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PaddleFL
未验证 提交 7eb82e3f 编写于 作者: Q Qinghe JING 提交者： GitHub
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Merge pull request #105 from jhjiangcs/smc612 

fix conflict bugs.
上级 280ebac9 3d195298
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Showing with 23 addition and 1569 deletion
core/paddlefl_mpc/data_utils/data_utils.cc
浏览文件 @ 7eb82e3f
<<<<<<< HEAD
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
......@@ -13,8 +12,6 @@ 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. */
=======
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
#include <atomic>
#include <set>
#include <string>
......@@ -90,7 +87,6 @@ PYBIND11_MODULE(mpc_data_utils, m)
{
// optional module docstring
m.doc() = "pybind11 paddle-mpc plugin: data_utils (share, reveal, psi)";
<<<<<<< HEAD
m.def("share", &share<long long, paddle::mpc::ABY3_SCALING_FACTOR>,
"split plaintext into three shares.");
......@@ -100,17 +96,6 @@ PYBIND11_MODULE(mpc_data_utils, m)
m.def("send_psi", &send_psi, "Send input in two party PSI.");
m.def("recv_psi", &recv_psi, "Send input and return PSI result as output in two party PSI.");
=======
m.def("share", &share<long long, paddle::mpc::ABY3_SCALING_FACTOR>,
"split plaintext into three shares.");
m.def("reveal", &reveal<long long, paddle::mpc::ABY3_SCALING_FACTOR>,
"combine three shares to reveal plaintext.");
m.def("send_psi", &send_psi, "Send input in two party PSI.");
m.def("recv_psi", &recv_psi, "Send input and return PSI result as output in two party PSI.");
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
m.attr("mpc_one_share") = (1 << paddle::mpc::ABY3_SCALING_FACTOR) / 3;
}
......
core/paddlefl_mpc/operators/mpc_relu_op.cc
浏览文件 @ 7eb82e3f
......@@ -25,11 +25,7 @@ class MpcReluOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {
auto in_dims = ctx->GetInputDim("X");
<<<<<<< HEAD
ctx->SetOutputDim("Out", in_dims);
=======
ctx->SetOutputDim("Y", in_dims);
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
ctx->SetOutputDim("Derivative", in_dims);
}
};
......@@ -39,11 +35,7 @@ class MpcReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input tensor.");
<<<<<<< HEAD
AddOutput("Out", "Output of relu_op");
=======
AddOutput("Y", "Output of relu_op");
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
AddOutput("Derivative", "Derivative of relu_op");
AddComment(R"DOC(
Mpc Relu Operator.
......@@ -71,15 +63,9 @@ public:
protected:
void Apply(GradOpPtr<T> grad) const override {
grad->SetType("mpc_relu_grad");
<<<<<<< HEAD
grad->SetInput("Out", this->Output("Out"));
grad->SetInput("Derivative", this->Output("Derivative"));
grad->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
=======
grad->SetInput("Y", this->Output("Y"));
grad->SetInput("Derivative", this->Output("Derivative"));
grad->SetInput(framework::GradVarName("Y"), this->OutputGrad("Y"));
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
grad->SetAttrMap(this->Attrs());
grad->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
}
......
core/paddlefl_mpc/operators/mpc_relu_op.h
浏览文件 @ 7eb82e3f
......@@ -25,11 +25,7 @@ class MpcReluKernel : public MpcOpKernel<T> {
public:
void ComputeImpl(const framework::ExecutionContext& ctx) const override {
const Tensor* in_t = ctx.Input<Tensor>("X");
<<<<<<< HEAD
Tensor* out_t = ctx.Output<Tensor>("Out");
=======
Tensor* out_t = ctx.Output<Tensor>("Y");
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
Tensor* der_t = ctx.Output<Tensor>("Derivative");
auto x = in_t->data<T>();
auto y = out_t->mutable_data<T>(ctx.GetPlace());
......@@ -45,13 +41,8 @@ template <typename DeviceContext, typename T>
class MpcReluGradKernel : public MpcOpKernel<T> {
public:
void ComputeImpl(const framework::ExecutionContext& ctx) const override {
<<<<<<< HEAD
auto* dy_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* y_t = ctx.Input<Tensor>("Out");
=======
auto* dy_t = ctx.Input<Tensor>(framework::GradVarName("Y"));
auto* y_t = ctx.Input<Tensor>("Y");
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
auto* der_t = ctx.Input<Tensor>("Derivative");
auto* dx_t = ctx.Output<Tensor>(framework::GradVarName("X"));
auto dx = dx_t->mutable_data<T>(ctx.GetPlace());
......
core/privc3/circuit_context.h
浏览文件 @ 7eb82e3f
......@@ -15,7 +15,6 @@
#include <algorithm>
#include <memory>
#include <algorithm>
#include "core/paddlefl_mpc/mpc_protocol/abstract_network.h"
#include "prng_utils.h"
......@@ -81,85 +80,6 @@ public:
void set_network(std::shared_ptr<AbstractNetwork> network) {
_network = network;
}
<<<<<<< HEAD
AbstractNetwork* network() {
return _network.get();
}
void set_random_seed(const block& seed, size_t idx) {
if (idx >= 3) {
// exception handling
}
_prng[idx].set_seed(seed);
}
size_t party() const {
return _party;
}
size_t pre_party() const {
return (_party + 3 - 1) % 3;
}
size_t next_party() const {
return (_party + 1) % 3;
}
template <typename T>
T gen_random(bool next) {
return _prng[next].get<T>();
}
template<typename T, template <typename> class Tensor>
void gen_random(Tensor<T>& tensor, bool next) {
std::for_each(tensor.data(), tensor.data() + tensor.numel(),
[this, next](T& val) {
val = this->template gen_random<T>(next);
});
}
template <typename T>
T gen_random_private() {
return _prng[2].get<T>();
}
template<typename T, template <typename> class Tensor>
void gen_random_private(Tensor<T>& tensor) {
std::for_each(tensor.data(), tensor.data() + tensor.numel(),
[this](T& val) {
val = this->template gen_random_private<T>();
});
}
template <typename T>
T gen_zero_sharing_arithmetic() {
return _prng[0].get<T>() - _prng[1].get<T>();
}
template<typename T, template <typename> class Tensor>
void gen_zero_sharing_arithmetic(Tensor<T>& tensor) {
std::for_each(tensor.data(), tensor.data() + tensor.numel(),
[this](T& val) {
val = this->template gen_zero_sharing_arithmetic<T>();
});
}
template <typename T>
T gen_zero_sharing_boolean() {
return _prng[0].get<T>() ^ _prng[1].get<T>();
}
template<typename T, template <typename> class Tensor>
void gen_zero_sharing_boolean(Tensor<T>& tensor) {
std::for_each(tensor.data(), tensor.data() + tensor.numel(),
[this](T& val) {
val = this->template gen_zero_sharing_boolean<T>();
});
}
template<typename T, template <typename> class Tensor>
=======
AbstractNetwork* network() {
return _network.get();
......@@ -237,7 +157,6 @@ public:
}
template<typename T, template <typename> class Tensor>
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
void ot(size_t sender, size_t receiver, size_t helper,
const Tensor<T>* choice, const Tensor<T>* m[2],
Tensor<T>* buffer[2], Tensor<T>* ret) {
......
core/privc3/fixedpoint_tensor.h
浏览文件 @ 7eb82e3f
......@@ -28,7 +28,6 @@ class FixedPointTensor {
public:
explicit FixedPointTensor(TensorAdapter<T>* share_tensor[2]);
<<<<<<< HEAD
explicit FixedPointTensor(TensorAdapter<T>* share_tensor_0,
TensorAdapter<T>* share_tensor_1);
......@@ -162,141 +161,6 @@ public:
size_t... N1>
void gt(const CTensor<T, N1...>* rhs, BooleanTensor<T>* ret) const;
=======
explicit FixedPointTensor(TensorAdapter<T>* share_tensor_0,
TensorAdapter<T>* share_tensor_1);
~FixedPointTensor() {};
//get mutable shape of tensor
TensorAdapter<T>* mutable_share(size_t idx);
const TensorAdapter<T>* share(size_t idx) const;
size_t numel() const {
return _share[0]->numel();
}
// reveal fixedpointtensor to one party
void reveal_to_one(size_t party, TensorAdapter<T>* ret) const;
// reveal fixedpointtensor to all parties
void reveal(TensorAdapter<T>* ret) const;
const std::vector<size_t> shape() const;
//convert TensorAdapter to shares
static void share(const TensorAdapter<T>* input,
TensorAdapter<T>* output_shares[3],
block seed = g_zero_block);
// element-wise add with FixedPointTensor
void add(const FixedPointTensor* rhs, FixedPointTensor* ret) const;
// element-wise add with TensorAdapter
void add(const TensorAdapter<T>* rhs, FixedPointTensor* ret) const;
// element-wise sub with FixedPointTensor
void sub(const FixedPointTensor* rhs, FixedPointTensor* ret) const;
// element-wise sub with TensorAdapter
void sub(const TensorAdapter<T>* rhs, FixedPointTensor* ret) const;
// negative
void negative(FixedPointTensor* ret) const;
// element-wise mul with FixedPointTensor using truncate1
void mul(const FixedPointTensor* rhs, FixedPointTensor* ret) const;
// element-wise mul with TensorAdapter
void mul(const TensorAdapter<T>* rhs, FixedPointTensor* ret) const;
// div by TensorAdapter
void div(const TensorAdapter<T>* rhs, FixedPointTensor* ret) const;
// div by FixedPointedTensor
// TODO@yqy : not surport operator rhs <= 0 now
void div(const FixedPointTensor* rhs, FixedPointTensor* ret,
size_t iter = 16, double x0 = pow(2, -15)) const;
// long div by boolean circuit
// res_int_len: estimated bit len of the integer part of result
void long_div(const FixedPointTensor* rhs,
FixedPointTensor* ret, size_t res_int_len = 20) const;
void inverse_square_root(FixedPointTensor* ret,
size_t iter = 16, double x0 = 0x1p-10) const;
// dot_mul
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void dot_mul(const CTensor<T, N1...>* rhs, FixedPointTensor* ret) const;
//sum all element
void sum(FixedPointTensor* ret) const;
// mat_mul with FixedPointTensor
void mat_mul(const FixedPointTensor* rhs, FixedPointTensor* ret) const;
// mat_mul with TensorAdapter
void mat_mul(const TensorAdapter<T>* rhs, FixedPointTensor* ret) const;
// exp approximate: exp(x) = \lim_{n->inf} (1+x/n)^n
// where n = 2^ite
void exp(FixedPointTensor* ret, size_t iter = 8) const;
// element-wise relu
void relu(FixedPointTensor* ret) const;
// element-wise relu with relu'
void relu_with_derivative(FixedPointTensor* ret, BooleanTensor<T>* derivative) const;
// element-wise sigmoid using 3 piecewise polynomials
void sigmoid(FixedPointTensor* ret) const;
// element-wise sigmoid using 5 pieces polynomial
// see paper [Privacy-preserving collaborative machine learning
