/
transformer.py
1484 lines (1322 loc) · 68.6 KB
/
transformer.py
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# Copyright (c) 2020 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.
# TODO: define the classes of Transformer neural network
import collections
import copy
import numpy as np
import paddle
from paddle.base.data_feeder import convert_dtype
from ... import tensor
from ...framework import ParamAttr
from .. import functional as F
from .common import Dropout, Linear
from .container import LayerList
from .layers import Layer
from .norm import LayerNorm
__all__ = []
def _convert_param_attr_to_list(param_attr, n):
"""
If `param_attr` is a list or tuple, convert every element in it to a
ParamAttr instance. Otherwise, repeat `param_attr` `n` times to
construct a list, and rename every one by appending a increasing index
suffix to avoid having same names when `param_attr` contains a name.
Parameters:
param_attr (list|tuple|ParamAttr): A list, tuple or something can be
converted to a ParamAttr instance by `ParamAttr._to_attr`.
n (int): The times to repeat to construct a list when `param_attr`
is not a list or tuple.
Returns:
list: A list composed of each including cell's `param_attr`.
"""
if isinstance(param_attr, (list, tuple)):
assert len(param_attr) == n, (
"length of param_attr should be %d when it is a list/tuple" % n
)
param_attrs = []
for attr in param_attr:
if isinstance(attr, bool):
if attr:
param_attrs.append(ParamAttr._to_attr(None))
else:
param_attrs.append(False)
else:
param_attrs.append(ParamAttr._to_attr(attr))
# param_attrs = [ParamAttr._to_attr(attr) for attr in param_attr]
elif isinstance(param_attr, bool):
param_attrs = []
if param_attr:
param_attrs = [ParamAttr._to_attr(None) for i in range(n)]
else:
param_attrs = [False] * n
else:
param_attrs = []
attr = ParamAttr._to_attr(param_attr)
for i in range(n):
attr_i = copy.deepcopy(attr)
if attr.name:
attr_i.name = attr_i.name + "_" + str(i)
param_attrs.append(attr_i)
return param_attrs
def _convert_attention_mask(attn_mask, dtype):
"""
Convert the attention mask to the target dtype we expect.
Parameters:
attn_mask (Tensor, optional): 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. It can be None when
nothing wanted or needed to be prevented attention to. Default None.
dtype (VarType): The target type of `attn_mask` we expect.
Returns:
Tensor: A Tensor with shape same as input `attn_mask`, with data type `dtype`.
"""
if attn_mask is not None and attn_mask.dtype != dtype:
attn_mask_dtype = convert_dtype(attn_mask.dtype)
if attn_mask_dtype == 'bool' or 'int' in attn_mask_dtype:
attn_mask = (paddle.cast(attn_mask, dtype) - 1.0) * 1e9
else:
attn_mask = paddle.cast(attn_mask, dtype)
return attn_mask
class MultiHeadAttention(Layer):
"""
Attention mapps queries and a set of key-value pairs to outputs, and
Multi-Head Attention performs multiple parallel attention to jointly attending
to information from different representation subspaces.
Please refer to `Attention Is All You Need <https://arxiv.org/pdf/1706.03762.pdf>`_
for more details.
Parameters:
embed_dim (int): The expected feature size in the input and output.
num_heads (int): The number of heads in multi-head attention.
dropout (float, optional): The dropout probability used on attention
weights to drop some attention targets. 0 for no dropout. Default 0
kdim (int, optional): The feature size in key. If None, assumed equal to
`embed_dim`. Default None.
vdim (int, optional): The feature size in value. If None, assumed equal to
`embed_dim`. Default None.
need_weights (bool, optional): Indicate whether to return the attention
weights. Default False.
weight_attr(ParamAttr, optional): To specify the weight parameter property.
Default: None, which means the default weight parameter property is used.
See usage for details in :code:`ParamAttr` .
bias_attr (ParamAttr|bool, optional): To specify the bias parameter property.
Default: None, which means the default bias parameter property is used.
If it is set to False, this layer will not have trainable bias parameter.
See usage for details in :code:`ParamAttr` .
