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[WS1][CUDA][Qwen3-Next] TP4 GDN, convolution and D=256 attention mixers on real weights (RFC #428 stack 6/8) - #507
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`test_registry_dispatches_rms_norm` asserts that the registry resolves
`rms_norm` to `RMSNormCudaOp` whenever CUDA and the compiled kernels are
both present, but `OpBackend` had no CUDA member for this operator and the
CUDA priority list contained only `PYTORCH_NATIVE_RMS_NORM`, so the assert
could never hold. The test therefore fails on any CUDA machine that builds
the native extension, and only passes when `_HAS_CUDA_RMSNORM` is false --
which is why an unbuilt CI has not caught it.
`RMSNormCudaOp` is already a first-class backend elsewhere: it is the
`"cuda"` candidate in `gtest/operator_specs.py` and is used directly by
`attention_preprocess.py`. Only the registry was missing it.
Add `OpBackend.CUDA_RMS_NORM` and put it ahead of the PyTorch reference in
the CUDA priority list. Because `_load_backend` only catches import errors
and this module imports cleanly without `_C`, a CUDA-first list would
otherwise hand out an op that raises at call time on an unbuilt install; so
`RMSNormCudaOp.__init__` now validates the extension and its three symbols,
matching `_require_cuda_activation` in the activation ops. The registry
already treats a backend whose construction raises as unavailable, so the
list degrades to `NativeRMSNormOp` as before.
Only the `cuda` priority map changes; rocm/musa/cpu/npu are untouched.
Verified on 2x B200 (sm_100, driver 580.126.20, torch 2.13.0+cu130) with the
extension rebuilt from source:
pytest tests/test_rms_norm.py -q
pytest tests/ rl_engine/tests/ -q -p no:randomly \
--ignore=tests/test_rocm_aiter_api_contract.py
tests/test_rms_norm.py passes, including test_registry_dispatches_rms_norm, which
fails on the merge-base. The full suite gains no failure. The ignored file fails
to import on the merge-base as well.
Absolute suite counts are reported in the PR description against a named base
commit, not here: they shift whenever a sibling test is added, so a count frozen
in a commit message goes stale the moment the branch is rebased.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…back Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…ires Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
The forward, backward-dx and both backward-dw launchers took the current CUDA stream without switching to the input's device, so a tensor on cuda:1 while cuda:0 is current launched on the wrong GPU. Add a device guard on the input's device in each launcher. Use at::cuda::OptionalCUDAGuard with the headers included unconditionally, as activation.cu does: those launchers compile in the ROCm build too, where the file's existing c10::cuda::CUDAGuard stays inside the !USE_ROCM block. A two-device test checks that the op runs on the input's device and matches the single-device result. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Advances RFC RL-Align#428 C1 on the CUDA track. Claim level: L0 repeatable and L1 batch-invariant. L2 is NOT claimed -- see "On exactness against vLLM" below. Qwen3-Next's decoder and final norms store a zero-centred weight and compute `x * rstd * (1 + w)`, with the `1 +` applied in fp32 after the upcast. Folding it into a bf16 weight beforehand rounds the offset away, so it has to reach the kernel as a parameter rather than being pre-applied by the caller. NativeRMSNormOp gains `weight_offset` (default 0.0) Qwen3NextRMSNormOp subclasses it, overriding only weight_offset = 1.0 Qwen3NextRMSNormGatedOp GDN gated norm: plain w, weight multiply in fp32 Qwen3NextRMSNormGatedHFOp the transformers convention, kept as a witness The offset is applied under `if cls.weight_offset:` rather than unconditionally, because `0.0 + w` rewrites -0.0 to +0.0. torch.equal does not notice that, but a bitwise comparison does, and the plain path must stay bit-for-bit what it was. A test pins it at the bit level. The gated pair exists because transformers and vLLM disagree on where the gated norm's weight multiply happens, and the gap is not a ULP: on bf16 / head_v_dim=128 they differ in 35% of elements with max|diff| = 6.25e-2. Isolating the cast order alone reproduces the gap (5.3e-2), so the cast order dominates rather than the reduction order. The two conventions share their validation and normalization and differ only in a `_scale_by_weight` hook. CUDA: `weight_offset` added to the forward and dx kernels, defaulting to 0.0 so every existing caller and binding is unaffected. The dw kernel is untouched: d/dw (offset + w) == d/dw w. weight_offset=1.0 vs an explicit fp32 (1 + w) weight bitwise equal weight_offset=1.0 vs a bf16-folded (1 + w) weight differs, as required default offset vs the previous kernel bitwise equal The first line is the correctness argument: the in-kernel offset is the same arithmetic