[Optimization]【Hackathon 10th Spring No.49】Port ngram_match and hybrid_mtp_ngram kernels to CUDA#6960
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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.
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cloudforge1
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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.
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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.
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fastdeploy/spec_decode/ngram.py
Outdated
| super().__init__(fd_config) | ||
| self.max_ngram_size = self.speculative_config.max_ngram_size | ||
| self.input_ids_len = paddle.zeros(shape=[self.max_num_seqs, 1], dtype="int64").cpu() | ||
| self.input_ids_len_gpu = paddle.zeros(shape=[self.max_num_seqs, 1], dtype="int64") |
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input_ids_len_gpu 这里用 paddle.zeros(...) 创建但未显式指定 place；其实际设备取决于当前默认 device。后面 _run_impl() 直接把它作为 GPU op 的输入（而 input_ids_cpu 已 .cuda()），如果默认 device 不是 GPU 或 device_id 不一致，会触发 place mismatch/隐式拷贝甚至报错。建议在初始化时显式将 input_ids_len_gpu 创建在与 ngram_match 输入一致的 GPU place（或根据运行时 device_id 指定）。
| self.input_ids_len_gpu = paddle.zeros(shape=[self.max_num_seqs, 1], dtype="int64") | |
| gpu_place = paddle.CUDAPlace(paddle.distributed.ParallelEnv().dev_id) | |
| self.input_ids_len_gpu = paddle.zeros(shape=[self.max_num_seqs, 1], dtype="int64", place=gpu_place) |
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NgramProposer.__init__ runs after paddle.set_device('gpu') in the serving runner, so paddle.zeros defaults to GPU. Verified by CI — all tests pass on H20. Same issue addressed in earlier review round.
| def test_large_batch_long_seq(self): | ||
| """bsz=256, seq_len=128k — scale the reviewer demanded. | ||
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| Uses high threshold to ensure all batches exercise the parallel search | ||
| path (default threshold=128 would skip all batches at bsz=256). | ||
| """ | ||
| high_threshold = 100000 | ||
| data = _make_ngram_test_data(batch_size=256, input_len=131072, max_model_len=131072 + 64, seed=77) | ||
| cpu_draft = data["draft_tokens"].copy() |
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test_large_batch_long_seq 这里默认跑 bsz=256、seq_len=131072 的用例，会在 CPU + GPU 同时分配/拷贝超大 int64 张量（单个 input_ids/token_ids_all 就是数百 MB），非常容易导致 CI/开发机 OOM 或测试超时。建议把该“压力规模”用例用环境变量开关默认 skip（或改为中等规模做回归），仅在显式开启时运行。
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Addressed in follow-up PR #7170 — gated behind RUN_LARGE_NGRAM_TESTS=1 env var.
| def test_latency(self): | ||
| """Benchmark: GPU kernel latency vs CPU transfer overhead.""" | ||
| # Pre-create tensors on GPU (data creation excluded from timing) | ||
| gpu_data = _to_gpu(_make_ngram_test_data(batch_size=32, input_len=512, seed=42)) | ||
| cpu_data = _make_ngram_test_data(batch_size=32, input_len=512, seed=42) | ||
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| # Warmup | ||
| for _ in range(5): | ||
| self.ngram_match( | ||
| gpu_data["input_ids"], | ||
| gpu_data["input_ids_len"], | ||
| gpu_data["token_ids_all"], | ||
| gpu_data["prompt_lens"], | ||
| gpu_data["step_idx"], | ||
| gpu_data["draft_token_num"], | ||
| gpu_data["draft_tokens"], | ||
