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llama : add llm_build_norm helper function

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ggml-ci
This commit is contained in:
Georgi Gerganov
2023-10-29 15:39:58 +02:00
parent 210e6e5d02
commit 7db9c96d8a
1 changed files with 175 additions and 258 deletions
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llama.cpp
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@@ -972,7 +972,7 @@ struct llama_mlock {
typedef void (*offload_func_t)(struct ggml_tensor * tensor); typedef void (*offload_func_t)(struct ggml_tensor * tensor);
static void ggml_offload_nop(struct ggml_tensor * tensor) { // don't offload by default static void ggml_offload_nop(struct ggml_tensor * tensor) {
(void) tensor; (void) tensor;
} }
@@ -3093,6 +3093,42 @@ static bool llama_model_load(
using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>; using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;
enum llm_norm_type {
LLM_NORM,
LLM_NORM_RMS,
};
static struct ggml_tensor * llm_build_norm(
struct ggml_context * ctx,
struct ggml_tensor * cur,
struct ggml_tensor * mw,
struct ggml_tensor * mb,
llm_norm_type type,
float eps,
const llm_build_cb & cb,
int il) {
switch (type) {
case LLM_NORM: cur = ggml_norm (ctx, cur, eps); break;
case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, eps); break;
};
if (mw || mb) {
cb(cur, "norm", il);
}
if (mw) {
cur = ggml_mul(ctx, cur, mw);
if (mb) {
cb(cur, "norm_w", il);
}
}
if (mb) {
cur = ggml_add(ctx, cur, mb);
}
return cur;
}
static struct ggml_cgraph * llm_build_llama( static struct ggml_cgraph * llm_build_llama(
llama_context & lctx, llama_context & lctx,
const llama_batch & batch, const llama_batch & batch,
@@ -3192,14 +3228,11 @@ static struct ggml_cgraph * llm_build_llama(
struct ggml_tensor * inpSA = inpL; struct ggml_tensor * inpSA = inpL;
// norm // norm
{ cur = llm_build_norm(ctx0, inpL,
cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps); model.layers[il].attn_norm,
cb(cur, "rms_norm_0", il); NULL,
LLM_NORM_RMS, norm_rms_eps, cb, il);
// cur = cur*attn_norm(broadcasted) cb(cur, "attn_norm", il);
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
cb(cur, "attn_norm_0", il);
}
// self-attention // self-attention
{ {
@@ -3307,15 +3340,11 @@ static struct ggml_cgraph * llm_build_llama(
// feed-forward network // feed-forward network
{ {
// norm cur = llm_build_norm(ctx0, inpFF,
{ model.layers[il].ffn_norm,
cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps); NULL,
cb(cur, "rms_norm_1", il); LLM_NORM_RMS, norm_rms_eps, cb, il);
// cur = cur*ffn_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
cb(cur, "ffn_norm", il); cb(cur, "ffn_norm", il);
}
struct ggml_tensor * tmp = ggml_mul_mat(ctx0, struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
model.layers[il].w3, model.layers[il].w3,
@@ -3349,15 +3378,11 @@ static struct ggml_cgraph * llm_build_llama(
cur = inpL; cur = inpL;
// norm cur = llm_build_norm(ctx0, cur,
{ model.output_norm,
cur = ggml_rms_norm(ctx0, cur, norm_rms_eps); NULL,
cb(cur, "rms_norm_2", -1); LLM_NORM_RMS, norm_rms_eps, cb, -1);
// cur = cur*norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.output_norm);
cb(cur, "result_norm", -1); cb(cur, "result_norm", -1);
}
// lm_head // lm_head
cur = ggml_mul_mat(ctx0, model.output, cur); cur = ggml_mul_mat(ctx0, model.output, cur);
@@ -3466,15 +3491,11 @@ static struct ggml_cgraph * llm_build_baichaun(
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL; struct ggml_tensor * inpSA = inpL;
// norm cur = llm_build_norm(ctx0, inpL,
{ model.layers[il].attn_norm,
cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps); NULL,
cb(cur, "rms_norm_0", il); LLM_NORM_RMS, norm_rms_eps, cb, il);
cb(cur, "attn_norm", il);
// cur = cur*attn_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
cb(cur, "attn_norm_0", il);
}
// self-attention // self-attention
{ {
@@ -3600,15 +3621,11 @@ static struct ggml_cgraph * llm_build_baichaun(
// feed-forward network // feed-forward network
{ {
// norm cur = llm_build_norm(ctx0, inpFF,
{ model.layers[il].ffn_norm,
cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps); NULL,
cb(cur, "rms_norm_1", il); LLM_NORM_RMS, norm_rms_eps, cb, il);
// cur = cur*ffn_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
cb(cur, "ffn_norm", il); cb(cur, "ffn_norm", il);
}
struct ggml_tensor * tmp = ggml_mul_mat(ctx0, struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
model.layers[il].w3, model.layers[il].w3,
@@ -3763,27 +3780,21 @@ static struct ggml_cgraph * llm_build_falcon(
struct ggml_tensor * attn_norm; struct ggml_tensor * attn_norm;
// self-attention // self-attention
// TODO: refactor into common function (shared with LLaMA)
{ {
attn_norm = ggml_norm(ctx0, inpL, norm_eps); attn_norm = llm_build_norm(ctx0, inpL,
cb(attn_norm, "attn_norm_0", il); model.layers[il].attn_norm,
model.layers[il].attn_norm_b,
LLM_NORM, norm_eps, cb, il);
cb(attn_norm, "attn_norm", il);
attn_norm = ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm); if (model.layers[il].attn_norm_2) {
cb(attn_norm, "attn_norm_0_w", il); // Falcon-40B
cur = llm_build_norm(ctx0, attn_norm,
attn_norm = ggml_add(ctx0, attn_norm, model.layers[il].attn_norm_b); model.layers[il].attn_norm_2,
cb(attn_norm, "attn_norm_0_wb", il); model.layers[il].attn_norm_2_b,
LLM_NORM, norm_eps, cb, il);
if (model.layers[il].attn_norm_2) { // Falcon-40B
cur = ggml_norm(ctx0, inpL, norm_eps);
cb(cur, "attn_norm_2", il); cb(cur, "attn_norm_2", il);
} else {
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm_2);
cb(cur, "attn_norm_2_w", il);
cur = ggml_add(ctx0, cur, model.layers[il].attn_norm_2_b);
cb(cur, "attn_norm_2_wb", il);
} else { // Falcon 7B
cur = attn_norm; cur = attn_norm;
} }
@@ -3925,16 +3936,11 @@ static struct ggml_cgraph * llm_build_falcon(
cur = inpL; cur = inpL;
// norm // norm
{ cur = llm_build_norm(ctx0, cur,
cur = ggml_norm(ctx0, cur, norm_eps); model.output_norm,
cb(cur, "out_norm_0", -1); model.output_norm_b,
LLM_NORM, norm_eps, cb, -1);
cur = ggml_mul(ctx0, cur, model.output_norm);
cb(cur, "out_norm_0_w", -1);
cur = ggml_add(ctx0, cur, model.output_norm_b);
cb(cur, "result_norm", -1); cb(cur, "result_norm", -1);
}
cur = ggml_mul_mat(ctx0, model.output, cur); cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1); cb(cur, "result_output", -1);
@@ -4024,17 +4030,11 @@ static struct ggml_cgraph * llm_build_starcoder(
cb(inpL, "inpL", -1); cb(inpL, "inpL", -1);
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
{ cur = llm_build_norm(ctx0, inpL,
