feat: support CCL AllToAll

[GordonYang1]

Summary

This PR adds AllToAll support to the shared CCL backend abstraction by composing provider-native grouped point-to-point operations through the existing NCCL and MCCL layers for the public infinicclAllToAll() API. It also keeps inter-only communicators on the existing OpenMPI staging path during mixed-backend bootstrap flows, corrects the shared OpenMPI/MPICH movement path to use byte counts for all data types, defines zero-count behavior, makes the existing MPI example validate every received source block and propagate failures, and includes CCL-only plus OpenMPI-assisted AllToAll example programs.

Changes

• Public API and Dispatch

  • Enable the existing infinicclAllToAll() API for configured CCL backends through generated bridge dispatch without changing its public signature.
  • Use a matching native CCL intra communicator when available, and otherwise delegate to the existing OpenMPI provider when the communicator has only an OpenMPI inter communicator.
  • Return success for a zero-count AllToAll after validating the communicator and data type, without requiring non-null buffers or entering a backend.
  • Reject non-zero in-place buffers on the native CCL path with NotSupported.

• Common CCL Implementation

  • Add a shared provider-oriented CCL AllToAll implementation using one grouped Send/Recv pair per peer, including self.
  • Validate the native communicator backend, device, rank, world size, and handle before dispatch.
  • Map InfiniCCL data types through the configured provider and check per-peer and total byte-size calculations for size_t overflow.
  • Preserve the first point-to-point error, continue issuing operations for the remaining peers, and always call GroupEnd after a successful GroupStart.

• Existing CCL Provider Bindings

  • Extend the existing NCCL and MCCL API wrappers with GroupStart, GroupEnd, Send, and Recv bindings.
  • Register AllToAll with the existing NCCL and MCCL provider layers without introducing a dependency on a vendor-specific AllToAll entry point.

• MPI Correctness and Safety

  • Treat AllToAll as a movement operation in the shared OpenMPI/MPICH implementation by using MPI_BYTE with count * type_size bytes per peer.
  • Add communicator, world-size, size_t multiplication, total-buffer-size, and MPI int count-range checks.
  • Manage host staging buffers with automatic cleanup across success and error paths.
  • Validate every received source block in the existing MPI example and exchange validation status so failures propagate through the process exit code.

• Examples and Validation

  • Add a thread-per-GPU single-node CCL AllToAll example using one shared unique ID and rank-based communicator initialization.
  • Add an OpenMPI-assisted CCL example that first uses the public AllToAll API through the OpenMPI inter communicator to distribute the native unique ID, then initializes the native CCL communicator for grouped GPU point-to-point exchange.
  • Validate the complete out-of-place receive layout with source/destination-specific values on every rank.
  • Propagate validation failures across all ranks through shared atomic state in the thread-per-GPU example and a second public AllToAll status exchange in the process-based examples.
  • Report per-peer and per-rank data sizes, rank-0 average time, algorithmic bandwidth as world_size * peer_bytes / time, and bus bandwidth as algorithmic bandwidth multiplied by (world_size - 1) / world_size.
  • Parse numeric options without exceptions and guard example buffer-size calculations against overflow.

Platform and Backend Affected

Platform

• CPU
• NVIDIA GPU
• Iluvatar GPU
• MetaX GPU
• Moore Threads GPU
• Cambricon MLU
• HYGON DCU

Backend

• OpenMPI
• MPICH
• NCCL
• MCCL

Performance Impact

• No performance impact
• Performance improved
• Performance regression possible

This adds GPU-native NCCL and MCCL paths for AllToAll, avoiding the existing MPI host-staging path when a supported CCL backend and matching native communicator are available. The native implementation uses grouped point-to-point operations because neither provider exposes a vendor AllToAll entry point, while the shared MPI fallback remains host-staged with corrected byte-count handling. Other collective operations are intended to remain unchanged. The validation-log timing output is reference data rather than a quantified cross-backend performance comparison.

