support KL2 multi-card training, refactor KL2 unittest, *test=kunlun (#41543)

python/paddle/fluid/tests/unittests/xpu/test_squeeze2_op_xpu.py

@@ -21,67 +21,86 @@ import numpy as np
 
 from op_test import OpTest
 from op_test_xpu import XPUOpTest
+from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
 import paddle
 
 paddle.enable_static()
 
 
-# Correct: General.
-class TestSqueezeOp(XPUOpTest):
-    def setUp(self):
-        self.op_type = "squeeze2"
-        self.use_xpu = True
-        self.use_mkldnn = False
-        self.init_test_case()
-        self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
-        self.init_attrs()
-        self.outputs = {
-            "Out": self.inputs["X"].reshape(self.new_shape),
-            "XShape": np.random.random(self.ori_shape).astype("float32")
-        }
-
-    def test_check_output(self):
-        if paddle.is_compiled_with_xpu():
+class XPUTestSqueeze2Op(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = "squeeze2"
+        self.use_dynamic_create_class = False
+
+    class TestSqueeze2Op(XPUOpTest):
+        def setUp(self):
+            self.op_type = "squeeze2"
+            self.use_mkldnn = False
+            self.init_dtype()
+            self.init_test_case()
+            self.inputs = {
+                "X": np.random.random(self.ori_shape).astype(self.dtype)
+            }
+            self.outputs = {
+                "Out": self.inputs["X"].reshape(self.new_shape),
+                "XShape": np.random.random(self.ori_shape).astype(self.dtype)
+            }
+            self.init_attrs()
+
+        def init_dtype(self):
+            self.dtype = self.in_type
+
+        def init_attrs(self):
+            self.attrs = {"axes": self.axes}
+
+        def init_test_case(self):
+            self.ori_shape = (1, 3, 1, 40)
+            self.axes = (0, 2)
+            self.new_shape = (3, 40)
+
+        def test_check_output(self):
             place = paddle.XPUPlace(0)
             self.check_output_with_place(place, no_check_set=['XShape'])
 
-    def test_check_grad(self):
-        if paddle.is_compiled_with_xpu():
+        def test_check_grad(self):
             place = paddle.XPUPlace(0)
-            self.check_grad_with_place(place, ['X'], 'Out')
-
-    def init_test_case(self):
-        self.ori_shape = (1, 3, 1, 40)
-        self.axes = (0, 2)
-        self.new_shape = (3, 40)
-
-    def init_attrs(self):
-        self.attrs = {"axes": self.axes}
-
-
-# Correct: There is mins axis.
-class TestSqueezeOp1(TestSqueezeOp):
-    def init_test_case(self):
-        self.ori_shape = (1, 20, 1, 5)
-        self.axes = (0, -2)
-        self.new_shape = (20, 5)
-
-
-# Correct: No axes input.
-class TestSqueezeOp2(TestSqueezeOp):
-    def init_test_case(self):
-        self.ori_shape = (1, 20, 1, 5)
-        self.axes = ()
-        self.new_shape = (20, 5)
-
-
-# Correct: Just part of axes be squeezed. 
-class TestSqueezeOp3(TestSqueezeOp):
-    def init_test_case(self):
-        self.ori_shape = (6, 1, 5, 1, 4, 1)
-        self.axes = (1, -1)
-        self.new_shape = (6, 5, 1, 4)
-
+            if self.dtype in [np.float32, np.float64]:
+                self.check_grad_with_place(place, ['X'], 'Out')
+            elif self.dtype == np.bool_:
+                return
+            else:
+                user_defined_grad_outputs = np.random.random(
+                    self.new_shape).astype(self.dtype)
+                self.check_grad_with_place(
+                    place, ['X'],
+                    'Out',
+                    user_defined_grad_outputs=user_defined_grad_outputs)
+
+    # Correct: There is mins axis.
+    class TestSqueeze2Op1(TestSqueeze2Op):
+        def init_test_case(self):
+            self.ori_shape = (1, 20, 1, 5)
+            self.axes = (0, -2)
+            self.new_shape = (20, 5)
+
+    # Correct: No axes input.
+    class TestSqueeze2Op2(TestSqueeze2Op):
+        def init_test_case(self):
+            self.ori_shape = (1, 20, 1, 5)
+            self.axes = ()
+            self.new_shape = (20, 5)
+
+    # Correct: Just part of axes be squeezed. 
+    class TestSqueeze2Op3(TestSqueeze2Op):
+        def init_test_case(self):
+            self.ori_shape = (6, 1, 5, 1, 4, 1)
+            self.axes = (1, -1)
+            self.new_shape = (6, 5, 1, 4)
+
+
+support_types = get_xpu_op_support_types("squeeze2")
+for stype in support_types:
+    create_test_class(globals(), XPUTestSqueeze2Op, stype)
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/xpu/test_unsqueeze2_op_xpu.py

@@ -23,209 +23,225 @@ import paddle
 import paddle.fluid as fluid
 from op_test import OpTest
 from op_test_xpu import XPUOpTest
+from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
 
 paddle.enable_static()
 
 
-# Correct: General.
-class TestUnsqueezeOp(XPUOpTest):
-    def setUp(self):
-        self.init_test_case()
-        self.use_xpu = True
-        self.use_mkldnn = False
-        self.op_type = "unsqueeze2"
-        self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
-        self.init_attrs()
-        self.outputs = {
-            "Out": self.inputs["X"].reshape(self.new_shape),
-            "XShape": np.random.random(self.ori_shape).astype("float32")
-        }
-
-    def test_check_output(self):
-        if paddle.is_compiled_with_xpu():
+class XPUTestUnsqueeze2Op(XPUOpTestWrapper):
+    def __init__(self):
+        self.op_name = "unsqueeze2"
+        self.use_dynamic_create_class = False
+
+    class TestUnsqueeze2Op(XPUOpTest):
+        def setUp(self):
+            self.op_type = "unsqueeze2"
+            self.use_mkldnn = False
+            self.init_dtype()
+            self.init_test_case()
+            self.inputs = {
+                "X": np.random.random(self.ori_shape).astype(self.dtype)
+            }
+            self.outputs = {
+                "Out": self.inputs["X"].reshape(self.new_shape),
+                "XShape": np.random.random(self.ori_shape).astype(self.dtype)
+            }
+            self.init_attrs()
+
+        def init_dtype(self):
+            self.dtype = self.in_type
+
+        def init_attrs(self):
+            self.attrs = {"axes": self.axes}
+
+        def init_test_case(self):
+            self.ori_shape = (3, 40)
+            self.axes = (1, 2)
+            self.new_shape = (3, 1, 1, 40)
+
+        def test_check_output(self):
             place = paddle.XPUPlace(0)
             self.check_output_with_place(place, no_check_set=['XShape'])
 