// on genomic data using TensorFlow]
void sigmoid_enhanced(FixedPointTensor* ret) const;
// element-wise sigmoid using Chebyshev polynomial approximation
// implemented with ref to tfe[https://github.com/tf-encrypted/tf-encrypted]
void sigmoid_chebyshev(FixedPointTensor* ret) const;
// softmax axis = -1
void softmax(FixedPointTensor* ret,
bool use_relu = false,
bool use_long_div = true) const;
// element-wise polynomial
void polynomial(const TensorAdapter<T>* coeff,
FixedPointTensor* ret) const;
// element-wise piecewise polynomial
void polynomial_piecewise(
const TensorAdapter<T>* coeff,
const TensorAdapter<T>* break_point,
FixedPointTensor* ret) const;
// element-wise compare
// <
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void lt(const CTensor<T, N1...>* rhs, BooleanTensor<T>* ret) const;
// <=
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void leq(const CTensor<T, N1...>* rhs, BooleanTensor<T>* ret) const;
// >
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void gt(const CTensor<T, N1...>* rhs, BooleanTensor<T>* ret) const;
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
// >=
template<template<typename U, size_t...> class CTensor,
size_t... N1>
......@@ -332,7 +196,6 @@ private:
static inline std::shared_ptr<CircuitContext> aby3_ctx() {
return paddle::mpc::ContextHolder::mpc_ctx();
}
<<<<<<< HEAD
static inline std::shared_ptr<TensorAdapterFactory> tensor_factory() {
return paddle::mpc::ContextHolder::tensor_factory();
......@@ -368,38 +231,6 @@ private:
return aby3_ctx()->next_party();
}
=======
static inline std::shared_ptr<TensorAdapterFactory> tensor_factory() {
return paddle::mpc::ContextHolder::tensor_factory();
}
static void truncate(const FixedPointTensor* op, FixedPointTensor* ret,
size_t scaling_factor);
template<typename MulFunc>
static void mul_trunc(const FixedPointTensor<T, N>* lhs,
const FixedPointTensor<T, N>* rhs,
FixedPointTensor<T, N>* ret,
MulFunc mul_func);
// reduce last dim
static void reduce(FixedPointTensor<T, N>* input,
FixedPointTensor<T, N>* ret);
static size_t party() {
return aby3_ctx()->party();
}
static size_t pre_party() {
return aby3_ctx()->pre_party();
}
static size_t next_party() {
return aby3_ctx()->next_party();
}
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
static void reshare(const TensorAdapter<T>* send_val,
TensorAdapter<T>* recv_val) {
if (party() == 0) {
......
core/privc3/fixedpoint_tensor_imp.h
浏览文件 @ 7eb82e3f
......@@ -208,7 +208,6 @@ void FixedPointTensor<T, N>::truncate(const FixedPointTensor<T, N>* op,
return;
}
<<<<<<< HEAD
// Protocol. `truncate3`
// P2 randomly generates r' \in (-2^62, 2^62), randomly generates r'_0, r_0, r_1 in Z_{2^64},
// P2 compute r'_1 = r' - r'_0, r_2 = r'/2^N - r_0 - r_1, let x2 = r_2
......@@ -550,249 +549,6 @@ void FixedPointTensor<T, N>::sigmoid_chebyshev(FixedPointTensor<T, N>* ret) cons
}
template< typename T, size_t N>
=======
template<typename T, size_t N>
template<typename MulFunc>
void FixedPointTensor<T, N>::mul_trunc(const FixedPointTensor<T, N>* lhs,
const FixedPointTensor<T, N>* rhs,
FixedPointTensor<T, N>* ret,
MulFunc mul_func) {
auto r_zero = tensor_factory()->template create<T>(ret->shape());
aby3_ctx()->gen_zero_sharing_arithmetic(*r_zero.get());
// temp = _share[0]->mul(rhs->_share[0]) +