as the fp32 reference, not an approximation. The second is a regression guard -- if it ever passes, the offset has stopped being fp32. On exactness against vLLM ------------------------- Measured over 40 seeds (bf16, head_v_dim=128, 512 rows): ours vs forward_native 6/40 seeds differ, worst 1.56e-2 ours vs forward_cuda 18/40 seeds differ, worst 3.91e-3 forward_native vs forward_cuda 21/40 seeds differ, worst 1.56e-2 vLLM's own two paths are not bitwise equal to each other, so "bitwise equal to vLLM" is undefined until a single provider is named. In fp32 the two paths differ on ~36% of elements, every one by an fp32 ULP -- the tree shapes differ, the semantics do not. What this reproduces is the convention (fp32 weight multiply, single trailing cast); the residual is the reduction tree. The reduction stays the repo's fixed 32-wide chunked sum, which is what buys L1. It was introduced for NPU but is needed on CUDA too: over 20 seeds at H=2048 in bf16 -- Qwen3-Next's own hidden_size and dtype -- a plain mean(-1) broke slice invariance on 1 of 20 while the chunked reduction broke on 0 of 20. Matching stock vLLM bitwise would mean adopting a reduction that is not itself batch-invariant, i.e. trading L1 for L2. tests/check_qwen3_next_norm_providers.py pins the dispatch facts and bounds the gap, asserting magnitudes rather than equality so a vLLM bump that changes the provider fails loudly. It is named `check_` rather than `test_`, following tests/distributed/check_*.py: it imports real vLLM, and tests/test_framework_operator_integrations.py asserts vllm is absent from sys.modules, an invariant any collected test importing vLLM would break for the whole session. Also: `rl_engine/_C.pyi` updated for the new `weight_offset` argument (CI runs mypy against it), `tests/test_qwen3_next_norm.py` added to the CI test list in .github/workflows/ci.yml, and an operator page added per docs/operators/README.md ("the documentation page is part of the operator contract"). Verified on 2x B200 (sm_100, driver 580.126.20, torch 2.13.0+cu130, triton 3.7.1, vllm 0.30.0, transformers 5.17.0): pytest tests/test_qwen3_next_norm.py -q pytest tests/check_qwen3_next_norm_providers.py -q pytest tests/ rl_engine/tests/ -q -p no:randomly \ --ignore=tests/test_rocm_aiter_api_contract.py Both new files pass and the full suite gains no failure. The ignored file fails to import on the merge-base as well. Absolute suite counts are reported in the PR description against a named base commit, not here: they shift whenever a sibling test is added, so a count frozen in a commit message goes stale the moment the branch is rebased. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
… VJPs Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Adds the CUDA kernel behind `Qwen3NextRMSNormGatedOp`, and registers both it and
the zero-centred decoder norm from the previous commit.
y = x * rstd * (weight_offset + weight) * act(gate)
Every multiply is fp32 with one cast at the store, matching vLLM's RMSNormGated
with norm_before_gate=True and group_size=None -- the only configuration the GDN
block constructs. Other configurations are rejected rather than approximated.
`expf` is used rather than the `__expf` intrinsic: the fast intrinsic trades
accuracy for speed and would move the result away from the fp32 reference.
Forward reuses the existing block_reduce_sum / choose_threads(H), so for a fixed
H the reduction tree is independent of the row count -- that is the L1 guarantee
-- and `rstd` comes out bitwise identical to the ungated kernel for the same x,
which is asserted.
Backward is assembled from deterministic pieces:
dx new kernel, the ungated dx with (w + offset) -> (w + offset) * act(z)
dweight reuses rmsnorm_dweight_rows_fp32 + the ascending-row fp32 left fold
dgate row-local and reduction-free, fp32 in the wrapper
Both backwards route through one `_fold_dweight_rows` helper so the file keeps a
single left-fold entrypoint, which tests/test_vjp_fp32.py pins, and one
`_require_cuda_symbols` helper so the module has a single availability contract.
The gated op is deliberately NOT a subclass of RMSNormCudaOp: it takes an extra
required tensor, so it cannot stand in for one. Same reasoning as on the PyTorch
side, and stated in its docstring so the question is not reopened.
Registration: `rms_norm_gated` and `qwen3_next_rms_norm` in OP_SPECS (both with a
cuda-sm90 candidate, as every other reduction spec carries), operator_inputs
builders, OpBackend members and priority maps on all five platforms,
test_dispatch assertions, and the WS1 registered-ops set in
tests/test_ws1_gtest_gpu.py. Operator pages added per docs/operators/README.md,
which states the page is part of the operator contract.
Claim level: L0 repeatable and L1 batch-invariant. NOT L2 -- see
tests/check_qwen3_next_norm_providers.py, which pins the dispatch facts and
bounds the gap against vLLM rather than asserting equality.
Verified on 2x B200 (sm_100, driver 580.126.20, torch 2.13.0+cu130, vllm 0.30.0):
pytest tests/test_qwen3_next_norm.py -q
pytest tests/check_qwen3_next_norm_providers.py -q
python scripts/check_operator.py --op rms_norm_gated --candidate cuda \
--device cuda --dtype bf16 --check-grad -> pass_rate=1.0000
python scripts/check_operator.py --op qwen3_next_rms_norm --candidate cuda \
--device cuda --dtype bf16 --check-grad -> pass_rate=1.0000
pytest tests/ rl_engine/tests/ -q -p no:randomly \
--ignore=tests/test_rocm_aiter_api_contract.py
Both new files pass, both operators report pass_rate=1.0000 on the CUDA
candidate, and the full suite gains no failure. The ignored file fails to import
on the merge-base as well.
Absolute suite counts are reported in the PR description against a named base
commit, not here: they shift whenever a sibling test is added, so a count frozen
in a commit message goes stale the moment the branch is rebased.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…t norms Tests: the low-precision and CUDA-vs-golden comparisons hard-coded atol=2e-2/rtol=1.6e-2, which is the contract's elementwise bf16 row. They now resolve forward_accuracy for op_class="reduction" from tolerance_contract.json. That loosens bf16 (5e-2/2e-2) and tightens fp16 (1e-3/1e-3, previously 2e-2); the fp16 cases still pass. The vLLM provider-gap bounds in tests/check_qwen3_next_norm_providers.py are labelled as gap bounds, not contract thresholds. Docs and docstrings: correct statements this branch had committed. - The operator page no longer says the op is registered or prints a check_operator command; the gtest spec and registry entry arrive with the gated-norm PR. - Withdrawn: "7 of 1048576 differ" (single seed, no script), the 35% / 5.3e-2 cast-order isolation (an fp32 round-trip is a no-op), "needed on CUDA as well", and "matching vLLM means trading L1 for L2" (one unreproduced observation). Replaced with the scoped claim levels and the probe results. - The provider check no longer claims to establish which path vLLM dispatches, or a per-element fp32 ULP bound. - The module docstring is cut to the contract and links to the operator page. Comments: why the kernel's offset add is guarded (signed zero, with the pinning test), and why parameter_vjp_contributions_fp32 passes the offset. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…atistics Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…ion fallback The zero-centred norm was registered CUDA-first alongside the gated one, but only rms_norm_gated had dispatch tests. Mirror them: pin the priority on all five platforms, and assert that the inherited RMSNormCudaOp.__init__ symbol check makes the registry fall through to the PyTorch reference. Each assertion was checked to fail on CPU with the behaviour removed: PyTorch first in the CUDA list, the ROCm entry dropped, and the __init__ check removed. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…ctivation, offset
The bitwise rstd identity was asserted for one configuration only (bf16, H=128,
silu, offset 0). Parametrize it over fp32/fp16/bf16 x H in {128, 2048, 5120} x
{silu, sigmoid} x offset in {0.0, 1.0}, 36 cases, with the plain kernel taking
the same offset.
All 36 are expected to hold by construction: both kernels run the same
sum-of-squares loop, block_reduce_sum and choose_threads(H) launch shape, and
neither the gate nor the offset enters the statistic. The H values cover both
block sizes (128 and 512 threads) and two per-thread serial lengths. Not yet run
on a GPU; the cases skip without the compiled extension.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…16, 17) R2-1 (private bf16 tolerances cited as the reduction row) was resolved in the preceding merge, which switched the gated tests to c1's _forward_tol. - R2-2 docs/operators/qwen3-next-rms-norm-gated.md: Accuracy now points to tolerance_contract.json (forward_accuracy, reduction x dtype); the 35% / 6.25e-2 HF-vs-vLLM figure is labelled a one-off with its setup (one seed, B200, bf16, head_v_dim=128, 512 rows, eager); the 40-seed figures are attributed to tests/check_qwen3_next_norm_providers.py. Withdrawn, as on the c1 page: the cast-order isolation, the per-element fp32 ULP wording, and "fails closed (RFC section 6 item 7)" for configurations the op has no parameter for. Claim levels now read L1 (prefix slices; batch size, chunking, padding, permutation in the C3/C4 gates), L0 not separately tested; the rstd sweep is described. - R2-7 csrc/ops.cpp: drop the dim and size TORCH_CHECKs that rmsnorm_check_weight already performs, in rmsnorm_forward, rmsnorm_backward_dx and rmsnorm_gated_check. Error messages for those cases now come from rmsnorm_check_weight; no test matches the removed messages. - R2-8 workload_report() in rl_engine/testing/ws1_workload.py replaces the payload block duplicated in both gate scripts. full_model_evidence is read from the manifest: False for the Qwen3-Next norm manifest, None (not declared) for the Dense manifest, which previously reported a hard-coded False. - R2-9 the gate scripts import validate_norm_dimensions at module level, and --manifest has a help string. - R2-12 the four dispatch tests become two, parametrized over both norms. The fallback test now resolves for device="cuda", so the CUDA-first list is walked even on a CPU-only host, and asserts the CUDA backend was tried and failed. Before, on CPU, get_op went through the CPU list and the assertion held trivially. - R2-13 SPDX headers on rl_engine/testing/qwen3_next_workload.py and tests/test_qwen3_next_workload.py. - R2-14 ci/run_ws1_gtest.sh runs the four Qwen3-Next gates as explicit commands instead of a loop over interpolated script names. - R2-16 Qwen3NextRMSNormGatedCudaOp takes activation as a constructor argument, validated before the extension check; the test no longer mutates the instance. "swish" stays as vLLM's alias for "silu", now asserted bitwise on CUDA. - R2-17 the Python dgate path adds weight_offset only when it is nonzero, like the kernels, so a -0.0 weight keeps its sign in dgate. New tests: activation rejected at construction (CPU), swish == silu (CUDA), dgate signed zero (CUDA, 3 dtypes). Checked on CPU that the fallback and constructor tests fail with the behaviour removed. The CUDA tests and the csrc change are not yet run on a GPU. Deferred: R2-6 (shared sum-of-squares device function), R2-10 (private validator imports), R2-11. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
… guard The six rmsnorm launchers compiled on both CUDA and ROCm (plain forward and dx, gated forward and dx, partial and reduce dweight) constructed a non-optional c10::cuda::CUDAGuard. No other file the ROCm build compiles uses that class outside a USE_ROCM guard, while activation.cu and deterministic_attention.cu use at::cuda::OptionalCUDAGuard. Match the guard used by activation.cu so the ROCm build relies only on patterns it already compiles. The guard follows the same tensor as before (x, or partial_dw for the reduce launcher). CUDA behaviour is unchanged: these tensors are always on a CUDA device, so the optional guard always sets the device. The left-fold launcher sits inside the USE_ROCM guard and keeps its c10::cuda::CUDAGuard. The ROCm build is still untested. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…th the stacked C1 branch Carried over from the former merge of the C1 branch into this one: the decoder-norm page again states the registration and the check_operator command, the gated tests use the contract's reduction tolerances through _forward_tol, and the reference docstrings name the gated page. Same final tree as before the re-stack. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
The trainer-side reference for a Qwen3-Next rollout decode step: the recurrent
Gated DeltaNet update and the causal-conv1d state update that precedes it.
Claim level: L0 repeatable and L1 batch-invariant; L2 is not claimed.
Design notes, measurements and the deferral list are in
docs/design/ws1-c6-428-gdn-recurrent-replay.md.
Provider
--------
`fused_recurrent_gated_delta_rule_packed_decode`, not
`fused_sigmoid_gating_delta_rule_update`. A decode-only, non-speculative batch
returns early at qwen_gdn_linear_attn.py:1295-1307 into the packed path because
VLLM_ENABLE_FLA_PACKED_RECURRENT_DECODE defaults to true; the sigmoid-gating
kernel is the fallback for mixed prefill+decode and spec decode. The env
defaults that decide this are asserted, so a vLLM bump fails loudly.
Three places the kernel and the HF model disagree, transcribed from the kernel:
* The gating is fused -- beta = sigmoid(b) and
g = -exp(A_log) * softplus(a + dt_bias) are computed in fp32 inside the
kernel with a threshold branch at 20, not as separate PyTorch ops.
* No repeat_interleave: the kernel indexes i_h = i_hv // (HV // H).
* The QK norm is an L2 norm over a plain sum, x / sqrt(sum(x*x) + 1e-6),
dividing by sqrt rather than multiplying by rsqrt. scale hits q after the
norm; k is never scaled.
A fourth candidate divergence turned out not to be one: prefill passes
use_qk_l2norm_in_kernel=False only because fused_post_conv_prep(apply_l2norm=True)
already normalized q/k. Both paths normalize exactly once.
Conv accumulation order is load-bearing: taps accumulate sequentially,
acc = acc + win[t] * w[t] from zero. With an fp32 cache that reproduces the
provider bitwise; a tree sum over the same four terms does not, and neither
does an FMA.
State ABI is mirrored: paged [num_blocks, HV, V, K] and
[num_blocks, dim, width-1], NULL_BLOCK_ID (<= 0) skipping, an fp32 accumulator,
and a store that rounds to the cache dtype -- fp32 or bf16, per
FUSED_GDN_STATE_DTYPES. Contractions run in the repo's fixed 32-wide chunk order
via one `_chunked_sum` primitive, which is what the L1 claim rests on.
