Remove name from all asyncio tasks (#90)

(needed for python 3.7 compatibility)

hivemind/__init__.py

@@ -3,4 +3,4 @@ from hivemind.dht import *
 from hivemind.server import *
 from hivemind.utils import *
 
-__version__ = '0.8.1'
+__version__ = '0.8.2'

hivemind/client/moe.py

@@ -80,7 +80,7 @@ class RemoteMixtureOfExperts(nn.Module):
 
         async def _search():
             coroutines = [asyncio.create_task(self.beam_search(
-                [dim_scores[i] for dim_scores in grid_scores], self.k_best), name=f'beam_search_{i}')
+                [dim_scores[i] for dim_scores in grid_scores], self.k_best))
                 for i in range(len(input))]
             return list(await asyncio.gather(*coroutines))
 
@@ -215,7 +215,7 @@ class _RemoteCallMany(torch.autograd.Function):
         async def _forward():
             # dispatch tasks to all remote experts, await responses
             pending_tasks = {
-                asyncio.create_task(cls._forward_one_expert((i, j), expert, flat_inputs_per_sample[i]), name=f'forward_expert_{j}_for_{i}')
+                asyncio.create_task(cls._forward_one_expert((i, j), expert, flat_inputs_per_sample[i]))
                 for i in range(num_samples) for j, expert in enumerate(experts_per_sample[i])
             }
             alive_grid_indices, alive_flat_outputs = await cls._wait_for_responses(
@@ -262,8 +262,7 @@ class _RemoteCallMany(torch.autograd.Function):
             for i, j, inputs_ij, grad_outputs_ij in zip(alive_ii.cpu().numpy(), alive_jj.cpu().numpy(),
                                                         inputs_per_expert, grad_outputs_per_expert):
                 pending_tasks.add(asyncio.create_task(
-                    cls._backward_one_expert((i, j), expert_per_sample[i.item()][j.item()], inputs_ij, grad_outputs_ij),
-                    name=f'backward_expert_{j}_for_{i}'))
+                    cls._backward_one_expert((i, j), expert_per_sample[i.item()][j.item()], inputs_ij, grad_outputs_ij)))
 
             backward_survivor_indices, survivor_grad_inputs = await cls._wait_for_responses(
                 pending_tasks, num_samples, backward_k_min, backward_timeout, timeout_after_k_min)