Source code for transformers.modeling_tf_ctrl

# coding=utf-8
# Copyright 2018 Salesforce and 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
""" TF 2.0 CTRL model."""

import numpy as np
import tensorflow as tf

from .configuration_ctrl import CTRLConfig
from .file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_callable
from .modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast
from .modeling_tf_utils import (
from .tokenization_utils import BatchEncoding
from .utils import logging

logger = logging.get_logger(__name__)


    # See all CTRL models at

def angle_defn(pos, i, d_model_size):
    angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model_size))
    return pos * angle_rates

def positional_encoding(position, d_model_size):
    # create the sinusoidal pattern for the positional encoding
    angle_rads = angle_defn(np.arange(position)[:, np.newaxis], np.arange(d_model_size)[np.newaxis, :], d_model_size)

    sines = np.sin(angle_rads[:, 0::2])
    cosines = np.cos(angle_rads[:, 1::2])

    # pos_encoding = tf.cast(np.concatenate([sines, cosines], axis=-1)[np.newaxis, ...], dtype=tf.float32)
    pos_encoding = tf.cast(np.concatenate([sines, cosines], axis=-1), dtype=tf.float32)
    return pos_encoding

def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=None):
    # calculate attention
    matmul_qk = tf.matmul(q, k, transpose_b=True)

    dk = tf.cast(shape_list(k)[-1], tf.float32)
    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)

    if mask is not None:
        scaled_attention_logits += mask * -1e4

    if attention_mask is not None:
        # Apply the attention mask
        scaled_attention_logits = scaled_attention_logits + attention_mask

    attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)

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

    output = tf.matmul(attention_weights, v)

    return output, attention_weights

class TFMultiHeadAttention(tf.keras.layers.Layer):
    def __init__(self, d_model_size, num_heads, output_attentions=False, **kwargs):
        self.num_heads = num_heads
        self.d_model_size = d_model_size
        self.output_attentions = output_attentions

        self.depth = int(d_model_size / self.num_heads)

        self.Wq = tf.keras.layers.Dense(d_model_size, name="Wq")
        self.Wk = tf.keras.layers.Dense(d_model_size, name="Wk")
        self.Wv = tf.keras.layers.Dense(d_model_size, name="Wv")

        self.dense = tf.keras.layers.Dense(d_model_size, name="dense")

    def split_into_heads(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, v, k, q, mask, layer_past, attention_mask, head_mask, use_cache, output_attentions, training=False):
        batch_size = shape_list(q)[0]

        q = self.Wq(q)
        k = self.Wk(k)
        v = self.Wv(v)

        q = self.split_into_heads(q, batch_size)
        k = self.split_into_heads(k, batch_size)
        v = self.split_into_heads(v, batch_size)

        if layer_past is not None:
            past_key, past_value = tf.unstack(layer_past, axis=0)
            k = tf.concat((past_key, k), axis=-2)
            v = tf.concat((past_value, v), axis=-2)

        if use_cache:
            present = tf.stack((k, v), axis=0)
            present = (None,)

        output = scaled_dot_product_attention(q, k, v, mask, attention_mask, head_mask)
        scaled_attention = tf.transpose(output[0], perm=[0, 2, 1, 3])
        attn = output[1]
        original_size_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model_size))
        output = self.dense(original_size_attention)
        outputs = (output, present)

        if output_attentions:
            outputs = outputs + (attn,)

        return outputs

class TFPointWiseFeedForwardLayer(tf.keras.layers.Layer):
    def __init__(self, d_model_size, dff, **kwargs):

        self.dense_0 = tf.keras.layers.Dense(dff, activation="relu", name="0")
        self.dense_2 = tf.keras.layers.Dense(d_model_size, name="2")

    def call(self, inputs, trainable=False):
        dense_0_output = self.dense_0(inputs)
        dense_2_output = self.dense_2(dense_0_output)

        return dense_2_output

class TFEncoderLayer(tf.keras.layers.Layer):
    def __init__(
        self, d_model_size, num_heads, dff, rate=0.1, layer_norm_epsilon=1e-6, output_attentions=False, **kwargs

        self.output_attentions = output_attentions

        self.multi_head_attention = TFMultiHeadAttention(
            d_model_size, num_heads, output_attentions=self.output_attentions, name="multi_head_attention"
        self.ffn = TFPointWiseFeedForwardLayer(d_model_size, dff, name="ffn")

