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roberta and distilbert

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thomwolf 2019-09-20 23:14:51 +02:00
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pytorch_transformers/modeling_tf_bert.py
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@ -145,7 +145,6 @@ class TFBertEmbeddings(tf.keras.layers.Layer):
def _embedding(self, inputs, training=False):
"""Applies embedding based on inputs tensor."""
# Create binary mask of size [batch_size, length]
input_ids, position_ids, token_type_ids = inputs
seq_length = tf.shape(input_ids)[1]

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pytorch_transformers/modeling_tf_distilbert.py Normal file
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@ -0,0 +1,714 @@
# coding=utf-8
# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
#
# 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.
""" TF 2.0 DistilBERT model
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import math
import copy
import sys
from io import open
import itertools
import numpy as np
import tensorflow as tf
from .configuration_distilbert import DistilBertConfig
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list
from .file_utils import add_start_docstrings
from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
logger = logging.getLogger(__name__)
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-tf_model.h5",
'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-tf_model.h5"
}
### UTILS AND BUILDING BLOCKS OF THE ARCHITECTURE ###
def gelu(x):
""" Gaussian Error Linear Unit.
Original Implementation of the gelu activation function in Google Bert repo when initialy created.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
Also see https://arxiv.org/abs/1606.08415
"""
cdf = 0.5 * (1.0 + tf.math.erf(x / tf.math.sqrt(2.0)))
return x * cdf
def gelu_new(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
`x` with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.tanh(
(np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
return x * cdf
def load_distilbert_pt_weights_in_tf2(tf_model, pytorch_checkpoint_path):
# build the network
inputs_list = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
attns_list = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
tf_inputs = [inputs_list, attns_list]
tfo = tf_model(tf_inputs, training=False)
return load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=tf_inputs)
class TFEmbeddings(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFEmbeddings, self).__init__(**kwargs)
self.vocab_size = config.vocab_size
self.dim = config.dim
self.word_embeddings = TFSharedEmbeddings(, name='word_embeddings') # padding_idx=0)
self.position_embeddings = tf.keras.layers.Embedding(config.max_position_embeddings, config.dim, name='position_embeddings')
if config.sinusoidal_embeddings:
raise NotImplementedError
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="LayerNorm")
self.dropout = tf.keras.layers.Dropout(config.dropout)
def build(self, input_shape):
"""Build shared word embedding layer """
with tf.name_scope("word_embeddings"):
# Create and initialize weights. The random normal initializer was chosen
# arbitrarily, and works well.
self.word_embeddings = self.add_weight(
"weight",
shape=[self.vocab_size, self.hidden_size],
initializer=tf.random_normal_initializer(
mean=0., stddev=self.hidden_size**-0.5))
super(TFEmbeddings, self).build(input_shape)
def call(self, inputs, mode="embedding", training=False):
"""Get token embeddings of inputs.
Args:
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
mode: string, a valid value is one of "embedding" and "linear".
Returns:
outputs: (1) If mode == "embedding", output embedding tensor, float32 with
shape [batch_size, length, embedding_size]; (2) mode == "linear", output
linear tensor, float32 with shape [batch_size, length, vocab_size].
Raises:
ValueError: if mode is not valid.
Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
if mode == "embedding":
return self._embedding(inputs, training=training)
elif mode == "linear":
return self._linear(inputs)
else:
raise ValueError("mode {} is not valid.".format(mode))
def _embedding(self, inputs, training=False):
"""
Parameters
----------
input_ids: torch.tensor(bs, max_seq_length)
The token ids to embed.
Outputs
-------
embeddings: torch.tensor(bs, max_seq_length, dim)
The embedded tokens (plus position embeddings, no token_type embeddings)
"""
input_ids, position_ids = inputs
seq_length = tf.shape(input_ids)[1]
if position_ids is None:
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
words_embeddings = tf.gather(self.word_embeddings, input_ids)
position_embeddings = self.position_embeddings(position_ids) # (bs, max_seq_length, dim)
embeddings = word_embeddings + position_embeddings # (bs, max_seq_length, dim)
embeddings = self.LayerNorm(embeddings) # (bs, max_seq_length, dim)
embeddings = self.dropout(embeddings, training=training) # (bs, max_seq_length, dim)
return embeddings
def _linear(self, inputs):
"""Computes logits by running inputs through a linear layer.
Args:
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
Returns:
float32 tensor with shape [batch_size, length, vocab_size].
"""
batch_size = tf.shape(inputs)[0]
length = tf.shape(inputs)[1]
x = tf.reshape(inputs, [-1, self.hidden_size])
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
return tf.reshape(logits, [batch_size, length, self.vocab_size])
class TFMultiHeadSelfAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFMultiHeadSelfAttention, self).__init__(**kwargs)
self.n_heads = config.n_heads
self.dim = config.dim
self.dropout = nn.Dropout(p=config.attention_dropout)
self.output_attentions = config.output_attentions
assert self.dim % self.n_heads == 0
self.q_lin = tf.keras.layers.Dense(config.dim, name="q_lin")
self.k_lin = tf.keras.layers.Dense(config.dim, name="k_lin")
self.v_lin = tf.keras.layers.Dense(config.dim, name="v_lin")
self.out_lin = tf.keras.layers.Dense(config.dim, name="out_lin")
self.pruned_heads = set()
def prune_heads(self, heads):
raise NotImplementedError
def call(self, inputs, training=False):
"""
Parameters
----------
query: torch.tensor(bs, seq_length, dim)
key: torch.tensor(bs, seq_length, dim)
value: torch.tensor(bs, seq_length, dim)
mask: torch.tensor(bs, seq_length)
Outputs
-------
weights: torch.tensor(bs, n_heads, seq_length, seq_length)
Attention weights
context: torch.tensor(bs, seq_length, dim)
Contextualized layer. Optional: only if `output_attentions=True`
"""
query, key, value, mask, head_mask = inputs
bs, q_length, dim = shape_list(query)
k_length = shape_list(key)[1]
# assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
# assert key.size() == value.size()
dim_per_head = self.dim // self.n_heads
assert 2 <= len(tf.shape(mask)) <= 3
causal = (len(tf.shape(mask)) == 3)
mask_reshp = [bs, 1, 1, k_length]
def shape(x):
""" separate heads """
return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))
def unshape(x):
""" group heads """
return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))
q = shape(self.q_lin(query)) # (bs, n_heads, q_length, dim_per_head)
k = shape(self.k_lin(key)) # (bs, n_heads, k_length, dim_per_head)
v = shape(self.v_lin(value)) # (bs, n_heads, k_length, dim_per_head)
q = q / math.sqrt(dim_per_head) # (bs, n_heads, q_length, dim_per_head)
scores = tf.matmul(q, k, transpose_b=True) # (bs, n_heads, q_length, k_length)
mask = tf.reshape(mask, mask_reshape) # (bs, n_heads, qlen, klen)
# scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, q_length, k_length)
scores = scores - 1e30 * (1.0 - mask)
weights = tf.nn.softmax(scores, axis=-1) # (bs, n_heads, qlen, klen)
weights = self.dropout(weights, training=training) # (bs, n_heads, qlen, klen)
# Mask heads if we want to
if head_mask is not None:
weights = weights * head_mask
context = tf.matmul(weights, v) # (bs, n_heads, qlen, dim_per_head)
context = unshape(context) # (bs, q_length, dim)
context = self.out_lin(context) # (bs, q_length, dim)
if self.output_attentions:
return (context, weights)
else:
return (context,)
class TFFFN(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFFFN, self).__init__(**kwargs)
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.lin1 = tf.keras.layers.Dense(config.hidden_dim, name="lin1")
self.lin2 = tf.keras.layers.Dense(config.dim, name="lin2")
assert config.activation in ['relu', 'gelu'], "activation ({}) must be in ['relu', 'gelu']".format(config.activation)
self.activation = tf.keras.layers.Activation(gelu) if config.activation=='gelu' else tf.keras.activations.relu
def call(self, input, training=False):
x = self.lin1(input)
x = self.activation(x)
x = self.lin2(x)
x = self.dropout(x, training=training)
return x
class TFTransformerBlock(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFTransformerBlock, self).__init__(**kwargs)
