TorchRec 2D Parallel (#2554)

Summary:
Pull Request resolved: #2554

In this diff we introduce a new parallelism strategy for scaling recommendation model training called 2D parallel. In this case, we scale model parallel through data parallel, hence, the 2D name.

Our new entry point, DMPCollection, subclasses DMP and is meant to be a drop in replacement to integrate 2D parallelism in distributed training. By setting the total number of GPUs to train across and the number of GPUs to locally shard across (aka one replication group), users can train their models in the same training loop but now over a larger number of GPUs.

The current implementation shards the model such that, for a given shard, its replicated shards lie on the ranks within the node. This significantly improves the performance of the all-reduce communication (parameter sync) by utilizing intra-node bandwidth.

Under this scheme the supported sharding types are RW, CW, and GRID. TWRW is not supported due to no longer being able to take advantage of the intra node bandwidth in the 2D scheme.

Example Use Case:
        Consider a setup with 2 nodes, each with 4 GPUs. The sharding groups could be:
            - Group 0, DMP 0: [0, 2, 4, 6]
            - Group 1, DMP 1: [1, 3, 5, 7]

        Each group receives an identical sharding plan for their local world size and ranks.
        If we have one table sharded in each DMP, with one shard on each rank in the group,
        each shard in DMP0 will have a duplicate shard on its corresponding rank in DMP1.
        The replication groups would be: [0, 1], [2, 3], [4, 5], [6, 7].

NOTE: We have to pass global process group to the DDPWrapper otherwise some of the unsharded parameters will not get optimizer applied to them. will result in numerically inaccurate results

Reviewed By: dstaay-fb

Differential Revision: D61643328

fbshipit-source-id: 7e3e447210cabe9a28b72c1acc48ae06b153d95d

Author: iamzainhuda

torchrec/distributed/batched_embedding_kernel.py

@@ -46,7 +46,7 @@
     PartiallyMaterializedTensor,
 )
 from torch import nn
-from torchrec.distributed.comm import get_local_rank, get_local_size
+from torchrec.distributed.comm import get_local_rank, get_node_group_size
 from torchrec.distributed.composable.table_batched_embedding_slice import (
     TableBatchedEmbeddingSlice,
 )
@@ -303,7 +303,7 @@ def get_optimizer_rowwise_shard_metadata_and_global_metadata(
             )
             # for grid sharding, the row dimension is replicated CW shard times
             grid_shard_nodes = (
-                len(table_global_shards_metadata) // get_local_size()
+                len(table_global_shards_metadata) // get_node_group_size()
                 if is_grid_sharded
                 else 1
             )
@@ -1445,7 +1445,6 @@ def __init__(
         fused_params = config.fused_params or {}
         if "cache_precision" not in fused_params:
             fused_params["cache_precision"] = weights_precision
-
         self._emb_module: SplitTableBatchedEmbeddingBagsCodegen = (
             SplitTableBatchedEmbeddingBagsCodegen(
                 embedding_specs=list(

torchrec/distributed/comm.py

@@ -13,13 +13,19 @@
 
 import torch
 import torch.distributed as dist
+from torchrec.distributed.types import ShardingEnv2D
 
 logger: logging.Logger = logging.getLogger(__name__)
 
 # Global, only should be accessed via intra_and_cross_node_pg()
 _INTRA_PG: Optional[dist.ProcessGroup] = None
 _CROSS_PG: Optional[dist.ProcessGroup] = None
 
+# For 2D parallel
+_INTRA_PG_2D: Optional[dist.ProcessGroup] = None
+_CROSS_PG_2D: Optional[dist.ProcessGroup] = None
+_NODE_GROUP_SIZE_2D: Optional[int] = None
+
 
 def _env2int(env_list: List[str], default: int = -1) -> int:
     for e in env_list:
@@ -54,6 +60,15 @@ def get_local_size(world_size: Optional[int] = None) -> int:
     return local_size
 
 
+def get_node_group_size(world_size: Optional[int] = None) -> int:
+    """
+    Get the local world size accounting for 2D environment, if not set, we fallback to global environment
+    """
+    if _NODE_GROUP_SIZE_2D is None:
+        return get_local_size(world_size)
+    return _NODE_GROUP_SIZE_2D
+
+
 def get_local_rank(world_size: Optional[int] = None, rank: Optional[int] = None) -> int:
     """
     Gets the local rank of the local processes (see https://pytorch.org/docs/stable/elastic/run.html)
@@ -151,3 +166,98 @@ def intra_and_cross_node_pg(
         dist.barrier()
 
