[Bench] Add external baselines for common ops

[superAngGao]

Summary

Draft PR for #1806.

• Add external baseline tags for common-op benchmarks: torch_inductor, torch_cudnn, torch_cufft, torch_native, and flashinfer.
• Add a shared torch_inductor_baseline() helper for benchmark-local PyTorch baselines compiled with torch.compile(..., fullgraph=True).
• Add FlashInfer baselines for RMSNorm, fused add RMSNorm, and semantic-equivalent RoPE variants.
• Keep optional third-party baselines local to benchmark files and preserve existing roofline reporting.
• Fix nightly report coloring to use the strongest competitive baseline when multiple baselines are present.

Validation

Official runner image: ghcr.io/tile-ai/tileops-runner:65dbc98-torch2.10.

• compileall and ruff check on changed files.
• pytest -q tests/test_nightly_report.py benchmarks/tests/test_benchmark_base.py --tb=short -> 19 passed.
• RoPE/Pool benchmark collect-only -> 52 cases collected.
• Targeted manifest checks for RoPE, RMSNorm, Conv/Pool, and FFT passed with advisory warnings only.
• TorchInductor and FlashInfer smoke checks passed.

Closes #1806.

[gemini-code-assist]

Caution

The consumer version of Gemini Code Assist on GitHub has been sunset. All code review activity has officially ceased.

[superAngGao]

Thanks for the careful review. I pushed a follow-up commit (1a28c9d) addressing all three points.

• Fixed RoPE FlashInfer semantics: cached FlashInfer baselines now receive variant-specific cos/sin caches. YaRN uses the benchmark-local YaRN interpolation formula, and the benchmark also checks FlashInfer output against the benchmark-local semantic reference once before timing. I also tightened the torch reference path so Neox, Non-Neox, Llama 3.1, YaRN, and LongRoPE all use semantic-equivalent references instead of geometry-only rotation.
• Renamed pooling baselines from torch_cudnn to torch_native, matching the kernels observed in the official runner. Convolution and BatchNorm remain tagged torch_cudnn.
• Fixed nightly report coloring for multi-baseline configs. Since ratio = baseline_latency / tileops_latency, the strongest competitor is the smallest ratio, not the largest. Added a small regression test for the FlashInfer=0.5 / Inductor=2.0 case.

Validation in ghcr.io/tile-ai/tileops-runner:65dbc98-torch2.10:

• compileall for the changed files
• pytest -q tests/test_nightly_report.py benchmarks/tests/test_benchmark_base.py --tb=short -> 19 passed
• pytest --collect-only -q benchmarks/ops/bench_rope.py benchmarks/ops/bench_pool.py --tb=short -> 52 benchmark cases collected
• ruff check on the changed files
• validate_manifest.py --check-op RopeYarnOp
• validate_manifest.py --check-op AvgPool2dFwdOp
• FlashInfer RoPE semantic smoke: Neox, Non-Neox, Llama 3.1, YaRN, and LongRoPE all matched the benchmark-local reference within fp16 tolerance; YaRN/Llama/LongRoPE local tables also matched the production formula helpers.