Examples:
.. code-block:: python
>>> import paddle
>>> # encoder input: [batch_size, sequence_length, d_model]
>>> query = paddle.rand((2, 4, 128))
>>> # self attention mask: [batch_size, num_heads, query_len, query_len]
>>> attn_mask = paddle.rand((2, 2, 4, 4))
>>> multi_head_attn = paddle.nn.MultiHeadAttention(128, 2)
>>> output = multi_head_attn(query, None, None, attn_mask=attn_mask)
>>> print(output.shape)
[2, 4, 128]
"""
Cache = collections.namedtuple("Cache", ["k", "v"])
StaticCache = collections.namedtuple("StaticCache", ["k", "v"])
def __init__(
self,
embed_dim,
num_heads,
dropout=0.0,
kdim=None,
vdim=None,
need_weights=False,
weight_attr=None,
bias_attr=None,
):
super().__init__()
assert embed_dim > 0, (
"Expected embed_dim to be greater than 0, "
f"but received {embed_dim}"
)
assert num_heads > 0, (
"Expected num_heads to be greater than 0, "
f"but received {num_heads}"
)
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.need_weights = need_weights
self.head_dim = embed_dim // num_heads
assert (
self.head_dim * num_heads == self.embed_dim
), "embed_dim must be divisible by num_heads"
self.q_proj = Linear(
embed_dim, embed_dim, weight_attr, bias_attr=bias_attr
)
self.k_proj = Linear(
self.kdim, embed_dim, weight_attr, bias_attr=bias_attr
)
self.v_proj = Linear(
self.vdim, embed_dim, weight_attr, bias_attr=bias_attr
)
self.out_proj = Linear(
embed_dim, embed_dim, weight_attr, bias_attr=bias_attr
)
def _prepare_qkv(self, query, key, value, cache=None):
r"""
Prepares linear projected queries, keys and values for usage of subsequent
multiple parallel attention. If `cache` is not None, using cached results
to reduce redundant calculations.
Parameters:
query (Tensor): The queries for multi-head attention. It is a
tensor with shape `[batch_size, query_length, embed_dim]`. The
data type should be float32 or float64.
key (Tensor): The keys for multi-head attention. It is
a tensor with shape `[batch_size, key_length, kdim]`. The
data type should be float32 or float64. If None, use `query` as
`key`.
value (Tensor): The values for multi-head attention. It
is a tensor with shape `[batch_size, value_length, vdim]`.
The data type should be float32 or float64. If None, use `query` as
`value`.
cache (MultiHeadAttention.Cache|MultiHeadAttention.StaticCache, optional):
It is a namedtuple with `k` and `v` as fields, and stores tensors
shaped `[batch_size, num_heads, length, embed_dim]` which are results
of linear projection, reshape and transpose calculations in
MultiHeadAttention. If is an instance of `Cache`, `k` and `v`
fields reserve intermediate results of previous positions, which
mostly used for decoder self attention. If it is an instance of
`StaticCache`, `key` and `value` args would be ignored, `k` and
`v` fields would be used as calculated results on `key` and
`value`, which mostly used for decoder-encoder cross attention.
It is only used for inference and should be None for training.
Default None.
Returns:
tuple: A tuple including linear projected keys and values. These two \
tensors have shapes `[batch_size, n_head, sequence_length, d_key]` \
and `[batch_size, n_head, sequence_length, d_value]` separately, \
and their data types are same as inputs.
"""
q = self.q_proj(query)
q = tensor.reshape(x=q, shape=[0, 0, self.num_heads, self.head_dim])
q = tensor.transpose(x=q, perm=[0, 2, 1, 3])
if isinstance(cache, self.StaticCache):
# for encoder-decoder attention in inference and has cached
k, v = cache.k, cache.v
else:
k, v = self.compute_kv(key, value)
if isinstance(cache, self.Cache):
# for decoder self-attention in inference
k = tensor.concat([cache.k, k], axis=2)
v = tensor.concat([cache.v, v], axis=2)
cache = self.Cache(k, v)
return (q, k, v) if cache is None else (q, k, v, cache)
def compute_kv(self, key, value):
r"""
Applies linear projection on input keys and values, then splits heads
(reshape and transpose) to get keys and values from different representation
subspaces. The results are used as key-values pairs for subsequent multiple
parallel attention.
It is part of calculations in multi-head attention, and is provided as
a method to pre-compute and prefetch these results, thus we can use them
to construct cache for inference.
Parameters:
key (Tensor): The keys for multi-head attention. It is a tensor
with shape `[batch_size, sequence_length, kdim]`. The data type
should be float32 or float64.
value (Tensor): The values for multi-head attention. It is a tensor
with shape `[batch_size, sequence_length, vdim]`. The data type
should be float32 or float64.