self.n_heads = config.n_heads
self.dim = config.dim
self.hidden_dim = config.hidden_dim
self.dropout = tf.keras.layers.Dropout(config.dropout)
self.activation = config.activation
self.output_attentions = config.output_attentions
assert config.dim % config.n_heads == 0
self.attention = TFMultiHeadSelfAttention(config, name="attention")
self.sa_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="sa_layer_norm")
self.ffn = TFFFN(config, name="ffn")
self.output_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="output_layer_norm")
def call(self, inputs, training=False): # removed: src_enc=None, src_len=None
"""
Parameters
----------
x: torch.tensor(bs, seq_length, dim)
attn_mask: torch.tensor(bs, seq_length)
Outputs
-------
sa_weights: torch.tensor(bs, n_heads, seq_length, seq_length)
The attention weights
ffn_output: torch.tensor(bs, seq_length, dim)
The output of the transformer block contextualization.
"""
x, attn_mask, head_mask = inputs
# Self-Attention
sa_output = self.attention([x, x, x, attn_mask, head_mask], training=training)
if self.output_attentions:
sa_output, sa_weights = sa_output # (bs, seq_length, dim), (bs, n_heads, seq_length, seq_length)
else: # To handle these `output_attention` or `output_hidden_states` cases returning tuples
# assert type(sa_output) == tuple
sa_output = sa_output[0]
sa_output = self.sa_layer_norm(sa_output + x) # (bs, seq_length, dim)
# Feed Forward Network
ffn_output = self.ffn(sa_output, training=training) # (bs, seq_length, dim)
ffn_output = self.output_layer_norm(ffn_output + sa_output) # (bs, seq_length, dim)
output = (ffn_output,)
if self.output_attentions:
output = (sa_weights,) + output
return output
class TFTransformer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFTransformer, self).__init__(**kwargs)
self.n_layers = config.n_layers
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.layer = [TFTransformerBlock(config, name='layer_._{}'.format(i))
for i in range(config.n_layers)]
def forward(self, inputs, training=False):
"""
Parameters
----------
x: torch.tensor(bs, seq_length, dim)
Input sequence embedded.
attn_mask: torch.tensor(bs, seq_length)
Attention mask on the sequence.
Outputs
-------
hidden_state: torch.tensor(bs, seq_length, dim)
Sequence of hiddens states in the last (top) layer
all_hidden_states: Tuple[torch.tensor(bs, seq_length, dim)]
Tuple of length n_layers with the hidden states from each layer.
Optional: only if output_hidden_states=True
all_attentions: Tuple[torch.tensor(bs, n_heads, seq_length, seq_length)]
Tuple of length n_layers with the attention weights from each layer
Optional: only if output_attentions=True
"""
x, attn_mask, head_mask = inputs
all_hidden_states = ()
all_attentions = ()
hidden_state = x
for i, layer_module in enumerate(self.layer):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_state,)
layer_outputs = layer_module([hidden_state, attn_mask, head_mask[i]], training=training)
hidden_state = layer_outputs[-1]
if self.output_attentions:
assert len(layer_outputs) == 2
attentions = layer_outputs[0]
all_attentions = all_attentions + (attentions,)
else:
assert len(layer_outputs) == 1
# Add last layer
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_state,)
outputs = (hidden_state,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.output_attentions:
outputs = outputs + (all_attentions,)
return outputs # last-layer hidden state, (all hidden states), (all attentions)
class TFDistilBertMainLayer(tf.keras.layers.Layer):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the output of the last layer of the model.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertModel.from_pretrained('distilbert-base-uncased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).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
"""
def __init__(self, config, **kwargs):
super(TFDistilBertMainLayer, self).__init__(**kwargs)
self.embeddings = TFEmbeddings(config, name="embeddings") # Embeddings
self.transformer = TFTransformer(config, name="transformer") # Encoder
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
def _prune_heads(self, heads_to_prune):
raise NotImplementedError
def call(self, inputs, training=False):
if not isinstance(inputs, (dict, tuple, list)):
input_ids = inputs
(attention_mask, head_mask) = None, None
elif isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else None
head_mask = inputs[2] if len(inputs) > 2 else None
assert len(inputs) <= 3, "Too many inputs."
