Improve parallelism in RoPE with pos_ids #609
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The previous kernel was not parallelised sufficiently well for low batch sizes. Similarly to the regular rotary kernel, now all qo/kv heads are split across separate blocks. In decode mode, the pos_ids kernel is now faster.
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Hi @nandor we use such parallelism mainly to save sin/cos computation time (same sin/cos can be reused for multiple heads). Would you mind running https://github.com/flashinfer-ai/flashinfer/blob/32d9510d67187f1f3a379cce81302cdd15a557d2/benchmarks/bench_rope.py ? |
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You are right - saving Unfortunately this sort of batching is a bit more convoluted to implement in CUDA than triton and internally we'll be relying on a Triton kernel instead. |
yzh119
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Sound good, thank you!
this sort of batching is a bit more convoluted to implement in CUDA than triton and internally we'll be relying on a Triton kernel instead.
Yes I agree, we plan to port most of the kernels (except for sampling and attention) to triton in v0.2.1 :)
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flashinfer-ai/flashinfer#609 potentially introduces correctness issues
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I'm actually seeing that this change causes a correctness issue wrt apply_rope_pos_ids. Here's a sample comparison script, passing prior to this commit hash (32d9510) but failing post-change: https://github.com/sgl-project/sglang/blob/dd0d2a3af4967880362e3bad9d95cd14572c89ea/scripts/playground/compare_flashinfer_vllm_rope.py |
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@james-p-xu I'll fix it, thank you! |
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Nov 20, 2024
As observed by @james-p-xu, #609 produce wrong results for some input shapes, this PR fixes the correctness issue, and add optimizations of dispatching to different parallelism modes for different input shapes. For large shape inputs, using the original implementation (re-use sin/cos for different heads) will be better. For small shape inputs, using head parallelism will be better. Some results: ``` Before #609 (no head-parallelism, re-use sin/cos value) ----------------- batch_size: 1, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 27us, throughput: 0.762GB/s batch_size: 1, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 22us, throughput: 0.919GB/s batch_size: 1, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 27us, throughput: 95.699GB/s batch_size: 1, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 28us, throughput: 95.244GB/s batch_size: 1, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 31us, throughput: 670.254GB/s batch_size: 1, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 31us, throughput: 667.253GB/s --- batch_size: 19, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 27us, throughput: 14.490GB/s batch_size: 19, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 27us, throughput: 14.466GB/s batch_size: 19, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 37us, throughput: 1344.086GB/s batch_size: 19, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 37us, throughput: 1344.902GB/s batch_size: 19, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 148us, throughput: 2699.475GB/s batch_size: 19, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 147us, throughput: 2701.897GB/s --- batch_size: 99, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 27us, throughput: 74.322GB/s batch_size: 99, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 27us, throughput: 74.568GB/s batch_size: 99, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 110us, throughput: 2352.352GB/s batch_size: 99, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 110us, throughput: 2365.580GB/s batch_size: 99, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 718us, throughput: 2893.608GB/s batch_size: 99, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 717us, throughput: 2894.859GB/s --- batch_size: 128, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 27us, throughput: 95.373GB/s batch_size: 128, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 27us, throughput: 95.810GB/s batch_size: 128, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 130us, throughput: 2583.872GB/s batch_size: 128, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 129us, throughput: 2595.944GB/s batch_size: 128, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 923us, throughput: 2907.408GB/s batch_size: 128, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 924us, throughput: 2905.533GB/s Head parallelism only (no dispatch) --------------------- batch_size: 1, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 6us, throughput: 3.321GB/s batch_size: 1, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 6us, throughput: 3.391GB/s batch_size: 1, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 7us, throughput: 358.862GB/s batch_size: 1, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 7us, throughput: 362.361GB/s batch_size: 1, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 15us, throughput: 1413.175GB/s batch_size: 1, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 15us, throughput: 1437.332GB/s --- batch_size: 19, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 6us, throughput: 60.526GB/s batch_size: 19, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 6us, throughput: 60.127GB/s batch_size: 19, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 26us, throughput: 1897.923GB/s batch_size: 19, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 24us, throughput: 2050.075GB/s batch_size: 19, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 164us, throughput: 2431.650GB/s batch_size: 19, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 147us, throughput: 2709.333GB/s --- batch_size: 99, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 7us, throughput: 284.641GB/s batch_size: 99, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 7us, throughput: 302.815GB/s batch_size: 99, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 109us, throughput: 2391.712GB/s batch_size: 99, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 97us, throughput: 2671.150GB/s batch_size: 99, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 860us, throughput: 2413.211GB/s batch_size: 99, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 828us, throughput: 2508.817GB/s --- batch_size: 128, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 7us, throughput: 349.795GB/s batch_size: 128, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 7us, throughput: 376.624GB/s batch_size: 128, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 139us, throughput: 2413.690GB/s batch_size: 128, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 124us, throughput: 2705.994GB/s batch_size: 128, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 1110us, throughput: 2417.480GB/s batch_size: 128, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 1063us, throughput: 2525.976GB/s This PR (shape dispatch) --------------------- batch_size: 1, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 28us, throughput: 0.728GB/s batch_size: 1, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 6us, throughput: 3.451GB/s batch_size: 1, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 7us, throughput: 359.759GB/s batch_size: 1, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 7us, throughput: 361.286GB/s batch_size: 1, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 15us, throughput: 1426.267GB/s batch_size: 1, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 15us, throughput: 1433.691GB/s --- batch_size: 19, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 6us, throughput: 60.390GB/s batch_size: 19, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 6us, throughput: 59.937GB/s batch_size: 19, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 26us, throughput: 1892.575GB/s batch_size: 19, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 24us, throughput: 2049.735GB/s batch_size: 19, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 148us, throughput: 2698.780GB/s batch_size: 19, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 147us, throughput: 2701.558GB/s --- batch_size: 99, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 7us, throughput: 285.335GB/s batch_size: 99, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 7us, throughput: 303.373GB/s batch_size: 99, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 110us, throughput: 2351.126GB/s batch_size: 99, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 110us, throughput: 2362.898GB/s batch_size: 99, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 717us, throughput: 2893.713GB/s batch_size: 99, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 717us, throughput: 2894.902GB/s --- batch_size: 128, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 7us, throughput: 350.720GB/s batch_size: 128, append_len: 1, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 7us, throughput: 376.690GB/s batch_size: 128, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 130us, throughput: 2584.221GB/s batch_size: 128, append_len: 128, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 129us, throughput: 2596.612GB/s batch_size: 128, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: False, latency: 924us, throughput: 2906.480GB/s batch_size: 128, append_len: 1024, num_qo_heads: 32, num_kv_heads: 8, head_dim: 128, use_cos_sin_cache: True, latency: 924us, throughput: 2905.134GB/s ``` cc @nandor @james-p-xu
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🤖 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]>
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The previous kernel was not parallelised sufficiently well for low batch sizes. Similarly to the regular rotary kernel, now all qo/kv heads are split across separate blocks.
In decode mode, the pos_ids kernel is now faster.