[Zero-Dim] fix create_scalar to create 0D (#51024)

paddle/fluid/pybind/eager_math_op_patch.cc

@@ -539,8 +539,8 @@ static PyObject* tensor__mul__method(TensorObject* self,
         CastPyArg2Scalar(other_obj, "__mul__", 0);
     if (PyComplex_Check(other_obj)) {
       eager_gil_scoped_release guard;
-      other_tensor =
-          full_ad_func({1}, value, DataType::COMPLEX64, self_tensor.place());
+      other_tensor = full_ad_func(
+          self_tensor.shape(), value, DataType::COMPLEX64, self_tensor.place());
     } else {
       eager_gil_scoped_release guard;
       other_tensor = full_ad_func(

python/paddle/fluid/layers/math_op_patch.py

@@ -98,8 +98,7 @@ def monkey_patch_variable():
         return var
 
     def create_scalar(block, value, dtype):
-        # TODO(zhouwei): will change to [] which is 0-D Tensor
-        return create_tensor(block, value, dtype, shape=[1])
+        return create_tensor(block, value, dtype, shape=[])
 
     def create_tensor_with_batchsize(ref_var, value, dtype):
         assert isinstance(ref_var, Variable)

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

@@ -139,12 +139,7 @@ class TestUnaryAPI(unittest.TestCase):
                 x = paddle.rand([])
                 x.stop_gradient = False
                 out = api(x)
-                # TODO(zhouwei):
-                # ScaleLossGradOp / append_backward set grad shape to [1]
-                # after output 0D, may change it to []
-                # use out.sum() to avoid this two problem now
-                loss = out.sum()
-                paddle.static.append_backward(loss)
+                paddle.static.append_backward(out)
 
                 fetch_list = [x, out]
                 if block.has_var(x.grad_name):
@@ -156,12 +151,10 @@ class TestUnaryAPI(unittest.TestCase):
                     self.assertEqual(item.shape, ())
 
                 # 2) Test CompiledProgram Program
-                expect_shape = ()
                 compile_prog = paddle.static.CompiledProgram(main_prog)
-
                 res = exe.run(compile_prog, fetch_list=fetch_list)
                 for item in res:
-                    self.assertEqual(item.shape, expect_shape)
+                    self.assertEqual(item.shape, ())
 
         paddle.disable_static()
 
@@ -229,7 +222,7 @@ class TestReduceAPI(unittest.TestCase):
                     x = paddle.rand([])
                 x.stop_gradient = False
                 out = api(x, None)
-                paddle.static.append_backward(out.sum())
+                paddle.static.append_backward(out)
 
                 out_empty_list = api(x, None)
                 self.assertEqual(out_empty_list.shape, ())
@@ -437,7 +430,7 @@ class TestBinaryAPI(unittest.TestCase):
                     self.assertEqual(out.shape, out_cls.shape)
                 else:
                     out = api(x, y)
-                paddle.static.append_backward(out.sum())
+                paddle.static.append_backward(out)
 
                 self.assertEqual(x.shape, ())
                 self.assertEqual(y.shape, ())
@@ -464,7 +457,7 @@ class TestBinaryAPI(unittest.TestCase):
                     self.assertEqual(out.shape, out_cls.shape)
                 else:
                     out = api(x, y)
-                paddle.static.append_backward(out.sum())
+                paddle.static.append_backward(out)
 
                 self.assertEqual(x.shape, ())
                 self.assertEqual(y.shape, (2, 3, 4))
@@ -491,7 +484,7 @@ class TestBinaryAPI(unittest.TestCase):
                     self.assertEqual(out.shape, out_cls.shape)
                 else:
                     out = api(x, y)
-                paddle.static.append_backward(out.sum())
+                paddle.static.append_backward(out)
 
                 self.assertEqual(x.shape, (2, 3, 4))
                 self.assertEqual(y.shape, ())
@@ -505,9 +498,6 @@ class TestBinaryAPI(unittest.TestCase):
                     self.assertEqual(y_grad.shape, ())
                     self.assertEqual(out_grad.shape, (2, 3, 4))
 
-                # TODO(zhouwei25):
-                # will open this UT after fix create_scalar in static graph
-                '''
                 # 4) x is 0D , y is scalar
                 x = paddle.rand([])
                 x.stop_gradient = False
@@ -516,17 +506,16 @@ class TestBinaryAPI(unittest.TestCase):
                     out = getattr(paddle.static.Variable, api['cls_method'])(
                         x, y
                     )
-                    paddle.static.append_backward(out.sum())
+                    paddle.static.append_backward(out)
 
                     self.assertEqual(x.shape, ())
                     self.assertEqual(out.shape, ())
-                    if block.has_var(x.name):
+                    if block.has_var(x.grad_name):
                         out_grad = block.var(out.grad_name)
                         x_grad = block.var(x.grad_name)
 
