Fix a problem with shared adam

my_optim.py

@@ -1,4 +1,5 @@
 import math
+import torch
 import torch.optim as optim
 
 class SharedAdam(optim.Adam):
@@ -12,13 +13,53 @@ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
         for group in self.param_groups:
             for p in group['params']:
                 state = self.state[p]
-                state['step'] = 0
+                state['step'] = torch.zeros(1)
                 state['exp_avg'] = p.data.new().resize_as_(p.data).zero_()
                 state['exp_avg_sq'] = p.data.new().resize_as_(p.data).zero_()
 
     def share_memory(self):
         for group in self.param_groups:
             for p in group['params']:
                 state = self.state[p]
+                state['step'].share_memory_()
                 state['exp_avg'].share_memory_()
                 state['exp_avg_sq'].share_memory_()
+
+    def step(self, closure=None):
+        """Performs a single optimization step.
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data
+                state = self.state[p]
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                state['step'] += 1
+
+                if group['weight_decay'] != 0:
+                    grad = grad.add(group['weight_decay'], p.data)
+
+                # Decay the first and second moment running average coefficient
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+
+                denom = exp_avg_sq.sqrt().add_(group['eps'])
+
+                bias_correction1 = 1 - beta1 ** state['step'][0]
+                bias_correction2 = 1 - beta2 ** state['step'][0]
+                step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
+
+                p.data.addcdiv_(-step_size, exp_avg, denom)
+
+        return loss

train.py

@@ -31,9 +31,6 @@ def train(rank, args, shared_model, optimizer=None):
 
     model.train()
 
-    values = []
-    log_probs = []
-
     state = env.reset()
     state = torch.from_numpy(state)
     done = True