[fix] OSS unit test to check data group (#129)

* new unit test to catch rank issues in OSS

.circleci/config.yml

@@ -101,7 +101,12 @@ run_oss_benchmark: &run_oss_benchmark
       name: Run OSS Benchmark
       command: |
         python benchmarks/oss.py --check_regression --world_size 4 --reference_speed 21.2 --reference_memory 4220 --reference_loss 0.63
-        python benchmarks/oss.py --gloo --optim_type oss
+
+run_oss_gloo: &run_oss_gloo
+- run:
+    name: Run OSS with Gloo
+    command: |
+      python benchmarks/oss.py --gloo --optim_type oss
 
 
 # -------------------------------------------------------------------------------------
@@ -254,6 +259,9 @@ jobs:
 
       - <<: *run_oss_benchmark
 
+      - <<: *run_oss_gloo
+
+
 
 
 workflows:

fairscale/optim/oss.py

@@ -194,7 +194,7 @@ class OSS(Optimizer):
                 device,
                 device_params,
             ) in self.per_device_params.items():  # all the params on this device (inc all ranks)
-                self._broadcast_params(self._broadcast_buffers[device], device_params, self.group, self.global_rank)
+                self._broadcast_params(self._broadcast_buffers[device], device_params)
 
         return loss
 
@@ -408,10 +408,7 @@ class OSS(Optimizer):
             global_rank = dist.distributed_c10d._get_global_rank(group, rank)  # type: ignore
         return global_rank
 
-    @staticmethod
-    def _broadcast_params(
-        buffers: List[torch.Tensor], per_rank_params: List[List[Parameter]], group: Any, self_rank: int
-    ) -> None:
+    def _broadcast_params(self, buffers: List[torch.Tensor], per_rank_params: List[List[Parameter]]) -> None:
         """Helper function to broadcast all the parameters from a given device
         """
         buffer_size = buffers[0].numel()
@@ -425,7 +422,7 @@ class OSS(Optimizer):
             if len(params) == 0:
                 continue
 
-            global_rank = OSS.get_global_rank(group, rank)
+            global_rank = OSS.get_global_rank(self.group, rank)
 
             # Copy small parameters into per-GPU buffers
             i_bucketed = 0  # the number of tensors packed in the buffer
@@ -434,14 +431,14 @@ class OSS(Optimizer):
             # Since all the parameters are already sorted per increasing size, we only need to consider the first ones.
             while i_bucketed < len(params) and offset + params[i_bucketed].numel() < buffer_size:
                 end = offset + params[i_bucketed].numel()
-                if global_rank == self_rank:
+                if global_rank == self.global_rank:
                     buffer[offset:end].copy_(params[i_bucketed].data.view(-1))  # type: ignore
                 offset = end
                 i_bucketed += 1
 
             if i_bucketed > 0:
-                future = dist.broadcast(tensor=buffer, src=global_rank, group=group, async_op=True)
-                if global_rank != self_rank:
+                future = dist.broadcast(tensor=buffer, src=global_rank, group=self.group, async_op=True)
+                if global_rank != self.global_rank:
                     # This request will need to be unrolled
                     bucket_requests.append((future, rank))
 
@@ -455,7 +452,7 @@ class OSS(Optimizer):
                     restore_require_grad.append(param)
                     param.requires_grad = False
 
-                requests.append(dist.broadcast(tensor=param, src=global_rank, group=group, async_op=True))
+                requests.append(dist.broadcast(tensor=param, src=global_rank, group=self.group, async_op=True))
 
         # Unroll the initial packed small parameters
         for gate, rank in bucket_requests:

tests/optim/test_oss.py

@@ -9,6 +9,7 @@
 
 import os
 
+import numpy as np
 import pytest
 import torch
 import torch.distributed as dist
@@ -334,3 +335,78 @@ def test_collect_shards():
     mp.spawn(
         run_test_collect_shards, args=(world_size, reference_rank), nprocs=world_size, join=True,
     )
+
+
+def run_test_multiple_groups(rank, world_size):
+    # Only work with the even ranks, to check that the global_rank indexing is properly used
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "29501"
+    dist.init_process_group(backend="gloo", rank=rank, world_size=world_size)
+    sub_group_ranks = [0, 2, 4]
+    process_group = torch.distributed.new_group(ranks=sub_group_ranks, backend="gloo")
+
+    # Make sure that all the ranks get different training data
+    # So that the sync check in between their models is meaningful
+    torch.manual_seed(rank)
+    np.random.seed(rank)
+
+    # Standard deep learning setup
+    device = "cpu"
+    epochs, batch, input_width, hidden, target_width = 5, 3, 20, 10, 5
+    loss_fn = torch.nn.L1Loss().to(device)
+
+    def check(optimizer):
+        # Just run a couple of epochs, check that the model is properly updated
+        for _ in range(epochs):
+            target = torch.rand((batch, target_width), device=device)
+            inputs = torch.rand((batch, input_width), device=device)
+
+            def closure():
+                optimizer.zero_grad()
+                output = model(inputs)
+                loss = loss_fn(output, target)
+                loss /= world_size
+                loss.backward()
+                dist.all_reduce(loss, group=process_group)  # Not strictly needed for the test below
+
+                return loss
+
+            _ = optimizer.step(closure=closure)
+
+            # Check that all the params are the same on all ranks
+            for pg in optimizer.param_groups:
+                for p in pg["params"]:
+                    receptacle = [p.clone() for _ in sub_group_ranks] if rank == 0 else []
+                    dist.gather(p, receptacle, dst=0, group=process_group)
+                    if rank == 0:
+                        for sync_p in receptacle[1:]:
+                            assert torch.all(torch.eq(receptacle[0], sync_p)), "Models differ in between ranks"
+
+    if rank in sub_group_ranks:
+        # Model fitting in the broadcast bucket
+        model = torch.nn.Sequential(torch.nn.Linear(input_width, hidden), torch.nn.Linear(hidden, target_width)).to(
+            device
+        )
+
+        # With SGD, Momentum is required to get a state to shard
+        optimizer = optim.OSS(
+            model.parameters(), lr=0.1, momentum=0.99, group=process_group, broadcast_buffer_size=2 ** 20
+        )
+        check(optimizer)
+
+        # Model not-fitting in the broadcast bucket
+        model = torch.nn.Sequential(torch.nn.Linear(input_width, hidden), torch.nn.Linear(hidden, target_width)).to(
+            device
+        )
+
+        # With SGD, Momentum is required to get a state to shard
+        optimizer = optim.OSS(model.parameters(), lr=0.1, momentum=0.99, group=process_group, broadcast_buffer_size=0)
+        check(optimizer)
+
+
+def test_multiple_groups():
+    world_size = 6
+
+    mp.spawn(
+        run_test_multiple_groups, args=(world_size,), nprocs=world_size, join=True,
+    )