diff --git a/python/paddle/cost_model/__init__.py b/python/paddle/cost_model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..65f2533032ae3a8850022faf945dc643cd35e909 --- /dev/null +++ b/python/paddle/cost_model/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# 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. + +from .cost_model import CostModel # noqa: F401 +__all__ = ['CostModel'] diff --git a/python/paddle/cost_model/cost_model.py b/python/paddle/cost_model/cost_model.py index 93c89d0c89297fcae6b4235f19d8db48e79f4e3d..e6a87468a1172c0761cdbd7fe2ca748715424f8d 100644 --- a/python/paddle/cost_model/cost_model.py +++ b/python/paddle/cost_model/cost_model.py @@ -15,6 +15,8 @@ import paddle import paddle.static as static import numpy as np +import json +import os from paddle.fluid import core @@ -36,7 +38,6 @@ class CostModel(): paddle.optimizer.SGD(learning_rate=0.01).minimize(loss) print("main program is: {}".format(main_program)) - #print("start up program is: {}".format(startup_program)) return startup_program, main_program @@ -44,7 +45,7 @@ class CostModel(): startup_program, main_program, device='gpu', - fetch_cost_list=['time', 'memory']): + fetch_cost_list=['time']): place = paddle.set_device('gpu') x = np.random.random(size=(10, 1)).astype('float32') @@ -53,17 +54,33 @@ class CostModel(): exe.run(startup_program) paddle.fluid.profiler.start_profiler("All") exe.run(main_program, feed={"X": x}, fetch_list=[]) - # core.CostModel.ProfileMeasure(main_program, device) - print("core:<<<<<<<") cost_model = core.CostModel() cost_data = cost_model.ProfileMeasure(device) - # cost_list = self.stop_cost_model() - # return cost_list + def static_cost_data(self): + static_cost_data_path = os.path.join( + os.path.dirname(__file__), "static_op_benchmark.json") + with open(static_cost_data_path, 'r') as load_f: + load_dict = json.load(load_f) + self._static_cost_data = load_dict + # return all static cost data + return load_dict -cost_model = CostModel() + def get_static_op_time(self, op_name, forward=True, dtype="float32"): + # if forward is True, return op forward time, otherwise return op backward time. + if op_name == None: + raise ValueError( + 'op_name should not be empty when you want to get static op time' + ) -startup_program, main_program = cost_model.build_program() + op_cost = {} + for op_data in self._static_cost_data: + if (op_data["op"] == op_name) and (dtype in op_data["config"]): + if (forward): + op_cost["op_time"] = op_data["paddle_gpu_time"] + else: + op_cost["op_time"] = op_data["paddle_gpu_time_backward"] + op_cost["config"] = op_data["config"] -cost_model.profile_measure(startup_program, main_program) + return op_cost diff --git a/python/paddle/cost_model/static_op_benchmark.json b/python/paddle/cost_model/static_op_benchmark.json new file mode 100644 index 0000000000000000000000000000000000000000..b516f810e53e8c6b4f6b6c42416956e945d98661 --- /dev/null +++ b/python/paddle/cost_model/static_op_benchmark.json @@ -0,0 +1 @@ +[{"name":"abs_0","op":"abs","op_count":2,"config":"x (Variable) - 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