# 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 # # 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 CTRL model.""" import logging 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_start_docstrings, add_start_docstrings_to_callable from .modeling_utils import Conv1D, PreTrainedModel logger = logging.getLogger(__name__) CTRL_PRETRAINED_MODEL_ARCHIVE_MAP = {"ctrl": "https://storage.googleapis.com/sf-ctrl/pytorch/seqlen256_v1.bin"} 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), d_model_size, ) sines = torch.sin(angle_rads[:, 0::2]) cosines = torch.cos(angle_rads[:, 1::2]) pos_encoding = torch.cat([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, output_attentions=False): super().__init__() self.output_attentions = output_attentions 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) 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(self, v, k, q, mask, layer_past=None, attention_mask=None, head_mask=None): 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 = torch.cat((past_key, k), dim=-2) v = torch.cat((past_value, v), dim=-2) present = torch.stack((k, v)) 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 self.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, output_attentions=False): super().__init__() self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads, output_attentions) 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): normed = self.layernorm1(x) attn_outputs = self.multi_head_attention( normed, normed, normed, mask, layer_past=layer_past, attention_mask=attention_mask, head_mask=head_mask ) 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 pretrained_model_archive_map = CTRL_PRETRAINED_MODEL_ARCHIVE_MAP 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 https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) CTRL_START_DOCSTRING = r""" 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. Parameters: 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. """ CTRL_INPUTS_DOCSTRING = r""" Args: input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using :class:`transformers.CTRLTokenizer`. See :func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.encode_plus` for details. `What are input IDs? <../glossary.html#input-ids>`__ past (: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` 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:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): 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`, defaults to :obj:`None`): 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`, defaults to :obj:`None`): 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`, defaults to :obj:`None`): 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**. input_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`): Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. """ @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.output_hidden_states = config.output_hidden_states self.output_attentions = config.output_attentions self.output_past = config.output_past 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, config.output_attentions) for _ in range(config.n_layer) ] ) self.layernorm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.init_weights() def get_input_embeddings(self): return self.w 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].attn.prune_heads(heads) @add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING) def forward( self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, ): r""" Return: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) and inputs: last_hidden_state (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the last layer of the model. past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`): Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) 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. hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: from transformers import CTRLTokenizer, CTRLModel import torch tokenizer = CTRLTokenizer.from_pretrained('ctrl') model = CTRLModel.from_pretrained('ctrl') input_ids = torch.tensor(tokenizer.encode("Links Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 outputs = model(input_ids) last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple """ 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]) elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if past is None: past_length = 0 past = [None] * len(self.h) else: past_length = past[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: attention_mask = attention_mask.view(-1, input_shape[-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 = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility attention_mask = (1.0 - attention_mask) * -10000.0 # 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: if head_mask.dim() == 1: head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1) head_mask = head_mask.expand(self.config.n_layer, -1, -1, -1, -1) elif head_mask.dim() == 2: head_mask = ( head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) ) # We can specify head_mask for each layer head_mask = head_mask.to( dtype=next(self.parameters()).dtype ) # switch to fload if need + fp16 compatibility else: head_mask = [None] * 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 = () all_hidden_states = () all_attentions = [] for i, (h, layer_past) in enumerate(zip(self.h, past)): if self.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] ) hidden_states, present = outputs[:2] if self.output_past: presents = presents + (present,) if self.output_attentions: all_attentions.append(outputs[2]) hidden_states = self.layernorm(hidden_states) hidden_states = hidden_states.view(*output_shape) if self.output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) outputs = (hidden_states,) if self.output_past: outputs = outputs + (presents,) if self.output_hidden_states: outputs = outputs + (all_hidden_states,) if self.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) outputs = outputs + (all_attentions,) return outputs @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() def get_output_embeddings(self): return self.lm_head 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": past} @add_start_docstrings_to_callable(CTRL_INPUTS_DOCSTRING) def forward( self, input_ids=None, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None, ): r""" labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`): Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set ``lm_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]`` Return: :obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.CTRLConfig`) and inputs: loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when ``labels`` is provided) Language modeling loss. prediction_scores (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past (:obj:`List[torch.FloatTensor]` of length :obj:`config.n_layers` with each tensor of shape :obj:`(2, batch_size, num_heads, sequence_length, embed_size_per_head)`): Contains pre-computed hidden-states (key and values in the attention blocks). Can be used (see `past` input) 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. hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``): Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape :obj:`(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``): Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Examples:: import torch from transformers import CTRLTokenizer, CTRLLMHeadModel tokenizer = CTRLTokenizer.from_pretrained('ctrl') model = CTRLLMHeadModel.from_pretrained('ctrl') input_ids = torch.tensor(tokenizer.encode("Links Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # Batch size 1 outputs = model(input_ids, labels=input_ids) loss, logits = outputs[:2] """ transformer_outputs = self.transformer( input_ids, past=past, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, ) hidden_states = transformer_outputs[0] lm_logits = self.lm_head(hidden_states) outputs = (lm_logits,) + transformer_outputs[1:] 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)) outputs = (loss,) + outputs return outputs # (loss), lm_logits, presents, (all hidden_states), (attentions)