add data layout (#6832)

* add data layout

* fix ci

paddle/framework/data_layout.h

+/* Copyright (c) 2016 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. */
+
+#pragma once
+
+namespace paddle {
+namespace framework {
+
+enum DataLayout {
+  kNHWC = 0,
+  kNCHW = 1,
+  kAnyLayout = 2,
+};
+
+inline DataLayout StringToDataLayout(const std::string& str) {
+  if (str == "NHWC" || str == "nhwc") {
+    return DataLayout::kNHWC;
+  } else if (str == "NCHW" || str == "nchw") {
+    return DataLayout::kNCHW;
+  } else {
+    PADDLE_THROW("Unknown storage order string: %s", str);
+  }
+}
+
+}  // namespace framework
+}  // namespace paddle

paddle/operators/batch_norm_op.cc

@@ -13,12 +13,14 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/batch_norm_op.h"
+#include "paddle/framework/data_layout.h"
 
 namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
+using DataLayout = framework::DataLayout;
 
 template <typename T>
 using EigenArrayMap =
@@ -60,15 +62,15 @@ class BatchNormOp : public framework::OperatorWithKernel {
                       "Variance and VarianceOut should share the same memory");
 
     const auto x_dims = ctx->GetInputDim("X");
-    const TensorFormat tensor_format =
-        StringToTensorFormat(ctx->Attrs().Get<std::string>("tensor_format"));
+    const DataLayout data_layout = framework::StringToDataLayout(
+        ctx->Attrs().Get<std::string>("data_layout"));
 
     PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
                    "Input X must have 2 to 5 dimensions.");
 
     const int C =
-        (tensor_format == TensorFormat::NCHW ? x_dims[1]
-                                             : x_dims[x_dims.size() - 1]);
+        (data_layout == DataLayout::kNCHW ? x_dims[1]
+                                          : x_dims[x_dims.size() - 1]);
 
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], C);
@@ -90,7 +92,7 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<bool>("is_test", "").SetDefault(false);
     AddAttr<float>("momentum", "").SetDefault(0.9);
     AddAttr<float>("epsilon", "").SetDefault(1e-5);
-    AddAttr<std::string>("tensor_format", "").SetDefault("NCHW");
+    AddAttr<std::string>("data_layout", "").SetDefault("NCHW");
     AddInput("X", "The input tensor");
     AddInput("Scale",
              "Scale is a 1-dimensional tensor of size C "
@@ -141,9 +143,9 @@ class BatchNormKernel<platform::CPUDeviceContext, T>
     const float epsilon = ctx.Attr<float>("epsilon");
     const float momentum = ctx.Attr<float>("momentum");
     const bool is_test = ctx.Attr<bool>("is_test");
-    const std::string tensor_format_str =
-        ctx.Attr<std::string>("tensor_format");
-    const TensorFormat tensor_format = StringToTensorFormat(tensor_format_str);
+    const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
+    const DataLayout data_layout =
+        framework::StringToDataLayout(data_layout_str);
 
     const auto *x = ctx.Input<Tensor>("X");
     const auto &x_dims = x->dims();
@@ -151,8 +153,8 @@ class BatchNormKernel<platform::CPUDeviceContext, T>
                    "The Input dim size should be between 2 and 5");
     const int N = x_dims[0];
     const int C =
-        (tensor_format == TensorFormat::NCHW ? x_dims[1]
-                                             : x_dims[x_dims.size() - 1]);
+        (data_layout == DataLayout::kNCHW ? x_dims[1]
+                                          : x_dims[x_dims.size() - 1]);
     const int sample_size = x->numel() / N / C;
 
     auto *y = ctx.Output<Tensor>("Y");
@@ -177,8 +179,8 @@ class BatchNormKernel<platform::CPUDeviceContext, T>
       saved_mean_e.setZero();
       saved_variance_e.setZero();
 
