From 74b31af51031c2b7cf15a2393abf024e1d8c3a21 Mon Sep 17 00:00:00 2001 From: rushichavda <82209751+rushichavda@users.noreply.github.com> Date: Thu, 3 Aug 2023 19:15:57 +0530 Subject: [PATCH 1/7] Uploading installaiton.md --- installation.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 installation.md diff --git a/installation.md b/installation.md new file mode 100644 index 00000000..b0310e49 --- /dev/null +++ b/installation.md @@ -0,0 +1,12 @@ +# Installation for Ubuntu VM +## Step-by-Step Guide + +If you are reading this, we suppose you have ready to run Ubuntu on VM, in your system. if not, then go to some recent youtube videos and make sure you have Ubuntu successfully running on VM. + +1. Open Your Virtual Machine +2. Open Terminal (ctrl + T) +3. Clone this repo https://github.com/google/CFU-Playground by using ```git clone https://github.com/google/CFU-Playground``` +4. Go to this directory "CFU-Playground/third_party/python/vizier/" using ```cd CFU-Playground/third_party/python/vizier/``` +5. Now you will see that there is a CFU-Playground. Now go to "CFU-Playground/third_party/python/vizier/" and see if vizier folder is empty or not! ![Alt text](./docs/installation_images/file_preview.png?raw=true "Title") + + From a81d0079137e7e24b96cbdf984a8fdc253027e50 Mon Sep 17 00:00:00 2001 From: rushichavda <82209751+rushichavda@users.noreply.github.com> Date: Thu, 3 Aug 2023 19:31:00 +0530 Subject: [PATCH 2/7] Create file_preview.png --- docs/installation_images/file_preview.png | 1 + 1 file changed, 1 insertion(+) create mode 100644 docs/installation_images/file_preview.png diff --git a/docs/installation_images/file_preview.png b/docs/installation_images/file_preview.png new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/docs/installation_images/file_preview.png @@ -0,0 +1 @@ + From 8661b2d0dc26fa35d465a89706e9fff84c256772 Mon Sep 17 00:00:00 2001 From: rushichavda <82209751+rushichavda@users.noreply.github.com> Date: Thu, 3 Aug 2023 19:31:33 +0530 Subject: [PATCH 3/7] uploaded file_preview --- 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zX`lUP_DZiD4=1DwPYZ^c5u@)I;dmh|M{WfJj0b>9gD-^3RHS(kAUB%Ejg4zxaMq8m zCO4v`hQ`e5OHcSprzW%`ZS!BnmigTq9EkZK4q7PXX}fxdZNB=|fx2ToOqK{g&#@41 zL7lSsO-yMgLNjUiljp{&I(-F;4ZL_W@bYwp+PSMvNt=_0-#sK)68r9BNY}R^&Jm00C8&;^}S0LbV@aj;d zI5K?zZ)-R$v&~I9H{xH@@{w=8)693!dFL70ZqoU!ZgBn!_($4)-2H6fE9}Vq{pI(M zf4=<2ciBJwEQjjJSBi_^qFY|IgUCF-VW2fvm;4zTtbkcLv^#8T}; zK#Ke;;F>E(zm0x9Aa(pc4^EA0D7Fa)GyHe^@A;WBI{2XppB@cSuEt-`4fn#c;+ z72kEl#;-IE!6FlMYsF}+dD9Pn_9<9F9vpeb!A+|+<>SE7Pv-&cOlEwu6Fh%Ee#@@` zgi6eDwHtSKQ9g~q10KMkQ=NTc$vj8~#~kB5KE?|hk2P-!?k7xvCj;Mn`4?4*4l$dn RfmHwi002ovPDHLkV1l#jJxc%p literal 1 Icmd-A000XB3jhEB From 6ca05ec1616ef1cd85d2921cb153cc0ca8785a22 Mon Sep 17 00:00:00 2001 From: rushichavda <82209751+rushichavda@users.noreply.github.com> Date: Thu, 3 Aug 2023 19:47:17 +0530 Subject: [PATCH 4/7] Update installation.md --- installation.md | 1 - 1 file changed, 1 deletion(-) diff --git a/installation.md b/installation.md index b0310e49..17b41851 100644 --- a/installation.md +++ b/installation.md @@ -9,4 +9,3 @@ If you are reading this, we suppose you have ready to run Ubuntu on VM, in your 4. Go to this directory "CFU-Playground/third_party/python/vizier/" using ```cd CFU-Playground/third_party/python/vizier/``` 5. Now you will see that there is a CFU-Playground. Now go to "CFU-Playground/third_party/python/vizier/" and see if vizier folder is empty or not! ![Alt text](./docs/installation_images/file_preview.png?raw=true "Title") - From fa4291dc9013f2dcdde11753cef611c7d1c24262 Mon Sep 17 00:00:00 2001 From: rushichavda <82209751+rushichavda@users.noreply.github.com> Date: Fri, 4 Aug 2023 00:47:41 +0530 Subject: [PATCH 5/7] Update installation.md --- installation.md | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/installation.md b/installation.md index 17b41851..7d236e50 100644 --- a/installation.md +++ b/installation.md @@ -7,5 +7,29 @@ If you are reading this, we suppose you have ready to run Ubuntu on VM, in your 2. Open Terminal (ctrl + T) 3. Clone this repo https://github.com/google/CFU-Playground by using ```git clone https://github.com/google/CFU-Playground``` 4. Go to this directory "CFU-Playground/third_party/python/vizier/" using ```cd CFU-Playground/third_party/python/vizier/``` -5. Now you will see that there is a CFU-Playground. Now go to "CFU-Playground/third_party/python/vizier/" and see if vizier folder is empty or not! ![Alt text](./docs/installation_images/file_preview.png?raw=true "Title") +5. Now you will see that there is a CFU-Playground folder. Now go to "CFU-Playground/third_party/python/vizier/" and see if vizier folder is empty or not! ![Alt text](./docs/installation_images/file_preview.png?raw=true "Title") +10. Go to python folder using terminal (location: CFU-Playground/third_party/python/) using command like ```cd CFU-Playground/third_party/python/``` +11. run ```rm -rf vizier``` +13. Clone vizier repo, run ```git clone https://github.com/ShvetankPrakash/vizier``` +15. ```cd CFU-Playground/``` -> ```cd scripts/``` -> Run setup_vizier.sh file using command ```./setup_vizier.sh``` +19. Might give some errors, therefore activate conda environment/ or create one, If you have existing environments, you can find the list using this command ```conda env list``` if you don't find one, create using following commands: Open anaconda terminal and run ```conda create -n myenv```. Replace myenv with the environment name. now activate that enviroment using ```conda activate myenv``` + 1. Install anaconda + 2. +22. Now run ```./setup_vizier.sh```again +23. Some Errors might occur due to version of python. +24. run ```sudo apt install build-essential``` +25. run ```pip install cvxopt``` +26. run ```export CVXOPT_BUILD_FFTW=1``` +27. ```pip install cvxopt --no-binary cvxopt``` +28. ```conda install -c conda-forge cvxopt``` +29. ```./setup_vizier.sh``` +30. Might give errors related to ale-py +31. Go to file requirements-benchmarks.txt: +32. third party -> python -> vizier -> vizier -> requirements-benchmarks.txt +33. Comment out all the lines +34. Now running setup_vizier.sh file would not give ale-py error +35. Now, try some other example - go to CFU-Playground -> proj -> dse_template -> vizier_dse.py +36. Go to line 40, comment it out and add this line +37. cycles, cells = 1, 1 +38. Run this file. It should run successfully without any errors. From 1cc7b19dd3cb5a1a823e0467aea308d28aa4f620 Mon Sep 17 00:00:00 2001 From: yash Date: Sun, 13 Aug 2023 05:12:05 +0000 Subject: [PATCH 6/7] Installation commands for setting up a new VM machine in arch-gym --- aco/DeepSwarm/deepswarm/backends.py | 2 +- arch_gym/envs/AstraSimEnv.py | 17 ++- bo/AstraSimEstimator.py | 225 ++++++++++++++++++++++++++++ new_VM_setup_archgym.md | 66 ++++++++ sims/AstraSim/run_general.sh | 2 +- sims/AstraSim/trainBOAstraSim.py | 122 +++++++++++++++ 6 files changed, 424 insertions(+), 10 deletions(-) create mode 100644 bo/AstraSimEstimator.py create mode 100644 new_VM_setup_archgym.md create mode 100644 sims/AstraSim/trainBOAstraSim.py diff --git a/aco/DeepSwarm/deepswarm/backends.py b/aco/DeepSwarm/deepswarm/backends.py index 4d541a5b..ace3edd2 100644 --- a/aco/DeepSwarm/deepswarm/backends.py +++ b/aco/DeepSwarm/deepswarm/backends.py @@ -364,7 +364,7 @@ def __init__(self, path, exp_name, traject_dir, log_dir, reward_formulation, use # SET UP ACTION DICT self.action_dict = {"network": {}, "workload": {}} self.action_dict["network"]['path'] = "3d_fc_ring_switch.json" - self.action_dict["workload"]['path'] = "gnmt_fp16_fused.txt" + self.action_dict["workload"]['path'] = "all_reduce/allreduce_0.65.txt" # PARSE SYSTEM FILE self.parse_system(self.system_file, self.action_dict) diff --git a/arch_gym/envs/AstraSimEnv.py b/arch_gym/envs/AstraSimEnv.py index f561e495..737c5b25 100644 --- a/arch_gym/envs/AstraSimEnv.py +++ b/arch_gym/envs/AstraSimEnv.py @@ -18,7 +18,7 @@ class AstraSimEnv(gym.Env): def __init__(self, rl_form="random_walker", max_steps=5, num_agents=1, reward_formulation="None", reward_scaling=1): # action space = set of all possible actions. Space.sample() returns a random action - self.action_space = gym.spaces.Discrete(2) + self.action_space = gym.spaces.Discrete(16) # observation space = set of all possible observations self.observation_space = gym.spaces.Discrete(1) @@ -107,9 +107,9 @@ def close(self): def calculate_reward(self, observations): print("Calculating reward...") print(observations) - sum = 0 + sum = 1.0 for obs in observations: - sum += ((float(obs[0]) - 1) ** 2) + sum += ((float(obs) - 1) ** 2) print(sum) return 1 / (sum ** 0.5) @@ -119,6 +119,7 @@ def step(self, action_dict): # write the three config files # with open(self.network_config, "w") as outfile: # outfile.write(json.dumps(action_dict['network'], indent=4)) + print(action_dict) if "path" in action_dict["network"]: self.network_config = action_dict["network"]["path"] @@ -178,17 +179,17 @@ def step(self, action_dict): len(detailed) == 0 or len(end_to_end) == 0 or len(sample_all_reduce_dimension_utilization) == 0)): # set reward to be extremely negative - reward = -100000 + reward = float("-inf") print("reward: ", reward) return [[], reward, self.done, {"useful_counter": self.useful_counter}, self.state] else: # only recording the first line because apparently they are all the same? TODO self.observations = [ backend_end_to_end["CommsTime"][0], - end_to_end["fwd compute"][0], - end_to_end["wg compute"][0], - end_to_end["ig compute"][0], - end_to_end["total exposed comm"][0] + # end_to_end["fwd compute"][0], + # end_to_end["wg compute"][0], + # end_to_end["ig compute"][0], + # end_to_end["total exposed comm"][0] ] reward = self.calculate_reward(self.observations) print("reward: ", reward) diff --git a/bo/AstraSimEstimator.py b/bo/AstraSimEstimator.py new file mode 100644 index 00000000..9e0b6c0a --- /dev/null +++ b/bo/AstraSimEstimator.py @@ -0,0 +1,225 @@ +from sklearn.base import BaseEstimator, ClassifierMixin +import os +os.sys.path.insert(0, os.path.abspath('/../arch_gym/envs/')) +os.sys.path.insert(0, os.path.abspath('/../')) +from configs import arch_gym_configs +import json +from arch_gym.envs.envHelpers import helpers +from arch_gym.envs.AstraSimEnv import AstraSimEnv +from arch_gym.envs.AstraSimWrapper import make_astraSim_env +import configparser +import envlogger +import sys +import numpy as np +import pandas as pd +import time + +from absl import logging +from absl import flags + + +class AstraSimEstimator(BaseEstimator): + + def __init__(self, scheduling_policy="FIFO", collective_optimization="baseline", + intra_dimension_scheduling="FIFO", inter_dimension_scheduling="baseline", + exp_name="test", traject_dir="traj"): + + ''' All the default values of AstraSim should be initialized here. + Take all the parameters here and write it to the config files + ''' + # To do: Implement some default parameters + self.env = AstraSimEnv() + self.helper = helpers() + self.action_dict = {} + + settings_file_path = os.path.realpath(__file__) + settings_dir_path = os.path.dirname(settings_file_path) + proj_root_path = os.path.join(settings_dir_path, '..') + astrasim_archgym = os.path.join(proj_root_path, "sims/AstraSim/astrasim-archgym") + + # TODO: V1 SPEC: + archgen_v1_knobs = os.path.join(astrasim_archgym, "dse/archgen_v1_knobs") + knobs_spec = os.path.join(archgen_v1_knobs, "archgen_v1_knobs_spec.py") + networks_folder = os.path.join(archgen_v1_knobs, "templates/network") + systems_folder = os.path.join(astrasim_archgym, "themis/inputs/system") + workloads_folder = os.path.join(astrasim_archgym, "themis/inputs/workload") + + + self.network_file = "4d_ring_fc_ring_switch.json" + self.system_file = os.path.join(systems_folder, "4d_ring_fc_ring_switch_baseline.txt") + self.workload_file = "all_reduce/allreduce_0.20.txt" + + # self.action_dict['network'] = {"path": self.network_file} + # self.action_dict['workload'] = {"path": self.workload_file} + + # self.parse_system(self.system_file, self.action_dict) + + # self.action_dict["system"]["scheduling-policy"] = scheduling_policy + self.action_dict["scheduling_policy"] = scheduling_policy + self.action_dict["collective_optimization"] = collective_optimization + self.action_dict["intra_dimension_scheduling"] = intra_dimension_scheduling + self.action_dict["inter_dimension_scheduling"] = inter_dimension_scheduling + + self.exp_name = exp_name + self.traject_dir = traject_dir + self.fitness_hist = [] + self.exp_log_dir = os.path.join(os.getcwd(), "bo_logs") + self.reward_formulation = 'power' + + print("[Experiment]: ", self.exp_name) + print("[Trajectory Log path]: ", self.traject_dir) + + + self.bo_steps=0 + + + def parse_system(self, system_file, action_dict): + # parse system_file (above is the content) into dict + action_dict['system'] = {} + with open(system_file, 'r') as file: + lines = file.readlines() + + for line in lines: + key, value = line.strip().split(': ') + action_dict['system'][key] = value + + + def wrap_in_envlogger(self, env, envlogger_dir, use_envlogger): + metadata = { + 'agent_type': 'Bayesian Optimization', + 'env_type': type(env).__name__, + } + if use_envlogger == 'True': + logging.info('Wrapping environment with EnvironmentLogger...') + env = envlogger.EnvLogger(env, + data_directory=envlogger_dir, + max_episodes_per_file=1000, + metadata=metadata) + logging.info('Done wrapping environment with EnvironmentLogger.') + return env + else: + print("Not using envlogger") + return env + + + def fit (self, X, y=None): + ''' + 1) Call the AstraSim simulator and return performance, power, and energy + 2) The parameter must be updated before the calling the AstraSim simulator + 3) X is the trace files (.e., Workload) + ''' + self.bo_steps += 1 + + def step_fn(unused_timestep, unused_action, unused_env): + return {'timestamp': time.time()} + + reward = 0 + self.fitness_hist = {} + + # read from the config file + config = configparser.ConfigParser() + config.read("exp_config.ini") + + # read the all the parameters from exp_config.ini + traj_dir = config.get("experiment_configuration", "trajectory_dir") + exp_name = config.get("experiment_configuration", "exp_name") + log_dir = config.get("experiment_configuration", "log_dir") + reward_formulation = config.get("experiment_configuration", "reward_formulation") + use_envlogger = config.get("experiment_configuration", "use_envlogger") + + env_wrapper = make_astraSim_env(reward_formulation = reward_formulation, + rl_form = 'bo') + + # check if trajectory directory exists + if use_envlogger == 'True': + if not os.path.exists(traj_dir): + os.makedirs(traj_dir) + # check