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[OMNIML-6284] Add a PTQ scale-comparison tool and hf_ptq --deterministic - #2995
Makes PTQ reproducibility measurable and controllable:
python -m modelopt.torch.quantization.utils.scale_compare diffs the quantization scales (input_scale, weight_scale, weight_scale_2, weight_scale_inv, k_scale, v_scale, pre_quant_scale, quantizer _amax) of two exported safetensors checkpoints, or of any two {name: tensor} mappings, per scale kind. It reports how many tensors differ and the median and worst relative difference, and exits non-zero when a scale differs by more than --rtol / --atol (default 0).
hf_ptq.py --deterministic pins what a run depends on besides the model and data: it requires an explicit --batch_size (the default 0 probes the calibration batch size from free GPU memory, so another GPU or memory state silently calibrates with another batch size), seeds every RNG (hf_ptq.py seeded only random and numpy), sets CUBLAS_WORKSPACE_CONFIG and selects deterministic torch kernels (torch.use_deterministic_algorithms(True, warn_only=True): an op without one only warns).
What the measurements below show, so reviewers can judge the flag: on main, repeating a run is already bit-identical, with or without --deterministic; what moves the scales is the calibration batch size. The flag does not remove measured noise, it pins the remaining inputs (and costs some run time).
Related: the AWQ side of the batch-size dependence is a separate PR (awq_lite token weighting, linked in a comment).
frommodelopt.torch.quantization.utils.scale_compareimportcompare_scalesdiff=compare_scales(scales_a, scales_b, rtol=1e-2) # two {name: tensor} mappingsprint(diff.summary())
Testing
Setup: Qwen3-30B-A3B (bf16), recipe general/ptq/nvfp4_default-kv_fp8_cast, --calib_size 128 --calib_seq 512, default calibration data, GB200 GPUs, torch 2.11.0a0, transformers 5.13.0, one export per run, compared with the tool in this PR. "differing" counts the input_scale tensors (18624) outside the tolerance; median is over the differing tensors. weight_scale and weight_scale_2 were identical (0/18624) in every pair.
What a run depends on, on main (no --deterministic):
Pair
What differs
rtol 0: differing
median / max rel
rtol 1e-2: differing
A1 vs A2
nothing (repeat, same node)
0
0 / 0
0
A1 vs A2b
node
0
0 / 0
0
A1 vs A3
batch size 8 vs 1
1349 (7.24%)
7.35e-3 / 2.90e-2
663 (3.56%)
A1 vs A4
batch size 8 vs auto-probed (128)
1454 (7.81%)
1.02e-2 / 2.10e-1
918 (4.93%)
A1 vs A5
1 GPU vs 2-rank FSDP2, both batch size 8
4 (0.02%)
7.35e-3 / 9.43e-2
1
--deterministic (the flag and helper of this PR on top of the same code as A1 to A5):
4/18624 input_scale: the same tensors and magnitudes as A1 vs A5 (a 1-GPU vs FSDP2 effect, not a kernel the flag misses)
Run
Wall time
A1, A2, A2b (same config, no flag)
65.1 s, 52.3 s, 73.0 s
D1 (--deterministic, 1 GPU)
80.1 s
A5 / D5 (2-rank FSDP2)
50.5 s / 52.6 s
The wall time of three runs of the same configuration without the flag spans 52 to 73 s and overlaps D1 (80 s), so on this short job the cost on one GPU is somewhere between about 10% and 50%, and a few percent on two ranks; a longer calibration would resolve it better. No "does not have a deterministic implementation" warning appeared in the D1 or D5 logs and nothing raised.
Tool cost on two EP4 Qwen3-30B-A3B FP8 exports (18624 input_scale, 18624 weight_scale, 96 k/v scale tensors): 31 s, 1.4 GiB peak RSS (CPU only); on two Qwen3-8B AWQ checkpoints (324 tensors): 1.8 GiB peak RSS.
Unit tests (CPU, 0.05 s in total): tests/unit/torch/quantization/test_scale_compare.py (tolerance semantics with fp8 scales, missing and reshaped tensors, the CLI over differently sharded checkpoints, each tensor loaded once, an empty comparison is not a pass), tests/unit/torch/utils/test_determinism.py (reseeding reproduces the draws, flags and env var set), and two cases in tests/examples/hf_ptq/test_hf_ptq_args.py (--deterministic needs an explicit --batch_size; main seeds before loading). The tests for the lazy loading, the empty comparison, the --deterministic validation and the main wiring were mutation-checked: each fails when its code is reverted.
