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fused_transformer.py
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fused_transformer.py
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# 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.
import numpy as np
import paddle
from paddle.fluid import core
from paddle.fluid.core import VarDesc
from paddle.fluid.dygraph import no_grad
from paddle.fluid.framework import convert_np_dtype_to_dtype_
from paddle.framework import in_dynamic_mode
from paddle.incubate.nn import functional as incubate_f
from paddle.nn import Layer
from paddle.nn.initializer import Constant
from paddle.nn.layer.transformer import (
_convert_attention_mask,
_convert_param_attr_to_list,
)
# for distributed tensor model parallel
def _set_var_distributed(var):
if var is None:
return
var.is_distributed = True
if not in_dynamic_mode():
# NOTE: use current_block and find_var_recursive to support while_loop
startup_block = paddle.static.default_startup_program().current_block()
main_block = paddle.static.default_main_program().current_block()
startup_block._find_var_recursive(var.name).is_distributed = True
main_block._find_var_recursive(var.name).is_distributed = True
def _to_dtype(t, dtype):
# this function is a prune of Layer._transform function to fix fused op under amp.decorator(O2)
if not paddle.is_floating_point(t):
return t
if type(dtype) is not VarDesc.VarType:
dtype = convert_np_dtype_to_dtype_(dtype)
if t.place.is_gpu_place():
size_dtype = core.size_of_dtype(dtype)
waiting_alloc_memory = (
((np.prod(t.shape) * size_dtype) / 256 + 1) * 256 * 1.2
)
gpu_memory_available = core.gpu_memory_available()
if gpu_memory_available < waiting_alloc_memory:
t_used = t._copy_to(paddle.CPUPlace(), False)
t.value().get_tensor()._clear()
else:
t_used = t
else:
t_used = t
if dtype is not None and dtype != t_used.dtype:
with paddle.fluid.framework._dygraph_place_guard(place=t_used.place):
t_casted = t_used.cast(dtype=dtype)
else:
t_casted = t_used
new_t = t_casted
dst_tensor = t.value().get_tensor()
src_tensor = new_t.value().get_tensor()
dst_tensor._share_data_with(src_tensor)
return t
class FusedBiasDropoutResidualLayerNorm(Layer):
"""
Applies fused_bias_dropout_residual_layer_norm operation.
Parameters:
embed_dim (int): The expected feature size in the input and output.
dropout_rate (float, optional): The dropout probability used on attention
weights to drop some attention targets for the dropout after attention.
0 for no dropout. Default 0.5.
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`.
epsilon (float, optional): The small value added to the variance to prevent
division by zero. Default: 1e-05.
Examples:
.. code-block:: python
# required: gpu
import paddle
# input: [batch_size, seq_len, embed_dim]
x = paddle.rand((2, 4, 128))
# residual: [batch_size, seq_len, embed_dim]
residual = paddle.rand((2, 4, 128))
fused_bias_dropout_residual_ln = paddle.incubate.nn.FusedBiasDropoutResidualLayerNorm(128)
output = fused_bias_dropout_residual_ln(x, residual) # [2, 4, 128]
"""
def __init__(
self,
embed_dim,
dropout_rate=0.5,
weight_attr=None,
bias_attr=None,
epsilon=1e-5,
name=None,
):
super().__init__()
assert embed_dim > 0, (
"Expected embed_dim to be greater than 0, "
"but received {}".format(embed_dim)
)
self._dtype = self._helper.get_default_dtype()
self._bias_attr = bias_attr
self._weight_attr = weight_attr
self.embed_dim = embed_dim
self.linear_bias = self.create_parameter(
shape=[embed_dim],
attr=self._bias_attr,
dtype=self._dtype,
is_bias=True,
)
self.ln_scale = self.create_parameter(
attr=self._weight_attr,
shape=[embed_dim],
default_initializer=Constant(value=1.0),
)
self.ln_bias = self.create_parameter(
attr=self._bias_attr, shape=[embed_dim], is_bias=True
)
self.dropout_rate = dropout_rate
self._epsilon = epsilon
self.name = name
def forward(self, x, residual):
"""
Applies fused_bias_dropout_residual_layer_norm operation.
Parameters:
x (Tensor): The input tensor. It is a tensor with shape
`[batch_size, seq_len, embed_dim]`. The data type should be
float32 or float64.
residual (Tensor, optional): The residual tensor. It is a tensor
with shape `[batch_size, value_length, vdim]`. The data type
should be float32 or float64.
Returns:
Tensor|tuple: It is a tensor that has the same shape and data type \
as `x`.
"""
out = incubate_f.fused_bias_dropout_residual_layer_norm(
x=x,
residual=residual,
bias=self.linear_bias,
ln_scale=self.ln_scale,
ln_bias=self.ln_bias,
dropout_rate=self.dropout_rate,
ln_epsilon=self._epsilon,
training=self.training,
mode='upscale_in_train',
name=self.name,
)
return out
def extra_repr(self):
name_str = f', name={self.name}' if self.name else ''
return 'embed_dim={}, seq_len={}, dropout_rate={}, epsilon={}, dtype={}{}'.format(
self.embed_dim,
self.seq_len,
self.dropout_rate,
self._epsilon,
self._dtype,
name_str,
)
class FusedMultiHeadAttention(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_rate (float, optional): The dropout probability used on attention
weights to drop some attention targets for the dropout after attention.
0 for no dropout. Default 0.5.
attn_dropout_rate (float, optional): The dropout probability used on attention
weights to drop some attention targets for the dropout in attention.
0 for no dropout. Default 0.5.
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.
normalize_before (bool, optional): Indicate whether it is pre_layer_norm
(True) or post_layer_norm architecture (False). Default False.
need_weights (bool, optional): Indicate whether to return the attention
weights. Now, only False is supported. Default False.
qkv_weight_attr(ParamAttr, optional): To specify the weight parameter property
for QKV projection computation. Default: None, which means the default weight
parameter property is used. See usage for details in :code:`ParamAttr`.
qkv_bias_attr(ParamAttr|bool, optional): To specify the bias parameter property
for QKV projection computation. The `False` value means the corresponding layer
would not have trainable bias parameter. Default: None, which means the
default bias parameter property is used. See usage for details in :code:`ParamAttr`.
linear_weight_attr(ParamAttr, optional): To specify the weight parameter property
for linear projection computation. Default: None, which means the default weight
parameter property is used. See usage for details in :code:`ParamAttr`.
linear_bias_attr(ParamAttr|bool, optional): To specify the bias parameter property
for linear projection computation. The `False` value means the corresponding layer would
not have trainable bias parameter. Default: None, which means the default bias
parameter property is used. See usage for details in :code:`ParamAttr`.
pre_ln_scale_attr(ParamAttr, optional): To specify the weight parameter property
for pre_layer_norm computation. Otherwise, all layers both use it as
`attr` to create parameters. Default: None, which means the default weight
parameter property is used. See usage for details in :code:`ParamAttr`.
pre_ln_bias_attr(ParamAttr|bool, optional): To specify the bias parameter property
for pre_layer_norm computation. The `False` value means the corresponding layer would
not have trainable bias parameter. Default: None, which means the default bias
parameter property is used. See usage for details in :code:`ParamAttr`.
ln_scale_attr(ParamAttr, optional): To specify the weight parameter property
for post_layer_norm computation. Default: None, which means the default weight
parameter property is used. See usage for details in :code:`ParamAttr`.
ln_bias_attr(ParamAttr|bool, optional): To specify the bias parameter property
for post_layer_norm computation. The `False` value means the corresponding layer would
not have trainable bias parameter. Default: None, which means the default bias
parameter property is used. See usage for details in :code:`ParamAttr`.
epsilon (float, optional): The small value added to the variance to prevent
division by zero. Default: 1e-05.
nranks (int, optional): Distributed tensor model parallel nranks. Default is 1, means not using tensor parallel.
ring_id (int, optional): For distributed tensor model parallel. Default is -1, means not using tensor parallel.
transpose_qkv_wb (bool, optional): Support input qkv matmul weight shape as
[hidden_size, 3 * hidden_size] and qkv matmul bias shape as [3 * hidden_size].
