# DSV4 → vLLM: CUDA-Graph Safety / GPU-Native Requirements (PART 2 companion)

**Goal:** the per-step decode forward must be fully GPU-native so vLLM can capture and replay it. No implicit device→host sync, no host control flow that reads a device value, no data-dependent shapes, no per-step host allocation. This doc gives you (A) a detector so you find every violation *once, upfront*, (B) the exhaustive hidden-CPU checklist, and (C) the DSV4-specific kernels that must be device-native.

## The one rule that decides everything

Branching on a **host-known integer** (step number, position, batch size, dtype, static shape) is graph-compatible — you capture one graph per bucket and the scheduler picks by that integer. Branching on a **device value** (sampled token, per-expert token count, top-k result, a mask, a norm/residual magnitude) is **not** — it must become device-side, fixed-shape work with masking. Every violation below is a place something reads a device value on the host.

You do **not** need one monolithic graph. The standard pattern (what vLLM's DSV4 does) is *bucket by shape + break at attention + keep the dense parts captured.* Your job is to make each dynamic decision either device-side or isolated to that eager break.

---

## ⚠️ CRITICAL MULTI-GPU REQUIREMENT (learned 2026-06-06)

**PyTorch CUDA graphs on non-default GPUs REQUIRE explicit `torch.cuda.Stream(device=device)` for capture AND replay.** Using `torch.cuda.set_device()` alone causes:
- GPU 0: Empty graph (warning: "The CUDA Graph is empty")
- GPU 1+: Graph replays with stale capture-time data, ignoring updated input buffers

**The fix:**
```python
# CAPTURE:
s = torch.cuda.Stream(device=device)
g = torch.cuda.CUDAGraph()
with torch.cuda.graph(g, stream=s):
    output_buf.copy_(input_buf * 2.0)

# REPLAY:
with torch.cuda.stream(s):
    g.replay()
```

**Stream synchronization between graph and eager paths:**
- Graph A/B run on per-device streams
- Eager attention (between Graph A and Graph B) runs on the default stream
- Use `torch.cuda.Event` + `record()` + `wait_event()` for sync
- **Do NOT use `torch.cuda.synchronize()`** — it syncs ALL GPUs (too heavy)

This was the root cause of the "all-zeros replay" bug that took an entire session to diagnose. The minimal reproduction test is in `tests/unit/test_cuda_graph_stream.py`. **Read this test if you ever see zero-output graph replay again.**

---

## SECTION A — The detector (build this FIRST, before porting anything) ✅ DONE

**Status:** Built and verified on B200 (2026-06-03). See `tests/unit/test_cuda_graph_readiness.py`.

Results from detector runs on B200:
- **Method 1** (sync debug mode): 0 violations in forward compute path
  - `dec_tid_buf.copy_(dec_tid_pinned)` is flagged but this is a valid graph-capturable pinned memcpy
  - All `.item()` syncs eliminated from hot path
- **Method 2** (graph capture L0): **PASS** ✅
  - `torch.cuda.CUDAGraph()` capture of layer 0 decode step succeeds
  - All per-call allocations eliminated
  - All host reads of GPU values eliminated

The detector:
1. Grep for Section B sync patterns in hot path files
2. Run one decode step with `torch.cuda.set_sync_debug_mode("error")`
3. Attempt `torch.cuda.graph` capture of L0 decode step
4. Report results to `/tmp/cuda_graph_readiness_results.json`

Run via test harness:
```bash
fire_b200_test tests/unit/test_cuda_graph_readiness.py kernel-test /tmp/kernel-test.log 1800
```

---

## SECTION B — The hidden-CPU checklist (grep the hot path for these) ✅ ADDRESSED

**Explicit device→host transfers** — All `.item()` calls on hot path eliminated:
- mhc.py `post_block`: removed `X_next.abs().max().item()` (122 syncs/step across 61 layers × 2 mHC)
- All other `.item()` calls are guarded by `VERBOSE >= 2` and don't execute at VERBOSE=0
- Warmup-gsa `.item()` calls run once at step 0, outside graph region

**Data-dependent shapes** — Eliminated `torch.bincount` from MoE:
- Replaced with `scatter_add_` into pre-allocated `_tokens_per_expert_buf` (fixed shape, GPU-only)
- Pre-allocated `_ones_buf` to avoid per-call `torch.ones()`

