236 lines
9.6 KiB
C++
236 lines
9.6 KiB
C++
#include "models.h"
|
|
|
|
void llama_model_mistral3::load_arch_hparams(llama_model_loader & ml) {
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);
|
|
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast, false);
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow, false);
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false);
|
|
|
|
hparams.f_attn_temp_offset = 0.0f;
|
|
|
|
// TODO: maybe add n_attn_temp_floor_scale as a separate KV?
|
|
if (hparams.f_attn_temp_scale != 0.0f) {
|
|
hparams.n_attn_temp_floor_scale = hparams.n_ctx_orig_yarn;
|
|
if (hparams.n_attn_temp_floor_scale == 0) {
|
|
throw std::runtime_error("invalid n_ctx_orig_yarn for attention temperature scaling");
|
|
}
|
|
}
|
|
|
|
switch (hparams.n_layer) {
|
|
case 26: type = LLM_TYPE_3B; break;
|
|
case 34: type = LLM_TYPE_8B; break;
|
|
case 40: type = LLM_TYPE_14B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
}
|
|
|
|
void llama_model_mistral3::load_arch_tensors(llama_model_loader &) {
|
|
LLAMA_LOAD_LOCALS;
|
|
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
|
|
|
// optional bias tensors
|
|
layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
|
|
layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
}
|
|
else {
|
|
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
}
|
|
|
|
if (n_expert == 0) {
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
|
|
// optional MLP bias
|
|
layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
|
|
} else {
|
|
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
|
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
|
|
|
|
// For Granite MoE Shared
|
|
if (hparams.n_ff_shexp > 0) {
|
|
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
|
|
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
|
|
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
std::unique_ptr<llm_graph_context> llama_model_mistral3::build_arch_graph(const llm_graph_params & params) const {
|
|
return std::make_unique<graph>(*this, params);
|
|
}
|
|
|
|
llama_model_mistral3::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
|
|
const int64_t n_embd_head = hparams.n_embd_head_v();
|
|
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
|
|
GGML_ASSERT(n_embd_head == n_rot);
|
|
|
|
ggml_tensor * cur;
|
|
ggml_tensor * inpL;
|
|
|
|
inpL = build_inp_embd(model.tok_embd);
|
|
|
|
// inp_pos - contains the positions
|
|
ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// (optional) temperature tuning
|
|
ggml_tensor * inp_attn_scale = nullptr;
|
|
if (hparams.f_attn_temp_scale != 0.0f) {
|
|
inp_attn_scale = build_inp_attn_scale();
|
|
}
|
|
|
|
auto * inp_attn = build_attn_inp_kv();
|
|
|
|
const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
|
|
|
|
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
cur = build_norm(inpL,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
// rope freq factors for llama3; may return nullptr for llama2 and other models
|
|
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
|
|
|
|
// compute Q and K and RoPE them
|
|
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
|
|
n_embd_head, n_head, n_head_kv, il);
|
|
|
|
Qcur = ggml_rope_ext(
|
|
ctx0, Qcur, inp_pos, rope_factors,
|
|
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
|
|
Kcur = ggml_rope_ext(
|
|
ctx0, Kcur, inp_pos, rope_factors,
|
|
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
if (inp_attn_scale) {
|
|
// apply llama 4 temperature scaling
|
|
Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
|
|
cb(Qcur, "Qcur_attn_temp_scaled", il);
|
|
}
|
|
|
|
cur = build_attn(inp_attn,
|
|
model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
|
|
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
|
cb(cur, "attn_out", il);
|
|
}
|
|
if (il == n_layer - 1 && inp_out_ids) {
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network (non-MoE)
|
|
if (model.layers[il].ffn_gate_inp == nullptr) {
|
|
|
|
cur = build_norm(ffn_inp,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = build_ffn(cur,
|
|
model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s,
|
|
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s,
|
|
model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
|
cb(cur, "ffn_out", il);
|
|
} else {
|
|
// MoE branch
|
|
cur = build_norm(ffn_inp,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = build_moe_ffn(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,
|
|
nullptr,
|
|
n_expert, n_expert_used,
|
|
LLM_FFN_SILU, true,
|
|
hparams.expert_weights_scale,
|
|
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
|
|
il,
|
|
nullptr, nullptr,
|
|
model.layers[il].ffn_up_exps_s,
|
|
model.layers[il].ffn_gate_exps_s,
|
|
model.layers[il].ffn_down_exps_s);
|
|
cb(cur, "ffn_moe_out", il);
|
|
}
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "ffn_out", il);
|
|
|
|
cur = build_cvec(cur, il);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
cur = inpL;
|
|
|
|
cur = build_norm(cur,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, -1);
|
|
|
|
cb(cur, "result_norm", -1);
|
|
res->t_embd = cur;
|
|
|
|
// lm_head
|
|
cur = build_lora_mm(model.output, cur, model.output_s);
|
|
|
|
cb(cur, "result_output", -1);
|
|
res->t_logits = cur;
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
}
|