paddlenlp.transformers.unified_transformer.modeling 源代码
# Copyright (c) 2021 PaddlePaddle Authors. 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.
"""Modeling classes for UnifiedTransformer model."""
from typing import Optional, Tuple
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import Tensor
from ...utils.env import CONFIG_NAME
from ...utils.log import logger
from .. import PretrainedModel, register_base_model
from ..model_outputs import CausalLMOutputWithCrossAttentions
from .configuration import (
UNIFIED_TRANSFORMER_PRETRAINED_INIT_CONFIGURATION,
UNIFIED_TRANSFORMER_PRETRAINED_RESOURCE_FILES_MAP,
UnifiedTransformerConfig,
)
__all__ = [
"UnifiedTransformerPretrainedModel",
"UnifiedTransformerModel",
"UnifiedTransformerLMHeadModel",
"UnifiedTransformerForMaskedLM",
]
[文档]class UnifiedTransformerPretrainedModel(PretrainedModel):
"""
An abstract class for pretrained UnifiedTransformer models. It provides UnifiedTransformer
related `model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
`pretrained_init_configuration`, `base_model_prefix` for downloading
and loading pretrained models.
See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
"""
model_config_file = CONFIG_NAME
pretrained_init_configuration = UNIFIED_TRANSFORMER_PRETRAINED_INIT_CONFIGURATION
pretrained_resource_files_map = UNIFIED_TRANSFORMER_PRETRAINED_RESOURCE_FILES_MAP
config_class = UnifiedTransformerConfig
base_model_prefix = "unified_transformer"
def _init_weights(self, layer):
# Initialization hook
if isinstance(layer, (nn.Linear, nn.Embedding)):
# In the dygraph mode, use the `set_value` to reset the parameter directly,
# and reset the `state_dict` to update parameter in static mode.
if isinstance(layer.weight, paddle.Tensor) and paddle.get_default_dtype() == "float32":
layer.weight.set_value(
# TODO(guosheng): `normal` does not support float16, and
# need to handle this when using fp16 as default dtype for
# big models.
paddle.tensor.normal(
mean=0.0,
std=self.config.initializer_range,
shape=layer.weight.shape,
)
)
class UnifiedTransformerEmbeddings(nn.Layer):
# Include embeddings from word, position and token_type.
def __init__(self, config: UnifiedTransformerConfig):
super(UnifiedTransformerEmbeddings, self).__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
self.role_embeddings = (
None if config.role_type_size is None else nn.Embedding(config.role_type_size, config.hidden_size)
)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.pad_token_id = config.pad_token_id
def forward(
self,
input_ids: Optional[Tensor] = None,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
role_ids: Optional[Tensor] = None,
input_embeddings: Optional[Tensor] = None,
):
if input_ids is None and input_embeddings is None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
inputs_shape = paddle.shape(input_ids)
elif input_embeddings is not None:
inputs_shape = paddle.shape(input_embeddings)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if input_embeddings is None:
input_embeddings = self.word_embeddings(input_ids)
if position_ids is None:
if self.pad_token_id is None:
position_ids = paddle.expand(paddle.arange(end=inputs_shape[1], dtype="int64"), inputs_shape)
else:
if input_ids is not None:
# NOTE: If there is a unk_token_id in input_ids, the following logic is wrong.
# In that case, the position_ids must be provided.
# And this is for left padding input_ids.
num_pad = paddle.sum((input_ids == self.pad_token_id).astype("float32"), axis=-1, keepdim=True)
position_ids = F.relu(
paddle.expand(paddle.arange(end=inputs_shape[1], dtype="int64"), inputs_shape) - num_pad
).astype("int64")
else:
logger.warning(
"Position_ids or pad_token_ids should be provided when input_embeds is specified, "
"otherwise an unexpected result may be returned since `[0, 1, ..., sequence length - 1]` will be generated as a default position_ids."
)
position_ids = paddle.expand(paddle.arange(end=inputs_shape[1], dtype="int64"), inputs_shape)
position_ids.stop_gradient = True
position_embeddings = self.position_embeddings(position_ids)
if token_type_ids is None:
token_type_ids = paddle.zeros_like(input_ids, dtype="int64")
token_type_ids.stop_gradient = True
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = input_embeddings + position_embeddings + token_type_embeddings
# A model with role_embeddings can generate without role_ids.
if role_ids is not None:
embeddings += self.role_embeddings(role_ids)
embeddings = self.dropout(embeddings)
return embeddings
[文档]@register_base_model
class UnifiedTransformerModel(UnifiedTransformerPretrainedModel):
"""
The bare UnifiedTransformer Model outputting raw hidden-states.
