Source code for transformers.models.flaubert.modeling_tf_flaubert

# coding=utf-8
# Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
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# 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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"""
 TF 2.0 Flaubert model.
"""

import itertools
from dataclasses import dataclass
from typing import Optional, Tuple

import tensorflow as tf

from ...activations_tf import get_tf_activation
from ...file_utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
)
from ...modeling_tf_outputs import TFBaseModelOutput
from ...modeling_tf_utils import (
    TFPreTrainedModel,
    TFSharedEmbeddings,
    get_initializer,
    input_processing,
    keras_serializable,
    shape_list,
)
from ...utils import logging
from ..xlm.modeling_tf_xlm import (
    TFXLMForMultipleChoice,
    TFXLMForQuestionAnsweringSimple,
    TFXLMForSequenceClassification,
    TFXLMForTokenClassification,
)
from .configuration_flaubert import FlaubertConfig


logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "FlaubertConfig"
_TOKENIZER_FOR_DOC = "FlaubertTokenizer"

TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
    # See all Flaubert models at https://huggingface.co/models?filter=flaubert
]

FLAUBERT_START_DOCSTRING = r"""

    This model inherits from :class:`~transformers.TFPreTrainedModel`. Check the superclass documentation for the
    generic methods the library implements for all its model (such as downloading or saving, resizing the input
    embeddings, pruning heads etc.)

    This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. Use
    it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage
    and behavior.

    .. note::

        TF 2.0 models accepts two formats as inputs:

        - having all inputs as keyword arguments (like PyTorch models), or
        - having all inputs as a list, tuple or dict in the first positional arguments.

        This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
        the tensors in the first argument of the model call function: :obj:`model(inputs)`.

        If you choose this second option, there are three possibilities you can use to gather all the input Tensors in
        the first positional argument :

        - a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
        - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
          :obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
        - a dictionary with one or several input Tensors associated to the input names given in the docstring:
          :obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

    Parameters:
        config (:class:`~transformers.FlaubertConfig`): 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.
"""

FLAUBERT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using :class:`~transformers.FlaubertTokenizer`. See
            :func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
            details.

            `What are input IDs? <../glossary.html#input-ids>`__
        attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` 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 tokens that are **masked**.

            `What are attention masks? <../glossary.html#attention-mask>`__
        langs (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            A parallel sequence of tokens to be used to indicate the language of each token in the input. Indices are
            languages ids which can be obtained from the language names by using two conversion mappings provided in
            the configuration of the model (only provided for multilingual models). More precisely, the `language name
            to language id` mapping is in :obj:`model.config.lang2id` (which is a dictionary string to int) and the
            `language id to language name` mapping is in :obj:`model.config.id2lang` (dictionary int to string).

            See usage examples detailed in the :doc:`multilingual documentation <../multilingual>`.
        token_type_ids (:obj:`tf.Tensor` or :obj:`Numpy array` 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:`tf.Tensor` or :obj:`Numpy array` 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>`__
        lengths (:obj:`tf.Tensor` or :obj:`Numpy array` of shape :obj:`(batch_size,)`, `optional`):
            Length of each sentence that can be used to avoid performing attention on padding token indices. You can
            also use `attention_mask` for the same result (see above), kept here for compatibility Indices selected in
            ``[0, ..., input_ids.size(-1)]``:
        cache (:obj:`Dict[str, tf.Tensor]`, `optional`):
            Dictionary string to ``tf.FloatTensor`` that contains precomputed hidden states (key and values in the
            attention blocks) as computed by the model (see :obj:`cache` output below). Can be used to speed up
            sequential decoding.

