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Fix q/k/v quantizer wiring in register_attention_for_kv_quant - #2618
register_attention_for_kv_quant collected the two matmul nodes with breadth-first ast.walk and patched them by list index, which assumes the attn @ v node comes first. Within one method that only holds when the score matmul is nested deeper in the AST than the output matmul. In a flat layout (scores = q @ k.T, probs = softmax(scores), probs @ v) the score matmul comes first, so v_bmm_quantizer was applied to k, k_bmm_quantizer to v and q_bmm_quantizer to the softmax output. The cause is the node order, not the transpose wrapper the issue's triage points at.
The nodes are now collected in source order (children before parents), which puts the score matmul first when both matmuls are in one method, and the two index pairs are swapped. The transpose wrapper on k and the quantizer-to-operand design are unchanged.
baddbmm is no longer matched. patch wires args[0] and args[1], but torch.baddbmm(input, batch1, batch2) takes the additive input first, so the q quantizer landed on the bias and k was never quantized (GPTNeoXJapaneseAttention), and keyword calls raised IndexError (BloomAttention). Those classes now return False and get the existing warning to register a custom quantized attention class.
Flat-layout attention classes will calibrate differently after this change. For them the k and v scales that export reads (get_kv_cache_scaling_factor) were swapped, so models calibrated or exported with the old wiring need to be recalibrated and re-exported. In the modeling_<model>.py modules of transformers 5.14.1 that have no ALL_ATTENTION_FUNCTIONS, 72 of the 178 *Attention classes other than T5Attention are accepted by this function; the generated code changes for 65 and is byte-identical for 7.
Usage
No API change.
Testing
New test_kv_quant_operand_wiring in tests/unit/torch/quantization/plugins/test_attention_quant.py: flat and nested layouts for torch.matmul, torch.bmm and @, with q, k and v of different scales, asserting that each quantizer's amax equals the abs-max of its own operand. On the base commit the three flat cases fail and the three nested cases pass; with the fix all six pass.
New test_kv_quant_rejects_baddbmm: positional and keyword baddbmm scores. Before the change the first registers with the wrong wiring and the second raises IndexError; with it both return False. On transformers 5.18, BloomAttention goes from IndexError to False and GPTNeoXJapaneseAttention from True to False; no other class that reaches this fallback calls baddbmm.
17 real classes (15 changed, 2 unchanged): the operands of the two matmul calls recorded from the original forward and compared with the calibrated amax. On the base the 15 changed ones are miswired (BarkSelfAttention and MptAttention, both nested, are correct); with the fix all 17 are correct. For example MegatronBertSelfAttention:
pre-commit run --all-files: every hook passes except generate-arguments-md, which cannot import the Hugging Face plugin on torch 2.14 here (CI pins torch<2.14 for that job); running its generator directly leaves examples/llm_qat/ARGUMENTS.md unchanged.
Not run: tests/gpu (no GPU). tests/gpu/torch/quantization/test_hadamard.py is the only GPU test that calls this function directly and it uses an SDPA-only class, which this change does not touch.
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Is this change backward compatible?: ❌ Flat-layout attention classes calibrate differently, as described above. A class that defines the score matmul in a helper placed after forward can change from correct to wrong (see Additional Information). BloomAttention and GPTNeoXJapaneseAttention no longer get KV-cache quantizers from this fallback.
If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
Not covered: real baddbmm support (matrix operands at args[1] and args[2], keyword arguments), and classes whose two matmuls sit in different methods, which are ordered by source position instead of call order. None of the 71 classes with two matmuls in the modeling_<model>.py modules of transformers 5.14.1 does this, but a helper defined after forward that was wired correctly by accident before would now be miswired.
Summary by CodeRabbit
Bug Fixes
Corrected query, key, and value quantizer assignments for supported attention layouts using fallback quantization, including separated and nested matrix multiplication, batch matrix multiplication, and @ operations. Quantizers now track their corresponding inputs.
Attention implementations using torch.baddbmm are no longer patched by the fallback and receive a warning to register a custom quantized attention class. Models quantized with the affected fallback must be recalibrated and re-exported. The SDPA quantization path is unchanged.
