独立infer进程

douzero/dmc/arguments.py

@@ -17,6 +17,8 @@ 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('--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,

douzero/dmc/dmc.py

@@ -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
 
@@ -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)
+        actor_model = Model(device="cpu", flags = flags, lite_model = flags.lite_model)
-        model.share_memory()
+    actor_model.eval()
-        model.eval()
-        models[device] = model
 
     # 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:
+                actor_model.get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])
-                    models[device].get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])
         stats = checkpoint_states["stats"]
 
         frames = checkpoint_states["frames"]
@@ -173,20 +161,35 @@ def train(flags):
         sync_onnx_model(frames)
         log.info(f"Resuming preempted job, current stats:\n{stats}")
 
-    # Starting actor processes
+    infer_queues = []
-    for device in device_iterator:
-        if device == 'cpu':
-            num_actors = flags.num_actors_cpu
-        else:
     num_actors = flags.num_actors
+    for i in range(num_actors):
+        infer_queues.append({
+            'input': ctx.Queue(), 'output': ctx.Queue()
+        })
+
+    infer_processes = []
+    for device in ['0']:
+        for i in range(flags.num_infer):
+            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]['input'], infer_queues[i]['output'], batch_queues, actor_model, flags))
         actor.daemon = True
         actor.start()
         actor_processes.append({
-                'device': device,
             'i': i,
             'actor': actor
         })
@@ -201,7 +204,7 @@ 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 +218,12 @@ def train(flags):
 
     threads = []
     locks = {}
-    for device in device_iterator:
+    locks['cpu'] = {'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_front': threading.Lock(), 'landlord_down': threading.Lock()}
-        locks[device] = {'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)
@@ -303,14 +305,14 @@ def train(flags):
                      position_fps['landlord_front'],
                      position_fps['landlord_down'],
                      pprint.pformat(stats))
-            for proc in actor_processes:
+            # for proc in actor_processes:
-                if not proc['actor'].is_alive():
+            #     if not proc['actor'].is_alive():
-                    actor = mp.Process(
+            #         actor = mp.Process(
-                        target=act,
+            #             target=act,
-                        args=(proc['i'], proc['device'], batch_queues, models[device], flags, onnx_frame))
+            #             args=(proc['i'], proc['device'], batch_queues, models[device], flags, onnx_frame))
-                    actor.daemon = True
+            #         actor.daemon = True
-                    actor.start()
+            #         actor.start()
-                    proc['actor'] = actor
+            #         proc['actor'] = actor
 
     except KeyboardInterrupt:
         flags.enable_upload = False

douzero/dmc/env_utils.py

@@ -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,
@@ -39,7 +32,7 @@ class Environment:
 
     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)
+        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(
@@ -58,7 +51,7 @@ class Environment:
             obs = self.env.reset(model, device, 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)
 

douzero/dmc/utils.py

@@ -1,4 +1,5 @@
 import os
+import queue
 import typing
 import logging
 import traceback
@@ -111,16 +112,44 @@ 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():
+                    agent_output = model.forward(task['position'], task['z_batch'], task['x_batch'], flags=flags)
+                _action_idx = int(agent_output['action'])
+                infer_queue['output'].put({
+                    'action': _action_idx
+                })
+                all_empty = False
+            except queue.Empty:
+                pass
+        if all_empty:
+            time.sleep(0.01)
+
+
+def act(i, input_queue, output_queue, batch_queues, actor_model, 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}
@@ -136,18 +165,16 @@ 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)
+        position, obs, env_output = env.initial(actor_model, device, flags=flags)
-        last_onnx_frame = -1
         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():
+                    input_queue.put({
-                        agent_output = model.forward(position, obs['z_batch'], obs['x_batch'], flags=flags)
+                        'position': position,
+                        'z_batch': obs['z_batch'],
+                        'x_batch': obs['x_batch']
+                    })
+                    agent_output = output_queue.get()
                     _action_idx = int(agent_output['action'])
                     action = obs['legal_actions'][_action_idx]
                 else:
@@ -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, actor_model, device, flags=flags)
                 size[position] += 1
                 if env_output['done']:
                     for p in positions: