model: move load_hparams and load_tensors to per-model definition (#22004)
* git-friendly migration * add build_graph * nits * exclude old code from build * wip * add llm_arch_model_i * prepare downstream functions * nits * nits * wip * wip * add back create_tensor_qkv * fix files missing include * enforce one llm_build per arch * cmake: use glob * missing model params * nits * wip * wip (2) * wip (3) * test-llama-archs is happy * improve switch case * move more stuff into llm_arch_model_i * fix downstream code * nits * nits (2) * fix order * llama_model_base * LLAMA_LOAD_LOCALS * small fix * fix build errors * auto * rm migration script and ifdef
This commit is contained in:
Xuan-Son Nguyen
+117
-1
@@ -1,7 +1,123 @@
|
||||
#include "models.h"
|
||||
|
||||
void llama_model_arwkv7::load_arch_hparams(llama_model_loader & ml) {
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps, false);
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
|
||||
ml.get_key(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size);
|
||||
ml.get_key(LLM_KV_ATTENTION_DECAY_LORA_RANK, hparams.n_lora_decay);
|
||||
ml.get_key(LLM_KV_ATTENTION_ICLR_LORA_RANK, hparams.n_lora_iclr);
|
||||
ml.get_key(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, hparams.n_lora_value_res_mix);
|
||||
ml.get_key(LLM_KV_ATTENTION_GATE_LORA_RANK, hparams.n_lora_gate, false);
|
||||
ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT, hparams.token_shift_count, false);
|
||||
|
||||
llm_build_arwkv7::llm_build_arwkv7(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv7_base(model, params) {
|
||||
switch (hparams.n_layer) {
|
||||
case 12:
|
||||
switch (hparams.n_embd) {
|
||||
case 768: type = LLM_TYPE_190M; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
} break;
|
||||
case 24:
|
||||
switch (hparams.n_embd) {
|
||||
case 1024: type = LLM_TYPE_450M; break;
|
||||
case 2048: type = LLM_TYPE_1_5B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
} break;
|
||||
case 28:
|
||||
switch (hparams.n_embd) {
|
||||
case 1536: type = LLM_TYPE_1_5B; break;
|
||||
case 3584: type = LLM_TYPE_7B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
} break;
|
||||
case 32:
|
||||
switch (hparams.n_embd) {
|
||||
case 2560: type = LLM_TYPE_2_9B; break;
|
||||
case 4096: type = LLM_TYPE_7B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
} break;
|
||||
case 61:
|
||||
switch (hparams.n_embd) {
|
||||
case 4096: type = LLM_TYPE_14B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
} break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
}
|
||||
|
||||
void llama_model_arwkv7::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}, 0);
|
||||
|
||||
const int n_lora_decay = hparams.n_lora_decay;
|
||||
const int n_lora_iclr = hparams.n_lora_iclr;
|
||||
const int n_lora_value_res_mix = hparams.n_lora_value_res_mix;
|
||||
const int n_lora_gate = hparams.n_lora_gate;
|
||||
const int attn_hidden_size = n_embd;
|
||||
|
||||
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);
|
||||
|
||||
layer.time_mix_w0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W0, "weight", i), {n_embd}, 0);
|
||||
layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, n_lora_decay}, 0);
|
||||
layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {n_lora_decay, n_embd}, 0);
|
||||
|
||||
layer.time_mix_a0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A0, "weight", i), {n_embd}, 0);
|
||||
layer.time_mix_a1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A1, "weight", i), {n_embd, n_lora_iclr}, 0);
|
||||
layer.time_mix_a2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A2, "weight", i), {n_lora_iclr, n_embd}, 0);
|
||||
|
||||
if (i == 0) {
|
||||
// actually not used
|
||||
layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
|
||||
layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_iclr}, 0);
|
||||
layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_iclr, n_embd}, 0);
|
||||
} else {
|
||||
layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
|
||||
layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix}, 0);
|
||||
layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd}, 0);
|
||||
}
|
||||
|
||||
layer.time_mix_g1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G1, "weight", i), {n_embd, n_lora_gate}, TENSOR_NOT_REQUIRED);
|
||||
layer.time_mix_g2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G2, "weight", i), {n_lora_gate, n_embd}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
try {
|
||||
layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 6}, 0);
|
||||
} catch(std::runtime_error & e) {
|
||||
// ARWKV models may not have gate tensors
|
||||
layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, 0);
|
||||
}
|
||||
|
||||
layer.time_mix_k_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_K, "weight", i), {attn_hidden_size}, 0);
|
||||
layer.time_mix_k_a = create_tensor(tn(LLM_TENSOR_TIME_MIX_K_A, "weight", i), {attn_hidden_size}, 0);
|
||||
layer.time_mix_r_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_R_K, "weight", i), {attn_hidden_size}, 0);
|
||||
|
||||
layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
|
||||
layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
|
||||
layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
|
||||
|
||||
layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
||||
layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
||||
layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 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);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
std::unique_ptr<llm_graph_context> llama_model_arwkv7::build_arch_graph(const llm_graph_params & params) const {
|
||||
return std::make_unique<graph>(*this, params);
|
||||
}
|
||||
|
||||
llama_model_arwkv7::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv7_base(model, params) {
|
||||
GGML_ASSERT(n_embd == hparams.n_embd_r());
|
||||
|
||||
ggml_tensor * cur;
|
||||
|
||||
Reference in New Issue
Block a user