finetuned-squad-config.json",
    'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-config.json",
}


def qk2attn(query, key, attention_mask, gamma):
    query = query / gamma
    attention_scores = torch.matmul(query, key.transpose(-1, -2))
    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
    return attention_scores.softmax(dim=-1)


class QK2Attention(nn.Module):
    def forward(self, query, key, attention_mask, gamma):
        return qk2attn(query, key, attention_mask, gamma)


LayerNormClass = torch.nn.LayerNorm


class BertSelfAttention(nn.Module):
    def __init__(self, config):
        super(BertSelfAttention, self).__init__()
        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                "The hidden size (%d) is not a multiple of the number of attention "
                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
        self.output_attentions = config.output_attentions

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.softmax = nn.Softmax(dim=-1)
        self.qk2attn = QK2Attention()

    def transpose_for_scores(self, x):
        if torch._C._get_tracing_state():
            # exporter is not smart enough to detect dynamic size for some paths
            x = x.view(x.shape[0], -1, self.num_attention_heads, self.attention_head_size)
        else:
            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, hidden_states, attention_mask, head_mask=None,
            history_state=None):
        if history_state is not None:
            x_states = torch.cat([history_state, hidden_states], dim=1)
            mixed_query_layer = self.query(hidden_states)
            mixed_key_layer = self.key(x_states)
            mixed_value_layer = self.value(x_states)
        else:
            mixed_query_layer = self.query(hidden_states)
            mixed_key_layer = self.key(hidden_states)
            mixed_value_layer = self.value(hidden_states)

        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)

        attention_probs = self.qk2attn(query_layer, key_layer, attention_mask, math.sqrt(self.attention_head_size))

        # 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 self.output_attentions else (context_layer,)
        return outputs


class BertSelfOutput(nn.Module):
    def __init__(self, config):
        super(BertSelfOutput, self).__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
        if not self.pre_norm:
            self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        if not self.pre_norm:
            hidden_states = self.LayerNorm(hidden_states + input_tensor)
        else:
            hidden_states = hidden_states + input_tensor
        return hidden_states


class BertAttention(nn.Module):
    def __init__(self, config):
        super(BertAttention, self).__init__()
        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
        if self.pre_norm:
            self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)
        self.self = BertSelfAttention(config)
        self.output = BertSelfOutput(config)

    def forward(self, input_tensor, attention_mask, head_mask=None,
            history_state=None):
        if self.pre_norm:
            self_outputs = self.self(self.LayerNorm(input_tensor), attention_mask, head_mask,
                    self.layerNorm(history_state) if history_state else history_state)
        else:
            self_outputs = self.self(input_tensor, attention_mask, head_mask,
                    history_state)
        attention_output = self.output(self_outputs[0], input_tensor)
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs


class BertIntermediate(nn.Module):
    def __init__(self, config):
        super(BertIntermediate, self).__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        assert config.hidden_act == 'gelu', 'Please implement other activation functions'
        self.intermediate_act_fn = _gelu_python

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


class BertOutput(nn.Module):
    def __init__(self, config):
        super(BertOutput, self).__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        if not self.pre_norm:
            self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        if not self.pre_norm:
            hidden_states = self.LayerNorm(hidden_states + input_tensor)
        else:
            hidden_states = hidden_states + input_tensor
        return hidden_states


class Mlp(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
        self.intermediate = BertIntermediate(config)
        if self.pre_norm:
            self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)
        self.output = BertOutput(config)

    def forward(self, attention_output):
        if not self.pre_norm:
            intermediate_output = self.intermediate(attention_output)
        else:
            intermediate_output = self.intermediate(self.LayerNorm(attention_output))
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output


class BertLayer(nn.Module):
    def __init__(self, config, use_act_checkpoint=True):
        super(BertLayer, self).__init__()
        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
        self.use_mlp_wrapper = hasattr(config, 'use_mlp_wrapper') and config.use_mlp_wrapper
        self.attention = BertAttention(config)
        self.use_act_checkpoint = use_act_checkpoint
        if self.use_mlp_wrapper:
            self.mlp = Mlp(config)
        else:
            self.intermediate = BertIntermediate(config)
            if self.pre_norm:
                self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)
            self.output = BertOutput(config)

