From b77d646751d01c0962bc203b6809e9d94f7d50b7 Mon Sep 17 00:00:00 2001 From: zql <37731799+zqlcode@users.noreply.github.com> Date: Mon, 27 Jul 2026 23:04:18 +0800 Subject: [PATCH] model: Add support for Nanbeige4.2 (#25994) * support nanbeige4.2 model * fix * fix flake8 Lint check * fix loop bound check and drop redundant head_dim --------- Co-authored-by: root --- conversion/__init__.py | 1 + conversion/nanbeige.py | 24 +++++ gguf-py/gguf/constants.py | 25 ++++- gguf-py/gguf/gguf_writer.py | 6 ++ src/llama-arch.cpp | 3 + src/llama-arch.h | 3 + src/llama-context.cpp | 1 + src/llama-model.cpp | 3 + src/models/models.h | 16 ++++ src/models/nanbeige.cpp | 184 ++++++++++++++++++++++++++++++++++++ 10 files changed, 265 insertions(+), 1 deletion(-) create mode 100644 conversion/nanbeige.py create mode 100644 src/models/nanbeige.cpp diff --git a/conversion/__init__.py b/conversion/__init__.py index b2bb7e516..45c001b78 100644 --- a/conversion/__init__.py +++ b/conversion/__init__.py @@ -167,6 +167,7 @@ TEXT_MODEL_MAP: dict[str, str] = { "ModernBertForMaskedLM": "bert", "ModernBertForSequenceClassification": "bert", "ModernBertModel": "bert", + "NanbeigeForCausalLM": "nanbeige", "NemotronForCausalLM": "nemotron", "NemotronHForCausalLM": "nemotron", "NeoBERT": "bert", diff --git a/conversion/nanbeige.py b/conversion/nanbeige.py new file mode 100644 index 000000000..f1fc425b3 --- /dev/null +++ b/conversion/nanbeige.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +from .base import ModelBase, gguf, logger +from .llama import LlamaModel + + +@ModelBase.register("NanbeigeForCausalLM") +class NanbeigeModel(LlamaModel): + model_arch = gguf.MODEL_ARCH.NANBEIGE + undo_permute = True + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + + n_loops = int(hparams.get("num_loops", 1) or 1) + if n_loops < 1: + n_loops = 1 + self.gguf_writer.add_num_loops(n_loops) + logger.info(f"gguf: num_loops = {n_loops}") + + skip_loop_final_norm = bool(hparams.get("skip_loop_final_norm", False)) + self.gguf_writer.add_skip_loop_final_norm(skip_loop_final_norm) + logger.info(f"gguf: skip_loop_final_norm = {skip_loop_final_norm}") diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 2071e3eaa..78e3c29a0 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -145,6 +145,8 @@ class Keys: TOKEN_SHIFT_COUNT = "{arch}.token_shift_count" INTERLEAVE_MOE_LAYER_STEP = "{arch}.interleave_moe_layer_step" FULL_ATTENTION_INTERVAL = "{arch}.full_attention_interval" + NUM_LOOPS = "{arch}.num_loops" + SKIP_LOOP_FINAL_NORM = "{arch}.skip_loop_final_norm" HASH_LAYER_COUNT = "{arch}.hash_layer_count" ACTIVATION_SPARSITY_SCALE = "{arch}.activation_sparsity_scale" ALTUP_ACTIVE_IDX = "{arch}.altup.active_idx" @@ -545,6 +547,7 @@ class MODEL_ARCH(IntEnum): KIMI_LINEAR = auto() TALKIE = auto() MELLUM = auto() + NANBEIGE = auto() class VISION_PROJECTOR_TYPE(IntEnum): @@ -1134,6 +1137,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.KIMI_LINEAR: "kimi-linear", MODEL_ARCH.TALKIE: "talkie", MODEL_ARCH.MELLUM: "mellum", + MODEL_ARCH.NANBEIGE: "nanbeige", } VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = { @@ -4505,7 +4509,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], - # TODO + MODEL_ARCH.NANBEIGE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], } # tensors that will not be serialized @@ -4572,6 +4591,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.NANBEIGE: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], } # diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index ba08f8d65..ecf1f17ee 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -908,6 +908,12 @@ class GGUFWriter: def add_token_shift_count(self, count: int) -> None: self.add_uint32(Keys.LLM.TOKEN_SHIFT_COUNT.format(arch=self.arch), count) + def add_num_loops(self, count: int) -> None: + self.add_uint32(Keys.LLM.NUM_LOOPS.format(arch=self.arch), count) + + def add_skip_loop_final_norm(self, value: bool) -> None: + self.add_bool(Keys.LLM.SKIP_LOOP_FINAL_NORM.format(arch=self.arch), value) + def add_interleave_moe_layer_step(self, value: int) -> None: self.add_uint32(Keys.LLM.INTERLEAVE_MOE_LAYER_STEP.format(arch=self.arch), value) diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 39bf2c795..c01706785 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -143,6 +143,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_KIMI_LINEAR, "kimi-linear" }, { LLM_ARCH_TALKIE, "talkie" }, { LLM_ARCH_MELLUM, "mellum" }, + { LLM_ARCH_NANBEIGE, "nanbeige" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -221,6 +222,8 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_TOKEN_SHIFT_COUNT, "%s.token_shift_count" }, { LLM_KV_INTERLEAVE_MOE_LAYER_STEP, "%s.interleave_moe_layer_step" }, { LLM_KV_FULL_ATTENTION_INTERVAL, "%s.full_attention_interval" }, + { LLM_KV_NUM_LOOPS, "%s.num_loops" }, + { LLM_KV_SKIP_LOOP_FINAL_NORM, "%s.skip_loop_final_norm" }, { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" }, { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index 2e3916a0b..1c9aebb0b 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -148,6 +148,7 @@ enum llm_arch { LLM_ARCH_EAGLE3, LLM_ARCH_MINIMAX_M3, LLM_ARCH_DFLASH, + LLM_ARCH_NANBEIGE, LLM_ARCH_UNKNOWN, }; @@ -226,6 +227,8 @@ enum llm_kv { LLM_KV_TOKEN_SHIFT_COUNT, LLM_KV_INTERLEAVE_MOE_LAYER_STEP, LLM_KV_FULL_ATTENTION_INTERVAL, + LLM_KV_NUM_LOOPS, + LLM_KV_SKIP_LOOP_FINAL_NORM, LLM_KV_ATTENTION_HEAD_COUNT, LLM_KV_ATTENTION_HEAD_COUNT_KV, diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 012894e13..9b399d609 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -2339,6 +2339,7 @@ uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const { model.arch == LLM_ARCH_QWEN35 || model.arch == LLM_ARCH_QWEN35MOE || model.arch == LLM_ARCH_DEEPSEEK4 || + model.arch == LLM_ARCH_NANBEIGE || model.arch == LLM_ARCH_MINIMAX_M3) { return std::max(n_tokens * 40, 32u * model.n_tensors()); } diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 074acbe1f..be0a0df55 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -85,6 +85,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_stablelm(params); case LLM_ARCH_MELLUM: return new llama_model_mellum(params); + case LLM_ARCH_NANBEIGE: + return new llama_model_nanbeige(params); case LLM_ARCH_QWEN: return new llama_model_qwen(params); case LLM_ARCH_QWEN2: @@ -2491,6 +2493,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_LLAMA_EMBED: case LLM_ARCH_MAINCODER: case LLM_ARCH_GLM_DSA: + case LLM_ARCH_NANBEIGE: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 diff --git a/src/models/models.h b/src/models/models.h index 916459e12..92ebfafa1 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -424,6 +424,22 @@ struct llama_model_mellum : public llama_model_base { std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; }; +struct llama_model_nanbeige : public llama_model_base { + llama_model_nanbeige(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + + int n_loops = 1; + int n_layer_phys = 0; + bool skip_loop_final_norm = false; + + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + }; + + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; + struct llama_model_qwen : public llama_model_base { llama_model_qwen(const struct llama_model_params & params) : llama_model_base(params) {} void load_arch_hparams(llama_model_loader & ml) override; diff --git a/src/models/nanbeige.cpp b/src/models/nanbeige.cpp new file mode 100644 index 000000000..3a546600f --- /dev/null +++ b/src/models/nanbeige.cpp @@ -0,0 +1,184 @@ +#include "models.h" + +void llama_model_nanbeige::load_arch_hparams(llama_model_loader & ml) { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + uint32_t n_loops_u = 1; + ml.get_key(LLM_KV_NUM_LOOPS, n_loops_u, false); + GGML_ASSERT(n_loops_u >= 1); + + skip_loop_final_norm = false; + ml.get_key(LLM_KV_SKIP_LOOP_FINAL_NORM, skip_loop_final_norm, false); + + n_layer_phys = (int) hparams.n_layer(); + + // Bound-check before casting: signed int mul can overflow and bypass the guard. + GGML_ASSERT((size_t) n_layer_phys * (size_t) n_loops_u <= (size_t) LLAMA_MAX_LAYERS); + n_loops = (int) n_loops_u; + + // Expand logical layer count before load_tensors() allocates layers / KV. + if (n_loops > 1) { + for (int j = 1; j < n_loops; ++j) { + for (int i = 0; i < n_layer_phys; ++i) { + const int dst = i + j * n_layer_phys; + hparams.n_head_arr[dst] = hparams.n_head_arr[i]; + hparams.n_head_kv_arr[dst] = hparams.n_head_kv_arr[i]; + hparams.n_ff_arr[dst] = hparams.n_ff_arr[i]; + hparams.is_swa_impl[dst] = hparams.is_swa_impl[i]; + hparams.is_recr_impl[dst] = hparams.is_recr_impl[i]; + } + } + hparams.n_layer_all = (uint32_t) ((size_t) n_layer_phys * (size_t) n_loops); + } + + type = LLM_TYPE_UNKNOWN; +} + +void llama_model_nanbeige::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_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED); + if (output == NULL) { + output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); + } + + const int n_phys = n_layer_phys > 0 ? n_layer_phys : n_layer; + for (int i = 0; i < n_phys; ++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_k_gqa, n_embd_v_gqa, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); + + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, + TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 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); + } + + // Share physical weights across loops; each slot still has its own KV index. + if (n_loops > 1) { + for (int j = 1; j < n_loops; ++j) { + for (int i = 0; i < n_phys; ++i) { + layers[i + j * n_phys] = layers[i]; + } + } + } +} + +std::unique_ptr llama_model_nanbeige::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +llama_model_nanbeige::graph::graph(const llama_model & model, const llm_graph_params & params) : + llm_graph_context(params) { + const auto & nb = static_cast(model); + + const int64_t n_embd_head = hparams.n_embd_head_v(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k()); + + const int n_phys = nb.n_layer_phys > 0 ? nb.n_layer_phys : (int) n_layer; + const int n_loops = nb.n_loops > 0 ? nb.n_loops : 1; + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + ggml_tensor * inp_pos = build_inp_pos(); + + auto * inp_attn = build_attn_inp_kv(); + + const float kq_scale = hparams.f_attention_scale == 0.0f + ? 1.0f / sqrtf(float(n_embd_head)) + : hparams.f_attention_scale; + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + { + ggml_tensor * rope_factors = model.get_rope_factors(cparams, il); + + auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, + n_embd_head, n_head, n_head_kv, il); + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, rope_factors, + 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, rope_factors, + 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, kq_scale, il); + cb(cur, "attn_out", 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); + + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + cur = build_ffn(cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s, + NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + inpL = cur; + + if (n_loops > 1 && + ((il + 1) % n_phys) == 0 && + (il + 1) < n_layer && + !nb.skip_loop_final_norm) { + cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, il); + cb(cur, "loop_norm", il); + inpL = cur; + } + } + + cur = inpL; + + cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + res->t_embd = cur; + + 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); +}