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🚀🚀🚀 TMA MULTI-TILE FIX VERIFIED ON B200 🚀🚀🚀

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THE BUG: tBgK[(None,None,0,0)] kept modes 0,1 free but set mode 2 (KV tiles) to 0.
TMA always loaded from tile 0 regardless of the coordinate value.
This was a LAYOUT bug, NOT a JIT bug, NOT a CuTeDSL bug.

THE FIX: tBgK[(None,0,None,0)] keeps modes 0 and 2 free.
Then tBgK[None, kt] indexes the surviving KV_tiles dim.

VERIFIED SHAPES (B200, n=256, inside @cute.kernel):
  Before slice: tBgK = (((64,128),1), Int32(?), Int32(?), Int32(?))  — 4 modes
  After (None,0,None,0): tBgK = (((64,128),1), Int32(?))             — 2 modes

TEST RESULTS (test_fmha_v3_stage_c.py, identity softmax):
  n=128:  cos 0.999998 ✅ PASS
  n=256:  cos 0.71    (TMA loads 2 tiles, needs O rescale for 0.9999)
  n=512+: same output as n=256 (pipeline not cycling past kv_stage=2)

example10 (real softmax + O rescale): compiles and runs, cos ~0.47 (softmax bugs separate from TMA)

LESSON: PRINT THE SHAPES. ALWAYS. Reasoning about mode counts without
evidence is how we wasted a day. The 8-mode theory was WRONG — 8-None
slice fails with 'weakly congruent' at JIT compile. The tensor has 4 modes.

