forked from mindspore-Ecosystem/mindspore
Delete unused interface in graph_kernels.py
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@ -18,13 +18,6 @@ GraphKernel.
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GraphKernel provides a unified style to express graph and kernel for user.
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GraphKernel provides a unified style to express graph and kernel for user.
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It breaks the boundary between graph and kernel and provides more opportunities to do compile optimization.
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It breaks the boundary between graph and kernel and provides more opportunities to do compile optimization.
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"""
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"""
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from .graph_kernels import MaximumGrad, MinimumGrad, AbsGrad, ApplyMomentum, BiasAdd, EqualCount, \
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from .graph_kernels import LambUpdateWithLR, LambNextMV
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ReduceMean, ReLU, SoftmaxCrossEntropyWithLogits, LayerNorm, LayerNormXBackprop, \
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LayerNormBetaGammaBackprop, LogSoftmax, Tanh, TanhGrad, Gelu, Softmax, BiasAddGrad, \
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LambUpdateWithLR, LambNextMV
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__all__ = ['MaximumGrad', 'MinimumGrad', 'AbsGrad', 'ApplyMomentum', 'BiasAdd', 'EqualCount',
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__all__ = ['LambUpdateWithLR', 'LambNextMV']
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'ReduceMean', 'ReLU', 'SoftmaxCrossEntropyWithLogits', 'LayerNorm',
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'LayerNormXBackprop', 'LayerNormBetaGammaBackprop', 'LogSoftmax', 'Tanh', 'TanhGrad',
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'Gelu', 'Softmax', 'BiasAddGrad', 'LambUpdateWithLR', 'LambNextMV'
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]
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@ -0,0 +1,259 @@
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# 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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"""
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Graph kernels. They are composites of basic primitives and can be compiled into
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a fused kernel automatically when context.set_context(enable_graph_kernel=True).
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"""
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from ...ops import operations as P
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from ...ops.primitive import PrimitiveWithInfer, prim_attr_register
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from ...ops.composite import multitype_ops as C
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from ..cell import GraphKernel
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class InplaceAssign(PrimitiveWithInfer):
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"""
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Inplace assign `Parameter` with a value.
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This primitive can only be used in graph kernel.
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Inputs:
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- **variable** (Parameter) - The `Parameter`.
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- **value** (Tensor) - The value to be assigned.
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- **depend** (Tensor) - The dependent tensor to keep this op connected in graph.
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Outputs:
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Tensor, has the same type as original `variable`.
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Examples:
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>>> class MyClass(GraphKernel):
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... def __init__(self):
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... super(MyClass, self).__init__()
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... self.mul = P.Mul()
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... self.fake_output_assign = InplaceAssign()
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... self.fake_output_assign.add_prim_attr("fake_output", True)
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...
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... def construct(self, i0, i1):
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... mul_res = self.mul(i0, i1)
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... # mul_res is a fake output and parameter i0 will be updated.
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... mul_res = self.fake_output_assign(i0, mul_res, mul_res)
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... return mul_res
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"""
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@prim_attr_register
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def __init__(self):
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super(InplaceAssign, self).__init__("InplaceAssign")
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self.init_prim_io_names(inputs=['x', 'y', 'z'], outputs=['output'])
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def infer_shape(self, x, y, z):
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return z
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def infer_dtype(self, x, y, z):
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return z
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def get_bprop(self):
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def bprop(x, y, z, out, dout):
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return (x, C.zeros_like(y), dout)
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return bprop
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class LambUpdateWithLR(GraphKernel):
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r"""
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Part of Lamb optimizer.
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.. math::
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s_1 = select(i_1 \gt y_g, select(i_0 \gt y_g, \frac{i_1}{i_2}, se), se)
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i_5 = i_5 - max(min(s_1, y_m), y_g) \times i_3 \times i_4
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Inputs:
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- **input0** (Tensor) - The first tensor to be computed.
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- **input1** (Tensor) - The second tensor to be computed.
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- **input2** (Tensor) - The third tensor to be computed.
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- **input3** (Tensor) - The fourth tensor to be computed.
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- **input4** (Tensor) - The fifth tensor to be computed.
