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quark-onnx-result-validator

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更新日2026年7月9日 06:10

Validate Quark ONNX quantization output using four lightweight checks: auxiliary file copy alignment, expected non-quantized initializer MD5 byte-identity (inline `raw_data` + external-data byte ranges), model metadata equality after stripping quantization-only opset entries / Quark domains, and fuzzy node-pattern + op-type + dtype summaries with QDQ / `com.amd.quark` custom-op presence. Intended for post-quantization inspection of `model.onnx` (with or without `model.onnx_data`). Trigger for "validate ONNX quantization result", "check quantized .onnx output", "verify ONNX initializers", "did QDQ insertion happen", "are the non-quantized weights byte-identical".

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name	quark-onnx-result-validator
description	Validate Quark ONNX quantization output using four lightweight checks: auxiliary file copy alignment, expected non-quantized initializer MD5 byte-identity (inline `raw_data` + external-data byte ranges), model metadata equality after stripping quantization-only opset entries / Quark domains, and fuzzy node-pattern + op-type + dtype summaries with QDQ / `com.amd.quark` custom-op presence. Intended for post-quantization inspection of `model.onnx` (with or without `model.onnx_data`). Trigger for "validate ONNX quantization result", "check quantized .onnx output", "verify ONNX initializers", "did QDQ insertion happen", "are the non-quantized weights byte-identical".
layer	l1-atomic
primary_artifact	validation_report.md
source_knowledge	["examples/onnx/yolo_quantization/quantize_yolo.py","examples/onnx/language_models/opt/quantize_model.py","examples/onnx/weights_only_quantization/int4_matmul_nbits/llama2/quantize_model.py","quark/onnx/quantization/api.py","quark/onnx/quantization/config/custom_config.py","quark/onnx/operators/custom_ops/__init__.py"]

quark-onnx-result-validator

Purpose

Run four lightweight checks on a completed Quark ONNX quantization output. Reads only ONNX graph headers (onnx.load(..., load_external_data=False)), initializer metadata, and small auxiliary files. Raw payload bytes are only touched for the bounded MD5 spot-check (and only for tensors matched by the user's exclude rules). Results feed a structured validation_report.md.

Runtime Assumptions

All scripts (quant_validation_onnx.py, run_validation.py) live in the same directory as this SKILL.md, under .claude/skills-impl/l1-atomic/onnx/quark-onnx-result-validator/.

Resolve SKILL_DIR from the repo root before running any command:

SKILL_DIR=.claude/skills-impl/l1-atomic/onnx/quark-onnx-result-validator

run_validation.py writes JSON to stdout; quant_validation_onnx.py diagnostics go to stderr with the prefix [quant-validation-onnx][tag]. Never treat stderr as structured output.

Requires the onnx Python package in the supported range (onnx>=1.21.0,<=1.22.0, per requirements.txt).

Contracts

Input: session_context.json, quant_plan.json (for exclude / op-type filters and model paths)
Output: validation_report.md
Schemas: shared/contracts/validation_report.schema.json

Inputs

Field	Source	Required
`source_model_path`	user or `session_context`	Step 2 + Step 3
`quantized_model_path`	user or `run_manifest`	All steps
`source_model_dir`	parent dir of source model (or `session_context`)	Step 1
`quantized_model_dir`	parent dir of quantized model (or `run_manifest`)	Step 1
`quant_config`	`quant_plan.json` or user-supplied JSON	Step 2 only

quant_config for step 2 supports the following keys (all optional unless noted):

Key	Purpose	Default
`exclude`	Glob list of initializer names expected to remain unchanged	—
`exclude_initializers`	Alias of `exclude`	—
`nodes_to_exclude`	Node names whose initializer inputs should remain unchanged	—
`op_types_to_quantize`	When set, any initializer not wired into one of these op types becomes an implicit exclude	—
`max_samples`	Random spot-check cap for large models	`200`
`random_seed` / `seed`	Deterministic sampling seed	`None`

At least one of exclude / exclude_initializers / nodes_to_exclude / op_types_to_quantize must be provided; otherwise step 2 is marked skipped.

Outputs

validation_report.md with one section per executed step. Unexecuted steps are marked skipped.

Interaction Flow

Confirm SKILL_DIR, source_model_path (if available), and quantized_model_path are resolvable.
Run the self-test to verify scripts are intact: python3 "$SKILL_DIR/run_validation.py" self-test
Execute steps in cheap-to-expensive order (4 → 1 → 3 → 2).
Collect JSON from stdout for each step; write validation_report.md.
Surface any ok: false steps with their errors / mismatches.

Four Validation Steps

Order	Function	CLI subcommand	Purpose
1	`check_auxiliary_files_copied`	`auxiliary`	Compare non-`.onnx`/non-`.onnx_data` auxiliary files between source and quantized directories
2	`check_non_quantized_initializers_md5_unchanged`	`md5`	MD5 spot-check initializer payload bytes (inline `raw_data` or external-data byte ranges) for tensors expected to remain non-quantized
3	`check_model_metadata_equal_except_quantization`	`metadata`	Compare IR version, producer, default-domain opset, and graph input/output signatures after stripping Quark-injected custom-op domains
4	`get_fuzzy_node_op_summary`	`fuzzy`	Header-only summary: op-type histogram, canonical node-name patterns, initializer dtype counts per pattern, QDQ / `com.amd.quark` custom-op presence

Run in cost order: 4 → 1 → 3 → 2.