Returns:
Tuple. A tuple including transformed keys and values. Their shapes
both are `[batch_size, num_heads, sequence_length, embed_dim // num_heads]`,
and their data types are same as inputs.
"""
k = self.k_proj(key)
v = self.v_proj(value)
k = tensor.reshape(x=k, shape=[0, 0, self.num_heads, self.head_dim])
k = tensor.transpose(x=k, perm=[0, 2, 1, 3])
v = tensor.reshape(x=v, shape=[0, 0, self.num_heads, self.head_dim])
v = tensor.transpose(x=v, perm=[0, 2, 1, 3])
return k, v
def gen_cache(self, key, value=None, type=Cache):
"""
Generates cache for `forward` usage in inference according to arguments.
The generated cache is an instance of `MultiHeadAttention.Cache` or an
instance of `MultiHeadAttention.StaticCache`.
`Cache` or `StaticCache` is namedtuple with `k` and `v` as fields,
and it stores tensors shaped `[batch_size, num_heads, length, embed_dim]`
which are results of linear projection, reshape and transpose calculations
in MultiHeadAttention.
If the generated cache is an instance of `Cache`, `k` and `v` fields
reserve intermediate result tensors of previous positions, and the tensors
are incremental among decoding steps, which mostly are used for decoder
decoder self attention.
If the generated cache is an instance of `StaticCache`, `k` and `v` fields
would be used as calculated result tensors on keys an values in `forward`,
and the tensors keep unchanged among decoding steps, which are mostly used
for decoder-encoder cross attention.
The cache is generated as follows:
1. If `type` is `StaticCache`, apply `compute_kv(key, value)` and use the
results to create an instance of `StaticCache`.
2. If `type` is `Cache` and `value` is None, generate empty tensors shaped
`[batch_size, num_heads, 0, embed_dim // num_heads]` and use the results
to create an instance of `Cache`, where `batch_size` is from the first
dimension of `key`.
3. If `type` is `Cache` and `value` is not None, use `key`, `value` to create
an instance of `Cache`.
Parameters:
key (Tensor): The keys for multi-head attention. It is
a tensor with shape `[batch_size, key_length, kdim]`. The
data type should be float32 or float64. If `value` is None,
it is only for batch size and data type reference.
value (Tensor, optional): The values for multi-head attention. It
is a tensor with shape `[batch_size, value_length, vdim]`.
The data type should be float32 or float64. If None, `key` is only
for batch size reference. Default None.
type (type): It should be `MultiHeadAttention.StaticCache` or
`MultiHeadAttention.Cache` to indicate the cache type to generate.
Returns:
namedtuple: an instance of `Cache` or `StaticCache` accordingly.
"""
if type == MultiHeadAttention.StaticCache: # static_kv
k, v = self.compute_kv(key, value)
return self.StaticCache(k, v)
elif value is None: # incremental_state
fill_shape = [-1, self.num_heads, 0, self.head_dim]
fill_shape[0] = paddle.shape(key)[0].item()
k = paddle.full(fill_shape, 0, key.dtype)
v = paddle.full(fill_shape, 0, key.dtype)
return self.Cache(k, v)
else:
# incremental_state with initial value, mainly for usage like UniLM
return self.Cache(key, value)
def forward(self, query, key=None, value=None, attn_mask=None, cache=None):
r"""
Applies multi-head attention to map queries and a set of key-value pairs
to outputs.
Parameters:
query (Tensor): The queries for multi-head attention. It is a
tensor with shape `[batch_size, query_length, embed_dim]`. The
data type should be float32 or float64.
key (Tensor, optional): The keys for multi-head attention. It is
a tensor with shape `[batch_size, key_length, kdim]`. The
data type should be float32 or float64. If None, use `query` as
`key`. Default None.
value (Tensor, optional): The values for multi-head attention. It
is a tensor with shape `[batch_size, value_length, vdim]`.
The data type should be float32 or float64. If None, use `query` as
`value`. Default None.
attn_mask (Tensor, optional): 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. It can be None when
nothing wanted or needed to be prevented attention to. Default None.
cache (MultiHeadAttention.Cache|MultiHeadAttention.StaticCache, optional):
It is a namedtuple with `k` and `v` as fields, and stores tensors
shaped `[batch_size, num_heads, length, embed_dim]` which are results
of linear projection, reshape and transpose calculations in
MultiHeadAttention. If it is an instance of `Cache`, `k` and `v`
fields reserve intermediate results of previous positions, which
mostly used for decoder self attention. If it is an instance of
`StaticCache`, `key` and `value` args would be ignored, `k` and
`v` fields would be used as calculated results on `key` and
`value`, which mostly used for decoder-encoder cross attention.
