Mirrors/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2025-01-15 06:40:45 +01:00
Code Issues Packages Projects Releases Wiki Activity

able to compile

Browse Source
This commit is contained in:
okada 2023-12-17 17:44:29 +09:00
parent 9d49236570
commit 4a3ef4f2a4
1 changed files with 143 additions and 57 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

200
llama.cpp
View File

@ -3705,15 +3705,15 @@ static void llm_load_tensors(
layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split); layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split); layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split); layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
if (backend == GGML_BACKEND_GPU) { if (backend == GGML_BACKEND_GPU) {
vram_weights += vram_weights +=
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) + ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) +
ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3); ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_up) + ggml_nbytes(layer.ffn_down);
} }
} }
} break; } break;
@ -5512,20 +5512,11 @@ struct llm_build_context {
} }
struct ggml_cgraph * build_plamo() { struct ggml_cgraph * build_plamo() {
//TODO
/*
struct ggml_cgraph * gf = ggml_new_graph(ctx0); struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_tensor * cur; struct ggml_tensor * cur;
struct ggml_tensor * inpL; struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_scale // KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
cb(KQ_scale, "KQ_scale", -1); cb(KQ_scale, "KQ_scale", -1);
@ -5534,76 +5525,172 @@ struct llm_build_context {
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);
// KQ_pos - contains the positions
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(KQ_pos, "KQ_pos", -1);
// shift the entire K-cache if needed // shift the entire K-cache if needed
if (do_rope_shift) { if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, hparams.n_rot, freq_base, freq_scale, cb); llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
} }
for (int il = 0; il < n_layer; ++il) { //for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL; for (int il = 0; il < 1; ++il) {
// norm // norm
cur = llm_build_norm(ctx0, inpL, hparams, cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, model.layers[il].attn_norm, NULL,
model.layers[il].attn_norm_b, LLM_NORM_RMS, cb, il);
LLM_NORM, cb, il); cb(cur, "attention_norm_0", il);
cb(cur, "attn_norm", il);
struct ggml_tensor * attention_norm = cur;
// self-attention // self-attention
{ {
// compute Q and K and RoPE them // compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
cb(Qcur, "Qcur", il); cb(tmpk, "tmpk", il);
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
cb(tmpq, "tmpq", il);
struct ggml_tensor * Kcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), KQ_pos,
n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Kcur, "Kcur", il); cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); struct ggml_tensor * Qcur = ggml_rope_custom(
cb(Vcur, "Vcur", il); ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), KQ_pos,
n_embd_head, 2, 0, n_orig_ctx, freq_base, freq_scale,
Qcur = ggml_rope_custom( ext_factor, attn_factor, beta_fast, beta_slow);
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); cb(Qcur, "Qcur", il);
Kcur = ggml_rope_custom( // store key and value to memory
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, // compute the transposed [n_tokens, n_embd] V matrix
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); struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
cb(tmpv, "tmpv", il);
cur = llm_build_kqv(ctx0, hparams, kv_self, struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
model.layers[il].wo, NULL, cb(Vcur, "Vcur", il);
Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
cb(cur, "kqv_out", il); //struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k_l[il], n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k_l[il])*n_embd_gqa)*(il*n_ctx + kv_head));
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k_l[il], n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k_l[il])*n_embd_gqa)*kv_head);
cb(k, "k", il);
/*
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
( n_ctx)*ggml_element_size(kv_self.v),
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
*/
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v_l[il], n_tokens, n_embd_gqa,
n_ctx*ggml_element_size(kv_self.v_l[il]),
kv_head*ggml_element_size(kv_self.v_l[il]));
cb(v, "v", il);
// important: storing RoPE-ed version of K in the KV cache!
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
}
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
cb(Q, "Q", il);
/*
struct ggml_tensor * K =
ggml_view_3d(ctx0, kv_self.k,
n_embd_head, n_kv, n_head_kv,
ggml_element_size(kv_self.k)*n_embd_gqa,
ggml_element_size(kv_self.k)*n_embd_head,
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
*/
struct ggml_tensor * K =
ggml_view_3d(ctx0, kv_self.k_l[il],
n_embd_head, n_kv, n_head_kv,
ggml_element_size(kv_self.k_l[il])*n_embd_gqa,
ggml_element_size(kv_self.k_l[il])*n_embd_head,
0);
cb(K, "K", il);
// K * Q
//struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
// we should avoid to repeat K but current ggml_mul_mat generates wrong values for grouped query att
struct ggml_tensor * K_repeated = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, K->ne[0], K->ne[1], Q->ne[2]);
cb(K_repeated, "K_repeated", il);
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, ggml_repeat(ctx0, K, K_repeated), Q);
cb(KQ, "KQ", il);
// KQ_scaled = KQ / sqrt(n_embd_head)
// KQ_scaled shape [n_kv, n_tokens, n_head, 1]
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
cb(KQ_scaled, "KQ_scaled", il);
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
cb(KQ_masked, "KQ_masked", il);
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
cb(KQ_soft_max, "KQ_soft_max", il);
// split cached V into n_head heads
/*
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v,
n_kv, n_embd_head, n_head_kv,
ggml_element_size(kv_self.v)*n_ctx,
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
*/
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v_l[il],
n_kv, n_embd_head, n_head_kv,
ggml_element_size(kv_self.v_l[il])*n_ctx,
ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head,
0);
cb(V, "V", il);
//struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
// we should avoid to repeat V but current ggml_mul_mat generates wrong values for grouped query att
struct ggml_tensor * V_repeated = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, V->ne[0], V->ne[1], Q->ne[2]);
cb(V_repeated, "V_repeated", il);
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, ggml_repeat(ctx0, V, V_repeated), KQ_soft_max);
cb(KQV, "KQV", il);
// KQV_merged = KQV.permute(0, 2, 1, 3)
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
cb(KQV_merged, "KQV_merged", il);
// cur = KQV_merged.contiguous().view(n_embd, n_tokens)
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
cb(cur, "KQV_merged_contiguous", il);
// projection (no bias)
cur = ggml_mul_mat(ctx0,
model.layers[il].wo,
cur);
cb(cur, "result_wo", il);
} }
struct ggml_tensor * sa_out = cur;
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); cur = attention_norm;
cb(ffn_inp, "ffn_inp", il);
// feed-forward network // feed-forward network
{ {
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur, cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL, model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL, model.layers[il].ffn_down, NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il); LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il); cb(cur, "mlp_out", il);
} }
cur = ggml_add(ctx0, cur, ffn_inp); cur = ggml_add(ctx0, cur, sa_out);
cb(cur, "l_out", il); cb(cur, "mlp_out + sa_out", il);
cur = ggml_add(ctx0, cur, inpL);
cb(cur, "mlp_out + sa_out + inpL", il);
// input for next layer // input for next layer
inpL = cur; inpL = cur;
@ -5626,7 +5713,6 @@ struct llm_build_context {
ggml_build_forward_expand(gf, cur); ggml_build_forward_expand(gf, cur);
return gf; return gf;
*/
} }
}; };