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add unittest for spectral_norm. test=develop

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align_pyramid
dengkaipeng 6 years ago
parent 3bf1ae9b59
commit 72509ec3bd
3 changed files with 82 additions and 32 deletions
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22
paddle/fluid/operators/spectral_norm_op.cu
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@ -0,0 +1,22 @@
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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/spectral_norm_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
spectral_norm,
ops::SpectralNormKernel<paddle::platform::CUDADeviceContext, float>,
ops::SpectralNormKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
spectral_norm_grad,
ops::SpectralNormGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::SpectralNormGradKernel<paddle::platform::CUDADeviceContext, double>);

52
paddle/fluid/operators/spectral_norm_op.h
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@ -46,47 +46,51 @@ static inline void CalcMatrixSigmaAndNormWeight(
Tensor* sigma, Tensor* u, Tensor* v, Tensor* weight, const int power_iters,
const float eps, const framework::ExecutionContext& ctx) {
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
auto blas = math::GetBlas<DeviceContext, T>(ctx);
auto sigma_t = EigenTensor<T, 2>::From(*sigma);
auto weight_t = EigenTensor<T, 2>::From(*weight);
auto u_t = EigenTensor<T, 1>::From(*u);
auto v_t = EigenTensor<T, 1>::From(*v);
auto u_t = EigenTensor<T, 2>::From(*u);
auto v_t = EigenTensor<T, 2>::From(*v);
const int h = weight->dims()[0];
const int w = weight->dims()[1];
Eigen::array<int, 2> perm = {1, 0};
Eigen::array<IndexPair, 1> product_dims = {IndexPair(1, 0)};
auto weight_trans_t = weight_t.shuffle(perm);
LOG(ERROR) << "weight: " << weight_t;
LOG(ERROR) << "weight_trans: " << weight_trans_t;
// LOG(ERROR) << "weight: " << weight_t;
// LOG(ERROR) << "weight_trans: " << weight_trans_t;
for (int i = 0; i < power_iters; i++) {
v_t.device(place) = weight_trans_t.contract(u_t, product_dims);
LOG(ERROR) << "iter v: " << v_t;
// v_t.device(place) = weight_trans_t.contract(u_t, product_dims);
blas.MatMul(*weight, true, *u, false, T(1), v, T(0));
// LOG(ERROR) << "iter v: " << v_t;
auto v_t_norm =
v_t.square().sum().sqrt().eval().reshape(Array1(1)).broadcast(
Array1(w));
LOG(ERROR) << "iter v_norm: " << v_t_norm;
// LOG(ERROR) << "iter v_norm: " << v_t_norm;
v_t.device(place) = v_t / (v_t_norm + v_t_norm.constant(eps));
LOG(ERROR) << "iter norm v: " << v_t;
u_t.device(place) = weight_t.contract(v_t, product_dims);
LOG(ERROR) << "iter u: " << u_t;
// LOG(ERROR) << "iter norm v: " << v_t;
// u_t.device(place) = weight_t.contract(v_t, product_dims);
blas.MatMul(*weight, false, *v, false, T(1), u, T(0));
// LOG(ERROR) << "iter u: " << u_t;
auto u_t_norm =
u_t.square().sum().sqrt().eval().reshape(Array1(1)).broadcast(
Array1(h));
u_t.device(place) = u_t / (u_t_norm + u_t_norm.constant(eps));
LOG(ERROR) << "iter norm u: " << u_t;
// LOG(ERROR) << "iter norm u: " << u_t;
}
LOG(ERROR) << "h" << h << "w" << w;
LOG(ERROR) << "u: " << u_t;
LOG(ERROR) << "v: " << v_t;
LOG(ERROR) << "weight_v: " << weight_t.contract(v_t, product_dims);
sigma_t.device(place) = (u_t * weight_t.contract(v_t, product_dims))
// LOG(ERROR) << "h" << h << "w" << w;
// LOG(ERROR) << "u: " << u_t;
// LOG(ERROR) << "v: " << v_t;
Tensor weight_v;
weight_v.mutable_data<T>({h, 1}, ctx.GetPlace());
blas.MatMul(*weight, false, *v, false, T(1), &weight_v, T(0));
auto weight_v_t = EigenTensor<T, 2>::From(weight_v);
// LOG(ERROR) << "weight_v: " << weight_v_t;
sigma_t.device(place) = (u_t * weight_v_t)
.sum()
.eval()
.reshape(Array2(1, 1))
.broadcast(Array2(h, w));
LOG(ERROR) << "weight: " << weight_t;
LOG(ERROR) << "sigma: " << sigma_t;
// LOG(ERROR) << "weight: " << weight_t;
// LOG(ERROR) << "sigma: " << sigma_t;
weight_t.device(place) = weight_t / sigma_t;
}
@ -103,6 +107,9 @@ class SpectralNormKernel : public framework::OpKernel<T> {
int power_iters = ctx.Attr<int>("power_iters");
float eps = ctx.Attr<float>("eps");
const int h = weight->dims()[0];
const int w = weight->dims()[1];
Tensor weight_mat;
TensorCopySync(*weight, ctx.GetPlace(), &weight_mat);
ResizeWeight(&weight_mat, dim);
@ -113,7 +120,8 @@ class SpectralNormKernel : public framework::OpKernel<T> {
TensorCopySync(*u, ctx.GetPlace(), &uu);
TensorCopySync(*v, ctx.GetPlace(), &vv);
CalcMatrixSigmaAndNormWeight<DeviceContext, T>(
&sigma, &uu, &vv, &weight_mat, power_iters, eps, ctx);
&sigma, &(uu.Resize({h, 1})), &(vv.Resize({w, 1})), &weight_mat,
power_iters, eps, ctx);
TensorCopySync(weight_mat, ctx.GetPlace(), out);
}
};

