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PaddleFL
提交 a8576fd5 编写于 作者: Y yangqingyou
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amend

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core/privc/fixedpoint_tensor_imp.h
浏览文件 @ a8576fd5
......@@ -95,8 +95,6 @@ void FixedPointTensor<T, N>::add(const FixedPointTensor<T, N>* rhs,
template<typename T, size_t N>
void FixedPointTensor<T, N>::add(const TensorAdapter<T>* rhs,
FixedPointTensor<T, N>* ret) const {
PADDLE_ENFORCE_EQ(N, rhs->scaling_factor(),
"no match scaling factor");
if (party() == 0) {
_share->add(rhs, ret->_share);
} else {
......@@ -113,8 +111,6 @@ void FixedPointTensor<T, N>::sub(const FixedPointTensor<T, N>* rhs,
template<typename T, size_t N>
void FixedPointTensor<T, N>::sub(const TensorAdapter<T>* rhs,
FixedPointTensor<T, N>* ret) const {
PADDLE_ENFORCE_EQ(N, rhs->scaling_factor(),
"no match scaling factor");
if (party() == 0) {
_share->sub(rhs, ret->_share);
} else {
......@@ -182,1132 +178,4 @@ void FixedPointTensor<T, N>::mul_impl(const FixedPointTensor<T, N>* rhs,
}
}
/*
template<typename T, size_t N>
void FixedPointTensor<T, N>::truncate(const FixedPointTensor<T, N>* op,
FixedPointTensor<T, N>* ret,
size_t scaling_factor) {
if (scaling_factor == 0) {
op->share(0)->copy(ret->mutable_share(0));
op->share(1)->copy(ret->mutable_share(1));
}
// implement ABY3's truncate1 algorithm
if (party() == 0) {
// party0
op->_share[0]->rshift(scaling_factor, ret->_share[0]);
privc_ctx()->network()->template recv(1, *(ret->_share[1]));
} else if (party() == 1) {
// party1
auto r_12 = tensor_factory()->template create<T>(op->shape());
privc_ctx()->template gen_random(*r_12.get(), true);
op->_share[0]->add(op->_share[1], ret->_share[0]);
// trunc from [SecureML, Thm.1]
ret->_share[0]->negative(ret->_share[0]);
ret->_share[0]->rshift(scaling_factor, ret->_share[0]);
ret->_share[0]->negative(ret->_share[0]);
ret->_share[0]->sub(r_12.get(), ret->_share[0]);
privc_ctx()->network()->template send(0, *(ret->_share[0]));
r_12->copy(ret->_share[1]);
} else {
// party2
op->_share[1]->rshift(scaling_factor, ret->_share[1]);
auto r_21 = tensor_factory()->template create<T>(op->shape());
privc_ctx()->template gen_random(*r_21.get(), false);
r_21->copy(ret->_share[0]);
}
return;
}
// 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
// P2 send r_0, r'_0 to P0, send r_1, r'_1 to P1
// P1 and P0 execute "reveal x - r' to P1"
// P1 compute x1 = (x - r') / 2^N + r_1
// P0 set x0 = r_0
// P0, P1, P2 invoke reshare() with inputs x0, x1, x2 respectively.
template<typename T, size_t N>
void FixedPointTensor<T, N>::truncate3(const FixedPointTensor<T, N>* op,
FixedPointTensor<T, N>* ret,
size_t scaling_factor) {
if (scaling_factor == 0) {
op->share(0)->copy(ret->mutable_share(0));
op->share(1)->copy(ret->mutable_share(1));
return;
}
std::vector<std::shared_ptr<TensorAdapter<T>>> temp;
if (party() == 2) {
for (int i = 0; i < 7; ++i) {
temp.emplace_back(
tensor_factory()->template create<T>(op->shape()));
}
// r', contraint in (-2^62, 2^62)
// notice : when r' is contrainted in (-2^62, 2^62),
// the SD (statistical distance) of x - r' between this
// and r' in Z_{2^64} is equal to |X| / (2^63 + |X|)
// according to http://yuyu.hk/files/ho2.pdf
privc_ctx()->template gen_random_private(*temp[0]);
int64_t contraint_upper = ~((uint64_t) 1 << 62);
int64_t contraint_low = (uint64_t) 1 << 62;
std::for_each(temp[0]->data(), temp[0]->data() + temp[0]->numel(),
[&contraint_upper, &contraint_low] (T& a) {
// contraint -2^62 < a < 2^62
if (a >= 0) {
a &= contraint_upper;
} else {
a |= contraint_low;
}
});
//r'_0, r'_1
privc_ctx()->template gen_random_private(*temp[1]);
temp[0]->sub(temp[1].get(), temp[2].get());
// r, r_0, r_1
temp[0]->rshift(scaling_factor, temp[3].get());
privc_ctx()->template gen_random_private(*temp[4]);
privc_ctx()->template gen_random_private(*temp[5]);
// r_2
temp[3]->sub(temp[4].get(), temp[6].get());
temp[6]->sub(temp[5].get(), temp[6].get());
privc_ctx()->network()->template send(1, *temp[2]);
privc_ctx()->network()->template send(1, *temp[5]);
privc_ctx()->network()->template send(0, *temp[1]);
privc_ctx()->network()->template send(0, *temp[4]);
temp[6]->copy(ret->mutable_share(0));
} else if (party() == 1) {
for (int i = 0; i < 4; ++i) {
temp.emplace_back(
tensor_factory()->template create<T>(op->shape()));
}
// r'_1, r_1
privc_ctx()->network()->template recv(2, *temp[0]);
privc_ctx()->network()->template recv(2, *temp[1]);
// recv x0 - r'_0 from party 0
privc_ctx()->network()->template recv(0, *temp[2]);
//reveal x - r' to party 1
op->share(0)->add(op->share(1), temp[3].get());
temp[3]->add(temp[2].get(), temp[3].get());
temp[3]->sub(temp[0].get(), temp[3].get());
// truncate x-r'
temp[3]->rshift(scaling_factor, temp[3].get());
temp[3]->add(temp[1].get(), ret->mutable_share(0));
} else {
for (int i = 0; i < 3; ++i) {
temp.emplace_back(
tensor_factory()->template create<T>(op->shape()));
}
// r'_0, r_0
privc_ctx()->network()->template recv(2, *temp[0]);
privc_ctx()->network()->template recv(2, *temp[1]);
//send x0 - r'_0 to party 1
op->share(0)->sub(temp[0].get(), temp[2].get());
privc_ctx()->network()->template send(1, *temp[2]);
temp[1]->copy(ret->mutable_share(0));
}
reshare(ret->share(0), ret->mutable_share(1));
// compensation for carry in
auto tensor_carry_in = tensor_factory()->template create<T>(ret->shape());
assign_to_tensor(tensor_carry_in.get(), (T)1);
tensor_carry_in->scaling_factor() = N;
ret->add(tensor_carry_in.get(), ret);
}
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());
privc_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]);
truncate3(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]);
truncate3(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]);
truncate3(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>
void FixedPointTensor<T, N>::sigmoid(FixedPointTensor<T, N>* ret) const {