Agent Execution Contract

Self-Test

SKILL_DIR=.claude/skills-impl/l1-atomic/onnx/quark-onnx-result-validator
python3 "$SKILL_DIR/run_validation.py" self-test

Exits 0 and prints exported symbols self-test (__all__): ok on success.

CLI Commands

SKILL_DIR=.claude/skills-impl/l1-atomic/onnx/quark-onnx-result-validator

# 4. get_fuzzy_node_op_summary (cheapest — header only)
python3 "$SKILL_DIR/run_validation.py" fuzzy \
  --model-path ./quantized/model.onnx

# 1. check_auxiliary_files_copied
python3 "$SKILL_DIR/run_validation.py" auxiliary \
  --source-model-dir ./source-dir --quantized-model-dir ./quantized-dir \
  --ignore 'README*'

# 3. check_model_metadata_equal_except_quantization
python3 "$SKILL_DIR/run_validation.py" metadata \
  --source-model-path ./source/model.onnx \
  --quantized-model-path ./quantized/model.onnx

# 2. check_non_quantized_initializers_md5_unchanged (most expensive)
python3 "$SKILL_DIR/run_validation.py" md5 \
  --source-model-path ./source/model.onnx \
  --output-model-path ./quantized/model.onnx \
  --quant-config '{"exclude":["*.bias","embeddings.*.weight"],"max_samples":50}'

For md5, --quant-config accepts a JSON string or a path to a JSON file. If SKILL_DIR or model paths cannot be resolved, mark the affected step skipped.

Public API (for direct Python import)

from quant_validation_onnx import (
    check_auxiliary_files_copied,
    check_non_quantized_initializers_md5_unchanged,
    check_model_metadata_equal_except_quantization,
    get_fuzzy_node_op_summary,
)

All four functions are in __all__. Other public-named helpers are internal utility surface.

Recovery

Failure	Recovery
Self-test exits non-zero	Report script integrity failure; do not run further steps
`onnx` import fails	Hand off to `quark-onnx-install`; do not run any step
`source_model_path` missing	Mark steps 2, 3 as skipped; run steps 1, 4 if quantized path is available
`quant_config` missing exclude rules	Mark step 2 as skipped
External-data file missing alongside `.onnx`	Recorded under `external_data_missing`; affected tensors marked `read_error` in step 2; step 4 still runs against the graph proto
Quantized model has zero QDQ / Quark-custom nodes	Step 4 emits a high-severity warning (`quantization_did_not_run`)

Report Template

## Validation Report — quark-onnx-result-validator

**Step 4 — fuzzy node / op summary**: ok / FAIL / skipped
  - op types: <count>, QDQ nodes: <count>, com.amd.quark nodes: <count>
  - Notable: <pattern> → <op_type_counts>
  - Quantization signal: present / **MISSING** / partial

**Step 1 — auxiliary files**: ok / FAIL / skipped
  - missing: <count>, mismatched: <count>, extra: <count>

**Step 3 — model metadata**: ok / FAIL / skipped
  - ir_version: <source> / <quantized>
  - opset_import (default domain): <source> / <quantized>
  - input/output signature diffs: <count>
  - Quark-injected opset domains: <list>

**Step 2 — MD5 spot-check (initializers)**: ok / FAIL / skipped
  - candidates: <count>, checked: <count>, sampled: true/false
  - mismatches: <count>
  - external_data_missing: <count>

Canonical Name Rules

Replace only pure numeric path segments with *: Conv_12 → Conv_*, model.layer.3.Conv → model.layer.*.Conv
Do not alter digits embedded in non-numeric names: Conv1, MatMul_w2 stay unchanged
op_type_counts and dtype_counts are aggregated per pattern; multiple op types / dtypes in one pattern signals partial or mixed-precision quantization

Optional Dtype Hints

Heuristics only — not mandatory pass/fail rules:

INT8 QDQ: initializers with INT8 / UINT8; nodes QuantizeLinear / DequantizeLinear
INT4 MatMulNBits: initializers with UINT8 packed-low-nibble shape; MatMulNBits op
BFP16 / MX / MXFP: com.amd.quark opset domain present; nodes BFPQuantizeDequantize / MXQuantizeDequantize / ExtendedQuantizeLinear
FP16 / BF16 keep: initializers with FLOAT16 / BFLOAT16 and no QDQ neighbors

ONNX-vs-Torch Behavioural Notes

Step 2 walks initializers (the ONNX analog of safetensors tensors), not safetensors entries. Inline tensors are read via tensor.raw_data; external-data tensors are read by (offset, length) from the external-data file declared in tensor.external_data.
Step 3 compares model-level metadata (IR / producer / opset) and graph I/O signature. ONNX has no config.json equivalent — opset comparison strips Quark's custom domains before equality so the only allowed diff is the addition of com.amd.quark (or similar) on the quantized side.
Step 4 is structurally similar to the Torch fuzzy summary: it groups nodes / initializers by canonical name pattern and reports op_type_counts / dtype_counts. Additionally surfaces whether quantization actually ran (QuantizeLinear / com.amd.quark presence).