diff --git a/autoparallel/api.py b/autoparallel/api.py
index a938d351..bd857381 100644
--- a/autoparallel/api.py
+++ b/autoparallel/api.py
@@ -43,6 +43,7 @@
     cleanup_graph,
     update_joint_with_descriptors,
 )
+from .graph_passes.make_collectives_contiguous import make_collectives_contiguous
 from .init_weights import hook_params_setters
 from .optimize_sharding import ShardingOptimizer
 from .shardings.placement_options import (
@@ -657,6 +658,7 @@ def _apply_placement_common(self, sharding_placement):
         # clean it up by removing the added aliases from previous pass
         # as well as redundant views
         cleanup_graph(parallel_gm, aggressive=True)
+        make_collectives_contiguous(parallel_gm)
         t_cleanup = time.perf_counter()
 
         trace_structured(
diff --git a/autoparallel/graph_passes/make_collectives_contiguous.py b/autoparallel/graph_passes/make_collectives_contiguous.py
new file mode 100644
index 00000000..d74e3c53
--- /dev/null
+++ b/autoparallel/graph_passes/make_collectives_contiguous.py
@@ -0,0 +1,48 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+
+_COLLECTIVES_REQUIRING_CONTIGUOUS = {
+    torch.ops._c10d_functional.all_gather_into_tensor.default,
+    torch.ops._c10d_functional.reduce_scatter_tensor.default,
+}
+
+
+def make_collectives_contiguous(gm: torch.fx.GraphModule) -> None:
+    """Insert clone(memory_format=contiguous) before collectives that require it.
+
+    NCCL collectives like all_gather_into_tensor and reduce_scatter_tensor
+    require contiguous input tensors. When AP inserts these collectives, the
+    input may be non-contiguous (e.g. after a transpose or view). This pass
+    walks the graph and inserts a contiguous clone on any such input.
+    """
+    graph = gm.graph
+    for node in list(graph.nodes):
+        if (
+            node.op != "call_function"
+            or node.target not in _COLLECTIVES_REQUIRING_CONTIGUOUS
+        ):
+            continue
+        tensor_arg = node.args[0]
+        if not isinstance(tensor_arg, torch.fx.Node):
+            continue
+        # Skip if the input is already a contiguous clone
+        if (
+            tensor_arg.op == "call_function"
+            and tensor_arg.target == torch.ops.aten.clone.default
+            and len(tensor_arg.kwargs) > 0
+            and tensor_arg.kwargs.get("memory_format") == torch.contiguous_format
+        ):
+            continue
+        with graph.inserting_before(node):
+            clone_node = graph.call_function(
+                torch.ops.aten.clone.default,
+                args=(tensor_arg,),
+                kwargs={"memory_format": torch.contiguous_format},
+            )
+            clone_node.meta.update(tensor_arg.meta)
+            node.replace_input_with(tensor_arg, clone_node)
+    gm.recompile()
diff --git a/tests/test_make_collectives_contiguous.py b/tests/test_make_collectives_contiguous.py
new file mode 100644
index 00000000..6afe90f6
--- /dev/null
+++ b/tests/test_make_collectives_contiguous.py
@@ -0,0 +1,129 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.fx
+
+from autoparallel.graph_passes.make_collectives_contiguous import (
+    make_collectives_contiguous,
+)
+
+
+def _count_ops(gm, target):
+    return len(gm.graph.find_nodes(op="call_function", target=target))
+
+
+def _build_graph_with_collective(collective_target):
+    """Build a simple FX graph: placeholder -> collective -> output."""
