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
GitHub
+107
-1
@@ -1,6 +1,112 @@
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#include "models.h"
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llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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void llama_model_afmoe::load_arch_hparams(llama_model_loader & ml) {
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
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ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
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ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
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// Set up interleaved sliding window attention (ISWA)
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// Pattern: 3 sliding - 1 full (global_attn_every_n_layers = 4)
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if (hparams.n_swa > 0) {
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hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
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uint32_t swa_period = 4;
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ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
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hparams.set_swa_pattern(swa_period);
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hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
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hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
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ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
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} else {
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hparams.swa_type = LLAMA_SWA_TYPE_NONE;
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}
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// Default to sigmoid if not set
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if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
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hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
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}
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switch (hparams.n_layer) {
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case 56: type = LLM_TYPE_6B; break;
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case 32: type = LLM_TYPE_26B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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}
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void llama_model_afmoe::load_arch_tensors(llama_model_loader &) {
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LLAMA_LOAD_LOCALS;
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const int64_t n_expert_shared = hparams.n_expert_shared;
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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// output
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
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// if output is NULL, init from the input tok embed
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if (output == NULL) {
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
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}
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const int64_t n_ff_exp = hparams.n_ff_exp;
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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// dual attention normalization
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
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// attention projections
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create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
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// Q/K normalization
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layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
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// attention gating
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layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
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// dual ffn normalization
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
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if (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) {
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// MoE layers
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
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layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
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// grouped expert weights
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layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
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// shared expert
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if (n_expert_shared > 0) {
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const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
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}
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} else {
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// Dense layers
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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}
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}
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}
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std::unique_ptr<llm_graph_context> llama_model_afmoe::build_arch_graph(const llm_graph_params & params) const {
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return std::make_unique<graph>(*this, params);
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}
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llama_model_afmoe::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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const int64_t n_embd_head = hparams.n_embd_head_v();
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
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