add UOut and VOut for SpectralNorm

paddle/fluid/operators/spectral_norm_op.cc

@@ -30,6 +30,8 @@ class SpectralNormOp : public framework::OperatorWithKernel {
     OP_INOUT_CHECK(ctx->HasInput("U"), "Input", "U", "SpectralNorm");
     OP_INOUT_CHECK(ctx->HasInput("V"), "Input", "V", "SpectralNorm");
     OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SpectralNorm");
+    OP_INOUT_CHECK(ctx->HasOutput("UOut"), "Output", "UOut", "SpectralNorm");
+    OP_INOUT_CHECK(ctx->HasOutput("VOut"), "Output", "VOut", "SpectralNorm");
 
     auto dim_weight = ctx->GetInputDim("Weight");
     auto rank_weight = dim_weight.size();
@@ -88,6 +90,8 @@ class SpectralNormOp : public framework::OperatorWithKernel {
     }
 
     ctx->SetOutputDim("Out", dim_weight);
+    ctx->SetOutputDim("UOut", dim_u);
+    ctx->SetOutputDim("VOut", dim_v);
     ctx->ShareLoD("Weight", /*->*/ "Out");
   }
 
@@ -126,6 +130,10 @@ class SpectralNormOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("Out",
               "The output weight tensor of spectral_norm operator, "
               "This tensor is in same shape with Input(Weight).");
+    AddOutput("UOut",
+              "The updated value of `U`");
+    AddOutput("VOut",
+              "The updated value of `V`");
 
     AddAttr<int>("dim",
                  "The index of dimension which should be permuted "
@@ -145,7 +153,6 @@ class SpectralNormOpMaker : public framework::OpProtoAndCheckerMaker {
                    "the denominator to aviod divide zero. "
                    "Default 1e-12.")
         .SetDefault(1e-12);
-
     AddComment(R"DOC(
           This layer calculates the spectral normalization value of weight of
           fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D

paddle/fluid/operators/spectral_norm_op.h

@@ -61,13 +61,11 @@ static inline void TransCompute(const int rank, const Tensor& in, Tensor* out,
 }
 
 template <typename DeviceContext, typename T>
-static inline void CalcMatrixSigmaAndNormWeight(
-    Tensor* sigma, Tensor* u, Tensor* v, Tensor* weight, const int power_iters,
+static inline void UpdateUandV(
+    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, 2>::From(*u);
   auto v_t = EigenTensor<T, 2>::From(*v);
 
@@ -88,6 +86,23 @@ static inline void CalcMatrixSigmaAndNormWeight(
             Array1(h));
     u_t.device(place) = u_t / (u_t_norm + u_t_norm.constant(eps));
   }
+}
+
+// CalcMatrixSigmaAndNormWeight will not update u and v
+template <typename DeviceContext, typename T>
+static inline void CalcMatrixSigmaAndNormWeight(
+    Tensor* sigma, const Tensor* u, const 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, 2>::From(*u);
+
+  const int h = weight->dims()[0];
+  const int w = weight->dims()[1];
+
   Tensor weight_v;
   weight_v.mutable_data<T>({h, 1}, ctx.GetPlace());
   blas.MatMul(*weight, false, *v, false, T(1), &weight_v, T(0));
@@ -109,6 +124,8 @@ class SpectralNormKernel : public framework::OpKernel<T> {
     auto u = ctx.Input<Tensor>("U");
     auto v = ctx.Input<Tensor>("V");
     auto out = ctx.Output<Tensor>("Out");
+    auto u_out = ctx.Output<Tensor>("UOut");
+    auto v_out = ctx.Output<Tensor>("VOut");
 
     int dim = ctx.Attr<int>("dim");
     int power_iters = ctx.Attr<int>("power_iters");
@@ -144,11 +161,13 @@ class SpectralNormKernel : public framework::OpKernel<T> {
 
