vbatts/llama.cpp
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Cleanup for review

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Galunid 2023-11-10 06:53:28 +01:00
parent a371a8b611
commit e87d709446
1 changed files with 1 additions and 25 deletions
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26
llama.cpp
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@ -3118,14 +3118,6 @@ static void llm_load_tensors(
ggml_backend_type backend_norm; ggml_backend_type backend_norm;
ggml_backend_type backend_output; ggml_backend_type backend_output;
// Don't allow for offloading of more than 33 layers.
// Offloading 34 layers causes model to respond with letter 'E'
// Offloading 35 layers doesn't work because of missing cuda implementation for rope:
// GGML_ASSERT: ggml-cuda.cu:6402: ne00 == n_dims && "ne00 != n_dims is not implemented for CUDA yet"
if (n_gpu_layers > 33) {
n_gpu_layers = 33;
}
if (n_gpu_layers > int(n_layer)) { if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU // on Windows however this is detrimental unless everything is on the GPU
@ -4323,7 +4315,7 @@ struct llm_build_context {
struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass); struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
cb(Kcur, "Kcur", il); cb(Kcur, "Kcur", il);
struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3)); struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3));
cb(Q, "Q", il); cb(Q, "Q", il);
Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
@ -4791,20 +4783,6 @@ struct llm_build_context {
Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
cb(Kcur, "Kcur", il); cb(Kcur, "Kcur", il);
// Qcur = ggml_rope_custom(
// ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
// hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
// ext_factor, attn_factor, beta_fast, beta_slow
// );
// cb(Qcur, "Qcur", il);
// Kcur = ggml_rope_custom(
// ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
// hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
// ext_factor, attn_factor, beta_fast, beta_slow
// );
// cb(Kcur, "Kcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il); llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, hparams, kv_self, cur = llm_build_kqv(ctx0, hparams, kv_self,
@ -5026,8 +5004,6 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
static llm_offload_trie k_offload_func_trie(k_offload_map); static llm_offload_trie k_offload_func_trie(k_offload_map);
static struct ggml_cgraph * llama_build_graph( static struct ggml_cgraph * llama_build_graph(
llama_context & lctx, llama_context & lctx,
const llama_batch & batch) { const llama_batch & batch) {