SP GRPO support + batch SP fixes (#2643)

* ctx manager for SP

* updates

* update

* further simplifying

* simplifying

* simplifying

* reorg

* batch api HF adapter for ring-flash-attn; cleanup and improvements

* update

* adding all batch ring-flash-attn methods via single adapter

* fix

* fixes for batch API funcs, simplify

* fix

* grpo sp support

* progress

* stronger subclassing of TRL GRPO trainer; custom distributed sampler

* subclassing constructor

* progress

* finalizing SP + GRPO trainer

* minimize diffs to GRPO trainer

* remove (most of) the custom GRPO trainer logic

* debug

* debug

* update

* update

* update

* progress

* cleanup

* cleanup

* minor changes

* update

* update

* update

* small changes

* updates

* cleanup; torch.compile ring_flash_attn functions to prevent numerical instability; lint

* spacing

* cleanup; log in pydantic model config only on main process

* remove comment

* fix sp sampler, update to latest upstream code, doc

* add docs

* update quartodoc autodoc contents

* fix, simplifications

* fixes + simplifications

* review comments

* lint

* removing main process only logs in favor of #2608

* fixes, additional smoke test

* updatse

* more tests

* update

* fix grad accum bug (sort of)

* lint, tests

* todo

docs/sequence_parallelism.qmd

@@ -3,8 +3,6 @@ title: Sequence Parallelism
 description: Train with long sequences split across multiple GPUs.
 ---
 
-# Sequence Parallelism
-
 Sequence parallelism is a technique that splits sequences across multiple GPUs,
 allowing you to train with very long sequences that wouldn't fit on a single GPU. Each
 GPU processes a different portion of the sequence, and the results are aggregated
@@ -27,7 +25,7 @@ To enable sequence parallelism, add the following to your configuration file:
 sequence_parallel_degree: 4  # Split sequences across 4 GPUs
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 heads_k_stride: 1
-# Optional; one of "varlen_llama3", "batch_ring", "batch_zigzag", "batch_stripe". Defaults to
+# Optional; one of "varlen_llama3" or "batch_ring". Defaults to
 # "varlen_llama3" when `sample_packing: true`, and "batch_ring" otherwise.
 ring_attn_func:
 ```