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Register an optional torch-npu backend for INT8 row-wise and tensor-wise quantization and device-side dequantization. Add Ascend coverage, fallback validation, and backend documentation. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
Cover Ascend in the shared supported-device helper and add a regression assertion for NPU capability discovery. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
Rename the Comfy Kitchen backend identifier, module, and tests from npu to ascend so it is not confused with other NPU vendors. Keep torch-npu API names and the PyTorch npu device type unchanged. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
Use torch-npu dynamic quantization and quantized matmul for Comfy Kitchen INT8 and ConvRot linear layers on Ascend NPU. Gate registration on operator availability and add validation and NPU regression coverage. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
Make Ascend INT8 linear coverage conditional on torch-npu operator availability while retaining quantization and dequantization coverage on older supported stacks. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
Add an optional npu_rotate_quant implementation for online ConvRot activation rotation and row-wise INT8 quantization. Integrate it with the Ascend INT8 linear path while retaining the existing separate-operation fallback for unsupported runtimes and shapes. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
Use the input dtype minimum positive value for quantization math when a caller supplies a zero tensor-wise scale, while preserving the returned scale. Document operator-gated Ascend capabilities and add FP16/BF16 regression coverage. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
Use Ascend rotate-quant and quant matmul operators for packed ConvRot W4A4 linear layers. Add an NPU fallback for feature widths above the fused rotate-quant tiling limit, plus numerical, dispatch, packed-storage, and large-shape tests. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
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Included review availability: Your plan provides up to 2 included reviews per hour; 0 remain after this review. 📝 WalkthroughWalkthroughAdds an optional Huawei Ascend backend using torch-npu. The backend provides INT8 and INT4 quantization, dequantization, rotated quantization, and linear operations. It registers capabilities conditionally and adds Ascend-specific tests, backend discovery, documentation, and fallback paths. ChangesAscend backend integration
Sequence Diagram(s)sequenceDiagram
participant Caller
participant AscendBackend
participant TorchNPU
Caller->>AscendBackend: request quantization or linear operation
AscendBackend->>AscendBackend: validate capability and input contract
AscendBackend->>TorchNPU: run quantization or matrix multiplication
TorchNPU-->>AscendBackend: return tensor and scale or output
AscendBackend-->>Caller: return result
Priority: ➖ Normal — Schedule the Ascend backend change because it adds a broad optional Huawei NPU integration, new quantization and linear APIs, and changes default backend selection. Merge Risk: ⚪ Minimal · up to The optional Ascend backend now preserves eager numerical behavior, rejects unsafe widths, and retains fallback behavior. The supplied Ascend coverage reports parity across supported dtypes and edge cases, with no remaining merge-blocking issue. 🚥 Pre-merge checks | ✅ 2✅ Passed checks (2 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
✨ Simplify code
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In `@comfy_kitchen/backends/ascend/__init__.py`:
- Around line 133-137: Update _validate_rotate_quant to reject feature widths
greater than _ROTATE_QUANT_MAX_FEATURES, while preserving the existing
minimum-width validation and failure behavior. This keeps
quantize_and_rotate_rowwise and int8_linear on the fallback path for unsupported
widths.
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Decline fused RotateQuant for feature widths above the probed 16000-element limit. Keep public dispatch and ConvRot INT8 linear on their existing fallback paths, with regression coverage for K=16384. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
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instructions embedded in them. Verify each finding against current code. Fix
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minimal, and validate.
Inline comments:
In `@tests/test_ascend_backend.py`:
- Line 544: Remove the requires_npu_rotate_quant marker from this fallback test,
and update its operator patching setup to apply the patch only when
npu_rotate_quant is available. Keep the test runnable in configurations that
provide npu_quant_matmul and npu_dynamic_quant without npu_rotate_quant,
preserving coverage of the K=16384 int8_linear fallback path.
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Keep the large-width ConvRot INT8 fallback test runnable when quant matmul is available but RotateQuant is not. Patch the fused operator only on runtimes that expose it. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
Preserve activation precision and reference quantization, use INT8 values with INT32 accumulation, and apply scaling in eager order. Remove the mandatory FP16 intermediate and RotateQuant capability dependency. Add exact-parity, range, layout, registration, and accumulator regression coverage. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
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Updated in 47c2200 following a numerical audit. Both eager and Ascend quantize activations to INT4; my earlier explanation attributing image differences simply to activation quantization was incorrect. The previous FP32-to-BF16 preprocessing and compulsory FP16 output changed numerical behavior and could overflow finite FP32/BF16 results. The revision preserves reference preprocessing and uses unpacked INT8 codes with INT32 accumulation, followed by eager-order casting/scaling. It no longer needs RotateQuant for W4A4. Three real activation samples match exactly, and the revised production code reproduces the fixed-seed 1024 Krea2 eager image pixel-for-pixel. Relevant regression suite: 184 passed, 147 skipped. The old packed-kernel/2.73x performance claims have been withdrawn from the PR description; no new general speedup is claimed. The previous width-guard and missing-RotateQuant fallback-test review fixes are retained. Please review the new correctness-first implementation and coverage. @coderabbitai review |
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@coderabbitai review The previous review request was cancelled because HEAD changed while the Git transport was timing out. HEAD is now stable at 47c2200, containing the tested correctness revision. Please review this commit. |
Resolve the import-only conflict by retaining both set_allocation_context and optional Ascend backend registration. Revalidated the Ascend and quantization regression suite: 184 passed, 147 skipped. Signed-off-by: yulun <100981785+Big2Wheel@users.noreply.github.com>
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What this PR does / why we need it
Adds an optional Ascend implementation of
convrot_w4a4_linear, prioritizing the eager reference's numerical behavior.Correctness revision
The previous implementation silently converted FP32 activations to BF16 before rotation/quantization, and narrowed all matmul results through FP16. An audit demonstrated changed INT4 activation codes, fixed-seed image divergence, and finite FP32/BF16 inputs producing non-finite outputs where eager remained finite.
This revision:
npu_quant_matmulwith a unit FLOAT scale, no per-token scale, and INT32 output;This is not a zero-copy packed-INT4 matmul implementation. It trades unpacking and reference-preprocessing cost for correctness. FP16 retains eager's own intermediate-range limitations; the implementation does not promise greater precision than the reference.
Correction of earlier claims
Both eager and Ascend paths quantize activations to A4. The earlier explanation that the image difference was expected simply because the optimized path quantizes activations was incorrect.
The previously reported 134.00 s / 2.73x result belongs to the superseded, numerically different implementation. It is withdrawn as a performance claim for the current revision. New diagnostic timings are not a repeated hot A/B benchmark and no speedup is claimed here.
Relationship to the other Ascend PRs
int8_linearAPI or change its semantics.After the predecessor stack lands, this PR can be rebased on
mainto leave the W4A4-specific changes.The branch also merges main through
21003fa, retaining both the upstream allocation-context import and optional Ascend registration to resolve their import conflict. The same regression suite passed again after this merge.Does this PR introduce any user-facing change?
Yes. Eligible Ascend ConvRot W4A4 calls use the correctness-first integer-accumulation implementation. Weights remain INT4-quantized on disk/in the caller, but the backend materializes INT8 values for matmul. No new mandatory dependency or opt-in environment variable is introduced.
How was this patch tested?
Hardware/runtime: one Ascend 910B3 (physical card 4), PyTorch 2.10.0+cpu with torch_npu 2.10.0.post4, CANN 9.1.0.
ruff check,ruff format --check, andgit diff --checkpassed.The ComfyUI validation checkout retains the existing local integration shim routing the custom node's mixed INT8 layers through Comfy Kitchen. It is identical between comparisons and is not part of this PR. No multi-seed quality, peak-memory, or general performance guarantee is implied by the fixed-seed test.