mpi-less baselines (#689)

* make baselines run without mpi wip

* squash-merged latest master

* further removing MPI references where unnecessary

* more MPI removal

* syntax and flake8

* MpiAdam becomes regular Adam if Mpi not present

* autopep8

* add assertion to test in mpi_adam; fix trpo_mpi failure without MPI on cartpole

* mpiless ddpg

Dockerfile

@@ -1,16 +1,7 @@
-FROM ubuntu:16.04
+FROM python:3.6
 
-RUN apt-get -y update && apt-get -y install git wget python-dev python3-dev libopenmpi-dev python-pip zlib1g-dev cmake python-opencv
+# RUN apt-get -y update && apt-get -y install git wget python-dev python3-dev libopenmpi-dev python-pip zlib1g-dev cmake python-opencv
 ENV CODE_DIR /root/code
-ENV VENV /root/venv
-
-RUN \
-    pip install virtualenv && \
-    virtualenv $VENV --python=python3 && \
-    . $VENV/bin/activate && \
-    pip install --upgrade pip
-
-ENV PATH=$VENV/bin:$PATH
 
 COPY . $CODE_DIR/baselines
 WORKDIR $CODE_DIR/baselines

baselines/common/mpi_adam.py

@@ -1,7 +1,11 @@
-from mpi4py import MPI
 import baselines.common.tf_util as U
 import tensorflow as tf
 import numpy as np
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
+
 
 class MpiAdam(object):
     def __init__(self, var_list, *, beta1=0.9, beta2=0.999, epsilon=1e-08, scale_grad_by_procs=True, comm=None):
@@ -16,16 +20,19 @@ class MpiAdam(object):
         self.t = 0
         self.setfromflat = U.SetFromFlat(var_list)
         self.getflat = U.GetFlat(var_list)
-        self.comm = MPI.COMM_WORLD if comm is None else comm
+        self.comm = MPI.COMM_WORLD if comm is None and MPI is not None else comm
 
     def update(self, localg, stepsize):
         if self.t % 100 == 0:
             self.check_synced()
         localg = localg.astype('float32')
-        globalg = np.zeros_like(localg)
+        if self.comm is not None:
-        self.comm.Allreduce(localg, globalg, op=MPI.SUM)
+            globalg = np.zeros_like(localg)
-        if self.scale_grad_by_procs:
+            self.comm.Allreduce(localg, globalg, op=MPI.SUM)
-            globalg /= self.comm.Get_size()
+            if self.scale_grad_by_procs:
+                globalg /= self.comm.Get_size()
+        else:
+            globalg = np.copy(localg)
 
         self.t += 1
         a = stepsize * np.sqrt(1 - self.beta2**self.t)/(1 - self.beta1**self.t)
@@ -35,11 +42,15 @@ class MpiAdam(object):
         self.setfromflat(self.getflat() + step)
 
     def sync(self):
+        if self.comm is None:
+            return
         theta = self.getflat()
         self.comm.Bcast(theta, root=0)
         self.setfromflat(theta)
 
     def check_synced(self):
+        if self.comm is None:
+            return
         if self.comm.Get_rank() == 0: # this is root
             theta = self.getflat()
             self.comm.Bcast(theta, root=0)
@@ -63,17 +74,30 @@ def test_MpiAdam():
     do_update = U.function([], loss, updates=[update_op])
 
     tf.get_default_session().run(tf.global_variables_initializer())
+    losslist_ref = []
     for i in range(10):
-        print(i,do_update())
+        l = do_update()
+        print(i, l)
+        losslist_ref.append(l)
+
+
 
     tf.set_random_seed(0)
     tf.get_default_session().run(tf.global_variables_initializer())
 
     var_list = [a,b]
-    lossandgrad = U.function([], [loss, U.flatgrad(loss, var_list)], updates=[update_op])
+    lossandgrad = U.function([], [loss, U.flatgrad(loss, var_list)])
     adam = MpiAdam(var_list)
 
