转换规则 No. 181-184 (#195)

* Add tests * Fix
PaddlePaddle · Jul 31, 2023 · 27ebdae · 27ebdae
1 parent cbc21e1
commit 27ebdae
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Showing 27 changed files with 649 additions and 102 deletions.
diff --git a/paconvert/api_alias_mapping.json b/paconvert/api_alias_mapping.json
@@ -1,4 +1,29 @@
 {
+  "torch.distributions.bernoulli.Bernoulli": "torch.distributions.Bernoulli",
+  "torch.distributions.beta.Beta": "torch.distributions.Beta",
+  "torch.distributions.binomial.Binomial": "torch.distributions.Binomial",
+  "torch.distributions.categorical.Categorical": "torch.distributions.Categorical",
+  "torch.distributions.cauchy.Cauchy": "torch.distributions.Cauchy",
+  "torch.distributions.continuous_bernoulli.ContinuousBernoulli": "torch.distributions.ContinuousBernoulli",
+  "torch.distributions.dirichlet.Dirichlet": "torch.distributions.Dirichlet",
+  "torch.distributions.exp_family.ExponentialFamily": "torch.distributions.ExponentialFamily",
+  "torch.distributions.exponential.Exponential": "torch.distributions.Exponential",
+  "torch.distributions.geometric.Geometric": "torch.distributions.Geometric",
+  "torch.distributions.gumbel.Gumbel": "torch.distributions.Gumbel",
+  "torch.distributions.independent.Independent": "torch.distributions.Independent",
+  "torch.distributions.laplace.Laplace": "torch.distributions.Laplace",
+  "torch.distributions.log_normal.LogNormal": "torch.distributions.LogNormal",
+  "torch.distributions.multinomial.Multinomial": "torch.distributions.Multinomial",
+  "torch.distributions.normal.Normal": "torch.distributions.Normal",
+  "torch.distributions.one_hot_categorical.OneHotCategorical": "torch.distributions.OneHotCategorical",
+  "torch.distributions.transformed_distribution.TransformedDistribution": "torch.distributions.TransformedDistribution",
+  "torch.distributions.transforms.ComposeTransform": "torch.distributions.ComposeTransform",
+  "torch.distributions.transforms.SigmoidTransform": "torch.distributions.SigmoidTransform",
+  "torch.distributions.transforms.SoftmaxTransform": "torch.distributions.SoftmaxTransform",
+  "torch.distributions.transforms.StackTransform": "torch.distributions.StackTransform",
+  "torch.distributions.transforms.StickBreakingTransform": "torch.distributions.StickBreakingTransform",
+  "torch.distributions.transforms.Transform": "torch.distributions.Transform",
+  "torch.distributions.uniform.Uniform": "torch.distributions.Uniform",
   "torch.nn.modules.GroupNorm": "torch.nn.GroupNorm",
   "torch.nn.modules.activation.ReLU": "torch.nn.ReLU",
   "torch.nn.modules.conv.Conv2d": "torch.nn.Conv2d",