    def forward(self, hidden_states, attention_mask, head_mask=None,
                history_state=None):
        if self.use_act_checkpoint:
            attention_outputs = checkpoint.checkpoint(self.attention, hidden_states,
                                                      attention_mask, head_mask, history_state)
        else:
            attention_outputs = self.attention(hidden_states, attention_mask,
                                               head_mask, history_state)
        attention_output = attention_outputs[0]
        if self.use_mlp_wrapper:
            layer_output = self.mlp(attention_output)
        else:
            if not self.pre_norm:
                intermediate_output = self.intermediate(attention_output)
            else:
                intermediate_output = self.intermediate(self.LayerNorm(attention_output))
            layer_output = self.output(intermediate_output, attention_output)
        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
        return outputs


class BertEncoder(nn.Module):
    def __init__(self, config, use_act_checkpoint=True):
        super(BertEncoder, self).__init__()
        self.output_attentions = config.output_attentions
        self.output_hidden_states = config.output_hidden_states
        self.layer = nn.ModuleList([BertLayer(config, use_act_checkpoint=use_act_checkpoint) for _ in range(config.num_hidden_layers)])
        self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
        if self.pre_norm:
            self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)

    def forward(self, hidden_states, attention_mask, head_mask=None,
                encoder_history_states=None):
        all_hidden_states = ()
        all_attentions = ()
        for i, layer_module in enumerate(self.layer):
            if self.output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            history_state = None if encoder_history_states is None else encoder_history_states[i]
            layer_outputs = layer_module(
                hidden_states, attention_mask,
                (None if head_mask is None else head_mask[i]),
                history_state,
            )
            hidden_states = layer_outputs[0]

            if self.output_attentions:
                all_attentions = all_attentions + (layer_outputs[1],)
        if self.pre_norm:
            hidden_states = self.LayerNorm(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

CONFIG_NAME = "config.json"

class PretrainedConfig(object):
    """ Base class for all configuration classes.
        Handle a few common parameters and methods for loading/downloading/saving configurations.
    """
    pretrained_config_archive_map = {}

    def __init__(self, **kwargs):
        self.finetuning_task = kwargs.pop('finetuning_task', None)
        self.num_labels = kwargs.pop('num_labels', 2)
        self.output_attentions = kwargs.pop('output_attentions', False)
        self.output_hidden_states = kwargs.pop('output_hidden_states', False)
        self.torchscript = kwargs.pop('torchscript', False)

    def save_pretrained(self, save_directory):
        """ Save a configuration object to a directory, so that it
            can be re-loaded using the `from_pretrained(save_directory)` class method.
        """
        assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"

        # If we save using the predefined names, we can load using `from_pretrained`
        output_config_file = os.path.join(save_directory, CONFIG_NAME)

        self.to_json_file(output_config_file)

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        r""" Instantiate a PretrainedConfig from a pre-trained model configuration.

        Params:
            **pretrained_model_name_or_path**: either:
                - a string with the `shortcut name` of a pre-trained model configuration to load from cache
                    or download and cache if not already stored in cache (e.g. 'bert-base-uncased').
                - a path to a `directory` containing a configuration file saved
                    using the `save_pretrained(save_directory)` method.
                - a path or url to a saved configuration `file`.
            **cache_dir**: (`optional`) string:
                Path to a directory in which a downloaded pre-trained model
                configuration should be cached if the standard cache should not be used.
            **return_unused_kwargs**: (`optional`) bool:
                - If False, then this function returns just the final configuration object.
                - If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs`
                is a dictionary consisting of the key/value pairs whose keys are not configuration attributes:
                ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
            **kwargs**: (`optional`) dict:
                Dictionary of key/value pairs with which to update the configuration object after loading.
                - The values in kwargs of any keys which are configuration attributes will be used
                to override the loaded values.
                - Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
                by the `return_unused_kwargs` keyword parameter.

        Examples::

            >>> config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
            >>> config = BertConfig.from_pretrained('./test/saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
            >>> config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
            >>> config = BertConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
            >>> assert config.output_attention == True
            >>> config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True,
            >>>                                                    foo=False, return_unused_kwargs=True)
            >>> assert config.output_attention == True
            >>> assert unused_kwargs == {'foo': False}

        """
        cache_dir = kwargs.pop('cache_dir', None)
        return_unused_kwargs = kwargs.pop('return_unused_kwargs', False)

        if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
            config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path]
        elif os.path.isdir(pretrained_model_name_or_path):
            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
        else:
            config_file = pretrained_model_name_or_path
        # redirect to the cache, if necessary
        try:
            resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
        except EnvironmentError:
            if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
                logger.error(
                    "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
                        config_file))
            else:
                logger.error(
                    "Model name '{}' was not found in model name list ({}). "
                    "We assumed '{}' was a path or url but couldn't find any file "
                    "associated to this path or url.".format(
                        pretrained_model_name_or_path,
                        ', '.join(cls.pretrained_config_archive_map.keys()),
                        config_file))
            return None
        if resolved_config_file == config_file:
            logger.info("loading configuration file {}".format(config_file))
        else:
            logger.info("loading configuration file {} from cache at {}".format(
                config_file, resolved_config_file))

