forked from huawei/mindspore2022
185 lines
3.6 KiB
Python
185 lines
3.6 KiB
Python
# Copyright 2020 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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"""builtin_operations"""
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import numpy as np
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from mindspore.ops import functional as F
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from mindspore.ops import composite as C
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from mindspore.common.tensor import Tensor
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import mindspore.common.dtype as mstype
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from mindspore.common.dtype import dtype_to_nptype, get_py_obj_dtype
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def ScalarAdd(x, y):
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"""Implement `scalar_add`."""
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return x + y
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def ScalarMul(x, y):
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"""Implement `scalar_mul`."""
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return x * y
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def ScalarMod(x, y):
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"""Implement `scalar_mul`."""
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return x % y
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def ScalarSub(x, y):
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"""Implement `scalar_sub`."""
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return x - y
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def ScalarUsub(x):
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"""Implement `scalar_usub`."""
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return -x
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def TupleGetItem(x, index):
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"""Implement `tuple_getitem`."""
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if isinstance(x, Tensor):
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x = x.asnumpy()
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y = x[index]
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return Tensor(y)
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return x[index]
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def scalar_gt(x, y):
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"""Implement `scalar_gt`."""
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return x > y
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def scalar_ne(x, y):
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"""Implement `scalar_ne`."""
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return x != y
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def scalar_eq(x, y):
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"""Implement `scalar_eq`."""
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return x == y
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def scalar_le(x, y):
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"""Implement `scalar_le`."""
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return x <= y
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def scalar_lt(x, y):
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"""Implement `scalar_lt`."""
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return x < y
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def identity(x):
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"""Implement `identity`."""
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return x
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def zeros_like_tensor(x):
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"""Implement `zeros_like_tensor`."""
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x = x.asnumpy()
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value = Tensor(np.zeros(x.shape).astype(np.float32))
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return value
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def Switch(c, x, y):
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"""Implement `switch`."""
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return x if c else y
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def list_getitem(data, item):
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"""Implement `list_getitem`."""
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return data[item]
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def bool_not(x):
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"""Implement `bool_not`."""
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return not x
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def bool_and(x, y):
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"""Implement `bool_and`."""
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return x and y
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def bool_or(x, y):
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"""Implement `bool_or`."""
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return x or y
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def make_list(*xs):
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"""Implement `make_list`."""
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return list(xs)
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def list_len(x):
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"""Implement `list_len`."""
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return len(x)
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def Depend(value, expr):
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"""Implement `Depend`."""
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return value
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def UpdateState(monad, *exprs):
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"""Implement `UpdateState`."""
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return monad
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def Load(value, u=None):
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"""Implement `Load`."""
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return value
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# only used in PyNative mode
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def make_ref(key, value, ref):
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return value
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def scalar_cast(x, t):
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"""Implement scalar_cast."""
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np_type = dtype_to_nptype(t)
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value = np_type(x)
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cast_value = np.ndarray.item(value)
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return cast_value
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def typeof(x):
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"""Implement typeof."""
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return get_py_obj_dtype(x)
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def tuple_to_array(x):
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"""Implement `tuple_to_array`."""
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return Tensor(np.array(x))
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def stop_gradient(x):
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"""Implement `stop_gradient`."""
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return x
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hyper_map = C.HyperMap()
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def mixed_precision_cast(dst_type, x):
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"""Implement `mixed_precision_cast`."""
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def cast_inner(data):
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if isinstance(data, Tensor) and data.dtype in (mstype.float32, mstype.float16, mstype.float64):
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return F.cast(data, dst_type)
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return data
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return hyper_map(cast_inner, x)
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