This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
Refer to the superclass documentation for the generic methods.
This model is also a `paddle.nn.Layer <https://www.paddlepaddle.org.cn
/documentation/docs/zh/api/paddle/nn/Layer_cn.html>`__
subclass. Use it as a regular Paddle Layer and refer to the Paddle
documentation for all matter related to general usage and behavior.
"""
def __init__(self, config: UnifiedTransformerConfig):
super(UnifiedTransformerModel, self).__init__(config)
self.unk_token_id = config.unk_token_id
self.pad_token_id = config.pad_token_id
self.bos_token_id = config.bos_token_id
self.eos_token_id = config.eos_token_id
self.mask_token_id = config.mask_token_id
self.initializer_range = config.initializer_range
self.embeddings = UnifiedTransformerEmbeddings(config)
encoder_layer = nn.TransformerEncoderLayer(
config.hidden_size,
config.num_attention_heads,
config.intermediate_size,
dropout=config.hidden_dropout_prob,
activation=config.hidden_act,
attn_dropout=config.attention_probs_dropout_prob,
act_dropout=0,
normalize_before=config.normalize_before,
)
encoder_norm = nn.LayerNorm(config.hidden_size)
self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers, encoder_norm)
[文档] def forward(
self,
input_ids: Optional[Tensor] = None,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
use_cache: Optional[bool] = None,
cache: Optional[Tuple[Tensor]] = None,
role_ids: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
The UnifiedTransformerModel forward method, overrides the special
:meth:`__call__` method.
Args:
input_ids (Tensor, optional):
Indices of input sequence tokens in the vocabulary. They are
numerical representations of tokens that build the input
sequence. It's data type should be `int64` and has a shape of
[batch_size, sequence_length].
token_type_ids (Tensor):
Segment token indices to indicate first and second portions of
the inputs. Indices can be either 0 or 1:
- 0 corresponds to a **sentence A** token,
- 1 corresponds to a **sentence B** token.
It's data type should be `int64` and has a shape of
[batch_size, sequence_length].
position_ids (Tensor):
The position indices of input sequence tokens. It's data type
should be `int64` and has a shape of [batch_size, sequence_length].
attention_mask (Tensor):
A tensor used in multi-head attention to prevents attention to
some unwanted positions, usually the paddings or the subsequent
positions. It is a tensor with shape broadcasted to
[batch_size, n_head, sequence_length, sequence_length].
- When the data type is bool, the unwanted positions have
`False` values and the others have `True` values.
- When the data type is int, the unwanted positions have 0
values and the others have 1 values.
- When the data type is float, the unwanted positions have
`-INF` values and the others have 0 values.
use_cache: (bool, optional):
Whether or not use the model cache to speed up decoding. Defaults
to False.
cache (list, optional):
It is a list, and each element in the list is `incremental_cache`
produced by :meth:`paddle.nn.TransformerEncoderLayer.gen_cache`
method. See :meth:`paddle.nn.TransformerEncoder.gen_cache`
method for more details. It is only used for inference and
should be None for training. Defaults to None.
role_ids (Tensor, optional):
Indices of role ids indicated different roles.
It's data type should be `int64` and has a shape of
[batch_size, sequence_length]. Defaults to None.
inputs_embeds (Tensor, optional):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation
of shape `(batch_size, sequence_length, hidden_size)`. This is useful if you want more control over
how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
Default to None.
output_attentions (bool, optional):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail. Defaults to `False`.
output_hidden_states (bool, optional):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail. Defaults to `False`.
return_dict (bool, optional):
Whether to return a :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPastAndCrossAttentions` object.
If `False`, the output will be a tuple of tensors. Defaults to `False`.
Returns:
An instance of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPastAndCrossAttentions` if
`return_dict=True`. Otherwise it returns a tuple of tensors corresponding
to ordered and not None (depending on the input arguments) fields of
:class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPastAndCrossAttentions`.
Especially, When `return_dict=output_hidden_states=output_attentions=False` and `cache=None`,
returns a tensor representing the output of :class:`UnifiedTransformerModel`, with
shape [batch_size, sequence_length, hidden_size]. The data type is
float32 or float64.
Example:
.. code-block::
from paddlenlp.transformers import UnifiedTransformerModel
from paddlenlp.transformers import UnifiedTransformerTokenizer
model = UnifiedTransformerModel.from_pretrained('plato-mini')
tokenizer = UnifiedTransformerTokenizer.from_pretrained('plato-mini')
history = '我爱祖国'
inputs = tokenizer.dialogue_encode(
history,
return_tensors=True,
is_split_into_words=False)
outputs = model(**inputs)
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if attention_mask is None:
if input_ids is not None:
attention_mask = (
(input_ids == self.pad_token_id).astype(paddle.get_default_dtype()) * -1e4
).unsqueeze([1, 2])
else:
logger.warning(
"Provided inputs_embeds while attention_mask is None, attention weights will not be masked during forwarding."