// Norm model.layers[il].attn_norm,
cur = ggml_norm(ctx0, inpL, norm_eps); model.layers[il].attn_norm_b,
cb(cur, "attn_norm_0", il); LLM_NORM, norm_eps, cb, il);
cb(cur, "attn_norm", il);
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
cb(cur, "attn_norm_0_w", il);
cur = ggml_add(ctx0, cur, model.layers[il].attn_norm_b);
cb(cur, "attn_norm_0_wb", il);
}
{ {
// Self Attention // Self Attention
@@ -4130,17 +4130,11 @@ static struct ggml_cgraph * llm_build_starcoder(
// FF // FF
{ {
// Norm cur = llm_build_norm(ctx0, inpFF,
{ model.layers[il].ffn_norm,
cur = ggml_norm(ctx0, inpFF, norm_eps); model.layers[il].ffn_norm_b,
cb(cur, "ffn_norm_0", il); LLM_NORM, norm_eps, cb, il);
cb(cur, "ffn_norm", il);
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
cb(cur, "ffn_norm_0_w", il);
cur = ggml_add(ctx0, cur, model.layers[il].ffn_norm_b);
cb(cur, "ffn_norm_0_wb", il);
}
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3); cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3);
cb(cur, "result_w3", il); cb(cur, "result_w3", il);
@@ -4161,17 +4155,11 @@ static struct ggml_cgraph * llm_build_starcoder(
} }
// Output Norm cur = llm_build_norm(ctx0, inpL,
{ model.output_norm,
cur = ggml_norm(ctx0, inpL, norm_eps); model.output_norm_b,
cb(cur, "out_norm_0", -1); LLM_NORM, norm_eps, cb, -1);
cur = ggml_mul(ctx0, cur, model.output_norm);
cb(cur, "out_norm_0_w", -1);
cur = ggml_add(ctx0, cur, model.output_norm_b);
cb(cur, "result_norm", -1); cb(cur, "result_norm", -1);
}
cur = ggml_mul_mat(ctx0, model.output, cur); cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1); cb(cur, "result_output", -1);
@@ -4271,16 +4259,11 @@ static struct ggml_cgraph * llm_build_persimmon(
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * residual = inpL; struct ggml_tensor * residual = inpL;
{ cur = llm_build_norm(ctx0, inpL,
cur = ggml_norm(ctx0, inpL, norm_eps); model.layers[il].attn_norm,
cb(cur, "attn_norm_0", il); model.layers[il].attn_norm_b,
LLM_NORM, norm_eps, cb, il);
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm); cb(cur, "attn_norm", il);
cb(cur, "attn_norm_0_w", il);
cur = ggml_add(ctx0, cur, model.layers[il].attn_norm_b);
cb(cur, "attn_norm_0_wb", il);
}
// self attention // self attention
{ {
@@ -4316,22 +4299,16 @@ static struct ggml_cgraph * llm_build_persimmon(
cb(tmpk, "tmpk", il); cb(tmpk, "tmpk", il);
// Q/K Layernorm // Q/K Layernorm
tmpq = ggml_norm(ctx0, tmpq, norm_eps); tmpq = llm_build_norm(ctx0, tmpq,
model.layers[il].attn_q_norm,
model.layers[il].attn_q_norm_b,
LLM_NORM, norm_eps, cb, il);
cb(tmpq, "tmpq", il); cb(tmpq, "tmpq", il);
tmpq = ggml_mul(ctx0, tmpq, model.layers[il].attn_q_norm); tmpk = llm_build_norm(ctx0, tmpk,
cb(tmpq, "tmpq", il); model.layers[il].attn_k_norm,
model.layers[il].attn_k_norm_b,
tmpq = ggml_add(ctx0, tmpq, model.layers[il].attn_q_norm_b); LLM_NORM, norm_eps, cb, il);
cb(tmpq, "tmpq", il);
tmpk = ggml_norm(ctx0, tmpk, norm_eps);
cb(tmpk, "tmpk", il);
tmpk = ggml_mul(ctx0, tmpk, model.layers[il].attn_k_norm);
cb(tmpk, "tmpk", il);
tmpk = ggml_add(ctx0, tmpk, model.layers[il].attn_k_norm_b);
cb(tmpk, "tmpk", il); cb(tmpk, "tmpk", il);
// RoPE the first n_rot of q/k, pass the other half, and concat. // RoPE the first n_rot of q/k, pass the other half, and concat.
@@ -4480,17 +4457,11 @@ static struct ggml_cgraph * llm_build_persimmon(
{ {
// MLP // MLP
{ cur = llm_build_norm(ctx0, inpFF,
// Norm model.layers[il].ffn_norm,
cur = ggml_norm(ctx0, inpFF, norm_eps); model.layers[il].ffn_norm_b,
cb(cur, "ffn_norm_0", il); LLM_NORM, norm_eps, cb, il);
cb(cur, "ffn_norm", il);
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
cb(cur, "ffn_norm_0_w", il);
cur = ggml_add(ctx0, cur, model.layers[il].ffn_norm_b);
cb(cur, "ffn_norm_0_wb", il);
}
cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur); cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
cb(cur, "result_w3", il); cb(cur, "result_w3", il);
@@ -4519,16 +4490,11 @@ static struct ggml_cgraph * llm_build_persimmon(
cur = inpL; cur = inpL;
{ cur = llm_build_norm(ctx0, cur,
cur = ggml_norm(ctx0, cur, norm_eps); model.output_norm,
cb(cur, "out_norm_0", -1); model.output_norm_b,
LLM_NORM, norm_eps, cb, -1);
cur = ggml_mul(ctx0, cur, model.output_norm);
cb(cur, "out_norm_0_w", -1);
cur = ggml_add(ctx0, cur, model.output_norm_b);
cb(cur, "result_norm", -1); cb(cur, "result_norm", -1);
}
cur = ggml_mul_mat(ctx0, model.output, cur); cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1); cb(cur, "result_output", -1);
@@ -4609,15 +4575,11 @@ static struct ggml_cgraph * llm_build_refact(
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL; struct ggml_tensor * inpSA = inpL;
// norm cur = llm_build_norm(ctx0, inpL,
{ model.layers[il].attn_norm,
cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps); NULL,
cb(cur, "rms_norm_0", il); LLM_NORM_RMS, norm_rms_eps, cb, il);
cb(cur, "attn_norm", il);
// cur = cur*attn_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
cb(cur, "attn_norm_0", il);
}
// self-attention // self-attention
{ {
@@ -4719,15 +4681,11 @@ static struct ggml_cgraph * llm_build_refact(
// feed-forward network // feed-forward network
{ {
// norm cur = llm_build_norm(ctx0, inpFF,
{ model.layers[il].ffn_norm,
cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps); NULL,
cb(cur, "rms_norm_1", il); LLM_NORM_RMS, norm_rms_eps, cb, il);
// cur = cur*ffn_norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
cb(cur, "ffn_norm", il); cb(cur, "ffn_norm", il);
}
struct ggml_tensor * tmp = ggml_mul_mat(ctx0, struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
model.layers[il].w3, model.layers[il].w3,
@@ -4761,15 +4719,11 @@ static struct ggml_cgraph * llm_build_refact(
cur = inpL; cur = inpL;
// norm cur = llm_build_norm(ctx0, cur,
{ model.output_norm,
cur = ggml_rms_norm(ctx0, cur, norm_rms_eps); NULL,
cb(cur, "rms_norm_2", -1); LLM_NORM_RMS, norm_rms_eps, cb, -1);
// cur = cur*norm(broadcasted)
cur = ggml_mul(ctx0, cur, model.output_norm);
cb(cur, "result_norm", -1); cb(cur, "result_norm", -1);
}
// lm_head // lm_head
cur = ggml_mul_mat(ctx0, model.output, cur); cur = ggml_mul_mat(ctx0, model.output, cur);
@@ -4851,30 +4805,18 @@ static struct ggml_cgraph * llm_build_bloom(
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1); cb(KQ_mask, "KQ_mask", -1);
// norm inpL = llm_build_norm(ctx0, embd,
{ model.tok_norm,
inpL = ggml_norm(ctx0, embd, norm_eps); model.tok_norm_b,
LLM_NORM, norm_eps, cb, -1);
cb(inpL, "inp_norm", -1); cb(inpL, "inp_norm", -1);
inpL = ggml_mul(ctx0, inpL, model.tok_norm);
cb(inpL, "inp_norm_w", -1);
inpL = ggml_add (ctx0, inpL, model.tok_norm_b);
cb(inpL, "inp_norm_wb", -1);
}
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
{ cur = llm_build_norm(ctx0, inpL,
// Norm model.layers[il].attn_norm,
cur = ggml_norm(ctx0, inpL, norm_eps); model.layers[il].attn_norm_b,
cb(cur, "attn_norm_0", il); LLM_NORM, norm_eps, cb, il);
cb(cur, "attn_norm", il);
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
cb(cur, "attn_norm_0_w", il);
cur = ggml_add(ctx0, cur, model.layers[il].attn_norm_b);
cb(cur, "attn_norm_0_wb", il);
}
{ {
// Self Attention // Self Attention
@@ -4984,17 +4926,11 @@ static struct ggml_cgraph * llm_build_bloom(
// FF // FF
{ {
// Norm cur = llm_build_norm(ctx0, inpFF,
{ model.layers[il].ffn_norm,
cur = ggml_norm(ctx0, inpFF, norm_eps); model.layers[il].ffn_norm_b,
cb(cur, "ffn_norm_0", il); LLM_NORM, norm_eps, cb, il);
cb(cur, "ffn_norm", il);
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
cb(cur, "ffn_norm_0_w", il);
cur = ggml_add(ctx0, cur, model.layers[il].ffn_norm_b);
cb(cur, "ffn_norm_0_wb", il);
}
cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur); cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
cb(cur, "result_w3", il); cb(cur, "result_w3", il);
@@ -5016,17 +4952,11 @@ static struct ggml_cgraph * llm_build_bloom(
cb(inpL, "inpFF_+_result_w2", il); cb(inpL, "inpFF_+_result_w2", il);
} }
// Output Norm cur = llm_build_norm(ctx0, inpL,
{ model.output_norm,
cur = ggml_norm(ctx0, inpL, norm_eps); model.output_norm_b,
cb(cur, "out_norm_0", -1); LLM_NORM, norm_eps, cb, -1);
cur = ggml_mul(ctx0, cur, model.output_norm);
cb(cur, "out_norm_0_w", -1);
cur = ggml_add(ctx0, cur, model.output_norm_b);
cb(cur, "result_norm", -1); cb(cur, "result_norm", -1);
}
cur = ggml_mul_mat(ctx0, model.output, cur); cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1); cb(cur, "result_output", -1);
@@ -5109,18 +5039,15 @@ static struct ggml_cgraph * llm_build_mpt(
for (int il = 0; il < n_layer; ++il) { for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * attn_norm; struct ggml_tensor * attn_norm;
attn_norm = llm_build_norm(ctx0, inpL,
model.layers[il].attn_norm,
NULL,
LLM_NORM, norm_eps, cb, il);
cb(attn_norm, "attn_norm", il);
// self-attention // self-attention
// TODO: refactor into common function (shared with LLaMA)
{ {
attn_norm = ggml_norm(ctx0, inpL, norm_eps);
cb(attn_norm, "attn_norm_0", il);
attn_norm = ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm);
cb(attn_norm, "attn_norm_0_w", il);
if (1) {
cur = attn_norm; cur = attn_norm;
}
// compute QKV // compute QKV
@@ -5230,14 +5157,11 @@ static struct ggml_cgraph * llm_build_mpt(
// feed forward // feed forward
{ {
// Norm cur = llm_build_norm(ctx0, attn_out,
{ model.layers[il].ffn_norm,
cur = ggml_norm(ctx0, attn_out, norm_eps); NULL,
cb(cur, "ffn_norm_0", il); LLM_NORM, norm_eps, cb, il);
cb(cur, "ffn_norm", il);
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
cb(cur, "ffn_norm_0_w", il);
}
cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur); cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
cb(cur, "result_w3", il); cb(cur, "result_w3", il);
@@ -5258,14 +5182,11 @@ static struct ggml_cgraph * llm_build_mpt(
cur = inpL; cur = inpL;
// norm cur = llm_build_norm(ctx0, cur,
{ model.output_norm,
cur = ggml_norm(ctx0, cur, norm_eps); NULL,
cb(cur, "out_norm_0", -1); LLM_NORM, norm_eps, cb, -1);
cur = ggml_mul(ctx0, cur, model.output_norm);
cb(cur, "result_norm", -1); cb(cur, "result_norm", -1);
}
cur = ggml_mul_mat(ctx0, model.output, cur); cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1); cb(cur, "result_output", -1);
@@ -5378,15 +5299,12 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
{ "inp_norm_w", OFFLOAD_FUNC_NR }, { "inp_norm_w", OFFLOAD_FUNC_NR },
{ "inp_norm_wb", OFFLOAD_FUNC_NR }, { "inp_norm_wb", OFFLOAD_FUNC_NR },
{ "rms_norm_0", OFFLOAD_FUNC }, { "norm", OFFLOAD_FUNC },
{ "norm_w", OFFLOAD_FUNC },
{ "attn_norm_0", OFFLOAD_FUNC }, { "norm_wb", OFFLOAD_FUNC },
{ "attn_norm_0_w", OFFLOAD_FUNC },
{ "attn_norm_0_wb", OFFLOAD_FUNC },
{ "attn_norm", OFFLOAD_FUNC },
{ "attn_norm_2", OFFLOAD_FUNC }, { "attn_norm_2", OFFLOAD_FUNC },
{ "attn_norm_2_w", OFFLOAD_FUNC },
{ "attn_norm_2_wb", OFFLOAD_FUNC },
{ "wqkv", OFFLOAD_FUNC_KQ }, { "wqkv", OFFLOAD_FUNC_KQ },
{ "bqkv", OFFLOAD_FUNC_KQ }, { "bqkv", OFFLOAD_FUNC_KQ },
@@ -5614,20 +5532,19 @@ static struct ggml_cgraph * llama_build_graph(
static const std::unordered_map<llm_offload_func_e, std::string, std::hash<int>> k_offload_func_name = { static const std::unordered_map<llm_offload_func_e, std::string, std::hash<int>> k_offload_func_name = {
{ OFFLOAD_FUNC_NOP, "CPU" }, { OFFLOAD_FUNC_NOP, "CPU" },
{ OFFLOAD_FUNC_OUT, "CPU" },
#ifdef GGML_USE_CUBLAS #ifdef GGML_USE_CUBLAS
{ OFFLOAD_FUNC, "GPU (CUDA)" }, { OFFLOAD_FUNC, "GPU (CUDA)" },
{ OFFLOAD_FUNC_KQ, "GPU (CUDA) KQ" }, { OFFLOAD_FUNC_KQ, "GPU (CUDA) KQ" },
{ OFFLOAD_FUNC_V, "GPU (CUDA) V" }, { OFFLOAD_FUNC_V, "GPU (CUDA) V" },
{ OFFLOAD_FUNC_NR, "GPU (CUDA) NR" }, { OFFLOAD_FUNC_NR, "GPU (CUDA) NR" },
{ OFFLOAD_FUNC_EMB, "GPU (CUDA) EMB" }, { OFFLOAD_FUNC_EMB, "GPU (CUDA) EMB" },
{ OFFLOAD_FUNC_OUT, "GPU (CUDA) OUT" },
#else #else
{ OFFLOAD_FUNC, "CPU" }, { OFFLOAD_FUNC, "CPU" },
{ OFFLOAD_FUNC_KQ, "CPU" }, { OFFLOAD_FUNC_KQ, "CPU" },
{ OFFLOAD_FUNC_V, "CPU" }, { OFFLOAD_FUNC_V, "CPU" },
{ OFFLOAD_FUNC_NR, "CPU" }, { OFFLOAD_FUNC_NR, "CPU" },
{ OFFLOAD_FUNC_EMB, "CPU" }, { OFFLOAD_FUNC_EMB, "CPU" },
{ OFFLOAD_FUNC_OUT, "CPU" },
#endif // GGML_USE_CUBLAS #endif // GGML_USE_CUBLAS
}; };