-      switch (tensor_format) {
-        case TensorFormat::NCHW: {
+      switch (data_layout) {
+        case DataLayout::kNCHW: {
           ConstEigenArrayMap<T> x_arr(x->data<T>(), sample_size, N * C);
           for (int nc = 0; nc < N * C; ++nc) {
             saved_mean_e(nc % C) += x_arr.col(nc).sum();
@@ -191,7 +193,7 @@ class BatchNormKernel<platform::CPUDeviceContext, T>
           saved_variance_e /= N * sample_size;
           break;
         }
-        case TensorFormat::NHWC: {
+        case DataLayout::kNHWC: {
           ConstEigenArrayMap<T> x_arr(x->data<T>(), C, N * sample_size);
           for (int i = 0; i < N * sample_size; ++i) {
             saved_mean_e += x_arr.col(i);
@@ -205,7 +207,7 @@ class BatchNormKernel<platform::CPUDeviceContext, T>
           break;
         }
         default:
-          PADDLE_THROW("Unknown storage order: %s", tensor_format_str);
+          PADDLE_THROW("Unknown storage order: %s", data_layout_str);
       }
 
       EigenVectorArrayMap<T> running_mean_arr(
@@ -247,8 +249,8 @@ class BatchNormKernel<platform::CPUDeviceContext, T>
     Eigen::Array<T, Eigen::Dynamic, 1> new_bias =
         bias_arr - mean_arr * inv_std * scale_arr;
 
-    switch (tensor_format) {
-      case TensorFormat::NCHW: {
+    switch (data_layout) {
+      case DataLayout::kNCHW: {
         EigenArrayMap<T> y_arr(y->mutable_data<T>(ctx.GetPlace()), sample_size,
                                N * C);
         ConstEigenArrayMap<T> x_arr(x->data<T>(), sample_size, N * C);
@@ -257,7 +259,7 @@ class BatchNormKernel<platform::CPUDeviceContext, T>
         }
         break;
       }
-      case TensorFormat::NHWC: {
+      case DataLayout::kNHWC: {
         EigenArrayMap<T>(y->mutable_data<T>(ctx.GetPlace()), C,
                          N * sample_size) =
             (ConstEigenArrayMap<T>(x->data<T>(), C, N * sample_size).colwise() *
@@ -267,7 +269,7 @@ class BatchNormKernel<platform::CPUDeviceContext, T>
         break;
       }
       default:
-        PADDLE_THROW("Unknown storage order: %d", tensor_format);
+        PADDLE_THROW("Unknown storage order: %d", data_layout);
     }
   }
 };
@@ -290,11 +292,11 @@ class BatchNormGradOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Bias")), "");
 
     const auto x_dims = ctx->GetInputDim("X");
-    const TensorFormat tensor_format =
-        StringToTensorFormat(ctx->Attrs().Get<std::string>("tensor_format"));
+    const DataLayout data_layout = framework::StringToDataLayout(
+        ctx->Attrs().Get<std::string>("data_layout"));
     const int C =
-        (tensor_format == TensorFormat::NCHW ? x_dims[1]
-                                             : x_dims[x_dims.size() - 1]);
+        (data_layout == DataLayout::kNCHW ? x_dims[1]
+                                          : x_dims[x_dims.size() - 1]);
 
     ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
     ctx->SetOutputDim(framework::GradVarName("Scale"), {C});
@@ -333,9 +335,9 @@ class BatchNormGradKernel<platform::CPUDeviceContext, T>
     const auto *saved_mean = ctx.Input<Tensor>("SavedMean");
     // SavedVariance have been reverted in forward operator
     const auto *saved_inv_variance = ctx.Input<Tensor>("SavedVariance");
-    const std::string tensor_format_str =
-        ctx.Attr<std::string>("tensor_format");
-    const TensorFormat tensor_format = StringToTensorFormat(tensor_format_str);
+    const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
+    const DataLayout data_layout =
+        framework::StringToDataLayout(data_layout_str);
 
     // Get the size for each dimension.
     // NCHW [batch_size, in_channels, in_height, in_width]
@@ -344,8 +346,8 @@ class BatchNormGradKernel<platform::CPUDeviceContext, T>
                    "The Input dim size should be between 2 and 5");
     const int N = x_dims[0];
     const int C =
-        (tensor_format == TensorFormat::NCHW ? x_dims[1]
-                                             : x_dims[x_dims.size() - 1]);
+        (data_layout == DataLayout::kNCHW ? x_dims[1]
+                                          : x_dims[x_dims.size() - 1]);
     const int sample_size = x->numel() / N / C;
 