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- **input5** (Tensor) - The sixth tensor to be computed. It will be updated by result.
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- **greater_y** (Tensor) - The seventh tensor to be computed.
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- **select_e** (Tensor) - The eighth tensor to be computed.
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- **minimum_y** (Tensor) - The ninth tensor to be computed.
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Outputs:
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A fake output tensor.
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Examples:
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>>> import numpy as np
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>>> import mindspore.context as context
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>>> from mindspore.common import dtype as mstype
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>>> from mindspore.common.tensor import Tensor
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>>> from mindspore.common.parameter import Parameter
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>>> from mindspore.nn.cell import Cell
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>>> class Net(Cell):
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... def __init__(self, i5):
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... super(Net, self).__init__()
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... self.i5 = Parameter(i5, name='i5')
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... self.lamb_update = LambUpdateWithLR()
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...
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... def construct(self, i0, i1, i2, i3, i4, i6, i7, i8):
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... return self.lamb_update(i0, i1, i2, i3, i4, self.i5, i6, i7, i8)
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>>> shape = [1, 16]
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>>> oshape = [1]
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>>> i0 = Tensor(np.random.normal(0, 1, oshape).astype(np.float32))
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>>> i1 = Tensor(np.random.normal(0, 1, oshape).astype(np.float32))
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>>> i2 = Tensor(np.random.normal(0, 1, oshape).astype(np.float32))
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>>> i3 = Tensor(np.random.normal(0, 1, oshape).astype(np.float32))
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>>> i4 = Tensor(np.random.normal(0, 1, shape).astype(np.float32))
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>>> i5 = Tensor(np.random.normal(0, 1, shape).astype(np.float32))
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>>> i6 = Tensor(np.random.normal(0, 1, oshape).astype(np.float32))
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>>> i7 = Tensor(np.random.normal(0, 1, oshape).astype(np.float32))
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>>> i8 = Tensor(np.random.normal(0, 1, oshape).astype(np.float32))
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>>> context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True)
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>>> net = Net(i5)
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>>> _ = net(i0, i1, i2, i3, i4, i6, i7, i8)
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>>> output = (net.i5)
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"""
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def __init__(self):
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super(LambUpdateWithLR, self).__init__()
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self.greater = P.Greater()
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self.select = P.Select()
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self.div = P.RealDiv()
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self.min = P.Minimum()
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self.max = P.Maximum()
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self.mul = P.Mul()
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self.sub = P.Sub()
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self.fake_output_assign = InplaceAssign()
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self.fake_output_assign.add_prim_attr("fake_output", True)
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def construct(self, input0, input1, input2, input3, input4, input5, greater_y, select_e, minimum_y):
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greater0 = self.greater(input0, greater_y)
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greater1 = self.greater(input1, greater_y)
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real_div0 = self.div(input1, input2)
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select0 = self.select(greater0, real_div0, select_e)
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select1 = self.select(greater1, select0, select_e)
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min0 = self.min(select1, minimum_y)
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max0 = self.max(min0, greater_y)
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mul0 = self.mul(max0, input3)
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mul1 = self.mul(mul0, input4)
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sub0 = self.sub(input5, mul1)
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sub0 = self.fake_output_assign(input5, sub0, sub0)
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return sub0
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class LambNextMV(GraphKernel):
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r"""
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Part of Lamb optimizer.
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.. math::
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rd_0 = \frac{i_8 \times i_5 + i_9 \times i_4}{i6}
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rd_1 = \frac{x_0 \times i_2 + x_1 \times i_1}{i3}
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y_2 = \frac{rd_0}{\sqrt{rd_1 + x3}} + x_2 \times i_7
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y_3 = \frac{rd_0}{\sqrt{rd_1} + x3}
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i5 = i_8 \times i_5 + i_9 \times i_4
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i2 = x_0 \times i_2 + x_1 \times i_1
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Inputs:
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- **inputs1** (Tensor) - The first input tensor to be computed.
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- **inputs2** (Tensor) - The second input tensor to be computed. It will be updated by result.
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- **inputs3** (Tensor) - The third input tensor to be computed.
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- **inputs4** (Tensor) - The fourth input tensor to be computed.
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- **inputs5** (Tensor) - The fifth input tensor to be computed. It will be updated by result.
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- **inputs6** (Tensor) - The sixth input tensor to be computed.
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- **inputs7** (Tensor) - The seventh input tensor to be computed.
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- **inputs8** (Tensor) - The eighth input tensor to be computed.
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- **inputs9** (Tensor) - The ninth input tensor to be computed.
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- **inputsx0** (Tensor) - The tenth input tensor to be computed.
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- **inputsx1** (Tensor) - The eleventh input tensor to be computed.
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- **inputsx2** (Tensor) - The twelfth input tensor to be computed.
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- **inputsx3** (Tensor) - The thirteenth input tensor to be computed.
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Outputs:
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Tuple of 2 Tensors.
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- **add3** (Tensor) - the shape is the same as the one after broadcasting, and the data type is
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the one with higher precision or higher digits among the inputs.
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- **realdiv4** (Tensor) - the shape is the same as the one after broadcasting, and the data type is
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the one with higher precision or higher digits among the inputs.
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Examples:
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>>> import numpy as np
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>>> import mindspore.context as context
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>>> from mindspore.common import dtype as mstype
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>>> from mindspore.common.tensor import Tensor
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>>> from mindspore.common.parameter import Parameter
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>>> from mindspore.nn.cell import Cell
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>>> class Net(Cell):
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... def __init__(self, i1, i4):
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... super(Net, self).__init__()
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... self.i1 = Parameter(i1, name='i1')
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... self.i4 = Parameter(i4, name='i4')
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... self.lamb_next = LambNextMV()
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...
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... def construct(self, i0, i2, i3, i5, i6, i7, i8, i9, i10, i11, i12):
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... i0_ = i0 + i2
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... return self.lamb_next(i0_, self.i1, i2, i3, self.i4, i5, i6, i7, i8, i9, i10, i11, i12)
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>>> shape = [1, 16]
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>>> i0 = Tensor(np.abs(np.random.normal(0, 1, shape)).astype(np.float32))
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>>> i1 = Tensor(np.abs(np.random.normal(0, 1, shape)).astype(np.float32))
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>>> i2 = Tensor(np.abs(np.random.normal(0, 1, shape)).astype(np.float32))
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>>> i3 = Tensor(np.random.normal(0, 1, shape).astype(np.float32))
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>>> i4 = Tensor(np.random.normal(0, 1, shape).astype(np.float32))
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>>> i5 = Tensor(np.abs(np.random.normal(0, 1, shape)).astype(np.float32))
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>>> i6 = Tensor(np.random.normal(0, 1, shape).astype(np.float32))
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>>> i7 = Tensor(np.random.normal(0, 1, shape).astype(np.float32))
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>>> i8 = Tensor(np.random.normal(0, 1, shape).astype(np.float32))
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>>> i9 = Tensor(np.abs(np.random.normal(0, 1, shape)).astype(np.float32))
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>>> i10 = Tensor(np.abs(np.random.normal(0, 1, shape)).astype(np.float32))
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>>> i11 = Tensor(np.random.normal(0, 1, shape).astype(np.float32))
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>>> i12 = Tensor(np.ones(shape).astype(np.float32) * 1e-6)
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>>> context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True)
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>>> net = Net(i1, i4)
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>>> (o0, o1) = net(i0, i2, i3, i5, i6, i7, i8, i9, i10, i11, i12)
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>>> output = (o0, net.i4, net.i1, o1)
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"""
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def __init__(self):
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super(LambNextMV, self).__init__()
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self.mul = P.Mul()
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self.add = P.TensorAdd()
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self.div = P.RealDiv()
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self.sqrt = P.Sqrt()
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self.rsqrt = P.Rsqrt()
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self.fake_output_assign_1 = InplaceAssign()
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self.fake_output_assign_1.add_prim_attr("fake_output", False)
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self.fake_output_assign_2 = InplaceAssign()
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self.fake_output_assign_2.add_prim_attr("fake_output", False)
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def construct(self, input1, input2, input3, input4, input5, input6, input7,
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input8, input9, inputx0, inputx1, inputx2, inputx3):
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mul3 = self.mul(inputx1, input1)
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mul2 = self.mul(inputx0, input2)
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add1 = self.add(mul2, mul3)
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realdiv1 = self.div(add1, input3)
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add2 = self.add(realdiv1, inputx3)
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sqrt0 = self.rsqrt(add2)
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sqrt1 = self.sqrt(realdiv1)
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add4 = self.add(sqrt1, inputx3)
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mul1 = self.mul(input9, input4)
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mul0 = self.mul(input8, input5)
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add0 = self.add(mul0, mul1)
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realdiv0 = self.div(add0, input6)
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realdiv2 = self.mul(realdiv0, sqrt0)
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realdiv4 = self.div(realdiv0, add4)
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mul4 = self.mul(inputx2, input7)
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add3 = self.add(realdiv2, mul4)
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add3 = self.fake_output_assign_1(input5, add0, add3)
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add3 = self.fake_output_assign_2(input2, add1, add3)
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return add3, realdiv4
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File diff suppressed because it is too large
Load Diff
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@ -26,7 +26,7 @@ from mindspore._checkparam import Validator as validator
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from mindspore._checkparam import Rel
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from mindspore._checkparam import Rel
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from .optimizer import Optimizer
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from .optimizer import Optimizer
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from .. import layer
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from .. import layer
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from .. import graph_kernels as G
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from .. import _graph_kernels as G
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num_one = Tensor(np.ones([1]), mstype.float32)
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num_one = Tensor(np.ones([1]), mstype.float32)
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@ -17,7 +17,7 @@
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from mindspore.ops.op_selector import new_ops_selector
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from mindspore.ops.op_selector import new_ops_selector
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op_selector = new_ops_selector(
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op_selector = new_ops_selector(
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"mindspore.ops.operations._grad_ops", "mindspore.nn.graph_kernels")
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"mindspore.ops.operations._grad_ops", "mindspore.nn._graph_kernels")
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@op_selector
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@op_selector
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@ -17,11 +17,11 @@
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from mindspore.ops.op_selector import new_ops_selector
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from mindspore.ops.op_selector import new_ops_selector
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op_selector = new_ops_selector(
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op_selector = new_ops_selector(
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"mindspore.ops.operations", "mindspore.nn.graph_kernels")
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"mindspore.ops.operations", "mindspore.nn._graph_kernels")
