ViperEkura
31ca357c61
- attention family -> astrai::attention; fp8 family -> astrai::fp8 - new common/reduce.cuh (warp/group reductions, atomic_max_float) - new common/cp_async.cuh (predicated cp_async_16, commit/wait group) - move MAX_SPLITS into attention/common.h; delete warp_utils.cuh - .cu bindings and pure C tests open family namespaces via using
66 lines
2.6 KiB
Plaintext
66 lines
2.6 KiB
Plaintext
#include "dispatchers.cuh"
|
|
#include "entry_utils.cuh"
|
|
|
|
using namespace astrai::attention;
|
|
|
|
torch::Tensor attn_decode(
|
|
torch::Tensor q,
|
|
torch::Tensor k,
|
|
torch::Tensor v,
|
|
c10::optional<torch::Tensor> mask,
|
|
int64_t causal_offset,
|
|
double scale,
|
|
int64_t layout,
|
|
c10::optional<torch::Tensor> o_part_buf,
|
|
c10::optional<torch::Tensor> ml_part_buf
|
|
) {
|
|
const at::cuda::OptionalCUDAGuard device_guard(device_of(q));
|
|
auto stream = at::cuda::getCurrentCUDAStream();
|
|
|
|
AttentionParams<bf16> p;
|
|
attn_pack_params(q, k, v, mask, causal_offset, scale, layout, p);
|
|
TORCH_CHECK(p.q_len == 1, "Q seq_len must be 1");
|
|
TORCH_CHECK(p.head_dim % 32 == 0, "head_dim must be multiple of 32");
|
|
|
|
auto O = torch::empty_strided(q.sizes(), q.strides(), q.options());
|
|
auto O_view = (layout == BLHD) ? O.transpose(1, 2) : O;
|
|
p.o_ptr = (bf16*)O_view.data_ptr();
|
|
|
|
if (o_part_buf.has_value() && ml_part_buf.has_value()
|
|
&& o_part_buf->defined() && ml_part_buf->defined()) {
|
|
TORCH_CHECK(o_part_buf->scalar_type() == torch::kFloat32, "o_part_buf must be f32");
|
|
TORCH_CHECK(ml_part_buf->scalar_type() == torch::kFloat32, "ml_part_buf must be f32");
|
|
int64_t o_needed = (int64_t)p.batch * p.q_head * MAX_SPLITS * p.head_dim;
|
|
int64_t ml_needed = (int64_t)p.batch * p.q_head * MAX_SPLITS * 2;
|
|
TORCH_CHECK(o_part_buf->numel() >= o_needed,
|
|
"o_part_buf too small: need ", o_needed, " got ", o_part_buf->numel());
|
|
TORCH_CHECK(ml_part_buf->numel() >= ml_needed,
|
|
"ml_part_buf too small: need ", ml_needed, " got ", ml_part_buf->numel());
|
|
TORCH_CHECK(o_part_buf->is_cuda() && ml_part_buf->is_cuda(),
|
|
"split buffers must be CUDA tensors");
|
|
TORCH_CHECK(o_part_buf->is_contiguous() && ml_part_buf->is_contiguous(),
|
|
"split buffers must be contiguous");
|
|
p.o_part = (float*)o_part_buf->data_ptr();
|
|
p.ml_part = (float*)ml_part_buf->data_ptr();
|
|
} else {
|
|
alloc_split_partials(p);
|
|
}
|
|
DISPATCH_HEAD_DIM(p.head_dim, dispatch_decode, p, stream);
|
|
C10_CUDA_CHECK(cudaGetLastError());
|
|
return O;
|
|
}
|
|
|
|
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
|
|
m.def("attn_decode", &attn_decode,
|
|
py::arg("q"),
|
|
py::arg("k"),
|
|
py::arg("v"),
|
|
py::arg("mask") = py::none(),
|
|
py::arg("causal_offset") = -1,
|
|
py::arg("scale") = 0.0,
|
|
py::arg("layout") = (int64_t)BHLD,
|
|
py::arg("o_part_buf") = py::none(),
|
|
py::arg("ml_part_buf") = py::none(),
|
|
"GQA decode (tensor-core head-packing on sm_80+, scalar fallback)");
|
|
}
|