//utilize polynomial_piecewise
// break_point = {-2.5, 2.5}
// coeff[0] = {10^-4, 0}, coeff[1] = {0.5, 0.17}
// coeff[2] = {1 - 10^-4, 0}
// break_point.shape = {2, this->shape}, coeff.shape = {3, 2, this->shape}
//construct break_point
auto shape_ = shape();
//construct break_point
auto b_shape = shape_;
b_shape.insert(b_shape.begin(), 2);
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;
}
for (size_t i = 0; i < break_point->numel() / 2; ++i) {
b_ptr[i] = (T) (-2.5 * pow(2, N));
}
for (size_t i = break_point->numel() / 2; i < break_point->numel(); ++i) {
b_ptr[i] = (T) (2.5 * pow(2, N));
}
break_point->scaling_factor() = N;
//contruct coeff
std::vector<size_t> c_shape = {3, 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();
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;
this->polynomial_piecewise(coeff.get(), break_point.get(), ret);
}
template< typename T, size_t N>
void FixedPointTensor<T, N>::sigmoid_enhanced(FixedPointTensor<T, N>* ret) const {
//utilize polynomial_piecewise
// break_point = {-5, -2.5, 2.5, 5}
// coeff[0] = {10^-4, 0}, coeff[1] = {0.145, 0.02776}
// coeff[2] = {0.5, 0.17}, coeff[3] = {0.85498, 0.02776}, coeff[4] = {0.9999, 0}
// break_point.shape = {4, this->shape}, coeff.shape = {5, 2, this->shape}
//construct break_point
auto shape_ = shape();
//construct break_point
auto b_shape = shape_;
b_shape.insert(b_shape.begin(), 4);
auto break_point = tensor_factory()->template create<T>(b_shape);
T* b_ptr = break_point->data();
auto numel = ret->numel();
double scale = std::pow(2, N);
for (size_t i = 0; i < numel; ++i) {
b_ptr[i] = (T) (-5 * scale);
b_ptr[i + numel] = (T) (-2.5 * scale);
b_ptr[i + 2 * numel] = (T) (2.5 * scale);
b_ptr[i + 3 * numel] = (T) (5 * scale);
}
break_point->scaling_factor() = N;
//contruct coeff
std::vector<size_t> c_shape = {5, 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 < numel; ++i) {
c_ptr[i] = 0.0001 * scale;
c_ptr[i + numel] = 0;
c_ptr[i + 2 * numel] = 0.145 * scale;
c_ptr[i + 3 * numel] = 0.02776 * scale;
c_ptr[i + 4 * numel] = 0.5 * scale;
c_ptr[i + 5 * numel] = 0.17 * scale;
c_ptr[i + 6 * numel] = 0.85498 * scale;
c_ptr[i + 7 * numel] = 0.02776 * scale;
c_ptr[i + 8 * numel] = 0.9999 * scale;
c_ptr[i + 9 * numel] = 0 * scale;
}
coeff->scaling_factor() = N;
this->polynomial_piecewise(coeff.get(), break_point.get(), ret);
}
template< typename T, size_t N>
void FixedPointTensor<T, N>::softmax(FixedPointTensor<T, N>* ret,
bool use_relu, bool use_long_div) const {
// softmax axis = -1
const size_t col = *(shape().end() - 1);
const size_t row = numel() / col;
std::vector<std::shared_ptr<TensorAdapter<T>>> temp;
// 11 for allocating temp tensor
for (size_t i = 0; i < 11; ++i) {
temp.emplace_back(
tensor_factory()->template create<T>());
}
temp[0]->reshape({row, col});
temp[1]->reshape({row, col});
FixedPointTensor<T, N> x(temp[0].get(), temp[1].get());
if (!use_relu) {
temp[2]->reshape({col, row});
temp[3]->reshape({col, row});
temp[4]->reshape({1, row});
temp[5]->reshape({1, row});
}
FixedPointTensor<T, N> x_t(temp[2].get(), temp[3].get());
FixedPointTensor<T, N> max_x_t(temp[4].get(), temp[5].get());
temp[6]->reshape({row, 1});
temp[7]->reshape({row, 1});
FixedPointTensor<T, N> max_x(temp[6].get(), temp[7].get());
temp[8]->reshape({row, col});
temp[9]->reshape({row, col});
