W1 needs the GPT to consume Whisper-projector outputs as a prefix of "soft
tokens" sitting in front of the text token embeddings (LLaVA-style). The
change is intentionally minimal:
- forward() takes a new keyword-only arg `audio_features` of shape
(B, T_a, n_embd). They must already be projected to n_embd by the caller
(Projector lives in nanochat.audio, kept out of GPT itself).
- The audio rows are normed (matches the post-wte norm convention) and
concatenated *after* smear so smear stays a strictly text-side op (its
prev-token semantics aren't defined for soft tokens, and revisiting that
belongs to a later phase).
- Rotary embeddings are re-sliced for T_a + T_text. Audio gets positions
0..T_a-1, text 0-shifts to T_a..T_full-1. The 10× over-allocated rotary
cache in __init__ already covers this.
- value_embeds lookup uses an idx padded with 0 for audio positions. They
feed the v residual but the gate (`ve_gate`) is input-dependent and will
learn to suppress the dummy rows; for W1 smoke this is fine.
- targets are auto-padded with -1 (ignore_index) over audio positions so
the LM is only graded on text predictions.
Not yet supported: audio_features with kv_cache. The KV-cache path is a
prefill+decode protocol that would need its own audio-aware semantics; W1
runs train-style forwards only, so we just assert.
Fam Zheng
d760915daa