Not run: other models or recipes, multi-node, Hopper, the Megatron-Bridge PTQ flow, accuracy evaluation.
Before your PR is "Ready for review"
Is this change backward compatible?: ✅ (new tool, new opt-in flag; the default path is unchanged)
If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
Did you write any new necessary tests?: ✅
Did you update Changelog?: ✅
Did you get Claude approval on this PR?: ❌ (to be requested with /claude review)
Additional Information
The run-to-run differences that motivated this were traced to the all-to-all MoE combine accumulating top-k partials in bf16 with index_add_; that is fixed at its source (#2738, fixed-order combine), after which two EP4 runs are bit-identical without any flag.
Summary by CodeRabbit
New Features
Added a tool to compare quantization scales across exported checkpoints, with configurable tolerances and a pass/fail result. Comparisons identify missing scales and shape mismatches.
Added a best-effort deterministic mode for post-training quantization. It seeds random number generators and selects deterministic operations where supported; an explicit batch size is required. Some operations may still be nondeterministic, and runs may take longer.
Documentation
Added guidance and examples for running reproducible quantization and comparing checkpoint scales.
Diffs the input, weight and KV scales of two exported safetensors
checkpoints (or any two name -> tensor mappings) per scale kind, reports how
many tensors differ and the median and worst relative difference, and exits
non-zero when they differ beyond a tolerance. It is the measuring stick for
how reproducible PTQ is across repeated runs, batch sizes and parallel
layouts.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
hf_ptq seeded only python and numpy, and its default --batch_size 0 probes
the calibration batch size from free GPU memory, so another GPU or memory
state calibrates with another batch size. The new switch also seeds torch,
selects deterministic torch kernels (an op without one only warns), sets
CUBLAS_WORKSPACE_CONFIG, and rejects --batch_size 0, through a small
enable_determinism helper.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Walkthrough
This pull request adds best-effort deterministic settings to hf_ptq.py and a utility to compare quantization scales from mappings or safetensors checkpoints.
Changes: Deterministic PTQ
Layer / File(s)
Summary
Determinism utility and validation modelopt/torch/utils/determinism.py, modelopt/torch/utils/__init__.py, tests/unit/torch/utils/test_determinism.py
Adds enable_determinism to seed Python, NumPy, and PyTorch; set CUBLAS_WORKSPACE_CONFIG when unset; and enable deterministic algorithms in warning-only mode. Tests check repeated random draws and restore global state.
Adds --deterministic, which requires an explicit nonzero --batch_size and calls enable_determinism with RAND_SEED. Tests check argument validation and call order. The documentation describes the option and scale comparison.
Checkpoint Scale Comparison
Layer / File(s)
Summary
Scale comparison results and tolerance checks modelopt/torch/quantization/utils/scale_compare.py, tests/unit/torch/quantization/test_scale_compare.py
Adds result records and comparison logic for missing names, shape mismatches, and values outside tolerance. Tests cover tolerance thresholds and scale differences.
Checkpoint loading and CLI modelopt/torch/quantization/utils/scale_compare.py, tests/unit/torch/quantization/test_scale_compare.py
Adds lazy loading of scale tensors from safetensors files or directories, plus a CLI that prints comparison results and returns a status based on tolerance. Tests cover sharded directories, tensor access, and empty comparisons.
sequenceDiagram
participant PTQ as hf_ptq.py
participant Utility as enable_determinism
participant Python
participant NumPy
participant PyTorch
PTQ->>Utility: call with RAND_SEED
Utility->>Python: seed random generator
Utility->>NumPy: seed random generator
Utility->>PyTorch: seed RNG and enable deterministic algorithms
Loading
sequenceDiagram
participant User
participant CLI as scale_compare CLI
participant Loader as load_checkpoint_scales
participant Comparator as compare_scales
User->>CLI: provide checkpoint paths and tolerances
CLI->>Loader: load scale tensors from each checkpoint
Loader-->>CLI: return named scale tensors
CLI->>Comparator: compare named tensors
Comparator-->>CLI: return comparison results
CLI-->>User: print summary and return exit status
Loading
Possibly related PRs
NVIDIA/Model-Optimizer#2864: Changes the --batch_size contract in hf_ptq.py, which is also relevant to the deterministic option’s explicit batch-size requirement.
