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added TF2 model and tests - updated templates

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This commit is contained in:
thomwolf
2019-11-08 11:35:03 +01:00
parent 8fda532c3c
commit 727a79b305
11 changed files with 646 additions and 381 deletions
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2
templates/adding_a_new_model/modeling_tf_xxx.py
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@@ -26,6 +26,8 @@ import logging
import math
import os
import sys
import copy
import itertools
from io import open
import numpy as np

2
templates/adding_a_new_model/modeling_xxx.py
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@@ -25,6 +25,8 @@ import logging
import math
import os
import sys
import copy
import itertools
from io import open
import torch

3
transformers/__init__.py
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@@ -158,6 +158,9 @@ if is_tf_available():
TFCTRLLMHeadModel,
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_tf_t5 import (TFT5PreTrainedModel, TFT5Model, TFT5WithLMHeadModel,
TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP)
# TF 2.0 <=> PyTorch conversion utilities
from .modeling_tf_pytorch_utils import (convert_tf_weight_name_to_pt_weight_name,
load_pytorch_checkpoint_in_tf2_model,

6
transformers/configuration_auto.py
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@@ -27,6 +27,7 @@ from .configuration_xlm import XLMConfig
from .configuration_roberta import RobertaConfig
from .configuration_distilbert import DistilBertConfig
from .configuration_ctrl import CTRLConfig
from .configuration_t5 import T5Config
logger = logging.getLogger(__name__)
@@ -64,6 +65,7 @@ class AutoConfig(object):
The configuration class to instantiate is selected as the first pattern matching
in the `pretrained_model_name_or_path` string (in the following order):
- contains `t5`: T5Config (T5 model)
- contains `distilbert`: DistilBertConfig (DistilBERT model)
- contains `bert`: BertConfig (Bert model)
- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
@@ -114,7 +116,9 @@ class AutoConfig(object):
assert unused_kwargs == {'foo': False}
"""
if 'distilbert' in pretrained_model_name_or_path:
if 't5' in pretrained_model_name_or_path:
return T5Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'distilbert' in pretrained_model_name_or_path:
return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
elif 'roberta' in pretrained_model_name_or_path:
return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)

3
transformers/configuration_t5.py
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@@ -27,8 +27,7 @@ from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
't5-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-uncased-config.json",
't5-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-uncased-config.json",
't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-config.json",
}

