# coding=utf-8
# Copyright 2020 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 math
from typing import Tuple
import tensorflow as tf
from transformers.modeling_tf_utils import TFWrappedEmbeddings
from ...activations_tf import get_tf_activation
from ...file_utils import (
DUMMY_INPUTS,
DUMMY_MASK,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPast,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFPreTrainedModel,
TFSharedEmbeddings,
input_processing,
keras_serializable,
shape_list,
)
from ...utils import logging
from .configuration_t5 import T5Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "T5Config"
_TOKENIZER_FOR_DOC = "T5Tokenizer"
TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST = [
"t5-small",
"t5-base",
"t5-large",
"t5-3b",
"t5-11b",
# See all T5 models at https://huggingface.co/models?filter=t5
]
####################################################
# 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 subtraction of mean.
"""
super().__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().build(input_shape)
def call(self, hidden_states):
variance = tf.math.reduce_mean(tf.math.square(hidden_states), axis=-1, keepdims=True)
hidden_states = hidden_states * tf.math.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states
class TFT5DenseReluDense(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__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):
hidden_states = self.wi(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.wo(hidden_states)
return hidden_states
class TFT5GatedGeluDense(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.wi_0 = tf.keras.layers.Dense(config.d_ff, use_bias=False, name="wi_0")
self.wi_1 = tf.keras.layers.Dense(config.d_ff, use_bias=False, name="wi_1")
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 = get_tf_activation("gelu_new")
def call(self, hidden_states, training=False):
hidden_gelu = self.act(self.wi_0(hidden_states))
hidden_linear = self.wi_1(hidden_states)
hidden_states = hidden_gelu * hidden_linear
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.wo(hidden_states)
return hidden_states
class TFT5LayerFF(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.feed_forward_proj == "relu":
self.DenseReluDense = TFT5DenseReluDense(config, name="DenseReluDense")
elif config.feed_forward_proj == "gated-gelu":
self.DenseReluDense = TFT5GatedGeluDense(config, name="DenseReluDense")
else:
raise ValueError(
f"{self.config.feed_forward_proj} is not supported. Choose between `relu` and `gated-gelu`"
)
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):
normed_hidden_states = self.layer_norm(hidden_states)
dense_output = self.DenseReluDense(normed_hidden_states, training=training)
hidden_states = hidden_states + self.dropout(dense_output, training=training)
return hidden_states
class TFT5Attention(tf.keras.layers.Layer):
NEW_ID = itertools.count()
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super().__init__(**kwargs)
self.layer_id = next(TFT5Attention.NEW_ID)
self.is_decoder = config.is_decoder
self.use_cache = config.use_cache
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.key_value_proj_dim = config.d_kv
self.n_heads = config.num_heads
self.inner_dim = self.n_heads * self.key_value_proj_dim
# 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)
"""
relative_buckets = 0
# n = -relative_position
if bidirectional:
num_buckets //= 2
relative_buckets += tf.dtypes.cast(tf.math.greater(relative_position, 0), tf.int32) * num_buckets
relative_position = tf.math.abs(relative_position)
else:
relative_position = -tf.math.minimum(relative_position, 0)
# now n is in the range [0, inf)
max_exact = num_buckets // 2
is_small = tf.math.less(relative_position, max_exact)
relative_position_if_large = max_exact + tf.dtypes.cast(
tf.math.log(tf.dtypes.cast(relative_position, tf.float32) / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact),
tf.int32,
)
relative_position_if_large = tf.math.minimum(relative_position_if_large, num_buckets - 1)
relative_buckets += tf.where(is_small, relative_position, relative_position_if_large)
return relative_buckets
def compute_bias(self, query_length, key_length):
""" Compute binned relative position bias """
context_position = tf.range(query_length)[:, None]
memory_position = tf.range(key_length)[None, :]
relative_position = memory_position - context_position # shape (query_length, key_length)
relative_position_bucket = self._relative_position_bucket(
relative_position,
bidirectional=(not self.is_decoder),
num_buckets=self.relative_attention_num_buckets,
)
values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
values = tf.expand_dims(
tf.transpose(values, [2, 0, 1]), axis=0
) # shape (1, num_heads, query_length, key_length)
return values
def call(
self,
hidden_states,
mask=None,
key_value_states=None,
position_bias=None,
