From de90576418dc2437f6d56b5aa343e2d5a3136088 Mon Sep 17 00:00:00 2001 From: xinpw8 Date: Wed, 7 Feb 2024 08:02:22 +0000 Subject: [PATCH] cool on-map dashboard, stream_wrapper to visualize live training at https://pwhiddy.github.io/pokerl-map-viz/ --- checkpoint_dict.txt | 2 + checkpoint_file_aggregator.py | 208 + checkpoint_stats.txt | 2 + clean_pufferl.py | 298 +- config.yaml | 21 +- cv_test.py | 384 + delete_image_test_files.sh | 8 + demo.py | 2 +- kanto_map_dsv.png | Bin 0 -> 5106703 bytes log_file_aggregator.py | 209 + mat_test.py | 0 pokemon_red_eval.py | 176 +- pokerl-map-viz | 1 + .../environments/pokemon_red/__init__.py | 1 - .../environments/pokemon_red/environment.py | 63 + pufferlib/environments/pokemon_red/torch.py | 2 +- pufferlib/extensions.c | 8128 ++++++++++++++++- pufferlib/frameworks/cleanrl.py | 152 +- run.sh | 5 + stream_agent_wrapper.py | 270 + 20 files changed, 9773 insertions(+), 159 deletions(-) create mode 100644 checkpoint_dict.txt create mode 100644 checkpoint_file_aggregator.py create mode 100644 checkpoint_stats.txt create mode 100644 cv_test.py create mode 100755 delete_image_test_files.sh create mode 100644 kanto_map_dsv.png create mode 100644 log_file_aggregator.py create mode 100644 mat_test.py create mode 160000 pokerl-map-viz create mode 100755 run.sh create mode 100644 stream_agent_wrapper.py diff --git a/checkpoint_dict.txt b/checkpoint_dict.txt new file mode 100644 index 00000000..fe0a6762 --- /dev/null +++ b/checkpoint_dict.txt @@ -0,0 +1,2 @@ +defaultdict(, {'Oaks Lab': '0.04', 'Viridian City': '12.20', 'Viridian Forest': '16.52'}) +defaultdict(. at 0x7f59cd40f060>, {'Oaks Lab': {'mean': '10.76', 'variance': '74.75', 'std_dev': '8.65'}, 'Viridian City': {'mean': '17.06', 'variance': '10.79', 'std_dev': '3.29'}, 'Viridian Forest': {'mean': '20.42', 'variance': '9.71', 'std_dev': '3.12'}}) \ No newline at end of file diff --git a/checkpoint_file_aggregator.py b/checkpoint_file_aggregator.py new file mode 100644 index 00000000..1e9dfcce --- /dev/null +++ b/checkpoint_file_aggregator.py @@ -0,0 +1,208 @@ +import os +from collections import defaultdict +from pokegym import game_map +import pdb as T +import numpy as np + +# from memory_profiler import profile + +LOGGABLE_LOCATIONS = {"Viridian City": (1, None), + "Viridian Forest Entrance": (51, None), + "Viridian Forest Exit": (47, None), + "Pewter City": (2, None), + "Badge 1": (None, None), + "Route 3": (14, None), + "Mt Moon Entrance (Route 3)": (59, None), + "Mt Moon B1F": (60, None), + "Mt Moon B2F": (61, None), + "Mt Moon Exit": (59, None), + "Route 4": (15, None), + "Cerulean City": (3, None), + "Badge 2": (None, None), + "Bill": (88, None), + "Vermilion City": (5, None), + "Vermilion Harbor": (94, None), + "SS Anne Start": (95, None), + "SS Anne Captains Office": (101, None)} + +# @profile +def read_checkpoint_logs(): + checkpoint_data = defaultdict(str) + stats_data = defaultdict(lambda: {'mean': 0, 'variance': 0, 'std_dev': 0}) + + base_dir = "experiments" + file_path = "experiments/running_experiment.txt" + + with open(file_path, "r") as pathfile: + exp_uuid8 = pathfile.readline().strip() + + sessions_path = os.path.join(base_dir, exp_uuid8, "sessions") + all_location_times = defaultdict(list) + current_location_times = defaultdict(lambda: float('inf')) + + try: + # Iterate over each log file and append times to all_location_times + for folder in os.listdir(sessions_path): + session_path = os.path.join(sessions_path, folder) + checkpoint_log_file = None + for file in os.listdir(session_path): + if file.startswith("checkpoint_log") and file.endswith(".txt"): + checkpoint_log_file = os.path.join(session_path, file) + break + + if checkpoint_log_file is not None: + with open(checkpoint_log_file, 'r') as log_file: + # lines = log_file.readlines() + + for line in log_file: + line = line.strip() + if line.startswith("Location ID:"): + current_location_id = line.split(":")[-1].strip() + if current_location_id.isdigit(): + current_location_id = int(current_location_id) + if current_location_id in [map_id for location, (map_id, _) in LOGGABLE_LOCATIONS.items() if map_id is not None]: + current_location = current_location_id + else: + current_location = 40 + else: + current_location = 40 + elif line.startswith("Time Visited:") and current_location is not None: + time_visited = float(line.split(":")[-1].strip()) + current_location_times[current_location] = min(current_location_times[current_location], time_visited) + all_location_times[current_location].append(time_visited) + # print(f'all loc: {all_location_times}') + + # Update checkpoint_data with minimum times + for location_id, time_visited in current_location_times.items(): + location_name = game_map.get_map_name_from_map_n(location_id) + formatted_time = '{:.2f}'.format(time_visited / 60) + checkpoint_data[location_name] = formatted_time + + # Calculate mean, variance, and standard deviation for each location + for location_id, times in all_location_times.items(): + if times: + location_name = game_map.get_map_name_from_map_n(location_id) + + mean = np.mean(times) / 60 # Convert mean to minutes + variance = np.var(times) / 3600 # Convert variance to minutes^2 + std_dev = np.sqrt(variance) # Standard deviation in minutes + stats_data[location_name]['mean'] = '{:.2f}'.format(mean) + stats_data[location_name]['variance'] = '{:.2f}'.format(variance) + stats_data[location_name]['std_dev'] = '{:.2f}'.format(std_dev) + + + + # # Calculate mean, variance, and standard deviation for each location + # for location_id, times in all_location_times.items(): + # if times: + # location_name = game_map.get_map_name_from_map_n(location_id) + + # mean = np.mean(times) + # variance = np.var(times) + # std_dev = np.sqrt(variance) + # stats_data[location_name]['mean'] = '{:.2f}'.format(mean) + # stats_data[location_name]['variance'] = '{:.2f}'.format(variance) + # stats_data[location_name]['std_dev'] = '{:.2f}'.format(std_dev) + + return checkpoint_data, stats_data + + except Exception as e: + print("An error occurred:", e) + # Set default values for 'Viridian City' + viridian_city_id = LOGGABLE_LOCATIONS['Viridian City'][0] + checkpoint_data[game_map.get_map_name_from_map_n(viridian_city_id)] = '0.00' + + stats_data[game_map.get_map_name_from_map_n(viridian_city_id)] = { + 'mean': '0.00', + 'variance': '0.00', + 'std_dev': '0.00' + } + return checkpoint_data, stats_data + +checkpoint_dict, stats_dict = read_checkpoint_logs() +if checkpoint_dict is not None and stats_dict is not None: + with open('checkpoint_dict.txt', 'w') as f: + f.write(str(checkpoint_dict)) + f.write('\n') + f.write(str(stats_dict)) + +# if mean_sigma_dict is not None: +# with open('mean_sigma_dict.txt', 'w') as f: +# f.write(str(mean_sigma_dict)) + +# def read_checkpoint_logs(): +# checkpoint_data = defaultdict(str) +# mean_sigma_data = defaultdict(lambda: {'mean': 0, 'sigma': 0}) + +# base_dir = "experiments" +# file_path = "experiments/running_experiment.txt" + +# with open(file_path, "r") as pathfile: +# exp_uuid8 = pathfile.readline().strip() + +# sessions_path = os.path.join(base_dir, exp_uuid8, "sessions") +# output_file_path = os.path.join(base_dir, exp_uuid8, "checkpoints_log.txt") + +# try: +# for folder in os.listdir(sessions_path): +# session_path = os.path.join(sessions_path, folder) +# checkpoint_log_file = None +# for file in os.listdir(session_path): +# if file.startswith("checkpoint_log") and file.endswith(".txt"): +# checkpoint_log_file = os.path.join(session_path, file) +# break + +# if checkpoint_log_file is not None: +# with open(checkpoint_log_file, 'r') as log_file: +# lines = log_file.readlines() + +# current_location_times = defaultdict(lambda: float('inf')) +# current_location_fns = defaultdict(lambda: float('inf')) # added for mean and sigma calc + +# for line in lines: +# line = line.strip() +# if line.startswith("Location ID:"): +# current_location_id = line.split(":")[-1].strip() +# if current_location_id.isdigit() and int(current_location_id) in [map_id for location, (map_id, _) in LOGGABLE_LOCATIONS.items() if map_id is not None]: +# current_location = int(current_location_id) +# else: +# current_location = None # Skip if not a valid location ID +# elif line.startswith("Time Visited:") and current_location is not None: +# time_visited = float(line.split(":")[-1].strip()) +# current_location_times[current_location] = min(current_location_times[current_location], time_visited) +# current_location_fns[current_location] = current_location_fns[current_location], time_visited # added for mean and sigma calc + +# for location_id, time_visited in current_location_times.items(): +# location_name = game_map.get_map_name_from_map_n(location_id) +# formatted_time = '{:.2f}'.format(time_visited) + +# if location_name in checkpoint_data: +# existing_time = float(checkpoint_data[location_name]) +# if time_visited < existing_time: +# checkpoint_data[location_name] = formatted_time +# else: +# checkpoint_data[location_name] = formatted_time + +# # Logic in this for loop (uncomment) +# # for location_id, time_visited in current_location_fns.items(): +# # location_name_fn = game_map.get_map_name_from_map_n(location_id) +# # formatted_time = '{:.2f}'.format(time_visited) + +# # if location_name_fn in checkpoint_data: +# # existing_time = float(checkpoint_data[location_name_fn]) +# # if time_visited < existing_time: +# # checkpoint_data[location_name_fn] = formatted_time +# # else: +# # checkpoint_data[location_name] = formatted_time + +# # print("Final checkpoint data:", checkpoint_data) +# return checkpoint_data + +# except Exception as e: +# print("An error occurred:", e) +# return None + +# checkpoint_dict = read_checkpoint_logs() +# if checkpoint_dict is not None: +# with open('checkpoint_dict.txt', 'w') as f: +# f.write(str(checkpoint_dict)) diff --git a/checkpoint_stats.txt b/checkpoint_stats.txt new file mode 100644 index 00000000..802d6e83 --- /dev/null +++ b/checkpoint_stats.txt @@ -0,0 +1,2 @@ +defaultdict(, {'Route 2 Gate Pewter': '7462.12', 'Viridian City': '424.64', 'Route 3': '15070.86', 'Pewter City': '7464.80', 'Viridian Forest': '787.27', 'Route 4': '45421.06', 'Mt Moon Route 3': '45606.91'}) +defaultdict(. at 0x7f6e7d6aa660>, {'Route 2 Gate Pewter': {'mean': '7956.67', 'variance': '0.00', 'std_dev': '0.00'}, 'Viridian City': {'mean': '639.68', 'variance': '0.00', 'std_dev': '0.00'}, 'Route 3': {'mean': '61713.76', 'variance': '0.00', 'std_dev': '0.00'}, 'Pewter City': {'mean': '7967.26', 'variance': '0.00', 'std_dev': '0.00'}, 'Viridian Forest': {'mean': '1619.69', 