+    losslist_test = []
     for i in range(10):
         l,g = lossandgrad()
         adam.update(g, stepsize)
         print(i,l)
+        losslist_test.append(l)
+
+    np.testing.assert_allclose(np.array(losslist_ref), np.array(losslist_test), atol=1e-4)
+
+
+if __name__ == '__main__':
+    test_MpiAdam()

baselines/common/mpi_running_mean_std.py

@@ -1,4 +1,8 @@
-from mpi4py import MPI
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
+
 import tensorflow as tf, baselines.common.tf_util as U, numpy as np
 
 class RunningMeanStd(object):
@@ -39,7 +43,8 @@ class RunningMeanStd(object):
         n = int(np.prod(self.shape))
         totalvec = np.zeros(n*2+1, 'float64')
         addvec = np.concatenate([x.sum(axis=0).ravel(), np.square(x).sum(axis=0).ravel(), np.array([len(x)],dtype='float64')])
-        MPI.COMM_WORLD.Allreduce(addvec, totalvec, op=MPI.SUM)
+        if MPI is not None:
+            MPI.COMM_WORLD.Allreduce(addvec, totalvec, op=MPI.SUM)
         self.incfiltparams(totalvec[0:n].reshape(self.shape), totalvec[n:2*n].reshape(self.shape), totalvec[2*n])
 
 @U.in_session

baselines/ddpg/ddpg.py

@@ -12,8 +12,11 @@ import baselines.common.tf_util as U
 
 from baselines import logger
 import numpy as np
-from mpi4py import MPI
 
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
 
 def learn(network, env,
           seed=None,
@@ -49,7 +52,11 @@ def learn(network, env,
     else:
         nb_epochs = 500
 
-    rank = MPI.COMM_WORLD.Get_rank()
+    if MPI is not None:
+        rank = MPI.COMM_WORLD.Get_rank()
+    else:
+        rank = 0
+
     nb_actions = env.action_space.shape[-1]
     assert (np.abs(env.action_space.low) == env.action_space.high).all()  # we assume symmetric actions.
 
@@ -199,7 +206,11 @@ def learn(network, env,
                             eval_episode_rewards_history.append(eval_episode_reward[d])
                             eval_episode_reward[d] = 0.0
 
-        mpi_size = MPI.COMM_WORLD.Get_size()
+        if MPI is not None:
+            mpi_size = MPI.COMM_WORLD.Get_size()
+        else:
+            mpi_size = 1
+
         # Log stats.
         # XXX shouldn't call np.mean on variable length lists
         duration = time.time() - start_time
@@ -233,7 +244,10 @@ def learn(network, env,
             else:
                 raise ValueError('expected scalar, got %s'%x)
 
-        combined_stats_sums = MPI.COMM_WORLD.allreduce(np.array([ np.array(x).flatten()[0] for x in combined_stats.values()]))
+        combined_stats_sums = np.array([ np.array(x).flatten()[0] for x in combined_stats.values()])
+        if MPI is not None:
+            combined_stats_sums = MPI.COMM_WORLD.allreduce(combined_stats_sums)
+
         combined_stats = {k : v / mpi_size for (k,v) in zip(combined_stats.keys(), combined_stats_sums)}
 
         # Total statistics.

baselines/ddpg/ddpg_learner.py

@@ -9,7 +9,10 @@ from baselines import logger
 from baselines.common.mpi_adam import MpiAdam
 import baselines.common.tf_util as U
 from baselines.common.mpi_running_mean_std import RunningMeanStd
-from mpi4py import MPI
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
 
 def normalize(x, stats):
     if stats is None:
@@ -358,6 +361,11 @@ class DDPG(object):
         return stats
 
     def adapt_param_noise(self):
+        try:
+            from mpi4py import MPI
+        except ImportError:
+            MPI = None
+
         if self.param_noise is None:
             return 0.
 
@@ -371,7 +379,16 @@ class DDPG(object):
             self.param_noise_stddev: self.param_noise.current_stddev,
         })
 
-        mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
+        if MPI is not None:
+            mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
+        else:
+            mean_distance = distance
+
+        if MPI is not None:
+            mean_distance = MPI.COMM_WORLD.allreduce(distance, op=MPI.SUM) / MPI.COMM_WORLD.Get_size()
+        else:
+            mean_distance = distance
+
         self.param_noise.adapt(mean_distance)
         return mean_distance
 

baselines/ppo2/ppo2.py

@@ -10,11 +10,15 @@ from baselines.common import explained_variance, set_global_seeds
 from baselines.common.policies import build_policy
 from baselines.common.runners import AbstractEnvRunner
 from baselines.common.tf_util import get_session, save_variables, load_variables
-from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer
 
