diff --git a/autoparallel/api.py b/autoparallel/api.py
index 5ecac40e..bdfb5491 100644
--- a/autoparallel/api.py
+++ b/autoparallel/api.py
@@ -63,6 +63,10 @@
 
 
 def _boxed_nop_preserve_node_meta(fx_g, example_inputs):
+    from autoparallel.graph_passes.bucket_collectives import bucket_collectives
+
+    bucket_collectives(fx_g)
+
     if torch._inductor.config.aten_distributed_optimizations.enable_overlap_scheduling:
         from torch._inductor.fx_passes.overlap_scheduling import (
             schedule_overlap_bucketing_from_inductor_configs,
@@ -275,6 +279,18 @@ def __enter__(self):
             )
             torch._inductor.config.comprehensive_padding = False
 
+            if self.compiler_fn is compile_fx_inner:
+                from autoparallel.graph_passes.bucket_collectives import (
+                    bucket_collectives,
+                )
+
+                self.old_post_grad_custom_post_pass = (
+                    torch._inductor.config.post_grad_custom_post_pass
+                )
+                torch._inductor.config.post_grad_custom_post_pass = (
+                    lambda gm: bucket_collectives(gm.owning_module)
+                )
+
             rescale_grad_comm_cost_for_mp = 1.0
             if self.mp_policy is not None:
                 param_size = self.mp_policy.param_dtype.itemsize
@@ -312,6 +328,10 @@ def __exit__(self, exc_type, exc_val, exc_tb):
         torch._inductor.config.comprehensive_padding = (
             self.old_inductor_comprehensive_padding
         )
+        if hasattr(self, "old_post_grad_custom_post_pass"):
+            torch._inductor.config.post_grad_custom_post_pass = (
+                self.old_post_grad_custom_post_pass
+            )
         self.active = None
         return self.stack.__exit__(exc_type, exc_val, exc_tb)
 
@@ -462,6 +482,13 @@ def _apply_placement_common(self, sharding_placement):
         self.parallel_gm = parallel_gm
         update_joint_with_descriptors(self.joint_with_descriptors, parallel_gm)
         fix_scatter_on_aliased_inputs(parallel_gm.graph)
+        # Tag FSDP/DDP collectives on the joint graph so the bucketing
+        # pass can identify them after partitioning into fw/bw subgraphs.
+        from autoparallel.graph_passes.bucket_collectives import (
+            tag_collectives_for_bucketing,
+        )
+
+        tag_collectives_for_bucketing(parallel_gm.graph)
         # Allow DCE to remove unused wait_tensor nodes in the backward graph.
         # Pushed onto self.stack so it's restored in AutoParallel.__exit__.
         self.stack.enter_context(_suppress_wait_tensor_side_effect())
diff --git a/autoparallel/apply_sharding.py b/autoparallel/apply_sharding.py
index 1766770b..67a237ce 100644
--- a/autoparallel/apply_sharding.py
+++ b/autoparallel/apply_sharding.py
@@ -310,12 +310,27 @@ def _lower_to_parallel_graph(gm, sharding_placement, local_args):
 
 def _copy_descriptors_and_rename_placeholders(source_gm, target_gm):
     """Copy node descriptors from source graph and rename placeholders to match."""
+    from torch.utils._pytree import tree_flatten
+
     for n1, n2 in zip(
         (n for n in source_gm.graph.nodes if n.op in ("placeholder", "output")),
         (n for n in target_gm.graph.nodes if n.op in ("placeholder", "output")),
     ):
-        n2.meta["desc"] = n1.meta["desc"]
-        if n2.op == "placeholder":
+        if n2.op == "output":
+            # get_all_input_and_grad_nodes iterates with zip(n.args[0], desc),
+            # so both must be flat. make_fx may nest the output args (e.g.,
+            # ((fw_outs...), (grads...))) while desc from the source is flat.
+            # Flatten the target output args to match, and copy the flat desc.
