comaniac commented on a change in pull request #6187: URL: https://github.com/apache/incubator-tvm/pull/6187#discussion_r464690412
########## File path: include/tvm/auto_scheduler/cost_model.h ########## @@ -0,0 +1,160 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you 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. + */ + +/*! + * \file tvm/auto_scheduler/cost_model.h + * \brief Cost models that estimate the performance of programs + */ + +#ifndef TVM_AUTO_SCHEDULER_COST_MODEL_H_ +#define TVM_AUTO_SCHEDULER_COST_MODEL_H_ + +#include <tvm/auto_scheduler/compute_dag.h> +#include <tvm/auto_scheduler/measure.h> +#include <tvm/node/node.h> +#include <tvm/runtime/packed_func.h> + +#include <vector> + +namespace tvm { +namespace auto_scheduler { + +using runtime::PackedFunc; +using runtime::TypedPackedFunc; + +/*! \brief The base class for cost model */ +class CostModelNode : public Object { + public: + /*! + * \brief Update the cost model according to new measurement results (training data). + * \param inputs The measure inputs + * \param results The measure results + */ + virtual void Update(const Array<MeasureInput>& inputs, const Array<MeasureResult>& results) = 0; + + /*! + * \brief Predict the scores of states + * \param task The search task of states + * \param states The input states + * \param scores The predicted scores for all states + */ + virtual void Predict(const SearchTask& task, const std::vector<State>& states, + std::vector<float>* scores) = 0; + + /*! + * \brief Predict the scores of all stages in states + * \param task The search task + * \param states The input states + * \param state_scores The predicted scores for all states + * \param stage_scores The predicted scores for all stages in all stages + */ + virtual void PredictStages(const SearchTask& task, const std::vector<State>& states, Review comment: @merrymercy would that be better to also make this as a virtual function, or setup a flag or something to indicate if this cost model supports `PredictStages` or not instead of just throwing an error? ########## File path: python/tvm/auto_scheduler/cost_model/cost_model.py ########## @@ -0,0 +1,142 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +""" Cost model that estimates the performance of programs """ +import ctypes +import numpy as np + +import tvm._ffi +from tvm.runtime import Object +from .. import _ffi_api + + +@tvm._ffi.register_object("auto_scheduler.CostModel") +class CostModel(Object): + """The base class for cost model""" + +@tvm._ffi.register_object("auto_scheduler.RandomModel") +class RandomModel(CostModel): + """A model returns random estimation for all inputs""" + def __init__(self): + self.__init_handle_by_constructor__(_ffi_api.RandomModel) + + def update(self, inputs, results): + """Update the cost model according to new measurement results (training data). + + Parameters + ---------- + inputs : List[MeasureInput] + The measurement inputs + results : List[MeasureResult] + The measurement results + """ + _ffi_api.CostModelUpdate(self, inputs, results) + + def predict(self, search_task, states): + """Predict the scores of states + + Parameters + ---------- + search_task : SearchTask + The search task of states + statse : List[State] + The input states + + Returns + ------- + scores: List[float] + The predicted scores for all states + """ + return [x.value for x in _ffi_api.CostModelPredict(self, search_task, states)] + + +@tvm._ffi.register_func("auto_scheduler.cost_model.random_number") +def random_number(n, return_ptr): + """ A random number generator func for c++'s RandomModel """ + if n == 0: + return + return_ptr = ctypes.cast(return_ptr, ctypes.POINTER(ctypes.c_float)) + array_wrapper = np.ctypeslib.as_array(return_ptr, shape=(n,)) + array_wrapper[:] = np.random.uniform(0, 1, (n,)) + + +@tvm._ffi.register_object("auto_scheduler.PythonBasedModel") +class PythonBasedModel(CostModel): + """Base class for cost models implemented in python""" + def __init__(self): + def update_func(inputs, results): + self.update(inputs, results) + + def predict_func(task, states, return_ptr): + return_ptr = ctypes.cast(return_ptr, ctypes.POINTER(ctypes.c_float)) + array_wrapper = np.ctypeslib.as_array(return_ptr, shape=(len(states),)) + array_wrapper[:] = self.predict(task, states) + + def predict_stage_func(task, states, return_ptr): + ret = self.predict_stages(task, states) + return_ptr = ctypes.cast(return_ptr, ctypes.POINTER(ctypes.c_float)) + array_wrapper = np.ctypeslib.as_array(return_ptr, shape=ret.shape) + array_wrapper[:] = ret + + self.__init_handle_by_constructor__(_ffi_api.PythonBasedModel, update_func, + predict_func, predict_stage_func) + + def update(self, inputs, results): + """Update the cost model according to new measurement results (training data). + + Parameters + ---------- + inputs : List[MeasureInput] + The measurement inputs + results : List[MeasureResult] + The measurement results + """ + raise NotImplementedError + + def predict(self, task, states): + """Predict the scores of states + + Parameters + ---------- + search_task : SearchTask + The search task of states + statse : List[State] + The input states + + Returns + ------- + scores: List[float] + The predicted scores for all states + """ + raise NotImplementedError + + def predict_stages(self, task, states): + """Predict the scores of states Review comment: ```suggestion """Predict the scores of states. Different from "predict", this function returns score breakdown for each stage. ``` ########## File path: python/tvm/auto_scheduler/cost_model/cost_model.py ########## @@ -0,0 +1,142 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +""" Cost model that estimates the performance of programs """ +import ctypes +import numpy as np + +import tvm._ffi +from tvm.runtime import Object +from .. import _ffi_api + + +@tvm._ffi.register_object("auto_scheduler.CostModel") +class CostModel(Object): + """The base class for cost model""" + +@tvm._ffi.register_object("auto_scheduler.RandomModel") +class RandomModel(CostModel): + """A model returns random estimation for all inputs""" + def __init__(self): + self.__init_handle_by_constructor__(_ffi_api.RandomModel) + + def update(self, inputs, results): + """Update the cost model according to new measurement results (training data). + + Parameters + ---------- + inputs : List[MeasureInput] + The measurement inputs + results : List[MeasureResult] + The measurement results + """ + _ffi_api.CostModelUpdate(self, inputs, results) + + def predict(self, search_task, states): + """Predict the scores of states + + Parameters + ---------- + search_task : SearchTask + The search task of states + statse : List[State] + The input states + + Returns + ------- + scores: List[float] + The predicted scores for all states + """ + return [x.value for x in _ffi_api.CostModelPredict(self, search_task, states)] + + +@tvm._ffi.register_func("auto_scheduler.cost_model.random_number") +def random_number(n, return_ptr): + """ A random number generator func for c++'s RandomModel """ + if n == 0: Review comment: Better to improve the naming of `n`. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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