ViperEkura
cd31f1f62f
- rename output pointer field o to o_ptr for consistency with q_ptr/k_ptr/v_ptr - regroup AttentionParams fields by responsibility and fix misleading comments - drop unused max_seq_len/total_q fields and paged decode max_seq_len arg - drop redundant group_size param from decode launchers (computed from p)
49 lines
1.5 KiB
Plaintext
49 lines
1.5 KiB
Plaintext
#include "attn_dispatchers.cuh"
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#include "attn_entry_utils.cuh"
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torch::Tensor attn_paged_prefill(
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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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torch::Tensor qo_indptr,
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c10::optional<torch::Tensor> mask,
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int64_t max_q_len,
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int64_t causal_offset,
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double scale
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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_prefill_params(q, k_cache, v_cache,
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req_to_token, req_pool_indices,
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kv_indptr, qo_indptr, mask,
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max_q_len, 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_ptr = (bf16*)O.data_ptr();
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DISPATCH_HEAD_DIM(p.head_dim, dispatch_paged_prefill, 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_prefill", &attn_paged_prefill,
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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("qo_indptr"),
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py::arg("mask") = py::none(),
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py::arg("max_q_len"),
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py::arg("causal_offset") = -1,
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py::arg("scale") = 0.0,
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"SGLang-style paged prefill: flat KV pool + ragged batch.");
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}
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