model: add Mellum architecture (#23966)
* model: support for Mellum architecture * model: improve mellum.py formatting * model: improve mellum.py formatting once again * deps: downgrade transformers to 4.57.6 (to fix CI) * deps: remove huggingface_hub dependency * deps: remove huggingface_hub from test requirements --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
Mikhail Podvitskii
co-authored by
Sigbjørn Skjæret
Sigbjørn Skjæret
parent
bfb4308b05
commit
4fb16eccce
@@ -0,0 +1,225 @@
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#include "models.h"
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void llama_model_mellum::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_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
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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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const auto res = ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
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if (res) {
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hparams.set_swa_pattern(swa_period);
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} else {
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ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
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}
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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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switch (hparams.n_layer) {
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case 28: type = LLM_TYPE_12B_A2_5B; 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_mellum::load_arch_tensors(llama_model_loader &) {
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LLAMA_LOAD_LOCALS;
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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}, 0);
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_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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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
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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.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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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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if (n_expert == 0) {
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throw std::runtime_error("n_expert must be > 0 for Mellum");
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}
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if (n_expert_used == 0) {
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throw std::runtime_error("n_expert_used must be > 0 for Mellum");
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}
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const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
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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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}
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}
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std::unique_ptr<llm_graph_context> llama_model_mellum::build_arch_graph(const llm_graph_params & params) const {
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if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
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return std::make_unique<graph<true>>(*this, params);
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}
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return std::make_unique<graph<false>>(*this, params);
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}
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template <bool iswa>
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llama_model_mellum::graph<iswa>::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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GGML_ASSERT(n_embd_head == n_rot);
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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = build_inp_embd(model.tok_embd);
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// inp_pos - contains the positions
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ggml_tensor * inp_pos = build_inp_pos();
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using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
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inp_attn_type * inp_attn = nullptr;
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if constexpr (iswa) {
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inp_attn = build_attn_inp_kv_iswa();
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} else {
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inp_attn = build_attn_inp_kv();
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}
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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// norm
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cur = build_norm(inpL,
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model.layers[il].attn_norm, nullptr,
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LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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// self_attention
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{
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// compute Q and K and RoPE them
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auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
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n_embd_head, n_head, n_head_kv, il);
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Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
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cb(Qcur, "Qcur_normed", il);
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Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
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cb(Kcur, "Kcur_normed", il);
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const bool is_swa = hparams.is_swa(il);
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if (is_swa) {
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// For sliding window layers, use regular rope with no yarn rope scaling.
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// This is achieved here by setting freq_scale and attn_factor to 1.
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// We also set ext_factor to 0 to avoid a few unnecessary computations.
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
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0.0, 1.0, beta_fast, beta_slow
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);
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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
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0.0, 1.0, beta_fast, beta_slow
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);
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} else {
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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}
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cur = build_attn(inp_attn,
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model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
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}
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if (il == n_layer - 1 && inp_out_ids) {
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// MoE
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cur = build_norm(ffn_inp,
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model.layers[il].ffn_norm, nullptr,
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LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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ggml_tensor * moe_out =
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build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, true,
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hparams.expert_weights_scale,
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LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
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il,
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nullptr, nullptr,
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model.layers[il].ffn_up_exps_s,
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model.layers[il].ffn_gate_exps_s,
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model.layers[il].ffn_down_exps_s);
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cb(moe_out, "ffn_moe_out", il);
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cur = moe_out;
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "ffn_out", il);
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cur = build_cvec(cur, il);
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cb(cur, "l_out", il);
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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cur = build_norm(cur,
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model.output_norm, nullptr,
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LLM_NORM_RMS, -1);
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cb(cur, "result_norm", -1);
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res->t_embd = cur;
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// lm_head
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cur = build_lora_mm(model.output, cur, model.output_s);
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cb(cur, "result_output", -1);
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res->t_logits = cur;
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ggml_build_forward_expand(gf, cur);
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}
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template struct llama_model_mellum::graph<false>;
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template struct llama_model_mellum::graph<true>;
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@@ -411,6 +411,18 @@ struct llama_model_stablelm : public llama_model_base {
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_mellum : public llama_model_base {
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llama_model_mellum(const struct llama_model_params & params) : llama_model_base(params) {}
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void load_arch_hparams(llama_model_loader & ml) override;
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void load_arch_tensors(llama_model_loader & ml) override;
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template <bool iswa>
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struct graph : public llm_graph_context {
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graph(const llama_model & model, const llm_graph_params & params);
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};
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std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
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};
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struct llama_model_qwen : public llama_model_base {
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llama_model_qwen(const struct llama_model_params & params) : llama_model_base(params) {}
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