e33c48e44c
NVFP4-1.1: Use nvvm.inline_ptx instead of llvm.inline_asm for f32→i32
...
llvm.inline_asm fails with 'LLVM ERROR: unsupported operation' in CuTeDSL
lowering pipeline. Switch to nvvm.inline_ptx which is native to the NVVM
dialect and lowers correctly.
- f32_to_i32_rni: cvt.rni.s32.f32 via nvvm.inline_ptx
- f32_to_i32_rz: cvt.rzi.s32.f32 via nvvm.inline_ptx
- f32_to_i32_rmi: cvt.rmi.s32.f32 via nvvm.inline_ptx
2026-05-28 04:42:33 +00:00
1cbb3cf752
NVFP4-1.1: Replace threshold rounding with inline PTX cvt.rni/rz/rmi
...
- Add f32_to_i32_rni (cvt.rni.s32.f32) for round-to-nearest-even
- Add f32_to_i32_rz (cvt.rzi.s32.f32) for round-toward-zero
- Add f32_to_i32_rmi (cvt.rmi.s32.f32) for round-to-minus-infinity
- Replace round_rne_u0_8 and abs_scaled_to_e2m1_idx threshold hacks
with proper PTX hardware rounding in fp8_e4m3_from_float32
- quantize_e2m1_nibble now uses f32_to_i32_rni + LUT logic for half_step
- Add test_ptx_convert.py for inline PTX conversion verification
- This is the CORRECT approach per NVFP4-1.1_INLINE_PTX_APPROACH.md option 1
2026-05-28 04:40:17 +00:00
d2aa93aad7
NVFP4-1.1: fix Int32 clamping — use comparisons instead of fmin/fmax (float-only ops)
2026-05-28 04:30:06 +00:00
dabcc415a8
NVFP4-1.1: threshold rounding for float-to-int — avoids CuTeDSL limitation
...
All float-to-int conversions replaced with threshold comparisons:
- round_rne_u0_8: mantissa rounding via Float32 comparisons → Int32 constants
- abs_scaled_to_e2m1_idx: direct |scaled| → E2M1 index (no half_step needed)
- Verified 0/500 trial failures against Python reference
Key thresholds (RNE boundaries):
- 0.25, 0.75, 1.25, 1.75, 2.75, 3.75, 5.25 with > vs >= for RNE tie-breaking
- Fixed: 2.75 must use >= (not >) to match round(5.5)=6 RNE
2026-05-28 04:26:40 +00:00
e565ebce91
NVFP4-1.1: replace cute.math.fmin with cute.arch.fmin (correct API)
2026-05-28 03:55:54 +00:00
20d5ddfa3d
NVFP4-1.1: fix indentation for @cute.jit decorators
2026-05-28 03:52:46 +00:00
f6f59d34cb
NVFP4-1.1: add @cute.jit decorator to fp4_quant functions for CuTeDSL if-block support
2026-05-28 03:50:11 +00:00
6f94925491
NVFP4-1.1: fix cute.math.fmax -> cute.arch.fmax (correct CuTeDSL API)
2026-05-28 03:48:51 +00:00
80b6b79f9e
NVFP4-1.1: FP4 quantization primitives for CuTeDSL kernels
...
- fp8_e4m3_from_float32: manual FP8 E4M3 cast (bias=7, exp 0-15 valid,
NaN guard for exp=15/mant=7, mantissa overflow handling)
- fp8_e4m3_to_float32: dequantize FP8 E4M3 bit pattern back to Float32
- half_step_to_e2m1_idx: E2M1 step mapping (0-12 → 0-7)
- quantize_e2m1_nibble: per-element E2M1 quantize + sign + pack
- Verified 0/500 trial failures against Python reference
- Key fixes discovered during validation:
1. FP8 E4M3 bias is 7, NOT 8
2. Exponent range is 0-15 (exp=15/mant=7 is NaN; others valid)
3. Subnormal formula: val = m * 2^(-9) = m/512 (NOT m/1024)
4. Round-to-nearest-even (not round-half-up) for half_step and mantissa
5. Mantissa overflow (round to 8) must increment exponent
2026-05-28 03:39:55 +00:00
b9f15c250f
Stage E: head-packed MQA/GQA, batch dim, custom_op, integration API
...
