From 71d8bd6480fb672fba2bf8cd45225d5658db1167 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Stanis=C5=82aw=20Szymczyk?= Date: Wed, 1 May 2024 09:43:19 +0200 Subject: [PATCH] Added support for the snowflake-arctic model. --- convert-hf-to-gguf.py | 113 +++++++++++++++ gguf-py/gguf/constants.py | 25 ++++ gguf-py/gguf/tensor_mapping.py | 66 ++++++++- llama.cpp | 246 ++++++++++++++++++++++++++++++++- 4 files changed, 447 insertions(+), 3 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 2f146d730..6f013a1d0 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1516,6 +1516,119 @@ class LlamaModel(Model): if len(experts) > 0: raise ValueError(f"Unprocessed experts: {experts.keys()}") +@Model.register("ArcticForCausalLM") +class ArcticModel(Model): + model_arch = gguf.MODEL_ARCH.ARCTIC + + def set_vocab(self): + self._set_vocab_llama_hf() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) + + # Same as super class, but permuting q_proj, k_proj + def write_tensors(self): + block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + n_head = self.hparams.get("num_attention_heads") + n_kv_head = self.hparams.get("num_key_value_heads") + n_experts = self.hparams.get("num_local_experts") + experts = dict() + for name, data_torch in self.get_tensors(): + # we don't need these + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.numpy() + + if name.endswith("q_proj.weight"): + data = permute(data, n_head, n_head) + if name.endswith("k_proj.weight"): + data = permute(data, n_head, n_kv_head) + + data = data.squeeze() + + # process the experts separately + if name.find("block_sparse_moe.experts") != -1: + experts[name] = data + if len(experts) >= n_experts: + # merge the experts into a single 3d tensor + for bid in range(block_count): + for wid in range(1, 4): + full = True + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight" + if ename not in experts: + full = False + break + if not full: + continue + + datas = [] + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight" + datas.append(experts[ename]) + del experts[ename] + + data = np.stack(datas, axis=0) + data_dtype = data.dtype + + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + if self.ftype == 1 and data_dtype == np.float32: + data = data.astype(np.float16) + + merged_name = f"layers.{bid}.feed_forward.experts.w{wid}.weight" + + new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + continue + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) + + # 1d tensors need to be converted to float32 + if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: + data = data.astype(np.float32) + + # if f16 desired, convert any float32 2-dim weight tensors to float16 + if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + + self.gguf_writer.add_tensor(new_name, data) + + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts.keys()}") + + @Model.register("GrokForCausalLM") class GrokModel(Model): diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 6d597bfd9..047882818 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -138,6 +138,7 @@ class MODEL_ARCH(IntEnum): COMMAND_R = auto() DBRX = auto() OLMO = auto() + ARCTIC = auto() class MODEL_TENSOR(IntEnum): @@ -180,6 +181,7 @@ class MODEL_TENSOR(IntEnum): SSM_A = auto() SSM_D = auto() SSM_OUT = auto() + FFN_NORM_EXP = auto() MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { @@ -215,6 +217,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.COMMAND_R: "command-r", MODEL_ARCH.DBRX: "dbrx", MODEL_ARCH.OLMO: "olmo", + MODEL_ARCH.ARCTIC: "arctic", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -257,6 +260,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", + MODEL_TENSOR.FFN_NORM_EXP: "blk.{bid}.ffn_norm_exps", } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { @@ -725,6 +729,27 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.ARCTIC: [ + 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_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_NORM_EXP, + ], # TODO } diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index e5750d419..6ecce589c 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -370,6 +370,64 @@ class TensorNameMap: "model.layers.{bid}.out_proj", "backbone.layers.{bid}.mixer.out_proj", ), + + } + + # architecture-specific block mappings + arch_block_mappings_cfg: dict[MODEL_ARCH, dict[MODEL_TENSOR, tuple[str, ...]]] = { + MODEL_ARCH.ARCTIC: { + MODEL_TENSOR.TOKEN_EMBD: ( + "model.embed_tokens", + ), + MODEL_TENSOR.OUTPUT_NORM: ( + "model.norm", + ), + MODEL_TENSOR.OUTPUT: ( + "lm_head", + ), + MODEL_TENSOR.ATTN_NORM: ( + "model.layers.{bid}.input_layernorm", + ), + MODEL_TENSOR.ATTN_Q: ( + "model.layers.{bid}.self_attn.q_proj", + ), + MODEL_TENSOR.ATTN_K: ( + "model.layers.{bid}.self_attn.k_proj", + ), + MODEL_TENSOR.ATTN_V: ( + "model.layers.{bid}.self_attn.v_proj", + ), + MODEL_TENSOR.ATTN_OUT: ( + "model.layers.{bid}.self_attn.o_proj", + ), + MODEL_TENSOR.FFN_GATE_INP: ( + "model.layers.{bid}.block_sparse_moe.gate", + ), + MODEL_TENSOR.FFN_NORM: ( + "model.layers.{bid}.residual_layernorm", + ), + MODEL_TENSOR.FFN_GATE: ( + "model.layers.{bid}.residual_mlp.w1", + ), + MODEL_TENSOR.FFN_DOWN: ( + "model.layers.{bid}.residual_mlp.w2", + ), + MODEL_TENSOR.FFN_UP: ( + "model.layers.{bid}.residual_mlp.w3", + ), + MODEL_TENSOR.FFN_GATE_EXP: ( + "layers.{bid}.feed_forward.experts.w1", + ), + MODEL_TENSOR.FFN_DOWN_EXP: ( + "layers.