Will transpose the weight to [3, num_head, head_dim, hidden_size] and transpose bias to
[3, num_head, hidden_size] in the fused_attention_op. Only support for GPU for now.
The default value is False, which is not do transpose to qkv_w and qkv_b.
name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
Examples:
.. code-block:: python
# required: gpu
import paddle
# input: [batch_size, sequence_length, embed_dim]
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.incubate.nn.FusedMultiHeadAttention(128, 2)
output = multi_head_attn(query, None, None, attn_mask=attn_mask) # [2, 4, 128]
"""
def __init__(
self,
embed_dim,
num_heads,
dropout_rate=0.5,
attn_dropout_rate=0.5,
kdim=None,
vdim=None,
normalize_before=False,
need_weights=False,
qkv_weight_attr=None,
qkv_bias_attr=None,
linear_weight_attr=None,
linear_bias_attr=None,
pre_ln_scale_attr=None,
pre_ln_bias_attr=None,
ln_scale_attr=None,
ln_bias_attr=None,
epsilon=1e-5,
nranks=1,
ring_id=-1,
transpose_qkv_wb=False,
name=None,
):
super().__init__()
assert embed_dim > 0, (
"Expected embed_dim to be greater than 0, "
"but received {}".format(embed_dim)
)
assert (
num_heads > 0
), "Expected nhead to be greater than 0, " "but received {}".format(
num_heads
)
self.normalize_before = normalize_before
self._dtype = self._helper.get_default_dtype()
self._epsilon = epsilon
self._ring_id = ring_id
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.kdim = kdim
self.vdim = vdim
self.need_weights = need_weights
assert (
self.head_dim * num_heads == embed_dim
), "embed_dim must be divisible by num_heads"
assert need_weights is False, "Only support need_weight is False now."
# tensor model parallel
assert num_heads % nranks == 0
self.num_heads = num_heads // nranks
self.transpose_qkv_wb = transpose_qkv_wb
if self.transpose_qkv_wb:
# For tensor model parallel, use num_head * head_dim to compute the real shape.
qkv_wight_shape = [embed_dim, 3 * self.num_heads * self.head_dim]
qkv_bias_shape = [3 * self.num_heads * self.head_dim]
else:
qkv_wight_shape = [3, self.num_heads, self.head_dim, embed_dim]
qkv_bias_shape = [3, self.num_heads, self.head_dim]
self.qkv_weight = self.create_parameter(
shape=qkv_wight_shape,
attr=qkv_weight_attr,
dtype=self._dtype,
is_bias=False,
)
self.qkv_bias = self.create_parameter(
shape=qkv_bias_shape,
attr=qkv_bias_attr,
dtype=self._dtype,
is_bias=True,
)
self.linear_weight = self.create_parameter(
shape=[self.num_heads * self.head_dim, embed_dim],
attr=linear_weight_attr,
dtype=self._dtype,
is_bias=False,
)
self.linear_bias = self.create_parameter(
shape=[embed_dim],
attr=linear_bias_attr,
dtype=self._dtype,
is_bias=True,
)
# tensor model parallel
if nranks > 1:
assert ring_id != -1
# column parallel
_set_var_distributed(self.qkv_weight)
_set_var_distributed(self.qkv_bias)
# row parallel
_set_var_distributed(self.linear_weight)
if normalize_before:
self.pre_ln_scale = self.create_parameter(
attr=pre_ln_scale_attr,
shape=[embed_dim],
default_initializer=Constant(value=1.0),
)
self.pre_ln_bias = self.create_parameter(
attr=pre_ln_bias_attr, shape=[embed_dim], is_bias=True
)
self.ln_scale = None
self.ln_bias = None
else:
self.pre_ln_scale = None
self.pre_ln_bias = None
self.ln_scale = self.create_parameter(
attr=ln_scale_attr,
shape=[embed_dim],
default_initializer=Constant(value=1.0),
)
self.ln_bias = self.create_parameter(
attr=ln_bias_attr, shape=[embed_dim], is_bias=True
)
self.dropout_rate = dropout_rate
self.attn_dropout_rate = attn_dropout_rate
self.name = name
def forward(self, query, key=None, value=None, attn_mask=None, cache=None):
"""
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):
Now, only None is supported. Default None.