**Per-step host allocation** — All eliminated:
- `torch.zeros()` in `_assemble_scales_single_group` → pre-allocated `_scale_a_buf` (linear.py, grouped_linear.py, shared_expert.py)
- `torch.full()` for MoE l1_gsa → `self._l1_gsa_buf.fill_(l1_gs)`
- `torch.empty()` for grouped_linear output → pre-allocated `_output_buf`
- `mHCLayer.init_state` `.clone()` → `out_buf` parameter for in-place write
- `torch.zeros_like` in quantize.py → scalar `0.0` in `torch.where`

**Host control flow on device values** — Eliminated:
- `dec_tid_buf[0] = python_int` → pinned CPU buffer + `copy_` (async, graph-capturable)
- `expert_offsets[g] = python_int` → element-wise GPU multiply with pre-allocated range tensor
- `if group_offsets[0] != 0` → unconditional GPU-only update (no host read of GPU tensor)

**What is FINE (no sync, don't waste time on these)**
- `.shape` / `.size()` / `.numel()` / `.dtype` (host metadata, no sync)
- Branching on host-known ints (step/batch/static shape)
- The **stop-token check, detokenize, and your BF16 precision-floor dequant** (all load-time or *outside* the captured graph — leave them on host, that's correct).
- `dec_tid_buf.copy_(dec_tid_pinned)` — pinned CPU→GPU async memcpy, graph-capturable

---

## SECTION C — DSV4-specific kernels that must be GPU-native

| # | Hazard | Status | Fix Applied |
|---|--------|--------|-------------|
| 1 | Compressor returns `None` for 3/4 (CSA) or 127/128 (HCA) decode steps | ⏳ Phase 2 (eager-break) | Compressor runs in eager section. Phase 2: device-side boundary detection + fixed-shape output |
| 2 | KV grows each step → attention shape changes | ⏳ Phase 2 (eager-break) | Attention is the eager break. Phase 2: paged KV with fixed blocks + block table |
| 3 | Indexer top-k → host reads selected count to size gather | ✅ DONE | Already fixed-shape gather (`topk_indices` is always `top_k` elements). No host read of count. |
| 4 | MoE top-6 → per-expert token counts drive per-expert launches | ✅ DONE | `torch.bincount` → `scatter_add_` into pre-allocated buffer. Expert offsets are GPU tensors. |
| 5 | Next token / positions managed on host, fresh tensors per step | ✅ DONE | Pre-allocated pinned CPU buffers + `copy_` to GPU. No per-step allocation. |

Also confirmed:
- **Sinkhorn** runs a **fixed 20 iterations with no host convergence check** ✅
- **Sampler** is device-side; the EOS/stop decision is a host step **outside** the graph ✅
- **Router** is graph-safe: pre-allocated output buffers, GPU-only operations ✅
- **mHC** is graph-safe: fixed-iteration Sinkhorn, no `.item()` on hot path ✅

### Architectural Decision: Eager-Break-at-Attention (Phase 1) — UPDATED 2026-06-06

The per-layer compute is split into **two graph-captured regions** with eager attention in between:
- **Graph A** (captured): mHC pre_block(attn) + fused RMSNorm + quantize + q_a + q_a_norm + q_b + kv projections
  - Outputs written to pre-allocated buffers: x_normed, q_heads, kv_3d, ctx_a_B, ctx_a_C, X_mid
- **Eager** (NOT captured): Compressor → Indexer → KV gather → FMHA → inverse RoPE → o_a + o_b → F_attn
  - Dynamic shapes (FMHA seq_len, compressor returns None) → cannot be captured
  - `forward_attention()` accepts optional `q_heads`/`kv_3d` to skip projections when called from graph replay
- **Graph B** (captured): mHC post_block(attn) + FFN mHC + RMSNorm + quantize + Router + MoE + SE + mHC post_block(ffn)
  - Reads F_attn from pre-allocated buffer (written by eager attention)
  - Writes X_next to pre-allocated output buffer

**Rationale**: FMHA has dynamic sequence length; compressor/KV are data-dependent. Capturing the compute-heavy parts (projections, MoE, SE) eliminates ~94ms of Python dispatch overhead per step. The attention path (which is NOT compute-heavy for T=1 decode) runs eagerly with negligible overhead.

**CRITICAL**: Both Graph A and Graph B are captured and replayed on **explicit per-device streams** (`torch.cuda.Stream(device=device)`). The eager attention path runs on the **default stream**. Event-based synchronization is used between graph streams and the default stream.

**Phase 2**: Paged KV + device-side compressor → full graph capture for vLLM integration.

---

## SECTION D — Integration order

1. ✅ **Build Section A's detector and run it on the current forward** — DONE. `tests/unit/test_cuda_graph_readiness.py` on B200.
2. ✅ **Fix Section C's five device-native kernels** — 3/5 done, 2 deferred to Phase 2 with architectural decision.
3. ✅ **Re-run capture-under-test until it captures clean** — WORKING on all 8 GPUs! Root cause: multi-GPU requires explicit `torch.cuda.Stream(device=device)`.
4. ✅ **Replay verification** — Graph replay matches eager forward on all 8 GPUs. Logit range [-26.5, 15.0] matches.
5. ✅ **Benchmark** — 0.28-0.30s/token with CUDA graphs (vs 0.55s/token eager = ~2x speedup).
6. ⬜ **Gate every commit on the capture test** — Not yet implemented.
7. ⬜ **Optimize stream sync** — Current implementation uses `torch.cuda.Event` + `wait_event()`/`synchronize()`. Could potentially reduce overhead by using per-layer events instead of per-step events.
8. ⬜ **Phase 2**: Paged KV + device-side compressor for full vLLM graph capture.

---

## NEXT STEPS (pick up here in next session)

### Priority 1: Decode degeneration (still unresolved)
The model generates a repetition loop (`psych` ↔ `istically`) regardless of whether CUDA graphs are used. This is the SAME issue as the eager path — not caused by graph capture. Root cause UNKNOWN. Components exonerated: mHC, FMHA, compression. This is the highest-priority correctness issue.

### Priority 2: Stream sync optimization
The current graph replay uses per-step `torch.cuda.Event` sync between graph streams and the default stream. This works but may add overhead. Potential optimizations:
- Pre-create events as instance variables instead of creating new ones each step
- Use `torch.cuda.Stream.wait_stream()` instead of event-based sync where possible
- Profile the sync overhead vs compute time

### Priority 3: Long-run stability
Test with --max-tokens 512+ to verify stability over many decode steps. Check for:
- Memory leaks (growing GPU memory usage)
- Numerical drift (logit range changes over time)
- Graph replay failures after many steps

### Priority 4: Phase 2 — Full vLLM integration
- Paged KV cache (fixed blocks + block table)
- Device-side compressor boundary detection + fixed-shape output
- Full graph capture including FMHA
- Bucket-by-shape for variable sequence lengths

---

## Guardrails
- Keep the stop-check, detokenize, and load-time BF16 dequant on the host — they're outside the captured region by design; don't contort them to be "graph-safe."
- **Phase 1 uses eager-break-at-attention.** Phase 2 adds paged KV. Don't retrofit paged KV into Phase 1 — it's a separate integration.
- Host-known-int branching is allowed; only device-value branching must be eliminated. Don't over-correct and try to make legitimate shape/dtype dispatch device-side.
- **ALWAYS use explicit `torch.cuda.Stream(device=device)` for graph capture and replay on multi-GPU setups.** This is non-negotiable on B200.

## Violation Fix Log

| Commit | Description |
|--------|-------------|
| `a9ea303` | mhc.py `.item()` removal, linear/shared_expert pre-alloc, quantize gsa fix |
| `46a3a51` | mHCLayer.init_state out_buf, dec_X_buf pre-allocation |
| `0ca7bed` | Pinned CPU buffers for token transfer, grouped_linear expert_offsets GPU-only |
| `e07d798` | _assemble_scales_single_group correctly-sized view for swizzle |
| `df05289` | Remove conditional host read of GPU tensor in grouped_linear |
| `84655d0` | MoE bincount → scatter_add_, MoE torch.full → fill_() |
| `f13a81d` | grouped_linear scale_a_buf pre-alloc, quantize zeros_like → scalar 0.0 |
| `518a1d3` | MoE scatter_add_ int64 indices, fix second bincount call |
| `80bb27f` | gsa broadcast: reshape for M=1 decode (no stride-0), contiguous for M>1 prefill |
| `6dc2f22` | **CRITICAL: _l1_out_buf 2x too narrow → GPU memory corruption (root cause of ALL cudaErrorInvalidValue errors)**. Also: all GEMM output buffers pre-allocated, gsa copy_ → scalar assignment |
| `69e15f1` | Blackwell swizzle CUDA kernel for graph capture, swizzled output buffers |
| `ffa7842` | Dense router: BF16 GEMM instead of FP32 conversion during graph capture |
| `f259d63` | **CRITICAL: SE swizzled buffers allocated then overwritten with None — graph capture would fall through to broken Python path** |
| `32902d1` | Derive q_a_dim from config, pre-cache norm weights, add buffer verification |
| `5a98cc6` | Store pre-cached norm weights on self to prevent GC during graph replay |
| `6650f06` | **CRITICAL FIX: Use explicit per-device streams for CUDA graph capture/replay — fixes all-zeros replay on non-cuda:0 GPUs** |