1#include "common.cuh"
2#include "gla.cuh"
3
4template<int HEAD_SIZE>
5static __global__ void gated_linear_attn_f32(const int B, const int T, const int C, const int H, const float scale,
6 const float * k, const float * v, const float * r, const float * td, const float * s, float * dst) {
7 const int tid = threadIdx.x;
8 const int bid = blockIdx.x;
9
10 const int head_size = HEAD_SIZE;
11 const int batch_i = bid / H;
12 const int head_i = bid % H;
13 const int state_size = C * head_size;
14 const int n_seq_tokens = T / B;
15
16 float state[head_size];
17 __shared__ float _k[head_size], _r[head_size], _td[head_size];
18
19 #pragma unroll
20 for (int i = 0; i < head_size; i++) {
21 state[i] = s[batch_i * state_size + head_i * head_size * head_size + i * head_size + tid];
22 }
23
24 for (int t = batch_i * n_seq_tokens * C + head_i * head_size + tid; t < (batch_i + 1) * n_seq_tokens * C + head_i * head_size + tid; t += C) {
25 __syncthreads();
26 _k[tid] = k[t];
27 _r[tid] = r[t];
28 _td[tid] = td[t];
29 __syncthreads();
30
31 const float _v = v[t];
32 float y = 0;
33 for (int j = 0; j < head_size; j += 4) {
34 const float4 & k = (float4 &)(_k[j]);
35 const float4 & r = (float4 &)(_r[j]);
36 const float4 & td = (float4 &)(_td[j]);
37 float4 & s = (float4 &)(state[j]);
38 float4 kv;
39
40 kv.x = k.x * _v;
41 kv.y = k.y * _v;
42 kv.z = k.z * _v;
43 kv.w = k.w * _v;
44
45 s.x = s.x * td.x + kv.x;
46 s.y = s.y * td.y + kv.y;
47 s.z = s.z * td.z + kv.z;
48 s.w = s.w * td.w + kv.w;
49
50 y += r.x * s.x;
51 y += r.y * s.y;
52 y += r.z * s.z;
53 y += r.w * s.w;
54 }
55 dst[t] = y * scale;
56 }
57
58 #pragma unroll
59 for (int i = 0; i < head_size; i++) {
60 dst[T * C + batch_i * state_size + head_i * head_size * head_size + i * head_size + tid] = state[i];
61 }
62}
63
64void ggml_cuda_op_gated_linear_attn(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
65 const float * k_d = (const float *)dst->src[0]->data;
66 const float * v_d = (const float *)dst->src[1]->data;
67 const float * r_d = (const float *)dst->src[2]->data;
68 const float * td_d = (const float *)dst->src[3]->data;
69 const float * s_d = (const float *)dst->src[4]->data;
70
71 const int64_t B = dst->src[4]->ne[1];
72 const int64_t T = dst->src[0]->ne[2];
73 const int64_t C = dst->ne[0];
74 const int64_t H = dst->src[0]->ne[1];
75
76 float scale;
77 memcpy(dest: &scale, src: (float*)dst->op_params, n: sizeof(float));
78
79 float * dst_d = (float *)dst->data;
80
81 cudaStream_t stream = ctx.stream();
82
83 GGML_ASSERT(dst->src[4]->type == GGML_TYPE_F32);
84 GGML_ASSERT(C % H == 0);
85 GGML_ASSERT(C / H == 64 || C / H == 128);
86
87
88 if (C / H == 64) {
89 gated_linear_attn_f32<64><<<gridDim: B * H, blockDim: C / H, sharedMem: 0, stream>>>(B, T, C, H, scale, k: k_d, v: v_d, r: r_d, td: td_d, s: s_d, dst: dst_d);
90 } else {
91 gated_linear_attn_f32<128><<<gridDim: B * H, blockDim: C / H, sharedMem: 0, stream>>>(B, T, C, H, scale, k: k_d, v: v_d, r: r_d, td: td_d, s: s_d, dst: dst_d);
92 }
93}
94