Create comm.py

comm.py

@@ -0,0 +1,132 @@
+import pickle
+
+import torch
+import torch.distributed as dist
+
+
+class Comm(object):
+    def __init__(self, local_rank=0):
+        self.local_rank = 0
+
+    @property
+    def world_size(self):
+        if not dist.is_available():
+            return 1
+        if not dist.is_initialized():
+            return 1
+        return dist.get_world_size()
+
+    @property
+    def rank(self):
+        if not dist.is_available():
+            return 0
+        if not dist.is_initialized():
+            return 0
+        return dist.get_rank()
+
+    @property
+    def local_rank(self):
+        if not dist.is_available():
+            return 0
+        if not dist.is_initialized():
+            return 0
+        return self._local_rank
+
+    @local_rank.setter
+    def local_rank(self, value):
+        if not dist.is_available():
+            self._local_rank = 0
+        if not dist.is_initialized():
+            self._local_rank = 0
+        self._local_rank = value
+
+    @property
+    def head(self):
+        return 'Rank[{}/{}]'.format(self.rank, self.world_size)
+   
+    def is_main_process(self):
+        return self.rank == 0
+
+    def synchronize(self):
+        """
+        Helper function to synchronize (barrier) among all processes when
+        using distributed training
+        """
+        if self.world_size == 1:
+            return
+        dist.barrier()
+
+
+comm = Comm()
+
+
+def all_gather(data):
+    """
+    Run all_gather on arbitrary picklable data (not necessarily tensors)
+    Args:
+        data: any picklable object
+    Returns:
+        list[data]: list of data gathered from each rank
+    """
+    world_size = comm.world_size
+    if world_size == 1:
+        return [data]
+
+    # serialized to a Tensor
+    buffer = pickle.dumps(data)
+    storage = torch.ByteStorage.from_buffer(buffer)
+    tensor = torch.ByteTensor(storage).to("cuda")
+
+    # obtain Tensor size of each rank
+    local_size = torch.LongTensor([tensor.numel()]).to("cuda")
+    size_list = [torch.LongTensor([0]).to("cuda") for _ in range(world_size)]
+    dist.all_gather(size_list, local_size)
+    size_list = [int(size.item()) for size in size_list]
+    max_size = max(size_list)
+
+    # receiving Tensor from all ranks
+    # we pad the tensor because torch all_gather does not support
+    # gathering tensors of different shapes
+    tensor_list = []
+    for _ in size_list:
+        tensor_list.append(torch.ByteTensor(size=(max_size,)).to("cuda"))
+    if local_size != max_size:
+        padding = torch.ByteTensor(size=(max_size - local_size,)).to("cuda")
+        tensor = torch.cat((tensor, padding), dim=0)
+    dist.all_gather(tensor_list, tensor)
+
+    data_list = []
+    for size, tensor in zip(size_list, tensor_list):
+        buffer = tensor.cpu().numpy().tobytes()[:size]
+        data_list.append(pickle.loads(buffer))
+
+    return data_list
+
+
+def reduce_dict(input_dict, average=True):
+    """
+    Args:
+        input_dict (dict): all the values will be reduced
+        average (bool): whether to do average or sum
+    Reduce the values in the dictionary from all processes so that process with rank
+    0 has the averaged results. Returns a dict with the same fields as
+    input_dict, after reduction.
+    """
+    world_size = comm.world_size
+    if world_size < 2:
+        return input_dict
+    with torch.no_grad():
+        names = []
+        values = []
+        # sort the keys so that they are consistent across processes
+        for k in sorted(input_dict.keys()):
+            names.append(k)
+            values.append(input_dict[k])
+        values = torch.stack(values, dim=0)
+        dist.reduce(values, dst=0)
+        if dist.get_rank() == 0 and average:
+            # only main process gets accumulated, so only divide by
+            # world_size in this case
+            values /= world_size
+        reduced_dict = {k: v for k, v in zip(names, values)}
+    return reduced_dict