Source code for transformers.modeling_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.
""" PyTorch CTRL model."""

import warnings

import numpy as np
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss

from .configuration_ctrl import CTRLConfig
from .file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_callable
from .modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from .modeling_utils import Conv1D, PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
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 / torch.pow(10000, (2 * (i // 2)) / d_model_size)
    return pos * angle_rates

def positional_encoding(position, d_model_size, dtype):
    # create the sinusoidal pattern for the positional encoding
    angle_rads = angle_defn(
        torch.arange(position, dtype=dtype).unsqueeze(1),
        torch.arange(d_model_size, dtype=dtype).unsqueeze(0),

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

    pos_encoding =[sines, cosines], dim=-1)
    return pos_encoding

def scaled_dot_product_attention(q, k, v, mask, attention_mask=None, head_mask=None):
    # calculate attention
    matmul_qk = torch.matmul(q, k.permute(0, 1, 3, 2))

    dk = k.shape[-1]
    scaled_attention_logits = matmul_qk / np.sqrt(dk)

    if mask is not None:
        nd, ns = scaled_attention_logits.size(-2), scaled_attention_logits.size(-1)
        scaled_attention_logits += mask[ns - nd : ns, :ns] * -1e4

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

    attention_weights = torch.softmax(scaled_attention_logits, dim=-1)

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

    output = torch.matmul(attention_weights, v)

    return output, attention_weights

class MultiHeadAttention(torch.nn.Module):
    def __init__(self, d_model_size, num_heads):
        self.num_heads = num_heads
        self.d_model_size = d_model_size

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

        self.Wq = torch.nn.Linear(d_model_size, d_model_size)
        self.Wk = torch.nn.Linear(d_model_size, d_model_size)
        self.Wv = torch.nn.Linear(d_model_size, d_model_size)

        self.dense = torch.nn.Linear(d_model_size, d_model_size)
        self.pruned_heads = set()

    def prune_heads(self, heads):
        attention_head_size = self.d_model_size // self.num_heads
        if len(heads) == 0:
        heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, attention_head_size, self.pruned_heads)

        # Prune linear layers
        self.Wq = prune_linear_layer(self.Wq, index)
        self.Wk = prune_linear_layer(self.Wk, index)
        self.Wv = prune_linear_layer(self.Wv, index)
        self.dense = prune_linear_layer(self.dense, index, dim=1)

        # Update hyper params
        self.num_heads = self.num_heads - len(heads)
        self.d_model_size = attention_head_size * self.num_heads
        self.pruned_heads = self.pruned_heads.union(heads)

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

    def forward(
        batch_size = q.shape[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 = layer_past[0], layer_past[1]
            k =, k), dim=-2)
            v =, v), dim=-2)

        if use_cache is True:
            present = torch.stack((k, v))
            present = (None,)

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

        outputs = (output, present)
        if output_attentions:
            outputs = outputs + (attn,)
        return outputs

def point_wise_feed_forward_network(d_model_size, dff):
    return torch.nn.Sequential(torch.nn.Linear(d_model_size, dff), torch.nn.ReLU(), torch.nn.Linear(dff, d_model_size))

class EncoderLayer(torch.nn.Module):
    def __init__(self, d_model_size, num_heads, dff, rate=0.1):

        self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads)
        self.ffn = point_wise_feed_forward_network(d_model_size, dff)

        self.layernorm1 = torch.nn.LayerNorm(d_model_size, eps=1e-6)
        self.layernorm2 = torch.nn.LayerNorm(d_model_size, eps=1e-6)

        self.dropout1 = torch.nn.Dropout(rate)
        self.dropout2 = torch.nn.Dropout(rate)

    def forward(
        self, x, mask, layer_past=None, attention_mask=None, head_mask=None, use_cache=False, output_attentions=False
        normed = self.layernorm1(x)
        attn_outputs = self.multi_head_attention(
        attn_output = attn_outputs[0]
        attn_output = self.dropout1(attn_output)
        out1 = x + attn_output

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

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

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

    config_class = CTRLConfig
    base_model_prefix = "transformer"

    def _init_weights(self, module):
        """Initialize the weights."""
        if isinstance(module, (nn.Linear, nn.Embedding, Conv1D)):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf
  , std=self.config.initializer_range)
            if isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
        elif isinstance(module, nn.LayerNorm):

    This model is a PyTorch `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.

        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:`torch.LongTensor` of shape :obj:`(batch_size, input_ids_length)`):
            :obj:`input_ids_length` = ``sequence_length`` if ``past_key_values`` is ``None`` else
            ``past_key_values[0].shape[-2]`` (``sequence_length`` of input past key value states).
            Indices of input sequence tokens in the vocabulary.

            If ``past_key_values`` 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 :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.__call__` for details.