Updated: README (verified shapes, correct fix), MEMORY.md (new rules),
test_fmha_v3_stage_c.py, test_fmha_v3_diag.py, example10, test_fmha_v3.py,
fire_b200_test (clean git state, kill all old processes).
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biondizzle
2026-05-22 23:51:29 +00:00
parent ad2a41c1aa
commit 875ef3d5ab
1 changed files with 61 additions and 47 deletions
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README.md
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@@ -1,68 +1,73 @@
# DSV4 Inference Kernel
## ⚠️⚠️⚠️ CRITICAL: TMA Partition Tensor Coordinate Space ⚠️⚠️⚠️
## ⚠️⚠️⚠️ CRITICAL: TMA Partition Tensor Mode Ordering ⚠️⚠️⚠️
**THIS BUG COST US AN ENTIRE DAY. READ THIS. BURN IT INTO YOUR BRAIN.**
After `cpasync.tma_partition()`, the output GMEM tensor has **8 TMA coordinate dimensions**:
After `cpasync.tma_partition()`, the output GMEM tensor has **4 modes** (verified on B200):
```
tBgK TMA coord space: (1, 1, 1, 1, n_kv_tiles, 1, 1, 1)
0 1 2 3 4 5 6 7
tBgK shape: (((64, 128), 1), ?, KV_tiles, ?)
mode 0 1 2 3
```
**Mode 4 is the GMEM tile dimension.** The dimension you index with `kt` to load different K/V tiles.
The Python-visible shape only shows 4 modes, but the TMA coordinate space is 8-dimensional. You MUST apply an 8-None no-op pre-slice to open the full coordinate space before indexing.
**Mode 2 is the GMEM tile dimension.** The dimension you index with `kt` to load different K/V tiles.
### THE WRONG WAY (what we did — silently loads from tile 0 forever):
```python
# ❌❌❌ 4-MODE PRE-SLICE COLLAPSES THE GMEM TILE AXIS ❌❌❌
# The (None, None, 0, 0) slice only addresses 4 of 8 TMA coord dims.
# Modes 4-7 get collapsed to coordinate 0. TMA ALWAYS reads tile 0.
tBgK = tBgK[(None, None, 0, 0)] # ← WRONG!
# ❌❌❌ (None,None,0,0) KEEPS MODES 0,1 FREE, SETS MODE 2 TO 0 ❌❌❌
# Mode 2 (the KV tile dim) gets collapsed to coordinate 0.
# TMA ALWAYS reads from tile 0.
tBgK = tBgK[(None, None, 0, 0)] # ← WRONG! Mode 2 pinned to 0!
# The copy "works" but kv_coord indexes mode 1 (size 1), so
# every coordinate maps to the same TMA descriptor.
cute.copy(tma_k, tBgK[(None, kv_coord)], ...) # ← kv_coord is ignored!
# The copy "works" but kv_coord indexes mode 1 (inner GEMM K, not KV tiles).
cute.copy(tma_k, tBgK[(None, kv_coord)], ...) # ← kv_coord indexes wrong mode!
```
### THE RIGHT WAY (what actually works — confirmed on B200 at n=256):
### THE RIGHT WAY (verified on B200 at n=128 and n=256):
```python
# ✅ 8-None no-op pre-slice opens the full TMA coordinate space
tBgK = tBgK[(None, None, None, None, None, None, None, None)]
# ✅ (None,0,None,0) keeps modes 0 and 2 free → 2D tensor
# Mode 2 (KV tiles) survives as the second mode.
tBgK = tBgK[(None, 0, None, 0)]
# ✅ Index mode 4 (the GMEM tile dim) in the copy call
cute.copy(tma_k, tBgK[None, None, None, None, kt, None, None, None], ...)
# ^^ MODE 4 — THE GMEM TILE DIM
# ✅ [None, kt] indexes the surviving mode 1 (originally mode 2 = KV tiles)
cute.copy(tma_k, tBgK[None, kt], ...)
# ^^ THIS IS THE KV TILE DIM
```
**Verified shapes on B200 (May 22, n=256, inside @cute.kernel):**
```
Before slice: tBgK = (((64,128),1), Int32(?), Int32(?), Int32(?)) — 4 modes
After (None,0,None,0): tBgK = (((64,128),1), Int32(?)) — 2 modes
```
### WHY THIS IS SO INSIDIOUS
1. **No error, no warning.** The slice `tBgK[(None,None,0,0)]` silently collapses modes 4-7.
2. **Single-tile (n=128) works perfectly.** With only 1 KV tile, mode 4 is size 1, so the bug is invisible.
3. **All multi-tile tests produce "reasonable" output.** The TMA loads from tile 0 every time, so you get a valid (but wrong) attention computation. Cosine similarity is 0.7-0.9, not NaN.
1. **No error, no warning.** The slice `tBgK[(None,None,0,0)]` silently sets mode 2 to 0.
2. **Single-tile (n=128) works perfectly.** With only 1 KV tile, mode 2 is size 1, so the bug is invisible.
3. **Multi-tile tests produce "reasonable" output.** The TMA loads from tile 0 every time, so you get a valid (but wrong) attention computation. Cosine similarity is 0.7-0.9, not NaN.
4. **The strides are all 0.** Printing `tBgK.layout.stride` shows all zeros for TMA tensors. You can't detect the bug from strides alone.
5. **`cute.printf` shows `kv_coord=0`.** We thought the JIT was constant-folding the variable. It wasn't — the variable was fine, but it was indexing the wrong mode.
6. **The 8-mode theory was wrong.** We assumed tma_partition produced 8 TMA coordinate dimensions. It produces 4. The 8-None no-op slice fails with "weakly congruent" at JIT compile.
### THE LESSON
**TMA tensors use a coordinate space, not a pointer space.** The TMA instruction at PTX level takes integer coordinates (`crd0, crd1, crd2, ...`), not pointers. CuTeDSL's `tma_partition` produces a tensor whose layout maps logical coordinates to TMA coordinate tuples. When you pre-slice with fewer dimensions than the TMA descriptor expects, the extra coordinate dimensions get collapsed to 0.
**PRINT THE SHAPES. ALWAYS.** Run `print(f"tBgK: shape={cute.shape(tBgK)}")` inside `@cute.kernel` at trace time. The shapes are your ground truth. Reasoning about mode counts without evidence is how we wasted a day.
**The 8-None no-op pre-slice is mandatory for multi-tile TMA.** Without it, the GMEM tile axis (mode 4) is invisible and unindexable.
**The correct pre-slice depends on which mode is the GMEM tile iteration axis.** For our `local_tile` + `partition_B` + `group_modes(0,3)` pattern, mode 2 is the KV tile axis. `(None,0,None,0)` keeps it free. `(None,None,0,0)` collapses it to 0.
```python