)
if attention_mask is not None:
attention_mask.stop_gradient = True
embedding_output = self.embeddings(
input_ids, token_type_ids, position_ids, role_ids=role_ids, input_embeddings=inputs_embeds
)
if use_cache and cache is None:
cache = self.encoder.gen_cache(embedding_output)
sequence_output = self.encoder(
embedding_output,
attention_mask,
cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return sequence_output
class UnifiedTransformerLMHead(nn.Layer):
def __init__(self, hidden_size, vocab_size, activation, embedding_weights=None):
super(UnifiedTransformerLMHead, self).__init__()
self.transform = nn.Linear(hidden_size, hidden_size)
self.activation = getattr(nn.functional, activation)
self.layer_norm = nn.LayerNorm(hidden_size)
self.decoder_weight = (
self.create_parameter(shape=[vocab_size, hidden_size], dtype=self.transform.weight.dtype, is_bias=False)
if embedding_weights is None
else embedding_weights
)
self.decoder_bias = self.create_parameter(shape=[vocab_size], dtype=self.decoder_weight.dtype, is_bias=True)
def forward(
self,
hidden_states: Tensor,
masked_positions: Optional[Tensor] = None,
):
if masked_positions is not None:
hidden_states = paddle.reshape(hidden_states, [-1, hidden_states.shape[-1]])
hidden_states = paddle.tensor.gather(hidden_states, masked_positions)
hidden_states = self.transform(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = self.layer_norm(hidden_states)
logits = paddle.tensor.matmul(hidden_states, self.decoder_weight, transpose_y=True) + self.decoder_bias
return logits
[文档]class UnifiedTransformerLMHeadModel(UnifiedTransformerPretrainedModel):
"""
The UnifiedTransformer Model with a language modeling head on top
for generation tasks.
Args:
unified_transformer (:class:`UnifiedTransformerModel`):
An instance of :class:`UnifiedTransformerModel`.
"""
def __init__(self, config: UnifiedTransformerConfig):
super(UnifiedTransformerLMHeadModel, self).__init__(config)
self.unified_transformer = UnifiedTransformerModel(config)
self.lm_head = UnifiedTransformerLMHead(
config.hidden_size,
config.vocab_size,
config.hidden_act,
self.unified_transformer.embeddings.word_embeddings.weight,
)
[文档] def forward(
self,
input_ids: Optional[Tensor] = None,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
masked_positions: Optional[Tensor] = None,
use_cache: Optional[bool] = None,
cache: Optional[Tuple[Tensor]] = None,
role_ids: Optional[Tensor] = None,
labels: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
The UnifiedTransformerLMHeadModel forward method, overrides the special
:meth:`__call__` method.
Args:
input_ids (Tensor, optional):
See :class:`UnifiedTransformerModel`.
token_type_ids (Tensor):
See :class:`UnifiedTransformerModel`.
position_ids (Tensor):
See :class:`UnifiedTransformerModel`.
attention_mask (Tensor):
See :class:`UnifiedTransformerModel`.
use_cache: (bool, optional):
See :class:`UnifiedTransformerModel`.
cache (list, optional):
See :class:`UnifiedTransformerModel`.
role_ids: (Tensor, optional):
See :class:`UnifiedTransformerModel`.
labels: (Tensor, optional):
Labels for computing the left-to-right language modeling loss. Indices should be in
`[-100, 0, ..., vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., vocab_size]`
inputs_embeds (Tensor, optional):
See :class:`UnifiedTransformerModel`.
output_attentions (bool, optional):
See :class: `UnifiedTransformerModel`
output_hidden_states (bool, optional):
See :class: `UnifiedTransformerModel`
return_dict (bool, optional):
See :class: `UnifiedTransformerModel`
Returns:
An instance of :class:`~paddlenlp.transformers.model_outputs.CausalLMOutputWithCrossAttentions` if
`return_dict=True`. Otherwise it returns a tuple of tensors corresponding
to ordered and not None (depending on the input arguments) fields of
:class:`~paddlenlp.transformers.model_outputs.CausalLMOutputWithCrossAttentions`.
Especially, When `return_dict=output_hidden_states=output_attentions=False` and `cache=labels=None`,
returns a tensor representing the output of :class:`UnifiedTransformerLMHeadModel`,
with shape [batch_size, sequence_length, vocab_size]. The data type
is float32 or float64.