                         self.assertEqual(out_grad.shape, ())
                         self.assertEqual(x_grad.shape, ())
-                '''
 
         for api in binary_int_api_list:
             main_prog = paddle.static.Program()
@@ -2154,10 +2143,10 @@ class TestSundryAPIStatic(unittest.TestCase):
     @prog_scope()
     def test_std(self):
         x = paddle.rand([])
+        x.stop_gradient = False
         out1 = paddle.std(x)
         out2 = paddle.std(x, [])
         paddle.static.append_backward(out1)
-        paddle.static.append_backward(out2)
 
         prog = paddle.static.default_main_program()
         res = self.exe.run(
@@ -2166,19 +2155,23 @@ class TestSundryAPIStatic(unittest.TestCase):
                 x,
                 out1,
                 out2,
+                x.grad_name,
+                out1.grad_name,
             ],
         )
         self.assertEqual(res[0].shape, ())
         self.assertEqual(res[1].shape, ())
         self.assertEqual(res[2].shape, ())
+        self.assertEqual(res[3].shape, ())
+        self.assertEqual(res[4].shape, ())
 
     @prog_scope()
     def test_var(self):
         x = paddle.rand([])
+        x.stop_gradient = False
         out1 = paddle.var(x)
         out2 = paddle.var(x, [])
         paddle.static.append_backward(out1)
-        paddle.static.append_backward(out2)
 
         prog = paddle.static.default_main_program()
         res = self.exe.run(
@@ -2187,11 +2180,15 @@ class TestSundryAPIStatic(unittest.TestCase):
                 x,
                 out1,
                 out2,
+                x.grad_name,
+                out1.grad_name,
             ],
         )
         self.assertEqual(res[0].shape, ())
         self.assertEqual(res[1].shape, ())
         self.assertEqual(res[2].shape, ())
+        self.assertEqual(res[3].shape, ())
+        self.assertEqual(res[4].shape, ())
 
     @prog_scope()
     def test_quantile(self):
@@ -3651,7 +3648,7 @@ class TestUnaryElementwiseAPIWithComplexInput(unittest.TestCase):
     def test_dygraph_unary(self):
         paddle.disable_static()
         for api in unary_apis_with_complex_input:
-            x = paddle.to_tensor(2.0 + 3.0j).squeeze()
+            x = paddle.rand([]) + 1j * paddle.rand([])
             x.stop_gradient = False
             x.retain_grads()
             out = api(x)
@@ -3668,7 +3665,6 @@ class TestUnaryElementwiseAPIWithComplexInput(unittest.TestCase):
 
     def test_static_unary(self):
         paddle.enable_static()
-
         for api in unary_apis_with_complex_input:
             main_prog = paddle.static.Program()
             block = main_prog.global_block()
@@ -3676,18 +3672,10 @@ class TestUnaryElementwiseAPIWithComplexInput(unittest.TestCase):
             with paddle.static.program_guard(
                 main_prog, paddle.static.Program()
             ):
-                # before full support for complex, we cannot create complex tensor with the same code as in dynamic graph
-                x = paddle.complex(
-                    paddle.to_tensor(2.0), paddle.to_tensor(2.0)
-                ).squeeze()
+                x = paddle.complex(paddle.rand([]), paddle.rand([]))
                 x.stop_gradient = False
                 out = api(x)
-                # TODO(zhouwei):
-                # ScaleLossGradOp / append_backward set grad shape to [1]
-                # after output 0D, may change it to []
-                # use out.sum() to avoid this two problem now
-                loss = out.sum()
-                paddle.static.append_backward(loss)
+                paddle.static.append_backward(out)
 
                 fetch_list = [x, out]
                 if block.has_var(x.grad_name):
@@ -3699,12 +3687,10 @@ class TestUnaryElementwiseAPIWithComplexInput(unittest.TestCase):
                     self.assertEqual(item.shape, ())
 
                 # 2) Test CompiledProgram Program
-                expect_shape = ()
                 compile_prog = paddle.static.CompiledProgram(main_prog)
-
                 res = exe.run(compile_prog, fetch_list=fetch_list)
                 for item in res:
-                    self.assertEqual(item.shape, expect_shape)
+                    self.assertEqual(item.shape, ())
 
         paddle.disable_static()
 
@@ -3712,8 +3698,7 @@ class TestUnaryElementwiseAPIWithComplexInput(unittest.TestCase):
 class TestAsReal(unittest.TestCase):
     def test_dygraph(self):
         paddle.disable_static()
-        for api in unary_apis_with_complex_input:
-            x = paddle.to_tensor(2.0 + 3.0j).squeeze()
+        x = paddle.rand([]) + 1j * paddle.rand([])
         x.stop_gradient = False
         x.retain_grads()
         out = paddle.as_real(x)
@@ -3731,47 +3716,70 @@ class TestAsReal(unittest.TestCase):
     def test_static(self):
         paddle.enable_static()
 