     Tensor sigma;
     sigma.mutable_data<T>(weight_mat.dims(), ctx.GetPlace());
-    Tensor uu, vv;
-    TensorCopySync(*u, ctx.GetPlace(), &uu);
-    TensorCopySync(*v, ctx.GetPlace(), &vv);
+    TensorCopySync(*u, ctx.GetPlace(), u_out);
+    TensorCopySync(*v, ctx.GetPlace(), v_out);
+    UpdateUandV<DeviceContext, T>(
+        &(u_out->Resize({h, 1})), &(v_out->Resize({w, 1})), &weight_mat,
+        power_iters, eps, ctx);
     CalcMatrixSigmaAndNormWeight<DeviceContext, T>(
-        &sigma, &(uu.Resize({h, 1})), &(vv.Resize({w, 1})), &weight_mat,
+        &sigma, &(u_out->Resize({h, 1})), &(v_out->Resize({w, 1})), &weight_mat,
         power_iters, eps, ctx);
 
     if (dim != 0) {
@@ -180,8 +199,8 @@ class SpectralNormGradKernel : public framework::OpKernel<T> {
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     auto blas = math::GetBlas<DeviceContext, T>(ctx);
     auto weight = ctx.Input<Tensor>("Weight");
-    auto u = ctx.Input<Tensor>("U");
-    auto v = ctx.Input<Tensor>("V");
+    auto u_out = ctx.Input<Tensor>("UOut");
+    auto v_out = ctx.Input<Tensor>("VOut");
     auto out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
     auto weight_grad = ctx.Output<Tensor>(framework::GradVarName("Weight"));
 
@@ -189,8 +208,12 @@ class SpectralNormGradKernel : public framework::OpKernel<T> {
     int power_iters = ctx.Attr<int>("power_iters");
     float eps = ctx.Attr<float>("eps");
 
-    const int h = u->dims()[0];
-    const int w = v->dims()[0];
+    const int h = u_out->dims()[0];
+    const int w = v_out->dims()[0];
+
+    Tensor u_mat, v_mat;
+    TensorCopySync(*u_out, ctx.GetPlace(), &u_mat);
+    TensorCopySync(*v_out, ctx.GetPlace(), &v_mat);
 
     Tensor weight_mat, out_grad_mat;
     auto dims = weight->dims();
@@ -225,16 +248,14 @@ class SpectralNormGradKernel : public framework::OpKernel<T> {
 
     Tensor sigma;
     sigma.mutable_data<T>(weight_mat.dims(), ctx.GetPlace());
-    Tensor uu, vv;
-    TensorCopySync(*u, ctx.GetPlace(), &uu);
-    TensorCopySync(*v, ctx.GetPlace(), &vv);
+
     CalcMatrixSigmaAndNormWeight<DeviceContext, T>(
-        &sigma, &(uu.Resize({h, 1})), &(vv.Resize({w, 1})), &weight_mat,
+        &sigma, &(u_mat.Resize({h, 1})), &(v_mat.Resize({w, 1})), &weight_mat,
         power_iters, eps, ctx);
 
     Tensor uv;
     uv.mutable_data<T>({h, w}, ctx.GetPlace());
-    blas.MatMul(uu.Resize({h, 1}), false, vv.Resize({w, 1}), false, T(1), &uv,
+    blas.MatMul(u_mat.Resize({h, 1}), false, v_mat.Resize({w, 1}), false, T(1), &uv,
                 T(0));
 
     Tensor weight_grad_mat;

python/paddle/fluid/dygraph/nn.py

@@ -1375,7 +1375,7 @@ class BatchNorm(layers.Layer):
 
         outputs = {
             "Y": [batch_norm_out],
-            "MeanOut": [mean_out],
+            "MeanOut": [],
             "VarianceOut": [variance_out],
             "SavedMean": [saved_mean],
             "SavedVariance": [saved_variance]
@@ -3031,9 +3031,11 @@ class SpectralNorm(layers.Layer):
         dim(int, optional): The index of dimension which should be permuted to the first before reshaping Input(Weight) to matrix, it should be set as 0 if Input(Weight) is the weight of fc layer, and should be set as 1 if Input(Weight) is the weight of conv layer. Default: 0.
         power_iters(int, optional): The number of power iterations to calculate spectral norm. Default: 1.
         eps(float, optional): The epsilon for numerical stability in calculating norms. Default: 1e-12.
+        fix_state(bool, optional): whether to update the two vectors `u` and `v`. Default: True.
         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` .
         dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32".
 
