[Cherry-Pick 2.5]Support output 0D for is_empty/as_complex/inner/dot/rank/tensordot/squeeze_/static.accuracy/static.auc/metric.accuracy

paddle/phi/infermeta/binary.cc

@@ -1152,8 +1152,9 @@ void DotInferMeta(const MetaTensor& x, const MetaTensor& y, MetaTensor* out) {
                         "with input tensor Y: %s",
                         x_dims.to_str(),
                         y_dims.to_str()));
-
-  x_dims[x_dims.size() - 1] = 1;
+  std::vector<int64_t> x_dims_vec = phi::vectorize(x_dims);
+  std::vector<int64_t> x_dims_vec_cut(x_dims_vec.begin(), x_dims_vec.end() - 1);
+  x_dims = phi::make_ddim(x_dims_vec_cut);
   out->set_dims(x_dims);
   out->set_dtype(x.dtype());
   out->set_layout(x.layout());

paddle/phi/infermeta/multiary.cc

@@ -479,7 +479,7 @@ void AucInferMeta(const MetaTensor& input,
       0,
       phi::errors::InvalidArgument("slide_steps must be natural number"));
 
-  auc->set_dims({1});
+  auc->set_dims(phi::make_ddim({}));
   auc->set_dtype(DataType::INT64);
 
   if (slide_steps) {

paddle/phi/infermeta/ternary.cc

@@ -66,11 +66,11 @@ void AccuracyInferMeta(const MetaTensor& out,
             label_dim[0]));
   }
 
-  accuracy->set_dims({1});
+  accuracy->set_dims(phi::make_ddim({}));
+  correct->set_dims(phi::make_ddim({}));
+  total->set_dims(phi::make_ddim({}));
   accuracy->set_dtype(out.dtype());
-  correct->set_dims({1});
   correct->set_dtype(out.dtype());
-  total->set_dims({1});
   total->set_dtype(out.dtype());
   accuracy->share_lod(out);
 }

paddle/phi/infermeta/unary.cc

@@ -1839,7 +1839,7 @@ void InverseInferMeta(const MetaTensor& x, MetaTensor* out) {
 }
 
 void IsEmptyInferMeta(const MetaTensor& x, MetaTensor* out) {
-  out->set_dims(phi::make_ddim({1}));
+  out->set_dims(phi::make_ddim({}));
   out->set_dtype(DataType::BOOL);
 }
 

paddle/phi/kernels/gpu/dot_kernel.cu

@@ -32,15 +32,15 @@ void DotKernel(const Context& dev_ctx,
                const DenseTensor& y,
                DenseTensor* out) {
   dev_ctx.template Alloc<T>(out);
-  if (1 == out->dims().size()) {
+  if (out->dims().size() == 0) {
     auto eigen_out = phi::EigenScalar<T>::From(*out);
     auto eigen_x = phi::EigenVector<T>::Flatten(x);
     auto eigen_y = phi::EigenVector<T>::Flatten(y);
 
     auto& dev = *dev_ctx.eigen_device();
     eigen_out.device(dev) = (eigen_x * eigen_y).sum();
   } else {
-    auto eigen_out = phi::EigenMatrix<T>::From(*out);
+    auto eigen_out = phi::EigenVector<T>::From(*out);
     auto eigen_x = phi::EigenMatrix<T>::From(x);
     auto eigen_y = phi::EigenMatrix<T>::From(y);
 

paddle/phi/kernels/impl/dot_grad_kernel_impl.h

@@ -46,7 +46,7 @@ struct DotGradFunction<DeviceContext, T, phi::funcs::EnableComplex<T>> {
                   DenseTensor* tensor_dy) {
     VLOG(1) << "enable route";
 #if defined(__NVCC__) || defined(__HIPCC__)
-    if (1 == tensor_dout->dims().size()) {
+    if (1 >= tensor_dout->dims().size()) {
       auto dout = EigenVector<T>::Flatten(*tensor_dout);
 
