slm_lab/env/vec_env.py
# Wrappers for parallel vector environments.
# Adapted from OpenAI Baselines (MIT) https://github.com/openai/baselines/tree/master/baselines/common/vec_env
from abc import ABC, abstractmethod
from collections import OrderedDict
from functools import partial
from gym import spaces
from slm_lab.env.wrapper import make_gym_env
from slm_lab.lib import logger
import contextlib
import ctypes
import gym
import numpy as np
import os
import torch.multiprocessing as mp
_NP_TO_CT = {
np.float32: ctypes.c_float,
np.int32: ctypes.c_int32,
np.int8: ctypes.c_int8,
np.uint8: ctypes.c_char,
np.bool: ctypes.c_bool,
}
# helper methods
@contextlib.contextmanager
def clear_mpi_env_vars():
'''
from mpi4py import MPI will call MPI_Init by default. If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang.
This context manager is a hacky way to clear those environment variables temporarily such as when we are starting multiprocessing Processes.
'''
removed_environment = {}
for k, v in list(os.environ.items()):
for prefix in ['OMPI_', 'PMI_']:
if k.startswith(prefix):
removed_environment[k] = v
del os.environ[k]
try:
yield
finally:
os.environ.update(removed_environment)
def copy_obs_dict(obs):
'''Deep-copy an observation dict.'''
return {k: np.copy(v) for k, v in obs.items()}
def dict_to_obs(obs_dict):
'''Convert an observation dict into a raw array if the original observation space was not a Dict space.'''
if set(obs_dict.keys()) == {None}:
return obs_dict[None]
return obs_dict
def obs_to_dict(obs):
'''Convert an observation into a dict.'''
if isinstance(obs, dict):
return obs
return {None: obs}
def obs_space_info(obs_space):
'''
Get dict-structured information about a gym.Space.
@returns (keys, shapes, dtypes)
- keys: a list of dict keys.
- shapes: a dict mapping keys to shapes.
- dtypes: a dict mapping keys to dtypes.
'''
if isinstance(obs_space, gym.spaces.Dict):
assert isinstance(obs_space.spaces, OrderedDict)
subspaces = obs_space.spaces
else:
subspaces = {None: obs_space}
keys = []
shapes = {}
dtypes = {}
for key, box in subspaces.items():
keys.append(key)
shapes[key] = box.shape
dtypes[key] = box.dtype
return keys, shapes, dtypes
def tile_images(img_nhwc):
'''
Tile N images into a rectangular grid for rendering
@param img_nhwc list or array of images, with shape (batch, h, w, c)
@returns bigim_HWc ndarray with shape (h',w',c)
'''
img_nhwc = np.asarray(img_nhwc)
N, h, w, c = img_nhwc.shape
H = int(np.ceil(np.sqrt(N)))
W = int(np.ceil(float(N) / H))
img_nhwc = np.array(list(img_nhwc) + [img_nhwc[0] * 0 for _ in range(N, H * W)])
img_HWhwc = img_nhwc.reshape(H, W, h, w, c)
img_HhWwc = img_HWhwc.transpose(0, 2, 1, 3, 4)
img_Hh_Ww_c = img_HhWwc.reshape(H * h, W * w, c)
return img_Hh_Ww_c
def subproc_worker(
pipe, parent_pipe, env_fn_wrapper,
obs_bufs, obs_shapes, obs_dtypes, keys):
'''
Control a single environment instance using IPC and shared memory. Used by ShmemVecEnv.
'''
def _write_obs(maybe_dict_obs):
flatdict = obs_to_dict(maybe_dict_obs)
for k in keys:
dst = obs_bufs[k].get_obj()
dst_np = np.frombuffer(dst, dtype=obs_dtypes[k]).reshape(obs_shapes[k])
np.copyto(dst_np, flatdict[k])
env = env_fn_wrapper.x()
parent_pipe.close()
try:
while True:
cmd, data = pipe.recv()
if cmd == 'reset':
pipe.send(_write_obs(env.reset()))
elif cmd == 'step':
obs, reward, done, info = env.step(data)
if done:
obs = env.reset()
pipe.send((_write_obs(obs), reward, done, info))
elif cmd == 'render':
pipe.send(env.render(mode='rgb_array'))
elif cmd == 'close':
pipe.send(None)
break
else:
raise RuntimeError(f'Got unrecognized cmd {cmd}')
except KeyboardInterrupt:
logger.exception('ShmemVecEnv worker: got KeyboardInterrupt')
finally:
env.close()
# vector environment wrappers
class CloudpickleWrapper(object):
'''
Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
'''
def __init__(self, x):
self.x = x
def __getstate__(self):
import cloudpickle
return cloudpickle.dumps(self.x)
def __setstate__(self, ob):
import pickle
self.x = pickle.loads(ob)
class VecEnv(ABC):
'''
An abstract asynchronous, vectorized environment.
Used to batch data from multiple copies of an environment, so that each observation becomes an batch of observations, and expected action is a batch of actions to be applied per-environment.
