diff --git a/docs/examples/op_fuser/op_fuser.rst b/docs/examples/op_fuser/op_fuser.rst
index dd17191e58..1466e38121 100644
--- a/docs/examples/op_fuser/op_fuser.rst
+++ b/docs/examples/op_fuser/op_fuser.rst
@@ -151,6 +151,132 @@ arguments and the extra outputs will be returned.
    the block has been split into two sections, each with one branching
    operation.
 
+Extra tensor channels
+"""""""""""""""""""""
+
+Branching operations can also route their extra inputs and outputs within the same ``Sequential`` via named channels. Extra output tensors with a specified channel can be consumed by other operations, in addition to being returned from the ``Sequential``. Extra input tensors with a specified channel are accessed internally instead of being provided as arguments to ``Seqential``.
+
+With a channel, the residual block above can be expressed using one
+``Sequential``:
+
+.. code-block:: python
+
+    import torch
+    import transformer_engine.pytorch as te
+
+    make_residual = te.ops.MakeExtraOutput()
+    add_residual = te.ops.AddExtraInput()
+    make_residual.set_extra_output_channel(0, "residual")
+    add_residual.set_extra_input_channel(0, "residual")
+
+    block = te.ops.Sequential(
+        te.ops.LayerNorm(4096),
+        make_residual,
+        te.ops.Linear(4096, 28672),
+        te.ops.SwiGLU(),
+        te.ops.Linear(14336, 4096),
+        add_residual,
+    )
+
+    # The residual is routed internally and is also returned to the caller.
+    x = torch.randn(16384, 4096, device="cuda")
+    y, residual = block(x)
+
+Channels are also useful for mixture-of-experts blocks. The following
+example assumes custom ``Dispatch`` and ``Combine`` basic operations.
+``Dispatch`` has one public extra input containing router probabilities
+and three extra outputs: split sizes, token probabilities, and a
+routing map. ``Combine`` consumes the routing map.
+
+.. code-block:: python
+
+    import transformer_engine.pytorch as te
+    from my_ops import Dispatch, Combine
+
+    num_experts = 8
+    hidden_size = 4096
+    ffn_size = 14336
+
+    dispatch = Dispatch(num_experts)
+    fc1 = te.ops.GroupedLinear(
+        num_experts, hidden_size, 2 * ffn_size, bias=False
+    )
+    activation = te.ops.ScaledSwiGLU()
+    fc2 = te.ops.GroupedLinear(
+        num_experts, ffn_size, hidden_size, bias=False
+    )
+    combine = Combine(num_experts)
+
+    # Dispatch extra outputs:
+    #   0: split sizes, 1: token probabilities, 2: routing map
+    dispatch.set_extra_output_channel(0, "m_splits")
+    dispatch.set_extra_output_channel(1, "probs")
+    dispatch.set_extra_output_channel(2, "routing_map")
+
+    fc1.set_extra_input_channel(0, "m_splits")
+    activation.set_extra_input_channel(0, "probs")
+    fc2.set_extra_input_channel(0, "m_splits")
+    combine.set_extra_input_channel(0, "routing_map")
+
+    moe = te.ops.Sequential(dispatch, fc1, activation, fc2, combine)
+
+    # Dispatch's extra input has no channel, so the caller passes router_probs.
+    # Channels supply all later extra inputs internally, while Dispatch's
+    # extra outputs are still returned in their original order.
+    y, m_splits, probs, routing_map = moe(x, router_probs)
+
+Channels cannot connect operations in different ``OperationFuser``
+instances. In particular, an ordinary PyTorch module inside a
+``Sequential`` splits the fusible operations on either side into
+separate fusers. The following channel connection is therefore not
+supported:
+
+.. code-block:: python
+
+    make_residual = te.ops.MakeExtraOutput()
+    add_residual = te.ops.AddExtraInput()
+    make_residual.set_extra_output_channel(0, "residual")
+    add_residual.set_extra_input_channel(0, "residual")
+
+    block = te.ops.Sequential(
+        make_residual,
+        torch.nn.Identity(),  # Splits the operations into separate fusers.
+        add_residual,
+    )
+
+Use the public extra output and extra input interfaces, as in the
+two-``Sequential`` example above, when the producer and consumer cannot
+be placed in the same ``OperationFuser``.
+
+The following conditions apply to extra tensor channels:
+
+- A producer must appear before all of its consumers. Backward edges
+  and cycles are not supported.
+- An output channel name has at most one producer, but its output may
+  fan out to multiple consumers.
+- A named output does not require a consumer. It is still returned as
+  a public extra output.
+- A channel is scoped to one ``OperationFuser``. In a ``Sequential``,
+  ordinary PyTorch modules split adjacent fusible operations into
+  separate fusers, and channels cannot cross that boundary.
+- The caller passes extra inputs that are not connected to an earlier
+  producer in the same fuser. Channel-connected extra input slots do
+  not appear in the ``Sequential`` arguments.
+- The caller receives every extra output in the original basic-operation
+  and slot order. This includes channel-bound outputs that are also
+  consumed internally. Gradients supplied for a returned output are
+  combined with gradients from its internal channel consumers.
+- Channel bindings are captured when an ``OperationFuser`` (or the
+  fusers inside a ``Sequential``) is first constructed. Changing
+  ``set_extra_input_channel`` / ``set_extra_output_channel`` afterward
+  requires constructing a new ``OperationFuser`` or ``Sequential``.
+
+Channel-connected basic operations may still be replaced by registered
+``FusedOperation`` implementations. If a fused operation contains both
+the producer and consumer of a channel, its ``fuser_forward`` and
+``fuser_backward`` implementations are responsible for routing the
+tensor and its gradient between those basic operations.
+
 Developer guide
 ---------------
 
diff --git a/tests/pytorch/test_fusible_ops.py b/tests/pytorch/test_fusible_ops.py
index 66857d8125..b50c9c3fc1 100644
--- a/tests/pytorch/test_fusible_ops.py
+++ b/tests/pytorch/test_fusible_ops.py
@@ -23,6 +23,7 @@
     OUTPUT_BUFFER_KEY,
     GRAD_INPUT_BUFFER_KEY,
 )
+from transformer_engine.pytorch.ops.fuser import OperationFuser
 from transformer_engine.pytorch._extra_state import UNSAFE_PICKLE_EXTRA_STATE_ENV
 
 from transformer_engine.pytorch.ops.fused import (
@@ -437,6 +438,417 @@ def test_extra_tensors(self, size: int = 16) -> None:
         torch.testing.assert_close(x4, x4_orig + x3)
 
 
+class _DualExtraOutput(te_ops.BasicOperation):
+    """Test helper: one op with two scaled extra outputs."""
+
+    num_extra_outputs = 2
+
+    def __init__(self, scales: tuple[float, float] = (1.0, 1.0)) -> None:
+        super().__init__()
+        self._scales = scales
+
+    def op_forward(self, *args, **kwargs):
+        raise RuntimeError("_DualExtraOutput uses fuser_forward")
+
+    def op_backward(self, *args, **kwargs):
+        raise RuntimeError("_DualExtraOutput uses fuser_backward")
+
+    def fuser_forward(self, basic_op_ctxs, input_, *, basic_op_extra_inputs, **unused):
+        del basic_op_ctxs, basic_op_extra_inputs
+        s0, s1 = self._scales
+        return input_, [(s0 * input_, s1 * input_)]
+
+    def fuser_backward(self, basic_op_ctxs, grad_output, *, basic_op_grad_extra_outputs):
+        del basic_op_ctxs
+        s0, s1 = self._scales
+        g0, g1 = basic_op_grad_extra_outputs[0]
+        grad_extra = torch.zeros_like(grad_output)
+        if g0 is not None:
+            grad_extra = grad_extra + s0 * g0
+        if g1 is not None:
+            grad_extra = grad_extra + s1 * g1
+        return grad_output + grad_extra, [()], [()]
+
+
+class TestExtraTensorChannels:
+    """Error handling and grad coverage for named extra-tensor channels."""
+
+    @pytest.mark.parametrize("with_extra_grad", (True, False))
+    def test_internal_residual_connection(
+        self,
+        with_extra_grad: bool,
+        size: int = 16,
+    ) -> None:
+        """A channel can keep a residual connection inside a Sequential."""
+        residual = te_ops.MakeExtraOutput()
+        body = te_ops.Bias(size=size, device="cpu")
+        add_residual = te_ops.AddExtraInput()
+        residual.set_extra_output_channel(0, "residual")
+        add_residual.set_extra_input_channel(0, "residual")
+
+        model = te_ops.Sequential(residual, body, add_residual)
+        x = torch.rand((size,), requires_grad=True)
+        y, residual_out = model(x)
+
+        torch.testing.assert_close(y, 2 * x + body.bias)
+        torch.testing.assert_close(residual_out, x)
+        dy = torch.rand_like(y)
+        if with_extra_grad:
+            dresidual = torch.rand_like(residual_out)
+            torch.autograd.backward((y, residual_out), (dy, dresidual))
+            expected_dx = 2 * dy + dresidual
+        else:
+            y.backward(dy)
+            expected_dx = 2 * dy
+        torch.testing.assert_close(x.grad, expected_dx)
+        torch.testing.assert_close(body.bias.grad, dy)
+
+    @pytest.mark.parametrize("fusion_kind", ("forward", "backward", "forward_backward"))
+    @pytest.mark.parametrize("with_extra_grad", (True, False))
+    def test_fused_internal_residual_connection(
+        self,
+        fusion_kind: str,
+        with_extra_grad: bool,
+        size: int = 16,
+    ) -> None:
+        """Forward, backward, and joint fusions can own an internal channel."""
+
+        class FusedResidual(te_ops.FusedOperation):
+            """Fuse MakeExtraOutput, Bias, and AddExtraInput."""
+
+            _enabled = True
+
+            def __init__(self, residual, body, add_residual) -> None:
+                super().__init__((residual, body, add_residual))
+
+            def fuser_forward(
+                self,
+                basic_op_ctxs,
+                input_,
+                *,
+                basic_op_extra_inputs,
+                **unused,
+            ):
+                del basic_op_ctxs
+                # The consumer slot is internal to this fusion, so the
+                # OperationFuser deliberately leaves it unset.
+                assert basic_op_extra_inputs[2][0] is None
+                return 2 * input_ + self.basic_ops[1].bias, [(input_,), (), ()]
+
+            def fuser_backward(
+                self,
+                basic_op_ctxs,
+                grad_output,
+                *,
+                basic_op_grad_extra_outputs,
+            ):
+                del basic_op_ctxs
+                # The fusion owns the internal residual edge. The fuser also
+                # supplies the gradient from the public residual output.
+                grad_residual = basic_op_grad_extra_outputs[0][0]
+                return (
+                    2 * grad_output
+                    + (torch.zeros_like(grad_output) if grad_residual is None else grad_residual),
+                    [(), (grad_output,), ()],
+                    [(), (), (grad_output,)],
+                )
+
+        def fuse_residual(ops, **unused):
+            if not FusedResidual._enabled:
+                return ops
+            if (
+                len(ops) == 3
+                and isinstance(ops[0], te_ops.MakeExtraOutput)
+                and isinstance(ops[1], te_ops.Bias)
+                and isinstance(ops[2], te_ops.AddExtraInput)
+            ):
+                # We want to enable this fusion just for this test.
+                FusedResidual._enabled = False
+                return [FusedResidual(*ops)]
+            return ops
+
+        residual = te_ops.MakeExtraOutput()
+        body = te_ops.Bias(size=size, device="cpu")
+        add_residual = te_ops.AddExtraInput()
+        residual.set_extra_output_channel(0, "residual")
+        add_residual.set_extra_input_channel(0, "residual")
+        model = te_ops.Sequential(residual, body, add_residual)
+
+        if fusion_kind == "forward":
+            te_ops.register_forward_fusion(fuse_residual, prepend=True)
+        elif fusion_kind == "backward":
+            te_ops.register_backward_fusion(fuse_residual, prepend=True)
+        else:
+            te_ops.register_forward_backward_fusion(fuse_residual, prepend=True)
+        x = torch.rand((size,), requires_grad=True)
+        y, residual_out = model(x)
+
+        forward_ops = model._module_groups[0]._forward_ops
+        backward_ops = model._module_groups[0]._backward_ops
+        if fusion_kind in ("forward", "forward_backward"):
+            assert len(forward_ops) == 1
+            assert isinstance(forward_ops[0][0], FusedResidual)
+        else:
+            assert len(forward_ops) == 3
+        if fusion_kind in ("backward", "forward_backward"):
+            assert len(backward_ops) == 1
+            assert isinstance(backward_ops[0][0], FusedResidual)
+        else:
+            assert len(backward_ops) == 3
+        if fusion_kind == "forward_backward":
+            assert backward_ops[0][0] is forward_ops[0][0]
+        torch.testing.assert_close(y, 2 * x + body.bias)
+        dy = torch.rand_like(y)
+        if with_extra_grad:
+            dresidual = torch.rand_like(residual_out)
+            torch.autograd.backward((y, residual_out), (dy, dresidual))
+            expected_dx = 2 * dy + dresidual
+        else:
+            y.backward(dy)
+            expected_dx = 2 * dy
+        torch.testing.assert_close(x.grad, expected_dx)
+        torch.testing.assert_close(body.bias.grad, dy)
+
+    def test_internal_extra_tensor_channel_fanout(self, size: int = 16) -> None:
+        """An internal extra output can feed multiple later consumers."""
+        producer = te_ops.MakeExtraOutput()
+        consumer1 = te_ops.AddExtraInput()
+        consumer2 = te_ops.AddExtraInput()
+        producer.set_extra_output_channel(0, "route")
+        consumer1.set_extra_input_channel(0, "route")
+        consumer2.set_extra_input_channel(0, "route")
+        model = te_ops.Sequential(producer, consumer1, consumer2)
+
+        x = torch.rand((size,), requires_grad=True)
+        y, route = model(x)
+
+        # Main path: x -> x + route -> x + route + route.
+        torch.testing.assert_close(y, 3 * x)
+        torch.testing.assert_close(route, x)
+        dy = torch.rand_like(y)
+        droute = torch.rand_like(route)
+        torch.autograd.backward((y, route), (dy, droute))
+        # The channel fan-out contributes two independent gradient paths.
+        torch.testing.assert_close(x.grad, 3 * dy + droute)
+
+        # Internal slots are unavailable before forward, so grad discovery
+        # must tolerate them when no public input requires gradients.
+        x_no_grad = x.detach()
+        y_no_grad, route_no_grad = model(x_no_grad)
+        torch.testing.assert_close(y_no_grad, 3 * x_no_grad)
+        torch.testing.assert_close(route_no_grad, x_no_grad)
+
+    def test_internal_and_external_extra_tensor_inputs(self, size: int = 16) -> None:
+        """Unbound slots remain public when other slots use internal channels."""
+        producer = te_ops.MakeExtraOutput()
+        internal_consumer = te_ops.AddExtraInput()
+        external_consumer = te_ops.AddExtraInput()
+        producer.set_extra_output_channel(0, "route")
+        internal_consumer.set_extra_input_channel(0, "route")
+        model = te_ops.Sequential(producer, internal_consumer, external_consumer)
+
+        x = torch.rand((size,), requires_grad=True)
+        extra = torch.rand((size,), requires_grad=True)
+        y, route = model(x, extra)
+
+        torch.testing.assert_close(y, 2 * x + extra)
+        torch.testing.assert_close(route, x)
+        dy = torch.rand_like(y)
+        y.backward(dy)
+        torch.testing.assert_close(x.grad, 2 * dy)
