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HuggingFace_transformer/src/transformers/modeling_tf_t5.py
alberduris dd4df80f0b Moved the encoded_prompts to correct device
2020-01-06 15:11:12 +01:00

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# coding=utf-8
# Copyright 2018 T5 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 T5 model. """
import copy
import itertools
import logging
import math
import tensorflow as tf
from .configuration_t5 import T5Config
from .file_utils import DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list
logger = logging.getLogger(__name__)
TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
"t5-small": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-tf_model.h5",
"t5-base": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-tf_model.h5",
"t5-large": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-tf_model.h5",
"t5-3b": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-3b-tf_model.h5",
"t5-11b": "https://s3.amazonaws.com/models.huggingface.co/bert/t5-11b-tf_model.h5",
}
####################################################
# TF 2.0 Models are constructed using Keras imperative API by sub-classing
# - tf.keras.layers.Layer for the layers and
# - TFPreTrainedModel for the models (it-self a sub-class of tf.keras.Model)
####################################################
class TFT5LayerNorm(tf.keras.layers.Layer):
def __init__(self, epsilon=1e-6, **kwargs):
""" Construct a layernorm module in the T5 style
No bias and no substraction of mean.
"""
super(TFT5LayerNorm, self).__init__(**kwargs)
self.variance_epsilon = epsilon
def build(self, input_shape):
"""Build shared word embedding layer """
self.weight = self.add_weight("weight", shape=(input_shape[-1],), initializer="ones")
super(TFT5LayerNorm, self).build(input_shape)
def call(self, x):
variance = tf.math.reduce_mean(tf.math.square(x), axis=-1, keepdims=True)
x = x * tf.math.rsqrt(variance + self.variance_epsilon)
return self.weight * x
class TFT5DenseReluDense(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFT5DenseReluDense, self).__init__(**kwargs)
self.wi = tf.keras.layers.Dense(config.d_ff, use_bias=False, name="wi")
self.wo = tf.keras.layers.Dense(config.d_model, use_bias=False, name="wo")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
self.act = tf.keras.activations.relu
def call(self, hidden_states, training=False):
h = self.wi(hidden_states)
h = self.act(h)
h = self.dropout(h, training=training)
h = self.wo(h)
return h
class TFT5LayerFF(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFT5LayerFF, self).__init__(**kwargs)
self.DenseReluDense = TFT5DenseReluDense(config, name="DenseReluDense")
self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
def call(self, hidden_states, training=False):
norm_x = self.layer_norm(hidden_states)
y = self.DenseReluDense(norm_x, training=training)
layer_output = hidden_states + self.dropout(y, training=training)
return layer_output
class TFT5Attention(tf.keras.layers.Layer):
NEW_ID = itertools.count()
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super(TFT5Attention, self).__init__(**kwargs)
self.layer_id = next(TFT5Attention.NEW_ID)
self.is_decoder = config.is_decoder
self.has_relative_attention_bias = has_relative_attention_bias
self.output_attentions = config.output_attentions
self.relative_attention_num_buckets = config.relative_attention_num_buckets
self.d_model = config.d_model
self.d_kv = config.d_kv
self.n_heads = config.num_heads
self.inner_dim = self.n_heads * self.d_kv
# Mesh TensorFlow initialization to avoid scaling before softmax
self.q = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name="q")
self.k = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name="k")
self.v = tf.keras.layers.Dense(self.inner_dim, use_bias=False, name="v")
self.o = tf.keras.layers.Dense(self.d_model, use_bias=False, name="o")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
if self.has_relative_attention_bias:
self.relative_attention_bias = tf.keras.layers.Embedding(
self.relative_attention_num_buckets, self.n_heads, name="relative_attention_bias"
)
self.pruned_heads = set()
def prune_heads(self, heads):
raise NotImplementedError
@staticmethod
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
"""
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
Translate relative position to a bucket number for relative attention.
The relative position is defined as memory_position - query_position, i.e.
the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are
invalid.
We use smaller buckets for small absolute relative_position and larger buckets
for larger absolute relative_positions. All relative positions >=max_distance
map to the same bucket. All relative positions <=-max_distance map to the
same bucket. This should allow for more graceful generalization to longer
sequences than the model has been trained on.
