vllm-project
[ROCm] Enable custom paged attention kernel for Navi3/4
Add additional custom paged attention kernels for AMD Navi 3x/4x GPU support based on PR: https://github.com/vllm-project/vllm/pull/12348 Due to the differences in architecture from MI, specific instructions and detailed logic have changed (mfma16 -> wmma16/wmma16_gfx12), so new kernels for each architecture has been added.
- Supports cases where head_size == 128 and block_size == 16.
- It does not support alibi_slopes and kv_cache_dtype == fp8.
- It supports gqa_ratio up to 16, and shows performance gains over the existing kernel when gqa_ratio is 3 or higher. Therefore, it is enabled for gqa_ratio values between 3 and 16.
- Fixed paged attention unit test to pass on Navi
Performance Gain Script: python ./benchmarks/benchmark_throughput.py --model <path_to_model> --trust-remote-code --dataset <ShareGPT_V3_unfiltered_cleaned_split.json> --num_prompts 1000 --max-model-len 4096 --gpu-memory-utilization 0.95
Navi 3
| Models | Num Heads | GQA Ratio | Output Token/s (original) | Output Token/s (custom) | Gain |
|---|---|---|---|---|---|
| glm-4-9b-chat | 32 | 16 | 991.43 | 1113.75 | 12.3% |
| chatglm3-6b | 32 | 16 | 1442.07 | 1554.23 | 7.8% |
| Meta-Llama-3.1-8B-Instruct | 32 | 4 | 1143.65 | 1221.75 | 6.8% |
| Llama-3.2-3B-Instruct | 24 | 3 | 2058.97 | 2146.62 | 4.3% |
| Qwen1.5-7B-Chat | 32 | 1 | 904.46 | 882.53 | -2.4% |
Navi 4
| Models | Num Heads | GQA Ratio | Output Token/s (original) | Output Token/s (custom) | Gain |
|---|---|---|---|---|---|
| glm-4-9b-chat | 32 | 16 | 1195.56 | 1433.13 | 19.9% |
| chatglm3-6b | 32 | 16 | 1750.34 | 1962.21 | 12.1% |
| Meta-Llama-3.1-8B-Instruct | 32 | 4 | 1405.42 | 1516.69 | 7.9% |
| Llama-3.2-3B-Instruct | 24 | 3 | 2419.31 | 2561.47 | 5.9% |
| Qwen1.5-7B-Chat | 32 | 1 | 765.6 | 761.3 | -0.6% |
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This pull request has merge conflicts that must be resolved before it can be merged. Please rebase the PR, @hyoon1.
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork
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This pull request has merge conflicts that must be resolved before it can be merged. Please rebase the PR, @hyoon1.
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork
Do we know how this stacks up against the new AMD triton kernels? (cc @SageMoore for direction to the kernels)
Unfortunately, that kernel is only integrated into V1 right now. We should definitely integrate that kernel into V0 and see what the performance is like, though.
Here's the kernel in question if you are interested: https://github.com/vllm-project/vllm/blob/main/vllm/attention/ops/chunked_prefill_paged_decode.py#L186
@tlrmchlsmth and @WoosukKwon, could you please help to review and approve this PR, which provides good e2e perf gain for SOTA models running on AMD Radeon GPUs using vLLM? Thanks.
Hi @hyoon1, thank you for your contribution.
I am hesitant to review and accept this PR, mainly because it only applies to vLLM V0. We are imminently going to switch to V1 by default starting with the 0.8.0 release, which will bring with it large performance improvements. V1 natively uses chunked-prefill, and as I understand it, this kernel doesn't fit easily into that case.
For V1 I think we have a good solution in the triton kernels added in https://github.com/vllm-project/vllm/pull/14152, but would also be interested in seeing how the kernels compare
Hi @tlrmchlsmth thanks for letting me know about new V1 kernel.
As you mentioned, there seem to be significant performance improvements in V1 due to the new prefill/decode method. As a result, I compared the performance of V0 with custom_paged_attention (CPA) applied to V1 with the new kernel.
The comparison showed that, as of now, the approach with CPA applied to V0 performs better on AMD Navi GPUs in terms of output token throughput. Although it seems that V1 is not yet fully optimized, we do have customers who desire high performance with AMD Navi GPUs. Therefore, until V1 surpasses the optimized V0 in performance, we want to offer the optimized V0 option. Additionally, we have other optimization elements that can further improve performance, and we plan to submit more pull requests.
Here are benchmark results from Navi3x/Navi4x GPU system:
python ./benchmarks/benchmark_serving.py --model /models/Llama-3.1-8B-Instruct --dataset-name sharegpt --dataset-path /sharegpt/ShareGPT_V3_unfiltered_cleaned_split.json
Navi4 GPU V1
============ Serving Benchmark Result ============
Successful requests: 1000
Benchmark duration (s): 229.43
Total input tokens: 215196
Total generated tokens: 197286
Request throughput (req/s): 4.36
Output token throughput (tok/s): 859.89
Total Token throughput (tok/s): 1797.85
---------------Time to First Token----------------
Mean TTFT (ms): 85488.14
Median TTFT (ms): 91342.26
P99 TTFT (ms): 192146.97
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 246.50
Median TPOT (ms): 192.40
P99 TPOT (ms): 786.78
---------------Inter-token Latency----------------
Mean ITL (ms): 181.05
Median ITL (ms): 101.11
P99 ITL (ms): 784.66
==================================================
V0 + this PR (CPA)
============ Serving Benchmark Result ============
Successful requests: 1000
Benchmark duration (s): 200.67
Total input tokens: 215196
Total generated tokens: 197999
Request throughput (req/s): 4.98
Output token throughput (tok/s): 986.71
Total Token throughput (tok/s): 2059.12
---------------Time to First Token----------------
Mean TTFT (ms): 69061.01
Median TTFT (ms): 64760.14
P99 TTFT (ms): 159312.67
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 296.71
Median TPOT (ms): 231.04
P99 TPOT (ms): 1870.42
---------------Inter-token Latency----------------
Mean ITL (ms): 213.11
Median ITL (ms): 134.48
P99 ITL (ms): 776.32
==================================================
Navi3 GPU V1
============ Serving Benchmark Result ============
Successful requests: 1000
Benchmark duration (s): 212.18
Total input tokens: 215196
Total generated tokens: 198024
Request throughput (req/s): 4.71
Output token throughput (tok/s): 933.27
Total Token throughput (tok/s): 1947.48
---------------Time to First Token----------------
Mean TTFT (ms): 43194.99
Median TTFT (ms): 40183.75
P99 TTFT (ms): 97066.20
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 532.21
Median TPOT (ms): 504.83
P99 TPOT (ms): 795.70
---------------Inter-token Latency----------------
Mean ITL (ms): 363.82
Median ITL (ms): 244.72
P99 ITL (ms): 818.46
==================================================
V0 + this PR (CPA)
============ Serving Benchmark Result ============
Successful requests: 1000
Benchmark duration (s): 204.61
Total input tokens: 215196
Total generated tokens: 197859
Request throughput (req/s): 4.89
Output token throughput (tok/s): 966.99
Total Token throughput (tok/s): 2018.71
---------------Time to First Token----------------
Mean TTFT (ms): 66983.01
Median TTFT (ms): 62685.91
P99 TTFT (ms): 156777.94
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 288.71
Median TPOT (ms): 231.92
P99 TPOT (ms): 1687.35
---------------Inter-token Latency----------------
Mean ITL (ms): 212.65
Median ITL (ms): 133.75
P99 ITL (ms): 811.25
==================================================
This pull request has merge conflicts that must be resolved before it can be merged. Please rebase the PR, @hyoon1.
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork
Closing this PR in favor of new v1 support: https://github.com/vllm-project/vllm/pull/17004