only use nightly pytorch in ci

Summary:
- change ci to only use nightly since block_current_stream is not in stable yet
- fix new errors in nightly version of pyre
  - remove fixme[29] about future not being a function
  - make reduce_scatter_quantized return Work object

Author: tushar00jain

.github/workflows/lint.yaml

@@ -26,6 +26,7 @@ jobs:
           pip install lintrunner lintrunner-adapters
           lintrunner init
 
+          pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128
           pip install .[dev] -v
       - name: Run lintrunner
         run: |

.github/workflows/unittest.yaml

@@ -15,11 +15,7 @@ jobs:
           - runs-on: "linux.2xlarge"
             gpu-arch-type: "cpu"
             gpu-arch-version: ""
-            torch-version: "stable"
-          - runs-on: "linux.g5.12xlarge.nvidia.gpu"
-            gpu-arch-type: "cuda"
-            gpu-arch-version: "12.4"
-            torch-version: "stable"
+            torch-version: "nightly"
           - runs-on: "linux.g5.12xlarge.nvidia.gpu"
             gpu-arch-type: "cuda"
             gpu-arch-version: "12.4"

torchft/collectives.py

@@ -162,7 +162,7 @@ def reduce_scatter_quantized(
     opts: ReduceScatterOptions | ReduceOp,
     process_group: "ProcessGroup",
     sync_stream: cuda.Stream | None = None,
-) -> Future[None]:
+) -> Work:
     """
     Performs a quantized reduce-scatter operation on a list of tensors.
 
@@ -196,10 +196,10 @@ def reduce_scatter_quantized(
     """
 
     if isinstance(opts, ReduceOp):
-        reducescatter_opts = ReduceScatterOptions()
+        reducescatter_opts: ReduceScatterOptions = ReduceScatterOptions()
         reducescatter_opts.reduceOp = opts
     else:
-        reducescatter_opts = opts
+        reducescatter_opts: ReduceScatterOptions = opts
 
     # Check if the reduceOp is AVG or SUM
     if reducescatter_opts.reduceOp not in {
@@ -211,15 +211,15 @@ def reduce_scatter_quantized(
             f"for quantized reduce-scatter, only AVG and SUM are supported"
         )
 
-    rank = process_group.rank()
-    world_size = process_group.size()
+    rank: int = process_group.rank()
+    world_size: int = process_group.size()
 
     reduce_output_sizes = [
         torch.Size((s[0] // world_size, *s[1:]))
         for s in get_padded_sizes(inputs, world_size)
     ]
     reduce_output_numels = [s.numel() for s in reduce_output_sizes]
-    reduce_outputs = [
+    reduce_outputs: list[torch.Tensor] = [
         o.view(s)
         for o, s in zip(
             output.split(reduce_output_numels),
@@ -240,48 +240,51 @@ def reduce_scatter_quantized(
         quantized_inputs = fused_quantize_into_fp8(inputs, world_size)
 
         # Allocate output tensor where all-reduce results will be stored
-        quantized_inputs_out = torch.zeros_like(quantized_inputs)
+        quantized_inputs_out: torch.Tensor = torch.zeros_like(quantized_inputs)
         # Collect chunks and their scales from other ranks
-        process_group.alltoall_base(
+        work = process_group.alltoall_base(
             quantized_inputs_out.view(world_size, -1),
             quantized_inputs.view(world_size, -1),
             [],
             [],
             _to_alltoall_options(reducescatter_opts),
-        ).wait()
-
-        # Reduce chunks locally in higher precision after dequantization.
-        # The output is again quantized.
-        fused_reduce_fp8(
-            inputs,
-            quantized_inputs_out,
-            world_size,
-            rank,
-            reducescatter_opts.reduceOp,
         )
+        work.wait()
 
-        # Get view into the output tensor that corresponds to the
-        # current rank
-        quantized_reduce_scatter = (
-            quantized_inputs_out.view(world_size, -1).split(1)[rank].squeeze(0)
-        )
-        # Dequantize the result back to the original precision for
-        # the current rank
-        fused_dequantize_from_fp8(
-            reduce_outputs,
-            quantized_reduce_scatter,
-            1,
-        )
+        fut = work.get_future()
 
-        # pyre-ignore[29]
-        return _QuantizedOpFuture(
-            sync_stream,
-            [
-                quantized_inputs,
-                quantized_inputs_out,
-            ],
-            [output],
-        )
+        def callback(fut: Future[list[torch.Tensor]]) -> None:
+            nonlocal inputs, quantized_inputs_out, world_size, sync_stream, rank, reduce_outputs, reducescatter_opts
+
+            with torch.cuda.stream(sync_stream):
+                # Setup stream dependency
+                fut.wait()
+                # Reduce chunks locally in higher precision after dequantization.
+                # The output is again quantized.
+                fused_reduce_fp8(
+                    inputs,
+                    quantized_inputs_out,
+                    world_size,
+                    rank,
+                    reducescatter_opts.reduceOp,
+                )
+
+                # Get view into the output tensor that corresponds to the
+                # current rank
+                quantized_reduce_scatter = (
+                    quantized_inputs_out.view(world_size, -1).split(1)[rank].squeeze(0)
+                )
+                # Dequantize the result back to the original precision for
+                # the current rank
+                fused_dequantize_from_fp8(
+                    reduce_outputs,
+                    quantized_reduce_scatter,
+                    1,
+                )
+
+        fut.add_done_callback(callback)
+
+        return work
 
 
 def allreduce_quantized(

torchft/collectives_test.py

@@ -141,8 +141,8 @@ def _run_reduce_scatter_collective(
                 opts = ReduceScatterOptions()
                 opts.reduceOp = reduce_op
 
-                fut = reduce_scatter_quantized(actual_output, tensors, opts, pg)
-                fut.wait()
+                work = reduce_scatter_quantized(actual_output, tensors, opts, pg)
+                work.get_future().wait()
 
                 padded_sizes = get_padded_sizes(tensors, world_size)
                 padded_numel = sum(s.numel() for s in padded_sizes)

torchft/futures.py

@@ -148,7 +148,6 @@ def register(self, fut: Future[T], timeout: timedelta) -> Future[T]:
 
         loop = self._maybe_start_event_loop()
 
-        # pyre-fixme[29]: Future is not a function
         timed_fut: Future[T] = Future()
         handle: _TimerHandle = _TimerHandle()
         loop.call_soon_threadsafe(

torchft/futures_test.py

@@ -24,38 +24,32 @@ def tearDown(self) -> None:
         _TIMEOUT_MANAGER._watchdog_interval = self._original_watchdog_interval
 
     def test_future_wait(self) -> None:
-        # pyre-fixme[29]: Future is not a function
         fut = Future()
         with self.assertRaisesRegex(TimeoutError, "future did not complete within"):
             future_wait(fut, timeout=timedelta(seconds=0.01))
 
-        # pyre-fixme[29]: Future is not a function
         fut = Future()
         fut.set_result(1)
         self.assertEqual(future_wait(fut, timeout=timedelta(seconds=1.0)), 1)
 
-        # pyre-fixme[29]: Future is not a function
         fut = Future()
         fut.set_exception(RuntimeError("test"))
         with self.assertRaisesRegex(RuntimeError, "test"):
             future_wait(fut, timeout=timedelta(seconds=1.0))
 
     def test_future_timeout(self) -> None:
-        # pyre-fixme[29]: Future is not a function
         fut = Future()
         timed_fut = future_timeout(fut, timeout=timedelta(seconds=0.01))
         with self.assertRaisesRegex(TimeoutError, "future did not complete within"):
             timed_fut.wait()
 
     def test_future_timeout_result(self) -> None:
-        # pyre-fixme[29]: Future is not a function
         fut = Future()
         timed_fut = future_timeout(fut, timeout=timedelta(seconds=10))
         fut.set_result(1)
         self.assertEqual(timed_fut.wait(), 1)
 
     def test_future_timeout_exception(self) -> None:
-        # pyre-fixme[29]: Future is not a function
         fut = Future()
         timed_fut = future_timeout(fut, timeout=timedelta(seconds=10))
         fut.set_exception(RuntimeError("test"))

torchft/manager_test.py

@@ -401,7 +401,7 @@ def test_allreduce_error(self, client_mock: MagicMock) -> None:
 
         self.assertFalse(manager._errored)
 
-        bad_fut = torch.futures.Future()  # pyre-fixme[29]: not a function
+        bad_fut = torch.futures.Future()
         bad_fut.set_exception(RuntimeError("injected failure"))
         manager._pg.allreduce.return_value.get_future.return_value = bad_fut
         manager.allreduce(torch.tensor([1.0])).wait()
@@ -542,7 +542,7 @@ def test_manager_wrap_future(self, client_mock: MagicMock) -> None:
 
         self.assertIsNone(manager.errored())
 
-        fut = torch.futures.Future()  # pyre-fixme[29]: not a function
+        fut = torch.futures.Future()
         wrapped_fut = manager.wrap_future(fut, 2)
         self.assertIsNone(manager.errored())
 
@@ -559,7 +559,7 @@ def test_manager_wrap_future_timeout(self, client_mock: MagicMock) -> None:
 
         self.assertFalse(manager.errored())
 
-        fut = torch.futures.Future()  # pyre-fixme[29]: not a function
+        fut = torch.futures.Future()
         wrapped_fut = manager.wrap_future(fut, 2)
         wrapped_fut.wait()
         error = manager.errored()

torchft/process_group.py

@@ -183,6 +183,7 @@ def alltoall_base(
         """
         raise NotImplementedError("not implemented")
 
+    # pyre-fixme[14]: inconsistent override
     def barrier(self, opts: BarrierOptions) -> Work:
         """
         Synchronizes all processes.
@@ -496,7 +497,7 @@ def alltoall_base(
                 opts,
             )
 
-    def barrier(self, opts: BarrierOptions) -> Work:
+    def barrier(self, opts: Optional[BarrierOptions] = None) -> Work:
         with self._run_context():
             return self._wrap_work(self.parent.barrier(self._opts_hook(opts)), opts)
 
@@ -866,7 +867,7 @@ def alltoall_base(
         self._work.append(res)
         return res
 
-    def barrier(self, opts: BarrierOptions) -> Work:
+    def barrier(self, opts: Optional[BarrierOptions] = None) -> Work:
         return _DummyWork(None)
 
     def broadcast(self, tensor_list: List[torch.Tensor], opts: object) -> Work:
@@ -1497,7 +1498,7 @@ def _get_future(
         self, op_id: int, stream: Optional[torch.cuda.Stream]
     ) -> Future[object]:
         with self._futures_lock:
-            fut = Future()  # pyre-fixme[29]: is not a function
+            fut = Future()
             self._futures[op_id] = _FutureMetadata(future=fut, stream=stream)
             assert self._pipe is not None
             self._pipe.send(("future", op_id))
@@ -1629,7 +1630,7 @@ def alltoall_base(
             opts,
         )
 
-    def barrier(self, opts: BarrierOptions) -> Work:
+    def barrier(self, opts: Optional[BarrierOptions] = None) -> Work:
         return self._run_func("barrier", opts)
 
     def broadcast(

torchft/work.py

@@ -10,7 +10,6 @@ class _DummyWork(dist._Work):
     def __init__(self, result: object) -> None:
         super().__init__()
         self.result_ = result
-        # pyre-fixme[29]: Future is not a function
         self.future_: torch.futures.Future[object] = torch.futures.Future()
         self.future_.set_result(result)