Cherry pick PR41990, add _grad_name and _grad_value for eager tensor (#41990) (#42079)

* add _grad_name and _grad_value for eager tensor

* fix paddle_enforce

* fix paddle_enforce 2

* fix grad_name

* _grad_value return lodtensor rather than tensor

* fix

paddle/fluid/pybind/eager_method.cc

@@ -1487,6 +1487,46 @@ static PyObject* tensor__offset(TensorObject* self, PyObject* args,
   EAGER_CATCH_AND_THROW_RETURN_NULL
 }
 
+static PyObject* tensor__grad_name(TensorObject* self, PyObject* args,
+                                   PyObject* kwargs) {
+  EAGER_TRY
+  paddle::experimental::Tensor* grad =
+      egr::EagerUtils::mutable_grad(self->tensor);
+  PADDLE_ENFORCE_EQ(grad != nullptr, true,
+                    platform::errors::InvalidArgument(
+                        "Detected NULL grad. Please check if you have manually "
+                        "cleared the grad inside autograd_meta"));
+  return ToPyObject(grad->name());
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
+static PyObject* tensor__grad_value(TensorObject* self, PyObject* args,
+                                    PyObject* kwargs) {
+  EAGER_TRY
+  paddle::experimental::Tensor* grad =
+      egr::EagerUtils::mutable_grad(self->tensor);
+  PADDLE_ENFORCE_EQ(grad != nullptr, true,
+                    platform::errors::InvalidArgument(
+                        "Detected NULL grad. Please check if you have manually "
+                        "cleared the grad inside autograd_meta"));
+
+  if (!grad->defined()) {
+    Py_IncRef(Py_None);
+    return Py_None;
+  }
+  if (grad->is_dense_tensor()) {
+    auto* grad_tensor =
+        static_cast<paddle::framework::LoDTensor*>(grad->impl().get());
+    return ToPyObject(grad_tensor);
+  } else {
+    PADDLE_THROW(paddle::platform::errors::Fatal(
+        "this method is only supported for DenseTensor"));
+    Py_IncRef(Py_None);
+    return Py_None;
+  }
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
 #if defined(PADDLE_WITH_CUDA)
 static PyObject* tensor_method__uva(TensorObject* self, PyObject* args,
                                     PyObject* kwargs) {
@@ -1628,6 +1668,10 @@ PyMethodDef variable_methods[] = {
      METH_VARARGS | METH_KEYWORDS, NULL},
     {"_offset", (PyCFunction)(void (*)(void))tensor__offset,
      METH_VARARGS | METH_KEYWORDS, NULL},
+    {"_grad_name", (PyCFunction)(void (*)(void))tensor__grad_name,
+     METH_VARARGS | METH_KEYWORDS, NULL},
+    {"_grad_value", (PyCFunction)(void (*)(void))tensor__grad_value,
+     METH_VARARGS | METH_KEYWORDS, NULL},
 #if defined(PADDLE_WITH_CUDA)
     {"_tensor_uva", (PyCFunction)(void (*)(void))tensor_method__uva,
      METH_VARARGS | METH_KEYWORDS, NULL},

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

@@ -1743,5 +1743,18 @@ class TestVarBaseCopyGradientFrom(unittest.TestCase):
         self.func_test_copy_gradient_from()
 
 
+class TestEagerTensorGradNameValue(unittest.TestCase):
+    def test_eager_tensor_grad_name_value(self):
+        with _test_eager_guard():
+            a_np = np.array([2, 3]).astype('float32')
+            a = paddle.to_tensor(a_np)
+            a.stop_gradient = False
+            b = a**2
+            self.assertEqual(a._grad_value(), None)
+            b.backward()
+            self.assertEqual('eager_tmp' in a._grad_name(), True)
+            self.assertNotEqual(a._grad_value(), None)
+
+
 if __name__ == '__main__':
     unittest.main()