提交 ba22624d 编写于 作者: G gmcather

position encoding && log loss

test=develop
上级 e3701ad7
......@@ -177,6 +177,8 @@ paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, k
paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
paddle.fluid.layers.add_position_encoding ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......
/* Copyright (c) 2018 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. */
#include "paddle/fluid/operators/add_position_encoding_op.h"
namespace paddle {
namespace operators {
class AddPositionEncodingOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"X(Input) of add_position_encoding_op should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("Out"),
"Out(Output) of add_position_encoding_op should not be null.");
auto x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class AddPositionEncodingOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "X(Input) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("Out"), "Out must not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Out@GRAD must not be null.");
auto out_dims = ctx->GetInputDim("Out");
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), out_dims);
}
}
};
class AddPositionEncodingOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "Input of AddPositionEncoding operator");
AddOutput("Out", "Output of AddPositionEncoding operator");
AddAttr<float>("alpha", "The scale of Original Embedding.")
.SetDefault(1.0f)
.AddCustomChecker([](const float& alpha) {
PADDLE_ENFORCE(alpha >= 0.0f, "'alpha' must be above 0.0.");
});
AddAttr<float>("beta", "The scale of Position Embedding.")
.SetDefault(1.0f)
.AddCustomChecker([](const float& beta) {
PADDLE_ENFORCE(beta >= 0.0f, "'beta' must be between 0.0.");
});
AddComment(R"DOC(
Add Position Encoding Operator.
The add position encoding calculates the output based on the input, alpha, beta.
The size of each dimension of the parameters checked in the infer-shape.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plt = paddle::platform;
REGISTER_OPERATOR(add_position_encoding, ops::AddPositionEncodingOp,
ops::AddPositionEncodingOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(add_position_encoding_grad, ops::AddPositionEncodingOpGrad);
REGISTER_OP_CPU_KERNEL(
add_position_encoding,
ops::AddPositionEncodingKernel<plt::CPUDeviceContext, float>,
ops::AddPositionEncodingKernel<plt::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
add_position_encoding_grad,
ops::AddPositionEncodingGradKernel<plt::CPUDeviceContext, float>,
ops::AddPositionEncodingGradKernel<plt::CPUDeviceContext, double>);
/* Copyright (c) 2018 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. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class AddPositionEncodingKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* X = context.Input<framework::LoDTensor>("X");
auto& x_lod = X->lod();
auto* src_ptr = X->data<T>();
auto* Out = context.Output<framework::LoDTensor>("Out");
auto* dst_ptr = Out->mutable_data<T>(context.GetPlace());
float alpha = context.Attr<float>("alpha");
float beta = context.Attr<float>("beta");
auto x_dim = X->dims();
int batch_size = 0;
int max_seq_len = 0;
int enc_size = 0;
if (x_lod.empty()) {
PADDLE_ENFORCE(
x_dim.size() == 3UL,
"The input X of Add Position Encoding should be 3-D Tensor!");
batch_size = x_dim[0];
max_seq_len = x_dim[1];
enc_size = x_dim[2];
} else {
PADDLE_ENFORCE(
x_dim.size() == 2UL,
"The input X of Add Position Encoding should be 2-D LoDTensor!");
PADDLE_ENFORCE(
x_lod.size() == 1UL,
"The Add Position Encoding Op only supports lod_level == 1!");
batch_size = x_lod[0].size() - 1;
max_seq_len = -1;
enc_size = x_dim[1];
}
PADDLE_ENFORCE(enc_size % 2 == 0, "Only support even encode size!");
const int half_size = enc_size / 2;
for (int i = 0; i < batch_size; ++i) {
const int max_length =
x_lod.empty() ? max_seq_len : x_lod[0][i + 1] - x_lod[0][i];
for (int j = 0; j < max_length; ++j) {
for (int k = 0; k < half_size; ++k) {
const double val = (half_size > 1)
? j / pow(10000.0, double(k) / (half_size - 1))
: j / 10000.0;
dst_ptr[k] = src_ptr[k] * alpha + sin(val) * beta;
dst_ptr[half_size + k] =
src_ptr[half_size + k] * alpha + cos(val) * beta;
}
src_ptr += enc_size;
dst_ptr += enc_size;
}
}
}
};
template <typename DeviceContext, typename T>
class AddPositionEncodingGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* dOut =
context.Input<framework::LoDTensor>(framework::GradVarName("Out"));
auto dout = framework::EigenVector<T>::Flatten(*dOut);
auto* dX =
context.Output<framework::LoDTensor>(framework::GradVarName("X"));
dX->mutable_data<T>(context.GetPlace());
auto dx = framework::EigenVector<T>::Flatten(*dX);
float alpha = context.Attr<float>("alpha");
auto* place =
context.template device_context<DeviceContext>().eigen_device();
dx.device(*place) = dout * static_cast<T>(alpha);
}
};
} // namespace operators
} // namespace paddle
......@@ -157,6 +157,8 @@ __all__ = [
'sequence_reverse',
'affine_channel',
'hash',
'log_loss',
'add_position_encoding',
]
......@@ -7580,3 +7582,99 @@ def hash(input, hash_size, num_hash=1, name=None):
attrs={'num_hash': num_hash,
'mod_by': hash_size})
return out
def log_loss(input, label, epsilon=1e-4, name=None):
"""
**Negative Log Loss Layer**
This layer accepts input predictions and target label and returns the
negative log loss.