Returns:
Tensor|tuple: It is a tensor that has the same shape and data type \
as `query`, representing attention output.
"""
if attn_mask is not None:
# Support bool or int mask
attn_mask = _convert_attention_mask(attn_mask, query.dtype)
out = incubate_f.fused_multi_head_attention(
x=query,
qkv_weight=self.qkv_weight,
linear_weight=self.linear_weight,
pre_layer_norm=self.normalize_before,
pre_ln_scale=self.pre_ln_scale,
pre_ln_bias=self.pre_ln_bias,
ln_scale=self.ln_scale,
ln_bias=self.ln_bias,
pre_ln_epsilon=self._epsilon,
qkv_bias=self.qkv_bias,
linear_bias=self.linear_bias,
cache_kv=cache,
attn_mask=attn_mask,
dropout_rate=self.dropout_rate,
attn_dropout_rate=self.attn_dropout_rate,
ln_epsilon=self._epsilon,
training=self.training,
ring_id=self._ring_id,
num_heads=self.num_heads,
transpose_qkv_wb=self.transpose_qkv_wb,
name=self.name,
)
return out
def extra_repr(self):
name_str = f', name={self.name}' if self.name else ''
return 'embed_dim={}, num_heads={}, dropout_rate={}, attn_dropout_rate={}, epsilon={}, kdim={}, vdim={}, normalize_before={}, need_weights={}, dtype={}{}'.format(
self.embed_dim,
self.num_heads,
self.dropout_rate,
self.attn_dropout_rate,
self._epsilon,
self.kdim,
self.vdim,
self.normalize_before,
self.need_weights,
self._dtype,
name_str,
)
def _amp_decorate(self, dtype):
# tmp fix for amp.decorator(O2)
layer_norm_params_id = []
if self.normalize_before:
layer_norm_params_id.append(id(self.pre_ln_scale))
layer_norm_params_id.append(id(self.pre_ln_bias))
else:
layer_norm_params_id.append(id(self.ln_scale))
layer_norm_params_id.append(id(self.ln_bias))
for key, param in self._parameters.items():
if id(param) in layer_norm_params_id:
continue
if param is not None:
with no_grad():
param_applied = _to_dtype(param, dtype)
self._dtype = dtype
class FusedFeedForward(Layer):
"""
Parameters:
d_model (int): The expected feature size in the input and output.
dim_feedforward (int): The hidden layer size.
dropout_rate (float, optional): The dropout probability used in pre-process
and post-precess. Default 0.1
epsilon (float, optional): he small value added to the variance to prevent
division by zero. Default: 1e-05.
activation (str, optional): The activation function. Default relu.
act_dropout_rate (float, optional): The dropout probability after activition.
If None, use the value of `dropout_rate`. Default None
normalize_before (bool, optional): Indicate whether to put layer normalization
into, preprocessing or postprocessing. Default False
linear1_weight_attr(ParamAttr, optional): To specify the weight parameter property
for FFN first linear. Default: None, which means the default weight
parameter property is used. See usage for details in :code:`ParamAttr`.
linear1_bias_attr(ParamAttr|bool, optional): To specify the bias parameter property
for FFN first linear. The `False` value means the corresponding layer would
not have trainable bias parameter. Default: None, which means the default bias
parameter property is used. See usage for details in :code:`ParamAttr`.
linear2_weight_attr(ParamAttr, optional): To specify the weight parameter property
for FFN second linear. Default: None, which means the default weight
parameter property is used. See usage for details in :code:`ParamAttr`.
linear2_bias_attr(ParamAttr|bool, optional): To specify the bias parameter property
for FFN second linear. The `False` value means the corresponding layer would
not have trainable bias parameter. Default: None, which means the default bias
parameter property is used. See usage for details in :code:`ParamAttr`.
ln1_scale_attr(ParamAttr, optional): To specify the weight parameter property
for FFN pre_layer_norm. Default: None, which means the default weight
parameter property is used. See usage for details in :code:`ParamAttr`.