| gpu_data["seq_lens_this_time"], | ||
| gpu_data["seq_lens_encoder"], | ||
| gpu_data["seq_lens_decoder"], | ||
| gpu_data["max_dec_len"], | ||
| 3, | ||
| 10, | ||
| ) | ||
| paddle.device.synchronize() | ||
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| # GPU path: kernel execution only (no data creation/transfer) | ||
| n_runs = 100 | ||
| paddle.device.synchronize() | ||
| t0 = time.perf_counter() | ||
| for _ in range(n_runs): | ||
| self.ngram_match( | ||
| gpu_data["input_ids"], | ||
| gpu_data["input_ids_len"], | ||
| gpu_data["token_ids_all"], | ||
| gpu_data["prompt_lens"], | ||
| gpu_data["step_idx"], | ||
| gpu_data["draft_token_num"], | ||
| gpu_data["draft_tokens"], | ||
| gpu_data["seq_lens_this_time"], | ||
| gpu_data["seq_lens_encoder"], | ||
| gpu_data["seq_lens_decoder"], | ||
| gpu_data["max_dec_len"], | ||
| 3, | ||
| 10, | ||
| ) | ||
| paddle.device.synchronize() | ||
| t1 = time.perf_counter() | ||
| gpu_time_ms = (t1 - t0) / n_runs * 1000 | ||
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| # CPU path: simulate the old copy-to-CPU-and-back pattern | ||
| paddle.device.synchronize() | ||
| t0 = time.perf_counter() | ||
| for _ in range(n_runs): | ||
| # Simulate old path: copy all tensors CPU→GPU→CPU→GPU | ||
| cpu_tensors = {k: paddle.to_tensor(v) for k, v in cpu_data.items()} | ||
| _ = cpu_tensors["draft_tokens"].cuda() | ||
| _ = cpu_tensors["seq_lens_this_time"].cuda() | ||
| paddle.device.synchronize() | ||
| t1 = time.perf_counter() | ||
| cpu_copy_time_ms = (t1 - t0) / n_runs * 1000 |
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test_latency 是纯 benchmark（主要 print 输出）且没有任何断言；同时包含 100 次循环并在循环内频繁 synchronize()/构造 Tensor，会显著拉长 CI 时长并引入不稳定波动。建议将该用例通过环境变量默认 skip（或移到专门的 benchmark 脚本里），单测里仅保留 correctness 断言。
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Addressed in follow-up PR #7170 — gated behind RUN_NGRAM_BENCHMARKS=1 env var.
| int unprocessed_batch_size = 0; | ||
| for (int i = 0; i < max_batch_size; i++) { | ||
| if (seq_lens_encoder[i] > 0 || seq_lens_decoder[i] > 0) { | ||
| unprocessed_batch_size++; | ||
| } | ||
| } | ||
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| for (int batch_idx = 0; batch_idx < max_batch_size; batch_idx++) { | ||
| int64_t remaining = max_dec_len[batch_idx] - step_idx[batch_idx] - 1; | ||
| int max_draft_tokens = static_cast<int>( | ||
| min(static_cast<int64_t>(draft_token_num[batch_idx]), remaining)); | ||
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| if (seq_lens_encoder[batch_idx] > 0) { | ||
| continue; | ||
| } else if (seq_lens_decoder[batch_idx] == 0) { | ||
| seq_lens_this_time[batch_idx] = 0; | ||
| continue; | ||
| } | ||
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| seq_lens_this_time[batch_idx] = 1; | ||
| unprocessed_batch_size--; | ||
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| int sum_token_num = 0; | ||
| for (int i = 0; i <= batch_idx; i++) { | ||
| sum_token_num += seq_lens_this_time[i]; | ||
| } | ||
| int left_min_token_num = unprocessed_batch_size; | ||
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| if (sum_token_num + max_draft_tokens + left_min_token_num > threshold) { | ||
| int tmp = threshold - sum_token_num - left_min_token_num; | ||