// _share[0]->mul(rhs->_share[1]) +
// _share[1]->mul(rhs->_share[0]) +
// r_zero
auto temp = tensor_factory()->template create<T>(ret->shape());
auto temp1 = tensor_factory()->template create<T>(ret->shape());
// use mul_func to fit both element_wise mul and mat mul
(lhs->share(0)->*mul_func)(rhs->share(0), temp.get());
(lhs->share(0)->*mul_func)(rhs->share(1), temp1.get());
temp1->add(temp.get(), temp1.get());
(lhs->share(1)->*mul_func)(rhs->share(0), temp.get());
temp1->add(r_zero.get(), temp1.get());
temp->add(temp1.get(), temp.get());
auto temp2 = tensor_factory()->template create<T>(ret->shape());
auto temp3 = tensor_factory()->template create<T>(ret->shape());
TensorAdapter<int64_t>* temp_array[2] = {temp2.get(), temp3.get()};
std::shared_ptr<FixedPointTensor<T, N>> ret_no_trunc =
std::make_shared<FixedPointTensor<T, N>>(temp_array);
temp->copy(ret_no_trunc->_share[0]);
reshare(temp.get(), ret_no_trunc->_share[1]);
truncate(ret_no_trunc.get(), ret, N);
}
template<typename T, size_t N>
void FixedPointTensor<T, N>::mul(const TensorAdapter<T>* rhs,
FixedPointTensor<T, N>* ret) const {
// PADDLE_ENFORCE_EQ(N, rhs->scaling_factor(),
// "no match scaling factor");
auto temp0 = tensor_factory()->template create<T>(this->shape());
auto temp1 = tensor_factory()->template create<T>(this->shape());
std::shared_ptr<FixedPointTensor<T, N>> temp =
std::make_shared<FixedPointTensor<T, N>>(temp0.get(), temp1.get());
_share[0]->mul(rhs, temp->_share[0]);
_share[1]->mul(rhs, temp->_share[1]);
truncate(temp.get(), ret, rhs->scaling_factor());
}
template<typename T, size_t N>
void FixedPointTensor<T, N>::sum(FixedPointTensor<T, N>* ret) const {
PADDLE_ENFORCE_EQ(ret->numel(), 1, "output size should be 1.");
T sum1 = (T) 0;
T sum2 = (T) 0;
T* iter_0 = _share[0]->data();
T* iter_1 = _share[1]->data();
for (int i = 0; i < this->numel(); ++i) {
sum1 += *(iter_0 + i);
sum2 += *(iter_1 + i);
}
assign_to_tensor(ret->_share[0], sum1);
assign_to_tensor(ret->_share[1], sum2);
}
template<typename T, size_t N>
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void FixedPointTensor<T, N>::dot_mul(const CTensor<T, N1...>* rhs,
FixedPointTensor<T, N>* ret) const {
PADDLE_ENFORCE_EQ(ret->numel(), 1, "output size should be 1.");
auto temp0 = tensor_factory()->template create<T>(this->shape());
auto temp1 = tensor_factory()->template create<T>(this->shape());
std::shared_ptr<FixedPointTensor<T, N>> temp =
std::make_shared<FixedPointTensor<T, N>>(temp0.get(), temp1.get());
this->mul(rhs, temp.get());
temp->sum(ret);
}
template<typename T, size_t N>
void FixedPointTensor<T, N>::mat_mul(const FixedPointTensor<T, N>* rhs,
FixedPointTensor<T, N>* ret) const {
mul_trunc(this, rhs, ret, &TensorAdapter<T>::mat_mul);
}
template<typename T, size_t N>
void FixedPointTensor<T, N>::mat_mul(const TensorAdapter<T>* rhs,
FixedPointTensor<T, N>* ret) const {
_share[0]->mat_mul(rhs, ret->_share[0]);
_share[1]->mat_mul(rhs, ret->_share[1]);
truncate(ret, ret, rhs->scaling_factor());
}
template< typename T, size_t N>
void FixedPointTensor<T, N>::div(const TensorAdapter<T>* rhs,
FixedPointTensor<T, N>* ret) const {
PADDLE_ENFORCE_EQ(N, rhs->scaling_factor(),
"no match scaling factor");
auto temp = tensor_factory()->template create<T>(this->shape());