Example:
.. code-block::
from paddlenlp.transformers import UnifiedTransformerLMHeadModel
from paddlenlp.transformers import UnifiedTransformerTokenizer
model = UnifiedTransformerLMHeadModel.from_pretrained('plato-mini')
tokenizer = UnifiedTransformerTokenizer.from_pretrained('plato-mini')
history = '我爱祖国'
inputs = tokenizer.dialogue_encode(
history,
return_tensors=True,
is_split_into_words=False)
logits = model(**inputs)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.unified_transformer(
input_ids,
token_type_ids,
position_ids,
attention_mask,
use_cache,
cache,
role_ids=role_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
input_type = type(input_ids) if input_ids is not None else type(inputs_embeds)
sequence_output = outputs if isinstance(outputs, input_type) else outputs[0]
logits = self.lm_head(sequence_output, masked_positions)
lm_loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
lm_loss = loss_fct(logits.reshape((-1, logits.shape[-1])), labels.reshape([-1]))
if not return_dict:
if isinstance(outputs, input_type):
return (lm_loss, logits) if lm_loss is not None else logits
else:
outputs = (logits,) + outputs[1:]
return ((lm_loss,) + outputs) if lm_loss is not None else outputs
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_fast_entry(self, kwargs):
from paddlenlp.ops import FasterUnifiedTransformer
use_fp16_decoding = kwargs.get("use_fp16_decoding", False)
decode_strategy = kwargs.get("decode_strategy")
if decode_strategy == "sampling" and kwargs.get("top_k") != 0 and kwargs.get("top_p") != 1:
raise AttributeError(
"Only topk sampling or topp sampling are supported. "
"Topk sampling and topp sampling cannot be both applied in the fast version."
)
if kwargs["repetition_penalty"] != 1.0:
# not support for repetition_penalty yet in the fast version
raise AttributeError("'repetition_penalty != 1' is not supported yet in the fast version")
if kwargs["forced_bos_token_id"] is not None:
# not support for min_length yet in the fast version
raise AttributeError("'forced_bos_token_id != None' is not supported yet in the fast version")
self._fast_entry = FasterUnifiedTransformer(self, use_fp16_decoding=use_fp16_decoding).forward
return self._fast_entry
def adjust_logits_during_generation(self, logits):
# pre-process distribution
logits[:, self.unified_transformer.unk_token_id] = -1e4
logits[:, self.unified_transformer.bos_token_id] = -1e4
logits[:, self.unified_transformer.mask_token_id] = -1e4
return logits
def prepare_inputs_for_generation(
self,
input_ids,
token_type_ids=None,
position_ids=None,
attention_mask=None,
use_cache=False,
cache=None,
**kwargs
):
role_ids = kwargs.get("role_ids", None)
if position_ids is None:
if self.pad_token_id is None:
position_ids = paddle.expand_as(
paddle.arange(end=paddle.shape(input_ids)[1], dtype="int64"), input_ids
)
else:
# NOTE: If there is a unk_token_id in input_ids, the following logic is wrong.
# In that case, the position_ids must be provided.
# And this is for left padding input_ids.
num_pad = paddle.sum((input_ids == self.pad_token_id).astype("float32"), axis=-1, keepdim=True)
position_ids = F.relu(
paddle.expand_as(paddle.arange(end=paddle.shape(input_ids)[1], dtype="float32"), input_ids)
- num_pad
).astype("int64")
position_ids.stop_gradient = True
if token_type_ids is None:
token_type_ids = paddle.zeros_like(input_ids, dtype="int64")
token_type_ids.stop_gradient = True
if attention_mask is None:
attention_mask = ((input_ids == self.pad_token_id).astype(paddle.get_default_dtype()) * -1e4).unsqueeze(
[1, 2]
)
attention_mask.stop_gradient = True
# only last token for inputs_ids if cache is defined in kwargs
if cache is not None:
input_ids = input_ids[:, -1:]
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -1:]
if position_ids is not None:
position_ids = position_ids[:, -1:]
if role_ids is not None:
role_ids = role_ids[:, -1:]
if attention_mask is not None:
attention_mask = attention_mask[:, :, -1:, :]
return {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"position_ids": position_ids,
"attention_mask": attention_mask,
"use_cache": use_cache,
"cache": cache,
"role_ids": role_ids,
}
def __getattr__(self, name):
try:
return super().__getattr__(name)
except AttributeError:
return getattr(getattr(self, self.base_model_prefix), name)
UnifiedTransformerForMaskedLM = UnifiedTransformerLMHeadModel