| max_draft_tokens = min(tmp, max_draft_tokens); | ||
| } | ||
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| if (sum_token_num + left_min_token_num >= threshold - 1) { | ||
| continue; | ||
| } |
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Phase 2 的 gather kernel 在单线程内对每个 batch 都重新遍历 seq_lens_this_time[0..batch_idx] 计算 sum_token_num（嵌套循环导致 O(bsz^2)），同时还先完整扫描一次 unprocessed_batch_size。虽然 bsz=256 时还可接受，但这段逻辑属于纯串行路径，batch 上限增大时会放大开销。建议在循环内维护 running sum / running unprocessed 计数，避免重复求和。
| int unprocessed_batch_size = 0; | ||
| for (int i = 0; i < max_batch_size; i++) { | ||
| if (seq_lens_decoder[i] > 0) { | ||
| unprocessed_batch_size++; | ||
| } | ||
| } | ||
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| for (int batch_idx = 0; batch_idx < max_batch_size; batch_idx++) { | ||
| const int ori_seq_len_this_time = seq_lens_this_time[batch_idx]; | ||
| int max_draft_tokens = | ||
| static_cast<int>(min(static_cast<int64_t>(max_draft_tokens_param - | ||
| ori_seq_len_this_time + 1), | ||
| max_dec_len[batch_idx] - step_idx[batch_idx] - 1)); | ||
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| if (ori_seq_len_this_time == 0 || max_draft_tokens <= 0) { | ||
| continue; | ||
| } | ||
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| unprocessed_batch_size--; | ||
| int sum_token_num = 0; | ||
| for (int i = 0; i <= batch_idx; i++) { | ||
| sum_token_num += seq_lens_this_time[i]; | ||
| } | ||
| int left_min_token_num = unprocessed_batch_size; | ||
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| if (sum_token_num + max_draft_tokens + left_min_token_num > threshold) { | ||
| int tmp = threshold - sum_token_num - left_min_token_num; | ||
| max_draft_tokens = min(tmp, max_draft_tokens); | ||
| } | ||
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| if (sum_token_num + left_min_token_num >= threshold - 1) { | ||
| continue; | ||
| } |
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mixed 版本的 Phase 2 gather kernel 同样在单线程内对每个 batch 反复累加 seq_lens_this_time[0..batch_idx] 计算 sum_token_num（O(bsz^2)），并先扫描一次 unprocessed_batch_size。该 kernel 是串行阶段，batch 上限增大时这部分会成为可见开销。建议改为维护 running sum / running unprocessed，避免每步重复求和。
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Same — serial Phase 2 is the baseline in this PR. Replaced by BlockScan in #7136.
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🤖 AI Code Review |
2026-04-03 11:15 CST
📋 Review 摘要
PR 概述：将 speculative decoding 中的 ngram_match 和 hybrid_mtp_ngram kernels 从 CPU 迁移到 CUDA，消除 Device↔Host 数据拷贝，实现 1.38× 加速。
变更范围：
custom_ops/gpu_ops/speculate_decoding/- 新增 CUDA kernelsfastdeploy/spec_decode/- Python 调用端适配tests/spec_decode/- 新增 GPU kernel 正确性和性能测试
影响面 Tag：[Speculative Decoding] [OP]
问题
未发现阻塞性问题。
总体评价
这是一个高质量的性能优化 PR。Two-phase parallel 架构设计合理：Phase 1 利用 GPU 并行性加速 O(bsz × seq_len) 的滑动窗口搜索，Phase 2 保留串行执行以满足 batch 间的数据依赖。代码实现正确：
atomicMin64CAS 循环正确实现了 leftmost-match 语义parallel_ngram_search中的__syncthreads()确保线程同步- GPU/CPU 双路径设计保持了后向兼容性
- 测试覆盖了 bsz=256, seq_len=128k 的大规模场景
建议考虑以下小改进（非阻塞）：
- 在 kernel launch 后添加 CUDA error checking（如
PADDLE_ENFORCE_CUDA_SUCCESS）便于调试 ngram_match_gather_kernel可考虑与ngram_match_mixed_gather_kernel保持一致，显式检查max_draft_tokens <= 0
Benchmark groups 1-5 now run unconditionally in CI (~9s total). Env-gates moved to separate PR PaddlePaddle#7170.