double scale = std::pow(2, rhs->scaling_factor());
auto inverse = [scale](T d) -> T {
return 1.0 * scale / d * scale; };
std::transform(rhs->data(), rhs->data() + rhs->numel(),
temp->data(), inverse);
temp->scaling_factor() = rhs->scaling_factor();
this->mul(temp.get(), ret);
}
template<typename T, size_t N>
void FixedPointTensor<T, N>::div(const FixedPointTensor<T, N>* rhs,
FixedPointTensor<T, N>* ret,
size_t iter, double x0) const {
auto temp0 = tensor_factory()->template create<T>(ret->shape());
auto temp1 = tensor_factory()->template create<T>(ret->shape());
std::shared_ptr<FixedPointTensor<T, N>> temp =
std::make_shared<FixedPointTensor<T, N>>(temp0.get(), temp1.get());
reciprocal(rhs, temp.get(), iter, x0);
this->mul(temp.get(), ret);
}
template<typename T, size_t N>
void FixedPointTensor<T, N>::exp(FixedPointTensor<T, N>* ret,
size_t iter) const {
// exp approximate: exp(x) = \lim_{n->inf} (1+x/n)^n
// where n = 2^ite
auto pow_iter = tensor_factory()->template create<T>(this->shape());
assign_to_tensor(pow_iter.get(), (T) (pow(2, N -iter)));
pow_iter->scaling_factor() = N;
auto tensor_one = tensor_factory()->template create<T>(this->shape());
assign_to_tensor(tensor_one.get(), (T) 1 << N);
tensor_one->scaling_factor() = N;
this->mul(pow_iter.get(), ret);
ret->add(tensor_one.get(), ret);
for (int i = 0; i < iter; ++i) {
ret->mul(ret, ret);
}
}
template< typename T, size_t N>
void FixedPointTensor<T, N>::relu(FixedPointTensor<T, N>* ret) const {
//utilize polynomial_piecewise
// break_point = {0}, coeff[0] = {0, 0}, coeff[1] = {0, 1}
// break_point.shape = {1, this->shape}, coeff.shape = {2, 2, this->shape}
auto shape_ = shape();
//construct break_point
auto b_shape = shape_;
b_shape.insert(b_shape.begin(), 1);
auto break_point = tensor_factory()->template create<T>(b_shape);
T* b_ptr = break_point->data();
for (size_t i = 0; i < break_point->numel(); ++i) {
b_ptr[i] = 0;
}
break_point->scaling_factor() = N;
//contruct coeff
std::vector<size_t> c_shape = {2, 2};
c_shape.insert(c_shape.end(), shape_.begin(), shape_.end());
auto coeff = tensor_factory()->template create<T>(c_shape);
T* c_ptr = coeff->data();
for (size_t i = 0; i < 3 * this->numel(); ++i) {
c_ptr[i] = 0;
}
for (size_t i = 3 * this->numel(); i < 4 * this->numel(); ++i) {
c_ptr[i] = (T) 1 << N;
}
coeff->scaling_factor() = N;
this->polynomial_piecewise(coeff.get(), break_point.get(), ret);
}
template< typename T, size_t N>
void FixedPointTensor<T, N>::relu_with_derivative(
FixedPointTensor<T, N>* ret, BooleanTensor<T>* derivative) const {
auto shape_ = shape();
auto zero = tensor_factory()->template create<T>(shape_);
assign_to_tensor(zero.get(), (T)0);
zero->scaling_factor() = N;
auto tmp0 = tensor_factory()->template create<T>(shape_);
auto tmp1 = tensor_factory()->template create<T>(shape_);
BooleanTensor<T> der(tmp0.get(), tmp1.get());
gt(zero.get(), &der);
der.mul(this, ret);
if (derivative) {
der.share(0)->copy(derivative->share(0));
der.share(1)->copy(derivative->share(1));
}
}
template< typename T, size_t N>
void FixedPointTensor<T, N>::sigmoid_chebyshev(FixedPointTensor<T, N>* ret) const {
//utilize Chebyshev polynomial approximation
// more accurate in small range, such as [-4, 4]
auto shape = ret->shape();
std::vector<size_t> shape_ = shape;
shape_.insert(shape_.begin(), 10);
auto numel = ret->numel();
auto coeff = tensor_factory()->template create<T>(shape_);
std::vector<double> w;
w.resize(10, 0.0f);
w[0] = 0.5;
w[1] = 0.2159198015;
w[3] = -0.0082176259;
w[5] = 0.0001825597;
w[7] = -0.0000018848;
w[9] = 0.0000000072;
for (int i = 0; i < 10; ++i) {
for (int j = 0; j < numel; ++j) {
*(coeff->data() + i * numel + j) = (T) (w[i] * pow(2, N));
}
}
coeff->scaling_factor() = N;
polynomial(coeff.get(), ret);
}
template< typename T, size_t N>
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
void FixedPointTensor<T, N>::sigmoid(FixedPointTensor<T, N>* ret) const {
//utilize polynomial_piecewise
// break_point = {-2.5, 2.5}
......@@ -823,25 +579,6 @@ void FixedPointTensor<T, N>::sigmoid(FixedPointTensor<T, N>* ret) const {
//contruct coeff
std::vector<size_t> c_shape = {3, 2};
c_shape.insert(c_shape.end(), shape_.begin(), shape_.end());
<<<<<<< HEAD
auto coeff = tensor_factory()->template create<T>(c_shape);
T* c_ptr = coeff->data();
size_t numel = this->numel();
double scale = std::pow(2, N);
for (size_t i = 0; i < numel; ++i) {
c_ptr[i] = 0.0001 * scale;
c_ptr[i + numel] = 0;
c_ptr[i + 2 * numel] = 0.5 * scale;
c_ptr[i + 3 * numel] = 0.17 * scale;
c_ptr[i + 4 * numel] = (1 - 0.0001) * scale;
c_ptr[i + 5 * numel] = 0;
}
coeff->scaling_factor() = N;
=======
auto coeff = tensor_factory()->template create<T>(c_shape);
......@@ -859,7 +596,6 @@ void FixedPointTensor<T, N>::sigmoid(FixedPointTensor<T, N>* ret) const {
}
coeff->scaling_factor() = N;
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
this->polynomial_piecewise(coeff.get(), break_point.get(), ret);
}
......@@ -947,41 +683,6 @@ void FixedPointTensor<T, N>::softmax(FixedPointTensor<T, N>* ret,
temp[8]->reshape({row, col});
temp[9]->reshape({row, col});
FixedPointTensor<T, N> max_x_broadcast(temp[8].get(), temp[9].get());
<<<<<<< HEAD
temp[10]->reshape({row, col});
auto exp_lower_bound = temp[10].get();
auto transpose = [](const TensorAdapter<T>* in, TensorAdapter<T>* out) {
// suppose input dims = 2
const size_t col = in->shape()[1];
const size_t row = in->shape()[0];
const size_t numel = in->numel();
for (size_t k = 0; k < numel; ++k) {
size_t i = k / row;
size_t j = k % row;
out->data()[k] = in->data()[j * col + i];
}
};
auto broadcast = [](const TensorAdapter<T>* in, TensorAdapter<T>* out) {
// suppose input dims = 2
// in shape = [row, 1]
const size_t col = out->shape()[1];
const size_t row = out->shape()[0];
for (size_t k = 0; k < out->numel(); ++k) {
size_t i = k / col;
out->data()[k] = in->data()[i];
}
};
share(0)->copy(x.mutable_share(0));
share(1)->copy(x.mutable_share(1));
if (use_relu) {
=======
temp[10]->reshape({row, col});
auto exp_lower_bound = temp[10].get();
......@@ -1015,7 +716,6 @@ void FixedPointTensor<T, N>::softmax(FixedPointTensor<T, N>* ret,
if (use_relu) {
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
x.relu(&x);
} else { // use exp
......@@ -1087,7 +787,6 @@ void FixedPointTensor<T, N>::long_div(const FixedPointTensor<T, N>* rhs,
assign_to_tensor(cmp_res_all.share(0), (T)0);
assign_to_tensor(cmp_res_all.share(1), (T)0);
<<<<<<< HEAD
const size_t msb = sizeof(T) * 8 - 1;
sign_lhs.bit_extract(msb, this);
......@@ -1121,41 +820,6 @@ void FixedPointTensor<T, N>::long_div(const FixedPointTensor<T, N>* rhs,
