perf: fix the performance issue of append_paged_kv_cache
#588
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
Awesome! Huge improvements! Thanks! 🙏 |
yzh119
added a commit
that referenced
this pull request
Dec 17, 2024
🤖 I have created a release *beep* *boop* --- ## [0.2.0](v0.1.6...v0.2.0) (2024-12-17) [Release Blog](https://flashinfer.ai/2024/12/16/flashinfer-v02-release.html). ### Features * add `rotary_dim` argument to rope APIs for partial apply rope ([#599](#599)) ([eb9bc71](eb9bc71)) * add a `use_softmax` field in variant class ([#533](#533)) ([d81af97](d81af97)) * add an option `non_blocking` to plan function ([#622](#622)) ([560af6f](560af6f)) * add gemma_rmsnorm and gemma_fused_add_rmsnorm ([#477](#477)) ([1a6b17e](1a6b17e)) * add group size 3 to GQA decode dispatch ([#558](#558)) ([6227562](6227562)) * add JIT compilation support for FA3 templates ([#672](#672)) ([d4e8d79](d4e8d79)) * allow the cascade kernels to be executed using varying sequence lenghts ([#627](#627)) ([92ac440](92ac440)) * CUDAGraph compatibility of multi-level cascade inference APIs ([#586](#586)) ([2332e8a](2332e8a)) * fix the maximal grid dimension in prefill planning with CUDA graphs ([#639](#639)) ([86ca89a](86ca89a)) * improve the precision of the FusedAddRMSNormKernel function ([#587](#587)) ([c7dc921](c7dc921)) * JIT compilation ([#507](#507)) ([3613a5b](3613a5b)) * modify group-gemm stage number ([#497](#497)) ([52dab1d](52dab1d)) * non-contiguous query with paged kv cache ([#553](#553)) ([89f2c4a](89f2c4a)) * pass a dynamic token count to the cascade kernels ([#635](#635)) ([5fe9f7d](5fe9f7d)) * simplify prefill JIT compilation ([#605](#605)) ([fe4f898](fe4f898)) * specify gemm backend ([#648](#648)) ([0cc1a51](0cc1a51)) * support cached cos/sin in rope APIs ([#585](#585)) ([83e541d](83e541d)) * support huggingface transformer style rope interface ([#568](#568)) ([4f40420](4f40420)) * support sm90 cutlass group gemm ([#509](#509)) ([794bdda](794bdda)) * torch custom_op fix for rope ([#569](#569)) ([3e104bc](3e104bc)) * torch custom_op support: norm ([#552](#552)) ([f6e0010](f6e0010)) * torch.compile and custom_op support ([#554](#554)) ([9bf916f](9bf916f)) * warmup for jit kernel tests ([#629](#629)) ([8f5f349](8f5f349)) ### Bug Fixes * AOT compiler flags on non-sm90 ([#522](#522)) ([0aa4726](0aa4726)) * batch decode kernel redundant store output to gmem ([#505](#505)) ([90e42a7](90e42a7)) * compatible with torch 2.2 ([#478](#478)) ([ac41d1b](ac41d1b)) * #452 ([b53a46f](b53a46f)) * remove redundant load ([#495](#495)) ([2de16b0](2de16b0)) * update bmm fp8 test ([#487](#487)) ([45eac04](45eac04)) ### Performance Improvements * accelerate JIT compilation speed ([#618](#618)) ([eaf73fd](eaf73fd)) * Dense and sparse customizable flashattention-3 template ([#667](#667)) ([51236c9](51236c9)) * fix prefill kernel performance degradation (step 1) ([#602](#602)) ([595cf60](595cf60)) * fix the performance issue of `append_paged_kv_cache` ([#588](#588)) ([e15f7c9](e15f7c9)) * improve parallelism in RoPE with pos_ids ([#609](#609)) ([ff05155](ff05155)) * improve plan performance by using non-blocking memcpy ([#547](#547)) ([41ebe6d](41ebe6d)) * reduce the read and write of shared memory in the FusedAddRMSNormKernel ([#592](#592)) ([2043ca2](2043ca2)) * reduce total_num_tiles_q by one ([#644](#644)) ([553ace5](553ace5)) * remove unnecessary contiguous operation in block sparse attention ([#561](#561)) ([7a7ad46](7a7ad46)) * speedup jit compilation of prefill attention kernels ([#632](#632)) ([a059586](a059586)) * use cuda-core implemention for io-bound block-sparse attention ([#560](#560)) ([3fbf028](3fbf028)) --- This PR was generated with [Release Please](https://github.com/googleapis/release-please). See [documentation](https://github.com/googleapis/release-please#release-please). --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Zihao Ye <[email protected]>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
The performance of
append_paged_kv_cacheis terrible for small batch size, which is a known issue that we haven't fixed for a long time, this PR fixes it. This PR also adds support for non-contiguous append keys/values (which could be sliced from fused qkv matrix).We first call a triton kernel to convert
append_indptrtobatch_indicesandpositions(which is similar to CSR2COO conversion in sparse matrix). After the conversion, we can use element parallelism instead of batch parallelism.It's also worth trying using triton for the second
AppendPagedKVCacheKernelkernel, I think the performance should be fine. I'll leave it for future work.Some todo items:
After this PR (reference number can be found at #583 ):
cc @abcdabcd987