fix cpu adamw problem for np.float64 (#35124)

paddle/fluid/operators/optimizers/adamw_op.h

@@ -31,19 +31,22 @@ class AdamWFunctor;
 template <typename T>
 class AdamWFunctor<T, CPUAdamW> {
  private:
-  const float coeff_;
-  const float learning_rate_;
+  const T coeff_;
+  const T* lr_;
   T* param_;
 
  public:
-  AdamWFunctor(const float& coeff, const float& learning_rate, T* param)
-      : coeff_(coeff), learning_rate_(learning_rate), param_(param) {}
+  AdamWFunctor(const T coeff, const T* lr, T* param)
+      : coeff_(coeff), lr_(lr), param_(param) {}
 
   inline HOSTDEVICE void operator()(size_t numel) const {
     Eigen::Map<Eigen::Array<T, 1, Eigen::Dynamic>> param{
         param_, static_cast<Eigen::Index>(numel)};
+
+    T lr = *lr_;
+
     // Calculation
-    param = param * (1.0f - learning_rate_ * coeff_);
+    param = param * (1 - lr * coeff_);
   }
 };
 
@@ -183,7 +186,7 @@ class AdamWOpKernel : public AdamOpKernel<DeviceContext, T> {
       return;
     }
 
-    float coeff = ctx.Attr<float>("coeff");
+    T coeff = static_cast<T>(ctx.Attr<float>("coeff"));
     auto* lr = ctx.Input<LoDTensor>("LearningRate");
 
     LoDTensor* param;
@@ -195,9 +198,7 @@ class AdamWOpKernel : public AdamOpKernel<DeviceContext, T> {
       param = const_cast<LoDTensor*>(ctx.Input<LoDTensor>("Param"));
     }
 
-    // AdamWFunctor(float coeff, const float* learning_rate, T* parma)
-    AdamWFunctor<T, CPUAdamW> functor(coeff, *lr->data<float>(),
-                                      param->data<T>());
+    AdamWFunctor<T, CPUAdamW> functor(coeff, lr->data<T>(), param->data<T>());
     functor(param->numel());
 
     AdamOpKernel<DeviceContext, T>::Compute(ctx);