lshift(&abs_rhs, i, &sub_rhs);
=======
const size_t msb = sizeof(T) * 8 - 1;
sign_lhs.bit_extract(msb, this);
sign_rhs.bit_extract(msb, rhs);
sign_lhs.bitwise_xor(&sign_rhs, &sign_ret);
auto lshift = [] (const FixedPointTensor<T, N>* in,
size_t rhs,
FixedPointTensor<T, N>* out) {
in->share(0)->lshift(rhs, out->mutable_share(0));
in->share(1)->lshift(rhs, out->mutable_share(1));
};
// abs = val - 2 * sign * val
auto abs = [lshift] (const FixedPointTensor<T, N>* in,
const BooleanTensor<T>* sign,
FixedPointTensor<T, N>* out) {
lshift(in, 1, out);
sign->mul(out, out);
in->sub(out, out);
};
auto out0 = tensor_factory()->template create<T>(ret->shape());
abs(this, &sign_lhs, &abs_lhs);
abs(rhs, &sign_rhs, &abs_rhs);
for (ssize_t i = int_len - 1; i >= 0; --i) {
lshift(&abs_rhs, i, &sub_rhs);
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
abs_lhs.gt(&sub_rhs, &cmp_res);
......@@ -1167,11 +831,7 @@ void FixedPointTensor<T, N>::long_div(const FixedPointTensor<T, N>* rhs,
}
for (size_t i = 1; i <= N; ++i) {
<<<<<<< HEAD
truncate3(&abs_rhs, &sub_rhs, i);
=======
truncate(&abs_rhs, &sub_rhs, i);
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
abs_lhs.gt(&sub_rhs, &cmp_res);
cmp_res.mul(&sub_rhs, &sub_rhs);
cmp_res.lshift(N - i, &cmp_res);
......@@ -1312,16 +972,6 @@ void FixedPointTensor<T, N>::polynomial_piecewise(
temp[temp_index++].get()));
msb[i]->bit_extract(sizeof(T) * 8 - 1, temp1[i].get());
}
<<<<<<< HEAD
// b[0] = msb[0], b[i + 1] = ~ msb[i] & msb[i + 1]
std::vector<std::shared_ptr<BooleanTensor<T>>> b;
b.emplace_back(std::make_shared<BooleanTensor<T>>(
temp[temp_index++].get(),
temp[temp_index++].get()));
b[0] = msb[0];
=======
// b[0] = msb[0], b[i + 1] = ~ msb[i] & msb[i + 1]
std::vector<std::shared_ptr<BooleanTensor<T>>> b;
......@@ -1330,7 +980,6 @@ void FixedPointTensor<T, N>::polynomial_piecewise(
temp[temp_index++].get()));
b[0] = msb[0];
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
for (int i = 0; i < len_break_point - 1; ++i) {
b.emplace_back(std::make_shared<BooleanTensor<T>>(
temp[temp_index++].get(),
......@@ -1535,11 +1184,7 @@ void FixedPointTensor<T, N>::inverse_square_root(const FixedPointTensor* op,
std::shared_ptr<FixedPointTensor<T, N>> x2 =
std::make_shared<FixedPointTensor<T, N>>(temp[2].get(), temp[3].get());
// x2 = 0.5 * op
<<<<<<< HEAD
truncate3(op, x2.get(), 1);
=======
truncate(op, x2.get(), 1);
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
assign_to_tensor(y->mutable_share(0), (T)(x0 * pow(2, N)));
assign_to_tensor(y->mutable_share(1), (T)(x0 * pow(2, N)));
......
python/paddle_fl/mpc/layers/ml.py
浏览文件 @ 7eb82e3f
......@@ -234,11 +234,7 @@ def relu(input, name=None):
type="mpc_relu",
inputs={"X": input},
outputs={
<<<<<<< HEAD
"Out": out,
=======
"Y": out,
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
"Derivative": derivative}
)
return out
......
python/paddle_fl/mpc/mpc_layer_helper.py
浏览文件 @ 7eb82e3f
......@@ -221,11 +221,7 @@ class MpcLayerHelper(LayerHelper):
self.append_op(
type="mpc_" + act_type,
inputs={"X": [input_var]},
<<<<<<< HEAD
outputs={"Out": [tmp],
=======
outputs={"Y": [tmp],
>>>>>>> 5a09665c36ffb7eae2288b3f837d3be18091c259
"Derivative": [derivative]},
attrs=act)
return tmp
......
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