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Reviewing files that changed from the base of the PR and between 91a7aae and f8ad6a9.
Files selected for processing: 1
modelopt/torch/quantization/plugins/attention.py
Files skipped from review as they are similar to previous changes: 1
modelopt/torch/quantization/plugins/attention.py
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Walkthrough
The attention quantization plugin now assigns Q/K and V quantizers to the intended matched operations. It recognizes matmul and bmm, but not baddbmm. Tests verify operand calibration and registration behavior.
Changes: Attention KV quantization
Layer / File(s)
Summary
AST matching and quantizer assignment modelopt/torch/quantization/plugins/attention.py
The plugin traverses AST nodes in post-order and matches matmul and bmm operations. For matched operation pairs, it assigns Q/K quantizers to the first operation and the V quantizer to the second. It no longer recognizes baddbmm.
Operand mapping validation and changelog tests/unit/torch/quantization/plugins/test_attention_quant.py, CHANGELOG.rst
Tests check calibrated quantizer maxima against the corresponding operands for six attention implementations and verify rejection of positional and keyword baddbmm forms. The changelog advises recalibration and re-export for affected models.
Some attention classes can still register with incorrect quantizer assignments, producing incorrect calibration. Reject these unsupported layouts or verify their operand roles before merging unless the risk is explicitly accepted.
Pre-merge checks: 5 · 1
Failed checks: 1 warning
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Passed checks: 5 passed
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Title check
The title clearly and concisely identifies the main change: correcting q/k/v quantizer wiring in register_attention_for_kv_quant.
Linked Issues check
Issue #1064 asks for correct q/k/v quantizer wiring in the simple attention pattern. register_attention_for_kv_quant now collects supported matmul nodes in child-first source order and applies the q…
Out of Scope Changes check
The changes remain within Issue #1064. Excluding unsupported baddbmm prevents incorrect wiring and is covered by focused regression tests. The changelog documents the related recalibration impact. N…
Security Anti-Patterns
PASS. The reviewed Python diff only changes attention AST traversal/quantizer wiring and adds tests. It adds no torch.load(..., weights_only=False), numpy.load(..., allow_pickle=True), hardcoded trust…
Full details: Linked Issues check
Explanation
Issue #1064 asks for correct q/k/v quantizer wiring in the simple attention pattern. register_attention_for_kv_quant now collects supported matmul nodes in child-first source order and applies the quantizers to the correct score and value operations. The new tests cover flat and nested torch.matmul, torch.bmm, and @ forms with distinct operand scales. The reported unit tests pass.
Full details: Out of Scope Changes check
Explanation
The changes remain within Issue #1064. Excluding unsupported baddbmm prevents incorrect wiring and is covered by focused regression tests. The changelog documents the related recalibration impact. No unrelated implementation is identified.
Full details: Security Anti-Patterns
Explanation
PASS. The reviewed Python diff only changes attention AST traversal/quantizer wiring and adds tests. It adds no torch.load(..., weights_only=False), numpy.load(..., allow_pickle=True), hardcoded trust_remote_code=True, eval()/exec() on external input, or # nosec comment. No dependency files changed.
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Inline comments:
Review comments at @modelopt/torch/quantization/plugins/attention.py:
- Around line 195-196: Update the attention matmul discovery and patching logic
around bmm_nodes so it identifies which operation computes scores and which
computes values across methods, rather than relying on source order; if that
relationship cannot be established, reject the class before registering
quantizers. Apply the same operand-role validation to the @ branch.
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The AST patcher collected the two matmul nodes with breadth-first ast.walk
and patched them by list index, so which quantizer landed on which operand
depended on AST nesting depth. A flat layout (scores = q @ k.T, probs =
softmax(scores), probs @ v) got k_bmm_quantizer on v, v_bmm_quantizer on k
and q_bmm_quantizer on the softmax output.
Collect the nodes in source order, children before parents, and swap the
two index pairs. The q/k score matmul is expected to be written before the
matmul that consumes its result. In the modeling_<model>.py modules of
transformers 5.14.1 the generated code changes for 65 of the 72 accepted
classes and is byte-identical for the other 7. A class that defines the
score matmul in a helper placed after forward can go from correct to
wrong, because the order is source position, not call order.
Signed-off-by: Javier de Jesus <javier.dejesusj9@gmail.com>
Thanks for the contribution! This PR adds its CHANGELOG.rst entry under the older 0.48.0 section. New entries on main go in the 0.49.0 section at the top of the file. Could you move the entry there when you update your branch with the latest main?
We've added the waiting for feedback label while we wait for your update.
@kevalmorabia97 Rebased onto current main. The only conflict was in CHANGELOG.rst, and since release/0.48.0 is already cut, the entry now sits under 0.49.0 Bug Fixes. The code is unchanged.
Re-checked on the new base: test_kv_quant_operand_wiring still fails for the three flat layouts on main and passes for all six with the fix, test_attention_quant.py passes 20/20, and ruff is clean. The CodeRabbit thread about matmuls split across methods is resolved as a follow-up (listed under "Not covered" in the description). Ready for another look.
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Approving: this is a small, correct fix. The new post-order AST walk puts the score matmul first in both flat and nested layouts, and the new parametrized test checks that each quantizer calibrates on its own operand.
No action needed:
walk_post_order visits children before parents, in source order. In the nested form, the inner q @ kᵀ call is returned before the outer · @ v. In the flat form, the score statement comes before the output statement. So index 0 is always the q/k matmul, and the swapped patch/patch_binop indices are right.
Behavior change: flat-layout classes now calibrate differently, so existing calibrations need redoing. The CHANGELOG entry tells users to recalibrate and re-export.
Known gaps, called out in the PR body:
baddbmm operand indexing is still wrong.
When the two matmuls sit in different methods, they're ordered by where they appear in the source, not by call order.
The test uses different scales for q, k and v (1×, 10×, 100×), so any swap shows up as a wrong amax. Existing tests are unchanged.
No licensing changes. The file's existing third-party Apache header isn't touched.
✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 79.18%. Comparing base (e19ba61) to head (f8ad6a9). ⚠️ Report is 13 commits behind head on main.
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Inline comments:
Review comments at @modelopt/torch/quantization/plugins/attention.py:
- Around line 195-204: Update the BMM matching logic in is_bmm to exclude
baddbmm, since patch assumes the first two arguments are the matrix operands.
Leave baddbmm unsupported so classes matching only baddbmm continue through the
existing count guard and return False.
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patch() wires node.args[0] and node.args[1], but torch.baddbmm(input,
batch1, batch2) takes the additive input first. A positional call put
q_bmm_quantizer on the bias and k_bmm_quantizer on q, leaving k
unquantized (GPTNeoXJapaneseAttention), and a keyword call raised
IndexError (BloomAttention).
Drop baddbmm from the matched ops so these classes fail the existing
count guard and get the warning to register a custom quantized
attention class. Correct baddbmm support needs operand offsets and
keyword handling in patch(), which is left for separate work.
Signed-off-by: javierdejesusda <javier.dejesusj9@gmail.com>
@kevalmorabia97 Addressed the new CodeRabbit comment in 91a7aae. baddbmm is no longer matched, because its operands were always wired wrong (or raised IndexError for Bloom's keyword call), so those classes now get the existing warning instead. Added test_kv_quant_rejects_baddbmm and updated the changelog entry and the description.
The GPU and example runs on 873ba48 were cancelled during setup, before any tests ran, so they need a fresh /ok to test. Unit, code-quality and regression checks passed.
The comment only described the torch.baddbmm(input, batch1, batch2) form. Bloom calls alibi.baddbmm(batch1=..., batch2=...) with keywords, so an args offset alone would not fix it.
Signed-off-by: javierdejesusda <javier.dejesusj9@gmail.com>
🪄 Fix comments: Fix CodeRabbit comments on this PR
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only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @modelopt/torch/quantization/plugins/attention.py:
- Around line 55-56: Update the attention class validation that uses bmm_ops so
it separately detects baddbmm nodes and returns False whenever any are present,
even if the class also has two matmul or bmm calls. Add a regression test for a
class containing both baddbmm and recognized operations.
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CHANGELOG.rst
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…CUDA crashes (#3059)
### What does this PR do?
Type of change: test
Reruns a test once when it fails with a known transient error, using one
mechanism for two kinds of flakiness:
- **Blackwell CUDA crashes in `gpu_megatron`.** These tests were skipped
on Blackwell; they now run again. This also covers
`test_dsa_kv_cache_quant`, which was failing unrelated PRs.
- **Hugging Face Hub, network and port-collision errors in every test
group.** Before, only example commands run through `run_example_command`
retried these, with their own loop. GPU tests that call
`from_pretrained`, `load_dataset` or `snapshot_download` directly had no
retry.
`tests/conftest.py` adds `@pytest.mark.flaky(reruns=1, reruns_delay=10,
only_rerun=[...])` from pytest-rerunfailures to every test. It reruns
only failures whose exception chain matches one of these:
- the Hub/network/port patterns, now shared in
`tests/_test_utils/transient_errors.py`;
- `TransientError`, which `run_example_command` now raises when a failed
command's output shows one of those patterns, instead of retrying
internally;
- on Blackwell, for `gpu_megatron` tests that use a worker pool only:
`illegal memory access`, `CUBLAS_STATUS_INTERNAL_ERROR`, or a worker
death where every dead rank exited with -6 (SIGABRT), the same crash
surfacing as an abort.
Any other failure fails with no rerun, including assertion errors and a
worker killed by OOM (-9). The same error twice in a row also fails.
**Why one mechanism.** Every retry is visible in the log, where the old
in-helper retry only printed a warning: a `RERUN` line for the attempt,
a count in the final line (`462 passed, 3 rerun`), and, with
`--rerun-show-tracebacks` now in `addopts`, a "rerun test summary info"
section with each rerun attempt's traceback. Without that flag nothing
showed why a test was rerun, since `-ra` and `--instafail` skip rerun
attempts. Each attempt gets its own timeout. The tradeoff: a transient
error in one step of a multi-step example test reruns the whole test,
not just that command.
**Matching fix.** For in-process example steps, the old retry searched
`traceback.format_exc()`, which includes the source lines of each frame,
so a pattern in code or a comment could trigger a retry. It now matches
the exception chain's messages and the step's printed output.
**Why a rerun works.** A failed job retires its worker pool, and the
function-scoped `dist_workers*` fixtures give the rerun a fresh one. The
crash depends on how many jobs a worker has run (see below), so a fresh
worker passes the test.
**Why all pool tests, not a marked list.** The crash lands on whichever
test reaches about the 13th job in a worker, so any `gpu_megatron` test
can hit it. A per-test marker would keep missing new victims. Tests that
run CUDA in the pytest process are left out: a crash there breaks the
process, and the rerun runs in the same one.
**Why bother.** Both GPU CI lanes run on RTX PRO 6000 (Blackwell), so
the 54 skipped test cases had no CI coverage. The skip reason also still
named the old nemo:26.06 stack, while CI now runs nemo:26.08.
**Tradeoff.** A real modelopt bug that causes an intermittent illegal
memory access would be hidden by the rerun. A deterministic one still
fails, since its rerun crashes too. The rerun count is in the final
summary line, with each error in the "rerun test summary info" section;
a rising count means a regression or a worse upstream stack.
Changes:
- **`tests/conftest.py`**: the rerun marker, replacing the
`flaky_cuda_crash_on_blackwell` marker and xfail hook from the earlier
commits of this PR.
- **`tests/_test_utils/transient_errors.py`**: the shared pattern list
(moved from `run_command.py`) and `TransientError`.
- **`run_example_command`**: raises `TransientError` instead of
retrying; the `hf_max_retries` and `hf_retry_delay_s` parameters are
gone.
- **`pyproject.toml`**: adds `pytest-rerunfailures` to `dev-test`, and
`--rerun-show-tracebacks` to `addopts`. `uv.lock` is left to the weekly
relock job.
- **The tests previously behind `skip_flaky_on_blackwell`**, plus
`test_dsa_kv_cache_quant`: no longer skipped.
- **`test_regular_state_dict[320]`**: stays skipped. It runs in the
pytest process itself, so a crash would break every later test.
- **`test_adapter_gradient_flow_freeze_lora_with_api`**: stays skipped.
Its crash is deterministic, so a rerun would not help.
- **`is_blackwell()`** in `tests/_test_utils/torch/misc.py` replaces
three copies of the same GPU check.
**New dependency license.** pytest-rerunfailures is MPL-2.0: weak
copyleft, applying only to its own files if modified and distributed. It
is used unmodified and only in the `dev-test` extra, and `tqdm`, a core
dependency, is already MPL-2.0 AND MIT. Matching along the exception
chain needs 16.7 (exception classes in `only_rerun` need 16.2); `~=16.7`
covers both. Per `CONTRIBUTING.md`, this still needs a check with the
setup codeowners.
**Root cause (still open; waiting for nemo:26.10).** The crash is not
from a recent commit. It happens on the Nth job a worker process runs,
whichever test that is:
- Repeating `test_dsa_kv_cache_quant` 40 times in one process on a 1-GPU
Blackwell runner crashes on exactly repetitions 13, 26 and 39 (each
crash replaces the worker). That held on every commit tried, back to
before #2618, #2981 and #2744.
- Repeating it on Ada never crashed (30/30).
- On 2 GPUs the crash hits the 15th job,
`test_homogeneous_sharded_state_dict[local-True-config5]`, which passes
alone. Skipping it moves the crash to the next case.
- With `CUDA_LAUNCH_BLOCKING=1`, the failing call is
`cublasGemmStridedBatchedEx` (bf16) returning
`CUBLAS_STATUS_INTERNAL_ERROR`. It comes from the `torch.einsum` in
Megatron's `absorbed_mla.py` (`qkv_up_proj_and_rope_apply`). The PEFT
test fails the same way in `cublasGemmEx`. cuBLAS is 13.5.1.
- With cuBLAS/cuBLASLt debug logging on, the crash did not happen (14/14
passed), so it is timing sensitive.
- Ruled out as the cause: destroying the NCCL groups in the worker
teardown, and Megatron's Gloo groups. GPU memory, live CUDA tensors and
process groups stay flat across jobs.
We will retest on the nemo:26.10 container and drop the rerun if the
crash is gone.
### Testing
- `tests/unit/test_example_run_command.py`, rewritten for
`TransientError`: passes in nemo:26.08. Its raising line has
`ConnectionError` in a comment, so the old traceback matching would fail
the non-transient case.
- Rerun check in nemo:26.08 on Ada, using a throwaway test file (not
committed):
- A Hub error wrapped in another exception was rerun once, then passed.
- An example step whose output showed "Too Many Requests" was rerun
once, then passed.
- A subprocess that kept printing "503 Server Error" was rerun once,
then failed with `TransientError`.
- An assertion error was not rerun.
- With `is_blackwell()` forced on: a CUDA crash that happens once was
rerun on a different worker and passed; one that happens every time was
rerun once, then failed.
- Collection with `is_blackwell()` forced on: all 502 `gpu_megatron`
tests get the marker, and the 241 worker-pool tests also get the CUDA
crash patterns.
- CI on a temp branch with the Blackwell rerun (before the worker-pool
filter and the Hub unification; runs 40588283353, 40588292684), 2 runs
of each job on Blackwell, all green:
| Job | Result per run | Reruns per run | Time |
|---|---|---|---|
| `gpu_megatron`, 1 GPU | 416 passed | 1 | 9-11 min |
| `gpu_megatron`, 2 GPUs, `--run-manual --run-nightly` | 462 passed | 3
| 12 min |
| `test_dsa_kv_cache_quant` ×40, 1 GPU | 40 passed | 0 | |
An earlier round without the -6 pattern had 2 of 6 jobs red from a
worker abort (and one from the unrelated `test_qkv_slicing_gqa_tp2`
race, which also fails on main).
- Not run locally: the example tests themselves; this PR's CI covers
them.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: ⚠️
`pytest-rerunfailures` is MPL-2.0 (test-only); justification above,
pending codeowner check
- Did you write any new necessary tests?: N/A
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A
- Did you get Claude approval on this PR?: N/A
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
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What does this PR do?
