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"