past_key_value=None,
head_mask=None,
query_length=None,
use_cache=False,
training=False,
output_attentions=False,
):
"""
Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
"""
# Input is (batch_size, query_length, dim)
# Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
# past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
batch_size, seq_length = shape_list(hidden_states)[:2]
real_seq_length = seq_length
if past_key_value is not None:
assert (
len(past_key_value) == 2
), "past_key_value should have 2 past states: keys and values. Got {} past states".format(
len(past_key_value)
)
real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length
key_length = real_seq_length if key_value_states is None else key_value_states.shape[1]
def shape(hidden_states):
""" projection """
return tf.transpose(
tf.reshape(hidden_states, (batch_size, -1, self.n_heads, self.key_value_proj_dim)), perm=(0, 2, 1, 3)
)
def unshape(hidden_states):
""" compute context """
return tf.reshape(tf.transpose(hidden_states, perm=(0, 2, 1, 3)), (batch_size, -1, self.inner_dim))
def project(hidden_states, proj_layer, key_value_states, past_key_value):
""" projects hidden states correctly to key/query states """
if key_value_states is None:
# self-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(hidden_states))
elif past_key_value is None:
# cross-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(key_value_states))
if past_key_value is not None:
if key_value_states is None:
# self-attn
# (batch_size, n_heads, key_length, dim_per_head)
hidden_states = tf.concat([past_key_value, hidden_states], axis=2)
else:
# cross-attn
hidden_states = past_key_value
return hidden_states
# get query
query_states = shape(self.q(hidden_states)) # (batch_size, n_heads, query_length, dim_per_head)
# get key/value
key_states = project(
hidden_states, self.k, key_value_states, past_key_value[0] if past_key_value is not None else None
)
value_states = project(
hidden_states, self.v, key_value_states, past_key_value[1] if past_key_value is not None else None
)
# to cope with keras serialization
if self.is_decoder and use_cache:
present_key_value_state = (key_states, value_states)
else:
present_key_value_state = None
scores = tf.einsum(
"bnqd,bnkd->bnqk", query_states, key_states
) # (batch_size, n_heads, query_length, key_length)
if position_bias is None:
if not self.has_relative_attention_bias:
position_bias = tf.zeros((1, self.n_heads, real_seq_length, key_length), dtype=tf.float32)
else:
position_bias = self.compute_bias(real_seq_length, key_length)
# if key and values are already calculated
# we want only the last query position bias
if past_key_value is not None:
position_bias = position_bias[:, :, -seq_length:, :]
if mask is not None:
position_bias = position_bias + mask # (batch_size, n_heads, query_length, key_length)
scores += position_bias
weights = tf.nn.softmax(scores, axis=-1) # (batch_size, n_heads, query_length, key_length)
weights = self.dropout(weights, training=training) # (batch_size, n_heads, query_length, key_length)
# Mask heads if we want to
if head_mask is not None:
weights = weights * head_mask
attn_output = tf.matmul(weights, value_states) # (batch_size, n_heads, query_length, dim_per_head)
attn_output = self.o(unshape(attn_output))
outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
if output_attentions:
outputs = outputs + (weights,)
return outputs
class TFT5LayerSelfAttention(tf.keras.layers.Layer):
def __init__(self, config, has_relative_attention_bias=False, **kwargs):
super().__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,
past_key_value=None,
use_cache=False,
output_attentions=False,
training=False,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.SelfAttention(
normed_hidden_states,
mask=attention_mask,
position_bias=position_bias,
head_mask=head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
)
hidden_states = hidden_states + self.dropout(attention_output[0], training=training)
outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them
return outputs
class TFT5LayerCrossAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.EncDecAttention = TFT5Attention(
config,
has_relative_attention_bias=False,
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,
key_value_states,
attention_mask=None,
position_bias=None,
head_mask=None,
past_key_value=None,
query_length=None,
use_cache=False,
output_attentions=False,
training=False,
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.EncDecAttention(
normed_hidden_states,
mask=attention_mask,
key_value_states=key_value_states,
position_bias=position_bias,
head_mask=head_mask,
past_key_value=past_key_value,
query_length=query_length,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
)
hidden_states = hidden_states + self.dropout(attention_output[0], training=training)