else:
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', None)
head_mask = inputs.get('head_mask', None)
assert len(inputs) <= 3, "Too many inputs."
if attention_mask is None:
attention_mask = tf.ones(shape_list(input_ids)) # (bs, seq_length)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.config.num_hidden_layers
embedding_output = self.embeddings(input_ids) # (bs, seq_length, dim)
tfmr_output = self.transformer([embedding_output, attention_mask, head_mask], training=training)
hidden_state = tfmr_output[0]
output = (hidden_state, ) + tfmr_output[1:]
return output # last-layer hidden-state, (all hidden_states), (all attentions)
### INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL ###
class TFDistilBertPreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
config_class = DistilBertConfig
pretrained_model_archive_map = TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP
load_pt_weights = load_distilbert_pt_weights_in_tf2
base_model_prefix = "distilbert"
DISTILBERT_START_DOCSTRING = r"""
DistilBERT is a small, fast, cheap and light Transformer model
trained by distilling Bert base. It has 40% less parameters than
`bert-base-uncased`, runs 60% faster while preserving over 95% of
Bert's performances as measured on the GLUE language understanding benchmark.
Here are the differences between the interface of Bert and DistilBert:
- DistilBert doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`)
- DistilBert doesn't have options to select the input positions (`position_ids` input). This could be added if necessary though, just let's us know if you need this option.
For more information on DistilBERT, please refer to our
`detailed blog post`_
.. _`detailed blog post`:
https://medium.com/huggingface/distilbert-8cf3380435b5
Parameters:
config (:class:`~pytorch_transformers.DistilBertConfig`): 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:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
DISTILBERT_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids** ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
The input sequences should start with `[CLS]` and end with `[SEP]` tokens.
For now, ONLY BertTokenizer(`bert-base-uncased`) is supported and you should use this tokenizer when using DistilBERT.
**attention_mask**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
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.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**.
"""
@add_start_docstrings("The bare DistilBERT encoder/transformer outputing raw hidden-states without any specific head on top.",
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
class DistilBertModel(DistilBertPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the output of the last layer of the model.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertModel.from_pretrained('distilbert-base-uncased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).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
"""
def __init__(self, config, *inputs, **kwargs):
super(DistilBertModel, self).__init__(config, *inputs, **kwargs)
self.distilbert = TFDistilBertMainLayer(config, name="distilbert") # Embeddings
def call(self, inputs, training=False):
outputs = self.distilbert(inputs, training=training)
return outputs
@add_start_docstrings("""DistilBert Model with a `masked language modeling` head on top. """,
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel):
r"""
**masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for computing the masked language modeling loss.
Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Masked language modeling loss.
**prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForMaskedLM.from_pretrained('distilbert-base-uncased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, masked_lm_labels=input_ids)
loss, prediction_scores = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFDistilBertForMaskedLM, self).__init__(config, *inputs, **kwargs)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.distilbert = TFDistilBertMainLayer(config, name="distilbert")
self.vocab_transform = tf.keras.layers.Dense(config.dim, name="vocab_transform")
self.act = tf.keras.layers.Activation(gelu)
self.vocab_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="vocab_layer_norm")
self.vocab_projector_weight = self.distilbert.embeddings
def build(self, input_shape):
self.vocab_projector_bias = self.add_weight(shape=(self.vocab_size,),
initializer='zeros',
trainable=True,
name='vocab_projector_._bias')
super(TFDistilBertForMaskedLM, self).build(input_shape)
def call(self, inputs, training=False):
dlbrt_output = self.distilbert(inputs, training=training)
hidden_states = dlbrt_output[0] # (bs, seq_length, dim)
prediction_logits = self.vocab_transform(hidden_states) # (bs, seq_length, dim)
prediction_logits = self.act(prediction_logits) # (bs, seq_length, dim)
prediction_logits = self.vocab_layer_norm(prediction_logits) # (bs, seq_length, dim)
prediction_logits = self.vocab_projector_weight(prediction_logits, mode='linear') + self.vocab_projector_bias
outputs = (prediction_logits, ) + dlbrt_output[1:]
return outputs # prediction_logits, (all hidden_states), (all attentions)
@add_start_docstrings("""DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
class TFDistilBertForSequenceClassification(TFDistilBertPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for computing the sequence classification/regression loss.
Indices should be in ``[0, ..., config.num_labels - 1]``.
If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification (or regression if config.num_labels==1) loss.
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFDistilBertForSequenceClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.distilbert = TFDistilBertModel(config, name="distilbert")
self.pre_classifier = tf.keras.layers.Dense(config.dim, activation='relu', name="pre_classifier")
self.classifier = tf.keras.layers.Dense(config.num_labels, name="classifier")
self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)
def call(self, inputs, training=False):
distilbert_output = self.distilbert(inputs, training=training)
hidden_state = distilbert_output[0] # (bs, seq_len, dim)
pooled_output = hidden_state[:, 0] # (bs, dim)
pooled_output = self.pre_classifier(pooled_output) # (bs, dim)
pooled_output = self.dropout(pooled_output, training=training) # (bs, dim)
logits = self.classifier(pooled_output) # (bs, dim)
outputs = (logits,) + distilbert_output[1:]
return outputs # logits, (hidden_states), (attentions)
@add_start_docstrings("""DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
the hidden-states output to compute `span start logits` and `span end logits`). """,
DISTILBERT_START_DOCSTRING, DISTILBERT_INPUTS_DOCSTRING)
class TFDistilBertForQuestionAnswering(TFDistilBertPreTrainedModel):
r"""
**start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
**end_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
**start_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-start scores (before SoftMax).
**end_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length,)``
Span-end scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForQuestionAnswering.from_pretrained('distilbert-base-uncased')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
start_positions = torch.tensor([1])
end_positions = torch.tensor([3])
outputs = model(input_ids, start_positions=start_positions, end_positions=end_positions)
loss, start_scores, end_scores = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFDistilBertForQuestionAnswering, self).__init__(config, *inputs, **kwargs)
self.distilbert = TFDistilBertModel(config, name="distilbert")
self.qa_outputs = tf.keras.layers.Dense(config.num_labels, name='qa_output')
assert config.num_labels == 2
self.dropout = tf.keras.layers.Dropout(config.qa_dropout)
def call(self, inputs, training=False):
distilbert_output = self.distilbert(inputs, training=training)
hidden_states = distilbert_output[0] # (bs, max_query_len, dim)
hidden_states = self.dropout(hidden_states, training=training) # (bs, max_query_len, dim)
logits = self.qa_outputs(hidden_states) # (bs, max_query_len, 2)
start_logits, end_logits = tf.split(logits, 2, axis=-1)
start_logits = tf.squeeze(start_logits, axis=-1)
end_logits = tf.squeeze(end_logits, axis=-1)
outputs = (start_logits, end_logits,) + distilbert_output[1:]
return outputs # start_logits, end_logits, (hidden_states), (attentions)

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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The 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.
