103 lines
11 KiB
Markdown
103 lines
11 KiB
Markdown
# DSV4 Audit — Decode Repetition + Precision / Tensor-Core Plan
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**Context:** post-cleanup `single_shot_inference.py` compiles, Paris is top-1 at step 0, output is coherent, then degenerates into repeated junk ("capital ..."). Defaults in effect: `temperature=0.6`, `repetition_penalty=1.1`, `--warmup-gsa` **off**, fused rmsnorm+quant **on**.
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**Read this first — what the symptom rules in/out.** The model is *sampling* (temp 0.6) with a penalty (1.1) and still loops a near-constant token. That is not "greedy with no penalty." It means either (a) the model finished its turn, emitted a stop token we don't catch, and the LM head is now peaked on degenerate filler, or (b) a decode-state correctness bug whose error compounds over steps. (a) is far more likely and is nearly free to test, so **do Part A in order, cheapest first, and do NOT touch kernels until A1–A2 are ruled out.** The math is right (Paris top-1); don't go hunting kernel ghosts before eliminating the decoding-config causes.
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Code is the source of truth. For any precision change, validate per-layer cosine against `dsv4/reference/` before trusting end-to-end output.
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---
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# PART A — Decode repetition (correctness). Do in order.
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## A1 — Stop set (HIGH priority, ~zero effort, most likely the whole bug)
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`single_shot_inference.py:1571` stops only on `next_id == tokenizer.eos_token_id`. DSV4 is a reasoning/chat model; an assistant turn ends with a special token (`<|end_of_sentence|>`, and the turn structure also uses USER=128803 / ASSISTANT=128804 / `</think>`=128822). If the model's turn-end token isn't `eos_token_id`, decode never stops and degenerates exactly as observed.
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**Diagnose (no code change):**
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1. Print `tokenizer.eos_token_id`, `tokenizer.eos_token`, and `tokenizer.special_tokens_map` once at startup.
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2. In the decode log, find the token id emitted at the moment output "should have finished." Decode it: `tokenizer.decode([id])`. If it's a special/end token not in the stop set, that's the bug.
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**Fix:** build an explicit stop set and break on membership, e.g.:
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```python
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STOP_IDS = {tokenizer.eos_token_id}
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for t in ("<|end_of_sentence|>",): # add the real turn-end token name(s) for this checkpoint
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tid = tokenizer.convert_tokens_to_ids(t)
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if tid is not None and tid >= 0: STOP_IDS.add(tid)
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STOP_IDS.add(USER_TOKEN) # model trying to open a new user turn = it's done
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# ... in the loop:
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if next_id in STOP_IDS:
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print(f" STOP ({next_id}) at step {step}", flush=True); break
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```
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**A/B:** if adding the stop set ends generation cleanly, the "bug" was never in the kernels. Stop here.
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## A2 — Sampler / penalty sanity (MEDIUM, cheap, diagnostic)
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A 1.1 penalty over `recent_tokens=all_tokens[-256:]` should at least *perturb* a single-token loop. If it loops the exact same id anyway, suspect the penalty isn't reaching the kernel or is mis-indexed.
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- **Test:** rerun with `--repetition-penalty 1.5`. If the loop is *unchanged*, the penalty path in `dsv4/model/sampler.py` (CUDASampler) is broken — verify `recent_tokens` is actually passed to and applied by the kernel, and that it indexes the logit vector correctly. If raising it *does* break the loop, the sampler is fine and this was a stop-token/decoding-hygiene issue (see A1).
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- Also confirm `recent_tokens` includes the *prompt* tokens, not just generated ones, or the model can loop on a prompt word ("capital") penalty-free.
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## A3 — Compressed/SWA visible-range parity (MEDIUM, architectural — verify vs reference)
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During decode the query attends to `[top-k compressed entries] ++ [SWA window]` (`forward_attention`, "5. Gather KV"). Two things to verify against the HF/`dsv4/reference` oracle, because an off-by-one here causes subtle wrongness that **compounds across decode steps** (coherent early, degenerate late — matches the symptom):
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1. **Which compressed blocks are visible to a decode query.** Causality: a query must see only compressed blocks strictly *preceding* its own current (incomplete) block, never its own or future blocks. Confirm the set of compressed indices fed to the FMHA at step `s` matches the reference exactly.
