转换规则 No.22 No.25 torch.linalg.matrix_rank torch.qr (#159)

* add convert rule No.22 and No.25

* fix ut error

* fixed

* fixed

paconvert/api_mapping.json

@@ -4784,6 +4784,25 @@
       "input": "x"
     }
   },
+  "torch.linalg.matrix_rank": {
+    "Matcher": "GenericMatcher",
+    "paddle_api": "paddle.linalg.matrix_rank",
+    "args_list": [
+      "A",
+      "tol",
+      "atol",
+      "rtol",
+      "hermitian",
+      "out"
+    ],
+    "kwargs_change": {
+      "A": "x"
+    },
+    "unsupport_args": [
+      "atol",
+      "rtol"
+    ]
+  },
   "torch.linalg.multi_dot": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.linalg.multi_dot",
@@ -8332,6 +8351,18 @@
       "dim": "axis"
     }
   },
+  "torch.qr": {
+    "Matcher": "QrMatcher",
+    "paddle_api": "paddle.linalg.qr",
+    "args_list": [
+      "input",
+      "some",
+      "out"
+    ],
+    "kwargs_change": {
+      "input": "x"
+    }
+  },
   "torch.rad2deg": {
     "Matcher": "GenericMatcher",
     "paddle_api": "paddle.rad2deg",

paconvert/api_matcher.py

@@ -3648,6 +3648,31 @@ def generate_code(self, kwargs):
         return GenericMatcher.generate_code(self, kwargs)
 
 
+class QrMatcher(BaseMatcher):
+    def generate_code(self, kwargs):
+        some_v = kwargs.pop("some") if "some" in kwargs else None
+        out_v = kwargs.pop("out") if "out" in kwargs else None
+
+        if some_v:
+            kwargs["mode"] = "'complete'" if some_v != "(False)" else "'reduced'"
+
+        if out_v:
+            kwargs["x"] = kwargs.pop("input")
+            API_TEMPLATE = textwrap.dedent(
+                """
+                tmp_q, tmp_r = {}({})
+                paddle.assign(tmp_q, {}[0]), paddle.assign(tmp_r, {}[1])
+                """
+            )
+
+            code = API_TEMPLATE.format(
+                self.get_paddle_api(), self.kwargs_to_str(kwargs), out_v, out_v
+            )
+            return code
+
+        return GenericMatcher.generate_code(self, kwargs)
+
+
 class RandomSplitMatcher(BaseMatcher):
     def generate_code(self, kwargs):
         API_TEMPLATE = textwrap.dedent(

tests/apibase.py

@@ -37,6 +37,7 @@ def run(
         compared_tensor_names=None,
         expect_paddle_code=None,
         check_value=True,
+        check_dtype=True,
         unsupport=False,
         reason=None,
         is_aux_api=False,
@@ -47,6 +48,7 @@ def run(
             compared_tensor_names: the list of variant name to be compared
             expect_paddle_code: the string of expect paddle code
             check_value: If false, the value will not be checked
+            check_dtype: If false, the dtype will not be checked
             unsupport: If true, conversion is not supported
             reason: the reason why it is not supported
             is_aux_api: the bool value for api that need Auxiliary code
@@ -81,7 +83,11 @@ def run(
             paddle_result = [loc[name] for name in compared_tensor_names]
             for i in range(len(compared_tensor_names)):
                 self.compare(
-                    self.pytorch_api, pytorch_result[i], paddle_result[i], check_value
+                    self.pytorch_api,
+                    pytorch_result[i],
+                    paddle_result[i],
+                    check_value,
+                    check_dtype,
                 )
 
         if expect_paddle_code:
@@ -90,14 +96,17 @@ def run(
                 convert_paddle_code == expect_paddle_code
             ), "[{}]: get unexpected code".format(self.pytorch_api)
 