""" TF 2.0 RoBERTa model. """
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import logging
import numpy as np
import tensorflow as tf
from .configuration_roberta import RobertaConfig
from .modeling_tf_utils import TFPreTrainedModel
from .file_utils import add_start_docstrings
from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
from .modeling_tf_bert import TFBertEmbeddings, TFBertMainLayer, gelu, gelu_new
logger = logging.getLogger(__name__)
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = {
'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-tf_model.h5",
'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-tf_model.h5",
'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-tf_model.h5",
}
def load_roberta_pt_weights_in_tf2(tf_model, pytorch_checkpoint_path):
# build the network
inputs_list = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
tf_inputs = tf.constant(inputs_list)
tfo = tf_model(tf_inputs, training=False)
return load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=tf_inputs)
class TFRobertaEmbeddings(TFBertEmbeddings):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config, **kwargs):
super(TFRobertaEmbeddings, self).__init__(config, **kwargs)
self.padding_idx = 1
def _embedding(self, inputs, training=False):
"""Applies embedding based on inputs tensor."""
input_ids, position_ids, token_type_ids = inputs
seq_length = tf.shape(input_ids)[1]
if position_ids is None:
position_ids = tf.range(self.padding_idx+1, seq_length+self.padding_idx+1, dtype=tf.int32)[tf.newaxis, :]
return super(TFRobertaEmbeddings, self)._embedding([input_ids, position_ids, token_type_ids], training=training)
class TFRobertaMainLayer(TFBertMainLayer):
"""
Same as TFBertMainLayer but uses TFRobertaEmbeddings.
"""
def __init__(self, config, **kwargs):
super(TFRobertaMainLayer, self).__init__(config, **kwargs)
self.embeddings = TFRobertaEmbeddings(config, name='embeddings')
def call(self, inputs, training=False):
# Check that input_ids starts with control token
if not isinstance(inputs, (dict, tuple, list)):
input_ids = inputs
elif isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
else:
input_ids = inputs.get('input_ids')
if tf.not_equal(tf.reduce_sum(input_ids[:, 0]), 0):
logger.warning("A sequence with no special tokens has been passed to the RoBERTa model. "
"This model requires special tokens in order to work. "
"Please specify add_special_tokens=True in your encoding.")
return super(TFRobertaMainLayer, self).call(inputs, training=training)
class TFRobertaPreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = RobertaConfig
pretrained_model_archive_map = TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP
load_pt_weights = load_roberta_pt_weights_in_tf2
base_model_prefix = "roberta"
ROBERTA_START_DOCSTRING = r""" The RoBERTa model was proposed in
`RoBERTa: A Robustly Optimized BERT Pretraining Approach`_
by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer,
Veselin Stoyanov. It is based on Google's BERT model released in 2018.
It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining
objective and training with much larger mini-batches and learning rates.
This implementation is the same as BertModel with a tiny embeddings tweak as well as a setup for Roberta pretrained
models.
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.
.. _`RoBERTa: A Robustly Optimized BERT Pretraining Approach`:
https://arxiv.org/abs/1907.11692
.. _`torch.nn.Module`:
https://pytorch.org/docs/stable/nn.html#module
Parameters:
config (:class:`~pytorch_transformers.RobertaConfig`): 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:`~pytorch_transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
ROBERTA_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
To match pre-training, RoBERTa input sequence should be formatted with <s> and </s> tokens as follows:
(a) For sequence pairs:
``tokens: <s> Is this Jacksonville ? </s> </s> No it is not . </s>``
(b) For single sequences:
``tokens: <s> the dog is hairy . </s>``
Fully encoded sequences or sequence pairs can be obtained using the RobertaTokenizer.encode function with
the ``add_special_tokens`` parameter set to ``True``.
RoBERTa is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
See :func:`pytorch_transformers.PreTrainedTokenizer.encode` and
:func:`pytorch_transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
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.
