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perf: move decode split partials to InferenceWorkspace

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- Replace per-.cu-file static cached tensors with workspace-managed pre-allocated buffers

- InferenceWorkspace now owns decode_o_part / decode_ml_part (mirrors FlashInfer's workspace pattern)

- KVCache carries the buffers through the backend -> C++ kernel chain

- C++ kernels accept optional pre-allocated buffers; fallback to alloc_split_partials for backward compat

- Pre-allocates once at Executor init, zero allocation in the decode hot loop

- Prerequisite for CUDA-graph capture (all kernel addresses are stable)
This commit is contained in:
ViperEkura
2026-08-06 19:12:09 +08:00
parent d0c5debbab
commit 6f67ba8942
10 changed files with 109 additions and 36 deletions
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csrc/kernels/attn_paged_decode.cu
+14 -10
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@@ -11,7 +11,9 @@ torch::Tensor attn_paged_decode(
int64_t max_seq_len,
c10::optional<torch::Tensor> mask,
int64_t causal_offset,
double scale
double scale,
torch::Tensor o_part_buf,
torch::Tensor ml_part_buf
) {
const at::cuda::OptionalCUDAGuard device_guard(device_of(q));
auto stream = at::cuda::getCurrentCUDAStream();
@@ -24,16 +26,16 @@ torch::Tensor attn_paged_decode(
auto O = torch::empty({q.size(0), q.size(1), q.size(2)}, q.options());
p.o = (bf16*)O.data_ptr();
{
static torch::Tensor s_o_part, s_ml_part;
if (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;
auto fopt = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA);
if (!s_o_part.defined() || s_o_part.numel() < o_needed) {
s_o_part = torch::empty({p.batch, p.q_head, MAX_SPLITS, p.head_dim}, fopt);
s_ml_part = torch::empty({p.batch, p.q_head, MAX_SPLITS, 2}, fopt);
}
p.o_part = (float*)s_o_part.data_ptr();
p.ml_part = (float*)s_ml_part.data_ptr();
TORCH_CHECK(o_part_buf.numel() >= o_needed,
"o_part_buf too small: need ", o_needed, " got ", o_part_buf.numel());
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());
@@ -52,5 +54,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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(),
"SGLang-style paged decode: flat KV pool + req_to_token + kv_indptr.");
}