ViperEkura
6f67ba8942
- 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)
61 lines
2.2 KiB
Plaintext
61 lines
2.2 KiB
Plaintext
#include "attn_dispatchers.cuh"
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#include "attn_entry_utils.cuh"
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torch::Tensor attn_paged_decode(
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torch::Tensor q,
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torch::Tensor k_cache,
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torch::Tensor v_cache,
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torch::Tensor req_to_token,
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torch::Tensor req_pool_indices,
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torch::Tensor kv_indptr,
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int64_t max_seq_len,
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c10::optional<torch::Tensor> mask,
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int64_t causal_offset,
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double scale,
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torch::Tensor o_part_buf,
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torch::Tensor ml_part_buf
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) {
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const at::cuda::OptionalCUDAGuard device_guard(device_of(q));
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auto stream = at::cuda::getCurrentCUDAStream();
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AttentionParams<bf16> p;
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attn_pack_paged_decode_params(q, k_cache, v_cache,
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req_to_token, req_pool_indices, kv_indptr,
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max_seq_len, mask, causal_offset, scale, p);
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auto O = torch::empty({q.size(0), q.size(1), q.size(2)}, q.options());
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p.o = (bf16*)O.data_ptr();
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if (o_part_buf.defined() && ml_part_buf.defined()) {
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TORCH_CHECK(o_part_buf.scalar_type() == torch::kFloat32, "o_part_buf must be f32");
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TORCH_CHECK(ml_part_buf.scalar_type() == torch::kFloat32, "ml_part_buf must be f32");
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int64_t o_needed = (int64_t)p.batch * p.q_head * MAX_SPLITS * p.head_dim;
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TORCH_CHECK(o_part_buf.numel() >= o_needed,
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"o_part_buf too small: need ", o_needed, " got ", o_part_buf.numel());
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p.o_part = (float*)o_part_buf.data_ptr();
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p.ml_part = (float*)ml_part_buf.data_ptr();
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} else {
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alloc_split_partials(p);
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}
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DISPATCH_HEAD_DIM(p.head_dim, dispatch_paged_decode, p, stream);
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C10_CUDA_CHECK(cudaGetLastError());
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return O;
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}
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("attn_paged_decode", &attn_paged_decode,
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py::arg("q"),
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py::arg("k_cache"),
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py::arg("v_cache"),
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py::arg("req_to_token"),
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py::arg("req_pool_indices"),
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py::arg("kv_indptr"),
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py::arg("max_seq_len"),
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py::arg("mask") = py::none(),
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py::arg("causal_offset") = -1,
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py::arg("scale") = 0.0,
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py::arg("o_part_buf") = py::none(),
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py::arg("ml_part_buf") = py::none(),
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"SGLang-style paged decode: flat KV pool + req_to_token + kv_indptr.");
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}
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