+    graph = torch.fx.Graph()
+    x = graph.placeholder("x")
+    x.meta["val"] = torch.randn(8)
+    collective = graph.call_function(collective_target, args=(x, 2, "0"))
+    collective.meta["val"] = torch.randn(16)
+    output = graph.output(collective)
+    output.meta["val"] = collective.meta["val"]
+    gm = torch.fx.GraphModule(torch.nn.Module(), graph)
+    return gm
+
+
+def test_all_gather_gets_contiguous_clone():
+    target = torch.ops._c10d_functional.all_gather_into_tensor.default
+    gm = _build_graph_with_collective(target)
+
+    assert _count_ops(gm, torch.ops.aten.clone.default) == 0
+    make_collectives_contiguous(gm)
+    assert _count_ops(gm, torch.ops.aten.clone.default) == 1
+
+    # The clone should be the input to the collective
+    for node in gm.graph.nodes:
+        if node.target == target:
+            clone_node = node.args[0]
+            assert clone_node.target == torch.ops.aten.clone.default
+            assert clone_node.kwargs["memory_format"] == torch.contiguous_format
+
+
+def test_reduce_scatter_gets_contiguous_clone():
+    target = torch.ops._c10d_functional.reduce_scatter_tensor.default
+    gm = _build_graph_with_collective(target)
+
+    make_collectives_contiguous(gm)
+    assert _count_ops(gm, torch.ops.aten.clone.default) == 1
+
+
+def test_already_contiguous_clone_is_not_duplicated():
+    """If the input is already a contiguous clone, don't insert another."""
+    target = torch.ops._c10d_functional.all_gather_into_tensor.default
+    graph = torch.fx.Graph()
+    x = graph.placeholder("x")
+    x.meta["val"] = torch.randn(8)
+    clone = graph.call_function(
+        torch.ops.aten.clone.default,
+        args=(x,),
+        kwargs={"memory_format": torch.contiguous_format},
+    )
+    clone.meta["val"] = x.meta["val"]
+    collective = graph.call_function(target, args=(clone, 2, "0"))
+    collective.meta["val"] = torch.randn(16)
+    output = graph.output(collective)
+    output.meta["val"] = collective.meta["val"]
+    gm = torch.fx.GraphModule(torch.nn.Module(), graph)
+
+    make_collectives_contiguous(gm)
+    # Should still be exactly 1 clone, not 2
+    assert _count_ops(gm, torch.ops.aten.clone.default) == 1
+
+
+def test_non_collective_ops_untouched():
+    """Ops that aren't collectives should not get a clone inserted."""
+    graph = torch.fx.Graph()
+    x = graph.placeholder("x")
+    x.meta["val"] = torch.randn(4, 4)
+    add = graph.call_function(torch.ops.aten.add.Tensor, args=(x, x))
+    add.meta["val"] = torch.randn(4, 4)
+    output = graph.output(add)
+    output.meta["val"] = add.meta["val"]
+    gm = torch.fx.GraphModule(torch.nn.Module(), graph)
+
+    make_collectives_contiguous(gm)
+    assert _count_ops(gm, torch.ops.aten.clone.default) == 0
+
+
+def test_multiple_collectives():
+    """Each collective gets its own contiguous clone."""
+    ag_target = torch.ops._c10d_functional.all_gather_into_tensor.default
+    rs_target = torch.ops._c10d_functional.reduce_scatter_tensor.default
+
+    graph = torch.fx.Graph()
+    x = graph.placeholder("x")
+    x.meta["val"] = torch.randn(8)
+    ag = graph.call_function(ag_target, args=(x, 2, "0"))
+    ag.meta["val"] = torch.randn(16)
+    rs = graph.call_function(rs_target, args=(ag, "sum", 2, "0"))
+    rs.meta["val"] = torch.randn(8)
+    output = graph.output(rs)
+    output.meta["val"] = rs.meta["val"]
+    gm = torch.fx.GraphModule(torch.nn.Module(), graph)
+
+    make_collectives_contiguous(gm)
+    assert _count_ops(gm, torch.ops.aten.clone.default) == 2
+
+
+def test_shared_input_gets_separate_clones():
+    """When two collectives share the same input, each gets its own clone."""
+    target = torch.ops._c10d_functional.all_gather_into_tensor.default
+
+    graph = torch.fx.Graph()
+    x = graph.placeholder("x")
+    x.meta["val"] = torch.randn(8)
+    ag1 = graph.call_function(target, args=(x, 2, "0"))
+    ag1.meta["val"] = torch.randn(16)
+    ag2 = graph.call_function(target, args=(x, 4, "1"))
+    ag2.meta["val"] = torch.randn(32)
+    output = graph.output((ag1, ag2))
+    output.meta["val"] = (ag1.meta["val"], ag2.meta["val"])
+    gm = torch.fx.GraphModule(torch.nn.Module(), graph)
+
+    make_collectives_contiguous(gm)
+    assert _count_ops(gm, torch.ops.aten.clone.default) == 2