It is only used for inference and should be None for training.
Default None.
Returns:
Tensor|tuple. It is a tensor that has the same shape and data type
as `query`, representing attention output. Or a tuple if
`need_weights` is True or `cache` is not None. If `need_weights`
is True, except for attention output, the tuple also includes
the attention weights tensor shaped `[batch_size, num_heads, query_length, key_length]`.
If `cache` is not None, the tuple then includes the new cache
having the same type as `cache`, and if it is `StaticCache`, it
is same as the input `cache`, if it is `Cache`, the new cache
reserves tensors concatenating raw tensors with intermediate
results of current query.
"""
key = query if key is None else key
value = query if value is None else value
# compute q ,k ,v
if cache is None:
q, k, v = self._prepare_qkv(query, key, value, cache)
else:
q, k, v, cache = self._prepare_qkv(query, key, value, cache)
# scale dot product attention
product = paddle.matmul(
x=q * (self.head_dim**-0.5), y=k, transpose_y=True
)
if attn_mask is not None:
# Support bool or int mask
attn_mask = _convert_attention_mask(attn_mask, product.dtype)
product = product + attn_mask
weights = F.softmax(product)
if self.dropout:
weights = F.dropout(
weights,
self.dropout,
training=self.training,
mode="upscale_in_train",
)
out = tensor.matmul(weights, v)
# combine heads
out = tensor.transpose(out, perm=[0, 2, 1, 3])
out = tensor.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
# project to output
out = self.out_proj(out)
outs = [out]
if self.need_weights:
outs.append(weights)
if cache is not None:
outs.append(cache)
return out if len(outs) == 1 else tuple(outs)
class TransformerEncoderLayer(Layer):
"""
TransformerEncoderLayer is composed of two sub-layers which are self (multi-head)
attention and feedforward network. Before and after each sub-layer, pre-process
and post-precess would be applied on the input and output accordingly. If
`normalize_before` is True, pre-process is layer normalization and post-precess
includes dropout, residual connection. Otherwise, no pre-process and post-precess
includes dropout, residual connection, layer normalization.
Parameters:
d_model (int): The expected feature size in the input and output.
nhead (int): The number of heads in multi-head attention(MHA).
dim_feedforward (int): The hidden layer size in the feedforward network(FFN).
dropout (float, optional): The dropout probability used in pre-process
and post-precess of MHA and FFN sub-layer. Default 0.1
activation (str, optional): The activation function in the feedforward
network. Default relu.
attn_dropout (float, optional): The dropout probability used
in MHA to drop some attention target. If None, use the value of
`dropout`. Default None
act_dropout (float, optional): The dropout probability used after FFN
activation. If None, use the value of `dropout`. Default None
normalize_before (bool, optional): Indicate whether to put layer normalization
into preprocessing of MHA and FFN sub-layers. If True, pre-process is layer
normalization and post-precess includes dropout, residual connection.
Otherwise, no pre-process and post-precess includes dropout, residual
connection, layer normalization. Default False
weight_attr(ParamAttr|list|tuple, optional): To specify the weight parameter property.
If it is a list/tuple, `weight_attr[0]` would be used as `weight_attr` for
MHA, and `weight_attr[1]` would be used as `weight_attr` for linear in FFN.
Otherwise, MHA and FFN both use it as `weight_attr` to create parameters.
Default: None, which means the default weight parameter property is used.
See usage for details in :code:`ParamAttr` .
bias_attr (ParamAttr|list|tuple|bool, optional): To specify the bias parameter property.
If it is a list/tuple, `bias_attr[0]` would be used as `bias_attr` for
MHA, and `bias_attr[1]` would be used as `bias_attr` for linear in FFN.
Otherwise, MHA and FFN both use it as `bias_attr` to create parameters.
The `False` value means the corresponding layer would not have trainable
bias parameter. See usage for details in :code:`ParamAttr` . Default: None,
which means the default bias parameter property is used.
layer_norm_eps: the eps value in layer normalization components. Default=1e-5.