Args:
relative_position: an int32 Tensor
bidirectional: a boolean - whether the attention is bidirectional
num_buckets: an integer
max_distance: an integer
Returns:
a Tensor with the same shape as relative_position, containing int32
values in the range [0, num_buckets)
"""
ret = 0
n = -relative_position
if bidirectional:
num_buckets //= 2
ret += tf.dtypes.cast(tf.math.less(n, 0), tf.int32) * num_buckets
n = tf.math.abs(n)
else:
n = tf.math.maximum(n, 0)
# now n is in the range [0, inf)
max_exact = num_buckets // 2
is_small = tf.math.less(n, max_exact)
val_if_large = max_exact + tf.dtypes.cast(
tf.math.log(tf.dtypes.cast(n, tf.float32) / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact),
tf.int32,
)
val_if_large = tf.math.minimum(val_if_large, num_buckets - 1)
ret += tf.where(is_small, n, val_if_large)
return ret
def compute_bias(self, qlen, klen):
""" Compute binned relative position bias """
context_position = tf.range(qlen)[:, None]
memory_position = tf.range(klen)[None, :]
relative_position = memory_position - context_position # shape (qlen, klen)
rp_bucket = self._relative_position_bucket(
relative_position, bidirectional=not self.is_decoder, num_buckets=self.relative_attention_num_buckets
)
values = self.relative_attention_bias(rp_bucket) # shape (qlen, klen, num_heads)
values = tf.expand_dims(tf.transpose(values, [2, 0, 1]), axis=0) # shape (1, num_heads, qlen, klen)
return values
def call(self, input, mask=None, kv=None, position_bias=None, cache=None, head_mask=None, training=False):
"""
Self-attention (if kv is None) or attention over source sentence (provided by kv).
"""
# Input is (bs, qlen, dim)
# Mask is (bs, klen) (non-causal) or (bs, klen, klen)
bs, qlen, dim = shape_list(input)
if kv is None:
klen = qlen if cache is None else cache["slen"] + qlen
else:
klen = shape_list(kv)[1]
def shape(x):
""" projection """
return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, self.d_kv)), perm=(0, 2, 1, 3))
def unshape(x):
""" compute context """
return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.inner_dim))
q = shape(self.q(input)) # (bs, n_heads, qlen, dim_per_head)
if kv is None:
k = shape(self.k(input)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v(input)) # (bs, n_heads, qlen, dim_per_head)
elif cache is None or self.layer_id not in cache:
k = v = kv
k = shape(self.k(k)) # (bs, n_heads, qlen, dim_per_head)
v = shape(self.v(v)) # (bs, n_heads, qlen, dim_per_head)
if cache is not None:
if self.layer_id in cache:
if kv is None:
k_, v_ = cache[self.layer_id]
k = tf.concat([k_, k], axis=2) # (bs, n_heads, klen, dim_per_head)
v = tf.concat([v_, v], axis=2) # (bs, n_heads, klen, dim_per_head)
else:
k, v = cache[self.layer_id]
cache[self.layer_id] = (k, v)
# q = q / math.sqrt(dim_per_head) # No scaling in T5
# scores = tf.matmul(q, k, transpose_b=True) # (bs, n_heads, qlen, klen)
scores = tf.einsum("bnqd,bnkd->bnqk", q, k) # (bs, n_heads, qlen, klen)
if position_bias is None:
if not self.has_relative_attention_bias:
raise ValueError("No position_bias provided and no weights to compute position_bias")
position_bias = self.compute_bias(qlen, klen)
if mask is not None:
position_bias = position_bias + mask
# mask = (mask == 0).expand_as(scores) # (bs, n_heads, qlen, klen)
# scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, qlen, klen)
scores += position_bias
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, qlen, dim)
context = self.o(context)
outputs = (context,)
if self.output_attentions:
outputs = outputs + (weights,)
if self.has_relative_attention_bias:
outputs = outputs + (position_bias,)
return outputs
class TFT5LayerSelfAttention(tf.keras.layers.Layer):
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super(TFT5LayerSelfAttention, self).__init__(**kwargs)
self.SelfAttention = TFT5Attention(
config, has_relative_attention_bias=has_relative_attention_bias, name="SelfAttention"
)
self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
def call(self, hidden_states, attention_mask=None, position_bias=None, head_mask=None, training=False):
norm_x = self.layer_norm(hidden_states)
attention_output = self.SelfAttention(
norm_x, mask=attention_mask, position_bias=position_bias, head_mask=head_mask, training=training
)
y = attention_output[0]
layer_output = hidden_states + self.dropout(y, training=training)
outputs = (layer_output,) + attention_output[1:] # add attentions if we output them
return outputs
class TFT5LayerCrossAttention(tf.keras.layers.Layer):