+            flat_desc, _ = tree_flatten(n1.meta["desc"])
+            flat_args, _ = tree_flatten(n2.args[0])
+            assert len(flat_desc) == len(flat_args), (
+                f"Output desc has {len(flat_desc)} leaves but output args has "
+                f"{len(flat_args)} leaves"
+            )
+            n2.args = (tuple(flat_args),)
+            n2.meta["desc"] = flat_desc
+        else:
+            n2.meta["desc"] = n1.meta["desc"]
             n2.target = n1.target
             # node renaming is needed for partitioner as it searches for tangent
             # nodes. See https://fburl.com/kc4jtc3t for one case where it's used
diff --git a/autoparallel/graph_passes/bucket_collectives.py b/autoparallel/graph_passes/bucket_collectives.py
new file mode 100644
index 00000000..d1b9821b
--- /dev/null
+++ b/autoparallel/graph_passes/bucket_collectives.py
@@ -0,0 +1,213 @@
+# 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 logging
+from collections import defaultdict
+
+import torch
+from torch._inductor.fx_passes.bucketing import (
+    collect_node_descendants,
+    is_all_gather_into_tensor,
+    is_all_reduce_tensor,
+    is_reduce_scatter_tensor,
+    merge_all_gather_bucket,
+    merge_all_reduce_bucket,
+    merge_reduce_scatter_bucket,
+)
+from torch._inductor.fx_passes.post_grad import stable_topological_sort
+from torch.utils._ordered_set import OrderedSet
+
+from .graph_utils import build_param_derived_set, build_terminal_derived_set
+
+logger = logging.getLogger(__name__)
+
+# Meta key used to tag collectives eligible for bucketing.
+# Set to "param" for param-derived (forward all-gathers) or
+# "terminal" for terminal-derived (backward reduce-scatters / all-reduces).
+AP_BUCKET_KEY = "ap_bucket_group"
+
+
+def tag_collectives_for_bucketing(graph: torch.fx.Graph) -> None:
+    """Tag FSDP/DDP collectives on the joint graph for later bucketing.
+
+    Must run on the joint graph where placeholder metadata is available.
+    The tags survive partitioning into fw/bw subgraphs via node_copy's
+    shallow copy of node.meta.
+    """
+    param_derived = build_param_derived_set(graph)
+    terminal_derived = build_terminal_derived_set(graph)
+
+    n_ag = 0
+    n_rs = 0
+    n_ar = 0
+    for node in graph.nodes:
+        if is_all_gather_into_tensor(node) and node in param_derived:
+            node.meta[AP_BUCKET_KEY] = "param"
+            n_ag += 1
+        elif is_reduce_scatter_tensor(node) and node in terminal_derived:
+            node.meta[AP_BUCKET_KEY] = "terminal"
+            n_rs += 1
+        elif is_all_reduce_tensor(node) and node in terminal_derived:
+            node.meta[AP_BUCKET_KEY] = "terminal"
+            n_ar += 1
+
+    logger.info(
+        "Tagged collectives for bucketing: AG %d, RS %d, AR %d",
+        n_ag,
+        n_rs,
+        n_ar,
+    )
+
+
+def _group_key(node: torch.fx.Node) -> tuple:
+    """Extract group key for any collective type.
+
+    The key ensures only collectives on the same process group / reduce op /
+    dtype can be bucketed together — the same constraints enforced by
+    PyTorch's merge functions.
+    """
+    if is_all_gather_into_tensor(node):
+        _, group_size, group_name = node.args
+        return (group_name,)
+    elif is_reduce_scatter_tensor(node):
+        _, reduce_op, group_size, group_name = node.args
+        dtype = node.meta["val"].dtype
+        return (group_name, reduce_op, dtype)
+    elif is_all_reduce_tensor(node):
+        _, reduce_op, group_name = node.args
+        dtype = node.meta["val"].dtype
+        return (group_name, reduce_op, dtype)
+    else:
+        raise ValueError(f"Unsupported collective type: {node.target}")
+
+
+def _greedy_bucket(
+    graph: torch.fx.Graph,
+    coll_nodes: list[torch.fx.Node],
+    bucket_cap_bytes: int,
+    node_descendants: dict[torch.fx.Node, OrderedSet[torch.fx.Node]],
+) -> list[list[torch.fx.Node]]:
+    """Group collectives into buckets up to bucket_cap_bytes.
+
+    Unlike PyTorch's greedy_bucket_collective_by_mb, this does not require
+    collectives to be adjacent in the graph — it only requires that no
+    collective is a descendant of another in the same bucket (to avoid
+    creating cycles when merged).
+    """
+    if not coll_nodes:
+        return []
+
+    groups: dict[tuple, list[torch.fx.Node]] = defaultdict(list)
+    for node in coll_nodes:
+        groups[_group_key(node)].append(node)
+
+    buckets: list[list[torch.fx.Node]] = []
+    for nodes in groups.values():
+        cur_bucket: list[torch.fx.Node] = []
+        cur_bucket_descendants: OrderedSet[torch.fx.Node] = OrderedSet()
+        cur_bucket_bytes = 0
+
+        for node in nodes:
+            if node in cur_bucket_descendants:
+                continue
+
+            val = node.meta["val"]
+            out_bytes = val.numel() * val.element_size()
+            in_val = node.all_input_nodes[0].meta["val"]
+            in_bytes = in_val.numel() * in_val.element_size()
+            size_bytes = max(out_bytes, in_bytes)
+
+            if cur_bucket_bytes + size_bytes > bucket_cap_bytes and cur_bucket:
+                if len(cur_bucket) > 1:
+                    buckets.append(cur_bucket)
+                cur_bucket = []
+                cur_bucket_bytes = 0
+                cur_bucket_descendants = OrderedSet()
+
+            cur_bucket_bytes += size_bytes
+            cur_bucket.append(node)
+            cur_bucket_descendants |= node_descendants[node]
+
+        if len(cur_bucket) > 1:
+            buckets.append(cur_bucket)
+
+    return buckets
+
+
+def bucket_collectives(
+    gm: torch.fx.GraphModule,
+    bucket_cap_mb: float = 25.0,
+) -> None:
+    """Bucket FSDP/DDP collectives before the overlap scheduler runs.
+
+    Reads the tags set by tag_collectives_for_bucketing() to identify
+    which collectives to merge. Merges per-parameter collectives into
+    larger bucketed collectives so the overlap scheduler sees fewer,
+    larger ops.
+    """
+    graph = gm.graph
+
+    fsdp_all_gathers: list[torch.fx.Node] = []
+    fsdp_reduce_scatters: list[torch.fx.Node] = []
+    ddp_all_reduces: list[torch.fx.Node] = []
+
+    for node in graph.nodes:
+        bucket_group = node.meta.get(AP_BUCKET_KEY)
+        if bucket_group is None:
+            continue
+        if is_all_gather_into_tensor(node):
+            fsdp_all_gathers.append(node)
+        elif is_reduce_scatter_tensor(node):
+            fsdp_reduce_scatters.append(node)
+        elif is_all_reduce_tensor(node):
+            ddp_all_reduces.append(node)
+
+    total = len(fsdp_all_gathers) + len(fsdp_reduce_scatters) + len(ddp_all_reduces)
+    if total < 2:
+        return
+
+    node_descendants = collect_node_descendants(graph)
+    bucket_cap_bytes = int(bucket_cap_mb * 1024 * 1024)
+
+    ag_buckets = _greedy_bucket(
+        graph, fsdp_all_gathers, bucket_cap_bytes, node_descendants
+    )
+    rs_buckets = _greedy_bucket(
+        graph, fsdp_reduce_scatters, bucket_cap_bytes, node_descendants
+    )
+    ar_buckets = _greedy_bucket(
+        graph, ddp_all_reduces, bucket_cap_bytes, node_descendants
+    )
+
+    n_merged = sum(len(b) for b in ag_buckets + rs_buckets + ar_buckets)
+    n_buckets = len(ag_buckets) + len(rs_buckets) + len(ar_buckets)
+    if n_buckets == 0:
+        return
+
+    logger.info(
+        "Bucketing %d collectives into %d buckets "
+        "(AG: %d->%d, RS: %d->%d, AR: %d->%d)",
+        n_merged,
+        n_buckets,
+        len(fsdp_all_gathers),
+        len(fsdp_all_gathers) - sum(len(b) for b in ag_buckets) + len(ag_buckets),
+        len(fsdp_reduce_scatters),
+        len(fsdp_reduce_scatters) - sum(len(b) for b in rs_buckets) + len(rs_buckets),
+        len(ddp_all_reduces),
+        len(ddp_all_reduces) - sum(len(b) for b in ar_buckets) + len(ar_buckets),
+    )
+
+    for bucket in ag_buckets:
+        merge_all_gather_bucket(graph, bucket)
+    for bucket in rs_buckets:
+        merge_reduce_scatter_bucket(graph, bucket)
+    for bucket in ar_buckets:
+        merge_all_reduce_bucket(graph, bucket)
+
+    # Bucketing can place new nodes (concat, split, etc.) at positions that
+    # break use-before-def ordering. Re-sort to fix this — same as PyTorch's
+    # post_grad_passes does after its own FX bucketing.
+    stable_topological_sort(graph)
+    gm.recompile()