Cosmos3 context parallel#14054
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Under sharded placement (device_map="balanced"), vae.encode() runs on the VAE's own device while the mean/inv_std buffers were pinned to x.device, causing a cross-device RuntimeError. Compute raw_mu first, then pin the normalization buffers to its device so all tensors share one device.
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Cosmos 3 cannot use diffusers' declarative `_cp_plan` CP path: it is grouped-query
attention (the shared Ulysses kernel assumes K/V share the query head count), its
understanding (causal) and generation (full) streams are separate packed sequences
(gen attends to cat(und, gen)), and per-pathway lengths are ragged. The model carries
no parallelism logic -- it exposes only small, CP-agnostic seams; all sharding lives
outside it, in a reusable example module.
Model (transformer_cosmos3.py): adds two default-None `forward` seams -- `_cp_shard_fn`
(shards und/gen + rotary before the decoder layers) and `_cp_gather_fn` (gathers/unpads
after the final norm) -- and extracts `Cosmos3AttnProcessor._run_attention` as an
override point. The non-parallel path is unchanged.
Helpers (examples/cosmos3/cosmos_parallel.py): one importable module, two orthogonal
and composable axes:
* Context parallelism (Ulysses) -- `enable_cosmos3_context_parallel`. Shards the
sequence; brackets the two attention pathways with all-to-all (DTensor redistribute),
repeats GQA KV heads, pads ragged lengths and masks padded generation keys.
* Tensor parallelism (Megatron) -- `enable_cosmos3_tensor_parallel`. Column/row-shards
the attention + MLP weights so a checkpoint that does not fit one GPU (Super, ~120 GB)
loads across several; weights load to CPU then shard layer by layer.
Both expand KV heads to the query-head count and call SDPA with enable_gqa=False so it
dispatches to the flash kernel; enable_gqa=True forces the math path, which materializes
the full [S, S] score matrix and OOMs on long videos. A dense `Cosmos3FlashAttnProcessor`
(`enable_cosmos3_flash_attention`) provides the same for TP without CP.
CLI (examples/cosmos3/inference_cosmos3.py): imports these helpers, so any modality
(text-to-image/video, image-to-video, sound, action) runs single- or multi-GPU via
`--tp-degree` / `--cp-degree` (their product must equal --nproc_per_node). Single-GPU
behavior is unchanged.
Docs + example README updated. Verified: CP attention core is bit-exact vs non-CP in
fp32 (max|d|=0), and a full 36-layer forward matches CP-on vs CP-off to ~1e-6 in fp32
(bf16 differs only by floating-point rounding).
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