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super(TFT5LayerCrossAttention, self).__init__(**kwargs)
self.EncDecAttention = TFT5Attention(
config, has_relative_attention_bias=has_relative_attention_bias, name="EncDecAttention"
)
self.layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
def call(self, hidden_states, kv, attention_mask=None, position_bias=None, head_mask=None, training=False):
norm_x = self.layer_norm(hidden_states)
attention_output = self.EncDecAttention(
norm_x, mask=attention_mask, kv=kv, position_bias=position_bias, head_mask=head_mask, training=training
)
y = attention_output[0]
layer_output = hidden_states + self.dropout(y, training=training)
outputs = (layer_output,) + attention_output[1:] # add attentions if we output them
return outputs
class TFT5Block(tf.keras.layers.Layer):
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super(TFT5Block, self).__init__(**kwargs)
self.is_decoder = config.is_decoder
self.layer = []
self.layer.append(
TFT5LayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias, name="layer_._0")
)
if self.is_decoder:
self.layer.append(
TFT5LayerCrossAttention(
config, has_relative_attention_bias=has_relative_attention_bias, name="layer_._1"
)
)
self.layer.append(TFT5LayerFF(config, name="layer_._2"))
else:
self.layer.append(TFT5LayerFF(config, name="layer_._1"))
def call(
self,
hidden_states,
attention_mask=None,
position_bias=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
head_mask=None,
training=False,
):
self_attention_outputs = self.layer[0](
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
head_mask=head_mask,
training=training,
)
hidden_states = self_attention_outputs[0]
outputs = self_attention_outputs[1:]
if not self.is_decoder:
hidden_states = self.layer[1](hidden_states, training=training)
else:
cross_attention_outputs = self.layer[1](
hidden_states,
kv=encoder_hidden_states,
attention_mask=encoder_attention_mask,
position_bias=encoder_decoder_position_bias,
head_mask=head_mask,
training=training,
)
hidden_states = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:]
hidden_states = self.layer[2](hidden_states, training=training)
outputs = (hidden_states,) + outputs # add attentions if we output them
return outputs # hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
####################################################
# The full model without a specific pretrained or finetuning head is
# provided as a tf.keras.layers.Layer usually called "TFT5MainLayer"
####################################################
class TFT5MainLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFT5MainLayer, self).__init__(**kwargs)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.is_decoder = config.is_decoder
self.config = config
self.num_hidden_layers = config.num_layers
self.block = [
TFT5Block(config, has_relative_attention_bias=bool(i == 0), name="block_._{}".format(i))
for i in range(config.num_layers)
]
self.final_layer_norm = TFT5LayerNorm(epsilon=config.layer_norm_epsilon, name="final_layer_norm")
self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models
def _prune_heads(self, heads_to_prune):
raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models
def call(
self,
hidden_states,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
training=False,
):
batch_size, seq_length = shape_list(hidden_states)[:2]
if attention_mask is None:
attention_mask = tf.fill((batch_size, seq_length), 1)
if self.is_decoder and encoder_attention_mask is None:
encoder_seq_length = encoder_hidden_states.shape[1]
encoder_attention_mask = tf.fill((batch_size, encoder_seq_length), 1)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
attention_mask = tf.cast(attention_mask, dtype=tf.float32)
num_dims_attention_mask = len(shape_list(attention_mask))
if num_dims_attention_mask == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif num_dims_attention_mask == 2:
# Provided a padding mask of dimensions [batch_size, seq_length]
# - if the model is a decoder, apply a causal mask in addition to the padding mask
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder:
seq_ids = tf.range(seq_length)
causal_mask = tf.less_equal(
tf.tile(seq_ids[None, None, :], (batch_size, seq_length, 1)), seq_ids[None, :, None]
)
causal_mask = tf.cast(causal_mask, dtype=tf.float32)
extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
else:
extended_attention_mask = attention_mask[:, None, None, :]