Known Issues & Future Work

• The CCL collective backend on this branch now covers AllReduce and AllToAll; other CCL collective operations remain future work.
• AllToAll inherits the backend/device combinations and data type support of the existing CCL providers; this PR does not add a new provider or device integration.
• The native CCL path uses one grouped Send/Recv pair per peer, including self, rather than a vendor-specific AllToAll entry point. The number of point-to-point operations therefore grows linearly per rank and quadratically across the communicator.
• Non-zero in-place buffers are not supported by the native CCL path.
• The server examples validate out-of-place Float32 payloads on the default stream. Additional data types, non-default streams, and runtime coverage on Iluvatar and Moore Threads remain future work.
• The shared OpenMPI/MPICH fallback remains host-staged with per-call allocations and rejects per-peer byte counts above the MPI int range; chunked transfers remain future work.

Test Results

The implementation commit is a4ada89dffe7b22afd595124df304efc0f5ce7ac, a single commit directly based on ef4045a2d99837c75c2acd90aae57dacb83172d2. The attached evidence contains exactly 15 hash-verified canonical logs from the current commit: all 15 targets passed, with Correct: YES 17 times and Correct: NO 0 times. The extra two positive results are the additional expected validation cases in the broadcast log.

All four current-commit AllToAll paths passed:

Path	Test topology	Data per peer / rank	Time	Alg BW	Bus BW
Pure NCCL	8 NVIDIA A100 GPUs	4.00 / 32.00 MiB	0.205 ms	163.82 GB/s	143.34 GB/s
OpenMPI + NCCL hybrid	8 NVIDIA A100 GPUs	4.00 / 32.00 MiB	0.211 ms	158.97 GB/s	139.10 GB/s
Heterogeneous OpenMPI	8 NVIDIA A100 + 8 MetaX C550 GPUs	1.00 / 16.00 MiB	34.164 ms	0.49 GB/s	0.46 GB/s
Pure MCCL	8 MetaX C550 GPUs	4.00 / 32.00 MiB	0.233 ms	144.11 GB/s	126.10 GB/s

The reported times are the rank-0 averages over 20 profiled payload calls after 2 warm-up calls. Algorithm bandwidth uses the total per-rank transfer size, and bus bandwidth applies the (world_size - 1) / world_size correction factor. The values were independently recomputed while accounting for the displayed time being rounded to three decimal places. They are included as execution evidence, not as a cross-platform performance comparison or an all-rank aggregate.

• The pure NCCL and OpenMPI+NCCL configurations each passed both selected targets, 2/2 and 2/2, on all 8 A100 GPUs.
• The heterogeneous OpenMPI configuration passed all 9 MPI targets on 16 ranks. Ranks 0-7 mapped to NVIDIA devices 0-7, and ranks 8-15 mapped to MetaX devices 0-7.
• The pure MCCL configuration passed both selected targets on all 8 MetaX C550 GPUs. The AllToAll log reports 8 target GPUs and validates all eight source blocks at every destination.
• All five formal invocations returned zero. There were no harness retries, MetaX vendor queue retries, timeouts, missing canonical logs, or recorded finalization errors.

Test Involved Platform

• CPU
• NVIDIA GPU
• Iluvatar GPU
• MetaX GPU
• Moore Threads GPU
• Cambricon MLU
• HYGON DCU

Test Involved Backend

• OpenMPI
• MPICH
• NCCL
• MCCL

Pure CCL (NCCL) on single-node NVIDIA:
ccl_all_reduce.log
ccl_all_to_all.log

CCL + MPI on single-node NVIDIA:
ccl_mpi_hybrid_all_reduce.log
ccl_mpi_hybrid_all_to_all.log

MPI on Heterogeneous Cluster:
mpi_all_gather.log
mpi_all_reduce.log
mpi_all_to_all.log
mpi_broadcast.log
mpi_gather.log
mpi_reduce.log
mpi_reduce_scatter.log
mpi_scatter.log
mpi_send_recv.log

Pure CCL (MCCL) on single-node MetaX:
ccl_all_reduce.log
ccl_all_to_all.log

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Scope and Design

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Testing

• All applicable example programs have been built and tested successfully on at least one supported heterogeneous cluster setup.

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