fix bug of adaptive pool2d_grad, *test=kunlun (#45031)

* fix bug of adaptive pool2d_grad, *test=kunlun

* fix bug of adaptive pool2d_grad, *test=kunlun

* fix bug of adaptive pool2d_grad, *test=kunlun

cmake/external/xpu.cmake

@@ -10,7 +10,7 @@ set(XPU_RT_LIB_NAME "libxpurt.so")
 if(NOT DEFINED XPU_BASE_URL)
   set(XPU_BASE_URL_WITHOUT_DATE
       "https://baidu-kunlun-product.cdn.bcebos.com/KL-SDK/klsdk-dev")
-  set(XPU_BASE_URL "${XPU_BASE_URL_WITHOUT_DATE}/20220802")
+  set(XPU_BASE_URL "${XPU_BASE_URL_WITHOUT_DATE}/20220810")
 else()
   set(XPU_BASE_URL "${XPU_BASE_URL}")
 endif()
@@ -19,7 +19,7 @@ endif()
 if(NOT DEFINED XPU_XDNN_BASE_URL)
   set(XPU_XDNN_BASE_URL_WITHOUT_DATE
       "https://klx-sdk-release-public.su.bcebos.com/xdnn/dev")
-  set(XPU_XDNN_BASE_URL "${XPU_XDNN_BASE_URL_WITHOUT_DATE}/20220802")
+  set(XPU_XDNN_BASE_URL "${XPU_XDNN_BASE_URL_WITHOUT_DATE}/20220810")
 else()
   set(XPU_XDNN_BASE_URL "${XPU_XDNN_BASE_URL}")
 endif()

paddle/fluid/operators/pool_op_xpu.cc

@@ -44,6 +44,15 @@ class PoolXPUKernel : public framework::OpKernel<T> {
         platform::errors::InvalidArgument(
             "The Pool2d XPU OP only support 2 dimension pooling!"));
 
+    std::string data_format = context.Attr<std::string>("data_format");
+    PADDLE_ENFORCE_EQ(
+        data_format,
+        "NCHW",
+        platform::errors::InvalidArgument("The Pool2d XPU OP only support"
+                                          "data_format is 'NCHW', but received "
+                                          "%s",
+                                          data_format));
+
     int* index_data = nullptr;
     bool global_pooling = context.Attr<bool>("global_pooling") ||
                           (adaptive && (ksize[0] * ksize[1] == 1));
@@ -173,6 +182,16 @@ class PoolGradXPUKernel : public framework::OpKernel<T> {
     bool exclusive = context.Attr<bool>("exclusive");
     bool adaptive = context.Attr<bool>("adaptive");
     bool ceil_mode = context.Attr<bool>("ceil_mode");
+
+    std::string data_format = context.Attr<std::string>("data_format");
+    PADDLE_ENFORCE_EQ(
+        data_format,
+        "NCHW",
+        platform::errors::InvalidArgument("The Pool2d_grad XPU OP only support"
+                                          "data_format is 'NCHW', but received "
+                                          "%s",
+                                          data_format));
+
     std::string padding_algorithm =
         context.Attr<std::string>("padding_algorithm");
     const int* index_data = nullptr;
@@ -202,13 +221,6 @@ class PoolGradXPUKernel : public framework::OpKernel<T> {
     const int out_h = out->dims()[2];
     const int out_w = out->dims()[3];
 
-    PADDLE_ENFORCE_EQ(!adaptive || (ksize[0] * ksize[1] == 1) ||
-                          (in_h % out_h == 0 && in_w % out_w == 0),
-                      true,
-                      platform::errors::InvalidArgument(
-                          "The Pool2d XPU OP does not support (adaptive == "
-                          "true && output_size != 1)"));
-
     framework::DDim data_dims;
 
     data_dims = phi::slice_ddim(in_x->dims(), 2, in_x->dims().size());
@@ -234,7 +246,8 @@ class PoolGradXPUKernel : public framework::OpKernel<T> {
     auto input_grad = reinterpret_cast<XPUType*>(in_x_grad->data<T>());
     auto& dev_ctx = context.template device_context<DeviceContext>();
     int r = xpu::Error_t::SUCCESS;
-    if (adaptive && in_h % out_h == 0 && in_w % out_w == 0) {
+    if (adaptive) {
+      // floor for stride
       strides = {in_h / out_h, in_w / out_w};
       int kh = in_h - (out_h - 1) * strides[0];
       int kw = in_w - (out_w - 1) * strides[1];
@@ -243,6 +256,7 @@ class PoolGradXPUKernel : public framework::OpKernel<T> {
     }
 
     if (pooling_type == "max") {
+      // TODO(zhanghuan05) to bind max_pool2d_grad_indices xpu api
       r = xpu::max_pool2d_grad<XPUType>(dev_ctx.x_context(),
                                         input,
                                         output,

python/paddle/fluid/tests/unittests/xpu/test_pool2d_op_xpu.py

@@ -341,6 +341,24 @@ class XPUTestPool2D_Op(XPUOpTestWrapper):
         def init_adaptive(self):
             self.adaptive = False
 
+    class TestAvgPoolAdaptive(TestPool2D_Op):
+
+        def init_adaptive(self):
+            self.adaptive = True
+
+    class TestAvgPoolAdaptiveAsyOutSize(TestPool2D_Op):
+
+        def init_adaptive(self):
+            self.adaptive = True
+
+        def init_shape(self):
+            self.shape = [8, 3, 6, 6]
+
+        def init_test_case(self):
+            self.ksize = [2, 3]
+            self.strides = [1, 1]
+            self.paddings = [0, 0, 0, 0]
+
     class TestCase1(TestPool2D_Op):
 
         def init_test_case(self):