+        torch.testing.assert_close(extra.grad, dy)
+
+    def test_external_named_extra_inputs_remain_separate(self, size: int = 16) -> None:
+        """Unmatched inputs with the same channel require separate public tensors."""
+        consumer1 = te_ops.AddExtraInput()
+        consumer2 = te_ops.AddExtraInput()
+        consumer1.set_extra_input_channel(0, "external")
+        consumer2.set_extra_input_channel(0, "external")
+        model = te_ops.Sequential(consumer1, consumer2)
+
+        x = torch.rand((size,), requires_grad=True)
+        extra = torch.rand((size,), requires_grad=True)
+        with pytest.raises(ValueError, match="Expected 2 extra inputs but got 1"):
+            model(x, extra)
+        y = model(x, extra, extra)
+        torch.testing.assert_close(y, x + 2 * extra)
+
+        dy = torch.rand_like(y)
+        y.backward(dy)
+        torch.testing.assert_close(x.grad, dy)
+        torch.testing.assert_close(extra.grad, 2 * dy)
+
+    def test_consumer_before_producer(self) -> None:
+        """Channels only connect forward; a later producer does not satisfy an earlier consumer."""
+        consumer = te_ops.AddExtraInput()
+        producer = te_ops.MakeExtraOutput()
+        consumer.set_extra_input_channel(0, "route")
+        producer.set_extra_output_channel(0, "route")
+        with pytest.raises(ValueError, match="has no earlier producer"):
+            OperationFuser([consumer, producer])
+
+    def test_set_extra_channel_rejects_invalid_index(self) -> None:
+        """Slot indices must be in range; negatives and OOB are rejected at bind time."""
+        producer = te_ops.MakeExtraOutput()
+        consumer = te_ops.AddExtraInput()
+
+        with pytest.raises(IndexError, match="out of range"):
+            producer.set_extra_output_channel(-1, "route")
+        with pytest.raises(IndexError, match="out of range"):
+            producer.set_extra_output_channel(1, "route")
+        with pytest.raises(IndexError, match="out of range"):
+            consumer.set_extra_input_channel(-1, "route")
+        with pytest.raises(IndexError, match="out of range"):
+            consumer.set_extra_input_channel(1, "route")
+
+    def test_set_extra_channel_rejects_invalid_name(self) -> None:
+        """Channel names must be non-empty strings."""
+        producer = te_ops.MakeExtraOutput()
+        consumer = te_ops.AddExtraInput()
+
+        with pytest.raises(ValueError, match="non-empty string"):
+            producer.set_extra_output_channel(0, "")
+        with pytest.raises(ValueError, match="non-empty string"):
+            consumer.set_extra_input_channel(0, "")
+        with pytest.raises(ValueError, match="non-empty string"):
+            producer.set_extra_output_channel(0, 123)  # type: ignore[arg-type]
+
+    def test_extra_channel_change_requires_new_sequential(self, size: int = 16) -> None:
+        """Sequential does not auto-rebuild after a channel configuration change."""
+        producer = te_ops.MakeExtraOutput()
+        consumer = te_ops.AddExtraInput()
+        producer.set_extra_output_channel(0, "route")
+        consumer.set_extra_input_channel(0, "route")
+        model = te_ops.Sequential(producer, consumer)
+
+        x = torch.rand((size,))
+        y, route = model(x)
+        torch.testing.assert_close(y, 2 * x)
+        torch.testing.assert_close(route, x)
+
+        consumer.set_extra_input_channel(0, None)
+        extra = torch.rand_like(x)
+        with pytest.raises(RuntimeError, match="Construct a new OperationFuser"):
+            model(x, extra)
+
+        model = te_ops.Sequential(producer, consumer)
+        y, route = model(x, extra)
+        torch.testing.assert_close(y, x + extra)
+        torch.testing.assert_close(route, x)
+
+    @pytest.mark.parametrize("layout", ("two_ops", "same_op"))
+    def test_duplicate_extra_output_channel_names(self, layout: str) -> None:
+        """A channel name may have at most one producer, across ops or slots."""
+        consumer = te_ops.AddExtraInput()
+        consumer.set_extra_input_channel(0, "route")
+        if layout == "two_ops":
+            producer1 = te_ops.MakeExtraOutput()
+            producer2 = te_ops.MakeExtraOutput()
+            producer1.set_extra_output_channel(0, "route")
+            producer2.set_extra_output_channel(0, "route")
+            ops = [producer1, producer2, consumer]
+        else:
+            producer = _DualExtraOutput()
+            producer.set_extra_output_channel(0, "route")
+            producer.set_extra_output_channel(1, "route")
+            ops = [producer, consumer]
+        with pytest.raises(ValueError, match="multiple producers"):
+            OperationFuser(ops)
+
+    def test_named_extra_output_without_consumer_is_public(self, size: int = 16) -> None:
+        """A named output remains public when its fuser has no consumer."""
+        producer = te_ops.MakeExtraOutput()
+        producer.set_extra_output_channel(0, "orphan")
+        x = torch.rand((size,), requires_grad=True)
+        y, extra = producer(x)
+        torch.testing.assert_close(y, x)
+        torch.testing.assert_close(extra, x)
+
+    def test_one_extra_input_has_single_source(self, size: int = 16) -> None:
+        """Rebinding selects one source and leaves the other output public."""
+        producer_a = te_ops.MakeExtraOutput()
+        producer_b = te_ops.MakeExtraOutput()
+        consumer = te_ops.AddExtraInput()
+        producer_a.set_extra_output_channel(0, "a")
+        producer_b.set_extra_output_channel(0, "b")
+        consumer.set_extra_input_channel(0, "a")
+        consumer.set_extra_input_channel(0, "b")
+        fuser = OperationFuser([producer_a, producer_b, consumer])
+        assert fuser._basic_op_extra_input_sources[2] == [(1, 0)]
+        assert fuser.num_extra_inputs == 0
+
+        x = torch.rand((size,))
+        y, output_a, output_b = fuser(x)
+        torch.testing.assert_close(y, 2 * x)
+        torch.testing.assert_close(output_a, x)
+        torch.testing.assert_close(output_b, x)
+
+    def test_mixed_channel_outputs_are_public(self, size: int = 16) -> None:
+        """Both internally consumed and unconsumed channel outputs are public."""
+        producer = _DualExtraOutput(scales=(2.0, 3.0))
+        consumer = te_ops.AddExtraInput()
+        producer.set_extra_output_channel(0, "internal")
+        producer.set_extra_output_channel(1, "public")
+        consumer.set_extra_input_channel(0, "internal")
+        model = te_ops.Sequential(producer, consumer)
+
+        x = torch.rand((size,), requires_grad=True)
+        y, internal, public = model(x)
+        torch.testing.assert_close(y, 3 * x)
+        torch.testing.assert_close(internal, 2 * x)
+        torch.testing.assert_close(public, 3 * x)
+
+        dy = torch.rand_like(y)
+        dinternal = torch.rand_like(internal)
+        dpublic = torch.rand_like(public)
+        torch.autograd.backward((y, internal, public), (dy, dinternal, dpublic))
+        torch.testing.assert_close(x.grad, 3 * dy + 2 * dinternal + 3 * dpublic)
+
+    def test_fresh_internal_output_preserves_grad_requirement(self) -> None:
+        """A fresh internal tensor requests its gradient from a scaled activation."""
+
+        # A BasicOperation with one extra output that is freshly computed instead of
+        # retrieved from a previous op's tensor.
+        class MakeScale(te_ops.BasicOperation):
+            num_extra_outputs = 1
+
+            def op_forward(self, *args, **kwargs):
+                raise RuntimeError("MakeScale uses fuser_forward")
+
+            def op_backward(self, *args, **kwargs):
+                raise RuntimeError("MakeScale uses fuser_backward")
+
+            def fuser_forward(self, basic_op_ctxs, input_, *, basic_op_extra_inputs, **unused):
+                del basic_op_extra_inputs
+                basic_op_ctxs[0].save_for_backward(input_)
+                return input_, [(input_.square().mean(dim=-1),)]
+
+            def fuser_backward(self, basic_op_ctxs, grad_output, *, basic_op_grad_extra_outputs):
+                (input_,) = basic_op_ctxs[0].saved_tensors
+                grad_scale = basic_op_grad_extra_outputs[0][0]
+                assert grad_scale is not None
+                grad_input = grad_output + grad_scale.unsqueeze(-1) * 2 * input_ / input_.size(-1)
+                return grad_input, [()], [()]
+
+        producer = MakeScale()
+        activation = te_ops.ScaledSReLU()
+        producer.set_extra_output_channel(0, "scale")
+        activation.set_extra_input_channel(0, "scale")
+        model = te_ops.Sequential(producer, activation)
+
+        x_ref = torch.randn((5, 8), device="cuda", requires_grad=True)
+        x_test = x_ref.detach().clone().requires_grad_(True)
+        scale_ref = x_ref.square().mean(dim=-1)
+        y_ref = torch.nn.functional.relu(x_ref).square() * scale_ref.unsqueeze(-1)
+        y_test, _scale_test = model(x_test)
+        torch.testing.assert_close(y_test, y_ref)
+
+        dy = torch.rand_like(y_ref)
+        y_ref.backward(dy)
+        y_test.backward(dy)
+        torch.testing.assert_close(x_test.grad, x_ref.grad)
+
+
 class TestFuser:
     """Tests for operation fusion infrastructure"""
 