'variance': '0.00', 'std_dev': '0.00'}, 'Route 4': {'mean': '62254.41', 'variance': '0.00', 'std_dev': '0.00'}, 'Mt Moon Route 3': {'mean': '45606.91', 'variance': '0.00', 'std_dev': '0.00'}}) \ No newline at end of file diff --git a/clean_pufferl.py b/clean_pufferl.py index 91c753ce..664a178f 100644 --- a/clean_pufferl.py +++ b/clean_pufferl.py @@ -1,14 +1,18 @@ from pdb import set_trace as T import numpy as np -import cv2 +import mediapy as media +import uuid +import matplotlib.pyplot as plt import os import random import time import uuid +from pathlib import Path from collections import defaultdict from datetime import timedelta +import multiprocessing import torch import torch.nn as nn @@ -21,6 +25,19 @@ import pufferlib.frameworks.cleanrl import pufferlib.policy_pool +from torch.profiler import profile, ProfilerActivity, schedule +import pokemon_red_eval as pre +# from memory_profiler import profile + +def f(input_queue, output_queue): + while True: + v = input_queue.get() + if v is None: # Termination signal + output_queue.put(None) # Signal the main process that the subprocess is exiting + break + data_image = pre.matplotlib_table_map_generate(v) + plt.close('all') + output_queue.put(data_image) @pufferlib.dataclass class Performance: @@ -41,7 +58,6 @@ class Performance: train_sps = 0 train_memory = 0 train_pytorch_memory = 0 - misc_time = 0 @pufferlib.dataclass class Losses: @@ -83,16 +99,29 @@ def init( if exp_name is None: exp_name = str(uuid.uuid4())[:8] - + + # Jerry-rigged logging to experiments/uuid8/sessions folder + exp_path = Path(f'running_experiment').with_suffix('.txt') + # exp_path = Path(f'test_exp').with_suffix('.txt') # for testing video writing BET + with open(config.data_dir / exp_path, 'w') as file: + file.write(f"{exp_name}") + wandb = None if track: import wandb - + #create process and queue + input_queue = multiprocessing.Queue() + output_queue = multiprocessing.Queue() + + worker = multiprocessing.Process(target=f, args=(input_queue, output_queue)) + worker.start() + start_time = time.time() seed_everything(config.seed, config.torch_deterministic) total_updates = config.total_timesteps // config.batch_size device = config.device + obs_device = 'cpu' if config.cpu_offload else device # Create environments, agent, and optimizer init_profiler = pufferlib.utils.Profiler(memory=True) @@ -131,17 +160,6 @@ def init( optimizer = optim.Adam(agent.parameters(), lr=config.learning_rate, eps=1e-5) - - uncompiled_agent = agent # Needed to save the model - if config.compile: - agent.get_action_and_value = torch.compile( - agent.get_action_and_value, mode=config.compile_mode) - agent = torch.compile(agent, mode=config.compile_mode) - - if config.verbose: - n_params = sum(p.numel() for p in agent.parameters() if p.requires_grad) - print(f"Model Size: {n_params//1000} K parameters") - opt_state = resume_state.get("optimizer_state_dict", None) if opt_state is not None: optimizer.load_state_dict(resume_state["optimizer_state_dict"]) @@ -164,22 +182,13 @@ def init( torch.zeros(shape).to(device), torch.zeros(shape).to(device), ) - obs=torch.zeros(config.batch_size + 1, *obs_shape) - actions=torch.zeros(config.batch_size + 1, *atn_shape, dtype=int) - logprobs=torch.zeros(config.batch_size + 1) - rewards=torch.zeros(config.batch_size + 1) - dones=torch.zeros(config.batch_size + 1) - truncateds=torch.zeros(config.batch_size + 1) - values=torch.zeros(config.batch_size + 1) - - obs_ary = np.asarray(obs) - actions_ary = np.asarray(actions) - logprobs_ary = np.asarray(logprobs) - rewards_ary = np.asarray(rewards) - dones_ary = np.asarray(dones) - truncateds_ary = np.asarray(truncateds) - values_ary = np.asarray(values) - + obs=torch.zeros(config.batch_size + 1, *obs_shape).to(obs_device) + actions=torch.zeros(config.batch_size + 1, *atn_shape, dtype=int).to(device) + logprobs=torch.zeros(config.batch_size + 1).to(device) + rewards=torch.zeros(config.batch_size + 1).to(device) + dones=torch.zeros(config.batch_size + 1).to(device) + truncateds=torch.zeros(config.batch_size + 1).to(device) + values=torch.zeros(config.batch_size + 1).to(device) storage_profiler.stop() #"charts/actions": wandb.Histogram(b_actions.cpu().numpy()), @@ -196,7 +205,6 @@ def init( config=config, pool = pool, agent = agent, - uncompiled_agent = uncompiled_agent, optimizer = optimizer, policy_pool = policy_pool, @@ -207,6 +215,8 @@ def init( losses = Losses(), init_performance = init_performance, performance = Performance(), + output_queue = output_queue, + input_queue = input_queue, # Storage sort_keys = [], @@ -217,19 +227,13 @@ def init( rewards = rewards, dones = dones, values = values, - obs_ary = obs_ary, - actions_ary = actions_ary, - logprobs_ary = logprobs_ary, - rewards_ary = rewards_ary, - dones_ary = dones_ary, - truncateds_ary = truncateds_ary, - values_ary = values_ary, # Misc total_updates = total_updates, update = update, global_step = global_step, device = device, + obs_device = obs_device, start_time = start_time, ) @@ -237,7 +241,9 @@ def init( def evaluate(data): config = data.config # TODO: Handle update on resume + if data.wandb is not None and data.performance.total_uptime > 0: + data.wandb data.wandb.log({ 'SPS': data.SPS, 'global_step': data.global_step, @@ -249,35 +255,33 @@ def