        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layernorm1")
        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=layer_norm_epsilon, name="layernorm2")

        self.dropout1 = tf.keras.layers.Dropout(rate)
        self.dropout2 = tf.keras.layers.Dropout(rate)

    def call(self, x, mask, layer_past, attention_mask, head_mask, use_cache, output_attentions, training=False):
        normed = self.layernorm1(x)
        attn_outputs = self.multi_head_attention(
        attn_output = attn_outputs[0]
        attn_output = self.dropout1(attn_output, training=training)
        out1 = x + attn_output

        out2 = self.layernorm2(out1)
        ffn_output = self.ffn(out2)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = out1 + ffn_output

        outputs = (out2,) + attn_outputs[1:]
        return outputs

class TFCTRLMainLayer(tf.keras.layers.Layer):
    config_class = CTRLConfig

    def __init__(self, config, **kwargs):
        self.output_hidden_states = config.output_hidden_states
        self.output_attentions = config.output_attentions
        self.use_cache = config.use_cache
        self.return_dict = config.use_return_dict

        self.d_model_size = config.n_embd
        self.num_layers = config.n_layer

        self.pos_encoding = positional_encoding(config.n_positions, self.d_model_size)

        self.w = TFSharedEmbeddings(
            config.vocab_size, config.n_embd, initializer_range=config.initializer_range, name="w"

        self.dropout = tf.keras.layers.Dropout(config.embd_pdrop)
        self.h = [
            for i in range(config.n_layer)
        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="layernorm")

    def get_input_embeddings(self):
        return self.w

    def set_input_embeddings(self, value):
        self.w.weight = value
        self.w.vocab_size = value.shape[0]

    def _resize_token_embeddings(self, new_num_tokens):
        raise NotImplementedError

    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}
        raise NotImplementedError

    def call(

        if isinstance(inputs, (tuple, list)):
            input_ids = inputs[0]
            past = inputs[1] if len(inputs) > 1 else past
            attention_mask = inputs[2] if len(inputs) > 2 else attention_mask
            token_type_ids = inputs[3] if len(inputs) > 3 else token_type_ids
            position_ids = inputs[4] if len(inputs) > 4 else position_ids
            head_mask = inputs[5] if len(inputs) > 5 else head_mask
            inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
            use_cache = inputs[7] if len(inputs) > 7 else use_cache
            output_attentions = inputs[8] if len(inputs) > 8 else output_attentions
            output_hidden_states = inputs[9] if len(inputs) > 9 else output_hidden_states
            return_dict = inputs[10] if len(inputs) > 10 else return_dict
            assert len(inputs) <= 11, "Too many inputs."
        elif isinstance(inputs, (dict, BatchEncoding)):
            input_ids = inputs.get("input_ids")
            past = inputs.get("past", past)
            attention_mask = inputs.get("attention_mask", attention_mask)
            token_type_ids = inputs.get("token_type_ids", token_type_ids)
            position_ids = inputs.get("position_ids", position_ids)
            head_mask = inputs.get("head_mask", head_mask)
            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
            use_cache = inputs.get("use_cache", use_cache)
            output_attentions = inputs.get("output_attentions", output_attentions)
            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
            return_dict = inputs.get("return_dict", return_dict)
            assert len(inputs) <= 11, "Too many inputs."
            input_ids = inputs

        output_attentions = output_attentions if output_attentions is not None else self.output_attentions
        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
        use_cache = use_cache if use_cache is not None else self.use_cache
        return_dict = return_dict if return_dict is not None else self.return_dict

        # If using past key value states, only the last tokens
        # should be given as an input
        if past is not None:
            if input_ids is not None:
                input_ids = input_ids[:, -1:]
            if inputs_embeds is not None:
                inputs_embeds = inputs_embeds[:, -1:]
            if token_type_ids is not None:
                token_type_ids = token_type_ids[:, -1:]

        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 = shape_list(input_ids)
            input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
        elif inputs_embeds is not None:
            input_shape = shape_list(inputs_embeds)[:-1]
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if past is None:
            past_length = 0
            past = [None] * len(self.h)
            past_length = shape_list(past[0][0])[-2]
        if position_ids is None:
            position_ids = tf.range(past_length, input_shape[-1] + past_length, dtype=tf.int32)[tf.newaxis, :]
            position_ids = tf.tile(position_ids, [input_shape[0], 1])

        # Attention mask.
        if attention_mask is not None:
            # We create a 3D attention mask from a 2D tensor mask.
            # Sizes are [batch_size, 1, 1, to_seq_length]
            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
            # this attention mask is more simple than the triangular masking of causal attention
            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
            attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]

            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
            # masked positions, this operation will create a tensor which is 0.0 for
            # positions we want to attend and -10000.0 for masked positions.
            # Since we are adding it to the raw scores before the softmax, this is
            # effectively the same as removing these entirely.