-    def compare(self, name, pytorch_result, paddle_result, check_value=True):
+    def compare(
+        self, name, pytorch_result, paddle_result, check_value=True, check_dtype=True
+    ):
         """
         compare tensors' data, shape, requires_grad, dtype
         args:
             name: pytorch api name
             pytorch_result: pytorch Tensor
             paddle_result: paddle Tensor
             check_value: If false, the value will not be checked
+            check_dtype: If false, the dtype will not be checked
         """
         if isinstance(pytorch_result, (tuple, list)):
             assert isinstance(
@@ -147,11 +156,12 @@ def compare(self, name, pytorch_result, paddle_result, check_value=True):
         ), "API ({}): shape mismatch, torch shape is {}, paddle shape is {}".format(
             name, pytorch_numpy.shape, paddle_numpy.shape
         )
-        assert (
-            pytorch_numpy.dtype == paddle_numpy.dtype
-        ), "API ({}): dtype mismatch, torch dtype is {}, paddle dtype is {}".format(
-            name, pytorch_numpy.dtype, paddle_numpy.dtype
-        )
+        if check_dtype:
+            assert (
+                pytorch_numpy.dtype == paddle_numpy.dtype
+            ), "API ({}): dtype mismatch, torch dtype is {}, paddle dtype is {}".format(
+                name, pytorch_numpy.dtype, paddle_numpy.dtype
+            )
         if check_value:
             assert np.allclose(
                 pytorch_numpy, paddle_numpy

tests/test_Generator.py

@@ -19,7 +19,9 @@
 
 
 class GeneratorAPIBase(APIBase):
-    def compare(self, name, pytorch_result, paddle_result, check_value=True):
+    def compare(
+        self, name, pytorch_result, paddle_result, check_value=True, check_dtype=True
+    ):
         if isinstance(paddle_result, paddle.fluid.libpaddle.Generator):
             return True
         return False

tests/test_cuda_current_stream.py

@@ -19,7 +19,9 @@
 
 
 class cudaCurrentStreamAPI(APIBase):
-    def compare(self, name, pytorch_result, paddle_result, check_value=True):
+    def compare(
+        self, name, pytorch_result, paddle_result, check_value=True, check_dtype=True
+    ):
         return pytorch_result == paddle_result or isinstance(
             paddle_result, paddle.fluid.libpaddle.CUDAStream
         )

tests/test_cuda_event.py

@@ -19,7 +19,9 @@
 
 
 class cudaEventAPI(APIBase):
-    def compare(self, name, pytorch_result, paddle_result, check_value=True):
+    def compare(
+        self, name, pytorch_result, paddle_result, check_value=True, check_dtype=True
+    ):
         return pytorch_result == paddle_result or isinstance(
             paddle_result, paddle.fluid.libpaddle.CUDAEvent
         )

tests/test_cuda_get_device_properites.py

@@ -19,7 +19,9 @@
 
 
 class cudaGetDeviceProperitesAPI(APIBase):
-    def compare(self, name, pytorch_result, paddle_result, check_value=True):
+    def compare(
+        self, name, pytorch_result, paddle_result, check_value=True, check_dtype=True
+    ):
         return pytorch_result == paddle_result or isinstance(
             paddle_result, paddle.fluid.libpaddle._gpuDeviceProperties
         )

tests/test_cuda_max_memory_allocated.py

@@ -18,7 +18,9 @@
 
 
 class cudaMaxMemoryAllocatedAPI(APIBase):
-    def compare(self, name, pytorch_result, paddle_result, check_value=True):
+    def compare(
+        self, name, pytorch_result, paddle_result, check_value=True, check_dtype=True
+    ):
         return pytorch_result == paddle_result
 
 

tests/test_device.py

@@ -18,7 +18,7 @@
 
 
 class DeviceAPIBase(APIBase):
-    def compare(self, name, pytorch_result, paddle_result, value):
+    def compare(self, name, pytorch_result, paddle_result, value, dtype):
         return str(pytorch_result) == str(paddle_result)
 