FixedPointTensor<T, N> max_x_broadcast(temp[8].get(), temp[9].get());
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) {
x.relu(&x);
} else { // use exp
transpose(x.share(0), x_t.mutable_share(0));
transpose(x.share(1), x_t.mutable_share(1));
// x = max(input - max(input), exp_lower_bound)
x_t.max_pooling(&max_x_t);
transpose(max_x_t.share(0), max_x.mutable_share(0));
transpose(max_x_t.share(1), max_x.mutable_share(1));
broadcast(max_x.share(0), max_x_broadcast.mutable_share(0));
broadcast(max_x.share(1), max_x_broadcast.mutable_share(1));
x.sub(&max_x_broadcast, &x);
// n = 64, see exp
assign_to_tensor(exp_lower_bound, (T)(-64 * (1 << N)));
exp_lower_bound->scaling_factor() = N;
x.sub(exp_lower_bound, &x);
x.relu(&x);
x.add(exp_lower_bound, &x);
x.exp(&x);
}
// reuse max_x as sum
reduce(&x, &max_x);
if (!use_long_div) { // invert sum by Newton's method
// divisor range = [1/col, 1.0]
// TODO: find better iter num & init val
reciprocal(&max_x, &max_x, 16, 0.5 / col);
}
broadcast(max_x.share(0), max_x_broadcast.mutable_share(0));
broadcast(max_x.share(1), max_x_broadcast.mutable_share(1));
if (use_long_div) {
x.long_div(&max_x_broadcast, &x, 1);
} else {
x.mul(&max_x_broadcast, &x);
}
x.share(0)->copy(ret->mutable_share(0));
x.share(1)->copy(ret->mutable_share(1));
}
template<typename T, size_t N>
void FixedPointTensor<T, N>::long_div(const FixedPointTensor<T, N>* rhs,
FixedPointTensor<T, N>* ret,
size_t int_len) const {
std::vector<std::shared_ptr<TensorAdapter<T>>> temp;
for (int i = 0; i < 16; ++i) {
temp.emplace_back(
tensor_factory()->template create<T>(ret->shape()));
}
BooleanTensor<T> sign_lhs(temp[0].get(), temp[1].get());
BooleanTensor<T> sign_rhs(temp[2].get(), temp[3].get());
BooleanTensor<T> sign_ret(temp[4].get(), temp[5].get());
FixedPointTensor<T, N> abs_lhs(temp[6].get(), temp[7].get());
FixedPointTensor<T, N> abs_rhs(temp[8].get(), temp[9].get());
FixedPointTensor<T, N> sub_rhs(temp[10].get(), temp[11].get());
BooleanTensor<T> cmp_res(temp[12].get(), temp[13].get());
BooleanTensor<T> cmp_res_all(temp[14].get(), temp[15].get());
assign_to_tensor(cmp_res_all.share(0), (T)0);
assign_to_tensor(cmp_res_all.share(1), (T)0);
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);
abs_lhs.gt(&sub_rhs, &cmp_res);
cmp_res.mul(&sub_rhs, &sub_rhs);
cmp_res.lshift(N + i, &cmp_res);
abs_lhs.sub(&sub_rhs, &abs_lhs);
cmp_res.bitwise_xor(&cmp_res_all, &cmp_res_all);
}
for (size_t i = 1; i <= N; ++i) {
truncate3(&abs_rhs, &sub_rhs, i);
abs_lhs.gt(&sub_rhs, &cmp_res);
cmp_res.mul(&sub_rhs, &sub_rhs);
cmp_res.lshift(N - i, &cmp_res);
abs_lhs.sub(&sub_rhs, &abs_lhs);
cmp_res.bitwise_xor(&cmp_res_all, &cmp_res_all);
}
// use abs_lhs as buffer
cmp_res_all.b2a(&abs_lhs);
abs(&abs_lhs, &sign_ret, ret);
}
// reduce last dim
template <typename T, size_t N>
void FixedPointTensor<T, N>::reduce(FixedPointTensor<T, N>* input,
FixedPointTensor<T, N>* ret) {
//enfoce shape: input->shape[0 ... (n-2)] == ret shape
auto& shape = input->shape();
size_t ite_size = shape[shape.size() - 1];
T* ret_begin_ptr_0 = ret->_share[0]->data();
T* ret_begin_ptr_1 = ret->_share[1]->data();
T* input_begin_ptr_0 = input->_share[0]->data();
T* input_begin_ptr_1 = input->_share[1]->data();
for (int j = 0; j < ret->numel(); ++j) {
*(ret_begin_ptr_0 + j) = *(input_begin_ptr_0 + j * ite_size);
*(ret_begin_ptr_1 + j) = *(input_begin_ptr_1 + j * ite_size);
for (int i = 1; i < ite_size; ++i) {
*(ret_begin_ptr_0 + j) +=
*(input_begin_ptr_0 + j * ite_size + i);
*(ret_begin_ptr_1 + j) +=
*(input_begin_ptr_1 + j * ite_size + i);
}
}
}
template< typename T, size_t N>
void FixedPointTensor<T, N>::polynomial(const TensorAdapter<T>* coeff,
FixedPointTensor<T, N>* ret) const {
// e.g., x.shape = {2, 3}, coeff.shape = {n, 2, 3} (n: polynomial power)
//TODO: improve performance: [ABY3]
std::vector<std::shared_ptr<TensorAdapter<T>>> temp;
for (int i = 0; i < 7; ++i) {
temp.emplace_back(
tensor_factory()->template create<T>(this->shape()));
}
std::shared_ptr<FixedPointTensor<T, N>> x_pow_i =
std::make_shared<FixedPointTensor<T, N>>(
temp[0].get(), temp[1].get());
std::shared_ptr<FixedPointTensor<T, N>> temp_fixed =
std::make_shared<FixedPointTensor<T, N>>(
temp[2].get(), temp[3].get());
std::shared_ptr<FixedPointTensor<T, N>> result =
std::make_shared<FixedPointTensor<T, N>>(
temp[5].get(), temp[6].get());
assign_to_tensor(result->_share[0], (T) 0);
assign_to_tensor(result->_share[1], (T) 0);
//x_pow_i.get() = 1;
assign_to_tensor(x_pow_i.get()->_share[0], (T) 0);
assign_to_tensor(x_pow_i.get()->_share[1], (T) 0);
assign_to_tensor(temp[4].get(), (T) 1 << N);
temp[4]->scaling_factor() = N;
x_pow_i->add(temp[4].get(), x_pow_i.get());
for (int i = 0; i < coeff->shape()[0]; ++i) {
auto t = tensor_factory()->template create<T>();
coeff->slice(i, i + 1, t.get());
auto t_shape = t->shape();
// remove leading 1
t_shape.erase(t_shape.begin());
t->reshape(t_shape);
x_pow_i->mul(t.get(), temp_fixed.get());
result->add(temp_fixed.get(), result.get());
x_pow_i->mul(this, x_pow_i.get());
}
result->share(0)->copy(ret->mutable_share(0));
result->share(1)->copy(ret->mutable_share(1));
}
template< typename T, size_t N>
void FixedPointTensor<T, N>::polynomial_piecewise(
const TensorAdapter<T>* coeff,
const TensorAdapter<T>* break_point,
FixedPointTensor<T, N>* ret) const {
// e.g., x.shape = {2, 3},
// break_point.shape = {k, 2, 3} (k: num of break point)
// coeff.shape = {k + 1, n, 2, 3} (n: poly power)
// copy ret
auto ret_cpy_s0 = tensor_factory()->create_int64_t(ret->share(0)->shape());
ret->share(0)->copy(ret_cpy_s0.get());
auto ret_cpy_s1 = tensor_factory()->create_int64_t(ret->share(1)->shape());
ret->share(1)->copy(ret_cpy_s1.get());
std::shared_ptr<FixedPointTensor<T, N>> ret_cpy{new FixedPointTensor<T, N>(ret_cpy_s0.get(), ret_cpy_s1.get())};
std::vector<std::shared_ptr<BooleanTensor<T>>> msb;
int len_break_point = break_point->shape()[0];
int len_coeff = coeff->shape()[0];
//number of temp tensor used
int temp_total = 4 * len_break_point + 2 +
2 * (len_break_point - 1) + 2 + 4 * len_coeff;
std::vector<std::shared_ptr<TensorAdapter<T>>> temp;
for (int i = 0; i < temp_total; ++i) {
temp.emplace_back(tensor_factory()->
template create<T>(this->shape()));
}
int temp_index = 0;
// std::vector<std::shared_ptr<TensorAdapter<T>>> paddle_t_break;
std::vector<std::shared_ptr<FixedPointTensor<T, N>>> temp1;
for (int i = 0; i < break_point->shape()[0]; ++i) {
// msb[i] = msb(x - break_point[i])
auto t_break = tensor_factory()->template create<T>();
break_point->slice(i, i + 1, t_break.get());
auto t_shape = t_break->shape();
// remove leading 1
t_shape.erase(t_shape.begin());
t_break->reshape(t_shape);
temp1.emplace_back(
std::make_shared<FixedPointTensor<T, N>>(
temp[temp_index++].get(),
temp[temp_index++].get()));
this->sub(t_break.get(), temp1[i].get());
msb.emplace_back(std::make_shared<BooleanTensor<T>>(
temp[temp_index++].get(),
temp[temp_index++].get()));
msb[i]->bit_extract(sizeof(T) * 8 - 1, temp1[i].get());
}
// 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];
for (int i = 0; i < len_break_point - 1; ++i) {
b.emplace_back(std::make_shared<BooleanTensor<T>>(
temp[temp_index++].get(),
temp[temp_index++].get()));
msb[i]->bitwise_not(b[i + 1].get());
b[i + 1]->bitwise_and(msb[i + 1].get(), b[i + 1].get());
}
b.emplace_back(std::make_shared<BooleanTensor<T>>(
temp[temp_index++].get(),
temp[temp_index++].get()));
msb[len_break_point - 1]->bitwise_not(b[len_break_point].get());
// ret += b[i].mul(polynomial(coeff[i]))
std::vector<std::shared_ptr<FixedPointTensor<T, N>>> temp_fixed;
std::vector<std::shared_ptr<FixedPointTensor<T, N>>> temp_fixed1;
assign_to_tensor(ret_cpy->_share[0], (T) 0);
assign_to_tensor(ret_cpy->_share[1], (T) 0);
for (int i = 0; i < len_coeff; ++i) {
temp_fixed.emplace_back(
std::make_shared<FixedPointTensor<T, N>>(
temp[temp_index++].get(),
temp[temp_index++].get()));
temp_fixed1.emplace_back(
std::make_shared<FixedPointTensor<T, N>>(
temp[temp_index++].get(),
temp[temp_index++].get()));
auto t = tensor_factory()->template create<T>();
coeff->slice(i, i + 1, t.get());
auto t_shape = t->shape();
// remove leading 1
t_shape.erase(t_shape.begin());
t->reshape(t_shape);;
this->polynomial(t.get(), temp_fixed[i].get());
b[i]->bit_extract(0, b[i].get());
b[i]->mul(temp_fixed[i].get(), temp_fixed1[i].get());
ret_cpy->add(temp_fixed1[i].get(), ret_cpy.get());
}
ret_cpy->share(0)->copy(ret->mutable_share(0));
ret_cpy->share(1)->copy(ret->mutable_share(1));
}
template<typename T, size_t N>
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void FixedPointTensor<T, N>::lt(const CTensor<T, N1...>* rhs,
BooleanTensor<T>* ret) const {
std::vector<std::shared_ptr<TensorAdapter<T>>> temp;
for (int i = 0; i < 2; ++i) {
temp.emplace_back(
tensor_factory()->template create<T>(this->shape()));
}
std::shared_ptr<FixedPointTensor<T, N>> sub_result =
std::make_shared<FixedPointTensor<T, N>>(
temp[0].get(), temp[1].get());
this->sub(rhs, sub_result.get());
ret->bit_extract(sizeof(T) * 8 - 1, sub_result.get());
}
template<typename T, size_t N>
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void FixedPointTensor<T, N>::leq(const CTensor<T, N1...>* rhs,
BooleanTensor<T>* ret) const {
this->gt(rhs, ret);
auto tensor_one = tensor_factory()->
template create<T>(this->shape());
assign_to_tensor(tensor_one.get(), (T) 1);
ret->bitwise_xor(tensor_one.get(), ret);
}
template<typename T, size_t N>
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void FixedPointTensor<T, N>::gt(const CTensor<T, N1...>* rhs,
BooleanTensor<T>* ret) const {
std::vector<std::shared_ptr<TensorAdapter<T>>> temp;
for (int i = 0; i < 2; ++i) {
temp.emplace_back(
tensor_factory()->template create<T>(this->shape()));
}
std::shared_ptr<FixedPointTensor<T, N>> sub_result =
std::make_shared<FixedPointTensor<T, N>>(
temp[0].get(), temp[1].get());
this->sub(rhs, sub_result.get());
sub_result->negative(sub_result.get());
ret->template bit_extract(sizeof(T) * 8 - 1, sub_result.get());
}
template<typename T, size_t N>
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void FixedPointTensor<T, N>::geq(const CTensor<T, N1...>* rhs,
BooleanTensor<T>* ret) const {
this->lt(rhs, ret);
auto tensor_one = tensor_factory()->
template create<T>(this->shape());
assign_to_tensor(tensor_one.get(), (T) 1);
ret->bitwise_xor(tensor_one.get(), ret);
}
template<typename T, size_t N>
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void FixedPointTensor<T, N>::eq(const CTensor<T, N1...>* rhs,
BooleanTensor<T>* ret) const {
this->neq(rhs, ret);
auto tensor_one = tensor_factory()->template create<T>(this->shape());
assign_to_tensor(tensor_one.get(), (T) 1);
ret->bitwise_xor(tensor_one.get(), ret);
}
template<typename T, size_t N>
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void FixedPointTensor<T, N>::neq(const CTensor<T, N1...>* rhs,
BooleanTensor<T>* ret) const {
std::vector<std::shared_ptr<TensorAdapter<T>>> temp;
for (int i = 0; i < 4; i ++) {
temp.emplace_back(tensor_factory()->
template create<T>(this->shape()));
}
std::shared_ptr<BooleanTensor<T>> lt =
std::make_shared<BooleanTensor<T>>(
temp[0].get(), temp[1].get());
std::shared_ptr<BooleanTensor<T>> gt =
std::make_shared<BooleanTensor<T>>(
temp[2].get(), temp[3].get());
this->lt(rhs, lt.get());
this->gt(rhs, gt.get());
lt->bitwise_or(gt.get(), ret);
}
template<typename T, size_t N>
void FixedPointTensor<T, N>::reciprocal(const FixedPointTensor<T, N>* op, FixedPointTensor<T, N>* ret,
size_t iter, double x0) {
auto temp0 = tensor_factory()->template create<T>(ret->shape());
auto temp1 = tensor_factory()->template create<T>(ret->shape());
auto temp2 = tensor_factory()->template create<T>(ret->shape());
auto temp3 = tensor_factory()->template create<T>(ret->shape());
std::shared_ptr<FixedPointTensor<T, N>> result =
std::make_shared<FixedPointTensor<T, N>>(temp0.get(), temp1.get());
std::shared_ptr<FixedPointTensor<T, N>> x_copy =
std::make_shared<FixedPointTensor<T, N>>(temp2.get(), temp3.get());
assign_to_tensor(result->mutable_share(0), (T) 0);
assign_to_tensor(result->mutable_share(1), (T) 0);
auto tensor_x0 = tensor_factory()->template create<T>(op->shape());
assign_to_tensor(tensor_x0.get(), (T)(x0 * pow(2, N)));
tensor_x0->scaling_factor() = N;
result->add(tensor_x0.get(), result.get());
auto tensor_2 = tensor_factory()->template create<T>(op->shape());
tensor_2->scaling_factor() = N;
assign_to_tensor(tensor_2.get(), (T)(2 << N));
for (int i = 0; i < iter; ++i) {
result->share(0)->copy(x_copy->mutable_share(0));
result->share(1)->copy(x_copy->mutable_share(1));
auto res_ptr = result.get();
op->mul(res_ptr, res_ptr);
result->negative(res_ptr);
result->add(tensor_2.get(), res_ptr);
x_copy->mul(res_ptr, res_ptr);
}
result->share(0)->copy(ret->mutable_share(0));
result->share(1)->copy(ret->mutable_share(1));
}
template<typename T, size_t N>
void FixedPointTensor<T, N>::inverse_square_root(FixedPointTensor* ret,
size_t iter,
double x0) const {
inverse_square_root(this, ret, iter, x0);
}
// Newton's method, var naming from Quake III Arena: Q_rsqrt
// float threehalfs = 1.5F;
// x2 = number * 0.5F;
// y = x0; // since 0x5f3759df does not fit fixed-point arithmetic
// y = y * ( threehalfs - ( x2 * y * y ) ); // iteration of Newton's method
template<typename T, size_t N>
void FixedPointTensor<T, N>::inverse_square_root(const FixedPointTensor* op,
FixedPointTensor* ret,
size_t iter,
