diff --git a/.gitignore b/.gitignore index 470db16..3e7370b 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,4 @@ dataset !requirements.txt *.yaml +workspace \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..a94af50 --- /dev/null +++ b/README.md @@ -0,0 +1,40 @@ +# Tiramisu GNN Autoscheduler + +This is a repository for the Tiramisu GNN Autoscheduler, a tool for training graph neural networks based reinforcement learning agents. The tool is built on top of the [Tiramisu](https://github.com/Tiramisu-Compiler/tiramisu), a polyhedral compiler for deep learning and scientific computing. + +The project uses [TiraLib](https://github.com/Tiramisu-Compiler/TiraLib) as the backend for communications with the Tiramisu compiler. TiraLib is a Python library that provides an interface to write and run Tiramisu programs. It uses [TiralibCpp](https://github.com/skourta/TiraLibCpp) as a backend to compile and run the Tiramisu programs. + +## Installation + +To install the Tiramisu GNN Autoscheduler, you need to install the following dependencies: +- [Tiramisu](https://github.com/Tiramisu-Compiler/tiramisu) +- [TiralibCpp](https://github.com/skourta/TiraLibCpp) +- [Poetry](https://python-poetry.org/) +After installing the dependencies, you can run the following commands to train an agent: + +- Clone the repository: +```bash +git clone https://github.com/Tiramisu-Compiler/gnn_rl +``` +- Install the requirements : +```bash +poetry install +``` + +- Create a config for training by copying the example config and make sure both the directory with the headers and library files for Tiramisu and TiraLibCpp are correctly added to the config: +```bash +cp config/config.yaml.temp config/config.yaml +``` + + +- Create a TraLib config just like in the [example template](https://github.com/Tiramisu-Compiler/TiraLib/blob/main/config.yaml.example) and save it in the same directory as the config.yaml +```bash +wget https://raw.githubusercontent.com/Tiramisu-Compiler/TiraLib/main/config.yaml.example -O config/tiralib_config.yaml +``` + +- Use the `rl_exec_job.sh.temp` script as a template to create a script that will run the training job. Make sure to update the paths to the config files and the Tiramisu GNN Autoscheduler repository in the script. + +- You can also run the training on the current machine by running the following command: +```bash +python train_ppo_gnn.py --num-nodes=$NBR_NODES --name$NAME_OF_TRAINING +``` \ No newline at end of file diff --git a/agent/graph_utils.py b/agent/graph_utils.py index 19c724f..9fd5c95 100644 --- a/agent/graph_utils.py +++ b/agent/graph_utils.py @@ -2,40 +2,76 @@ import re -def isl_to_write_matrix(isl_map): - comp_iterators_str = re.findall(r"\[(.*)\]\s*->", isl_map)[0] - buffer_iterators_str = re.findall(r"->\s*\w*\[(.*)\]", isl_map)[0] - buffer_iterators_str = re.sub(r"\w+'\s=", "", buffer_iterators_str) - comp_iter_names = re.findall(r"(?:\s*(\w+))+", comp_iterators_str) - buf_iter_names = re.findall(r"(?:\s*(\w+))+", buffer_iterators_str) - matrix = np.zeros([len(buf_iter_names), len(comp_iter_names) + 1]) - for i, buf_iter in enumerate(buf_iter_names): - for j, comp_iter in enumerate(comp_iter_names): - if buf_iter == comp_iter: - matrix[i, j] = 1 - break - return matrix - - -def iterators_to_vectors(annotations): - it_dict = {} - iter_vector_size = 718 - size_of_comp_vector = 709 - for it in annotations["iterators"]: - single_iter_vector = -np.ones(iter_vector_size) - single_iter_vector[0] = 0 - single_iter_vector[-9:] = 0 - # lower value - single_iter_vector[size_of_comp_vector + 1] = annotations["iterators"][it][ - "lower_bound" - ] - # upper value - single_iter_vector[size_of_comp_vector + 2] = annotations["iterators"][it][ - "upper_bound" - ] - it_dict[it] = single_iter_vector - - return it_dict +def parse_isl_map(isl_map: str): + # Extract the computation and buffer parts + match = re.match(r".*{\s*(\w+)\[([^\]]+)\]\s*->\s*(\w+)\[([^\]]+)\]\s*}", isl_map) + if not match: + raise ValueError("Invalid ISL map format") + + _ = match.group(1) + comp_iterators = match.group(2).split(",") + _ = match.group(3) + buffer_accesses = match.group(4).split(",") + + # Strip any spaces around iterators or accesses + comp_iterators = [it.strip() for it in comp_iterators] + buffer_accesses = [access.strip() for access in buffer_accesses] + + return comp_iterators, buffer_accesses + + +def extract_affine_coefficients(access, comp_iterators): + """ + Extract coefficients of iterators in the affine expression. + Example: access = 'i1 + 2*j2', comp_iterators = ['i1', 'j2', 'k'] + Output: [1, 2, 0, 0] (coefficients for i1, j2, k, and scalar term) + """ + # remove all spaces + access = access.replace(" ", "") + + # Initialize coefficients (including one for the scalar term) + coefficients = [0] * (len(comp_iterators) + 1) + + # Match terms like '2*i1' or 'i1' or '-i1', and extract the coefficients + for i, it in enumerate(comp_iterators): + # Modify the pattern to match iterator names with numbers or underscores + term_pattern = re.compile(r"([+-]?\d*)\s*\*?\s*(" + re.escape(it) + r")(\b|$)") + match = term_pattern.search(access) + if match: + coeff = match.group(1) + coefficients[i] = ( + int(coeff) + if coeff and coeff != "+" and coeff != "-" + else 1 + if coeff == "" or coeff == "+" + else -1 + ) + + # Handle the scalar part, which is just a constant not attached to any iterator + scalar_pattern = re.compile( + r"([+-]?\b\d+\b)(?![\*\w])" + ) # Match isolated constants (scalars) not tied to iterators + scalar_match = scalar_pattern.search(access) + + if scalar_match: + coefficients[-1] = int(scalar_match.group(1)) + + return coefficients + + +def isl_map_to_write_access_matrix(isl_map: str): + # Parse the ISL map + comp_iterators, buffer_accesses = parse_isl_map(isl_map) + + # Initialize the access matrix (rows = buffer dimensions, columns = iterators + scalar) + access_matrix = [] + + # Process each buffer access and extract affine coefficients + for access in buffer_accesses: + row = extract_affine_coefficients(access, comp_iterators) + access_matrix.append(row) + + return np.array(access_matrix) def pad_access_matrix(access_matrix, max_depth): @@ -53,121 +89,3 @@ def encode_data_type(data_type): return [0, 1, 0] elif data_type == "float64": return [0, 0, 1] - - -def comps_to_vectors(annotations): - comp_vector_size = 718 - max_depth = 5 - dict_comp = {} - for comp in annotations["computations"]: - single_comp_vector = -np.ones(comp_vector_size) - # This means that this vector has data related to a computation and not an iterator - single_comp_vector[0] = 1 - comp_dict = annotations["computations"][comp] - # This field represents the absolute order of execution of computations - single_comp_vector[1] = comp_dict["absolute_order"] - # a vector of one-hot encoding of possible 3 data-types - single_comp_vector[2:5] = encode_data_type(comp_dict["data_type"]) - single_comp_vector[5] = +comp_dict["comp_is_reduction"] - # The write-to buffer id - single_comp_vector[6] = +comp_dict["write_buffer_id"] - # We add a vector of write access - write_matrix = isl_to_write_matrix(comp_dict["write_access_relation"]) - padded_matrix = pad_access_matrix(write_matrix, max_depth).reshape(-1) - single_comp_vector[7 : 7 + padded_matrix.shape[0]] = padded_matrix - # We add vector of read access - for index, read_access_dict in enumerate(comp_dict["accesses"]): - read_access_matrix = pad_access_matrix( - np.array(read_access_dict["access_matrix"]), max_depth - ).reshape(-1) - read_access_matrix = np.append( - read_access_matrix, +read_access_dict["access_is_reduction"] - ) - read_access_matrix = np.append( - read_access_matrix, read_access_dict["buffer_id"] + 1 - ) - read_access_size = read_access_matrix.shape[0] - single_comp_vector[ - 49 + index * read_access_size : 49 + (index + 1) * read_access_size - ] = read_access_matrix - dict_comp[comp] = single_comp_vector - return dict_comp - - -def build_graph(annotations): - it_vector_dict = iterators_to_vectors(annotations) - comp_vector_dict = comps_to_vectors(annotations) - it_index = {} - comp_index = {} - num_iterators = len(annotations["iterators"]) - for i, it in enumerate(it_vector_dict): - it_index[it] = i - for i, comp in enumerate(comp_vector_dict): - comp_index[comp] = i - - edge_index = [] - node_feats = None - - for it in annotations["iterators"]: - for child_it in annotations["iterators"][it]["child_iterators"]: - edge_index.append([it_index[it], it_index[child_it]]) - - for child_comp in annotations["iterators"][it]["computations_list"]: - edge_index.append([it_index[it], num_iterators + comp_index[child_comp]]) - node_feats = np.stack( - [ - *[arr for arr in it_vector_dict.values()], - *[arr for arr in comp_vector_dict.values()], - ], - ) - return node_feats, np.array(edge_index), it_index, comp_index - - -def apply_parallelization(iterator, node_feats, it_index): - index = it_index[iterator] - node_feats[index][-6] = 1 - - -def apply_reversal(iterator, node_feats, it_index): - index = it_index[iterator] - node_feats[index][-5] = 1 - - -def apply_unrolling(iterator, unrolling_factor, node_feats, it_index): - index = it_index[iterator] - node_feats[index][-4] = unrolling_factor - - -def apply_tiling(iterators, tile_sizes, node_feats, it_index): - for it, tile in zip(iterators, tile_sizes): - index = it_index[it] - node_feats[index][-3] = tile - - -def apply_skewing(iterators, skewing_factors, node_feats, it_index): - for it in iterators: - index = it_index[it] - node_feats[index][-2:] = skewing_factors - - -def apply_interchange(iterators, edge_index, it_index): - it1, it2 = it_index[iterators[0]], it_index[iterators[1]] - for edge in edge_index: - if edge[0] == it1: - edge[0] = it2 - elif edge[0] == it2: - edge[0] = it1 - if edge[1] == it1: - edge[1] = it2 - elif edge[1] == it2: - edge[1] = it1 - - -def focus_on_iterators(iterators, node_feats, it_index): - # We reset the value for all the nodes - node_feats[: len(it_index), -9:-8] = 0 - # We focus on the branches' iterators - for it in iterators: - index = it_index[it] - node_feats[index][-9] = 1 - return node_feats diff --git a/agent/policy_value_nn.py b/agent/policy_value_nn.py index eb2a191..c44124a 100644 --- a/agent/policy_value_nn.py +++ b/agent/policy_value_nn.py @@ -134,10 +134,10 @@ def shared_layers(self, data): def forward(self, data, actions_mask=None, action=None): weights = self.shared_layers(data) logits = self.π(weights) - if actions_mask != None: + if actions_mask is not None: logits = logits - actions_mask * 1e8 probs = Categorical(logits=logits) - if action == None: + if action is None: action = probs.sample() value = self.v(weights) return action, probs.log_prob(action), probs.entropy(), value diff --git a/agent/rollout_worker.py b/agent/rollout_worker.py index 4e5939c..51e1763 100644 --- a/agent/rollout_worker.py +++ b/agent/rollout_worker.py @@ -1,321 +1,23 @@ from collections import namedtuple +from dataclasses import dataclass, field +import json +import logging +from pathlib import Path +from time import time import ray +import numpy as np +import ray.actor import torch import torch.nn as nn import math from torch_geometric.data import Data -from agent.graph_utils import * -from config.config import Config -from env_api.tiramisu_api import TiramisuEnvAPI - - -def apply_flattened_action( - tiramisu_api: TiramisuEnvAPI, - action, - node_feats, - edge_index, - it_index, - worker_id="0", -): - done = False - if action < 4: - loop_level = action - # Interchange of loops (0,1) (1,2) (2,3) (3,4) - ( - speedup, - legality, - actions_mask, - ) = tiramisu_api.interchange( - loop_level1=loop_level, - loop_level2=loop_level + 1, - env_id=action, - worker_id=worker_id, - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_interchange( - [its[loop_level], its[loop_level + 1]], edge_index, it_index - ) - elif action < 9: - loop_level = action - 4 - # Reversal from 0 to 4 - ( - speedup, - legality, - actions_mask, - ) = tiramisu_api.reverse( - loop_level=loop_level, env_id=action, worker_id=worker_id - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_reversal(its[loop_level], node_feats, it_index) - elif action < 12: - loop_level = action - 9 - # Skewing 0,1 to 2,3 - speedup, legality, actions_mask = tiramisu_api.skew( - loop_level1=loop_level, - loop_level2=loop_level + 1, - env_id=action, - worker_id=worker_id, - ) - if legality: - skewing_params = ( - tiramisu_api.scheduler_service.schedule_object.schedule_list[-1].params[ - -2: - ] - ) - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_skewing( - [its[loop_level], its[loop_level + 1]], - skewing_params, - node_feats, - it_index, - ) - elif action < 14: - loop_level = action - 12 - # For parallelization 0 and 1 - ( - speedup, - legality, - actions_mask, - ) = tiramisu_api.parallelize( - loop_level=loop_level, env_id=action, worker_id=worker_id - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_parallelization(its[loop_level], node_feats, it_index) - elif action < 18: - loop_level = action - 14 - size = 32 - speedup, legality, actions_mask = tiramisu_api.tile2D( - loop_level1=loop_level, - loop_level2=loop_level + 1, - size_x=size, - size_y=size, - env_id=action, - worker_id=worker_id, - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_tiling( - [its[loop_level], its[loop_level + 1]], - [size, size], - node_feats, - it_index, - ) - elif action < 22: - loop_level = action - 18 - size = 64 - speedup, legality, actions_mask = tiramisu_api.tile2D( - loop_level1=loop_level, - loop_level2=loop_level + 1, - size_x=size, - size_y=size, - env_id=action, - worker_id=worker_id, - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_tiling( - [its[loop_level], its[loop_level + 1]], - [size, size], - node_feats, - it_index, - ) - elif action < 26: - loop_level = action - 22 - size = 128 - speedup, legality, actions_mask = tiramisu_api.tile2D( - loop_level1=loop_level, - loop_level2=loop_level + 1, - size_x=size, - size_y=size, - env_id=action, - worker_id=worker_id, - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_tiling( - [its[loop_level], its[loop_level + 1]], - [size, size], - node_feats, - it_index, - ) - elif action < 30: - loop_level = action - 26 - size_x = 32 - size_y = 64 - - speedup, legality, actions_mask = tiramisu_api.tile2D( - loop_level1=loop_level, - loop_level2=loop_level + 1, - size_x=size_x, - size_y=size_y, - env_id=action, - worker_id=worker_id, - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_tiling( - [its[loop_level], its[loop_level + 1]], - [size_x, size_y], - node_feats, - it_index, - ) - elif action < 34: - loop_level = action - 30 - size_x = 32 - size_y = 128 - - speedup, legality, actions_mask = tiramisu_api.tile2D( - loop_level1=loop_level, - loop_level2=loop_level + 1, - size_x=size_x, - size_y=size_y, - env_id=action, - worker_id=worker_id, - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_tiling( - [its[loop_level], its[loop_level + 1]], - [size_x, size_y], - node_feats, - it_index, - ) - elif action < 38: - loop_level = action - 34 - size_x = 64 - size_y = 32 - - speedup, legality, actions_mask = tiramisu_api.tile2D( - loop_level1=loop_level, - loop_level2=loop_level + 1, - size_x=size_x, - size_y=size_y, - env_id=action, - worker_id=worker_id, - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_tiling( - [its[loop_level], its[loop_level + 1]], - [size_x, size_y], - node_feats, - it_index, - ) - elif action < 42: - loop_level = action - 38 - size_x = 64 - size_y = 128 - - speedup, legality, actions_mask = tiramisu_api.tile2D( - loop_level1=loop_level, - loop_level2=loop_level + 1, - size_x=size_x, - size_y=size_y, - env_id=action, - worker_id=worker_id, - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_tiling( - [its[loop_level], its[loop_level + 1]], - [size_x, size_y], - node_feats, - it_index, - ) - elif action < 46: - loop_level = action - 42 - size_x = 128 - size_y = 32 - - speedup, legality, actions_mask = tiramisu_api.tile2D( - loop_level1=loop_level, - loop_level2=loop_level + 1, - size_x=size_x, - size_y=size_y, - env_id=action, - worker_id=worker_id, - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_tiling( - [its[loop_level], its[loop_level + 1]], - [size_x, size_y], - node_feats, - it_index, - ) - elif action < 50: - loop_level = action - 46 - size_x = 128 - size_y = 64 - - speedup, legality, actions_mask = tiramisu_api.tile2D( - loop_level1=loop_level, - loop_level2=loop_level + 1, - size_x=size_x, - size_y=size_y, - env_id=action, - worker_id=worker_id, - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_tiling( - [its[loop_level], its[loop_level + 1]], - [size_x, size_y], - node_feats, - it_index, - ) - elif action < 55: - factor = action - 49 - speedup, legality, actions_mask = tiramisu_api.unroll( - unrolling_factor=2**factor, env_id=action, worker_id=worker_id - ) - if legality: - branch = tiramisu_api.scheduler_service.current_branch - its = tiramisu_api.scheduler_service.branches[branch].common_it - apply_unrolling(its[-1], 2**factor, node_feats, it_index) - else: - # Next case - next_branch_mask = tiramisu_api.scheduler_service.next_branch() - if not (isinstance(next_branch_mask, np.ndarray)): - speedup, legality, actions_mask = ( - 1, - True, - np.zeros(56), - ) - done = True - else: - speedup, legality, actions_mask = ( - 1, - True, - next_branch_mask, - ) - branch = tiramisu_api.scheduler_service.current_branch - node_feats = focus_on_iterators( - tiramisu_api.scheduler_service.branches[branch].common_it, - node_feats, - it_index, - ) - - return speedup, node_feats, edge_index, legality, actions_mask, done +from agent.tiramisu_interface import TiramisuInterface, program_compatible_with_model +from config.config import AutoSchedulerConfig, Config +from utils.dataset_actor import DatasetActor +logger = logging.getLogger(__name__) Transition = namedtuple( "Transition", @@ -323,12 +25,39 @@ def apply_flattened_action( ) -@ray.remote +@dataclass +class TimePerStage: + graph: float = 0.0 + get_next_function: float = 0.0 + tiramisu_interface_init: float = 0.0 + model_eval: float = 0.0 + get_mask: list[float] = field(default_factory=list) + get_action: list[float] = field(default_factory=list) + apply_action: list[float] = field(default_factory=list) + reward_process: list[float] = field(default_factory=list) + get_next_state: list[float] = field(default_factory=list) + rollout: float = 0.0 + update_dataset: float = 0.0 + + def __repr__(self): + return json.dumps(self.__dict__, indent=4) # pragma: no cover + + def __str__(self): + return json.dumps(self.__dict__) + + class RolloutWorker: - def __init__(self, dataset_worker, config, worker_id=0): + def __init__( + self, + dataset_worker: DatasetActor, + config: AutoSchedulerConfig, + worker_id: int = 0, + function_name: str | None = None, + ): Config.config = config - self.tiramisu_api = TiramisuEnvAPI(local_dataset=False) self.dataset_worker = dataset_worker + self.tiramisu_interface: TiramisuInterface | None = None + self.tiralib_config_path = config.tiralib_config_path # Variables related to workers and the environment self.worker_id = worker_id @@ -336,50 +65,85 @@ def __init__(self, dataset_worker, config, worker_id=0): # Variables related to the RL+Tiramisu train cycle self.state = None - self.actions_mask = None self.previous_speedup = None self.steps = None + self.times_per_stages = TimePerStage() # Initializing values and the episode - self.reset() - - def reset(self): - actions_mask = None - while not isinstance(actions_mask, np.ndarray): - prog_infos = ray.get(self.dataset_worker.get_next_function.remote()) - actions_mask = self.tiramisu_api.set_program( - *prog_infos, worker_id=str(self.worker_id) - ) - - self.current_program = prog_infos[0] + self.reset(function_name) + + def reset(self, function_name: str | None = None): + assert Config.config is not None, "Config is not initialized" + self.times_per_stages = TimePerStage() + start_time = time() + is_program_model_compatible = False + init_crashed = False + while not is_program_model_compatible or init_crashed: + logger.info("Getting next function") + if function_name and not isinstance( + self.dataset_worker, ray.actor.ActorHandle + ): + function_name, function_data, cpp_code = ( + self.dataset_worker.get_function_by_name(function_name) + ) + else: + function_name, function_data, cpp_code = ray.get( + self.dataset_worker.get_next_function.remote() # type: ignore + ) - self.actions_mask = torch.tensor(actions_mask) - annotations = ( - self.tiramisu_api.scheduler_service.schedule_object.prog.annotations - ) - node_feats, edge_index, it_index, comp_index = build_graph(annotations) + annotations = function_data.program_annotation + is_program_model_compatible = program_compatible_with_model(annotations) + end_time = time() + self.times_per_stages.get_next_function = end_time - start_time + start_time = end_time + + self.current_program = function_name + try: + print(f"Compiling Function : {function_name}") + self.tiramisu_interface = TiramisuInterface( + cpp_code, + self.tiralib_config_path, + cache=function_data, + machine=Config.config.machine, + ) + except Exception as e: + init_crashed = True + logger.error( + f"Error while creating TiramisuInterface for function {function_name}" + ) + logger.error(e) + end_time = time() + self.times_per_stages.tiramisu_interface_init = end_time - start_time + start_time = end_time - node_feats = focus_on_iterators( - self.tiramisu_api.scheduler_service.branches[0].common_it, - node_feats, - it_index, - ) + node_feats, edge_index, it_index, comp_index = self.tiramisu_interface.graph self.previous_speedup = 1 self.steps = 0 self.state = (node_feats, edge_index, it_index) - self.previous_action = None + self.times_per_stages.graph = time() - start_time def rollout(self, model: nn.Module, device: str): + rollout_start_time = time() + start_time = rollout_start_time model.to(device) model.eval() trajectory = [] done = False log_trajectory = "#" * 50 log_trajectory += f"\nFunction : {self.current_program}" - + print("\n") + print("#" * 50) + print(f"Function : {self.current_program}") + print(f"trajectory : {trajectory}") + end_time = time() + self.times_per_stages.model_eval = end_time - start_time + start_time = end_time while not done: - prev_actions_mask = self.actions_mask + prev_actions_mask = self.tiramisu_interface.get_mask() + end_time = time() + self.times_per_stages.get_mask.append(end_time - start_time) + start_time = end_time self.steps += 1 (node_feats, edge_index, it_index) = self.state data = Data( @@ -391,29 +155,31 @@ def rollout(self, model: nn.Module, device: str): with torch.no_grad(): action, action_log_prob, entropy, value = model( - data, self.actions_mask.to(device) + data, torch.tensor(prev_actions_mask).to(device) ) action = action.item() action_log_prob = action_log_prob.item() value = value.item() - ( - total_speedup, - new_node_feats, - new_edge_index, - legality, - actions_mask, - done, - ) = apply_flattened_action( - self.tiramisu_api, - action, - np.copy(node_feats), - np.copy(edge_index), - it_index, - worker_id=str(self.worker_id), - ) - self.actions_mask = torch.tensor(actions_mask) + end_time = time() + self.times_per_stages.get_action.append(end_time - start_time) + start_time = end_time + + print(f"Running Action : {action}") + + result = self.tiramisu_interface.apply_action(action) + end_time = time() + self.times_per_stages.apply_action.append(end_time - start_time) + start_time = end_time - reward = self.reward_process(action, legality, total_speedup) + done = result.done + if result.crashed: + logger.info("Crashed applying the action. Skipping this action") + continue + + reward = self.reward_process(action, result.is_legal, result.speedup) + end_time = time() + self.times_per_stages.reward_process.append(end_time - start_time) + start_time = end_time trajectory.append( ( @@ -426,37 +192,63 @@ def rollout(self, model: nn.Module, device: str): prev_actions_mask, ) ) - self.state = (new_node_feats, new_edge_index, it_index) + if self.steps == 40: + done = True + + if not done: + new_node_feats, new_edge_index, it_index, _ = ( + self.tiramisu_interface.graph + ) - log_trajectory += ( + self.state = (new_node_feats, new_edge_index, it_index) + end_time = time() + self.times_per_stages.get_next_state.append(end_time - start_time) + start_time = time() + + current_log = ( f"\nStep : {self.steps}" + f"\nAction ID : {action}" - + f"\nLegality : {legality}" - + f"\nActions Sequence So far : {self.tiramisu_api.scheduler_service.schedule_object.schedule_str}" + + f"\nLegality : {result.is_legal}" + + f"\nActions Sequence So far : {self.tiramisu_interface.action_indices}" + "\n" ) - - if self.steps == 40: - done = True + print(current_log) + log_trajectory += current_log else: - schedule_object = self.tiramisu_api.scheduler_service.schedule_object - - tiramisu_program_dict = ( - self.tiramisu_api.get_current_tiramisu_program_dict() - ) - ray.get( - self.dataset_worker.update_dataset.remote( - self.current_program, tiramisu_program_dict + start_time = time() + self.times_per_stages.rollout = start_time - rollout_start_time + if type(self.dataset_worker) is DatasetActor: + self.dataset_worker.update_dataset( + self.current_program, self.tiramisu_interface.cache.to_dict() ) - ) - - return { - "trajectory": trajectory, - "speedup": self.previous_speedup, - "schedule_object": schedule_object, - "log_trajectory": log_trajectory, - } + else: + ray.get( # pragma: no cover + self.dataset_worker.update_dataset.remote( + self.current_program, + self.tiramisu_interface.cache.to_dict(), + ) + ) + self.times_per_stages.update_dataset = time() - start_time + + # clean up created files + # delete files with the filename in workspace + if Config.config.clean_files: + for filename in Path(Config.config.tiramisu.workspace).glob("*"): + if self.current_program in filename.name: + filename.unlink() + print(f"End of episode : {self.current_program}") + print(f"Schedule : {self.tiramisu_interface.schedule}") + print(f"actions : {self.tiramisu_interface.action_indices}") + print(f"Speedup : {self.previous_speedup}") + print(f"Time per stage for {self.current_program} : {self.times_per_stages}") + return RolloutResult( + self.current_program, + trajectory, + self.previous_speedup, + str(self.tiramisu_interface.schedule), + log_trajectory, + ) def reward_process(self, action, legality, total_speedup): switching_branch_penality = 1 @@ -467,7 +259,6 @@ def reward_process(self, action, legality, total_speedup): if legality: if action != 55: # If the action is not Next - self.previous_action = action instant_speedup = total_speedup / self.previous_speedup self.previous_speedup = total_speedup else: @@ -478,5 +269,28 @@ def reward_process(self, action, legality, total_speedup): instant_speedup = np.clip(instant_speedup, 0, max_speedup) reward = math.log(instant_speedup, log_base) + print( + f"Reward : {reward}, Total Speedup : {total_speedup}, instant_speedup : {instant_speedup}" + ) return reward + + +@ray.remote +class RolloutWorkerRemote(RolloutWorker): + def __init__( + self, + dataset_worker: DatasetActor, + config: AutoSchedulerConfig, + worker_id: int = 0, + ): + super().__init__(dataset_worker, config, worker_id) # pragma: no cover + + +@dataclass +class RolloutResult: + function_name: str + trajectory: list[Transition] + speedup: float + schedule: str + log_trajectory: str diff --git a/agent/tiramisu_interface.py b/agent/tiramisu_interface.py new file mode 100644 index 0000000..29efca9 --- /dev/null +++ b/agent/tiramisu_interface.py @@ -0,0 +1,641 @@ +from collections import namedtuple +import numpy as np +from ray import logger +import tiralib.tiramisu as tiralib +import tiralib.config as tiralib_config +from agent.graph_utils import ( + encode_data_type, + isl_map_to_write_access_matrix, + pad_access_matrix, +) +from utils.dataset_actor import TiramisuProgramCache + + +NEXT_ACTION_INDEX = 55 +SECOND = 1000 +MAX_ITERATOR_DEPTH = 5 +# BUFFER_ACCESS_EMBEDDING_START = (MAX_ITERATOR_DEPTH + 1) * (MAX_ITERATOR_DEPTH +2) +# + 1 (VECTOR_TYPE) + 3 (DATA_TYPE_ENCODING) + 1 (REDUCTION) + 1 (WRITE_BUFFER_ID) +BUFFER_ACCESS_EMBEDDING_START = 49 +VECTOR_SIZE = 720 + + +class TiramisuInterface: + def __init__( + self, + cpp_code: str, + tiralib_config_path: str, + cache: TiramisuProgramCache | None = None, + machine: str = "jubail", + use_server: bool = True, + ): + tiralib_config.BaseConfig.init(tiralib_config_path) + self.current_branch_index = 0 + self.action_indices: list[int] = [] + self._initial_execution_time: float | None = None + self.cache = cache + self.machine = machine + self.use_server = use_server + if self.cache: + self.tiramisu_program = tiralib.TiramisuProgram.from_annotations( + self.cache.program_annotation, cpp_code=cpp_code, load_tree=True + ) + else: + if not self.use_server: + raise ValueError("Server must be used when cache is not provided") + self.tiramisu_program = tiralib.TiramisuProgram.init_server( + cpp_code=cpp_code, + load_isl_ast=True, + load_tree=True, + load_annotations=True, + reuse_server=True, + ) + + self.schedule = tiralib.Schedule(self.tiramisu_program) + assert self.initial_execution_time, "Getting initial execution time failed" + + # self.branches = self.schedule_branches + + @property + def server(self): + if not self.tiramisu_program.server: + self.tiramisu_program.server = tiralib.FunctionServer( + self.tiramisu_program, reuse_server=True + ) + return self.tiramisu_program.server + + @property + def initial_execution_time(self): + if not self._initial_execution_time: + if self.cache: + self._initial_execution_time = self.cache.execution_time( + self.machine, "empty" + ) + if not self._initial_execution_time: + self.init_server() + self._initial_execution_time = median_execution_time(self.schedule) + if self.cache: + self.cache.add_execution_time( + self.machine, + "empty", + self._initial_execution_time, + ) + return self._initial_execution_time + + @property + def branches(self) -> list[list[tiralib.IteratorIdentifier]]: + branches = [] + + for sections in self.tree.get_candidate_sections().values(): + branches.extend(sections) + + return branches + + @property + def current_branch(self) -> list[tiralib.IteratorIdentifier]: + return self.branches[self.current_branch_index] + + @property + def tree(self): + if self.schedule.tree is None: + raise ValueError("Schedule tree is not initialized") + return self.schedule.tree + + def init_server(self): + if self.use_server: + _ = self.server + + def get_mask(self, mask_size: int = 56): + mask = np.zeros(mask_size) + + # TODO break out of this order + for optim in self.schedule.optims_list: + match type(optim): + case tiralib.tiramisu_actions.Skewing: + # Mask interchange, Reversal, and Skewing + mask[ActionSlices.INTERCHANGE] = 1 + mask[ActionSlices.REVERSAL] = 1 + mask[ActionSlices.SKEWING] = 1 + # Mask Tiling2D + mask[ActionSlices.TILING2D] = 1 + case tiralib.tiramisu_actions.Parallelization: + # Mask interchange, Reversal, and Skewing + mask[ActionSlices.INTERCHANGE] = 1 + mask[ActionSlices.REVERSAL] = 1 + mask[ActionSlices.SKEWING] = 1 + case tiralib.tiramisu_actions.Tiling2D: + # Mask interchange, Reversal, Skewing, Parallelization, and Tiling2D + mask[ActionSlices.INTERCHANGE] = 1 + mask[ActionSlices.REVERSAL] = 1 + mask[ActionSlices.SKEWING] = 1 + mask[ActionSlices.PARALLELIZATION] = 1 + mask[ActionSlices.TILING2D] = 1 + case tiralib.tiramisu_actions.Unrolling: + # Mask all actions + mask[0:55] = 1 + case ( + tiralib.tiramisu_actions.Interchange + | tiralib.tiramisu_actions.Reversal + ): + # No masks + pass + case _: + raise ValueError(f"Unsupported action type {type(optim)}") + + # hide all previous actions + for action_index in self.action_indices: + if action_index == NEXT_ACTION_INDEX: + continue + mask[action_index] = 1 + + if len(self.current_branch) == 1: + mask[ActionSlices.INTERCHANGE] = 1 + mask[ActionSlices.SKEWING] = 1 + mask[ActionSlices.TILING2D] = 1 + + # check if the most in depth node in the branch is a leaf iterator + # if node has children then mask Unrolling + iterator = self.tree.iterators[self.current_branch[-1]] + if iterator.child_iterators: + mask[ActionSlices.UNROLLING] = 1 + + # mask levels that are not in current branch + # TODO this is a temporary solution, we need to find a better way handle iterator depth + levels = [level for level in range(MAX_ITERATOR_DEPTH)] + for iterator in self.current_branch: + # if iterator[1] not in levels: + # print(f"iterator {iterator} not in levels {levels}") + levels.remove(iterator[1]) + + for level in levels: + # REVERSAL has actions that work on a single iterator + mask[ActionSlices.REVERSAL.start + level] = 1 + # UNROLLING is always applied to the leaf iterator + # the parameter is thus used for factor not level + + # only 2 actions for parallelization + if level < 2: + mask[ActionSlices.PARALLELIZATION.start + level] = 1 + + # interchange and tiling2d have actions that work on successive tuples + # (0,1), (1,2), (2,3), etc. + tuple_actions_start_indices = [ + ActionSlices.INTERCHANGE.start, + ActionSlices.SKEWING.start, + ] + [ + i + for i in range( + ActionSlices.TILING2D.start, ActionSlices.TILING2D.stop, 4 + ) + ] + + for tuple_action_start_index in tuple_actions_start_indices: + for action_index in _get_level_action_indices_tuple_actions( + level, tuple_action_start_index + ): + mask[action_index] = 1 + return mask + + def _tree_to_iterator_vectors(self): + schedule_tree = self.tree + it_dict = {} + for it in schedule_tree.iterators: + single_iter_vector = -np.ones(VECTOR_SIZE) + # Type 0 for iterators + single_iter_vector[IteratorTags.TYPE_TAG] = 0 + # Initialize the non paddings tags to 0 + # The focus tag is the first valid tag + single_iter_vector[IteratorTags.FOCUS_TAG :] = 0 + + lower_bound_is_int = isinstance( + schedule_tree.iterators[it].lower_bound, int + ) + # TODO Create a better embedding for non rectangular domains + single_iter_vector[IteratorTags.LOWER_BOUND_IS_INT_TAG] = ( + 1 if lower_bound_is_int else 0 + ) + single_iter_vector[IteratorTags.LOWER_BOUND_VALUE_TAG] = ( + (schedule_tree.iterators[it].lower_bound) if lower_bound_is_int else 0 + ) + upper_bound_is_int = isinstance( + schedule_tree.iterators[it].upper_bound, int + ) + # TODO Create a better embedding for non rectangular domains + single_iter_vector[IteratorTags.UPPER_BOUND_IS_INT_TAG] = ( + 1 if upper_bound_is_int else 0 + ) + single_iter_vector[IteratorTags.UPPER_BOUND_VALUE_TAG] = ( + (schedule_tree.iterators[it].upper_bound) if upper_bound_is_int else 0 + ) + it_dict[it] = single_iter_vector + + return it_dict + + # TODO recompute the annotations in tiramisu after schedule is applied + def _get_comp_annotations(self, comp: str): + if comp in self.annotations["computations"]: + return self.annotations["computations"][comp] + else: + for initial_comp in self.annotations["computations"]: + if initial_comp in comp: + return self.annotations["computations"][initial_comp] + raise ValueError(f"Computation {comp} not found in annotations") + + def _annotations_to_comps_vectors(self): + max_depth = MAX_ITERATOR_DEPTH + dict_comp = {} + comps = self.tree.computations + for comp in comps: + single_comp_vector = -np.ones(VECTOR_SIZE) + # vector type 0 for iterators and 1 for computations + single_comp_vector[0] = 1 + + comp_dict = self._get_comp_annotations(comp) + # This field represents the absolute order of execution of computations + single_comp_vector[1] = self.tree.computations_absolute_order[comp] + # a vector of one-hot encoding of possible 3 data-types + single_comp_vector[2:5] = encode_data_type(comp_dict["data_type"]) + single_comp_vector[5] = +comp_dict["comp_is_reduction"] + # The write-to buffer id + single_comp_vector[6] = +comp_dict["write_buffer_id"] + # We add a vector of write access + write_matrix = isl_map_to_write_access_matrix( + comp_dict["write_access_relation"] + ) + padded_matrix = pad_access_matrix(write_matrix, max_depth).reshape(-1) + single_comp_vector[7 : 7 + padded_matrix.shape[0]] = padded_matrix + # We add vector of read access + for index, read_access_dict in enumerate(comp_dict["accesses"]): + read_access_matrix = pad_access_matrix( + np.array(read_access_dict["access_matrix"]), max_depth + ).reshape(-1) + read_access_matrix = np.append( + read_access_matrix, +read_access_dict["access_is_reduction"] + ) + read_access_matrix = np.append( + read_access_matrix, read_access_dict["buffer_id"] + 1 + ) + read_access_size = read_access_matrix.shape[0] + single_comp_vector[ + BUFFER_ACCESS_EMBEDDING_START + + index * read_access_size : BUFFER_ACCESS_EMBEDDING_START + + (index + 1) * read_access_size + ] = read_access_matrix + dict_comp[comp] = single_comp_vector + return dict_comp + + @property + def graph(self): + it_vector_dict = self._tree_to_iterator_vectors() + comp_vector_dict = self._annotations_to_comps_vectors() + it_index = {} + comp_index = {} + tree = self.tree + num_iterators = len(tree.iterators) + for i, iterator_id in enumerate(it_vector_dict): + it_index[iterator_id] = i + for i, comp in enumerate(comp_vector_dict): + comp_index[comp] = i + + edge_index = [] + node_feats = None + + for iterator_id in tree.iterators: + iterator_node = tree.iterators[iterator_id] + for child_it in iterator_node.child_iterators: + edge_index.append([it_index[iterator_id], it_index[child_it]]) + + for child_comp in iterator_node.computations_list: + edge_index.append( + [it_index[iterator_id], num_iterators + comp_index[child_comp]] + ) + node_feats = np.stack( + [ + *[arr for arr in it_vector_dict.values()], + *[arr for arr in comp_vector_dict.values()], + ], + ) + + # focus on current branch + for iterator in self.current_branch: + index = it_index[iterator] + node_feats[index][IteratorTags.FOCUS_TAG] = 1 + + return node_feats, np.array(edge_index), it_index, comp_index + + def apply_action(self, action: int): + done = False + mask = self.get_mask() + if np.all(mask): + logger.warning("All actions are masked") + return ApplyActionResult(is_legal=True, speedup=1, done=True, crashed=False) + self.action_indices.append(action) + tmp_schedule = self.schedule.copy() + + # if action is next action, move to next branch + if action == NEXT_ACTION_INDEX: + if self.current_branch_index + 1 >= len(self.branches): + return ApplyActionResult( + is_legal=True, speedup=1, done=True, crashed=False + ) + self.current_branch_index += 1 + return ApplyActionResult( + is_legal=True, speedup=1, done=False, crashed=False + ) + + # if action is not next action, apply the action + try: + tmp_schedule.add_optimizations( + [self.action_index_to_tiralib_action(action)] + ) + is_legal = self.schedule_is_legal(tmp_schedule) + if not is_legal: + return ApplyActionResult( + is_legal=False, speedup=1, done=False, crashed=False + ) + tmp_schedule_str = str(tmp_schedule) + if self.cache and ( + cached_exec_time := self.cache.execution_time( + self.machine, tmp_schedule_str + ) + is not None + ): + current_execution_time = cached_exec_time + else: + self.init_server() + try: + current_execution_time = median_execution_time(tmp_schedule) + except tiralib.function_server.ServerExecutionFailedError: + return ApplyActionResult( + is_legal=False, speedup=1, done=False, crashed=True + ) + if self.cache: + self.cache.add_execution_time( + self.machine, tmp_schedule_str, current_execution_time + ) + + speedup = self.initial_execution_time / current_execution_time + self.schedule = tmp_schedule + + # TODO Handle the depth of the tree dyamically or in a better way + if self.schedule.tree.depth > MAX_ITERATOR_DEPTH: + done = True + + return ApplyActionResult( + is_legal=True, speedup=speedup, done=done, crashed=False + ) + except Exception as e: + logger.error(f"Error applying action {action}: {e}") + return ApplyActionResult( + is_legal=False, speedup=1, done=False, crashed=True + ) + + def action_index_to_tiralib_action(self, action_index: int): + computation = self.current_branch[0][0] + if ( + ActionSlices.INTERCHANGE.start + <= action_index + < ActionSlices.INTERCHANGE.stop + ): + level = action_index - ActionSlices.INTERCHANGE.start + # check if level is in current branch we exclude the last iterator because the action uses + # 2 successive iterators + if level not in [iterator[1] for iterator in self.current_branch[:-1]]: + raise ValueError( + f"Invalid level {level} for interchange: current branch {self.current_branch} and level {level}" + ) + return tiralib.tiramisu_actions.Interchange( + params=[(computation, level), (computation, level + 1)] + ) + elif ActionSlices.REVERSAL.start <= action_index < ActionSlices.REVERSAL.stop: + level = action_index - ActionSlices.REVERSAL.start + if level not in [iterator[1] for iterator in self.current_branch]: + raise ValueError( + f"Invalid level {level} for reversal: current branch {self.current_branch} and level {level}" + ) + return tiralib.tiramisu_actions.Reversal(params=[(computation, level)]) + elif ActionSlices.SKEWING.start <= action_index < ActionSlices.SKEWING.stop: + level = action_index - ActionSlices.SKEWING.start + # action uses 2 successive iterators + if level not in [iterator[1] for iterator in self.current_branch[:-1]]: + raise ValueError( + f"Invalid level {level} for skewing: current branch {self.current_branch} and level {level}" + ) + return tiralib.tiramisu_actions.Skewing( + params=[(computation, level), (computation, level + 1), 0, 0] + ) + elif ( + ActionSlices.PARALLELIZATION.start + <= action_index + < ActionSlices.PARALLELIZATION.stop + ): + level = action_index - ActionSlices.PARALLELIZATION.start + if level not in [iterator[1] for iterator in self.current_branch]: + raise ValueError( + f"Invalid level {level} for parallelization: current branch {self.current_branch} and level {level}" + ) + return tiralib.tiramisu_actions.Parallelization( + params=[(computation, level)] + ) + elif ActionSlices.TILING2D.start <= action_index < ActionSlices.TILING2D.stop: + level = (action_index - ActionSlices.TILING2D.start) % 4 + if level not in [iterator[1] for iterator in self.current_branch[:-1]]: + raise ValueError( + f"Invalid level {level} for tiling2D: current branch {self.current_branch} and level {level}" + ) + return tiralib.tiramisu_actions.Tiling2D( + params=[ + (computation, level), + (computation, level + 1), + *ActionSlices.tiling_size(action_index), + ] + ) + + elif ActionSlices.UNROLLING.start <= action_index < ActionSlices.UNROLLING.stop: + factor = action_index - ActionSlices.UNROLLING.start + 1 + # check if leaf of current branch does not have child iterators + iterator_id = self.current_branch[-1] + iterator = self.tree.get_iterator_of_computation(*iterator_id) + if iterator.child_iterators: + raise ValueError( + f"Cannot unroll iterator {iterator.name} with child iterators {iterator.child_iterators}" + ) + return tiralib.tiramisu_actions.Unrolling(params=[iterator_id, 2**factor]) + else: + raise ValueError(f"Invalid action index {action_index}") + + @property + def annotations(self): + return self.tiramisu_program.annotations + + def schedule_is_legal(self, schedule: tiralib.Schedule): + """ + Checks if the schedule is legal. + + Returns + ------- + Boolean indicating if the schedule is legal. + """ + schedule_str = str(schedule) + is_legal = None + if self.cache: + is_legal = self.cache.schedules_legality.get(schedule_str, None) + isl_ast_str = self.cache.isl_ast.get(schedule_str, None) + skewing_factors = self.cache.schedules_solver.get(schedule_str, None) + + if is_legal is None: + if self.use_server: + result = self.server.run("legality", schedule) + is_legal: bool = result.legality + isl_ast_str: str = result.isl_ast + skewing_factors = None + if ( + result.additional_info + and "skewing_factors" in result.additional_info + ): + skewing_factors = [ + int(factor) + for factor in result.additional_info.replace( + "skewing_factors:", "" + ).split(",") + ] + if self.cache: + self.cache.add(schedule_str, is_legal, isl_ast_str, skewing_factors) + else: + is_legal = schedule.is_legal(with_ast=True) + isl_ast_str = schedule.tree.get_isl_ast_string() + if ( + schedule.optims_list[-1].is_skewing() + and schedule.optims_list[-1].params[2] == 0 + ): + skewing_action: tiralib.tiramisu_actions.Skewing = ( + schedule.optims_list[-1] + ) + copy_schedule = schedule.copy() + copy_schedule.optims_list.pop() + result = tiralib.tiramisu_actions.Skewing.get_factors( + copy_schedule, + [iterator[1] for iterator in skewing_action.iterators], + skewing_action.comps, + ) + skewing_factors = list(result) if result else None + + schedule.legality = is_legal + schedule.tree = tiralib.tiramisu_tree.TiramisuTree.from_isl_ast_string_list( + isl_ast_string_list=isl_ast_str.split("\n") + ) + # Update the skewing factors if they are not set + if skewing_factors: + for action in schedule.optims_list: + if action.type == tiralib.tiramisu_actions.TiramisuActionType.SKEWING: + if action.params[2] == 0: + action.params[2] = skewing_factors[0] + action.params[3] = skewing_factors[1] + action.factors = skewing_factors + action.set_string_representations(schedule.tree) + return is_legal + + +def median_execution_time( + schedule: tiralib.Schedule, + min_runs: int = 1, + max_runs: int = 30, + time_budget_in_seconds: int = 30, +): + execution_times = schedule.execute( + min_runs=min_runs, + max_runs=max_runs, + time_budget=time_budget_in_seconds * SECOND, + ) + return float(np.median(execution_times)) + + +class ActionSlices: + INTERCHANGE = slice(0, 4) # (0,1), (1,2), (2,3), (3,4) + REVERSAL = slice(4, 9) # 0, 1, 2, 3, 4 + SKEWING = slice(9, 12) # (0,1), (1,2), (2,3) + PARALLELIZATION = slice(12, 14) # 0, 1 + TILING2D = slice(14, 50) # (0,1), (1,2), (2,3), (3,4) * + # [(32, 32), (64, 64), (128, 128), (32, 64), (32, 128), (64, 32), (64, 128), (128, 32), (128, 64)] + UNROLLING = slice(50, 55) # 2, 4, 8, 16, 32 + + @classmethod + def tiling_size(cls, action_index: int): + size_dict = { + (14, 18): (32, 32), + (18, 22): (64, 64), + (22, 26): (128, 128), + (26, 30): (32, 64), + (30, 34): (32, 128), + (34, 38): (64, 32), + (38, 42): (64, 128), + (42, 46): (128, 32), + (46, 50): (128, 64), + } + for start, stop