dbed1c5d94
adding conversion script adding first draft of modeling & tokenization adding placeholder for test files bunch of changes registering the tokenizer/model/etc tests change link; something is very VERY wrong here weird end-of-word thingy going on i think the tokenization works now ; wrote the unit tests overall structure works;load w next the monster is alive! works after some cleanup as well adding emacs autosave to gitignore currently only supporting the 48 layer one; seems to infer fine on my macbook cleanup fixing some documentation fixing some documentation tests passing? now works on CUDA also adding greedy? adding greedy sampling works well
388 lines
17 KiB
Python
388 lines
17 KiB
Python
# coding=utf-8
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# Copyright 2018 Salesforce and HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch CTRL model."""
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from __future__ import absolute_import, division, print_function, unicode_literals
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import collections
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import json
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import logging
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import math
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import os
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import sys
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from io import open
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import numpy as np
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import torch
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import torch.nn as nn
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import pdb
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from torch.nn import CrossEntropyLoss
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from torch.nn.parameter import Parameter
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from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
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from .configuration_ctrl import CTRLConfig
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from .file_utils import add_start_docstrings
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logger = logging.getLogger(__name__)
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CTRL_PRETRAINED_MODEL_ARCHIVE_MAP = {"ctrl": "https://storage.googleapis.com/sf-ctrl/pytorch/seqlen256_v1.bin"}
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def angle_defn(pos, i, d_model_size):
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angle_rates = 1 / torch.pow(10000, (2 * (i//2)) / d_model_size)
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return pos * angle_rates
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def positional_encoding(position, d_model_size, dtype):
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# create the sinusoidal pattern for the positional encoding
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angle_rads = (angle_defn(torch.arange(position, dtype=dtype).unsqueeze(1), torch.arange(d_model_size, dtype=dtype).unsqueeze(0), d_model_size))
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sines = torch.sin(angle_rads[:, 0::2])
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cosines = torch.cos(angle_rads[:, 1::2])
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pos_encoding = torch.cat([sines, cosines], dim=-1).unsqueeze(0)
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return pos_encoding
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def scaled_dot_product_attention(q, k, v, mask):
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# calculate attention
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matmul_qk = torch.matmul(q, k.permute(0,1,3,2))
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dk = k.shape[-1]
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scaled_attention_logits = matmul_qk / np.sqrt(dk)
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if mask is not None:
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scaled_attention_logits += (mask * -1e4)
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attention_weights = torch.softmax(scaled_attention_logits, dim=-1)
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output = torch.matmul(attention_weights, v)
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return output, attention_weights
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class MultiHeadAttention(torch.nn.Module):
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def __init__(self, d_model_size, num_heads):
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super(MultiHeadAttention, self).__init__()
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self.num_heads = num_heads
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self.d_model_size = d_model_size
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self.depth = int(d_model_size / self.num_heads)
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self.Wq = torch.nn.Linear(d_model_size, d_model_size)
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self.Wk = torch.nn.Linear(d_model_size, d_model_size)
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self.Wv = torch.nn.Linear(d_model_size, d_model_size)
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self.dense = torch.nn.Linear(d_model_size, d_model_size)
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def split_into_heads(self, x, batch_size):
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x = x.reshape(batch_size, -1, self.num_heads, self.depth)
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return x.permute([0, 2, 1, 3])
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def forward(self, v, k, q, mask):
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batch_size = q.shape[0]
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q = self.Wq(q)
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k = self.Wk(k)
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v = self.Wv(v)
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q = self.split_into_heads(q, batch_size)
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k = self.split_into_heads(k, batch_size)
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v = self.split_into_heads(v, batch_size)
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output = scaled_dot_product_attention(q, k, v, mask)
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scaled_attention = output[0].permute([0, 2, 1, 3])
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attn = output[1]
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original_size_attention = scaled_attention.reshape(batch_size, -1, self.d_model_size)
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output = self.dense(original_size_attention)
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return output, attn
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def point_wise_feed_forward_network(d_model_size, dff):
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return torch.nn.Sequential(torch.nn.Linear(d_model_size, dff), torch.nn.ReLU(), torch.nn.Linear(dff, d_model_size))
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class EncoderLayer(torch.nn.Module):
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def __init__(self, d_model_size, num_heads, dff, rate=0.1):
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super(EncoderLayer, self).__init__()
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self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads)
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self.ffn = point_wise_feed_forward_network(d_model_size, dff)
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self.layernorm1 = torch.nn.LayerNorm(d_model_size, eps=1e-6)
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self.layernorm2 = torch.nn.LayerNorm(d_model_size, eps=1e-6)
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self.dropout1 = torch.nn.Dropout(rate)
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self.dropout2 = torch.nn.Dropout(rate)
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def forward(self, x, mask):
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normed = self.layernorm1(x)
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attn_output, attn = self.multi_head_attention(normed, normed, normed, mask)
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attn_output = self.dropout1(attn_output)
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out1 = x + attn_output
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out2 = self.layernorm2(out1)
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ffn_output = self.ffn(out2)
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ffn_output = self.dropout2(ffn_output)
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out2 = out1 + ffn_output
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return out2, attn
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class CTRLPreTrainedModel(PreTrainedModel):
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""" An abstract class to handle weights initialization and
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a simple interface for dowloading and loading pretrained models.
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"""
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config_class = CTRLConfig
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pretrained_model_archive_map = CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
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base_model_prefix = "transformer"
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def __init__(self, *inputs, **kwargs):
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super(CTRLPreTrainedModel, self).__init__(*inputs, **kwargs)
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def _init_weights(self, module):
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""" Initialize the weights.
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"""
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if isinstance(module, (nn.Linear, nn.Embedding, Conv1D)):
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# Slightly different from the TF version which uses truncated_normal for initialization
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# cf https://github.com/pytorch/pytorch/pull/5617
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
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if isinstance(module, (nn.Linear, Conv1D)) and module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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CTRL_START_DOCSTRING = r""" CTRL model was proposed in
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`CTRL: A Conditional Transformer Language Model for Controllable Generation`_
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by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
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It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
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corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
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This model is a PyTorch `torch.nn.Module`_ sub-class. Use it as a regular PyTorch Module and
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refer to the PyTorch documentation for all matter related to general usage and behavior.
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.. _`CTRL: A Conditional Transformer Language Model for Controllable Generation`:
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https://www.github.com/salesforce/ctrl
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.. _`torch.nn.Module`:
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https://pytorch.org/docs/stable/nn.html#module
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Parameters:
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config (:class:`~transformers.CTRLConfig`): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the configuration.
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Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
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"""
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CTRL_INPUTS_DOCSTRING = r""" Inputs:
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**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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Indices of input sequence tokens in the vocabulary.
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CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on
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the right rather than the left.
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Indices can be obtained using :class:`transformers.CTRLTokenizer`.
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See :func:`transformers.PreTrainedTokenizer.encode` and
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:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
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**past**:
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list of ``torch.FloatTensor`` (one for each layer):
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that contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
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(see `past` output below). Can be used to speed up sequential decoding.
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**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
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Mask to avoid performing attention on padding token indices.
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Mask values selected in ``[0, 1]``:
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``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
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**token_type_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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A parallel sequence of tokens (can be used to indicate various portions of the inputs).
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The embeddings from these tokens will be summed with the respective token embeddings.
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Indices are selected in the vocabulary (unlike BERT which has a specific vocabulary for segment indices).
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**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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Indices of positions of each input sequence tokens in the position embeddings.
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Selected in the range ``[0, config.max_position_embeddings - 1]``.
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**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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Mask to nullify selected heads of the self-attention modules.
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Mask values selected in ``[0, 1]``:
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``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
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"""
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@add_start_docstrings("The bare CTRL Model transformer outputting raw hidden-states without any specific head on top.",
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CTRL_START_DOCSTRING, CTRL_INPUTS_DOCSTRING)
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class CTRLModel(CTRLPreTrainedModel):
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r"""
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Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
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**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
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Sequence of hidden-states at the last layer of the model.
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**past**:
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list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
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that contains pre-computed hidden-states (key and values in the attention blocks).
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Can be used (see `past` input) to speed up sequential decoding.
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**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
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list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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of shape ``(batch_size, sequence_length, hidden_size)``:
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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**attentions**: (`optional`, returned when ``config.output_attentions=True``)
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list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
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Examples::
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tokenizer = CTRLTokenizer.from_pretrained('ctrl')
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model = CTRLModel.from_pretrained('ctrl')
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input_ids = torch.tensor(tokenizer.encode("Links Hello, my dog is cute")).unsqueeze(0) # Batch size 1
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outputs = model(input_ids)
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last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
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"""
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def __init__(self, config):
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super(CTRLModel, self).__init__(config)
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self.output_hidden_states = config.output_hidden_states
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self.d_model_size = config.n_embd
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self.num_layers = config.n_layer
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self.pos_encoding = positional_encoding(config.n_positions, self.d_model_size, torch.float)
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self.output_attentions = config.output_attentions
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self.w = nn.Embedding(config.vocab_size, config.n_embd)
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self.dropout = nn.Dropout(config.embd_pdrop)
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self.h = nn.ModuleList([EncoderLayer(config.n_embd, config.n_head, config.dff, config.resid_pdrop) for _ in range(config.n_layer)])
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self.layernorm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
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self.init_weights()
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def _resize_token_embeddings(self, new_num_tokens):
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self.w = self._get_resized_embeddings(self.w, new_num_tokens)
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return self.w
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def _prune_heads(self, heads_to_prune):
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""" Prunes heads of the model.