Agreement, B200, Qwen3-Next dims (H=16, HV=32, K=V=128)
-------------------------------------------------------
recurrent, fp32 state, B=1..64 max|d out| 1.5e-08..6.1e-05, state <= 3.0e-07
recurrent, bf16 state, B=1..64 max|d out| 3.7e-09..3.1e-05, state <= 2.0e-03
conv: the rolled state is BITWISE exact in every configuration
conv, fp32 cache: output bitwise but for 15 of 524288 elements at B=64
conv, bf16 cache: output agrees on ~63%, each disagreement one bf16 ULP
L1: a sequence's output and state block are bitwise identical alone or at any
position in a batch of 64, for both state dtypes
Where the provider rounds in the bf16-conv-cache case is not reproduced;
recorded as an open gap rather than guessed at.
Decode versus chunked prefill
-----------------------------
T=8 max|d| 3.66e-04 fp32 state / 5.49e-04 bf16 (7.0e-03 / 1.0e-02 rel)
T=64 4.88e-04 / 5.49e-04 (6.6e-03 / 7.5e-03 rel)
T=256 3.66e-04 / 3.66e-04 (5.0e-03 / 5.0e-03 rel)
~0.5-1% relative and flat in T. The recurrence is contracting -- per-step decay
exp(g) averages ~0.47 -- so old rounding error is forgotten at roughly the rate
old signal is, and the fp32-vs-bf16 state drift saturates at ~2% relative on the
state (2.67e-03 at step 1, 1.52e-02 at 64, 2.47e-02 at 1024: a 16x longer run
past step 64 grows it 1.6x).
So the two paths are not bitwise, and ~1% relative on logits is still material
for RL importance ratios, but this is a bounded error rather than a divergence.
An fp32 recurrent state remains the right choice; the reason is the plateau, not
a blow-up. Separately, the chunked prefill kernel refuses fp32 q/k/v outright
(chunk.py:213), and causal_conv1d_update does not bounds-check
conv_state_indices under its default validate_data=False.
tests/check_gdn_recurrent_golden.py is named check_ rather than test_, following
tests/distributed/check_*.py: it imports real vLLM, and
tests/test_framework_operator_integrations.py asserts vllm is absent from
sys.modules.
pytest tests/check_gdn_recurrent_golden.py -q
pytest tests/ rl_engine/tests/ -q -p no:randomly \
--ignore=tests/test_rocm_aiter_api_contract.py
The new file passes and the full suite gains no failure.
Absolute suite counts are reported in the PR description against a named base
commit, not here: they shift whenever a sibling test is added, so a count frozen
in a commit message goes stale the moment the branch is rebased.
Not covered, deliberately: speculative decode and MTP, a backward for the
recurrence, and the provider bridge. Reasons in the design doc.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…touched test_null_block_id_is_skipped_by_both checked that both sides write zeros for a NULL_BLOCK_ID row, but asserted "block untouched" only for the golden. The provider returns before any store for state_idx <= 0 (fused_recurrent.py:299-303 in vllm 0.30.0); assert it on the provider's state as well so the test name matches what it checks. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
ws1-gtest-gpu.yml: 158fc75 added four Qwen3-Next/GDN test files to the pull_request and push path filters together with a step in ci/run_ws1_gtest.sh that ran the two check_ files. 03176a3 moved that step to qwen3-next-provider-gpu.yml but left the filters, so editing those files triggered a RunPod job that never executed them. Remove the filters; the file is back to its state on feat/cuda-qwen3-next-gated-rmsnorm. qwen3-next-provider-gpu.yml: drop rl_engine/integrations/qwen3_next_gdn.py and ci/run_qwen3_next_operator_gates.sh from the path filter. Neither file exists on this branch. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
The design note quoted provider-vs-golden figures (max|d out|, max|d state|, conv mismatch counts) that no committed code printed, so they could not be reproduced. scripts/ws1_gdn_provider_agreement.py prints them as JSON, using tests/check_gdn_recurrent_golden.py's own input helpers and seeds (imported, not copied): - recurrent: per (batch, state dtype), max|d out|, max|d state| and bitwise mismatch counts, next to the bounds the check file asserts; - conv: per (batch, cache dtype), output mismatch count, max|diff| and whether the rolled state is bitwise equal; - the conv comparison again with triton.knobs.language.default_fp_fusion off, plus the fma.rn.f32 count in each compiled variant of the provider's conv-update kernel, to test whether the fp32-cache mismatches are FMA contraction. Provenance (git commit and dirty flag, torch/triton/vllm versions, device) is recorded with the results. Requires CUDA and vLLM; not run in CI. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Review after 03176a3 found nine statements in the note that the source does not support. Fix them: - §1: the decode path is chosen per engine step and by the model, not by env defaults alone. Tabulate decode-only / mixed / draft-token steps with their conv and recurrent kernels. Qwen3-Next's gqa_interleaved_layout=True makes the fused CUDA decode unsupported, so the layer falls back to triton and fused_gdn_decode_post_conv_mtp is unreachable (:1834). The env-default assertion guards the packed-decode default but not VLLM_GDN_DECODE_KERNEL. - §2: the golden follows the kernel's arithmetic but is not a transcription everywhere: softplus uses log1p where the kernel uses log(1 + exp), and FLA_USE_FAST_OPS swaps in fast exp/log. - §3: split NULL_BLOCK_ID semantics. The recurrent provider skips <= 0 and writes zeros; the conv provider skips only == null_block_id (0) and does not write the output row; both goldens skip <= 0 and write zeros. - §4: replace the "measured" table, which had no runner, with the bounds the check file asserts and point to scripts/ws1_gdn_provider_agreement.py. State the fp32-cache conv mismatch and the FMA hypothesis the runner tests. - §5: add the mixed decode-and-prefill caveat on the provider side, including vllm-project/vllm#49827, and state that validate_data=True does not check index values either. - §6: restate the MTP deferral against RFC RL-Align#428 §2.2 and the actual Qwen3-Next MTP path. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
