[Feature][AutoDeploy]: Support custom sharding config

[greg-kwasniewski1]

🚀 The feature, motivation and pitch

Enable specifying sharding config manually, similarly to factory's base_model_tp_plan. The feature should enable specifying priority order between custom sharding config, factory, and heuristics.

Alternatives

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Additional context

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[coderabbitai]

A summary of the changes CodeRabbit can apply:

• Implement custom sharding support by adding a CUSTOM option to ShardingConfigSource (tensorrt_llm/_torch/auto_deploy/models/factory.py), extending ShardingConfig with custom_config, sharding_priority, _validate_custom_config, get_active_config and refactored validation (tensorrt_llm/_torch/auto_deploy/utils/sharding_utils.py), adding custom_sharding_config and sharding_priority fields to ShardingTransformConfig (tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py), updating Sharding._apply to accept and set custom_sharding_config into shared_config.sharding_config and use priority-based get_active_config logic, and renaming/updating detect_sharding_from_factory_config to detect_sharding_from_config to accept both HUGGINGFACE and CUSTOM — enabling user-provided sharding with configurable precedence while remaining backward compatible.

• Update tensorrt_llm/_torch/auto_deploy/models/factory.py to document and add a CUSTOM ShardingConfigSource enum value, and modify tensorrt_llm/_torch/auto_deploy/utils/sharding_utils.py to introduce custom_config and sharding_priority fields, refactor validate_config to choose and validate configs by priority, add _validate_factory_config and _validate_custom_config helpers, and add get_active_config to return the active sharding plan (falling back to heuristics) — enabling user-provided sharding, prioritized selection, and clearer validation and logging.

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[greg-kwasniewski1]

PR #9143