Deprecate horovod in favor of torch.distributed

README.md

@@ -143,6 +143,14 @@ poetry run python runner.py --train --file rl_games/configs/atari/ppo_breakout_t
 ```
 
 
+## Multi GPU
+
+We use `torchrun` to orchestrate any multi-gpu runs.
+
+```bash
+torchrun --standalone --nnodes=1 --nproc_per_node=2 runner.py --train --file rl_games/configs/ppo_cartpole.yaml
+```
+
 ## Config Parameters
 
 | Field                  | Example Value                             | Default  | Description                                                                                            |

rl_games/algos_torch/a2c_continuous.py

@@ -48,7 +48,6 @@ def __init__(self, base_name, params):
                 'writter' : self.writer,
                 'max_epochs' : self.max_epochs,
                 'multi_gpu' : self.multi_gpu,
-                'hvd': self.hvd if self.multi_gpu else None
             }
             self.central_value_net = central_value.CentralValueTrain(**cv_config).to(self.ppo_device)
 

rl_games/algos_torch/a2c_discrete.py

@@ -49,7 +49,6 @@ def __init__(self, base_name, params):
                 'writter' : self.writer,
                 'max_epochs' : self.max_epochs,
                 'multi_gpu' : self.multi_gpu,
-                'hvd': self.hvd if self.multi_gpu else None
             }
             self.central_value_net = central_value.CentralValueTrain(**cv_config).to(self.ppo_device)
 

rl_games/algos_torch/central_value.py

@@ -1,5 +1,6 @@
 import torch
 from torch import nn
+import torch.distributed as dist
 import gym
 import numpy as np
 from rl_games.algos_torch import torch_ext
@@ -9,7 +10,7 @@
 from rl_games.common import schedulers
 
 class CentralValueTrain(nn.Module):
-    def __init__(self, state_shape, value_size, ppo_device, num_agents, horizon_length, num_actors, num_actions, seq_len, normalize_value,network, config, writter, max_epochs, multi_gpu, hvd):
+    def __init__(self, state_shape, value_size, ppo_device, num_agents, horizon_length, num_actors, num_actions, seq_len, normalize_value,network, config, writter, max_epochs, multi_gpu):
         nn.Module.__init__(self)
         self.ppo_device = ppo_device
         self.num_agents, self.horizon_length, self.num_actors, self.seq_len = num_agents, horizon_length, num_actors, seq_len
@@ -19,7 +20,6 @@ def __init__(self, state_shape, value_size, ppo_device, num_agents, horizon_leng
         self.value_size = value_size
         self.max_epochs = max_epochs
         self.multi_gpu = multi_gpu
-        self.hvd = hvd
         self.truncate_grads = config.get('truncate_grads', False)
         self.config = config
         self.normalize_input = config['normalize_input']
@@ -77,8 +77,8 @@ def __init__(self, state_shape, value_size, ppo_device, num_agents, horizon_leng
 
     def update_lr(self, lr):
         if self.multi_gpu:
-            lr_tensor = torch.tensor([lr])
-            self.hvd.broadcast_value(lr_tensor, 'cv_learning_rate')
+            lr_tensor = torch.tensor([lr], device=self.device)
+            dist.broadcast(lr_tensor, 0)
             lr = lr_tensor.item()
 
         for param_group in self.optimizer.param_groups:

rl_games/common/a2c_common.py

@@ -20,6 +20,7 @@
 from tensorboardX import SummaryWriter
 import torch 
 from torch import nn
+import torch.distributed as dist
 
 from time import sleep
 
@@ -68,11 +69,15 @@ def __init__(self, base_name, params):
         self.rank_size = 1
         self.curr_frames = 0
         if self.multi_gpu:
-            from rl_games.distributed.hvd_wrapper import HorovodWrapper
-            self.hvd = HorovodWrapper()
-            self.config = self.hvd.update_algo_config(config)
-            self.rank = self.hvd.rank
-            self.rank_size = self.hvd.rank_size
+            self.rank = int(os.getenv("LOCAL_RANK", "0"))
+            self.rank_size = int(os.getenv("WORLD_SIZE", "1"))
+            dist.init_process_group("nccl", rank=self.rank, world_size=self.rank_size)
+
+            self.device_name = 'cuda:' + str(self.rank)
+            config['device'] = self.device_name
+            if self.rank != 0:
+                config['print_stats'] = False
+                config['lr_schedule'] = None
 
         self.use_diagnostics = config.get('use_diagnostics', False)
 
@@ -251,19 +256,27 @@ def __init__(self, base_name, params):
 
     def trancate_gradients_and_step(self):
         if self.multi_gpu:
-            self.optimizer.synchronize()
+            # batch allreduce ops: see https://github.com/entity-neural-network/incubator/pull/220
+            all_grads_list = []
+            for param in self.model.parameters():
+                if param.grad is not None:
+                    all_grads_list.append(param.grad.view(-1))
+            all_grads = torch.cat(all_grads_list)
+            dist.all_reduce(all_grads, op=dist.ReduceOp.SUM)
+            offset = 0
+            for param in self.model.parameters():
+                if param.grad is not None:
+                    param.grad.data.copy_(
+                        all_grads[offset : offset + param.numel()].view_as(param.grad.data) / self.rank_size
+                    )
+                    offset += param.numel()
 
         if self.truncate_grads:
             self.scaler.unscale_(self.optimizer)
             nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_norm)
 
-        if self.multi_gpu:
-            with self.optimizer.skip_synchronize():
-                self.scaler.step(self.optimizer)
-                self.scaler.update()
-        else:
-            self.scaler.step(self.optimizer)
-            self.scaler.update()
+        self.scaler.step(self.optimizer)
+        self.scaler.update()
 
     def load_networks(self, params):
         builder = model_builder.ModelBuilder()
@@ -308,8 +321,8 @@ def set_train(self):
 
     def update_lr(self, lr):
         if self.multi_gpu:
-            lr_tensor = torch.tensor([lr])
-            self.hvd.broadcast_value(lr_tensor, 'learning_rate')
+            lr_tensor = torch.tensor([lr], device=self.device)
+            dist.broadcast(lr_tensor, 0)
             lr = lr_tensor.item()
 
         for param_group in self.optimizer.param_groups:
@@ -802,7 +815,8 @@ def train_epoch(self):
 
             av_kls = torch_ext.mean_list(ep_kls)
             if self.multi_gpu:
-                av_kls = self.hvd.average_value(av_kls, 'ep_kls')
+                dist.all_reduce(av_kls, op=dist.ReduceOp.SUM)
+                av_kls /= self.rank_size
 
             self.last_lr, self.entropy_coef = self.scheduler.update(self.last_lr, self.entropy_coef, self.epoch_num, 0, av_kls.item())
             self.update_lr(self.last_lr)
@@ -887,19 +901,20 @@ def train(self):
         self.obs = self.env_reset()
 
         if self.multi_gpu:
-            self.hvd.setup_algo(self)
+            torch.cuda.set_device(self.rank)
+            print("====================broadcasting parameters")
+            model_params = [self.model.state_dict()]
+            dist.broadcast_object_list(model_params, 0)
+            self.model.load_state_dict(model_params[0])
 
         while True:
             epoch_num = self.update_epoch()
             step_time, play_time, update_time, sum_time, a_losses, c_losses, entropies, kls, last_lr, lr_mul = self.train_epoch()
 
-            if self.multi_gpu:
-                self.hvd.sync_stats(self)
-
             # cleaning memory to optimize space
             self.dataset.update_values_dict(None)
             total_time += sum_time
-            curr_frames = self.curr_frames
+            curr_frames = self.curr_frames * self.rank_size if self.multi_gpu else self.curr_frames
             self.frame += curr_frames
             should_exit = False
             if self.rank == 0:
@@ -959,9 +974,9 @@ def train(self):
                     should_exit = True                              
                 update_time = 0
             if self.multi_gpu:
-                    should_exit_t = torch.tensor(should_exit).float()
-                    self.hvd.broadcast_value(should_exit_t, 'should_exit')
-                    should_exit = should_exit_t.bool().item()
+                should_exit_t = torch.tensor(should_exit).float()
