[Eager] Support admax, fill_diagonal, fill_diagonal_tensor_, to_list, ... in eager mode (#41117)

* Update ResNet test cases

* [Eager] Support uva, adamax, fill_diagonal_, to_list and so on.

* Fix CI

* Updated CUDA defined statement

* Fix CI

* Update headers, Fix CI

* Remove useless setting

* Updated func name to Fix windows-CI

* Remove tensor uva related codes

* Remove uva related code

* recover original test

paddle/fluid/pybind/eager_functions.cc

@@ -26,6 +26,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/convert_utils.h"
 #include "paddle/fluid/framework/custom_operator.h"
 #include "paddle/fluid/framework/op_meta_info_helper.h"
+#include "paddle/fluid/framework/python_headers.h"
 #include "paddle/fluid/memory/allocation/allocator.h"
 #include "paddle/fluid/memory/memcpy.h"
 #include "paddle/fluid/platform/device/gpu/gpu_info.h"
@@ -35,6 +36,7 @@ limitations under the License. */
 #include "paddle/fluid/pybind/eager.h"
 #include "paddle/fluid/pybind/eager_utils.h"
 #include "paddle/fluid/pybind/exception.h"
+#include "paddle/fluid/pybind/tensor_py.h"
 #include "paddle/phi/api/ext/op_meta_info.h"
 #include "paddle/phi/api/lib/utils/allocator.h"
 #include "paddle/phi/api/lib/utils/storage.h"
@@ -771,6 +773,7 @@ static PyObject* eager_api_async_write(PyObject* self, PyObject* args,
   EAGER_CATCH_AND_THROW_RETURN_NULL
 }
 #endif
+
 PyMethodDef variable_functions[] = {
     // TODO(jiabin): Remove scale when we have final state tests
     {"scale", (PyCFunction)(void (*)(void))eager_api_scale,
@@ -794,13 +797,13 @@ PyMethodDef variable_functions[] = {
     {"sparse_csr_tensor",
      (PyCFunction)(void (*)(void))eager_api_sparse_csr_tensor,
      METH_VARARGS | METH_KEYWORDS, NULL},
+/**sparse functions**/
 #if defined(PADDLE_WITH_CUDA)
     {"async_read", (PyCFunction)(void (*)(void))eager_api_async_read,
      METH_VARARGS | METH_KEYWORDS, NULL},
     {"async_write", (PyCFunction)(void (*)(void))eager_api_async_write,
      METH_VARARGS | METH_KEYWORDS, NULL},
 #endif
-    /**sparse functions**/
     {NULL, NULL, 0, NULL}};
 
 void BindFunctions(PyObject* module) {

python/paddle/fluid/framework.py

@@ -173,9 +173,13 @@ def _test_eager_guard(place=None):
         monkey_patch_math_varbase()
 
         # Ugly setting
-        from paddle.tensor.manipulation import fill_, zero_
+        from paddle.tensor.manipulation import fill_, zero_, fill_diagonal_, fill_diagonal_tensor_, tolist
         setattr(core.eager.Tensor, 'fill_', fill_)
         setattr(core.eager.Tensor, 'zero_', zero_)
+        setattr(core.eager.Tensor, 'fill_diagonal_', fill_diagonal_)
+        setattr(core.eager.Tensor, 'fill_diagonal_tensor_',
+                fill_diagonal_tensor_)
+        setattr(core.eager.Tensor, 'tolist', tolist)
 
         _already_patch_eager_tensor = True
     try:

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

@@ -18,10 +18,11 @@ import unittest
 import numpy as np
 import paddle
 import paddle.fluid.core as core
+from paddle.fluid.framework import _test_eager_guard
 
 
 class TensorTypeTest(unittest.TestCase):
-    def test_type_totensor(self):
+    def func_type_totensor(self):
         paddle.disable_static()
         inx = np.array([1, 2])
         tensorx = paddle.to_tensor(inx)
@@ -29,7 +30,12 @@ class TensorTypeTest(unittest.TestCase):
         expectx = "<class 'paddle.Tensor'>"
         self.assertEqual((typex_str == expectx), True)
 
-    def test_type_Tensor(self):
+    def test_type_totensor(self):
+        with _test_eager_guard():
+            self.func_type_totensor()
+        self.func_type_totensor()
+
+    def func_type_Tensor(self):
         paddle.disable_static()
         inx = np.array([1, 2])
         tensorx = paddle.Tensor(inx)
@@ -43,7 +49,12 @@ class TensorTypeTest(unittest.TestCase):
         expectx = "<class 'paddle.Tensor'>"
         self.assertEqual((typex_str == expectx), True)
 
-    def test_type_core(self):
+    def test_type_Tensor(self):
+        with _test_eager_guard():
+            self.func_type_Tensor()
+        self.func_type_Tensor()
+
+    def func_type_core(self):
         paddle.disable_static()
         inx = np.array([1, 2])
         tensorx = core.VarBase(inx)
@@ -56,6 +67,11 @@ class TensorTypeTest(unittest.TestCase):
         expectx = "<class 'paddle.Tensor'>"
         self.assertEqual((typex_str == expectx), True)
 
+    def test_type_core(self):
+        with _test_eager_guard():
+            pass
+        self.func_type_core()
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -19,10 +19,11 @@ import numpy as np
 from op_test import OpTest
 import paddle
 import paddle.fluid as fluid
+from paddle.fluid.framework import _test_eager_guard
 
 
 class TestAdamaxAPI(unittest.TestCase):
-    def test_adamax_api_dygraph(self):
+    def func_adamax_api_dygraph(self):
         paddle.disable_static()
         value = np.arange(26).reshape(2, 13).astype("float32")
         a = paddle.to_tensor(value)
@@ -36,7 +37,12 @@ class TestAdamaxAPI(unittest.TestCase):
         adam.step()
         adam.clear_gradients()
 
-    def test_adamax_api(self):
+    def test_adamax_api_dygraph(self):
+        with _test_eager_guard():
+            self.func_adamax_api_dygraph()
+        self.func_adamax_api_dygraph()
+
+    def func_adamax_api(self):
         paddle.enable_static()
         place = fluid.CPUPlace()
         shape = [2, 3, 8, 8]
@@ -63,9 +69,14 @@ class TestAdamaxAPI(unittest.TestCase):
         rets = exe.run(train_prog, feed={"data": data_np}, fetch_list=[loss])
         assert rets[0] is not None
 
+    def test_adamax_api(self):
+        with _test_eager_guard():
+            self.func_adamax_api()
+        self.func_adamax_api()
+
 
 class TestAdamaxAPIGroup(TestAdamaxAPI):
-    def test_adamax_api_dygraph(self):
+    def func_adamax_api_dygraph(self):
         paddle.disable_static()
         value = np.arange(26).reshape(2, 13).astype("float32")
         a = paddle.to_tensor(value)
@@ -89,6 +100,11 @@ class TestAdamaxAPIGroup(TestAdamaxAPI):
         adam.step()
         adam.clear_gradients()
 
+    def test_adamax_api_dygraph(self):
+        with _test_eager_guard():
+            self.func_adamax_api_dygraph()
+        self.func_adamax_api_dygraph()
+
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -17,10 +17,11 @@ import unittest
 import numpy as np
 import six
 import paddle
+from paddle.fluid.framework import _test_eager_guard
 
 
 class TensorFillDiagonal_Test(unittest.TestCase):
-    def test_dim2_normal(self):
+    def func_dim2_normal(self):
         expected_np = np.array(
             [[1, 2, 2], [2, 1, 2], [2, 2, 1]]).astype('float32')
         expected_grad = np.array(
@@ -50,7 +51,12 @@ class TensorFillDiagonal_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True)
 
-    def test_offset(self):
+    def test_dim2_normal(self):
+        with _test_eager_guard():
+            self.func_dim2_normal()
+        self.func_dim2_normal()
+
+    def func_offset(self):
         expected_np = np.array(
             [[2, 2, 1], [2, 2, 2], [2, 2, 2]]).astype('float32')
         expected_grad = np.array(
@@ -80,7 +86,12 @@ class TensorFillDiagonal_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True)
 