-    def test_check_grad(self):
-        if paddle.is_compiled_with_xpu():
+        def test_check_grad(self):
             place = paddle.XPUPlace(0)
-            self.check_grad_with_place(place, ['X'], 'Out')
-
-    def init_test_case(self):
-        self.ori_shape = (3, 40)
-        self.axes = (1, 2)
-        self.new_shape = (3, 1, 1, 40)
-
-    def init_attrs(self):
-        self.attrs = {"axes": self.axes}
-
-
-# Correct: Single input index.
-class TestUnsqueezeOp1(TestUnsqueezeOp):
-    def init_test_case(self):
-        self.ori_shape = (20, 5)
-        self.axes = (-1, )
-        self.new_shape = (20, 5, 1)
-
-
-# Correct: Mixed input axis.
-class TestUnsqueezeOp2(TestUnsqueezeOp):
-    def init_test_case(self):
-        self.ori_shape = (20, 5)
-        self.axes = (0, -1)
-        self.new_shape = (1, 20, 5, 1)
-
-
-# Correct: There is duplicated axis.
-class TestUnsqueezeOp3(TestUnsqueezeOp):
-    def init_test_case(self):
-        self.ori_shape = (10, 2, 5)
-        self.axes = (0, 3, 3)
-        self.new_shape = (1, 10, 2, 1, 1, 5)
-
-
-# Correct: Reversed axes.
-class TestUnsqueezeOp4(TestUnsqueezeOp):
-    def init_test_case(self):
-        self.ori_shape = (10, 2, 5)
-        self.axes = (3, 1, 1)
-        self.new_shape = (10, 1, 1, 2, 5, 1)
-
-
-# axes is a list(with tensor)
-class TestUnsqueezeOp_AxesTensorList(XPUOpTest):
-    def setUp(self):
-        self.init_test_case()
-        self.use_xpu = True
-        self.use_mkldnn = False
-        self.op_type = "unsqueeze2"
-
-        axes_tensor_list = []
-        for index, ele in enumerate(self.axes):
-            axes_tensor_list.append(("axes" + str(index), np.ones(
-                (1)).astype('int32') * ele))
-
-        self.inputs = {
-            "X": np.random.random(self.ori_shape).astype("float32"),
-            "AxesTensorList": axes_tensor_list
-        }
-        self.init_attrs()
-        self.outputs = {
-            "Out": self.inputs["X"].reshape(self.new_shape),
-            "XShape": np.random.random(self.ori_shape).astype("float32")
-        }
-
-    def test_check_output(self):
-        if paddle.is_compiled_with_xpu():
+            if self.dtype in [np.float32, np.float64]:
+                self.check_grad_with_place(place, ['X'], 'Out')
+            elif self.dtype == np.bool_:
+                return
+            else:
+                user_defined_grad_outputs = np.random.random(
+                    self.new_shape).astype(self.dtype)
+                self.check_grad_with_place(
+                    place, ['X'],
+                    'Out',
+                    user_defined_grad_outputs=user_defined_grad_outputs)
+
+    # Correct: Single input index.
+    class TestUnsqueeze2Op1(TestUnsqueeze2Op):
+        def init_test_case(self):
+            self.ori_shape = (20, 5)
+            self.axes = (-1, )
+            self.new_shape = (20, 5, 1)
+
+    # Correct: Mixed input axis.
+    class TestUnsqueeze2Op2(TestUnsqueeze2Op):
+        def init_test_case(self):
+            self.ori_shape = (20, 5)
+            self.axes = (0, -1)
+            self.new_shape = (1, 20, 5, 1)
+
+    # Correct: There is duplicated axis.
+    class TestUnsqueeze2Op3(TestUnsqueeze2Op):
+        def init_test_case(self):
+            self.ori_shape = (10, 2, 5)
+            self.axes = (0, 3, 3)
+            self.new_shape = (1, 10, 2, 1, 1, 5)
+
+    # Correct: Reversed axes.
+    class TestUnsqueeze2Op4(TestUnsqueeze2Op):
+        def init_test_case(self):
+            self.ori_shape = (10, 2, 5)
+            self.axes = (3, 1, 1)
+            self.new_shape = (10, 1, 1, 2, 5, 1)
+
+    # axes is a list(with tensor)
+    class TestUnsqueeze2Op_AxesTensorList(XPUOpTest):
+        def setUp(self):
+            self.op_type = "unsqueeze2"
+            self.use_mkldnn = False
+            self.init_dtype()
+            self.init_test_case()
+
+            axes_tensor_list = []
+            for index, ele in enumerate(self.axes):
+                axes_tensor_list.append(("axes" + str(index), np.ones(
+                    (1)).astype('int32') * ele))
+
+            self.inputs = {
+                "X": np.random.random(self.ori_shape).astype(self.dtype),
+                "AxesTensorList": axes_tensor_list
+            }
+            self.init_attrs()
+            self.outputs = {
+                "Out": self.inputs["X"].reshape(self.new_shape),
+                "XShape": np.random.random(self.ori_shape).astype(self.dtype)
+            }
+
+        def init_dtype(self):
+            self.dtype = self.in_type
+
+        def test_check_output(self):
             place = paddle.XPUPlace(0)
             self.check_output_with_place(place, no_check_set=['XShape'])
 
