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179 lines
6.6 KiB
179 lines
6.6 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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import os
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import paddle.fluid.framework as framework
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from paddle.fluid.optimizer import Optimizer
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import paddle.fluid.core as core
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import numpy as np
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from . import ascend_parser
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class AscendIRParser(object):
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def __init__(self):
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self.graph_idx = 0
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def _construct_input_map(self, input_varlist):
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ret_map = {}
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ge_in_operator = []
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for id, var in enumerate(input_varlist):
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if var.is_data: # input data
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ge_input = core.GEOperatorFactory.create_operator(var.name, "Data").set_attr_int32("index", id)
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ret_map[var.name] = ge_input
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ge_in_operator.append(ge_input)
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else: # param, learning ...
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ge_input = core.GEOperatorFactory.create_operator(var.name, "Variable")
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ge_input.update_output_desc("y", core.GETensorDesc(core.GEShape(var.shape), core.GEFormat.FORMAT_ND, core.GEDataType.DT_FLOAT))
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ret_map[var.name] = ge_input
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return ge_in_operator, ret_map
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def parse_op(self, op):
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if op.type in ascend_parser.registerd_op:
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print("Op[%s] has been registered, begin to parse it" % (op.type))
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op_parser = self.parser_factory.create_parse(ascend_parser.registerd_op[op.type])
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op_parser.apply(op)
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else:
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print("Op[%s] has not been registered, so we have to skip it" % (op.type))
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def _parse_program(self, graph_name, program, input_varlist=[], fetch_list=[]):
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begin_graph_idx = self.graph_idx
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ge_in_operator = []
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ge_out_operator = []
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self.var2geop = {}
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block = program.global_block()
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if len(block.ops) == 0:
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print("There is no ops in program %s" % (graph_name))
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return []
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graph = core.GEGraph(graph_name)
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ge_in_operator, self.var2geop = self._construct_input_map(input_varlist)
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self.parser_factory = ascend_parser.AscendParserFactory(graph, self.var2geop)
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for i, curop in list(enumerate(block.ops)):
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self.parse_op(curop)
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# Set fetch_var for GE
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for e in fetch_list:
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name = e
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if not isinstance(e, str):
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name = e.name
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ge_out_operator.append(self.var2geop[name])
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# (Debug) If you want to print back prop vars, append/assign the varname in ge_out_operator here, such as:
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# if graph_name == "main":
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# ge_out_operator.append(self.var2geop["reduce_sum_0.tmp_0@GRAD"])
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# Add ops that may be input of a graph, such as const.
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for varname, geop in self.var2geop.items():
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if varname.startswith("geinput"):
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ge_in_operator.append(geop)
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graph.set_inputs(ge_in_operator).set_outputs(ge_out_operator)
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# Remove ops of origin program
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op_num = len(block.ops)
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for i in range(op_num - 1, -1, -1):
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block._remove_op(i)
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input_varlist = [var for var in input_varlist if var.is_data]
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block.append_op(
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type="ascend_trigger",
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inputs={"FeedList": input_varlist},
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outputs={"FetchList": fetch_list},
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attrs={'graph_idx': self.graph_idx})
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self.graph_idx += 1
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return graph
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def parse_program(self, startup_program, main_program, input_varlist, fetch_list):
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startup_graph = self._parse_program("startup", startup_program)
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main_graph = self._parse_program("main", main_program, input_varlist, fetch_list)
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return startup_graph, main_graph
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# AscendOptimizer is a wrapper for basic optimizer now
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# We will make it part of fleet meta_optimizer in the future
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class AscendOptimizer(Optimizer):
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def __init__(self, optimizer, fetch_list=[]):
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self.inner_opt = optimizer
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self.fetch_list = fetch_list
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def __del__(self):
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core.ge_finalize()
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def _can_apply(self):
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if not self.user_defined_strategy.ascend:
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return False
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# TODO(hutuxian): other check here
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return True
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def _disable_strategy(self, dist_strategy):
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dist_strategy.ascend = False
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dist_strategy.ascend_configs = {}
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def _get_input_varlist(self, program):
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ret_list = []
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for var in program.list_vars():
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if var.is_data or var.persistable:
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ret_list.append(var)
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return ret_list
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def minimize(self,
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loss,
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startup_program=None,
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parameter_list=None,
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no_grad_set=None,
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auto_dp=False):
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minimized = self.inner_opt.minimize(loss, startup_program=startup_program)
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self.ascend_instance = core.AscendInstance()
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from paddle.distributed import fleet
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if auto_dp and fleet.worker_num() > 1:
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from paddle.fluid.transpiler import ascend_transpiler
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t = ascend_transpiler.AscendTranspiler(startup_program, loss.block.program)
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t.transpile()
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print(loss.block.program)
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# Config about Graph Engine can be found in https://support.huaweicloud.com/
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config = {
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"ge.exec.deviceId": str(fleet.rank_in_node()),
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"ge.graphRunMode": "1",
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"ge.exec.precision_mode": "must_keep_origin_dtype",
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# if multi mode
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"ge.exec.rankTableFile": os.getenv("RANK_TABLE_FILE"),
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"ge.exec.rankId": str(fleet.worker_index()),
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"ge.exec.isUseHcom": "1",
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"ge.exec.deployMode": "0",
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}
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print("ge_initialize config:", config)
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core.ge_initialize(config)
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# Init Session
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self.ascend_instance.init_global_resources()
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main_block = loss.block
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self.parser = AscendIRParser()
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input_varlist = self._get_input_varlist(main_block.program)
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startup_graph, main_graph = self.parser.parse_program(
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startup_program, main_block.program, input_varlist, self.fetch_list)
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self.ascend_instance.add_ascend_subgraph(0, startup_graph)
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self.ascend_instance.add_ascend_subgraph(1, main_graph)
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return minimized
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