-    def test_bool(self):
+    def test_offset(self):
+        with _test_eager_guard():
+            self.func_offset()
+        self.func_offset()
+
+    def func_bool(self):
         expected_np = np.array(
             [[False, True, True], [True, False, True], [True, True, False]])
 
@@ -101,7 +112,12 @@ class TensorFillDiagonal_Test(unittest.TestCase):
 
                 self.assertEqual((x.numpy() == expected_np).all(), True)
 
-    def test_dim2_unnormal_wrap(self):
+    def test_bool(self):
+        with _test_eager_guard():
+            self.func_bool()
+        self.func_bool()
+
+    def func_dim2_unnormal_wrap(self):
         expected_np = np.array([[1, 2, 2], [2, 1, 2], [2, 2, 1], [2, 2, 2],
                                 [1, 2, 2], [2, 1, 2],
                                 [2, 2, 1]]).astype('float32')
@@ -133,7 +149,12 @@ class TensorFillDiagonal_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True)
 
-    def test_dim2_unnormal_unwrap(self):
+    def test_dim2_unnormal_wrap(self):
+        with _test_eager_guard():
+            self.func_dim2_unnormal_wrap()
+        self.func_dim2_unnormal_wrap()
+
+    def func_dim2_unnormal_unwrap(self):
         expected_np = np.array([[1, 2, 2], [2, 1, 2], [2, 2, 1], [2, 2, 2],
                                 [2, 2, 2], [2, 2, 2],
                                 [2, 2, 2]]).astype('float32')
@@ -165,7 +186,12 @@ class TensorFillDiagonal_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True)
 
-    def test_dim_larger2_normal(self):
+    def test_dim2_unnormal_unwrap(self):
+        with _test_eager_guard():
+            self.func_dim2_unnormal_unwrap()
+        self.func_dim2_unnormal_unwrap()
+
+    def func_dim_larger2_normal(self):
         expected_np = np.array([[[1, 2, 2], [2, 2, 2], [2, 2, 2]], [[2, 2, 2], [
             2, 1, 2
         ], [2, 2, 2]], [[2, 2, 2], [2, 2, 2], [2, 2, 1]]]).astype('float32')
@@ -198,6 +224,11 @@ class TensorFillDiagonal_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True)
 
+    def test_dim_larger2_normal(self):
+        with _test_eager_guard():
+            self.func_dim_larger2_normal()
+        self.func_dim_larger2_normal()
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -18,6 +18,7 @@ import unittest
 import numpy as np
 import six
 import paddle
+from paddle.fluid.framework import _test_eager_guard
 
 
 class TensorFillDiagTensor_Test(unittest.TestCase):
@@ -27,7 +28,7 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
         if fluid.core.is_compiled_with_cuda():
             self.places.append(fluid.CUDAPlace(0))
 
-    def test_dim2(self):
+    def func_dim2(self):
         expected_np = np.array(
             [[1, 2, 2], [2, 1, 2], [2, 2, 1], [2, 2, 2]]).astype('float32')
         expected_grad = np.array(
@@ -54,7 +55,12 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True)
 
-    def test_dim2_offset_1(self):
+    def test_dim2(self):
+        with _test_eager_guard():
+            self.func_dim2()
+        self.func_dim2()
+
+    def func_dim2_offset_1(self):
         expected_np = np.array(
             [[2, 2, 2], [1, 2, 2], [2, 1, 2], [2, 2, 1]]).astype('float32')
         expected_grad = np.array(
@@ -81,7 +87,12 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True)
 
-    def test_dim2_offset1(self):
+    def test_dim2_offset_1(self):
+        with _test_eager_guard():
+            self.func_dim2_offset_1()
+        self.func_dim2_offset_1()
+
+    def func_dim2_offset1(self):
         expected_np = np.array(
             [[2, 1, 2], [2, 2, 1], [2, 2, 2], [2, 2, 2]]).astype('float32')
         expected_grad = np.array(
@@ -108,7 +119,12 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True)
 
-    def test_dim4(self):
+    def test_dim2_offset1(self):
+        with _test_eager_guard():
+            self.func_dim2_offset1()
+        self.func_dim2_offset1()
+
+    def func_dim4(self):
         expected_np = np.array(
             [[[[0, 3], [2, 2], [2, 2]], [[2, 2], [1, 4], [2, 2]],
               [[2, 2], [2, 2], [2, 5]], [[2, 2], [2, 2], [2, 2]]],
@@ -144,7 +160,12 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
                     (y.grad.numpy().astype('float32') == expected_grad).all(),
                     True)
 