ln1_bias_attr(ParamAttr|bool, optional): To specify the bias parameter property
for FFN pre_layer_norm. The `False` value means the corresponding layer would
not have trainable bias parameter. Default: None, which means the default bias
parameter property is used. See usage for details in :code:`ParamAttr`.
ln2_scale_attr(ParamAttr, optional): To specify the weight parameter property
for FFN post_layer_norm. Default: None, which means the default weight
parameter property is used. See usage for details in :code:`ParamAttr`.
ln2_bias_attr(ParamAttr|bool, optional): To specify the bias parameter property
for FFN layer_norm. The `False` value means the corresponding layer would
not have trainable bias parameter. Default: None, which means the default bias
parameter property is used. See usage for details in :code:`ParamAttr`.
nranks (int, optional): Distributed tensor model parallel nranks. Default is 1, means not using tensor parallel.
ring_id (int, optional): For distributed tensor model parallel. Default is -1, means not using tensor parallel.
name (str, optional): The default value is None. Normally there is no need for user to set
this property. For more information, please refer to :ref:`api_guide_Name`.
Examples:
.. code-block:: python
# required: gpu
import paddle
from paddle.incubate.nn import FusedFeedForward
fused_feedforward_layer = FusedFeedForward(8, 8)
x = paddle.rand((1, 8, 8))
out = fused_feedforward_layer(x)
print(out.shape)
# [1, 8, 8]
"""
def __init__(
self,
d_model,
dim_feedforward,
dropout_rate=0.1,
epsilon=1e-05,
activation="relu",
act_dropout_rate=None,
normalize_before=False,
linear1_weight_attr=None,
linear1_bias_attr=None,
linear2_weight_attr=None,
linear2_bias_attr=None,
ln1_scale_attr=None,
ln1_bias_attr=None,
ln2_scale_attr=None,
ln2_bias_attr=None,
nranks=1,
ring_id=-1,
name=None,
):
super().__init__()
assert (
d_model > 0
), "Expected d_model to be greater than 0, but received {}".format(
d_model
)
assert (
dim_feedforward > 0
), "Expected dim_feedforward to be greater than 0, but received {}".format(
dim_feedforward
)
self._dtype = self._helper.get_default_dtype()
self._d_model = d_model
assert dim_feedforward % nranks == 0
dim_feedforward = dim_feedforward // nranks
self._dim_feedforward = dim_feedforward
self._dropout_rate = dropout_rate
self._act_dropout_rate = (
dropout_rate if act_dropout_rate is None else act_dropout_rate
)
self._act_method = activation
self._normalize_before = normalize_before
self._epsilon = epsilon
self._ring_id = ring_id
self._linear1_weight = self.create_parameter(
shape=[d_model, dim_feedforward],
attr=linear1_weight_attr,
dtype=self._dtype,
is_bias=False,
)
self._linear1_bias = self.create_parameter(
shape=[dim_feedforward],
attr=linear1_bias_attr,
dtype=self._dtype,
is_bias=True,
)
self._linear2_weight = self.create_parameter(
shape=[dim_feedforward, d_model],
attr=linear2_weight_attr,
dtype=self._dtype,
is_bias=False,
)
self._linear2_bias = self.create_parameter(
shape=[d_model],
attr=linear2_bias_attr,
dtype=self._dtype,
is_bias=True,
)
if nranks > 1:
assert ring_id != -1
# column parallel
_set_var_distributed(self._linear1_weight)
_set_var_distributed(self._linear1_bias)
_set_var_distributed(self._linear2_weight)
if normalize_before:
self._ln1_scale = self.create_parameter(
shape=[d_model],
attr=ln1_scale_attr,
is_bias=False,
default_initializer=Constant(1.0),
)
self._ln1_bias = self.create_parameter(
shape=[d_model], attr=ln1_bias_attr, is_bias=True
)
self._ln2_scale = None
self._ln2_bias = None
else:
self._ln1_scale = None
self._ln1_bias = None
self._ln2_scale = self.create_parameter(
shape=[d_model],
attr=ln2_scale_attr,
is_bias=False,
default_initializer=Constant(1.0),
)
self._ln2_bias = self.create_parameter(
shape=[d_model], attr=ln2_bias_attr, is_bias=True
)
self.name = name
def forward(self, src, cache=None):
out = incubate_f.fused_feedforward(
src,
self._linear1_weight,
self._linear2_weight,
self._linear1_bias,
self._linear2_bias,
self._ln1_scale,
self._ln1_bias,
self._ln2_scale,
self._ln2_bias,