Type of change: Bug fix
register_attention_for_kv_quantcollected the two matmul nodes with breadth-firstast.walkand patched them by list index, which assumes theattn @ vnode comes first. Within one method that only holds when the score matmul is nested deeper in the AST than the output matmul. In a flat layout (scores = q @ k.T,probs = softmax(scores),probs @ v) the score matmul comes first, sov_bmm_quantizerwas applied to k,k_bmm_quantizerto v andq_bmm_quantizerto the softmax output. The cause is the node order, not the transpose wrapper the issue's triage points at.The nodes are now collected in source order (children before parents), which puts the score matmul first when both matmuls are in one method, and the two index pairs are swapped. The
transposewrapper on k and the quantizer-to-operand design are unchanged.baddbmmis no longer matched.patchwiresargs[0]andargs[1], buttorch.baddbmm(input, batch1, batch2)takes the additive input first, so the q quantizer landed on the bias and k was never quantized (GPTNeoXJapaneseAttention), and keyword calls raisedIndexError(BloomAttention). Those classes now returnFalseand get the existing warning to register a custom quantized attention class.Flat-layout attention classes will calibrate differently after this change. For them the k and v scales that export reads (
get_kv_cache_scaling_factor) were swapped, so models calibrated or exported with the old wiring need to be recalibrated and re-exported. In themodeling_<model>.pymodules of transformers 5.14.1 that have noALL_ATTENTION_FUNCTIONS, 72 of the 178*Attentionclasses other thanT5Attentionare accepted by this function; the generated code changes for 65 and is byte-identical for 7.Usage
No API change.
Testing
test_kv_quant_operand_wiringintests/unit/torch/quantization/plugins/test_attention_quant.py: flat and nested layouts fortorch.matmul,torch.bmmand@, with q, k and v of different scales, asserting that each quantizer'samaxequals the abs-max of its own operand. On the base commit the three flat cases fail and the three nested cases pass; with the fix all six pass.test_kv_quant_rejects_baddbmm: positional and keywordbaddbmmscores. Before the change the first registers with the wrong wiring and the second raisesIndexError; with it both returnFalse. On transformers 5.18,BloomAttentiongoes fromIndexErrortoFalseandGPTNeoXJapaneseAttentionfromTruetoFalse; no other class that reaches this fallback callsbaddbmm.amax. On the base the 15 changed ones are miswired (BarkSelfAttentionandMptAttention, both nested, are correct); with the fix all 17 are correct. For exampleMegatronBertSelfAttention:pytest tests/unit: 4375 passed, 28 skipped, 13 xfailed.pre-commit run --all-files: every hook passes exceptgenerate-arguments-md, which cannot import the Hugging Face plugin on torch 2.14 here (CI pinstorch<2.14for that job); running its generator directly leavesexamples/llm_qat/ARGUMENTS.mdunchanged.tests/gpu(no GPU).tests/gpu/torch/quantization/test_hadamard.pyis the only GPU test that calls this function directly and it uses an SDPA-only class, which this change does not touch.Before your PR is "Ready for review"
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trust_remote_code=True,torch.load(..., weights_only=False),pickle, etc.).forwardcan change from correct to wrong (see Additional Information).BloomAttentionandGPTNeoXJapaneseAttentionno longer get KV-cache quantizers from this fallback.CONTRIBUTING.md: N/AAdditional Information
Fixes #1064.
Not covered: real
baddbmmsupport (matrix operands atargs[1]andargs[2], keyword arguments), and classes whose two matmuls sit in different methods, which are ordered by source position instead of call order. None of the 71 classes with two matmuls in themodeling_<model>.pymodules of transformers 5.14.1 does this, but a helper defined afterforwardthat was wired correctly by accident before would now be miswired.Summary by CodeRabbit
@operations. Quantizers now track their corresponding inputs.torch.baddbmmare no longer patched by the fallback and receive a warning to register a custom quantized attention class. Models quantized with the affected fallback must be recalibrated and re-exported. The SDPA quantization path is unchanged.