Add reduce-scatter coalescing for FSDP/ZeRO1

test/test_mp_reduce_scatter.py

@@ -10,6 +10,7 @@ def _mp_fn(index):
   scale = 1 / world_size
   scatter_dim = 1
   shard_size = 2
+  input_list_size = 5
 
   if xm.xla_device_hw(device) in ['TPU', 'CUDA']:
     rand = torch.rand((32, shard_size * world_size, 32))
@@ -25,8 +26,35 @@ def _mp_fn(index):
     expected = expected_world.cpu().index_select(scatter_dim, slice_idx)
 
     assert res.cpu().allclose(expected)
-
     xm.rendezvous('test_reduce_scatter')
+
+    # Testing reduce-scatter with list input
+    rand_list = [
+        torch.rand((32, shard_size * world_size, 32))
+        for _ in range(input_list_size)
+    ]
+    xrand_list = [rand.to(device) for rand in rand_list]
+
+    # TODO: fix the broken case with pin_layout=True
+    res_list = xm.reduce_scatter(
+        xm.REDUCE_SUM,
+        xrand_list,
+        scale,
+        scatter_dim,
+        world_size,
+        pin_layout=False)
+
+    for i, res in enumerate(res_list):
+      expected_world = xm.all_reduce(xm.REDUCE_SUM, xrand_list[i], scale)
+      xm.mark_step()
+
+      slice_idx = torch.tensor(
+          list(range(index * shard_size, (index + 1) * shard_size)))
+      expected = expected_world.cpu().index_select(scatter_dim, slice_idx)
+      assert res.cpu().allclose(expected)
+
+    xm.rendezvous('test_reduce_scatter_list_input')
+
   else:
     print(
         'Default device {} is not a TPU device'.format(device), file=sys.stderr)

test/test_torch_distributed_xla_backend.py

@@ -134,6 +134,27 @@ def test_reduce_scatter(self):
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([output])
     hlo_matches(hlo, reduce_scatter_pattern)
 
+  @skipIf(xr.device_type() == 'CPU',
+          "UNIMPLEMENTED: ReduceScatter is not implemented on CPU.")
+  def test_reduce_scatter_coalesced(self):
+    device = xm.xla_device()
+    tensor = torch.arange(2, device=device) + 1 + 2 * dist.get_rank()
+    tensor2 = torch.arange(5, device=device) + 1 + 2 * dist.get_rank()
+    input_tensors_list = [[tensor, tensor], [tensor2, tensor2]]
+    output_list = [torch.zeros_like(tensor), torch.zeros_like(tensor2)]
+    pg_xla = get_process_group_xla(rank=0, size=len(input_tensors_list[0]))
+    opts = dist.ReduceScatterOptions()
+    opts.reduceOp = dist.ReduceOp.SUM
+    reduce_scatter_pattern = (
+        r'%reduce\-scatter\.\d+ = \(s64\[2]\{0}, s64\[5]\{0}, s64\[]\) '
+        r'reduce\-scatter\(s64\[4]\{0} %.+\.\d+, s64\[10]\{0} %.+\.\d+, '
+        r's64\[] %.+\.\d+\)')
+    pg_xla.reduce_scatter_coalesced(output_list, input_tensors_list, opts)
+    hlo = torch_xla._XLAC._get_xla_tensors_hlo(output_list)
+    hlo_matches(hlo, reduce_scatter_pattern)
+    # purge all computations attached the device.
+    xm.mark_step()
+
   @patch_world(0, 6)
   def test_send(self):
     device = xm.xla_device()

torch_xla/core/xla_model.py

@@ -740,16 +740,18 @@ def reduce_scatter(reduce_type,
     reduce_type (string): One of ``xm.REDUCE_SUM``, ``xm.REDUCE_MUL``,
       ``xm.REDUCE_AND``, ``xm.REDUCE_OR``, ``xm.REDUCE_MIN`` and
       ``xm.REDUCE_MAX``.
-    input: A single `torch.Tensor` all reduce + scatter op to.
+    input: (torch.Tensor or a list of torch.Tensor): The input. If it's a list, then
+      it will also be the output.
     scale (float): A default scaling value to be applied after the reduce.
     scatter_dim (int): Dimension number to which apply scatter operation.
     shard_count (int): The number of ways to split up the scatter_dim in.
     groups (list): A list of list, representing the replica groups for
-      the `all_reduce()` operation. Example: `[[0, 1, 2, 3], [4, 5, 6, 7]]`
+      the `reduce_scatter()` operation. Example: `[[0, 1, 2, 3], [4, 5, 6, 7]]`
         defines two groups, one with the `[0, 1, 2, 3]` replicas and one with
         the `[4, 5, 6, 7]` replicas. If `None` there will be only one group with
         all the replicas in it.
-    output: Optional output tensor
+    output: Optional output tensor if `input` is a torch.Tensor, or a list of
+      torch.Tensor if `input` is a list of torch.Tensor.
     pin_layout (bool, optional): whether to pin the layout for this communication op.
       Layout pining can prevent potential data corruption when each process that
       participate in the communication has slightly different program, but it might
@@ -762,21 +764,43 @@ def reduce_scatter(reduce_type,
     the same as the input.
   """
   token, devctx = _get_all_reduce_token()
-  if output != None:
-    # Call the out of place version of the reduce_scatter
-    new_token = torch_xla._XLAC._xla_reduce_scatter_out(reduce_type, output,
-                                                        input, token, scale,
-                                                        scatter_dim,
-                                                        shard_count, groups or
-                                                        [], pin_layout)
-    torch_xla._XLAC._set_all_reduce_token(devctx.device, new_token)
-    return output
-
-  result = torch_xla._XLAC._xla_reduce_scatter(reduce_type, input, token, scale,
-                                               scatter_dim, shard_count,
-                                               groups or [], pin_layout)
-  torch_xla._XLAC._set_all_reduce_token(devctx.device, result[1])
-  return result[0]
+
+  if isinstance(input, torch.Tensor):
+    if output != None:
+      # Call the out of place version of the reduce_scatter
+      new_token = torch_xla._XLAC._xla_reduce_scatter_out(
+          reduce_type, output, input, token, scale, scatter_dim, shard_count,
+          groups or [], pin_layout)
+      torch_xla._XLAC._set_all_reduce_token(devctx.device, new_token)
+      return output
+
+    result = torch_xla._XLAC._xla_reduce_scatter(reduce_type, input, token,
+                                                 scale, scatter_dim,
+                                                 shard_count, groups or [],
+                                                 pin_layout)
+    torch_xla._XLAC._set_all_reduce_token(devctx.device, result[1])
+    return result[0]
+
+  # Now the input should be a list of Tensors.
+  elif isinstance(input, list) and all(
+      isinstance(v, torch.Tensor) for v in input):
+    if output != None:
+      if not isinstance(output, list) or any(
+          not isinstance(v, torch.Tensor) for v in output):
+        raise TypeError(
+            f"`output` needs to be a list of Tensors, but given {type(output)}."
+        )
+      if len(output) != len(input):
+        raise ValueError("`output` length doesn't match `input` length: "
+                         f"{len(output)} vs {len(input)}.")
+    result = torch_xla._XLAC._xla_reduce_scatter_coalesced(
+        reduce_type, output or [], input, token, scale, scatter_dim,
+        shard_count, groups or [], pin_layout)
+    torch_xla._XLAC._set_all_reduce_token(devctx.device, result[-1])
+    return result[:-1]
+  else:
+    raise TypeError("`input` needs to be a Tensor or a list of Tensors, but "
+                    f"given {type(input)}.")
 
 
 def add_step_closure(closure, args=(), run_async=False):

torch_xla/csrc/cross_replica_reduces.cpp

@@ -348,6 +348,54 @@ ReduceScatterResult BuildReduceScatter(
   return {reduce_result, token_handler.GetNewToken(reduce_result)};
 }
 
+ReduceScatterResultCoalesced BuildReduceScatterCoalesced(
+    AllReduceType reduce_type, absl::Span<const xla::XlaOp> inputs,
+    xla::XlaOp token, double scale, int64_t scatter_dim, int64_t shard_count,
+    const std::vector<std::vector<int64_t>>& groups, bool pin_layout) {
+  std::vector<xla::ReplicaGroup> cc_groups = CreateReduceGroups(groups);
+  TokenHandler token_handler(token);
+  // TODO: We use pseudo-tokens ATM, which are real values. This need to be
+  // switched to use the real XLA Token once support has been added to XLA
+  // ReduceScatter().
+  ReduceContext cc_ctx = GetReduceContext(inputs);
+  std::vector<xla::XlaOp> result(inputs.size());
+  for (auto& type_ctx : cc_ctx.contexts) {
+    xla::XlaOp reduce_result;
+    type_ctx.second.ops[0] = token_handler.GetInput(
+        type_ctx.second.ops[0], &type_ctx.second.operand_shapes[0]);
+    if (pin_layout) {
+      reduce_result = xla::ReduceScatter(
+          xla::Tuple(inputs[0].builder(), type_ctx.second.ops),
+          GetReduceComutation(reduce_type, type_ctx.first), scatter_dim,
+          shard_count, cc_groups, /*channel_id=*/absl::nullopt,
+          /*layout=*/
+          MakeReduceShape(type_ctx.second.operand_shapes).layout());
+    } else {
+      reduce_result = xla::ReduceScatter(
+          xla::Tuple(inputs[0].builder(), type_ctx.second.ops),
+          GetReduceComutation(reduce_type, type_ctx.first), scatter_dim,
+          shard_count, cc_groups);
+    }
+    for (size_t i = 0; i < type_ctx.second.indices.size(); ++i) {
+      size_t op_idx = type_ctx.second.indices[i];
+      xla::XlaOp gte;
+      if (ShapeHelper::ShapeOfXlaOp(reduce_result).rank() == 0) {
+        gte = xla::GetTupleElement(reduce_result, i);
+      } else {
+        gte = reduce_result;
+      }
+      if (scale != 1.0) {
+        xla::XlaOp scaling_value = XlaHelpers::ScalarValue<float>(
+            scale, type_ctx.second.operand_shapes[i].element_type(),
+            gte.builder());
+        gte = gte * scaling_value;
+      }
+      result[op_idx] = gte;
+    }
+  }
+  return {result, token_handler.GetNewToken(result[0])};
+}
+
 std::vector<torch::lazy::Value> GetOperandListWithToken(
     c10::ArrayRef<torch::lazy::Value> operands,
     const torch::lazy::Value& token) {

torch_xla/csrc/cross_replica_reduces.h

@@ -55,6 +55,11 @@ struct ReduceScatterResult {
   xla::XlaOp token;
 };
 