in size_dict: + if start <= action_index < stop: + return size_dict[start, stop] + + raise ValueError(f"Invalid action index {action_index} for tiling2D") + + +# named tuple to hold the return of apply_action result +# is_legal: bool, is the schedule legal after applying the action +# speedup: float, the speedup after applying the action +# done: bool, is the schedule done +# crashed: bool, did the schedule crash after applying the action +ApplyActionResult = namedtuple( + "ApplyActionResult", ["is_legal", "speedup", "done", "crashed"] +) + + +def program_compatible_with_model(annotations): + max_accesses = 15 + min_accesses = 0 + max_iterators = MAX_ITERATOR_DEPTH + computations_dict = annotations["computations"] + + # Making sure every computation doesn't exceed the limit of the cost model , if the model is updated change the conditions + for comp_name in computations_dict: + comp_dict = computations_dict[comp_name] + if ( + len(comp_dict["accesses"]) > max_accesses + or len(comp_dict["accesses"]) < min_accesses + ): + return False + if len(comp_dict["iterators"]) > max_iterators: + return False + + return True + + +class IteratorTags: + """The tags of the iterator embeddings. + + The embedding is of size VECTOR_SIZE. The rest of the tags are set to -1 as padding.""" + + TYPE_TAG = 0 + FOCUS_TAG = -11 + LOWER_BOUND_IS_INT_TAG = -10 + LOWER_BOUND_VALUE_TAG = -9 + UPPER_BOUND_IS_INT_TAG = -8 + UPPER_BOUND_VALUE_TAG = -7 + PARALLELIZATION_TAG = -6 + REVERSAL_TAG = -5 + UNROLLING_FACTOR_TAG = -4 + TILE_SIZE_TAG = -3 + SKEWING_FACTOR_1_TAG = -2 + SKEWING_FACTOR_2_TAG = -1 + + +def _get_level_action_indices_tuple_actions(level: int, start_index: int): + size_of_action = 3 if start_index == ActionSlices.SKEWING.start else 4 + if level > size_of_action: + return [] + + if level == 0: + return [start_index] + if level == size_of_action: + return [start_index + level - 1] + + return [start_index + level - 1, start_index + level] diff --git a/config/config.py b/config/config.py index fcff193..9f25f13 100644 --- a/config/config.py +++ b/config/config.py @@ -5,22 +5,6 @@ # Enum for the dataset format -class DatasetFormat: - # Both the CPP code and the data of the functions are loaded from PICKLE files - PICKLE = "PICKLE" - # We look for informations in the pickle files, if something is missing we get it from cpp files in a dynamic way - HYBRID = "HYBRID" - - @staticmethod - def from_string(s): - if s == "PICKLE": - return DatasetFormat.PICKLE - elif s == "HYBRID": - return DatasetFormat.HYBRID - else: - raise ValueError("Unknown dataset format") - - @dataclass class TiramisuConfig: workspace: str = "./experiment_dir/workspace/" @@ -29,7 +13,6 @@ class TiramisuConfig: @dataclass class DatasetConfig: - dataset_format: DatasetFormat = DatasetFormat.HYBRID cpps_path: str = "" dataset_path: str = "" save_path: str = "" @@ -39,9 +22,6 @@ class DatasetConfig: tags: list[str] = field(default_factory=list) def __init__(self, dataset_config_dict: Dict): - self.dataset_format = DatasetFormat.from_string( - dataset_config_dict["dataset_format"] - ) self.cpps_path = dataset_config_dict["cpps_path"] self.dataset_path = dataset_config_dict["dataset_path"] self.save_path = dataset_config_dict["save_path"] @@ -78,18 +58,8 @@ class AutoSchedulerConfig: code_deps: CodeDeps test: Test machine: str = "jubail" - - def __post_init__(self): - if isinstance(self.tiramisu, dict): - self.tiramisu = TiramisuConfig(**self.tiramisu) - if isinstance(self.dataset, dict): - self.dataset = DatasetConfig(self.dataset) - if isinstance(self.experiment, dict): - self.experiment = Experiment(**self.experiment) - if isinstance(self.code_deps, dict): - self.code_deps = CodeDeps(**self.code_deps) - if isinstance(self.test, dict): - self.test = Test(**self.test) + tiralib_config_path: str = "./config/tiralib_config.yaml" + clean_files: bool = True def read_yaml_file(path): @@ -108,7 +78,20 @@ def dict_to_config(parsed_yaml: Dict[Any, Any]) -> AutoSchedulerConfig: code_deps = CodeDeps(**parsed_yaml["code_deps"]) test = Test(**parsed_yaml["test"]) machine = parsed_yaml.get("machine", "jubail") - return AutoSchedulerConfig(tiramisu, dataset, experiment, code_deps, test, machine) + clean_files = parsed_yaml.get("clean_files", True) + tiralib_config_path = parsed_yaml.get( + "tiralib_config_path", "./config/tiralib_config.yaml" + ) + return AutoSchedulerConfig( + tiramisu, + dataset, + experiment, + code_deps, + test, + machine, + tiralib_config_path, + clean_files, + ) class Config(object): diff --git a/config/config.yaml.temp b/config/config.yaml.temp index 460d439..0120fbe 100644 --- a/config/config.yaml.temp +++ b/config/config.yaml.temp @@ -25,3 +25,5 @@ experiment: test : skip_execute_schedules : False + +tiralib_config_path: path to tiralib config file \ No newline at end of file diff --git a/env_api/core/models/tiramisu_program.py b/env_api/core/models/tiramisu_program.py deleted file mode 100644 index 1daa97f..0000000 --- a/env_api/core/models/tiramisu_program.py +++ /dev/null @@ -1,163 +0,0 @@ -import re -import random -from config.config import Config -from env_api.utils.wrapper_code import WrappersCode - - -class TiramisuProgram: - def __init__(self, code: str): - self.annotations = None - self.comps = None - self.name = None - self.schedules_legality = {} - self.schedules_solver = {} - self.execution_times = {} - self.original_str = None - if code: - code = """"#include -#include -#include -#include "function_153_wrapper.h" - -using namespace tiramisu; - -int main(int argc, char **argv) -{ - tiramisu::init("function_153"); - - int c00(322), c01(130), c02(5); - var i00("i00", 0, c00) , i01("i01", 0, c01) , i02("i02", 0, c02) ; - - // computations - input input00("input00", {i00, i01, i02}, p_int32); - input input01("input01", {i02}, p_int32); - - computation comp02("comp02", {i00, i01, i02}, p_int32); - comp02.set_expression(comp02(i00, i01, i02) + input00(i00, i01, i02) * input01(i02)); - - // buffers - buffer buf02("buf02", {322, 130}, p_int32, a_output); - buffer buf00("buf00", {322, 130, 5}, p_int32, a_input); - buffer buf01("buf01", {5}, p_int32, a_input); - - comp02.store_in(&buf02, {i00, i01}); - input00.store_in(&buf00); - input01.store_in(&buf01); - - tiramisu::codegen({&buf02, &buf00, &buf01}, "./function_153.o"); - return 0; -}""" - self.load_code_lines(original_str=code) - self.wrapper_cpp_code, self.wrapper_h_code = self.build_wrappers() - - # Since there is no factory constructors in python, I am creating this class method to replace the factory pattern - @classmethod - def from_dict(cls, name: str, data: dict, original_str: str = None): - # Initiate an instante of the TiramisuProgram class - tiramisu_prog = cls(None) - tiramisu_prog.name = name - tiramisu_prog.annotations = data["program_annotation"] - if tiramisu_prog.annotations: - tiramisu_prog.comps = list(tiramisu_prog.annotations["computations"].keys()) - tiramisu_prog.schedules_legality = data["schedules_legality"] - if "schedules_solver" not in data: - data["schedules_solver"] = {} - tiramisu_prog.schedules_solver = data["schedules_solver"] - if "execution_times" in data: - tiramisu_prog.execution_times = data["execution_times"] - - if Config.config.machine not in tiramisu_prog.execution_times: - tiramisu_prog.execution_times[Config.config.machine] = {} - - tiramisu_prog.load_code_lines(original_str) - - # tiramisu_prog.wrapper_cpp_code, tiramisu_prog.wrapper_h_code = ( - # tiramisu_prog.build_wrappers() - # ) - - # After taking the neccessary fields return the instance - return tiramisu_prog - - def load_code_lines(self, original_str: str = None): - """ - This function loads the file code , it is necessary to generate legality check code and annotations - """ - if original_str: - self.original_str = original_str - else: - return - - self.body = re.findall( - r"(tiramisu::init(?s:.)+)tiramisu::codegen", self.original_str - )[0] - self.name = re.findall(r"tiramisu::init\(\"(\w+)\"\);", self.original_str)[0] - # Remove the wrapper include from the original string - self.wrapper_str = f'#include "{self.name}_wrapper.h"' - - self.original_str = self.original_str.replace( - self.wrapper_str, f"// {self.wrapper_str}" - ) - - self.comps = re.findall(r"computation (\w+)\(", self.original_str) - self.code_gen_line = re.findall(r"tiramisu::codegen\({.+;", self.original_str)[ - 0 - ] - buffers_vect = re.findall(r"{(.+)}", self.code_gen_line)[0] - self.IO_buffer_names = re.findall(r"\w+", buffers_vect) - self.buffer_sizes = [] - for buf_name in self.IO_buffer_names: - sizes_vect = re.findall( - r"buffer " + buf_name + ".*{(.*)}", self.original_str - )[0] - self.buffer_sizes.append(re.findall(r"\d+", sizes_vect)) - - def build_wrappers(tiramisu_program): - buffers_init_lines = "" - for i, buffer_name in enumerate(tiramisu_program.IO_buffer_names): - buffers_init_lines += f""" - double *c_{buffer_name} = (double*)malloc({'*'.join(tiramisu_program.buffer_sizes[i][::-1])}* sizeof(double)); - parallel_init_buffer(c_{buffer_name}, {'*'.join(tiramisu_program.buffer_sizes[i][::-1])}, (double){str(random.randint(1,10))}); - Halide::Buffer {buffer_name}(c_{buffer_name}, {','.join(tiramisu_program.buffer_sizes[i][::-1])}); - """ - if tiramisu_program.name is None: - raise Exception("TiramisuProgram.name is None") - - wrapper_cpp_code = WrappersCode.wrapper_cpp_template.replace( - "$func_name$", tiramisu_program.name - ) - wrapper_cpp_code = wrapper_cpp_code.replace( - "$buffers_init$", buffers_init_lines - ) - - wrapper_cpp_code = wrapper_cpp_code.replace( - "$func_params$", - ",".join( - [name + ".raw_buffer()" for name in tiramisu_program.IO_buffer_names] - ), - ) - - wrapper_h_code = WrappersCode.wrapper_h_template.replace( - "$func_name$", tiramisu_program.name - ) - wrapper_h_code = wrapper_h_code.replace( - "$func_params$", - ",".join( - [ - "halide_buffer_t *" + name - for name in tiramisu_program.IO_buffer_names - ] - ), - ) - - return wrapper_cpp_code, wrapper_h_code - - def get_execution_time( - self, schedule_str: str = "initial_execution", machine: str = "jubail" - ): - if ( - machine in self.execution_times - and schedule_str in self.execution_times[machine] - ): - return self.execution_times[machine][schedule_str] - - return None diff --git a/env_api/core/services/compiling_service.py b/env_api/core/services/compiling_service.py deleted file mode 100644 index 3dc581e..0000000 --- a/env_api/core/services/compiling_service.py +++ /dev/null @@ -1,495 +0,0 @@ -import subprocess -import re -import copy -import logging -from typing import List -from env_api.core.models.tiramisu_program import TiramisuProgram -from env_api.scheduler.models.action import Action, Tiling, Unrolling -from env_api.scheduler.models.schedule import Schedule -from config.config import Config -from pathlib import Path - - -class CompilingService: - @classmethod - def compile_legality( - cls, - schedule_object: Schedule, - optims_list: List[Action], - worker_id: str | None, - ): - if worker_id != None: - worker = worker_id - else: - worker = str(optims_list[-1].worker_id) - - tiramisu_program = schedule_object.prog - workspace = Path(Config.config.tiramisu.workspace) - output_path = ( - workspace / f"{tiramisu_program.name}legal{optims_list[-1].worker_id}" - ) - - cpp_code = cls.get_legality_code( - schedule_object=schedule_object, optims_list=optims_list - ) - cpp_code = cls.get_legality_code( - schedule_object=schedule_object, optims_list=optims_list - ) - - return cls.run_cpp_code(cpp_code=cpp_code, output_path=output_path) - - @classmethod - def get_legality_code( - cls, - schedule_object: Schedule, - optims_list: List[Action], - ): - cpp_code = schedule_object.prog.original_str - comps_dict = {} - for comp in schedule_object.prog.annotations["computations"]: - comps_dict[comp] = copy.deepcopy( - schedule_object.prog.annotations["computations"][comp]["iterators"] - ) - # Add code to the original file to get legality result - legality_check_lines = """ - prepare_schedules_for_legality_checks(true); - perform_full_dependency_analysis(); - bool is_legal=true;""" - - updated_fusion = "" - unrolling_legality = "" - tiling_in_actions = False - - for optim in optims_list: - if isinstance(optim, Unrolling): - unrolling_legality += optim.legality_code_str - else: - legality_check_lines += optim.legality_code_str - - if isinstance(optim, Tiling): - tiling_in_actions = True - loop_levels_size = len(optim.params) // 2 - # Add the tiling new loops to comps_dict - for impacted_comp in optim.comps: - for loop_index in optim.params[:loop_levels_size]: - comps_dict[impacted_comp].insert( - loop_levels_size + loop_index, f"t{loop_index}" - ) - - if tiling_in_actions: - updated_fusion, cpp_code = cls.fuse_tiling_loops( - code=cpp_code, comps_dict=comps_dict - ) - legality_check_lines += "\n\tclear_implicit_function_sched_graph();" - - legality_check_lines += f""" - {updated_fusion} - {unrolling_legality} - prepare_schedules_for_legality_checks(true); - is_legal &= check_legality_of_function(); - std::cout << is_legal; - """ - - # Paste the lines responsable of checking legality of schedule in the cpp file - cpp_code = cpp_code.replace( - schedule_object.prog.code_gen_line, legality_check_lines - ) - return cpp_code - - @classmethod - def run_cpp_code(cls, cpp_code: str, output_path: str): - libs = ":".join(Config.config.code_deps.libs) - shell_script = [ - f"export LD_LIBRARY_PATH={libs}", - f"export LIBRARY_PATH={libs}", - f"export CPATH={':'.join(Config.config.code_deps.includes)}", - # Compile intermidiate tiramisu file - "$CXX -Wl,--no-as-needed -ldl -g -fno-rtti -lpthread -fopenmp -std=c++17 -O0 -o {}.o -c -x c++ -".format( - output_path - ), - # Link generated file with executer - "$CXX -Wl,--no-as-needed -ldl -g -fno-rtti -lpthread -fopenmp -std=c++17 -O0 {}.o -o {}.out -ltiramisu -ltiramisu_auto_scheduler -lHalide -lisl".format( - output_path, output_path - ), - # Run the program - "{}.out".format(output_path), - # Clean generated files - "rm {}.out {}.o".format(output_path, output_path), - ] - try: - compiler = subprocess.run( - ["\n".join(shell_script)], - input=cpp_code, - capture_output=True, - text=True, - shell=True, - check=True, - ) - return compiler.stdout if compiler.stdout != "" else "0" - except subprocess.CalledProcessError as e: - print("Process terminated with error code", e.returncode) - print("Error output:", e.stderr) - return "0" - except Exception as e: - logging.log(e) - return "0" - - @classmethod - def call_skewing_solver( - cls, schedule_object, optim_list, action: Action, worker_id: str | None - ): - if worker_id is not None: - worker = worker_id - else: - worker = str(action.worker_id) - - params = action.params - legality_cpp_code = cls.get_legality_code(schedule_object, optim_list) - to_replace = re.findall(r"std::cout << is_legal;", legality_cpp_code)[0] - legality_cpp_code = legality_cpp_code.replace( - "is_legal &= check_legality_of_function();", "" - ) - legality_cpp_code = legality_cpp_code.replace("bool is_legal=true;", "") - legality_cpp_code = re.sub( - r"is_legal &= loop_parallelization_is_legal.*\n", "", legality_cpp_code - ) - legality_cpp_code = re.sub( - r"is_legal &= loop_unrolling_is_legal.*\n", "", legality_cpp_code - ) - - solver_lines = ( - "function * fct = tiramisu::global::get_implicit_function();\n" - + "\n\tauto auto_skewing_result = fct->skewing_local_solver({" - + ", ".join([f"&{comp}" for comp in action.comps]) - + "}" - + ",{},{},1);\n".format(*params) - ) - - solver_lines += """ - std::vector> outer1, outer2,outer3; - tie( outer1, outer2, outer3 )= auto_skewing_result; - if (outer1.size()>0){ - std::cout << outer1.front().first; - std::cout << ","; - std::cout << outer1.front().second; - std::cout << ","; - }else { - std::cout << "None,None,"; - } - if(outer2.size()>0){ - std::cout << outer2.front().first; - std::cout << ","; - std::cout << outer2.front().second; - std::cout << ","; - }else { - std::cout << "None,None,"; - } - if(outer3.size()>0){ - std::cout << outer3.front().first; - std::cout << ","; - std::cout << outer3.front().second; - std::cout << ","; - }else { - std::cout << "None,None,"; - } - - """ - - solver_code = legality_cpp_code.replace(to_replace, solver_lines) - output_path = ( - Path(Config.config.tiramisu.workspace) - / worker - / f"{schedule_object.prog.name}skew_solver{action.worker_id}" - ) - result_str = cls.run_cpp_code(cpp_code=solver_code, output_path=output_path) - if not result_str: - return None - # Refer to function run_cpp_code to see from where the "0" comes from - elif result_str == "0": - return None - result_str = result_str.split(",") - # Skewing Solver returns 3 solutions in form of tuples, the first tuple is for outer parallelism , - # second is for inner parallelism , and last one is for locality, we are going to use the first preferably - # if availble , else , we are going to use the scond one if available, this policy of choosing factors may change - # in later versions! - # The compiler in our case returns a tuple of type : (fac0,fac1,fac2,fac3,fac4,fac5) each 2 factors represent the - # solutions mentioned above - if result_str[0] != "None": - # Means we have a solution for outer parallelism - fac1 = int(result_str[0]) - fac2 = int(result_str[1]) - return fac1, fac2 - if result_str[2] != "None": - # Means we have a solution for inner parallelism - fac1 = int(result_str[2]) - fac2 = int(result_str[3]) - return fac1, fac2 - else: - return None - - @classmethod - def fuse_tiling_loops(cls, code: str, comps_dict: dict): - fusion_code = "" - # This pattern will detect lines that looks like this : - # ['comp00.then(comp01, i2)', - # '.then(comp02, i1)', - # '.then(comp03, i3)', - # '.then(comp04, i1);'] - regex_first_comp = r"(\w+)\.then\(" - matching = re.search(regex_first_comp, code) - - if matching is None: - return fusion_code, code - - # comps will contain all the computations that are fused together - comps = [matching.group(1)] - - # regex rest of the thens - regex_rest = r"\.then\(([\w]+)," - # results will contain all the lines that match the regex - for result in re.findall(regex_rest, code): - comps.append(result) - - # levels indicates which loop level the 2 comps will be seperated in - levels = [] - # updated_lines will contain new lines of code with the new seperated levels - updated_lines = [] - # Defining intersection between comps' iterators - for i in range(len(comps) - 1): - level = 0 - while True: - if comps_dict[comps[i]][level] == comps_dict[comps[i + 1]][level]: - if ( - level + 1 == comps_dict[comps[i]].__len__() - or level + 1 == comps_dict[comps[i + 1]].__len__() - ): - levels.append(level) - break - level += 1 - else: - levels.append(level - 1) - break - if levels[-1] == -1: - updated_lines.append(f".then({comps[i+1]}, computation::root)") - else: - updated_lines.append(f".then({comps[i+1]},{levels[-1]})") - - updated_lines[0] = comps[0] + updated_lines[0] - updated_lines[-1] = updated_lines[-1] + ";" - - for line in range(len(comps) - 1): - # code = code.replace(results[line],"") - fusion_code += updated_lines[line] - - return fusion_code, code - - @classmethod - def get_schedule_code( - cls, - tiramisu_program: TiramisuProgram, - optims_list: List[Action], - ): - cpp_code = tiramisu_program.original_str - comps_dict = {} - for comp in tiramisu_program.annotations["computations"]: - comps_dict[comp] = copy.deepcopy( - tiramisu_program.annotations["computations"][comp]["iterators"] - ) - - updated_fusion = "" - unrolling_updated = "" - schedule_code = "" - tiling_in_actions = False - - for optim in optims_list: - if isinstance(optim, Unrolling): - unrolling_updated += optim.execution_code_str - else: - schedule_code += optim.execution_code_str - - if isinstance(optim, Tiling): - tiling_in_actions = True - loop_levels_size = len(optim.params) // 2 - # Add the tiling new loops to comps_dict - for impacted_comp in optim.comps: - for loop_index in optim.params[:loop_levels_size]: - comps_dict[impacted_comp].insert( - loop_levels_size + loop_index, f"t{loop_index}" - ) - - if tiling_in_actions: - updated_fusion, cpp_code = cls.fuse_tiling_loops( - code=cpp_code, comps_dict=comps_dict - ) - schedule_code += "\n\tclear_implicit_function_sched_graph();" - - schedule_code += f""" - {updated_fusion} - {unrolling_updated} - """ - - # Add code gen line to the schedule code - schedule_code += "\n\t" + tiramisu_program.code_gen_line + "\n" - # Paste the lines responsable of checking legality of schedule in the cpp file - cpp_code = tiramisu_program.original_str.replace( - tiramisu_program.code_gen_line, schedule_code - ) - cpp_code = cpp_code.replace( - f"// {tiramisu_program.wrapper_str}", tiramisu_program.wrapper_str - ) - return cpp_code - - @classmethod - def write_cpp_code(cls, cpp_code: str, output_path: str): - with open(output_path + ".cpp", "w") as f: - f.write(cpp_code) - - @classmethod - def execute_code( - cls, - tiramisu_program: TiramisuProgram, - optims_list: List[Action], - worker_id: str | None = None, - timeout: int | None = None, - ): - if worker_id is not None: - worker = worker_id - else: - worker = str(optims_list[-1].worker_id) - - path = Path().joinpath(Config.config.tiramisu.workspace, worker) - - if not path.exists(): - path.mkdir(parents=True) - - execution_time = None - - cpp_code = cls.get_schedule_code( - tiramisu_program=tiramisu_program, - optims_list=optims_list, - ) - - output_path = f"{str(path)}/{tiramisu_program.name}" - - cpp_file_path = output_path + "_schedule.cpp" - with open(cpp_file_path, "w") as file: - file.write(cpp_code) - - wrapper_cpp, wrapper_h = tiramisu_program.build_wrappers() - - wrapper_cpp_path = output_path + "_wrapper.cpp" - wrapper_h_path = output_path + "_wrapper.h" - - with open(wrapper_cpp_path, "w") as file: - file.write(wrapper_cpp) - - with open(wrapper_h_path, "w") as file: - file.write(wrapper_h) - - object_name = f"{tiramisu_program.name}.o" - out_name = f"{tiramisu_program.name}.out" - cpp_file_name = f"{tiramisu_program.name}_schedule.cpp" - wrapper_file_name = f"{tiramisu_program.name}_wrapper.cpp" - - wrapper_exec = f"{tiramisu_program.name}_wrapper" - - libs = ":".join(Config.config.code_deps.libs) - shell_script = [ - f"export LD_LIBRARY_PATH={libs}", - f"export LIBRARY_PATH={libs}", - f"export CPATH={':'.join(Config.config.code_deps.includes)}", - f"cd {str(path)}", - # Compile intermidiate tiramisu file - f"$CXX -Wl,--no-as-needed -ldl -g -fno-rtti -lpthread -fopenmp -std=c++17 -O0 -o {object_name} -c {cpp_file_name}", - # Link generated file with executer - f"$CXX -Wl,--no-as-needed -ldl -g -fno-rtti -lpthread -fopenmp -std=c++17 -O0 {object_name} -o {out_name} -ltiramisu -ltiramisu_auto_scheduler -lHalide -lisl", - # Run the generator - f"./{out_name}", - # compile the wrapper - f"$CXX -shared -o {object_name}.so {object_name}", - f"$CXX -std=c++17 -fno-rtti -o {wrapper_exec} -ltiramisu -lHalide -ldl -lpthread -fopenmp -lm {wrapper_file_name} ./{object_name}.so -ltiramisu -lHalide -ldl -lpthread -fopenmp -lm -lisl", - ] - - try: - compiler = subprocess.run( - [" \n ".join(shell_script)], - capture_output=True, - text=True, - shell=True, - check=True, - ) - except subprocess.CalledProcessError as e: - logging.error(f"Process terminated with error code: {e.returncode}") - logging.error(f"Error output: {e.stderr}") - logging.error(f"Output: {e.stdout}") - - run_script = [ - f"export LD_LIBRARY_PATH={libs}", - f"export LIBRARY_PATH={libs}", - f"export CPATH={':'.join(Config.config.code_deps.includes)}", - # cd to the workspace - f"cd {str(path)}", - # # set the env variables - "export DYNAMIC_RUNS=0", - "export MAX_RUNS=5", - "export NB_EXEC=5", - # run the wrapper - f"./{wrapper_exec}", - ] - try: - compiler = subprocess.run( - [" ; ".join(run_script)], - capture_output=True, - text=True, - shell=True, - check=True, - timeout=timeout, - ) - numbers = compiler.stdout.split("\n")[-2].split(" ")[:-1] - for i in range(len(numbers)): - numbers[i] = float(numbers[i]) - if numbers: - execution_time = min(numbers) - - except subprocess.CalledProcessError as e: - logging.error(f"Process terminated with error code: {e.returncode}") - logging.error(f"Error output: {e.stderr}") - logging.error(f"Output: {e.stdout}") - - except subprocess.TimeoutExpired: - logging.error("Timeout Error") - raise subprocess.TimeoutExpired(compiler.args, timeout) - - except Exception as e: - logging.error(e) - - # Deleting all files of the program - subprocess.run( - [f"rm {output_path}*"], - capture_output=True, - text=True, - shell=True, - check=True, - ) - return execution_time - - @classmethod - def compile_annotations(cls, tiramisu_program): - # TODO : add getting tree structure object from executing the file instead of building it - # output_path = Config.config.tiramisu.workspace + tiramisu_program.name + "annot" - output_path = ( - Path(Config.config.tiramisu.workspace) / f"{tiramisu_program.name}annot" - ) - # Add code to the original file to get json annotations - - get_json_lines = """ - auto ast = tiramisu::auto_scheduler::syntax_tree(tiramisu::global::get_implicit_function(), {}); - std::string program_json = tiramisu::auto_scheduler::evaluate_by_learning_model::get_program_json(ast); - std::cout << program_json; - """ - - # Paste the lines responsable of generating the program json tree in the cpp file - cpp_code = tiramisu_program.original_str.replace( - tiramisu_program.code_gen_line, get_json_lines - ) - return cls.run_cpp_code(cpp_code=cpp_code, output_path=output_path) diff --git a/env_api/core/services/tiramisu_service.py b/env_api/core/services/tiramisu_service.py deleted file mode 100644 index beaba10..0000000 --- a/env_api/core/services/tiramisu_service.py +++ /dev/null @@ -1,39 +0,0 @@ -from ..models.tiramisu_program import TiramisuProgram -from .compiling_service import CompilingService -import json - - -class TiramisuService: - def __init__(self): - pass - - def set_from_compil(self, code: str): - tiramisu_prog = TiramisuProgram(code=code) - tiramisu_prog.annotations = self.get_annotations(tiramisu_prog) - return tiramisu_prog - - def set_from_data(self, name: str, data: dict, original_str: str = None): - # This function fetched all the data from an offline dataset - tiramisu_prog = TiramisuProgram.from_dict( - name=name, data=data, original_str=original_str - ) - return tiramisu_prog - - def get_annotations(self, prog: TiramisuProgram): - max_accesses = 15 - min_accesses = 0 - max_iterators = 5 - result = CompilingService.compile_annotations(prog) - prog.annotations = json.loads(result) - computations_dict = prog.annotations["computations"] - # Making sure every computation doesn't exceed the limit of the cost model , if the model is updated change the conditions - for comp_name in computations_dict: - comp_dict = computations_dict[comp_name] - if ( - len(comp_dict["accesses"]) > max_accesses - or len(comp_dict["accesses"]) < min_accesses - ): - return None - if len(comp_dict["iterators"]) > max_iterators: - return None - return prog.annotations diff --git a/env_api/data/__init__.py b/env_api/data/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/env_api/data/data_service.py b/env_api/data/data_service.py deleted file mode 100644 index 1725b7b..0000000 --- a/env_api/data/data_service.py +++ /dev/null @@ -1,51 +0,0 @@ -import pickle -from datetime import date - - -class DataSetService: - def __init__(self, cpps_dataset_path=None, schedules_dataset_path=None): - self.cpps_dataset_path = cpps_dataset_path - self.schedules_dataset_path = schedules_dataset_path - self.schedules_dataset = None - self.cpps_dataset = None - try: - with open(cpps_dataset_path, "rb") as file: - self.cpps_dataset = pickle.load(file) - except FileNotFoundError: - print( - "[Error] : Offline dataset path is not valid => Reading from cpp files on disk" - ) - - if schedules_dataset_path is not None: - try: - with open(schedules_dataset_path, "rb") as file: - self.schedules_dataset = pickle.load(file) - except FileNotFoundError: - print( - "[Error] : Offline dataset path is not valid => Reading from cpp files on disk" - ) - - def in_schedule_dataset(self, func_name): - exist_offline = False - # Check if the file exists in the offline dataset - if self.schedules_dataset: - # Checking the function name - exist_offline = func_name in self.schedules_dataset - return exist_offline - - def get_offline_prog_data(self, name: str): - return self.schedules_dataset[name] - - def get_prog_code(self, name: str): - return self.cpps_dataset[name] - - def store_offline_dataset(self, suffix: str = ""): - if self.schedules_dataset: - with open( - self.schedules_dataset_path[:-4] - + date.today().__str__() - + suffix - + ".pkl", - "wb", - ) as file: - pickle.dump(self.schedules_dataset, file) diff --git a/env_api/scheduler/__init__.py b/env_api/scheduler/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/env_api/scheduler/models/action.py b/env_api/scheduler/models/action.py deleted file mode 100644 index 20ef34e..0000000 --- a/env_api/scheduler/models/action.py +++ /dev/null @@ -1,232 +0,0 @@ -from typing import List - - -class Action: - def __init__( - self, - params: list, - name: str, - comps: list = [], - env_id: int = None, - worker_id="", - loops=[], - factors=[], - ): - self.params = params - self.name = name - # List of computations concerned by this action - self.comps = comps - # The ID of the action inside the RL system, this ID helps in masking actions and matching RL with env_api - self.env_id = env_id - # In distributed training we want to know which worker is applying this action in order to distinguish the compilation - # of the same function by different workers - self.worker_id = worker_id - - self.legality_code_str = "" - self.execution_code_str = "" - self.comps_schedule = {} - self.loops: int = loops - self.factors: int = factors - - def set_comps(self, comps): - self.comps = comps - - def set_impacted_branches(self, branches): - self.impacted_brs = branches - - def apply_on_branches(self, branches): - for comp in self.comps: - for branch in branches: - # Check for the branches that needs to be updated - if comp in branch.comps: - # Update the actions mask - branch.update_actions_mask(action=self) - - -class AffineAction(Action): - def __init__( - self, - params: list, - name: str, - comps: list = [], - env_id: int = None, - worker_id="", - ): - super().__init__(params, name, comps, env_id, worker_id) - - def set_comps(self, comps): - super().set_comps(comps) - - def apply_on_branches(self, branches): - super().apply_on_branches(branches) - - -class Reversal(AffineAction): - def __init__(self, params: list, env_id: int = None, worker_id=""): - super().__init__(params, name="Reversal", env_id=env_id, worker_id=worker_id) - - def set_comps(self, comps): - super().set_comps(comps) - loop_level = self.params[0] - optim_str = "" - for comp in self.comps: - self.comps_schedule[comp] = f"R(L{loop_level})" - optim_str += f"\n\t{comp}.loop_reversal({loop_level});" - - self.legality_code_str = self.execution_code_str = optim_str - - -class Interchange(AffineAction): - def __init__(self, params: list, env_id: int = None, worker_id=""): - super().__init__(params, name="Interchange", env_id=env_id, worker_id=worker_id) - - def set_comps(self, comps): - super().set_comps(comps) - loop_level1, loop_level2 = self.params - optim_str = "" - for comp in self.comps: - self.comps_schedule[comp] = f"I(L{loop_level1},L{loop_level2})" - optim_str += f"\n\t{comp}.interchange({loop_level1}, {loop_level2});" - - self.legality_code_str = self.execution_code_str = optim_str - - -class Skewing(AffineAction): - def __init__(self, params: list, env_id: int = None, worker_id=""): - super().__init__(params, name="Skewing", env_id=env_id, worker_id=worker_id) - - def set_comps(self, comps): - super().set_comps(comps) - - def set_factors(self, factors): - self.params.extend(factors) - - loop_level1, loop_level2, factor1, factor2 = self.params - optim_str = "" - for comp in self.comps: - self.comps_schedule[comp] = ( - f"S(L{loop_level1},L{loop_level2},{factor1},{factor2})" - ) - optim_str += ( - f"\n\t{comp}.skew({loop_level1}, {loop_level2}, {factor1}, {factor2});" - ) - - self.legality_code_str = self.execution_code_str = optim_str - - -class Parallelization(Action): - def __init__(self, params: list, env_id: int = None, worker_id=""): - super().__init__( - params, name="Parallelization", env_id=env_id, worker_id=worker_id - ) - - def set_comps(self, comps): - super().set_comps(comps) - - loop_level = self.params[0] - - for comp in self.comps: - self.comps_schedule[comp] = f"P(L{loop_level})" - - comp = self.comps[0] - - self.execution_code_str = f"\n\t{comp}.tag_parallel_level({loop_level});" - - self.legality_code_str = f"""is_legal &= loop_parallelization_is_legal({loop_level}, {{&{",&".join(self.comps)}}}); - {self.execution_code_str} - """ - - -class Unrolling(Action): - def __init__(self, params: list, env_id: int = None, worker_id=""): - super().__init__(params, name="Unrolling", env_id=env_id, worker_id=worker_id) - - def set_comps(self, comps): - super().set_comps(comps) - - loop_level, factor = self.params - optim_str = "" - for comp in self.comps: - self.comps_schedule[comp] = f"U(L{loop_level},{factor})" - optim_str += f"\n\t{comp}.unroll({loop_level},{factor});" - - self.execution_code_str = optim_str - - self.legality_code_str = f"""is_legal &= loop_unrolling_is_legal({loop_level}, {{&{",&".join(self.comps)}}}); - {self.execution_code_str} - """ - - def set_params(self, additional_loops): - loop_level, factor = self.params - loop_level += additional_loops - optim_str = "" - for comp in self.comps: - self.comps_schedule[comp] = f"U(L{loop_level},{factor})" - optim_str += f"\n\t{comp}.unroll({loop_level},{factor});" - - self.execution_code_str = optim_str - - self.legality_code_str = f"""is_legal &= loop_unrolling_is_legal({loop_level}, {{&{",&".join(self.comps)}}}); - {self.execution_code_str} - """ - - def apply_on_branches(self, branches, current_branch: int): - branches[current_branch].update_actions_mask(action=self) - - -class Tiling(Action): - def __init__( - self, params: list, env_id: int = None, worker_id="", loops=[], factors=[] - ): - super().__init__( - params, - name="Tiling", - env_id=env_id, - worker_id=worker_id, - loops=loops, - factors=factors, - ) - - def set_comps(self, comps): - super().set_comps(comps) - - size = len(self.params) // 2 - loop_args = "".join([f"L{c}," for c in self.params[:size]]) - factor_args = ",".join([f"{c}" for c in self.params[size:]]) - optim_str = "" - for comp in self.comps: - self.comps_schedule[comp] = f"T{size}({loop_args}{factor_args})" - optim_str += f"\n\t{comp}.tile({','.join([str(p) for p in self.params])});" - - self.execution_code_str = self.legality_code_str = optim_str - - def apply_on_branches(self, branches, schedule_list: List[Action]): - tiling_depth = len(self.params) // 2 - for comp in self.comps: - for branch in branches: - # Check for the branches that needs to be updated - if comp in branch.comps: - # Update the actions mask - branch.update_actions_mask(action=self) - branch.additional_loops = tiling_depth - - for optim in schedule_list: - if isinstance(optim, Unrolling): - if comp in optim.comps: - optim.set_params(additional_loops=tiling_depth) - - -class Fusion(Action): - def __init__(self, params: list, env_id: int = None, worker_id=""): - super().__init__(params, name="Fusion", env_id=env_id, worker_id=worker_id) - - # Fusion will fuse the current branch with the next one - # I will try to implement it as long as it is legal - # It takes no parameters - # Before legality I need to generate the new .then tree - # If legal I should apply it to internal representations - - def get_fused_tree(self, branches, current_branch: int): - if (current_branch + 1) > len(branches): - return None - branches[current_branch]["annotations"]["iterators"] diff --git a/env_api/scheduler/models/branch.py b/env_api/scheduler/models/branch.py deleted file mode 100644 index 3042d15..0000000 --- a/env_api/scheduler/models/branch.py +++ /dev/null @@ -1,9 +0,0 @@ -from env_api.core.models.tiramisu_program import TiramisuProgram -from env_api.scheduler.models.schedule import Schedule - - -class Branch(Schedule): - def __init__(self, program: TiramisuProgram): - super().__init__(program) - # For a branch we don't need to recalculate the common iterators because all of them are common. - self.common_it = list(program.annotations["iterators"].keys()) diff --git a/env_api/scheduler/models/schedule.py b/env_api/scheduler/models/schedule.py deleted file mode 100644 index 96585df..0000000 --- a/env_api/scheduler/models/schedule.py +++ /dev/null @@ -1,124 +0,0 @@ -import numpy as np, copy -from config.config import Config -from env_api.core.models.tiramisu_program import TiramisuProgram -from env_api.scheduler.models.action import * - - -class Schedule: - def __init__(self, program: TiramisuProgram): - self.schedule_str = "" - self.prog = program - # List of computations of the program - self.comps = self.prog.comps - # List of branches of the program tree - self.branches = [] - # List of common iterators - self.common_it = [] - # self.schedule_list is an array that contains a list of optimizations that has been applied on the program - # This list has objects of type `Action` - self.schedule_list = [] - # Additional loops when Tiling is applied - self.additional_loops = 0 - - self.actions_mask = None - - if (type(self).__name__) == "Schedule": - self.__set_action_mask() - self.__form_branches() - else: - self.__set_action_mask() - - def __set_action_mask(self): - self.actions_mask = np.zeros(56) - - def __form_branches(self): - branches = [] - iterators = copy.deepcopy(self.prog.annotations["iterators"]) - computations = copy.deepcopy(self.prog.annotations["computations"]) - it = {} - for computation in computations: - iterators = copy.deepcopy( - self.prog.annotations["computations"][computation]["iterators"] - ) - if iterators[-1] in it: - it[iterators[-1]]["comps"].append(computation) - else: - it[iterators[-1]] = {"comps": [computation], "iterators": iterators} - - for iterator in it: - branches.append( - { - "comps": it[iterator]["comps"], - "iterators": it[iterator]["iterators"], - "annotations": {}, - } - ) - - for branch in branches: - branch_annotations = {"computations": {}, "iterators": {}} - for comp in branch["comps"]: - branch_annotations["computations"][comp] = copy.deepcopy( - self.prog.annotations["computations"][comp] - ) - # extract the branch specific iterators annotations - for iterator in branch["iterators"]: - branch_annotations["iterators"][iterator] = copy.deepcopy( - self.prog.annotations["iterators"][iterator] - ) - if self.prog.annotations["iterators"][iterator]["parent_iterator"]: - # Making sure that the parent node has the actual node as the only child - # It may happen that the parent node has many children but in a branch it is only allowed - # to have a single child to form a straight-forward branch from top to bottom - parent = branch_annotations["iterators"][iterator][ - "parent_iterator" - ] - branch_annotations["iterators"][parent]["child_iterators"] = ( - copy.deepcopy([iterator]) - ) - branch_annotations["iterators"][parent]["computations_list"] = [] - branch["annotations"] = copy.deepcopy(branch_annotations) - - self.branches = branches - - # print(3 * "\n" + "Branches") - # pprint(self.branches) - - def build_sched_string(self) -> str: - # Prepare a dictionary of computations name to fill it with each action applied on every comp - comps = {} - # Map the schedules applied one by one - for schedule in self.schedule_list: - # schedule has comps_schedule which includes the comps that was invloved in the optimisation - for key in schedule.comps_schedule.keys(): - # Add the data from that schedule to the global comps dictionnary - if not key in comps or not comps[key]: - comps[key] = "" - comps[key] += schedule.comps_schedule[key] - # Prepare the string and form it from the comps dictionary - schedule_string = "" - for key in comps.keys(): - schedule_string += "{" + key + "}:" + comps[key] - return schedule_string - - def update_actions_mask(self, action: Action, applied: bool = True): - # Whether an action is legal or not we should mask it to not use it again - self.actions_mask[action.env_id] = 1 - - if applied and Config.config.experiment.beam_search_order: - self.apply_beam_search_conditions(action=action) - - def apply_beam_search_conditions(self, action: Action): - # The order of actions in beam search : - # Fusion, [Interchange, reversal, skewing], parallelization, tiling, unrolling - if isinstance(action, Skewing): - self.actions_mask[0:12] = 1 - self.actions_mask[14:50] = 1 - - elif isinstance(action, Parallelization): - self.actions_mask[0:12] = 1 - - elif isinstance(action, Tiling): - self.actions_mask[0:50] = 1 - - elif isinstance(action, Unrolling): - self.actions_mask[0:55] = 1 diff --git a/env_api/scheduler/services/legality_service.py b/env_api/scheduler/services/legality_service.py deleted file mode 100644 index b48904d..0000000 --- a/env_api/scheduler/services/legality_service.py +++ /dev/null @@ -1,199 +0,0 @@ -from typing import List -from env_api.core.services.compiling_service import CompilingService -from env_api.scheduler.models.action import * -from env_api.scheduler.models.schedule import Schedule -import copy - - -class LegalityService: - def __init__(self): - """ - The legality service is responsible to evaluate the legality of tiramisu programs given a specific schedule - """ - pass - - def is_action_legal( - self, - schedule_object: Schedule, - branches: List[Schedule], - current_branch: int, - action: Action, - ): - """ - Checks the legality of action - input : - - an action that represents an optimization from the 7 types : Parallelization,Skewing,Interchange,Fusion,Reversal,Tiling,Unrolling - output : - - legality_check : bool - """ - branches[current_branch].update_actions_mask(action=action, applied=False) - # Check first if the iterator(s) level(s) is(are) included in the current iterators - # If not then the action is illegal by default - exceeded_iterators = self.check_iterators( - branches=branches, current_branch=current_branch, action=action - ) - if exceeded_iterators: - return False - - # The legality of Skewing is different than the others , we need to get the skewing params from the solver - # If there are any , this means that skewing is legal , if the solver fails , it means that skewing is illegal - if isinstance(action, Skewing): - # check if results of skewing solver exist in the dataset - schdule_str = schedule_object.build_sched_string() - if schdule_str in schedule_object.prog.schedules_solver: - factors = schedule_object.prog.schedules_solver[schdule_str] - - else: - if not schedule_object.prog.original_str: - # Loading function code lines - schedule_object.prog.load_code_lines() - # Call the skewing solver - factors = CompilingService.call_skewing_solver( - schedule_object=schedule_object, - optim_list=schedule_object.schedule_list, - action=action, - worker_id=action.worker_id, - ) - # Save the results of skewing solver in the dataset - schedule_object.prog.schedules_solver[schdule_str] = factors - if factors == None: - # The solver fails to find solutions => illegal action - return False - else: - # Adding the factors to the params - action.set_factors(factors) - # Add the command to the array of schedule - schedule_object.schedule_list.append(action) - # Storing the schedule string - schedule_object.schedule_str = schedule_object.build_sched_string() - return True - - # Add the command to the array of schedule - schedule_object.schedule_list.append(action) - # Building schedule string - schdule_str = schedule_object.build_sched_string() - # Check if the action is legal or no to be applied on schedule_object.prog - # prog.schedules_legality only has data when it is fetched from the offline dataset so no need to compile to get the legality - if schdule_str in schedule_object.prog.schedules_legality: - legality_check = int(schedule_object.prog.schedules_legality[schdule_str]) - else: - # To run the legality we need the original function code to generate legality code - if not schedule_object.prog.original_str: - # Loading function code lines - schedule_object.prog.load_code_lines() - try: - legality_check = int( - CompilingService.compile_legality( - schedule_object=schedule_object, - optims_list=schedule_object.schedule_list, - worker_id=action.worker_id, - ) - ) - - # Saving the legality of the new schedule - schedule_object.prog.schedules_legality[schdule_str] = ( - legality_check == 1 - ) - - except ValueError as e: - legality_check = 0 - print("Legality error :", e) - if legality_check != 1: - # If the action is not legal , remove it from the schedule list - schedule_object.schedule_list.pop() - # Rebuild the scedule string after removing the action - schdule_str = schedule_object.build_sched_string() - # Storing the schedule string to use it later - schedule_object.schedule_str = schdule_str - return legality_check == 1 - - def check_iterators( - self, branches: List[Schedule], current_branch: int, action: Action - ): - params = [] - # Before checking legality from dataset or by compiling , we see if the iterators are included in the common iterators - if isinstance(action, Unrolling): - # We look for the last iterator of each computation and save it in the params - unrolling_factor = action.params[0] - - innermost_iterator = list( - branches[current_branch].prog.annotations["iterators"].keys() - )[-1] - lower_bound = int( - branches[current_branch].prog.annotations["iterators"][ - innermost_iterator - ]["lower_bound"] - ) - upper_bound = int( - branches[current_branch].prog.annotations["iterators"][ - innermost_iterator - ]["upper_bound"] - ) - if abs(upper_bound - lower_bound) < unrolling_factor: - return True - - loop_level = ( - len(branches[current_branch].common_it) - - 1 - + branches[current_branch].additional_loops - ) - action.params = copy.deepcopy([loop_level, unrolling_factor]) - action.set_comps(copy.deepcopy(branches[current_branch].comps)) - return False - else: - num_iter = branches[current_branch].common_it.