vm for PopulationCount

mindspore/ops/_op_impl/tbe/__init__.py

@@ -284,3 +284,4 @@ from .scatter_div import _scatter_div_tbe
 from .mod import _mod_tbe
 from .max_pool_grad_grad import _max_pool_grad_grad_tbe
 from .max_pool_grad_grad_with_argmax import _max_pool_grad_grad_with_argmax_tbe
+from .population_count import _population_count_tbe

mindspore/ops/_op_impl/tbe/population_count.py

@@ -0,0 +1,38 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""PopulationCount op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+population_count_op_info = TBERegOp("PopulationCount") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("population_count.so") \
+    .compute_cost(10) \
+    .kernel_name("population_count") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.I16_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.I16_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U16_5HD, DataType.U8_5HD) \
+    .dtype_format(DataType.U16_Default, DataType.U8_Default) \
+    .get_op_info()
+
+
+@op_info_register(population_count_op_info)
+def _population_count_tbe():
+    """PopulationCount TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -76,7 +76,7 @@ from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, Appl
                      ApplyAdaMax, ApplyAdadelta, ApplyAdagrad, ApplyAdagradV2,
                      ApplyAddSign, ApplyPowerSign, ApplyGradientDescent, ApplyProximalGradientDescent,
                      ApplyRMSProp, ApplyCenteredRMSProp, BasicLSTMCell, InTopK)
-from .other_ops import (Assign, IOU, BoundingBoxDecode, BoundingBoxEncode,
+from .other_ops import (Assign, IOU, BoundingBoxDecode, BoundingBoxEncode, PopulationCount,
                         CheckValid, MakeRefKey, Partial, Depend, CheckBprop)
 from .thor_ops import *
 
@@ -328,7 +328,8 @@ __all__ = [
     "InplaceUpdate",
     "InTopK",
     "LRN",
-    "Mod"
+    "Mod",
+    "PopulationCount"
 ]
 
 __all__.sort()

mindspore/ops/operations/other_ops.py

@@ -51,6 +51,7 @@ class Assign(PrimitiveWithInfer):
         ('variable', sig_rw.RW_WRITE, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T),
         ('value', sig_rw.RW_READ, sig_kind.KIND_POSITIONAL_KEYWORD, sig_kind.KIND_EMPTY_DEFAULT_VALUE, sig_dtype.T)
     )
+
     @prim_attr_register
     def __init__(self):
         self.init_prim_io_names(inputs=['ref', 'value'], outputs=['output'])
@@ -324,6 +325,7 @@ class Partial(Primitive):
         partial_func = functools.partial(func, *args[1:])
         return partial_func
 
+
 class Depend(Primitive):
     """
     Depend is used for process side-effect operations.
@@ -457,3 +459,32 @@ class ConfusionMatrix(PrimitiveWithInfer):
         args = {"labels": labels, "predictions": predictions}
         validator.check_tensor_type_same(args, (mstype.number_type), self.name)
         return labels
+
+
+class PopulationCount(PrimitiveWithInfer):
+    r"""
+    Calculate population count.
+
+    Inputs:
+        - **input** (Tensor) -  The data type should be int16 or uint16.
+
+    Outputs:
+        Tensor, with shape same as the input.
+
+    Examples:
+        >>> population_count = P.PopulationCount()
+        >>> x_input = Tensor([0, 1, 3], mindspore.int16)
+        >>> population_count(x_input)
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        pass
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        args = {"x": x_dtype}
+        validator.check_tensor_type_same(args, (mstype.int16, mstype.uint16,), self.name)
+        return mstype.tensor_type(mstype.uint8)

tests/ut/python/ops/test_ops.py

@@ -2133,7 +2133,10 @@ test_case_other_ops = [
         'desc_inputs': [Tensor(np.array([1.1]).astype(np.float32)),
                         Tensor(np.array([1.2]).astype(np.float32))],
         'skip': ['backward']}),
-
+    ('PopulationCount', {
+        'block': P.PopulationCount(),
+        'desc_inputs': [Tensor(np.array([1, 2, 3]).astype(np.int16))],
+        'skip': ['backward']}),
 ]
 
 test_case_quant_ops = [