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refactor: reorganize CUDA kernels into per-family directories

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- move attention kernels to csrc/kernels/attention/ and rotary to rotary/
- add shared common/mma.cuh (mma_sync, ldmatrix) and device.cuh (sm checks)
- split fp8_mm into three-layer fp8/common.h, gemm.cuh, mm.cu
- fix fused FP8 GEMM ldmatrix lane indexing to fix OOB shared reads
- update extension ops, loader, and kernel tests
This commit is contained in:
ViperEkura
2026-08-22 20:40:31 +08:00
parent cb21af38ba
commit 16a55bb474
30 changed files with 1956 additions and 1235 deletions
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csrc/kernels/attention/paged_decode.cu
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#include "dispatchers.cuh"
#include "entry_utils.cuh"
torch::Tensor attn_paged_decode(
torch::Tensor q,
torch::Tensor k_cache,
torch::Tensor v_cache,
torch::Tensor req_to_token,
torch::Tensor req_pool_indices,
torch::Tensor kv_indptr,
c10::optional<torch::Tensor> new_k,
c10::optional<torch::Tensor> new_v,
c10::optional<torch::Tensor> mask,
int64_t causal_offset,
double scale,
c10::optional<torch::Tensor> o_part_buf,
c10::optional<torch::Tensor> ml_part_buf,
c10::optional<torch::Tensor> out_buf
) {
const at::cuda::OptionalCUDAGuard device_guard(device_of(q));
auto stream = at::cuda::getCurrentCUDAStream();
AttentionParams<bf16> p;
attn_pack_paged_decode_params(q, k_cache, v_cache,
req_to_token, req_pool_indices, kv_indptr,
new_k, new_v,
mask, causal_offset, scale, p);
torch::Tensor O;
if (out_buf.has_value() && out_buf->defined()) {
TORCH_CHECK(out_buf->dtype() == q.dtype(), "out_buf dtype must match q");
TORCH_CHECK(out_buf->is_cuda() && out_buf->is_contiguous(),
"out_buf must be a contiguous CUDA tensor");
TORCH_CHECK(out_buf->size(0) >= q.size(0), "out_buf batch too small");
TORCH_CHECK(out_buf->size(1) == q.size(1), "out_buf heads must match q");
TORCH_CHECK(out_buf->size(2) == q.size(2), "out_buf head_dim must match q");
TORCH_CHECK(q.is_contiguous(),
"q must be contiguous when out_buf is provided");
O = out_buf.value().slice(0, 0, q.size(0));
} else {
O = torch::empty({q.size(0), q.size(1), q.size(2)}, q.options());
}
p.o_ptr = (bf16*)O.data_ptr();
if (o_part_buf.has_value() && ml_part_buf.has_value()
&& o_part_buf->defined() && ml_part_buf->defined()) {
TORCH_CHECK(o_part_buf->scalar_type() == torch::kFloat32, "o_part_buf must be f32");
TORCH_CHECK(ml_part_buf->scalar_type() == torch::kFloat32, "ml_part_buf must be f32");
int64_t o_needed = (int64_t)p.batch * p.q_head * MAX_SPLITS * p.head_dim;
int64_t ml_needed = (int64_t)p.batch * p.q_head * MAX_SPLITS * 2;
TORCH_CHECK(o_part_buf->numel() >= o_needed,
"o_part_buf too small: need ", o_needed, " got ", o_part_buf->numel());
TORCH_CHECK(ml_part_buf->numel() >= ml_needed,
"ml_part_buf too small: need ", ml_needed, " got ", ml_part_buf->numel());
TORCH_CHECK(o_part_buf->is_cuda() && ml_part_buf->is_cuda(),
"split buffers must be CUDA tensors");
TORCH_CHECK(o_part_buf->is_contiguous() && ml_part_buf->is_contiguous(),
"split buffers must be contiguous");
p.o_part = (float*)o_part_buf->data_ptr();
p.ml_part = (float*)ml_part_buf->data_ptr();
} else {
alloc_split_partials(p);
}
DISPATCH_HEAD_DIM(p.head_dim, dispatch_paged_decode, p, stream);
C10_CUDA_CHECK(cudaGetLastError());
return O;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("attn_paged_decode", &attn_paged_decode,
py::arg("q"),
py::arg("k_cache"),
py::arg("v_cache"),
py::arg("req_to_token"),
py::arg("req_pool_indices"),
py::arg("kv_indptr"),
py::arg("new_k") = py::none(),
py::arg("new_v") = py::none(),
py::arg("mask") = py::none(),
py::arg("causal_offset") = -1,
py::arg("scale") = 0.0,
py::arg("o_part_buf") = py::none(),
py::arg("ml_part_buf") = py::none(),
py::arg("out_buf") = py::none(),
"SGLang-style paged decode: flat KV pool + req_to_token + kv_indptr.");
}