# 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.
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposistion
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
# extended_attention_mask = tf.math.equal(extended_attention_mask,
# tf.transpose(extended_attention_mask, perm=(-1, -2)))
extended_attention_mask = (1.0 - extended_attention_mask) * -1e9
if self.is_decoder:
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastabe to [batch_size, num_heads, seq_length, seq_length]
encoder_attention_mask = tf.cast(encoder_attention_mask, dtype=tf.float32)
num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask))
if num_dims_encoder_attention_mask == 3:
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
if num_dims_encoder_attention_mask == 2:
encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposistion
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask,
# tf.transpose(encoder_extended_attention_mask, perm=(-1, -2)))
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -1e9
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# 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.num_hidden_layers
# head_mask = tf.constant([0] * self.num_hidden_layers)
all_hidden_states = ()
all_attentions = ()
position_bias = None
encoder_decoder_position_bias = None
for i, layer_module in enumerate(self.block):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states,
attention_mask=extended_attention_mask,
position_bias=position_bias,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
encoder_decoder_position_bias=encoder_decoder_position_bias,
head_mask=head_mask[i],
training=training,
)
hidden_states = layer_outputs[0]
if i == 0:
# We share the position biases between the layers - the first layer store them
# layer_outputs = hidden-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
position_bias = layer_outputs[2 if self.output_attentions else 1]
if self.is_decoder:
encoder_decoder_position_bias = layer_outputs[4 if self.output_attentions else 2]
if self.output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
# Add last layer
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
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)
####################################################
# TFT5PreTrainedModel is a sub-class of tf.keras.Model
# which take care of loading and saving pretrained weights
# and various common utilities.
# Here you just need to specify a few (self-explanatory)
# pointers for your model.
####################################################
class TFT5PreTrainedModel(TFPreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
config_class = T5Config
pretrained_model_archive_map = TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "transformer"
@property
def dummy_inputs(self):
input_ids = tf.constant(DUMMY_INPUTS)
input_mask = tf.constant(DUMMY_MASK)
dummy_inputs = {
"decoder_input_ids": input_ids,
"encoder_input_ids": input_ids,
"decoder_attention_mask": input_mask,
}
return dummy_inputs
T5_START_DOCSTRING = r""" The T5 model was proposed in
`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`_
by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting.
This model is a tf.keras.Model `tf.keras.Model`_ sub-class. Use it as a regular TF 2.0 Keras Model and
refer to the TF 2.0 documentation for all matter related to general usage and behavior.
.. _`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer`:
https://arxiv.org/abs/1910.10683
.. _`tf.keras.Model`:
https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model
Note on the model inputs:
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is usefull when using `tf.keras.Model.fit()` method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :
- a single Tensor with input_ids only and nothing else: `model(inputs_ids)
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associaed to the input names given in the docstring:
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~transformers.T5Config`): 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.
"""
T5_INPUTS_DOCSTRING = r"""
Inputs:
**input_ids**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Indices of input sequence tokens in the vocabulary.
To match pre-training, T5 input sequence should be formatted with [CLS] and [SEP] tokens as follows:
(a) For sequence pairs:
``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
(b) For single sequences:
``tokens: [CLS] the dog is hairy . [SEP]``
T5 is a model with relative position embeddings so you should be able to pad the inputs on
the right or the left.
Indices can be obtained using :class:`transformers.T5Tokenizer`.
See :func:`transformers.PreTrainedTokenizer.encode` and
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
**attention_mask**: (`optional`) ``Numpy array`` or ``tf.Tensor`` 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`) ``Numpy array`` or ``tf.Tensor`` 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 T5 Model transformer outputting raw hidden-states" "without any specific head on top.",
T5_START_DOCSTRING,
T5_INPUTS_DOCSTRING,
)
class TFT5Model(TFT5PreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**last_hidden_state**: ``tf.Tensor`` 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 ``tf.Tensor`` (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 ``tf.Tensor`` (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::
import tensorflow as tf
from transformers import T5Tokenizer, TFT5Model
tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = TFT5Model.from_pretrained('t5-small')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids=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(TFT5Model, self).__init__(config, *inputs, **kwargs)
self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, name="shared")
encoder_config = copy.deepcopy(config)
self.encoder = TFT5MainLayer(encoder_config, name="encoder")
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
self.decoder = TFT5MainLayer(decoder_config, name="decoder")
def get_input_embeddings(self):
return self.shared
def get_output_embeddings(self):
return self.shared
def call(self, decoder_input_ids, **kwargs):
# We allow two types of multi-inputs:
# - traditional keyword arguments in the call method
# - all the arguments provided as a dict in the first positional argument of call
# The last option is useful to use the tf.keras fit() method.
if isinstance(decoder_input_ids, dict):
kwargs.update(decoder_input_ids)
else:
kwargs["decoder_input_ids"] = decoder_input_ids
kwargs_common = dict(
(k, v) for k, v in kwargs.items() if not k.startswith("encoder_") and not k.startswith("decoder_")
)
kwargs_encoder = kwargs_common.copy()
kwargs_decoder = kwargs_common.copy()
kwargs_encoder.update(dict((k[len("encoder_") :], v) for k, v in kwargs.items() if k.startswith("encoder_")))
kwargs_decoder.update(dict((k[len("decoder_") :], v) for k, v in kwargs.items() if k.startswith("decoder_")))
# Encode if needed (training, first prediction pass)
encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
if encoder_hidden_states is None:
# Convert encoder inputs in embeddings if needed
hidden_states = kwargs_encoder.pop("inputs_embeds", None)
if hidden_states is None:
encoder_inputs_ids = kwargs_encoder.pop("input_ids")
hidden_states = self.shared(encoder_inputs_ids) # Convert inputs in embeddings
encoder_outputs = self.encoder(hidden_states, **kwargs_encoder)
encoder_hidden_states = encoder_outputs[0]
else:
encoder_outputs = ()
# Decode
# Convert decoder inputs in embeddings if needed
hidden_states = kwargs_decoder.pop("inputs_embeds", None)
if hidden_states is None:
decoder_inputs_ids = kwargs_decoder.pop("input_ids")
hidden_states = self.shared(decoder_inputs_ids)
kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None)
decoder_outputs = self.decoder(hidden_states, **kwargs_decoder)
return decoder_outputs + encoder_outputs
@add_start_docstrings("""T5 Model with a `language modeling` head on top. """, T5_START_DOCSTRING, T5_INPUTS_DOCSTRING)
class TFT5WithLMHeadModel(TFT5PreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**prediction_scores**: ``Numpy array`` or ``tf.Tensor`` 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 ``Numpy array`` or ``tf.Tensor`` (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 ``Numpy array`` or ``tf.Tensor`` (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::
import tensorflow as tf
from transformers import T5Tokenizer, TFT5WithLMHeadModel
tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = TFT5WithLMHeadModel.from_pretrained('t5-small')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids=input_ids)
prediction_scores = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFT5WithLMHeadModel, self).__init__(config, *inputs, **kwargs)
self.model_dim = config.d_model
self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, name="shared")
encoder_config = copy.deepcopy(config)
self.encoder = TFT5MainLayer(encoder_config, name="encoder")
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
self.decoder = TFT5MainLayer(decoder_config, name="decoder")
def get_input_embeddings(self):
return self.shared
def get_output_embeddings(self):
return self.shared
def call(self, decoder_input_ids, **kwargs):
# We allow two types of multi-inputs:
# - traditional keyword arguments in the call method
# - all the arguments provided as a dict in the first positional argument of call
# The last option is useful to use the tf.keras fit() method.
if isinstance(decoder_input_ids, dict):
kwargs.update(decoder_input_ids)
else:
kwargs["decoder_input_ids"] = decoder_input_ids
kwargs_common = dict(
(k, v) for k, v in kwargs.items() if not k.startswith("encoder_") and not k.startswith("decoder_")
)
kwargs_encoder = kwargs_common.copy()
kwargs_decoder = kwargs_common.copy()
kwargs_encoder.update(dict((k[len("encoder_") :], v) for k, v in kwargs.items() if k.startswith("encoder_")))
kwargs_decoder.update(dict((k[len("decoder_") :], v) for k, v in kwargs.items() if k.startswith("decoder_")))
# Encode if needed (training, first prediction pass)
encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
if encoder_hidden_states is None:
# Convert encoder inputs in embeddings if needed
hidden_states = kwargs_encoder.pop("inputs_embeds", None)
if hidden_states is None:
encoder_inputs_ids = kwargs_encoder.pop("input_ids")
hidden_states = self.shared(encoder_inputs_ids) # Convert inputs in embeddings
encoder_outputs = self.encoder(hidden_states, **kwargs_encoder)
encoder_hidden_states = encoder_outputs[0]
else:
encoder_outputs = ()
# Decode
# Convert decoder inputs in embeddings if needed
hidden_states = kwargs_decoder.pop("inputs_embeds", None)
if hidden_states is None:
decoder_inputs_ids = kwargs_decoder.pop("input_ids")
hidden_states = self.shared(decoder_inputs_ids)
kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
kwargs_decoder["encoder_attention_mask"] = kwargs_encoder.get("attention_mask", None)
decoder_outputs = self.decoder(hidden_states, **kwargs_decoder)
sequence_output = decoder_outputs[0] * (self.model_dim ** -0.5)
lm_logits = self.shared(sequence_output, mode="linear")
decoder_outputs = (lm_logits,) + decoder_outputs[1:]
return decoder_outputs + encoder_outputs
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