[6508436] Fix BF16 FP8 ONNX export - #2314
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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>
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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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Co-Authored-By: Codex <noreply@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
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Co-Authored-By: Codex <noreply@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2314 +/- ##
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- Misses 13075 20133 +7058
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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>
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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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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>
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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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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>
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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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.
Co-Authored-By: Codex <codex@openai.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
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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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| raise AssertionError( | ||
| "Converting a BF16 FP8 ONNX graph to FP16 is not supported yet " | ||
| f"(source floating dtypes: {source_floating_dtype_names})" | ||
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🩺 Stability & Availability | 🟡 Minor | ⚡ Quick win
Clean up the temporary export directory before this error.
A BF16 FP8 export with weights_dtype="fp16" reaches this new raise after line 589 creates onnx_path. The normal cleanup at lines 720-721 does not run. Rejected exports therefore leave ONNX files in the temporary directory.
Move compatibility validation before temporary-path creation, or wrap the export flow in try/finally and remove onnx_path on failure.
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In `@modelopt/torch/_deploy/utils/torch_onnx.py` around lines 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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What does this PR do?
Type of change: Bug fix
Fix FP8 ONNX export for BF16 models during real-weight compression. The FP8 exporter preserves BF16 initializer bits when bridging GraphSurgeon NumPy arrays to Torch for weights, scales, and Conv weights.
BF16 normalization now uses FP32 arithmetic before FP8 rounding. Conv scales and dequantized outputs retain the surrounding source dtype, including BF16, and tiny scales are rounded upward to avoid FP8 overflow.
BF16-to-BF16 export is allowed for already-BF16 FP8 models. Actual quantized FP32-to-BF16 conversion, mixed source precision, and mixed INT4, MXFP8, or INT8 graphs remain unsupported. A minimal GraphSurgeon compatibility fix preserves an integer BF16 dtype on graph value information.
Usage
Testing
DequantizeLinearnodes, and 12 BF16 initializers.9730260: 49 passed, 5 skipped, and 3 failed. Two child jobs hit the same shared cuDNN runner outage, and their dependent aggregate failed; all functional unit, GPU, regression, code-quality, documentation, and remaining example jobs passed. Codecov passed.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.Summary by CodeRabbit
Bug Fixes
Tests