forked from mindspore-Ecosystem/mindspore
303 lines
6.5 KiB
Python
303 lines
6.5 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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"""Generate vm_impl function for math ops"""
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import copy
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import numpy as np
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from mindspore.common.dtype import dtype_to_nptype
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from mindspore.common.tensor import Tensor
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from mindspore.ops import operations as P
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from mindspore.ops.vm_impl_registry import vm_impl_registry as vm_impl_getters
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from .vm_interface import vm
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# pylint: disable=unused-argument
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@vm_impl_getters.register(P.Add)
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def vm_impl_tensor_add(self):
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"""Generate vm_impl function for TensorAdd."""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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return Tensor(x + y)
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return vm_impl
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# pylint: disable=used-before-assignment
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@vm_impl_getters.register(P.LogicalNot)
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def vm_impl_logical_not(self):
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def vm_impl(x):
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x = x.asnumpy()
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out = vm.logical_not(x)
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(P.MatMul)
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def vm_impl_mat_mul(self):
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"""Generate vm_impl function for MatMul."""
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def vm_impl(x, w):
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x = x.asnumpy()
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w = w.asnumpy()
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if self.transpose_a:
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x = x.transpose()
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if self.transpose_b:
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w = w.transpose()
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z = x @ w
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return Tensor(z)
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return vm_impl
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@vm_impl_getters.register(P.AddN)
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def vm_impl_addn(self):
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"""Generate vm_impl function for AddN."""
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def vm_impl(inputs):
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added = copy.deepcopy(inputs[0].asnumpy())
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for x in inputs[1:]:
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added += x.asnumpy()
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return Tensor(added)
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return vm_impl
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@vm_impl_getters.register(P.Neg)
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def vm_impl_neg(self):
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"""Generate vm_impl function for Neg."""
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def vm_impl(x):
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x = x.asnumpy()
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return Tensor(-x)
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return vm_impl
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@vm_impl_getters.register(P.Sub)
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def vm_impl_Sub(self):
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"""Generate vm_impl function for Sub."""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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return Tensor(x - y)
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return vm_impl
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@vm_impl_getters.register(P.Mul)
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def vm_impl_mul(self):
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"""Generate vm_impl function for Mul."""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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return Tensor(x * y)
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return vm_impl
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@vm_impl_getters.register(P.Square)
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def vm_impl_square(self):
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"""Generate vm_impl function for Square."""
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def vm_impl(x):
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x = x.asnumpy()
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return Tensor(x * x)
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return vm_impl
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@vm_impl_getters.register(P.Sqrt)
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def vm_impl_sqrt(self):
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"""Generate vm_impl function for Sqrt."""
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def vm_impl(x):
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x = x.asnumpy()
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res = vm.sqrt(x)
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return Tensor(res)
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return vm_impl
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@vm_impl_getters.register(P.Pow)
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def vm_impl_pow(self):
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"""Generate vm_impl function for Pow."""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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res = vm.power(x, y)
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return Tensor(res)
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return vm_impl
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@vm_impl_getters.register(P.Exp)
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def vm_impl_exp(self):
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"""Generate vm_impl function for Exp."""
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def vm_impl(x):
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x = x.asnumpy()
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res = vm.exp(x)
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return Tensor(res)
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return vm_impl
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@vm_impl_getters.register(P.RealDiv)
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def vm_impl_real_div(self):
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"""Generate vm_impl function for RealDiv."""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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out = x / y
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out = np.array(out, x.dtype)
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(P.Div)
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def vm_impl_div(self):
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"""Generate vm_impl function for Div."""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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return Tensor(x / y)
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return vm_impl
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@vm_impl_getters.register(P.ReduceMean)
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def vm_impl_reduce_mean(self):
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"""Generate vm_impl function for ReduceMean."""
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def vm_impl(x, axis):
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x = x.asnumpy()
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out = vm.mean(x, axis)
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(P.ReduceMax)
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def vm_impl_reduce_max(self):
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"""Generate vm_impl function for ReduceMean."""
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def vm_impl(x, axis):
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x = x.asnumpy()
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if axis == ():
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axis = None
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out = np.amax(x, axis)
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(P.Equal)
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def vm_impl_equal(self):
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"""Generate vm_impl function for Equal."""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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out = vm.equal(x, y)
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return Tensor(np.array(out))
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return vm_impl
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@vm_impl_getters.register(P.NotEqual)
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def vm_impl_not_equal(self):
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"""Generate vm_impl function for NotEqual."""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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out = vm.not_equal(x, y)
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return Tensor(np.array(out))
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return vm_impl
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@vm_impl_getters.register(P.Greater)
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def vm_impl_greater(self):
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"""Generate vm_impl function for Greater."""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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out = vm.greater(x, y)
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return Tensor(np.array(out))
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return vm_impl
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@vm_impl_getters.register(P.Maximum)
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def vm_impl_maximum(self):
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"""Generate vm_impl function for Maximum."""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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out = vm.maximum(x, y)
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(P.Minimum)
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def vm_impl_minimum(self):
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"""Generate vm_impl function for Minimum."""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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out = vm.minimum(x, y)
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return Tensor(out)
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return vm_impl
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@vm_impl_getters.register(P.Less)
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def vm_impl_less(self):
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"""Generate vm_impl function for Less"""
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def vm_impl(x, y):
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x = x.asnumpy()
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y = y.asnumpy()
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out = vm.less(x, y)
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return Tensor(np.array(out))
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return vm_impl
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@vm_impl_getters.register(P.ScalarCast)
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def vm_impl_scalar_cast(self):
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"""Generate vm_impl function for ScalarCast"""
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def vm_impl(x, t):
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np_type = dtype_to_nptype(t)
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value = np_type(x)
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cast_value = value.item()
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return cast_value
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return vm_impl
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