单测case覆盖2 (#455)

* case add2

* case add2

* fix MaxMinMatcher

* fix testcase

paconvert/api_mapping.json

@@ -8218,7 +8218,7 @@
     "paddle_api": "paddle.linalg.matrix_rank",
     "min_input_args": 1,
     "args_list": [
-      "A",
+      "input",
       "tol",
       "hermitian",
       "*",
@@ -8228,7 +8228,7 @@
       "out"
     ],
     "kwargs_change": {
-      "A": "x",
+      "input": "x",
       "atol": "tol",
       "rtol": ""
     }

paconvert/api_matcher.py

@@ -1003,6 +1003,7 @@ def get_paddle_nodes(self, args, kwargs):
             else:
                 code = "{}({})".format(paddle_api, self.kwargs_to_str(new_kwargs))
 
+            self.api_mapping["args_list"] = ["input", "dim", "keepdim", "*", "out"]
             return ast.parse(code).body
 
         # the case of one tensor

tests/test_autograd_grad.py

@@ -127,3 +127,65 @@ def test_case_7():
         """
     )
     obj.run(pytorch_code, ["result"])
+
+
+def test_case_8():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1.1, 2.2, 3.3], requires_grad=True)
+        z = torch.tensor([1.1, 2.2, 3.3], requires_grad=True)
+        grad = torch.tensor(2.0)
+        y = x * x + z
+
+        result = torch.autograd.grad(outputs=[y.sum()], inputs=[x, z], grad_outputs=grad, retain_graph=True,
+            create_graph=False, only_inputs=True, allow_unused=True, is_grads_batched=False, materialize_grads=False)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle dose not support 'only_inputs' now!",
+    )
+
+
+def test_case_9():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1.1, 2.2, 3.3], requires_grad=True)
+        z = torch.tensor([1.1, 2.2, 3.3], requires_grad=True)
+        grad = torch.tensor(2.0)
+        y = x * x + z
+
+        result = torch.autograd.grad([y.sum()], [x, z], grad, True, False, True, True, False, False)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle dose not support 'only_inputs' now!",
+    )
+
+
+def test_case_10():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        x = torch.tensor([1.1, 2.2, 3.3], requires_grad=True)
+        z = torch.tensor([1.1, 2.2, 3.3], requires_grad=True)
+        grad = torch.tensor(2.0)
+        y = x * x + z
+
+        result = torch.autograd.grad(outputs=[y.sum()], inputs=[x, z], retain_graph=True, allow_unused=True,
+            create_graph=False, only_inputs=True, is_grads_batched=False, grad_outputs=grad, materialize_grads=False)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle dose not support 'only_inputs' now!",
+    )

tests/test_bernoulli.py

@@ -115,3 +115,43 @@ def test_case_8():
         """
     )
     obj.run(pytorch_code, ["result", "out"])
+
+
+def test_case_9():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.ones(3, 3)
+        out = torch.zeros(3, 3)
+        result = torch.bernoulli(generator=torch.Generator(), input=a, out=out)
+        """
+    )
+    obj.run(pytorch_code, ["result", "out"])
+
+
+def test_case_10():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.ones(3, 3)
+        out = torch.zeros(3, 3)
+        result = torch.bernoulli(input=a, generator=torch.Generator(), out=out)
+        """
+    )
+    obj.run(pytorch_code, ["result", "out"])
+
+
+def test_case_11():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        a = torch.rand(3, 3)
+        result = torch.bernoulli(input=a, p=0.0, generator=torch.Generator())
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["a", "result"],
+        unsupport=True,
+        reason="paddle not support parameter 'p' ",
+    )

tests/test_chain_matmul.py

@@ -69,3 +69,14 @@ def test_case_4():
         unsupport=True,
         reason="paddle does not support variable parameter",
     )
+
+
+def test_case_5():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        v = torch.tensor([[3., 6, 9], [1, 3, 5], [2, 2, 2]])
+        result = torch.chain_matmul(v)
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_cumulative_trapezoid.py

@@ -84,3 +84,37 @@ def test_case_6():
         """
     )
     obj.run(pytorch_code, ["result"])
+
+
+def test_case_7():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        y = torch.tensor([1, 1, 1, 0, 1]).type(torch.float32)
+        result = torch.cumulative_trapezoid(y=y, dx=0.05, dim=0)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_8():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        y = torch.tensor([1, 1, 1, 0, 1]).type(torch.float32)
+        result = torch.cumulative_trapezoid(y, dx=0.05, dim=0)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_9():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        y = torch.tensor([1, 1, 1, 0, 1]).type(torch.float32)
+        x = torch.tensor([1, 2, 3, 0, 1]).type(torch.float32)
+        result = torch.cumulative_trapezoid(dim=0, y=y, x=x)
+        """
+    )
+    obj.run(pytorch_code, ["result"])

tests/test_distributions_Bernoulli.py

@@ -89,3 +89,71 @@ def test_case_6():
         """
     )
     obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_7():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        m = torch.distributions.Bernoulli(probs=torch.tensor([0.3]), logits=None, validate_args=False)
+        result = m.sample([100])
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle does not support logits temporarily",
+    )
+
+
+def test_case_8():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        m = torch.distributions.Bernoulli(torch.tensor([0.3]), None, False)
+        result = m.sample([100])
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle does not support logits temporarily",
+    )
+
+
+def test_case_9():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        m = torch.distributions.Bernoulli(probs=torch.tensor([0.3]), validate_args=False, logits=None)
+        result = m.sample([100])
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle does not support logits temporarily",
+    )
+
+
+def test_case_10():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        m = torch.distributions.Bernoulli(probs=None, validate_args=False, logits=3.5)
+        result = m.sample([100])
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle does not support logits temporarily",
+    )

tests/test_distributions_Categorical.py

@@ -78,3 +78,71 @@ def test_case_5():
         """
     )
     obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_6():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        m = torch.distributions.Categorical(probs=None, logits=torch.tensor([0.25, 0.25, 0.25, 0.25]), validate_args=False)
+        result = m.sample([1])
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle does not support probs temporarily",
+    )
+
+
+def test_case_7():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        m = torch.distributions.Categorical(None, torch.tensor([0.25, 0.25, 0.25, 0.25]), False)
+        result = m.sample([1])
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle does not support probs temporarily",
+    )
+
+
+def test_case_8():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        m = torch.distributions.Categorical(probs=None, validate_args=False, logits=torch.tensor([0.25, 0.25, 0.25, 0.25]))
+        result = m.sample([1])
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle does not support probs temporarily",
+    )
+
+
+def test_case_9():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        m = torch.distributions.Categorical(probs=torch.tensor([0.25, 0.25, 0.25, 0.25]), validate_args=False,logits=None)
+        result = m.sample([1])
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_value=False,
+        unsupport=True,
+        reason="paddle does not support probs temporarily",
+    )