support dflash2

[jiapingW]

Motivation

Add end-to-end support for training DFlash 2 draft models in SpecForge.

DFlash 2 reuses the existing DFlash capture and training pipeline while extending the draft architecture with grouped dynamic convolutions and candidate-path selection. This PR
also ensures that exported checkpoints follow the public SGLang DFlash 2 parameter and configuration contract.

• Add and register DFlash2DraftModel as a DFlash-compatible architecture.
• Add grouped dynamic depthwise convolutions around each attention and MLP sublayer.
• Add a low-rank candidate selector for reranking top-k token candidates using predecessor-token transitions.
• Support DFlash 2 unary-logit transformations, including output_multiplier and final_logit_softcapping.
• Extend the DFlash objective with a configurable selector loss:
  • add training.dflash2_selector_loss_alpha;
  • insert the gold token during training when it is missing from the top-k candidates;
  • report selector loss, accuracy, and original top-k coverage.
• Persist DFlash 2 architecture and selector settings in the resume contract.
• Allow the DFlash provider and draft registry to load both DFlashDraftModel and DFlash2DraftModel.
• Support block_size from either the top-level draft config or dflash_config, with conflict validation during export.
• Preserve the DFlash2DraftModel architecture during Hugging Face export and validate all required DFlash 2 fields.
• Add a Qwen3.6-27B DFlash 2 draft config and a managed two-GPU disaggregated training recipe.
• Document DFlash 2 training, configuration, export, and serving behavior.
• Add unit tests covering:
  • model registration and construction;
  • grouped-convolution behavior and gradients;
  • candidate scoring and lattice construction;
  • selector supervision and gradient propagation;
  • resume metadata;
  • export normalization and validation;
  • configuration schema and example topology.

Modifications

Related Issues

Accuracy Test

Benchmark & Profiling

{
  "passed": true,
  "input_format": "training_jsonl",
  "spec_accept_length": 8.0,
  "target_prefix_match_tokens": 24,
  "generated_tokens": 24,
  "target_tokens": 3785,
  "clean_block_tokens": 8,
  "errors": [],
  "result_path": "/disk3/wjp/projects/codex-run/SpecForge/outputs/Qwen3.6-27B-Dflash-overfit/serving-gate.json"
}

Checklist

• [] Format your code according to the Code Formatting with Pre-Commit.
• Add unit tests as outlined in the Running Unit Tests.
• Update documentation / docstrings / example tutorials as needed, according to Writing Documentation.
• Provide throughput / latency benchmark results and accuracy evaluation results as needed, according to Benchmark and Profiling and Accuracy Results.
• For reviewers: If you haven't made any contributions to this PR and are only assisting with merging the main branch, please remove yourself as a co-author when merging the PR.
• Please feel free to join our Slack channel at https://sgl-fru7574.slack.com/archives/C09784E3EN6 to discuss your PR.