Dynamo model list for different scopes

[libohao1201]

Add dynamo model list for different test scopes.

[Copilot]

Pull request overview

This PR adds Dynamo model lists for different benchmark test scopes (p0, p1, p2) across three model suites: TorchBench, TIMM, and HuggingFace. The lists are organized by priority/scope level to support different testing configurations.

Changes:

• Added TorchBench model lists for training and inference modes in p2 scope
• Added TIMM and HuggingFace model lists for p0, p1, and p2 scopes
• Created structured text files with numbered model entries for benchmark testing

Reviewed changes

Copilot reviewed 10 out of 10 changed files in this pull request and generated 1 comment.

Show a summary per file

File	Description
.ci/benchmarks/p2/torchbench_models_list_train.txt	28 TorchBench models for training scope in p2 tests
.ci/benchmarks/p2/torchbench_models_list_infer.txt	35 TorchBench models for inference scope in p2 tests
.ci/benchmarks/p2/timm_models_list.txt	27 TIMM models with batch sizes for p2 scope
.ci/benchmarks/p2/huggingface_models_list.txt	6 HuggingFace models with batch sizes for p2 scope
.ci/benchmarks/p1/torchbench_models_list.txt	29 TorchBench models for p1 scope
.ci/benchmarks/p1/timm_models_list.txt	20 TIMM models with batch sizes for p1 scope
.ci/benchmarks/p1/huggingface_models_list.txt	20 HuggingFace models with batch sizes for p1 scope
.ci/benchmarks/p0/torchbench_models_list.txt	16 TorchBench models for p0 scope
.ci/benchmarks/p0/timm_models_list.txt	16 TIMM models with batch sizes for p0 scope
.ci/benchmarks/p0/huggingface_models_list.txt	16 HuggingFace models with batch sizes for p0 scope

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[Copilot]

The model entry 'res2net101_26w_4s' is missing a batch size parameter, while all other entries in the file include one (e.g., '128', '512'). For consistency and to prevent potential parsing errors, add a batch size value.

Suggested change

	res2net101_26w_4s
	res2net101_26w_4s 128

[github-actions]

Performance outliers, please check!

• 🟡 [80%, 90%), may be fluctuations

Category	Model	Target vs. Baseline [Eager]	Target vs. Baseline [Inductor]
torchbench_bfloat16_training	resnet18	0.94539	0.815009

[github-actions]

Performance outliers, please check!

• 🔴 [-1, 80%), should be regression

Category	Model	Target vs. Baseline [Eager]	Target vs. Baseline [Inductor]
torchbench_bfloat16_training	resnet18	0.92947	0.765747

• 🟡 [80%, 90%), may be fluctuations

Category	Model	Target vs. Baseline [Eager]	Target vs. Baseline [Inductor]
torchbench_bfloat16_training	mnasnet1_0	1.011198	0.825302