__len__() - if isinstance(action, Tiling): - # First we verify if the tiling size is bigger than the loops extent - # TODO : remove this strategy later - tiling_size = max(action.params) - for iterator in branches[current_branch].prog.annotations["iterators"]: - lower_bound = int( - branches[current_branch].prog.annotations["iterators"][ - iterator - ]["lower_bound"] - ) - upper_bound = int( - branches[current_branch].prog.annotations["iterators"][ - iterator - ]["upper_bound"] - ) - if abs(upper_bound - lower_bound) < tiling_size: - return True - # Becuase the second half of action.params contains tiling size, so we need only the first half of the vector - params = action.params[: len(action.params) // 2] - else: - params = action.params - # Checking if the big param is smaller than the number of existing iterators - if params[-1] >= num_iter: - return True - - # We have the current branch - concerned_iterators = [branches[current_branch].common_it[it] for it in params] - concerned_comps = [] - match len(concerned_iterators): - case 1: - for branch in branches: - if concerned_iterators[0] in branch.common_it: - concerned_comps.extend(branch.comps) - case 2: - for branch in branches: - if concerned_iterators[0] in branch.common_it: - if concerned_iterators[1] in branch.common_it: - concerned_comps.extend(branch.comps) - else: - # If for some branch , we have the parent iterator shared with current branch but - # the child is different , this means that only the parent is shared and the children are different - return True - case 3: - for branch in branches: - if concerned_iterators[0] in branch.common_it: - if concerned_iterators[1] in branch.common_it: - if concerned_iterators[2] in branch.common_it: - concerned_comps.extend(branch.comps) - else: - return True - else: - return True - action.set_comps(copy.deepcopy(concerned_comps)) - - return False diff --git a/env_api/scheduler/services/prediction_service.py b/env_api/scheduler/services/prediction_service.py deleted file mode 100644 index 5e6e386..0000000 --- a/env_api/scheduler/services/prediction_service.py +++ /dev/null @@ -1,78 +0,0 @@ -from env_api.core.services.compiling_service import CompilingService -from env_api.scheduler.models.schedule import Schedule -from env_api.utils.exceptions import ExecutingFunctionException -import subprocess -from config.config import Config - - -class PredictionService: - def get_initial_time(self, schedule_object: Schedule, worker_id: str): - INIT_TIMEOUT = ( - 5 * Config.config.experiment.max_time_in_minutes * 60 + 4 - ) # number of executions * minutes * seconds + seconds of starting the script - - initial_execution = schedule_object.prog.get_execution_time( - "initial_execution", Config.config.machine - ) - if initial_execution is None: - try: - # We need to run the program to get the value - if Config.config.test.skip_execute_schedules: - initial_execution = 1 - else: - initial_execution = CompilingService.execute_code( - tiramisu_program=schedule_object.prog, - optims_list=[], - timeout=INIT_TIMEOUT, - worker_id=worker_id, - ) - if initial_execution: - schedule_object.prog.execution_times[Config.config.machine][ - "initial_execution" - ] = initial_execution - else: - raise ExecutingFunctionException - except subprocess.TimeoutExpired: - return None - except ExecutingFunctionException: - return None - except Exception: - return None - - return initial_execution - - def get_real_speedup(self, schedule_object: Schedule, worker_id): - SLOWDOWN_TIMEOUT = Config.config.experiment.max_slowdown - - initial_execution = self.get_initial_time(schedule_object, worker_id) - - schedule_execution = schedule_object.prog.get_execution_time( - schedule_object.schedule_str, Config.config.machine - ) - if schedule_execution is None: - try: - # We need to run the program to get the value - if Config.config.test.skip_execute_schedules: - schedule_execution = 1 - else: - schedule_execution = CompilingService.execute_code( - tiramisu_program=schedule_object.prog, - optims_list=schedule_object.schedule_list, - worker_id=worker_id, - timeout=((initial_execution / 1000) * SLOWDOWN_TIMEOUT) * 5 + 4, - ) - - if schedule_execution: - schedule_object.prog.execution_times[Config.config.machine][ - schedule_object.schedule_str - ] = schedule_execution - else: - raise ExecutingFunctionException - - except subprocess.TimeoutExpired: - schedule_execution = initial_execution * SLOWDOWN_TIMEOUT - - except Exception: - raise ExecutingFunctionException - - return initial_execution / schedule_execution diff --git a/env_api/scheduler/services/scheduler_service.py b/env_api/scheduler/services/scheduler_service.py deleted file mode 100644 index 968a6ee..0000000 --- a/env_api/scheduler/services/scheduler_service.py +++ /dev/null @@ -1,109 +0,0 @@ -from typing import List -from env_api.core.models.tiramisu_program import TiramisuProgram -from env_api.scheduler.models.branch import Branch -from env_api.scheduler.services.legality_service import LegalityService -from env_api.scheduler.services.prediction_service import PredictionService -from env_api.utils.exceptions import ExecutingFunctionException -from ..models.schedule import Schedule -from ..models.action import * -from config.config import Config - - -class SchedulerService: - def __init__(self): - # The Schedule object contains all the informations of a program : annotatons , tree representation ... - self.schedule_object: Schedule = None - # The branches generated from the program tree - self.branches: List[Branch] = [] - self.current_branch = 0 - # The prediction service is an object that has a value estimator `get_predicted_speedup(schedule)` of the speedup that a schedule will have - # This estimator is a recursive model that needs the schedule representation to give speedups - self.prediction_service = PredictionService() - # A schedules-legality service - self.legality_service = LegalityService() - - def set_schedule(self, schedule_object: Schedule): - """ - The `set_schedule` function is called first in `tiramisu_api` to initialize the fields when a new program is fetched from the dataset. - input : - - schedule_object : contains all the inforamtions on a program and the schedule - output : - - a tuple of vectors that represents the main program and the current branch , in addition to their respective actions mask - """ - self.schedule_object = schedule_object - # We create the branches of the program - self.create_branches() - # Init the index to the 1st branch - self.current_branch = 0 - return self.branches[self.current_branch].actions_mask - - def create_branches(self): - # Make sure to clear the branches of the previous function if there are ones - self.branches.clear() - for branch in self.schedule_object.branches: - # Create a mock-up of a program from the data of a branch - program_data = { - "program_annotation": branch["annotations"], - "schedules_legality": {}, - "schedules_solver": {}, - } - # The Branch is an inherited class from Schedule, it has all its characteristics - new_branch = Branch( - TiramisuProgram.from_dict( - self.schedule_object.prog.name, data=program_data, original_str="" - ) - ) - # The branch needs the original cpp code of the main function to calculate legality of schedules - new_branch.prog.load_code_lines(self.schedule_object.prog.original_str) - self.branches.append(new_branch) - - def next_branch(self): - # Switch to the next branch to optimize it - self.current_branch += 1 - if self.current_branch == len(self.branches): - # This matks the finish of exploring the branches - return None - # Using the model to embed the program and the branch in a 180 sized vector each - return self.branches[self.current_branch].actions_mask - - def apply_action(self, action: Action): - """ - input : - - an action that represents an optimization from the 7 types : Parallelization,Skewing,Interchange,Fusion,Reversal,Tiling,Unrolling - output : - - speedup : float , representation : tuple(tensor) , legality_check : bool - """ - legality_check = self.legality_service.is_action_legal( - schedule_object=self.schedule_object, - branches=self.branches, - current_branch=self.current_branch, - action=action, - ) - speedup = 1 - if legality_check: - try: - speedup = self.prediction_service.get_real_speedup( - schedule_object=self.schedule_object, worker_id=action.worker_id - ) - - if isinstance(action, Tiling): - action.apply_on_branches( - self.branches, self.schedule_object.schedule_list - ) - - elif isinstance(action, Unrolling): - action.apply_on_branches(self.branches, self.current_branch) - - else: - action.apply_on_branches(self.branches) - - except ExecutingFunctionException as e: - # If the execution went wrong remove it from the schedule list - self.schedule_object.schedule_list.pop() - # Rebuild the scedule string after removing the action - schdule_str = self.schedule_object.build_sched_string() - # Storing the schedule string to use it later - self.schedule_object.schedule_str = schdule_str - legality_check = False - - return speedup, legality_check, self.branches[self.current_branch].actions_mask diff --git a/env_api/tiramisu_api.py b/env_api/tiramisu_api.py deleted file mode 100644 index 6051f01..0000000 --- a/env_api/tiramisu_api.py +++ /dev/null @@ -1,200 +0,0 @@ -import traceback -from env_api.data.data_service import DataSetService -from env_api.core.services.tiramisu_service import * -from env_api.scheduler.models.action import * -from env_api.scheduler.models.schedule import Schedule -from env_api.scheduler.services.scheduler_service import SchedulerService -from config.config import Config - - -class TiramisuEnvAPI: - def __init__(self, local_dataset=True): - """ - local_dataset : TiramisuEnvAPI has an internal dataset service to manage offline dataset stored in self.dataset_service - when this variable is False , it means we are using an external service to manage dataset or pure - compilation. - """ - self.local_dataset = local_dataset - # Make the root to tiramisu root path explicit to the env in order to compile programs - # The services of the environment - self.scheduler_service: SchedulerService = SchedulerService() - self.tiramisu_service: TiramisuService = TiramisuService() - # Init database service with 2 paths : - # - cpps_dataset_path : pkl file containing the cpps code - # - offline_path : pkl file containing the schedules dictionnary - # This step of initializing the database service must be executed first in the init of tiramisu api - self.dataset_service = DataSetService( - cpps_dataset_path=Config.config.dataset.cpps_path, - schedules_dataset_path=( - Config.config.dataset.dataset_path if local_dataset else None - ), - ) - # The following attribute is independent of RL env , it is used for debugging don't remove it - self.programs = None - # The list of program names of the dataset - if local_dataset: - self.programs = self.get_programs() - - # This method is used Outside of the RL env for independent testing , don't remove it in order - # to make tiramisu_api_tutorial work - def get_programs(self): - if self.programs is None: - self.programs = list(self.dataset_service.schedules_dataset.keys()) - return sorted(self.programs) - - def set_program( - self, name: str, data: dict = None, cpp_code: str = None, worker_id="init" - ): - if data: - # If data is provided externally (From ray dataset actor) then we don't need to use internal - # dataset service nor compile to get annotations of a program - tiramisu_prog = self.tiramisu_service.set_from_data( - name=name, data=data, original_str=cpp_code - ) - else: - code = self.dataset_service.get_prog_code(name=name) - # This case applies when data == None, or we are using tiramisu api in isolation mode from rl env - annotations_exitst = self.dataset_service.in_schedule_dataset(name) - # if annotations_exitst is True , then we can fetch the data from the offline dataset if the program name is saved there - if annotations_exitst: - data = self.dataset_service.get_offline_prog_data(name=name) - tiramisu_prog = self.tiramisu_service.set_from_data( - name=name, data=data, original_str=code - ) - else: - # Load the Tiramisu model from the code string - try: - tiramisu_prog = self.tiramisu_service.set_from_compil(code=code) - except Exception as e: - if isinstance(e, LoopsDepthException): - print("Program has an unsupported loop level") - elif isinstance(e, NbAccessException): - print("Program has an unsupported number of access matrices") - print("Traceback of the error : " + 60 * "-") - print(traceback.print_exc()) - print(80 * "-") - return None - - # Create a Schedule object for the Tiramisu model - schedule = Schedule(tiramisu_prog) - - # Use the Scheduler service to set the schedule for the Tiramisu model - actions_mask = self.scheduler_service.set_schedule(schedule_object=schedule) - - initial_exec = self.scheduler_service.prediction_service.get_initial_time( - schedule, worker_id - ) - if not initial_exec: - return None - - return actions_mask - - def parallelize(self, loop_level: int, env_id: int = None, worker_id=""): - # Create a Parallelization action with the given loop level - parallelization = Parallelization( - params=[loop_level], env_id=env_id, worker_id=worker_id - ) - # Use the Scheduler service to apply the Parallelization action to the schedule - return self.scheduler_service.apply_action(parallelization) - - def reverse(self, loop_level: int, env_id: int = None, worker_id=""): - # Create a Reversal action with given loop level - reversal = Reversal(params=[loop_level], env_id=env_id, worker_id=worker_id) - # Use the Scheduler service to apply the Reversal action to the schedule - return self.scheduler_service.apply_action(reversal) - - def interchange( - self, loop_level1: int, loop_level2: int, env_id: int = None, worker_id="" - ): - # Create an Interchange action with given loop levels 1 and 2 - interchange = Interchange( - params=[loop_level1, loop_level2], env_id=env_id, worker_id=worker_id - ) - # Use the Scheduler service to apply the Interchange action to the schedule - return self.scheduler_service.apply_action(interchange) - - def skew( - self, loop_level1: int, loop_level2: int, env_id: int = None, worker_id="" - ): - # Create a skewing action for loop levels 1 and 2 - skewing = Skewing( - params=[loop_level1, loop_level2], env_id=env_id, worker_id=worker_id - ) - # Use the Scheduler to apply Skewing and return the speedup and legality - return self.scheduler_service.apply_action(skewing) - - def fuse(self, loop_level: int, env_id: int = None, worker_id=""): - # Create a Fusion action with given loop level 1 - fusion = Fusion(params=[loop_level], env_id=env_id, worker_id=worker_id) - # Use the Scheduler service to apply the Fusion action to the schedule - return self.scheduler_service.apply_action(fusion) - - def tile2D( - self, - loop_level1: int, - loop_level2: int, - size_x: int, - size_y: int, - env_id: int = None, - worker_id="", - ): - # Create a 2 dimensions Tiling action with given loop levels 1 and 2 , and 2D tile size (size_x,size_y) - tiling2D = Tiling( - params=[loop_level1, loop_level2, size_x, size_y], - env_id=env_id, - worker_id=worker_id, - ) - # Use the Scheduler service to apply the Tiling action to the schedule - return self.scheduler_service.apply_action(tiling2D) - - def tile3D( - self, - loop_level1: int, - loop_level2: int, - loop_level3: int, - size_x: int, - size_y: int, - size_z: int, - env_id: int = None, - worker_id="", - ): - # Create a 3 dimensions Tiling action with given loop levels 1 , 2 and 3, and 3D tile size (size_x,size_y,size_z) - tiling3D = Tiling( - params=[loop_level1, loop_level2, loop_level3, size_x, size_y, size_z], - env_id=env_id, - worker_id=worker_id, - ) - # Use the Scheduler service to apply the Tiling action to the schedule - return self.scheduler_service.apply_action(tiling3D) - - def unroll(self, unrolling_factor: int, env_id: int = None, worker_id=""): - # Create an Unrolling action with given unrolling factor , the loop level is not given - # because we suppose that unrollong innermost loop is more beneficial ,so this action is applied - # on the innermost loop level - unrolling = Unrolling( - params=[unrolling_factor], env_id=env_id, worker_id=worker_id - ) - # Use the Scheduler service to apply the Unrolling action to the schedule - return self.scheduler_service.apply_action(unrolling) - - def save_legality_dataset(self, suffix: str = ""): - self.dataset_service.store_offline_dataset(suffix=suffix) - - def get_current_tiramisu_program_dict(self) -> dict[str, any]: - return { - "program_annotation": self.scheduler_service.schedule_object.prog.annotations, - "schedules_legality": self.scheduler_service.schedule_object.prog.schedules_legality, - "schedules_solver": self.scheduler_service.schedule_object.prog.schedules_solver, - "execution_times": self.scheduler_service.schedule_object.prog.execution_times, - } - - def final_speedup(self): - speedup, sch_str = self.scheduler_service.get_current_speedup() - print( - "Function : ", - self.scheduler_service.schedule_object.prog.name, - "\nFinal Schedule :", - sch_str, - "\nFinal Speedup :", - speedup, - ) diff --git a/env_api/utils/exceptions.py b/env_api/utils/exceptions.py deleted file mode 100644 index e726932..0000000 --- a/env_api/utils/exceptions.py +++ /dev/null @@ -1,14 +0,0 @@ -class NbMatricesException(Exception): - pass - - -class NbAccessException(Exception): - pass - - -class LoopsDepthException(Exception): - pass - - -class ExecutingFunctionException(Exception): - pass diff --git a/env_api/utils/wrapper_code.py b/env_api/utils/wrapper_code.py deleted file mode 100644 index 5ad919a..0000000 --- a/env_api/utils/wrapper_code.py +++ /dev/null @@ -1,112 +0,0 @@ -class WrappersCode: - wrapper_cpp_template = """#include "Halide.h" - #include "$func_name$_wrapper.h" - #include "tiramisu/utils.h" - #include - #include - #include - #include - - using namespace std::chrono; - using namespace std; - - int main(int, char **argv){ - - $buffers_init$ - - //halide_set_num_threads(48); - - int nb_execs = get_nb_exec(); - - double duration; - - for (int i = 0; i < nb_execs; ++i) { - auto begin = std::chrono::high_resolution_clock::now(); - $func_name$($func_params$); - auto end = std::chrono::high_resolution_clock::now(); - - duration = std::chrono::duration_cast(end-begin).count() / (double)1000000; - std::cout << duration << " "; - - } - std::cout << std::endl; - return 0; - }""" - wrapper_h_template = """#include - #include - #include - #include - #include - - #define NB_THREAD_INIT 48 - struct args { - double *buf; - unsigned long long int part_start; - unsigned long long int part_end; - double value; - }; - - void *init_part(void *params) - { - double *buffer = ((struct args*) params)->buf; - unsigned long long int start = ((struct args*) params)->part_start; - unsigned long long int end = ((struct args*) params)->part_end; - double val = ((struct args*) params)->value; - for (unsigned long long int k = start; k < end; k++){ - buffer[k]=val; - } - pthread_exit(NULL); - } - - void parallel_init_buffer(double* buf, unsigned long long int size, double value){ - pthread_t threads[NB_THREAD_INIT]; - struct args params[NB_THREAD_INIT]; - for (int i = 0; i < NB_THREAD_INIT; i++) { - unsigned long long int start = i*size/NB_THREAD_INIT; - unsigned long long int end = std::min((i+1)*size/NB_THREAD_INIT, size); - params[i] = (struct args){buf, start, 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["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "~3.10" -content-hash = "9ad1794d86e0b734c33693a60033be3f0d1bf47cee797b1d9346897af7c748b7" +content-hash = "e1814018739ff5c8250f6d81959bf52bee024ed46e6ebe2bb14c725f58a3bf36" diff --git a/pyproject.toml b/pyproject.toml index 470b0bf..cd5c698 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,13 +11,29 @@ python = "~3.10" mlflow = "2.8.1" torch = "1.12.0" torch-geometric = "2.4.0" -ray = {version = "2.3.0", extras = ["rllib"]} +ray = { version = "2.3.0", extras = ["rllib"] } +tiralib = { git = "https://github.com/Tiramisu-Compiler/TiraLib" } [tool.poetry.group.dev.dependencies] pytest = "^8.3.3" -ruff = "^0.7.4" +ruff = "^0.8" +ipykernel = "^6.29.5" +pytest-cov = "^6.0.0" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" + + +[tool.coverage.run] +branch = true +source = ["agent", "config", "utils"] + +# [tool.coverage.report] +# # omit main.py from coverage report +# omit = ["tiralib/main.py"] + +[tool.pytest.ini_options] +addopts = "--cov --cov-report=lcov:lcov.info --cov-report=term" +testpaths = ["tests"] diff --git a/random_exploration.py b/random_exploration.py index 0b6e6eb..afa8dad 100644 --- a/random_exploration.py +++ b/random_exploration.py @@ -7,8 +7,8 @@ import ray import torch from config.config import Config -from agent.rollout_worker import RolloutWorker, Transition -from utils.dataset_actor.dataset_actor import DatasetActor +from agent.rollout_worker import RolloutWorker +from utils.dataset_actor import DatasetActor class RandomAgent(nn.Module): diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..3456100 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,71 @@ +from unittest.mock import patch +from pytest import fixture +from agent.policy_value_nn import GAT +from agent.tiramisu_interface import TiramisuInterface +from config.config import Config +from utils.dataset_actor import DatasetActor + + +@fixture +def conf_init(): + Config.init("tests/config.yaml") + + +@fixture +def dataset_actor(conf_init): + return DatasetActor(Config.config.dataset) + + +@fixture +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[2.0]) +def ti_cvt(_, dataset_actor): + function_name, cache, cpp = dataset_actor.get_function_by_name( + "function_cvtcolor_MEDIUM" + ) + return TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + cache=cache, + machine=Config.config.machine, + ) + + +@fixture +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[2.0]) +def ti_mvt(_, dataset_actor): + function_name, cache, cpp = dataset_actor.get_function_by_name( + "function_mvt_MEDIUM" + ) + return TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + cache=cache, + machine=Config.config.machine, + ) + + +@fixture +def test_dataset_actor(conf_init): + config = Config.config.dataset + config.dataset_path = "tests/benchmarks/test_programs.pkl" + config.cpps_path = "tests/benchmarks/test_programs_cpps.pkl" + return DatasetActor(config) + + +@fixture +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[2.0]) +def _1_node_branch_ti(_, test_dataset_actor): + function_name, cache, cpp = test_dataset_actor.get_function_by_name( + "function014733" + ) + return TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + cache=cache, + machine=Config.config.machine, + ) + + +@fixture +def gnn_model(): + return GAT(input_size=720, num_heads=4, hidden_size=128, num_outputs=56).to("cpu") diff --git a/tests/dataset_actor/test_actor.py b/tests/dataset_actor/test_actor.py