.. math::
Out = -label * \\log{(input + \\epsilon)}
- (1 - label) * \\log{(1 - input + \\epsilon)}
Args:
input (Variable|list): a 2-D tensor with shape [N x 1], where N is the
batch size. This input is a probability computed
by the previous operator.
label (Variable|list): the ground truth which is a 2-D tensor with
shape [N x 1], where N is the batch size.
epsilon (float): epsilon
name (string): the name of log_loss
Returns:
Variable: A 2-D tensor with shape [N x 1], the negative log loss.
Examples:
.. code-block:: python
prob = fluid.layers.sigmoid(net)
cost = fluid.layers.log_loss(input=prob, label=label)
"""
helper = LayerHelper('log_loss', **locals())
if name is None:
loss = helper.create_variable_for_type_inference(dtype=input.dtype)
else:
loss = helper.create_variable(
name=name, dtype=input.dtype, persistable=False)
helper.append_op(
type='log_loss',
inputs={'Predicted': [input],
'Labels': [label]},
outputs={'Loss': [loss]},
attrs={'epsilon': epsilon})
return loss
def add_position_encoding(input, alpha, beta, name=None):
"""
**Add Position Encoding Layer**
This layer accepts an input 3D-Tensor of shape [N x M x P], and return an
output Tensor of shape [N x M x P] with positional encoding value.
Refer to `Attention Is All You Need<http://arxiv.org/pdf/1706.03762.pdf>`_ .
.. math::
PE(pos, 2i) = \\sin{(pos / 10000^{2i / P})} \\\\
PE(pos, 2i + 1) = \\cos{(pos / 10000^{2i / P})} \\\\
Out(:, pos, i) = \\alpha * input(:, pos, i) + \\beta * PE(pos, i)
Where:
* PE(pos, 2i): the increment for the number at even position
* PE(pos, 2i + 1): the increment for the number at odd position
Args:
input (Variable): 3-D input tensor with shape [N x M x P]
alpha (float): multiple of Input Tensor
beta (float): multiple of Positional Encoding Tensor
name (string): the name of position encoding layer
Returns:
Variable: A 3-D Tensor of shape [N x M x P] with positional encoding.
Examples:
.. code-block:: python
position_tensor = fluid.layers.add_position_encoding(input=tensor)
"""
helper = LayerHelper('add_position_encoding', **locals())
dtype = helper.input_dtype()
if name is None:
out = helper.create_variable_for_type_inference(dtype=dtype)
else:
out = helper.create_variable(name=name, dtype=dtype, persistable=False)
helper.append_op(
type="add_position_encoding",
inputs={"X": input},
outputs={"Out": out},
attrs={"alpha": alpha,
"beta": beta})
return out
# Copyright (c) 2018 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 unittest
import numpy as np
import math
import paddle.fluid.core as core
from op_test import OpTest
class TestAddPositionEncodingTensorOp(OpTest):
"""
This class is to test the AddPositionEncodingOp
"""
def setUp(self):
"""
the prepared section for add position encoding op
"""
self.op_type = "add_position_encoding"
self.dtype = np.float32
self.init_input_output()
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(self.x), }
self.outputs = {'Out': self.out}
self.attrs = {'alpha': self.alpha, 'beta': self.beta}
def test_check_output(self):
"""
check the correctness of output
"""
self.check_output()
def test_check_grad(self):
"""
check the correctness of grad
"""
self.check_grad(['X'], 'Out', max_relative_error=0.005)
def init_input_output(self):
"""
init the input and output for test cases
"""
self.alpha = 0.6
self.beta = 0.5
self.x = np.random.uniform(0.1, 1, [2, 4, 4]).astype(self.dtype)
self.out = np.copy(self.x)
batch_size = self.x.shape[0]
max_length = self.x.shape[1]
enc_size = self.x.shape[2]
half_shape = int(enc_size / 2)
for i in range(batch_size):
for j in range(max_length):
for k in range(half_shape):
val = j / pow(10000.0, k / (
half_shape - 1)) if half_shape > 1 else j / 10000.0
self.out[i, j, k] = \
self.x[i, j, k] * self.alpha + math.sin(val) * self.beta
self.out[i, j, half_shape + k] = \
self.x[i, j, half_shape + k] * self.alpha + math.cos(val) * self.beta
class TestAddPositionEncodingLoDTensorOp(OpTest):
"""
This class is to test the AddPositionEncodingLoDTensorOp
"""
def setUp(self):
"""
the prepared section for add position encoding LoDTensor op
"""
self.op_type = "add_position_encoding"
self.dtype = np.float32
self.init_input_output()
self.inputs = {'X': (self.x, self.lod), }
self.outputs = {'Out': (self.out, self.lod)}
self.attrs = {'alpha': self.alpha, 'beta': self.beta}
def test_check_output(self):
"""
check the correctness of output
"""
self.check_output()
def test_check_grad(self):
"""
check the correctness of grad
"""
self.check_grad(['X'], 'Out', max_relative_error=0.005)
def init_input_output(self):
"""
init the input and output for test cases
"""
self.alpha = 0.6
self.beta = 0.5
self.x = np.random.uniform(0.1, 1, [10, 4]).astype(self.dtype)
self.lod = [[3, 7]]
self.out = np.copy(self.x)
batch_size = len(self.lod[0])
enc_size = self.x.shape[1]
start = 0
half_shape = int(enc_size / 2)
for i in range(batch_size):
max_length = self.lod[0][i]
for j in range(max_length):
for k in range(half_shape):
val = j / pow(10000.0, k / (
half_shape - 1)) if half_shape > 1 else j / 10000.0
pos = start + j
self.out[pos, k] = \
self.x[pos, k] * self.alpha + math.sin(val) * self.beta
self.out[pos, half_shape + k] = \
self.x[pos, half_shape + k] * self.alpha + math.cos(val) * self.beta
start += max_length
if __name__ == '__main__':
unittest.main()
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