feat(train): parallel training framework — FSDP2, HSDP, gradient accumulation, and DCP checkpoints (#4010)

* feat(train): parallel training engine with FSDP2, HSDP, and DCP checkpoints

Replace the FSDP1 training path with a config-owned parallel-training
engine:

- Topology and runtime configs (--parallelism.*, --accelerator.*):
  dp_replicate x dp_shard degrees select single-process, DDP (unchanged
  default), FSDP2, or HSDP; mixed precision, first-class gradient
  accumulation, and FSDP/DDP tuning knobs are mirrored as plain
  dataclasses that build the accelerate objects at runtime, so every
  run is reproducible from its train_config.json alone. Accelerate env
  vars are guarded against configuring the engine behind the config
  system's back.
- Declarative policy surface: policies declare FSDP2 wrap units
  (_fsdp_wrap_modules) and non-forward entry points
  (_fsdp_forward_methods); a shared engine resolves them around
  accelerator.prepare(). Context-parallel fields are reserved and
  validated to 1.
- Checkpoints: selectable --checkpoint_format (safetensors | dcp |
  safetensors_dcp); the sharded optimizer channel is always DCP;
  two-phase resume (step+RNG before prepare, DCP model/optimizer after)
  reshards across GPU-topology changes; lerobot-convert-dcp merges DCP
  shards into a distributable model.safetensors offline.
- Publishing: PreTrainedPolicy.push_model_to_hub is replaced by the
  free publish_trained_model (model + processors + card + train config,
  all-ranks gather with main-rank writes);
  PreTrainedPolicy._save_pretrained gathers state dicts internally,
  removing the state_dict= threading from save_pretrained.
- lerobot_train is restructured around the engine: optimizer built
  before the single prepare() call, deferred weight load on DCP
  resumes, collective save_checkpoint with no call-site rank branches,
  dp-world-size-based sample accounting.

Breaking changes: FSDP checkpoints from lerobot <= 0.6.x are not
resumable (weights stay loadable via from_pretrained; pin
lerobot==0.6.x to finish old runs); the `accelerate launch
--config_file` yaml flow is superseded by the config flags; training
autocast is owned exclusively by --accelerator.mixed_precision
(policy.dtype only casts parameters).

Also fixes: reward-model hub publishing crash (TypeError on extra
kwargs).

Verified by ~200 new CPU tests (config round-trips, checkpoint
round-trips per format, two-phase resume, publisher contracts,
converter equivalence, accelerate canaries), a 5-test 4-GPU suite
(FSDP2 save/resume bit-exactness, HSDP/DDP loss parity,
changed-topology resume, all-ranks save_pretrained, grad-accum
equivalence), and end-to-end ACT (1/4/8 GPUs) + FastWAM 6B
(FSDP2 + HSDP) training runs.

docs/source/torch_accelerators.mdx

@@ -40,3 +40,15 @@ lerobot-eval \
 ```
 
 However, in most cases, presence of an accelerator is detected automatically and `policy.device` parameter can be omitted from CLI commands.
+
+## Mixed precision
+
+Training precision is owned by `--accelerator.mixed_precision`, which accepts `no` (default) and `bf16`:
+
+```bash
+lerobot-train \
+    --policy.type=act \
+    --accelerator.mixed_precision=bf16 ...
+```
+
+`bf16` requires an accelerator that supports it.