dropout1_rate=self._act_dropout_rate,
dropout2_rate=self._dropout_rate,
activation=self._act_method,
ln1_epsilon=self._epsilon,
ln2_epsilon=self._epsilon,
pre_layer_norm=self._normalize_before,
training=self.training,
ring_id=self._ring_id,
name=self.name,
)
return out
def extra_repr(self):
name_str = f', name={self.name}' if self.name else ''
return 'd_model={}, dim_feedforward={}, dropout_rate={}, epsilon={}, activation={}, act_dropout_rate={}, normalize_before={}, dtype={}{}'.format(
self._d_model,
self._dim_feedforward,
self._dropout_rate,
self._epsilon,
self._act_method,
self._act_dropout_rate,
self._normalize_before,
self._dtype,
name_str,
)
def _amp_decorate(self, dtype):
# tmp fix for amp.decorator(O2)
layer_norm_params_id = []
if self._normalize_before:
layer_norm_params_id.append(id(self._ln1_scale))
layer_norm_params_id.append(id(self._ln1_bias))
else:
layer_norm_params_id.append(id(self._ln2_scale))
layer_norm_params_id.append(id(self._ln2_bias))
for key, param in self._parameters.items():
if id(param) in layer_norm_params_id:
continue
if param is not None:
with no_grad():
param_applied = _to_dtype(param, dtype)
self._dtype = dtype
class FusedTransformerEncoderLayer(Layer):
"""
FusedTransformerEncoderLayer 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_rate (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_rate (float, optional): The dropout probability used
in MHA to drop some attention target. If None, use the value of
`dropout`. Default None
act_dropout_rate (float, optional): The dropout probability used after FFN
activition. 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.
Examples:
.. code-block:: python
# required: gpu
import paddle
from paddle.incubate.nn import FusedTransformerEncoderLayer
# 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 = FusedTransformerEncoderLayer(128, 2, 512)
enc_output = encoder_layer(enc_input, attn_mask) # [2, 4, 128]
"""
def __init__(
self,
d_model,
nhead,
dim_feedforward,
dropout_rate=0.1,
activation="relu",
attn_dropout_rate=None,
act_dropout_rate=None,
normalize_before=False,
weight_attr=None,
bias_attr=None,
):
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, " "but received {}".format(
d_model
)
assert (
nhead > 0
), "Expected nhead to be greater than 0, " "but received {}".format(
nhead
)
assert dim_feedforward > 0, (
"Expected dim_feedforward to be greater than 0, "
"but received {}".format(dim_feedforward)
)
attn_dropout_rate = (
dropout_rate if attn_dropout_rate is None else attn_dropout_rate
)
act_dropout_rate = (
dropout_rate if act_dropout_rate is None else act_dropout_rate
)
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.fused_attn = FusedMultiHeadAttention(
d_model,
nhead,
dropout_rate=dropout_rate,
attn_dropout_rate=attn_dropout_rate,
normalize_before=self.normalize_before,
qkv_weight_attr=weight_attrs[0],
qkv_bias_attr=bias_attrs[0],
linear_weight_attr=weight_attrs[0],
linear_bias_attr=bias_attrs[0],
pre_ln_scale_attr=weight_attrs[0],
pre_ln_bias_attr=bias_attrs[0],
ln_scale_attr=weight_attrs[0],
ln_bias_attr=bias_attrs[0],
)
self.ffn = FusedFeedForward(
d_model,
dim_feedforward,
dropout_rate=dropout_rate,
activation=activation,
act_dropout_rate=act_dropout_rate,
normalize_before=self.normalize_before,
linear1_weight_attr=weight_attrs[1],
linear1_bias_attr=bias_attrs[1],
linear2_weight_attr=weight_attrs[1],
linear2_bias_attr=bias_attrs[1],
)
def forward(self, src, src_mask=None, cache=None):
"""
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 :ref:`api_paddle_nn_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)
if cache is None:
attn_out = self.fused_attn(src, attn_mask=src_mask)
else:
attn_out, incremental_cache = self.fused_attn(
src, attn_mask=src_mask, cache=cache
)
ffn_out = self.ffn(attn_out)
return ffn_out if cache is None else (ffn_out, incremental_cache)
class FusedTransformer(Layer):
"""
A Transformer model composed of an instance of `TransformerEncoder` and an
instance of `TransformerDecoder`. While the embedding layer and output layer
are not included.
