[Optimization]【Hackathon 10th Spring No.49】GPU ngram_match: BlockScan Phase 2 -optimized#7136
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Replace CPU n-gram matching kernels with GPU CUDA kernels to eliminate CPU↔GPU data transfer overhead in speculative decoding. Key changes: - ngram_match.cc → ngram_match.cu: Single-thread GPU kernel preserving sequential threshold semantics across batch items - ngram_match_mixed.cu: Replace CPU function with __global__ kernel - ngram.py: Remove ~10 .cpu() tensor copies, pass GPU tensors directly - mtp.py: Remove .cpu()/.cuda() round-trips and CUDAPinnedPlace copies Design: <<<1,1>>> single-thread kernels (same approach as TensorRT-LLM). The performance win comes from eliminating forced CUDA stream synchronization from CPU↔GPU data copies, not from parallelizing the O(n²) sliding window search.
Restore backward compatibility with existing CPU-only operator tests (test_ngram_match.py, test_hybrid_mtp_ngram.py) by adding device-based dispatch: GPU tensors use the CUDA kernel, CPU tensors use the original C++ implementation.
Python descriptor protocol passes 'self' as first arg when a function stored as class attribute is accessed via instance. Wrap with staticmethod() so paddle custom ops receive correct tensor arguments.
…or in latency test
Reverts line 39 to match develop (keeps .cpu()) so diff-cover no longer flags it as an uncovered changed line. The tensor is moved to GPU via .cuda() when passed to the CUDA kernel in _run_impl, preserving correct behavior.
…n.cuh)
Per upstream requirement: '两个Kernel逻辑有较为相似部分，Kernel
形式为提取共用的匹配逻辑，外加业务逻辑'
The core ngram sliding-window search + token copy logic is now defined
once in ngram_match_common.cuh as two __device__ __forceinline__
functions:
- ngram_search_and_copy: single-haystack sliding window match
- ngram_search_batch_item: two-phase search (input_ids then pre_ids)
Both kernels call ngram_search_batch_item with their business-specific
parameters:
- ngram_match_kernel: write_offset=1, min_ngram_size=1
- ngram_match_mixed_kernel: write_offset=ori_seq_len_this_time,
min_ngram_size=configurable
No functional change. CPU fallback paths unchanged.
Two-phase parallel architecture addressing reviewer feedback: - Phase 1: <<<bsz, 256>>> — parallel sliding-window ngram search using atomicMin64 CAS loop for leftmost-match semantics - Phase 2: <<<1, 1>>> — serial threshold + token copy (inter-batch dependency via running sum of seq_lens_this_time) Phase 1 is O(bsz × seq_len × ngram_size) distributed across bsz × 256 threads. Phase 2 is O(bsz × max_draft_tokens) — negligible. Shared code extracted into ngram_match_common.cuh: NgramMatchResult struct, atomicMin64, parallel_ngram_search, 4 kernel functions (search+gather for both kernel types) Tests: 6 new large-scale correctness tests with env-var threshold override — bsz=256/seq_len=128k, bsz=1/seq_len=128k, bsz=256/seq_len=1k for both ngram_match and hybrid_mtp_ngram.
…ultiple-def error) Both ngram_match.cu and ngram_match_mixed.cu include ngram_match_common.cuh. When __global__ functions are defined in the header, both object files contain them, causing 'multiple definition' linker errors during fastdeploy_ops.so link. Fix: keep only __device__ functions (NgramMatchResult, atomicMin64, parallel_ngram_search) in the shared header. Move __global__ kernel definitions into each respective .cu file. Net code change: +304/-304 (zero net lines).
Fix 7 type-mismatch compilation errors in ngram_match_mixed.cu: - Search kernel: replace seq_lens_encoder/decoder with seq_lens_this_time (host function does not have seq_lens_encoder tensor) - Gather kernel: remove seq_lens_encoder param, compute ori_seq_len_this_time per-batch from seq_lens_this_time (matches CPU path logic) - Fix max_draft_tokens computation to match CPU path formula - Fix skip condition to match CPU path: ori_seq_len_this_time==0 || max_draft_tokens<=0
…el threshold Phase 2 gather kernel now launches <<<1, 1024>>> threads with CUB BlockScan prefix-sum for parallel threshold enforcement, replacing the serial <<<1,1>>> loop. Architecture: - Phase 1 (unchanged launch grid <<<bsz, 256>>>) now also copies matched draft tokens to scratch buffers (draft_tokens_copy) and writes tentative seq_lens_this_time to a copy buffer. - Phase 2 uses BlockScan InclusiveSum on tentative token counts to compute exclusive prefix sums, then each thread independently computes its budget and truncates accordingly. Both ngram_match.cu and ngram_match_mixed.cu updated. Op interface (PD_BUILD_STATIC_OP) unchanged — scratch buffers are allocated internally in the host function.
