Register forward and backward operations of Ceil

mindspore/ops/_grad/grad_math_ops.py

@@ -254,6 +254,21 @@ def get_bprop_floor(self):
     shape_ = P.Shape()
     dtype_ = P.DType()
 
+    def bprop(x, out, dout):
+        bc_x = fill_(dtype_(x), shape_(x), 0.)
+        return (bc_x,)
+
+
+    return bprop
+
+
+@bprop_getters.register(P.Ceil)
+def get_bprop_ceil(self):
+    """Grad definition for `ceil` operation."""
+    fill_ = P.Fill()
+    shape_ = P.Shape()
+    dtype_ = P.DType()
+
     def bprop(x, out, dout):
         bc_x = fill_(dtype_(x), shape_(x), 0.)
         return (bc_x,)

mindspore/ops/_op_impl/tbe/__init__.py

@@ -178,6 +178,7 @@ from .space_to_batch import _space_to_batch_tbe
 from .depth_to_space import _depth_to_space_tbe
 from .space_to_depth import _space_to_depth_tbe
 from .floor import _floor_tbe
+from .ceil import _ceil_tbe
 from .log1p import _log1p_tbe
 from .resize_bilinear import _resize_bilinear_tbe
 from .resize_bilinear_grad import _resize_bilinear_grad_tbe

mindspore/ops/_op_impl/tbe/ceil.py

@@ -0,0 +1,36 @@
+# 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.
+# ============================================================================
+
+"""Ceil op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+ceil_op_info = TBERegOp("Ceil") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("ceil.so") \
+    .compute_cost(10) \
+    .kernel_name("ceil") \
+    .partial_flag(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+
+@op_info_register(ceil_op_info)
+def _ceil_tbe():
+    """Ceil TBE register"""
+    return

mindspore/ops/operations/__init__.py

@@ -43,8 +43,8 @@ from .inner_ops import ScalarCast
 from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul, BitwiseAnd, BitwiseOr,
                        BitwiseXor,
                        ReduceMax, ReduceMin, ReduceMean, ReduceSum, ReduceAll, ReduceProd, CumProd,
-                       Cos, Div, DivNoNan, Equal, EqualCount, Exp, Expm1, Erf, Erfc, Floor, FloorDiv, FloorMod, Acosh,
-                       Greater, GreaterEqual, Less, LessEqual, Log, Log1p, LogicalAnd,
+                       Cos, Div, DivNoNan, Equal, EqualCount, Exp, Expm1, Erf, Erfc, Floor, FloorDiv, FloorMod, Ceil,
+                       Acosh, Greater, GreaterEqual, Less, LessEqual, Log, Log1p, LogicalAnd,
                        LogicalNot, LogicalOr, MatMul, Maximum,
                        Minimum, Mul, Neg, NMSWithMask, NotEqual,
                        NPUAllocFloatStatus, NPUClearFloatStatus,
@@ -243,6 +243,7 @@ __all__ = [
     'SigmoidCrossEntropyWithLogits',
     'FloorDiv',
     'FloorMod',
+    'Ceil',
     'Acosh',
     'Asinh',
     "PReLU",

mindspore/ops/operations/math_ops.py

@@ -1401,6 +1401,35 @@ class FloorMod(_MathBinaryOp):
     """
 
 
+class Ceil(PrimitiveWithInfer):
+    """
+    Round a tensor up to the closest integer element-wise.
+
+    Inputs:
+        - **input_x** (Tensor) - The input tensor. Its element data type must be float.
+
+    Outputs:
+        Tensor, has the same shape as `input_x`.
+
+    Examples:
+        >>> input_x = Tensor(np.array([1.1, 2.5, -1.5]), mindspore.float32)
+        >>> ceil_op = P.Ceil()
+        >>> ceil_op(input_x)
+        [2.0, 3.0, -1.0]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        self.init_prim_io_names(inputs=['x'], outputs=['y'])
+
+    def infer_shape(self, x_shape):
+        return x_shape
+
+    def infer_dtype(self, x_dtype):
+        validator.check_tensor_type_same({"x": x_dtype}, mstype.float_type, self.name)
+        return x_dtype
+
+
 class Acosh(PrimitiveWithInfer):
     """
     Compute inverse hyperbolic cosine of x element-wise.

tests/ut/python/ops/test_ops.py

@@ -402,6 +402,11 @@ test_case_math_ops = [
         'desc_inputs': [[2, 512, 56, 56]],
         'desc_bprop': [[2, 512, 56, 56]],
         'skip': ['backward']}),
+    ('Ceil', {
+        'block': P.Ceil(),
+        'desc_inputs': [[2, 512, 56, 56]],
+        'desc_bprop': [[2, 512, 56, 56]],
+        'skip': ['backward']}),
     ('ACos', {
         'block': P.ACos(),
         'desc_inputs': [Tensor(np.array([2., 3.]).astype(np.float32))],