Fix torch tensor non-contiguous issue (#4855)

* add contiguous

* preserve tensor device type

ml-agents/mlagents/trainers/torch/attention.py

         with torch.no_grad():
             # Generate the masking tensors for each entities tensor (mask only if all zeros)
             key_masks: List[torch.Tensor] = [
-                (torch.sum(ent ** 2, axis=2) < 0.01).type(torch.FloatTensor)
-                for ent in observations
+                (torch.sum(ent ** 2, axis=2) < 0.01).float() for ent in observations
             ]
         return key_masks
 

ml-agents/mlagents/trainers/torch/encoders.py

         if not exporting_to_onnx.is_exporting():
             visual_obs = visual_obs.permute([0, 3, 1, 2])
         hidden = self.conv_layers(visual_obs)
-        hidden = hidden.view([-1, self.final_flat])
+        hidden = hidden.reshape([-1, self.final_flat])
         return self.dense(hidden)
 
 
         if not exporting_to_onnx.is_exporting():
             visual_obs = visual_obs.permute([0, 3, 1, 2])
         batch_size = visual_obs.shape[0]
-        hidden = self.sequential(visual_obs).contiguous()
-        before_out = hidden.view(batch_size, -1)
+        hidden = self.sequential(visual_obs)
+        before_out = hidden.reshape(batch_size, -1)
         return torch.relu(self.dense(before_out))

ml-agents/mlagents/trainers/torch/networks.py

         if self.use_lstm and memories is not None:
             # Use only the back half of memories for critic and actor
             actor_mem, critic_mem = torch.split(memories, self.memory_size // 2, dim=-1)
+            actor_mem, critic_mem = actor_mem.contiguous(), critic_mem.contiguous()
         else:
             critic_mem = None
             actor_mem = None