-    def test_largedim(self):
+    def test_func_dim4(self):
+        with _test_eager_guard():
+            self.func_dim4()
+        self.func_dim4()
+
+    def func_largedim(self):
         #large dim only test on gpu because the cpu version is too slow for ci test, and the memory is limited
         if len(self.places) > 1:
             bsdim = 1024
@@ -168,6 +189,11 @@ class TensorFillDiagTensor_Test(unittest.TestCase):
                 self.assertEqual((y == expected_pred).all(), True)
                 self.assertEqual((y.grad == expected_grad).all(), True)
 
+    def test_largedim(self):
+        with _test_eager_guard():
+            self.func_largedim()
+        self.func_largedim()
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -17,13 +17,14 @@ import unittest
 import numpy as np
 import six
 import paddle
+from paddle.fluid.framework import _test_eager_guard
 
 
 class TensorToListTest(unittest.TestCase):
     def setUp(self):
         self.shape = [11, 25, 32, 43]
 
-    def test_tensor_tolist(self):
+    def func_tensor_tolist(self):
         places = [fluid.CPUPlace()]
         if fluid.core.is_compiled_with_cuda():
             places.append(fluid.CUDAPlace(0))
@@ -39,6 +40,11 @@ class TensorToListTest(unittest.TestCase):
 
             self.assertEqual(tensorlist, expectlist)
 
+    def test_tensor_tolist(self):
+        with _test_eager_guard():
+            self.func_tensor_tolist()
+        self.func_tensor_tolist()
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/optimizer/adamax.py

@@ -16,6 +16,7 @@ from .optimizer import Optimizer
 from ..fluid import core
 from ..fluid import framework
 from ..fluid.framework import Variable, name_scope
+from paddle import _C_ops
 
 __all__ = []
 
@@ -190,30 +191,38 @@ class Adamax(Optimizer):
                                          param_and_grad[0])
         beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
                                               param_and_grad[0])
-        # create the adamax optimize op
-        adamax_op = block.append_op(
-            type=self.type,
-            inputs={
-                "Param": param_and_grad[0],
-                "Grad": param_and_grad[1],
-                "LearningRate": self._create_param_lr(param_and_grad),
-                "Moment": moment,
-                "InfNorm": inf_norm,
-                "Beta1Pow": beta1_pow_acc
-            },
-            outputs={
-                "ParamOut": param_and_grad[0],
-                "MomentOut": moment,
-                "InfNormOut": inf_norm
-            },
-            attrs={
-                "beta1": self._beta1,
-                "beta2": self._beta2,
-                "epsilon": self._epsilon
-            },
-            stop_gradient=True)
 
-        return adamax_op
+        if framework._non_static_mode():
+            _C_ops.adamax(param_and_grad[0], param_and_grad[1],
+                          self._create_param_lr(param_and_grad), moment,
+                          inf_norm, beta1_pow_acc, param_and_grad[0], moment,
+                          inf_norm, "beta1", self._beta1, "beta2", self._beta2,
+                          "epsilon", self._epsilon)
+        else:
+            # create the adamax optimize op
+            adamax_op = block.append_op(
+                type=self.type,
+                inputs={
+                    "Param": param_and_grad[0],
+                    "Grad": param_and_grad[1],
+                    "LearningRate": self._create_param_lr(param_and_grad),
+                    "Moment": moment,
+                    "InfNorm": inf_norm,
+                    "Beta1Pow": beta1_pow_acc
+                },
+                outputs={
+                    "ParamOut": param_and_grad[0],
+                    "MomentOut": moment,
+                    "InfNormOut": inf_norm
+                },
+                attrs={
+                    "beta1": self._beta1,
+                    "beta2": self._beta2,
+                    "epsilon": self._epsilon
+                },
+                stop_gradient=True)
+
+            return adamax_op
 
     def _finish_update(self, block, parameters_and_grads):
         """Update Beta1 Power accumulator