            The dictionary object will be modified in-place during the forward pass to add newly computed
            hidden-states.
        head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` 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]``:

            - ``1`` indicates the head is **not masked**,
            - ``0`` indicates the head is **masked**.

        inputs_embeds (:obj:`tf.Tensor` 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 :obj:`input_ids` indices into associated
            vectors than the model's internal embedding lookup matrix.
        output_attentions (:obj:`bool`, `optional`):
            Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
            tensors for more detail.
        output_hidden_states (:obj:`bool`, `optional`):
            Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
            more detail.
        return_dict (:obj:`bool`, `optional`):
            Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
        training (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to use the model in training mode (some modules like dropout modules have different
            behaviors between training and evaluation).
"""


def get_masks(slen, lengths, causal, padding_mask=None, dtype=tf.float32):
    """
    Generate hidden states mask, and optionally an attention mask.
    """
    bs = shape_list(lengths)[0]
    if padding_mask is not None:
        mask = padding_mask
    else:
        # assert lengths.max().item() <= slen
        alen = tf.range(slen)
        mask = tf.math.less(alen, lengths[:, tf.newaxis])

    # attention mask is the same as mask, or triangular inferior attention (causal)
    if causal:
        attn_mask = tf.less_equal(
            tf.tile(alen[tf.newaxis, tf.newaxis, :], (bs, slen, 1)), alen[tf.newaxis, :, tf.newaxis]
        )
    else:
        attn_mask = mask

    # sanity check
    # assert shape_list(mask) == [bs, slen]
    tf.debugging.assert_equal(shape_list(mask), [bs, slen])
    assert causal is False or shape_list(attn_mask) == [bs, slen, slen]

    mask = tf.cast(mask, dtype=dtype)
    attn_mask = tf.cast(attn_mask, dtype=dtype)

    return mask, attn_mask


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

    config_class = FlaubertConfig
    base_model_prefix = "transformer"

    @property
    def dummy_inputs(self):
        # Sometimes XLM has language embeddings so don't forget to build them as well if needed
        inputs_list = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
        attns_list = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
        if self.config.use_lang_emb and self.config.n_langs > 1:
            langs_list = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
        else:
            langs_list = None
        return {"input_ids": inputs_list, "attention_mask": attns_list, "langs": langs_list}