_git() returned the string "unavailable: ..." on failure, so
bool(_git("status", "--porcelain")) turned "git missing / not a checkout"
into git_dirty=True. Return None on failure and record git_dirty and
git_commit as null in that case, so the provenance distinguishes unknown
from dirty.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
The §6 row deferred backward because "a naive BPTT through a sequential recurrence is not batch-invariant". RFC RL-Align#428 does not ask backward to be: §2.2 item 4 says backward need not match rollout, only be correct for the replayed forward, and §9.1 makes the GDN backward its own work item, C7. Cite those instead, matching the PR description. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
A run of the runner at d66e595 showed both of its FMA probes were ineffective: - The fusion-off arm reused the fused kernel. Triton 3.7.1 keys its in-memory kernel cache on the launch kwargs; the default_fp_fusion knob is read in parse_options only after a cache miss. Flipping the knob in-process never missed, so all six recorded variants had enable_fp_fusion=True and the "off" counts were the "on" counts again. Run the fusion-off arm in a child process with TRITON_DEFAULT_FP_FUSION=0 and a private TRITON_CACHE_DIR instead. - fma.rn.f32 = 0 in the PTX does not rule out FMA. With fusion on, Triton emits plain mul.f32/add.f32 and lets ptxas contract them into FFMA; with fusion off it passes --fmad=false. Count plain and .rn PTX ops, and FFMA/FMUL/FADD in the SASS (Triton's bundled cuobjdump). Report per-arm kernel variants under conv_kernels and add fusion_check, which says whether the fusion-off variants were actually compiled without fusion, so a silent no-op cannot pass for a result again. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
The runner on d66e595 (B200, clean checkout) reproduced the recurrent figures the note had withdrawn for lack of a runner, so restore them as a per-batch table with their provenance and bitwise output-mismatch counts. The conv counts quoted in test_conv_output_matches_provider_with_fp32_cache's docstring (1 of 8192 at B=1, 15 of 524288 at B=64) predate the product- rounding fix; the runner measures 0, 0, 1 and 5 for B = 1, 4, 17, 64. Replace them, and record the bf16-cache counts in the note. State plainly that the FMA explanation for the fp32-cache mismatches is not determined: that run's fusion-off arm reused the fused kernel, and a PTX without fma.rn.f32 cannot exclude ptxas contraction. The runner now tests both (previous commit). Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…tches The fixed runner (b54e50f) on 7622616, B200, settles the FMA hypothesis left open in §4. Its fusion-off arm took effect: all six variants compiled with enable_fp_fusion=False, and their PTX uses only .rn-qualified f32 mul/add, which ptxas may not contract. With contraction verifiably off, the fp32-cache mismatch counts (0, 0, 1, 5 for B = 1, 4, 17, 64) and max|diff| are identical to the fused run, and neither arm's SASS has an FFMA. So FP contraction is ruled out; the actual cause stays open. The note bases the conclusion on the A/B outcome, not on the opcode counts: each variant's PTX has only two f32 multiplies, too few to be the four tap products, so the counters do not show where the products are computed. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
§5 said a mixed decode-and-prefill step leaves the decode row's bf16 output equal. That holds only at TP1 head counts (H=16, HV=32). At TP4 per-rank head counts (H=4, HV=8) the same step already moves one output element (4.8e-05 relative) and 32,663 of 131,072 state elements (1.7e-07), with the same numbers for all three prefill-bearing compositions. Tabulate both head counts and state the measurement's limits: one standalone layer from config.json, synthetic parameters and cache, single process with no engine, scheduler or CUDA graph, metadata built by the test. The 4-of-20 propagation figure is TP1-only and does not carry over to real weights. Conv output and state matched bitwise, so the difference is in the recurrent kernels. vllm-project/vllm#49827's two commits on 0.30.0 close the decode-row gap at both head counts (scheduler part untested); with packed decode disabled the decode row is bitwise equal in every composition. Source: the GDN evidence branch's committed results (not yet published). Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…rouped expert GEMM RFC RL-Align#428 moe_route_combine_contract (CUDA). - Routing: FP32 router through the pinned vLLM batch-invariant GEMM, a fixed-order softmax, stable descending argsort (ties go to the lower expert id) and FP32 renormalisation of the ten selected weights. - Experts: per-route gate_up and down projections through vLLM 0.30.0's fused_moe Triton kernel with the batch-invariant tile passed explicitly (BLOCK 64/64/32, SPLIT_K=1); no routed-weight multiply and no sum inside the kernel, so every (token, route) row has one writer. Bitwise equal to the previous per-expert GEMM loop at TP4 and TP1 widths; fails closed if VLLM_TRITON_USE_TD is enabled. - Combine: ten ordered FP32 multiply-adds and one BF16 cast; no atomics. - Backward: per-expert pinned GEMMs into one dense gradient buffer; dx is summed in ascending expert order. - TP4 block: HF load/export for all 512 experts, replicated router and shared-expert gate, Megatron-style copy/reduce autograd boundaries moved into qwen3_next_tp.py. - Prior-art runner and plot comparing HF transformers, vLLM fused_moe (BI=0/1) and FlashInfer cutlass_fused_moe; TP4 real-weight gate script. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Prep for RFC RL-Align#428 full_model_chain; serves the gdn_state_bridge row. - GDN: packed recurrent prefill/decode through the pinned vLLM 0.30.0 provider with an explicit state boundary (qwen3_next_gdn.py), the depthwise causal convolution with cache handoff (qwen3_next_conv.py), their combined shared core (qwen3_next_provider.py) and the TP4 block with HF GQA-interleaved weight mapping (qwen3_next_tp_gdn.py), now on the shared TP boundaries in qwen3_next_tp.py. - Attention: the deterministic CUDA attention kernel accepts head dim 256 (ROCm stays 128) and validates backward inputs; shared_attention and TP4FullAttention with the KV head replicated on each rank pair, partial RoPE at absolute positions and explicit per-slot KV state. - Real-weight TP4 gates for GDN (layer 0) and attention (layer 3), sharing scripts/qwen3_next_gate_common.py with the MoE gate. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…nce (B200, 2e950f3) Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Compares RL-Kernel's deterministic attention with torch SDPA, vLLM FlashAttention-2 (num_splits auto and 1), vLLM's Triton unified attention and FlashInfer's ragged prefill: batch, chunked-prefill and decode invariance, backward repeatability, accuracy against FP64 and latency. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…nce (B200, 9e6c4f2/6517077) Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…r-art runner - Megatron-core 0.16 MoELayer with Transformer Engine grouped GEMM and fused permute, configured as VIME runs Qwen3-Next (FP32 softmax router, top-10, all-to-all dispatcher), including its backward. - SGLang 0.5.21 Triton fused_moe in default and deterministic-inference mode. sgl_kernel cannot load next to torch 2.13, so its two kernels on this path are replaced by SGLang's own Triton moe_sum_reduce and JIT moe_align_block_size; any other sgl_kernel call fails loudly. - --only and --merge combine reports from different Python environments. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…ured (B200, c58816b) Every library in the reuse table is now measured: Megatron-core 0.16 + TE 2.16 as VIME configures Qwen3-Next, and SGLang 0.5.21 in default and deterministic-inference mode. The TP4 gate evidence is unchanged (2e950f3, identical MoE code). Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…gatron-core attention Add three engines to the attention prior-art runner: - FlashAttention-4 (CuTe DSL) varlen as vendored by vLLM, forward and backward. - Transformer Engine DotProductAttention on THD-packed sequences, in inference mode (cuDNN fused) and in training mode (on SM100 TE has no fused head-dim-256 backward and falls back to its unfused path). - Megatron-core's local DotProductAttention, one launch per sequence. The backward check now compares the target's dq/dk/dv alone against the same sequence packed first and last, so engines with a packed backward are checked for batch invariance rather than repeatability. A prefill/decode crash is recorded per candidate without skipping its other checks. --only and --merge combine reports from different Python environments. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
The decode and forward+backward latency sections are keyed by candidate directly, the prefill section by size first; merge them at any depth. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…egatron-core measured (B200, 703c6dc) Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
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…p as E402-exempt The gate scripts insert the repo root into sys.path before importing rl_engine and tools, which flake8 reports as E402. Mark those imports with noqa, as the other tools/ scripts do. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…p as E402-exempt The gate scripts insert the repo root into sys.path before importing rl_engine and tools, which flake8 reports as E402. Mark those imports with noqa, as the other tools/ scripts do. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Annotate the latency samples and accept the default-argument lambdas used as timed calls. No behaviour change. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
… runner Annotate the latency samples and accept the default-argument lambdas used as timed calls. No behaviour change. Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
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Repository refactor update
Merged
RL-Align/RL-Kernel:test-qwennextata5a92ecf6cbf29fdcfc862846e53db5fa244c35eas requested in RFC #428. This PR still targetstest-qwennext.Implementation changes now live in the canonical
rl_engine/backends,rl_engine/reference,rl_engine/runtime, andrl_engine/validationdirectories. Where present, model assembly and vLLM training bridges userl_engine/models/qwen3_nextandrl_engine/integrations/engines/train/vllm, respectively. Tests usetests/models/qwen3_next; evidence and validation commands usetools/validation/modelsandtools/validation/operators. Existing upstream compatibility entry points and pinned operator identifiers are preserved. CI commands and path filters follow the new layout, including thetest-qwennexttarget.Validation after migration: 366 passed in the relevant regression suites. CUDA tests used extensions built from the matching migrated native sources on B200. Syntax, undefined-name/duplicate-import checks, formatting, and
git diff --checkpassed.Additional checks: layout/packaging compatibility 32 passed, 1 skipped; workload/registry/validation compatibility 76 passed; both norms passed the canonical C3 forward and C4 gradient invariance gates. The provider suite passed 113 tests with one GPU; its mandatory cross-device test also passed when rerun with both B200 GPUs available (114 passed across the two runs). The historical four-GPU TP4 measurements below were not rerun during this migration (this host has two GPUs).