+struct ReduceScatterResultCoalesced {
+  std::vector<xla::XlaOp> result;
+  xla::XlaOp token;
+};
+
 std::vector<xla::XlaOp> BuildAllReduce(
     AllReduceType reduce_type, absl::Span<const xla::XlaOp> operands,
     xla::XlaOp token, double scale,
@@ -91,6 +96,11 @@ ReduceScatterResult BuildReduceScatter(
     int64_t scatter_dim, int64_t shard_count,
     const std::vector<std::vector<int64_t>>& groups, bool pin_layout);
 
+ReduceScatterResultCoalesced BuildReduceScatterCoalesced(
+    AllReduceType reduce_type, absl::Span<const xla::XlaOp> inputs,
+    xla::XlaOp token, double scale, int64_t scatter_dim, int64_t shard_count,
+    const std::vector<std::vector<int64_t>>& groups, bool pin_layout);
+
 std::vector<torch::lazy::Value> GetOperandListWithToken(
     c10::ArrayRef<torch::lazy::Value> operands,
     const torch::lazy::Value& token);

torch_xla/csrc/init_python_bindings.cpp

@@ -236,6 +236,29 @@ std::pair<at::Tensor, std::shared_ptr<torch::lazy::Value>> ReduceScatter(
       std::make_shared<torch::lazy::Value>(new_token));
 }
 
+std::pair<std::vector<at::Tensor>, std::shared_ptr<torch::lazy::Value>>
+ReduceScatterCoalesced(const std::string& reduce_type,
+                       const std::vector<at::Tensor>& outputs,
+                       const std::vector<at::Tensor>& inputs,
+                       const std::shared_ptr<torch::lazy::Value>& token,
+                       double scale, int64_t scatter_dim, int64_t shard_count,
+                       const std::vector<std::vector<int64_t>>& replica_groups,
+                       bool pin_layout) {
+  std::vector<XLATensorPtr> xtensors_out =
+      GetXlaTensors(outputs, /*want_all=*/true);
+  std::vector<XLATensorPtr> xtensors = GetXlaTensors(inputs, /*want_all=*/true);
+  std::vector<XLATensorPtr> result;
+  torch::lazy::Value new_token;
+  std::tie(result, new_token) = tensor_methods::reduce_scatter_coalesced(
+      xtensors_out, xtensors, *token, GetReduceType(reduce_type), scale,
+      scatter_dim, shard_count, replica_groups, pin_layout);
+  std::vector<at::Tensor> aten_result;
+  for (auto& xt : result) {
+    aten_result.emplace_back(bridge::AtenFromXlaTensor(std::move(xt)));
+  }
+  return {aten_result, std::make_shared<torch::lazy::Value>(new_token)};
+}
+
 std::shared_ptr<torch::lazy::Value> ReduceScatterOut(
     const std::string& reduce_type, at::Tensor& output, const at::Tensor& input,
     const std::shared_ptr<torch::lazy::Value>& token, double scale,
@@ -1320,6 +1343,30 @@ void InitXlaModuleBindings(py::module m) {
           result_tuple[1] = new_token;
           return result_tuple;
         });
+  m.def("_xla_reduce_scatter_coalesced",
+        [](const std::string& reduce_type, std::vector<at::Tensor>& outputs,
+           const std::vector<at::Tensor>& inputs,
+           const std::shared_ptr<torch::lazy::Value>& token, double scale,
+           int64_t scatter_dim, int64_t shard_count, const py::list& groups,
+           bool pin_layout) {
+          std::vector<std::vector<int64_t>> replica_groups =
+              CreateReduceGroups(groups);
+          std::vector<at::Tensor> result;
+          std::shared_ptr<torch::lazy::Value> new_token;
+          {
+            NoGilSection nogil;
+            std::tie(result, new_token) = ReduceScatterCoalesced(
+                reduce_type, outputs, inputs, token, scale, scatter_dim,
+                shard_count, replica_groups, pin_layout);
+          }
+          auto result_list = py::list(result.size() + 1);
+          for (int i = 0; i < result.size(); ++i) {
+            result_list[i] = torch::autograd::make_variable(
+                result[i], /*requires_grad=*/result[i].requires_grad());
+          }
+          result_list[result.size()] = new_token;
+          return result_list;
+        });
   m.def("_xla_reduce_scatter_out",
         [](const std::string& reduce_type, at::Tensor& output,
            const at::Tensor& input,