-    def test_check_grad(self):
-        if paddle.is_compiled_with_xpu():
+        def test_check_grad(self):
             place = paddle.XPUPlace(0)
-            self.check_grad_with_place(place, ['X'], 'Out')
-
-    def init_test_case(self):
-        self.ori_shape = (20, 5)
-        self.axes = (1, 2)
-        self.new_shape = (20, 1, 1, 5)
-
-    def init_attrs(self):
-        self.attrs = {}
-
-
-class TestUnsqueezeOp1_AxesTensorList(TestUnsqueezeOp_AxesTensorList):
-    def init_test_case(self):
-        self.ori_shape = (20, 5)
-        self.axes = (-1, )
-        self.new_shape = (20, 5, 1)
-
-
-class TestUnsqueezeOp2_AxesTensorList(TestUnsqueezeOp_AxesTensorList):
-    def init_test_case(self):
-        self.ori_shape = (20, 5)
-        self.axes = (0, -1)
-        self.new_shape = (1, 20, 5, 1)
-
-
-class TestUnsqueezeOp3_AxesTensorList(TestUnsqueezeOp_AxesTensorList):
-    def init_test_case(self):
-        self.ori_shape = (10, 2, 5)
-        self.axes = (0, 3, 3)
-        self.new_shape = (1, 10, 2, 1, 1, 5)
-
-
-class TestUnsqueezeOp4_AxesTensorList(TestUnsqueezeOp_AxesTensorList):
-    def init_test_case(self):
-        self.ori_shape = (10, 2, 5)
-        self.axes = (3, 1, 1)
-        self.new_shape = (10, 1, 1, 2, 5, 1)
-
-
-# axes is a Tensor
-class TestUnsqueezeOp_AxesTensor(XPUOpTest):
-    def setUp(self):
-        self.init_test_case()
-        self.use_xpu = True
-        self.use_mkldnn = False
-        self.op_type = "unsqueeze2"
-
-        self.inputs = {
-            "X": np.random.random(self.ori_shape).astype("float32"),
-            "AxesTensor": np.array(self.axes).astype("int32")
-        }
-        self.init_attrs()
-        self.outputs = {
-            "Out": self.inputs["X"].reshape(self.new_shape),
-            "XShape": np.random.random(self.ori_shape).astype("float32")
-        }
-
-    def test_check_output(self):
-        if paddle.is_compiled_with_xpu():
+            if self.dtype in [np.float32, np.float64]:
+                self.check_grad_with_place(place, ['X'], 'Out')
+            else:
+                return
+
+        def init_test_case(self):
+            self.ori_shape = (20, 5)
+            self.axes = (1, 2)
+            self.new_shape = (20, 1, 1, 5)
+
+        def init_attrs(self):
+            self.attrs = {}
+
+    class TestUnsqueeze2Op1_AxesTensorList(TestUnsqueeze2Op_AxesTensorList):
+        def init_test_case(self):
+            self.ori_shape = (20, 5)
+            self.axes = (-1, )
+            self.new_shape = (20, 5, 1)
+
+    class TestUnsqueeze2Op2_AxesTensorList(TestUnsqueeze2Op_AxesTensorList):
+        def init_test_case(self):
+            self.ori_shape = (20, 5)
+            self.axes = (0, -1)
+            self.new_shape = (1, 20, 5, 1)
+
+    class TestUnsqueeze2Op3_AxesTensorList(TestUnsqueeze2Op_AxesTensorList):
+        def init_test_case(self):
+            self.ori_shape = (10, 2, 5)
+            self.axes = (0, 3, 3)
+            self.new_shape = (1, 10, 2, 1, 1, 5)
+
+    class TestUnsqueeze2Op4_AxesTensorList(TestUnsqueeze2Op_AxesTensorList):
+        def init_test_case(self):
+            self.ori_shape = (10, 2, 5)
+            self.axes = (3, 1, 1)
+            self.new_shape = (10, 1, 1, 2, 5, 1)
+
+    # axes is a Tensor
+    class TestUnsqueeze2Op_AxesTensor(XPUOpTest):
+        def setUp(self):
+            self.op_type = "unsqueeze2"
+            self.use_mkldnn = False
+            self.init_test_case()
+            self.init_dtype()
+
+            self.inputs = {
+                "X": np.random.random(self.ori_shape).astype(self.dtype),
+                "AxesTensor": np.array(self.axes).astype("int32")
+            }
+            self.init_attrs()
+            self.outputs = {
+                "Out": self.inputs["X"].reshape(self.new_shape),
+                "XShape": np.random.random(self.ori_shape).astype(self.dtype)
+            }
+
+        def init_dtype(self):
+            self.dtype = self.in_type
+
+        def test_check_output(self):
             place = paddle.XPUPlace(0)
             self.check_output_with_place(place, no_check_set=['XShape'])
 
