97 lines
3.1 KiB
Plaintext
97 lines
3.1 KiB
Plaintext
#include "gqa_prefill_attn.cuh"
|
|
#include <torch/extension.h>
|
|
|
|
#ifndef ASTRAI_NO_MMA
|
|
#include "gqa_prefill_attn_mma.cuh"
|
|
#endif
|
|
|
|
template <int HEAD_DIM>
|
|
static void dispatch_prefill(GQAParams& p) {
|
|
#ifndef ASTRAI_NO_MMA
|
|
constexpr int WARPS = 4, BC = 32, BR = 16;
|
|
dim3 grid((p.q_len + BR * WARPS - 1) / (BR * WARPS), p.q_head, p.batch);
|
|
dim3 block(WARPS * 32, 1, 1);
|
|
int smem = (2 * BC * HEAD_DIM + WARPS * BR * HEAD_DIM) * (int)sizeof(bf16);
|
|
cudaFuncSetAttribute(gqa_prefill_attn_mma_kernel<HEAD_DIM, WARPS, BC>,
|
|
cudaFuncAttributeMaxDynamicSharedMemorySize, smem);
|
|
gqa_prefill_attn_mma_kernel<HEAD_DIM, WARPS, BC><<<grid, block, smem>>>(p);
|
|
#else
|
|
constexpr int G = 8, ROWS = 32, P_BC = 32;
|
|
dim3 grid((p.q_len + ROWS - 1) / ROWS, p.q_head, p.batch);
|
|
dim3 block(G, ROWS, 1);
|
|
size_t smem = 2 * P_BC * HEAD_DIM * sizeof(bf16);
|
|
gqa_prefill_attn_kernel_t<HEAD_DIM, G, ROWS, P_BC><<<grid, block, smem>>>(p);
|
|
#endif
|
|
}
|
|
|
|
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 % 16 == 0, "head_dim must be multiple of 16");
|
|
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();
|
|
|
|
switch (p.head_dim) {
|
|
case 64:
|
|
dispatch_prefill<64>(p);
|
|
break;
|
|
case 128:
|
|
dispatch_prefill<128>(p);
|
|
break;
|
|
case 256:
|
|
dispatch_prefill<256>(p);
|
|
break;
|
|
default:
|
|
TORCH_CHECK(false, "prefill: unsupported head_dim ", p.head_dim,
|
|
" (supported: 64,128,256)");
|
|
}
|
|
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 (tensor-core mma on sm_80+, scalar fallback)");
|
|
}
|