outputs = (hidden_states,) + 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().__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,
name="layer_._1",
)
)
self.layer.append(TFT5LayerFF(config, name="layer_._{}".format(len(self.layer))))
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,
past_key_value=None,
use_cache=False,
output_attentions=False,
training=False,
):
if past_key_value is not None:
assert self.is_decoder, "Only decoder can use `past_key_values`"
expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
error_message = "There should be {} past states. 2 (past / key) for self attention.{} Got {} past key / value states".format(
expected_num_past_key_values,
"2 (past / key) for cross attention" if expected_num_past_key_values == 4 else "",
len(past_key_value),
)
assert len(past_key_value) == expected_num_past_key_values, error_message
self_attn_past_key_value = past_key_value[:2]
cross_attn_past_key_value = past_key_value[2:]
else:
self_attn_past_key_value, cross_attn_past_key_value = None, None
self_attention_outputs = self.layer[0](
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
head_mask=head_mask,
past_key_value=self_attn_past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
)
hidden_states, present_key_value_state = self_attention_outputs[:2]
attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights
if self.is_decoder and encoder_hidden_states is not None:
# the actual query length is unknown for cross attention
# if using past key value states. Need to inject it here
if present_key_value_state is not None:
query_length = shape_list(present_key_value_state[0])[2]
else:
query_length = None
cross_attention_outputs = self.layer[1](
hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
position_bias=encoder_decoder_position_bias,
head_mask=head_mask,
past_key_value=cross_attn_past_key_value,
query_length=query_length,
use_cache=use_cache,
output_attentions=output_attentions,
training=training,
)
hidden_states = cross_attention_outputs[0]
# Combine self attn and cross attn key value states
if present_key_value_state is not None:
present_key_value_state = present_key_value_state + cross_attention_outputs[1]
# Keep cross-attention outputs and relative position weights
attention_outputs = attention_outputs + cross_attention_outputs[2:]
# Apply Feed Forward layer
hidden_states = self.layer[-1](hidden_states, training=training)
outputs = (hidden_states,)
# Add attentions if we output them
outputs = outputs + (present_key_value_state,) + attention_outputs
return outputs # hidden-states, present_key_value_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"
####################################################
@keras_serializable
class TFT5MainLayer(tf.keras.layers.Layer):
config_class = T5Config
def __init__(self, config, embed_tokens=None, **kwargs):
super().__init__(**kwargs)
self.config = config
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.use_cache = config.use_cache
self.embed_tokens = embed_tokens
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 get_input_embeddings(self):
return self.embed_tokens
def set_embed_tokens(self, embed_tokens):
self.embed_tokens = embed_tokens
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,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=None,
head_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
) -> Tuple:
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(
f"You cannot specify both {err_msg_prefix}inputs and {err_msg_prefix}inputs_embeds at the same time"
)
elif inputs["input_ids"] is not None:
input_shape = shape_list(inputs["input_ids"])
inputs["input_ids"] = tf.reshape(inputs["input_ids"], (-1, input_shape[-1]))
elif inputs["inputs_embeds"] is not None:
input_shape = shape_list(inputs["inputs_embeds"])[:-1]
else:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(f"You have to specify either {err_msg_prefix}inputs or {err_msg_prefix}inputs_embeds")
if inputs["inputs_embeds"] is None:
assert self.embed_tokens is not None, "You have to intialize the model with valid token embeddings"
inputs["inputs_embeds"] = self.embed_tokens(inputs["input_ids"])
batch_size, seq_length = input_shape
# required mask seq length can be calculated via length of past
mask_seq_length = (
shape_list(inputs["past_key_values"][0][0])[2] + seq_length
if inputs["past_key_values"] is not None
else seq_length
)
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.fill((batch_size, mask_seq_length), 1)
if (
self.is_decoder
and inputs["encoder_attention_mask"] is None
and inputs["encoder_hidden_states"] is not None
):
encoder_seq_length = shape_list(inputs["encoder_hidden_states"])[1]
inputs["encoder_attention_mask"] = tf.fill((batch_size, encoder_seq_length), 1)
# initialize past_key_values with `None` if past does not exist
if inputs["past_key_values"] is None:
inputs["past_key_values"] = [None] * len(self.block)
# 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.
inputs["attention_mask"] = tf.cast(inputs["attention_mask"], dtype=tf.float32)
num_dims_attention_mask = len(shape_list(inputs["attention_mask"]))
if num_dims_attention_mask == 3:
extended_attention_mask = inputs["attention_mask"][:, None, :, :]
elif num_dims_attention_mask == 2:
# Provided a padding mask of dimensions [batch_size, mask_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, mask_seq_length, mask_seq_length]
if self.is_decoder:
seq_ids = tf.range(mask_seq_length)
causal_mask = tf.less_equal(
tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)),
seq_ids[None, :, None],
)
causal_mask = tf.cast(causal_mask, dtype=tf.float32)
extended_attention_mask = causal_mask[:, None, :, :] * inputs["attention_mask"][:, None, None, :]
if inputs["past_key_values"][0] is not None:
extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :]
else:
extended_attention_mask = inputs["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 -1e9 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 transposition
# 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 and inputs["encoder_attention_mask"] is not None:
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length]
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
inputs["encoder_attention_mask"] = tf.cast(inputs["encoder_attention_mask"], dtype=tf.float32)
num_dims_encoder_attention_mask = len(shape_list(inputs["encoder_attention_mask"]))
if num_dims_encoder_attention_mask == 3:
encoder_extended_attention_mask = inputs["encoder_attention_mask"][:, None, :, :]
if num_dims_encoder_attention_mask == 2:
encoder_extended_attention_mask = inputs["encoder_attention_mask"][:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# 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
assert inputs["head_mask"] is None, "Head mask not supported"
inputs["head_mask"] = [None] * self.num_hidden_layers
present_key_value_states = () if inputs["use_cache"] and self.is_decoder else None
all_hidden_states = () if inputs["output_hidden_states"] else None
all_attentions = () if inputs["output_attentions"] else None
position_bias = None
encoder_decoder_position_bias = None
hidden_states = self.dropout(inputs["inputs_embeds"], training=inputs["training"])
for i, (layer_module, past_key_value) in enumerate(zip(self.block, inputs["past_key_values"])):
if inputs["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=inputs["encoder_hidden_states"],
encoder_attention_mask=encoder_extended_attention_mask,
encoder_decoder_position_bias=encoder_decoder_position_bias,
head_mask=inputs["head_mask"][i],
past_key_value=past_key_value,
use_cache=inputs["use_cache"],
output_attentions=inputs["output_attentions"],
training=inputs["training"],
)
# layer_outputs is a tuple with:
# hidden-states, key-value-states, (self-attention weights), (self-attention position bias), (cross-attention weights), (cross-attention position bias)
hidden_states, present_key_value_state = layer_outputs[:2]
# We share the position biases between the layers - the first layer store them
# layer_outputs = hidden-states, past_key_values, (self-attention weights),
# (self-attention position bias), (cross-attention position bias), (cross-attention weights),
position_bias = layer_outputs[2]
if self.is_decoder and inputs["encoder_hidden_states"] is not None:
encoder_decoder_position_bias = layer_outputs[4 if inputs["output_attentions"] else 3]
# append next layer key value states
if present_key_value_state is not None and inputs["use_cache"] and self.is_decoder:
present_key_value_states = present_key_value_states + (present_key_value_state,)
if inputs["output_attentions"]:
all_attentions = all_attentions + (layer_outputs[3],)
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.dropout(hidden_states, training=inputs["training"])
# Add last layer
if inputs["output_hidden_states"]:
all_hidden_states = all_hidden_states + (hidden_states,)
if not inputs["return_dict"]:
outputs = (hidden_states,)
# need to check if is decoder here as well for special cases when using keras compile
if inputs["use_cache"] and self.is_decoder:
outputs = outputs + (present_key_value_states,)
if inputs["output_hidden_states"]:
outputs = outputs + (all_hidden_states,)
if inputs["output_attentions"]:
outputs = outputs + (all_attentions,)
return outputs # last-layer hidden state, (all hidden states), (all attentions)
if self.is_decoder:
return TFBaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=present_key_value_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
else:
return TFBaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=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 downloading and loading pretrained
models.