if log directory exists + if not os.path.exists(log_dir): + os.makedirs(log_dir) + + env = self.wrap_in_envlogger(env_wrapper, self.exp_log_dir, use_envlogger) + env.reset() + print("Action dict: ", self.action_dict) + + # convert the action dict to a list with the same order + # action_list = [] + + actual_action = {} + actual_action['network'] = {"path": self.network_file} + actual_action['workload'] = {"path": self.workload_file} + self.parse_system(self.system_file, actual_action) + + actual_action["system"]["scheduling-policy"] = self.action_dict["scheduling_policy"] + actual_action["system"]["collective-optimization"] = self.action_dict["collective_optimization"] + actual_action["system"]["intra-dimension-scheduling"] = self.action_dict["intra_dimension_scheduling"] + actual_action["system"]["inter-dimension-scheduling"] = self.action_dict["inter_dimension_scheduling"] + + _, reward, _, info = env.step(actual_action) + + self.fitness_hist['reward'] = reward + self.fitness_hist['action'] = self.action_dict + self.fitness_hist['obs'] = info + + fitness_filename = os.path.join(self.exp_name) + + # logging twice due to the cv. So we will track the bo_steps and log only once + if self.bo_steps == 1: + self.log_fitness_to_csv(log_dir) + + # clear the self.fitness_hist + self.fitness_hist = [] + + return reward + + def predict(self, X, y): + return NotImplementedError + + def score(self,X, y=None): + return NotImplementedError + + def get_params(self, deep=False): + return { + "scheduling_policy": self.action_dict["scheduling_policy"], + "collective_optimization": self.action_dict["collective_optimization"], + "intra_dimension_scheduling": self.action_dict["intra_dimension_scheduling"], + "inter_dimension_scheduling": self.action_dict["inter_dimension_scheduling"] + } + + def set_params(self, **params): + """ + scheduling-policy: LIFO + endpoint-delay: 1 + active-chunks-per-dimension: 1 + preferred-dataset-splits: 64 + boost-mode: 1 + all-reduce-implementation: direct_ring_halvingDoubling + all-gather-implementation: direct_ring_halvingDoubling + reduce-scatter-implementation: direct_ring_halvingDoubling + all-to-all-implementation: direct_direct_direct + collective-optimization: localBWAware + intra-dimension-scheduling: FIFO + inter-dimension-scheduling: baseline + """ + _params = params + self.action_dict["scheduling_policy"] = _params["scheduling_policy"] + self.action_dict["collective_optimization"] = _params["collective_optimization"] + self.action_dict["intra_dimension_scheduling"] = _params["intra_dimension_scheduling"] + self.action_dict["inter_dimension_scheduling"] = _params["inter_dimension_scheduling"] + + return self + + + def calculate_reward(self, energy, latency): + sum = ((float(latency) - 1) ** 2) + return 1 / (sum ** 0.5) + + + def log_fitness_to_csv(self, filename): + df = pd.DataFrame([self.fitness_hist['reward']]) + csvfile = os.path.join(filename, "fitness.csv") + df.to_csv(csvfile, index=False, header=False, mode='a') + + df = pd.DataFrame([self.fitness_hist]) + csvfile = os.path.join(filename, "actions.csv") + df.to_csv(csvfile, index=False, header=False, mode='a') diff --git a/new_VM_setup_archgym.md b/new_VM_setup_archgym.md new file mode 100644 index 00000000..2e943bf4 --- /dev/null +++ b/new_VM_setup_archgym.md @@ -0,0 +1,66 @@ +# Setting Up a New Virtual Machine and Arch Gym Env Installation ! + +Follow the below instruction to setup a remote access of virtual machine and peform the necessary intallation for creating arch-gym enviroment + + +## Initializing and Starting VM +1. Generating the ssh public and private key using : `ssh-keygen -t rsa -b 2048 -C [USERNAME]` +2. Get the ssh access from adminstrator +3. Open terminal and run :`ssh -i ` . Example : `ssh -i C:\Users\yashc\.ssh/id_rsa yashc@34.83.98.10` +4. Open VS code and download Remote-SSH extension by microsoft +5. Press F1 select Remote-SSH: Connect to Host...use the same `USERNAME@IP_ADDRESS` as in step 2 +6. New VS Code window will be opened and If VS Code cannot automatically detect the type of server you are connecting to, you will be asked to select the type manually. + +## Installing Conda +In terminal run the following commands to install conda for your remote virtual machine +1. `curl -O https://repo.anaconda.com/archive/Anaconda3-2023.07-1-Linux-x86_64.sh` +2. `sha256sum Anaconda3-2023.07-1-Linux-x86_64.sh` +3. `bash Anaconda3-2023.07-1-Linux-x86_64.sh ( press yes and enter for all steps )` +4. `source ~/.bashrc` + + +## Creating Arch-Gym Enviroment +Follow the below steps for setting up vizier and arch-gym. In terminal +1. `git clone https://github.com/srivatsankrishnan/oss-arch-gym.git` +2. `cd oss-arch-gym/` +3. `conda env create -f environment.yml` +4. `conda activate arch-gym` +5. `cd ..` +6. `git clone https://github.com/ShvetankPrakash/vizier.git` +7. cd into vizier directory +8. `sudo apt-get install -y libprotobuf-dev` +9. `pip install -r requirements.txt --use-deprecated=legacy-resolver` ( you may see some package compatibility issues, ignore them ) +10. `pip install -e .` ( you may see some package compatibility issues, ignore them ) +11. `./build_protos.sh` +12. `pip install -r requirements-algorithms.txt` (you may probably end up with gcc compiler issue, ignore as of now) +13. `pip install -r requirements-benchmarks.txt` ( you may see some package compatibility issues, ignore them ) +14. Open VS code and make a copy of this script: [https://github.com/google/CFU-Playground/blob/main/proj/dse_template/vizier_dse.py](https://github.com/google/CFU-Playground/blob/main/proj/dse_template/vizier_dse.py) +15. Remove line 10 of your local copy +16. Go to line 40, comment it out and add this line `cycles, cells = 1, 1` +17. In terminal Run `python vizier_dse.py` to test working. Note : all this should be done with arch-gym virtual env activated only +18. If you get ModuleNotFoundError: No module named 'emukit'. Run `pip install emukit` +19. If pip install emukit throws error related to gcc compiler, then try to install it using this - `sudo apt update && sudo apt install -y build-essential` +20. Run `pip install emukit` again +21. Run `python vizier_dse.py` to test its working +The output should look like +`Suggested Parameters (bypass, cfu, dCacheSize, hardwareDiv, iCacheSize, mulDiv, prediction, safe, singleCycleShift, singleCycleMulDiv): True False 8192.0 True 4096.0 True static False True False.............` + + +## Testing Overall Installation + +Come out of vizier directory in terminal using `cd ..` + +1. `cd oss-arch-gym/acme` +2. `pip install .[jax,tf,testing,envs]` +3. `which python` + Output eg : `/home/yashc/anaconda3/envs/arch-gym/bin/python` + Replace `bin/python` with `lib` and copy it : `/home/yashc/anaconda3/envs/arch-gym/lib` +#### In VS Code +1. Go to .bashrc file inside your username folder ( for me its yashc) +2. Paste this in last : `export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/home//anaconda3/envs/arch-gym/lib/"` + +#### In terminal +Remember that your arch-gym env should be activated all the times +1. Run `sudo apt-get install libgmp-dev` +2. `cd oss-arch-gym/sims/customenv` +3. Rull all the Python files ( all should run without error ) \ No newline at end of file diff --git a/sims/AstraSim/run_general.sh b/sims/AstraSim/run_general.sh index 740af128..72b54b54 100755 --- a/sims/AstraSim/run_general.sh +++ b/sims/AstraSim/run_general.sh @@ -8,7 +8,7 @@ SCRIPT_DIR=$(dirname "$(realpath $0)") BINARY="${SCRIPT_DIR:?