diff --git a/transformer_engine/pytorch/ops/basic/activation.py b/transformer_engine/pytorch/ops/basic/activation.py
index 8137051322..a974d41ef9 100644
--- a/transformer_engine/pytorch/ops/basic/activation.py
+++ b/transformer_engine/pytorch/ops/basic/activation.py
@@ -6,7 +6,7 @@
 
 from __future__ import annotations
 import abc
-from collections.abc import Iterable
+from collections.abc import Iterable, Sequence
 from typing import Any, Optional
 
 import torch
@@ -401,7 +401,7 @@ def fuser_forward(
         prev_op_grad_output_quantizer: Optional[Quantizer],  # pylint: disable=unused-argument
         next_op_input_quantizer: Optional[Quantizer],  # pylint: disable=unused-argument
         basic_op_kwargs: list[dict[str, Any]],  # pylint: disable=unused-argument
-    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Sequence[Sequence[torch.Tensor]]]:
         if self.activation_recompute_in_mlp:
             raise RuntimeError(
                 f"{self.__class__.__name__}(activation_recompute_in_mlp=True) requires the "
diff --git a/transformer_engine/pytorch/ops/basic/add_extra_input.py b/transformer_engine/pytorch/ops/basic/add_extra_input.py
index fc3ca9cade..9af399f2dc 100644
--- a/transformer_engine/pytorch/ops/basic/add_extra_input.py
+++ b/transformer_engine/pytorch/ops/basic/add_extra_input.py
@@ -5,7 +5,7 @@
 """Fusible operation for adding extra input tensor."""
 
 from __future__ import annotations
-from collections.abc import Iterable
+from collections.abc import Iterable, Sequence
 from typing import Any, Optional
 
 import torch
@@ -67,7 +67,7 @@ def fuser_forward(
         prev_op_grad_output_quantizer: Optional[Quantizer],
         next_op_input_quantizer: Optional[Quantizer],
         basic_op_kwargs: list[dict[str, Any]],
-    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Sequence[Sequence[torch.Tensor]]]:
         extra_input = basic_op_extra_inputs[0][0]
         if self._in_place:
             extra_input = extra_input.detach()
diff --git a/transformer_engine/pytorch/ops/basic/grouped_linear.py b/transformer_engine/pytorch/ops/basic/grouped_linear.py
index e1980d2943..27213fe7cf 100644
--- a/transformer_engine/pytorch/ops/basic/grouped_linear.py
+++ b/transformer_engine/pytorch/ops/basic/grouped_linear.py
@@ -147,11 +147,11 @@ def __init__(
         delay_wgrad_compute: bool = False,
         scale_bias: bool = False,
     ) -> None:
-        super().__init__()
-
+        # Decide before BasicOperation.__init__ sizes _extra_input_channels.
         self._scale_bias: bool = scale_bias and bias
         if self._scale_bias:
             self.num_extra_inputs = 2
+        super().__init__()
 
         self.wgrad_store = WeightGradStore(delay_wgrad_compute)
         self.wgrad_accumulation_and_reduce_hooks: list = []
@@ -1003,7 +1003,7 @@ def fuser_forward(
         prev_op_grad_output_quantizer: Optional[Quantizer],
         next_op_input_quantizer: Optional[Quantizer],
         basic_op_kwargs: list[dict[str, Any]],
-    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Sequence[Sequence[torch.Tensor]]]:
         num_groups = self.num_groups
         weight_param = self.weight if self.single_grouped_weight else self.weight0
         device = weight_param.device
diff --git a/transformer_engine/pytorch/ops/basic/make_extra_output.py b/transformer_engine/pytorch/ops/basic/make_extra_output.py
index 0d9c870262..5ad00e2fd1 100644
--- a/transformer_engine/pytorch/ops/basic/make_extra_output.py
+++ b/transformer_engine/pytorch/ops/basic/make_extra_output.py
@@ -5,7 +5,7 @@
 """Make extra tensor output in operation fuser."""
 