       if (tensor_dx) {
@@ -144,7 +144,7 @@ struct DotGradFunction<DeviceContext, T, phi::funcs::DisableComplex<T>> {
                   DenseTensor* tensor_dx,
                   DenseTensor* tensor_dy) {
 #if defined(__NVCC__) || defined(__HIPCC__)
-    if (1 == tensor_dout->dims().size()) {
+    if (1 >= tensor_dout->dims().size()) {
       auto dout = EigenVector<T>::Flatten(*tensor_dout);
       if (tensor_dx) {
         auto y = EigenVector<T>::Flatten(*tensor_y);
@@ -236,7 +236,7 @@ struct DotDoubleGradFunction<DeviceContext, T, phi::funcs::EnableComplex<T>> {
     const DenseTensor* tensor_ddx = tensor_ddx_opt->get_ptr();
     const DenseTensor* tensor_ddy = tensor_ddy_opt->get_ptr();
 #if defined(__NVCC__) || defined(__HIPCC__)
-    if (1 == tensor_dout->dims().size()) {
+    if (1 >= tensor_dout->dims().size()) {
       DenseTensor tensor_dout_help;
       auto& dev = *ctx.eigen_device();
       if (tensor_dx || tensor_dy) {
@@ -431,7 +431,7 @@ struct DotDoubleGradFunction<DeviceContext, T, phi::funcs::DisableComplex<T>> {
     const DenseTensor* tensor_ddx = tensor_ddx_opt->get_ptr();
     const DenseTensor* tensor_ddy = tensor_ddy_opt->get_ptr();
 #if defined(__NVCC__) || defined(__HIPCC__)
-    if (1 == tensor_dout->dims().size()) {
+    if (1 >= tensor_dout->dims().size()) {
       auto& dev = *ctx.eigen_device();
       auto x = EigenVector<T>::Flatten(*tensor_x);
       auto y = EigenVector<T>::Flatten(*tensor_y);
@@ -621,7 +621,7 @@ struct DotTripleGradFunction<DeviceContext, T, phi::funcs::EnableComplex<T>> {
     const DenseTensor* in_tensor_d_dy = in_tensor_d_dy_opt->get_ptr();
     const DenseTensor* in_tensor_d_ddout = in_tensor_d_ddout_opt->get_ptr();
 #if defined(__NVCC__) || defined(__HIPCC__)
-    if (1 == in_tensor_dout->dims().size()) {
+    if (1 >= in_tensor_dout->dims().size()) {
       auto& dev = *ctx.eigen_device();
       DenseTensor in_tensor_x_help = Conj<T, DeviceContext>(ctx, *in_tensor_x);
       DenseTensor in_tensor_y_help = Conj<T, DeviceContext>(ctx, *in_tensor_y);
@@ -1015,7 +1015,7 @@ struct DotTripleGradFunction<DeviceContext, T, phi::funcs::DisableComplex<T>> {
     const DenseTensor* in_tensor_d_dy = in_tensor_d_dy_opt->get_ptr();
     const DenseTensor* in_tensor_d_ddout = in_tensor_d_ddout_opt->get_ptr();
 #if defined(__NVCC__) || defined(__HIPCC__)
-    if (1 == in_tensor_dout->dims().size()) {
+    if (1 >= in_tensor_dout->dims().size()) {
       auto& dev = *ctx.eigen_device();
       bool d_dout_flag = false;
       bool d_ddx_flag = false;

python/paddle/fluid/tests/unittests/check_nan_inf_base.py

@@ -97,7 +97,7 @@ def check(use_cuda):
                 step += 1
                 print(
                     'iter={:.0f},cost={},acc1={}'.format(
-                        step, outs[1][0], outs[2][0]
+                        step, outs[1][0], outs[2]
                     )
                 )
 

python/paddle/fluid/tests/unittests/test_dot_op.py

@@ -106,8 +106,7 @@ def test_2d_input(self):
         x = paddle.to_tensor(np.reshape(data, [0, 0]), dtype='float32')
         y = paddle.to_tensor(np.reshape(data, [0, 0]), dtype='float32')
         pd_out = paddle.dot(x, y)
-
-        self.assertEqual(pd_out.shape, (0, 1))
+        self.assertEqual(pd_out.shape, (0,))
 