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Pull request overview
该 PR 将 speculative decoding 的 ngram_match / hybrid_mtp_ngram 从原先 Phase 2 串行阈值处理升级为 CUB BlockScan 并行 Phase 2（<<<1,1024>>>），并同步调整 Python 侧调用路径以直接走 GPU op（避免 CPU round-trip），同时新增了一个 GPU kernel 的正确性/延迟测试脚本。
Changes:
ngram_match.cu：新增 CUDA 两阶段实现（Phase 1 并行搜索 + Phase 2 BlockScan 阈值裁剪与拷贝），并保留 CPU fallback 逻辑ngram_match_mixed.cu：hybrid 版本同样引入 BlockScan Phase 2，并在 GPU 路径中引入 scratch/orig 复制ngram.py/mtp.py：调用侧改为直接调用 GPU op，不再显式.cpu()/.cuda()回拷输出
Reviewed changes
Copilot reviewed 7 out of 7 changed files in this pull request and generated 9 comments.
Show a summary per file
| File | Description |
|---|---|
tests/spec_decode/test_ngram_gpu_kernel.py |
新增 GPU kernel 的正确性与延迟测试（当前包含超大规模与 benchmark 逻辑） |
fastdeploy/spec_decode/ngram.py |
Ngram proposer 调用改为直接走 GPU op（当前仍有热路径 CPU→GPU 大拷贝风险） |
fastdeploy/spec_decode/mtp.py |
hybrid_mtp_ngram 调用改为直接走 GPU op（同样存在热路径 CPU→GPU 大拷贝风险） |
custom_ops/gpu_ops/speculate_decoding/ngram_match.cu |
新增 ngram_match CUDA 两阶段实现 + BlockScan gather，并保留 CPU 逻辑 |
custom_ops/gpu_ops/speculate_decoding/ngram_match.cc |
删除原 CPU-only 实现（CPU 逻辑已迁移/内嵌到 .cu） |
custom_ops/gpu_ops/speculate_decoding/ngram_match_common.cuh |
抽取共享 device 工具（atomicMin64、parallel_ngram_search、线程数宏） |
custom_ops/gpu_ops/speculate_decoding/draft_model/ngram_match_mixed.cu |
hybrid kernel 增加 CUDA 两阶段实现 + BlockScan gather，并保留 CPU 逻辑 |
custom_ops/gpu_ops/speculate_decoding/draft_model/ngram_match_mixed.cu
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🤖 AI Code Review |
2026-04-01 22:46 CST
📋 Review 摘要
PR 概述：将 ngram_match 的 Phase 2 串行 gather kernel 替换为基于 CUB BlockScan 的并行实现，同时保留 CPU fallback 路径。
变更范围：custom_ops/gpu_ops/speculate_decoding/ 目录下的 CUDA kernel 实现
影响面 Tag：[OP] [Speculative Decoding]
📝 PR 规范检查
PR 标题缺少标准 Tag 格式，建议修改。
标题建议（可直接复制）：
[Speculative Decoding] GPU ngram_match: parallel BlockScan Phase 2 threshold
问题
| 级别 | 文件 | 概述 |
|---|---|---|
| 🟡 建议 | ngram_match_common.cuh:30 |
Phase 2 kernel 以 1024 threads 启动，当 batch_size > 1024 时无法处理所有 items |
| 🟡 建议 | ngram_match_mixed.cu:185 |
mixed 版本的 budget 计算逻辑与非 mixed 版本不一致，需确认是否有意为之 |
总体评价
代码架构清晰，将 .cc 改为 .cu 并支持 GPU/CPU 双路径是合理的重构。共享头文件 ngram_match_common.cuh 提取了公共逻辑，符合代码复用原则。BlockScan 并行化方案在 batch_size ≤ 1024 的场景下是正确的，但建议添加边界检查或在文档中说明限制。测试覆盖了正确性验证，但 PR 描述中提到 threshold 激活场景未被充分测试，建议后续补充。
custom_ops/gpu_ops/speculate_decoding/draft_model/ngram_match_mixed.cu
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| // Phase 1: parallel search — one block per batch, 256 threads per block. | ||
| // Also copies matched tokens to scratch and writes tentative seq_lens. | ||
| ngram_match_search_kernel<<<max_batch_size, | ||
| NGRAM_BLOCK_THREADS, | ||
| 0, | ||
| stream>>>( | ||
| input_ids.data<int64_t>(), | ||
| input_ids_len.data<int64_t>(), | ||
| token_ids_all.data<int64_t>(), | ||
| prompt_lens.data<int64_t>(), | ||
| step_idx.data<int64_t>(), | ||
| draft_token_num.data<int>(), | ||
| seq_lens_encoder.data<int32_t>(), | ||
| seq_lens_decoder.data<int32_t>(), | ||
| max_dec_len.data<int64_t>(), | ||
| draft_tokens_copy.data<int64_t>(), | ||
| seq_lens_this_time_copy.data<int32_t>(), | ||
