You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
119 lines
4.3 KiB
119 lines
4.3 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
""" test sparse feature bprop """
|
|
import numpy as np
|
|
|
|
import mindspore as ms
|
|
import mindspore.nn as nn
|
|
from mindspore import context
|
|
from mindspore.common import dtype as mstype
|
|
from mindspore.common.tensor import Tensor
|
|
from mindspore.ops import composite as C
|
|
from mindspore.ops.operations.comm_ops import AllReduce, _MirrorOperator
|
|
from mindspore.ops._grad.grad_base import bprop_getters
|
|
from mindspore._checkparam import Validator as validator
|
|
from mindspore._checkparam import Rel
|
|
from mindspore.ops.primitive import prim_attr_register, PrimitiveWithInfer
|
|
from mindspore.common.api import _executor
|
|
from mindspore.communication.management import HCCL_WORLD_COMM_GROUP
|
|
|
|
class GradWrap(nn.Cell):
|
|
def __init__(self, network):
|
|
super(GradWrap, self).__init__()
|
|
self.network = network
|
|
|
|
def construct(self, x):
|
|
return C.grad_all(self.network)(x)
|
|
|
|
class VirtualGatherV2(PrimitiveWithInfer):
|
|
@prim_attr_register
|
|
def __init__(self):
|
|
"""init index_select"""
|
|
super(VirtualGatherV2, self).__init__('VirtualGatherV2')
|
|
self.init_prim_io_names(inputs=['params', 'indices', 'axis'], outputs=['output'])
|
|
|
|
def __infer__(self, params, indices, axis):
|
|
validator.check_subclass("params", params['dtype'], mstype.tensor, self.name)
|
|
validator.check_tensor_type_same({"indices": indices['dtype']}, mstype.int_type, self.name)
|
|
validator.check_subclass("axis", axis['dtype'], mstype.int_, self.name)
|
|
axis_v = axis['value']
|
|
params_shp = params['shape']
|
|
rank = len(params_shp)
|
|
validator.check_int_range("axis", axis_v, -rank, rank, Rel.INC_LEFT, self.name)
|
|
if axis_v < 0:
|
|
axis_v += rank
|
|
out_shape = params_shp[:axis_v] + indices['shape'] + params_shp[axis_v + 1:]
|
|
out = {'shape': out_shape,
|
|
'dtype': params['dtype'],
|
|
'value': None}
|
|
return out
|
|
|
|
@bprop_getters.register(VirtualGatherV2)
|
|
def get_bprop_gather_v2(self):
|
|
"""Generate bprop for GatherV2"""
|
|
|
|
def bprop(x, indices, axis, out, dout):
|
|
return (indices, dout, x), axis, out
|
|
|
|
return bprop
|
|
|
|
def test_bprop_with_sparse_feature_allreduce():
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="hybrid_parallel")
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self, axis=0, shape=None):
|
|
super(Net, self).__init__()
|
|
if shape is None:
|
|
shape = [8, 8]
|
|
self.all_reduce = AllReduce()
|
|
self.gatherv2 = VirtualGatherV2()
|
|
self.index = Tensor(np.ones(shape), dtype=ms.int32)
|
|
self.axis = axis
|
|
|
|
def construct(self, x):
|
|
out = self.all_reduce(x)
|
|
out = self.gatherv2(out, self.index, self.axis)
|
|
|
|
return out
|
|
|
|
net = GradWrap(Net())
|
|
x = Tensor(np.ones([64, 64]), dtype=ms.float32)
|
|
|
|
_executor.compile(net, x)
|
|
|
|
def test_bprop_with_sparse_feature_mirror():
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="hybrid_parallel")
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self, axis=0, shape=None):
|
|
super(Net, self).__init__()
|
|
if shape is None:
|
|
shape = [8, 8]
|
|
self.mirror = _MirrorOperator(group=HCCL_WORLD_COMM_GROUP)
|
|
self.gatherv2 = VirtualGatherV2()
|
|
self.index = Tensor(np.ones(shape), dtype=ms.int32)
|
|
self.axis = axis
|
|
|
|
def construct(self, x):
|
|
out = self.mirror(x)
|
|
out = self.gatherv2(out, self.index, self.axis)
|
|
|
|
return out
|
|
|
|
net = GradWrap(Net())
|
|
x = Tensor(np.ones([64, 64]), dtype=ms.float32)
|
|
|
|
_executor.compile(net, x)
|