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Author	SHA1	Message	Date
Gahow Wang	cfbd64d206	cuda: fix int32 overflow in MoE dense kernels; surface launch errors in release The dense MoE kernels (moe_replicate, moe_bias_add_3d, moe_weighted_sum) computed total / expert_stride / element indices in int32. gpt-oss prefill runs the whole prompt through the dense path unchunked (SPARSE_MAX_TOKENS=8), so local_expertsnum_tokenshidden (and batchnum_tokensdim, and local_id*expert_stride) overflow int32 at ~3.6k-23k prefill tokens (TP-dependent) — well inside the supported context window. The launch then fails silently because CUDA_CHECK_LAST_ERROR was ((void)0) under NDEBUG, so the bias / weighted-sum simply never runs and the forward pass is corrupted with no error reported. Fix: switch the three kernels and their launchers to long long, mirroring the (long long) indexing already used in moe_sparse.cu. Also make CUDA_CHECK_LAST_ERROR always-on — cudaGetLastError does not sync, so the per-launch host cost is negligible, and a silent launch failure is exactly the class of bug this one was. Verified on dash5 (RTX 5090): a direct kernel test at 2.21B elements (>2^31) for both moe_replicate and moe_bias_add_3d produces correct results with no launch error; bench-gpt-oss TP=2 holds at 5.9ms TPOT, output unchanged. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-01 12:37:21 +08:00
Gahow Wang	4c3f914459	kernels/cuda: paged-attention kernel, dispatch, pinned host memory CUDA layer for the paged-KV + swap work: - csrc: new paged_attention.cu plus updates across attention/gemm/norm/ activation/embedding/reduce kernels and common.cuh. - xserv-kernels: new dispatch module and kernel-binding updates. - xserv-cuda: cudaMallocHost/FreeHost bindings + PinnedBuffer (host swap pool backing) and offset-aware D2H/H2D copies used to move KV blocks between the GPU pool and pinned host memory. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>	2026-05-28 19:58:36 +08:00
Gahow Wang	c8e8153702	phase 4: transformer core kernels CUDA kernels (csrc/): - common.cuh: shared warp_reduce_sum/max, block_reduce_sum/max - normalization/rmsnorm.cu: RMSNorm (F32 + BF16) - normalization/layernorm.cu: LayerNorm with Welford (F32 + BF16) - activation/activations.cu: GELU tanh-approx + SiLU (F32 + BF16) - reduce/softmax.cu: safe softmax, 3-pass (F32 + BF16) - embedding/embedding.cu: gather lookup (F32 + BF16) - embedding/rope.cu: RoPE in-place + precomputed cos/sin cache (F32 + BF16) Rust wrappers (xserv-kernels/src/): - rmsnorm.rs, layernorm.rs, activation.rs, softmax.rs, embedding.rs, rope.rs - RopeCache struct with GPU-side precomputation Tests: 12 new tests (ops_test.rs), all passing with good precision: - F32: max_err 1e-6 ~ 1e-9 - BF16: max_err 2e-3 ~ 7e-3 Total: 29 kernel tests + 27 prior = 56 tests passing Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-05-21 21:07:24 +08:00

Author

SHA1

Message

Date

Gahow Wang

cfbd64d206

cuda: fix int32 overflow in MoE dense kernels; surface launch errors in release

The dense MoE kernels (moe_replicate, moe_bias_add_3d, moe_weighted_sum)
computed total / expert_stride / element indices in int32. gpt-oss prefill
runs the whole prompt through the dense path unchunked (SPARSE_MAX_TOKENS=8),
so local_experts*num_tokens*hidden (and batch*num_tokens*dim, and
local_id*expert_stride) overflow int32 at ~3.6k-23k prefill tokens
(TP-dependent) — well inside the supported context window. The launch then
fails silently because CUDA_CHECK_LAST_ERROR was ((void)0) under NDEBUG, so
the bias / weighted-sum simply never runs and the forward pass is corrupted
with no error reported.

Fix: switch the three kernels and their launchers to long long, mirroring the
(long long) indexing already used in moe_sparse.cu. Also make
CUDA_CHECK_LAST_ERROR always-on — cudaGetLastError does not sync, so the
per-launch host cost is negligible, and a silent launch failure is exactly
the class of bug this one was.

Verified on dash5 (RTX 5090): a direct kernel test at 2.21B elements (>2^31)
for both moe_replicate and moe_bias_add_3d produces correct results with no
launch error; bench-gpt-oss TP=2 holds at 5.9ms TPOT, output unchanged.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

2026-07-01 12:37:21 +08:00

Gahow Wang

4c3f914459

kernels/cuda: paged-attention kernel, dispatch, pinned host memory

CUDA layer for the paged-KV + swap work:
- csrc: new paged_attention.cu plus updates across attention/gemm/norm/
  activation/embedding/reduce kernels and common.cuh.
- xserv-kernels: new dispatch module and kernel-binding updates.
- xserv-cuda: cudaMallocHost/FreeHost bindings + PinnedBuffer (host swap
  pool backing) and offset-aware D2H/H2D copies used to move KV blocks
  between the GPU pool and pinned host memory.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

2026-05-28 19:58:36 +08:00

Gahow Wang

c8e8153702

phase 4: transformer core kernels

CUDA kernels (csrc/):
- common.cuh: shared warp_reduce_sum/max, block_reduce_sum/max
- normalization/rmsnorm.cu: RMSNorm (F32 + BF16)
- normalization/layernorm.cu: LayerNorm with Welford (F32 + BF16)
- activation/activations.cu: GELU tanh-approx + SiLU (F32 + BF16)
- reduce/softmax.cu: safe softmax, 3-pass (F32 + BF16)
- embedding/embedding.cu: gather lookup (F32 + BF16)
- embedding/rope.cu: RoPE in-place + precomputed cos/sin cache (F32 + BF16)

Rust wrappers (xserv-kernels/src/):
- rmsnorm.rs, layernorm.rs, activation.rs, softmax.rs, embedding.rs, rope.rs
- RopeCache struct with GPU-side precomputation

Tests: 12 new tests (ops_test.rs), all passing with good precision:
- F32: max_err 1e-6 ~ 1e-9
- BF16: max_err 2e-3 ~ 7e-3
Total: 29 kernel tests + 27 prior = 56 tests passing

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-05-21 21:07:24 +08:00

3 Commits