        # Load config
        config = cls.from_json_file(resolved_config_file)

        # Update config with kwargs if needed
        to_remove = []
        for key, value in kwargs.items():
            if hasattr(config, key):
                setattr(config, key, value)
                to_remove.append(key)
        # add img_layer_norm_eps, use_img_layernorm
        if "img_layer_norm_eps" in kwargs:
            setattr(config, "img_layer_norm_eps", kwargs["img_layer_norm_eps"])
            to_remove.append("img_layer_norm_eps")
        if "use_img_layernorm" in kwargs:
            setattr(config, "use_img_layernorm", kwargs["use_img_layernorm"])
            to_remove.append("use_img_layernorm")
        for key in to_remove:
            kwargs.pop(key, None)

        logger.info("Model config %s", config)
        if return_unused_kwargs:
            return config, kwargs
        else:
            return config

    @classmethod
    def from_dict(cls, json_object):
        """Constructs a `Config` from a Python dictionary of parameters."""
        config = cls(vocab_size_or_config_json_file=-1)
        for key, value in json_object.items():
            config.__dict__[key] = value
        return config

    @classmethod
    def from_json_file(cls, json_file):
        """Constructs a `BertConfig` from a json file of parameters."""
        with open(json_file, "r", encoding='utf-8') as reader:
            text = reader.read()
        return cls.from_dict(json.loads(text))

    def __eq__(self, other):
        return self.__dict__ == other.__dict__

    def __repr__(self):
        return str(self.to_json_string())

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"

    def to_json_file(self, json_file_path):
        """ Save this instance to a json file."""
        with open(json_file_path, "w", encoding='utf-8') as writer:
            writer.write(self.to_json_string())


class BertConfig(PretrainedConfig):
    r"""
        :class:`~pytorch_transformers.BertConfig` is the configuration class to store the configuration of a
        `BertModel`.


        Arguments:
            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
            hidden_size: Size of the encoder layers and the pooler layer.
            num_hidden_layers: Number of hidden layers in the Transformer encoder.
            num_attention_heads: Number of attention heads for each attention layer in
                the Transformer encoder.
            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
                layer in the Transformer encoder.
            hidden_act: The non-linear activation function (function or string) in the
                encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
            hidden_dropout_prob: The dropout probabilitiy for all fully connected
                layers in the embeddings, encoder, and pooler.
            attention_probs_dropout_prob: The dropout ratio for the attention
                probabilities.
            max_position_embeddings: The maximum sequence length that this model might
                ever be used with. Typically set this to something large just in case
                (e.g., 512 or 1024 or 2048).
            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
                `BertModel`.
            initializer_range: The sttdev of the truncated_normal_initializer for
                initializing all weight matrices.
            layer_norm_eps: The epsilon used by LayerNorm.
    """
    pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP

    def __init__(self,
                 vocab_size_or_config_json_file=30522,
                 hidden_size=768,
                 num_hidden_layers=12,
                 num_attention_heads=12,
                 intermediate_size=3072,
                 hidden_act="gelu",
                 hidden_dropout_prob=0.1,
                 attention_probs_dropout_prob=0.1,
                 max_position_embeddings=512,
                 type_vocab_size=2,
                 initializer_range=0.02,
                 layer_norm_eps=1e-12,
                 **kwargs):
        super(BertConfig, self).__init__(**kwargs)
        if isinstance(vocab_size_or_config_json_file, str):
            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
                json_config = json.loads(reader.read())
            for key, value in json_config.items():
                self.__dict__[key] = value
        elif isinstance(vocab_size_or_config_json_file, int):
            self.vocab_size = vocab_size_or_config_json_file
            self.hidden_size = hidden_size
            self.num_hidden_layers = num_hidden_layers
            self.num_attention_heads = num_attention_heads
            self.hidden_act = hidden_act
            self.intermediate_size = intermediate_size
            self.hidden_dropout_prob = hidden_dropout_prob
            self.attention_probs_dropout_prob = attention_probs_dropout_prob
            self.max_position_embeddings = max_position_embeddings
            self.type_vocab_size = type_vocab_size
            self.initializer_range = initializer_range
            self.layer_norm_eps = layer_norm_eps
        else:
            raise ValueError("First argument must be either a vocabulary size (int)"
                             "or the path to a pretrained model config file (str)")


def _gelu_python(x):

    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))