"""
config_class = T5Config
base_model_prefix = "transformer"
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"decoder\Wblock[\W_0]+layer[\W_1]+EncDecAttention\Wrelative_attention_bias"]
@property
def dummy_inputs(self):
inputs = tf.constant(DUMMY_INPUTS)
input_mask = tf.constant(DUMMY_MASK)
dummy_inputs = {
"input_ids": inputs,
"decoder_input_ids": inputs,
"decoder_attention_mask": input_mask,
}
return dummy_inputs
def _shift_right(self, input_ids):
decoder_start_token_id = self.config.decoder_start_token_id
pad_token_id = self.config.pad_token_id
assert (
decoder_start_token_id is not None
), "self.model.config.decoder_start_token_id has to be defined. In TF T5 it is usually set to the pad_token_id. See T5 docs for more information"
shifted_input_ids = tf.cast(input_ids, tf.int32)
shifted_input_ids = tf.roll(shifted_input_ids, 1, axis=-1)
start_tokens = tf.fill((shape_list(shifted_input_ids)[0], 1), decoder_start_token_id)
shifted_input_ids = tf.concat([start_tokens, shifted_input_ids[:, 1:]], -1)
assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined."
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids = tf.where(
shifted_input_ids == -100, tf.fill(shape_list(shifted_input_ids), pad_token_id), shifted_input_ids
)
# "Verify that `labels` has only positive values and -100"
assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.cast(0, tf.int32))
# Make sure the assertion op is called by wrapping the result in an identity no-op
with tf.control_dependencies([assert_gte0]):
shifted_input_ids = tf.identity(shifted_input_ids)
return shifted_input_ids
T5_START_DOCSTRING = r"""
The T5 model was proposed in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
<https://arxiv.org/abs/1910.10683>`__ 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 inherits from :class:`~transformers.TFPreTrainedModel`. Check the superclass documentation for the
generic methods the library implements for all its model (such as downloading or saving, resizing the input
embeddings, pruning heads etc.)
This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass. 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.
.. note::
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 useful when using :meth:`tf.keras.Model.fit` method which currently requires having all
the tensors in the first argument of the model call function: :obj:`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 :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
:obj:`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"""
Args:
inputs (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. 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.BertTokenizer`. See
:func:`transformers.PreTrainedTokenizer.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
details.
To know more on how to prepare :obj:`inputs` for pretraining take a look at `T5 Training
<./t5.html#training>`__.
decoder_input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`):
Provide for sequence to sequence training. T5 uses the :obj:`pad_token_id` as the starting token for
:obj:`decoder_input_ids` generation. If :obj:`past_key_values` is used, optionally only the last
:obj:`decoder_input_ids` have to be input (see :obj:`past_key_values`).