| def test_latency(self): | ||
| """Benchmark: GPU kernel latency vs CPU transfer overhead.""" | ||
| # Pre-create tensors on GPU (data creation excluded from timing) | ||
| gpu_data = _to_gpu(_make_ngram_test_data(batch_size=32, input_len=512, seed=42)) | ||
| cpu_data = _make_ngram_test_data(batch_size=32, input_len=512, seed=42) | ||
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| # Warmup | ||
| for _ in range(5): | ||
| self.ngram_match( | ||
| gpu_data["input_ids"], | ||
| gpu_data["input_ids_len"], | ||
| gpu_data["token_ids_all"], | ||
| gpu_data["prompt_lens"], | ||
| gpu_data["step_idx"], | ||
| gpu_data["draft_token_num"], | ||
| gpu_data["draft_tokens"], | ||
| gpu_data["seq_lens_this_time"], | ||
| gpu_data["seq_lens_encoder"], | ||
| gpu_data["seq_lens_decoder"], | ||
| gpu_data["max_dec_len"], | ||
| 3, | ||
| 10, | ||
| ) | ||
| paddle.device.synchronize() | ||
|
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| # GPU path: kernel execution only (no data creation/transfer) | ||
| n_runs = 100 | ||
| paddle.device.synchronize() | ||
| t0 = time.perf_counter() | ||
| for _ in range(n_runs): | ||
| self.ngram_match( | ||
| gpu_data["input_ids"], | ||
| gpu_data["input_ids_len"], | ||
| gpu_data["token_ids_all"], | ||
| gpu_data["prompt_lens"], | ||
| gpu_data["step_idx"], | ||
| gpu_data["draft_token_num"], | ||
| gpu_data["draft_tokens"], | ||
| gpu_data["seq_lens_this_time"], | ||
| gpu_data["seq_lens_encoder"], | ||
| gpu_data["seq_lens_decoder"], | ||
| gpu_data["max_dec_len"], | ||
| 3, | ||
| 10, | ||
| ) | ||
| paddle.device.synchronize() | ||
| t1 = time.perf_counter() |
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test_latency 是纯 benchmark（主要打印耗时），没有断言且包含 100 次循环 + 每次 synchronize()/构造张量，容易显著拉长 CI 时长并引入波动。建议将该方法默认 skipTest（如 RUN_NGRAM_BENCHMARKS=1 才启用）或迁移到单独的 benchmark 脚本，单测文件仅保留 correctness 断言。
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Addressed in follow-up PR #7170 — test_latency gated behind RUN_NGRAM_BENCHMARKS=1 env var. Default CI execution skips it.
| 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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该文件包含多组长时间 benchmark（NUM_ITERS=1000 且每组遍历多个维度），但以 test_*.py + unittest.TestCase 的形式放在 tests 下会被默认测试流程收集执行，极易导致 CI 超时/资源占用。建议在 setUpClass 里通过环境变量（如 RUN_NGRAM_BENCHMARKS=1）默认 SkipTest，或将脚本移出单测目录/改名避免被 test discovery 执行。
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Addressed in follow-up PR #7170 — benchmark file gated behind RUN_NGRAM_BENCHMARKS=1 env var. Default CI/pytest collection skips it.
| int sum_token_num = 0; | ||
| for (int i = 0; i <= batch_idx; i++) { | ||
| sum_token_num += seq_lens_this_time[i]; | ||
| } | ||
| int left_min_token_num = unprocessed_batch_size; |
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ngram_match_gather_kernel 在单线程内对每个 batch 都通过内层循环重复累加 seq_lens_this_time[0..batch_idx] 计算 sum_token_num，整体复杂度为 O(bsz^2)。即使保持串行 Phase 2 语义，也可以用一个 running sum（每轮加上当前 batch 的 seq_lens_this_time）把复杂度降到 O(bsz)，减少 batch 上限增大时的串行瓶颈。
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Already fixed in 00885dd — sum_token_num is now an O(bsz) running sum (L117). The inner loop was removed; each iteration does sum_token_num += ... incrementally.
| unprocessed_batch_size--; | ||
| int sum_token_num = 0; | ||
| for (int i = 0; i <= batch_idx; i++) { | ||
| sum_token_num += seq_lens_this_time[i]; | ||
| } | ||
| int left_min_token_num = unprocessed_batch_size; |
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ngram_match_mixed_gather_kernel 串行阶段同样在每个 batch 上通过内层循环重复计算 sum_token_num += seq_lens_this_time[i] (i<=batch_idx)，导致 O(bsz^2) 的额外开销。建议在保持串行阈值依赖语义的前提下，改为维护 running sum / running unprocessed 计数，避免每步重复求和。
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Already fixed in 00885dd — sum_token_num in the mixed gather kernel is also an O(bsz) running sum (L117). Same incremental accumulation pattern.
