stage2 tuning for 4090D and 5090

[WANDY666]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @WANDY666, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on enhancing the performance of the _fwd_kernel_flash_decode_diverse_stage1 kernel by introducing a wide array of optimized configurations. These new settings are tailored for specific NVIDIA GPU architectures, namely the RTX 4090D and the upcoming RTX 5090, ensuring improved efficiency across different operational parameters such as GQA group sizes, batch sizes, and output data types.

Highlights

• New Kernel Configurations: Introduced a comprehensive set of new kernel configurations for the _fwd_kernel_flash_decode_diverse_stage1 kernel.
• GPU Specific Tuning: Added specific performance tuning parameters for NVIDIA GeForce RTX 4090D and 5090 GPUs.
• Diverse Parameter Support: Configurations cover various gqa_group_size, max_batch_group_size, and out_dtype settings, including torch.bfloat16 and torch.float16.

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[gemini-code-assist]

Code Review

This pull request adds kernel tuning configurations for NVIDIA GeForce RTX 4090D and 5090 GPUs. While adding these configurations is a good step for performance, I have identified two main issues. First, the configuration files for bfloat16 seem to be duplicates of the float16 files, which is likely incorrect and could lead to suboptimal performance. It's recommended to either perform separate tuning for bfloat16 or remove these files. Second, all the new JSON files are unformatted, which harms readability and maintainability. I've suggested formatting them for consistency and easier reviews in the future. Additionally, the very long filenames could potentially cause issues on some filesystems like Windows due to path length limitations.

[gemini-code-assist]

This configuration file for bfloat16 is identical to its float16 counterpart. Based on the tuning script test/kernel/llama_gqa_diverse_decode_stage1_tuning.py, it appears tuning was only performed for torch.half (float16), and the results were then copied for both float16 and bfloat16. This means the bfloat16 configurations are likely not optimal and could lead to performance degradation. It is recommended to either run the tuning specifically for bfloat16 and update these files, or remove the bfloat16 configuration files for now to avoid confusion and potential performance issues. This applies to all bfloat16 config files in this PR.

[gemini-code-assist]

This JSON configuration file is not formatted, which makes it difficult to read and review. For better maintainability, please format the JSON content with indentation and add a newline at the end of the file. This should be applied to all new JSON files in this pull request.

{
  "4096": {
    "8": {
      "BLOCK_N": 16,
      "num_warps": 4,
      "num_stages": 1
    },
    "32": {
      "BLOCK_N": 16,
      "num_warps": 4,
      "num_stages": 1
    },
    "128": {
      "BLOCK_N": 16,
      "num_warps": 2,
      "num_stages": 1
    },
    "256": {
      "BLOCK_N": 16,
      "num_warps": 2,
      "num_stages": 1
    }
  },
  "8192": {
    "8": {
      "BLOCK_N": 16,
      "num_warps": 2,
      "num_stages": 1
    },
    "32": {
      "BLOCK_N": 16,
      "num_warps": 2,
      "num_stages": 1
    },
    "128": {
      "BLOCK_N": 16,
      "num_warps": 2,
      "num_stages": 1
    },
    "256": {
      "BLOCK_N": 16,
      "num_warps": 2,
      "num_stages": 1
    }
  }
}