Examples:
.. code-block:: python
>>> import paddle
>>> from paddle.nn import TransformerEncoderLayer
>>> # encoder input: [batch_size, src_len, d_model]
>>> enc_input = paddle.rand((2, 4, 128))
>>> # self attention mask: [batch_size, n_head, src_len, src_len]
>>> attn_mask = paddle.rand((2, 2, 4, 4))
>>> encoder_layer = TransformerEncoderLayer(128, 2, 512)
>>> enc_output = encoder_layer(enc_input, attn_mask)
>>> print(enc_output.shape)
[2, 4, 128]
"""
def __init__(
self,
d_model,
nhead,
dim_feedforward,
dropout=0.1,
activation="relu",
attn_dropout=None,
act_dropout=None,
normalize_before=False,
weight_attr=None,
bias_attr=None,
layer_norm_eps=1e-5,
):
self._config = locals()
self._config.pop("self")
self._config.pop("__class__", None) # py3
super().__init__()
assert d_model > 0, (
"Expected d_model to be greater than 0, " f"but received {d_model}"
)
assert nhead > 0, (
"Expected nhead to be greater than 0, " f"but received {nhead}"
)
assert dim_feedforward > 0, (
"Expected dim_feedforward to be greater than 0, "
f"but received {dim_feedforward}"
)
attn_dropout = dropout if attn_dropout is None else attn_dropout
act_dropout = dropout if act_dropout is None else act_dropout
self.normalize_before = normalize_before
weight_attrs = _convert_param_attr_to_list(weight_attr, 2)
bias_attrs = _convert_param_attr_to_list(bias_attr, 2)
self.self_attn = MultiHeadAttention(
d_model,
nhead,
dropout=attn_dropout,
weight_attr=weight_attrs[0],
bias_attr=bias_attrs[0],
)
self.linear1 = Linear(
d_model, dim_feedforward, weight_attrs[1], bias_attr=bias_attrs[1]
)
self.dropout = Dropout(act_dropout, mode="upscale_in_train")
self.linear2 = Linear(
dim_feedforward, d_model, weight_attrs[1], bias_attr=bias_attrs[1]
)
self.norm1 = LayerNorm(d_model, layer_norm_eps)
self.norm2 = LayerNorm(d_model, layer_norm_eps)
self.dropout1 = Dropout(dropout, mode="upscale_in_train")
self.dropout2 = Dropout(dropout, mode="upscale_in_train")
self.activation = getattr(F, activation)
def forward(self, src, src_mask=None, cache=None):
r"""
Applies a Transformer encoder layer on the input.
Parameters:
src (Tensor): The input of Transformer encoder layer. It is
a tensor with shape `[batch_size, sequence_length, d_model]`.
The data type should be float32 or float64.
src_mask (Tensor, optional): 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. It can be None when
nothing wanted or needed to be prevented attention to. Default None.
cache (Tensor, optional): It is an instance of `MultiHeadAttention.Cache`.
See `TransformerEncoderLayer.gen_cache` for more details. It is
only used for inference and should be None for training. Default
None.
Returns:
Tensor|tuple: It is a tensor that has the same shape and data type \
as `enc_input`, representing the output of Transformer encoder \
layer. Or a tuple if `cache` is not None, except for encoder \
layer output, the tuple includes the new cache which is same \
as input `cache` argument but `incremental_cache` has an \
incremental length. See `MultiHeadAttention.gen_cache` and \
`MultiHeadAttention.forward` for more details.
"""
src_mask = _convert_attention_mask(src_mask, src.dtype)
residual = src
if self.normalize_before:
src = self.norm1(src)
# Add cache for encoder for the usage like UniLM
if cache is None:
src = self.self_attn(src, src, src, src_mask)
else:
src, incremental_cache = self.self_attn(
src, src, src, src_mask, cache
)
src = residual + self.dropout1(src)
if not self.normalize_before:
src = self.norm1(src)
residual = src
if self.normalize_before:
src = self.norm2(src)
src = self.linear2(self.dropout(self.activation(self.linear1(src))))
src = residual + self.dropout2(src)
if not self.normalize_before:
src = self.norm2(src)
return src if cache is None else (src, incremental_cache)
def gen_cache(self, src):
r"""
Generates cache for `forward` usage. The generated cache is an
instance of `MultiHeadAttention.Cache`.
Parameters:
src (Tensor): The input of Transformer encoder. It is a tensor
with shape `[batch_size, source_length, d_model]`. The data
type should be float32 or float64.