79
transformers/modeling_t5.py
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@@ -41,8 +41,7 @@ logger = logging.getLogger(__name__)
# for the pretrained weights provided with the models
####################################################
T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
't5-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-uncased-pytorch_model.bin",
't5-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-uncased-pytorch_model.bin",
't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-pytorch_model.bin",
}
####################################################
@@ -442,7 +441,7 @@ class T5PreTrainedModel(PreTrainedModel):
if isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(factor*1.0)
elif isinstance(module, T5Model):
elif isinstance(module, (T5Model, T5WithLMHeadModel)):
# Mesh TensorFlow embeddings initialization
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L1624
module.shared.weight.data.normal_(mean=0.0, std=factor*1.0)
@@ -502,11 +501,10 @@ class T5Stack(T5PreTrainedModel):
# ourselves in which case we just need to make it broadcastable to all heads.
if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 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 attention_mask.dim() == 2:
if self.config.is_decoder:
seq_ids = torch.arange(seq_length, device=hidden_states.device)
causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
@@ -593,7 +591,7 @@ class T5Stack(T5PreTrainedModel):
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 pre-trained transformer.
It's an encoder decoder transformer pre-trained in a text-to-text denoising generative setting.
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.
@@ -634,16 +632,13 @@ T5_INPUTS_DOCSTRING = r"""
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.
**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 single stack (encoder or decoder) of a T5 Model transformer outputting raw hidden-states"
@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 T5Model(T5PreTrainedModel):
@@ -661,8 +656,8 @@ class T5Model(T5PreTrainedModel):
Examples::
tokenizer = T5Tokenizer.from_pretrained('t5-base-uncased')
model = T5Model.from_pretrained('t5-base-uncased')
tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = T5Model.from_pretrained('t5-small')
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
@@ -752,8 +747,8 @@ class T5WithLMHeadModel(T5PreTrainedModel):
Examples::
tokenizer = T5Tokenizer.from_pretrained('t5-base-uncased')
model = T5WithLMHeadModel.from_pretrained('t5-base-uncased')
tokenizer = T5Tokenizer.from_pretrained('t5-small')
model = T5WithLMHeadModel.from_pretrained('t5-small')
input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute")).unsqueeze(0) # Batch size 1
outputs = model(input_ids, lm_labels=input_ids)
loss, prediction_scores = outputs[:2]
@@ -763,31 +758,73 @@ class T5WithLMHeadModel(T5PreTrainedModel):
super(T5WithLMHeadModel, self).__init__(config)
self.model_dim = config.d_model
self.transformer = T5Model(config)
self.shared = nn.Embedding(config.vocab_size, config.d_model)
encoder_config = copy.deepcopy(config)
self.encoder = T5Stack(encoder_config)
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
self.decoder = T5Stack(decoder_config)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.init_weights()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, new_embeddings):
self.shared = new_embeddings
def get_output_embeddings(self):
return self.lm_head
def forward(self, **kwargs):
lm_labels = kwargs.pop('decoder_lm_labels', None)
outputs = self.transformer(**kwargs)
# keyword arguments come in 3 flavors: encoder-specific (prefixed by
# `encoder_`), decoder-specific (prefixed by `decoder_`) and those
# that apply to the model as whole.
# We let the specific kwargs override the common ones in case of conflict.
sequence_output = outputs[0]
lm_labels = kwargs.pop('decoder_lm_labels', None)
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:
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
decoder_inputs_ids = kwargs_decoder.pop("input_ids")
hidden_states = self.shared(decoder_inputs_ids) # Convert inputs in embeddings
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]
# Rescale output before projecting on vocab
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
sequence_output = sequence_output * (self.model_dim ** -0.5)
lm_logits = self.lm_head(sequence_output)
outputs = (lm_logits,) + outputs[1:] # Add hidden states and attention if they are here
decoder_outputs = (lm_logits,) + decoder_outputs[1:] # Add hidden states and attention if they are here
if lm_labels is not None:
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = lm_labels[..., 1:].contiguous()
loss_fct = CrossEntropyLoss(ignore_index=-1)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1))
outputs = (loss,) + outputs # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
decoder_outputs = (loss,) + decoder_outputs # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
return outputs # (lm_loss), lm_logits, (hidden_states), (attentions)
return decoder_outputs + encoder_outputs

4
transformers/modeling_tf_pytorch_utils.py
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@@ -156,7 +156,7 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
e.args += (symbolic_weight.shape, array.shape)
raise e
logger.info("Initialize TF weight {}".format(symbolic_weight.name))
# logger.warning("Initialize TF weight {}".format(symbolic_weight.name))
weight_value_tuples.append((symbolic_weight, array))
all_pytorch_weights.discard(name)
@@ -269,7 +269,7 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
e.args += (pt_weight.shape, array.shape)
raise e
logger.info("Initialize PyTorch weight {}".format(pt_weight_name))
# logger.warning("Initialize PyTorch weight {}".format(pt_weight_name))
new_pt_params_dict[pt_weight_name] = torch.from_numpy(array)
loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array)