double x0) {
std::vector<std::shared_ptr<TensorAdapter<T>>> temp;
for (int i = 0; i < 7; ++i) {
temp.emplace_back(
tensor_factory()->template create<T>(op->shape()));
}
std::shared_ptr<FixedPointTensor<T, N>> y =
std::make_shared<FixedPointTensor<T, N>>(temp[0].get(), temp[1].get());
std::shared_ptr<FixedPointTensor<T, N>> x2 =
std::make_shared<FixedPointTensor<T, N>>(temp[2].get(), temp[3].get());
// x2 = 0.5 * op
truncate3(op, x2.get(), 1);
assign_to_tensor(y->mutable_share(0), (T)(x0 * pow(2, N)));
assign_to_tensor(y->mutable_share(1), (T)(x0 * pow(2, N)));
// threehalfs
temp[4]->scaling_factor() = N;
assign_to_tensor(temp[4].get(), T(1.5 * pow(2, N)));
std::shared_ptr<FixedPointTensor<T, N>> y_copy =
std::make_shared<FixedPointTensor<T, N>>(temp[5].get(), temp[6].get());
for (int i = 0; i < iter; ++i) {
y->share(0)->copy(y_copy->mutable_share(0));
y->share(1)->copy(y_copy->mutable_share(1));
y->mul(y.get(), y.get());
y->mul(x2.get(), y.get());
y->negative(y.get());
y->add(temp[4].get(), y.get());
y_copy->mul(y.get(), y.get());
}
y->share(0)->copy(ret->mutable_share(0));
y->share(1)->copy(ret->mutable_share(1));
}
template<typename T, size_t N>
template<template<typename U, size_t...> class CTensor,
size_t... N1>
void FixedPointTensor<T, N>::max(const CTensor<T, N1...>* rhs,
FixedPointTensor* ret,
BooleanTensor<T>* cmp) const {
// max = lhs + (rhs - lhs) if rhs > lhs else lhs
std::vector<std::shared_ptr<TensorAdapter<T>>> temp;
bool output_cmp = cmp != nullptr;
// if cmp is not null, store cmp results in cmp
// else, store them in tmp tensors
for (int i = 0; i < 2 + 2 * (!output_cmp); ++i) {
temp.emplace_back(
tensor_factory()->template create<T>(this->shape()));
}
FixedPointTensor<T, N> delta(temp[0].get(), temp[1].get());
sub(rhs, &delta);
BooleanTensor<T> sign;
if (output_cmp) {
sign = *cmp;
} else {
sign = BooleanTensor<T>(temp[2].get(), temp[3].get());
}
sign.template bit_extract(sizeof(T) * 8 - 1, &delta);
delta.negative(&delta);
sign.mul(&delta, &delta);
add(&delta, ret);
}
template<typename T, size_t N>
void FixedPointTensor<T, N>::max_pooling(FixedPointTensor* ret,
BooleanTensor<T>* pos) const {
size_t k = shape()[0];
std::vector<std::shared_ptr<TensorAdapter<T>>> tmp;
for (int i = 0; i < 4; ++i) {
tmp.emplace_back(
tensor_factory()->template create<T>());
}
FixedPointTensor now(tmp[0].get(), tmp[1].get());
BooleanTensor<T> cmp(tmp[2].get(), tmp[3].get());
auto cmp_ptr = pos ? &cmp : nullptr;
share(0)->slice(0, 1, tmp[0].get());
share(1)->slice(0, 1, tmp[1].get());
tmp[0]->copy(ret->mutable_share(0));
tmp[1]->copy(ret->mutable_share(1));
if (pos) {
pos->share(0)->slice(0, 1, tmp[2].get());
pos->share(1)->slice(0, 1, tmp[3].get());
// set init 1, slice_0 is larger than null
if (party() == 0 || party() == 2) {
size_t idx = 2 + (party() == 2);
assign_to_tensor(tmp[idx].get(), T(1));
assign_to_tensor(tmp[5 - idx].get(), T(0));
} else {
assign_to_tensor(tmp[2].get(), T(0));
assign_to_tensor(tmp[3].get(), T(0));
}
}
for (size_t i = 1; i < k; ++i) {
share(0)->slice(i, i + 1, tmp[0].get());
share(1)->slice(i, i + 1, tmp[1].get());
if (pos) {
pos->share(0)->slice(i, i + 1, tmp[2].get());
pos->share(1)->slice(i, i + 1, tmp[3].get());
}
ret->max(&now, ret, cmp_ptr);
}
if (pos) {
pos->onehot_from_cmp();
}
}
*/
} // namespace privc
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