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…brate Capture script: disable check_for_nan_in_loss_and_grad. Its per-microbatch torch.isnan/isinf check in loss_func forces a device->host sync that the profiler bills as a multi-ms stall kernel under the loss/rerun frames; in a single-layer capture this per-step cost was wrongly attributed per layer and multiplied by the layer count. Capture script: move the profiler window into the steady state. The first ~9 iters are warm-up (Triton/hipBLASLt autotune + compile) with noisy, inflated per-iter times that also desync and inflate the EP comm kernels; from ~iter 10 the iteration time is stable. PROFILE_STEP_START/END and TRAIN_ITERS are now env-overridable (defaults 16/19/22) so the captured ProfilerSteps are uniform (~185ms). Regenerate site/data/flash.json from freshly captured cr0/cr4/cr128 traces (clean window, no per-step loss-validation stall). MoE cross-cr spread 0.4%; the remaining cr4 attention 'other' is the real per-layer Indexer CatArrayBatchedCopy, which the measured flash-16L anchor confirms is genuine (removing it would push calibFactor to ~1.17). app.js: calibFactor 0.93 -> 0.91, re-derived against the measured anchor (6665 ms, 636 TFLOP/s/GPU). Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Ports the perf-projection benchmark V4 support from dev/john/dsv4 (64407fe). The in-repo benchmark built a stock GPT model (SelfAttention) instead of the V4 model, so use_v4_triton_attention / compress_ratios were ignored and attention timing was unrepresentative. Routes non-gpt model_type to its builder and makes the harness V4-aware: - V4 model routing in trainer.py (!= "gpt" instead of == "mamba"). - v4_module_inputs helper for V4 forward signatures (K-stream mHC [B,S,K,D], keyword-only position_ids/token_ids). - per-cr representative layer selection (PRIMUS_PROJ_MAX_LAYERS / PRIMUS_PROJ_COMPRESS_RATIOS) -- one HCA (cr=128) + one CSA (cr=4). - run_dsv4_projection_1gpu.sh. This is the in-repo module-benchmark projection (primus/core/projection/), a separate subsystem from the trace-based projection site (deepseek-v4/projection/); they share no code and coexist. Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…ers (#777) Ports the DeepSeek-V4 low-precision training work from dev/john/dsv4 onto dev/tas/deepseek-v4. All precision paths default-OFF. Quantization recipe (paper-aligned, flag-gated): - FP8 tensorwise training for V4 Pro/Flash + single-GPU launchers. - FP8 (mxfp8) attention/dense projections via Primus-Turbo (USE_TURBO_PARALLEL_LINEAR). - MXFP4 grouped experts via per-group native FP4 (hipBLASLt). - Per-module recipe: experts MXFP4 / rest FP8 (moe_experts_fp4). - FP4 CSA-indexer QK + paper-recipe FP8/FP4 attention, real-FP4 indexer GEMM, MXFP8 expert-weight cache. - Fused grouped-O into one grouped_gemm_fp8. Indexer reconciliation (keep-both): preserves FP8-E4M3 indexer QK and adds FP4 QK as a selectable mode; mutually exclusive (both default-OFF -> BF16 QK), FP4 takes precedence as a dedicated real-GEMM branch. Training + launchers: - Muon mxfp8 fix: auto-enable LR warmup, plumb NS knobs. - Default seq_length 4096. - gfx1250 launcher: opt-in tuned hipBLASLt (stock default), 3-layer default to fit the 432GB card. gfx1250-only. Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
DeepSeek-V4 attention kernel work (flydsl for gfx950, and triton tunning), flydsl default-OFF;. - FlyDSL attention backend: opt-in alternate kernel suite (gfx950/MI355X) structured like the sibling _tilelang package. Ships the 13-module import closure (fwd SWA/HCA/CSA + bwd dKV/dQ). Soft-dependency: imports zero FlyDSL on the default-off path, falls back to Triton when the runtime is absent. Config flags use_v4_flydsl_attention / use_v4_flydsl_csa_attention plumbed through DeepseekV4Attention as use_flydsl=. - MI355X Triton tuning: three shape-gated, env-overridable launch-param defaults (bwd dKV NUM_HEAD_GROUPS 1->2; fwd HCA BLOCK_M 64->128; bwd HCA pool BLOCK_M 32->16). No kernel-logic change; cos-gated. - Test: set the FlyDSL flags in the _make_bare_attn dispatch-precedence helper (it builds the attn via __new__, bypassing __init__). Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Switch V4-Pro Muon launcher to V4 Triton attention backend as default Fix gfx1250 WMMA tile collapse in V4 CSA fwd + indexer score kernels Add fused Triton HyperConnection expand kernel
Enable PRIMUS_SINKHORN_TRITON, PRIMUS_HC_TRITON, PRIMUS_ROPE_TRITON and PRIMUS_V4_ROUTER_TRITON by default (:-1). These collapse the eager small-kernel floods (Sinkhorn-Knopp loop, mHC HyperMixer glue, RoPE, MoE router) into fused Triton kernels.
Co-authored-by: Cursor <cursoragent@cursor.com>
On gfx1250 the mxfp8 Triton quant mishandles all-zero blocks: PrimusGroupedMLP's quantization_padding inserts zero-padded rows whose all-zero quant blocks (amax==0 -> ZERO_AMAX_TO_ONE) yield garbage fp8 and inf in the grouped-expert GEMM, diverging to NaN at iteration 1. grouped_gemm_fp8 handles unpadded tokens, so gate the padding off on gfx1250 via a cached _gfx1250_skip_expert_pad(); other archs keep the padding. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ization The submodule was pinned to a v0.2.0-era commit while moun.py targets the >=0.4.0a0 API (tp_mode, keyword-only nesterov, deepseekv4 coefficients); re-pin to 0.4.0a0 so those resolve natively. Also orthogonalize the consolidated 3D grouped-expert weight [E,N,K] per expert slice in TensorParallelMuon.orthogonalize, since the upstream default handles only 2D. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…e knob - install the real primus_turbo wheel + put flydsl on PYTHONPATH/volume so the turbo fp8 path runs in the gfx1250 container - default torch_profiler_use_gzip on and pass it explicitly (perfetto/chrome read .gz directly) - forward TORCH_COMPILE_DISABLE / TORCHINDUCTOR_COMPILE_THREADS to skip the cold MoE-permute torch.compile Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…#805) At TP=1 the consolidated [E, N, K] grouped-expert momentum can be orthogonalized in one batched Newton-Schulz call instead of an E-long per-expert Python loop: emerging_optimizers>=0.4.0a0 newton_schulz accepts 3-D input natively (batched_newton_schulz_step via torch.baddbmm), so one call replaces E per-expert calls -- folding E rounds of Python op-dispatch and the per-expert GEMM launches into a single batched launch. The per-window scale_factor keys on (N, K), which is shared across experts, so the single scalar applies to the whole batch; the result is numerically equivalent to the per-slice loop. Added a parity unit test asserting this. Gated to the non-partitioned (TP=1) path -- partitioned experts (TP>1) keep the per-expert loop, since the batched + partition_dim path is not exercised here. PRIMUS_MUON_BATCHED_NS=0 forces the loop as an escape hatch. Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