40
python/paddle/fluid/tests/unittests/test_spectral_norm_op.py
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@ -21,17 +21,36 @@ from op_test import OpTest
from paddle.fluid import core
def spectral_norm(weight, u, v, dim, power_iters, eps):
h = w = 1
for i, d in enumerate(weight.shape):
if i <= dim:
h *= d
else:
w *= d
weight_mat = weight.reshape((h, w))
u = u.reshape((h, 1))
v = v.reshape((w, 1))
for i in range(power_iters):
v = np.matmul(weight_mat.T, u)
v_norm = np.sqrt((v * v).sum())
v = v / (v_norm + eps)
u = np.matmul(weight_mat, v)
u_norm = np.sqrt((u * u).sum())
u = u / (u_norm + eps)
sigma = (u * np.matmul(weight_mat, v)).sum()
return (weight_mat / sigma).reshape(weight.shape)
class TestSpectralNormOp(OpTest):
def setUp(self):
self.initTestCase()
self.op_type = 'spectral_norm'
# weight = np.random.random(self.weight_shape).astype('float32')
# u = np.random.random(self.u_shape).astype('float32')
# v = np.random.random(self.u_shape).astype('float32')
weight = np.ones(self.weight_shape).astype('float32')
weight[1, :] = 2.
u = np.ones(self.u_shape).astype('float32')
v = np.ones(self.v_shape).astype('float32')
weight = np.random.random(self.weight_shape).astype('float32')
u = np.random.random(self.u_shape).astype('float32')
v = np.random.random(self.v_shape).astype('float32')
self.attrs = {
"dim": self.dim,
@ -45,8 +64,9 @@ class TestSpectralNormOp(OpTest):
"V": v,
}
output = weight
self.outputs = {"Out": weight, }
output = spectral_norm(weight, u, v, self.dim, self.power_iters,
self.eps)
self.outputs = {"Out": output}
def test_check_output(self):
self.check_output()
@ -56,7 +76,7 @@ class TestSpectralNormOp(OpTest):
self.u_shape = (2, )
self.v_shape = (3, )
self.dim = 0
self.power_iters = 1
self.power_iters = 2
self.eps = 1e-12

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