[Refactor] Promote DFly to a first-class DFlash-family drafter and add an end-to-end multi-step TV loss

[ali-88123]

Summary

This PR promotes DFly from an architecture variant
that piggy-backed on the DSpark code path into a standalone, first-class member
of the DFlash drafter family. It gets its own config, model, training wrapper,
and trainer, and no longer depends on DSpark in any way.

Alongside the refactor, this PR:

• Removes the dead treeflash_dspark_dflare drafter and its test.
• Slims down DSpark by moving the shared TreeFlash hidden-states correction (and related knobs) out of dspark.py and into dfly.py, where it now belongs.
• Adds an optional end-to-end multi-step TV loss to the DFlash composable loss, replacing the now-unused KL/LK temperature knobs.

The net effect is a cleaner architecture graph (DFly no longer "rides" DSpark), less coupling between drafters, and a large reduction in DSpark's surface area.

Motivation

Previously, DFly was selected via DSparkConfig + model_arch == "dfly" and was
dispatched through DSparkTrainer / DSparkModel. This meant:

• DFly's behavior was implicit and hard to discover (hidden behind a string flag).
• DSpark carried a lot of machinery (hidden-states correction, position-adaptive
  alpha, etc.) that only DFly actually used.
• The auto dispatch logic had brittle special-case branches keyed on
  model_arch string comparison.

Making DFly a proper config/model/trainer triple removes the cross-routing,
makes the dispatch type-based, and lets each drafter own only what it needs.

Changes

New — DFly as a first-class drafter

• angelspec/models/dfly.py (new): DFlyModel training wrapper. Subclasses
  DFlashModel and overrides _compute_draft_logits to apply the optional
  TreeFlash hidden-states correction (formula (1)) before the LM head.
• angelspec/training/dfly_trainer.py (new): DFlyTrainer, a thin subclass
  of DFlashTrainer that only overrides the two model-build seams
  (_build_draft_model / _build_training_wrapper). Reads the dflash_*
  hyperparameter namespace.
• angelspec/models/draft/dfly.py: now defines its own DFlyConfig
  (extends DFlashConfig, model_type = "qwen3") and owns the
  HiddenStatesCorrection module / build_hidden_correction helper (moved here
  from dspark.py). DFlyDraftModel.config_class is now DFlyConfig.

Dispatch / registration

• angelspec/models/draft/auto.py: register DFlyConfig → DFlyDraftModel
  and architecture "Qwen3DFlyModel" → DFlyConfig. Removed the
  model_arch == "dfly" and model_arch == "dflare" (TreeFlash) special-case
  branches.
• angelspec/training/trainer_actor.py: add a DFlyConfig dispatch branch.
  Since both DSparkConfig and DFlyConfig subclass DFlashConfig, they are
  checked before the DFlashConfig branch.
• angelspec/models/__init__.py / angelspec/models/draft/__init__.py:
  export DFlyModel; drop the TreeflashDSparkDFlareDraftModel export.

DSpark slim-down

• angelspec/models/draft/dspark.py: removed the hidden-states correction,
  PositionAdaptiveAlpha, position-adaptive Markov knobs, and related
  parameters — DSpark now only carries the Markov head and confidence head.
• angelspec/models/dspark.py: corresponding wrapper cleanup.

End-to-end multi-step TV loss

• angelspec/models/dflash.py: add _compute_e2e_tv_loss, an independent
  γ-step MTP TV term added on top of the total loss (not mutually exclusive with
  KL/LK), gated on e2e_tv_loss_weight > 0 and the presence of target
  last_hidden_states. Emits e2e_tv_loss in loss_components.

  L_e2e = 1 - (1/γ) * Σ_{j=1..γ} Π_{i=1..j} α_i
  

• angelspec/config/train_config.py: add dflash_e2e_tv_loss_weight
  (default 0.0, disabled) and DatasetConfig.num_proc (default 64); remove
  the now-unused dflash_kl_temperature, dflash_kl_topk_renormalize, and
  dflash_lk_temperature.

Removals

• angelspec/models/draft/treeflash_dspark_dflare.py (deleted).
• tests/test_treeflash.py (deleted).
• angelspec/config/dflare_dspark_treeflash_qwen3_4b_draft_config.json (deleted).

Configs

• angelspec/config/dfly_*_draft_config.json: switch from
  architectures: ["DSparkDraftModel"] / model_type: "dspark" /
  model_arch: "dfly" to architectures: ["Qwen3DFlyModel"] /
  model_type: "qwen3"; drop the DSpark-only markov_rank /
  enable_confidence_head / confidence_head_with_markov keys.
• configs/vllm_qwen3_8b_dfly.yaml, configs/vllm_hy3_dfly.yaml: fix
  lm_head_key to lm_head.weight, migrate dspark_* hyperparameters to the
  dflash_* namespace, and set up the two-stage loss schedule (cold-start
  lk-loss → final e2e_tv_loss).

Docs & tests

• docs/concepts/dfly.md: document DFly as an independent DFlyConfig /
  Qwen3DFlyModel / DFlyTrainer drafter; update the comparison table.
• docs/concepts/dspark.md, dflash.md, draft_model_family.md:
  minor updates reflecting the moved correction module and the new loss term.
• tests/test_dfly.py: updated to build via DFlyConfig and exercise the
  DFlyModel wrapper; asserts a plain DSparkConfig still routes to
  DSparkDraftModel (no cross-routing).

Compatibility / migration notes

• Breaking config change: existing DFly checkpoints/configs using
  architectures: ["DSparkDraftModel"] + model_arch: "dfly" must be migrated
  to architectures: ["Qwen3DFlyModel"] (see updated dfly_*_draft_config.json).
• The removed dflash_kl_temperature / dflash_kl_topk_renormalize /
  dflash_lk_temperature training args are no longer accepted.
• treeflash_dspark_dflare is gone; any references must be removed.

Testing

• tests/test_dfly.py covers auto-dispatch, model structure (shared-KV layers,
  re-added context_proj, inherited fusion), the zero-init identity of the
  hidden correction, a tiny forward through DFlyModel (finite loss, correct
  loss_components, correction actually affects the loss), and state-dict keys.

[yghstill]

这个文件删除

[yghstill]

check下Hy3也使用Qwen3DFlyModel？