-        for api in unary_apis_with_complex_input:
         main_prog = paddle.static.Program()
         block = main_prog.global_block()
         exe = paddle.static.Executor()
-            with paddle.static.program_guard(
-                main_prog, paddle.static.Program()
-            ):
-                # before full support for complex, we cannot create complex tensor with the same code as in dynamic graph
-                x = paddle.complex(
-                    paddle.to_tensor(2.0), paddle.to_tensor(2.0)
-                ).squeeze()
+        with paddle.static.program_guard(main_prog, paddle.static.Program()):
+            x = paddle.complex(paddle.rand([]), paddle.rand([]))
             x.stop_gradient = False
             out = paddle.as_real(x)
             self.assertEqual(x.shape, ())
             self.assertEqual(out.shape, (2,))
-                # TODO(zhouwei):
-                # ScaleLossGradOp / append_backward set grad shape to [1]
-                # after output 0D, may change it to []
-                # use out.sum() to avoid this two problem now
-                loss = out.abs().sum()
-                paddle.static.append_backward(loss)
+            paddle.static.append_backward(out.sum())
 
             fetch_list = [x, out]
             if block.has_var(x.grad_name):
                 fetch_list.extend([x.grad_name, out.grad_name])
 
-                # 1) Test Program
             res = exe.run(main_prog, fetch_list=fetch_list)
             self.assertEqual(res[0].shape, ())
             self.assertEqual(res[1].shape, (2,))
             self.assertEqual(res[2].shape, ())
             self.assertEqual(res[3].shape, (2,))
 
-                # 2) Test CompiledProgram Program
-                expect_shapes = (), (2,), (), (2,)
-                compile_prog = paddle.static.CompiledProgram(main_prog)
+        paddle.disable_static()
 
-                res = exe.run(compile_prog, fetch_list=fetch_list)
-                print(res)
-                for actual, expect in zip(res, expect_shapes):
-                    self.assertEqual(actual.shape, expect)
+
+class TestAsComplex(unittest.TestCase):
+    def test_dygraph(self):
+        paddle.disable_static()
+        x = paddle.rand([2])
+        x.stop_gradient = False
+        x.retain_grads()
+        out = paddle.as_complex(x)
+        out.retain_grads()
+        out.backward()
+
+        self.assertEqual(x.shape, [2])
+        self.assertEqual(out.shape, [])
+        if x.grad is not None:
+            self.assertEqual(x.grad.shape, [2])
+            self.assertEqual(out.grad.shape, [])
+
+        paddle.enable_static()
+
+    def test_static(self):
+        paddle.enable_static()
+        main_prog = paddle.static.Program()
+        block = main_prog.global_block()
+        exe = paddle.static.Executor()
+        with paddle.static.program_guard(main_prog, paddle.static.Program()):
+            x = paddle.rand([2])
+            x.stop_gradient = False
+            out = paddle.as_complex(x)
+            self.assertEqual(x.shape, (2,))
+            self.assertEqual(out.shape, ())
+            paddle.static.append_backward(out.sum())
+
+            fetch_list = [x, out]
+            if block.has_var(x.grad_name):
+                fetch_list.extend([x.grad_name, out.grad_name])
+
+            res = exe.run(main_prog, fetch_list=fetch_list)
+            self.assertEqual(res[0].shape, (2,))
+            self.assertEqual(res[1].shape, ())
+            self.assertEqual(res[2].shape, (2,))
+            self.assertEqual(res[3].shape, ())
 
         paddle.disable_static()
 

python/paddle/tensor/math.py

@@ -4469,19 +4469,15 @@ def gcd(x, y, name=None):
     y = paddle.broadcast_to(y, shape)
     x = paddle.abs(x)
     y = paddle.abs(y)
-    # TODO(zhouwei25): Support 0D for not_equal tensor with scalar
-    zero = paddle.full([], 0)
 
     def _gcd_cond_fn(x, y):
-        # return paddle.any(y != 0)
-        return paddle.any(y != zero)
+        return paddle.any(y != 0)
 
     def _gcd_body_fn(x, y):
         # paddle.mod will raise an error when any element of y is 0. To avoid
         # that, we change those zeros to ones. Their values don't matter because
         # they won't be used.
-        # y_not_equal_0 = y != 0
-        y_not_equal_0 = y != zero
+        y_not_equal_0 = y != 0
         y_safe = paddle.where(y_not_equal_0, y, paddle.ones(y.shape, y.dtype))
         x, y = (
             paddle.where(y_not_equal_0, y, x),