Returns:
incremental_cache: It is an instance of `MultiHeadAttention.Cache` \
produced by `self_attn.gen_cache`, it reserves two tensors
shaped `[batch_size, nhead, 0, d_model // nhead]`. See \
`MultiHeadAttention.gen_cache` and `MultiHeadAttention.forward` \
for more details.
"""
incremental_cache = self.self_attn.gen_cache(
src, type=self.self_attn.Cache
)
return incremental_cache
class TransformerEncoder(Layer):
"""
TransformerEncoder is a stack of N encoder layers.
Parameters:
encoder_layer (Layer): an instance of the `TransformerEncoderLayer`. It
would be used as the first layer, and the other layers would be created
according to the configurations of it.
num_layers (int): The number of encoder layers to be stacked.
norm (LayerNorm, optional): the layer normalization component. If provided,
apply layer normalization on the output of last encoder layer.
Examples:
.. code-block:: python
>>> import paddle
>>> from paddle.nn import TransformerEncoderLayer, TransformerEncoder
>>> # encoder input: [batch_size, src_len, d_model]
>>> enc_input = paddle.rand((2, 4, 128))
>>> # self attention mask: [batch_size, n_head, src_len, src_len]
>>> attn_mask = paddle.rand((2, 2, 4, 4))
>>> encoder_layer = TransformerEncoderLayer(128, 2, 512)
>>> encoder = TransformerEncoder(encoder_layer, 2)
>>> enc_output = encoder(enc_input, attn_mask)
>>> print(enc_output.shape)
[2, 4, 128]
"""
def __init__(self, encoder_layer, num_layers, norm=None):
super().__init__()
self.layers = LayerList(
[
(
encoder_layer
if i == 0
else type(encoder_layer)(**encoder_layer._config)
)
for i in range(num_layers)
]
)
self.num_layers = num_layers
self.norm = norm
def forward(self, src, src_mask=None, cache=None):
r"""
Applies a stack of N Transformer encoder layers on inputs. If `norm` is
provided, also applies layer normalization on the output of last encoder
layer.
Parameters:
src (Tensor): The input of Transformer encoder. It is a tensor
with shape `[batch_size, sequence_length, d_model]`. The data
type should be float32 or float64.
src_mask (Tensor, optional): 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. It can be None when
nothing wanted or needed to be prevented attention to. Default None.
cache (list, optional): It is a list, and each element in the list
is `incremental_cache` produced by `TransformerEncoderLayer.gen_cache`.
See `TransformerEncoder.gen_cache` for more details. It is only
used for inference and should be None for training. Default None.
Returns:
Tensor|tuple: It is a tensor that has the same shape and data type \
as `src`, representing the output of Transformer encoder. \
Or a tuple if `cache` is not None, except for encoder output, \
the tuple includes the new cache which is same as input `cache` \
argument but `incremental_cache` in it has an incremental length. \
See `MultiHeadAttention.gen_cache` and `MultiHeadAttention.forward` \
for more details.
"""
src_mask = _convert_attention_mask(src_mask, src.dtype)
output = src
new_caches = []
for i, mod in enumerate(self.layers):
if cache is None:
output = mod(output, src_mask=src_mask)
else:
output, new_cache = mod(
output, src_mask=src_mask, cache=cache[i]
)
new_caches.append(new_cache)
if self.norm is not None:
output = self.norm(output)
return output if cache is None else (output, new_caches)
def gen_cache(self, src):
r"""
Generates cache for `forward` usage. The generated cache is a list, and
each element in it is `incremental_cache` produced by
`TransformerEncoderLayer.gen_cache`. See `TransformerEncoderLayer.gen_cache`
for more details.
Parameters:
src (Tensor): The input of Transformer encoder. It is a tensor
with shape `[batch_size, source_length, d_model]`. The data type
should be float32 or float64.
Returns:
list: It is a list, and each element in the list is `incremental_cache`
produced by `TransformerEncoderLayer.gen_cache`. See
`TransformerEncoderLayer.gen_cache` for more details.
"""
cache = [layer.gen_cache(src) for layer in self.layers]
return cache
class TransformerDecoderLayer(Layer):
"""
TransformerDecoderLayer is composed of three sub-layers which are decoder
self (multi-head) attention, decoder-encoder cross attention and feedforward
network. Before and after each sub-layer, pre-process and post-precess would
be applied on the input and output accordingly. If `normalize_before` is True,
pre-process is layer normalization and post-precess includes dropout, residual
connection. Otherwise, no pre-process and post-precess includes dropout, residual
connection, layer normalization.