The compressed-attention branch's _build_compressed_pool ran a burst of many tiny eager kernels every forward, per compressed layer: it rebuilt the compress-base RoPE cos/sin table (arange -> outer-product -> cos/sin) and ran the per-window-softmax pool (add + cast + softmax + cast + mul + reduce) as separate launches. Two fusions, both gated default-on with eager fallback and numerically equivalent to the prior path: * RoPECache.forward_arange: memoise the cos/sin table for the deterministic arange(P) positions (P fixed per run), keyed by (n, device). Bit-identical to forward(arange(n)). PRIMUS_COMPRESS_ROPE_CACHE=0 disables. * compressor_pool.py: a Triton kernel fusing score+ape + softmax(dim=window) + weighted-sum into one launch (custom autograd Function: fused fwd, analytic eager bwd). PRIMUS_COMPRESS_POOL_TRITON=0 (or non-CUDA / unsupported dtype) falls back to eager. Handles HCA (no overlap) and CSA (overlap) windows. Tests: test_rope_arange_cache.py (cache parity + memoise + bypass, CPU), test_compressor_pool.py (fused-vs-eager fwd + dkv/dscore/dape parity, GPU-gated). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…E), opt-in PrimusTurboGroupedLinear's fp8 path routes the expert weight gradient through _WeightGradBridge, which adds it into the consolidated [E,N,K] main_grad with a separate aten::add_ per micro-batch. With PRIMUS_TURBO_FUSE_GROUPED_WGRAD=1, wrap the grouped GEMM in primus_turbo's fused_grouped_wgrad so the backward accumulates each expert's wgrad straight into main_grad (beta=1) and returns grad_b=None; the bridge backward then just flags grad_added_to_main_grad (skips the add_). Per-expert _MainGradShim handles point at the contiguous 2-D slices main_grad[i] of the consolidated block -- what fused_grouped_wgrad / _expert_main_grad_view expect. Gated default-off: grad_b=None is only safe with no gradient all-reduce / reduce-scatter (single-GPU TP=1/DP=1/EP=1). Requires a turbo wheel carrying fused_grouped_wgrad (falls back to the eager bridge add_ otherwise). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ntainer PrimusTurboGroupedLinear.forward_internal gates the grouped-wgrad fusion on PRIMUS_TURBO_FUSE_GROUPED_WGRAD, which defaults off. The 1gpu launcher omitted it from the docker --env forwarding set, so a host-side PRIMUS_TURBO_FUSE_GROUPED_WGRAD=1 never reached the container: inside, the gate read the default "0" and the fused path silently fell back to the per-expert main_grad.add_ bridge (the opt was never actually exercised end to end). Add it to ENV_ARGS, together with PRIMUS_TURBO_FUSE_WGRAD_DEBUG and the default-on toggles PRIMUS_MUON_BATCHED_NS / PRIMUS_COMPRESS_ROPE_CACHE / PRIMUS_COMPRESS_POOL_TRITON, so every opt is controllable from the launch env. Also add a gated diagnostic (PRIMUS_TURBO_FUSE_WGRAD_DEBUG=1) in forward_internal that logs the gate decision and whether _expert_main_grad_view accepts the consolidated [E,N,K] main_grad. Off by default; the functional path is unchanged. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…tention The V4 attention fwd/bwd Triton kernels' hard-coded BLOCK_M/BLOCK_N/num_warps/ num_stages and dKV head-split defaults are the gfx950 / MI355X R1/R2 sweep winners. On gfx1250 (MI450, CDNA-next) the optimum differs systematically -- the fwd wants a larger BLOCK_M with fewer warps/stages, and the MQA/HQ>=64 bwd wants far more dKV head-split -- so a gfx950-tuned default is well off on gfx1250. Add `_v4_attn_tuning.py`, which detects the GPU arch (via gcnArchName) and returns per-arch defaults, and wire it into the fwd launcher (BLOCK_M/BLOCK_N/WARPS/STAGES) and the bwd launcher (DKV_HEAD_GROUPS). The PRIMUS_V4_ATTN_* env knobs still override everything, and gfx950 + not-yet-swept shapes keep their historical defaults so nothing regresses. gfx1250 values come from node-safe microbench sweeps (ab_sweep/opt7b_hca_bwd_sweep.py, ab_sweep/opt7c_fwd_sweep.py). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A gfx1250 sweep of the HCA (cr=128) fwd shape (ab_sweep/opt7d_hca_fwd_sweep.py, exact _hca_extra_kv shapes: HK=HQ, k/v=cat([local S, pool P]), pool causal mask) found the same BLOCK_M=128/BLOCK_N=32/num_warps=4/num_stages=1 config wins there too, vs the gfx950 HCA winner BM=128/BN=16/W=8/S=1. Drop the SWA-only guard in fwd_attn_defaults so the gfx1250 tuned config applies to both the SWA and HCA fwd shapes. gfx950 defaults + env overrides unchanged. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…for V4-Pro MHA) V4-Pro expands the single-latent KV to all H heads (deepseek_v4_attention.py), so the attention runs MHA (HK==HQ) at the kernel level: the dKV head-split default is gated on HQ>HK in the launcher and is therefore never reached, so the gfx1250 HG=32 value does not apply to this model. A re-sweep at the real MHA shape (ab_sweep/opt7e_mha_bwd_sweep.py) confirms the gfx950 bwd defaults are already optimal on gfx1250 (every alternative config regresses) -- there is no bwd retune for V4-Pro. The real gfx1250 attention win is the FWD (fwd_attn_defaults). Document this in bwd_dkv_head_groups_default; the default is kept as it is still correct for a genuine MQA config. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Launch-count reductions across the DeepSeek-V4 compressed-attention paths (HCA, CSA, and the CSA indexer), plus launcher infra. All model changes are **gated default-on with eager fallbacks** and **numerically equivalent** (end-to-end loss `12.01 -> 8.78` matches baseline to bf16 noise; fp32-exact / bit-identical microbenches). --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Delete run_deepseek_v4_flash_proxy_1gpu.sh and run_deepseek_v4_pro_fp8_paper.sh. Co-authored-by: Cursor <cursoragent@cursor.com>
Add a Layer-timing mode switch (trace-derived vs manual) to the projection UI. In manual mode the user types per-cr fwd/bwd times (stored per GPU, prefilled from the trace baseline) which override the breakdown-derived per-layer times; all downstream PP/bubble/GA/optimizer/throughput math is reused unchanged. FLOPs stay analytic so TFLOP/s/GPU remains meaningful.
## Summary
Adds fused single-latent sparse-MLA attention backends for DeepSeek-V4,
reorganizes the attention-kernel package into versioned folders, unifies
backend selection behind two string selectors, and makes **`gluon`** the
default.
- **New backends**
- `gluon` — hand-tuned gfx950 (CDNA4) fused single-latent (K=V)
sparse-MLA.
- `triton_v2` — fused single-latent sparse-MLA in plain Triton (`tl.dot`
/ MFMA).
- Both cover all three V4 layer kinds (dense/SWA `cr=0`, CSA `cr=4`, HCA
`cr=128`) via a kernel-agnostic adapter.
- **Package reorg** — `v4_attention_kernels/` split into versioned
packages (`_eager`, `_triton_common`, `_triton_v0_deprecated`,
`_triton_v1`, `_triton_v2`, `_flydsl_v0_deprecated`, `_flydsl_v1`,
`_gluon_dsa`); every backend entry re-exported from the package
`__init__`.
- **Unified selectors** — replace the per-backend boolean flags with two
strings: `use_v4_attention_backend` (dense/HCA) and
`use_v4_csa_attention_backend` (CSA). **Default = `gluon`** (`triton_v1`
remains a supported opt-in).
- **Docs / scripts / tests** — new kernels `README.md`, run scripts +
example YAMLs migrated to the string selectors, unified single-table
benchmark (`bench_v4_attention.py`), and rewritten dispatch tests.
## Performance
### Attention kernel microbench
MI355X (gfx950), seq=4096, mbs=1, bf16, swa=128, sink on. `fwd ms | bwd
ms` (lower better); TFLOP/s over useful work (head_dim=512).
**V4-Flash (H=64)**
| cr | triton_v1 | gluon | triton_v2 |
|----|-----------|-------|-----------|
| 0 (SWA) | 0.44 / 1.98 | **0.33 / 1.20** | 0.33 / 2.77 |
| 4 (CSA) | 1.35 / 5.15 | **0.96** / 5.27 | 1.13 / 12.35 |
| 128 (HCA) | 0.65 / 2.98 | **0.40 / 1.76** | 0.46 / 4.03 |
**V4-Pro (H=128)**
| cr | triton_v1 | gluon | triton_v2 |
|----|-----------|-------|-----------|
| 0 (SWA) | 0.83 / 3.85 | **0.61 / 1.89** | 0.63 / 7.97 |
| 4 (CSA) | 3.98 / 15.05 | **3.04 / 14.51** | 3.74 / 60.42 |
| 128 (HCA) | 1.26 / 5.77 | **0.77 / 2.69** | 0.88 / 10.74 |
`gluon` is fastest fwd **and** bwd everywhere (~1.3–1.6× fwd, up to ~2×
bwd vs `triton_v1`). `triton_v2` matches gluon on forward but its
backward is untuned. Legacy `flydsl_v0` is 15–260× slower; `flydsl_v1`
forward is still WIP.
### End-to-end proxy (triton_v1 vs gluon)
8-layer V4-Flash proxy, FP8, EP=8 on 8×MI355X, GBS=8, seq=4096,
steady-state:
| backend | ms/iter | TFLOP/s/GPU | tokens/s/GPU | peak HBM/rank |
|---------|--------:|------------:|-------------:|--------------:|
| triton_v1 | 395.3 | 677.9 | 10361 | 229.2 GB |
| **gluon** | **392.7** | **682.4** | 10431 | 230.7 GB |
Both exit cleanly with matching loss trajectory. `gluon` is marginally
faster end-to-end (~0.66%); attention is a small slice of the step here
(dominated by MoE / DeepEP / Sinkhorn).
## Test plan
- [x] Dispatch unit tests `test_v4_p25_dispatch` /
`test_v4_p26_dispatch` pass (8/8).
- [x] EP=8 proxy smoke green for both `triton_v1` and `gluon` (exit 0,
loss parity).
- [x] Unified attention benchmark runs across all backends ×
`cr={0,4,128}` × {flash, pro}.
---
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
…wd split (#834) ## Summary Two commits on the DeepSeek-V4 performance-projection tool (`deepseek-v4/projection`): 1. **feat: iteration-timeline view** (`3502b68b`) — a new 3-level view that visually decomposes one training iteration's time, bottom-up: - **Level 1 · layer** — one representative layer per compression ratio (`cr=0/4/128`), forward and backward split into `attn / mlp / a2a`. - **Level 2 · pipeline ranks** — each PP rank's chunks at layer granularity (coloured by `cr`, recomputed layers hatched); identical ranks are deduped and the critical stage (max fwd/bwd) is highlighted. - **Level 3 · schedule** — a 1F1B pipeline-schedule Gantt (interleaved when `VPP>1`), styled after the Megatron-2 paper (Fig. 4), with a zoom slider and SVG/PNG export. - Tabbed / stacked layouts with cross-level drill-down linkage, a sticky Projection-controls sidebar, and manual per-layer overrides extended to `embedding / output / loss / MTP`. Methodology in `design/07-iteration-timeline.md`. 2. **fix: correct fwd/bwd split in the trace parser** (`1a07b807`) — the kernel→phase attribution defaulted unlinked kernels to *forward*, leaking backward compute (incl. the MoE dgrad/wgrad grouped GEMMs) into the forward total and skewing the per-module fwd/bwd ratio. The phase is now determined in priority order: (1) `_fwd_`/`_bwd_` tag in the kernel name; (2) for linked kernels, whether the launching CPU op falls inside an autograd `evaluate_function` interval; (3) for unlinked kernels, whether the GPU timestamp lies in the reconstructed backward GPU-time window. Implausible one-off device stalls billed to compute kernels are dropped. The flash/pro breakdown JSON is regenerated with the fixed parser. (A13; see `design/02`.) ## Changes - `site/` — `index.html`, `assets/app.js`, `assets/style.css`: timeline view, layouts, linkage, sidebar, manual non-layer overrides. - `site/data/flash.json`, `site/data/pro.json`: regenerated breakdowns with the corrected fwd/bwd split. - `tools/parse_trace.py`: fixed-phase kernel→module attribution. - `design/02-assumptions.md` (A13), `design/04-projection-math.md`, `design/07-iteration-timeline.md`: docs. ## Test plan - [ ] `python3 -m http.server -d deepseek-v4/projection/site 8000`; open `?model=pro` and `?model=flash`. - [ ] Level 1 shows sane `attn/mlp/a2a` fwd/bwd bars; ratios look correct after the parser fix. - [ ] Level 2 dedups identical PP ranks and highlights the critical stage. - [ ] Level 3: default fits with no scrollbar; zoom reveals microbatch numbers; set `VPP>1` to see interleaving; Export SVG/PNG works. - [ ] Tabbed↔Stacked, cross-level linkage, and manual per-layer/non-layer overrides behave as expected. - [ ] `parse_trace.py` re-run reproduces the committed JSON.
Run pre-commit over previously unlinted changes: isort/autoflake/black formatting, trailing-whitespace and end-of-file fixes, and shellcheck fixes (SC2155 in p19 scripts, SC2148 shebang, SC2086 quoting). Co-authored-by: Cursor <cursoragent@cursor.com>
# perf(v4-attn): optimize `triton_v2` sparse-MLA forward + backward
Optimizes the plain-Triton fused single-latent sparse-MLA backend
(`triton_v2`)
so it beats the hand-tuned `gluon` backend on **every** V4 attention
shape, for
both forward and backward, while matching eager accuracy. No changes to
any other
backend (`gluon` / `triton` / `flydsl` untouched).
Target GPU: **MI355X (gfx950)**. Shapes: `cr={0,4,128}` × `{flash H=64,
pro H=128}`,
`S=4096`, `mbs=1`, bf16, sink on, `swa=128`.
## What changed
`primus/backends/megatron/core/transformer/v4_attention_kernels/_triton_v2/`
- `dsa_bwd_v4_triton.py`, new `dsa_bwd_kernels.py` (owned bwd compute
kernels)
- `dsa_fwd_v4_triton.py`
**Backward**
- Reuse `dS`/`P` from the dQ kernel instead of recomputing `S/P/dS` in
the dKV kernel.
- Load `q`/`do` untransposed via a strided `[D, BLOCK_H]` index → drop
the
`.transpose(1,2).contiguous()` HBM copies.
- Skip the provably-zero rope compute (V4 bakes RoPE in-place; the
64-dim rope pad
is zero and its grad is discarded by the adapter).
- Adaptive dKV tiling for `H<=64`; single-chunk `R_CHUNK` for `H>=128`.
- Fuse `Delta = rowsum(O*dO)` into the dQ kernel (drop the separate
preprocess kernel).
**Forward**
- Skip the provably-zero rope QK term.
- Widen then focus the autotune space (`TILE_K=16` + `num_stages=3` win
→ latency-bound).
## Backward performance trend (geomean bwd TFLOPS / speedup vs gluon)
Reference gluon bwd geomean: **160.7 TFLOPS**.
| Round | Status | Change | Bwd geomean TFLOPS | vs gluon | Shapes won |
|------:|--------|--------|-------------------:|---------:|:----------:|
| 1 | BASELINE | recompute-dKV + external transpose copies | 52.1 |
0.324 | 0/6 |
| 2 | ACCEPTED | store dS/P in dQ, reuse in dKV (no recompute) | 105.5 |
0.658 | 0/6 |
| 3 | ACCEPTED | drop q/do transpose copies (load untransposed) | 154.1
| 0.967 | 1/6 |
| 4 | ROLLBACK | config sweep (BLOCK_H/TILE_K/warps/wpe/stages) | 154.1
| 0.967 | 1/6 |
| 5 | ACCEPTED | skip provably-zero rope compute (V4 zero-pad) | 160.9 |
1.016 | 4/6 |
| 6 | ROLLBACK | num_stages=2 pipelining | 160.9 | 1.016 | 4/6 |
| 7 | ACCEPTED | adaptive dKV tiling (H<=64 & R_CHUNK%128==0) | 168.2 |
1.033 | 6/6 |
| 8 | ROLLBACK | atomic dKV (drop interm+gather+CSR) | 55.6 | 0.345 |
0/6 |
| 9 | ACCEPTED | adaptive R_CHUNK: single-chunk for H>=128 | 172.1 |
1.075 | 6/6 |
| 10 | ACCEPTED | fuse Delta=rowsum(O*dO) into dQ (drop preprocess) |
**175.6** | **1.099** | **6/6** |
Net: **0.32× → 1.10× gluon** (≈3.4× vs the original triton_v2 bwd); `pro
cr4` bwd
reaches 284 TFLOPS (1.34×).
## Forward performance trend (geomean fwd TFLOPS / speedup vs gluon)
Reference gluon fwd geomean: **257 TFLOPS**. (Goal was 1.2×; not reached
— see note.)
| Round | Status | Change | Fwd geomean TFLOPS | vs gluon | Shapes ≥1.2×
|
|------:|--------|--------|-------------------:|---------:|:------------:|
| 1 | BASELINE | plain flash kernel, narrow autotune, computes zero rope
| 230 | 0.896 | 0/6 |
| 2 | ACCEPTED | skip zero-rope QK term (V4 zero-pad) | 243 | 0.945 |
0/6 |
| 3 | ACCEPTED | expand autotune {BH:16/32/64, TK:16/32/64/128,
ns:1/2/3, wpe:0/1/2} | 277 | 1.088 | 0/6 |
| 4 | ACCEPTED | focus autotune {BH:32/64, TK:16/32, ns:2/3/4, wpe:0/1}
(24 cfg, 5× faster tune) | ~276 | ~1.085 | 0/6 |
| 5 | ROLLBACK | add num_warps=8 | ~273 | ~1.074 | 0/6 |
Net: **0.90× → ~1.08× gluon** (triton_v2 fwd went from slower-than-gluon
to beating
gluon on all 6 shapes). The 1.2× target is not achievable in plain
Triton: profiling
shows the fwd is occupancy/register-bound (~13.6% occupancy,
`MemUnitStalled≈0`,
`MfmaUtil≈20%`, `VALUBusy≈33%`) — the `acc[BLOCK_H,512]` fp32
accumulator caps
occupancy. This is the same wall gluon hits, which gluon overcomes only
via its
hand-scheduled async double-buffered LDS pipeline (not expressible in
plain Triton).
## Final benchmark — all backends
MI355X (gfx950), `S=4096`, `mbs=1`, bf16, sink on, `swa=128`, `--warmup
5 --iters 20`.
Cell = `latency ms | TFLOP/s` (useful-work FLOPs, same formula for every
backend).
**Forward**
| variant | cr (layer) | triton | flydsl | gluon | triton_v2 | flydsl_v1
|
|---------|-----------|--------|--------|-------|-----------|-----------|
| flash | 0 (SWA) | 0.47 \| 147.6 | — | 0.35 \| 197.4 | **0.31 \|
221.0** | FAIL |
| flash | 4 (CSA) | 1.40 \| 244.9 | 15.51 \| 22.2 | 1.01 \| 341.8 |
**0.93 \| 369.4** | FAIL |
| flash | 128 (HCA) | 0.68 \| 126.4 | — | 0.42 \| 205.9 | **0.39 \|
219.8** | FAIL |
| pro | 0 (SWA) | 0.84 \| 163.0 | — | 0.63 \| 219.5 | **0.58 \| 237.6**
| FAIL |
| pro | 4 (CSA) | 4.01 \| 308.2 | 54.20 \| 22.8 | 3.05 \| 405.1 | **2.98
\| 414.5** | FAIL |
| pro | 128 (HCA) | 1.28 \| 133.8 | — | 0.77 \| 221.8 | **0.73 \|
236.8** | FAIL |
**Backward**
| variant | cr (layer) | triton | flydsl | gluon | triton_v2 | flydsl_v1
|
|---------|-----------|--------|--------|-------|-----------|-----------|
| flash | 0 (SWA) | 2.04 \| 84.4 | — | 1.21 \| 142.1 | **1.16 \| 148.6**
| FAIL |
| flash | 4 (CSA) | 5.18 \| 165.8 | 2909.43 \| 0.3 | 5.28 \| 162.7 |
**5.13 \| 167.6** | FAIL |
| flash | 128 (HCA) | 3.04 \| 70.6 | — | 1.78 \| 120.9 | **1.72 \|
124.9** | FAIL |
| pro | 0 (SWA) | 3.94 \| 87.3 | — | 1.89 \| 182.1 | **1.79 \| 192.4** |
FAIL |
| pro | 4 (CSA) | 15.02 \| 205.8 | 9413.50 \| 0.3 | 14.55 \| 212.6 |
**10.87 \| 284.5** | FAIL |
| pro | 128 (HCA) | 5.92 \| 72.5 | — | 2.71 \| 158.4 | **2.48 \| 173.4**
| FAIL |
`triton_v2` is fastest on all 6 shapes for both fwd and bwd. `flydsl`
(legacy) is
cr=4-only and orders of magnitude slower. `flydsl_v1` forward is an
upstream WIP
(`NotImplementedError`).
## Correctness
`test_v4_gluon_dsa_attention.py -k triton_v2` → **9/9 passed**
(cr=0/4/128,
dense/HCA/CSA, fwd+bwd vs fp32 eager). All optimizations are
kernel-internal (no
`id(...)`-keyed caches, no data-distribution assumptions) and transfer
1:1 to real
training. The rope-skip relies on the V4 invariant that the rope pad is
structurally
zero (adapter discards its gradient).
## Notes
- FlyDSL benchmarking needs the source at `/workspace/FlyDSL-amd`
(`git clone --depth 1 --branch v0.2.2 https://github.com/ROCm/FlyDSL`) +
`pip install flydsl`.
- Full backend table also added at
`deepseek-v4/benchmark/bench_v4_attention_results.md`.
Co-authored-by: Cursor <cursoragent@cursor.com>
…v2 training LDS/ping-pong fix (#839) ## Summary Two related pieces of work on the DeepSeek-V4 fused single-latent (K==V) sparse-MLA attention, on top of `dev/tas/deepseek-v4`: 1. **New `flydsl_v1` backend** — a fully native FlyDSL MFMA implementation of the sparse-MLA attention (native forward + native dQ backward + shared Triton dKV), replacing the previous NotImplemented forward and the `triton_v2`-aliased backward. Depends only on the installed `flydsl` pip package (gfx950 / CDNA4) and is loaded **lazily**, so no other backend or build pays for it. 2. **`triton_v2` made training-ready + tuned** — root-caused and fixed the dKV shared-memory (LDS) overflow that crashed `triton_v2` in training, disabled the AMD ping-pong / async-copy knobs (a measured pessimization) for the whole `triton_v2` fwd+bwd, and wired all fused backends into the training dispatch. ## Root cause — dKV LDS overflow in training but NOT the benchmark Same kernel, same API, **same shape** (in-process probe confirmed: H=64, TOPK=128, d_qk=576, R_CHUNK=256, BH=64/TK=128, num_warps=4). The overflow is **not** a shape or `num_stages` effect — it is a global AMD Triton knob. `primus_turbo`'s `set_triton_knobs_gfx950()` flips on `TRITON_HIP_USE_BLOCK_PINGPONG` + `TRITON_HIP_USE_ASYNC_COPY`, which double-buffer (ping-pong) the LDS operand tiles and ~double the kernel's shared memory: | num_stages | standalone (bench ctx) | training ctx | |---|---|---| | 2 | fits (≤163840 B) | **347904 B** | | 3 | 327680 B | **521984 B** | The standalone benchmark never imports `primus_turbo`, so the knobs stay off → no overflow. **Closed-loop proof:** setting those two env vars in the standalone sweep reproduces the training bytes exactly. These knobs are read at compile time and are **not** part of Triton's compile cache key (`HIPOptions.hash` omits them), so they can be toggled per-kernel. ## Ping-pong is a pessimization for these V4 kernels Benchmarked with the knobs forced on vs off (isolated Triton caches). Ping-pong is uniformly **slower**, never faster: | op (flash / pro, cr=4) | ping-pong ON | ping-pong OFF | |---|---|---| | fwd flash | 1.17 ms | **0.91–0.95** (−19–29%) | | fwd pro | 3.91 ms | **2.97** (−24%) | | bwd flash | 5.57–5.59 | **5.40–5.41** | | bwd pro | 11.55 | **10.88** (~6%) | **Fix:** `_triton_v2/_amd_knobs.py::amd_pingpong_disabled` — a scoped context manager that compiles the **entire** `triton_v2` fwd and bwd (dQ / dKV-intermediate / gather) with the knobs disabled and restores them on exit, leaving every other kernel untouched. Because the bwd now compiles ping-pong-off, the faster **wide 64/128** dKV-intermediate tiling fits in training too, so it is the default again where it applies (H≤64, 128-aligned chunk). ## Standalone kernel benchmark (MI355X, seq=4096, bf16, sink on — `ms | TFLOP/s`) **Forward** | variant | cr | triton | gluon | triton_v2 | flydsl_v1 | |---|---|---|---|---|---| | flash | 0 | 0.49 \| 139 | 0.33 \| 209 | **0.31 \| 225** | 0.45 \| 154 | | flash | 4 | 1.45 \| 238 | 0.94 \| 365 | **0.91 \| 377** | 1.35 \| 255 | | flash | 128 | 0.68 \| 126 | 0.41 \| 210 | **0.38 \| 228** | 0.56 \| 154 | | pro | 0 | 0.91 \| 152 | 0.62 \| 223 | **0.59 \| 235** | 0.97 \| 142 | | pro | 4 | 4.02 \| 307 | 3.04 \| 408 | **2.96 \| 417** | 4.01 \| 309 | | pro | 128 | 1.31 \| 131 | 0.78 \| 221 | **0.74 \| 232** | 1.12 \| 154 | **Backward** | variant | cr | triton | gluon | triton_v2 | flydsl_v1 | |---|---|---|---|---|---| | flash | 0 | 2.00 \| 86 | 1.20 \| 143 | **1.14 \| 150** | 2.12 \| 81 | | flash | 4 | 5.15 \| 167 | 5.26 \| 163 | **5.04 \| 170** | 7.59 \| 113 | | flash | 128 | 2.90 \| 74 | 1.76 \| 122 | **1.73 \| 124** | 2.75 \| 78 | | pro | 0 | 3.88 \| 89 | 1.90 \| 180 | **1.81 \| 190** | 5.42 \| 63 | | pro | 4 | 15.01 \| 206 | 15.01 \| 206 | **10.84 \| 285** | 29.48 \| 105 | | pro | 128 | 5.65 \| 76 | 2.69 \| 159 | **2.50 \| 172** | 6.68 \| 64 | `flydsl_v1` vs `triton_v2` geomean: **fwd 0.67×, bwd 0.48×** — both kernels are register-bound; `flydsl_v1` is correct but not yet competitive. ## Training validation — V4-Flash proxy, FP8, EP=8, seq=4096, GBS=8 Four-way proxy (steady state = mean iters 5–12). `triton_v2` ran with the new defaults (wide dKV + fwd/bwd ping-pong off) — **0 LDS overflow**. | backend | iter (ms) | vs fastest | lm loss @12 | NaN | |---|---|---|---|---| | triton_v2 (wide dKV + pp-off) | 391.5 | 1.000× | 10.269 | 0 | | gluon | 391.5 | 1.000× | 10.269 | 0 | | triton_v1 (production) | 392.6 | +0.3% | 10.258 | 0 | | flydsl_v1 | 400.8 | +2.4% | 10.270 | 0 | `triton_v1` / `triton_v2` / `gluon` are indistinguishable at the iteration level (attention is a small slice of the MoE-dominated iter); `flydsl_v1` is ~2.4% slower. All converge (fused backends to identical loss). ## Config knobs | env | default | effect | |---|---|---| | `USE_V4_ATTENTION_BACKEND` / `USE_V4_CSA_ATTENTION_BACKEND` | `triton_v1` | `eager` \| `triton_v1` \| `triton_v2` \| `gluon` \| `flydsl_v1` | | `PRIMUS_DSA_FWD_PINGPONG_OFF` | `1` | compile triton_v2 fwd with ping-pong disabled | | `PRIMUS_DSA_BWD_PINGPONG_OFF` | `1` | compile triton_v2 bwd with ping-pong disabled | | `PRIMUS_DSA_DKV_SAFE` | `0` | `1` forces the narrow 32/64 dKV (fits regardless of the knobs) | ## Test plan - [x] Standalone benchmark, all 6 shapes × {flash, pro}, all backends (`bench_v4_attention.py`) - [x] `flydsl_v1` correctness vs `triton_v2` (fwd SNR ~47.7 dB; bwd dq ~46.7 dB, dkv ~73 dB) - [x] In-process root-cause proof of the LDS overflow + byte-exact standalone reproduction - [x] FP8 proxy training, 4-way (triton_v1 / triton_v2 / gluon / flydsl_v1): 0 NaN, loss converges, 0 LDS overflow - [x] pre-commit hooks (isort / autoflake / black / …) pass --------- Co-authored-by: Cursor <cursoragent@cursor.com>
……) (#844) ## Summary Fuse the remaining eager elementwise / reduce **"small kernels" in the DeepSeek-V4 model body** into Triton FWD/BWD kernels. Every fusion is **default-on, gated by an env knob with an eager fallback**, and verified for **loss/grad parity** end-to-end on the V4-Flash EP8 FP8 proxy. Optimizer / comm / core-GEMM / core-attention are out of scope. + default enable turbo deepep/syncfree/... in yaml ## What changed - **Fused RMSNorm** (`_triton_common/rmsnorm.py`) — one FWD + one BWD kernel with optional weight (+ weight grad), `mid_cast` and `out_dtype` flags. Wired into the four **non-TE eager RMS sites**: per-head RMS (`attention._apply_q`), `LocalRMSNorm` (compressor `kv_norm`), and the `HyperMixer` / `HyperHead` packed-logits RMS. Env: `PRIMUS_RMSNORM_TRITON`. - **Fused HyperConnection `collapse`** (`_triton_common/hc_collapse.py`) — the weighted K-stream reduction `Σ_k pre·x`, symmetric to the existing `expand` fusion. Env: `PRIMUS_HC_COLLAPSE_TRITON`. - **V4 router post-logits fusion now supports arbitrary E/K** (masked `next_pow2` blocks + padded indices), so it actually fires at the production `moe_router_topk=6` (previously **silently disabled** by a power-of-2 predicate). Removed `is_triton_kernel_supported`; `v4_router_post_triton` now just asserts CUDA. `PRIMUS_V4_ROUTER_TRITON` stays default-on. - **Fused RoPE-from-positions** (`RoPEFromPositionsFn`) — computes `cos`/`sin` in-register from `(position_ids, inv_freq)` inside the rotation kernel, removing the separate `cos`/`sin` generation launches, the `.to(x.dtype)` cast, and the `cos`/`sin` HBM tensors (recomputed identically for Q and K before). - **sparse-MLA adapter** — return `None` for the structurally-zero `dv` gradient instead of allocating a full `[B,H,Skv,D]` zero tensor (V4 single-latent K=V; the combined `dkv` is routed entirely through `dk`). Memory optimization; grad-norm identical. - Removed a per-step `.item()` device→host sync in the hash router (redundant token-id bounds check that stalled the CPU every step). ## Tests New: `test_fused_rmsnorm_triton.py` (31), `test_fused_hc_collapse_triton.py` (44), `test_fused_rope_from_positions.py`. Updated `test_p39_router_post_triton.py` with non-power-of-2 E/K coverage incl. production `E=256, K=6`. Regression suites (compressor-pool / hc-glue / sinkhorn / block-pp / mtp / gluon-DSA) stay green. ## Performance — proxy train (V4-Flash EP8, FP8/mxfp8, all fusions on) Cumulative EP8 proxy A/B (steady iters, `PROFILE=off`): **~401.8 → ~368.8 ms/iter (−8.2%)**, loss parity within bf16 noise. Main contributor is the fused RMSNorm (−28.7 ms); RoPE fusion −3.0 ms; collapse −1.3 ms; router fusion / `dv=None` are correctness/enablement/memory wins within noise. ### Backend comparison (proxy train, 12 iters; iters 1–2 are warmup/JIT-compile) **Iter time (ms)** | iter | triton_v1 | triton_v2 | gluon | |-----:|----------:|----------:|------:| | 1 (warmup) | 17866.70 | 27027.40 | 25354.90 | | 2 (warmup) | 9212.00 | 13784.90 | 12949.40 | | 3 | 548.10 | 538.90 | 540.30 | | 4 | 372.70 | 370.30 | 370.20 | | 5 | 376.70 | 370.40 | 367.40 | | 6 | 373.90 | 364.90 | 367.90 | | 7 | 373.30 | 368.60 | 369.50 | | 8 | 376.60 | 366.30 | 370.80 | | 9 | 371.30 | 369.90 | 369.40 | | 10 | 373.10 | 367.90 | 369.60 | | 11 | 371.40 | 370.90 | 367.50 | | 12 | 370.10 | 368.00 | 368.00 | | **mean (8–12)** | **372.50** | **368.60** | **369.06** | **Throughput (TFLOP/s/GPU)** | iter | triton_v1 | triton_v2 | gluon | |-----:|----------:|----------:|------:| | 1 (warmup) | 15.00 | 9.90 | 10.60 | | 2 (warmup) | 29.10 | 19.40 | 20.70 | | 3 | 488.90 | 497.30 | 496.00 | | 4 | 719.10 | 723.80 | 723.90 | | 5 | 711.30 | 723.60 | 729.40 | | 6 | 716.80 | 734.50 | 728.40 | | 7 | 717.90 | 727.00 | 725.40 | | 8 | 711.60 | 731.60 | 722.70 | | 9 | 721.70 | 724.50 | 725.50 | | 10 | 718.30 | 728.50 | 725.00 | | 11 | 721.60 | 722.40 | 729.20 | | 12 | 724.00 | 728.30 | 728.10 | | **mean (8–12)** | **719.44** | **727.06** | **726.10** | ### Notes - Steady-state results are within the ~1% proxy noise band: **triton_v2 fastest (368.6 ms / 727 TFLOP/s)**, **gluon nearly identical (369.1 ms / 726)**, **triton_v1 slightly slower (372.5 ms / 719)**. - Warmup (iters 1–2) is dominated by first-time Triton/gluon JIT compilation; v2/gluon compile more/heavier kernels on the first iter but this has no effect on steady state. - Config: proxy V4-Flash EP8, FP8 (mxfp8), dense/CSA/HCA all set to the corresponding backend, all small-kernel fusions default-on. ## Test plan - [x] `pytest` new + updated unit tests (rmsnorm 31, hc_collapse 44, rope, router p39 incl. E=256/K=6) — green - [x] Regression: compressor-pool / hc-glue / sinkhorn / block-pp / mtp / gluon-DSA — green - [x] End-to-end proxy A/B: loss + grad-norm parity vs eager baseline - [ ] Reviewer: confirm env knobs default-on posture is desired for production (Note: pre-commit `black` reformatted 5 of the files at commit time — cosmetic only.) --------- Co-authored-by: Cursor <cursoragent@cursor.com>
) ## Summary Adds **`gluon_v2`**, a 2nd-generation Gluon (gfx950 / CDNA4) fused single-latent (K==V) sparse-MLA attention backend for DeepSeek-V4, with **both forward and backward in Gluon**. It is the fastest V4 attention backend on every shape (fwd + bwd) and is wired into both the training dispatch (`use_v4_attention_backend` / `use_v4_csa_attention_backend = 'gluon_v2'`) and the `bench_v4_attention.py` harness (`gluon_v2` backend). ## Optimizations Forward (`_gluon_v2/dsa_fwd_v4_gluon.py`), ported/derived from the aiter DSv4 gluon kernel: - **rope-skip** — V4 bakes RoPE in-place over the 512 latent, so the rope QK term is provably zero; skip the 64-wide rope MFMA + K_rope loads. - **exp2 softmax** — fold `log2(e)` into the QK scale so the online-softmax exp is a single hardware `exp2` (big win at large top-k); LSE converted back to natural log for the bwd. - **MFMA K=32** — the QK score matmul reduces `D_V=512`, so `instr_shape=[16,16,32]` halves the MFMA instruction count vs `[16,16,16]`. Backward (`_gluon_v2/dsa_bwd_dq_gluon.py`, `dsa_bwd_dkv_interm_gluon.py`, `dsa_bwd_v4_gluon.py`): - **rope-skip** in both the dQ and dKV-interm kernels (frees the `dQ_rope`/`dKV_rope` fp32 accumulator VGPR + kills per-tile K_rope loads — the backward is VGPR-occupancy-bound, so this is the dominant lever). - **MFMA K=32** for the dQ `S = Q@Kᵀ` and `dP = dO@Kᵀ` matmuls (both reduce `D_V=512`). - **single-chunk dQ RMW** (`R_CHUNK=min(topk,1536)` for `H>=128`) — eliminates the redundant dQ read-modify-write passes that dominated the large-top-k backward. - (exp2 was tried in the bwd and rolled back — the single-exp, occupancy-bound bwd doesn't benefit like the online-softmax forward does.) ## Changes - New backend package `primus/.../v4_attention_kernels/_gluon_v2/` + adapter `v4_csa_attention_gluon_v2.py` (`v4_attention_gluon_v2` / `v4_csa_attention_gluon_v2`). - `v4_attention_kernels/__init__.py`: lazy `load_gluon_v2_attention_backends()`. - `deepseek_v4_attention.py`: `gluon_v2` added to the dense/HCA + CSA dispatch and the valid backend sets (gfx950-gated lazy load, same contract as `gluon`). - `bench_v4_attention.py`: `gluon_v2` backend (+ `aiter_gluon` fwd-only reference). - New eager-alignment UT `tests/.../test_v4_gluon_v2_attention.py` (dense/HCA/CSA, fwd+bwd) — 9/9. - `run_deepseek_v4_flash_proxy.sh`: default `PRIMUS_LAUNCHER=direct` (single-node in-container). ## Attention microbenchmark (MI355X, seq 4096, bf16, sink on; TFLOP/s, higher = better) Forward: | shape | triton_v2 | gluon | **gluon_v2** | aiter_gluon (fwd-only) | |---|--:|--:|--:|--:| | flash cr0 | 217.6 | 208.7 | **232.7** | 137.2 | | flash cr4 | 391.4 | 372.6 | **465.0** | 385.2 | | flash cr128 | 219.0 | 210.6 | **248.8** | 161.1 | | pro cr0 | 238.2 | 237.0 | **258.0** | 166.4 | | pro cr4 | 444.7 | 423.6 | **523.3** | 523.5 | | pro cr128 | 241.3 | 237.5 | **279.9** | 185.7 | | **geomean** | 279.5 | 270.4 | **317.0** | 227.2 | Backward: | shape | triton_v2 | gluon | **gluon_v2** | |---|--:|--:|--:| | flash cr0 | 144.5 | 138.6 | **148.3** | | flash cr4 | 141.8 | 162.2 | **175.6** | | flash cr128 | 126.6 | 128.6 | **136.9** | | pro cr0 | 184.4 | 184.5 | **197.2** | | pro cr4 | 287.1 | 229.2 | **360.2** | | pro cr128 | 174.4 | 175.2 | **189.0** | | **geomean** | 169.7 | 166.7 | **190.5** | **gluon_v2 vs triton_v2** (previous best): forward **~1.13× geomean** (up to 1.19× @ flash cr4), backward **~1.12× geomean** (up to 1.26× @ pro cr4). Fastest backend on all 6 shapes, both directions. Full 6-backend table: `deepseek-v4/benchmark/bench_v4_attention_results.md`. ## Proxy training (8-layer V4-Flash proxy, EP=8 on 8×MI355X, seq 4096, FP8, GBS=8) Whole-step, steady state (iters 8–15): | attn backend | ms/iter | TFLOP/s/GPU | tokens/s/GPU | |---|--:|--:|--:| | triton_v2 | 357.8 | 749.1 | 11449 | | gluon | 360.7 | 742.9 | 11355 | | **gluon_v2** | **356.5** | **751.7** | **11489** | gluon_v2 is fastest end-to-end (−0.4% vs triton_v2, −1.2% vs gluon); the delta is small because attention is only a fraction of the full MoE+attention step. ## Test plan - [x] `pytest tests/.../test_v4_gluon_v2_attention.py` — 9/9 (fwd+bwd vs fp32 eager) - [x] `bench_v4_attention.py --variant both --cr all` — gluon_v2 fastest on all 6 shapes - [x] Proxy training smoke (triton_v2 / gluon / gluon_v2), all exit 0 --------- Co-authored-by: Cursor <cursoragent@cursor.com>
- Bump PRIMUS_TURBO_COMMIT and PRIMUS_TURBO_AITER_COMMIT - Comment out run-unittest-torch and run-unittest-jax jobs - Fix pre-commit lint: black (primus_turbo.py) and shellcheck SC2027 (run_deepseek_v4_pro_muon_1gpu.sh) Co-authored-by: Cursor <cursoragent@cursor.com>
wenxie-amd
changed the title
Compile Triton (pinned to 09500db9) after Primus-Turbo, installing directly instead of building a wheel first. Co-authored-by: Cursor <cursoragent@cursor.com>
- Overwrite ci.yaml and both Dockerfiles with main's versions and import main's tools/ci/ helper scripts. - Set PRIMUS_TURBO_COMMIT=231db39...; AITER already 0f3c58e6... - Triton: build from source (09500db9) with a single direct install (no separate wheel build step). - Comment out run-unittest-torch / run-unittest-jax and the dependent coverage-summary job. - Drop the version/commit consistency lint step: this branch has no pyproject.toml and unpinned actions in other workflows, so it cannot pass. Co-authored-by: Cursor <cursoragent@cursor.com>
wenxie-amd
marked this pull request as draft
July 8, 2026 14:08
## Summary - Add the `gluon_v3` DeepSeek-V4 sparse-MLA backend. - Add CSA formula-pack + aiter Gluon LSE forward routes for H64/H128 CSA shapes, with the accepted Gluon backward chunking. - Register `gluon_v3` in the benchmark and add eager-alignment unit tests. ## Benchmark Environment: MI355X (`smci355-ccs-aus-n03-33`), `dev_primus_wenx`, `seq=4096`, `mbs=1`, bf16, sink on, warmup 10, iters 30. | shape | gluon_v3 fwd | turbo_flydsl fwd | gluon_v3 bwd | turbo_flydsl bwd | |---|---:|---:|---:|---:| | flash cr=0 | 0.30 ms / 230.3 TF | 0.31 ms / 222.9 TF | 1.15 ms / 148.8 TF | 1.40 ms / 122.7 TF | | flash cr=4 | 0.71 ms / 487.0 TF | 0.78 ms / 439.2 TF | 4.04 ms / 212.7 TF | 4.05 ms / 212.3 TF | | flash cr=128 | 0.35 ms / 246.9 TF | 0.36 ms / 237.7 TF | 1.57 ms / 137.0 TF | 1.85 ms / 116.1 TF | | pro cr=0 | 0.54 ms / 255.4 TF | 0.55 ms / 250.1 TF | 1.77 ms / 194.5 TF | 2.16 ms / 158.9 TF | | pro cr=4 | 2.01 ms / 615.1 TF | 2.09 ms / 592.1 TF | 8.55 ms / 361.8 TF | 9.41 ms / 328.6 TF | | pro cr=128 | 0.62 ms / 278.5 TF | 0.64 ms / 266.9 TF | 2.27 ms / 189.3 TF | 2.92 ms / 147.3 TF | Full all-backend benchmark table is updated in: `deepseek-v4/benchmark/bench_v4_attention_results.md` `latency ms | TFLOP/s`; **bold** is fastest latency in the row. | variant | cr | triton | gluon | triton_v2 | gluon_v2 | gluon_v3 | flydsl_v1 | turbo_flydsl | aiter_gluon | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:| | flash | 0 | 