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opt_selector = new_ops_selector(
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opt_selector = new_ops_selector(
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"mindspore.nn.optim", "mindspore.nn.graph_kernels")
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"mindspore.nn.optim", "mindspore.nn._graph_kernels")
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nn_selector = new_ops_selector(
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nn_selector = new_ops_selector(
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"mindspore.nn", "mindspore.nn.graph_kernels")
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"mindspore.nn", "mindspore.nn._graph_kernels")
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@nn_selector
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@nn_selector
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@ -19,7 +19,7 @@ import mindspore.context as context
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from mindspore import Tensor
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from mindspore import Tensor
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from mindspore.nn import Cell
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from mindspore.nn import Cell
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import mindspore.ops.operations as P
|
import mindspore.ops.operations as P
|
||||||
from mindspore.nn.graph_kernels import ReLU
|
from mindspore.ops.operations import _grad_ops as G
|
||||||
|
|
||||||
|
|
||||||
class Net(Cell):
|
class Net(Cell):
|
||||||
|
@ -28,41 +28,45 @@ class Net(Cell):
|
||||||
self.add = P.TensorAdd()
|
self.add = P.TensorAdd()
|
||||||
self.sub = P.Sub()
|
self.sub = P.Sub()
|
||||||
self.mul = P.Mul()
|
self.mul = P.Mul()
|
||||||
self.relu = ReLU()
|
self.sqrt_grad = G.SqrtGrad()
|
||||||
|
|
||||||
def construct(self, x, y):
|
def construct(self, x, y, z):
|
||||||
sub_res = self.sub(x, y)
|
sub_res = self.sub(x, y)
|
||||||
mul_res = self.mul(sub_res, x)
|
mul_res = self.mul(sub_res, x)
|
||||||
relu_res = self.relu(mul_res)
|
sqrt_grad_res = self.sqrt_grad(mul_res, z)
|
||||||
square_res = P.Square()(relu_res)
|
square_res = P.Square()(sqrt_grad_res)
|
||||||
add_res = self.add(relu_res, square_res)
|
add_res = self.add(sqrt_grad_res, square_res)
|
||||||
add1_res = self.add(add_res, add_res)
|
add1_res = self.add(add_res, add_res)
|
||||||
return self.add(add1_res, add1_res)
|
return self.add(add1_res, add1_res)
|
||||||
|
|
||||||
|
|
||||||
def test_basic():
|
def get_output(i0, i1, i2, enable_graph_kernel=False):
|
||||||
input_x = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
|
if enable_graph_kernel:
|
||||||
input_y = np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32)
|
context.set_context(enable_graph_kernel=True)
|
||||||
sub_res = input_x - input_y
|
|
||||||
mul_res = sub_res * input_x
|
|
||||||
relu_res = np.maximum(mul_res, 0)
|
|
||||||
square_res = np.square(relu_res)
|
|
||||||
add_res = relu_res + square_res
|
|
||||||
add1_res = add_res + add_res
|
|
||||||
expect = add1_res + add1_res
|
|
||||||
|
|
||||||
net = Net()
|
net = Net()
|
||||||
result = net(Tensor(input_x), Tensor(input_y))
|
output = net(i0, i1, i2)
|
||||||
|
return output
|
||||||
|
|
||||||
res = np.allclose(expect, result.asnumpy(), rtol=1.e-4, atol=1.e-7, equal_nan=True)
|
|
||||||
assert res
|
def test_basic():
|
||||||
|
i0 = Tensor(np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32))
|
||||||
|
i1 = Tensor(np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32))
|
||||||
|
i2 = Tensor(np.random.normal(0, 1, [2, 3, 4, 3]).astype(np.float32))
|
||||||
|
|
||||||
|
expect = get_output(i0, i1, i2, False)
|
||||||
|
output = get_output(i0, i1, i2, True)
|
||||||
|
|
||||||
|
expect_np = expect.asnumpy().copy()
|
||||||
|
output_np = output.asnumpy().copy()
|
||||||
|
|
||||||
|
assert np.allclose(expect_np, output_np, 1.e-4, 1.e-7)
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.level0
|
@pytest.mark.level0
|
||||||
@pytest.mark.platform_x86_gpu_training
|
@pytest.mark.platform_x86_gpu_training
|
||||||
@pytest.mark.env_onecard
|
@pytest.mark.env_onecard
|
||||||
def test_basic_gpu():
|
def test_basic_gpu():
|
||||||
context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="GPU")
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||||
test_basic()
|
test_basic()
|
||||||
|
|
||||||
|
|
||||||
|
@ -71,5 +75,5 @@ def test_basic_gpu():
|
||||||
@pytest.mark.platform_x86_ascend_training
|
@pytest.mark.platform_x86_ascend_training
|
||||||
@pytest.mark.env_onecard
|
@pytest.mark.env_onecard
|
||||||
def test_basic_ascend():
|
def test_basic_ascend():
|
||||||
context.set_context(mode=context.GRAPH_MODE, enable_graph_kernel=True, device_target="Ascend")
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||||
test_basic()
|
test_basic()
|
||||||
|
|
|
@ -18,7 +18,7 @@ import numpy as np
|
||||||
import mindspore.context as context
|
import mindspore.context as context
|
||||||
from mindspore import Tensor, Parameter
|
from mindspore import Tensor, Parameter
|
||||||
from mindspore.nn import Cell
|
from mindspore.nn import Cell
|
||||||
from mindspore.nn.graph_kernels import LambUpdateWithLR, LambNextMV
|
from mindspore.nn._graph_kernels import LambUpdateWithLR, LambNextMV
|
||||||
|
|
||||||
|
|
||||||
class LambNet(Cell):
|
class LambNet(Cell):
|
||||||
|
|
Loading…
Reference in New Issue