Parameters:
d_model (int): The expected feature size in the input and output.
nhead (int): The number of heads in multi-head attention(MHA).
dim_feedforward (int): The hidden layer size in the feedforward network(FFN).
dropout (float, optional): The dropout probability used in pre-process
and post-precess of MHA and FFN sub-layer. Default 0.1
activation (str, optional): The activation function in the feedforward
network. Default relu.
attn_dropout (float, optional): The dropout probability used
in MHA to drop some attention target. If None, use the value of
`dropout`. Default None
act_dropout (float, optional): The dropout probability used after FFN
activation. If None, use the value of `dropout`. Default None
normalize_before (bool, optional): Indicate whether to put layer normalization
into preprocessing of MHA and FFN sub-layers. If True, pre-process is layer
normalization and post-precess includes dropout, residual connection.
Otherwise, no pre-process and post-precess includes dropout, residual
connection, layer normalization. Default False
weight_attr(ParamAttr|list|tuple, optional): To specify the weight parameter property.
If it is a list/tuple, `weight_attr[0]` would be used as `weight_attr` for
self attention, `weight_attr[1]` would be used as `weight_attr` for
cross attention, and `weight_attr[2]` would be used as `weight_attr`
for linear in FFN. Otherwise, the three sub-layers all uses it as
`weight_attr` to create parameters. Default: None, which means the
default weight parameter property is used. See usage for details
in :ref:`api_paddle_base_param_attr_ParamAttr` .
bias_attr (ParamAttr|list|tuple|bool, optional): To specify the bias parameter property.
If it is a list/tuple, `bias_attr[0]` would be used as `bias_attr` for
self attention, `bias_attr[1]` would be used as `bias_attr` for
cross attention, and `bias_attr[2]` would be used as `bias_attr`
for linear in FFN. Otherwise, the three sub-layers all uses it as
`bias_attr` to create parameters. The `False` value means the
corresponding layer would not have trainable bias parameter. See
usage for details in :code:`ParamAttr` . Default: None,which means
the default bias parameter property is used.
layer_norm_eps: the eps value in layer normalization components. Default=1e-5.
Examples:
.. code-block:: python
>>> import paddle
>>> from paddle.nn import TransformerDecoderLayer
>>> # decoder input: [batch_size, tgt_len, d_model]
>>> dec_input = paddle.rand((2, 4, 128))
>>> # encoder output: [batch_size, src_len, d_model]
>>> enc_output = paddle.rand((2, 6, 128))
>>> # self attention mask: [batch_size, n_head, tgt_len, tgt_len]
>>> self_attn_mask = paddle.rand((2, 2, 4, 4))
>>> # cross attention mask: [batch_size, n_head, tgt_len, src_len]
>>> cross_attn_mask = paddle.rand((2, 2, 4, 6))
>>> decoder_layer = TransformerDecoderLayer(128, 2, 512)
>>> output = decoder_layer(dec_input,
... enc_output,
... self_attn_mask,
... cross_attn_mask)
>>> print(output.shape)
[2, 4, 128]
"""
def __init__(
self,
d_model,
nhead,
dim_feedforward,
dropout=0.1,
activation="relu",
attn_dropout=None,
act_dropout=None,
normalize_before=False,
weight_attr=None,
bias_attr=None,
layer_norm_eps=1e-5,
):
self._config = locals()
self._config.pop("self")
self._config.pop("__class__", None) # py3
super().__init__()
assert d_model > 0, (
"Expected d_model to be greater than 0, " f"but received {d_model}"
)
assert nhead > 0, (
"Expected nhead to be greater than 0, " f"but received {nhead}"
)
assert dim_feedforward > 0, (
"Expected dim_feedforward to be greater than 0, "
f"but received {dim_feedforward}"
)
attn_dropout = dropout if attn_dropout is None else attn_dropout
act_dropout = dropout if act_dropout is None else act_dropout
self.normalize_before = normalize_before
weight_attrs = _convert_param_attr_to_list(weight_attr, 3)
bias_attrs = _convert_param_attr_to_list(bias_attr, 3)
self.self_attn = MultiHeadAttention(
d_model,
nhead,
dropout=attn_dropout,
weight_attr=weight_attrs[0],
bias_attr=bias_attrs[0],
)
self.cross_attn = MultiHeadAttention(
d_model,
nhead,
dropout=attn_dropout,
weight_attr=weight_attrs[1],
bias_attr=bias_attrs[1],
)
self.linear1 = Linear(
d_model, dim_feedforward, weight_attrs[2], bias_attr=bias_attrs[2]
)
self.dropout = Dropout(act_dropout, mode="upscale_in_train")
self.linear2 = Linear(
dim_feedforward, d_model, weight_attrs[2], bias_attr=bias_attrs[2]
)
self.norm1 = LayerNorm(d_model, layer_norm_eps)
self.norm2 = LayerNorm(d_model, layer_norm_eps)
self.norm3 = LayerNorm(d_model, layer_norm_eps)
self.dropout1 = Dropout(dropout, mode="upscale_in_train")
self.dropout2 = Dropout(dropout, mode="upscale_in_train")
self.dropout3 = Dropout(dropout, mode="upscale_in_train")
self.activation = getattr(F, activation)
def forward(self, tgt, memory, tgt_mask=None, memory_mask=None, cache=None):
r"""
Applies a Transformer decoder layer on the input.
