fix merge
ggml-ci
This commit is contained in:
slaren
parent
42003fdc32
commit
fb168ac5f7
1 changed files with 153 additions and 13 deletions
166
llama.cpp
166
llama.cpp
|
|
@ -6103,6 +6103,7 @@ static struct ggml_tensor * llm_build_moe_ffn(
|
|||
int64_t n_expert,
|
||||
int64_t n_expert_used,
|
||||
llm_ffn_op_type type_op,
|
||||
bool norm_w,
|
||||
const llm_build_cb & cb,
|
||||
int il) {
|
||||
int64_t n_embd = cur->ne[0];
|
||||
|
|
@ -6125,11 +6126,13 @@ static struct ggml_tensor * llm_build_moe_ffn(
|
|||
|
||||
weights = ggml_reshape_2d(ctx, weights, n_expert_used, n_tokens);
|
||||
|
||||
ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights); // [1, n_tokens]
|
||||
cb(weights_sum, "ffn_moe_weights_sum", il);
|
||||
if (norm_w) {
|
||||
ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights); // [1, n_tokens]
|
||||
cb(weights_sum, "ffn_moe_weights_sum", il);
|
||||
|
||||
weights = ggml_div(ctx, weights, weights_sum); // [n_expert_used, n_tokens]
|
||||
cb(weights, "ffn_moe_weights_norm", il);
|
||||
weights = ggml_div(ctx, weights, weights_sum); // [n_expert_used, n_tokens]
|
||||
cb(weights, "ffn_moe_weights_norm", il);
|
||||
}
|
||||
|
||||
cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
|
||||
ggml_tensor * up = ggml_mul_mat_id(ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
|
||||
|
|
@ -6180,6 +6183,7 @@ static struct ggml_tensor * llm_build_moe_ffn(
|
|||
|
||||
return moe_out;
|
||||
}
|
||||
|
||||
// if max_alibi_bias > 0 then apply ALiBi
|
||||
static struct ggml_tensor * llm_build_kqv(
|
||||
struct ggml_context * ctx,
|
||||
|
|
@ -6729,7 +6733,8 @@ struct llm_build_context {
|
|||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, cb, il);
|
||||
LLM_FFN_SILU, true,
|
||||
cb, il);
|
||||
cb(cur, "ffn_moe_out", il);
|
||||
}
|
||||
|
||||
|
|
@ -7215,7 +7220,8 @@ struct llm_build_context {
|
|||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_GELU, cb, il);
|
||||
LLM_FFN_GELU, true,
|
||||
cb, il);
|
||||
cb(cur, "ffn_moe_out", il);
|
||||
|
||||
// Grok
|
||||
|
|
@ -7243,6 +7249,138 @@ struct llm_build_context {
|
|||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
|
||||
// Grok
|
||||
// multiply logits by output_multiplier_scale of 0.5773502691896257
|
||||
|
||||
cur = ggml_scale(ctx0, cur, 0.5773502691896257f);
|
||||
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_dbrx() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
// mutable variable, needed during the last layer of the computation to skip unused tokens
|
||||
int32_t n_tokens = this->n_tokens;
|
||||
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
struct ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
struct ggml_tensor * inpSA = inpL;
|
||||
|
||||
// norm
|
||||
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||
model.layers[il].attn_norm, NULL,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
struct ggml_tensor * Qcur = nullptr;
|
||||
struct ggml_tensor * Kcur = nullptr;
|
||||
struct ggml_tensor * Vcur = nullptr;
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
||||
cb(cur, "wqkv", il);
|
||||
|
||||
cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
|
||||
cb(cur, "wqkv_clamped", il);
|
||||
|
||||
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
||||
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
||||
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Qcur = ggml_rope_custom(
|
||||
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
||||
n_rot, rope_type, 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,
|
||||
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
||||
model.layers[il].wo, NULL,
|
||||
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1) {
|
||||
// skip computing output for unused tokens
|
||||
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
n_tokens = n_outputs;
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// feed-forward network
|
||||
// MoE branch
|
||||
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||
model.layers[il].attn_out_norm, NULL,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "attn_out_norm", il);
|
||||
|
||||
cur = llm_build_moe_ffn(ctx0, cur,
|
||||
model.layers[il].ffn_gate_inp,
|
||||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, true,
|
||||
cb, il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
|
||||
if (layer_dir != nullptr) {
|
||||
cur = ggml_add(ctx0, cur, layer_dir);
|
||||
}
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM, cb, -1);
|
||||
|
|
@ -8397,12 +8535,6 @@ struct llm_build_context {
|
|||
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
// these nodes are added to the graph together so that they are not reordered
|
||||
// by doing so, the number of splits in the graph is reduced
|
||||
ggml_build_forward_expand(gf, Qcur);
|
||||
ggml_build_forward_expand(gf, Kcur);
|
||||
ggml_build_forward_expand(gf, Vcur);
|
||||
|
||||
Qcur = ggml_rope_custom(
|
||||
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
||||
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
||||
|
|
@ -8553,7 +8685,15 @@ struct llm_build_context {
|
|||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
ggml_tensor * moe_out = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, false, il);
|
||||
ggml_tensor * moe_out =
|
||||
llm_build_moe_ffn(ctx0, cur,
|
||||
model.layers[il].ffn_gate_inp,
|
||||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, false,
|
||||
cb, il);
|
||||
|
||||
// FFN shared expert
|
||||
{
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue