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)
40 lines
1.2 KiB
Plaintext
40 lines
1.2 KiB
Plaintext
#include "attn_dispatchers.cuh"
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#include "attn_entry_utils.cuh"
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torch::Tensor attn_prefill(
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torch::Tensor q,
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torch::Tensor k,
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torch::Tensor v,
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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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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_params(q, k, v, mask, causal_offset, scale, layout, p);
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TORCH_CHECK(p.head_dim % 16 == 0, "head_dim must be multiple of 16");
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auto O = torch::empty_strided(q.sizes(), q.strides(), q.options());
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auto O_view = (layout == BLHD) ? O.transpose(1, 2) : O;
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p.o_ptr = (bf16*)O_view.data_ptr();
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DISPATCH_HEAD_DIM(p.head_dim, dispatch_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_prefill", &attn_prefill,
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py::arg("q"),
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py::arg("k"),
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py::arg("v"),
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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") = (int64_t)BHLD,
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"GQA prefill (tensor-core mma on sm_80+, scalar fallback)");
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}
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