 

tests/test_linalg_lstsq.py

@@ -19,7 +19,9 @@
 
 
 class LstsqAPI(APIBase):
-    def compare(self, name, pytorch_result, paddle_result, check_value=True):
+    def compare(
+        self, name, pytorch_result, paddle_result, check_value=True, check_dtype=True
+    ):
         if isinstance(pytorch_result, (tuple, list)):
             for i in range(len(pytorch_result)):
                 self.compare(self.pytorch_api, pytorch_result[i], paddle_result[i])

tests/test_linalg_matrix_rank.py

@@ -0,0 +1,141 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import textwrap
+
+from apibase import APIBase
+
+obj = APIBase("torch.linalg.matrix_rank")
+
+
+def test_case_1():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        A = torch.eye(10)
+        A[0, 0] = 0
+        result = torch.linalg.matrix_rank(A)
+        """
+    )
+    # NOTE: torch dtype is int64, paddle dtype is int32
+    obj.run(pytorch_code, ["result"], check_dtype=False)
+
+
+def test_case_2():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        A = torch.tensor([[[[-1.1079, -0.4803,  0.2296],
+          [ 0.3198, -0.2976, -0.0585],
+          [-1.6931, -0.3353, -0.2893]],
+         [[ 1.9757, -0.5959, -1.2041],
+          [ 0.8443, -0.4916, -1.6574],
+          [-0.2654, -1.0447, -0.8138]],
+         [[-0.4111,  1.0973,  0.2275],
+          [ 1.1851,  1.8233,  0.8187],
+          [-1.4107, -0.5473,  1.1431]],
+         [[ 0.0327, -0.8295,  0.0457],
+          [-0.6286, -0.2507,  0.7292],
+          [ 0.4075, -1.3918, -0.5015]]],
+        [[[-2.1256,  0.9310,  1.0743],
+          [ 1.9577, -0.1513,  0.1668],
+          [-0.1404,  1.6647,  0.7108]],
+         [[ 0.9001,  1.6930, -0.4966],
+          [-1.0432, -1.0742,  1.2273],
+          [-0.2711, -0.4740, -0.6381]],
+         [[-1.3099, -1.7540,  0.5443],
+          [ 0.3565, -2.3821,  0.8638],
+          [-1.3840,  0.8216,  0.2761]],
+         [[-0.5989, -0.4732,  1.3252],
+          [-0.7614,  1.0493,  0.8488],
+          [-0.1300,  0.1287,  0.6234]]]])
+        result = torch.linalg.matrix_rank(A, hermitian=True)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_dtype=False)
+
+
+def test_case_3():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        A = torch.tensor([[[[-1.1079, -0.4803,  0.2296],
+          [ 0.3198, -0.2976, -0.0585],
+          [-1.6931, -0.3353, -0.2893]],
+         [[ 1.9757, -0.5959, -1.2041],
+          [ 0.8443, -0.4916, -1.6574],
+          [-0.2654, -1.0447, -0.8138]],
+         [[-0.4111,  1.0973,  0.2275],
+          [ 1.1851,  1.8233,  0.8187],
+          [-1.4107, -0.5473,  1.1431]],
+         [[ 0.0327, -0.8295,  0.0457],
+          [-0.6286, -0.2507,  0.7292],
+          [ 0.4075, -1.3918, -0.5015]]],
+        [[[-2.1256,  0.9310,  1.0743],
+          [ 1.9577, -0.1513,  0.1668],
+          [-0.1404,  1.6647,  0.7108]],
+         [[ 0.9001,  1.6930, -0.4966],
+          [-1.0432, -1.0742,  1.2273],
+          [-0.2711, -0.4740, -0.6381]],
+         [[-1.3099, -1.7540,  0.5443],
+          [ 0.3565, -2.3821,  0.8638],
+          [-1.3840,  0.8216,  0.2761]],
+         [[-0.5989, -0.4732,  1.3252],
+          [-0.7614,  1.0493,  0.8488],
+          [-0.1300,  0.1287,  0.6234]]]])
+        result = torch.linalg.matrix_rank(A, atol=1.0, rtol=0.0)
+        """
+    )
+    obj.run(
+        pytorch_code,
+        ["result"],
+        check_dtype=False,
+        unsupport=True,
+        reason="paddle does not support `atol` and `rtol`",
+    )
+
+
+def test_case_4():
+    pytorch_code = textwrap.dedent(
+        """
+        import torch
+        A = torch.tensor([[[[-1.1079, -0.4803,  0.2296],
+          [ 0.3198, -0.2976, -0.0585],
+          [-1.6931, -0.3353, -0.2893]],
+         [[ 1.9757, -0.5959, -1.2041],
+          [ 0.8443, -0.4916, -1.6574],
+          [-0.2654, -1.0447, -0.8138]],
+         [[-0.4111,  1.0973,  0.2275],
+          [ 1.1851,  1.8233,  0.8187],
+          [-1.4107, -0.5473,  1.1431]],
+         [[ 0.0327, -0.8295,  0.0457],
+          [-0.6286, -0.2507,  0.7292],
+          [ 0.4075, -1.3918, -0.5015]]],
+        [[[-2.1256,  0.9310,  1.0743],
+          [ 1.9577, -0.1513,  0.1668],
+          [-0.1404,  1.6647,  0.7108]],
+         [[ 0.9001,  1.6930, -0.4966],
+          [-1.0432, -1.0742,  1.2273],
+          [-0.2711, -0.4740, -0.6381]],
+         [[-1.3099, -1.7540,  0.5443],
+          [ 0.3565, -2.3821,  0.8638],
+          [-1.3840,  0.8216,  0.2761]],
+         [[-0.5989, -0.4732,  1.3252],
+          [-0.7614,  1.0493,  0.8488],
+          [-0.1300,  0.1287,  0.6234]]]])
+        result = torch.empty((2, 4), dtype=torch.int64)
+        torch.linalg.matrix_rank(A, hermitian=True, out=result)
+        """
+    )
+    obj.run(pytorch_code, ["result"], check_dtype=False)