**token_type_ids**: (`optional` need to be trained) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Optional segment token indices to indicate first and second portions of the inputs.
This embedding matrice is not trained (not pretrained during RoBERTa pretraining), you will have to train it
during finetuning.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
(see `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**position_ids**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1[``.
**head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
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**.
"""
@add_start_docstrings("The bare RoBERTa Model transformer outputing raw hidden-states without any specific head on top.",
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class TFRobertaModel(TFRobertaPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, hidden_size)``
Sequence of hidden-states at the output of the last layer of the model.
**pooler_output**: ``torch.FloatTensor`` of shape ``(batch_size, hidden_size)``
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
objective during Bert pretraining. This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).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
"""
def __init__(self, config, *inputs, **kwargs):
super(TFRobertaModel, self).__init__(config, *inputs, **kwargs)
self.roberta = TFRobertaMainLayer(config, name='roberta')
def call(self, inputs, training=False):
outputs = self.roberta(inputs, training=training)
return outputs
class TFRobertaLMHead(tf.keras.layers.Layer):
"""Roberta Head for masked language modeling."""
def __init__(self, config, input_embeddings, **kwargs):
super(TFRobertaLMHead, self).__init__(**kwargs)
self.vocab_size = config.vocab_size
self.dense = tf.keras.layers.Dense(config.hidden_size, name='dense')
self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name='layer_norm')
self.act = tf.keras.layers.Activation(gelu)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = input_embeddings
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.vocab_size,),
initializer='zeros',
trainable=True,
name='bias')
super(TFRobertaLMHead, self).build(input_shape)
def call(self, features):
x = self.dense(features)
x = self.act(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = self.decoder(x, mode="linear") + self.bias
return x
@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """,
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class TFRobertaForMaskedLM(TFRobertaPreTrainedModel):
r"""
**masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
Labels for computing the masked language modeling loss.
Indices should be in ``[-1, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
Tokens with indices set to ``-1`` are ignored (masked), the loss is only computed for the tokens with labels
in ``[0, ..., config.vocab_size]``
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``masked_lm_labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Masked language modeling loss.
**prediction_scores**: ``torch.FloatTensor`` of shape ``(batch_size, sequence_length, config.vocab_size)``
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForMaskedLM.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, masked_lm_labels=input_ids)
loss, prediction_scores = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFRobertaForMaskedLM, self).__init__(config, *inputs, **kwargs)
self.roberta = TFRobertaMainLayer(config, name="roberta")
self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head")
def call(self, inputs, training=False):
outputs = self.roberta(inputs, training=training)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
return outputs # prediction_scores, (hidden_states), (attentions)
class TFRobertaClassificationHead(tf.keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config, **kwargs):
super(TFRobertaClassificationHead, self).__init__(config, **kwargs)
self.dense = tf.keras.layers.Dense(config.hidden_size, activation='tanh', name="dense")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.out_proj = tf.keras.layers.Dense(config.num_labels, name="out_proj")
def call(self, features, training=False):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x, training=training)
x = self.dense(x)
x = self.dropout(x, training=training)
x = self.out_proj(x)
return x
@add_start_docstrings("""RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer
on top of the pooled output) e.g. for GLUE tasks. """,
ROBERTA_START_DOCSTRING, ROBERTA_INPUTS_DOCSTRING)
class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel):
r"""
**labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
Labels for computing the sequence classification/regression loss.
Indices should be in ``[0, ..., config.num_labels]``.
If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**loss**: (`optional`, returned when ``labels`` is provided) ``torch.FloatTensor`` of shape ``(1,)``:
Classification (or regression if config.num_labels==1) loss.
**logits**: ``torch.FloatTensor`` of shape ``(batch_size, config.num_labels)``
Classification (or regression if config.num_labels==1) scores (before SoftMax).