Parameters:
tgt (Tensor): The input of Transformer decoder layer. It is a tensor
with shape `[batch_size, target_length, d_model]`. The data type
should be float32 or float64.
memory (Tensor): The output of Transformer encoder. It is a tensor
with shape `[batch_size, source_length, d_model]`. The data type
should be float32 or float64.
tgt_mask (Tensor, optional): A tensor used in self attention
to prevents attention to some unwanted positions, usually the
the subsequent positions. It is a tensor with shape broadcasted
to `[batch_size, n_head, target_length, target_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. It can be None when
nothing wanted or needed to be prevented attention to. Default None.
memory_mask (Tensor, optional): A tensor used in decoder-encoder
cross attention to prevents attention to some unwanted positions,
usually the paddings. It is a tensor with shape broadcasted to
`[batch_size, n_head, target_length, source_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. It can be None when
nothing wanted or needed to be prevented attention to. Default None.
cache (tuple, optional): It is a tuple( :code:`(incremental_cache, static_cache)` ),
`incremental_cache` is an instance of `MultiHeadAttention.Cache`,
`static_cache` is an instance of `MultiHeadAttention.StaticCache.
See `TransformerDecoderLayer.gen_cache` for more details. It is
only used for inference and should be None for training. Default
None.
Returns:
Tensor|tuple: It is a tensor that has the same shape and data type \
as `tgt`, representing the output of Transformer decoder layer. \
Or a tuple if `cache` is not None, except for decoder layer output, \
the tuple includes the new cache which is same as input `cache` \
argument but `incremental_cache` in it has an incremental length. \
See `MultiHeadAttention.gen_cache` and `MultiHeadAttention.forward` \
for more details.
"""
tgt_mask = _convert_attention_mask(tgt_mask, tgt.dtype)
memory_mask = _convert_attention_mask(memory_mask, memory.dtype)
residual = tgt
if self.normalize_before:
tgt = self.norm1(tgt)
if cache is None:
tgt = self.self_attn(tgt, tgt, tgt, tgt_mask, None)
else:
tgt, incremental_cache = self.self_attn(
tgt, tgt, tgt, tgt_mask, cache[0]
)
tgt = residual + self.dropout1(tgt)
if not self.normalize_before:
tgt = self.norm1(tgt)
residual = tgt
if self.normalize_before:
tgt = self.norm2(tgt)
if cache is None:
tgt = self.cross_attn(tgt, memory, memory, memory_mask, None)
else:
tgt, static_cache = self.cross_attn(
tgt, memory, memory, memory_mask, cache[1]
)
tgt = residual + self.dropout2(tgt)
if not self.normalize_before:
tgt = self.norm2(tgt)
residual = tgt
if self.normalize_before:
tgt = self.norm3(tgt)
tgt = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = residual + self.dropout3(tgt)
if not self.normalize_before:
tgt = self.norm3(tgt)
return (
tgt if cache is None else (tgt, (incremental_cache, static_cache))
)
def gen_cache(self, memory):
r"""
Generates cache for `forward` usage. The generated cache is a tuple
composed of an instance of `MultiHeadAttention.Cache` and an instance
of `MultiHeadAttention.StaticCache`.
Parameters:
memory (Tensor): The output of Transformer encoder. It is a tensor
with shape `[batch_size, source_length, d_model]`. The data type
should be float32 or float64.