feat: add MobiusModelBuilder Olive pass for mobius-backed ONNX export

[justinchuby]

Summary

Adds a new Olive pass (MobiusModelBuilder) that wraps mobius build() to produce ONNX models from HuggingFace model IDs.

• Single-component models (LLMs) → ONNXModelHandler
• Multi-component models (VLMs, encoder-decoders) → CompositeModelHandler
• EP auto-detected from Olive accelerator spec (cpu/cuda/dml/webgpu)
• Precision: fp32 (default), fp16, bf16
• Registered in olive_config.json as MobiusModelBuilder
• Example pipeline config: examples/gemma4/gemma4_int4_cuda.json
• 14 unit tests covering single/multi-component, EP detection, and error cases

Validated: Gemma4 INT4 Quantization Pipeline

Successfully tested MobiusModelBuilder → OnnxBlockWiseRtnQuantization on google/gemma-4-E2B-it:

Quantized ops per component

Component	Total nodes	MatMulNBits	GatherBlockQuantized	Other ops
decoder	1,277	277	39	961
audio	1,465	135	0	1,330
vision	1,488	114	66	1,308
embedding	24	0	1	23

Weight quantization coverage

Component	Quantized (UINT8/INT4)	Non-quantized (FP16)	% quantized by size
decoder	316 tensors (2.4G elements)	585 tensors (71M elements)	97%
audio	135 tensors (154M elements)	768 tensors (2.8M elements)	98%
embedding	1 tensor (201M elements)	4 tensors (3.1M elements)	98%
vision	147 tensors (90M elements)	698 tensors (1.7M elements)	98%

Output structure (2.8GB total, down from ~5GB fp16)

models/gemma4-e2b-int4-cuda/
├── decoder.onnx      (853K) + decoder.onnx.data   (2.4G)
├── audio.onnx        (1.2M) + audio.onnx.data     (152M)
├── embedding.onnx    (8.5K) + embedding.onnx.data  (199M)
├── vision.onnx       (1.2M) + vision.onnx.data     (89M)
└── model_config.json

Pipeline timing

Pass	Time
MobiusModelBuilder (fp16 build)	77s
OnnxBlockWiseRtnQuantization (int4)	129s
Total	~3.5 min

[Copilot]

Pull request overview

Adds a new ONNX pass (MobiusModelBuilder) that uses the mobius package to build ONNX models directly from HuggingFace model IDs, returning either a single ONNXModelHandler or a CompositeModelHandler for multi-component exports.

Changes:

• Introduces olive/passes/onnx/mobius_model_builder.py implementing the new pass (EP mapping, precision mapping, trust_remote_code passthrough).
• Registers the pass in olive/olive_config.json and adds two Gemma4 example run configs.
• Adds unit tests for single-component, multi-component, EP selection, and error paths.

Reviewed changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 6 comments.

Show a summary per file

File	Description
olive/passes/onnx/mobius_model_builder.py	New pass wrapping mobius.build() and emitting Olive model handlers.
olive/olive_config.json	Registers MobiusModelBuilder and declares extras for its dependencies.
examples/gemma4/gemma4_int4_pipeline.json	Example pipeline: mobius export (fp16 CUDA) then INT4 quantization.
examples/gemma4/gemma4_fp32_cpu.json	Example pipeline: mobius export (fp32 CPU).
test/passes/onnx/test_mobius_model_builder.py	New unit tests for config, handler types, EP mapping, and missing dependency behavior.

[Copilot]

Pull request overview

Copilot reviewed 5 out of 5 changed files in this pull request and generated 4 comments.

[jambayk]

we could create an enum of the supported eps for automatic validation like in

Olive/olive/passes/pytorch/autoawq.py

Line 27 in 8b1957e

class ModelDtype(StrEnumBase):

.
unless you think the options might keep growing and it would be hard to keep it in sync across versions

[justinchuby]

Done.

[xiaoyu-work]

will mobius generate genai_config.json and related files for ort genai? Also, does mobius support customized naming for different component for multi components model? I can see all component models are named as "model.onnx"

[justinchuby]

@xiaoyu-work The models are <component_name>/model.onnx. Is it good enough or are there cases when we want a flat layout / renamed models?

[devang-ml]

Incorrect URL

[devang-ml]

We should not have pass level execution provider user choices.
The Olive engine is running a series of passes and user selects the EP for a given engine run.

[devang-ml]

See another relevant comment below _run_for_config

[devang-ml]

Olive Engine expects a pass to raise an error if accelerator spec provided EP is not supported by the pass. Alternatively return on modified input model in cases where input model type is same as output model type.