forked from mindspore-Ecosystem/mindspore
142 lines
4.6 KiB
Python
142 lines
4.6 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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"""graph kernel split"""
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import json
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import getopt
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import sys
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import model
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def print_usage():
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print('Usage: graph_kernel_split.py [OPTION] <JSON_FILE>')
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print('Options:')
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print(' -s <config/auto>\tsplit graph with config')
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print(' -e \t\testimate graph')
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print(' -i \t\tnaive estimate')
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print(' -o <prefix>\toutput split graphs')
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print(' -v \t\tverbose mode')
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print(' -h \t\tprint this help')
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class Option:
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"""Options"""
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def __init__(self):
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self.split = None
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self.estimate = False
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self.estimate_naive = False
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self.output = None
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self.verbose = False
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self.help = False
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def parse(self, options):
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"""parse options"""
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for name, val in options:
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if name == '-h':
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self.help = True
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elif name == '-v':
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self.verbose = True
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elif name == '-o':
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self.output = val
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elif name == '-e':
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self.estimate = True
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elif name == '-s':
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self.split = val
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elif name == '-i':
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self.estimate_naive = True
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opt = Option()
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def estimate(graph_in, parts_in, naive):
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"""estimate graphs costs"""
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def _print_cost(name, c):
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print("%s\tdma_ratio=%f, saturation=%f, mix_saturation=%f, type=%s" %
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(name, c.dma_ratio(), c.saturation(), c.mix_saturation(), c.cost_type()))
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main_cost, _ = model.estimate(graph_in, naive)
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split_cost, sub_costs = model.estimate(parts_in, naive) if parts_in else (None, None)
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_print_cost("MainGraph:", main_cost)
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if parts_in:
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_print_cost("Subgraphs:", split_cost)
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if opt.verbose:
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for i, sub_cost in enumerate(sub_costs):
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_print_cost(" |_%d:\t" % (i), sub_cost)
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def split_graph(graph_in, config):
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"""split graph"""
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if config == 'auto':
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return model.split(graph_in)
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subgraphs = []
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all_tensors = []
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subgraph_idx = 0
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config_parts = config.split('|')
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for part in config_parts:
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tensor_names = part.split(',')
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graph_name = "%s_%d" % (graph_in.name, subgraph_idx)
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g = graph_in.extract_subgraph(graph_name, tensor_names)
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assert len(g.ops) == len(tensor_names)
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subgraphs.append(g)
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all_tensors += tensor_names
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subgraph_idx += 1
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if len(all_tensors) < len(graph_in.ops):
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graph_name = "%s_%d" % (graph_in.name, subgraph_idx)
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g = graph_in.extract_subgraph(graph_name, all_tensors, True)
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subgraphs.append(g)
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return subgraphs
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def main():
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opts, args = getopt.getopt(sys.argv[1:], 'heivo:s:')
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opt.parse(opts)
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if len(args) != 1 or opt.help:
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print_usage()
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sys.exit(0)
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in_file = args[0]
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with open(in_file, 'r') as f:
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desc = json.loads(f.read())
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comp = model.load_composite(desc)
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graph = comp.graph
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parts = []
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# 1. split sub-graphs
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if opt.split is not None:
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parts = split_graph(graph, opt.split)
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if opt.verbose:
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print('----------- main graph --------------')
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print(graph)
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for i, _ in enumerate(parts):
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print('---------------- sub graph %d ---------------' % (i))
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print(parts[i])
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# 2. estimate cost
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if opt.estimate:
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print('------------- cost --------------')
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estimate(graph, parts, False)
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if opt.estimate_naive:
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print('------------- naive cost --------------')
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estimate(graph, parts, True)
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# 3. output parts
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if opt.output is not None:
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for graph_part in parts:
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desc = comp.dump(graph_part)
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s_desc = json.dumps(desc)
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fname = "%s_%s.json" % (opt.output, graph_part.name)
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with open(fname, 'w', encoding='utf-8') as of:
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of.write(s_desc)
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if __name__ == '__main__':
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main()
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