modify custom_ops to pass pynative mode

update dtype for device

delete used funcs

mindspore/nn/probability/distribution/_utils/custom_ops.py

@@ -17,6 +17,7 @@ import numpy as np
 from mindspore.ops import operations as P
 from mindspore.common import dtype as mstype
 
+
 def exp_by_step(input_x):
     """
     Log op on Ascend doesn't supprot int types.
@@ -24,23 +25,18 @@ def exp_by_step(input_x):
     """
     exp = P.Exp()
     cast = P.Cast()
-    dtype = P.DType()
-    checktype = P.IsSubClass()
 
-    if checktype(dtype(input_x), mstype.int_):
-        input_x = cast(input_x, mstype.float32)
-    elif checktype(dtype(input_x), mstype.float_):
-        pass
-    else:
-        return None
+    input_x = cast(input_x, mstype.float32)
     return exp(input_x)
 
+
 def expm1_by_step(input_x):
     """
     Expm1 ops under GPU context.
     """
     return exp_by_step(input_x) - 1.0
 
+
 def log_by_step(input_x):
     """
     Log op on Ascend is calculated as log(abs(x)).
@@ -56,14 +52,8 @@ def log_by_step(input_x):
     dtype = P.DType()
     shape = P.Shape()
     select = P.Select()
-    checktype = P.IsSubClass()
 
-    if checktype(dtype(input_x), mstype.int_):
-        input_x = cast(input_x, mstype.float32)
-    elif checktype(dtype(input_x), mstype.float_):
-        pass
-    else:
-        return None
+    input_x = cast(input_x, mstype.float32)
     nan = fill(dtype(input_x), shape(input_x), np.nan)
     inf = fill(dtype(input_x), shape(input_x), np.inf)
     neg_x = less(input_x, 0.0)
@@ -72,6 +62,7 @@ def log_by_step(input_x):
     result = select(nonpos_x, -inf, log_x)
     return select(neg_x, nan, result)
 
+
 def log1p_by_step(x):
     """
     Log1p ops on GPU device or when device_target == GPU.

mindspore/nn/probability/distribution/_utils/utils.py

@@ -14,15 +14,15 @@
 # ============================================================================
 """Utitly functions to help distribution class."""
 import numpy as np
-from mindspore.ops import _utils as utils
-from mindspore.ops.primitive import constexpr, PrimitiveWithInfer, prim_attr_register
+from mindspore import context
 from mindspore._checkparam import Validator as validator
 from mindspore.common.tensor import Tensor
 from mindspore.common.parameter import Parameter
 from mindspore.common import dtype as mstype
-from mindspore.ops import operations as P
+from mindspore.ops import _utils as utils
 from mindspore.ops import composite as C
-from mindspore import context
+from mindspore.ops import operations as P
+from mindspore.ops.primitive import constexpr, PrimitiveWithInfer, prim_attr_register
 import mindspore.nn as nn
 import mindspore.nn.probability as msp
 
@@ -82,6 +82,24 @@ def convert_to_batch(t, batch_shape, required_type):
     return Tensor(np.broadcast_to(t.asnumpy(), batch_shape), dtype=required_type)
 
 
+def cast_type_for_device(dtype):
+    """
+    use the alternative dtype supported by the device.
+    Args:
+        dtype (mindspore.dtype): input dtype.
+    Returns:
+        mindspore.dtype.
+    """
+    if context.get_context("device_target") == "GPU":
+        if dtype in mstype.uint_type or dtype == mstype.int8:
+            return mstype.int16
+        if dtype == mstype.int64:
+            return mstype.int32
+        if dtype == mstype.float64:
+            return mstype.float32
+    return dtype
+
+
 def check_scalar_from_param(params):
     """
     Check if params are all scalars.
@@ -293,10 +311,10 @@ def raise_not_impl_error(name):
 def check_distribution_name(name, expected_name):
     if name is None:
         raise ValueError(
-            f"Distribution should be a constant which is not None.")
+            f"Input dist should be a constant which is not None.")
     if name != expected_name:
         raise ValueError(
-            f"Expected distribution name is {expected_name}, but got {name}.")
+            f"Expected dist input is {expected_name}, but got {name}.")
 
 
 class CheckTuple(PrimitiveWithInfer):

mindspore/nn/probability/distribution/distribution.py

@@ -16,9 +16,10 @@
 from mindspore.nn.cell import Cell
 from mindspore._checkparam import Validator as validator
 from mindspore._checkparam import Rel
-from ._utils.utils import calc_broadcast_shape_from_param, check_scalar_from_param
+from ._utils.utils import calc_broadcast_shape_from_param, check_scalar_from_param, cast_type_for_device
 from ._utils.utils import CheckTuple, CheckTensor
 
+
 class Distribution(Cell):
     """
     Base class for all mathematical distributions.
@@ -43,12 +44,12 @@ class Distribution(Cell):
         new distribution specified by the dist_spec_args. But it won't change the
         original distribuion.
     """
+
     def __init__(self,
                  seed,
                  dtype,
                  name,
                  param):
-
         """
         Constructor of distribution class.
         """
@@ -58,7 +59,7 @@ class Distribution(Cell):
 
         self._name = name
         self._seed = seed
-        self._dtype = dtype
+        self._dtype = cast_type_for_device(dtype)
         self._parameters = {}
         # parsing parameters
         for k in param.keys():
@@ -436,7 +437,6 @@ class Distribution(Cell):
         """
         return self._sample(*args, **kwargs)
 
-
     def construct(self, name, *args, **kwargs):
         """
         Override construct in Cell.