     ConstEigenVectorArrayMap<T> scale_arr(scale->data<T>(), C);
@@ -376,8 +378,8 @@ class BatchNormGradKernel<platform::CPUDeviceContext, T>
 
     const auto scale_inv_var_nhw = scale_arr * inv_var_arr / (N * sample_size);
 
-    switch (tensor_format) {
-      case TensorFormat::NCHW: {
+    switch (data_layout) {
+      case DataLayout::kNCHW: {
         ConstEigenArrayMap<T> x_arr(x->data<T>(), sample_size, N * C);
         ConstEigenArrayMap<T> d_y_arr(d_y->data<T>(), sample_size, N * C);
         EigenArrayMap<T> d_x_arr(d_x->mutable_data<T>(ctx.GetPlace()),
@@ -400,7 +402,7 @@ class BatchNormGradKernel<platform::CPUDeviceContext, T>
         }
         break;
       }
-      case TensorFormat::NHWC: {
+      case DataLayout::kNHWC: {
         ConstEigenArrayMap<T> x_arr(x->data<T>(), C, N * sample_size);
         ConstEigenArrayMap<T> d_y_arr(d_y->data<T>(), C, N * sample_size);
         EigenArrayMap<T> d_x_arr(d_x->mutable_data<T>(ctx.GetPlace()), C,
@@ -425,7 +427,7 @@ class BatchNormGradKernel<platform::CPUDeviceContext, T>
         break;
       }
       default:
-        PADDLE_THROW("Unknown storage order: %s", tensor_format_str);
+        PADDLE_THROW("Unknown storage order: %s", data_layout_str);
     }
   }
 };

paddle/operators/batch_norm_op.cu.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/operators/batch_norm_op.h"
+#include "paddle/framework/data_layout.h"
 
 #include <cfloat>
 #include "paddle/operators/math/math_function.h"
@@ -22,12 +23,12 @@ namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
+using DataLayout = framework::DataLayout;
 template <typename T>
 using CudnnDataType = platform::CudnnDataType<T>;
 
-void ExtractNCWHD(const framework::DDim &dims,
-                  const TensorFormat &tensor_format, int *N, int *C, int *H,
-                  int *W, int *D) {
+void ExtractNCWHD(const framework::DDim &dims, const DataLayout &data_layout,
+                  int *N, int *C, int *H, int *W, int *D) {
   *N = dims[0];
   if (dims.size() == 2) {
     *C = dims[1];
@@ -35,13 +36,13 @@ void ExtractNCWHD(const framework::DDim &dims,
     *W = 1;
     *D = 1;
   } else {
-    *C = tensor_format == TensorFormat::NCHW ? dims[1] : dims[dims.size() - 1];
-    *H = tensor_format == TensorFormat::NCHW ? dims[2] : dims[1];
+    *C = data_layout == DataLayout::kNCHW ? dims[1] : dims[dims.size() - 1];
+    *H = data_layout == DataLayout::kNCHW ? dims[2] : dims[1];
     *W = dims.size() > 3
-             ? (tensor_format == TensorFormat::NCHW ? dims[3] : dims[2])
+             ? (data_layout == DataLayout::kNCHW ? dims[3] : dims[2])
              : 1;
     *D = dims.size() > 4
-             ? (tensor_format == TensorFormat::NCHW ? dims[4] : dims[3])
+             ? (data_layout == DataLayout::kNCHW ? dims[4] : dims[3])
              : 1;
   }
 }
@@ -56,9 +57,9 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
     double epsilon = static_cast<double>(ctx.Attr<float>("epsilon"));
     const float momentum = ctx.Attr<float>("momentum");
     const bool is_test = ctx.Attr<bool>("is_test");
-    const std::string tensor_format_str =
-        ctx.Attr<std::string>("tensor_format");
-    const TensorFormat tensor_format = StringToTensorFormat(tensor_format_str);
+    const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
+    const DataLayout data_layout =
+        framework::StringToDataLayout(data_layout_str);
 
     // Get the size for each dimension.
     // NCHW [batch_size, in_channels, in_height, in_width]
@@ -67,7 +68,7 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
     PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
                    "The Input dim size should be between 2 and 5");
     int N, C, H, W, D;
-    ExtractNCWHD(x_dims, tensor_format, &N, &C, &H, &W, &D);
+    ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);
 
     // ------------------- cudnn descriptors ---------------------
     cudnnTensorDescriptor_t data_desc_;
@@ -93,7 +94,7 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
     VLOG(1) << "Setting descriptors.";
     std::vector<int> dims;
     std::vector<int> strides;
-    if (tensor_format == TensorFormat::NCHW) {
+    if (data_layout == DataLayout::kNCHW) {
       dims = {N, C, H, W, D};
       strides = {C * H * W * D, H * W * D, W * D, D, 1};
     } else {
@@ -180,9 +181,9 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                    "It must use GPUPlace.");
     double epsilon = static_cast<double>(ctx.Attr<float>("epsilon"));
-    const std::string tensor_format_str =
-        ctx.Attr<std::string>("tensor_format");
-    const TensorFormat tensor_format = StringToTensorFormat(tensor_format_str);
+    const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
+    const DataLayout data_layout =
+        framework::StringToDataLayout(data_layout_str);
     const auto *x = ctx.Input<Tensor>("X");
     const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
     const auto *scale = ctx.Input<Tensor>("Scale");
@@ -192,7 +193,7 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
     PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
                    "The Input dim size should be between 2 and 5");
     int N, C, H, W, D;
-    ExtractNCWHD(x_dims, tensor_format, &N, &C, &H, &W, &D);
+    ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);
 
     PADDLE_ENFORCE_EQ(scale->dims().size(), 1UL);
     PADDLE_ENFORCE_EQ(scale->dims()[0], C);
@@ -219,7 +220,7 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
 
     std::vector<int> dims;
     std::vector<int> strides;
-    if (tensor_format == TensorFormat::NCHW) {
+    if (data_layout == DataLayout::kNCHW) {
       dims = {N, C, H, W, D};
       strides = {C * H * W * D, H * W * D, W * D, D, 1};
     } else {

paddle/operators/batch_norm_op.h

@@ -19,21 +19,6 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-enum TensorFormat {
-  NHWC = 0,
-  NCHW = 1,
-};
-
-inline TensorFormat StringToTensorFormat(const std::string& str) {
-  if (str == "NHWC" || str == "nhwc") {
-    return TensorFormat::NHWC;
-  } else if (str == "NCHW" || str == "nchw") {
-    return TensorFormat::NCHW;
-  } else {
-    PADDLE_THROW("Unknown storage order string: %s", str);
-  }
-}
-
 template <typename DeviceContext, typename T>
 class BatchNormKernel : public framework::OpKernel<T> {
  public:

python/paddle/v2/fluid/tests/test_batch_norm_op.py

@@ -208,7 +208,7 @@ class TestBatchNormOp(OpTest):
         print 'python: NHWC, NCHW, backward checking passed'
 
     def test_forward_backward(self):
-        def test_with_place(place, tensor_format, shape):
+        def test_with_place(place, data_layout, shape):
             # attr
             epsilon = 0.00001
             momentum = 0.9
@@ -292,7 +292,7 @@ class TestBatchNormOp(OpTest):
                 SavedVariance="saved_variance",
                 # attrs
                 is_test=False,
-                tensor_format=tensor_format,
+                data_layout=data_layout,
                 momentum=momentum,
                 epsilon=epsilon)
 
@@ -311,7 +311,7 @@ class TestBatchNormOp(OpTest):
                 atol = 1e-4
             self.__assert_close(variance_out_tensor, variance_out,
                                 "variance_out", atol)
-            print "op test forward passed: ", str(place), tensor_format
+            print "op test forward passed: ", str(place), data_layout
 
             # run backward
             batch_norm_op_grad = get_backward_op(scope, batch_norm_op, set())
@@ -336,7 +336,7 @@ class TestBatchNormOp(OpTest):
             self.__assert_close(x_grad_tensor, x_grad_ref, "x_grad")
             self.__assert_close(scale_grad_tensor, scale_grad_ref, "scale_grad")
             self.__assert_close(bias_grad_tensor, bias_grad_ref, "bias_grad")
-            print "op test backward passed: ", str(place), tensor_format
+            print "op test backward passed: ", str(place), data_layout
 
         places = [core.CPUPlace()]
         if core.is_compile_gpu() and core.op_support_gpu("batch_norm"):