 from __future__ import annotations
-from collections.abc import Iterable
+from collections.abc import Iterable, Sequence
 from typing import Any, Optional
 
 import torch
@@ -72,7 +72,7 @@ def fuser_forward(
         prev_op_grad_output_quantizer: Optional[Quantizer],
         next_op_input_quantizer: Optional[Quantizer],
         basic_op_kwargs: list[dict[str, Any]],
-    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Sequence[Sequence[torch.Tensor]]]:
         return input_, [(input_,)]
 
     def fuser_backward(
diff --git a/transformer_engine/pytorch/ops/basic/swiglu.py b/transformer_engine/pytorch/ops/basic/swiglu.py
index fb663c0480..598126e2cb 100644
--- a/transformer_engine/pytorch/ops/basic/swiglu.py
+++ b/transformer_engine/pytorch/ops/basic/swiglu.py
@@ -5,7 +5,7 @@
 """Fusible operation for SwiGLU and variants."""
 
 from __future__ import annotations
-from collections.abc import Iterable
+from collections.abc import Iterable, Sequence
 from typing import Any, Optional
 
 import torch
@@ -429,7 +429,7 @@ def fuser_forward(
         prev_op_grad_output_quantizer: Optional[Quantizer],
         next_op_input_quantizer: Optional[Quantizer],
         basic_op_kwargs: list[dict[str, Any]],
-    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Sequence[Sequence[torch.Tensor]]]:
         if self.activation_recompute_in_mlp:
             raise RuntimeError(
                 f"{self.__class__.__name__}(activation_recompute_in_mlp=True) requires the "
diff --git a/transformer_engine/pytorch/ops/fused/forward_linear_bias_activation.py b/transformer_engine/pytorch/ops/fused/forward_linear_bias_activation.py
index 8df929f799..6fa63675b4 100644
--- a/transformer_engine/pytorch/ops/fused/forward_linear_bias_activation.py
+++ b/transformer_engine/pytorch/ops/fused/forward_linear_bias_activation.py
@@ -5,7 +5,7 @@
 """Fused operation for forward GEMM + bias + activation."""
 
 from __future__ import annotations
-from collections.abc import Iterable
+from collections.abc import Sequence
 from typing import Any, Optional
 
 import torch
@@ -59,7 +59,7 @@ def fuser_forward(
         prev_op_grad_output_quantizer: Optional[Quantizer],
         next_op_input_quantizer: Optional[Quantizer],
         basic_op_kwargs: list[dict[str, Any]],
-    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Sequence[Sequence[torch.Tensor]]]:
 
         # Get basic operations
         idx = self._op_idxs["linear"]
diff --git a/transformer_engine/pytorch/ops/fused/forward_linear_bias_add.py b/transformer_engine/pytorch/ops/fused/forward_linear_bias_add.py
index 5376a7d264..28586360f5 100644
--- a/transformer_engine/pytorch/ops/fused/forward_linear_bias_add.py
+++ b/transformer_engine/pytorch/ops/fused/forward_linear_bias_add.py
@@ -5,7 +5,7 @@
 """Fused operation for forward GEMM + bias + add."""
 
 from __future__ import annotations
-from collections.abc import Iterable
+from collections.abc import Sequence
 from typing import Any, Optional
 
 import torch
@@ -57,7 +57,7 @@ def fuser_forward(
         prev_op_grad_output_quantizer: Optional[Quantizer],
         next_op_input_quantizer: Optional[Quantizer],
         basic_op_kwargs: list[dict[str, Any]],
-    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Sequence[Sequence[torch.Tensor]]]:
 
         # Get basic operations
         idx = self._op_idxs["linear"]
diff --git a/transformer_engine/pytorch/ops/fused/forward_linear_scale_add.py b/transformer_engine/pytorch/ops/fused/forward_linear_scale_add.py
index abeb39adfa..277263e0ec 100644
--- a/transformer_engine/pytorch/ops/fused/forward_linear_scale_add.py
+++ b/transformer_engine/pytorch/ops/fused/forward_linear_scale_add.py
@@ -5,7 +5,7 @@
 """Fused operation for forward GEMM + scale + add."""
 
 from __future__ import annotations
-from collections.abc import Iterable
+from collections.abc import Sequence
 from typing import Any, Optional
 
 import torch
@@ -47,7 +47,7 @@ def fuser_forward(
         prev_op_grad_output_quantizer: Optional[Quantizer],
         next_op_input_quantizer: Optional[Quantizer],
         basic_op_kwargs: list[dict[str, Any]],
-    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Sequence[Sequence[torch.Tensor]]]:
 
         # Get basic operations
         linear_op = self.basic_ops[0]
diff --git a/transformer_engine/pytorch/ops/fused/grouped_mlp.py b/transformer_engine/pytorch/ops/fused/grouped_mlp.py
index 76d51673f0..e618ef89c3 100644
--- a/transformer_engine/pytorch/ops/fused/grouped_mlp.py
+++ b/transformer_engine/pytorch/ops/fused/grouped_mlp.py
@@ -6,7 +6,7 @@
 
 from __future__ import annotations
 
-from collections.abc import Callable, Iterable
+from collections.abc import Callable, Iterable, Sequence
 import functools
 import os
 from importlib.metadata import PackageNotFoundError, version as get_pkg_version
@@ -975,7 +975,7 @@ def fuser_forward(
         prev_op_grad_output_quantizer: Optional[Quantizer],
         next_op_input_quantizer: Optional[Quantizer],
         basic_op_kwargs: list[dict[str, Any]],
-    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Sequence[Sequence[torch.Tensor]]]:
         # Get basic operations
         fc1_op, activation_op, fc2_op = self.basic_ops
         fc1_ctx, _activation_ctx, fc2_ctx = basic_op_ctxs
diff --git a/transformer_engine/pytorch/ops/fused/userbuffers_forward_linear.py b/transformer_engine/pytorch/ops/fused/userbuffers_forward_linear.py
index 3a8ff5438d..cb5f8a16e6 100644
--- a/transformer_engine/pytorch/ops/fused/userbuffers_forward_linear.py
+++ b/transformer_engine/pytorch/ops/fused/userbuffers_forward_linear.py
@@ -5,7 +5,7 @@
 """Linear layer forward with Userbuffers communication."""
 
 from __future__ import annotations
-from collections.abc import Iterable
+from collections.abc import Sequence
 from typing import Any, Optional
 
 import torch
@@ -286,7 +286,7 @@ def fuser_forward(
         prev_op_grad_output_quantizer: Optional[Quantizer],
         next_op_input_quantizer: Optional[Quantizer],
         basic_op_kwargs: list[dict[str, Any]],
-    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Sequence[Sequence[torch.Tensor]]]:
 
         # Get basic operations
         idx = self._op_idxs["linear"]
diff --git a/transformer_engine/pytorch/ops/fuser.py b/transformer_engine/pytorch/ops/fuser.py
index 09ffb004dd..3c95845c80 100644
--- a/transformer_engine/pytorch/ops/fuser.py
+++ b/transformer_engine/pytorch/ops/fuser.py
@@ -102,12 +102,17 @@ def forward(
         for tensor in (input_,) + params_and_extra_inputs:
             tensor._do_not_clear = True
 
-        # Unflatten list of parameters and extra tensor inputs
-        extra_inputs = params_and_extra_inputs[-fuser.num_extra_inputs :]
-        basic_op_extra_inputs = []
-        for op in fuser._basic_ops:
-            xs, extra_inputs = _split_tuple(extra_inputs, op.num_extra_inputs)
-            basic_op_extra_inputs.append(xs)
+        # Place user provided extra inputs into their basic-op slots. Slots bound to
+        # internal channels are filled lazily as their producers execute.
+        extra_inputs = params_and_extra_inputs[len(fuser._flat_basic_op_params) :]
+        basic_op_extra_inputs: list[list[Optional[torch.Tensor]]] = [
+            [None] * op.num_extra_inputs for op in fuser._basic_ops
+        ]
+        for tensor, (op_idx, input_idx) in zip(
+            extra_inputs,
+            fuser._external_extra_input_slots,
+        ):
+            basic_op_extra_inputs[op_idx][input_idx] = tensor
 
         # Apply forward ops
         x = input_
@@ -118,7 +123,22 @@ def forward(
             for idx in basic_op_idxs:
                 basic_op_ctxs[idx].requires_grad = idx >= fuser.first_op_requiring_backward
 
-            # Forward op
+            # Resolve internal channel inputs from outputs of
+            # earlier basic ops. When a fusion contains both producer and
+            # consumer, leave the consumer slot unset so the fused op can
+            # wire the channel itself
+            for idx in basic_op_idxs:
+                for input_idx, source in enumerate(fuser._basic_op_extra_input_sources[idx]):
+                    if source is None:
+                        continue
+                    producer_idx, output_idx = source
+                    if producer_idx in basic_op_idxs:
+                        # fused op will wire the channel itself internally
+                        continue
+                    producer_outputs = extra_outputs[producer_idx]
+                    basic_op_extra_inputs[idx][input_idx] = producer_outputs[output_idx]
+
+            # Prepare args for op forward
             extra_inputs = [basic_op_extra_inputs[idx] for idx in basic_op_idxs]
             prev_op_idx = basic_op_idxs[0] - 1
             prev_op = fuser._basic_ops[prev_op_idx] if prev_op_idx >= 0 else None
@@ -139,24 +159,30 @@ def forward(
                 next_op_input_quantizer=next_op_input_quantizer,
                 basic_op_kwargs=[basic_op_kwargs[idx] for idx in basic_op_idxs],
             )
+            if len(fused_op_extra_outputs) != len(basic_op_idxs):
+                raise RuntimeError(
+                    f"Expected {type(op).__name__} to generate extra outputs for "
+                    f"{len(basic_op_idxs)} basic operations, "
+                    f"but got {len(fused_op_extra_outputs)}"
+                )
             for idx, ys in zip(basic_op_idxs, fused_op_extra_outputs):
-                for y in ys:
-                    if set_output_requires_grad:
-                        y.requires_grad_(idx >= fuser.first_op_requiring_backward)
+                num_extra_outputs = fuser._basic_ops[idx].num_extra_outputs
+                if len(ys) != num_extra_outputs:
+                    raise RuntimeError(
+                        f"Expected op {idx} to generate {num_extra_outputs} extra outputs, "
+                        f"but got {len(ys)}"
+                    )
+                for output_idx, y in enumerate(ys):
+                    if (
+                        set_output_requires_grad
+                        and idx >= fuser.first_op_requiring_backward
+                        and y.is_floating_point()
+                    ):
+                        y.requires_grad_(True)
                 extra_outputs[idx] = ys
 
         # Flatten list of extra outputs
-        extra_outputs_flat = []
-        for idx, ys in enumerate(extra_outputs):
-            ys = list(ys)
-            num_extra_outputs = fuser._basic_ops[idx].num_extra_outputs
-            if len(ys) != num_extra_outputs:
-                raise RuntimeError(
-                    f"Expected op {idx} to generate "
-                    "{num_extra_outputs} extra inputs, "
-                    f"but got {len(ys)}"
-                )
-            extra_outputs_flat.extend(ys)
+        extra_outputs_flat = [y for ys in extra_outputs for y in ys]
 
         # Save context for backward pass
         if func_ctx is not None:
@@ -186,8 +212,11 @@ def forward(
             func_ctx.basic_ops = fuser._basic_ops
             func_ctx.basic_op_ctxs = basic_op_ctxs
             func_ctx.basic_op_num_params = fuser._basic_op_num_params
-            func_ctx.num_extra_inputs = fuser.num_extra_inputs
             func_ctx.num_extra_outputs = len(extra_outputs_flat)
+            func_ctx.external_extra_input_slots = fuser._external_extra_input_slots
+            func_ctx.basic_op_extra_output_channels = fuser._basic_op_extra_output_channels
+            func_ctx.basic_op_extra_output_is_internal = fuser._basic_op_extra_output_is_internal
+            func_ctx.basic_op_extra_input_sources = fuser._basic_op_extra_input_sources
             func_ctx.is_first_module = is_first_module
 
         # Mark output tensors as not deletable in backward
@@ -224,21 +253,31 @@ def backward(
             ctx.saved_tensors = saved_tensors[slice(*ctx._saved_tensors_range)]
             ctx._saved_tensors_range = None
 
-        # Unflatten list of extra tensor output grads
+        # Channel wiring saved from forward
+        basic_op_extra_output_channels = func_ctx.basic_op_extra_output_channels
+        basic_op_extra_output_is_internal = func_ctx.basic_op_extra_output_is_internal
+        basic_op_extra_input_sources = func_ctx.basic_op_extra_input_sources
+
+        # Place caller-provided extra-output grads into their basic-op slots.
+        # Gradients from internal channel consumers are added during backward.
         if len(grad_extra_outputs) != func_ctx.num_extra_outputs:
             raise ValueError(
                 f"Expected grads for {func_ctx.num_extra_outputs} extra tensor outputs, "
                 f"but got {len(grad_extra_outputs)}"
             )
-        basic_op_grad_extra_outputs = []
+        basic_op_grad_extra_outputs: list[list[Optional[torch.Tensor]]] = []
         for op in basic_ops:
-            dys, grad_extra_outputs = _split_tuple(grad_extra_outputs, op.num_extra_outputs)
-            basic_op_grad_extra_outputs.append(dys)
+            grads, grad_extra_outputs = _split_tuple(
+                grad_extra_outputs,
+                op.num_extra_outputs,
+            )
+            basic_op_grad_extra_outputs.append(list(grads))
 
         # Apply backward ops
         dx = grad_output
         grad_params = [None for _ in range(len(basic_ops))]
         grad_extra_inputs = [None for _ in range(len(basic_ops))]
+        channel_grads: dict[str, torch.Tensor] = {}
         for op, basic_op_idxs in reversed(backward_ops):
 
             # Stop if no more gradients are required
@@ -246,7 +285,17 @@ def backward(
                 dx = None
                 break
 
-            # Backward op
+            # Backward op. Supply gradients accumulated from every consumer of
+            # each internal channel.
+            for idx in basic_op_idxs:
+                for output_idx, channel in enumerate(basic_op_extra_output_channels[idx]):
+                    if basic_op_extra_output_is_internal[idx][output_idx]:
+                        channel_grad = channel_grads.get(channel)
+                        if channel_grad is not None:
+                            output_grad = basic_op_grad_extra_outputs[idx][output_idx]
+                            basic_op_grad_extra_outputs[idx][output_idx] = (
+                                channel_grad if output_grad is None else output_grad + channel_grad
+                            )
             grad_extra_outputs = [basic_op_grad_extra_outputs[idx] for idx in basic_op_idxs]
             dx, fused_op_grad_params, fused_op_grad_extra_inputs = op.fuser_backward(
                 [basic_op_ctxs[idx] for idx in basic_op_idxs],
@@ -258,6 +307,18 @@ def backward(
                 basic_op_ctxs[idx].saved_tensors = None
             for idx, dxs in zip(basic_op_idxs, fused_op_grad_extra_inputs):
                 grad_extra_inputs[idx] = dxs
+                for input_idx, grad in enumerate(dxs):
+                    source = basic_op_extra_input_sources[idx][input_idx]
+                    if source is None or grad is None:
+                        continue
+                    producer_idx, output_idx = source
+                    # Producer already ran inside this fusion; the fused op
+                    # must apply these grads itself rather than via channel_grads.
+                    if producer_idx in basic_op_idxs:
+                        continue
+                    channel = basic_op_extra_output_channels[producer_idx][output_idx]
+                    previous_grad = channel_grads.get(channel)
+                    channel_grads[channel] = grad if previous_grad is None else previous_grad + grad
 
         # Flatten list of parameter gradients
         grad_params_flat = []
@@ -275,20 +336,22 @@ def backward(
             grad_params_flat.extend(dparams)
 
         # Flatten list of parameter gradients
-        grad_extra_inputs_flat = []
         for idx, dxs in enumerate(grad_extra_inputs):
             num_extra_inputs = basic_ops[idx].num_extra_inputs
             if dxs is None:
-                dxs = [None for _ in range(num_extra_inputs)]
-            else:
-                dxs = list(dxs)
-            if len(dxs) != num_extra_inputs:
+                grad_extra_inputs[idx] = (None,) * num_extra_inputs
+            elif len(dxs) != num_extra_inputs:
                 raise RuntimeError(
                     f"Expected op {idx} to generate grads "
                     f"for {num_extra_inputs} extra inputs, "
                     f"but got {len(dxs)}"
                 )
-            grad_extra_inputs_flat.extend(dxs)
+
+        # Collect the gradient for each public extra input.
+        grad_extra_inputs_flat = [
+            grad_extra_inputs[op_idx][input_idx]
+            for op_idx, input_idx in func_ctx.external_extra_input_slots
+        ]
 
         # Update FP8 scaling factors
         if func_ctx.is_first_module and not _is_graph_capturing():
@@ -339,10 +402,93 @@ def __init__(
                 basic_ops.append(op)
         self._num_basic_ops: int = len(basic_ops)
         self._basic_ops: list[BasicOperation] = basic_ops
+        self._basic_op_extra_channels_versions = [
+            op._extra_channels_version for op in self._basic_ops
+        ]
 
         # Number of extra tensor inputs
         self._basic_op_num_extra_inputs: list[int] = list(op.num_extra_inputs for op in basic_ops)
-        self.num_extra_inputs: int = sum(self._basic_op_num_extra_inputs)
+        self._basic_op_extra_input_sources: list[list[Optional[tuple[int, int]]]] = [
+            [None] * op.num_extra_inputs for op in basic_ops
+        ]
+        self._basic_op_extra_output_channels: list[list[Optional[str]]] = [
+            list(op._extra_output_channels) for op in basic_ops
+        ]
+        self._basic_op_extra_output_is_internal: list[list[bool]] = [
+            [False] * op.num_extra_outputs for op in basic_ops
+        ]
+        self._external_extra_input_slots: list[tuple[int, int]] = []
+
+        # Find channel producers and reject ambiguous names.
+        channel_producers: dict[str, tuple[int, int]] = {}
+        for op_idx, op in enumerate(basic_ops):
+            for output_idx, channel in enumerate(self._basic_op_extra_output_channels[op_idx]):
+                if channel is None:
+                    continue
+                if channel in channel_producers:
+                    producer_idx, _ = channel_producers[channel]
+                    raise ValueError(
+                        f"Extra tensor channel {channel!r} has multiple producers "
+                        f"(ops {producer_idx} and {op_idx})"
+                    )
+                channel_producers[channel] = (op_idx, output_idx)
+
+        # Resolve inputs. A channel with an earlier producer is internal.
+        # Every input without an earlier producer is a separate public input.
+        consumed_channels: set[str] = set()
+        for op_idx, op in enumerate(basic_ops):
+            for input_idx, channel in enumerate(op._extra_input_channels):
+                if channel is None:
+                    self._external_extra_input_slots.append((op_idx, input_idx))
+                    continue
+                producer = channel_producers.get(channel)
+                # If no producer for named channel, this is a public input.
+                if producer is None:
+                    self._external_extra_input_slots.append((op_idx, input_idx))
+                    continue
+                producer_idx, _ = producer
+                if producer_idx >= op_idx:
+                    raise ValueError(
+                        f"Extra tensor channel {channel!r} consumed by op {op_idx} "
+                        f"({type(op).__name__}) has no earlier producer"
+                    )
+                self._basic_op_extra_input_sources[op_idx][input_idx] = producer
+                consumed_channels.add(channel)
+
+        # All extra outputs remain public, including outputs consumed internally.
+        for op_idx, op in enumerate(basic_ops):
+            for output_idx, channel in enumerate(self._basic_op_extra_output_channels[op_idx]):
+                if channel is not None and channel in consumed_channels:
+                    self._basic_op_extra_output_is_internal[op_idx][output_idx] = True
+
+        # Every channel-bound extra input must be wired to a matching producer
+        # extra output. External slots remain unbound (source is None).
+        for op_idx, sources in enumerate(self._basic_op_extra_input_sources):
+            op = basic_ops[op_idx]
+            for input_idx, source in enumerate(sources):
+                channel = op._extra_input_channels[input_idx]
+                if channel is None:
+                    if source is not None:
+                        raise RuntimeError(
+                            f"Extra input {input_idx} of op {op_idx} "
+                            f"({type(op).__name__}) is external but has a "
+                            f"producer source {source}"
+                        )
+                    continue
+                if source is None:
+                    continue
+                producer_idx, output_idx = source
+                producer_channel = self._basic_op_extra_output_channels[producer_idx][output_idx]
+                if producer_channel != channel:
+                    raise ValueError(
+                        f"Extra input {input_idx} of op {op_idx} "
+                        f"({type(op).__name__}) is bound to channel {channel!r}, "
+                        f"but producer op {producer_idx} extra output {output_idx} "
+                        f"is bound to {producer_channel!r}"
+                    )
+        # Used by Sequential to determine the number of extra inputs
+        # needed for each OperationFuser module in the sequence.
+        self.num_extra_inputs = len(self._external_extra_input_slots)
 
         # Ops for forward and backward pass, will be populated in maybe_fuse_ops
         self._forward_ops: list[tuple[FusibleOperation, list[int]]]
@@ -359,6 +505,16 @@ def __init__(
         self._basic_op_num_params = list(map(len, self._basic_op_params))
         self._flat_basic_op_params = sum(self._basic_op_params, [])
 
+    def has_stale_op_channels(self) -> bool:
+        """Whether an operation's extra tensor channels have changed."""
+        return any(
+            op._extra_channels_version != version
+            for op, version in zip(
+                self._basic_ops,
+                self._basic_op_extra_channels_versions,
+            )
+        )
+
     @staticmethod
     def _apply_fusions(
         ops: Iterable[FusibleOperation],
@@ -432,7 +588,7 @@ def maybe_fuse_ops(
             first_op_requiring_backward = self._num_basic_ops
             for op_idx in range(self._num_basic_ops):
                 op_inputs = itertools.chain(self._basic_op_params[op_idx], extra_inputs[op_idx])
-                if any(tensor.requires_grad for tensor in op_inputs):
+                if any(tensor is not None and tensor.requires_grad for tensor in op_inputs):
                     first_op_requiring_backward = op_idx
                     break
 
@@ -507,6 +663,12 @@ def __call__(
         *extra_inputs: torch.Tensor,
         basic_op_kwargs: Optional[list[dict[str, Any]]] = None,
     ) -> torch.Tensor | tuple[torch.Tensor, ...]:
+        if self.has_stale_op_channels():
+            raise RuntimeError(
+                "Extra tensor channels changed after this OperationFuser captured "
+                "its routing. Construct a new OperationFuser."
+            )
+
         # Verify extra input count
         if len(extra_inputs) != self.num_extra_inputs:
             raise ValueError(
@@ -517,12 +679,13 @@ def __call__(
         if basic_op_kwargs is None:
             basic_op_kwargs = [{}] * self._num_basic_ops
 
-        # Unflatten list of extra tensor inputs
-        extra_inputs_copy = list(extra_inputs)
-        basic_op_extra_inputs = []
-        for op in self._basic_ops:
-            xs, extra_inputs_copy = _split_tuple(extra_inputs_copy, op.num_extra_inputs)
-            basic_op_extra_inputs.append(xs)
+        # Place public extra inputs into their basic-op slots. Internal slots
+        # are not available until forward executes their producers.
+        basic_op_extra_inputs: list[list[Optional[torch.Tensor]]] = [
+            [None] * op.num_extra_inputs for op in self._basic_ops
+        ]
+        for tensor, (op_idx, input_idx) in zip(extra_inputs, self._external_extra_input_slots):
+            basic_op_extra_inputs[op_idx][input_idx] = tensor
 
         # Get environment state
         recipe = None
diff --git a/transformer_engine/pytorch/ops/op.py b/transformer_engine/pytorch/ops/op.py
index 5106ec9e0a..85e5641078 100644
--- a/transformer_engine/pytorch/ops/op.py
+++ b/transformer_engine/pytorch/ops/op.py
@@ -6,7 +6,7 @@
 
 from __future__ import annotations
 import abc
-from collections.abc import Iterable
+from collections.abc import Iterable, Sequence
 import dataclasses
 import pickle
 from typing import Any, Optional
@@ -89,7 +89,7 @@ def fuser_forward(
         prev_op_grad_output_quantizer: Optional[Quantizer],
         next_op_input_quantizer: Optional[Quantizer],
         basic_op_kwargs: list[dict[str, Any]],
-    ) -> tuple[torch.Tensor, Iterable[Iterable[torch.Tensor]]]:
+    ) -> tuple[torch.Tensor, Sequence[Sequence[torch.Tensor]]]:
         """Forward pass
 
         This op is either a basic op or the fusion of basic ops, so
@@ -118,7 +118,7 @@ def fuser_forward(
         -------
         torch.Tensor:
             Output tensor.
-        Iterable of torch.Tensor:
+        Sequence of torch.Tensor:
             Extra tensor outputs from basic operations.
 
         """
@@ -187,10 +187,55 @@ class BasicOperation(FusibleOperation, metaclass=abc.ABCMeta):
     def __init__(self) -> None:
         super().__init__()
 
+        # Optional names for extra-tensor channels internal to an OperationFuser.
+        # Unbound slots remain public inputs/outputs, preserving the original API.
+        self._extra_input_channels: list[Optional[str]] = [None] * self.num_extra_inputs
+        self._extra_output_channels: list[Optional[str]] = [None] * self.num_extra_outputs
+        self._extra_channels_version = 0
+
         # Objects for quantization
         self._fp8_metas: Optional[dict[str, dict[str, Any]]] = None
         self._quantizers: Optional[dict[str, list[Quantizer]]] = None
 
+    def set_extra_input_channel(self, index: int, channel: Optional[str]) -> BasicOperation:
+        """Bind an extra input slot to an internal fuser channel.
+
+        A bound slot receives the matching extra output from an earlier
+        operation in the same fuser instead of consuming a public extra input.
+        Passing ``None`` removes the binding.
+        """
+        if not 0 <= index < self.num_extra_inputs:
+            raise IndexError(
+                f"Extra input index {index} is out of range for "
+                f"{type(self).__name__} with {self.num_extra_inputs} extra inputs"
+            )
+        if channel is not None and (not isinstance(channel, str) or not channel):
+            raise ValueError("Extra input channel must be a non-empty string or None")
+        if self._extra_input_channels[index] == channel:
+            return self
+        self._extra_input_channels[index] = channel
+        self._extra_channels_version += 1
+        return self
+
+    def set_extra_output_channel(self, index: int, channel: Optional[str]) -> BasicOperation:
+        """Bind an extra output slot to an internal fuser channel.
+
+        A bound slot can feed one or more later operations and is not returned
+        as a public extra output. Passing ``None`` removes the binding.
+        """
+        if not 0 <= index < self.num_extra_outputs:
+            raise IndexError(
+                f"Extra output index {index} is out of range for "
+                f"{type(self).__name__} with {self.num_extra_outputs} extra outputs"
+            )
+        if channel is not None and (not isinstance(channel, str) or not channel):
+            raise ValueError("Extra output channel must be a non-empty string or None")
+        if self._extra_output_channels[index] == channel:
+            return self
+        self._extra_output_channels[index] = channel
+        self._extra_channels_version += 1
+        return self
+
     @property
     def is_fused_op(self) -> bool:
         return False