[CUDA] Support bfloat16 in AllReduce, AllGather and AllToAll

[tianleiwu]

Description

Adds bfloat16 support to the CUDA collective ops AllReduce, AllGather and AllToAll.

Three things blocked bf16 today:

1. GetNcclDataType had no BFloat16 case, so any bf16 tensor reaching a collective failed at runtime with Tensor type not supported in NCCL. Now mapped to ncclBfloat16.
2. The AllReduce CUDA kernel used DataTypeImpl::AllIEEEFloatTensorTypes(), which excludes BFloat16 because bf16 is not an IEEE format. Replaced with an explicit float / double / MLFloat16 / BFloat16 list.
3. The op schemas did not list tensor(bfloat16). Added to all three. Note that the AllGather and AllToAll kernels already register DataTypeImpl::AllTensorTypes(), so for those two the schema was the only thing rejecting bf16 — no kernel change was needed.

Motivation and Context

bf16 is the native dtype of most current LLMs, so a tensor-parallel graph built from one is bf16 end to end. Hitting an fp32-or-fp16-only AllReduce at every layer boundary forces either a cast pair around each collective (extra kernels and bandwidth on the critical path, twice per layer) or exporting the whole model in fp16.

This came up while bringing up a tensor-parallel + expert-parallel DeepSeek-V4 export on 8×H200, where each of the 43 layers issues two AllReduce on bf16 activations.

Testing

Validated end to end rather than by unit test, since the distributed collective tests require a multi-GPU host and are opt-in:

• 8-rank TP=8 / EP=8 inference of a 284B bf16 model, 2 × bf16 AllReduce per layer × 43 layers, producing correct token sequences.
• A full 800-sample MMLU-Pro run over that deployment.

Happy to add a bf16 case to the existing distributed collective test if reviewers would like it in this PR.

Note for reviewers

ncclBfloat16 requires NCCL ≥ 2.10 (mid-2021). CMake currently only version-checks NCCL for the USE_NCCL_P2P define (≥ 2.7) and does not enforce a floor, so in principle a build against NCCL 2.7–2.9 would now fail to compile this file. Every CUDA 12/13 toolchain ships far newer NCCL, so I left it unguarded rather than adding #if NCCL_VERSION_CODE >= NCCL_VERSION(2,10,0) clutter — but let me know if you would prefer the guard or a hard CMake floor.