{bid}.feed_forward.experts.w2", + ), + MODEL_TENSOR.FFN_UP_EXP: ( + "layers.{bid}.feed_forward.experts.w3", + ), + MODEL_TENSOR.FFN_NORM_EXP: ( + "model.layers.{bid}.post_attention_layernorm", + ), + }, } mapping: dict[str, tuple[MODEL_TENSOR, str]] @@ -383,12 +441,16 @@ class TensorNameMap: self.mapping[tensor_name] = (tensor, tensor_name) for key in keys: self.mapping[key] = (tensor, tensor_name) + if arch in self.arch_block_mappings_cfg: + block_mappings = self.arch_block_mappings_cfg[arch] + else: + block_mappings = self.block_mappings_cfg for bid in range(n_blocks): - for tensor, keys in self.block_mappings_cfg.items(): + for tensor, keys in block_mappings.items(): if tensor not in MODEL_TENSORS[arch]: continue # TODO: make this configurable - n_experts = 60 + n_experts = 128 for xid in range(n_experts): tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid) self.mapping[tensor_name] = (tensor, tensor_name) diff --git a/llama.cpp b/llama.cpp index 18d6297ce..ee5a3226e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -106,7 +106,7 @@ #endif #define LLAMA_MAX_NODES 8192 -#define LLAMA_MAX_EXPERTS 60 +#define LLAMA_MAX_EXPERTS 128 // // logging @@ -224,6 +224,7 @@ enum llm_arch { LLM_ARCH_COMMAND_R, LLM_ARCH_DBRX, LLM_ARCH_OLMO, + LLM_ARCH_ARCTIC, LLM_ARCH_UNKNOWN, }; @@ -260,6 +261,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_COMMAND_R, "command-r" }, { LLM_ARCH_DBRX, "dbrx" }, { LLM_ARCH_OLMO, "olmo" }, + { LLM_ARCH_ARCTIC, "arctic" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -457,6 +459,7 @@ enum llm_tensor { LLM_TENSOR_FFN_DOWN_EXPS, // merged experts LLM_TENSOR_FFN_GATE_EXPS, LLM_TENSOR_FFN_UP_EXPS, + LLM_TENSOR_FFN_NORM_EXPS, LLM_TENSOR_FFN_DOWN_SHEXP, LLM_TENSOR_FFN_GATE_SHEXP, LLM_TENSOR_FFN_UP_SHEXP, @@ -1027,6 +1030,28 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_ARCTIC, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { 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_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + { LLM_TENSOR_FFN_NORM_EXPS, "blk.%d.ffn_norm_exps" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1803,6 +1828,7 @@ enum e_model { MODEL_8x7B, MODEL_8x22B, MODEL_16x12B, + MODEL_10B_128x3_66B, }; static const size_t kiB = 1024; @@ -1975,6 +2001,7 @@ struct llama_layer { struct ggml_tensor * ffn_norm_b; struct ggml_tensor * layer_out_norm; struct ggml_tensor * layer_out_norm_b; + struct ggml_tensor * ffn_norm_exps; // ff struct ggml_tensor * ffn_gate; // w1 @@ -3734,6 +3761,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_8x7B: return "8x7B"; case MODEL_8x22B: return "8x22B"; case MODEL_16x12B: return "16x12B"; + case MODEL_10B_128x3_66B: return "10B+128x3.66B"; default: return "?B"; } } @@ -4196,6 +4224,20 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_ARCTIC: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + if (hparams.n_expert == 128) { + switch (hparams.n_layer) { + case 35: model.type = e_model::MODEL_10B_128x3_66B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } else { + model.type = e_model::MODEL_UNKNOWN; + } + } break; + default: (void)0; } @@ -5932,6 +5974,55 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; + case LLM_ARCH_ARCTIC: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + // optional bias tensors + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_embd}); + + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + layer.ffn_norm_exps = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}); + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); + layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); + layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + } + } break; + default: throw std::runtime_error("unknown architecture"); } @@ -10682,6 +10773,154 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_arctic() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + 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); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, 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); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, 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); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, 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_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + struct ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp); + cb(ffn_out, "ffn_out", il); + + // MoE + cur = llm_build_norm(ctx0, inpSA, hparams, + model.layers[il].ffn_norm_exps, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm_exps", il); + + cur = llm_build_moe_ffn(ctx0, 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, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + cb, il); + cb(cur, "ffn_moe_out", il); + + cur = ggml_add(ctx0, cur, ffn_out); + cb(cur, "ffn_out", il); + + ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); + if (layer_dir != nullptr) { + cur = ggml_add(ctx0, cur, layer_dir); + } + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -10895,6 +11134,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_olmo(); } break; + case LLM_ARCH_ARCTIC: + { + result = llm.build_arctic(); + } break; default: GGML_ASSERT(false); } @@ -15783,6 +16026,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_XVERSE: case LLM_ARCH_COMMAND_R: case LLM_ARCH_OLMO: + case LLM_ARCH_ARCTIC: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2