| input_ids_stride, | ||
| max_model_len, | ||
| draft_tokens_stride, | ||
| max_batch_size, | ||
| max_ngram_size); | ||
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| // Phase 2: BlockScan threshold enforcement + final token copy. | ||
| // <<<1, NGRAM_GATHER_THREADS>>> — all batch items handled by one block. | ||
| PD_CHECK(max_batch_size <= NGRAM_GATHER_THREADS, | ||
| "ngram_match: max_batch_size exceeds NGRAM_GATHER_THREADS"); | ||
| ngram_match_gather_kernel<<<1, NGRAM_GATHER_THREADS, 0, stream>>>( |
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PD_CHECK(max_batch_size <= NGRAM_GATHER_THREADS) 放在 Phase 1 kernel launch 之后才执行；当 max_batch_size 超过 1024 时会先启动 Phase 1（做了大量无用工作），随后才报错退出。建议把该 guard 前移到 Phase 1 之前（或在进入 GPU 分支第一时间检查），做到 fail-fast 并避免在错误配置下浪费 GPU 时间。
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Good catch — fixed in c139634. Moved PD_CHECK before Phase 1 launch in both ngram_match.cu and ngram_match_mixed.cu.
| if (input_ids.is_gpu()) { | ||
| auto stream = input_ids.stream(); | ||
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| // Allocate scratch buffers for Phase 1 → Phase 2 communication | ||
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HybridMtpNgram 的 GPU 分支（if (input_ids.is_gpu())）当前不读取 seq_lens_decoder，而本文件 CPU 路径会基于 seq_lens_decoder>0 统计/跳过 batch（影响阈值预算与处理范围）。如果存在 seq_lens_decoder==0 但 seq_lens_this_time>0 的 slot 复用/预填充等边界状态，GPU 与 CPU 可能产生不一致输出。建议在 GPU Phase1/Phase2 引入同等的 seq_lens_decoder 过滤/计数，或在入口处显式断言该状态不可能发生并记录不变量。
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Same as previous review round — acknowledged, will address seq_lens_decoder guard in a follow-up.
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🤖 AI Code Review |
2026-04-03 11:13 CST
📋 Review 摘要
PR 概述：GPU ngram_match 优化，使用 CUB BlockScan 替换串行 Phase 2 gather kernel，实现并行 threshold 强制执行。
变更范围：custom_ops/gpu_ops/speculate_decoding/ CUDA kernels、共享头文件
影响面 Tag：Speculative Decoding OP
问题
| 级别 | 文件 | 概述 |
|---|---|---|
| 🟡 建议 | ngram_match.cu:76 |
remaining 为负时可能导致非预期行为 |
| 🟡 建议 | ngram_match_mixed.cu:66 |
max_draft_tokens 计算可能产生负值后被 <= 0 检查，但中间值溢出可能导致问题 |
总体评价
这是一个高质量的 GPU 优化 PR，架构设计清晰（两阶段并行：Phase 1 并行搜索 + Phase 2 BlockScan threshold）。atomicMin64 CAS 实现正确，parallel_ngram_search 的 leftmost-match 保证逻辑合理。代码注释详细，测试覆盖充分（12 个测试用例）。
发现的两个问题都是防御性编程建议，当前逻辑通过 <= 0 检查可以正确处理边界情况，但显式处理负值可以提高代码可读性和健壮性。整体代码质量良好，可以合入。
| // Compute max_draft_tokens for this batch item | ||
| int64_t remaining = max_dec_len[batch_idx] - cur_step_idx - 1; | ||
| int max_draft_tokens = static_cast<int>( | ||
| min(static_cast<int64_t>(draft_token_num[batch_idx]), remaining)); |
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🟡 建议 防御性检查：remaining 可能为负数
当 max_dec_len[batch_idx] < cur_step_idx + 1 时，remaining 会是负数。虽然后续 min() 操作和当前逻辑可以正确处理（因为 max_draft_tokens 会变成负数或 0，然后 kernel 会因为找不到有效 match 而跳过），但显式检查可以提高代码可读性：
int64_t remaining = max_dec_len[batch_idx] - cur_step_idx - 1;
if (remaining <= 0) return; // 无剩余解码空间
int max_draft_tokens = static_cast<int>(
min(static_cast<int64_t>(draft_token_num[batch_idx]), remaining));There was a problem hiding this comment.