Deterministic PTQ may still produce nondeterministic cuBLAS results under an unsupported inherited workspace configuration.
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The title clearly and concisely identifies both primary changes: the PTQ scale-comparison tool and the hf_ptq --deterministic option.
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Related: the AWQ side of the batch-size dependence measured here (awq_lite averaged per-batch means, so its scales moved with the batch split) is in #2996. The two PRs are independent and can merge in either order.
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Inline comments:
Review comments at @examples/hf_ptq/README.md:
- Line 793: Update the deterministic-mode description in the README to frame
identical scales as an observed result for tested configurations, not a
guarantee for every run; retain the explanation that PyTorch’s warning-only
deterministic mode may allow operations without deterministic implementations to
run.
Review comments at @modelopt/torch/quantization/utils/scale_compare.py:
- Around line 155-157: Update the file-processing loop that populates
self._handles to detect scale names already present from earlier checkpoint
files and raise an error instead of allowing self._handles.update to overwrite
them; keep adding names that are unique across the files.
Review comments at @modelopt/torch/utils/determinism.py:
- Line 40: Update the determinism setup around CUBLAS_WORKSPACE_CONFIG to
validate any inherited value and reject values other than :4096:8 or :16:8
before PTQ starts, while retaining the default when the variable is unset.
Review comments at @tests/unit/torch/utils/test_determinism.py:
- Around line 27-33: Update the restore_global_state fixture to save the Python,
NumPy, and Torch RNG states before yielding, then restore those states during
teardown alongside the existing environment and deterministic-algorithm
settings.
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❌ Patch coverage is 97.47899% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 78.80%. Comparing base (0638a27) to head (133fb1d). ⚠️ Report is 3 commits behind head on main.
A scale name that appeared in two files of a checkpoint directory was
silently taken from the later file, so a stale or overlapping export could
pass a comparison without every tensor being checked; it is an error now.
The README presented identical scales as a guarantee, but --deterministic
only warns for an op without a deterministic kernel, so it now says the
result was observed on the measured configurations. The determinism test
also restores the RNG states it reseeds.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Report all nonzero differences in ScaleDiff.summary.
When a shared tensor differs within tolerance, differing excludes it because the filter uses outside_tolerance. The summary then reports zero differing tensors, a zero median, and omits the tensor from worst. Keep tolerance filtering for pass/fail, but calculate reporting statistics from every tensor whose max_rel is nonzero.
Suggested fix
- differing = [t.max_rel for t in ts if t.outside_tolerance]+ differing = [t.max_rel for t in ts if t.max_rel > 0]
...
- worst = sorted((t for t in self.tensors if t.outside_tolerance), key=lambda t: -t.max_rel)+ worst = sorted((t for t in self.tensors if t.max_rel > 0), key=lambda t: -t.max_rel)
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Review comment at @modelopt/torch/quantization/utils/scale_compare.py around
lines 86 - 98:
Update ScaleDiff.summary so the differing statistics and worst-tensor list
include every tensor with a nonzero max_rel, including differences within
tolerance. Keep outside_tolerance filtering for pass/fail behavior unchanged.
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.
Outside diff comments:
Review comments at @modelopt/torch/quantization/utils/scale_compare.py:
- Around line 86-98: Update ScaleDiff.summary so the differing statistics and
worst-tensor list include every tensor with a nonzero max_rel, including
differences within tolerance. Keep outside_tolerance filtering for pass/fail
behavior unchanged.
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So block-scaled FP8 exports (the weight_scale_inv layout, e.g. models that go through a conversion mapping from a native FP8 blockwise source) write their per-block weight scales under *.weight_scale_inv, and this regex skips every one of them.
Why it matters: the skip is silent and biases toward a false pass. On such a checkpoint the tool compares only input_scale (and any weight_scale), prints result: within tolerance, and main exits 0 — while the blockwise weight scales, the tensors a reproducibility check most wants to see, were never loaded. That is exactly the failure mode the tool exists to rule out, and within_tolerance's bool(self.tensors) guard does not catch it because other scale kinds are present.
Suggested fix — add the suffix and the comment above it:
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Fixed in 133fb1d: the pattern is now (_scale(_2|_inv)?|_amax)$, so weight_scale_inv is loaded and compared. test_weight_scale_inv_is_compared fails without it (the loader then finds no scale tensors in a checkpoint that only has that kind).
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[SUGGESTION]setdefault + warn_only=True + an unconditional success message can claim determinism the run does not have.
cuBLAS only pins its reduction split for CUBLAS_WORKSPACE_CONFIG in :4096:8 or :16:8. setdefault deliberately respects a value the user already exported — good — but it also respects a value that is not one of those two (a stale :0:0, or anything else inherited from a container image). In that case torch's alertCuBLASConfigNotDeterministic fires, warn_only=True downgrades it to a warning that is easy to lose in PTQ's log volume, and line 42 still prints Deterministic mode: ... deterministic torch kernels. The user then compares two runs, sees scales move, and has no signal pointing at the env var.
Cheap fix — warn when the inherited value isn't deterministic-compatible, rather than silently accepting it:
deterministic_configs= (":4096:8", ":16:8")
config=os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", deterministic_configs[0])
ifconfignotindeterministic_configs:
warn_rank_0(
f"CUBLAS_WORKSPACE_CONFIG={config} is not one of {deterministic_configs}; ""cuBLAS reductions stay nondeterministic."
)
(warn_rank_0 is already next to print_rank_0 in modelopt/torch/utils/logging.py.)
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Done in 133fb1d: enable_determinism still keeps a CUBLAS_WORKSPACE_CONFIG the user already set, but now warns (rank 0) when it is not :4096:8 or :16:8 and says cuBLAS reductions are then not made deterministic. Covered in test_enable_determinism.
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[SUGGESTION]enable_determinism runs before setup_distributed_args(args), so its print_rank_0 is not yet rank-aware: dist.is_master() returns True on every rank until torch.distributed is initialized, and under torchrun all N ranks print the "Deterministic mode" line.
Harmless, but it's the one line of this feature's output and duplicating it per rank is the opposite of what print_rank_0 is there for. Moving the call just after setup_distributed_args(args) keeps it comfortably before the first CUDA matmul (the constraint the docstring actually cares about) and makes the message appear once.
The ordering in tests/examples/hf_ptq/test_hf_ptq_args.py::test_main_enables_determinism_before_loading ("before loading") still holds either way, since load_model comes well after.
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Moved in 133fb1d: the seeding block now runs right after setup_distributed_args(args), so the "Deterministic mode" line is printed once instead of on every rank under torchrun. It is still before ensure_local_checkpoint, the model load and the first matmul.
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[SUGGESTION] Two columns in the table mean something narrower than their headers suggest, and the docstring on summary doesn't say which:
median rel is the median of per-tensor maxima, taken over the differing tensors only — not a median over elements, and not over all tensors. With rtol large enough that nothing differs it reads 0.00e+00, which looks like "no difference anywhere" rather than "nothing exceeded the tolerance".
max rel is over all tensors of the kind, including those inside the tolerance — so a kind can show differing 0 next to a non-zero max rel. That combination is correct but reads like a contradiction.
The PR description spells both out; the tool's own output is what a user will have in front of them months later. One extra clause on the summary docstring would carry it:
defsummary(self, top: int=5) ->str:
"""Per-kind table (kind = last component of the tensor name) and the worst tensors. ``median rel`` is the median over the differing tensors of each tensor's largest relative difference; ``max rel`` is the largest over all tensors of the kind. """
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Done in 133fb1d: the summary docstring now says that differing counts the tensors outside the tolerance, median rel is the median of their largest relative differences, and max rel is the largest over all tensors of the kind, including those inside the tolerance.
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[SUGGESTION]_CheckpointScales keeps one safe_open handle per shard alive for the object's whole lifetime and offers no way to release them.
For the CLI that's fine — the process exits. For the documented library use (load_checkpoint_scales is in __all__), a caller comparing a many-shard MoE export holds every shard's file descriptor and mmap until the object is garbage-collected, and has no deterministic point to drop them. A __enter__/__exit__ pair closing set(self._handles.values()) would make load_checkpoint_scales usable as a context manager without changing the CLI path.
Minor, separate: when path is a non-existent file the FileNotFoundError("No .safetensors files in ...") branch is skipped (files = [path]) and the user gets safetensors' lower-level error instead of this module's message. A path.exists() check at the top would route both cases to the clear message.
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Partly done in 133fb1d: a path that does not exist now raises FileNotFoundError("... does not exist") (covered in test_nothing_to_compare_is_not_a_pass). I am not adding a context manager. The shard handles are released as soon as the mapping is dropped (CPython reference counting), the CLI exits right after, and I would rather not add API surface for a long-lived caller that does not exist yet.
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Claude review
Scope: full review (the trigger comment carried no scoping instructions). 9 changed files, +489/-3. Reviewed all of modelopt/ (scale_compare.py, determinism.py, utils/__init__.py) and examples/hf_ptq/ (hf_ptq.py, README.md), then the three test files and CHANGELOG.rst. Nothing deliberately skipped — the PR is small enough to cover end to end.
Note on the diff: this checkout's HEAD is behind origin/main, so a two-dot git diff origin/main HEAD also surfaces unrelated main-only commits (the --vllm_fakequant_export / Megatron-Bridge work). I scoped every diff to the 9 files GitHub reports for this PR; none of that other churn is yours.
setdefault accepts a non-deterministic CUBLAS_WORKSPACE_CONFIG, then logs success anyway
3
SUGGESTION
hf_ptq.py:1520
enable_determinism runs pre-setup_distributed_args, so print_rank_0 prints on every rank
4
SUGGESTION
scale_compare.py:82-95
median rel / max rel column semantics aren't stated in summary's docstring
5
SUGGESTION
scale_compare.py:148-176
Shard handles never closed; no context manager on the public load_checkpoint_scales
The one that matters
#1.modelopt/torch/export/quant_aware_conversion.py:92-97 declares .weight_scale_inv as one of the four scale leaves the unified HF export carries through rename/un-fuse, so block-scaled FP8 exports write their per-block weight scales under that suffix. r"(_scale|_scale_2|_amax)$" doesn't match it.
The skip is silent and biases toward a pass: on such a checkpoint the tool compares only input_scale, prints result: within tolerance, and main exits 0, while the blockwise weight scales were never loaded. within_tolerance's bool(self.tensors) guard doesn't help, because other scale kinds are present. For a tool whose entire contract is "exit 0 means the scales agree", a format-dependent false pass is the failure mode worth closing before merge. One-token fix (_scale(_2|_inv)?) plus a test case.
What held up
I traced the comparison math rather than just reading it, and it is careful work:
_diff upcasts fp8/bf16 to fp32 (exact, so the default rtol=atol=0 really is a bit-identity check), handles NaN-vs-NaN via the same mask, inf == inf through that same mask ahead of the isfinite guard, and 0/0 → nan_to_num(..., nan=inf) masked back to 0. The symmetric rtol * max(|a|, |b|) is documented at compare_scales.
compare_scales builds plain name sets before indexing, with the comment explaining why — a lazy mapping's in would load the tensor. test_compare_scales_loads_each_tensor_once pins it.
within_tolerance requiring bool(self.tensors) so an empty comparison is not a pass, and the cross-shard duplicate-name rejection, are both the right call and both tested.
The --deterministic / --batch_size validation is complete: I checked every probing site (hf_ptq.py:495, :986, and the nested min(..., sum(args.calib_size)) at :498 and :1045) and all of them sit under an args.batch_size == 0 guard, so an explicit batch size is never overwritten downstream.
enable_determinism lands early in main(), before ensure_local_checkpoint / load_model / any matmul, which is what the CUBLAS_WORKSPACE_CONFIG note requires. torch.manual_seed covers the CUDA generators, so no separate manual_seed_all is needed.
scale_compare is not star-imported by modelopt/torch/quantization/utils/__init__.py, so the new safetensors top-level import adds no eager dependency to import modelopt.torch.quantization. determinism.py importing .logging introduces no cycle.
Both CHANGELOG entries are user-facing, one or two sentences, filed under the existing *Quantization* sub-section.
I did not execute the new unit tests — the sandbox declined the pytest invocation — so the pass/fail evidence here is the PR's own report, not mine. Everything above is from reading the code and its call sites.
Risk
Low, with one caveat. Both additions are opt-in and leave the default PTQ path byte-for-byte unchanged, the public surface is two new names (enable_determinism, the scale_compare module) with no signature or schema changes, and no modelopt_state / mode-registration / export-format surface is touched, so there's no restore or backward-compatibility exposure. The caveat is finding #1: a diagnostic that can report a pass it hasn't earned is worse than no diagnostic, and that one is worth fixing before this lands.
The scale pattern missed weight_scale_inv, which the unified HF export
emits for block-scaled FP8, so a comparison of such checkpoints skipped
those scales and could pass without having looked at them. A missing
checkpoint path now says so, and the summary documents what its columns
count.
enable_determinism keeps a CUBLAS_WORKSPACE_CONFIG the user already set but
warns when cuBLAS does not treat it as deterministic. hf_ptq seeds after
setup_distributed_args, so the "Deterministic mode" line is printed once
and not on every rank.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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What does this PR do?
Type of change: new feature
Makes PTQ reproducibility measurable and controllable:
python -m modelopt.torch.quantization.utils.scale_comparediffs the quantization scales (input_scale,weight_scale,weight_scale_2,weight_scale_inv,k_scale,v_scale,pre_quant_scale, quantizer_amax) of two exported safetensors checkpoints, or of any two{name: tensor}mappings, per scale kind. It reports how many tensors differ and the median and worst relative difference, and exits non-zero when a scale differs by more than--rtol/--atol(default 0).hf_ptq.py --deterministicpins what a run depends on besides the model and data: it requires an explicit--batch_size(the default0probes the calibration batch size from free GPU memory, so another GPU or memory state silently calibrates with another batch size), seeds every RNG (hf_ptq.pyseeded onlyrandomandnumpy), setsCUBLAS_WORKSPACE_CONFIGand selects deterministic torch kernels (torch.use_deterministic_algorithms(True, warn_only=True): an op without one only warns).What the measurements below show, so reviewers can judge the flag: on main, repeating a run is already bit-identical, with or without
--deterministic; what moves the scales is the calibration batch size. The flag does not remove measured noise, it pins the remaining inputs (and costs some run time).Related: the AWQ side of the batch-size dependence is a separate PR (
awq_litetoken weighting, linked in a comment).Usage
Testing
Setup: Qwen3-30B-A3B (bf16), recipe
general/ptq/nvfp4_default-kv_fp8_cast,--calib_size 128 --calib_seq 512, default calibration data, GB200 GPUs, torch 2.11.0a0, transformers 5.13.0, one export per run, compared with the tool in this PR. "differing" counts theinput_scaletensors (18624) outside the tolerance; median is over the differing tensors.weight_scaleandweight_scale_2were identical (0/18624) in every pair.What a run depends on, on main (no
--deterministic):--deterministic(the flag and helper of this PR on top of the same code as A1 to A5):input_scale, 0/18624weight_scale)input_scale: the same tensors and magnitudes as A1 vs A5 (a 1-GPU vs FSDP2 effect, not a kernel the flag misses)--deterministic, 1 GPU)The wall time of three runs of the same configuration without the flag spans 52 to 73 s and overlaps D1 (80 s), so on this short job the cost on one GPU is somewhere between about 10% and 50%, and a few percent on two ranks; a longer calibration would resolve it better. No "does not have a deterministic implementation" warning appeared in the D1 or D5 logs and nothing raised.
Tool cost on two EP4 Qwen3-30B-A3B FP8 exports (18624
input_scale, 18624weight_scale, 96k/vscale tensors): 31 s, 1.4 GiB peak RSS (CPU only); on two Qwen3-8B AWQ checkpoints (324 tensors): 1.8 GiB peak RSS.Unit tests (CPU, 0.05 s in total):
tests/unit/torch/quantization/test_scale_compare.py(tolerance semantics with fp8 scales, missing and reshaped tensors, the CLI over differently sharded checkpoints, each tensor loaded once, an empty comparison is not a pass),tests/unit/torch/utils/test_determinism.py(reseeding reproduces the draws, flags and env var set), and two cases intests/examples/hf_ptq/test_hf_ptq_args.py(--deterministicneeds an explicit--batch_size;mainseeds before loading). The tests for the lazy loading, the empty comparison, the--deterministicvalidation and themainwiring were mutation-checked: each fails when its code is reverted.Not run: other models or recipes, multi-node, Hopper, the Megatron-Bridge PTQ flow, accuracy evaluation.
Before your PR is "Ready for review"
CONTRIBUTING.md: N/A/claude review)Additional Information
The run-to-run differences that motivated this were traced to the all-to-all MoE combine accumulating top-k partials in bf16 with
index_add_; that is fixed at its source (#2738, fixed-order combine), after which two EP4 runs are bit-identical without any flag.Summary by CodeRabbit