781
transformers/modeling_tf_t5.py
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@@ -22,24 +22,21 @@ import logging
import math
import os
import sys
import copy
import itertools
from io import open
import numpy as np
import tensorflow as tf
from .configuration_t5 import T5Config
from .modeling_tf_utils import TFPreTrainedModel, get_initializer
from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list, get_initializer, DUMMY_INPUTS
from .file_utils import add_start_docstrings
logger = logging.getLogger(__name__)
####################################################
# This dict contrains shortcut names and associated url
# for the pretrained weights provided with the models
####################################################
TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
't5-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-base-uncased-tf_model.h5",
't5-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-large-uncased-tf_model.h5",
't5-small': "https://s3.amazonaws.com/models.huggingface.co/bert/t5-small-tf_model.h5",
}
####################################################
@@ -48,33 +45,294 @@ TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP = {
# - TFPreTrainedModel for the models (it-self a sub-class of tf.keras.Model)
####################################################
####################################################
# Here is an example of typical layer in a TF 2.0 model of the library
# The classes are usually identical to the PyTorch ones and prefixed with 'TF'.
#
# Note that class __init__ parameters includes **kwargs (send to 'super').
# This let us have a control on class scope and variable names:
# More precisely, we set the names of the class attributes (lower level layers) to
# to the equivalent attributes names in the PyTorch model so we can have equivalent
# class and scope structure between PyTorch and TF 2.0 models and easily load one in the other.
#
# See the conversion methods in modeling_tf_pytorch_utils.py for more details
####################################################
class TFT5Layer(tf.keras.layers.Layer):
class TFT5DenseReluDense(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super(TFT5Layer, self).__init__(**kwargs)
self.attention = TFT5Attention(config, name='attention')
self.intermediate = TFT5Intermediate(config, name='intermediate')
self.transformer_output = TFT5Output(config, name='output')
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, inputs, training=False):
hidden_states, attention_mask, head_mask = inputs
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
attention_outputs = self.attention([hidden_states, attention_mask, head_mask], training=training)
attention_output = attention_outputs[0]
intermediate_output = self.intermediate(attention_output)
layer_output = self.transformer_output([intermediate_output, attention_output], training=training)
outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them
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 = tf.keras.layers.LayerNormalization(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.dim = config.d_model
self.d_kv = config.d_kv
self.n_heads = config.num_heads
assert self.dim % self.n_heads == 0
assert self.dim // self.n_heads == self.d_kv
# Mesh TensorFlow initialization to avoid scaling before softmax
self.q = tf.keras.layers.Dense(self.dim, use_bias=False, name='q')
self.k = tf.keras.layers.Dense(self.dim, use_bias=False, name='k')
self.v = tf.keras.layers.Dense(self.dim, use_bias=False, name='v')
self.o = tf.keras.layers.Dense(self.dim, 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]
# assert dim == self.dim, 'Dimensions do not match: %s input vs %s configured' % (dim, self.dim)
n_heads = self.n_heads
dim_per_head = self.dim // n_heads
def shape(x):
""" projection """
return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))
def unshape(x):
""" compute context """
return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))
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)
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)
scores += position_bias
if mask is not None:
scores += mask
# mask = (mask == 0).expand_as(scores) # (bs, n_heads, qlen, klen)
# scores.masked_fill_(mask, -float('inf')) # (bs, n_heads, qlen, klen)
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 = tf.keras.layers.LayerNormalization(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 = tf.keras.layers.LayerNormalization(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 = cross_attention_outputs[1:] + outputs
hidden_states = self.layer[2](hidden_states, training=training)
outputs = (hidden_states,) + outputs # add attentions if we output them
return outputs
@@ -85,6 +343,19 @@ class TFT5Layer(tf.keras.layers.Layer):
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 = tf.keras.layers.LayerNormalization(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
@@ -92,51 +363,56 @@ class TFT5MainLayer(tf.keras.layers.Layer):
def _prune_heads(self, heads_to_prune):
raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models
def call(self, inputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
# We allow three 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
# - all the arguments provided as a list/tuple (ordered) in the first positional argument of call
# The last two options are useful to use the tf.keras fit() method.
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
assert len(inputs) <= 5, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
attention_mask = inputs.get('attention_mask', attention_mask)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
assert len(inputs) <= 5, "Too many inputs."
else:
input_ids = inputs
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(tf.shape(input_ids), 1)
if token_type_ids is None:
token_type_ids = tf.fill(tf.shape(input_ids), 0)
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 create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]
# 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.
extended_attention_mask = tf.cast(extended_attention_mask, tf.float32)
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
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, :]
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0
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
@@ -148,14 +424,44 @@ class TFT5MainLayer(tf.keras.layers.Layer):
head_mask = [None] * self.num_hidden_layers
# head_mask = tf.constant([0] * self.num_hidden_layers)
##################################
# Replace this with your model code
embedding_output = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids)
encoder_outputs = self.encoder([embedding_output, extended_attention_mask, head_mask], training=training)
sequence_output = encoder_outputs[0]
outputs = (sequence_output,) + encoder_outputs[1:] # add hidden_states and attentions if they are here
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,)
return outputs # sequence_output, (hidden_states), (attentions)
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:
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)
layer_output = 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)
####################################################
@@ -173,18 +479,26 @@ class TFT5PreTrainedModel(TFPreTrainedModel):
pretrained_model_archive_map = TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP
base_model_prefix = "transformer"
@property
def dummy_inputs(self):
input_ids = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
input_mask = tf.constant([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]])
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 XXX model was proposed in
`XXX: Pre-training of Deep Bidirectional Transformers for Language Understanding`_
by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It's a bidirectional transformer
pre-trained using a combination of masked language modeling objective and next sentence prediction
on a large corpus comprising the Toronto Book Corpus and Wikipedia.
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.
.. _`XXX: Pre-training of Deep Bidirectional Transformers for Language Understanding`:
https://arxiv.org/abs/1810.04805
.. _`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
@@ -206,67 +520,50 @@ T5_START_DOCSTRING = r""" The XXX model was proposed in
`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
Parameters:
config (:class:`~transformers.XxxConfig`): Model configuration class with all the parameters of the model.
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.
"""
XXX_INPUTS_DOCSTRING = r"""
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, XXX input sequence should be formatted with [CLS] and [SEP] tokens as follows:
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]``
``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1``
(b) For single sequences:
``tokens: [CLS] the dog is hairy . [SEP]``
``token_type_ids: 0 0 0 0 0 0 0``
Xxx is a model with absolute position embeddings so it's usually advised to pad the inputs on
the right rather than the left.
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.XxxTokenizer`.
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.
**token_type_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length)``:
Segment token indices to indicate first and second portions of the inputs.
Indices are selected in ``[0, 1]``: ``0`` corresponds to a `sentence A` token, ``1``
corresponds to a `sentence B` token
(see `XXX: Pre-training of Deep Bidirectional Transformers for Language Understanding`_ for more details).
**position_ids**: (`optional`) ``Numpy array`` or ``tf.Tensor`` 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`) ``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 Xxx Model transformer outputing raw hidden-states without any specific head on top.",
XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING)
class TFXxxModel(TFXxxPreTrainedModel):
@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.
**pooler_output**: ``tf.Tensor`` 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 Xxx 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 ``tf.Tensor`` (one for the output of each layer + the output of the embeddings)
of shape ``(batch_size, sequence_length, hidden_size)``:
@@ -278,27 +575,68 @@ class TFXxxModel(TFXxxPreTrainedModel):
Examples::
import tensorflow as tf
from transformers import XxxTokenizer, TFXxxModel
from transformers import T5Tokenizer, TFT5Model
tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased')
model = TFXxxModel.from_pretrained('xxx-base-uncased')
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)
last_hidden_states = outputs[0] # The last hidden-state is the first element of the output tuple
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXxxModel, self).__init__(config, *inputs, **kwargs)
self.transformer = TFXxxMainLayer(config, name='transformer')
super(TFT5Model, self).__init__(config, *inputs, **kwargs)
self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model,
name='shared')
def call(self, inputs, **kwargs):
outputs = self.transformer(inputs, **kwargs)
return outputs
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 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:
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
decoder_inputs_ids = kwargs_decoder.pop("input_ids")
hidden_states = self.shared(decoder_inputs_ids) # Convert inputs in embeddings
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("""Xxx Model with a `language modeling` head on top. """,
XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING)
class TFXxxForMaskedLM(TFXxxPreTrainedModel):
@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)``
@@ -314,183 +652,66 @@ class TFXxxForMaskedLM(TFXxxPreTrainedModel):
Examples::
import tensorflow as tf
from transformers import XxxTokenizer, TFXxxForMaskedLM
from transformers import T5Tokenizer, TFT5WithLMHeadModel
tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased')
model = TFXxxForMaskedLM.from_pretrained('xxx-base-uncased')
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)
prediction_scores = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXxxForMaskedLM, self).__init__(config, *inputs, **kwargs)
super(TFT5WithLMHeadModel, self).__init__(config, *inputs, **kwargs)
self.model_dim = config.d_model
self.transformer = TFXxxMainLayer(config, name='transformer')
self.mlm = TFXxxMLMHead(config, self.transformer.embeddings, name='mlm')
self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model,
name='shared')
def call(self, inputs, **kwargs):
outputs = self.transformer(inputs, **kwargs)
encoder_config = copy.deepcopy(config)
self.encoder = TFT5MainLayer(encoder_config, name='encoder')
sequence_output = outputs[0]
prediction_scores = self.mlm(sequence_output, training=kwargs.get('training', False))
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
self.decoder = TFT5MainLayer(decoder_config, name='decoder')
outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
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.
return outputs # prediction_scores, (hidden_states), (attentions)
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_")))
@add_start_docstrings("""Xxx Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING)
class TFXxxForSequenceClassification(TFXxxPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**logits**: ``Numpy array`` or ``tf.Tensor`` 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 ``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.
# Encode if needed (training, first prediction pass)
encoder_hidden_states = kwargs_encoder.pop("hidden_states", None)
if encoder_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 = ()
Examples::
# Decode
decoder_inputs_ids = kwargs_decoder.pop("input_ids")
hidden_states = self.shared(decoder_inputs_ids) # Convert inputs in embeddings
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)
import tensorflow as tf
from transformers import XxxTokenizer, TFXxxForSequenceClassification
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:]
tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased')
model = TFXxxForSequenceClassification.from_pretrained('xxx-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
logits = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXxxForSequenceClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFXxxMainLayer(config, name='transformer')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name='classifier')
def call(self, inputs, **kwargs):
outputs = self.transformer(inputs, **kwargs)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output, training=kwargs.get('training', False))
logits = self.classifier(pooled_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
return outputs # logits, (hidden_states), (attentions)
@add_start_docstrings("""Xxx Model with a token classification head on top (a linear layer on top of
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING)
class TFXxxForTokenClassification(TFXxxPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length, config.num_labels)``
Classification scores (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 XxxTokenizer, TFXxxForTokenClassification
tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased')
model = TFXxxForTokenClassification.from_pretrained('xxx-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
scores = outputs[0]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXxxForTokenClassification, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFXxxMainLayer(config, name='transformer')
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name='classifier')
def call(self, inputs, **kwargs):
outputs = self.transformer(inputs, **kwargs)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output, training=kwargs.get('training', False))
logits = self.classifier(sequence_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
return outputs # scores, (hidden_states), (attentions)
@add_start_docstrings("""Xxx 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`). """,
XXX_START_DOCSTRING, XXX_INPUTS_DOCSTRING)
class TFXxxForQuestionAnswering(TFXxxPreTrainedModel):
r"""
Outputs: `Tuple` comprising various elements depending on the configuration (config) and inputs:
**start_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
Span-start scores (before SoftMax).
**end_scores**: ``Numpy array`` or ``tf.Tensor`` of shape ``(batch_size, sequence_length,)``
Span-end scores (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 XxxTokenizer, TFXxxForQuestionAnswering
tokenizer = XxxTokenizer.from_pretrained('xxx-base-uncased')
model = TFXxxForQuestionAnswering.from_pretrained('xxx-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
outputs = model(input_ids)
start_scores, end_scores = outputs[:2]
"""
def __init__(self, config, *inputs, **kwargs):
super(TFXxxForQuestionAnswering, self).__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.transformer = TFXxxMainLayer(config, name='transformer')
self.qa_outputs = tf.keras.layers.Dense(config.num_labels,
kernel_initializer=get_initializer(config.initializer_range),
name='qa_outputs')
def call(self, inputs, **kwargs):
outputs = self.transformer(inputs, **kwargs)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
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,) + outputs[2:]
return outputs # start_logits, end_logits, (hidden_states), (attentions)
return decoder_outputs + encoder_outputs

4
transformers/modeling_utils.py
View File

@@ -160,7 +160,6 @@ class PreTrainedModel(nn.Module):
base_model.vocab_size = new_num_tokens
# Tie weights again if needed
if hasattr(self, 'tie_weights'):
self.tie_weights()
return model_embeds
@@ -458,8 +457,7 @@ class PreTrainedModel(nn.Module):
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
model.__class__.__name__, "\n\t".join(error_msgs)))
if hasattr(model, 'tie_weights'):
model.tie_weights() # make sure word embedding weights are still tied
model.tie_weights() # make sure word embedding weights are still tied if needed
# Set model in evaluation mode to desactivate DropOut modules by default
model.eval()

21
transformers/tests/modeling_tf_common_test.py
View File

@@ -69,6 +69,7 @@ class TFCommonTestCases:
test_torchscript = True
test_pruning = True
test_resize_embeddings = True
is_encoder_decoder = False
def test_initialization(self):
pass
@@ -156,6 +157,10 @@ class TFCommonTestCases:
def test_compile_tf_model(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if self.is_encoder_decoder:
input_ids = {'decoder_input_ids': tf.keras.Input(batch_shape=(2, 2000), name='decoder_input_ids', dtype='int32'),
'encoder_input_ids': tf.keras.Input(batch_shape=(2, 2000), name='encoder_input_ids', dtype='int32')}
else:
input_ids = tf.keras.Input(batch_shape=(2, 2000), name='input_ids', dtype='int32')
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
@@ -189,7 +194,7 @@ class TFCommonTestCases:
outputs_dict = model(inputs_dict)
inputs_keywords = copy.deepcopy(inputs_dict)
input_ids = inputs_keywords.pop('input_ids')
input_ids = inputs_keywords.pop('input_ids', inputs_keywords.pop('decoder_input_ids'))
outputs_keywords = model(input_ids, **inputs_keywords)
output_dict = outputs_dict[0].numpy()
@@ -216,12 +221,24 @@ class TFCommonTestCases:
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
out_len = len(outputs)
if self.is_encoder_decoder:
self.assertEqual(out_len % 2, 0)
decoder_attentions = outputs[(out_len // 2)-1]
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads,
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
# Check attention is always last and order is fine
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config)
outputs = model(inputs_dict)
self.assertEqual(out_len+1, len(outputs))
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)