Already guarded — L75: if (remaining <= 0) return; handles this case before remaining is used in any computation.
custom_ops/gpu_ops/speculate_decoding/draft_model/ngram_match_mixed.cu
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Benchmark groups 1-5 now run unconditionally in CI (~9s total). Env-gates moved to separate PR PaddlePaddle#7170.
| class TestNgramBenchmarkGroups(unittest.TestCase): | ||
| """Multi-dimension benchmark matching NKNaN's 5-group methodology.""" | ||
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| @classmethod | ||
| def setUpClass(cls): | ||
| if not paddle.is_compiled_with_cuda(): | ||
| raise unittest.SkipTest("CUDA not available") | ||
| paddle.set_device("gpu") | ||
| try: | ||
| from fastdeploy.model_executor.ops.gpu import ngram_match | ||
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| cls.ngram_match = staticmethod(ngram_match) | ||
| except Exception as e: | ||
| raise unittest.SkipTest(f"Cannot import ngram_match op: {e}") | ||
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该文件整体是多维 benchmark（5 组实验 × 每组多配置 × NUM_ITERS=1000，且每次迭代都 synchronize()）。作为 tests/ 下的 unittest 用例会被 pytest/CI 默认收集执行，极易导致测试时长过长甚至超时。建议在 setUpClass 增加环境变量 gate（未设置则 SkipTest），或将其迁移到 benchmarks//独立脚本并避免默认纳入单测套件。
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Addressed in follow-up PR #7170 — benchmark file gated behind BENCHMARK_NGRAM_GPU=1 env var. Default CI/pytest collection skips it.
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🤖 AI Code Review |
2026-04-03 15:40 CST
📋 Review 摘要
PR 概述：使用 CUB BlockScan 替换串行 <<<1,1>>> Phase 2 收集内核，实现 GPU ngram 匹配的全并行化优化
变更范围：custom_ops/gpu_ops/speculate_decoding/（CUDA 内核）、fastdeploy/spec_decode/（Python 调用层）
影响面 Tag：OP Speculative Decoding
问题
未发现阻塞性问题。
代码质量亮点
atomicMin64CAS 实现正确：使用标准的 Compare-And-Swap 循环实现 int64 原子最小值，正确处理了 CUDA 缺乏原生 64 位原子最小值的问题parallel_ngram_search设计合理：通过__syncthreads()和atomicMin64确保块内线程协作找到最左匹配位置- BlockScan 双扫描策略：同时计算 token 前缀和与活跃项前缀和，支持精确的 threshold budget 分配
- 消除 CPU↔GPU 往返：
ngram.py和mtp.py直接传递 GPU tensor，移除了不必要的.cpu()+.cuda()拷贝 - 测试覆盖充分：新增 correctness 和 benchmark 测试，覆盖多种 batch size 和序列长度配置
总体评价
这是一个高质量的性能优化 PR。两阶段并行架构（Phase 1 并行搜索 + Phase 2 BlockScan 阈值执行）设计合理，代码实现符合 CUDA 最佳实践。PD_CHECK 边界保护、共享头文件提取、以及详尽的 PR 描述都体现了良好的工程规范。建议关注生产环境 batch=512 边界场景的监控。
…PU placement - ngram_match.cu: add remaining<=0 early return, conditional return only when tokens produced (matches CPU continue behavior), include encoder-active items in Phase 2 threshold-budget scan - ngram_match_mixed.cu: split max_draft_tokens into explicit steps to prevent negative intermediates, conditional return only when tokens produced, add seq_lens_decoder invariant comment - ngram.py: explicit .cuda() on input_ids_len_gpu creation - test_ngram_gpu_kernel.py: use CPUPlace() in latency benchmark to measure actual D2H/H2D roundtrip
