[Feat] Enable PDL automatically on Hopper architecture

[PopSoda2002]

Motivation

In previous versions, FlashInfer 0.2.5 supports norm's PDL, but currently, norm's PDL is disabled by default. This PR modifies the code to enable it automatically on Hopper architecture.

Modifications

1. Add is_hopper_arch() utility function to detect Hopper architecture (compute capability >= 9.0)
2. Modify rmsnorm, fused_add_rmsnorm, gemma_rmsnorm and gemma_fused_add_rmsnorm functions to auto-enable PDL on Hopper
3. Update documentation to reflect these changes

This implementation automatically enables PDL optimization on Hopper GPUs while maintaining backward compatibility by allowing explicit override through the parameter.

Checklist

• [x] Format your code according to the Code Formatting with Pre-Commit.
• [ ] Add unit tests as outlined in the Running Unit Tests.
• [ ] Update documentation / docstrings / example tutorials as needed, according to Writing Documentation.
• [ ] Provide throughput / latency benchmark results and accuracy evaluation results as needed, according to Benchmark and Profiling and Accuracy Results.
• [ ] For reviewers: If you haven't made any contributions to this PR and are only assisting with merging the main branch, please remove yourself as a co-author when merging the PR.
• [ ] Please feel free to join our Slack channel at https://slack.sglang.ai to discuss your PR.

[zhyncs]

Also please provide the performance benchmark after this enhancement

[PopSoda2002]

Also please provide the performance benchmark after this enhancement

Yes, there is another guy who is testing the performance!

[hebiao064]

Also please provide the performance benchmark after this enhancement

Yes, there is another guy who is testing the performance!

Hi, how's the progress on the benchmark?

[PopSoda2002]

Still working bro

Also please provide the performance benchmark after this enhancement

Yes, there is another guy who is testing the performance!

Hi, how's the progress on the benchmark?

[PopSoda2002]

Here is my benchmark for testing(test on H100): command:

python3 -m sglang.bench_one_batch --model-path meta-llama/Llama-3.1-8B-Instruct --attention-backend fa3 --batch 16 --input-len 1024 --output-len 10

Before this PR:

After:

Thanks @Fridge003 for helping!

[FlamingoPg]

batch_size	hidden_size	dtype	w/o pdl	w/ pdl
1	111	torch.float16	12.224000	9.632000
1	111	torch.bfloat16	10.976000	11.040000
1	500	torch.float16	11.008000	10.144000
1	500	torch.bfloat16	11.424000	9.632000
1	1024	torch.float16	11.360000	9.696000
1	1024	torch.bfloat16	11.392000	9.984000
1	3072	torch.float16	12.544000	10.304000
1	3072	torch.bfloat16	11.136000	11.264000
1	3584	torch.float16	11.616000	10.016000
1	3584	torch.bfloat16	11.296000	10.368000
1	4096	torch.float16	11.648000	11.456000
1	4096	torch.bfloat16	11.680000	11.456000
1	8192	torch.float16	13.248000	10.880000
1	8192	torch.bfloat16	12.128000	11.936000
1	16384	torch.float16	12.864000	11.904000
1	16384	torch.bfloat16	13.216000	11.584000
19	111	torch.float16	11.776000	11.584000
19	111	torch.bfloat16	12.864000	11.584000
19	500	torch.float16	12.544000	9.920000
19	500	torch.bfloat16	11.584000	9.920000
19	1024	torch.float16	11.776000	10.496000
19	1024	torch.bfloat16	12.672000	10.048000
19	3072	torch.float16	12.832000	11.520000
19	3072	torch.bfloat16	11.456000	10.528000
19	3584	torch.float16	12.992000	10.272000
19	3584	torch.bfloat16	12.992000	10.272000
19	4096	torch.float16	12.032000	11.744000
19	4096	torch.bfloat16	13.088000	10.336000
19	8192	torch.float16	12.576000	11.264000
19	8192	torch.bfloat16	12.672000	11.392000
19	16384	torch.float16	13.856000	12.704000
19	16384	torch.bfloat16	14.048000	13.728000
99	111	torch.float16	13.088000	10.464000
99	111	torch.bfloat16	13.088000	10.848000
99	500	torch.float16	11.392000	10.400000
99	500	torch.bfloat16	11.584000	10.016000
99	1024	torch.float16	11.552000	10.688000
99	1024	torch.bfloat16	12.960000	10.688000
99	3072	torch.float16	12.288000	10.688000
99	3072	torch.bfloat16	13.376000	11.136000
99	3584	torch.float16	12.640000	12.352000
99	3584	torch.bfloat16	12.640000	11.328000
99	4096	torch.float16	12.704000	11.552000
99	4096	torch.bfloat16	14.080000	11.552000
99	8192	torch.float16	16.192000	14.016000
99	8192	torch.bfloat16	16.160000	14.752000
99	16384	torch.float16	18.208001	15.776001
99	16384	torch.bfloat16	18.176001	17.120000
989	111	torch.float16	12.864000	11.648000
989	111	torch.bfloat16	12.896000	11.680000
989	500	torch.float16	13.408000	13.280000
989	500	torch.bfloat16	14.528000	12.224000
989	1024	torch.float16	18.975999	17.696001
989	1024	torch.bfloat16	18.975999	17.664000
989	3072	torch.float16	23.647999	22.399999
989	3072	torch.bfloat16	23.712000	22.431999
989	3584	torch.float16	24.831999	23.520000
989	3584	torch.bfloat16	24.831999	23.456000
989	4096	torch.float16	23.808001	22.368001
989	4096	torch.bfloat16	23.680000	21.152001
989	8192	torch.float16	35.808001	33.408001
989	8192	torch.bfloat16	34.880001	34.015998
989	16384	torch.float16	64.800002	63.616000
989	16384	torch.bfloat16	64.576000	64.032003

benchmark down cc @zhyncs @hebiao064

[hebiao064]

LFG!

[PopSoda2002]

Hi @zhyncs, can you help to review this PR? I think it is ready to merge