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291 lines
12 KiB
291 lines
12 KiB
# 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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@File : test_indexed_slices.py
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@Author:
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@Date : 2020-06-08
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@Desc : test mindspore indexed_slices's operation
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"""
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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from mindspore.ops.composite.multitype_ops.zeros_like_impl import zeros_like
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from mindspore.ops.primitive import constexpr
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from mindspore.ops._grad.grad_base import bprop_getters
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from mindspore import Tensor, IndexedSlices, context
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from mindspore.common.parameter import Parameter, ParameterTuple
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from mindspore.common import dtype as mstype
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from mindspore._checkparam import Validator as validator
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from mindspore._checkparam import Rel
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from mindspore.nn import Optimizer
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from mindspore.nn import TrainOneStepCell, WithLossCell
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reduce_sum = P.ReduceSum()
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unsorted_segment_sum = P.UnsortedSegmentSum()
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transpose = P.Transpose()
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shape_op = P.Shape()
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reshape = P.Reshape()
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size_op = P.Size()
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invert_permutation = P.InvertPermutation()
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logical_and = P.LogicalAnd()
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context.set_context(mode=context.GRAPH_MODE, enable_sparse=True)
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@constexpr
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def _generate_shape_index(out_shape, indices_shape, axis):
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out_rank = len(out_shape)
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ind_rank = len(indices_shape)
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if axis < 0:
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axis += out_rank - ind_rank + 1
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perm_part1 = tuple(range(axis, axis + ind_rank))
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index = tuple(range(out_rank))
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perm = perm_part1 + index[:axis] + index[axis + ind_rank:]
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return perm
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@constexpr
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def _generate_inverse_index(x_shape, axis):
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x_rank = len(x_shape)
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index = tuple(range(x_rank))
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if axis < 0:
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axis += x_rank
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perm = index[1:1 + axis] + (0,) + index[1 + axis:]
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return perm
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class MySparseGatherV2(P.GatherV2):
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"""
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For test
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"""
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@bprop_getters.register(MySparseGatherV2)
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def get_bprop_sparse_gather_v2(self):
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"""Generate bprop for MySparseGatherV2"""
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def bprop(x, indices, axis, out, dout):
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x_shp = shape_op(x)
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if axis == 0:
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indices_size = (size_op(indices),)
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x_tail_shp = x_shp[1:]
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values_shape = indices_size + x_tail_shp
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values = reshape(dout, values_shape)
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indices = reshape(indices, indices_size)
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return IndexedSlices(indices, values, x_shp), zeros_like(indices), zeros_like(axis)
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if F.rank(dout) == 0:
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dout = P.ExpandDims()(dout, -1)
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if F.rank(indices) == 0:
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indices = P.ExpandDims()(indices, -1)
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out_shp = shape_op(dout)
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ind_shp = shape_op(indices)
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# Example: out_shape:(3,2,3) axis 1 -> (1,0,2)
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perm_1 = _generate_shape_index(out_shp, ind_shp, axis)
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values_transpose = transpose(dout, perm_1)
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params_grad = unsorted_segment_sum(values_transpose, indices, shape_op(x)[axis])
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# Example: out_shape:(3,2,3) axis 2 -> (1,2,0)
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perm_2 = _generate_inverse_index(x_shp, axis)
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params_grad = transpose(params_grad, perm_2)
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return params_grad, zeros_like(indices), zeros_like(axis)
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return bprop
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adam_opt_for_map = C.MultitypeFuncGraph("adam_opt_for_map")
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@adam_opt_for_map.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
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"Tensor", "Tensor", "Tensor", "Undetermined", "Bool")
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def _update_run_op_for_map(beta1, beta2, eps, lr, weight_decay_tensor, param, m, v, gradient, decay_flag):
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if gradient.is_indexed_slices():
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return gradient.values()
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op_mul = P.Mul()
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op_square = P.Square()
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op_sqrt = P.Sqrt()
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op_cast = P.Cast()
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op_reshape = P.Reshape()
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op_shape = P.Shape()
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param_fp32 = op_cast(param, mstype.float32)
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m_fp32 = op_cast(m, mstype.float32)
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v_fp32 = op_cast(v, mstype.float32)
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gradient_fp32 = op_cast(gradient, mstype.float32)
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next_m = op_mul(beta1, m_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32) - beta1, gradient_fp32)
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next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32)
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- beta2, op_square(gradient_fp32))
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update = next_m / (op_sqrt(next_v) + eps)
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if decay_flag:
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update = update + op_mul(weight_decay_tensor, param_fp32)
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update_with_lr = op_mul(lr, update)
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next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))
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next_v = F.depend(next_v, F.assign(param, next_param))
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next_v = F.depend(next_v, F.assign(m, next_m))
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next_v = F.depend(next_v, F.assign(v, next_v))
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return next_v
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def _check_param_value(beta1, beta2, eps, weight_decay, prim_name):
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"""Check the type of inputs."""
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validator.check_value_type("beta1", beta1, [float], prim_name)
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validator.check_value_type("beta2", beta2, [float], prim_name)
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validator.check_value_type("eps", eps, [float], prim_name)
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validator.check_value_type("weight_dacay", weight_decay, [float], prim_name)
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validator.check_number_range("beta1", beta1, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
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validator.check_number_range("beta2", beta2, 0.0, 1.0, Rel.INC_NEITHER, prim_name)
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validator.check_number_range("eps", eps, 0.0, float("inf"), Rel.INC_NEITHER, prim_name)
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validator.check_number_range("weight_decay", weight_decay, 0.0, float("inf"), Rel.INC_LEFT, prim_name)
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class AdamWeightDecaySparse(Optimizer):
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def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0,
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decay_filter=lambda x: 'beta' not in x.name and 'gamma' not in x.name):
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super(AdamWeightDecaySparse, self).__init__(learning_rate, params)
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if self.is_group:
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raise RuntimeError(f"The {self.cls_name} optimizer cannot support group setting.")
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_check_param_value(beta1, beta2, eps, weight_decay, self.cls_name)
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self.beta1 = Tensor(np.array([beta1]).astype(np.float32))
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self.beta2 = Tensor(np.array([beta2]).astype(np.float32))
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self.eps = Tensor(np.array([eps]).astype(np.float32))
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self.weight_decay_tensor = Tensor(np.array([weight_decay]).astype(np.float32))
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self.params = self.parameters
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self.moments1 = self.params.clone(prefix="adam_m", init='zeros')
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self.moments2 = self.params.clone(prefix="adam_v", init='zeros')
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self.decay_flag = tuple(decay_filter(x) for x in self.params)
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self.map = C.Map()
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def construct(self, gradients):
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lr = self.get_lr()
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updated_velocity = self.map(F.partial(adam_opt_for_map, self.beta1, self.beta2, self.eps, lr,
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self.weight_decay_tensor),
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self.params, self.moments1, self.moments2, gradients, self.decay_flag)
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return updated_velocity
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def test_indexed_slices_make_indexed_slices():
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class MakeIndexedSlices(nn.Cell):
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def __init__(self):
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super(MakeIndexedSlices, self).__init__()
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self.dense_shape = (3, 4)
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def construct(self, indices, values):
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ret = (IndexedSlices(indices, values, self.dense_shape),)
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return ret[0].is_indexed_slices()
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indices = Tensor([[0, 0], [1, 2]])
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values = Tensor([1, 2], dtype=ms.float32)
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MakeIndexedSlices()(indices, values)
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def test_indexed_slices_attr():
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class IndexedSlicesGetAttr(nn.Cell):
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def __init__(self):
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super(IndexedSlicesGetAttr, self).__init__()
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self.dense_shape = (3, 4)
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def construct(self, indices, values):
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x = IndexedSlices(indices, values, self.dense_shape)
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return x.values(), x.indices(), x.dense_shape()
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indices = Tensor([[0, 0], [1, 2]])
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values = Tensor([1, 2], dtype=ms.float32)
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IndexedSlicesGetAttr()(indices, values)
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def test_indexed_slices_sparse_gatherv2_grad_all():
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grad_all = C.GradOperation('get_all', get_all=True)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x, y):
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grad = grad_all(self.network)(x, y)
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return grad, grad[0].is_indexed_slices(), grad[1].is_indexed_slices()
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class SparseGatherV2(nn.Cell):
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def __init__(self):
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super(SparseGatherV2, self).__init__()
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self.sparse_gatherv2 = MySparseGatherV2()
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self.axis = 0
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def construct(self, params, indices):
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return self.sparse_gatherv2(params, indices, self.axis)
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params = Tensor(np.ones([3, 1, 2]).astype(np.int32))
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indices = Tensor(np.array([0, 1]).astype(np.int32))
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GradWrap(SparseGatherV2())(params, indices)
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def test_indexed_slices_sparse_gatherv2_grad_with_pram():
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grad_by_list = C.GradOperation('get_by_list', get_by_list=True)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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self.weights = ParameterTuple(filter(lambda x: x.requires_grad, network.get_parameters()))
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def construct(self, x):
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weights = self.weights
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grad = grad_by_list(self.network, weights)(x)
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x = grad[0]
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return x.is_indexed_slices(), x.values(), x.indices(), x.dense_shape()
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class SparseGatherV2(nn.Cell):
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def __init__(self):
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super(SparseGatherV2, self).__init__()
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self.sparse_gatherv2 = MySparseGatherV2()
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self.axis = 0
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self.params = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.int32)),
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name="params", has_indexed_slices_grad=True)
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def construct(self, indices):
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return self.sparse_gatherv2(self.params, indices, self.axis)
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indices = Tensor(np.array([0, 1]).astype(np.int32))
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network = GradWrap(SparseGatherV2())
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network(indices)
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def test_indexed_slices_is_indexed_slices():
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class MakeIndexedSlices(nn.Cell):
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def __init__(self):
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super(MakeIndexedSlices, self).__init__()
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self.dense_shape = (3, 4)
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def construct(self, indices, values):
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indexed_slices = IndexedSlices(indices, values, self.dense_shape)
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ret = indexed_slices.is_indexed_slices()
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return ret
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indices = Tensor([[0, 0], [1, 2]])
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values = Tensor([1, 2], dtype=ms.float32)
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MakeIndexedSlices()(indices, values)
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def test_indexed_slices_env_get():
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class Loss(nn.Cell):
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def __init__(self):
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super(Loss, self).__init__()
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def construct(self, base, target):
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return base
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class NetWithSparseGatherV2(nn.Cell):
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def __init__(self):
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super(NetWithSparseGatherV2, self).__init__()
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self.w1 = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="w1", has_indexed_slices_grad=True)
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self.w2 = Parameter(Tensor(np.ones([2, 1, 2]).astype(np.float32)), name="w2")
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self.gatherv2 = MySparseGatherV2()
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self.axis = 0
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def construct(self, indices):
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return self.gatherv2(self.w1, indices, self.axis) * self.w2
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inputs = Tensor(np.array([0, 1]).astype(np.int32))
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label = Tensor(np.zeros([2, 1, 2]).astype(np.float32))
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net = NetWithSparseGatherV2()
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net.set_train()
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loss = Loss()
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optimizer = AdamWeightDecaySparse(net.trainable_params())
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net_with_loss = WithLossCell(net, loss)
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train_network = TrainOneStepCell(net_with_loss, optimizer)
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train_network(inputs, label)
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