dist: coalesce grads into buckets for all-reduce (KI-5)

Replace the per-parameter eager all-reduce (~150 tiny serial NCCL calls
for dim512, DDP's dominant cost after T10's batched forward) with a
coalesced bucketed all-reduce: pack grads into a few large contiguous
scratch buffers, all-reduce each bucket once (fused via ncclGroupStart/
End), fold the 1/world average into one per-bucket scale, unpack back.

The packed buffer is the concatenation of the grad tensors, so NCCL's
element-wise sum over a bucket equals the per-tensor sums — bit-identical
to the un-bucketed path; only launch/latency overhead is removed. DDP
cross-rank param identity + loss-match are preserved.

Adds xtrain_cuda::device::copy_d2d (cudaMemcpy D2D) for the pack/unpack.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

crates/xtrain-cuda/src/ffi.rs

@@ -5,6 +5,7 @@ pub type CudaStream = *mut c_void;
 
 pub const CUDA_MEMCPY_H2D: i32 = 1;
 pub const CUDA_MEMCPY_D2H: i32 = 2;
+pub const CUDA_MEMCPY_D2D: i32 = 3;
 
 pub const CUDA_SUCCESS: i32 = 0;
 pub const CUDA_ERROR_OUT_OF_MEMORY: i32 = 2;