new file mode 100644 index 0000000..48b4c27 --- /dev/null +++ b/tests/dataset_actor/test_actor.py @@ -0,0 +1,202 @@ +from unittest.mock import patch +import pytest +from config.config import Config +from utils.dataset_actor import DatasetActor + + +@pytest.fixture +def conf_init(): + Config.init("tests/config.yaml") + + +@pytest.fixture +def dataset_actor(conf_init): + dataset_actor = DatasetActor(Config.config.dataset) + return dataset_actor + + +def test_get_next_function(dataset_actor): + first_function = dataset_actor.get_next_function() + assert first_function is not None + + second_function = dataset_actor.get_next_function() + assert second_function is not None + + third_function = dataset_actor.get_next_function() + assert third_function is not None + + +def test_get_next_function_random_same_seed(dataset_actor): + first_function = dataset_actor.get_next_function() + assert first_function is not None + + second_function = dataset_actor.get_next_function() + assert second_function is not None + + third_function = dataset_actor.get_next_function() + assert third_function is not None + + sequence = [first_function[0], second_function[0], third_function[0]] + + dataset_actor = DatasetActor(Config.config.dataset) + first_function_again = dataset_actor.get_next_function() + second_function_again = dataset_actor.get_next_function() + third_function_again = dataset_actor.get_next_function() + + assert sequence == [ + first_function_again[0], + second_function_again[0], + third_function_again[0], + ] + + +def test_get_next_function_random_different_seed(dataset_actor): + first_function = dataset_actor.get_next_function() + assert first_function is not None + + second_function = dataset_actor.get_next_function() + assert second_function is not None + + third_function = dataset_actor.get_next_function() + assert third_function is not None + + sequence = [first_function[0], second_function[0], third_function[0]] + + Config.config.dataset.seed = 42 + dataset_actor = DatasetActor(Config.config.dataset) + first_function_again = dataset_actor.get_next_function() + second_function_again = dataset_actor.get_next_function() + third_function_again = dataset_actor.get_next_function() + + assert sequence != [ + first_function_again[0], + second_function_again[0], + third_function_again[0], + ] + + Config.config.dataset.seed = None + dataset_actor = DatasetActor(Config.config.dataset) + first_function_again = dataset_actor.get_next_function() + second_function_again = dataset_actor.get_next_function() + third_function_again = dataset_actor.get_next_function() + + assert sequence != [ + first_function_again[0], + second_function_again[0], + third_function_again[0], + ] + + +def test_get_next_function_not_random(conf_init): + Config.config.dataset.shuffle = False + dataset_actor = DatasetActor(Config.config.dataset) + first_function = dataset_actor.get_next_function() + assert first_function is not None + + second_function = dataset_actor.get_next_function() + assert second_function is not None + + third_function = dataset_actor.get_next_function() + assert third_function is not None + + sequence = [first_function[0], second_function[0], third_function[0]] + + dataset_actor = DatasetActor(Config.config.dataset) + first_function_again = dataset_actor.get_next_function() + second_function_again = dataset_actor.get_next_function() + third_function_again = dataset_actor.get_next_function() + + assert sequence == [ + first_function_again[0], + second_function_again[0], + third_function_again[0], + ] + Config.config.dataset.seed = 13 + dataset_actor = DatasetActor(Config.config.dataset) + first_function_again = dataset_actor.get_next_function() + second_function_again = dataset_actor.get_next_function() + third_function_again = dataset_actor.get_next_function() + + assert sequence == [ + first_function_again[0], + second_function_again[0], + third_function_again[0], + ] + + +def test_get_next_function_random(conf_init): + dataset_actor = DatasetActor(Config.config.dataset) + first_function = dataset_actor.get_next_function() + assert first_function is not None + + second_function = dataset_actor.get_next_function() + assert second_function is not None + + third_function = dataset_actor.get_next_function() + assert third_function is not None + + sequence = [first_function[0], second_function[0], third_function[0]] + + dataset_actor = DatasetActor(Config.config.dataset) + first_function_again = dataset_actor.get_next_function(random=True) + second_function_again = dataset_actor.get_next_function(random=True) + third_function_again = dataset_actor.get_next_function(random=True) + + assert sequence != [ + first_function_again[0], + second_function_again[0], + third_function_again[0], + ] + + +def test_dataset_filtered_by_tags(conf_init): + tag = "icml" + Config.config.dataset.tags = [tag] + dataset_actor = DatasetActor(Config.config.dataset) + + assert ( + dataset_actor.dataset_size == 8 + ), f"Expected 8, got {dataset_actor.dataset_size}" + + assert all( + tag in dataset_actor.dataset[function]["tags"] + for function in dataset_actor.function_names + ) + + +def test_tags(conf_init, dataset_actor): + tag = "full" + all_same_tag = True + for i in range(dataset_actor.dataset_size): + _, function_data, _ = dataset_actor.get_next_function() + if tag not in function_data.tags: + all_same_tag = False + break + + assert not all_same_tag + + +def test_save_dataset(dataset_actor): + with ( + patch("pickle.dump") as pickle_dump, + patch("pathlib.Path.open") as path_open, + ): + dataset_actor.save_dataset_to_disk() + path_open.assert_called_once_with("wb") + pickle_dump.assert_called_once() + + +def test_update_dataset(dataset_actor): + function_name, function_cache, _ = dataset_actor.get_next_function() + with patch( + "utils.dataset_actor.DatasetActor.save_dataset_to_disk" + ) as save_dataset_to_disk: + dataset_actor.update_dataset(function_name, function_cache.to_dict()) + save_dataset_to_disk.assert_not_called() + dataset_actor.nbr_updates = dataset_actor.saving_frequency - 1 + dataset_actor.update_dataset(function_name, function_cache.to_dict()) + save_dataset_to_disk.assert_called_once_with(version=2) + dataset_actor.nbr_updates = 2 * dataset_actor.saving_frequency - 1 + dataset_actor.update_dataset(function_name, function_cache.to_dict()) + save_dataset_to_disk.call_count == 2 + save_dataset_to_disk.call_args == {"version": 1} diff --git a/tests/dataset_actor/test_pickle_actor.py b/tests/dataset_actor/test_pickle_actor.py deleted file mode 100644 index c934ba5..0000000 --- a/tests/dataset_actor/test_pickle_actor.py +++ /dev/null @@ -1,57 +0,0 @@ -import pytest -import ray -from config.config import Config -from utils.dataset_actor.dataset_actor import DatasetActor - - -@pytest.fixture -def conf_init(): - Config.init("config/config.yaml") - - -@pytest.fixture -def dataset_actor(conf_init): - dataset_actor = DatasetActor.remote(Config.config.dataset) - return dataset_actor - - -def test_get_next_function(dataset_actor): - first_function = ray.get(dataset_actor.get_next_function.remote()) - assert first_function is not None - - second_function = ray.get(dataset_actor.get_next_function.remote()) - assert second_function is not None - - third_function = ray.get(dataset_actor.get_next_function.remote()) - assert third_function is not None - - dataset_actor = DatasetActor.remote(Config.config.dataset) - first_function_again = ray.get(dataset_actor.get_next_function.remote()) - assert first_function_again is not None and first_function_again == first_function - - second_function_again = ray.get(dataset_actor.get_next_function.remote()) - assert ( - second_function_again is not None and second_function_again == second_function - ) - - # random - third_function_random = ray.get(dataset_actor.get_next_function.remote(True)) - assert third_function_random is not None and third_function_random != third_function - - -def test_tags(conf_init, dataset_actor): - tag = "10k" - all_same_tag = True - for i in range(10): - _, function_data, _ = ray.get(dataset_actor.get_next_function.remote()) - if tag not in function_data["tags"]: - all_same_tag = False - break - - assert not all_same_tag - - Config.config.dataset.tags = [tag] - dataset_actor = DatasetActor.remote(Config.config.dataset) - for i in range(10): - _, function_data, _ = ray.get(dataset_actor.get_next_function.remote()) - assert tag in function_data["tags"] diff --git a/env_api/__init__.py b/tests/rollout_worker/__init__.py similarity index 100% rename from env_api/__init__.py rename to tests/rollout_worker/__init__.py diff --git a/tests/rollout_worker/test_rollout_worker.py b/tests/rollout_worker/test_rollout_worker.py new file mode 100644 index 0000000..58c6c76 --- /dev/null +++ b/tests/rollout_worker/test_rollout_worker.py @@ -0,0 +1,160 @@ +from unittest.mock import MagicMock, patch + +from agent.rollout_worker import RolloutWorker, TimePerStage +from agent.tiramisu_interface import ApplyActionResult +from config.config import Config + + +@patch("agent.rollout_worker.RolloutWorker.reset") +def test_init(reset, dataset_actor): + worker = RolloutWorker(dataset_worker=dataset_actor, config=Config.config) + assert worker.dataset_worker == dataset_actor + assert worker.tiramisu_interface is None + assert worker.tiralib_config_path == Config.config.tiralib_config_path + assert worker.worker_id == 0 + assert worker.current_program is None + assert worker.times_per_stages == TimePerStage() + assert worker.state is None + assert worker.previous_speedup is None + reset.assert_called_once() + + +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[3.0, 2.0, 5.0]) +def test_init_with_reset(_, dataset_actor): + worker = RolloutWorker( + dataset_worker=dataset_actor, + config=Config.config, + function_name="function_cvtcolor_MEDIUM", + ) + assert worker.dataset_worker == dataset_actor + assert worker.current_program == "function_cvtcolor_MEDIUM" + assert worker.tiramisu_interface + assert worker.state + + +@patch("ray.get", lambda x: x) +@patch("tiralib.tiramisu.schedule.Schedule.execute") +def test_init_with_reset_remote_dataset_actor(_, dataset_actor): + remote_dataset_actor = MagicMock() + remote_dataset_actor.get_next_function.remote.return_value = ( + dataset_actor.get_function_by_name("function_cvtcolor_MEDIUM") + ) + + worker = RolloutWorker( + dataset_worker=remote_dataset_actor, + config=Config.config, + ) + assert worker.dataset_worker == remote_dataset_actor + assert worker.current_program == "function_cvtcolor_MEDIUM" + assert worker.tiramisu_interface + assert worker.state + + +@patch("tiralib.tiramisu.schedule.Schedule.execute") +def test_reward_process(_, dataset_actor): + worker = RolloutWorker( + dataset_worker=dataset_actor, + config=Config.config, + function_name="function_cvtcolor_MEDIUM", + ) + worker.previous_speedup = 1 + assert worker.reward_process(0, True, 2) == 0.5 + assert worker.previous_speedup == 2 + + assert worker.reward_process(55, True, 2) == 0 + assert worker.previous_speedup == 2 + + assert worker.reward_process(0, False, 2) == 0 + assert worker.previous_speedup == 2 + + +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[2.0]) +def test_rollout_with_model(_, gnn_model, dataset_actor): + worker = RolloutWorker( + dataset_worker=dataset_actor, + config=Config.config, + function_name="function_cvtcolor_MEDIUM", + ) + + result = worker.rollout(gnn_model, "cpu") + assert result.function_name == "function_cvtcolor_MEDIUM" + assert result.trajectory + assert result.speedup + assert result.schedule + assert result.log_trajectory + + +@patch( + "agent.tiramisu_interface.TiramisuInterface.apply_action", + return_value=ApplyActionResult( + done=True, + crashed=True, + speedup=0, + is_legal=True, + ), +) +@patch("agent.rollout_worker.RolloutWorker.reward_process") +def test_rollout_action_crashes(reward, apply_action, dataset_actor): + worker = RolloutWorker( + dataset_worker=dataset_actor, + config=Config.config, + function_name="function_cvtcolor_MEDIUM", + ) + + model = MagicMock() + model.to = MagicMock() + model.eval = MagicMock() + action = MagicMock() + action.item = MagicMock(return_value=0) + model.return_value = (action, MagicMock(), MagicMock(), MagicMock()) + + result = worker.rollout(model, "cpu") + assert result.function_name == "function_cvtcolor_MEDIUM" + assert reward.call_count == 0 + + +@patch( + "agent.tiramisu_interface.TiramisuInterface.apply_action", + return_value=ApplyActionResult( + done=False, + crashed=False, + speedup=2, + is_legal=True, + ), +) +def test_rollout_action_stopped_at_40(apply_action, dataset_actor): + worker = RolloutWorker( + dataset_worker=dataset_actor, + config=Config.config, + function_name="function_cvtcolor_MEDIUM", + ) + + model = MagicMock() + model.to = MagicMock() + model.eval = MagicMock() + action = MagicMock() + action.item = MagicMock(return_value=0) + model.return_value = (action, MagicMock(), MagicMock(), MagicMock()) + + result = worker.rollout(model, "cpu") + assert result.function_name == "function_cvtcolor_MEDIUM" + assert worker.steps == 40 + + +@patch("pathlib.Path.unlink") +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[2.0]) +def test_rollout_with_model_clean_files(_, unlink, gnn_model, dataset_actor): + Config.config.clean_files = True + worker = RolloutWorker( + dataset_worker=dataset_actor, + config=Config.config, + function_name="function_cvtcolor_MEDIUM", + ) + + result = worker.rollout(gnn_model, "cpu") + assert result.function_name == "function_cvtcolor_MEDIUM" + assert result.trajectory + assert result.speedup + assert result.schedule + assert result.log_trajectory + assert unlink.call_count > 0 diff --git a/env_api/core/__init__.py b/tests/tiramisu_interface/__init__.py similarity index 100% rename from env_api/core/__init__.py rename to tests/tiramisu_interface/__init__.py diff --git a/tests/tiramisu_interface/test_apply_action.py b/tests/tiramisu_interface/test_apply_action.py new file mode 100644 index 0000000..db4b845 --- /dev/null +++ b/tests/tiramisu_interface/test_apply_action.py @@ -0,0 +1,336 @@ +from unittest.mock import MagicMock, PropertyMock, patch +import pytest + +from agent.tiramisu_interface import ActionSlices, ApplyActionResult, TiramisuInterface +from tiralib.tiramisu import Schedule + +from config.config import Config + + +def tilings_2d(): + tilings = [] + for i in range(14, 50, 4): + tilings.extend( + [ + ( + i + j, + f"T2(L{j},L{j + 1},{ActionSlices.tiling_size(i)[0]},{ActionSlices.tiling_size(i)[1]},comps=['comp02'])", + ) + if j < 2 + else (i + j, "raise") + for j in range(0, 4) + ] + ) + + return tilings + + +@pytest.mark.parametrize( + "action, expected_action_str", + [ + (0, "I(L0,L1,comps=['comp02'])"), + (1, "I(L1,L2,comps=['comp02'])"), + (2, "raise"), + (3, "raise"), + (4, "R(L0,comps=['comp02'])"), + (5, "R(L1,comps=['comp02'])"), + (6, "R(L2,comps=['comp02'])"), + (7, "raise"), + (8, "raise"), + (9, "S(L0,L1,0,0,comps=['comp02'])"), + (10, "S(L1,L2,0,0,comps=['comp02'])"), + (11, "raise"), + (12, "P(L0,comps=['comp02'])"), + (13, "P(L1,comps=['comp02'])"), + ] + + tilings_2d() + + [ + (50, "U(L2,2,comps=['comp02'])"), + (51, "U(L2,4,comps=['comp02'])"), + (52, "U(L2,8,comps=['comp02'])"), + (53, "U(L2,16,comps=['comp02'])"), + (54, "U(L2,32,comps=['comp02'])"), + (55, "raise"), + ], +) +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[3.0, 2.0, 5.0]) +def test_action_index_to_tiralib_action( + mock_execute, dataset_actor, action, expected_action_str +): + _, cache, cpp = dataset_actor.get_function_by_name("function_cvtcolor_MEDIUM") + ti_cvt = TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + cache=cache, + machine=Config.config.machine, + ) + + if expected_action_str == "raise": + with pytest.raises(ValueError): + action_obj = ti_cvt.action_index_to_tiralib_action(action) + else: + action_obj = ti_cvt.action_index_to_tiralib_action(action) + action_obj.initialize_action_for_tree(ti_cvt.tree) + assert str(action_obj) == expected_action_str + + +def test_action_index_to_tiralib_action_invalid_unrolling_and_parallelization( + _1_node_branch_ti, +): + with pytest.raises(ValueError): + _1_node_branch_ti.action_index_to_tiralib_action(50) + + _1_node_branch_ti.current_branch_index = 1 + + with pytest.raises(ValueError): + _1_node_branch_ti.action_index_to_tiralib_action(13) + + +@patch("agent.tiramisu_interface.TiramisuInterface.get_mask", return_value=[1] * 56) +def test_apply_action_all_actions_masked(get_mask, ti_cvt): + result = ti_cvt.apply_action(0) + assert result == ApplyActionResult( + is_legal=True, speedup=1, done=True, crashed=False + ) + + +def test_apply_action_next_branch(_1_node_branch_ti): + ti = _1_node_branch_ti + result = ti.apply_action(55) + assert result == ApplyActionResult( + is_legal=True, speedup=1, done=False, crashed=False + ) + assert ti.current_branch_index == 1 + + result = ti.apply_action(55) + assert result == ApplyActionResult( + is_legal=True, speedup=1, done=False, crashed=False + ) + assert ti.current_branch_index == 2 + + result = ti.apply_action(55) + assert result == ApplyActionResult( + is_legal=True, speedup=1, done=True, crashed=False + ) + assert ti.current_branch_index == 2 + + +@patch( + "agent.tiramisu_interface.TiramisuInterface.schedule_is_legal", return_value=False +) +def test_apply_action_illegal_action(schedule_is_legal, _1_node_branch_ti): + ti = _1_node_branch_ti + result = ti.apply_action(0) + assert result == ApplyActionResult( + is_legal=False, speedup=1, done=False, crashed=False + ) + + +@patch( + "agent.tiramisu_interface.TiramisuInterface.schedule_is_legal", return_value=True +) +def test_apply_action_legal_action_with_cache(schedule_is_legal, _1_node_branch_ti): + ti = _1_node_branch_ti + ti.cache = MagicMock() + ti.cache.execution_time = lambda _, y: 2.0 if y == "empty" else 1.0 + result = ti.apply_action(0) + assert result == ApplyActionResult( + is_legal=True, speedup=2, done=False, crashed=False + ) + + +@patch( + "agent.tiramisu_interface.TiramisuInterface.schedule_is_legal", return_value=True +) +@patch("agent.tiramisu_interface.median_execution_time", lambda x: 1.0 if x else 2.0) +def test_apply_action_legal_action_without_cache(schedule_is_legal, _1_node_branch_ti): + ti = _1_node_branch_ti + ti.schedule + ti.cache = None + result = ti.apply_action(0) + assert result == ApplyActionResult( + is_legal=True, speedup=2, done=False, crashed=False + ) + + +@patch( + "agent.tiramisu_interface.TiramisuInterface.schedule_is_legal", + side_effect=ValueError("Execution crashed"), +) +def test_apply_action_execution_crashed(schedule_is_legal, _1_node_branch_ti): + ti = _1_node_branch_ti + ti.schedule + ti.cache = None + result = ti.apply_action(0) + assert result == ApplyActionResult( + is_legal=False, speedup=1, done=False, crashed=True + ) + + +@patch( + "agent.tiramisu_interface.TiramisuInterface.schedule_is_legal", return_value=True +) +@patch("agent.tiramisu_interface.median_execution_time", lambda x: 1.0 if x else 2.0) +@patch( + "tiralib.tiramisu.tiramisu_tree.TiramisuTree.depth", + new_callable=PropertyMock(return_value=6), +) +def test_apply_action_new_tree_exceeds_depth( + depth, schedule_is_legal, _1_node_branch_ti +): + ti = _1_node_branch_ti + ti.schedule + result = ti.apply_action(0) + assert result == ApplyActionResult( + is_legal=True, speedup=2, done=True, crashed=False + ) + + +@patch("tiralib.tiramisu.tiramisu_tree.TiramisuTree.from_isl_ast_string_list") +def test_schedule_is_legal_cache_hit(isl_ast, ti_cvt): + ti = ti_cvt + ti.cache = MagicMock() + ti.cache.schedules_legality = { + "I(L0,L1,comps=['comp02'])": True, + } + ti.schedule = Schedule.from_sched_str( + "I(L0,L1,comps=['comp02'])", ti.tiramisu_program + ) + assert ti.schedule_is_legal(ti.schedule) + + +@patch("tiralib.tiramisu.tiramisu_tree.TiramisuTree.from_isl_ast_string_list") +@patch( + "tiralib.tiramisu.function_server.FunctionServer.run", + return_value=MagicMock( + name="cvt", legality=True, isl_ast="isl_ast", exec_times=[2.0], success=True + ), +) +def test_schedule_is_legal_no_cache(run, isl_ast, ti_cvt): + ti = ti_cvt + ti.cache = None + ti.schedule = Schedule.from_sched_str( + "I(L0,L1,comps=['comp02'])", ti.tiramisu_program + ) + assert ti.schedule_is_legal(ti.schedule) + + +@patch("tiralib.tiramisu.tiramisu_tree.TiramisuTree.from_isl_ast_string_list") +@patch( + "tiralib.tiramisu.function_server.FunctionServer.run", + return_value=MagicMock( + name="cvt", + legality=True, + isl_ast="isl_ast", + exec_times=[2.0], + success=True, + additional_info="skewing_factors:1,1", + ), +) +def test_schedule_is_legal_skewing(run, isl_ast, ti_cvt): + ti = ti_cvt + ti.cache = None + ti.schedule = Schedule.from_sched_str( + "S(L0,L1,0,0,comps=['comp02'])", ti.tiramisu_program + ) + assert ti.schedule_is_legal(ti.schedule) + assert ti.schedule.optims_list[0].factors == [1, 1] + + +@patch("tiralib.tiramisu.tiramisu_tree.TiramisuTree.from_isl_ast_string_list") +@patch( + "tiralib.tiramisu.function_server.FunctionServer.run", + return_value=MagicMock( + name="cvt", + legality=True, + isl_ast="isl_ast", + exec_times=[2.0], + success=True, + additional_info="skewing_factors:1,1", + ), +) +def test_schedule_is_legal_skewing_factors_set_by_user(run, isl_ast, ti_cvt): + ti = ti_cvt + ti.cache = None + ti.schedule = Schedule.from_sched_str( + "S(L0,L1,2,2,comps=['comp02'])", ti.tiramisu_program + ) + assert ti.schedule_is_legal(ti.schedule) + assert ti.schedule.optims_list[0].factors == [2, 2] + + +@patch("tiralib.tiramisu.tiramisu_tree.TiramisuTree.from_isl_ast_string_list") +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[2.0]) +def test_schedule_is_legal_no_server(_, isl_ast, dataset_actor): + function_name, cache, cpp = dataset_actor.get_function_by_name( + "function_mvt_MEDIUM" + ) + ti = TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + cache=cache, + machine=Config.config.machine, + use_server=False, + ) + ti.cache = MagicMock() + ti.cache.schedules_legality = { + "S(L0,L1,1,1,comps=['comp03', 'comp04'])": None, + } + ti.schedule = Schedule.from_sched_str( + "S(L0,L1,0,0,comps=['comp03', 'comp04'])", ti.tiramisu_program + ) + with ( + patch( + "tiralib.tiramisu.function_server.FunctionServer.run", + ) as run, + patch( + "tiralib.tiramisu.compiling_service.CompilingService.compile_legality", + return_value=(True, MagicMock()), + ), + patch( + "tiralib.tiramisu.compiling_service.CompilingService.call_skewing_solver", + return_value=(1, 1), + ) as skewing_solver, + ): + assert ti.schedule_is_legal(ti.schedule) + run.assert_not_called() + skewing_solver.assert_called_once() + assert ti.schedule.optims_list[0].factors == [1, 1] + + +@patch("tiralib.tiramisu.tiramisu_tree.TiramisuTree.from_isl_ast_string_list") +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[2.0]) +def test_schedule_is_legal_no_server_skewing_set_by_user(_, isl_ast, dataset_actor): + function_name, cache, cpp = dataset_actor.get_function_by_name( + "function_mvt_MEDIUM" + ) + ti = TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + cache=cache, + machine=Config.config.machine, + use_server=False, + ) + ti.cache = MagicMock() + ti.cache.schedules_legality = { + "S(L0,L1,1,1,comps=['comp03', 'comp04'])": None, + } + ti.schedule = Schedule.from_sched_str( + "S(L0,L1,2,2,comps=['comp03', 'comp04'])", ti.tiramisu_program + ) + with ( + patch( + "tiralib.tiramisu.function_server.FunctionServer.run", + ) as run, + patch( + "tiralib.tiramisu.compiling_service.CompilingService.compile_legality", + return_value=(True, MagicMock()), + ), + patch( + "tiralib.tiramisu.compiling_service.CompilingService.call_skewing_solver", + ) as skewing_solver, + ): + assert ti.schedule_is_legal(ti.schedule) + run.assert_not_called() + skewing_solver.assert_not_called() + assert ti.schedule.optims_list[0].factors == [2, 2] diff --git a/tests/tiramisu_interface/test_embeddings.py b/tests/tiramisu_interface/test_embeddings.py new file mode 100644 index 0000000..8f5dffd --- /dev/null +++ b/tests/tiramisu_interface/test_embeddings.py @@ -0,0 +1,237 @@ +from unittest.mock import patch +import numpy as np +import pytest + +from agent.graph_utils import ( + encode_data_type, + isl_map_to_write_access_matrix, + pad_access_matrix, +) +from agent.tiramisu_interface import ( + BUFFER_ACCESS_EMBEDDING_START, + MAX_ITERATOR_DEPTH, + VECTOR_SIZE, +) + + +def test_tree_to_iterator_vectors(ti_cvt): + iterator_vectors = ti_cvt._tree_to_iterator_vectors() + assert len(iterator_vectors) == 3 + + i00 = -np.ones(VECTOR_SIZE) + # Type of embedding 0 for vector 1 for computation + i00[0] = 0 + # 11 valid tags all at the end of the embedding + i00[-11:] = [0, 1, 0, 1, 322, 0, 0, 0, 0, 0, 0] + assert np.array_equal(iterator_vectors[("comp02", 0)], i00) + + i01 = -np.ones(VECTOR_SIZE) + i01[0] = 0 + i01[-11:] = [0, 1, 0, 1, 130, 0, 0, 0, 0, 0, 0] + assert np.array_equal(iterator_vectors[("comp02", 1)], i01) + + i02 = -np.ones(VECTOR_SIZE) + i02[0] = 0 + i02[-11:] = [0, 1, 0, 1, 5, 0, 0, 0, 0, 0, 0] + assert np.array_equal(iterator_vectors[("comp02", 2)], i02) + + +@patch("agent.tiramisu_interface.median_execution_time", return_value=1.5) +def test_get_comp_annotations(_, ti_cvt): + comp02 = ti_cvt._get_comp_annotations("comp02") + assert comp02.get("absolute_order") == 1 + assert comp02.get("comp_is_reduction") + ti_cvt.apply_action(51) + updated_comp02 = ti_cvt._get_comp_annotations("_comp02_update_0") + assert updated_comp02 == comp02 + + # inexistent comp + with pytest.raises(ValueError): + ti_cvt._get_comp_annotations("comp03") + + +def test_encode_data_type(): + assert np.array_equal([1, 0, 0], encode_data_type("int32")) + assert np.array_equal([0, 1, 0], encode_data_type("float32")) + assert np.array_equal([0, 0, 1], encode_data_type("float64")) + assert encode_data_type("whatever") is None + + +@pytest.mark.parametrize( + "access_matrix, padded_access_matrix, max_depth", + [ + ( + np.array([[1, 2], [3, 4]]), + np.array([[1, 2, -1, -1], [3, 4, -1, -1], [-1, -1, -1, -1]]), + 2, + ), + ( + np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), + np.array( + [ + [1, 2, 3, -1, -1], + [4, 5, 6, -1, -1], + [7, 8, 9, -1, -1], + [-1, -1, -1, -1, -1], + ] + ), + 3, + ), + ( + np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]), + np.array( + [ + [1, 0, 0, 0], + [0, 1, 0, 0], + [0, 0, 1, 0], + ] + ), + 2, + ), + ], +) +def test_pad_access_matrix(access_matrix, padded_access_matrix, max_depth): + access_matrix = np.array(access_matrix) + padded_access_matrix = np.array(padded_access_matrix) + assert np.array_equal( + pad_access_matrix(access_matrix, max_depth), padded_access_matrix + ) + + +def test_isl_map_to_write_access_matrix(): + isl_map = "{ comp02[i00, i01, i02] -> buf02[i00, i01] }" + matrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]]) + assert np.array_equal(isl_map_to_write_access_matrix(isl_map), matrix) + + isl_map = "{ comp02[i00, i01, i02] -> buf02[i00, i01, i02] }" + matrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]) + assert np.array_equal(isl_map_to_write_access_matrix(isl_map), matrix) + + isl_map = "{ comp02[i00] -> buf02[i00, i01, i02, i03] }" + matrix = np.array([[1, 0], [0, 0], [0, 0], [0, 0]]) + assert np.array_equal(isl_map_to_write_access_matrix(isl_map), matrix) + + isl_map = "{ comp02[i00] -> buf02[i00 + 2, i01, i02, i03] }" + matrix = np.array([[1, 2], [0, 0], [0, 0], [0, 0]]) + assert np.array_equal(isl_map_to_write_access_matrix(isl_map), matrix) + + isl_map = "{ comp02[i00, i01] -> buf02[3*i00, i01 + 2, i02, i03] }" + matrix = np.array([[3, 0, 0], [0, 1, 2], [0, 0, 0], [0, 0, 0]]) + assert np.array_equal(isl_map_to_write_access_matrix(isl_map), matrix) + + with pytest.raises(ValueError): + isl_map_to_write_access_matrix("whatever") + + +def test_annotations_to_comps_vectors(ti_cvt): + comp_vectors = ti_cvt._annotations_to_comps_vectors() + assert len(comp_vectors) == 1 + + comp02 = -np.ones(VECTOR_SIZE) + comp02[0] = 1 + comp02[1] = 1 + comp02[2:5] = [1, 0, 0] + comp02[5] = 1 + comp02[6] = 2 + write_padded_matrix = pad_access_matrix( + np.array([[1, 0, 0, 0], [0, 1, 0, 0]]), MAX_ITERATOR_DEPTH + ).reshape(-1) + comp02[7:BUFFER_ACCESS_EMBEDDING_START] = write_padded_matrix + buf_2 = np.concatenate( + [ + pad_access_matrix( + np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]), MAX_ITERATOR_DEPTH + ).reshape(-1), + [1], + [2 + 1], + ] + ) + buf_0 = np.concatenate( + [ + pad_access_matrix( + np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]), MAX_ITERATOR_DEPTH + ).reshape(-1), + [0], + [0 + 1], + ] + ) + buf_1 = np.concatenate( + [ + pad_access_matrix(np.array([[0, 0, 1, 0]]), MAX_ITERATOR_DEPTH).reshape(-1), + [0], + [1 + 1], + ] + ) + access_embedding = np.concatenate([buf_2, buf_0, buf_1]) + comp02[ + BUFFER_ACCESS_EMBEDDING_START : BUFFER_ACCESS_EMBEDDING_START + + access_embedding.shape[0] + ] = access_embedding + + assert np.array_equal(comp_vectors["comp02"], comp02) + + +def test_graph(ti_cvt): + i00 = -np.ones(VECTOR_SIZE) + i00[0] = 0 + i00[-11:] = [1, 1, 0, 1, 322, 0, 0, 0, 0, 0, 0] + + i01 = -np.ones(VECTOR_SIZE) + i01[0] = 0 + i01[-11:] = [1, 1, 0, 1, 130, 0, 0, 0, 0, 0, 0] + + i02 = -np.ones(VECTOR_SIZE) + i02[0] = 0 + i02[-11:] = [1, 1, 0, 1, 5, 0, 0, 0, 0, 0, 0] + + comp02 = -np.ones(VECTOR_SIZE) + comp02[0] = 1 + comp02[1] = 1 + comp02[2:5] = [1, 0, 0] + comp02[5] = 1 + comp02[6] = 2 + write_padded_matrix = pad_access_matrix( + np.array([[1, 0, 0, 0], [0, 1, 0, 0]]), MAX_ITERATOR_DEPTH + ).reshape(-1) + comp02[7:BUFFER_ACCESS_EMBEDDING_START] = write_padded_matrix + buf_2 = np.concatenate( + [ + pad_access_matrix( + np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]), MAX_ITERATOR_DEPTH + ).reshape(-1), + [1], + [2 + 1], + ] + ) + buf_0 = np.concatenate( + [ + pad_access_matrix( + np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]), MAX_ITERATOR_DEPTH + ).reshape(-1), + [0], + [0 + 1], + ] + ) + buf_1 = np.concatenate( + [ + pad_access_matrix(np.array([[0, 0, 1, 0]]), MAX_ITERATOR_DEPTH).reshape(-1), + [0], + [1 + 1], + ] + ) + access_embedding = np.concatenate([buf_2, buf_0, buf_1]) + comp02[ + BUFFER_ACCESS_EMBEDDING_START : BUFFER_ACCESS_EMBEDDING_START + + access_embedding.shape[0] + ] = access_embedding + + node_feats, edge_index, it_index, comp_index = ti_cvt.graph + assert len(it_index) == 3 + assert len(comp_index) == 1 + assert node_feats.shape[0] == 4 + assert edge_index.shape[0] == 3 + + stacked_node_feats = np.stack([i00, i01, i02, comp02]) + + assert np.array_equal(node_feats, stacked_node_feats) + assert np.array_equal(edge_index, np.array([[0, 1], [1, 2], [2, 3]])) diff --git a/tests/tiramisu_interface/test_helpers.py b/tests/tiramisu_interface/test_helpers.py new file mode 100644 index 0000000..96900cf --- /dev/null +++ b/tests/tiramisu_interface/test_helpers.py @@ -0,0 +1,102 @@ +import pytest + +from agent.tiramisu_interface import ActionSlices, program_compatible_with_model + + +@pytest.mark.parametrize( + "index, params", + [ + (14, (32, 32)), + (18, (64, 64)), + (22, (128, 128)), + (26, (32, 64)), + (30, (32, 128)), + (34, (64, 32)), + (38, (64, 128)), + (42, (128, 32)), + (46, (128, 64)), + (50, "raise"), + ], +) +def test_ActionSlices(index, params): + if params == "raise": + with pytest.raises(ValueError): + ActionSlices.tiling_size(index) + else: + assert ActionSlices.tiling_size(index) == params + + +# def program_compatible_with_model(annotations): +# max_accesses = 15 +# min_accesses = 0 +# max_iterators = MAX_ITERATOR_DEPTH +# computations_dict = annotations["computations"] + +# # Making sure every computation doesn't exceed the limit of the cost model , if the model is updated change the conditions +# for comp_name in computations_dict: +# comp_dict = computations_dict[comp_name] +# if ( +# len(comp_dict["accesses"]) > max_accesses +# or len(comp_dict["accesses"]) < min_accesses +# ): +# return False +# if len(comp_dict["iterators"]) > max_iterators: +# return False + +# return True + + +@pytest.mark.parametrize( + "annotations, expected", + [ + ( + { + "computations": { + "comp01": {"accesses": [1, 2, 3], "iterators": [1, 2, 3]}, + "comp02": {"accesses": [1, 2, 3], "iterators": [1, 2, 3]}, + } + }, + True, + ), + ( + { + "computations": { + "comp01": { + "accesses": [ + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + ], + "iterators": [1, 2, 3], + }, + "comp02": {"accesses": [1, 2, 3], "iterators": [1, 2, 3]}, + } + }, + False, + ), + ( + { + "computations": { + "comp01": {"accesses": [1, 2, 3], "iterators": [1, 2, 3]}, + "comp02": {"accesses": [1, 2, 3], "iterators": [1, 2, 3, 4, 5, 6]}, + } + }, + False, + ), + ], +) +def test_program_compatible_with_model(annotations, expected): + assert program_compatible_with_model(annotations) == expected diff --git a/tests/tiramisu_interface/test_mask.py b/tests/tiramisu_interface/test_mask.py new file mode 100644 index 0000000..9ad2161 --- /dev/null +++ b/tests/tiramisu_interface/test_mask.py @@ -0,0 +1,364 @@ +from unittest.mock import patch +import pytest +from agent.tiramisu_interface import ( + NEXT_ACTION_INDEX, + ActionSlices, + _get_level_action_indices_tuple_actions, +) +from tiralib.tiramisu import Schedule + + +@pytest.mark.parametrize( + "level, start_index, expected", + [ + (0, ActionSlices.SKEWING.start, [ActionSlices.SKEWING.start]), + (0, ActionSlices.INTERCHANGE.start, [ActionSlices.INTERCHANGE.start]), + (0, ActionSlices.TILING2D.start, [ActionSlices.TILING2D.start]), + (0, ActionSlices.TILING2D.start + 12, [ActionSlices.TILING2D.start + 12]), + ( + 1, + ActionSlices.SKEWING.start, + [ActionSlices.SKEWING.start, ActionSlices.SKEWING.start + 1], + ), + ( + 1, + ActionSlices.INTERCHANGE.start, + [ActionSlices.INTERCHANGE.start, ActionSlices.INTERCHANGE.start + 1], + ), + ( + 1, + ActionSlices.TILING2D.start, + [ActionSlices.TILING2D.start, ActionSlices.TILING2D.start + 1], + ), + ( + 1, + ActionSlices.TILING2D.start + 12, + [ActionSlices.TILING2D.start + 12, ActionSlices.TILING2D.start + 13], + ), + ( + 2, + ActionSlices.SKEWING.start, + [ActionSlices.SKEWING.start + 1, ActionSlices.SKEWING.start + 2], + ), + ( + 2, + ActionSlices.INTERCHANGE.start, + [ActionSlices.INTERCHANGE.start + 1, ActionSlices.INTERCHANGE.start + 2], + ), + ( + 2, + ActionSlices.TILING2D.start, + [ActionSlices.TILING2D.start + 1, ActionSlices.TILING2D.start + 2], + ), + ( + 2, + ActionSlices.TILING2D.start + 12, + [ActionSlices.TILING2D.start + 13, ActionSlices.TILING2D.start + 14], + ), + (3, ActionSlices.SKEWING.start, [ActionSlices.SKEWING.stop - 1]), + ( + 3, + ActionSlices.INTERCHANGE.start, + [ActionSlices.INTERCHANGE.start + 2, ActionSlices.INTERCHANGE.start + 3], + ), + ( + 3, + ActionSlices.TILING2D.start, + [ActionSlices.TILING2D.start + 2, ActionSlices.TILING2D.start + 3], + ), + ( + 3, + ActionSlices.TILING2D.start + 12, + [ActionSlices.TILING2D.start + 14, ActionSlices.TILING2D.start + 15], + ), + (4, ActionSlices.SKEWING.start, []), + (4, ActionSlices.INTERCHANGE.start, [ActionSlices.INTERCHANGE.start + 3]), + (4, ActionSlices.TILING2D.start, [ActionSlices.TILING2D.start + 3]), + (4, ActionSlices.TILING2D.start + 12, [ActionSlices.TILING2D.start + 15]), + (5, ActionSlices.SKEWING.start, []), + (5, ActionSlices.INTERCHANGE.start, []), + (5, ActionSlices.TILING2D.start, []), + (5, ActionSlices.TILING2D.start + 12, []), + ], +) +def test_get_level_action_indices_tuple_actions(level, start_index, expected): + assert _get_level_action_indices_tuple_actions(level, start_index) == expected + + +def test_mask_empty_schedule(ti_cvt): + mask = ti_cvt.get_mask() + + interchange_part = [0, 0, 1, 1] + reversal_part = [0, 0, 0, 1, 1] + skewing_part = [0, 0, 1] + parallelize_part = [0, 0] + tiling2d_part = [0, 0, 1, 1] * 9 + unrolling_part = [0, 0, 0, 0, 0] + next_action = [0] + + assert ( + mask[ActionSlices.INTERCHANGE] == interchange_part + ).all(), "Interchange mask is wrong" + assert ( + mask[ActionSlices.REVERSAL] == reversal_part + ).all(), "Reversal mask is wrong" + assert (mask[ActionSlices.SKEWING] == skewing_part).all(), "Skewing mask is wrong" + assert ( + mask[ActionSlices.PARALLELIZATION] == parallelize_part + ).all(), "Parallelize mask is wrong" + assert ( + mask[ActionSlices.TILING2D] == tiling2d_part + ).all(), "Tiling2D mask is wrong" + assert ( + mask[ActionSlices.UNROLLING] == unrolling_part + ).all(), "Unrolling mask is wrong" + assert (mask[55] == next_action).all(), "Next action mask is wrong" + + +def test_mask_after_skewing_illegal(ti_cvt): + result = ti_cvt.apply_action(9) + assert result.is_legal is False + mask = ti_cvt.get_mask() + + interchange_part = [0, 0, 1, 1] + reversal_part = [0, 0, 0, 1, 1] + skewing_part = [1, 0, 1] + parallelize_part = [0, 0] + tiling2d_part = [0, 0, 1, 1] * 9 + unrolling_part = [0, 0, 0, 0, 0] + next_action = [0] + + assert ( + mask[ActionSlices.INTERCHANGE] == interchange_part + ).all(), "Interchange mask is wrong" + assert ( + mask[ActionSlices.REVERSAL] == reversal_part + ).all(), "Reversal mask is wrong" + assert (mask[ActionSlices.SKEWING] == skewing_part).all(), "Skewing mask is wrong" + assert ( + mask[ActionSlices.PARALLELIZATION] == parallelize_part + ).all(), "Parallelize mask is wrong" + assert ( + mask[ActionSlices.TILING2D] == tiling2d_part + ).all(), "Tiling2D mask is wrong" + assert ( + mask[ActionSlices.UNROLLING] == unrolling_part + ).all(), "Unrolling mask is wrong" + assert (mask[55] == next_action).all(), "Next action mask is wrong" + + +@patch("agent.tiramisu_interface.median_execution_time", return_value=1.5) +def test_mask_after_skewing_legal(_, ti_mvt): + result = ti_mvt.apply_action(9) + assert result.is_legal is True + mask = ti_mvt.get_mask() + + interchange_part = [1, 1, 1, 1] + reversal_part = [1, 1, 1, 1, 1] + skewing_part = [1, 1, 1] + parallelize_part = [0, 0] + tiling2d_part = [1, 1, 1, 1] * 9 + unrolling_part = [0, 0, 0, 0, 0] + next_action = [0] + + assert ( + mask[ActionSlices.INTERCHANGE] == interchange_part + ).all(), "Interchange mask is wrong" + assert ( + mask[ActionSlices.REVERSAL] == reversal_part + ).all(), "Reversal mask is wrong" + assert (mask[ActionSlices.SKEWING] == skewing_part).all(), "Skewing mask is wrong" + assert ( + mask[ActionSlices.PARALLELIZATION] == parallelize_part + ).all(), "Parallelize mask is wrong" + assert ( + mask[ActionSlices.TILING2D] == tiling2d_part + ).all(), "Tiling2D mask is wrong" + assert ( + mask[ActionSlices.UNROLLING] == unrolling_part + ).all(), "Unrolling mask is wrong" + assert (mask[55] == next_action).all(), "Next action mask is wrong" + + +@patch("agent.tiramisu_interface.median_execution_time", return_value=1.5) +def test_mask_after_parallelisation(_, ti_mvt): + result = ti_mvt.apply_action(12) + assert result.is_legal is True + mask = ti_mvt.get_mask() + + interchange_part = [1, 1, 1, 1] + reversal_part = [1, 1, 1, 1, 1] + skewing_part = [1, 1, 1] + parallelize_part = [1, 0] + tiling2d_part = [0, 1, 1, 1] * 9 + unrolling_part = [0] * 5 + next_action = [0] + + assert ( + mask[ActionSlices.INTERCHANGE] == interchange_part + ).all(), "Interchange mask is wrong" + assert ( + mask[ActionSlices.REVERSAL] == reversal_part + ).all(), "Reversal mask is wrong" + assert (mask[ActionSlices.SKEWING] == skewing_part).all(), "Skewing mask is wrong" + assert ( + mask[ActionSlices.PARALLELIZATION] == parallelize_part + ).all(), "Parallelize mask is wrong" + assert ( + mask[ActionSlices.TILING2D] == tiling2d_part + ).all(), "Tiling2D mask is wrong" + assert ( + mask[ActionSlices.UNROLLING] == unrolling_part + ).all(), "Unrolling mask is wrong" + assert (mask[55] == next_action).all(), "Next action mask is wrong" + + +@patch("agent.tiramisu_interface.median_execution_time", return_value=1.5) +def test_mask_after_parallelisation_and_tiling(_, ti_mvt): + result = ti_mvt.apply_action(12) + assert result.is_legal is True + result = ti_mvt.apply_action(14) + mask = ti_mvt.get_mask() + + interchange_part = [1, 1, 1, 1] + reversal_part = [1, 1, 1, 1, 1] + skewing_part = [1, 1, 1] + parallelize_part = [1, 1] + tiling2d_part = [1, 1, 1, 1] * 9 + unrolling_part = [0] * 5 + next_action = [0] + + assert ( + mask[ActionSlices.INTERCHANGE] == interchange_part + ).all(), "Interchange mask is wrong" + assert ( + mask[ActionSlices.REVERSAL] == reversal_part + ).all(), "Reversal mask is wrong" + assert (mask[ActionSlices.SKEWING] == skewing_part).all(), "Skewing mask is wrong" + assert ( + mask[ActionSlices.PARALLELIZATION] == parallelize_part + ).all(), "Parallelize mask is wrong" + assert ( + mask[ActionSlices.TILING2D] == tiling2d_part + ).all(), "Tiling2D mask is wrong" + assert ( + mask[ActionSlices.UNROLLING] == unrolling_part + ).all(), "Unrolling mask is wrong" + assert (mask[55] == next_action).all(), "Next action mask is wrong" + + +@patch("agent.tiramisu_interface.median_execution_time", return_value=1.5) +@patch( + "agent.tiramisu_interface.TiramisuInterface.schedule_is_legal", return_value=True +) +def test_mask_after_unrolling(_, schedule_is_legal, ti_mvt): + result = ti_mvt.apply_action(51) + assert result.is_legal is True + mask = ti_mvt.get_mask() + + interchange_part = [1, 1, 1, 1] + reversal_part = [1, 1, 1, 1, 1] + skewing_part = [1, 1, 1] + parallelize_part = [1, 1] + tiling2d_part = [1, 1, 1, 1] * 9 + unrolling_part = [1, 1, 1, 1, 1] + next_action = [0] + + assert ( + mask[ActionSlices.INTERCHANGE] == interchange_part + ).all(), "Interchange mask is wrong" + assert ( + mask[ActionSlices.REVERSAL] == reversal_part + ).all(), "Reversal mask is wrong" + assert (mask[ActionSlices.SKEWING] == skewing_part).all(), "Skewing mask is wrong" + assert ( + mask[ActionSlices.PARALLELIZATION] == parallelize_part + ).all(), "Parallelize mask is wrong" + assert ( + mask[ActionSlices.TILING2D] == tiling2d_part + ).all(), "Tiling2D mask is wrong" + assert ( + mask[ActionSlices.UNROLLING] == unrolling_part + ).all(), "Unrolling mask is wrong" + assert (mask[55] == next_action).all(), "Next action mask is wrong" + + +@patch("agent.tiramisu_interface.median_execution_time", return_value=1.5) +def test_mask_1_node_branch(_, _1_node_branch_ti): + ti = _1_node_branch_ti + ti.apply_action(NEXT_ACTION_INDEX) + assert len(ti.current_branch) == 1, "Branch is not of length 1" + mask = ti.get_mask() + + interchange_part = [1, 1, 1, 1] + reversal_part = [1, 1, 0, 1, 1] + skewing_part = [1, 1, 1] + parallelize_part = [1, 1] + tiling2d_part = [1, 1, 1, 1] * 9 + unrolling_part = [0, 0, 0, 0, 0] + next_action = [0] + + assert ( + mask[ActionSlices.INTERCHANGE] == interchange_part + ).all(), "Interchange mask is wrong" + assert ( + mask[ActionSlices.REVERSAL] == reversal_part + ).all(), "Reversal mask is wrong" + assert (mask[ActionSlices.SKEWING] == skewing_part).all(), "Skewing mask is wrong" + assert ( + mask[ActionSlices.PARALLELIZATION] == parallelize_part + ).all(), "Parallelize mask is wrong" + assert ( + mask[ActionSlices.TILING2D] == tiling2d_part + ).all(), "Tiling2D mask is wrong" + assert ( + mask[ActionSlices.UNROLLING] == unrolling_part + ).all(), "Unrolling mask is wrong" + assert (mask[55] == next_action).all(), "Next action mask is wrong" + + +@patch("agent.tiramisu_interface.median_execution_time", return_value=1.5) +@patch("tiralib.tiramisu.schedule.Schedule.update_tree_from_isl_ast") +def test_mask_unsupported_action(_, update, ti_cvt): + ti = ti_cvt + ti.schedule = Schedule.from_sched_str( + "T3(L0,L1,L2,32,32,32,comps=['comp02'])", ti.tiramisu_program + ) + with pytest.raises(ValueError): + ti.apply_action(50) + + +@patch("agent.tiramisu_interface.median_execution_time", return_value=1.5) +@patch( + "agent.tiramisu_interface.TiramisuInterface.schedule_is_legal", return_value=True +) +def test_mask_after_interchange(_, schedule_is_legal, ti_cvt): + result = ti_cvt.apply_action(0) + assert result.is_legal is True + mask = ti_cvt.get_mask() + + interchange_part = [1, 0, 1, 1] + reversal_part = [0, 0, 0, 1, 1] + skewing_part = [0, 0, 1] + parallelize_part = [0, 0] + tiling2d_part = [0, 0, 1, 1] * 9 + unrolling_part = [0, 0, 0, 0, 0] + next_action = [0] + + assert ( + mask[ActionSlices.INTERCHANGE] == interchange_part + ).all(), "Interchange mask is wrong" + assert ( + mask[ActionSlices.REVERSAL] == reversal_part + ).all(), "Reversal mask is wrong" + assert (mask[ActionSlices.SKEWING] == skewing_part).all(), "Skewing mask is wrong" + assert ( + mask[ActionSlices.PARALLELIZATION] == parallelize_part + ).all(), "Parallelize mask is wrong" + assert ( + mask[ActionSlices.TILING2D] == tiling2d_part + ).all(), "Tiling2D mask is wrong" + assert ( + mask[ActionSlices.UNROLLING] == unrolling_part + ).all(), "Unrolling mask is wrong" + assert (mask[55] == next_action).all(), "Next action mask is wrong" diff --git a/tests/tiramisu_interface/test_tiramisu_interface.py b/tests/tiramisu_interface/test_tiramisu_interface.py new file mode 100644 index 0000000..33a14aa --- /dev/null +++ b/tests/tiramisu_interface/test_tiramisu_interface.py @@ -0,0 +1,149 @@ +from unittest.mock import patch + +import pytest +from agent.tiramisu_interface import TiramisuInterface +from config.config import Config + + +@patch("agent.tiramisu_interface.median_execution_time", return_value=1.5) +def test_cache(_, dataset_actor): + function_name, cache, cpp = dataset_actor.get_function_by_name( + "function_cvtcolor_MEDIUM" + ) + assert function_name == "function_cvtcolor_MEDIUM" + assert cache is not None + assert cpp is not None + + ti = TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + cache=cache, + machine=Config.config.machine, + ) + assert ti.tiramisu_program.name == "function_cvtcolor_MEDIUM" + schedule_str = "I(L0,L1,comps=['comp02'])" + assert schedule_str not in ti.cache.schedules_legality + assert schedule_str not in ti.cache.isl_ast + # INTERCHANGE 0,1 + ti.apply_action(0) + assert schedule_str in ti.cache.schedules_legality + assert ti.cache.schedules_legality[schedule_str] is True + assert schedule_str in ti.cache.isl_ast + assert schedule_str in ti.cache.execution_times[Config.config.machine] + + +def test_init_without_cache(dataset_actor): + function_name, cache, cpp = dataset_actor.get_function_by_name( + "function_cvtcolor_MEDIUM" + ) + assert function_name == "function_cvtcolor_MEDIUM" + assert cache is not None + assert cpp is not None + + with patch( + "tiralib.tiramisu.schedule.Schedule.execute", return_value=[3.0, 2.0, 5.0] + ): + ti = TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + machine=Config.config.machine, + ) + assert ti.tiramisu_program.name == "function_cvtcolor_MEDIUM" + assert ti.cache is None + assert ti.tiramisu_program.server is not None + assert ti.machine == Config.config.machine + assert ti.initial_execution_time == 3.0 + + +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[3.0, 2.0, 5.0]) +def test_init_with_cache(mock_execute, dataset_actor): + function_name, cache, cpp = dataset_actor.get_function_by_name( + "function_cvtcolor_MEDIUM" + ) + assert function_name == "function_cvtcolor_MEDIUM" + + ti = TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + machine=Config.config.machine, + cache=cache, + ) + assert ti.tiramisu_program.name == "function_cvtcolor_MEDIUM" + assert ti.cache is not None + assert ti.machine == Config.config.machine + assert ti._initial_execution_time == 3.0 + assert ti.cache.execution_time(Config.config.machine, "empty") + assert ti.initial_execution_time == 3.0 + mock_execute.assert_called_once() + assert ti.initial_execution_time == 3.0 + mock_execute.assert_called_once() + assert ti.cache.execution_time(Config.config.machine, "empty") == 3.0 + + ti = TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + machine=Config.config.machine, + cache=cache, + ) + assert ti.initial_execution_time == 3.0 + + +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[3.0, 2.0, 5.0]) +def test_branches(mock_execute, ti_cvt, dataset_actor): + print(ti_cvt.tiramisu_program.tree) + assert len(ti_cvt.branches) == 1 + assert ti_cvt.current_branch_index == 0 + assert ti_cvt.current_branch == [("comp02", 0), ("comp02", 1), ("comp02", 2)] + + function_name, cache, cpp = dataset_actor.get_function_by_name( + "function_blur_MEDIUM" + ) + ti = TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + cache=cache, + machine=Config.config.machine, + ) + assert len(ti.branches) == 1 + assert ti.current_branch_index == 0 + + +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[3.0, 2.0, 5.0]) +def test_init_with_cache_no_server(mock_execute, dataset_actor): + function_name, cache, cpp = dataset_actor.get_function_by_name( + "function_cvtcolor_MEDIUM" + ) + assert function_name == "function_cvtcolor_MEDIUM" + + ti = TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + machine=Config.config.machine, + cache=cache, + use_server=False, + ) + assert ti.tiramisu_program.name == "function_cvtcolor_MEDIUM" + assert ti.cache is not None + assert ti.tiramisu_program.server is None + assert ti.machine == Config.config.machine + assert ti.initial_execution_time == 3.0 + mock_execute.assert_called_once() + assert ti.initial_execution_time == 3.0 + mock_execute.assert_called_once() + assert ti.tiramisu_program.server is None + + +@patch("tiralib.tiramisu.schedule.Schedule.execute", return_value=[3.0, 2.0, 5.0]) +def test_init_no_cache_no_server(mock_execute, dataset_actor): + function_name, cache, cpp = dataset_actor.get_function_by_name( + "function_cvtcolor_MEDIUM" + ) + assert function_name == "function_cvtcolor_MEDIUM" + + with pytest.raises(ValueError): + TiramisuInterface( + cpp, + tiralib_config_path=Config.config.tiralib_config_path, + machine=Config.config.machine, + use_server=False, + ) diff --git a/tiramisu_api_tutorial.py b/tiramisu_api_tutorial.py deleted file mode 100644 index 0476635..0000000 --- a/tiramisu_api_tutorial.py +++ /dev/null @@ -1,105 +0,0 @@ -from env_api.tiramisu_api import TiramisuEnvAPI -from config.config import Config -from env_api.utils.exceptions import * -import random, traceback, time -from env_api.core.services.compiling_service import CompilingService -from agent.graph_utils import build_graph -from torch_geometric.data import Data -import torch -from agent.policy_value_nn import GAT - -if __name__ == "__main__": - torch.set_default_dtype(torch.float32) - start = time.time() - # Init global config to run the Tiramisu env - Config.init() - t_api = TiramisuEnvAPI(local_dataset=True) - # Get a list of the program names in the database - programs = t_api.get_programs() - try: - # Select a program randomly for example program = "function025885" - program: str = random.choice(programs) - print("Selected f unction : ", program) - # set_program(str) creates all the necessary objects to start doing operations on a program - # it returns an encoded representation specific to the RL system - # This representation has a shape and type of torch.Size([180]) - # ast = t_api.initialize_optimzer(name=program) - # print(t_api.optimizer.tiramisu_prog.original_str) - # print(ast) - # init_exec_time = t_api.optimizer.execute_original_code() - # t_api.apply_tiling(loops=[0,1], sizes=[2,2]) - # ast.get_fusion_levels() - - actions_mask = t_api.set_program(name=program) - # There is some programs that are not supported so we need to check our representation first - if True: - # After setting a program and checking if it is fully supported by our RL system, you can apply any action on it in any order - # And expect to get the speedup of the whole schedule, the representation and the result of legality check of the last operation - - # (speedup, legality, actions_mask) = t_api.skew( - # loop_level1=0, loop_level2=1, env_id=2, worker_id="0" - # ) - - # (speedup, - # legality,actions_mask) = t_api.skew(loop_level1=0,loop_level2=1,env_id=2) - # ( - # speedup, - # legality, - # actions_mask, - # ) = t_api.interchange(loop_level1=1,loop_level2=2, env_id=7) - # ( - # speedup, - # legality, - # actions_mask, - # ) = t_api.parallelize(loop_level=1, env_id=1, worker_id = "test") - # t_api.scheduler_service.next_branch() - # (speedup, legality, actions_mask) = t_api.tile2D( - # loop_level1=1, loop_level2=2, size_x=6, size_y=6, env_id=4 - # ) - # ( - # speedup, - # legality, - # actions_mask, - # ) = t_api.reverse(loop_level=2, env_id=1) - # n, e, i, c = build_graph( - # t_api.scheduler_service.schedule_object.prog.annotations - # ) - - # data = Data( - # x=torch.tensor(n, dtype=torch.float32), - # edge_index=torch.tensor(e, dtype=torch.int) - # .transpose(0, 1) - # .contiguous(), - # ) - - # ppo_agent = GAT(input_size=718, num_heads=4, hidden_size=198, num_outputs=56) - - # print(ppo_agent(data)) - - # ( - # speedup, - # legality, - # actions_mask, - # ) = t_api.parallelize(loop_level=0, env_id=1) - ( - speedup, - legality, - actions_mask, - ) = t_api.unroll(unrolling_factor=10, env_id=7, worker_id="1") - - # t_api.scheduler_service.next_branch() - # (speedup, - # legality,actions_mask) = t_api.tile2D(loop_level1=0,loop_level2=1,size_x=32,size_y=64,env_id=4) - # (speedup, embedding_tensor, legality, actions_mask, - # ) = t_api.unroll(unrolling_factor=16, env_id=7) - - # # (speedup, embedding_tensor, - # # legality,actions_mask) = t_api.tile3D(loop_level1=0 , loop_level2=1,loop_level3=2, - # # size_x=128,size_y=128,size_z=128,env_id=17) - print("Speedup : ", speedup, " ", "Legality : ", legality) - - print("Time : ", time.time() - start) - except Exception as e: - print("Traceback of the error : " + 60 * "-") - print(traceback.print_exc()) - print(80 * "-") diff --git a/train_ppo_gnn.py b/train_ppo_gnn.py index f2b0ecf..73008f4 100644 --- a/train_ppo_gnn.py +++ b/train_ppo_gnn.py @@ -3,9 +3,9 @@ import torch import torch.nn as nn from agent.policy_value_nn import GAT -from agent.rollout_worker import RolloutWorker, Transition +from agent.rollout_worker import RolloutWorkerRemote, Transition from config.config import Config -from utils.dataset_actor.dataset_actor import DatasetActor +from utils.dataset_actor import DatasetActorRemote import numpy as np import ray import math @@ -51,10 +51,10 @@ print(f"Number of CPUs detected by ray: {ray.cluster_resources()['CPU']}") - dataset_worker = DatasetActor.remote(Config.config.dataset) + dataset_worker = DatasetActorRemote.remote(Config.config.dataset) device = "cuda:0" if torch.cuda.is_available() else "cpu" - ppo_agent = GAT(input_size=718, num_heads=4, hidden_size=128, num_outputs=56).to( + ppo_agent = GAT(input_size=720, num_heads=4, hidden_size=128, num_outputs=56).to( device ) optimizer = torch.optim.Adam( @@ -80,7 +80,7 @@ num_cpus = int(ray.cluster_resources()["CPU"]) // NUM_ROLLOUT_WORKERS rollout_workers = [ - RolloutWorker.options( + RolloutWorkerRemote.options( num_cpus=num_cpus, num_gpus=0, scheduling_strategy="SPREAD" ).remote(dataset_worker, Config.config, worker_id=i) for i in range(NUM_ROLLOUT_WORKERS) @@ -148,9 +148,9 @@ ) for result in results: - b_speedups.append(math.log(result["speedup"], 4)) - trajectory_len = len(result["trajectory"]) - full_trajectory = Transition(*zip(*result["trajectory"])) + b_speedups.append(math.log(result.speedup, 4)) + trajectory_len = len(result.trajectory) + full_trajectory = Transition(*zip(*result.trajectory)) avg_episode_length = (m * avg_episode_length) / ( m + 1 ) + trajectory_len / (m + 1) diff --git a/utils/dataset_actor.py b/utils/dataset_actor.py new file mode 100644 index 0000000..de6142d --- /dev/null +++ b/utils/dataset_actor.py @@ -0,0 +1,176 @@ +from dataclasses import asdict, dataclass +from pathlib import Path +import pickle +import random +from typing import Any + +import numpy as np +import config.config as cfg +import ray + + +@dataclass +class TiramisuProgramCache: + execution_times: dict[str, dict[str, float]] + program_annotation: dict[str, Any] + schedules_legality: dict[str, bool] + schedules_solver: dict[str, tuple[int, int]] + tags: set[str] + isl_ast: dict[str, str] + + def add( + self, + schedule_str: str, + is_legal: bool, + isl_ast_str: str, + skewing_factors: tuple[int, int] | None = None, + ): + self.schedules_legality[schedule_str] = is_legal + self.isl_ast[schedule_str] = isl_ast_str + if skewing_factors is not None: + self.schedules_solver[schedule_str] = skewing_factors + + def add_execution_time( + self, machine: str, schedule_str: str, execution_time: float + ): + if machine not in self.execution_times: + self.execution_times[machine] = {} + self.execution_times[machine][schedule_str] = execution_time + + def execution_time(self, machine: str, schedule_str: str) -> float | None: + return self.execution_times.get(machine, {}).get(schedule_str, None) + + def to_dict(self): + return asdict(self) + + +class DatasetActor: + """ + DatasetActor is a class that is used to read the dataset and update it. + It is used to read the dataset from disk and update it with the new functions. + It is also used to save the dataset to disk. + + """ + + def __init__( + self, + config: cfg.DatasetConfig, + ): + self.dataset_path = config.dataset_path + self.path_to_save_dataset = config.save_path + self.shuffle = config.shuffle + self.seed = config.seed + self.saving_frequency = config.saving_frequency + + self.dataset = {} + self.function_names = [] + self.current_function_index = 0 + self.nbr_updates = 0 + self.dataset_name = config.dataset_path.split("/")[-1].split(".")[0] + + self.cpps_path = config.cpps_path + self.cpps = {} + self.tags = config.tags + + print( + f"reading dataset in full pkl format: dataset pkl from {self.dataset_path} and cpps pkl from {self.cpps_path}" + ) + + with open(self.dataset_path, "rb") as f: + self.dataset: dict[str, dict] = pickle.load(f) + self.function_names: list[str] = list(self.dataset.keys()) + if self.tags: + print(f"Filtering dataset by tags: {self.tags}") + filtered_function_names: list[str] = [] + for program in self.function_names: + if any(tag in self.dataset[program]["tags"] for tag in self.tags): + filtered_function_names.append(program) + self.function_names = filtered_function_names + + with open(self.cpps_path, "rb") as f: + self.cpps: dict[str, str] = pickle.load(f) + + # Shuffle the dataset (can be used with random sampling turned off to get a random order) + if self.shuffle: + # Set the seed if specified (for reproducibility) + if self.seed is not None: + random.seed(self.seed) + random.shuffle(self.function_names) + + @property + def dataset_size(self): + return len(self.function_names) + + def get_next_function(self, random=False): + if random: + function_name: str = np.random.choice(self.function_names) + # Choose the next function sequentially + else: + function_name = self.function_names[ + self.current_function_index % self.dataset_size + ] + self.current_function_index += 1 + + return ( + function_name, + TiramisuProgramCache(**self.dataset[function_name]), + self.cpps[function_name], + ) + + # Update the dataset with the new function + def update_dataset(self, function_name: str, function_dict: dict) -> bool: + """ + Update the dataset with the new function + + Arguments: + function_name (str): name of the function + function_dict (dict): dictionary containing the function schedules + + Returns: + bool: True if the dataset was saved successfully + """ + for key in function_dict.keys(): + self.dataset[function_name][key] = function_dict[key] + + self.nbr_updates += 1 + # print(f"# updates: {self.nbr_updates}") + if self.nbr_updates % self.saving_frequency == 0: + if self.nbr_updates % (2 * self.saving_frequency): + return self.save_dataset_to_disk(version=2) + else: + return self.save_dataset_to_disk(version=1) + return False + + def get_function_by_name(self, function_name: str): + return ( + function_name, + TiramisuProgramCache(**self.dataset[function_name]), + self.cpps[function_name], + ) + + def save_dataset_to_disk(self, version=1) -> bool: + """ + Save the dataset to disk + :param version: version of the dataset to save (1 or 2) + :return: True if the dataset was saved successfully + """ + print("[Start] Save the legality_annotations_dict to disk") + updated_dataset_path = ( + Path(self.path_to_save_dataset) + / f"{self.dataset_name}_updated_{version}.pkl" + ) + + with updated_dataset_path.open("wb") as f: + pickle.dump(self.dataset, f, protocol=pickle.HIGHEST_PROTOCOL) + + print("[Done] Save the legality_annotations_dict to disk") + return True + + +@ray.remote +class DatasetActorRemote(DatasetActor): + def __init__( + self, + config: cfg.DatasetConfig, + ): + super().__init__(config) # pragma: no cover diff --git a/utils/dataset_actor/dataset_actor.py b/utils/dataset_actor/dataset_actor.py deleted file mode 100644 index 44452d5..0000000 --- a/utils/dataset_actor/dataset_actor.py +++ /dev/null @@ -1,55 +0,0 @@ -import ray -import config.config as cfg -import env_api.core.models.tiramisu_program as tiramisu_program -from utils.dataset_actor.services.hybrid_data_service import HybridDataService -from utils.dataset_actor.services.pickle_data_service import PickleDataService - -# Frequency at which the dataset is saved to disk -SAVING_FREQUENCY = 10000 - - -@ray.remote -class DatasetActor: - """ - DatasetActor is a class that is used to read the dataset and update it. - It is used to read the dataset from disk and update it with the new functions. - It is also used to save the dataset to disk. - - """ - - def __init__( - self, - config: cfg.DatasetConfig, - ): - if config.dataset_format == cfg.DatasetFormat.PICKLE: - self.dataset_service = PickleDataService( - config.dataset_path, - config.cpps_path, - config.save_path, - config.shuffle, - config.seed, - config.saving_frequency, - config.tags, - ) - elif config.dataset_format == cfg.DatasetFormat.HYBRID: - self.dataset_service = HybridDataService( - config.dataset_path, - config.cpps_path, - config.save_path, - config.shuffle, - config.seed, - config.saving_frequency, - ) - else: - raise ValueError("Unknown dataset format") - - def get_next_function(self, random=False) -> tiramisu_program.TiramisuProgram: - return self.dataset_service.get_next_function(random) - - # Update the dataset with the new function - def update_dataset(self, function_name: str, function_dict: dict) -> bool: - return self.dataset_service.update_dataset(function_name, function_dict) - - # Get dataset size - def get_dataset_size(self) -> int: - return self.dataset_service.dataset_size diff --git a/utils/dataset_actor/services/base_data_service.py b/utils/dataset_actor/services/base_data_service.py deleted file mode 100644 index 483c56b..0000000 --- a/utils/dataset_actor/services/base_data_service.py +++ /dev/null @@ -1,74 +0,0 @@ -from abc import abstractmethod -from pathlib import Path -import pickle -from typing import Tuple - -from env_api.core.services.tiramisu_service import TiramisuService - - -class BaseDataService: - def __init__( - self, - dataset_path: str, - path_to_save_dataset: str, - shuffle: bool = False, - seed: int = None, - saving_frequency: int = 10000, - ) -> None: - self.dataset_path = dataset_path - self.path_to_save_dataset = path_to_save_dataset - self.shuffle = shuffle - self.seed = seed - self.saving_frequency = saving_frequency - - self.dataset = {} - self.function_names = [] - self.dataset_size = 0 - self.current_function_index = 0 - self.nbr_updates = 0 - self.dataset_name = dataset_path.split("/")[-1].split(".")[0] - self.tiramisu_service = TiramisuService() - - @abstractmethod - def get_next_function(self, random=False) -> Tuple: - pass - - # Update the dataset with the new function - def update_dataset(self, function_name: str, function_dict: dict) -> bool: - """ - Update the dataset with the new function - :param function_name: name of the function - :param function_dict: dictionary containing the function schedules - :return: True if the dataset was saved successfully - """ - for key in function_dict.keys(): - self.dataset[function_name][key] = function_dict[key] - - self.nbr_updates += 1 - # print(f"# updates: {self.nbr_updates}") - if self.nbr_updates % self.saving_frequency == 0: - if self.nbr_updates % (2 * self.saving_frequency): - return self.save_dataset_to_disk(version=2) - else: - return self.save_dataset_to_disk(version=1) - return False - - # Save the dataset to disk - def save_dataset_to_disk(self, version=1) -> bool: - """ - Save the dataset to disk - :param version: version of the dataset to save (1 or 2) - :return: True if the dataset was saved successfully - """ - print("[Start] Save the legality_annotations_dict to disk") - - updated_dataset_path = ( - Path(self.path_to_save_dataset) - / f"{self.dataset_name}_updated_{version}.pkl" - ) - - with updated_dataset_path.open("wb") as f: - pickle.dump(self.dataset, f, protocol=pickle.HIGHEST_PROTOCOL) - - print("[Done] Save the legality_annotations_dict to disk") - return True diff --git a/utils/dataset_actor/services/hybrid_data_service.py b/utils/dataset_actor/services/hybrid_data_service.py deleted file mode 100644 index 480fe9f..0000000 --- a/utils/dataset_actor/services/hybrid_data_service.py +++ /dev/null @@ -1,66 +0,0 @@ -import pickle -import random -import os -import numpy as np -from env_api.core.models.tiramisu_program import TiramisuProgram -from utils.dataset_actor.services.base_data_service import ( - BaseDataService, -) - - -class HybridDataService(BaseDataService): - def __init__( - self, - dataset_path: str, - cpps_path: str, - path_to_save_dataset: str, - shuffle: bool = False, - seed: int = None, - saving_frequency: int = 10000, - ): - super().__init__( - dataset_path=dataset_path, - path_to_save_dataset=path_to_save_dataset, - shuffle=shuffle, - seed=seed, - saving_frequency=saving_frequency, - ) - self.cpps_path = cpps_path - self.cpps = {} - - print( - f"reading dataset in Hybrid format: dataset pkl from {self.dataset_path} and cpps from {self.cpps_path}" - ) - - with open(self.dataset_path, "rb") as f: - self.dataset = pickle.load(f) - self.function_names = os.listdir(self.cpps_path) - - # Shuffle the dataset (can be used with random sampling turned off to get a random order) - if self.shuffle: - # Set the seed if specified (for reproducibility) - if self.seed is not None: - random.seed(self.seed) - random.shuffle(self.function_names) - - self.dataset_size = len(self.function_names) - - # Returns next function name, function data, and function cpps - def get_next_function(self, random=False) -> TiramisuProgram: - if random: - function_name = np.random.choice(self.function_names) - # Choose the next function sequentially - else: - function_name = self.function_names[ - self.current_function_index % self.dataset_size - ] - self.current_function_index += 1 - - # print( - # f"Selected function with index: {self.current_function_index}, name: {function_name}" - # ) - if function_name in self.dataset: - # If the function doesn't exit in the dataset it will be compiled automatically - return function_name, self.dataset[function_name], None - else: - return function_name, None, None diff --git a/utils/dataset_actor/services/pickle_data_service.py b/utils/dataset_actor/services/pickle_data_service.py deleted file mode 100644 index b9e22a3..0000000 --- a/utils/dataset_actor/services/pickle_data_service.py +++ /dev/null @@ -1,75 +0,0 @@ -import pickle -import random - -import numpy as np -from env_api.core.models.tiramisu_program import TiramisuProgram -from utils.dataset_actor.services.base_data_service import ( - BaseDataService, -) - - -class PickleDataService(BaseDataService): - def __init__( - self, - dataset_path: str, - cpps_path: str, - path_to_save_dataset: str, - shuffle: bool = False, - seed: int = None, - saving_frequency: int = 10000, - tags: list[str] = [], - ): - super().__init__( - dataset_path=dataset_path, - path_to_save_dataset=path_to_save_dataset, - shuffle=shuffle, - seed=seed, - saving_frequency=saving_frequency, - ) - self.cpps_path = cpps_path - self.cpps = {} - self.tags = tags - - print( - f"reading dataset in full pkl format: dataset pkl from {self.dataset_path} and cpps pkl from {self.cpps_path}" - ) - - with open(self.dataset_path, "rb") as f: - self.dataset = pickle.load(f) - self.function_names = list(self.dataset.keys()) - if self.tags: - print(f"Filtering dataset by tags: {self.tags}") - filtered_function_names = [] - for program in self.function_names: - if any(tag in self.dataset[program]["tags"] for tag in self.tags): - filtered_function_names.append(program) - self.function_names = filtered_function_names - - with open(self.cpps_path, "rb") as f: - self.cpps = pickle.load(f) - - # Shuffle the dataset (can be used with random sampling turned off to get a random order) - if self.shuffle: - # Set the seed if specified (for reproducibility) - if self.seed is not None: - random.seed(self.seed) - random.shuffle(self.function_names) - - self.dataset_size = len(self.function_names) - - # Returns next function name, function data, and function cpps - def get_next_function(self, random=False) -> TiramisuProgram: - if random: - function_name = np.random.choice(self.function_names) - # Choose the next function sequentially - else: - function_name = self.function_names[ - self.current_function_index % self.dataset_size - ] - self.current_function_index += 1 - - # print( - # f"Selected function with index: {self.current_function_index}, name: {function_name}" - # ) - - return function_name, self.dataset[function_name], self.cpps[function_name]