     def test_3d_input_error(self):
         data = np.array([], dtype=np.float32)
@@ -127,7 +126,7 @@ def init_input_output(self):
         self.y = (
             np.random.uniform(1, 3, [132]).astype(self.dtype).reshape([11, 12])
         )
-        self.out = np.sum(self.x * self.y, axis=1).reshape([11, 1])
+        self.out = np.sum(self.x * self.y, axis=1)
 
     def test_check_grad_normal(self):
         self.check_grad(['X', 'Y'], 'Out')
@@ -180,7 +179,7 @@ def test_dygraph(self):
                 np.array([[2, 5], [6, 8]]).astype(np.float32)
             )
             np.testing.assert_array_equal(
-                paddle.dot(x1, y1).numpy(), np.array([[17], [58]])
+                paddle.dot(x1, y1).numpy(), np.array([17, 58])
             )
 
 
@@ -211,7 +210,7 @@ def init_input_output(self):
         self.out = np.dot(self.x, self.y)
 
     def init_grad_input_output(self):
-        self.grad_out = np.ones(1, self.dtype) + 1j * np.ones(1, self.dtype)
+        self.grad_out = np.ones([], self.dtype) + 1j * np.ones([], self.dtype)
         self.grad_x = self.grad_out * np.conj(self.y)
         self.grad_y = self.grad_out * np.conj(self.x)
 
@@ -269,12 +268,10 @@ def init_input_output(self):
         self.y = np.random.random((2, 100)).astype(
             self.dtype
         ) + 1j * np.random.random((2, 100)).astype(self.dtype)
-        self.out = np.diag(np.dot(self.x, self.y.T)).reshape(-1, 1)
+        self.out = np.diag(np.dot(self.x, self.y.T)).reshape(-1)
 
     def init_grad_input_output(self):
-        self.grad_out = np.ones((2, 1), self.dtype) + 1j * np.ones(
-            (2, 1), self.dtype
-        )
+        self.grad_out = np.ones((2), self.dtype) + 1j * np.ones((2), self.dtype)
         self.grad_x = self._get_grad(self.grad_out, self.y)
         self.grad_y = self._get_grad(self.grad_out, self.x)
 
@@ -381,7 +378,7 @@ def init_input_output(self):
         self.y = (
             np.random.uniform(1, 3, [132]).astype(self.dtype).reshape([11, 12])
         )
-        self.out = np.sum(self.x * self.y, axis=1).reshape([11, 1])
+        self.out = np.sum(self.x * self.y, axis=1)
 
 
 @unittest.skipIf(
@@ -468,7 +465,7 @@ def init_input_output(self):
         self.y = (
             np.random.uniform(1, 3, [132]).astype(np.float32).reshape([11, 12])
         )
-        self.out = np.sum(self.x * self.y, axis=1).reshape([11, 1])
+        self.out = np.sum(self.x * self.y, axis=1)
 
     def test_check_grad_normal(self):
         if core.is_compiled_with_cuda():

python/paddle/fluid/tests/unittests/test_layers.py

@@ -1426,8 +1426,9 @@ def test_accuracy(self):
             exe.run(fluid.default_startup_program())
             # x = np.random.rand(3, 32, 32).astype("float32")
             # y = np.array([[1], [0], [1]])
+
             static_out = exe.run(
-                feed={"input": x, "label": y}, fetch_list=result[0]
+                feed={"input": x, "label": y}, fetch_list=result
             )
 
         with self.dynamic_graph(force_to_use_cpu=True):

python/paddle/fluid/tests/unittests/test_nan_inf.py

@@ -50,11 +50,15 @@ def check_nan_inf(self):
         assert (out + err).find(b'There are NAN or INF') != -1
 
     def test_nan_inf_in_static_mode(self):
-        self._python_interp += " check_nan_inf_base.py"
+        self._python_interp += (
+            " " + os.path.dirname(__file__) + "/check_nan_inf_base.py"
+        )
         self.check_nan_inf()
 
     def test_nan_inf_in_dynamic_mode(self):
-        self._python_interp += " check_nan_inf_base_dygraph.py"
+        self._python_interp += (
+            " " + os.path.dirname(__file__) + "/check_nan_inf_base_dygraph.py"
+        )
         self.check_nan_inf()