-from mpi4py import MPI
+try:
+    from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer
+    from mpi4py import MPI
+    from baselines.common.mpi_util import sync_from_root
+except ImportError:
+    MPI = None
+
 from baselines.common.tf_util import initialize
-from baselines.common.mpi_util import sync_from_root
 
 class Model(object):
     """
@@ -93,7 +97,10 @@ class Model(object):
         # 1. Get the model parameters
         params = tf.trainable_variables('ppo2_model')
         # 2. Build our trainer
-        trainer = MpiAdamOptimizer(MPI.COMM_WORLD, learning_rate=LR, epsilon=1e-5)
+        if MPI is not None:
+            trainer = MpiAdamOptimizer(MPI.COMM_WORLD, learning_rate=LR, epsilon=1e-5)
+        else:
+            trainer = tf.train.AdamOptimizer(learning_rate=LR, epsilon=1e-5)
         # 3. Calculate the gradients
         grads_and_var = trainer.compute_gradients(loss, params)
         grads, var = zip(*grads_and_var)
@@ -136,10 +143,12 @@ class Model(object):
         self.save = functools.partial(save_variables, sess=sess)
         self.load = functools.partial(load_variables, sess=sess)
 
-        if MPI.COMM_WORLD.Get_rank() == 0:
+        if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
             initialize()
         global_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="")
-        sync_from_root(sess, global_variables) #pylint: disable=E1101
+
+        if MPI is not None:
+            sync_from_root(sess, global_variables) #pylint: disable=E1101
 
 class Runner(AbstractEnvRunner):
     """
@@ -392,9 +401,9 @@ def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2
             logger.logkv('time_elapsed', tnow - tfirststart)
             for (lossval, lossname) in zip(lossvals, model.loss_names):
                 logger.logkv(lossname, lossval)
-            if MPI.COMM_WORLD.Get_rank() == 0:
+            if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
                 logger.dumpkvs()
-        if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and MPI.COMM_WORLD.Get_rank() == 0:
+        if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and (MPI is None or MPI.COMM_WORLD.Get_rank() == 0):
             checkdir = osp.join(logger.get_dir(), 'checkpoints')
             os.makedirs(checkdir, exist_ok=True)
             savepath = osp.join(checkdir, '%.5i'%update)

baselines/trpo_mpi/trpo_mpi.py

@@ -4,7 +4,6 @@ import baselines.common.tf_util as U
 import tensorflow as tf, numpy as np
 import time
 from baselines.common import colorize
-from mpi4py import MPI
 from collections import deque
 from baselines.common import set_global_seeds
 from baselines.common.mpi_adam import MpiAdam
@@ -13,6 +12,11 @@ from baselines.common.input import observation_placeholder
 from baselines.common.policies import build_policy
 from contextlib import contextmanager
 
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
+
 def traj_segment_generator(pi, env, horizon, stochastic):
     # Initialize state variables
     t = 0
@@ -146,9 +150,12 @@ def learn(*,
 
     '''
 
-
+    if MPI is not None:
-    nworkers = MPI.COMM_WORLD.Get_size()
+        nworkers = MPI.COMM_WORLD.Get_size()
-    rank = MPI.COMM_WORLD.Get_rank()
+        rank = MPI.COMM_WORLD.Get_rank()
+    else:
+        nworkers = 1
+        rank = 0
 
     cpus_per_worker = 1
     U.get_session(config=tf.ConfigProto(
@@ -237,9 +244,13 @@ def learn(*,
 
     def allmean(x):
         assert isinstance(x, np.ndarray)
-        out = np.empty_like(x)
+        if MPI is not None:
-        MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
+            out = np.empty_like(x)
-        out /= nworkers
+            MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
+            out /= nworkers
+        else:
+            out = np.copy(x)
+
         return out
 
     U.initialize()
@@ -247,7 +258,9 @@ def learn(*,
         pi.load(load_path)
 
     th_init = get_flat()
-    MPI.COMM_WORLD.Bcast(th_init, root=0)
+    if MPI is not None:
+        MPI.COMM_WORLD.Bcast(th_init, root=0)
+
     set_from_flat(th_init)
     vfadam.sync()
     print("Init param sum", th_init.sum(), flush=True)
@@ -353,7 +366,11 @@ def learn(*,
         logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret))
 
         lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
-        listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
+        if MPI is not None:
+            listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
+        else:
+            listoflrpairs = [lrlocal]
+
         lens, rews = map(flatten_lists, zip(*listoflrpairs))
         lenbuffer.extend(lens)
         rewbuffer.extend(rews)

setup.py

@@ -15,6 +15,9 @@ extras = {
     ],
     'bullet': [
         'pybullet',
+    ],
+    'mpi': [
+        'mpi4py'
     ]
 }
 
@@ -34,7 +37,6 @@ setup(name='baselines',
           'joblib',
           'dill',
           'progressbar2',
-          'mpi4py',
           'cloudpickle',
           'click',
           'opencv-python'