fix momentum ops (#36452)

paddle/fluid/operators/optimizers/momentum_op.h

@@ -173,14 +173,15 @@ class CPUDenseMomentumFunctor {
   }
 };
 
-template <typename T, typename MT, typename UpdateMethod>
+template <typename T, typename MT, RegularizationType kRegType,
+          typename UpdateMethod>
 class DenseMomentumFunctor;
 
 // NOTE(dzh) for performance.
 // avoid if/else in inside kernel, implement GPU UseNesterov/NoNesterov as two
 // functor.
-template <typename T, typename MT>
-class DenseMomentumFunctor<T, MT, UseNesterov> {
+template <typename T, typename MT, RegularizationType kRegType>
+class DenseMomentumFunctor<T, MT, kRegType, UseNesterov> {
  private:
   const T* param_;
   const T* grad_;
@@ -193,7 +194,6 @@ class DenseMomentumFunctor<T, MT, UseNesterov> {
   T* param_out_;
   MT* velocity_out_;
   MT* master_param_out_;
-  const RegularizationType regularization_flag_;
   const MT regularization_coeff_;
 
  public:
@@ -201,7 +201,6 @@ class DenseMomentumFunctor<T, MT, UseNesterov> {
                        const MultiPrecisionType<MT>* learning_rate,
                        const MT* master_param, const MT mu,
                        const MT rescale_grad, const int64_t num,
-                       const RegularizationType regularization_flag,
                        const MT regularization_coeff, T* param_out,
                        MT* velocity_out, MT* master_param_out)
       : param_(param),
@@ -215,7 +214,6 @@ class DenseMomentumFunctor<T, MT, UseNesterov> {
         param_out_(param_out),
         velocity_out_(velocity_out),
         master_param_out_(master_param_out),
-        regularization_flag_(regularization_flag),
         regularization_coeff_(regularization_coeff) {}
   inline HOSTDEVICE void operator()(size_t i) const {
     // put memory access in register
@@ -225,9 +223,9 @@ class DenseMomentumFunctor<T, MT, UseNesterov> {
     const MT lr = static_cast<MT>(lr_[0]);
     const MT velocity = velocity_[i];
 
-    grad = regularization_flag_ == RegularizationType::kL2DECAY
-               ? grad + regularization_coeff_ * param
-               : grad;
+    if (kRegType == RegularizationType::kL2DECAY) {
+      grad += regularization_coeff_ * param;
+    }
 
     MT velocity_out = velocity * mu_ + grad;
     MT param_out = param - (grad + velocity_out * mu_) * lr;
@@ -240,8 +238,8 @@ class DenseMomentumFunctor<T, MT, UseNesterov> {
   }
 };
 
-template <typename T, typename MT>
-class DenseMomentumFunctor<T, MT, NoNesterov> {
+template <typename T, typename MT, RegularizationType kRegType>
+class DenseMomentumFunctor<T, MT, kRegType, NoNesterov> {
  private:
   const T* param_;
   const T* grad_;
@@ -254,7 +252,6 @@ class DenseMomentumFunctor<T, MT, NoNesterov> {
   T* param_out_;
   MT* velocity_out_;
   MT* master_param_out_;
-  const RegularizationType regularization_flag_;
   const MT regularization_coeff_;
 
  public:
@@ -262,7 +259,6 @@ class DenseMomentumFunctor<T, MT, NoNesterov> {
                        const MultiPrecisionType<MT>* learning_rate,
                        const MT* master_param, const MT mu,
                        const MT rescale_grad, const int64_t num,
-                       const RegularizationType regularization_flag,
                        const MT regularization_coeff, T* param_out,
                        MT* velocity_out, MT* master_param_out)
       : param_(param),
@@ -276,7 +272,6 @@ class DenseMomentumFunctor<T, MT, NoNesterov> {
         param_out_(param_out),
         velocity_out_(velocity_out),
         master_param_out_(master_param_out),
-        regularization_flag_(regularization_flag),
         regularization_coeff_(regularization_coeff) {}
   inline HOSTDEVICE void operator()(size_t i) const {
     // put memory access in register
@@ -286,9 +281,9 @@ class DenseMomentumFunctor<T, MT, NoNesterov> {
     const MT lr = static_cast<MT>(lr_[0]);
     const MT velocity = velocity_[i];
 
-    grad = regularization_flag_ == RegularizationType::kL2DECAY
-               ? grad + regularization_coeff_ * param
-               : grad;
+    if (kRegType == RegularizationType::kL2DECAY) {
+      grad += regularization_coeff_ * param;
+    }
 
     MT velocity_out = velocity * mu_ + grad;
     MT param_out = param - lr * velocity_out;
@@ -522,23 +517,31 @@ class MomentumOpKernel : public framework::OpKernel<T> {
         platform::ForRange<DeviceContext> for_range(
             static_cast<const DeviceContext&>(ctx.device_context()),
             param->numel());
-        if (use_nesterov) {
-          DenseMomentumFunctor<T, MT, UseNesterov> functor(
-              param->data<T>(), grad->data<T>(), velocity->data<MT>(),
-              learning_rate->data<MPDType>(), master_in_data, mu, rescale_grad,
-              param->numel(), regularization_flag, regularization_coeff,
-              param_out->mutable_data<T>(ctx.GetPlace()),
-              velocity_out->mutable_data<MT>(ctx.GetPlace()), master_out_data);
-          for_range(functor);
+#define PADDLE_LAUNCH_DENSE_MOMENTUM_KERNEL(__nesterov, __reg_type)     \
+  DenseMomentumFunctor<T, MT, __reg_type, __nesterov> functor(          \
+      param->data<T>(), grad->data<T>(), velocity->data<MT>(),          \
+      learning_rate->data<MPDType>(), master_in_data, mu, rescale_grad, \
+      param->numel(), regularization_coeff,                             \
+      param_out->mutable_data<T>(ctx.GetPlace()),                       \
+      velocity_out->mutable_data<MT>(ctx.GetPlace()), master_out_data); \
+  for_range(functor);
 
+        if (use_nesterov) {
+          if (regularization_flag == RegularizationType::kL2DECAY) {
+            PADDLE_LAUNCH_DENSE_MOMENTUM_KERNEL(UseNesterov,
+                                                RegularizationType::kL2DECAY);
+          } else {
+            PADDLE_LAUNCH_DENSE_MOMENTUM_KERNEL(UseNesterov,
+                                                RegularizationType::kNONE);
+          }
         } else {
-          DenseMomentumFunctor<T, MT, NoNesterov> functor(
-              param->data<T>(), grad->data<T>(), velocity->data<MT>(),
-              learning_rate->data<MPDType>(), master_in_data, mu, rescale_grad,
-              param->numel(), regularization_flag, regularization_coeff,
-              param_out->mutable_data<T>(ctx.GetPlace()),
-              velocity_out->mutable_data<MT>(ctx.GetPlace()), master_out_data);
-          for_range(functor);
+          if (regularization_flag == RegularizationType::kL2DECAY) {
+            PADDLE_LAUNCH_DENSE_MOMENTUM_KERNEL(NoNesterov,
+                                                RegularizationType::kL2DECAY);
+          } else {
+            PADDLE_LAUNCH_DENSE_MOMENTUM_KERNEL(NoNesterov,
+                                                RegularizationType::kNONE);
+          }
         }
       }
 

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

@@ -102,7 +102,7 @@ def run_momentum_op(params,
                     'Param': p,
                     'Grad': g,
                     'Velocity': v,
-                    'LearningRate': lr_var
+                    'LearningRate': lr_var,
                 }
                 outputs = {'ParamOut': p, 'VelocityOut': v}
                 if multi_precision:
@@ -115,7 +115,7 @@ def run_momentum_op(params,
                 'Param': param_vars,
                 'Grad': grad_vars,
                 'Velocity': velocity_vars,
-                'LearningRate': lr_var
+                'LearningRate': lr_var,
             }
             outputs = {'ParamOut': param_vars, 'VelocityOut': velocity_vars}
             if multi_precision:
@@ -176,7 +176,10 @@ class TestMergedMomentum(unittest.TestCase):
         outs2 = run_op(False)
         self.assertEqual(len(outs1), len(outs2))
         for i, (out1, out2) in enumerate(zip(outs1, outs2)):
-            self.assertTrue(np.allclose(out1, out2, atol=1e-7))
+            if isinstance(place, paddle.CUDAPlace):
+                self.assertTrue(np.array_equal(out1, out2))
+            else:
+                self.assertTrue(np.allclose(out1, out2, atol=1e-7))
 
     def get_places(self):
         places = [paddle.CPUPlace()]