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model : add support for SmallThinker series (#14898)
* support smallthinker * support 20b softmax, 4b no sliding window * new build_moe_ffn_from_probs, and can run 4b * fix 4b rope bug * fix python type check * remove is_moe judge * remove set_dense_start_swa_pattern function and modify set_swa_pattern function * trim trailing whitespace * remove get_vocab_base of SmallThinkerModel in convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * better whitespace Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * use GGML_ASSERT for expert count validation Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Improve null pointer check for probs Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * use template parameter for SWA attention logic * better whitespace Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * move the creation of inp_out_ids before the layer loop * remove redundant judge for probs --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Dongliang Wei
GitHub
parent
afc0e89698
commit
6c6e397aff
10 changed files with 443 additions and 6 deletions
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@ -1768,6 +1768,29 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_SMALLTHINKER:
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{
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const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
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if (found_swa && hparams.n_swa > 0) {
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hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
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hparams.n_swa = 4096;
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hparams.set_swa_pattern(4, true);
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} else {
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hparams.swa_type = LLAMA_SWA_TYPE_NONE;
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hparams.n_no_rope_layer_step = hparams.n_layer;
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}
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
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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_GATING_FUNC, hparams.expert_gating_func, false);
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switch (hparams.n_layer) {
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case 32: type = LLM_TYPE_4B; break;
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case 52: type = LLM_TYPE_20B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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default: throw std::runtime_error("unsupported model architecture");
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}
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@ -5165,6 +5188,42 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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}
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}
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} break;
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case LLM_ARCH_SMALLTHINKER:
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{
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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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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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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, 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.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
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GGML_ASSERT(n_expert > 0 && "n_expert must be > 0 for SMALLTHINKER");
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GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for SMALLTHINKER");
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// MoE branch
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const int64_t n_ff_exp = hparams.n_ff_exp;
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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_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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} break;
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default:
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throw std::runtime_error("unknown architecture");
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}
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@ -5490,6 +5549,11 @@ void llama_model::print_info() const {
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LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
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}
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if (arch == LLM_ARCH_SMALLTHINKER) {
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LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
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LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
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}
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vocab.print_info();
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}
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@ -17011,6 +17075,119 @@ struct llm_build_lfm2 : public llm_graph_context {
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}
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};
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template <bool iswa>
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struct llm_build_smallthinker : public llm_graph_context{
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llm_build_smallthinker(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 == hparams.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_unified_iswa, llm_graph_input_attn_kv_unified>;
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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_unified_iswa();
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} else {
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inp_attn = build_attn_inp_kv_unified();
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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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ggml_tensor * probs = nullptr;
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probs = build_lora_mm(model.layers[il].ffn_gate_inp, inpL); // [n_expert, n_tokens]
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cb(probs, "ffn_moe_logits", il);
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// norm
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cur = build_norm(inpL,model.layers[il].attn_norm, NULL, 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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struct ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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struct ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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struct ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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if (hparams.n_no_rope_layer_step == n_layer || il % hparams.n_no_rope_layer_step != 0) {
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Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, 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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Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, 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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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cur = build_attn(inp_attn,
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model.layers[il].wo, model.layers[il].bo,
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Qcur, Kcur, Vcur, 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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probs = ggml_get_rows(ctx0, probs, 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 branch
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cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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ggml_tensor * ffn_out = build_moe_ffn_from_probs(cur, probs, model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
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nullptr, n_expert, n_expert_used,
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static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func), il);
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cb(ffn_out, "ffn_out", il);
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cur = ffn_out;
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cur = ggml_add(ctx0, cur, ffn_inp);
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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, model.output_norm, NULL, LLM_NORM_RMS, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = build_lora_mm(model.output, cur);
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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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};
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llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const {
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llama_memory_i * res;
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@ -17449,6 +17626,14 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
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{
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llm = std::make_unique<llm_build_lfm2>(*this, params);
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} break;
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case LLM_ARCH_SMALLTHINKER:
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{
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if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
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llm = std::make_unique<llm_build_smallthinker<true>> (*this, params);
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} else {
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llm = std::make_unique<llm_build_smallthinker<false>>(*this, params);
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}
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} break;
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default:
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GGML_ABORT("fatal error");
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}
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@ -17647,6 +17832,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
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case LLM_ARCH_DOTS1:
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case LLM_ARCH_HUNYUAN_MOE:
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case LLM_ARCH_LFM2:
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case LLM_ARCH_SMALLTHINKER:
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return LLAMA_ROPE_TYPE_NEOX;
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case LLM_ARCH_QWEN2VL:
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