[Zero-Dim] fix cases to Support paddle.all/any/min/max/prod/logsumexp…

…/amax/amin output 0D

framework/api/distribution/apibase.py

@@ -501,7 +501,7 @@ def compute_grad(self, res, data=None, **kwargs):
                     shape = v.numpy().shape
                     for i in range(len(v.numpy().flatten())):
                         g = self._get_sigle_grad(v, i, k)
-                        grad.append(g[0])
+                        grad.append(g.item())
                         self.kwargs[k] = v
                     numeric_grad[k] = np.array(grad).reshape(shape)
                 elif isinstance(v, (list, tuple)) and isinstance(v[0], paddle.Tensor):
@@ -512,7 +512,7 @@ def compute_grad(self, res, data=None, **kwargs):
                         shape = v[n].shape
                         for i in range(len(v[n].flatten())):
                             g = self._get_sigle_grad(v[n], i, k, n)
-                            grad.append(g[0])
+                            grad.append(g.item())
                             self.kwargs[k][n] = v[n]
                         tmp.append(np.array(grad).reshape(shape))
                     numeric_grad[k] = tmp
@@ -532,7 +532,7 @@ def compute_grad(self, res, data=None, **kwargs):
                     self.data.stop_gradient = False
                 loss_delta = self._numeric_grad()
                 g = (loss_delta - loss) / self.gap
-                grad.append(g[0])
+                grad.append(g.item())
                 # recover v to self.kwargs
                 self.data = data
             numeric_grad["data"] = np.array(grad).reshape(shape)

framework/api/fft/apibase.py

@@ -502,7 +502,7 @@ def compute_grad(self, res, data=None, **kwargs):
                         loss_delta = self._numeric_grad()
                         g = (loss_delta - loss) / self.gap
                         # print("-----> {}".format(g))
-                        grad.append(g[0])
+                        grad.append(g.item())
                         # recover v to self.kwargs
                         self.kwargs[k] = v
                     numeric_grad[k] = np.array(grad).reshape(shape)
@@ -521,7 +521,7 @@ def compute_grad(self, res, data=None, **kwargs):
                     self.data.stop_gradient = False
                 loss_delta = self._numeric_grad()
                 g = (loss_delta - loss) / self.gap
-                grad.append(g[0])
+                grad.append(g.item())
                 # recover v to self.kwargs
                 self.data = data
             numeric_grad["data"] = np.array(grad).reshape(shape)

framework/api/incubate/apibase.py

@@ -501,7 +501,7 @@ def compute_grad(self, res, data=None, **kwargs):
                     shape = v.numpy().shape
                     for i in range(len(v.numpy().flatten())):
                         g = self._get_sigle_grad(v, i, k)
-                        grad.append(g[0])
+                        grad.append(g.item())
                         self.kwargs[k] = v
                     numeric_grad[k] = np.array(grad).reshape(shape)
                 elif isinstance(v, (list, tuple)) and isinstance(v[0], paddle.Tensor):
@@ -512,7 +512,7 @@ def compute_grad(self, res, data=None, **kwargs):
                         shape = v[n].shape
                         for i in range(len(v[n].flatten())):
                             g = self._get_sigle_grad(v[n], i, k, n)
-                            grad.append(g[0])
+                            grad.append(g.item())
                             self.kwargs[k][n] = v[n]
                         tmp.append(np.array(grad).reshape(shape))
                     numeric_grad[k] = tmp
@@ -532,7 +532,7 @@ def compute_grad(self, res, data=None, **kwargs):
                     self.data.stop_gradient = False
                 loss_delta = self._numeric_grad()
                 g = (loss_delta - loss) / self.gap
-                grad.append(g[0])
+                grad.append(g.item())
                 # recover v to self.kwargs
                 self.data = data
             numeric_grad["data"] = np.array(grad).reshape(shape)

