[6508436] Fix BF16 FP8 ONNX export - #2314
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📝 WalkthroughWalkthroughFP8 ONNX export preserves BF16 weights and scales during compression. ONNX value-info export validates datatype enums. Torch ONNX export checks floating-point parameter and buffer dtypes. Tests cover valid exports and rejected mixed-dtype conversions. ChangesBF16 FP8 ONNX export
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🔵 Low · up to Unsupported BF16 FP8 exports requested as FP16 can leave temporary ONNX artifacts behind after failing. This is bounded cleanup debt but should be addressed before broad repeated use. Suggested reviewers: 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (5 passed)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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ajrasane
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2314 +/- ##
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- Coverage 79.31% 78.82% -0.50%
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Files 527 527
Lines 61487 61511 +24
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- Hits 48770 48483 -287
- Misses 12717 13028 +311
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Small, focused bug fix (6 files, +184/-15) that unblocks FP8 ONNX export for BF16 source models. No new subsystem/abstraction is introduced (the design-review gate fired only on directory span): the only new construct is a 4-line _torch_from_numpy helper, and I checked the existing alternatives — modelopt/onnx/utils.py has bfloat16_to_float32 / read_f16_tensor_as_fp32 for the TensorProto→fp32 direction but nothing for the GraphSurgeon-numpy→torch bit-preserving direction, so the helper isn't a duplicate. The gs_patching fix (avoiding the eagerly-evaluated getattr default that crashed on ONNX enum dtypes) is a genuine improvement, and the added tests (unit exporter test, positive/negative export tests) are meaningful. No licensing surface touched, and no injection attempts in the PR text.
Blocking concern: _quantize_conv_weights_to_fp8 now accepts BF16 conv weights but still emits a hard-coded FP16 scale constant, which produces a DequantizeLinear whose output type (FP16) doesn't match the surrounding BF16 activations in the newly-enabled BF16 path — and there is no Conv-based BF16 test to catch it. A few smaller items on precision of the BF16 division, the bare DataType.Name(dtype) validation call, and the first-parameter dtype heuristic are noted inline.
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Inline comments:
In `@modelopt/torch/_deploy/utils/torch_onnx.py`:
- Line 647: Update the dtype validation used by is_bf16_fp8_noop to inspect
floating-point registered buffers as well as model parameters, preventing
referenced FP32 buffers from being treated as a BF16 no-op conversion. Add a
regression test covering the existing assertion with BF16 parameters and an FP32
buffer, ensuring conversion is not skipped incorrectly.
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modelopt/onnx/export/fp8_exporter.pymodelopt/onnx/quantization/gs_patching.pymodelopt/torch/_deploy/utils/torch_onnx.pytests/unit/onnx/quantization/test_qdq_utils.pytests/unit/torch/deploy/utils/test_torch_onnx_utils.py
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Design gate (directory-span trigger): re-confirmed this is a contained bug fix, not a new subsystem. Problem: torch.from_numpy rejects ml_dtypes.bfloat16 arrays produced by GraphSurgeon, so FP8 real-weight compression crashed for BF16 source models. Alternatives checked: modelopt/onnx/utils.py already has bfloat16_to_float32 / read_f16_tensor_as_fp32, but those go TensorProto→FP32, not numpy→torch bit-preserving, so the 4-line _torch_from_numpy helper is not a duplicate; onnx.numpy_helper/convert_to_f16 don't cover this direction either. No second system introduced. No licensing surface. No injection attempts in the PR text.
Previous comments — status
- (critical) Hard-coded FP16 Conv scale under the newly-enabled BF16 path — addressed. 💬 Author (
a2afc68): scale now derives fromweight_input.values.dtype, the inserted DQ output carries that dtype/shape, and a BF16 Conv end-to-end regression withcheck_model(full_check=True)was added. - (critical) BF16-precision division before FP8 rounding — addressed: BF16 weights/scale are promoted to FP32 in both
compress_weightsand the Conv path; the magic-byte assertion was updated0x3a → 0x3baccordingly. - (critical) First-parameter dtype heuristic — addressed: replaced with the full set of floating parameter and buffer dtypes, gated on
== {torch.bfloat16}, with mixed-parameter and mixed-buffer regressions. - (minor) Discarded
DataType.Name(dtype)validation call — addressed with an explicitDataType.values()membership check and descriptiveValueError, plus a unit test for the ONNX-enum dtype path. - (minor) CHANGELOG placement — addressed (moved to 0.47 Bug Fixes).
Residual items for the owner
- Conv +
weights_dtype="fp16"is now untested and behaviourally changed. The deleted comment ("DQ output type matches scale dtype, must match activation type") existed because the FP32→FP16 conversion blocksDequantizeLinear; the Conv scale is now emitted FP32 and only becomes FP16 viafold_dq_fp32_to_fp16_castsdownstream. That chain looks like it still converges, but the new parametrization covers(bf16, "bf16", conv=True)and(fp32, "fp16", conv=False)only — adding(fp32, "fp16", conv=True)would pin the previously-working path. - BF16 source +
weights_dtype="fp16": this now falls through toconvert_float_to_float16, which only rewritesFLOATtensors. Before this PR the path crashed intorch.from_numpy, so it was unreachable; it may now silently emit a mixed BF16/FP16 graph rather than raising. Worth either asserting/raising explicitly or covering with a test. source_floating_dtypes == {torch.bfloat16}is strict by design (good for correctness) but rejects common real BF16 models that keep FP32 buffers (e.g. HF rotaryinv_freq), which will hit "Converting a quantized ONNX graph to BF16 is not supported yet". Not a regression versusmain, but the message doesn't hint at the actual cause; consider naming the offending dtypes in the assertion.- gcunhase's follow-up ("any accuracy concerns with BF16→INT16→BF16?") appears unanswered in the thread. For the record the two
.view()calls are bit-preserving reinterpretations (no numeric cast), so there is no accuracy loss — but a human reviewer's question is still open. - Nit: the
np.nextafter(...)upward-rounding block in_quantize_conv_weights_to_fp8has no comment explaining why (keepingmax|w|/scale ≤ 448so the FP8 cast can't produce NaN); one line would help the next reader, especially since it's exercised only indirectly by the1e-38Conv fixture.
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Inline comments:
In `@modelopt/torch/_deploy/utils/torch_onnx.py`:
- Around line 650-653: Ensure the BF16 FP8 to FP16 compatibility validation
occurs before creating the temporary ONNX path in the export flow, or guarantee
cleanup through a finally block when the AssertionError is raised; preserve
normal export behavior and remove any created onnx_path for rejected exports.
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modelopt/onnx/export/fp8_exporter.pymodelopt/torch/_deploy/utils/torch_onnx.pytests/unit/torch/deploy/utils/test_torch_onnx_utils.py
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- modelopt/onnx/export/fp8_exporter.py
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Re-review #3 of the BF16 FP8 ONNX export fix (6 files, +298/-19). Design gate (directory-span trigger) remains settled from earlier rounds: this is a contained bug fix, the only new construct is the 4-line _torch_from_numpy bridge (no in-repo equivalent — modelopt/onnx/utils.py only covers TensorProto→FP32), no second system, no licensing surface, no injection attempts in the PR text.
Previously-flagged items — status
- (critical) Hard-coded FP16 Conv scale under the BF16 path — addressed (scale derives from
weight_input.values.dtype, DQ output carries dtype/shape, BF16 Conv e2e regression withcheck_model(full_check=True)). - (critical) BF16 arithmetic before FP8 rounding — addressed (FP32 promotion in both
compress_weightsand the Conv path; magic byte updated to0x3b). - (critical) First-parameter dtype heuristic / FP32 buffers — addressed (
source_floating_dtypesover parameters and buffers, with mixed-param, mixed-buffer and mixed-format regressions). - (minor) Discarded
DataType.Name()validation, CHANGELOG placement,np.nextafterwhy-comment,(fp32, "fp16", conv=True)coverage, silent BF16→FP16 fallthrough — all addressed (explicit enum membership check, 0.47 Bug Fixes entry, comment added, new parametrization case, explicit rejection + test). - (minor) gcunhase's
BF16→INT16→BF16accuracy question — answered in-thread (both.view()s are bit-preserving reinterpretations, no numeric cast).
Why a human should still glance before merge
- 💬 Author (
9730260/4c6a904): the no-op gate now inspects floating buffers too and the error names the detected dtypes — still worth a look because the gate issource_floating_dtypes == {torch.bfloat16}, so any FP32 buffer (HF rotaryinv_freq, and potentially ModelOpt's own_amax/_pre_quant_scaleif a calibrator stores FP32) makes the newly-enabled BF16 path unreachable and surfaces as "Converting a quantized ONNX graph to BF16 is not supported yet". The unit tests only use tinynn.Linear/nn.Conv2dmodels, so they can't confirm the path fires for realistic BF16 checkpoints. Not a regression versusmain, but it determines whether this fix actually helps users. - Unaddressed minor from CodeRabbit: the new
raise AssertionError(...)for BF16-FP8 → FP16 fires aftertempfile.mkdtemp(...)and aftertorch.onnx.exporthas written the model, so the rejected export leaks the temp directory (theshutil.rmtree(onnx_path)at the end is skipped). Moving the check before the export — the inputs it needs (weights_dtype,source_floating_dtypes,is_fp8_quantized(model)) are all available up front — would also avoid doing a full export just to reject it. Related nit: raisingAssertionErrorexplicitly (rather thanValueError) is unusual, even if it keeps the tests'pytest.raises(AssertionError)consistent with the neighbouringassert.
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Replying to the latest substantive review:
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Support BF16 initializers during FP8 weight compression and skip redundant precision conversion when the requested dtype already matches the source model. Add focused exporter and end-to-end regression coverage. Co-Authored-By: Codex <noreply@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Co-Authored-By: Codex <noreply@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Co-Authored-By: Codex <noreply@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Revert the universal target-precision expansion while retaining the BF16 FP8 real-weight compression fix and its required GraphSurgeon compatibility. The broader multi-format precision contract will be delivered separately. Co-Authored-By: Codex <noreply@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Keep Conv dequantization type-consistent, normalize BF16 values in FP32, validate ONNX dtype enums explicitly, and reject mixed-source precision no-ops. Co-Authored-By: Codex <noreply@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Require both floating parameters and registered buffers to be BF16 before treating FP8 export as a no-op conversion. Co-Authored-By: Codex <noreply@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Co-Authored-By: Codex <codex@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Keep the implementation and regression matrix focused on BF16 FP8 weight export while retaining the reviewed Conv and conversion safeguards. Co-Authored-By: Codex <codex@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Calibrate the combined Conv and Linear fixture before injecting the tiny BF16 Conv weight so the test isolates exporter scale handling across Torch versions. Co-Authored-By: Codex <codex@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Place the BF16 FP8 ONNX export fix under the correct release section. Co-Authored-By: Codex <noreply@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
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What does this PR do?
Type of change: Bug fix
Fix FP8 ONNX export for BF16 models during real-weight compression without changing the public API or the
weights_dtype="fp32"default.The FP8 exporter preserves BF16 initializer bits when bridging GraphSurgeon NumPy arrays to Torch, widens BF16 values exactly to FP32 for normalization, and leaves existing FP16/FP32 handling unchanged. Conv scales and dequantized outputs retain the source dtype, and scales round upward when needed so serialized values cannot cause FP8 overflow.
weights_dtype="bf16"is accepted as a no-op only for FP8-only models whose floating parameters are all BF16. Registered buffers do not affect this weight-focused decision. Unsupported BF16 FP8-to-FP16 conversion is rejected before temporary export paths are created, while unsupported FP32 or mixed-parameter conversion to BF16 retains its existing assertion contract. A narrow GraphSurgeon fix preserves integer BF16 value-info dtypes.Usage
Testing
f21d62a: exit code 0; ONNX checker passed; 6 FP8 initializers, 3 nativeDequantizeLinearnodes, and 12 BF16 initializers.f21d62a: 50 passed and 1 skipped. Unit, GPU, and regression required aggregates and Codecov passed. Two ONNX example leaves failed because the runner could not load a cuDNN sublibrary; their dependent example aggregate consequently failed.Before your PR is "Ready for review"
Make sure you read and follow Contributor guidelines and your commits are signed (
git commit -s -S).Make sure you read and follow the Security Best Practices (e.g. avoiding hardcoded
trust_remote_code=True,torch.load(..., weights_only=False),pickle, etc.).CONTRIBUTING.md: N/AAdditional Information
native/FP32/FP16/BF16 ONNX export across all quantized formats in follow-up pull requests.