ViperEkura
f7a16efc9d
- Merge 3 duplicated dispatch blocks into single attn_dispatchers.cuh - Merge compute_num_splits from attn_utils.cuh into dispatcher header - All dim3 grid/block declarations and <<<>>> launches are single-line - Production .cu files (35-42 loc) only handle torch wrapping + pybind11 - Test files include dispatcher header directly, removing all #ifndef ASTRAI_NO_MMA duplication
43 lines
1.3 KiB
Plaintext
43 lines
1.3 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 page_table,
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torch::Tensor k_cache,
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torch::Tensor v_cache,
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int64_t page_size,
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int64_t kv_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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int64_t layout
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) {
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PagedAttentionParams<bf16> p;
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attn_pack_paged_params(q, page_table, k_cache, v_cache,
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page_size, kv_len, mask, causal_offset, scale, layout, p);
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auto O = torch::empty_strided(q.sizes(), q.strides(), q.options());
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auto O_view = (layout == 1) ? O.transpose(1, 2) : O;
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p.o = (bf16*)O_view.data_ptr();
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alloc_split_partials(p);
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DISPATCH_HEAD_DIM(p.head_dim, dispatch_paged_decode, p);
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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("page_table"),
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py::arg("k_cache"),
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py::arg("v_cache"),
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py::arg("page_size"),
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py::arg("kv_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("layout") = 0,
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"Paged GQA decode — split-KV with direct page-table access.");
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}
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