To know more on how to prepare :obj:`decoder_input_ids` for pretraining take a look at `T5 Training
<./t5.html#training>`__. If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset,
:obj:`decoder_input_ids` takes the value of :obj:`input_ids`.
attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
decoder_attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`):
Default behavior: generate a tensor that ignores pad tokens in :obj:`decoder_input_ids`. Causal mask will
also be used by default.
encoder_outputs (:obj:`tuple(tuple(tf.FloatTensor)`, `optional`):
Tuple consists of (:obj:`last_hidden_state`, :obj:`optional`: `hidden_states`, :obj:`optional`:
`attentions`) :obj:`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)` is a
sequence of hidden states at the output of the last layer of the encoder. Used in the cross-attention of
the decoder.
past_key_values (:obj:`tuple(tuple(tf.Tensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
decoder_inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded
representation. If :obj:`past_key_values` is used, optionally only the last :obj:`decoder_inputs_embeds`
have to be input (see :obj:`past_key_values`). This is useful if you want more control over how to convert
:obj:`decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
If :obj:`decoder_input_ids` and :obj:`decoder_inputs_embeds` are both unset, :obj:`decoder_inputs_embeds`
takes the value of :obj:`inputs_embeds`.
head_mask: (:obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
T5_ENCODER_INPUTS_DOCSTRING = r"""
Args:
inputs (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. 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.__call__` and :func:`transformers.PreTrainedTokenizer.encode` for
details.
To know more on how to prepare :obj:`inputs` for pre-training take a look at `T5 Training
<./t5.html#training>`__.
attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
inputs_embeds (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
head_mask: (:obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
[docs]@add_start_docstrings(
"The bare T5 Model transformer outputting raw hidden-states" "without any specific head on top.",
T5_START_DOCSTRING,
)
class TFT5Model(TFT5PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, name="shared")
# retrieve correct absolute scope for embed token wrapper
with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
encoder_config = copy.deepcopy(config)
encoder_config.use_cache = False
self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
self.decoder = TFT5MainLayer(decoder_config, embed_tokens, name="decoder")
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared.weight = new_embeddings
self.shared.vocab_size = self.shared.weight.shape[0]
# retrieve correct absolute scope for embed token wrapper
with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
self.encoder.set_embed_tokens(embed_tokens)
self.decoder.set_embed_tokens(embed_tokens)
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
[docs] @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
past_key_values=None,
head_mask=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
r"""
Returns:
Examples::
>>> from transformers import T5Tokenizer, TFT5Model
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> model = TFT5Model.from_pretrained('t5-small')
>>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="tf").input_ids # Batch size 1
>>> decoder_input_ids = tokenizer("Studies show that", return_tensors="tf").input_ids # Batch size 1
>>> outputs = model(input_ids, decoder_input_ids=decoder_input_ids)
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
# Encode if needed (training, first prediction pass)
if inputs["encoder_outputs"] is None:
inputs["encoder_outputs"] = self.encoder(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=inputs["inputs_embeds"],
head_mask=inputs["head_mask"],
past_key_values=None,
use_cache=False,
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
hidden_states = inputs["encoder_outputs"][0]
# Decode
decoder_outputs = self.decoder(
inputs["decoder_input_ids"],
attention_mask=inputs["decoder_attention_mask"],
encoder_hidden_states=hidden_states,
encoder_attention_mask=inputs["attention_mask"],
inputs_embeds=inputs["decoder_inputs_embeds"],
head_mask=inputs["head_mask"],
past_key_values=inputs["past_key_values"],
use_cache=inputs["use_cache"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
past = (inputs["encoder_outputs"], decoder_outputs[1]) if inputs["use_cache"] else None
if not inputs["return_dict"]:
if past is not None:
decoder_outputs = decoder_outputs[:1] + (past,) + decoder_outputs[2:]
return decoder_outputs + inputs["encoder_outputs"]
return TFSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=past,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
encoder_last_hidden_state=inputs["encoder_outputs"].last_hidden_state,
encoder_hidden_states=inputs["encoder_outputs"].hidden_states,
encoder_attentions=inputs["encoder_outputs"].attentions,
)
[docs]@add_start_docstrings("""T5 Model with a `language modeling` head on top. """, T5_START_DOCSTRING)
class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.model_dim = config.d_model
self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, name="shared")
# retrieve correct absolute scope for embed token wrapper
with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
encoder_config = copy.deepcopy(config)
encoder_config.use_cache = False
self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
self.decoder = TFT5MainLayer(decoder_config, embed_tokens, name="decoder")
if not config.tie_word_embeddings:
self.lm_head = tf.keras.layers.Dense(config.vocab_size, use_bias=False, name="lm_head")
def get_input_embeddings(self):
return self.shared
def get_output_embeddings(self):
if self.config.tie_word_embeddings:
return self.shared
else:
return self.lm_head
def set_input_embeddings(self, new_embeddings):
self.shared.weight = new_embeddings
# retrieve correct absolute scope for embed token wrapper
with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
self.encoder.set_embed_tokens(embed_tokens)
self.decoder.set_embed_tokens(embed_tokens)
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
[docs] @add_start_docstrings_to_model_forward(T5_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
encoder_outputs=None,
past_key_values=None,
head_mask=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
config.vocab_size - 1]``.
Returns:
Examples::
>>> from transformers import T5Tokenizer, TFT5ForConditionalGeneration
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> model = TFT5ForConditionalGeneration.from_pretrained('t5-small')
>>> inputs = tokenizer('The <extra_id_0> walks in <extra_id_1> park', return_tensors='tf').input_ids
>>> labels = tokenizer('<extra_id_0> cute dog <extra_id_1> the <extra_id_2> </s>', return_tensors='tf').input_ids
>>> outputs = model(inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
>>> inputs = tokenizer("summarize: studies have shown that owning a dog is good for you ", return_tensors="tf").input_ids # Batch size 1
>>> result = model.generate(inputs)
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
# Encode if needed (training, first prediction pass)
if inputs["encoder_outputs"] is None:
inputs["encoder_outputs"] = self.encoder(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
inputs_embeds=inputs["inputs_embeds"],
head_mask=inputs["head_mask"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
hidden_states = inputs["encoder_outputs"][0]
if (
inputs["labels"] is not None
and inputs["decoder_input_ids"] is None
and inputs["decoder_inputs_embeds"] is None
):
# get decoder inputs from shifting lm labels to the right
inputs["decoder_input_ids"] = self._shift_right(inputs["labels"])
# Decode
decoder_outputs = self.decoder(
inputs["decoder_input_ids"],
attention_mask=inputs["decoder_attention_mask"],
encoder_hidden_states=hidden_states,
encoder_attention_mask=inputs["attention_mask"],
inputs_embeds=inputs["decoder_inputs_embeds"],
head_mask=inputs["head_mask"],
past_key_values=inputs["past_key_values"],
use_cache=inputs["use_cache"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
sequence_output = decoder_outputs[0]
# T5v1.1 does not tie output word embeddings and thus does not require downscaling
if self.config.tie_word_embeddings:
sequence_output = sequence_output * (self.model_dim ** -0.5)
logits = self.get_output_embeddings()(sequence_output, mode="linear")
else:
logits = self.get_output_embeddings()(sequence_output)
loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
past = (inputs["encoder_outputs"], decoder_outputs[1]) if inputs["use_cache"] else None
if not inputs["return_dict"]:
if past is not None:
decoder_outputs = decoder_outputs[:1] + (past,) + decoder_outputs[2:]
output = (logits,) + decoder_outputs[1:] + inputs["encoder_outputs"]
return ((loss,) + output) if loss is not None else output
# If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
elif isinstance(inputs["encoder_outputs"], tuple):
last_hidden_state = inputs["encoder_outputs"][0]
hidden_states = None
attentions = None
idx = 0
if inputs["output_hidden_states"]:
idx += 1
hidden_states = inputs["encoder_outputs"][idx]
if inputs["output_attentions"]:
idx += 1
attentions = inputs["encoder_outputs"][idx]
inputs["encoder_outputs"] = TFBaseModelOutput(
last_hidden_state=last_hidden_state,
hidden_states=hidden_states,
attentions=attentions,
)
return TFSeq2SeqLMOutput(
loss=loss,
logits=logits,
past_key_values=past,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
encoder_last_hidden_state=inputs["encoder_outputs"].last_hidden_state,
encoder_hidden_states=inputs["encoder_outputs"].hidden_states,
encoder_attentions=inputs["encoder_outputs"].attentions,
)
def prepare_inputs_for_generation(self, inputs, past, attention_mask, use_cache, **kwargs):
assert past is not None, "past has to be defined for encoder_outputs"
# first step
if len(past) < 2:
encoder_outputs, past_key_values = past, None
else:
encoder_outputs, past_key_values = past[0], past[1]
# cut decoder_input_ids if past is used
if past_key_values is not None:
inputs = inputs[:, -1:]
return {
"input_ids": None, # inputs don't have to be defined, but still need to be passed to make Keras.layer.__call__ happy
"decoder_input_ids": inputs, # inputs are the decoder_input_ids
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"attention_mask": attention_mask,
"use_cache": use_cache,
}
def _reorder_cache(self, past, beam_idx) -> Tuple:
# if decoder past is not included in output
# speedy decoding is disabled and no need to reorder
if len(past) < 2:
logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
return past
decoder_past = past[1]
past = (past[0],)
reordered_decoder_past = ()
for layer_past_states in decoder_past:
# get the correct batch idx from layer past batch dim
# batch dim of `past` is at 2nd position
reordered_layer_past_states = ()
for layer_past_state in layer_past_states:
# need to set correct `past` for each of the four key / value states
reordered_layer_past_states = reordered_layer_past_states + (tf.gather(layer_past_state, beam_idx),)
assert shape_list(reordered_layer_past_states[0]) == shape_list(layer_past_states[0])
assert len(reordered_layer_past_states) == len(layer_past_states)
reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
return past + (reordered_decoder_past,)
[docs]@add_start_docstrings(
"The bare T5 Model transformer outputting encoder's raw hidden-states" "without any specific head on top.",
T5_START_DOCSTRING,
)
class TFT5EncoderModel(TFT5PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, name="shared")
# retrieve correct absolute scope for embed token wrapper
with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
encoder_config = copy.deepcopy(config)
encoder_config.use_cache = False
self.encoder = TFT5MainLayer(encoder_config, embed_tokens, name="encoder")
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared.weight = new_embeddings
self.shared.vocab_size = self.shared.weight.shape[0]
# retrieve correct absolute scope for embed token wrapper
with tf.compat.v1.variable_scope("shared") as shared_abs_scope_name:
pass
# Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
self.encoder.set_embed_tokens(embed_tokens)
def get_encoder(self):
return self.encoder
[docs] @add_start_docstrings_to_model_forward(T5_ENCODER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids,
attention_mask=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
**kwargs,
):
r"""
Returns:
Examples::
>>> from transformers import T5Tokenizer, TFT5Model
>>> tokenizer = T5Tokenizer.from_pretrained('t5-small')
>>> model = TFT5EncoderModel.from_pretrained('t5-small')
>>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="tf").input_ids # Batch size 1
>>> outputs = model(input_ids)
"""
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
encoder_outputs = self.encoder(
input_ids,
attention_mask=inputs["attention_mask"],
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=inputs["inputs_embeds"],
head_mask=head_mask,
past_key_values=None,
use_cache=False,
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
if not inputs["return_dict"]:
return encoder_outputs
return TFBaseModelOutput(
last_hidden_state=encoder_outputs.last_hidden_state,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)