116
transformers/tests/modeling_tf_t5_test.py
View File

@@ -26,7 +26,7 @@ from .configuration_common_test import ConfigTester
from transformers import T5Config, is_tf_available
if False: # is_tf_available():
if is_tf_available():
import tensorflow as tf
from transformers.modeling_tf_t5 import (TFT5Model, TFT5WithLMHeadModel,TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP)
else:
@@ -35,7 +35,8 @@ else:
class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
all_model_classes = (TFT5Model, TFT5WithLMHeadModel) if False else () # is_tf_available() else ()
is_encoder_decoder = True
all_model_classes = (TFT5Model, TFT5WithLMHeadModel) if is_tf_available() else ()
class TFT5ModelTester(object):
@@ -45,22 +46,16 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
n_positions=14,
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,
d_ff=37,
relative_attention_num_buckets=8,
dropout_rate=0.1,
initializer_factor=0.002,
scope=None,
):
self.parent = parent
@@ -68,22 +63,16 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
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.n_positions = n_positions
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.d_ff = d_ff
self.relative_attention_num_buckets = relative_attention_num_buckets
self.dropout_rate = dropout_rate
self.initializer_factor = initializer_factor
self.scope = scope
def prepare_config_and_inputs(self):
@@ -93,61 +82,53 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
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)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = T5Config(
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)
n_positions=self.n_positions,
d_model=self.hidden_size,
d_ff=self.d_ff,
d_kv=self.hidden_size // self.num_attention_heads,
num_layers=self.num_hidden_layers,
num_heads=self.num_attention_heads,
relative_attention_num_buckets=self.relative_attention_num_buckets,
dropout_rate=self.dropout_rate,
initializer_factor=self.initializer_factor)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
return (config, input_ids, input_mask, token_labels)
def create_and_check_t5_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
def create_and_check_t5_model(self, config, input_ids, input_mask, token_labels):
model = TFT5Model(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
sequence_output, pooled_output = model(inputs)
inputs = {'encoder_input_ids': input_ids,
'decoder_input_ids': input_ids,
'decoder_attention_mask': input_mask}
encoder_output, decoder_output = model(inputs)
inputs = [input_ids, input_mask]
sequence_output, pooled_output = model(inputs)
sequence_output, pooled_output = model(input_ids)
encoder_output, decoder_output = model(input_ids,
decoder_attention_mask=input_mask,
encoder_input_ids=input_ids)
result = {
"sequence_output": sequence_output.numpy(),
"pooled_output": pooled_output.numpy(),
"encoder_output": encoder_output.numpy(),
"decoder_output": decoder_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape),
list(result["encoder_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(
list(result["decoder_output"].shape),
[self.batch_size, self.seq_length, self.hidden_size])
self.parent.assertListEqual(list(result["pooled_output"].shape), [self.batch_size, self.hidden_size])
def create_and_check_t5_with_lm_head(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
def create_and_check_t5_with_lm_head(self, config, input_ids, input_mask, token_labels):
model = TFT5WithLMHeadModel(config=config)
inputs = {'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids}
prediction_scores, = model(inputs)
inputs = {'encoder_input_ids': input_ids,
'decoder_input_ids': input_ids,
'decoder_attention_mask': input_mask}
prediction_scores, decoder_output = model(inputs)
result = {
"prediction_scores": prediction_scores.numpy(),
}
@@ -158,14 +139,15 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
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}
(config, input_ids, input_mask, token_labels) = config_and_inputs
inputs_dict = {'encoder_input_ids': input_ids,
'decoder_input_ids': input_ids,
'decoder_attention_mask': input_mask}
return config, inputs_dict
def setUp(self):
self.model_tester = TFT5ModelTest.TFT5ModelTester(self)
self.config_tester = ConfigTester(self, config_class=T5Config, hidden_size=37)
self.config_tester = ConfigTester(self, config_class=T5Config, d_model=37)
def test_config(self):
self.config_tester.run_common_tests()
@@ -181,7 +163,7 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/transformers_test/"
for model_name in ['t5-base']:
for model_name in ['t5-small']:
model = TFT5Model.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)