542258ac9d
Derive MLA dimension constants (q_lora_rank, qk_nope_head_dim, etc.)
from tensor shapes at runtime instead of hardcoding DeepSeek V3 values.
This enables the mla_preprocess fused op to work with both DeepSeek V3
and GLM5 models without Python API changes.
- Add 9 dimension fields to MlaTilingData with DeepSeek V3 defaults
- Add OpParam fields and dynamize all host-side tiling functions
- Derive dimensions from wuk, gamma1, kv_cache_rope tensor shapes
- Replace 310+ hardcoded constants across 4 kernel .hpp files
- Remove unused MMSIZE1/MMSIZE2 constants
### What this PR does / why we need it?
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: liuchenbing <chenliumail@163.com>
Co-authored-by: liuchenbing <chenliumail@163.com>
113 lines
3.5 KiB
C++
113 lines
3.5 KiB
C++
// Adapted from
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// https://gitee.com/ascend/ascend-transformer-boost
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//
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// Copyright (c) Huawei Technologies Co., Ltd. 2025. All rights reserved.
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// This file is a part of the CANN Open Software.
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// Licensed under CANN Open Software License Agreement Version 1.0 (the "License").
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// Please refer to the License for details. You may not use this file except in compliance with the License.
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// THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
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// INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
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// See LICENSE in the root of the software repository for the full text of the License.
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//
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#ifndef MLAPREPROCESS_TILING_H
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#define MLAPREPROCESS_TILING_H
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#include <cstdint>
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struct PpMatmulTilingData {
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uint32_t numBatch{0};
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uint32_t m{0};
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uint32_t k{0};
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uint32_t n{0};
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uint32_t m0{0};
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uint32_t k0{0};
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uint32_t n0{0};
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uint32_t mLoop{0};
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uint32_t kLoop{0};
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uint32_t nLoop{0};
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uint32_t coreLoop{0};
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uint32_t swizzleCount{0};
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uint32_t swizzleDirect{0};
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uint32_t enShuffleK{0};
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uint32_t blockDim{0};
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uint32_t enLoadAllAmat{0};
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uint32_t b0matPingPongBufferLen{0};
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};
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struct MlaTilingData {
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uint32_t tilingKey{0};
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uint64_t userWorkspaceSize{0};
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uint64_t s1Offset{0};
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uint64_t s2Offset{0};
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uint64_t s3Offset{0};
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uint64_t s4Offset{0};
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uint64_t s5Offset{0};
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uint32_t numCore{0};
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uint32_t n{0};
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uint32_t perTaskNum{0};
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uint32_t resTaskNum{0};
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PpMatmulTilingData mm1;
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PpMatmulTilingData mm2;
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PpMatmulTilingData mm3;
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// rms1
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uint32_t rmsNumCore1{0};
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uint32_t rmsNumCol1{0};
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uint32_t rmsNumRow1{0};
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uint32_t rmsQuantMin1{0};
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// rms2
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uint32_t rmsNumCore2{0};
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uint32_t rmsNumCol2{0};
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uint32_t rmsNumRow2{0};
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uint32_t rmsQuantMin2{0};
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uint32_t hiddenSizeQ{0};
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uint32_t headNumQ{0};
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uint32_t headDim{0};
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uint32_t concatSize{0};
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uint32_t rotaryCoeff{0};
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uint32_t ntokens{0};
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uint32_t realCore{0};
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uint32_t nlCoreRun{0};
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uint32_t lCoreRun{0};
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uint32_t maxNPerLoopForUb{0};
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uint32_t preCoreLoopTime{0};
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uint32_t preCoreLoopNLast{0};
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uint32_t lastCoreLoopTime{0};
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uint32_t lastCoreLoopNLast{0};
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// EinSumQuant
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uint32_t esqFrontCore{0};
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uint32_t esqTailCore{0};
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uint32_t esqFrontCoreBatch{0};
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uint32_t esqTailCoreBatch{0};
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uint32_t esqHeadNum{0};
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uint32_t esqColNum{0};
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uint32_t esqUbHeadLoop{0};
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uint32_t esqHeadPerLoop{0};
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uint32_t esqHeadTail{0};
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uint32_t esqColLoop{0};
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uint32_t esqColTail{0};
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// hidden state dimension
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uint32_t hiddenStateDim{7168};
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uint32_t isWeightQuantized{1};
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// Model-specific MLA dimensions (derived from tensor shapes)
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uint32_t mm1OutSize{2112}; // q_lora_rank + kv_lora_rank + qk_rope_head_dim
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uint32_t splitSizeOne{576}; // kv_lora_rank + qk_rope_head_dim
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uint32_t splitSizeTwo{1536}; // q_lora_rank
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uint32_t splitRmsNormSizeOne{512}; // kv_lora_rank
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uint32_t splitRmsNormSizeTwo{64}; // qk_rope_head_dim
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uint32_t ropeSplitSizeOne{64}; // qk_rope_head_dim
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uint32_t ropeSplitSizeTwo{128}; // qk_nope_head_dim
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uint32_t hiddenStrideRope{192}; // qk_nope_head_dim + qk_rope_head_dim
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uint32_t qkNopeHeadDim{128}; // for RoPE offset calc
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float avgFactor{0.000651041666f}; // 1/splitSizeTwo (1/qLoraRank), for RmsNorm avg
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};
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#endif // MLAPREPROCESS_TILING_H
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