[Roadmap] FlashInfer v0.2 to v0.3

[yzh119]

Milestones

Our tentative roadmap includes the following milestones:

• SageAttention-2 in FlashAttention3: Implement SageAttention-2 in FlashAttention3 template
• Flex-Attention Compatible Interface: standarize JIT interface @shadowpa0327
• SM89 Kernel Optimization: Leverage Ada FP8 Tensor Cores for better performance on Ada6000 & 4090.
• Template Refactoring: Refactor FA-2 and MLA templates using CuTE.
• MLA Acceleration: Optimize Multi-Level Attention (MLA) with Tensor Core support, follow up of feat: support MLA decode #551 . Tracking issue [Tracing Issue] Multi-head Latent Attention #792
• Triton Porting: Migrate elementwise, normalization, and other kernels (that are not on critical path) to Triton.
• API Standardization: Simplify and standardize the attention APIs for better usability.
• POD-Attention Integration: Implement POD-Attention for improved efficiency of chunked-prefill.
• Nanoflow Parallelism: Expose python-level APIs for performing GEMM and Attention on a subset of SMs, which is required for nanoflow style parallelism, see #591.
• Fused Tree Speculative Sampling: follow up of sampling: fused speculative sampling kernels #259 , we should support tree-speculative sampling as well, we will port the implementation of fused tree-speculative sampling written by @spectrometerHBH from https://github.com/mlc-ai/mlc-llm to accelerate eagle and medusa etc.
• Improvements on Existing Top-P/K Sampling Operators: change the algorithm to guarantee all samples are successful after 32 rounds.
• PyPI wheels: upload wheels to PyPI (pending issue: PEP 541 Request: flashinfer pypi/support#5355)
• RoPE positions in batch attention interface: Fused-RoPE Attention with q_offset and k_offset #701
• FP32 output for bf16 input: Different sequence numbers calculate inconsistent results #696
• Per Head Scale Quantized KV-Cache: How to use low bit KV Cache  #721

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We welcome your feedback and suggestions!
Let us know what features you'd like to see in FlashInfer.

[johnnynunez]

Initial support blackwell:
#747
10.0 blackwell b100/b200
12.0 blackwell rtx50
super: flex attention

[AgrawalAmey]

Looking forward to Pod-Attention support!

[AgrawalAmey]

To add more context, we have the following piece of code in mneomsyne codebase:

def _arrange_sequences_for_execution(
        self,
        seq_schedule_metadata_list: List[SequenceScheduleMetadata],
    ) -> List[SequenceScheduleMetadata]:
        """
        We need to arrange sequences in a way that allows us to perform
        attention computation in an efficient manner. Due to poor handling of mixed batches
        in attention kernels. We need to split the first split the sequences into prefill and decode:
        | prefill seqs | decode seqs |

        Secondly, when we mix sequences of different lengths, the attention kernel parallelization
        heuristics fail, and results in high latency. Thus, we need to further split the sequences:
        | long seqs | short seqs |

        Furthermore, within each group, we can have kvp sequences. Some of these kvp
        sequences might not require kv cache to be saved. So, within each group, we need to further
        organize sequences as follows:
        | non kvp seqs | kvp seqs w/ save_kv_cache | kvp seqs w/o save_kv_cache |
        """

In essence, we create make 4 different instances of flashinfer prefill attention wrapper and call the kernel 4 times 😢 cc @yzh119