paddle.distribution.StudentT improve input data type and fix returned dimension 易用性提升

python/paddle/distribution/distribution.py

@@ -306,3 +306,44 @@ def _logits_to_probs(
             if is_binary
             else paddle.nn.functional.softmax(logits, axis=-1)
         )
+
+    def _broadcast_all(
+        self, *args: TensorLike | NestedNumbericSequence
+    ) -> tuple[Tensor, ...]:
+        r"""
+        Returns a list where each arg is broadcasted. Scalar args are upcast to tensors
+        having the same data type as the first Tensor passed to `args`.  If all the
+        args are scalars, then they are upcasted to Tensors with paddle default data type.
+
+        Args:
+            value (float, list, numpy.ndarray, Tensor)
+
+        Returns:
+            Broadcasted Tensor of args.
+        """
+        for arg in args:
+            if not isinstance(
+                arg,
+                (float, list, tuple, np.ndarray, Variable, paddle.pir.Value),
+            ):
+                raise TypeError(
+                    f"Type of input args must be float, list, tuple, numpy.ndarray or Tensor, but received type {type(arg)}"
+                )
+        if not all(
+            isinstance(arg, (Variable, paddle.pir.Value)) for arg in args
+        ):
+            dtype = paddle.get_default_dtype()
+            for arg in args:
+                if isinstance(arg, (Variable, paddle.pir.Value)):
+                    dtype = arg.dtype
+                    break
+            new_args = [
+                (
+                    arg
+                    if isinstance(arg, (Variable, paddle.pir.Value))
+                    else paddle.to_tensor(arg, dtype=dtype)
+                )
+                for arg in args
+            ]
+            return paddle.broadcast_tensors(new_args)
+        return paddle.broadcast_tensors(args)

python/paddle/distribution/student_t.py

@@ -18,7 +18,7 @@
 from typing import TYPE_CHECKING
 
 import paddle
-from paddle.base.data_feeder import check_type, convert_dtype
+from paddle.base.data_feeder import check_type
 from paddle.base.framework import Variable
 from paddle.distribution import Gamma, distribution
 from paddle.framework import in_dynamic_mode
@@ -135,18 +135,7 @@ def __init__(
             )
 
         self.name = name if name is not None else 'StudentT'
-        self.dtype = paddle.get_default_dtype()
-
-        if self._validate_args(df, loc, scale):
-            self.df = df
-            self.loc = loc
-            self.scale = scale
-            self.df, self.loc, self.scale = paddle.broadcast_tensors(
-                [self.df, self.loc, self.scale]
-            )
-            self.dtype = convert_dtype(df.dtype)
-        else:
-            self.df, self.loc, self.scale = self._to_tensor(df, loc, scale)
+        self.df, self.loc, self.scale = self._broadcast_all(df, loc, scale)
 
         if not self._check_nonnegative(self.df):
             raise ValueError(
@@ -157,10 +146,6 @@ def __init__(
                 'Every element of input parameter `scale` should be nonnegative.'
             )
 
-        if self.df.shape == []:
-            self.df = self.df.reshape([1])
-            self.loc = self.loc.reshape([1])
-            self.scale = self.scale.reshape([1])
         batch_shape = self.df.shape
         super().__init__(batch_shape)
         self._chi2 = Gamma(0.5 * self.df, paddle.full_like(self.df, 0.5))
@@ -222,7 +207,7 @@ def sample(self, shape: Sequence[int] = ()) -> Tensor:
             raise TypeError('sample shape must be Sequence object.')
 
         output_shape = self._extend_shape(shape)
-        z = paddle.cast(paddle.normal(shape=output_shape), self.dtype)
+        z = paddle.normal(shape=output_shape)
         chi2 = self._chi2.sample(shape)
         x = z * paddle.rsqrt(chi2 / self.df)
         return self.loc + self.scale * x

test/distribution/test_distribution_student_t.py

@@ -187,6 +187,47 @@ def _np_entropy(self):
         return scipy.stats.t.entropy(df, loc, scale)
 
 
+@parameterize.place(config.DEVICES)
+@parameterize.parameterize_cls(
+    (parameterize.TEST_CASE_NAME, 'df', 'loc', 'scale'),
+    [
+        (
+            'float-tensor',
+            10.0,
+            paddle.to_tensor(1.0),
+            2.0,
+        ),
+        (
+            'float-tensor1',
+            10.0,
+            parameterize.xrand((2, 3), dtype='float32', min=1, max=10),
+            2.0,
+        ),
+        (
+            'float-tensor2',
+            parameterize.xrand((2, 1), dtype='float64', min=4, max=30),
+            parameterize.xrand((2, 3), dtype='float64', min=1, max=10),
+            2.0,
+        ),
+        (
+            'float-tensor3',
+            parameterize.xrand((2, 1), dtype='float64', min=4, max=30),
+            1.0,
+            parameterize.xrand((2, 1), dtype='float64', min=0.1, max=3),
+        ),
+        (
+            'float-tensor4',
+            5.0,
+            parameterize.xrand((2, 1), dtype='float32', min=-1, max=-10),
+            parameterize.xrand((2, 3), dtype='float32', min=0.1, max=3),
+        ),
+    ],
+)
+class TestStudentT2(TestStudentT):
+    def setUp(self):
+        self._dist = StudentT(self.df, self.loc, self.scale)
+
+
 @parameterize.place(config.DEVICES)
 @parameterize.parameterize_cls(
     (parameterize.TEST_CASE_NAME, 'df', 'loc', 'scale', 'value'),
@@ -247,6 +288,31 @@ def test_log_prob(self):
         )
 
 
+@parameterize.place(config.DEVICES)
+@parameterize.parameterize_cls(
+    (parameterize.TEST_CASE_NAME, 'df', 'loc', 'scale', 'value'),
+    [
+        (
+            'float-tensor1',
+            10.0,
+            parameterize.xrand((2, 1), dtype='float32', min=-10, max=10),
+            1.0,
+            np.array(3.3).astype("float32"),
+        ),
+        (
+            'float-tensor2',
+            parameterize.xrand((2, 1), dtype='float64', min=4, max=30),
+            1.0,
+            parameterize.xrand((2, 1), dtype='float64', min=0.1, max=5),
+            parameterize.xrand((2, 4), dtype='float64', min=-10, max=10),
+        ),
+    ],
+)
+class TestStudentTProbs2(TestStudentTProbs):
+    def setUp(self):
+        self._dist = StudentT(self.df, self.loc, self.scale)
+
+
 @parameterize.place(config.DEVICES)
 @parameterize_cls([TEST_CASE_NAME], ['StudentTTestError'])
 class StudentTTestError(unittest.TestCase):