forked from huawei/mindspore2022
234 lines
7.8 KiB
Python
234 lines
7.8 KiB
Python
# This is the Python adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
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#
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""The names of functional part are summarized here."""
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from mindspore.common._register_for_tensor import tensor_operator_registry
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from mindspore.ops import _constants
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from .primitive import Primitive
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from . import operations as P
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from .operations import _grad_ops
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typeof = Primitive('typeof')
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hastype = Primitive('hastype')
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cast = P.Cast()
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dtype = P.DType()
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isconstant = Primitive('is_constant')
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isconstant.set_const_prim(True)
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issubclass_ = P.IsSubClass()
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isinstance_ = P.IsInstance()
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eye = P.Eye()
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fill = P.Fill()
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tile = P.Tile()
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select = P.Select()
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size = P.Size()
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ones_like = P.OnesLike()
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shape = P.Shape()
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rank = P.Rank()
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reshape = P.Reshape()
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# control_depend: represent dependency between two operators
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def control_depend(src, dst):
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control_depend_op = P.ControlDepend()
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return control_depend_op(src, dst)
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merge = P.Merge()
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geswitch = P.GeSwitch()
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addn = P.AddN()
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absolute = P.Abs()
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tensor_add = P.Add()
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add = tensor_add
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neg_tensor = P.Neg()
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tensor_lt = P.Less()
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less = tensor_lt
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tensor_le = P.LessEqual()
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le = tensor_le
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tensor_gt = P.Greater()
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gt = tensor_gt
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tensor_ge = P.GreaterEqual()
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ge = tensor_ge
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tensor_sub = P.Sub()
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sub = tensor_sub
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tensor_mul = P.Mul()
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mul = tensor_mul
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tensor_div = P.RealDiv()
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div = tensor_div
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tensor_floordiv = P.FloorDiv()
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floordiv = tensor_floordiv
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tensor_pow = P.Pow()
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pows = tensor_pow
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tensor_mod = P.FloorMod()
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floormod = tensor_mod
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tensor_exp = P.Exp()
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exp = tensor_exp
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tensor_expm1 = P.Expm1()
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strided_slice = P.StridedSlice()
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same_type_shape = P.SameTypeShape()
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check_bprop = P.CheckBprop()
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equal = P.Equal()
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not_equal = P.NotEqual()
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isfinite = P.IsFinite()
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assign_sub = P.AssignSub()
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assign_add = P.AssignAdd()
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assign = P.Assign()
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square = P.Square()
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sqrt = P.Sqrt()
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log = P.Log()
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reduce_sum = P.ReduceSum()
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tensor_slice = P.Slice()
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maximum = P.Maximum()
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minimum = P.Minimum()
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floor = P.Floor()
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scalar_to_array = P.ScalarToArray()
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scalar_to_tensor = P.ScalarToTensor()
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tuple_to_array = P.TupleToArray()
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scalar_cast = P.ScalarCast()
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print_ = P.Print()
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expand_dims = P.ExpandDims()
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transpose = P.Transpose()
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squeeze = P.Squeeze()
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scatter_nd = P.ScatterNd()
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gather = P.Gather()
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gather_d = P.GatherD()
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gather_nd = P.GatherNd()
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scatter_update = P.ScatterUpdate()
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scatter_nd_update = P.ScatterNdUpdate()
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stack = P.Stack()
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def pack(x):
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print("WARNING: 'pack' is deprecated from version 1.1 and will be removed in a future version, use 'stack' instead"
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".")
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return stack(x)
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partial = P.Partial()
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# depend: mount a node to another node
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depend = P.Depend()
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identity = P.identity()
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tuple_setitem = Primitive('tuple_setitem')
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tuple_getitem = Primitive(_constants.kTupleGetItem)
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list_getitem = Primitive('list_getitem')
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list_setitem = Primitive('list_setitem')
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dict_getitem = Primitive('dict_getitem')
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dict_setitem = Primitive('dict_setitem')
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tuple_div = Primitive("tuple_div")
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tuple_len = Primitive("tuple_len")
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list_len = Primitive("list_len")
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tuple_reversed = Primitive("tuple_reversed")
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make_range = Primitive("make_range")
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make_tuple = Primitive('MakeTuple')
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make_dict = Primitive('make_dict')
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make_list = Primitive('make_list')
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make_slice = Primitive('make_slice')
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tuple_equal = Primitive("tuple_equal")
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list_equal = Primitive("list_equal")
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make_ref = Primitive("make_ref")
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scalar_add = Primitive(_constants.kScalarAdd)
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scalar_mul = Primitive(_constants.kScalarMul)
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scalar_sub = Primitive(_constants.kScalarSub)
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scalar_div = Primitive(_constants.kScalarDiv)
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scalar_floordiv = Primitive(_constants.kScalarFloordiv)
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scalar_log = Primitive('scalar_log')
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scalar_pow = Primitive(_constants.kScalarPow)
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scalar_gt = Primitive('scalar_gt')
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scalar_ge = Primitive('scalar_ge')
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scalar_le = Primitive('scalar_le')
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scalar_lt = Primitive('scalar_lt')
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scalar_eq = Primitive('scalar_eq')
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scalar_ne = Primitive('scalar_ne')
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scalar_uadd = Primitive(_constants.kScalarUadd)
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scalar_usub = Primitive(_constants.kScalarUsub)
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scalar_mod = Primitive(_constants.kScalarMod)
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string_eq = Primitive('string_equal')
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string_concat = Primitive('string_concat')
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bool_not = Primitive("bool_not")
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bool_or = Primitive("bool_or")
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bool_and = Primitive("bool_and")
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bool_eq = Primitive("bool_eq")
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logical_and = P.LogicalAnd()
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logical_or = P.LogicalOr()
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logical_not = P.LogicalNot()
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array_to_scalar = Primitive('array_to_scalar')
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is_ = Primitive("is_")
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is_not = Primitive("is_not")
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in_dict = Primitive("in_dict")
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not_in_dict = Primitive("not_in_dict")
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mixed_precision_cast = Primitive("mixed_precision_cast")
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broadcast_gradient_args = Primitive('BroadcastGradientArgs')
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array_reduce = Primitive('array_reduce')
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zeros_like = P.ZerosLike()
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distribute = Primitive('distribute')
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embed = Primitive('embed')
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ref_to_embed = _grad_ops.RefToEmbed()
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env_setitem = Primitive('env_setitem')
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env_getitem = Primitive('env_getitem')
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env_add = Primitive('env_add')
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J = Primitive('J')
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switch = Primitive('Switch')
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switch_layer = Primitive('switch_layer')
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# for sum bprop
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reduced_shape = Primitive("reduced_shape")
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# shape_mul:input mush be shape multiply elemts in tuple(shape)
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shape_mul = Primitive("shape_mul")
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# a primitive to compare between tuple.
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stop_gradient = Primitive("stop_gradient")
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make_row_tensor = Primitive('MakeRowTensor')
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row_tensor_get_values = Primitive('RowTensorGetValues')
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row_tensor_get_indices = Primitive('RowTensorGetIndices')
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row_tensor_get_dense_shape = Primitive('RowTensorGetDenseShape')
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row_tensor_add = Primitive('RowTensorAdd')
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make_sparse_tensor = Primitive('MakeSparseTensor')
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sparse_tensor_get_values = Primitive('SparseTensorGetValues')
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sparse_tensor_get_indices = Primitive('SparseTensorGetIndices')
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sparse_tensor_get_dense_shape = Primitive('SparseTensorGetDenseShape')
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tensor_operator_registry.register('__add__', tensor_add)
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tensor_operator_registry.register('__sub__', tensor_sub)
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tensor_operator_registry.register('__mul__', tensor_mul)
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tensor_operator_registry.register('__truediv__', tensor_div)
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tensor_operator_registry.register('__mod__', tensor_mod)
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tensor_operator_registry.register('__pow__', tensor_pow)
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tensor_operator_registry.register('__floordiv__', tensor_floordiv)
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tensor_operator_registry.register('all', P.ReduceAll)
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tensor_operator_registry.register('any', P.ReduceAny)
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tensor_operator_registry.register('abs', P.Abs)
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tensor_operator_registry.register('mean', P.ReduceMean)
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tensor_operator_registry.register('reshape', P.Reshape)
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tensor_operator_registry.register('transpose', P.Transpose)
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tensor_operator_registry.register('broadcast_to', P.BroadcastTo)
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# ms cannot support Tensor(True) compare
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tensor_operator_registry.register('__eq__', equal)
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tensor_operator_registry.register('__ne__', not_equal)
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tensor_operator_registry.register('__neg__', neg_tensor)
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tensor_operator_registry.register('__lt__', tensor_lt)
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tensor_operator_registry.register('__le__', tensor_le)
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tensor_operator_registry.register('__gt__', tensor_gt)
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tensor_operator_registry.register('__ge__', tensor_ge)
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tensor_operator_registry.register('__logical_not__', logical_not)
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tensor_operator_registry.register('shape', shape)
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tensor_operator_registry.register('squeeze', squeeze)
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# support GE backend for no compare operators
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tensor_operator_registry.register('cast', cast)
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__all__ = [name for name in dir() if name[0] != "_"]
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__all__.remove('Primitive')
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