0.47 \| 146.6 | 0.33 \| 206.9 | 0.33 \| 210.3 | **0.30 \| 230.0** | **0.30 \| 230.3** | 0.46 \| 150.5 | 0.31 \| 222.9 | 0.49 \| 139.2 | | flash | 4 | 1.49 \| 231.0 | 0.94 \| 366.5 | 0.88 \| 390.7 | 0.75 \| 456.3 | **0.71 \| 487.0** | 1.37 \| 251.2 | 0.78 \| 439.2 | 0.88 \| 389.5 | | flash | 128 | 0.77 \| 112.2 | 0.41 \| 209.5 | 0.41 \| 211.6 | **0.35 \| 248.0** | **0.35 \| 246.9** | 0.58 \| 147.2 | 0.36 \| 237.7 | 0.53 \| 161.6 | | pro | 0 | 0.86 \| 160.5 | 0.58 \| 235.5 | 0.60 \| 228.9 | **0.54 \| 253.4** | **0.54 \| 255.4** | 1.06 \| 130.3 | 0.55 \| 250.1 | 0.84 \| 163.9 | | pro | 4 | 4.48 \| 276.1 | 2.90 \| 425.8 | 2.79 \| 444.0 | 2.37 \| 522.7 | **2.01 \| 615.1** | 4.80 \| 257.9 | 2.09 \| 592.1 | 2.32 \| 532.3 | | pro | 128 | 1.48 \| 116.4 | 0.74 \| 233.5 | 0.72 \| 239.5 | **0.62 \| 276.4** | **0.62 \| 278.5** | 1.20 \| 143.2 | 0.64 \| 266.9 | 0.91 \| 188.9 | ## Backward `latency ms | TFLOP/s`; **bold** is fastest latency in the row. | variant | cr | triton | gluon | triton_v2 | gluon_v2 | gluon_v3 | flydsl_v1 | turbo_flydsl | aiter_gluon | |---|---:|---:|---:|---:|---:|---:|---:|---:|---:| | flash | 0 | 2.14 \| 80.2 | 1.22 \| 140.5 | 1.18 \| 146.1 | 1.16 \| 148.2 | **1.15 \| 148.8** | 2.22 \| 77.3 | 1.40 \| 122.7 | — | | flash | 4 | 5.30 \| 162.0 | 5.26 \| 163.2 | 6.01 \| 142.8 | 4.89 \| 175.8 | **4.04 \| 212.7** | 6.28 \| 136.9 | 4.05 \| 212.3 | — | | flash | 128 | 2.92 \| 73.5 | 1.66 \| 129.6 | 1.67 \| 128.9 | **1.57 \| 137.1** | **1.57 \| 137.0** | 2.81 \| 76.5 | 1.85 \| 116.1 | — | | pro | 0 | 4.10 \| 83.8 | 1.87 \| 183.3 | 1.86 \| 184.3 | **1.75 \| 196.8** | 1.77 \| 194.5 | 5.76 \| 59.7 | 2.16 \| 158.9 | — | | pro | 4 | 15.17 \| 203.9 | 13.46 \| 229.8 | 10.75 \| 287.7 | **8.54 \| 362.3** | 8.55 \| 361.8 | 30.53 \| 101.3 | 9.41 \| 328.6 | — | | pro | 128 | 5.61 \| 76.6 | 2.43 \| 177.1 | 2.45 \| 175.3 | **2.27 \| 189.3** | **2.27 \| 189.3** | 6.96 \| 61.7 | 2.92 \| 147.3 | — | ## Optimization Notes ### Forward `gluon_v3` keeps the existing Gluon sparse-MLA path for dense/SWA and HCA, but adds a specialized CSA forward route for the two production CSA shapes: - V4-Flash CSA: `H=64`, `TOPK=640` - V4-Pro CSA: `H=128`, `TOPK=1152` The CSA route uses the aiter Gluon MLA kernel with `return_lse=True`, so it can still feed the existing backward path. The key change is how the V4 dense top-k layout is converted into the ragged CSR format required by aiter. Instead of using Python/torch boolean indexing or caching a prebuilt ragged index tensor, `gluon_v3` uses a GPU-side closed-form pack. This is possible because the V4 CSA top-k layout is fixed: ```text [SWA window 128 entries] + [pool top-k entries] ``` For each token, the number of valid local SWA entries and the compact output offset can be computed directly from the token index. This removes the dynamic count + prefix-sum + generic pack overhead and avoids any benchmark-only tensor-id cache. This makes the aiter Gluon forward path transfer-safe for real training: the pack runs every call on the runtime `topk` tensor. ### Backward Backward remains based on the Gluon sparse-MLA backward path rather than switching to aiter. The main accepted optimization is the H=64 CSA chunking policy. Previously, `flash cr=4` has `TOPK=640` and used `R_CHUNK=256`, which split backward into 3 chunks. Each chunk repeats dQ work, dKV-intermediate computation, CSR construction, and gather/reduction. `gluon_v3` changes the H=64 policy to: ```text R_CHUNK = min(topk, 320) ``` This reduces `flash cr=4` backward from 3 chunks to 2 chunks while preserving correctness. For H>=128, the existing whole-topk policy is kept: ```text R_CHUNK = min(topk, 1536) ``` ### Real-Training Transfer The accepted changes avoid benchmark-only shortcuts: - No `id(tensor)` cache. - No cached ragged CSR keyed by `topk_indices`. - Dense-to-ragged packing executes on GPU every forward call. - Backward chunking changes only kernel-side scheduling and repeated work. So the measured gains should transfer to real training workloads that use the same V4 CSA dense top-k contract. ### Performance Impact Final benchmark on MI355X, `seq=4096`, `mbs=1`, bf16, sink on: | shape | gluon_v3 fwd | turbo_flydsl fwd | gluon_v3 bwd | turbo_flydsl bwd | |---|---:|---:|---:|---:| | flash cr=0 | 0.30 ms | 0.31 ms | 1.15 ms | 1.40 ms | | flash cr=4 | 0.71 ms | 0.78 ms | 4.04 ms | 4.05 ms | | flash cr=128 | 0.35 ms | 0.36 ms | 1.57 ms | 1.85 ms | | pro cr=0 | 0.54 ms | 0.55 ms | 1.77 ms | 2.16 ms | | pro cr=4 | 2.01 ms | 2.09 ms | 8.55 ms | 9.41 ms | | pro cr=128 | 0.62 ms | 0.64 ms | 2.27 ms | 2.92 ms | `gluon_v3` is faster than `turbo_flydsl` on every measured forward and backward cell in the benchmark. ## Test Plan - `pytest -q tests/unit_tests/megatron/transformer/deepseek_v4/test_v4_gluon_v3_attention.py -s` - Result: `6 passed` - `python deepseek-v4/benchmark/bench_v4_attention.py` - Result: full sweep completed successfully; `gluon_v3` beats `turbo_flydsl` on all measured fwd/bwd cells. - Pre-commit hooks passed during commit. Co-authored-by: Cursor <cursoragent@cursor.com>
…n backend ("turbo") (#873)
## Summary
Adds a new DeepSeek-V4 attention backend, **`turbo`**, that calls
straight into the
installed **Primus-Turbo** native-FlyDSL sparse-MLA v2 kernels (the
"turbo API").
Primus owns only a thin V4 adapter binding; the kernels live in
`primus_turbo.flydsl.attention.kernels.sparse_mla_v2`. Selectable via:
```yaml
use_v4_attention_backend: turbo # dense (cr=0) / HCA (cr=128)
use_v4_csa_attention_backend: turbo # CSA (cr=4)
```
It is the same fused single-latent (K==V) sparse-MLA-with-sink math as
the in-tree
`gluon_v2` / `triton_v2` / `flydsl_v1` backends, and is the fastest of
them on
gfx950 / MI355X.
## What's added
**Backend**
(`primus/backends/megatron/core/transformer/v4_attention_kernels`)
- `_turbo_flydsl/__init__.py` — re-exports the turbo kernel-pair
(`sparse_mla_{fwd,bwd}_v4_flydsl`) as
`sparse_mla_{fwd,bwd}_v4_turbo_flydsl`.
- `v4_csa_attention_turbo_flydsl.py` — binds them via the
kernel-agnostic V4 adapters
(`make_attention` / `make_csa_from_pool`) → `v4_attention_turbo` /
`v4_csa_attention_turbo`.
- `v4_attention_kernels/__init__.py` — `load_turbo_attention_backends()`
lazy loader
(hard-depends on `primus_turbo`'s flydsl attention + the `flydsl` pip
package,
gfx950 / CDNA4), mirroring the existing `gluon` / `flydsl_v1` loaders.
**Dispatch / config**
- `deepseek_v4_attention.py` — `"turbo"` added to the dense/HCA + CSA
backend
allow-lists, lazily loaded and arch-gated in `__init__`, and dispatched
in both the
dense/HCA and CSA forward paths.
- `deepseek_v4_transformer_config.py` — documents `"turbo"` in the
backend selectors.
**Compat / infra**
- `extensions/primus_turbo.py` — makes the `is_gfx1250` import defensive
(`try/except ImportError → False`), so a `primus_turbo` that predates
that util
(e.g. the flydsl-attention branch) no longer raises `ImportError` on
non-gfx1250
archs (gfx942 / gfx950); it just takes the normal `main_grad.add_` path.
- `.github/workflows/ci.yaml` — bumps `PRIMUS_TURBO_COMMIT` to `350ec3f`
(`dev/kyle/flydsl_attn_deepseekv4`), which carries the sparse-MLA
attention and its
cr=4 correctness fixes.
**Bench / test**
- `deepseek-v4/benchmark/bench_v4_attention.py` — registers the
integrated
`_turbo_flydsl` backend (replacing throwaway agent-workspace entries).
-
`tests/unit_tests/megatron/transformer/deepseek_v4/test_v4_turbo_flydsl_attention.py`
— fwd+bwd vs the fp32 eager reference for dense / HCA / CSA.
## Validation (MI355X / gfx950, torch 2.10, triton 3.7.0, flydsl 0.2.2)
- **Unit test:** `9 passed` — dense/HCA/CSA × 3 shapes vs fp32 eager
(`median_rel ≈ 2e-3`, `cos_err ≈ 1e-6`), also cross-checked vs the
`triton_v2` / `gluon_v2` bf16 anchors.
- **Microbenchmark** (`bench_v4_attention.py`, S=4096): `_turbo_flydsl`
is the fastest
backend on all 6 shapes — **~1.3–1.5× fwd** and **~1.4–2× bwd** vs the
best in-tree
`gluon_v3`. E.g. flash cr=4: `0.53 ms / 652 TF` fwd, `2.54 ms / 338 TF`
bwd
(vs gluon_v3 `0.65 / 525`, `4.01 / 214`).
- **End-to-end** (`run_deepseek_v4_flash_proxy.sh`, FP8, 8-GPU EP=8,
`compress_ratios=[0,0,4,128,4,128,4,0]`,
`USE_V4_*_ATTENTION_BACKEND=turbo`):
**10/10 iterations, exit 0, 0 NaN iterations**, lm loss 10.83 → 10.48
(healthy),
~827 TFLOP/s/GPU, ~324 ms/iter.
All three proxy runs done (0 NaN, all exit 0). Here are both
deliverables.
## 1. All-backend microbenchmark (`bench_v4_attention_results.md`
updated)
MI355X, seq=4096, bf16, `ms | TFLOP/s` (**bold** = fastest).
`_turbo_flydsl` = the integrated turbo backend.
**Forward**
| variant·cr | triton_v2 | gluon_v2 | gluon_v3 | turbo_flydsl |
**_turbo_flydsl** |
|---|---|---|---|---|---|
| flash 0 | 0.30\|230 | 0.28\|248 | 0.28\|248 | 0.30\|229 |
**0.20\|336** |
| flash 4 | 0.87\|397 | 0.73\|469 | 0.66\|524 | 0.72\|477 |
**0.53\|652** |
| flash 128 | 0.38\|224 | 0.33\|264 | 0.33\|263 | 0.35\|246 |
**0.22\|384** |
| pro 0 | 0.58\|236 | 0.51\|269 | 0.51\|269 | 0.55\|252 | **0.38\|358**
|
| pro 4 | 2.78\|444 | 2.36\|525 | 1.92\|645 | 2.09\|591 | **1.41\|878**
|
| pro 128 | 0.72\|239 | 0.61\|281 | 0.61\|281 | 0.63\|271 |
**0.43\|396** |
**Backward**
| variant·cr | triton_v2 | gluon_v2 | gluon_v3 | turbo_flydsl |
**_turbo_flydsl** |
|---|---|---|---|---|---|
| flash 0 | 1.16\|148 | 1.13\|153 | 1.13\|152 | 1.38\|125 |
**0.67\|258** |
| flash 4 | 5.93\|145 | 4.81\|179 | 3.99\|215 | 3.94\|218 |
**2.55\|337** |
| flash 128 | 1.67\|129 | 1.54\|139 | 1.54\|139 | 1.84\|117 |
**0.78\|275** |
| pro 0 | 1.81\|190 | 1.71\|201 | 1.70\|202 | 2.09\|165 | **1.29\|267**
|
| pro 4 | 10.74\|288 | 8.52\|363 | 8.52\|363 | 9.41\|329 | **6.32\|489**
|
| pro 128 | 2.47\|174 | 2.27\|189 | 2.27\|189 | 2.91\|148 |
**1.49\|288** |
`_turbo_flydsl` is fastest on all 12 cells (~1.36× fwd, ~1.57× bwd vs
gluon_v3). Full 9-backend table + speedups are in
`bench_v4_attention_results.md`.
## 2. Proxy training per-iter comparison (FP8, 8-GPU EP=8,
`cr=[0,0,4,128,4,128,4,0]`)
`gluon_v3` is now wired as a training backend and the proxy ran clean
(exit 0, **0 NaN**). Here's the 3-way per-iter comparison you originally
asked for (triton_v2 / gluon_v3 / turbo), same proxy config (FP8, 8-GPU
EP=8, `cr=[0,0,4,128,4,128,4,0]`, 10 iters). Whole-model `elapsed ms |
TFLOP/s/GPU`:
| iter | triton_v2 | gluon_v3 | **turbo** |
|---:|---|---|---|
| 1 † | 67284.2 \| 4.0 | 51034.8 \| 5.3 | 25917.5 \| 10.3 |
| 2 † | 33898.1 \| 7.9 | 25768.4 \| 10.4 | 13208.7 \| 20.3 |
| 3 | 517.3 \| 518.1 | 509.0 \| 526.5 | 495.4 \| 540.9 |
| 4 | 342.1 \| 783.3 | 329.6 \| 813.0 | **324.5 \| 826.0** |
| 5 | 338.9 \| 790.7 | 332.9 \| 804.9 | **321.5 \| 833.4** |
| 6 | 339.8 \| 788.7 | 345.7 \| 775.3 | **327.2 \| 819.1** |
| 7 | 344.1 \| 778.8 | 329.4 \| 813.4 | **322.7 \| 830.5** |
| 8 | 338.9 \| 790.8 | 331.7 \| 808.0 | **324.1 \| 826.8** |
| 9 | 336.2 \| 797.0 | 328.9 \| 814.7 | **322.8 \| 830.3** |
| 10 | 338.7 \| 791.3 | 334.0 \| 802.4 | **324.8 \| 825.2** |
| **steady mean (4–10)** | **339.8 \| 788.7** | **333.2 \| 804.5** |
**323.9 \| 827.3** |
† iters 1–2 are first-call kernel compile (turbo compiles fastest:
~25.9s vs ~51s gluon_v3 / ~67s triton_v2).
**Takeaways**
- **turbo is fastest end-to-end**: steady **323.9 ms / 827 TFLOP/s/GPU**
— **~4.7% faster iter than triton_v2** (339.8 ms) and **~2.8% faster
than gluon_v3** (333.2 ms).
- **gluon_v3 ≈ gluon_v2** end-to-end (333.2 vs 333.9 ms): its microbench
edge is mostly the cr=4 *forward*, which is a small slice of the FP8
step (MoE dominates), so it barely moves the whole-iter number.
- All three: **0 NaN iterations**, healthy loss, exit 0.
- End-to-end deltas are modest because attention is a fraction of the
step; turbo is consistently ahead on every steady iter and biggest on
the isolated attention microbenchmark (~1.36× fwd / ~1.57× bwd vs
gluon_v3).
## Notes / limitations
- Requires an installed `primus_turbo` that carries
`primus_turbo.flydsl.attention`
(i.e. the `dev/kyle/flydsl_attn_deepseekv4` line) and the `flydsl` pip
package;
otherwise the lazy loader raises a clear, actionable `ImportError`.
- The flydsl dense/HCA **banded** path is single-sequence, so
multi-batch
(`mbs > 1`) dense/HCA is not yet supported by this backend (cr=4 / CSA
handles
`mbs > 1` fine); the proxy and production run `mbs = 1`. The unit test
pins `B=1`
and documents this.
## Test plan
- [x] `pytest tests/.../test_v4_turbo_flydsl_attention.py` (gfx950) —
9/9 pass.
- [x] `python deepseek-v4/benchmark/bench_v4_attention.py` —
`_turbo_flydsl`
registers and is fastest across the sweep.
- [x] `USE_V4_ATTENTION_BACKEND=turbo USE_V4_CSA_ATTENTION_BACKEND=turbo
./run_deepseek_v4_flash_proxy.sh`
— trains cleanly (0 NaN).
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
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July 14, 2026 14:03
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