From ffa22a5f905daa7b7a4a9e4e6a1e3f17b9fea073 Mon Sep 17 00:00:00 2001
From: Kexin Zhao <zhaokexin01@baidu.com>
Date: Sun, 18 Mar 2018 20:01:27 -0700
Subject: [PATCH] fix scaling param type

---
 paddle/fluid/operators/batch_norm_op.cu.cc | 40 ++++++++++++----------
 paddle/fluid/platform/cudnn_helper.h       |  5 ---
 2 files changed, 21 insertions(+), 24 deletions(-)

diff --git a/paddle/fluid/operators/batch_norm_op.cu.cc b/paddle/fluid/operators/batch_norm_op.cu.cc
index 5e976788624..2de935d087a 100644
--- a/paddle/fluid/operators/batch_norm_op.cu.cc
+++ b/paddle/fluid/operators/batch_norm_op.cu.cc
@@ -18,6 +18,7 @@ limitations under the License. */
 #include <cfloat>
 #include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/platform/cudnn_helper.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
@@ -27,7 +28,7 @@ using DataLayout = framework::DataLayout;
 template <typename T>
 using CudnnDataType = platform::CudnnDataType<T>;
 template <typename T>
-using bn_param_type = CudnnDataType<T>::bn_param_type;
+using ScalingParamType = typename CudnnDataType<T>::ScalingParamType;
 
 void ExtractNCWHD(const framework::DDim &dims, const DataLayout &data_layout,
                   int *N, int *C, int *H, int *W, int *D) {
@@ -121,15 +122,15 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
 
     // alloc memory
     y->mutable_data<T>(ctx.GetPlace());
-    mean_out->mutable_data<bn_param_type<T>>(ctx.GetPlace());
-    variance_out->mutable_data<bn_param_type<T>>(ctx.GetPlace());
-    saved_mean->mutable_data<bn_param_type<T>>(ctx.GetPlace());
-    saved_variance->mutable_data<bn_param_type<T>>(ctx.GetPlace());
+    mean_out->mutable_data<ScalingParamType<T>>(ctx.GetPlace());
+    variance_out->mutable_data<ScalingParamType<T>>(ctx.GetPlace());
+    saved_mean->mutable_data<ScalingParamType<T>>(ctx.GetPlace());
+    saved_variance->mutable_data<ScalingParamType<T>>(ctx.GetPlace());
 
     auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
-    math::SetConstant<platform::CUDADeviceContext, bn_param_type<T>> functor;
-    functor(dev_ctx, saved_mean, static_cast<bn_param_type<T>>(0));
-    functor(dev_ctx, saved_variance, static_cast<bn_param_type<T>>(0));
+    math::SetConstant<platform::CUDADeviceContext, ScalingParamType<T>> functor;
+    functor(dev_ctx, saved_mean, static_cast<ScalingParamType<T>>(0));
+    functor(dev_ctx, saved_variance, static_cast<ScalingParamType<T>>(0));
 
     auto handle = dev_ctx.cudnn_handle();
 
@@ -150,10 +151,10 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
           CUDNN_BATCHNORM_SPATIAL, CudnnDataType<T>::kOne(),
           CudnnDataType<T>::kZero(), data_desc_, x->template data<T>(),
           data_desc_, y->template mutable_data<T>(ctx.GetPlace()),
-          bn_param_desc_, scale->template data<bn_param_type<T>>(),
-          bias->template data<bn_param_type<T>>(),
-          est_mean->template data<bn_param_type<T>>(),
-          est_var->template data<bn_param_type<T>>(), epsilon));
+          bn_param_desc_, scale->template data<ScalingParamType<T>>(),
+          bias->template data<ScalingParamType<T>>(),
+          est_mean->template data<ScalingParamType<T>>(),
+          est_var->template data<ScalingParamType<T>>(), epsilon));
     } else {
       // Run training mode.
       // obtain running mean and running inv var, and see if we need to
@@ -164,13 +165,14 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
           handle, mode_, CudnnDataType<T>::kOne(), CudnnDataType<T>::kZero(),
           data_desc_, x->template data<T>(), data_desc_,
           y->template mutable_data<T>(ctx.GetPlace()), bn_param_desc_,
-          scale->template data<bn_param_type<T>>(),
-          bias->template data<bn_param_type<T>>(), this_factor,
-          mean_out->template mutable_data<bn_param_type<T>>(ctx.GetPlace()),
-          variance_out->template mutable_data<bn_param_type<T>>(ctx.GetPlace()),
-          epsilon,
-          saved_mean->template mutable_data<bn_param_type<T>>(ctx.GetPlace()),
-          saved_variance->template mutable_data<bn_param_type<T>>(
+          scale->template data<ScalingParamType<T>>(),
+          bias->template data<ScalingParamType<T>>(), this_factor,
+          mean_out->template mutable_data<ScalingParamType<T>>(ctx.GetPlace()),
+          variance_out->template mutable_data<ScalingParamType<T>>(
+              ctx.GetPlace()),
+          epsilon, saved_mean->template mutable_data<ScalingParamType<T>>(
+                       ctx.GetPlace()),
+          saved_variance->template mutable_data<ScalingParamType<T>>(
               ctx.GetPlace())));
     }
 
diff --git a/paddle/fluid/platform/cudnn_helper.h b/paddle/fluid/platform/cudnn_helper.h
index a40c3662419..7e001ecc561 100644
--- a/paddle/fluid/platform/cudnn_helper.h
+++ b/paddle/fluid/platform/cudnn_helper.h
@@ -85,9 +85,6 @@ template <>
 class CudnnDataType<float16> {
  public:
   static const cudnnDataType_t type = CUDNN_DATA_HALF;
-  // cudnn batch norm requires that Scale, Bias, Mean, and Variance
-  // to be FLOAT tensors when the input x is HALF tensor
-  static const cudnnDataType_t bn_param_type = CUDNN_DATA_FLOAT;
   // The scaling param type is float for HALF and FLOAT tensors
   typedef const float ScalingParamType;
   static ScalingParamType* kOne() {
@@ -104,7 +101,6 @@ template <>
 class CudnnDataType<float> {
  public:
   static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
-  static const cudnnDataType_t bn_param_type = CUDNN_DATA_FLOAT;
   typedef const float ScalingParamType;
   static ScalingParamType* kOne() {
     static ScalingParamType v = 1.0;
@@ -120,7 +116,6 @@ template <>
 class CudnnDataType<double> {
  public:
   static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
-  static const cudnnDataType_t bn_param_type = CUDNN_DATA_DOUBLE;
   typedef const double ScalingParamType;
   static ScalingParamType* kOne() {
     static ScalingParamType v = 1.0;
-- 
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