'''
closed = False
viewer = None
metadata = {
'render.modes': ['human', 'rgb_array']
}
def __init__(self, num_envs, observation_space, action_space):
self.num_envs = num_envs
self.observation_space = observation_space
self.action_space = action_space
@abstractmethod
def reset(self):
'''
Reset all the environments and return an array of observations, or a dict of observation arrays.
If step_async is still doing work, that work will be cancelled and step_wait() should not be called until step_async() is invoked again.
'''
pass
@abstractmethod
def step_async(self, actions):
'''
Tell all the environments to start taking a step with the given actions.
Call step_wait() to get the results of the step.
You should not call this if a step_async run is already pending.
'''
pass
@abstractmethod
def step_wait(self):
'''
Wait for the step taken with step_async().
@returns (obs, rews, dones, infos)
- obs: an array of observations, or a dict of arrays of observations.
- rews: an array of rewards
- dones: an array of 'episode done' booleans
- infos: a sequence of info objects
'''
pass
def close_extras(self):
'''
Clean up the extra resources, beyond what's in this base class.
Only runs when not self.closed.
'''
pass
def close(self):
if self.closed:
return
if self.viewer is not None:
self.viewer.close()
self.close_extras()
self.closed = True
def step(self, actions):
'''
Step the environments synchronously.
This is available for backwards compatibility.
'''
self.step_async(actions)
return self.step_wait()
def render(self, mode='human'):
imgs = self.get_images()
bigimg = tile_images(imgs)
if mode == 'human':
self.get_viewer().imshow(bigimg)
return self.get_viewer().isopen
elif mode == 'rgb_array':
return bigimg
else:
raise NotImplementedError
def get_images(self):
'''Return RGB images from each environment'''
raise NotImplementedError
@property
def unwrapped(self):
if isinstance(self, VecEnvWrapper):
return self.venv.unwrapped
else:
return self
def get_viewer(self):
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.SimpleImageViewer()
return self.viewer
class DummyVecEnv(VecEnv):
'''
VecEnv that does runs multiple environments sequentially, that is, the step and reset commands are send to one environment at a time.
Useful when debugging and when num_envs == 1 (in the latter case, avoids communication overhead)
'''
def __init__(self, env_fns):
'''
@param env_fns iterable of functions that build environments
'''
self.envs = [fn() for fn in env_fns]
env = self.envs[0]
VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
obs_space = env.observation_space
self.keys, shapes, dtypes = obs_space_info(obs_space)
self.buf_obs = {k: np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k]) for k in self.keys}
self.buf_dones = np.zeros((self.num_envs,), dtype=np.bool)
self.buf_rews = np.zeros((self.num_envs,), dtype=np.float32)
self.buf_infos = [{} for _ in range(self.num_envs)]
self.actions = None
self.spec = self.envs[0].spec
def step_async(self, actions):
listify = True
try:
if len(actions) == self.num_envs:
listify = False
except TypeError:
pass
if not listify:
self.actions = actions
else:
assert self.num_envs == 1, f'actions {actions} is either not a list or has a wrong size - cannot match to {self.num_envs} environments'
self.actions = [actions]
def step_wait(self):
for e in range(self.num_envs):
action = self.actions[e]
obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(action)
if self.buf_dones[e]:
obs = self.envs[e].reset()
self._save_obs(e, obs)
return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones),
self.buf_infos.copy())
def reset(self):
for e in range(self.num_envs):
obs = self.envs[e].reset()
self._save_obs(e, obs)
return self._obs_from_buf()
def _save_obs(self, e, obs):
for k in self.keys:
if k is None:
self.buf_obs[k][e] = obs
else:
self.buf_obs[k][e] = obs[k]
def _obs_from_buf(self):
return dict_to_obs(copy_obs_dict(self.buf_obs))
def get_images(self):
return [env.render(mode='rgb_array') for env in self.envs]
def render(self, mode='human'):
if self.num_envs == 1:
return self.envs[0].render(mode=mode)
else:
return super().render(mode=mode)
class VecEnvWrapper(VecEnv):
'''
An environment wrapper that applies to an entire batch of environments at once.
'''
def __init__(self, venv, observation_space=None, action_space=None):
self.venv = venv
observation_space = observation_space or venv.observation_space
action_space = action_space or venv.action_space
VecEnv.__init__(self, venv.num_envs, observation_space, action_space)
def step_async(self, actions):
self.venv.step_async(actions)
@abstractmethod
def reset(self):
pass
@abstractmethod
def step_wait(self):
pass
def close(self):
return self.venv.close()
def render(self, mode='human'):
return self.venv.render(mode=mode)
def get_images(self):
return self.venv.get_images()
class ShmemVecEnv(VecEnv):
'''
Optimized version of SubprocVecEnv that uses shared variables to communicate observations.
'''
def __init__(self, env_fns, context='spawn'):
ctx = mp.get_context(context)
dummy = env_fns[0]()
observation_space, action_space = dummy.observation_space, dummy.action_space
self.spec = dummy.spec
dummy.close()
del dummy
VecEnv.__init__(self, len(env_fns), observation_space, action_space)
self.obs_keys, self.obs_shapes, self.obs_dtypes = obs_space_info(observation_space)
self.obs_bufs = [
{k: ctx.Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in self.obs_keys}
for _ in env_fns]
self.parent_pipes = []
self.procs = []
with clear_mpi_env_vars():
for env_fn, obs_buf in zip(env_fns, self.obs_bufs):
wrapped_fn = CloudpickleWrapper(env_fn)
parent_pipe, child_pipe = ctx.Pipe()
proc = ctx.Process(
target=subproc_worker,
args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes, self.obs_dtypes, self.obs_keys))
proc.daemon = True
self.procs.append(proc)
self.parent_pipes.append(parent_pipe)
proc.start()
child_pipe.close()
self.waiting_step = False
self.viewer = None
def reset(self):
if self.waiting_step:
logger.warning('Called reset() while waiting for the step to complete')
self.step_wait()
for pipe in self.parent_pipes:
pipe.send(('reset', None))
return self._decode_obses([pipe.recv() for pipe in self.parent_pipes])
def step_async(self, actions):
assert len(actions) == len(self.parent_pipes)
for pipe, act in zip(self.parent_pipes, actions):
pipe.send(('step', act))
def step_wait(self):
outs = [pipe.recv() for pipe in self.parent_pipes]
obs, rews, dones, infos = zip(*outs)
return self._decode_obses(obs), np.array(rews), np.array(dones), infos
def close_extras(self):
if self.waiting_step:
self.step_wait()
for pipe in self.parent_pipes:
pipe.send(('close', None))
for pipe in self.parent_pipes:
pipe.recv()
pipe.close()
for proc in self.procs:
proc.join()
def get_images(self, mode='human'):
for pipe in self.parent_pipes:
pipe.send(('render', None))
return [pipe.recv() for pipe in self.parent_pipes]
def _decode_obses(self, obs):
result = {}
for k in self.obs_keys:
bufs = [b[k] for b in self.obs_bufs]
o = [np.frombuffer(b.get_obj(), dtype=self.obs_dtypes[k]).reshape(self.obs_shapes[k]) for b in bufs]
result[k] = np.array(o)
return dict_to_obs(result)
class VecFrameStack(VecEnvWrapper):
'''Frame stack wrapper for vector environment'''
def __init__(self, venv, frame_op, frame_op_len):
self.venv = venv
assert frame_op in ('concat', 'stack'), 'Invalid frame_op mode'
self.is_stack = frame_op == 'stack'
self.frame_op_len = frame_op_len
self.spec = venv.spec
wos = venv.observation_space # wrapped ob space
if self.is_stack:
self.shape_dim0 = 1
low = np.repeat(np.expand_dims(wos.low, axis=0), self.frame_op_len, axis=0)
high = np.repeat(np.expand_dims(wos.high, axis=0), self.frame_op_len, axis=0)
else: # concat
self.shape_dim0 = wos.shape[0]
low = np.repeat(wos.low, self.frame_op_len, axis=0)
high = np.repeat(wos.high, self.frame_op_len, axis=0)
self.stackedobs = np.zeros((venv.num_envs,) + low.shape, low.dtype)
observation_space = spaces.Box(low=low, high=high, dtype=venv.observation_space.dtype)
VecEnvWrapper.__init__(self, venv, observation_space=observation_space)
def step_wait(self):
obs, rews, news, infos = self.venv.step_wait()
self.stackedobs[:, :-self.shape_dim0] = self.stackedobs[:, self.shape_dim0:]
for (i, new) in enumerate(news):
if new:
self.stackedobs[i] = 0
if self.is_stack:
obs = np.expand_dims(obs, axis=1)
self.stackedobs[:, -self.shape_dim0:] = obs
return self.stackedobs.copy(), rews, news, infos
def reset(self):
obs = self.venv.reset()
self.stackedobs[...] = 0
if self.is_stack:
obs = np.expand_dims(obs, axis=1)
self.stackedobs[:, -self.shape_dim0:] = obs
return self.stackedobs.copy()
def make_gym_venv(name, num_envs=4, seed=0, frame_op=None, frame_op_len=None, image_downsize=None, reward_scale=None, normalize_state=False, episode_life=True):
'''General method to create any parallel vectorized Gym env; auto wraps Atari'''
venv = [
# don't concat frame or clip reward on individual env; do that at vector level
partial(make_gym_env, name, seed + i, frame_op=None, frame_op_len=None, image_downsize=image_downsize, reward_scale=reward_scale, normalize_state=normalize_state, episode_life=episode_life)
for i in range(num_envs)
]
if len(venv) > 1:
venv = ShmemVecEnv(venv, context='fork')
else:
venv = DummyVecEnv(venv)
if frame_op is not None:
venv = VecFrameStack(venv, frame_op, frame_op_len)
return venv