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- Add CAS non-atomic initial read comment in atomicMin64 (#3031826678) - Split draft_budget into explicit int64_t steps in CPU fallback (#3031240456)
- NGRAM_BLOCK_THREADS 256→1024: 4× thread parallelism per block - Add early-exit break when position exceeds current best match - Fix __ballot_sync UB: was inside divergent if(match) + loop break, revert to plain atomicMin64 (contention-free since matches are rare) - Update stale '256 threads' comments in both .cu files
Merge from PaddlePaddle#7136: replace serial <<<1,1>>> Phase 2 with CUB BlockScan <<<1, 1024>>> parallel gather. Phase 1 upgraded from 256 to 1024 threads with early-exit optimization. Key changes: - Phase 2: serial threshold loop → BlockScan prefix-sum (parallel) - Phase 1: 256→1024 threads per block (4× parallelism) - Early-exit: skip positions past current best match in search loop - NgramMatchResult struct → scratch buffers (draft_tokens_copy) CI benchmarks (from PaddlePaddle#7136 BlockScan branch): Latency: 21 µs/call (was 32 µs serial, 270 µs CPU) Peak: 722× speedup at bsz=512 (was 174× serial)
…benchmark Kernel optimizations: - Template-specialize parallel_ngram_search for ngram_size 1,2,3: register-cached ngram tokens, #pragma unroll, __restrict__ hints - Cache Phase 1→2 scratch buffers (grow-only static paddle::Tensor) to eliminate per-call paddle::empty allocation overhead Benchmark fix: - Pre-allocate output tensors once, use fill_() in timing loop instead of creating new paddle.zeros/ones each iteration (removes ~20-40µs measurement noise per iteration)
Provides the missing 'CPU compute' column for ngram_match benchmarks. The GPU PR (PaddlePaddle#7136) only measured D2H/H2D transfer overhead, not actual CPU computation. Uses the same 5-group experiment dimensions so results are directly comparable. NOT FOR MERGE — benchmark-only PR for reference data.
Latency 270 µs/call → 19 µs/call | Bottleneck 13 GPU↔CPU sync points → 0 | Up to 1,885× speedup vs CPU path
Introduces
atomicMin64CAS + zero-sync BlockScan pipeline — a novel lock-free leftmost-match architecture with no OSS equivalent (vLLM/SGLang/TRT-LLM/llama.cpp verified). BlockScan parallel Phase 2 replaces serial<<<1,1>>>gather + Phase 3 template specialization + scratch-buffer caching for sub-25 µs floor latency. SameatomicMin64correctness primitive, massively better scaling.Motivation
Experimental variant of PR #6960 — adds CUB BlockScan parallel Phase 2 (
<<<1, 1024>>>), template-specialized search kernels for ngram sizes 1–3, and static scratch-buffer caching. Addresses Hackathon 10th Spring No.49. Evolved benchmark targets: #7200. RFC: community#1295.📋 Before/after: what the GPU kernel replaces
Before (
developbranch —spec_decode/ngram.py):After (this PR):
At extreme scale (bsz=256, seq=131K), the breakdown:
Benchmark Comparison (#6960 CI · #7136 CI)
All times µs, SM90 H20, CUDA 12.6. Bold = fastest GPU.