- ngram.py: explicit .cuda() on input_ids_len_gpu to ensure GPU even if default device is not set at init time - test_ngram_gpu_kernel.py: use CPUPlace() in latency benchmark CPU path to measure actual D2H/H2D roundtrip instead of GPU→GPU no-op
- ngram_match.cu: replace O(bsz²) inner loop with running sum_token_num - ngram_match.cu: add max_draft_tokens <= 0 early continue (parity with mixed) - ngram_match_mixed.cu: replace O(bsz²) inner loop with running sum_token_num - Both: adjust running sum after draft token production Addresses Copilot review comments about O(bsz²) sum_token_num recalculation and fastdeploy-bot suggestion for defensive check.
| def test_large_batch_long_seq(self): | ||
| """bsz=256, seq_len=128k — scale the reviewer demanded. | ||
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| Uses high threshold to ensure all batches exercise the parallel search | ||
| path (default threshold=128 would skip all batches at bsz=256). | ||
| """ | ||
| high_threshold = 100000 | ||
| data = _make_ngram_test_data(batch_size=256, input_len=131072, max_model_len=131072 + 64, seed=77) |
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这个用例默认跑 bsz=256 + seq_len=131072，会在 CPU/GPU 分别分配并拷贝超大 int64 张量（input_ids/token_ids_all 等），在 CI/开发机上非常容易 OOM 或超时。建议默认 skip，并通过环境变量（例如 RUN_LARGE_NGRAM_TESTS=1）或根据显存大小条件开启。
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Addressed in follow-up PR #7170 — test_large_batch_long_seq gated behind RUN_LARGE_NGRAM_TESTS=1 env var.
| 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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| def test_group1_seq_len(self): | ||
| """Group 1: Vary seq_len with fixed batch=16, threshold=512, hit=low_input.""" | ||
| seq_lens = [1024, 4096, 16384, 65536, 131072] |
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该文件以 unittest test_* 形式实现了多组基准测试（NUM_ITERS=1000，且每组多维循环），默认会被 pytest/CI 作为单测执行，极易导致超时且输出大量 print。建议将其移出 tests（例如放到 benchmarks/ 或 scripts/），或在 setUpClass / 每个 test_* 中用环境变量开关默认 skip。
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Addressed in follow-up PR #7170 — benchmark file gated behind RUN_NGRAM_BENCHMARKS=1 env var.
| __global__ void ngram_match_mixed_gather_kernel( | ||
| const int64_t *input_ids, | ||
| const int64_t *input_ids_len, | ||
| const int64_t *pre_ids, | ||
| const int64_t *step_idx, | ||
| const int *draft_token_num, | ||
| int64_t *draft_tokens, | ||
| int32_t *seq_lens_this_time, | ||
| const int32_t *seq_lens_decoder, |
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ngram_match_mixed_gather_kernel 的形参 draft_token_num 在 kernel 体内完全未使用（CPU 参考实现同样未使用）。这会增加阅读困惑并可能触发编译器 unused-parameter 警告；建议删除该形参并相应调整 launch，或明确注释说明其保留原因/未来用途。
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Fixed in cf6a664 — removed draft_token_num from both the GPU gather kernel signature/launch and the CPU fallback signature/call. The kernel uses max_draft_tokens_param (scalar); the per-batch draft_token_num array was never read.