- production.py: head-packed M dimension for MQA/GQA (q_per_kv*T rows
in single launch per KV group, eliminating redundant K/V TMA loads)
- production.py: batch dimension support (outer Python loop)
- production.py: warmup_attention_kernels() for pre-compilation
- production.py: dsv4_attention_per_head() for exact per-head sink bias
- __init__.py: sparse_fmha_with_swa, dense_fmha_with_swa, swa_only_fmha
integration functions bridging AttentionSubBlock → production FMHA
- custom_ops.py: dsv4::sparse_fmha_with_swa custom_op registration
- test_production.py: comprehensive tests (MHA/MQA/GQA, head-packed vs
per-head parity, multi-segment KV, SWA+causal+sink, batch, edge cases)
2026-05-27 15:15:03 +00:00
2412a5431b
MQA/GQA: batch Q heads into kernel batch dim, shared K/V per KV group
2026-05-27 08:31:23 +00:00
778d9d4f4f
Compile with row_sums tensor so kernel writes per-row row_sums
2026-05-27 07:10:00 +00:00
0736a04d9b
Fix KV merge: use NORMALIZED O (O_unnorm/row_sum) with LSE
2026-05-27 07:07:51 +00:00
06e7f7ab48
Debug: print LSE values for 2-segment merge
2026-05-27 07:04:39 +00:00
8f8d14c300
Match tensor slicing exactly to test_d1_kv_merge (2D slices, 3D unsqueeze)
2026-05-27 06:58:28 +00:00
6ee61717c0
Match tensor shapes from working test_d1_kv_merge
2026-05-27 06:56:04 +00:00
36a6f07a7e
Fix: unsqueeze k/v when dim==2
2026-05-27 06:52:43 +00:00
fc4172937c
Clean production wrapper: always normalize=False + KV merge
2026-05-27 06:51:14 +00:00
8f87109f86
Single-segment: use normalize=False + per-row normalization from row_sums
2026-05-27 06:48:56 +00:00
fe55bf23a0
Split single-segment (normalized) and multi-segment (KV merge) paths
2026-05-27 06:46:30 +00:00
b70ab2a6ee
Return o_accum directly (un-normalized merge result)
2026-05-27 06:42:58 +00:00
6111db571c
Match working test: don't pass row_sums to kernel
2026-05-27 06:41:44 +00:00
312ac52d15
Normalize O_accum by exp(lse) before returning
2026-05-27 06:39:36 +00:00
ddc701af9b
Use exact merge formula from working test_d1_kv_merge.py
2026-05-27 06:38:04 +00:00
8321ccf9c1
Fix production KV merge: use normalized O for log-sum-exp merge
2026-05-27 06:36:24 +00:00
98c93c1cd8
Stage E: production attention wrapper + Python KV merge, clean fmha_smem_acc
2026-05-27 06:34:10 +00:00
51e456df44
Slice MMA tile coords from tOgO for TMA copy
2026-05-27 05:39:42 +00:00
1caa737b09
Move sC_flat_staged creation before const_expr guard
2026-05-27 05:38:39 +00:00
3c9dbc0c5d
Staged sC_flat with (128, pv_n_tile//2, 2) to match TMA atom
2026-05-27 05:37:05 +00:00
de2028b106
Split sC_flat into staged layout to match TMA atom decomposition
2026-05-27 05:35:56 +00:00
a0e9f7534b
Use tCgC_epi (transformed) for GMEM side of TMA partition
2026-05-27 05:34:40 +00:00
b02e103ac0
Add c_simple GMEM tensor (non-dynamic) for SMEM accumulator TMA store
2026-05-27 05:33:30 +00:00
2438826eee
Use tma_partition with group_modes on both sC_flat and gO
2026-05-27 05:31:47 +00:00
603f52de78
Fix gO creation: use slice_(pv_mma_tiler) like fmha.py
2026-05-27 05:30:50 +00:00
b39d7f1a14
Try cute.copy(tma_c, sC_flat, gO) directly
2026-05-27 05:29:51 +00:00
2af767a90c
Try full tensor TMA copy without slicing
2026-05-27 05:28:43 +00:00
7d14a2f764
sC_flat with simple (128, pv_n_tile) layout for full epi_tile coverage
2026-05-27 05:27:51 +00:00
6fb0e6a417
Use sC_flat (non-swizzled epi_s layout) for TMA store from SMEM accumulator
2026-05-27 05:26:50 +00:00
4a2a06f9e1
Fix gO slice: use separate Int32(0) instead of tuple
2026-05-27 05:25:33 +00:00
bf36979a8d
Use CUTLASS FMHA reference pattern for sC->GMEM TMA store (flat_divide + tma_partition)
2026-05-27 05:24:39 +00:00
97bc6d8d2f
Add c_direct GMEM tensor for direct writes in SMEM accumulator path
2026-05-27 05:15:47 +00:00
3d349b497b
SME accumulator: direct GMEM write from sO_acc (bypass TMA for multi-kt)
2026-05-27 05:14:31 +00:00
7d1e0a605d
Different coordinate dims for bSG_sC (2D) and bSG_gC (3D)
2026-05-27 05:13:38 +00:00
75b272c5f2
2D coordinate for bSG_sC TMA copy
2026-05-27 05:12:58 +00:00
72dff90165
3D coordinate for bSG_sC/gC TMA copy
2026-05-27 05:12:11 +00:00
b8b6e8cc0b
Slice bSG_gC MMA tile coords for TMA copy
2026-05-27 05:11:26 +00:00
754740d5e5
Try bSG_sC[(None, 0)] for TMA copy coordinate
2026-05-27 05:10:40 +00:00
23a2b49daf
Add SMEM accumulator for n_kv_tiles>1: O load from TMEM, accumulate in sO_acc, TMA store from sC
2026-05-27 05:09:54 +00:00
2e262d2b99
Reset fmha_smem_acc.py to working fmha.py base
2026-05-27 05:05:41 +00:00
b43ffe9dac
Guard sO_acc allocation/zero-init with n_kv_tiles>1
2026-05-27 05:05:01 +00:00