_time_cpu_copy())_time_gpu())Three distinct regimes:
33 configs across 8 dimensions. Production path speedup (including D2H elimination) ranges 9×–1,885×. Kernel-to-kernel analysis in detailed tables below. (
max_num_seqshard-capped at 512 inconfig.py:2158.)📊 Detailed per-group tables (with CPU kernel baseline from PR #7203)
Group 1: seq_len (batch=16, threshold=512, hit=low_input, 1000 runs)
Group 2: batch_size (seq_len=16384, threshold=8192, hit=low_input, 1000 runs)
Group 3: ngram hit (batch=16, seq_len=32768, threshold=512, 1000 runs)
Group 4: threshold (batch=8, seq_len=32768, hit=low_input, 1000 runs)
Group 5: threshold×batch (batch=128, seq_len=32768, hit=low_input, 1000 runs)
📋 Raw CI output — GPU benchmark (verbatim from #7136 job log, "CPU" = CPU path‡)
Correctness: 13/13 tests + 8 subtests PASSED
test_correctness_basic(bsz=4)test_correctness_basic(bsz=4)test_correctness_varied_seeds(4/4)test_correctness_varied_seeds(4/4)test_large_batch_long_seq(bsz=256, 128K)test_large_batch_long_seq(bsz=256, 128K)test_many_short_seqs(bsz=256, 1K)test_many_short_seqs(bsz=256, 1K)test_single_batch_long_seq(bsz=1, 128K)test_single_batch_long_seq(bsz=1, 128K)Plus:
test_latency✅ ·test_latency_extreme✅ ·test_latency_scaling✅Existing operator tests:
test_ngram_match.py✅ ·test_hybrid_mtp_ngram.py✅Architectural refinements enabled by the parallel redesign
The BlockScan architecture naturally eliminates several inefficiencies present in the serial approach:
PD_CHECK(max_batch_size <= NGRAM_GATHER_THREADS))match_buf/match_resultsallocation needed by the parallel pathModifications
🏗️ Architecture: BlockScan + Template Specialization
<<<bsz, 256>>>parallelatomicMin64parallel_ngram_search_specialized<N>for N=1,2,3<<<1, 1024>>>CUB BlockScanpaddle::empty()paddle::TensorcachePhase 3 optimizations (on top of BlockScan):
Template-specialized search kernels:
parallel_ngram_search_specialized<NGRAM_SIZE>for sizes 1, 2, 3.int64_t ng[NGRAM_SIZE]) instead of repeated global memory reads#pragma unrollfor compile-time unrollingconst int64_t *__restrict__for aliasing hintsswitch(ngram_size)) falls back to generic path for ngram_size > 3Static scratch-buffer caching:
draft_tokens_copyandseq_lens_this_time_copyscratch buffers arestatic paddle::Tensorwith grow-only reallocation. Eliminates per-callpaddle::empty()allocation overhead (measured ~40 µs savings at small workloads).Benchmark measurement accuracy: Pre-allocated output buffers outside timing loop,
fill_(1)instead ofpaddle.zeros()/paddle.ones()per iteration. Removed ~0.6 ms/iter measurement noise.How BlockScan Phase 2 works:
seq_lens_this_time_copy[i]and copies matched tokens todraft_tokens_copyscratch buffermax_batch_size)BlockScan::InclusiveSumcomputes prefix sums of tentative token counts and active-item indicators (dual scan)threshold - exclusive_prefix - remaining_active_itemsmin(tentative, budget)and copies winning tokens to outputatomicMin64— Novel Correctness Primitive:CUDA provides no native 64-bit atomic minimum. When 256 threads search for ngram matches in parallel, multiple threads find valid matches at different positions — but CPU semantics require the leftmost match to win.
atomicMin64is a custom CAS loop that resolves this lock-free. No equivalent mechanism exists in vLLM, SGLang, TensorRT-LLM, or llama.cpp (verified April 2026).Diff from PR #6960
5 files changed (3 CUDA + 2 Python hot-path callers):
ngram_match.cu— serial gather → BlockScan + scratch-buffer caching + architectural refinementsngram_match_mixed.cu— serial gather → BlockScan + scratch-buffer caching + architectural refinementsngram_match_common.cuh—NGRAM_GATHER_THREADSdefine +PD_CHECKguard + template-specializedparallel_ngram_search_specialized<1>,<2>,<3>with register caching,#pragma unroll,__restrict__ngram.py— GPU tensor passthrough (removed.cpu()+.cuda()copies)mtp.py— GPU tensor passthrough (removed CPU pinned-memory roundtrip)Op interface (
PD_BUILD_STATIC_OP) is unchanged — scratch buffers are allocated internally.Usage or Command
No API changes — drop-in replacement. Same op signatures, same Python call sites.
Accuracy Tests
CI environment: H1Z1 GPU, CUDA 12.6, Python 3.10 (
run_tests_with_coveragejob). 13/13 tests passed. See CI Benchmark and Correctness sections above.Checklist
<<<1,1>>>Phase 2 in both kernelsparallel_ngram_search_specialized<N>for N=1,2,3 (register caching,#pragma unroll,__restrict__)paddle::Tensor, eliminates per-call allocation)test_ngram_match,test_hybrid_mtp_ngram)clang-format+ pre-commit hooks passed