support nhwc format for kunlun conv/batch_norm (#42195)

* support nhwc format for kunlun conv/batch_norm
*test=kunlun

* minor
*test=kunlun

cmake/external/xpu.cmake

@@ -17,7 +17,7 @@ endif()
 # ubuntu and centos: use output by XDNN API team
 if(NOT DEFINED XPU_XDNN_BASE_URL)
   SET(XPU_XDNN_BASE_URL_WITHOUT_DATE "https://klx-sdk-release-public.su.bcebos.com/xdnn/dev")
-  SET(XPU_XDNN_BASE_URL "${XPU_XDNN_BASE_URL_WITHOUT_DATE}/20220412")
+  SET(XPU_XDNN_BASE_URL "${XPU_XDNN_BASE_URL_WITHOUT_DATE}/20220425")
 else()
   SET(XPU_XDNN_BASE_URL "${XPU_XDNN_BASE_URL}")
 endif()

paddle/fluid/operators/batch_norm_op_xpu.cc

@@ -53,8 +53,12 @@ class BatchNormXPUKernel : public framework::OpKernel<T> {
             "But received: the size of input's dimensions is [%d]",
             x_dims.size()));
 
-    int N, C, H, W, D;
+    int N = -1, C = -1, H = -1, W = -1, D = -1;
     ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);
+    N = (N == 0) ? 1 : N;
+    C = (C == 0) ? 1 : C;
+    H = (H == 0) ? 1 : H;
+    W = (W == 0) ? 1 : W;
 
     const auto *scale = ctx.Input<Tensor>("Scale");
     const auto *bias = ctx.Input<Tensor>("Bias");
@@ -103,12 +107,6 @@ class BatchNormXPUKernel : public framework::OpKernel<T> {
                             "The batch_norm XPU API return wrong value[%d %s]",
                             r, XPUAPIErrorMsg[r]));
     } else {
-      PADDLE_ENFORCE_EQ(
-          data_layout_str == "NCHW", true,
-          platform::errors::InvalidArgument(
-              "The batch_norm_infer 'data_layout' attribute must be NCHW. "
-              "But recevived 'data_layout' is [%s].",
-              data_layout_str));
       const auto *mean = ctx.Input<Tensor>("Mean");
       const auto *variance = ctx.Input<Tensor>("Variance");
       const auto *mean_data = mean->data<float>();
@@ -222,8 +220,12 @@ class BatchNormGradXPUKernel : public framework::OpKernel<T> {
             "But received: the size of input's dimensions is [%d]",
             x_dims.size()));
 
-    int N, C, H, W, D;
+    int N = -1, C = -1, H = -1, W = -1, D = -1;
     ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);
+    N = (N == 0) ? 1 : N;
+    C = (C == 0) ? 1 : C;
+    H = (H == 0) ? 1 : H;
+    W = (W == 0) ? 1 : W;
 
     const auto *x_data = x->data<T>();
     const auto *d_y_data = d_y->data<T>();

paddle/fluid/operators/conv_op_xpu.cc

@@ -38,9 +38,10 @@ class GemmConvXPUKernel : public framework::OpKernel<T> {
     const std::string padding_algorithm =
         context.Attr<std::string>("padding_algorithm");
 
-    PADDLE_ENFORCE_EQ(data_format == "NHWC" || data_format == "NDHWC", false,
+    PADDLE_ENFORCE_EQ(
+        data_format == "NDHWC", false,
         platform::errors::InvalidArgument(
-                          ("XPU do support data_format is NCHW in conv op.")));
+            ("XPU does not support data_format is NDHWC in conv op.")));
 
     framework::DDim in_data_dims =
         phi::slice_ddim(input->dims(), 2, input->dims().size());
@@ -50,11 +51,18 @@ class GemmConvXPUKernel : public framework::OpKernel<T> {
     UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
                              in_data_dims, strides, ksize);
 
-    const int batch_size = static_cast<int>(input->dims()[0]);
-    const int img_c = static_cast<int>(input->dims()[1]);
-    const int img_h = static_cast<int>(input->dims()[2]);
-    const int img_w = static_cast<int>(input->dims()[3]);
-    const int f = static_cast<int>(filter.dims()[0]);
+    int batch_size = static_cast<int>(input->dims()[0]);
+    int img_c = static_cast<int>(input->dims()[1]);
+    int img_h = static_cast<int>(input->dims()[2]);
+    int img_w = static_cast<int>(input->dims()[3]);
+    int f = static_cast<int>(filter.dims()[0]);
+    bool is_nchw = true;
+    if (data_format == "NHWC") {
+      img_c = static_cast<int>(input->dims()[3]);
+      img_h = static_cast<int>(input->dims()[1]);
+      img_w = static_cast<int>(input->dims()[2]);
+      is_nchw = false;
+    }
 
     const XPUT *input_data = reinterpret_cast<const XPUT *>(input->data<T>());
     const XPUT *filter_data = reinterpret_cast<const XPUT *>(filter.data<T>());
@@ -64,7 +72,7 @@ class GemmConvXPUKernel : public framework::OpKernel<T> {
     int r = xpu::conv2d<XPUT, XPUT, XPUT, int16_t>(
         dev_ctx.x_context(), input_data, filter_data, output_data, batch_size,
         img_c, img_h, img_w, f, ksize, strides, paddings, dilations, groups,
-        nullptr, nullptr, nullptr, true);
+        nullptr, nullptr, nullptr, is_nchw);
     PADDLE_ENFORCE_EQ(
         r, XPU_SUCCESS,
         platform::errors::External("XPU conv kernel return wrong value[%d %s]",
@@ -99,9 +107,9 @@ class GemmConvGradXPUKernel : public framework::OpKernel<T> {
         context.Attr<std::string>("padding_algorithm");
 
     PADDLE_ENFORCE_EQ(
-        data_format == "NHWC" || data_format == "NDHWC", false,
+        data_format == "NDHWC", false,
         platform::errors::InvalidArgument(
-            ("XPU do support data_format is NCHW in conv grad op.")));
+            ("XPU doesn't support data_format is NDHWC in conv grad op.")));
 
     framework::DDim in_data_dims =
         phi::slice_ddim(input->dims(), 2, input->dims().size());
@@ -111,11 +119,18 @@ class GemmConvGradXPUKernel : public framework::OpKernel<T> {
     UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
                              in_data_dims, strides, ksize);
 
-    const int batch_size = static_cast<int>(input->dims()[0]);
-    const int img_c = static_cast<int>(input->dims()[1]);
-    const int img_h = static_cast<int>(input->dims()[2]);
-    const int img_w = static_cast<int>(input->dims()[3]);
-    const int f = static_cast<int>(filter.dims()[0]);
+    int batch_size = static_cast<int>(input->dims()[0]);
+    int img_c = static_cast<int>(input->dims()[1]);
+    int img_h = static_cast<int>(input->dims()[2]);
+    int img_w = static_cast<int>(input->dims()[3]);
+    int f = static_cast<int>(filter.dims()[0]);
+    bool is_nchw = true;
+    if (data_format == "NHWC") {
+      img_c = static_cast<int>(input->dims()[3]);
+      img_h = static_cast<int>(input->dims()[1]);
+      img_w = static_cast<int>(input->dims()[2]);
+      is_nchw = false;
+    }
 
     const XPUT *input_data = reinterpret_cast<const XPUT *>(input->data<T>());
     const XPUT *filter_data = reinterpret_cast<const XPUT *>(filter.data<T>());
@@ -136,7 +151,7 @@ class GemmConvGradXPUKernel : public framework::OpKernel<T> {
         dev_ctx.x_context(), input_data, filter_data, output_grad_data,
         input_grad_data, filter_grad_data, batch_size, img_c, img_h, img_w, f,
         ksize, strides, paddings, dilations, groups, nullptr, nullptr, nullptr,
-        nullptr, nullptr, true);
+        nullptr, nullptr, is_nchw);
     PADDLE_ENFORCE_EQ(
         r, XPU_SUCCESS,
         platform::errors::External("XPU conv kernel return wrong value[%d %s]",