+                dist.broadcast(should_exit_t, 0)
+                should_exit = should_exit_t.bool().item()
             if should_exit:
                 return self.last_mean_rewards, epoch_num
 
@@ -1040,9 +1055,10 @@ def train_epoch(self):
                 self.dataset.update_mu_sigma(cmu, csigma)
 
             av_kls = torch_ext.mean_list(ep_kls)
-
             if self.multi_gpu:
-                av_kls = self.hvd.average_value(av_kls, 'ep_kls')
+                dist.all_reduce(av_kls, op=dist.ReduceOp.SUM)
+                av_kls /= self.rank_size
+
             self.last_lr, self.entropy_coef = self.scheduler.update(self.last_lr, self.entropy_coef, self.epoch_num, 0, av_kls.item())
             self.update_lr(self.last_lr)
 
@@ -1128,16 +1144,18 @@ def train(self):
         self.curr_frames = self.batch_size_envs
 
         if self.multi_gpu:
-            self.hvd.setup_algo(self)
+            # 
+            print("====================broadcasting parameters")
+            model_params = [self.model.state_dict()]
+            dist.broadcast_object_list(model_params, 0)
+            self.model.load_state_dict(model_params[0])
 
         while True:
             epoch_num = self.update_epoch()
             step_time, play_time, update_time, sum_time, a_losses, c_losses, b_losses, entropies, kls, last_lr, lr_mul = self.train_epoch()
             total_time += sum_time
             frame = self.frame // self.num_agents
 
-            if self.multi_gpu:
-                self.hvd.sync_stats(self)
             # cleaning memory to optimize space
             self.dataset.update_values_dict(None)
             should_exit = False
@@ -1147,7 +1165,7 @@ def train(self):
                 # do we need scaled_time?
                 scaled_time = self.num_agents * sum_time
                 scaled_play_time = self.num_agents * play_time
-                curr_frames = self.curr_frames
+                curr_frames = self.curr_frames * self.rank_size if self.multi_gpu else self.curr_frames
                 self.frame += curr_frames
                 if self.print_stats:
                     fps_step = curr_frames / step_time
@@ -1204,9 +1222,9 @@ def train(self):
 
                 update_time = 0
             if self.multi_gpu:
-                    should_exit_t = torch.tensor(should_exit).float()
-                    self.hvd.broadcast_value(should_exit_t, 'should_exit')
-                    should_exit = should_exit_t.float().item()
+                should_exit_t = torch.tensor(should_exit).float()
+                dist.broadcast(should_exit_t, 0)
+                should_exit = should_exit_t.float().item()
             if should_exit:
                 return self.last_mean_rewards, epoch_num
 

rl_games/torch_runner.py

@@ -1,3 +1,4 @@
+import os
 import time
 import numpy as np
 import random
@@ -60,10 +61,7 @@ def load_config(self, params):
             self.seed = int(time.time())
 
         if params["config"].get('multi_gpu', False):
-            import horovod.torch as hvd
-
-            hvd.init()
-            self.seed += hvd.rank()
+            self.seed += int(os.getenv("LOCAL_RANK", "0"))
         print(f"self.seed = {self.seed}")
 
         self.algo_params = params['algo']
@@ -82,7 +80,7 @@ def load_config(self, params):
                     params['config']['env_config']['seed'] = self.seed
                 else:
                     if params["config"].get('multi_gpu', False):
-                        params['config']['env_config']['seed'] += hvd.rank()
+                        params['config']['env_config']['seed'] += int(os.getenv("LOCAL_RANK", "0"))
 
         config = params['config']
         config['reward_shaper'] = tr_helpers.DefaultRewardsShaper(**config['reward_shaper'])

runner.py

@@ -52,7 +52,8 @@
         except yaml.YAMLError as exc:
             print(exc)
 
-    if args["track"]:
+    rank = int(os.getenv("LOCAL_RANK", "0"))
+    if args["track"] and rank == 0:
         import wandb
 
         wandb.init(
@@ -67,4 +68,6 @@
     runner.run(args)
 
     ray.shutdown()
-
+
+    if args["track"] and rank == 0:
+        wandb.finish()