**hidden_states**: (`optional`, returned when ``config.output_hidden_states=True``)
list of ``torch.FloatTensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions**: (`optional`, returned when ``config.output_attentions=True``)
list of ``torch.FloatTensor`` (one for each layer) of shape ``(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::
tokenizer = RoertaTokenizer.from_pretrained('roberta-base')
model = RobertaForSequenceClassification.from_pretrained('roberta-base')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(input_ids, labels=labels)
loss, logits = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFRobertaForSequenceClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.roberta = TFRobertaMainLayer(config, name="roberta")
self.classifier = TFRobertaClassificationHead(config, name="classifier")
def call(self, inputs, training=False):
outputs = self.roberta(inputs, training=training)
sequence_output = outputs[0]
logits = self.classifier(sequence_output, training=training)
outputs = (logits,) + outputs[2:]
return outputs # logits, (hidden_states), (attentions)

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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import shutil
import pytest
from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
from .configuration_common_test import ConfigTester
from pytorch_transformers import RobertaConfig, is_tf_available
if is_tf_available():
import tensorflow as tf
import numpy
from pytorch_transformers.modeling_tf_roberta import (TFRobertaModel, TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
pytestmark = pytest.mark.skip("Require TensorFlow")
class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFRobertaModel,TFRobertaForMaskedLM,
TFRobertaForSequenceClassification) if is_tf_available() else ()
class TFRobertaModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = RobertaConfig(
vocab_size_or_config_json_file=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_roberta_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels,
token_labels, choice_labels):
model = TFRobertaModel(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
sequence_output = model(inputs)[0]
inputs = [input_ids, input_mask]
sequence_output = model(inputs)[0]
sequence_output = model(input_ids)[0]
result = {
"sequence_output": sequence_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
def create_and_check_roberta_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels,
token_labels, choice_labels):
model = TFRobertaForMaskedLM(config=config)
prediction_scores = model([input_ids, input_mask, token_type_ids])[0]
result = {
"prediction_scores": prediction_scores.numpy(),
}
self.parent.assertListEqual(
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
def setUp(self):
self.model_tester = TFRobertaModelTest.TFRobertaModelTester(self)
self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_roberta_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_roberta_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_roberta_for_masked_lm(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/pytorch_transformers_test/"
for model_name in list(TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = TFRobertaModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
class TFRobertaModelIntegrationTest(unittest.TestCase):
@pytest.mark.slow
def test_inference_masked_lm(self):
model = TFRobertaForMaskedLM.from_pretrained('roberta-base')
input_ids = tf.constant([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
expected_shape = [1, 11, 50265]
self.assertEqual(
list(output.numpy().shape),
expected_shape
)
# compare the actual values for a slice.
expected_slice = tf.constant(
[[[33.8843, -4.3107, 22.7779],
[ 4.6533, -2.8099, 13.6252],
[ 1.8222, -3.6898, 8.8600]]]
)
self.assertTrue(
numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3)
)
@pytest.mark.slow
def test_inference_no_head(self):
model = TFRobertaModel.from_pretrained('roberta-base')
input_ids = tf.constant([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
# compare the actual values for a slice.
expected_slice = tf.constant(
[[[-0.0231, 0.0782, 0.0074],
[-0.1854, 0.0539, -0.0174],
[ 0.0548, 0.0799, 0.1687]]]
)
self.assertTrue(
numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3)
)
@pytest.mark.slow
def test_inference_classification_head(self):
model = TFRobertaForSequenceClassification.from_pretrained('roberta-large-mnli')
input_ids = tf.constant([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
expected_shape = [1, 3]
self.assertEqual(
list(output.numpy().shape),
expected_shape
)
expected_tensor = tf.constant([[-0.9469, 0.3913, 0.5118]])
self.assertTrue(
numpy.allclose(output.numpy(), expected_tensor.numpy(), atol=1e-3)
)
if __name__ == "__main__":
unittest.main()