Please refer to `Attention is all you need <http://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf>`_ ,
and see `TransformerEncoder` and `TransformerDecoder` for more details.
Users can configurate the model architecture with corresponding parameters.
Note the usage of `normalize_before` representing where to apply layer
normalization (in pre-process or post-precess of multi-head attention or FFN),
and some transformer like models are different on this, such as
`BERT <https://arxiv.org/abs/1810.04805>`_ and `GPT2 <https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf>`_ .
The default architecture here places layer normalization in post-process and
applies another layer normalization on the output of last encoder/decoder layer.
Parameters:
d_model (int, optional): The expected feature size in the encoder/decoder input
and output. Default 512
nhead (int, optional): The number of heads in multi-head attention(MHA). Default 8
num_encoder_layers (int, optional): The number of layers in encoder. Default 6
num_decoder_layers (int, optional): The number of layers in decoder. Default 6
dim_feedforward (int, optional): The hidden layer size in the feedforward network(FFN). Default 2048
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
activition. 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, the length of `weight_attr` could be 1, 2 or 3. If it is 3,
`weight_attr[0]` would be used as `weight_attr` for self attention, `weight_attr[1]`
would be used as `weight_attr` for cross attention of `TransformerDecoder`,
and `weight_attr[2]` would be used as `weight_attr` for linear in FFN.
If it is 2, `weight_attr[0]` would be used as `weight_attr` both for self attention
and cross attntion and `weight_attr[1]` would be used as `weight_attr` for
linear in FFN. If it is 1, `weight_attr[0]` would be used as `weight_attr`
for self attention, cross attention and 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 :code:`ParamAttr` .
bias_attr (ParamAttr|list|tuple|bool, optional): To specify the bias parameter property.
If it is a list/tuple, the length of `bias_attr` could be 1, 2 or 3. If it is 3,
`bias_attr[0]` would be used as `bias_attr` for self attention, `bias_attr[1]`
would be used as `bias_attr` for cross attention of `TransformerDecoder`,
and `bias_attr[2]` would be used as `bias_attr` for linear in FFN.
If it is 2, `bias_attr[0]` would be used as `bias_attr` both for self attention
and cross attntion and `bias_attr[1]` would be used as `bias_attr` for
linear in FFN. If it is 1, `bias_attr[0]` would be used as `bias_attr`
for self attention, cross attention and 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.
custom_encoder (Layer, optional): If custom encoder is provided, use it as the encoder.
Default None
custom_decoder (Layer, optional): If custom decoder is provided, use it as the decoder.
Default None
Examples:
.. code-block:: python
import paddle
from paddle.nn import Transformer
# src: [batch_size, tgt_len, d_model]
enc_input = paddle.rand((2, 4, 128))
# tgt: [batch_size, src_len, d_model]
dec_input = paddle.rand((2, 6, 128))
# src_mask: [batch_size, n_head, src_len, src_len]
enc_self_attn_mask = paddle.rand((2, 2, 4, 4))
# tgt_mask: [batch_size, n_head, tgt_len, tgt_len]
dec_self_attn_mask = paddle.rand((2, 2, 6, 6))
# memory_mask: [batch_size, n_head, tgt_len, src_len]
cross_attn_mask = paddle.rand((2, 2, 6, 4))
transformer = Transformer(128, 2, 4, 4, 512)
output = transformer(enc_input,
dec_input,
enc_self_attn_mask,
dec_self_attn_mask,
cross_attn_mask) # [2, 6, 128]
"""
def __init__(
self,
d_model=512,