evaluate(data): **{f'skillrank/{policy}': elo for policy, elo in data.policy_pool.ranker.ratings.items()}, }) - + data.policy_pool.update_policies() performance = defaultdict(list) env_profiler = pufferlib.utils.Profiler() inference_profiler = pufferlib.utils.Profiler() eval_profiler = pufferlib.utils.Profiler(memory=True, pytorch_memory=True).start() - misc_profiler = pufferlib.utils.Profiler() ptr = step = padded_steps_collected = agent_steps_collected = 0 infos = defaultdict(lambda: defaultdict(list)) - while True: + while True: step += 1 if ptr == config.batch_size + 1: break - + with env_profiler: o, r, d, t, i, env_id, mask = data.pool.recv() - with misc_profiler: - i = data.policy_pool.update_scores(i, "return") + i = data.policy_pool.update_scores(i, "return") with inference_profiler, torch.no_grad(): o = torch.as_tensor(o) r = torch.as_tensor(r).float().to(data.device).view(-1) d = torch.as_tensor(d).float().to(data.device).view(-1) - agent_steps_collected += sum(mask) - padded_steps_collected += len(mask) - + agent_steps_collected += sum(mask) + padded_steps_collected += len(mask) + with inference_profiler, torch.no_grad(): # Multiple policies will not work with new envpool next_lstm_state = data.next_lstm_state if next_lstm_state is not None: @@ -296,37 +300,31 @@ def evaluate(data): value = value.flatten() - - with misc_profiler: - actions = actions.cpu().numpy() - - # Index alive mask with policy pool idxs... - # TODO: Find a way to avoid having to do this - learner_mask = torch.Tensor(mask * data.policy_pool.mask) - - # Ensure indices do not exceed batch size - indices = torch.where(learner_mask)[0][:config.batch_size - ptr + 1].numpy() - end = ptr + len(indices) - - # Batch indexing - data.obs_ary[ptr:end] = o.cpu().numpy()[indices] - data.values_ary[ptr:end] = value.cpu().numpy()[indices] - data.actions_ary[ptr:end] = actions[indices] - data.logprobs_ary[ptr:end] = logprob.cpu().numpy()[indices] - data.rewards_ary[ptr:end] = r.cpu().numpy()[indices] - data.dones_ary[ptr:end] = d.cpu().numpy()[indices] - data.sort_keys.extend([(env_id[i], step) for i in indices]) - - # Update pointer - ptr += len(indices) - - for policy_name, policy_i in i.items(): - for agent_i in policy_i: - for name, dat in unroll_nested_dict(agent_i): - infos[policy_name][name].append(dat) + # Index alive mask with policy pool idxs... + # TODO: Find a way to avoid having to do this + learner_mask = mask * data.policy_pool.mask + + for idx in np.where(learner_mask)[0]: + if ptr == config.batch_size + 1: + break + data.obs[ptr] = o[idx] + data.values[ptr] = value[idx] + data.actions[ptr] = actions[idx] + data.logprobs[ptr] = logprob[idx] + data.sort_keys.append((env_id[idx], step)) + if len(d) != 0: + data.rewards[ptr] = r[idx] + data.dones[ptr] = d[idx] + ptr += 1 + + + for policy_name, policy_i in i.items(): + for agent_i in policy_i: + for name, dat in unroll_nested_dict(agent_i): + infos[policy_name][name].append(dat) with env_profiler: - data.pool.send(actions) + data.pool.send(actions.cpu().numpy()) eval_profiler.stop() @@ -345,34 +343,61 @@ def evaluate(data): perf.eval_sps = int(padded_steps_collected / eval_profiler.elapsed) perf.eval_memory = eval_profiler.end_mem perf.eval_pytorch_memory = eval_profiler.end_torch_mem - perf.misc_time = misc_profiler.elapsed data.stats = {} + + # @Leanke: Add your infos['learner']['x'] etc for k, v in infos['learner'].items(): - if 'Task_eval_fn' in k: - # Temporary hack for NMMO competition - continue if 'pokemon_exploration_map' in k: - import cv2 - from pokemon_red_eval import make_pokemon_red_overlay - bg = cv2.imread('kanto_map_dsv.png') - overlay = make_pokemon_red_overlay(bg, sum(v)) - if data.wandb is not None: - data.stats['Media/exploration_map'] = data.wandb.Image(overlay) - # @Leanke: Add your infos['learner']['x'] etc + # Send input data to worker for processing + data.input_queue.put(sum(v)) + # Get output data_image from worker + data_image = data.output_queue.get() + else: + data_image = None try: # TODO: Better checks on log data types data.stats[k] = np.mean(v) except: continue + # Upload to wandb if available + if data.wandb is not None and data_image is not None: + data.stats['Media/exploration_map'] = data.wandb.Image(data_image) + data_image = None + plt.close('all') + + +# # multiprocessing below +# for k, v in infos['learner'].items(): +# if 'pokemon_exploration_map' in k: +# # overlay = pre.make_pokemon_red_overlay(sum(v)) +# data_image = pre.matplotlib_table_map_generate(sum(v)) + +# # Upload to wandb if available +# if data.wandb is not None: +# data.stats['Media/exploration_map'] = data.wandb.Image(data_image) + +# # @Leanke: Add your infos['learner']['x'] etc +# try: # TODO: Better checks on log data types +# data.stats[k] = np.mean(v) +# except: +# continue + + # Close all open + +# multiprocessing above + if config.verbose: print_dashboard(data.stats, data.init_performance, data.performance) return data.stats, infos +# @profile @pufferlib.utils.profile def train(data): if done_training(data): + assert data.output_queue.get() is None, "Expected termination signal from subprocess" + data.wandb_process.join() raise RuntimeError( f"Max training updates {data.total_updates} already reached") @@ -381,6 +406,25 @@ def train(data): train_profiler = pufferlib.utils.Profiler(memory=True, pytorch_memory=True) train_profiler.start() + # # Anneal learning rate + # # Cosine annealing test + # # torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max, eta_min=0, last_epoch=-1, verbose=False) + # cosine_anneal = torch.optim.lr_scheduler.CosineAnnealingLR(data.optimizer, data.total_updates, eta_min=0.000047, last_epoch=-1, verbose=True) + # # print(f'cosine_anneal.get_last_lr()={cosine_anneal.get_last_lr()}') + # lrnow = int(cosine_anneal.get_last_lr()[0]) * int(config.learning_rate) + # data.optimizer.param_groups[0]["lr"] = lrnow + + # if config.anneal_lr: + # cosine_anneal = torch.optim.lr_scheduler.CosineAnnealingLR(data.optimizer, data.total_updates, eta_min=0.000047, last_epoch=-1, verbose=True) + # # print(f'cosine_anneal.get_last_lr()={cosine_anneal.get_last_lr()}') + # lrnow = int(cosine_anneal.get_last_lr()[0]) * int(config.learning_rate) + # data.optimizer.param_groups[0]["lr"] = lrnow + + # Default lr + frac = 1.0 - (data.update - 1.0) / data.total_updates + lrnow = frac * config.learning_rate + data.optimizer.param_groups[0]["lr"] = lrnow + if config.anneal_lr: frac = 1.0 - (data.update - 1.0) / data.total_updates lrnow = frac * config.learning_rate @@ -414,15 +458,11 @@ def train(data): ) # Flatten the batch - data.b_obs = b_obs = torch.Tensor(data.obs_ary[b_idxs]) - b_actions = torch.Tensor(data.actions_ary[b_idxs] - ).to(data.device, non_blocking=True) - b_logprobs = torch.Tensor(data.logprobs_ary[b_idxs] - ).to(data.device, non_blocking=True) - b_dones = torch.Tensor(data.dones_ary[b_idxs] - ).to(data.device, non_blocking=True) - b_values = torch.Tensor(data.values_ary[b_idxs] - ).to(data.device, non_blocking=True) + data.b_obs = b_obs = data.obs[b_idxs] + b_actions = data.actions[b_idxs] + b_logprobs = data.logprobs[b_idxs] + b_dones = data.dones[b_idxs] + b_values = data.values[b_idxs] b_advantages = advantages.reshape( config.batch_rows, num_minibatches, config.bptt_horizon ).transpose(0, 1) @@ -431,22 +471,36 @@ def train(data): # Optimizing the policy and value network train_time = time.time() pg_losses, entropy_losses, v_losses, clipfracs, old_kls, kls = [], [], [], [], [], [] - mb_obs_buffer = torch.zeros_like(b_obs[0], pin_memory=True) - + # mb_obs_buffer = torch.zeros_like(b_obs[0], pin_memory=True) + mb_obs_buffer = torch.zeros_like(b_obs[0], pin_memory=(data.device=="cuda")) + for epoch in range(config.update_epochs): + # with profile( + # activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], + # schedule=schedule(wait=1, warmup=0, active=2, repeat=1), + # with_modules=True, + # with_stack=True, + # ) as prof: lstm_state = None for mb in range(num_minibatches): - mb_obs_buffer.copy_(b_obs[mb], non_blocking=True) - mb_obs = mb_obs_buffer.to(data.device, non_blocking=True) - #mb_obs = b_obs[mb].to(data.device, non_blocking=True) + # mb_obs = b_obs[mb].to(self.device, non_blocking=True) + mb_obs_buffer.copy_(b_obs[mb]) # sorry thatguy, non_blocking=True) + mb_obs = mb_obs_buffer.to(data.device) # sorry thatguy , non_blocking=True) mb_actions = b_actions[mb].contiguous() mb_values = b_values[mb].reshape(-1) mb_advantages = b_advantages[mb].reshape(-1) mb_returns = b_returns[mb].reshape(-1) - if hasattr(data.agent, 'lstm'): - _, newlogprob, entropy, newvalue, lstm_state = data.agent.get_action_and_value( - mb_obs, state=lstm_state, action=mb_actions) + if hasattr(data.agent, "lstm"): + ( + _, + newlogprob, + entropy, + newvalue, + lstm_state, + ) = data.agent.get_action_and_value( + mb_obs, state=lstm_state, action=mb_actions + ) lstm_state = (lstm_state[0].detach(), lstm_state[1].detach()) else: _, newlogprob, entropy, newvalue = data.agent.get_action_and_value( @@ -506,6 +560,9 @@ def train(data): nn.utils.clip_grad_norm_(data.agent.parameters(), config.max_grad_norm) data.optimizer.step() + # prof.step() + # prof.export_chrome_trace("train.json") + # raise if config.target_kl is not None: if approx_kl > config.target_kl: break @@ -541,6 +598,35 @@ def train(data): if data.update % config.checkpoint_interval == 0 or done_training(data): save_checkpoint(data) + # BET + infos = defaultdict(lambda: defaultdict(list)) + data_image = None + if data.update % 10 == 0 or done_training: + # Jerry-rigged logging cuz lazy + exp_path = Path(f'run_stats').with_suffix('.txt') + with open(config.data_dir / exp_path, 'w') as file: + file.write(f"{perf.epoch_sps}") + + # @Leanke: Add your infos['learner']['x'] etc + for k, v in infos['learner'].items(): + if 'pokemon_exploration_map' in k: + # Send input data to worker for processing + data.input_queue.put(sum(v)) + # Get output data_image from worker + data_image = data.output_queue.get() + else: + data_image = None + try: # TODO: Better checks on log data types + data.stats[k] = np.mean(v) + except: + continue + + # Upload to wandb if available + if data.wandb is not None and data_image is not None: + data.stats['Media/exploration_map'] = data.wandb.Image(data_image) + data_image = None + plt.close('all') + def close(data): data.pool.close() @@ -607,7 +693,7 @@ def save_checkpoint(data): if os.path.exists(model_path): return model_path - torch.save(data.uncompiled_agent, model_path) + torch.save(data.agent, model_path) state = { "optimizer_state_dict": data.optimizer.state_dict(), diff --git a/config.yaml b/config.yaml index 139469d1..560d401f 100644 --- a/config.yaml +++ b/config.yaml @@ -6,7 +6,7 @@ train: learning_rate: 2.5e-4 num_steps: 128 anneal_lr: True - gamma: 0.99 + gamma: 0.999 # 0.998 # 0.99 #BET testing 2/6/24 gae_lambda: 0.95 num_minibatches: 4 update_epochs: 4 @@ -24,7 +24,7 @@ train: env_pool: True verbose: True data_dir: experiments - checkpoint_interval: 200 + checkpoint_interval: 100 pool_kernel: [0] batch_size: 1024 batch_rows: 32 @@ -668,15 +668,16 @@ connect4: pokemon_red: package: pokemon_red train: - total_timesteps: 100_000_000 - num_envs: 128 - envs_per_worker: 4 - envs_per_batch: 48 - update_epochs: 3 - gamma: 0.998 - batch_size: 32768 - batch_rows: 128 + total_timesteps: 300_000_000 + num_envs: 128 # 128 # 256 # 128 + envs_per_worker: 8 # 4 + envs_per_batch: 32 # envpool batch size + update_epochs: 3 # 3 + gamma: 0.998 # 0.998 + batch_size: 65536 # 32768 + batch_rows: 128 # 128 compile: True + cpu_offload: True env: name: pokemon_red pokemon-red: diff --git a/cv_test.py b/cv_test.py new file mode 100644 index 00000000..8b9bafe0 --- /dev/null +++ b/cv_test.py @@ -0,0 +1,384 @@ +import cv2 +import numpy as np +# import pathlib as Path +from checkpoint_file_aggregator import read_checkpoint_logs +import matplotlib.pyplot as plt +import pandas as pd +from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas + +# import matplotlib.pyplot as plt +# import pandas as pd +# from matplotlib.gridspec import GridSpec +# import io +# from PIL import Image + +# from memory_profiler import profile +# import tracemalloc + +# tracemalloc.start() +# snapshot1=tracemalloc.take_snapshot() +bg = cv2.imread('kanto_map_dsv.png') # 142 MiB + +# @profile + +# Code previously being used to write the aggregated checkpoints data to image using cv2.imwrite +def create_data_image(width, height): + data_image = np.zeros((height, width, 3), dtype=np.uint8) + return data_image + +def get_text_width(text, fontdict): + # A simple approximation: assume each character is equal width + # This is a simplification and may not be accurate for all fonts + # You can replace this with a more sophisticated method if needed + return len(text) * fontdict['fontsize'] * 1.5 # Adjust the multiplier as needed + +# snapshot2=tracemalloc.take_snapshot() +# @profile +def make_pokemon_red_overlay(counts): + nonzero = np.where(counts > 0, 1, 0) + scaled = np.clip(counts, 0, 1000) / 1000.0 + + # Convert counts to hue map + hsv = np.zeros((*counts.shape, 3)) + hsv[..., 0] = (240.0 / 360) - scaled * (240.0 / 360.0) # bad heatmap with too much icky light green 2*(1-scaled)/3 + hsv[..., 1] = nonzero + hsv[..., 2] = nonzero + + # Convert the HSV image to RGB + import matplotlib.colors as mcolors + overlay = 255*mcolors.hsv_to_rgb(hsv) + + # Upscale to 16x16 + kernel = np.ones((16, 16, 1), dtype=np.uint8) + overlay = np.kron(overlay, kernel).astype(np.uint8) + mask = np.kron(nonzero, kernel[..., 0]).astype(np.uint8) + mask = np.stack([mask, mask, mask], axis=-1).astype(bool) + + # Combine with background + render = bg.copy().astype(np.int32) + render[mask] = 0.2*render[mask] + 0.8*overlay[mask] + render = np.clip(render, 0, 255).astype(np.uint8) + return render +# snapshot3=tracemalloc.take_snapshot() + +import random +# snapshot5=tracemalloc.take_snapshot() +# @profile +counts_map = np.zeros((444, 436)) +# Iterate over the range for x and y separately +for x in range(1, 164): # Range for x + for y in range(318, 418): # Range for y + # Increment counts_map at position (x, y) by a random number between 10 and 1000 + counts_map[x, y] += random.randint(10, 1000) + +def matplotlib_table_map_generate(counts): + i = 0 + while i < 1: + # Read the checkpoints data + try: + time_checkpoint, stats_checkpoint = read_checkpoint_logs() + except Exception as e: + print(f"Failed to read checkpoint logs: {e}") + time_checkpoint, stats_checkpoint = {}, {} + + # Read the epoch sps data + try: + with open("experiments/run_stats.txt", "r") as file: + epoch_sps = file.readline().strip() + except Exception as e: + print(f"Failed to read epoch sps data: {e}") + epoch_sps = "Unavailable" + + # Assuming there's data in the checkpoints, proceed to create the DataFrame + if time_checkpoint and stats_checkpoint: + # Extract data for table + milestones = list(time_checkpoint.keys()) + times = [time_checkpoint[milestone] for milestone in milestones] + means = [stats_checkpoint[milestone]['mean'] for milestone in milestones] + # variances = [stats_checkpoint[milestone]['variance'] for milestone in milestones] + std_devs = [stats_checkpoint[milestone]['std_dev'] for milestone in milestones] + + data = { + 'Milestone': milestones, + 'Time (min)': times, + 'Mean': means, + # 'Variance': variances, + 'Std Dev': std_devs + } + df = pd.DataFrame(data) + else: + print("Checkpoint data is empty. Creating an empty DataFrame.") + df = pd.DataFrame() + + plt.style.use("dark_background") + fig, (table_ax, img_ax) = plt.subplots( + 1, 2, figsize=(32, 22), gridspec_kw={'width_ratios': [1, 2]} + ) + + # Print the Epoch SPS at the top left of the whole image + fig.text(0.005, 0.995, f'Epoch SPS: {epoch_sps}', color='0.35', fontsize=40, ha='left', va='top') + + table_ax.axis("off") + font_size = 30 + + # fontdict = {'fontsize': 30} + get_font_dict = lambda x: {'fontsize': x} + + # Calculate relative column widths + widths = [] + # widths_1 = [] + for col in df.columns: + # max_width_1 = max([get_text_width_1(str(x), get_font_dict(font_size)) for x in df[col].tolist() + [col]]) + max_width = max([get_text_width(str(x), get_font_dict(font_size)) for x in df[col].tolist() + [col]]) + # print(f'max_width={max_width}') + # print(f'max_width_1={max_width_1}') + widths.append(max_width) + # widths_1.append(max_width_1) + + total_width = sum(widths) + # total_width_1 = sum(widths_1) + rel_widths = [w / total_width for w in widths] + # rel_widths_1 = [w / total_width_1 for w in widths_1] + + rel_widths[0] = rel_widths[0] * 1.1 + rel_widths[2] = rel_widths[2] * 1.1 + # rel_widths_1[0] = rel_widths_1[0] * 1.1 + + # print(f'rel_widths = {rel_widths}') + # print(f'rel_widths_1 = {rel_widths_1}') + + cell_height = 0.035 # Convert font size in points to inches + + # Create the table with relative column widths + the_table = table_ax.table(cellText=df.values, colLabels=df.columns, loc='upper center', colWidths=rel_widths) + + # Set table style + the_table.auto_set_font_size(False) + the_table.set_fontsize(font_size) + + # Define the colors for headings and different columns + heading_color = '#ff7f0e' # Deep blue for the headings + column_colors = ['#1f77b4', '#2ca02c', '#9467bd', '#8c564b'] # Orange, Green, Purple, Brown + + edge_color = '0.75' + + # Iterate over the cells and set colors + for (row, col), cell in the_table.get_celld().items(): + if row == 0: # This is a heading + cell.get_text().set_color(heading_color) + cell.set_facecolor('black') # Heading background color + cell.set_edgecolor('white') + else: # These are data cells + cell_color = column_colors[col] if col < len(column_colors) else 'black' # Default to black if no color is defined + cell.get_text().set_color(cell_color) + cell.set_facecolor('black') # Data cell background color + cell.set_edgecolor(f'{edge_color}') + + cell.set_height(cell_height) + + # Image subplot + # img = plt.imread("kanto_map_dsv.png") + img = make_pokemon_red_overlay(counts) + img_ax.imshow(img) + img_ax.axis("off") + + fig.tight_layout() + + # Save the figure to a NumPy array + fig.canvas.draw() + width, height = fig.get_size_inches() * fig.get_dpi() + table_image_rgba = np.frombuffer(fig.canvas.buffer_rgba(), dtype=np.uint8) + table_image_rgba = table_image_rgba.reshape(int(height), int(width), -1) + + plt.close('all') + cv2.destroyAllWindows() + return table_image_rgba + +# Run the function and save the image +print_table_image = matplotlib_table_map_generate(counts_map) +cv2.imwrite('image_test_data_image_cv_test_NEW.png', print_table_image) +cv2.waitKey(1) +# # Attempt to create the figure with the data image and table +# plt.style.use("dark_background") +# fig, (table_ax, img_ax) = plt.subplots( +# 1, 2, figsize=(32, 22), width_ratios=[1.1, 3.25], subplot_kw={"anchor": "N"} +# ) + +# # Add the text above the table +# if epoch_sps: +# fig.text(0.05, 0.95, f'Epoch SPS: {epoch_sps}', color='white', fontsize=12, ha='left', va='top') + +# # First subplot: Table +# table_ax.axis("off") + +# # Calculate the offset in figure coordinate space so table isn't edge-magnetic +# pixel_offset = -50 # The desired pixel offset, positive value to move right +# dpi = fig.get_dpi() # Get the DPI of the figure to convert pixels to inches +# inch_offset = pixel_offset / dpi # Convert pixel offset to inches +# # Get the current bounds of the table subplot +# pos = table_ax.get_position() +# # Adjust the left bound by the inch_offset +# new_pos = [pos.x0 + inch_offset / fig.get_figwidth(), pos.y0, pos.width, pos.height] +# # Set the new position of the table subplot +# table_ax.set_position(new_pos) + +# fontdict = {'fontsize': 20} # Customize as needed + +# # F +# # font_dict = {'fontsize': 20} +# text_width = get_text_width('text_here', fontdict) +# text_width = int(text_width) + +# widths = [] +# for col in df.columns: +# # Get the width of each text entry in the column, including the header +# text_widths = [get_text_width(str(x), fontdict) for x in df[col].tolist() + [col]] +# # Find the maximum width for the column +# max_width = max(text_widths) +# widths.append(max_width) + +# # Normalize the widths to sum to 1 to get relative column widths +# total_width = sum(widths) +# rel_widths = [w / total_width for w in widths] + +# # widths = [] +# # for col in df.columns: +# # max_width = max([get_text_width(str(x), fontdict) for x in df[col].tolist() + [col]]) +# # widths.append(max_width) +# # widths.append(text_width) + +# print(f'text_width: {text_width}') +# # Normalize the widths to the sum to get relative column widths +# print(f'widths: {widths}') +# # total_width = sum(widths) +# # rel_widths = [w / total_width for w in widths] +# print(f'rel_widths: {rel_widths}') + + +# # Calculate relative column widths to size columns to largest cell contents +# # widths = [] +# # t_widths = [] +# # total_width = 0 +# # t_total_width = 0 +# # for col in df.columns: +# # text_width = [max(get_text_width(len(str(x)), font_dict)) for x in df[col]] +# # max_width = max([len(str(x)) for x in df[col]]) + +# # widths.append(max_width) +# # t_widths.append(text_width) + +# # print(f'widths: {widths}') +# # print(f't_widths: {t_widths}') + +# # total_width += (max_width) +# # t_total_width += int((t_widths)) + +# # rel_widths = [w/total_width for w in widths] +# # t_rel_widths = [w/t_total_width for w in t_widths] + +# # print(f'rel_widths: {rel_widths}') +# # print(f't_rel_widths: {t_rel_widths}') + + # rel_widths[0] = rel_widths[0] * 0.74 + # t_rel_widths[0] = rel_widths[0] * 1 + +# # print(f'rel_widths={rel_widths}') +# # print(f't_rel_widths={t_rel_widths}') + +# rel_widths: [0.36106340937668403, 0.2083707562421412, 0.1905873899766481, 0.1904077600143704] +# # Create the table with relative column widths +# the_table = table_ax.table(cellText=df.values, colLabels=df.columns, loc="upper center", colWidths=rel_widths) + +# cell_height = 0.025 # Convert font size in points to inches + +# # Set table style for white text on black background +# the_table.auto_set_font_size(False) +# the_table.set_fontsize(30) +# for key, cell in the_table.get_celld().items(): +# cell.get_text().set_color('white') +# cell.set_facecolor('black') +# cell.set_edgecolor('white') + +# for pos, cell in the_table.get_celld().items(): +# cell.set_height(cell_height) + +# # Adjust the scale of the table to fit the content +# the_table.scale(1.35, 1.35) + +# # Second subplot: Image +# img = plt.imread("kanto_map_dsv.png") +# img_ax.imshow(img) +# img_ax.axis("off") + +# fig.tight_layout() + +# # Convert the figure to a NumPy array using buffer_rgba +# fig.canvas.draw() +# width, height = fig.get_size_inches() * fig.get_dpi() +# table_image_rgba = np.frombuffer(fig.canvas.buffer_rgba(), dtype=np.uint8) +# table_image_rgba = table_image_rgba.reshape(int(height), int(width), -1) + +# i += 1 +# return table_image_rgba + +# plt.close('all') +# cv2.destroyAllWindows() +# table_image_rgba = None + +# print_table_image = matplotlib_table_map_generate() +# cv2.imwrite(f'image_test_data_image_cv_test_NEW.png', print_table_image) +# cv2.waitKey(1) + + + # fig.savefig("image_thatguy.png") + + + # import pokemon_red_eval as pre + # # bg = cv2.imread('kanto_map_dsv.png') + # counts_map = np.zeros((444, 436)) + # overlay = make_pokemon_red_overlay(counts_map) + + # # BET + # di_height = 6976 * 1.5 + + # # Make the underlay that contains the dashboard to go to left of map + # # data_image = np.zeros((int(di_height), 7104, 3), dtype=np.uint8) + # data_image = create_data_image(int(di_height), 7104) + + # # Define the position where the data image will be rendered + # x_position = 6976 + # y_position = 7104 + + # # Calculate the starting position for rendering the data image + # start_x = x_position - data_image.shape[1] + # start_y = y_position - data_image.shape[0] + + # # Ensure the data image fits within the bounds of the background image + # end_x = min(start_x + data_image.shape[1], overlay.shape[1]) + # end_y = min(start_y + data_image.shape[0], overlay.shape[0]) + + # # Calculate the region of interest on the background image + # roi_start_x = max(start_x, 0) + # roi_end_x = min(end_x, overlay.shape[1]) + # roi_start_y = max(start_y, 0) + # roi_end_y = min(end_y, overlay.shape[0]) + + # # Calculate the corresponding region on the data image + # data_roi_start_x = roi_start_x - start_x + # data_roi_end_x = data_roi_start_x + (roi_end_x - roi_start_x) + # data_roi_start_y = roi_start_y - start_y + # data_roi_end_y = data_roi_start_y + (roi_end_y - roi_start_y) + + # # Overlay the background image onto the dashboard underlay + # # bg[roi_start_y:roi_end_y, roi_start_x:roi_end_x] += data_image[data_roi_start_y:data_roi_end_y, data_roi_start_x:data_roi_end_x] + # data_image[data_roi_start_y:data_roi_end_y, data_roi_start_x:data_roi_end_x] += overlay[roi_start_y:roi_end_y, roi_start_x:roi_end_x] + + # # Place matplotlib table onto the combined image + # data_image = matplotlib_table_map_generate(data_image) + # cv2.imwrite(f'image_test_data_image_cv_test_NEW.png',data_image) + # cv2.waitKey(1) + # try: + # data_image = matplotlib_table_map_generate(data_image) + # except: + # pass + # return data_image \ No newline at end of file diff --git a/delete_image_test_files.sh b/delete_image_test_files.sh new file mode 100755 index 00000000..7e644f9f --- /dev/null +++ b/delete_image_test_files.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +# Directory where the files are located +DIRECTORY="/puffertank/pufferlib" + +# Delete files starting with "image_test_data" and ending with ".png" +find "$DIRECTORY" -maxdepth 1 -type f -name 'image_test_data*.png' -delete +# find "$DIRECTORY" -maxdepth 1 -type f -name 'image_test_data*.png' -exec echo "Deleting file: {}" \; \ No newline at end of file diff --git a/demo.py b/demo.py index 8d104667..5ff78217 100644 --- a/demo.py +++ b/demo.py @@ -153,7 +153,7 @@ def train(args, env_module, make_env): parser.add_argument('--no-render', action='store_true', help='Disable render during evaluate') parser.add_argument('--exp-name', type=str, default=None, help="Resume from experiment") parser.add_argument('--vectorization', type=str, default='serial', help='Vectorization method (serial, multiprocessing, ray)') - parser.add_argument('--wandb-entity', type=str, default='jsuarez', help='WandB entity') + parser.add_argument('--wandb-entity', type=str, default='xinpw8', help='WandB entity') 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