-    def test_check_grad(self):
-        if paddle.is_compiled_with_xpu():
+        def test_check_grad(self):
             place = paddle.XPUPlace(0)
-            self.check_grad_with_place(place, ['X'], 'Out')
-
-    def init_test_case(self):
-        self.ori_shape = (20, 5)
-        self.axes = (1, 2)
-        self.new_shape = (20, 1, 1, 5)
-
-    def init_attrs(self):
-        self.attrs = {}
-
-
-class TestUnsqueezeOp1_AxesTensor(TestUnsqueezeOp_AxesTensor):
-    def init_test_case(self):
-        self.ori_shape = (20, 5)
-        self.axes = (-1, )
-        self.new_shape = (20, 5, 1)
-
-
-class TestUnsqueezeOp2_AxesTensor(TestUnsqueezeOp_AxesTensor):
-    def init_test_case(self):
-        self.ori_shape = (20, 5)
-        self.axes = (0, -1)
-        self.new_shape = (1, 20, 5, 1)
-
-
-class TestUnsqueezeOp3_AxesTensor(TestUnsqueezeOp_AxesTensor):
-    def init_test_case(self):
-        self.ori_shape = (10, 2, 5)
-        self.axes = (0, 3, 3)
-        self.new_shape = (1, 10, 2, 1, 1, 5)
-
-
-class TestUnsqueezeOp4_AxesTensor(TestUnsqueezeOp_AxesTensor):
-    def init_test_case(self):
-        self.ori_shape = (10, 2, 5)
-        self.axes = (3, 1, 1)
-        self.new_shape = (10, 1, 1, 2, 5, 1)
-
+            if self.dtype in [np.float32, np.float64]:
+                self.check_grad_with_place(place, ['X'], 'Out')
+            else:
+                return
+
+        def init_test_case(self):
+            self.ori_shape = (20, 5)
+            self.axes = (1, 2)
+            self.new_shape = (20, 1, 1, 5)
+
+        def init_attrs(self):
+            self.attrs = {}
+
+    class TestUnsqueeze2Op1_AxesTensor(TestUnsqueeze2Op_AxesTensor):
+        def init_test_case(self):
+            self.ori_shape = (20, 5)
+            self.axes = (-1, )
+            self.new_shape = (20, 5, 1)
+
+    class TestUnsqueeze2Op2_AxesTensor(TestUnsqueeze2Op_AxesTensor):
+        def init_test_case(self):
+            self.ori_shape = (20, 5)
+            self.axes = (0, -1)
+            self.new_shape = (1, 20, 5, 1)
+
+    class TestUnsqueeze2Op3_AxesTensor(TestUnsqueeze2Op_AxesTensor):
+        def init_test_case(self):
+            self.ori_shape = (10, 2, 5)
+            self.axes = (0, 3, 3)
+            self.new_shape = (1, 10, 2, 1, 1, 5)
+
+    class TestUnsqueeze2Op4_AxesTensor(TestUnsqueeze2Op_AxesTensor):
+        def init_test_case(self):
+            self.ori_shape = (10, 2, 5)
+            self.axes = (3, 1, 1)
+            self.new_shape = (10, 1, 1, 2, 5, 1)
+
+
+support_types = get_xpu_op_support_types("unsqueeze2")
+for stype in support_types:
+    create_test_class(globals(), XPUTestUnsqueeze2Op, stype)
 
 if __name__ == "__main__":
     unittest.main()