From d7da8dc83a03b30e1ec10317080082ea76840c38 Mon Sep 17 00:00:00 2001 From: Bartowski <3266127+bartowski1182@users.noreply.github.com> Date: Mon, 16 Jun 2025 00:04:06 +0100 Subject: [PATCH] model : Add support for Arcee AI's upcoming AFM model (#14185) * Add Arcee AFM support * Add draft update code * Fix linter and update URL, may still not be final * Update src/llama-model.cpp Co-authored-by: Xuan-Son Nguyen * Remote accidental blank line --------- Co-authored-by: Xuan-Son Nguyen --- convert_hf_to_gguf.py | 14 +++ convert_hf_to_gguf_update.py | 1 + gguf-py/gguf/constants.py | 19 +++- src/llama-arch.cpp | 19 ++++ src/llama-arch.h | 1 + src/llama-model.cpp | 181 +++++++++++++++++++++++++++++++++++ src/llama-vocab.cpp | 1 + 7 files changed, 235 insertions(+), 1 deletion(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index cff72c85f..2232a7d82 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2020,6 +2020,20 @@ class LlamaModel(TextModel): raise ValueError(f"Unprocessed experts: {experts}") +@ModelBase.register("ArceeForCausalLM") +class ArceeModel(LlamaModel): + model_arch = gguf.MODEL_ARCH.ARCEE + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self._try_set_pooling_type() + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) + + @ModelBase.register( "LlavaForConditionalGeneration", # pixtral "Mistral3ForConditionalGeneration", # mistral small 3.1 diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 2f733f097..fae4f7260 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -128,6 +128,7 @@ models = [ {"name": "llama4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", }, {"name": "pixtral", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistral-community/pixtral-12b", }, {"name": "seed-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", }, + {"name": "arcee", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/arcee-ai/AFM-4.5B", }, # TODO confirm final URL ] # some models are known to be broken upstream, so we will skip them as exceptions diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 8de2f7a53..9b2143c7c 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -344,6 +344,7 @@ class MODEL_ARCH(IntEnum): PLM = auto() BAILINGMOE = auto() DOTS1 = auto() + ARCEE = auto() class VISION_PROJECTOR_TYPE(IntEnum): @@ -624,7 +625,8 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec", MODEL_ARCH.PLM: "plm", MODEL_ARCH.BAILINGMOE: "bailingmoe", - MODEL_ARCH.DOTS1: "dots1" + MODEL_ARCH.DOTS1: "dots1", + MODEL_ARCH.ARCEE: "arcee", } VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = { @@ -2070,6 +2072,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.FFN_UP_SHEXP, ], + MODEL_ARCH.ARCEE: [ + 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_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index f8f76eedd..a3e7c861c 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -73,6 +73,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_PLM, "plm" }, { LLM_ARCH_BAILINGMOE, "bailingmoe" }, { LLM_ARCH_DOTS1, "dots1" }, + { LLM_ARCH_ARCEE, "arcee" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -244,6 +245,24 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, }, }, + { + LLM_ARCH_ARCEE, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_LLAMA4, { diff --git a/src/llama-arch.h b/src/llama-arch.h index 18f6d6b94..168fdcb40 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -77,6 +77,7 @@ enum llm_arch { LLM_ARCH_PLM, LLM_ARCH_BAILINGMOE, LLM_ARCH_DOTS1, + LLM_ARCH_ARCEE, LLM_ARCH_UNKNOWN, }; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index fdd5fefd6..dcc8b0be7 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -599,6 +599,16 @@ void llama_model::load_hparams(llama_model_loader & ml) { hparams.use_kq_norm = false; } } break; + case LLM_ARCH_ARCEE: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + // Arcee uses the same structure as Llama + switch (hparams.n_layer) { + case 36: type = LLM_TYPE_4B; break; + default: type = LLM_TYPE_UNKNOWN; + } + } break; case LLM_ARCH_DECI: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -4190,6 +4200,37 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } } } break; + case LLM_ARCH_ARCEE: + { + 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}, TENSOR_NOT_REQUIRED); + + // if output is NULL, init from the input tok embed + if (output == NULL) { + output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); + } + + 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); + + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, 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.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {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_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); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -13411,6 +13452,141 @@ struct llm_build_dots1 : public llm_graph_context { } }; +struct llm_build_arcee : public llm_graph_context { + llm_build_arcee(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : 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 == hparams.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(); + + auto * inp_attn = build_attn_inp_kv_unified(); + + const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + // norm + cur = build_norm(inpL, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // rope freq factors for llama3; may return nullptr for llama2 and other models + ggml_tensor * rope_factors = model.get_rope_factors(cparams, il); + + // compute Q and K and RoPE them + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + 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, gf, + model.layers[il].wo, model.layers[il].bo, + Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + ggml_tensor * inp_out_ids = build_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); + + // feed-forward network + // ARCEE uses relu^2 instead of silu + 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, NULL, NULL, + NULL, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_RELU_SQR, LLM_FFN_SEQ, 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); + + // input for next layer + 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; + + // lm_head + cur = build_lora_mm(model.output, cur); + + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); + } +}; + llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const { llama_memory_i * res; @@ -13753,6 +13929,10 @@ llm_graph_result_ptr llama_model::build_graph( { llm = std::make_unique(*this, params, gf); } break; + case LLM_ARCH_ARCEE: + { + llm = std::make_unique(*this, params, gf); + } break; default: GGML_ABORT("fatal error"); } @@ -13902,6 +14082,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_GRANITE_MOE: case LLM_ARCH_CHAMELEON: case LLM_ARCH_BAILINGMOE: + case LLM_ARCH_ARCEE: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 905d7c428..dd2251ef3 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1987,6 +1987,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { || t.first == "<|eom_id|>" || t.first == "" || t.first == "_" + || t.first == "<|end_of_text|>" ) { special_eog_ids.insert(t.second); if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {