prism-v6: dspark Path A, converge on upstream dflash with log-SNR conditioning and drafter-own embeddings

[bri-prism]

What

Path A of the dspark re-port: instead of re-porting our standalone dspark arch, converge on upstream's merged dflash implementation and re-apply the two drafter-side deltas it lacks.

1. GIDD log-SNR conditioning (LogSnrEmbed). GIDD-trained drafters ship a log_snr_embed module: a sinusoidal featurization of a per-position log-SNR value through a 2-layer SiLU MLP, added to the draft noise embedding before the backbone (anchor position of each block at max_log_snr, masked positions at min_log_snr). The feature matrix is a pure function of quantities known at graph-build time, so it is precomputed host-side and staged through a new llm_graph_input_dspark_logsnr input. New optional metadata dflash.log_snr_conditioning plus dflash.min_log_snr/max_log_snr (required and validated once the flag is set); new tensors log_snr_fc1/fc2.{weight,bias}, required when the flag is set so a broken conversion fails loudly instead of silently running unconditioned.
2. Optional drafter-own token_embd/output. Loaded as TENSOR_NOT_REQUIRED; the decoder graph already prefers model-own tensors and only borrows the target's via ctx_other when absent. Carrying the drafter's own full-precision embeddings and head protects accept rate when the target is heavily quantized, and lets such drafters initialize without ctx_other during memory fitting.

Drafters without either feature convert and load exactly as before.

Why

Keeping a standalone arch means carrying a parallel implementation of the same math forever. Upstream's dflash is in-graph, multi-seq, and block-size-sweepable; measured head-to-heads showed our accept advantage came from these two drafter-side deltas, not the algorithm, so this re-applies just the deltas.

How verified

• Builds clean; loader accepts a converted GIDD drafter (79 tensors including the 4 log_snr tensors and own embd/head) and rejects one whose metadata flag is set without the tensors
• End-to-end llama-speculative-simple --spec-type draft-dspark run on Metal: graph builds, spec loop executes, coherent output
• Accept-rate validation (llama-server probe, M5 Pro, GIDD drafter x binary Q1_0 target, greedy): 51.7% accept without conditioning (upstream-equivalent stripped drafter) vs 82.6% accept, mean len 3.43 with log-SNR conditioning active. An embd-less conditioned variant scores the identical 82.6%, so the accept gain is entirely log-SNR. Upstream master measures 41.6% with the stripped drafter on H100, bit-identical at the DSpark merge commit and current master: no upstream regression, and the base path in this branch is healthy (stripped drafter scores the same 51.7% here as on the prism-v6 tip).
• Do NOT evaluate this with llama-speculative-simple: it never sets ctx_other on the draft context, so embd-less drafters segfault and embd-carrying drafters silently draft without target features (2-6% accept). Use llama-server or the framework path. Details on the fork status board.

Rebased onto prism-v6 tip. Remaining follow-ups tracked on the status board: gguf-py/HF-converter mapping for fresh checkpoints, window-fix fold-in, CUDA speedup numbers.