}"/astrasim-archgym/astra-sim/build/astra_analytical/build/AnalyticalAstra/bin/AnalyticalAstra SYSTEM="${SCRIPT_DIR:?}"/general_system.txt NETWORK="${SCRIPT_DIR:?}"/astrasim-archgym/themis/inputs/network/analytical/$1 -WORKLOAD="${SCRIPT_DIR:?}"/astrasim-archgym/themis/inputs/workload/realworld_workloads/$3 +WORKLOAD="${SCRIPT_DIR:?}"/astrasim-archgym/themis/inputs/workload/$3 echo "SH NETWORK: ${NETWORK}" echo "SH SYSTEM: ${SYSTEM}" diff --git a/sims/AstraSim/trainBOAstraSim.py b/sims/AstraSim/trainBOAstraSim.py new file mode 100644 index 00000000..dee3daf7 --- /dev/null +++ b/sims/AstraSim/trainBOAstraSim.py @@ -0,0 +1,122 @@ +#!/usr/bin/env python3 +from skopt import BayesSearchCV +from skopt.space import Real, Categorical, Integer +from sklearn.metrics import make_scorer +import os +import sys +os.sys.path.insert(0, os.path.abspath('../../')) +os.sys.path.insert(0, os.path.abspath('../../arch_gym')) +from arch_gym.envs.envHelpers import helpers +from bo.AstraSimEstimator import AstraSimEstimator + +import configparser +import numpy as np +import time +import pandas as pd +from absl import flags +from absl import app + +flags.DEFINE_string('workload', 'resnet18', 'Workload trace file') +flags.DEFINE_integer('layer_id', 2, 'Layer id') +flags.DEFINE_integer('num_iter', 16, 'Number of training steps.') +flags.DEFINE_integer('random_state', 2, 'Random state.') +flags.DEFINE_string('traject_dir', 'bo_trajectories', 'Directory to store data.') +flags.DEFINE_string('exp_config_file', 'exp_config.ini', 'Experiment config file.') +flags.DEFINE_string('summary_dir', ".", 'Directory to store data.') +flags.DEFINE_string('reward_formulation', 'power', 'Reward formulation') +flags.DEFINE_bool('use_envlogger', True, 'Use EnvLogger to log environment data.') +FLAGS = flags.FLAGS + + +def scorer(estimator, X, y=None): + """ + Custom scorer for AstraSimEstimator. Default is to maximize the fitness. + If the reward formulation is to maximize, then the fitness is returned as is. + If the reward formulation is to minimize, then the fitness is returned as -1*fitness. + """ + # definition of "good" score is minimum + # but default is higher score is better so * -1 for our purposes + return 1 * estimator.fit(X, y) + + +def find_best_params_test(X, parameters, n_iter, seed, exp_name, traject_dir, exp_log_dir): + + model = AstraSimEstimator(scheduling_policy = parameters['scheduling_policy'], + collective_optimization = parameters['collective_optimization'], + intra_dimension_scheduling = parameters['intra_dimension_scheduling'], + inter_dimension_scheduling = parameters['inter_dimension_scheduling'], + exp_name= exp_name, traject_dir= traject_dir) + + # use config parser to update its parameters + config = configparser.ConfigParser() + config.read(FLAGS.exp_config_file) + config.set("experiment_configuration", "exp_name", str(exp_name)) + config.set("experiment_configuration", "trajectory_dir", str(traject_dir)) + config.set("experiment_configuration", "log_dir", str(exp_log_dir)) + config.set("experiment_configuration", "reward_formulation", str(FLAGS.reward_formulation)) + config.set("experiment_configuration", "use_envlogger", str(FLAGS.use_envlogger)) + + # write the updated config file + with open(FLAGS.exp_config_file, 'w') as configfile: + config.write(configfile) + + # Note need to use scipy=1.5.2 & scikit-learn=0.23.2 for this, see: + # https://github.com/scikit-optimize/scikit-optimize/issues/978 + opt = BayesSearchCV( + estimator=model, + search_spaces = parameters, + n_iter=FLAGS.num_iter, + random_state=FLAGS.random_state, + scoring=scorer, + n_jobs=1, + cv = 2, + ) + + # executes bayesian optimization + opt.fit(X) + print(opt.best_params_) + + return opt.best_params_ + + +def main(_): + + # To do : Configure the workload trace here + dummy_X = np.array([1,2,3,4,5,6]) + + # helper + h = helpers() + + # get the dimensions of the layers + astrasim_helpers = helpers() + dimension,_ = astrasim_helpers.get_dimensions(FLAGS.workload, FLAGS.layer_id) + + # define architectural parameters to search over + parameters = {"scheduling_policy": Categorical(["FIFO", "LIFO"]), + "collective_optimization": Categorical(["localBWAware", "baseline"]), + "intra_dimension_scheduling": Categorical(["FIFO", "SCF"]), + "inter_dimension_scheduling": Categorical(["baseline", "themis"]) + } + + # Construct the exp name from seed and num_iter + exp_name = str(FLAGS.workload) + "_random_state_" + str(FLAGS.random_state) + "_num_iter_" + str(FLAGS.num_iter) + + # get the current working directory and append the exp name + traject_dir = os.path.join(FLAGS.summary_dir, FLAGS.traject_dir, FLAGS.reward_formulation, exp_name) + + # log directories for storing exp csvs + exp_log_dir = os.path.join(FLAGS.summary_dir, "bo_logs", FLAGS.reward_formulation, exp_name) + + print("Trajectory directory: " + traject_dir) + + find_best_params_test(dummy_X, parameters, + FLAGS.num_iter, + FLAGS.random_state, + exp_name, + traject_dir, + exp_log_dir + ) + + +if __name__ == '__main__': + app.run(main) \ No newline at end of file From 11d9c64914bc11c8d69d2a1e0f309070c55152dd Mon Sep 17 00:00:00 2001 From: yash Date: Thu, 17 Aug 2023 05:50:23 +0000 Subject: [PATCH 7/7] Updated installation instruction --- new_VM_setup_archgym.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/new_VM_setup_archgym.md b/new_VM_setup_archgym.md index 2e943bf4..51363c88 100644 --- a/new_VM_setup_archgym.md +++ b/new_VM_setup_archgym.md @@ -6,9 +6,9 @@ Follow the below instruction to setup a remote access of virtual machine and pef ## Initializing and Starting VM 1. Generating the ssh public and private key using : `ssh-keygen -t rsa -b 2048 -C [USERNAME]` 2. Get the ssh access from adminstrator -3. Open terminal and run :`ssh -i ` . Example : `ssh -i C:\Users\yashc\.ssh/id_rsa yashc@34.83.98.10` -4. Open VS code and download Remote-SSH extension by microsoft -5. Press F1 select Remote-SSH: Connect to Host...use the same `USERNAME@IP_ADDRESS` as in step 2 +3. Open terminal and run :`ssh -i ` +4. Open VS code and download Remote-SSH extension by microsoft +5. Press F1 select Remote-SSH: Connect to Host...use the same `USERNAME@IP_ADDRESS` as in step 2 6. New VS Code window will be opened and If VS Code cannot automatically detect the type of server you are connecting to, you will be asked to select the type manually. ## Installing Conda @@ -53,10 +53,10 @@ Come out of vizier directory in terminal using `cd ..` 1. `cd oss-arch-gym/acme` 2. `pip install .[jax,tf,testing,envs]` 3. `which python` - Output eg : `/home/yashc/anaconda3/envs/arch-gym/bin/python` - Replace `bin/python` with `lib` and copy it : `/home/yashc/anaconda3/envs/arch-gym/lib` + Output eg : `/home//anaconda3/envs/arch-gym/bin/python` + Replace `bin/python` with `lib` and copy it : `/home//anaconda3/envs/arch-gym/lib` #### In VS Code -1. Go to .bashrc file inside your username folder ( for me its yashc) +1. Go to .bashrc file inside your username folder 2. Paste this in last : `export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/home//anaconda3/envs/arch-gym/lib/"` #### In terminal