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heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
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"""
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for layer, heads in heads_to_prune.items():
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self.h[layer].attn.prune_heads(heads)
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def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
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labels=None):
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embedded = self.w(input_ids)
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x = embedded.unsqueeze(0) if len(input_ids.shape)<2 else embedded
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seq_len = input_ids.shape[1]
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mask = torch.triu(torch.ones(seq_len, seq_len), 1).to(x.device)
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x *= np.sqrt(self.d_model_size)
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x += self.pos_encoding[:, :seq_len, :].to(x.device)
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x = self.dropout(x)
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all_hidden_states = ()
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all_attentions = []
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for i in range(self.num_layers):
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if self.output_hidden_states:
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all_hidden_states = all_hidden_states + (x,)
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x, attn = self.h[i](x, mask)
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if self.output_attentions:
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all_attentions.append(attn)
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x = self.layernorm(x)
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if self.output_hidden_states:
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all_hidden_states = all_hidden_states + (x,)
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outputs = (x, None)
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if self.output_hidden_states:
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outputs = outputs + (all_hidden_states,)
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if self.output_attentions:
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outputs = outputs + (all_attentions,)
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return outputs
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@add_start_docstrings("""The CTRL Model transformer with a language modeling head on top
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(linear layer with weights tied to the input embeddings). """, CTRL_START_DOCSTRING, CTRL_INPUTS_DOCSTRING)
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class CTRLLMHeadModel(CTRLPreTrainedModel):
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r"""
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**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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Labels for language modeling.
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Note that the labels **are shifted** inside the model, i.e. you can set ``lm_labels = input_ids``
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Indices are selected in ``[-1, 0, ..., config.vocab_size]``
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All labels set to ``-1`` are ignored (masked), the loss is only
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computed for labels in ``[0, ..., config.vocab_size]``
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Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
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**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
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Language modeling loss.
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**prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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**past**:
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list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
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that contains pre-computed hidden-states (key and values in the attention blocks).
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Can be used (see `past` input) to speed up sequential decoding.
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**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
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list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
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of shape ``(batch_size, sequence_length, hidden_size)``:
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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**attentions**: (`optional`, returned when ``config.output_attentions=True``)
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list of ``torch.FloatTensor`` (one for each layer) of shape ``(batch_size, num_heads, sequence_length, sequence_length)``:
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
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Examples::
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import torch
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from transformers import CTRLTokenizer, CTRLLMHeadModel
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tokenizer = CTRLTokenizer.from_pretrained('ctrl')
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model = CTRLLMHeadModel.from_pretrained('ctrl')
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input_ids = torch.tensor(tokenizer.encode("Links Hello, my dog is cute")).unsqueeze(0) # Batch size 1
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outputs = model(input_ids, labels=input_ids)
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loss, logits = outputs[:2]
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"""
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def __init__(self, config):
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super(CTRLLMHeadModel, self).__init__(config)
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self.transformer = CTRLModel(config)
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self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=True)
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self.init_weights()
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self.tie_weights()
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def tie_weights(self):
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""" Make sure we are sharing the input and output embeddings.
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Export to TorchScript can't handle parameter sharing so we are cloning them instead.
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"""
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self._tie_or_clone_weights(self.lm_head,
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self.transformer.w)
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#self._tie_or_clone_weights(self.lm_head.bias,
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# self.transformer.w.bias)
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def forward(self, input_ids, past=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None,
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labels=None):
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transformer_outputs = self.transformer(input_ids)
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hidden_states = transformer_outputs[0]
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lm_logits = self.lm_head(hidden_states)
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outputs = (lm_logits,) + transformer_outputs[1:]
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if labels is not None:
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# Shift so that tokens < n predict n
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shift_logits = lm_logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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loss_fct = CrossEntropyLoss(ignore_index=-1)
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loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
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shift_labels.view(-1))
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outputs = (loss,) + outputs
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return outputs # (loss), lm_logits, presents, (all hidden_states), (attentions)
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