            `What are input IDs? <../glossary.html#input-ids>`__
        past_key_values (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers`):
            Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
            (see ``past_key_values`` output below). Can be used to speed up sequential decoding.
            The ``input_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:`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`):
            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.
            If ``past_key_values`` is used, optionally only the last `inputs_embeds` have to be input (see ``past_key_values``).
        use_cache (:obj:`bool`):
            If `use_cache` is True, ``past_key_values`` key value states are returned and
            can be used to speed up decoding (see ``past_key_values``). Defaults to `True`.
        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 CTRL Model transformer outputting raw hidden-states without any specific head on top.", CTRL_START_DOCSTRING, ) class CTRLModel(CTRLPreTrainedModel): def __init__(self, config): super().__init__(config) 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, torch.float) self.w = nn.Embedding(config.vocab_size, config.n_embd) self.dropout = nn.Dropout(config.embd_pdrop) self.h = nn.ModuleList( [EncoderLayer(config.n_embd, config.n_head, config.dff, config.resid_pdrop) for _ in range(config.n_layer)] ) self.layernorm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.init_weights()
[docs] def get_input_embeddings(self): return self.w
[docs] def set_input_embeddings(self, new_embeddings): self.w = new_embeddings
def _prune_heads(self, heads_to_prune): """Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} """ for layer, heads in heads_to_prune.items(): self.h[layer].multi_head_attention.prune_heads(heads)
[docs] @add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING) @add_code_sample_docstrings( tokenizer_class=_TOKENIZER_FOR_DOC, checkpoint="ctrl", output_type=BaseModelOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, past_key_values=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, **kwargs, ): if "past" in kwargs: warnings.warn( "The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) past_key_values = kwargs.pop("past") assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}." output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions use_cache = use_cache if use_cache is not None else self.config.use_cache 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() input_ids = input_ids.view(-1, input_shape[-1]) batch_size = input_ids.shape[0] elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] batch_size = inputs_embeds.shape[0] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if past_key_values is None: past_length = 0 past_key_values = [None] * len(self.h) else: past_length = past_key_values[0][0].size(-2) if position_ids is None: device = input_ids.device if input_ids is not None else inputs_embeds.device position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) # Attention mask. if attention_mask is not None: assert batch_size > 0, "batch_size has to be defined and > 0" attention_mask = attention_mask.view(batch_size, -1) # 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.unsqueeze(1).unsqueeze(2) # 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 = # fp16 compatibility attention_mask = (1.0 - attention_mask) * -10000.0 # Prepare head mask if needed head_mask = self.get_head_mask(head_mask, self.config.n_layer) if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, input_shape[-1]) token_type_embeds = self.w(token_type_ids) token_type_embeds *= np.sqrt(self.d_model_size) else: token_type_embeds = 0 position_ids = position_ids.view(-1, input_shape[-1]) if inputs_embeds is None: inputs_embeds = self.w(input_ids) # inputs_embeds = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded seq_len = input_shape[-1] mask = torch.triu(torch.ones(seq_len + past_length, seq_len + past_length), 1).to(inputs_embeds.device) inputs_embeds *= np.sqrt(self.d_model_size) pos_embeds = self.pos_encoding[position_ids, :].to(inputs_embeds.device) hidden_states = inputs_embeds + pos_embeds + token_type_embeds hidden_states = self.dropout(hidden_states) output_shape = input_shape + (inputs_embeds.size(-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_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states.view(*output_shape),) outputs = h( hidden_states, mask, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, ) hidden_states, present = outputs[:2] if use_cache is True: presents = presents + (present,) if output_attentions: all_attentions.append(outputs[2]) hidden_states = self.layernorm(hidden_states) hidden_states = hidden_states.view(*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,) + all_attentions[0].shape[-2:] all_attentions = tuple(t.view(*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 BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_attentions, )
[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 CTRLLMHeadModel(CTRLPreTrainedModel): def __init__(self, config): super().__init__(config) self.transformer = CTRLModel(config) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=True) self.init_weights()
[docs] def get_output_embeddings(self): return self.lm_head
[docs] def prepare_inputs_for_generation(self, input_ids, past, **kwargs): # only last token for inputs_ids if past is defined in kwargs if past: input_ids = input_ids[:, -1].unsqueeze(-1) return {"input_ids": input_ids, "past_key_values": 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=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC, ) def forward( self, input_ids=None, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, use_cache=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 language modeling. Note that the labels **are shifted** inside the model, i.e. you can set ``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]`` """ if "past" in kwargs: warnings.warn( "The `past` argument is deprecated and will be removed in a future version, use `past_key_values` instead.", FutureWarning, ) past_key_values = kwargs.pop("past") assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}." return_dict = return_dict if return_dict is not None else self.config.use_return_dict transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, 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, ) hidden_states = transformer_outputs[0] lm_logits = self.lm_head(hidden_states) loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = lm_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) if not return_dict: output = (lm_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, )