[docs]@add_start_docstrings( "The bare Flaubert Model transformer outputting raw hidden-states without any specific head on top.", FLAUBERT_START_DOCSTRING, ) class TFFlaubertModel(TFFlaubertPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFFlaubertMainLayer(config, name="transformer")
[docs] @add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="jplu/tf-flaubert-small-cased", output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None, lengths=None, cache=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs, ): inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, langs=langs, token_type_ids=token_type_ids, position_ids=position_ids, lengths=lengths, cache=cache, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, kwargs_call=kwargs, ) outputs = self.transformer( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], langs=inputs["langs"], token_type_ids=inputs["token_type_ids"], position_ids=inputs["position_ids"], lengths=inputs["lengths"], cache=inputs["cache"], head_mask=inputs["head_mask"], inputs_embeds=inputs["inputs_embeds"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) return outputs
# Copied from transformers.models.xlm.modeling_tf_xlm.TFXLMMultiHeadAttention with XLM->Flaubert class TFFlaubertMultiHeadAttention(tf.keras.layers.Layer): NEW_ID = itertools.count() def __init__(self, n_heads, dim, config, **kwargs): super().__init__(**kwargs) self.layer_id = next(TFFlaubertMultiHeadAttention.NEW_ID) self.dim = dim self.n_heads = n_heads self.output_attentions = config.output_attentions assert self.dim % self.n_heads == 0 self.q_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="q_lin") self.k_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="k_lin") self.v_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="v_lin") self.out_lin = tf.keras.layers.Dense(dim, kernel_initializer=get_initializer(config.init_std), name="out_lin") self.dropout = tf.keras.layers.Dropout(config.attention_dropout) self.pruned_heads = set() def prune_heads(self, heads): raise NotImplementedError def call(self, input, mask, kv, cache, head_mask, output_attentions, training=False): """ Self-attention (if kv is None) or attention over source sentence (provided by kv). """ # Input is (bs, qlen, dim) # Mask is (bs, klen) (non-causal) or (bs, klen, klen) bs, qlen, dim = shape_list(input) if kv is None: klen = qlen if cache is None else cache["slen"] + qlen else: klen = shape_list(kv)[1] # assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim) dim_per_head = tf.math.divide(self.dim, self.n_heads) dim_per_head = tf.cast(dim_per_head, dtype=tf.int32) mask_reshape = (bs, 1, qlen, klen) if len(shape_list(mask)) == 3 else (bs, 1, 1, klen) def shape(x): """ projection """ return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3)) def unshape(x): """ compute context """ return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head)) q = shape(self.q_lin(input)) # (bs, n_heads, qlen, dim_per_head) if kv is None: k = shape(self.k_lin(input)) # (bs, n_heads, qlen, dim_per_head) v = shape(self.v_lin(input)) # (bs, n_heads, qlen, dim_per_head) elif cache is None or self.layer_id not in cache: k = v = kv k = shape(self.k_lin(k)) # (bs, n_heads, qlen, dim_per_head) v = shape(self.v_lin(v)) # (bs, n_heads, qlen, dim_per_head) if cache is not None: if self.layer_id in cache: if kv is None: k_, v_ = cache[self.layer_id] k = tf.concat([k_, k], axis=2) # (bs, n_heads, klen, dim_per_head) v = tf.concat([v_, v], axis=2) # (bs, n_heads, klen, dim_per_head) else: k, v = cache[self.layer_id] cache[self.layer_id] = (k, v) q = tf.cast(q, dtype=tf.float32) q = tf.multiply(q, tf.math.rsqrt(tf.cast(dim_per_head, dtype=tf.float32))) # (bs, n_heads, qlen, dim_per_head) k = tf.cast(k, dtype=q.dtype) scores = tf.matmul(q, k, transpose_b=True) # (bs, n_heads, qlen, klen) mask = tf.reshape(mask, mask_reshape) # (bs, n_heads, qlen, klen) # scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, qlen, klen) mask = tf.cast(mask, dtype=scores.dtype) scores = scores - 1e30 * (1.0 - mask) weights = tf.nn.softmax(scores, axis=-1) # (bs, n_heads, qlen, klen) weights = self.dropout(weights, training=training) # (bs, n_heads, qlen, klen) # Mask heads if we want to if head_mask is not None: weights = weights * head_mask context = tf.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head) context = unshape(context) # (bs, qlen, dim) outputs = (self.out_lin(context),) if output_attentions: outputs = outputs + (weights,) return outputs # Copied from transformers.models.xlm.modeling_tf_xlm.TFXLMTransformerFFN class TFFlaubertTransformerFFN(tf.keras.layers.Layer): def __init__(self, in_dim, dim_hidden, out_dim, config, **kwargs): super().__init__(**kwargs) self.lin1 = tf.keras.layers.Dense(dim_hidden, kernel_initializer=get_initializer(config.init_std), name="lin1") self.lin2 = tf.keras.layers.Dense(out_dim, kernel_initializer=get_initializer(config.init_std), name="lin2") self.act = get_tf_activation("gelu") if config.gelu_activation else get_tf_activation("relu") self.dropout = tf.keras.layers.Dropout(config.dropout) def call(self, input, training=False): x = self.lin1(input) x = self.act(x) x = self.lin2(x) x = self.dropout(x, training=training) return x @keras_serializable class TFFlaubertMainLayer(tf.keras.layers.Layer): config_class = FlaubertConfig def __init__(self, config, **kwargs): super().__init__(**kwargs) self.config = config self.n_heads = config.n_heads self.n_langs = config.n_langs self.dim = config.emb_dim self.hidden_dim = self.dim * 4 self.n_words = config.n_words self.pad_index = config.pad_index self.causal = config.causal self.n_layers = config.n_layers self.use_lang_emb = config.use_lang_emb self.layerdrop = getattr(config, "layerdrop", 0.0) self.pre_norm = getattr(config, "pre_norm", False) self.output_attentions = config.output_attentions self.output_hidden_states = config.output_hidden_states self.return_dict = config.use_return_dict self.dropout = tf.keras.layers.Dropout(config.dropout) self.position_embeddings = tf.keras.layers.Embedding( config.max_position_embeddings, self.dim, embeddings_initializer=get_initializer(config.embed_init_std), name="position_embeddings", ) if config.n_langs > 1 and config.use_lang_emb: self.lang_embeddings = tf.keras.layers.Embedding( self.n_langs, self.dim, embeddings_initializer=get_initializer(config.embed_init_std), name="lang_embeddings", ) self.embeddings = TFSharedEmbeddings( self.n_words, self.dim, initializer_range=config.embed_init_std, name="embeddings" ) self.layer_norm_emb = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm_emb") self.attentions = [] self.layer_norm1 = [] self.ffns = [] self.layer_norm2 = [] for i in range(self.n_layers): self.attentions.append( TFFlaubertMultiHeadAttention(self.n_heads, self.dim, config=config, name="attentions_._{}".format(i)) ) self.layer_norm1.append( tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm1_._{}".format(i)) ) # if self.is_decoder: # self.layer_norm15.append(nn.LayerNorm(self.dim, eps=config.layer_norm_eps)) # self.encoder_attn.append(MultiHeadAttention(self.n_heads, self.dim, dropout=self.attention_dropout)) self.ffns.append( TFFlaubertTransformerFFN( self.dim, self.hidden_dim, self.dim, config=config, name="ffns_._{}".format(i) ) ) self.layer_norm2.append( tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm2_._{}".format(i)) ) def get_input_embeddings(self): return self.embeddings def call( self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None, lengths=None, cache=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs, ): # removed: src_enc=None, src_len=None inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, langs=langs, token_type_ids=token_type_ids, position_ids=position_ids, lengths=lengths, cache=cache, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, kwargs_call=kwargs, ) if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif inputs["input_ids"] is not None: bs, slen = shape_list(inputs["input_ids"]) elif inputs["inputs_embeds"] is not None: bs, slen = shape_list(inputs["inputs_embeds"])[:2] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs["lengths"] is None: if inputs["input_ids"] is not None: inputs["lengths"] = tf.reduce_sum( tf.cast(tf.not_equal(inputs["input_ids"], self.pad_index), dtype=tf.int32), axis=1 ) else: inputs["lengths"] = tf.convert_to_tensor([slen] * bs, tf.int32) # mask = input_ids != self.pad_index # check inputs # assert shape_list(lengths)[0] == bs tf.debugging.assert_equal( shape_list(inputs["lengths"])[0], bs ), f"Expected batch size {shape_list(inputs['lengths'])[0]} and received batch size {bs} mismatched" # assert lengths.max().item() <= slen # input_ids = input_ids.transpose(0, 1) # batch size as dimension 0 # assert (src_enc is None) == (src_len is None) # if src_enc is not None: # assert self.is_decoder # assert src_enc.size(0) == bs # generate masks mask, attn_mask = get_masks(slen, inputs["lengths"], self.causal, padding_mask=inputs["attention_mask"]) # if self.is_decoder and src_enc is not None: # src_mask = torch.arange(src_len.max(), dtype=torch.long, device=lengths.device) < src_len[:, None] # position_ids if inputs["position_ids"] is None: inputs["position_ids"] = tf.expand_dims(tf.range(slen), axis=0) else: # assert shape_list(position_ids) == [bs, slen] # (slen, bs) tf.debugging.assert_equal( shape_list(inputs["position_ids"]), [bs, slen] ), f"Position id shape {shape_list(inputs['position_ids'])} and input shape {[bs, slen]} mismatched" # position_ids = position_ids.transpose(0, 1) # langs if inputs["langs"] is not None: # assert shape_list(langs) == [bs, slen] # (slen, bs) tf.debugging.assert_equal( shape_list(inputs["langs"]), [bs, slen] ), f"Lang shape {shape_list(inputs['langs'])} and input shape {[bs, slen]} mismatched" # langs = langs.transpose(0, 1) # 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 qlen x klen] if inputs["head_mask"] is not None: raise NotImplementedError else: inputs["head_mask"] = [None] * self.n_layers # do not recompute cached elements if inputs["cache"] is not None and inputs["input_ids"] is not None: _slen = slen - inputs["cache"]["slen"] inputs["input_ids"] = inputs["input_ids"][:, -_slen:] inputs["position_ids"] = inputs["position_ids"][:, -_slen:] if inputs["langs"] is not None: inputs["langs"] = inputs["langs"][:, -_slen:] mask = mask[:, -_slen:] attn_mask = attn_mask[:, -_slen:] # embeddings if inputs["inputs_embeds"] is None: inputs["inputs_embeds"] = self.embeddings(inputs["input_ids"]) tensor = inputs["inputs_embeds"] + self.position_embeddings(inputs["position_ids"]) if inputs["langs"] is not None and self.use_lang_emb: tensor = tensor + self.lang_embeddings(inputs["langs"]) if inputs["token_type_ids"] is not None: tensor = tensor + self.embeddings(inputs["token_type_ids"]) tensor = self.layer_norm_emb(tensor) tensor = self.dropout(tensor, training=inputs["training"]) tensor = tensor * mask[..., tf.newaxis] # hidden_states and attentions cannot be None in graph mode. hidden_states = () attentions = () # transformer layers for i in range(self.n_layers): # LayerDrop dropout_probability = tf.random.uniform([1], 0, 1) if inputs["training"] and tf.less(dropout_probability, self.layerdrop): continue if inputs["output_hidden_states"]: hidden_states = hidden_states + (tensor,) # self attention if not self.pre_norm: attn_outputs = self.attentions[i]( tensor, attn_mask, None, inputs["cache"], inputs["head_mask"][i], inputs["output_attentions"], training=inputs["training"], ) attn = attn_outputs[0] if inputs["output_attentions"]: attentions = attentions + (attn_outputs[1],) attn = self.dropout(attn, training=inputs["training"]) tensor = tensor + attn tensor = self.layer_norm1[i](tensor) else: tensor_normalized = self.layer_norm1[i](tensor) attn_outputs = self.attentions[i]( tensor_normalized, attn_mask, None, inputs["cache"], inputs["head_mask"][i], inputs["output_attentions"], training=inputs["training"], ) attn = attn_outputs[0] if output_attentions: attentions = attentions + (attn_outputs[1],) attn = self.dropout(attn, training=inputs["training"]) tensor = tensor + attn # encoder attention (for decoder only) # if self.is_decoder and src_enc is not None: # attn = self.encoder_attn[i](tensor, src_mask, kv=src_enc, cache=cache) # attn = F.dropout(attn, p=self.dropout, training=self.training) # tensor = tensor + attn # tensor = self.layer_norm15[i](tensor) # FFN if not self.pre_norm: tensor = tensor + self.ffns[i](tensor) tensor = self.layer_norm2[i](tensor) else: tensor_normalized = self.layer_norm2[i](tensor) tensor = tensor + self.ffns[i](tensor_normalized) tensor = tensor * mask[..., tf.newaxis] # Add last hidden state if inputs["output_hidden_states"]: hidden_states = hidden_states + (tensor,) # update cache length if inputs["cache"] is not None: inputs["cache"]["slen"] += tensor.size(1) # move back sequence length to dimension 0 # tensor = tensor.transpose(0, 1) # Set to None here if the output booleans are at False hidden_states = hidden_states if inputs["output_hidden_states"] else None attentions = attentions if inputs["output_attentions"] else None if not inputs["return_dict"]: return tuple(v for v in [tensor, hidden_states, attentions] if v is not None) return TFBaseModelOutput(last_hidden_state=tensor, hidden_states=hidden_states, attentions=attentions) # Copied from transformers.models.xlm.modeling_tf_xlm.TFXLMPredLayer class TFFlaubertPredLayer(tf.keras.layers.Layer): """ Prediction layer (cross_entropy or adaptive_softmax). """ def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.asm = config.asm self.n_words = config.n_words self.pad_index = config.pad_index if config.asm is False: self.input_embeddings = input_embeddings else: raise NotImplementedError # self.proj = nn.AdaptiveLogSoftmaxWithLoss( # in_features=dim, # n_classes=config.n_words, # cutoffs=config.asm_cutoffs, # div_value=config.asm_div_value, # head_bias=True, # default is False # ) def build(self, input_shape): # The output weights are the same as the input embeddings, but there is an output-only bias for each token. self.bias = self.add_weight(shape=(self.n_words,), initializer="zeros", trainable=True, name="bias") super().build(input_shape) def call(self, hidden_states): hidden_states = self.input_embeddings(hidden_states, mode="linear") hidden_states = hidden_states + self.bias return hidden_states @dataclass class TFFlaubertWithLMHeadModelOutput(ModelOutput): """ Base class for :class:`~transformers.TFFlaubertWithLMHeadModel` outputs. Args: logits (:obj:`tf.Tensor` 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(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``): Tuple of :obj:`tf.Tensor` (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(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``): Tuple of :obj:`tf.Tensor` (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. """ logits: tf.Tensor = None hidden_states: Optional[Tuple[tf.Tensor]] = None attentions: Optional[Tuple[tf.Tensor]] = None
[docs]@add_start_docstrings( """ The Flaubert Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, FLAUBERT_START_DOCSTRING, ) class TFFlaubertWithLMHeadModel(TFFlaubertPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFFlaubertMainLayer(config, name="transformer") self.pred_layer = TFFlaubertPredLayer(config, self.transformer.embeddings, name="pred_layer_._proj") def get_output_embeddings(self): return self.pred_layer.input_embeddings def get_output_layer_with_bias(self): return self.pred_layer def get_prefix_bias_name(self): return self.name + "/" + self.pred_layer.name def prepare_inputs_for_generation(self, inputs, **kwargs): mask_token_id = self.config.mask_token_id lang_id = self.config.lang_id effective_batch_size = inputs.shape[0] mask_token = tf.ones((effective_batch_size, 1), dtype=tf.int32) * mask_token_id inputs = tf.concat([inputs, mask_token], axis=1) if lang_id is not None: langs = tf.ones_like(inputs) * lang_id else: langs = None return {"input_ids": inputs, "langs": langs}
[docs] @add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="jplu/tf-flaubert-small-cased", output_type=TFFlaubertWithLMHeadModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids=None, attention_mask=None, langs=None, token_type_ids=None, position_ids=None, lengths=None, cache=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs, ): inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, langs=langs, token_type_ids=token_type_ids, position_ids=position_ids, lengths=lengths, cache=cache, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, kwargs_call=kwargs, ) transformer_outputs = self.transformer( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], langs=inputs["langs"], token_type_ids=inputs["token_type_ids"], position_ids=inputs["position_ids"], lengths=inputs["lengths"], cache=inputs["cache"], head_mask=inputs["head_mask"], inputs_embeds=inputs["inputs_embeds"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) output = transformer_outputs[0] outputs = self.pred_layer(output) if not inputs["return_dict"]: return (outputs,) + transformer_outputs[1:] return TFFlaubertWithLMHeadModelOutput( logits=outputs, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions )
[docs]@add_start_docstrings( """ Flaubert Model with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, FLAUBERT_START_DOCSTRING, ) class TFFlaubertForSequenceClassification(TFXLMForSequenceClassification): config_class = FlaubertConfig def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFFlaubertMainLayer(config, name="transformer")
[docs]@add_start_docstrings( """ Flaubert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, FLAUBERT_START_DOCSTRING, ) class TFFlaubertForQuestionAnsweringSimple(TFXLMForQuestionAnsweringSimple): config_class = FlaubertConfig def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFFlaubertMainLayer(config, name="transformer")
[docs]@add_start_docstrings( """ Flaubert 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. """, FLAUBERT_START_DOCSTRING, ) class TFFlaubertForTokenClassification(TFXLMForTokenClassification): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFFlaubertMainLayer(config, name="transformer")
[docs]@add_start_docstrings( """ Flaubert 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. """, FLAUBERT_START_DOCSTRING, ) class TFFlaubertForMultipleChoice(TFXLMForMultipleChoice): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFFlaubertMainLayer(config, name="transformer")