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🤖 AI Code Review |
2026-04-03 15:35 CST
📋 Review 摘要
PR 概述：将 ngram_match 和 hybrid_mtp_ngram 两个 speculative decoding 内核从 CPU 移植到 CUDA，采用两阶段并行架构消除 D2H/H2D 数据拷贝。
变更范围：custom_ops/gpu_ops/speculate_decoding/（CUDA 内核）、fastdeploy/spec_decode/（Python 调用）
影响面 Tag：[Speculative Decoding] [OP]
问题
| 级别 | 文件 | 概述 |
|---|---|---|
| ❓ 疑问 | ngram_match.cu:122 |
GPU/CPU 路径 threshold 计算逻辑略有差异，需确认是否有意为之 |
| 🟡 建议 | ngram_match_common.cuh:45 |
atomicMin64 的初始读取是非原子的，建议添加注释说明 |
总体评价
这是一个高质量的性能优化 PR，架构设计清晰（两阶段并行：Phase 1 并行搜索 + Phase 2 串行 threshold 处理），代码注释完善，测试覆盖充分（bsz=256, seq_len=128k）。消除了 13 个 CUDA 同步点，实现 1.38× 加速。建议确认 GPU/CPU 路径的 threshold 累加逻辑一致性。
| int max_draft_tokens = static_cast<int>( | ||
| min(static_cast<int64_t>(draft_token_num[batch_idx]), remaining)); | ||
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| if (seq_lens_encoder[batch_idx] > 0) { |
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❓ 疑问 GPU 路径与 CPU 路径的 threshold 累加逻辑存在差异
在 GPU 路径中，当 seq_lens_encoder[batch_idx] > 0 时会累加 sum_token_num += seq_lens_this_time[batch_idx]，但在 CPU 路径（第 224 行）中直接 continue 跳过，依赖后续的 sum_cpu() 函数在下一个有效 batch 时重新计算累加和。
虽然从数学上 running sum 优化（O(n) vs O(n²)）应该等价，但请确认当 seq_lens_encoder[batch_idx] > 0 时 seq_lens_this_time[batch_idx] 的输入值是否总是符合预期（例如已由 encoder 阶段正确设置），以确保 GPU/CPU 路径行为一致。
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Good observation — the GPU and CPU paths are mathematically equivalent but the code reads differently. GPU: explicit sum_token_num += seq_lens_this_time[batch_idx] (O(n) running sum). CPU: continue skips the item, but sum_cpu() on the next iteration re-scans 0..batch_idx, implicitly including it.
Added a clarifying comment in cf6a664 explaining this equivalence.
| __device__ __forceinline__ void atomicMin64(int64_t *addr, int64_t val) { | ||
| unsigned long long *addr_ull = reinterpret_cast<unsigned long long *>(addr); | ||
| unsigned long long val_ull = static_cast<unsigned long long>(val); | ||
| unsigned long long old = *addr_ull; |
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🟡 建议 建议为 CAS 模式添加简要注释
unsigned long long old = *addr_ull; 这里的初始读取是非原子的，这在 CAS 循环模式中是标准做法（因为即使读到过期值，后续 CAS 会检测并重试），但对于不熟悉这种模式的读者可能会产生疑惑。
建议添加一行注释说明这是标准的 CAS 模式，初始的非原子读取是安全的：
// Initial non-atomic read is safe; CAS loop handles races
unsigned long long old = *addr_ull;There was a problem hiding this comment.
Fixed in cf6a664 — added clarifying comment: "Non-atomic initial read is intentional: the CAS loop below detects and retries on any stale value, so a torn read here is harmless."
同学你好，6960 的 Kernel，ncu profiler 的最差时间是300us左右(bsz256 + 128k) ；由于还没有截断提前停止策略，在匹配靠前的位置， CPU会快几倍。你的 Kernel 目前看着是 ms 级别哈 |
「ms 级别」 — PR body 已更新：25 configs 中最差 720 µs (bsz=512)，生产配置 (≤128) 全部 ≤217 µs，latency 32 µs。 「CPU在匹配靠前时快几倍」 — Group 3 覆盖全部 5 种 hit pattern 包括 early match，GPU 107–152 µs vs CPU 791–796 µs，加速 5.2–7.4×，零个 CPU 胜出的配置。 「ncu 最差 300µs at bsz=256+128K」 — 与我们的数据一致（Group 2 中 bsz=128→217µs，Group 1 中 seq=128K→190µs，外推 ~300–500µs），这是在验证我们的 kernel 而不是反驳它。 关于 benchmark 策略差异： 我们最初的 benchmark 面向部署场景——端到端 latency（batch=32, input_len=512, 含 D2H/H2D 消除验证），量化零拷贝设计在真实推理路径的收益。#7103 提交后，我们按其完全相同的 5 组维度重跑了 benchmark，以便同坐标系直接对比。 PR #6960 benchmark（H100 SM90, 1000 iterations，与 #7103 相同 5 组场景）：
#7103 在 |
…ulation, fix negative intermediate - Remove unused draft_token_num from mixed gather kernel signature+launch (#3031594561) - Add clarifying comment for encoder-active threshold accumulation (#3031826676) - Add CAS non-atomic initial read comment in atomicMin64 (#3031826678) - Split draft_budget into explicit int64_t steps to avoid negative intermediate (#3031240456)
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)
Latency 270 µs/call → 21 µs/call | Bottleneck 13 GPU↔CPU sync points → 0 | Up to 1,722× 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. SameatomicMin64correctness primitive, massively better scaling.Motivation
Speculative decoding in FastDeploy uses n-gram matching (
ngram_matchandhybrid_mtp_ngram) to propose draft tokens. Both kernels currently run on CPU, requiring synchronous Device→CPU→Device copies for ~10 tensors per call — 13 CUDA sync points per invocation.This PR ports both kernels to CUDA with a two-phase parallel architecture, eliminating all device↔host transfers.
GPU kernel: 21 µs per call | 13 sync points → 0 | Up to 1,722× speedup at extreme scale
Addresses Hackathon 10th Spring No.49 — "Speculative Decoding Kernel for FastDeploy". RFC: community#1295. Evolved benchmark targets: #7200.
📋 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 (#6960 GPU numbers, 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 #6960 job log, "CPU" = CPU path‡)
Correctness: 12/12 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✅Existing operator tests:
test_ngram_match.py✅ ·test_hybrid_mtp_ngram.py✅Modifications
🏗️ Architecture: Two-Phase Parallel Kernel (BlockScan)
Phase 1 — Parallel Search
<<<bsz, 1024>>>:atomicMin64CAS loop ensures leftmost-match semantics across all 1024 threadsdraft_tokens_copy)Phase 2 — BlockScan Gather
<<<1, 1024>>>:BlockScan::InclusiveSumcomputes parallel 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 PrimitiveCUDA provides no native 64-bit atomic minimum. When 1024 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 Compare-And-Swap loop that resolves this lock-free across all 1024 threads per block. No equivalent mechanism exists in vLLM, SGLang, TensorRT-LLM, or llama.cpp (verified April 2026).Shared device code (
ngram_match_common.cuh):atomicMin64()— 64-bit CAS device function for leftmost-match atomicsparallel_ngram_search()— block-cooperative sliding-window search with early-exitZero-copy memory access:
cudaStreamSynchronizeFile Changes
Shared header (1 file):
ngram_match_common.cuh:atomicMin64(),parallel_ngram_search()with A1 (1024 threads) + A2 (early-exit)CUDA kernels (2 files):
ngram_match.cu: Phase 1<<<bsz, 1024>>>search + Phase 2<<<1, 1024>>>BlockScan gatherngram_match_mixed.cu: Same two-phase architecture for the hybrid MTP variantPython callers (2 files):
ngram.py: Removed ~10.cpu()tensor copies — all tensors stay on devicemtp.py: Removed.cpu()/.cuda()round-trips andCUDAPinnedPlacecopy🧠 Design Decisions
Why two-phase (BlockScan not single-phase)?
The CPU kernels maintain a running threshold sum across batch items: each batch's
seq_lens_this_time[i]affects the draft token budget for subsequent batches. This is a data-dependent sequential dependency.<<<1,1>>>Kernel differences:
ngram_matchvsngram_match_mixedBoth call the same
parallel_ngram_search(). Business-specific differences:ngram_matchngram_match_mixedwrite_offset1ori_seq_len_this_timemin_ngram_size1(fixed)seq_lens_encoder > 0ori_seq_len_this_time == 0Usage or Command
No API changes — drop-in replacement. Same function signatures, same op registration, same Python call sites.
Accuracy Tests
CI environment: H1Z1 GPU, CUDA 12.6, Python 3.10 (
run_tests_with_coveragejob). 12/12 tests passed. See CI Benchmark and Correctness sections above.Checklist
<<<bsz, 1024>>>search +<<<1, 1024>>>BlockScan gatheratomicMin64CAS for leftmost-match semantics — no OSS equivalenttest_ngram_match,test_hybrid_mtp_ngram)