            attention_mask = tf.cast(attention_mask, tf.float32)
            attention_mask = (1.0 - attention_mask) * -10000.0
            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
        # head_mask has shape n_layer x batch x n_heads x N x N
        if head_mask is not None:
            raise NotImplementedError
            head_mask = [None] * self.num_layers

        if token_type_ids is not None:
            token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
            token_type_embeds = self.w(token_type_ids, mode="embedding")
            token_type_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, tf.float32))
            token_type_embeds = 0
        position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])

        if inputs_embeds is None:
            inputs_embeds = self.w(input_ids, mode="embedding")
        seq_len = input_shape[-1]
        mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)

        inputs_embeds *= tf.math.sqrt(tf.cast(self.d_model_size, tf.float32))

        pos_embeds = tf.gather(self.pos_encoding, position_ids)

        hidden_states = inputs_embeds + pos_embeds + token_type_embeds

        hidden_states = self.dropout(hidden_states, training=training)

        output_shape = input_shape + [shape_list(hidden_states)[-1]]
        presents = () if use_cache else None
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for i, (h, layer_past) in enumerate(zip(self.h, past)):
            if output_hidden_states:
                all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
            outputs = h(
            hidden_states, present = outputs[:2]

            if use_cache:
                presents = presents + (present,)

            if output_attentions:
                all_attentions = all_attentions + (outputs[2],)

        hidden_states = self.layernorm(hidden_states)
        hidden_states = tf.reshape(hidden_states, output_shape)
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if output_attentions:
            # let the number of heads free (-1) so we can extract attention even after head pruning
            attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:]
            all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions)

        if not return_dict:
            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None)

        return TFBaseModelOutputWithPast(

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

    config_class = CTRLConfig
    base_model_prefix = "transformer"


    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 <>`__ 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:`` 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})`

        config (:class:`~transformers.CTRLConfig`): 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.

        input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(batch_size, input_ids_length)`):
            :obj:`input_ids_length` = ``sequence_length`` if ``past`` is ``None`` else ``past[0].shape[-2]``
            (``sequence_length`` of input past key value states).

            Indices of input sequence tokens in the vocabulary.

            If :obj:`past` is used, only input IDs that do not have their past calculated should be passed as

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

            `What are input IDs? <../glossary.html#input-ids>`__
        past (:obj:`List[tf.Tensor]` of length :obj:`config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
            (see :obj:`past` output below). Can be used to speed up sequential decoding.
            The token ids which have their past given to this model
            should not be passed as input ids as they have already been computed.
        attention_mask (:obj:`tf.Tensor` or :obj:`Numpy array` 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 **maked**.

            `What are attention masks? <../glossary.html#attention-mask>`__
        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>`__
        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]``:

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

        inputs_embeds (:obj:`tf.Tensor` or :obj:`Numpy array` 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.
        use_cache (:obj:`bool`, `optional`):
            If set to :obj:`True`, ``past`` key value states are returned and can be used to speed up
            decoding (see ``past``).
        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).

[docs]@add_start_docstrings( "The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.", CTRL_START_DOCSTRING, ) class TFCTRLModel(TFCTRLPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFCTRLMainLayer(config, name="transformer")
[docs] @add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="ctrl", output_type=TFBaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def call(self, inputs, **kwargs): outputs = self.transformer(inputs, **kwargs) return outputs
class TFCTRLLMHead(tf.keras.layers.Layer): def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.vocab_size = config.vocab_size # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. self.input_embeddings = input_embeddings def build(self, input_shape): self.bias = self.add_weight(shape=(self.vocab_size,), 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
[docs]@add_start_docstrings( """The CTRL Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, CTRL_START_DOCSTRING, ) class TFCTRLLMHeadModel(TFCTRLPreTrainedModel, TFCausalLanguageModelingLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.transformer = TFCTRLMainLayer(config, name="transformer") self.lm_head = TFCTRLLMHead(config, self.transformer.w, name="lm_head") def get_output_embeddings(self): return self.lm_head.input_embeddings def prepare_inputs_for_generation(self, inputs, past, **kwargs): # only last token for inputs_ids if past is defined in kwargs if past: inputs = tf.expand_dims(inputs[:, -1], -1) return {"inputs": inputs, "past": past, "use_cache": kwargs["use_cache"]}
[docs] @add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="ctrl", output_type=TFCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def call( self, inputs, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, labels=None, training=False, ): r""" labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): Labels for computing the cross entropy classification loss. Indices should be in ``[0, ..., config.vocab_size - 1]``. """ return_dict = return_dict if return_dict is not None else self.transformer.return_dict if isinstance(inputs, (tuple, list)): labels = inputs[11] if len(inputs) > 11 else labels if len(inputs) > 11: inputs = inputs[:11] elif isinstance(inputs, (dict, BatchEncoding)): labels = inputs.pop("labels", labels) transformer_outputs = self.transformer( inputs, past=past, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) hidden_states = transformer_outputs[0] logits = self.lm_head(hidden_states) loss = None if labels is not None: # shift labels to the left and cut last logit token logits = logits[:, :-1] labels = labels[:, 1:] loss = self.compute_loss(labels, logits) if not return_dict: output = (logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return TFCausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, )