AstrAI/csrc/kernels/gqa_prefill_attn.cu

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#include "gqa_prefill_attn.cuh"
#include <torch/extension.h>
torch::Tensor gqa_prefill_attn(
torch::Tensor q,
torch::Tensor k,
torch::Tensor v,
c10::optional<torch::Tensor> mask,
bool is_causal = false,
int64_t causal_offset = 0,
c10::optional<double> scale = c10::nullopt
) {
TORCH_CHECK(q.is_cuda() && k.is_cuda() && v.is_cuda());
TORCH_CHECK(q.dtype() == torch::kBFloat16);
TORCH_CHECK(k.dtype() == torch::kBFloat16);
TORCH_CHECK(v.dtype() == torch::kBFloat16);
GQAParams p;
p.batch = q.size(0);
p.q_head = q.size(1);
p.kv_head = k.size(1);
p.q_len = q.size(2);
p.kv_len = k.size(2);
p.head_dim = q.size(3);
TORCH_CHECK(p.head_dim % 32 == 0, "head_dim must be multiple of 32");
p.use_mask = mask.has_value();
p.is_causal = (int)is_causal;
p.causal_offset = (int)causal_offset;
p.scale = scale.has_value() ? (float)scale.value() : 1.0f / sqrtf((float)p.head_dim);
p.q = (const bf16*)q.data_ptr();
p.k = (const bf16*)k.data_ptr();
p.v = (const bf16*)v.data_ptr();
if (p.use_mask) {
TORCH_CHECK(mask.value().dtype() == torch::kBool);
TORCH_CHECK(mask.value().dim() == 2);
TORCH_CHECK(mask.value().size(0) == p.batch);
TORCH_CHECK(mask.value().size(1) == p.kv_len);
p.mask = mask.value().data_ptr<bool>();
} else {
p.mask = nullptr;
}
auto O = torch::empty_like(q);
p.o = (bf16*)O.data_ptr();
dim3 grid((p.q_len + Br - 1) / Br, p.q_head, p.batch);
dim3 block(32, Br, 1);
size_t smem = 2 * Bc * p.head_dim * sizeof(bf16);
gqa_prefill_attn_kernel<<<grid, block, smem>>>(p);
return O;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("gqa_prefill_attn", &gqa_prefill_attn,
py::arg("q"),
py::arg("k"),
py::arg("v"),
py::arg("mask") = py::none(),
py::arg("is_causal") = false,
py::arg("causal_offset") = 0,
py::arg("scale") = py::none(),
"GQA prefill (tiled, K+V smem)");
}