+
     Returns:
         None
 
@@ -3055,10 +3057,12 @@ class SpectralNorm(layers.Layer):
                  dim=0,
                  power_iters=1,
                  eps=1e-12,
+                 fix_state=True,
                  dtype='float32'):
         super(SpectralNorm, self).__init__()
         self._power_iters = power_iters
         self._eps = eps
+        self._fix_state = fix_state
         self._dim = dim
         self._dtype = dtype
 
@@ -3080,10 +3084,31 @@ class SpectralNorm(layers.Layer):
             default_initializer=Normal(0., 1.))
         self.weight_v.stop_gradient = True
 
+        if fix_state:
+            self.out_weight_u = self.create_parameter(
+                attr=ParamAttr(),
+                shape=[h],
+                dtype=self._dtype,
+                default_initializer=Normal(0., 1.))
+            self.out_weight_u.stop_gradient = True
+
+            self.out_weight_v = self.create_parameter(
+                attr=ParamAttr(),
+                shape=[w],
+                dtype=self._dtype,
+                default_initializer=Normal(0., 1.))
+            self.out_weight_v.stop_gradient = True
+        else:
+            self.out_weight_u = self.weight_u
+            self.out_weight_v = self.weight_v
+
     def forward(self, weight):
         check_variable_and_dtype(weight, "weight", ['float32', 'float64'],
                                  'SpectralNorm')
-        inputs = {'Weight': weight, 'U': self.weight_u, 'V': self.weight_v}
+        inputs = {
+            'Weight': weight, 'U': self.weight_u, 'V': self.weight_v,
+            'UOut': self.out_weight_u, 'VOut': self.out_weight_v,
+        }
         out = self._helper.create_variable_for_type_inference(self._dtype)
         self._helper.append_op(
             type="spectral_norm",

python/paddle/fluid/tests/unittests/test_spectral_norm_op.py

@@ -44,7 +44,7 @@ def spectral_norm(weight, u, v, dim, power_iters, eps):
         u = u / (u_norm + eps)
 
     sigma = (u * np.matmul(weight_mat, v)).sum()
-    return weight / sigma
+    return weight / sigma, u, v
 
 
 @skip_check_grad_ci(
@@ -63,6 +63,7 @@ class TestSpectralNormOpNoGrad(OpTest):
             "dim": self.dim,
             "power_iters": self.power_iters,
             "eps": self.eps,
+            "fix_state": self.fix_state,
         }
 
         self.inputs = {
@@ -71,9 +72,9 @@ class TestSpectralNormOpNoGrad(OpTest):
             "V": v,
         }
 
-        output = spectral_norm(weight, u, v, self.dim, self.power_iters,
-                               self.eps)
-        self.outputs = {"Out": output}
+        output, new_u, new_v = spectral_norm(weight, u, v, self.dim, self.power_iters,
+                                             self.eps)
+        self.outputs = {"Out": output, "UOut": new_u, "VOut": new_v}
 
     def test_check_output(self):
         self.check_output()
@@ -85,6 +86,7 @@ class TestSpectralNormOpNoGrad(OpTest):
         self.dim = 0
         self.power_iters = 5
         self.eps = 1e-12
+        self.fix_state = True
 
 
 @skip_check_grad_ci(
@@ -99,6 +101,7 @@ class TestSpectralNormOpNoGrad2(TestSpectralNormOpNoGrad):
         self.dim = 1
         self.power_iters = 10
         self.eps = 1e-12
+        self.fix_state = True
 
 
 class TestSpectralNormOp(TestSpectralNormOpNoGrad):
@@ -115,6 +118,7 @@ class TestSpectralNormOp(TestSpectralNormOpNoGrad):
         self.dim = 0
         self.power_iters = 0
         self.eps = 1e-12
+        self.fix_state = True
 
 
 class TestSpectralNormOp2(TestSpectralNormOp):
@@ -125,6 +129,7 @@ class TestSpectralNormOp2(TestSpectralNormOp):
         self.dim = 1
         self.power_iters = 0
         self.eps = 1e-12
+        self.fix_state = True
 
 
 class TestSpectralNormOpError(unittest.TestCase):