framework/api/linalg/apibase.py

@@ -498,7 +498,7 @@ def compute_grad(self, res, data=None, **kwargs):
                         loss_delta = self._numeric_grad()
                         g = (loss_delta - loss) / self.gap
                         # print("-----> {}".format(g))
-                        grad.append(g[0])
+                        grad.append(g.item())
                         # recover v to self.kwargs
                         self.kwargs[k] = v
                     numeric_grad[k] = np.array(grad).reshape(shape)
@@ -517,7 +517,7 @@ def compute_grad(self, res, data=None, **kwargs):
                     self.data.stop_gradient = False
                 loss_delta = self._numeric_grad()
                 g = (loss_delta - loss) / self.gap
-                grad.append(g[0])
+                grad.append(g.item())
                 # recover v to self.kwargs
                 self.data = data
             numeric_grad["data"] = np.array(grad).reshape(shape)

framework/api/linalg/test_cond.py

@@ -37,7 +37,7 @@ def test_cond_base():
     base
     """
     x = np.array([[1.0, 0, -1], [0, 1, 0], [1, 0, 1]])
-    res = np.array([np.linalg.cond(x)])
+    res = np.linalg.cond(x)
     obj.base(res=res, x=x)
 
 
@@ -48,7 +48,7 @@ def test_cond0():
     """
     x = randtool("float", -2, 4, [3, 4])
     # x = np.random.rand(3, 4)
-    res = np.array([np.linalg.cond(x)])
+    res = np.linalg.cond(x)
     # print(res)
     obj.run(res=res, x=x)
 
@@ -59,7 +59,7 @@ def test_cond1():
     m!=n, p=-2
     """
     x = randtool("float", -2, 4, [6, 4])
-    res = np.array([np.linalg.cond(x, p=-2)])
+    res = np.linalg.cond(x, p=-2)
     obj.run(res=res, x=x, p=-2)
 
 
@@ -70,7 +70,7 @@ def test_cond2():
     x: multiple dimension
     """
     x = randtool("float", -20, 40, [6, 2, 4, 3, 4])
-    res = np.array(np.linalg.cond(x))
+    res = np.linalg.cond(x)
     obj.run(res=res, x=x)
 
 
@@ -80,7 +80,7 @@ def test_cond3():
     x: n*n； p=2
     """
     x = randtool("float", -20, 40, [4, 4])
-    res = np.array([np.linalg.cond(x)])
+    res = np.linalg.cond(x)
     obj.run(res=res, x=x)
 
 
@@ -90,7 +90,7 @@ def test_cond4():
     x: n*n； p=-2
     """
     x = randtool("float", -2, 40, [4, 4])
-    res = np.array([np.linalg.cond(x, p=-2)])
+    res = np.linalg.cond(x, p=-2)
     obj.run(res=res, x=x, p=-2)
 
 
@@ -100,7 +100,7 @@ def test_cond5():
     x: n*n； p=-2
     """
     x = randtool("float", -2, 40, [4, 4])
-    res = np.array([np.linalg.cond(x, p=-2)])
+    res = np.linalg.cond(x, p=-2)
     obj.run(res=res, x=x, p=-2)
 
 
@@ -130,7 +130,7 @@ def test_cond8():
     x: n*n； p=1
     """
     x = randtool("float", -2, 40, [4, 4])
-    res = np.array([np.linalg.cond(x, p=1)])
+    res = np.linalg.cond(x, p=1)
     obj.run(res=res, x=x, p=1)
 
 
@@ -140,7 +140,7 @@ def test_cond9():
     x: n*n； p=-1
     """
     x = randtool("float", -4, 4, [4, 2, 4, 4])
-    res = np.array(np.linalg.cond(x, p=-1))
+    res = np.linalg.cond(x, p=-1)
     obj.run(res=res, x=x, p=-1)
 
 
@@ -150,7 +150,7 @@ def test_cond10():
     x: n*n； p=inf
     """
     x = randtool("float", -4, 4, [4, 2, 4, 4])
-    res = np.array(np.linalg.cond(x, p=np.inf))
+    res = np.linalg.cond(x, p=np.inf)
     obj.run(res=res, x=x, p=np.inf)
 
 
@@ -160,5 +160,5 @@ def test_cond11():
     x: n*n； p=-inf
     """
     x = randtool("float", -4, 4, [4, 2, 4, 4])
-    res = np.array(np.linalg.cond(x, p=-np.inf))
+    res = np.linalg.cond(x, p=-np.inf)
     obj.run(res=res, x=x, p=-np.inf)

framework/api/linalg/test_multi_dot.py

@@ -98,7 +98,7 @@ def numerical_grad(**kwargs):
             kwargs[k] = tmp
             loss_delta = cal_loss(**kwargs)
             g = (loss_delta - loss) / gap
-            grad.append(g[0])
+            grad.append(g.item())
             # recover v to self.kwargs
             kwargs[k] = v
         numeric_grad[k] = np.array(grad).reshape(shape)

framework/api/linalg/test_norm.py

@@ -91,7 +91,7 @@ def test_norm_base():
     base
     """
     x = randtool("float", -10, 10, [2, 3, 4])
-    res = [28.43878906]
+    res = np.array(28.43878906)
     obj.base(res=res, x=x)
 
 
@@ -102,7 +102,7 @@ def test_norm():
     """
     np.random.seed(33)
     x = randtool("float", -10, 10, [3, 3, 3])
-    res = [31.5736]
+    res = np.array(31.5736)
     obj.run(res=res, x=x, axis=None)
 
 
@@ -222,7 +222,7 @@ def test_norm10():
     x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
     pord = "fro"
     axis = None
-    res = [16.8819]
+    res = np.array(16.8819)
     obj.run(res=res, x=x, axis=axis, p=pord)
 
 
@@ -234,7 +234,7 @@ def test_norm11():
     x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]).reshape(3, 3)
     pord = "fro"
     axis = None
-    res = [16.8819]
+    res = np.array(16.8819)
     obj.run(res=res, x=x, axis=axis, p=pord)
 
 

framework/api/loss/apibase.py

@@ -502,7 +502,7 @@ def compute_grad(self, res, data=None, **kwargs):
                         loss_delta = self._numeric_grad()
                         g = (loss_delta - loss) / self.gap
                         # print("-----> {}".format(g))
-                        grad.append(g[0])
+                        grad.append(g.item())
                         # recover v to self.kwargs
                         self.kwargs[k] = v
                     numeric_grad[k] = np.array(grad).reshape(shape)
@@ -521,7 +521,7 @@ def compute_grad(self, res, data=None, **kwargs):
                     self.data.stop_gradient = False
                 loss_delta = self._numeric_grad()
                 g = (loss_delta - loss) / self.gap
-                grad.append(g[0])
+                grad.append(g.item())
                 # recover v to self.kwargs
                 self.data = data
             numeric_grad["data"] = np.array(grad).reshape(shape)

framework/api/nn/apibase.py

@@ -502,7 +502,7 @@ def compute_grad(self, res, data=None, **kwargs):
                         loss_delta = self._numeric_grad()
                         g = (loss_delta - loss) / self.gap
                         # print("-----> {}".format(g))
-                        grad.append(g[0])
+                        grad.append(g.item())
                         # recover v to self.kwargs
                         self.kwargs[k] = v
                     numeric_grad[k] = np.array(grad).reshape(shape)
@@ -521,7 +521,7 @@ def compute_grad(self, res, data=None, **kwargs):
                     self.data.stop_gradient = False
                 loss_delta = self._numeric_grad()
                 g = (loss_delta - loss) / self.gap
-                grad.append(g[0])
+                grad.append(g.item())
                 # recover v to self.kwargs
                 self.data = data
             numeric_grad["data"] = np.array(grad).reshape(shape)

framework/api/paddlebase/apibase.py

@@ -502,7 +502,7 @@ def compute_grad(self, res, data=None, **kwargs):
                         loss_delta = self._numeric_grad()
                         g = (loss_delta - loss) / self.gap
                         # print("-----> {}".format(g))
-                        grad.append(g[0])
+                        grad.append(g.item())
                         # recover v to self.kwargs
                         self.kwargs[k] = v
                     numeric_grad[k] = np.array(grad).reshape(shape)
@@ -521,7 +521,7 @@ def compute_grad(self, res, data=None, **kwargs):
                     self.data.stop_gradient = False
                 loss_delta = self._numeric_grad()
                 g = (loss_delta - loss) / self.gap
-                grad.append(g[0])
+                grad.append(g.item())
                 # recover v to self.kwargs
                 self.data = data
             numeric_grad["data"] = np.array(grad).reshape(shape)

framework/api/paddlebase/test_all.py

@@ -37,7 +37,7 @@ def test_all0():
     default
     """
     x = np.random.randint(-4, 4, (10,))
-    res = np.array([np.all(x)])
+    res = np.all(x)
     obj.base(res=res, x=x)
 
 
@@ -47,7 +47,7 @@ def test_all1():
     x: 2d-tensor
     """
     x = np.random.randint(-4, 4, (10, 10))
-    res = np.array([np.all(x)])
+    res = np.all(x)
     obj.base(res=res, x=x)
 
 
@@ -57,7 +57,7 @@ def test_all2():
     x: 3d-tensor
     """
     x = np.random.randint(-4, 4, (3, 4, 2))
-    res = np.array([np.all(x)])
+    res = np.all(x)
     obj.base(res=res, x=x)
 
 
@@ -67,7 +67,7 @@ def test_all3():
     x: 4d-tensor
     """
     x = np.random.randint(-4, 4, (2, 4, 4, 2))
-    res = np.array([np.all(x)])
+    res = np.all(x)
     obj.base(res=res, x=x)
 
 

framework/api/paddlebase/test_any.py

@@ -37,7 +37,7 @@ def test_any0():
     default
     """
     x = np.random.randint(-4, 4, (10,))
-    res = np.array([np.any(x)])
+    res = np.any(x)
     obj.base(res=res, x=x)
 
 
@@ -47,7 +47,7 @@ def test_any1():
     x: 2d-tensor
     """
     x = np.random.randint(-4, 4, (10, 10))
-    res = np.array([np.any(x)])
+    res = np.any(x)
     obj.base(res=res, x=x)
 
 
@@ -57,7 +57,7 @@ def test_any2():
     x: 3d-tensor
     """
     x = np.random.randint(-4, 4, (3, 4, 2))
-    res = np.array([np.any(x)])
+    res = np.any(x)
     obj.base(res=res, x=x)
 
 
@@ -67,7 +67,7 @@ def test_any3():
     x: 4d-tensor
     """
     x = np.random.randint(-4, 4, (2, 4, 4, 2))
-    res = np.array([np.any(x)])
+    res = np.any(x)
     obj.base(res=res, x=x)
 
 

framework/api/paddlebase/test_max.py

@@ -37,7 +37,7 @@ def test_max_base():
     max_base
     """
     x_data = np.arange(6).reshape(2, 3).astype(np.float32)
-    res = np.array([5])
+    res = np.max(x_data)
     obj.base(res=res, x=x_data)
 
 
@@ -47,7 +47,7 @@ def test_max_2D_tensor():
     max_2D_tensor
     """
     x_data = np.arange(6).reshape(2, 3).astype(np.float32)
-    res = np.array([5])
+    res = np.max(x_data)
     obj.run(res=res, x=x_data)
 
 
@@ -112,5 +112,5 @@ def test_max_1():
     special input
     """
     x_data = np.array([[-1.00595951, -0.20009832], [-0.35623679, -0.95880121]])
-    res = np.array([-0.20009832])
+    res = np.max(x_data)
     obj.run(res=res, x=x_data, axis=[-2, 1], keepdim=False)