Status
What
qwen3_next_gdn.py,qwen3_next_conv.py,qwen3_next_provider.py): packed recurrent prefill and decode through the pinned vLLM 0.30.0 provider, the depthwise causal convolution with its cache handoff, and one shared core whose prompt state crosses an explicit boundary into decode and into the trainer's gradient path.TP4GDN(qwen3_next_tp_gdn.py): HF GQA-interleaved weight mapping per rank, explicitGDNState, Megatron-style TP boundaries fromqwen3_next_tp.py.csrc/cuda/attention/deterministic_attention.cuaccepts head dim 256 on CUDA (ROCm stays 128) and validates backward inputs;shared_attentionandTP4FullAttentionwith the KV head replicated on each rank pair, partial RoPE (64 of 256 dims) at absolute positions and per-slot KV state.scripts/qwen3_next_tp_{gdn,attention,moe}_check.pyon a sharedscripts/qwen3_next_gate_common.py.Prior art & reuse decision
GDN: reused as surveyed in #469 (the decode step runs vLLM's own FLA kernels; RL-Kernel supplies the state boundary and the golden). Attention, measured here (one TP4 rank: 4 Q heads, 1 KV head, D = 256, causal):
num_splits=1num_splitsautoDotProductAttention¹ Default backward; its
deterministic=Truebackward asserts for head dim 256 on SM100.² TE's unfused THD path raises a shape error when the query is shorter than the KV.
³ No packed-sequence path: one launch per sequence, so batch invariance is by construction.
Decision: keep RL-Kernel's deterministic attention on both sides in this PR. It is the only candidate here that is RL-Kernel-owned, covers forward and backward with one documented reduction order, and already backs the WS1 attention contract; train and rollout must run the same kernel. FlashAttention-4 is the strongest reuse candidate for the follow-up on the slow forward: batch-invariant forward and packed backward, and 30–500× faster at 2048–8192 tokens. Adopting it means pinning vLLM's vendored CuTe build on both sides and accepting a backward that has no deterministic mode at this head dim; both are out of scope here. TE fused is invariant for inference only; Megatron-core's local path only by launching per sequence.
Results (4 × B200, official checkpoint)
Evidence (rank JSONs, attention report and figure, README with the exact commands) is in
docs/usage/evidence/qwen3-next-tp4-mixers-b200/, measured from clean clones of9e6c4f2(gates) and703c6dc(attention runner).Tests
python setup.py build_ext --inplace python -m pytest -q tests/test_qwen3_next_tp_gdn.py tests/test_qwen3_next_tp_blocks.py tests/test_qwen3_next_forward_contract.py TRITON_F32_DEFAULT=ieee python -m pytest -q tests/check_qwen3_next_{attention,gdn_bridge,gdn_sequence,conv_bridge,core_matrix,shared_core,forward}.py torchrun --nproc-per-node 4 scripts/qwen3_next_tp_gdn_check.py --checkpoint <ckpt> --output <dir> torchrun --nproc-per-node 4 scripts/qwen3_next_tp_attention_check.py --checkpoint <ckpt> --output <dir> python scripts/qwen3_next_attention_prior_art.py --only <all but te_fused> --out <dir>/report-main.json python scripts/qwen3_next_attention_prior_art.py --only te_fused --merge <dir>/report-main.json --out <dir>/report.jsontest_attention_dispatch, also onmainThe
check_files import vLLM and run in theQwen3-Next-provider-GPUworkflow;test_qwen3_next_tp_gdn.pyjoinsci.yml.Scope
CUDA, TP4. ROCm keeps D = 128. No engine adapter or model-level claim; those are
full_model_chain.