root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-25 02:44:36 +00:00
Code Issues Actions 9 Packages Projects Releases Wiki Activity

falcon : fix CUDA inference by making K and Q contiguous (#2830)

Browse Source 
* falcon : fix CUDA inference by making K and Q contiguous

ggml-ci

* cuda : add assert to guard from non-cont ropes
This commit is contained in:
Georgi Gerganov 2023-08-27 16:40:48 +03:00 committed by GitHub
parent da7455d046
commit eaa13a48ff
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 8 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
ggml-cuda.cu
View File

@ -6337,9 +6337,11 @@ void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml
void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
const int mode = ((int32_t *) dst->op_params)[2]; const int mode = ((int32_t *) dst->op_params)[2];
const bool is_glm = mode & 4; const bool is_glm = mode & 4;
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, !is_glm); // flatten support not implemented for glm ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, !is_glm); // flatten support not implemented for glm
} }

10
llama.cpp
View File

@ -2642,18 +2642,20 @@ static struct ggml_cgraph * llm_build_falcon(
const size_t wsize = ggml_type_size(cur->type); const size_t wsize = ggml_type_size(cur->type);
struct ggml_tensor * tmpq = ggml_view_3d( // TODO: these 2 ggml_conts are technically not needed, but we add them until CUDA support for
// non-contiguous views is added for the rope operator
struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_3d(
ctx0, cur, n_embd_head, n_head, N, ctx0, cur, n_embd_head, n_head, N,
wsize * n_embd_head, wsize * n_embd_head,
wsize * n_embd_head * (n_head + 2 * n_head_kv), wsize * n_embd_head * (n_head + 2 * n_head_kv),
0); 0));
offload_func_kq(tmpq); offload_func_kq(tmpq);
struct ggml_tensor * tmpk = ggml_view_3d( struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_3d(
ctx0, cur, n_embd_head, n_head_kv, N, ctx0, cur, n_embd_head, n_head_kv, N,
wsize * n_embd_head, wsize * n_embd_head,
wsize * n_embd_head * (n_head + 2 * n_head_kv), wsize * n_embd_head * (n_head + 2 * n_head_kv),
wsize * n_embd_head * n_head); wsize * n_embd_head * n_head));
offload_func_kq(tmpk); offload_func_kq(tmpk);
struct ggml_tensor * tmpv = ggml_view_3d( struct ggml_tensor * tmpv = ggml_view_3d(