--- /dev/null
+from __future__ import annotations
+
+from typing import Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+ from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("MellumForCausalLM")
+class MellumModel(TextModel):
+ model_arch = gguf.MODEL_ARCH.MELLUM
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+ self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+ logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
+
+ use_sliding_window = self.hparams.get("use_sliding_window")
+ sliding_window = self.hparams.get("sliding_window")
+ if (use_sliding_window is True or use_sliding_window is None) and sliding_window is not None:
+ self.gguf_writer.add_sliding_window(sliding_window)
+ logger.info(f"gguf: sliding window = {sliding_window}")
+ self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in self.hparams["layer_types"]])
+ logger.info(f"gguf: sliding window pattern length = {len(self.hparams['layer_types'])}")
+
+ _experts: list[dict[str, Tensor]] | None = None
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ if name.find("experts") != -1:
+ n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+ assert bid is not None
+
+ if self._experts is None:
+ self._experts = [{} for _ in range(self.block_count)]
+
+ self._experts[bid][name] = data_torch
+
+ if len(self._experts[bid]) >= n_experts * 3:
+ for w_name in ["down_proj", "gate_proj", "up_proj"]:
+ datas: list[Tensor] = []
+
+ for xid in range(n_experts):
+ ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+ datas.append(self._experts[bid][ename])
+ del self._experts[bid][ename]
+
+ data_torch = torch.stack(datas, dim=0)
+
+ merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+ yield from super().modify_tensors(data_torch, merged_name, bid)
+ return
+ else:
+ return
+
+ yield from super().modify_tensors(data_torch, name, bid)
--- /dev/null
+#include "models.h"
+
+void llama_model_mellum::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_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+ ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+
+ if (hparams.n_swa > 0) {
+ hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+
+ uint32_t swa_period = 4;
+ const auto res = ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+ if (res) {
+ hparams.set_swa_pattern(swa_period);
+ } else {
+ ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
+ }
+
+ hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
+ hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
+
+ ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+ } else {
+ hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+ }
+
+ switch (hparams.n_layer) {
+ case 28: type = LLM_TYPE_12B_A2_5B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+}
+
+void llama_model_mellum::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);
+
+ 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_gqa, n_embd_gqa, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+ layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+ layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+ layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+
+ if (n_expert == 0) {
+ throw std::runtime_error("n_expert must be > 0 for Mellum");
+ }
+ if (n_expert_used == 0) {
+ throw std::runtime_error("n_expert_used must be > 0 for Mellum");
+ }
+
+ const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+ layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
+ layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
+ layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
+ }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_mellum::build_arch_graph(const llm_graph_params & params) const {
+ if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
+ return std::make_unique<graph<true>>(*this, params);
+ }
+ return std::make_unique<graph<false>>(*this, params);
+}
+
+template <bool iswa>
+llama_model_mellum::graph<iswa>::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();
+
+ using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
+ inp_attn_type * inp_attn = nullptr;
+
+ if constexpr (iswa) {
+ inp_attn = build_attn_inp_kv_iswa();
+ } else {
+ inp_attn = build_attn_inp_kv();
+ }
+
+ 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, nullptr,
+ LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ // self_attention
+ {
+ // 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 = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Qcur, "Qcur_normed", il);
+
+ Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Kcur, "Kcur_normed", il);
+
+ const bool is_swa = hparams.is_swa(il);
+
+ if (is_swa) {
+ // For sliding window layers, use regular rope with no yarn rope scaling.
+ // This is achieved here by setting freq_scale and attn_factor to 1.
+ // We also set ext_factor to 0 to avoid a few unnecessary computations.
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
+ 0.0, 1.0, beta_fast, beta_slow
+ );
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
+ 0.0, 1.0, beta_fast, beta_slow
+ );
+ } else {
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr,
+ 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, nullptr,
+ 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);
+
+ 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, 1.0f/sqrtf(float(n_embd_head)), 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);
+
+ // MoE
+ cur = build_norm(ffn_inp,
+ model.layers[il].ffn_norm, nullptr,
+ LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ ggml_tensor * moe_out =
+ 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(moe_out, "ffn_moe_out", il);
+ cur = moe_out;
+
+ 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, nullptr,
+ 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);
+}
+
+template struct llama_model_mellum::graph<false>;
+template struct llama_model_mellum::graph<true>;
overview / pkg / ggml / sources / llama.cpp / commitdiff