[DOCS ]Add annotated partitioning documentation

[yuslepukhin]

This pull request introduces a new documentation page, PartitioningWithAnnotationsAndMemoryConstraints.md, which explains advanced ONNX Runtime features for partitioning model graphs across devices with explicit control. The doc covers how to annotate model layers for device assignment, collect per-node memory statistics, and enforce GPU memory budgets during partitioning. These features enable precise control over device placement and memory usage for large models.

The most important changes are:

New Documentation: Advanced Partitioning Features

• Adds a comprehensive guide (PartitioningWithAnnotationsAndMemoryConstraints.md) describing how to use ONNX Runtime’s layer annotation and memory constraint features for graph partitioning.

Layer Assignment via Annotations

• Explains how to annotate ONNX model nodes with layer_ann metadata, including manual annotation and automated annotation using Olive’s CaptureLayerAnnotations pass.
• Provides configuration examples for mapping annotation patterns to devices at runtime using the session.layer_assignment_settings session option.

Capacity-Aware Partitioning

• Details a two-phase workflow for profiling per-node memory usage and then enforcing a memory budget with the session.resource_cuda_partitioning_settings session option.
• Covers both profiling-based and ad-hoc (estimation-only) approaches for memory-constrained partitioning. (docs/annotated_partitioning/PartitioningWithAnnotationsAndMemoryConstraints.mdR1-R267

This is a follow up for #27595

[Copilot]

Pull request overview

Adds a new documentation page describing advanced ONNX Runtime graph partitioning features—layer annotation–based device assignment and CUDA capacity-aware partitioning—intended to guide users through annotation, profiling, and constrained partitioning workflows.

Changes:

• Documented layer_ann node annotations and runtime mapping via session.layer_assignment_settings.
• Documented memory-stat collection (session.collect_node_memory_stats_to_file) and CUDA memory-budget partitioning (session.resource_cuda_partitioning_settings).
• Included end-to-end examples for combining annotation-based assignment with memory-constrained partitioning.

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[Copilot]

Pull request overview

Copilot reviewed 1 out of 1 changed files in this pull request and generated 5 comments.

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[Copilot]

Pull request overview

Copilot reviewed 1 out of 1 changed files in this pull request and generated 3 comments.

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[yuslepukhin]

/azp run web_Release / build_onnxruntime_web

[azure-pipelines]

No pipelines are associated with this pull request.