tests/test_nn_AdaptiveMaxPool1d.py

@@ -19,7 +19,9 @@
 
 
 class MaxPoolAPI(APIBase):
-    def compare(self, name, pytorch_result, paddle_result, check_value=True):
+    def compare(
+        self, name, pytorch_result, paddle_result, check_value=True, check_dtype=True
+    ):
         if isinstance(pytorch_result, (tuple, list)):
             for i in range(len(pytorch_result)):
                 self.compare(self.pytorch_api, pytorch_result[i], paddle_result[i])

tests/test_nn_AdaptiveMaxPool2d.py

@@ -19,7 +19,9 @@
 
 
 class MaxPoolAPI(APIBase):
-    def compare(self, name, pytorch_result, paddle_result, check_value=True):
+    def compare(
+        self, name, pytorch_result, paddle_result, check_value=True, check_dtype=True
+    ):
         if isinstance(pytorch_result, (tuple, list)):
             for i in range(len(pytorch_result)):
                 self.compare(self.pytorch_api, pytorch_result[i], paddle_result[i])

tests/test_nn_AdaptiveMaxPool3d.py

@@ -19,7 +19,9 @@
 
 
 class MaxPoolAPI(APIBase):
-    def compare(self, name, pytorch_result, paddle_result, check_value=True):
+    def compare(
+        self, name, pytorch_result, paddle_result, check_value=True, check_dtype=True
+    ):
         if isinstance(pytorch_result, (tuple, list)):
             for i in range(len(pytorch_result)):
                 self.compare(self.pytorch_api, pytorch_result[i], paddle_result[i])