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Merge branch 'main' of ssh://git@git.zaneyork.cn:2222/douzero/Douzero_Resnet.git into main

This commit is contained in:
ZaneYork 2022-01-05 09:57:37 +08:00
parent 7c420ebc9a 2b8586303b
commit afa295e42b
6 changed files with 156 additions and 121 deletions
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10
douzero/dmc/arguments.py
View File

@ -13,15 +13,17 @@ parser.add_argument('--objective', default='adp', type=str, choices=['adp', 'wp'
# Training settings
parser.add_argument('--onnx_sync_interval', default=120, type=int,
help='Time interval (in seconds) at which to sync the onnx model')
parser.add_argument('--actor_device_cpu', action='store_true',
help='Use CPU as actor device')
parser.add_argument('--gpu_devices', default='0', type=str,
help='Which GPUs to be used for training')
parser.add_argument('--infer_devices', default='0', type=str,
help='Which device to be used for infer')
parser.add_argument('--num_infer', default=2, type=int,
help='The number of process used for infer')
parser.add_argument('--num_actor_devices', default=1, type=int,
help='The number of devices used for simulation')
parser.add_argument('--num_actors', default=2, type=int,
parser.add_argument('--num_actors', default=3, type=int,
help='The number of actors for each simulation device')
parser.add_argument('--num_actors_cpu', default=1, type=int,
parser.add_argument('--num_actors_thread', default=4, type=int,
help='The number of actors for each simulation device')
parser.add_argument('--training_device', default='0', type=str,
help='The index of the GPU used for training models. `cpu` means using cpu')

83
douzero/dmc/dmc.py
View File

@ -14,7 +14,7 @@ import douzero.dmc.models
import douzero.env.env
from .file_writer import FileWriter
from .models import Model, OldModel
from .utils import get_batch, log, create_env, create_optimizers, act
from .utils import get_batch, log, create_env, create_optimizers, act, infer_logic
import psutil
import shutil
import requests
@ -25,7 +25,7 @@ def compute_loss(logits, targets):
loss = ((logits.squeeze(-1) - targets)**2).mean()
return loss
def learn(position, actor_models, model, batch, optimizer, flags, lock):
def learn(position, actor_model, model, batch, optimizer, flags, lock):
"""Performs a learning (optimization) step."""
position_index = {"landlord": 31, "landlord_up": 32, 'landlord_front': 33, "landlord_down": 34}
print("Learn", position)
@ -60,7 +60,6 @@ def learn(position, actor_models, model, batch, optimizer, flags, lock):
optimizer.step()
if not flags.enable_onnx:
for actor_model in actor_models.values():
actor_model.get_model(position).load_state_dict(model.state_dict())
return stats
@ -71,9 +70,9 @@ def train(flags):
Then it will start subprocesses as actors. Then, it will call
learning function with multiple threads.
"""
if not flags.actor_device_cpu or flags.training_device != 'cpu':
if flags.training_device != 'cpu' or flags.infer_devices != 'cpu':
if not torch.cuda.is_available():
raise AssertionError("CUDA not available. If you have GPUs, please specify the ID after `--gpu_devices`. Otherwise, please train with CPU with `python3 train.py --actor_device_cpu --training_device cpu`")
raise AssertionError("CUDA not available. If you have GPUs, please specify the ID after `--gpu_devices`. Otherwise, please train with CPU with `python3 train.py --infer_devices cpu --training_device cpu`")
plogger = FileWriter(
xpid=flags.xpid,
xp_args=flags.__dict__,
@ -85,22 +84,12 @@ def train(flags):
T = flags.unroll_length
B = flags.batch_size
if flags.actor_device_cpu:
device_iterator = ['cpu']
else:
device_iterator = range(flags.num_actor_devices) #[0, 'cpu']
assert flags.num_actor_devices <= len(flags.gpu_devices.split(',')), 'The number of actor devices can not exceed the number of available devices'
# Initialize actor models
models = {}
for device in device_iterator:
if flags.old_model:
model = OldModel(device="cpu", flags = flags, lite_model = flags.lite_model)
actor_model = OldModel(device="cpu", flags = flags, lite_model = flags.lite_model)
else:
model = Model(device="cpu", flags = flags, lite_model = flags.lite_model)
model.share_memory()
model.eval()
models[device] = model
actor_model = Model(device="cpu", flags = flags, lite_model = flags.lite_model)
actor_model.eval()
# Initialize queues
actor_processes = []
@ -114,9 +103,6 @@ def train(flags):
else:
learner_model = Model(device=flags.training_device, lite_model = flags.lite_model)
# Create optimizers
optimizers = create_optimizers(flags, learner_model)
# Stat Keys
stat_keys = [
'mean_episode_return_landlord',
@ -155,6 +141,9 @@ def train(flags):
)
onnx_frame.value = frames
# Create optimizers
optimizers = create_optimizers(flags, learner_model)
# Load models if any
if flags.load_model and os.path.exists(checkpointpath):
checkpoint_states = torch.load(
@ -164,8 +153,7 @@ def train(flags):
learner_model.get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])
optimizers[k].load_state_dict(checkpoint_states["optimizer_state_dict"][k])
if not flags.enable_onnx:
for device in device_iterator:
models[device].get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])
actor_model.get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])
stats = checkpoint_states["stats"]
frames = checkpoint_states["frames"]
@ -173,20 +161,36 @@ def train(flags):
sync_onnx_model(frames)
log.info(f"Resuming preempted job, current stats:\n{stats}")
# Starting actor processes
for device in device_iterator:
if device == 'cpu':
num_actors = flags.num_actors_cpu
else:
infer_queues = []
num_actors = flags.num_actors
for j in range(flags.num_actors_thread):
for i in range(num_actors):
infer_queues.append({
'input': ctx.Queue(maxsize=100), 'output': ctx.Queue(maxsize=100)
})
infer_processes = []
for device in flags.infer_devices.split(','):
for i in range(flags.num_infer if device != 'cpu' else 1):
infer = mp.Process(
target=infer_logic,
args=(i, device, infer_queues, actor_model, flags, onnx_frame))
infer.daemon = True
infer.start()
infer_processes.append({
'device': device,
'i': i,
'infer': infer
})
# Starting actor processes
for i in range(num_actors):
actor = mp.Process(
target=act,
args=(i, device, batch_queues, models[device], flags, onnx_frame))
args=(i, infer_queues[i * 4: (i + 1) * 4], batch_queues, flags))
actor.daemon = True
actor.start()
actor_processes.append({
'device': device,
'i': i,
'actor': actor
})
@ -201,7 +205,7 @@ def train(flags):
nonlocal frames, position_frames, stats
while frames < flags.total_frames:
batch = get_batch(batch_queues, position, flags, local_lock)
_stats = learn(position, models, learner_model.get_model(position), batch,
_stats = learn(position, actor_model, learner_model.get_model(position), batch,
optimizers[position], flags, position_lock)
with lock:
for k in _stats:
@ -215,13 +219,12 @@ def train(flags):
threads = []
locks = {}
for device in device_iterator:
locks[device] = {'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_front': threading.Lock(), 'landlord_down': threading.Lock()}
locks['cpu'] = {'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_front': threading.Lock(), 'landlord_down': threading.Lock()}
for i in range(flags.num_threads):
for position in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']:
thread = threading.Thread(
target=batch_and_learn, name='batch-and-learn-%d' % i, args=(i,position,locks[device][position],position_locks[position]))
target=batch_and_learn, name='batch-and-learn-%d' % i, args=(i,position, locks['cpu'][position],position_locks[position]))
thread.setDaemon(True)
thread.start()
threads.append(thread)
@ -305,13 +308,23 @@ def train(flags):
pprint.pformat(stats))
for proc in actor_processes:
if not proc['actor'].is_alive():
i = proc['i']
actor = mp.Process(
target=act,
args=(proc['i'], proc['device'], batch_queues, models[device], flags, onnx_frame))
args=(i, infer_queues[i * 4: (i + 1) * 4], batch_queues, flags))
actor.daemon = True
actor.start()
proc['actor'] = actor
for proc in infer_processes:
if not proc['infer'].is_alive():
infer = mp.Process(
target=infer_logic,
args=(proc['i'], proc['device'], infer_queues, actor_model, flags, onnx_frame))
infer.daemon = True
infer.start()
proc['infer'] = actor
except KeyboardInterrupt:
flags.enable_upload = False
checkpoint(frames)

21
douzero/dmc/env_utils.py
View File

@ -6,21 +6,14 @@ the environment, we do it automatically.
import numpy as np
import torch
def _format_observation(obs, device, flags):
def _format_observation(obs):
"""
A utility function to process observations and
move them to CUDA.
"""
position = obs['position']
if flags.enable_onnx:
x_batch = obs['x_batch']
z_batch = obs['z_batch']
else:
if not device == "cpu":
device = 'cuda:' + str(device)
device = torch.device(device)
x_batch = torch.from_numpy(obs['x_batch']).to(device)
z_batch = torch.from_numpy(obs['z_batch']).to(device)
x_no_action = torch.from_numpy(obs['x_no_action'])
z = torch.from_numpy(obs['z'])
obs = {'x_batch': x_batch,
@ -37,9 +30,9 @@ class Environment:
self.device = device
self.episode_return = None
def initial(self, model, device, flags=None):
obs = self.env.reset(model, device, flags=flags)
initial_position, initial_obs, x_no_action, z = _format_observation(obs, self.device, flags)
def initial(self, flags=None):
obs = self.env.reset(flags=flags)
initial_position, initial_obs, x_no_action, z = _format_observation(obs)
self.episode_return = torch.zeros(1, 1)
initial_done = torch.ones(1, 1, dtype=torch.bool)
return initial_position, initial_obs, dict(
@ -49,16 +42,16 @@ class Environment:
obs_z=z,
)
def step(self, action, model, device, flags=None):
def step(self, action, flags=None):
obs, reward, done, _ = self.env.step(action)
self.episode_return = reward
episode_return = self.episode_return
if done:
obs = self.env.reset(model, device, flags=flags)
obs = self.env.reset(flags=flags)
self.episode_return = torch.zeros(1, 1)
position, obs, x_no_action, z = _format_observation(obs, self.device, flags)
position, obs, x_no_action, z = _format_observation(obs)
# reward = torch.tensor(reward).view(1, 1)
done = torch.tensor(done).view(1, 1)

13
douzero/dmc/models.py
View File

@ -493,8 +493,11 @@ model_dict_new_lite['landlord_up'] = GeneralModelLite
model_dict_new_lite['landlord_front'] = GeneralModelLite
model_dict_new_lite['landlord_down'] = GeneralModelLite
def forward_logic(self_model, position, z, x, return_value=False, flags=None):
def forward_logic(self_model, position, z, x, device='cpu', return_value=False, flags=None):
legal_count = len(z)
if not flags.enable_onnx:
z = torch.tensor(z, device=device)
x = torch.tensor(x, device=device)
if legal_count >= 80:
partition_count = int(legal_count / 40)
sub_z = np.array_split(z, partition_count)
@ -577,8 +580,8 @@ class OldModel:
def get_onnx_params(self, position):
self.models[position].get_onnx_params(self.device)
def forward(self, position, z, x, return_value=False, flags=None):
return forward_logic(self, position, z, x, return_value, flags)
def forward(self, position, z, x, device='cpu', return_value=False, flags=None):
return forward_logic(self, position, z, x, device, return_value, flags)
def share_memory(self):
if self.models['landlord'] is not None:
@ -646,8 +649,8 @@ class Model:
def get_onnx_params(self, position):
self.models[position].get_onnx_params(self.device)
def forward(self, position, z, x, return_value=False, flags=None, debug=False):
return forward_logic(self, position, z, x, return_value, flags)
def forward(self, position, z, x, device='cpu', return_value=False, flags=None):
return forward_logic(self, position, z, x, device, return_value, flags)
def share_memory(self):
if self.models['landlord'] is not None:

65
douzero/dmc/utils.py
View File

@ -1,4 +1,6 @@
import os
import queue
import threading
import typing
import logging
import traceback
@ -111,16 +113,42 @@ def create_optimizers(flags, learner_model):
return optimizers
def act(i, device, batch_queues, model, flags, onnx_frame):
def infer_logic(i, device, infer_queues, model, flags, onnx_frame):
positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
if not flags.enable_onnx:
for pos in positions:
model.models[pos].to(torch.device(device if device == "cpu" else ("cuda:"+str(device))))
last_onnx_frame = -1
log.info('Infer %i started.', i)
while True:
# print("posi", position)
if flags.enable_onnx and onnx_frame.value != last_onnx_frame:
last_onnx_frame = onnx_frame.value
model.set_onnx_model(device)
all_empty = True
for infer_queue in infer_queues:
try:
task = infer_queue['input'].get_nowait()
with torch.no_grad():
result = model.forward(task['position'], task['z_batch'], task['x_batch'], device=device, return_value=True, flags=flags)
infer_queue['output'].put({
'values': result['values']
})
all_empty = False
except queue.Empty:
pass
if all_empty:
time.sleep(0.01)
def act_queue(i, infer_queue, batch_queues, flags):
positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
try:
T = flags.unroll_length
log.info('Device %s Actor %i started.', str(device), i)
log.info('Actor %i started.', i)
env = create_env(flags)
device = 'cpu'
env = Environment(env, device)
done_buf = {p: [] for p in positions}
@ -136,19 +164,18 @@ def act(i, device, batch_queues, model, flags, onnx_frame):
position_index = {"landlord": 31, "landlord_up": 32, "landlord_front": 33, "landlord_down": 34}
position, obs, env_output = env.initial(model, device, flags=flags)
last_onnx_frame = -1
position, obs, env_output = env.initial(flags=flags)
while True:
# print("posi", position)
if flags.enable_onnx and onnx_frame.value != last_onnx_frame:
last_onnx_frame = onnx_frame.value
model.set_onnx_model(device)
while True:
if len(obs['legal_actions']) > 1:
with torch.no_grad():
agent_output = model.forward(position, obs['z_batch'], obs['x_batch'], flags=flags)
_action_idx = int(agent_output['action'])
infer_queue['input'].put({
'position': position,
'z_batch': obs['z_batch'],
'x_batch': obs['x_batch']
})
result = infer_queue['output'].get()
action = np.argmax(result['values'], axis=0)[0]
_action_idx = int(action)
action = obs['legal_actions'][_action_idx]
else:
action = obs['legal_actions'][0]
@ -162,7 +189,7 @@ def act(i, device, batch_queues, model, flags, onnx_frame):
x_batch = env_output['obs_x_no_action'].float()
obs_x_batch_buf[position].append(x_batch)
type_buf[position].append(position_index[position])
position, obs, env_output = env.step(action, model, device, flags=flags)
position, obs, env_output = env.step(action, flags=flags)
size[position] += 1
if env_output['done']:
for p in positions:
@ -216,6 +243,18 @@ def act(i, device, batch_queues, model, flags, onnx_frame):
print()
raise e
def act(i, infer_queues, batch_queues, flags):
threads = []
for x in range(len(infer_queues)):
thread = threading.Thread(
target=act_queue, name='act_queue-%d-%d' % (i, x),
args=(x, infer_queues[x], batch_queues, flags))
thread.setDaemon(True)
thread.start()
threads.append(thread)
for thread in threads:
thread.join()
def _cards2tensor(list_cards, compress_form = False):
"""
Convert a list of integers to the tensor

17
douzero/env/env.py vendored
View File

@ -91,7 +91,7 @@ class Env:
self.total_round = 0
self.infoset = None
def reset(self, model, device, flags=None):
def reset(self, flags=None):
"""
Every time reset is called, the environment
will be re-initialized with a new deck of cards.
@ -100,21 +100,6 @@ class Env:
self._env.reset()
# Randomly shuffle the deck
if model is None:
_deck = deck.copy()
np.random.shuffle(_deck)
card_play_data = {'landlord': _deck[:33],
'landlord_up': _deck[33:58],
'landlord_front': _deck[58:83],
'landlord_down': _deck[83:108],
# 'three_landlord_cards': _deck[17:20],
}
for key in card_play_data:
card_play_data[key].sort()
self._env.card_play_init(card_play_data)
self.infoset = self._game_infoset
return get_obs(self.infoset, self.use_general, self.lite_model)
else:
self.total_round += 1
_deck = deck.copy()
np.random.shuffle(_deck)