fix_ci_problem3 (#41484)

* fix_ci_problem3

* support windows no default error

paddle/fluid/eager/backward.cc

@@ -22,10 +22,10 @@
 #include "paddle/fluid/platform/profiler.h"
 #include "paddle/fluid/platform/profiler/event_tracing.h"
 
+#include "glog/logging.h"
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/errors.h"
-
-#include "glog/logging.h"
+#include "paddle/phi/kernels/autotune/switch_autotune.h"
 
 namespace egr {
 
@@ -799,6 +799,7 @@ void Backward(
   paddle::platform::RecordEvent backward_record_event(
       "backward", paddle::platform::TracerEventType::Operator, 1);
   RunBackward(tensors, grad_tensors, retain_graph);
+  phi::autotune::AutoTuneStatus::Instance().Update();
 }
 
 std::vector<paddle::experimental::Tensor> Grad(

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

@@ -46,7 +46,7 @@ class TestGraphKhopSampler(unittest.TestCase):
         self.sample_sizes = [5, 5]
         self.dst_src_dict = dst_src_dict
 
-    def test_sample_result(self):
+    def func_sample_result(self):
         paddle.disable_static()
         row = paddle.to_tensor(self.row)
         colptr = paddle.to_tensor(self.colptr)
@@ -79,13 +79,25 @@ class TestGraphKhopSampler(unittest.TestCase):
             # Ensure the correct sample neighbors.
             self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
 
-    def test_uva_sample_result(self):
+    def test_sample_result(self):
+        with fluid.framework._test_eager_guard():
+            self.func_sample_result()
+        self.func_sample_result()
+
+    def func_uva_sample_result(self):
         paddle.disable_static()
         if paddle.fluid.core.is_compiled_with_cuda():
-            row = paddle.fluid.core.to_uva_tensor(
-                self.row.astype(self.row.dtype))
-            sorted_eid = paddle.fluid.core.to_uva_tensor(
-                self.sorted_eid.astype(self.sorted_eid.dtype))
+            row = None
+            if fluid.framework.in_dygraph_mode():
+                row = paddle.fluid.core.eager.to_uva_tensor(
+                    self.row.astype(self.row.dtype), 0)
+                sorted_eid = paddle.fluid.core.eager.to_uva_tensor(
+                    self.sorted_eid.astype(self.sorted_eid.dtype), 0)
+            else:
+                row = paddle.fluid.core.to_uva_tensor(
+                    self.row.astype(self.row.dtype))
+                sorted_eid = paddle.fluid.core.to_uva_tensor(
+                    self.sorted_eid.astype(self.sorted_eid.dtype))
             colptr = paddle.to_tensor(self.colptr)
             nodes = paddle.to_tensor(self.nodes)
 
@@ -114,6 +126,11 @@ class TestGraphKhopSampler(unittest.TestCase):
                 in_neighbors = np.isin(edge_src_n.numpy(), self.dst_src_dict[n])
                 self.assertTrue(np.sum(in_neighbors) == in_neighbors.shape[0])
 
+    def test_uva_sample_result(self):
+        with fluid.framework._test_eager_guard():
+            self.func_uva_sample_result()
+        self.func_uva_sample_result()
+
     def test_sample_result_static_with_eids(self):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program()):

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

@@ -87,12 +87,22 @@ class TestDygraphAutoTuneStatus(TestAutoTune):
                 }
                 self.check_status(expected_res)
 
-    def test_enable_autotune(self):
+    def func_enable_autotune(self):
         self.run_program(enable_autotune=True)
 
-    def test_disable_autotune(self):
+    def test_enable_autotune(self):
+        with paddle.fluid.framework._test_eager_guard():
+            self.func_enable_autotune()
+        self.func_enable_autotune()
+
+    def func_disable_autotune(self):
         self.run_program(enable_autotune=False)
 
+    def test_disable_autotune(self):
+        with paddle.fluid.framework._test_eager_guard():
+            self.func_disable_autotune()
+        self.func_disable_autotune()
+
 
 class TestStaticAutoTuneStatus(TestAutoTune):
     def run_program(self, enable_autotune):
@@ -136,12 +146,22 @@ class TestStaticAutoTuneStatus(TestAutoTune):
                 self.check_status(expected_res)
         paddle.disable_static()
 
-    def test_enable_autotune(self):
+    def func_enable_autotune(self):
         self.run_program(enable_autotune=True)
 
-    def test_disable_autotune(self):
+    def test_enable_autotune(self):
+        with paddle.fluid.framework._test_eager_guard():
+            self.func_enable_autotune()
+        self.func_enable_autotune()
+
+    def func_disable_autotune(self):
         self.run_program(enable_autotune=False)
 
+    def test_disable_autotune(self):
+        with paddle.fluid.framework._test_eager_guard():
+            self.func_disable_autotune()
+        self.func_disable_autotune()
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_zeropad2d.py

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import unittest
 import numpy as np
+import paddle
 from paddle import to_tensor
 from paddle.nn.functional import zeropad2d
 from paddle.nn import ZeroPad2D
@@ -33,7 +34,7 @@ class TestZeroPad2dAPIError(unittest.TestCase):
         self.shape = [4, 3, 224, 224]
         self.unsupport_dtypes = ['bool', 'int8']
 
-    def test_unsupport_dtypes(self):
+    def func_unsupport_dtypes(self):
         """
         test unsupport dtypes.
         """
@@ -43,6 +44,11 @@ class TestZeroPad2dAPIError(unittest.TestCase):
             x_tensor = to_tensor(x).astype(dtype)
             self.assertRaises(TypeError, zeropad2d, x=x_tensor, padding=pad)
 
+    def test_unsupport_dtypes(self):
+        with paddle.fluid.framework._test_eager_guard():
+            self.func_unsupport_dtypes()
+        self.func_unsupport_dtypes()
+
 
 class TestZeroPad2dAPI(unittest.TestCase):
     """
@@ -56,7 +62,7 @@ class TestZeroPad2dAPI(unittest.TestCase):
         self.shape = [4, 3, 224, 224]
         self.support_dtypes = ['float32', 'float64', 'int32', 'int64']
 
-    def test_support_dtypes(self):
+    def func_support_dtypes(self):
         """
         test support types
         """
@@ -69,7 +75,12 @@ class TestZeroPad2dAPI(unittest.TestCase):
             ret_res = zeropad2d(x_tensor, [pad, pad, pad, pad]).numpy()
             self.assertTrue(np.allclose(expect_res, ret_res))
 
-    def test_support_pad2(self):
+    def test_support_dtypes(self):
+        with paddle.fluid.framework._test_eager_guard():
+            self.func_support_dtypes()
+        self.func_support_dtypes()
+
+    def func_support_pad2(self):
         """
         test the type of 'pad' is list.
         """
@@ -82,7 +93,12 @@ class TestZeroPad2dAPI(unittest.TestCase):
         ret_res = zeropad2d(x_tensor, pad).numpy()
         self.assertTrue(np.allclose(expect_res, ret_res))
 
-    def test_support_pad3(self):
+    def test_support_pad2(self):
+        with paddle.fluid.framework._test_eager_guard():
+            self.func_support_pad2()
+        self.func_support_pad2()
+
+    def func_support_pad3(self):
         """
         test the type of 'pad' is tuple.
         """
@@ -95,7 +111,12 @@ class TestZeroPad2dAPI(unittest.TestCase):
         ret_res = zeropad2d(x_tensor, pad).numpy()
         self.assertTrue(np.allclose(expect_res, ret_res))
 
-    def test_support_pad4(self):
+    def test_support_pad3(self):
+        with paddle.fluid.framework._test_eager_guard():
+            self.func_support_pad3()
+        self.func_support_pad3()
+
+    def func_support_pad4(self):
         """
         test the type of 'pad' is paddle.Tensor.
         """
@@ -109,6 +130,11 @@ class TestZeroPad2dAPI(unittest.TestCase):
         ret_res = zeropad2d(x_tensor, pad_tensor).numpy()
         self.assertTrue(np.allclose(expect_res, ret_res))
 
+    def test_support_pad4(self):
+        with paddle.fluid.framework._test_eager_guard():
+            self.func_support_pad4()
+        self.func_support_pad4()
+
 
 class TestZeroPad2DLayer(unittest.TestCase):
     """
@@ -124,12 +150,17 @@ class TestZeroPad2DLayer(unittest.TestCase):
                                  [[0, 0], [0, 0], [self.pad[2], self.pad[3]],
                                   [self.pad[0], self.pad[1]]])
 
-    def test_layer(self):
+    def func_layer(self):
         self.assertTrue(
             np.allclose(
                 zeropad2d(to_tensor(self.x), self.pad).numpy(),
                 self.padLayer(to_tensor(self.x))))
 
+    def test_layer(self):
+        with paddle.fluid.framework._test_eager_guard():
+            self.func_layer()
+        self.func_layer()
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/nn/functional/common.py

@@ -1356,29 +1356,31 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None):
                 unsqueezed_dim = [1]
                 x = unsqueeze(x, axis=unsqueezed_dim)
 
-    if in_dynamic_mode():
+    if in_dygraph_mode():
         if isinstance(pad, Variable):
-            pad = pad.numpy()
+            pad = pad.numpy().tolist()
+        out = _C_ops.final_state_pad3d(x, pad, mode, value, data_format)
+    else:
         if _in_legacy_dygraph():
+            if isinstance(pad, Variable):
+                pad = pad.numpy().tolist()
             out = _C_ops.pad3d(x, "paddings", pad, "mode", mode, "value", value,
                                "data_format", data_format, "name", name)
         else:
-            out = _C_ops.final_state_pad3d(x, pad, mode, value, data_format)
-    else:
-        attrs = {'mode': mode, 'value': value, 'data_format': data_format}
-        inputs = {'X': [x]}
-        if isinstance(pad, Variable):
-            inputs['Paddings'] = [pad]
-            attrs['paddings'] = []
-        else:
-            attrs['paddings'] = pad
+            attrs = {'mode': mode, 'value': value, 'data_format': data_format}
+            inputs = {'X': [x]}
+            if isinstance(pad, Variable):
+                inputs['Paddings'] = [pad]
+                attrs['paddings'] = []
+            else:
+                attrs['paddings'] = pad
 
-        helper = LayerHelper('pad3d', **locals())
+            helper = LayerHelper('pad3d', **locals())
 
-        dtype = helper.input_dtype(input_param_name='input')
-        out = helper.create_variable_for_type_inference(dtype)
-        helper.append_op(
-            type='pad3d', inputs=inputs, outputs={"Out": out}, attrs=attrs)
+            dtype = helper.input_dtype(input_param_name='input')
+            out = helper.create_variable_for_type_inference(dtype)
+            helper.append_op(
+                type='pad3d', inputs=inputs, outputs={"Out": out}, attrs=attrs)
 
     if len(unsqueezed_dim) != 0:
         out = squeeze(out, axis=unsqueezed_dim)
@@ -1531,38 +1533,50 @@ def linear(x, weight, bias=None, name=None):
           #     [0.9440598  0.9440598  0.9440598  0.9440598 ]
           #     [2.1077576  2.1077576  2.1077576  2.1077576 ]]
     """
-    if in_dynamic_mode():
-        pre_bias = _C_ops.matmul_v2(x, weight, 'trans_x', False, 'trans_y',
-                                    False)
+    if in_dygraph_mode():
+        pre_bias = _C_ops.final_state_matmul(x, weight, False, False)
 
         if bias is None:
             return pre_bias
 
-        return _C_ops.elementwise_add(pre_bias, bias)
+        return _C_ops.final_state_add(pre_bias, bias)
     else:
-        helper = LayerHelper('linear', **locals())
-        dtype = x.dtype
+        if _in_legacy_dygraph():
+            pre_bias = _C_ops.matmul_v2(x, weight, 'trans_x', False, 'trans_y',
+                                        False)
 
-        check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
-                                 'linear')
-        check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'], 'linear')
+            if bias is None:
+                return pre_bias
 
-        inputs = {'X': [x], 'Y': [weight]}
-        attrs = {'trans_x': False, 'trans_y': False}
-        tmp = helper.create_variable_for_type_inference(dtype)
-        helper.append_op(
-            type='matmul_v2', inputs=inputs, outputs={'Out': tmp}, attrs=attrs)
-        if bias is not None:
-            res = helper.create_variable_for_type_inference(dtype)
-            helper.append_op(
-                type='elementwise_add',
-                inputs={'X': [tmp],
-                        'Y': [bias]},
-                outputs={'Out': [res]},
-                attrs={'axis': len(x.shape) - 1})
+            return _C_ops.elementwise_add(pre_bias, bias)
         else:
-            res = tmp
-        return res
+            helper = LayerHelper('linear', **locals())
+            dtype = x.dtype
+
+            check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
+                                     'linear')
+            check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'],
+                        'linear')
+
+            inputs = {'X': [x], 'Y': [weight]}
+            attrs = {'trans_x': False, 'trans_y': False}
+            tmp = helper.create_variable_for_type_inference(dtype)
+            helper.append_op(
+                type='matmul_v2',
+                inputs=inputs,
+                outputs={'Out': tmp},
+                attrs=attrs)
+            if bias is not None:
+                res = helper.create_variable_for_type_inference(dtype)
+                helper.append_op(
+                    type='elementwise_add',
+                    inputs={'X': [tmp],
+                            'Y': [bias]},
+                    outputs={'Out': [res]},
+                    attrs={'axis': len(x.shape) - 1})
+            else:
+                res = tmp
+            return res
 
 
 def label_smooth(label, prior_dist=None, epsilon=0.1, name=None):