# After tma_partition, ALWAYS apply the 8-None no-op pre-slice:
tBgK = tBgK[(None, None, None, None, None, None, None, None)]
tVgV = tVgV[(None, None, None, None, None, None, None, None)]
# ALWAYS verify the shape at trace time:
print(f"tBgK shape: {cute.shape(tBgK)}") # 4 modes
print(f"tBgK after slice: {cute.shape(tBgK[(None,0,None,0)])}") # 2 modes
# Then index mode 4 in cute.copy:
cute.copy(tma_k, tBgK[None, None, None, None, kt, None, None, None], ...)
# Then index the 2D tensor:
cute.copy(tma_k, tBgK[None, kt], ...)
```
**IF YOU SKIP THE 8-NONE PRE-SLICE, MULTI-TILE TMA WILL BE SILENTLY BROKEN.**
**IF YOU USE (None,None,0,0) INSTEAD OF (None,0,None,0), MULTI-TILE TMA WILL BE SILENTLY BROKEN.**
---
@@ -140,7 +145,7 @@ Summary
|-------|--------|-------------|
| A | ✅ COMPLETE | Q@K^T via tcgen05.mma → TMEM → GMEM |
| B | ✅ COMPLETE | QK → identity softmax → P@V pipeline (TMEM alias, KV-tile interleaving) |
| C | ⚠️ MULTI-TILE IN PROGRESS | Single-tile cos 0.999998. TMA fix: n=256 cos 0.9956. Need O rescale + pipeline cycling. |
| C | ⚠️ MULTI-TILE TMA FIXED | n=128 cos 0.999998 ✅. TMA fix: n=256 loads 2 tiles. Pipeline cycling needed for n≥384. O rescale needed. |
| C' | 🔨 IN PROGRESS | Multi-tile TMA indexing fix + correction warps. See below. |
| D | TODO | Full decode attention: paged KV cache, multi-query, causal mask |
| E | TODO | Production kernel: extract into dsv4/kernels/attention/, PyTorch custom op, vLLM bridge |
@@ -294,30 +299,39 @@ What it does:
### Multi-Tile TMA Fix (RESOLVED — was a LAYOUT bug, not a JIT bug)
After `cpasync.tma_partition()`, the output GMEM tensor has **8 TMA coordinate dimensions**:
After `cpasync.tma_partition()`, the output GMEM tensor has **4 modes**: `(((64,128),1), ?, KV_tiles, ?)`.
```
tBgK TMA coord space: (1, 1, 1, 1, n_kv_tiles, 1, 1, 1)
0 1 2 3 4 5 6 7
```
**Mode 2 is the GMEM tile dimension.** Our old pre-slice `tBgK[(None, None, 0, 0)]` kept modes 0,1 free and set mode 2 to 0, so TMA always read tile 0. The bug looked like "JIT constant-folding" but was purely a layout error.
**Mode 4 is the GMEM tile dimension.** Our old pre-slice `tBgK[(None, None, 0, 0)]` collapsed modes 4-7 to coordinate 0, so TMA always read tile 0. The bug looked like "JIT constant-folding" but was purely a layout error.
**The fix:** 8-None no-op pre-slice + 8-mode indexing with `kt` at mode 4:
**The fix:** `(None,0,None,0)` keeps modes 0,2 free, then `[None, kt]` indexes KV tiles:
```python
tBgK = tBgK[(None, None, None, None, None, None, None, None)]
cute.copy(tma_k, tBgK[None, None, None, None, kt, None, None, None], ...)
tBgK = tBgK[(None, 0, None, 0)]
cute.copy(tma_k, tBgK[None, kt], ...)
```
**Results after fix:**
**Results after TMA fix (verified on B200, May 22):**
- n=128: cos 0.999998 ✅
- n=256: cos 0.9956 ✅ (lower because no O rescale yet)
- n=256: cos 0.71 (TMA loads 2 tiles correctly, needs O rescale for 0.9999)
- n=512/1024: output identical to n=256 — pipeline not cycling past kv_stage=2
**Verified tensor shapes (diag prints inside @cute.kernel on B200, n=256):**
```
Before (None,0,None,0) pre-slice:
tAgQ: (((64,128),1), Int32(?), Int32(?), Int32(?)) — 4 modes
tBgK: (((64,128),1), Int32(?), Int32(?), Int32(?)) — 4 modes
tVgV: (((64,128),1), 1, 1, 1) — 4 modes
After (None,0,None,0) pre-slice:
tAgQ: (((64,128),1), Int32(?)) — 2 modes, mode 1 = KV tiles
tBgK: (((64,128),1), Int32(?)) — 2 modes, mode 1 = KV tiles
tVgV: (((64,128),1), 1) — 2 modes, mode 1 = 1 (static)
```
### Remaining for Multi-Tile
1. O rescale between tiles: `O *= exp2(old_max - new_max)` — needed for n=256+ to hit 0.9999
2. Pipeline state cycling for n≥384 (3+ tiles with 2 pipeline stages)
2. Pipeline state cycling for n≥384 (3+ tiles with 2 pipeline stages) — output identical for all n>256, meaning only 2 KV tiles are loaded
3. Correction warps for production (separate softmax/correction/epilogue)
4. 12-warp layout
@@ -325,7 +339,7 @@ cute.copy(tma_k, tBgK[None, None, None, None, kt, None, None, None], ...)
| File | Status | Notes |
|------|--------|-------|
| `fmha_v3_stage_c_example10.py` | 🔨 CURRENT | 8-mode TMA, combined K+V pipeline, O rescale, final normalize |
| `fmha_v3_stage_c_example10.py` | 🔨 CURRENT | (None,0,None,0) TMA, combined K+V pipeline, O rescale, final normalize |
| `test_fmha_v3_stage_c_full.py` | OK n=128 | Working real softmax + O normalization |
| `fmha_v3_stage_c_example1.py` | BROKEN multi-tile | First fix attempt, TMA still loads tile 0 |
| `fmha_v3_stage_c_example2.py` | DEADLOCK | Combined K+V barrier, compiles but deadlocks |
@@ -347,7 +361,7 @@ Warps 0-3: Softmax, Warps 4-7: Correction, Warp 8: MMA, Warp 9: TMA, Warp 10: Ep
1. `vectorize=True` loops: ONLY load/store/print
2. `.reduce(cute.ReductionOp.MAX)`: reduces ENTIRE C-fragment to scalar — global max, not per-row
3. `cute.arch.fmax`: impure for vectorizer — use plain `range()` loop
4. `tBgK`/`tVgV` have 8 TMA coord dims after tma_partition — 8-None no-op pre-slice required, mode 4 is GMEM tile dim
4. `tBgK`/`tVgV` have 4 modes after tma_partition — (None,0,None,0) keeps mode 2 (KV tiles) free, [None, kt] indexes it
5. `tBgK[(None, 0, None, 0)]` hardcodes GMEM iteration to tile 0
6. `softmax_done_bar` NamedBarrier is reusable across tiles