diff --git a/paconvert/api_mapping.json b/paconvert/api_mapping.json
@@ -3948,7 +3948,7 @@
       "pg_options"
     ]
   },
-  "torch.distributions.bernoulli.Bernoulli": {
+  "torch.distributions.Bernoulli": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Bernoulli",
     "args_list": [
@@ -3963,7 +3963,7 @@
       "logits"
     ]
   },
-  "torch.distributions.beta.Beta": {
+  "torch.distributions.Beta": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Beta",
     "args_list": [
@@ -3976,7 +3976,7 @@
       "concentration0": "beta"
     }
   },
-  "torch.distributions.categorical.Categorical": {
+  "torch.distributions.Categorical": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Categorical",
     "args_list": [
@@ -3991,7 +3991,7 @@
       "probs"
     ]
   },
-  "torch.distributions.cauchy.Cauchy": {
+  "torch.distributions.Cauchy": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Cauchy",
     "args_list": [
@@ -4003,7 +4003,19 @@
       "validate_args": ""
     }
   },
-  "torch.distributions.dirichlet.Dirichlet": {
+  "torch.distributions.ComposeTransform": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.ChainTransform",
+    "args_list": [
+      "parts",
+      "cache_size"
+    ],
+    "kwargs_change": {
+      "parts": "transforms",
+      "cache_size": ""
+    }
+  },
+  "torch.distributions.Dirichlet": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Dirichlet",
     "args_list": [
@@ -4014,7 +4026,7 @@
       "validate_args": ""
     }
   },
-  "torch.distributions.exp_family.ExponentialFamily": {
+  "torch.distributions.ExponentialFamily": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.ExponentialFamily",
     "args_list": [
@@ -4026,7 +4038,7 @@
       "validate_args": ""
     }
   },
-  "torch.distributions.geometric.Geometric": {
+  "torch.distributions.Geometric": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Geometric",
     "args_list": [
@@ -4041,7 +4053,7 @@
       "logits"
     ]
   },
-  "torch.distributions.gumbel.Gumbel": {
+  "torch.distributions.Gumbel": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Gumbel",
     "args_list": [
@@ -4053,7 +4065,7 @@
       "validate_args": ""
     }
   },
-  "torch.distributions.independent.Independent": {
+  "torch.distributions.Independent": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Independent",
     "args_list": [
@@ -4067,27 +4079,7 @@
       "validate_args": ""
     }
   },
-  "torch.distributions.kl.kl_divergence": {
-    "Matcher": "GenericMatcher",
-    "paddle_api": "paddle.distribution.kl_divergence",
-    "args_list": [
-      "p",
-      "q"
-    ]
-  },
-  "torch.distributions.kl.register_kl": {
-    "Matcher": "GenericMatcher",
-    "paddle_api": "paddle.distribution.kl.register_kl",
-    "args_list": [
-      "type_p",
-      "type_q"
-    ],
-    "kwargs_change": {
-      "type_p": "cls_p",
-      "type_q": "cls_q"
-    }
-  },
-  "torch.distributions.laplace.Laplace": {
+  "torch.distributions.Laplace": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Laplace",
     "args_list": [
@@ -4099,7 +4091,7 @@
       "validate_args": ""
     }
   },
-  "torch.distributions.log_normal.LogNormal": {
+  "torch.distributions.LogNormal": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.LogNormal",
     "args_list": [
@@ -4111,7 +4103,7 @@
       "validate_args": ""
     }
   },
-  "torch.distributions.multinomial.Multinomial": {
+  "torch.distributions.Multinomial": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Multinomial",
     "args_list": [
@@ -4127,7 +4119,7 @@
       "logits"
     ]
   },
-  "torch.distributions.normal.Normal": {
+  "torch.distributions.Normal": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.Normal",
     "args_list": [
@@ -4139,7 +4131,17 @@
       "validate_args": ""
     }
   },
-  "torch.distributions.transforms.SoftmaxTransform": {
+  "torch.distributions.SigmoidTransform": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.SigmoidTransform",
+    "args_list": [
+      "cache_size"
+    ],
+    "kwargs_change": {
+      "cache_size": ""
+    }
+  },
+  "torch.distributions.SoftmaxTransform": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.SoftmaxTransform",
     "args_list": [
@@ -4149,7 +4151,7 @@
       "cache_size": ""
     }
   },
-  "torch.distributions.transforms.StackTransform": {
+  "torch.distributions.StackTransform": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.StackTransform",
     "args_list": [
@@ -4163,7 +4165,7 @@
       "cache_size": ""
     }
   },
-  "torch.distributions.transforms.StickBreakingTransform": {
+  "torch.distributions.StickBreakingTransform": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.distribution.StickBreakingTransform",
     "args_list": [
@@ -4173,6 +4175,61 @@
       "cache_size": ""
     }
   },
+  "torch.distributions.Transform": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.Transform",
+    "args_list": [
+      "cache_size"
+    ],
+    "kwargs_change": {
+      "cache_size": ""
+    }
+  },
+  "torch.distributions.TransformedDistribution": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.TransformedDistribution",
+    "args_list": [
+      "base_distribution",
+      "transforms",
+      "validate_args"
+    ],
+    "kwargs_change": {
+      "base_distribution": "base",
+      "validate_args": ""
+    }
+  },
+  "torch.distributions.Uniform": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.Uniform",
+    "args_list": [
+      "low",
+      "high",
+      "validate_args"
+    ],
+    "kwargs_change": {
+      "validate_args": ""
+    }
+  },
+  "torch.distributions.kl.kl_divergence": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.kl_divergence",
+    "args_list": [
+      "p",
+      "q"
+    ]
+  },
+  "torch.distributions.kl.register_kl": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.distribution.kl.register_kl",
+    "args_list": [
+      "type_p",
+      "type_q"
+    ],
+    "kwargs_change": {
+      "type_p": "cls_p",
+      "type_q": "cls_q"
+    }
+  },
   "torch.div": {
     "Matcher": "DivMatcher",
     "args_list": [

diff --git a/...test_distributions_bernoulli_Bernoulli.py → tests/test_distributions_Bernoulli.py b/...test_distributions_bernoulli_Bernoulli.py → tests/test_distributions_Bernoulli.py
@@ -16,14 +16,14 @@
 
 from apibase import APIBase
 
-obj = APIBase("torch.distributions.bernoulli.Bernoulli")
+obj = APIBase("torch.distributions.Bernoulli")
 
 
 def test_case_1():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        m = torch.distributions.bernoulli.Bernoulli(torch.tensor([0.3]))
+        m = torch.distributions.Bernoulli(torch.tensor([0.3]))
         result = m.sample([100])
         """
     )
@@ -34,7 +34,7 @@ def test_case_2():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        m = torch.distributions.bernoulli.Bernoulli(probs=torch.tensor([0.3]), logits=None)
+        m = torch.distributions.Bernoulli(probs=torch.tensor([0.3]), logits=None)
         result = m.sample([100])
         """
     )
@@ -48,6 +48,17 @@ def test_case_2():
 
 
 def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        m = torch.distributions.Bernoulli(0.3, validate_args=False)
+        result = m.sample([100])
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_value=False)
+
+
+def test_case_4():
     pytorch_code = textwrap.dedent(
         """
         import torch

diff --git a/tests/test_distributions_beta_Beta.py → tests/test_distributions_Beta.py b/tests/test_distributions_beta_Beta.py → tests/test_distributions_Beta.py
@@ -16,10 +16,22 @@
 
 from apibase import APIBase
 
-obj = APIBase("torch.distributions.beta.Beta")
+obj = APIBase("torch.distributions.Beta")
 
 
 def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        m = torch.distributions.Beta(torch.tensor([0.5]), torch.tensor([0.5]))
+        n = torch.distributions.Beta(torch.tensor([0.3]), torch.tensor([0.7]))
+        result = torch.distributions.kl.kl_divergence(m, n)
+        """
+    )
+    obj.run(pytorch_code, ["result"])
+
+
+def test_case_2():
     pytorch_code = textwrap.dedent(
         """
         import torch

diff --git a/...s/test_distributions_binomial_Binomial.py → tests/test_distributions_Binomial.py b/...s/test_distributions_binomial_Binomial.py → tests/test_distributions_Binomial.py
@@ -16,14 +16,14 @@
 
 from apibase import APIBase
 
-obj = APIBase("torch.distributions.binomial.Binomial")
+obj = APIBase("torch.distributions.Binomial")
 
 
 def test_case_1():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        m = torch.distributions.binomial.Binomial(100, torch.tensor([0, .2, .8, 1]))
+        m = torch.distributions.Binomial(100, torch.tensor([0, .2, .8, 1]))
         result = m.sample()
         """
     )
@@ -39,7 +39,7 @@ def test_case_2():
     pytorch_code = textwrap.dedent(
         """
         import torch
-        m = torch.distributions.binomial.Binomial(1, probs=torch.tensor([0.3]), logits=None)
+        m = torch.distributions.Binomial(1, probs=torch.tensor([0.3]), logits=None)
         result = m.sample()
         """
     )
@@ -52,6 +52,22 @@ def test_case_2():
 
 
 def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        m = torch.distributions.Binomial(1, 0.3, validate_args=False)
+        result = m.sample()
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        unsupport=True,
+        reason="paddle does not support this function temporarily",
+    )
+
+
+def test_case_4():
     pytorch_code = textwrap.dedent(
         """
         import torch