     return _INTRA_PG, _CROSS_PG
+
+
+def intra_and_cross_node_pg_2D(
+    env: ShardingEnv2D,
+    device: Optional[torch.device] = None,
+) -> Tuple[Optional[dist.ProcessGroup], Optional[dist.ProcessGroup]]:
+    """
+    Creates sub process groups (intra and cross node) under 2D parallelism scheme
+    The concept of "intra" and "cross" node is lost under a 2D parallelism scheme
+    due to the ranks that exist under a sharding group do not have gurantee of the typical
+    node topology. And as such there are no guarantees of "intra" group exploiting intra node bandwidth.
+
+    NOTE:
+    These process groups are created for sharding schemes (ie: GRID) that were designed to exploit
+    intra node bandwidth for optimized comms. There will be future work to redesign the comms for GRID
+    sharding to be optimized under a 2D setup.
+
+    Example::
+    Here is what "intra" and "cross" groups look like in a 2D environment,
+    Sharding Groups:
+        Group 0: [0, 2, 4, 6]
+        Group 1: [1, 3, 5, 7]
+    devices_per_node = 2:
+    "intra" groups for each sharding group,
+        Group 0: [0, 2], [4, 6]
+        Group 1: [1, 3], [5, 7]
+    "cross" groups for each sharding group,
+        Group 0: [0, 4], [2, 6]
+        Group 1: [1, 5], [3, 7]
+
+    We can see as this scales to real world topologies how the "intra" and "cross" node ideas in a traditional
+    sense are not applicable here.
+    """
+    if device is not None and device.type == "meta":
+        return None, None
+
+    global _INTRA_PG_2D
+    global _CROSS_PG_2D
+    global _NODE_GROUP_SIZE_2D
+
+    backend = dist.get_backend(env.sharding_pg)
+    my_rank = dist.get_rank()
+
+    sharding_group_size = dist.get_world_size(
+        env.sharding_pg
+    )  # Local replica group world size
+    world_size = dist.get_world_size()  # Global world size
+    step = world_size // sharding_group_size
+    devices_per_node = (
+        env.node_group_size if env.node_group_size else get_local_size(world_size)
+    )
+    _NODE_GROUP_SIZE_2D = devices_per_node
+
+    assert (
+        sharding_group_size % devices_per_node == 0
+    ), f"node group size is not divisible by sharding group size, {devices_per_node=}, {sharding_group_size=}"
+    intra_pg_groups: List[List[List[int]]] = [[] for _ in range(step)]
+
+    if _INTRA_PG_2D is None:
+        for group_rank in range(step):
+            sharding_pg_peers = [
+                step * r + group_rank for r in range(sharding_group_size)
+            ]
+            for group in range(len(sharding_pg_peers) // devices_per_node):
+                intra_pg_peers = sharding_pg_peers[
+                    group * devices_per_node : (group + 1) * devices_per_node
+                ]
+                intra_pg_groups[group_rank].append(intra_pg_peers)
+                curr_intra_pg = dist.new_group(backend=backend, ranks=intra_pg_peers)
+                if my_rank in intra_pg_peers:
+                    logger.warning(
+                        f"[Connection] 2D rank {my_rank} -> intra_pg_peers {intra_pg_peers}"
+                    )
+                    _INTRA_PG_2D = curr_intra_pg
+        assert _INTRA_PG_2D is not None, "INTRA_PG_2D is not initialized!"
+        dist.barrier()
+
+    if _CROSS_PG_2D is None:
+        for group_rank in range(step):
+            intra_pg_group = intra_pg_groups[group_rank]
+            for cross_group_rank in range(devices_per_node):
+                cross_pg_peers = [
+                    intra_pg_group[j][cross_group_rank]
+                    for j in range(len(intra_pg_group))
+                ]
+                curr_cross_pg = dist.new_group(backend=backend, ranks=cross_pg_peers)
+                if my_rank in cross_pg_peers:
+                    logger.warning(
+                        f"[Connection] 2D rank {my_rank} -> cross_pg_peers {cross_pg_peers}"
+                    )
+                    _CROSS_PG_2D = curr_cross_pg
+        assert _CROSS_PG_2D is not None, "CROSS_PG_2D is not initialized!"
+        dist.barrier()
+
+    return _INTRA_PG_2D, _CROSS_PG_2D

torchrec/distributed/embeddingbag.py

@@ -65,6 +65,7 @@
     QuantizedCommCodecs,
     ShardedTensor,
     ShardingEnv,
+    ShardingEnv2D,
     ShardingType,
     ShardMetadata,
     TensorProperties,
@@ -149,6 +150,7 @@ def create_embedding_bag_sharding(
     EmbeddingShardingContext, KeyedJaggedTensor, torch.Tensor, torch.Tensor
 ]:
     sharding_type = sharding_infos[0].param_sharding.sharding_type
+
     if device is not None and device.type == "meta":
         replace_placement_with_meta_device(sharding_infos)
     if sharding_type == ShardingType.TABLE_WISE.value:
@@ -949,10 +951,14 @@ def _initialize_torch_state(self) -> None:  # noqa
                 )
 
                 self._model_parallel_name_to_sharded_tensor[table_name] = (
-                    ShardedTensor._init_from_local_shards_and_global_metadata(
-                        local_shards=local_shards,
-                        sharded_tensor_metadata=metadata,
-                        process_group=none_throws(self._env.process_group),
+                    ShardedTensor._init_from_local_shards(
+                        local_shards,
+                        self._name_to_table_size[table_name],
+                        process_group=(
+                            self._env.sharding_pg
+                            if isinstance(self._env, ShardingEnv2D)
+                            else self._env.process_group
+                        ),
                     )
                 )