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2. **SWA / compressed overlap.** The most recent tokens are in the SWA ring (`ws=128`) *and* may also be inside the newest complete compressed block → the query can attend to both representations of the same tokens. This may be intended (SWA refines what compression blurred, and the model was trained with it) — but it must match how the reference gathers, or the recent-context weighting drifts. Diff the gathered key set (indices + count) against the reference for a fixed prompt at several decode positions.
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> Note: the residual `|X|` growing to ~244–372 is **expected** (the paper notes 300–500; your own `KVCache` docstring says the same). It is not by itself the bug. See B5 only if A1–A3 don't resolve it.
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## A4 — (verify, likely fine) Inverse RoPE
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`forward_attention:783` applies `_apply_rope(attn_out, positions, ..., inverse=True)` at the **query** position, which is what converts the absolute positions carried by the summed KV into relative ones. This looked correct. Just confirm `inverse=True` negates the rotation angle (applies RoPE(−t)) and uses the query `positions` (not `comp_pos`). Only revisit if A1–A3 are clean and degeneration persists.
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---
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# PART B — Precision / NVFP4 / tensor-core (do AFTER Part A; never optimize a broken decode)
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Goal: native NVFP4 where the math allows, FP8_E4M3 where it doesn't, BF16/FP32 only where required. Validate each change with per-layer cosine vs `dsv4/reference` before trusting it.
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## B0 — What's already optimal: DO NOT "fix" the MoE
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`dsv4/layers/moe.py` already runs **native NVFP4**: expert weights and activations are `float4_e2m1fn_x2`, block scales are `float8_e4m3fn`. This matches the paper (routed experts in FP4). Leave it. The remaining wins are in **attention** and the **indexer**, not MoE.
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## B1 — FP8_E4M3 FMHA (BIG win; perf + memory + native Blackwell)
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Today: KV is *stored* mixed (FP8 nope + BF16 rope), then in "5. Gather KV" it's **dequantized to BF16** into `gbuf`, and the FMHA runs in **BF16**. That throws away the FP8 you stored and runs the heaviest kernel at half the tensor-core throughput Blackwell offers.
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NVFP4 KV is correctly ruled out — your own `KVCache` docstring shows 4-bit KV values cost ~0.4%/round-trip that compounds fatally over 61 layers. **FP8_E4M3 is the right target**, and you already store the nope dims in it. Plan:
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- Feed FP8 nope dims to the FMHA **directly** (skip the FP8→BF16 dequant in `comp_nope_selective`/`comp_nope_all`). Keep the 64 rope dims in BF16 (precision-sensitive) → a split-precision FMHA, or quantize rope to FP8 too and measure cos.
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- Quantize `q` to FP8 before the FMHA (it's BF16 now; see B3). Blackwell FP8 MMA consumes FP8×FP8.
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- Wins: removes the per-entry dequant, **halves `gbuf` bandwidth** (the per-step gather is on the decode hot path), and uses FP8 tensor cores. The DeepGEMM reference `fp8_mqa_logits` / FP8 attention paths are the template.
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- Gate it behind a cos check vs the BF16 FMHA per layer; if rope-in-FP8 drops cos, keep rope BF16.
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## B2 — Indexer scoring on FP8/FP4 tensor cores (BIG at long context; native FP4)
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`single_shot_inference.py` indexer scoring is `torch.einsum('tnd,cd->tnc', q_idx.float(), k_idx.float())` → **full FP32 einsum on CUDA cores over all `n_comp` entries, every CSA layer, every decode step.** At long context this is the dominant indexer cost and it's the *opposite* of native-FP4. The indexer keys are already FP8 in cache. Replace with a tensor-core **weighted-ReLU MQA-logits kernel** in FP8 (or FP4 for the QK path, as the paper does: "lightning indexer ... FP4"). Mirror DeepGEMM `fp8_fp4_mqa_logits`. This is both the long-context perf unlock and a native-FP4 conversion. (The dead `dsv4/kernels/indexer/*.cu` is not this — write it fresh against the DeepGEMM kernel, score in FP8/FP4, top-k with a warp-local reduction, no global lock.)
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## B3 — Fused rmsnorm→quant for q_a_norm / kv_norm (small, removes BF16 round-trips)
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The fused `rmsnorm_quantize_nvfp4` path is used for the mHC attn/ffn input norms (good), but `q_a_norm` and `kv_norm` in `forward_attention` still call plain `rmsnorm` (returns BF16) and then re-quantize downstream. This is exactly the "produce BF16, immediately re-cast" pattern. Extend the fused fp32→(FP4/FP8) norm+quant to `q_a_norm` (feeds `q_b`) and `kv_norm` (feeds the FMHA), emitting the consumer's target dtype directly and skipping the intermediate BF16 materialization. Modest but on the per-token hot path.
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## B4 — General "producer BF16 → consumer FP32" sweep (the user's pattern)
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Find and fix places that cast up immediately after producing a narrower dtype:
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```bash
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grep -nE "\.float\(\)" single_shot_inference.py dsv4/layers/*.py dsv4/ops/*.py
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```
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For each hit, check the producing line just above. The rule: **emit the dtype the next consumer needs.** Two directions:
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- Producer makes BF16, consumer's first act is `.float()` → make the producer emit FP32 (or fuse), skip the cast.
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- Producer makes FP32 only to be quantized to FP4/FP8 next → fuse the quant into the producing kernel (as B3).
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Do **not** apply this to the compression boundaries: the compressor *should* emit FP32 then downcast to FP8/BF16 for storage — that downcast is the architecture's memory budget, not a wasted step.
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## B5 — Residual-stream precision (low priority; only if A-items don't fully resolve degeneration)
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The mHC residual `X` is BF16 at `|X|≈300`, where BF16 ULP ≈ 2. This is probably fine (matches the reference / paper's expected magnitude, and mHC's doubly-stochastic B is non-expansive). But if late-decode degeneration survives Part A, A/B test the residual stream in FP32 for a few layers and watch whether the repetition onset moves. If it does, the residual precision is a contributor; if not, rule it out. Keep this last — FP32 residual doubles mHC activation memory/bandwidth, against the concurrency goal.
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---
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# PART C — Guardrails for the agent
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1. **Order matters:** finish Part A (correctness) before any Part B (perf). A faster wrong decode is still wrong.
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2. **Every precision change is gated by a per-layer cosine vs `dsv4/reference`** for a fixed prompt, *before* judging end-to-end output. Record the cos in the commit message.
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3. **One change per commit**, with the A/B result. If a change drops end-to-end coherence, the per-layer cos tells you which layer/op regressed.
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4. **Don't re-create the dead indexer.** B2 is a new FP8/FP4 kernel; the `dsv4/kernels/indexer/*.cu` files are archived/dead — confirm with `helpers/import_closure.py` before reusing anything there.
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5. **Re-validate the stop fix (A1) on a long generation** (≥512 tokens) and a multi-turn prompt, not just "capital of France" — the turn-end token differs by prompt type.
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## Suggested sequence
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A1 (stop set) → A2 (penalty test) → if still broken: A3 (visible-range parity vs reference) → A4 (inverse-RoPE check). Then B1 (FP8 FMHA) → B2 (FP8/FP4 indexer) → B3 (fused norm+quant) → B4 (cast sweep) → B5 only if needed.
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---
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# PART D — Dangling TODOS
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- It is mentioned in `/home/openclaw/dev/nvfp4-megamoe-kernel/docs/PERFORMANCE_AUDIT.md` that P5 (Fuse mHC pre_block + RMSNorm into a single op) is done but kernel, pending integration. Please wire that up if you have not done so already |