mindspore/tests/st/graph_kernel/model/test_graph_parallel.py

46 lines
1.9 KiB
Python

# Copyright 2021 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 graph parallel case"""
import model
def injective_graph(shape):
gb = model.GraphBuilder()
with gb.graph_scope('injective') as _:
a1 = gb.tensor(shape, 'float32')
a2 = gb.emit('Abs', a1)
a3 = gb.emit('Abs', a2)
gb.emit('Abs', a3)
return gb.get()[0]
def reduce_graph(shape, reduce_axis):
gb = model.GraphBuilder()
with gb.graph_scope('reduce') as _:
a1 = gb.tensor(shape, 'float32')
a2 = gb.emit('Abs', a1)
a3 = gb.emit('Abs', a2)
gb.emit('ReduceSum', a3, 'C', attrs={'reduce_axis': reduce_axis})
return gb.get()[0]
def block_fusion(graphs):
gain = model.parallel_estimate(graphs)
print("fusion = {}, bottleneck = {}, gain = {}".format(gain.fusion_type, gain.bottleneck, gain.gain))
return gain.fusion_type == "block_fusion" and gain.gain > 0
if __name__ == "__main__":
assert block_fusion([injective_graph([40, 1024]), injective_graph([40, 1024])])
assert block_fusion([reduce_graph([1024, 1024], [1]), injective_graph([24, 1024])])
assert not block_fusion([reduce_graph([1024, 1024], [1]), injective_graph([50, 1024])])
assert not block_fusion([reduce_graph([1024, 1024], [0, 1]), injective_graph([1024, 1024])])