diff --git a/conversion/__init__.py b/conversion/__init__.py index cd6f8e6b93..00192cf33a 100644 --- a/conversion/__init__.py +++ b/conversion/__init__.py @@ -40,6 +40,7 @@ TEXT_MODEL_MAP: dict[str, str] = { "ChatGLMModel": "chatglm", "CodeShellForCausalLM": "codeshell", "CogVLMForCausalLM": "cogvlm", + "Cohere2MoeForCausalLM": "command_r", "Cohere2ForCausalLM": "command_r", "CohereForCausalLM": "command_r", "DbrxForCausalLM": "dbrx", diff --git a/conversion/base.py b/conversion/base.py index 9d81c19b46..c872bcbb3c 100644 --- a/conversion/base.py +++ b/conversion/base.py @@ -1195,7 +1195,7 @@ class TextModel(ModelBase): self.gguf_writer.add_embedding_length(n_embd) logger.info(f"gguf: embedding length = {n_embd}") - if (n_ff := self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None: + if (n_ff := self.find_hparam(["prefix_dense_intermediate_size", "intermediate_size", "n_inner", "hidden_dim"], optional=True)) is not None: self.gguf_writer.add_feed_forward_length(n_ff) logger.info(f"gguf: feed forward length = {n_ff}") @@ -1280,7 +1280,7 @@ class TextModel(ModelBase): self.gguf_writer.add_expert_group_used_count(n_group_used) logger.info(f"gguf: expert groups used count = {n_group_used}") - if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func"], optional=True)) is not None: + if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func", "expert_selection_fn"], optional=True)) is not None: if score_func == "sigmoid": self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) elif score_func == "softmax": @@ -1495,6 +1495,9 @@ class TextModel(ModelBase): if chkhsh == "d772b220ace2baec124bed8cfafce0ead7d6c38a4b65ef11261cf9d5d62246d1": # ref: https://huggingface.co/CohereLabs/tiny-aya-base res = "tiny_aya" + if chkhsh == "52df12b4c8d4176e7481aab4b6e8454d1fd0a210a04a574f6d4e067d10e23c3e": + # ref: https://huggingface.co/CohereLabs/North-Mini-Code-1.0 + res = "cohere2moe" if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea": # ref: https://huggingface.co/Qwen/Qwen1.5-7B res = "qwen2" diff --git a/conversion/command_r.py b/conversion/command_r.py index 603288d165..118565c669 100644 --- a/conversion/command_r.py +++ b/conversion/command_r.py @@ -1,5 +1,6 @@ from __future__ import annotations +import re from typing import Iterable, TYPE_CHECKING import torch @@ -55,3 +56,122 @@ class Cohere2Model(TextModel): return yield from super().modify_tensors(data_torch, name, bid) + + +@ModelBase.register("Cohere2MoeForCausalLM") +class Cohere2MoeModel(TextModel): + model_arch = gguf.MODEL_ARCH.COHERE2MOE + _n_main_layers: int | None = None + _expert_tensor_re = re.compile( + r"model\.layers\.(\d+)\.mlp\.experts\.(\d+)\.(down_proj|gate_proj|up_proj)\.weight" + ) + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if (n_nextn := int(self.hparams.get("num_nextn_predict_layers", 0) or 0)) > 0 and not self.no_mtp: + self.block_count += n_nextn + self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + self._experts: list[dict[str, Tensor]] = [{} for _ in range(self.block_count)] + + def _set_vocab_gpt2(self) -> None: + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + hparams = self.hparams + expert_intermediate_size = hparams["intermediate_size"] + mlp_layer_types = hparams.get("mlp_layer_types") + n_dense_lead = hparams.get("first_k_dense_replace", 0) + if mlp_layer_types is not None: + n_dense_lead = next((i for i, t in enumerate(mlp_layer_types) if t != "dense"), len(mlp_layer_types)) + + super().set_gguf_parameters() + + self.gguf_writer.add_logit_scale(hparams["logit_scale"]) + self.gguf_writer.add_sliding_window(hparams["sliding_window"]) + self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size) + self.gguf_writer.add_leading_dense_block_count(n_dense_lead) + self.gguf_writer.add_expert_weights_norm(hparams.get("norm_topk_prob", False)) + if (num_shared_experts := hparams.get("num_shared_experts", 0)) > 0: + if hparams.get("shared_expert_combination_strategy", "average") != "average": + raise ValueError("Cohere2 MoE only supports average shared expert combination") + self.gguf_writer.add_expert_shared_count(num_shared_experts) + self.gguf_writer.add_expert_shared_feed_forward_length(expert_intermediate_size * num_shared_experts) + if (n_nextn := hparams.get("num_nextn_predict_layers", 0)) > 0 and not self.no_mtp: + self.gguf_writer.add_nextn_predict_layers(n_nextn) + self.gguf_writer.add_rope_dimension_count(hparams["head_dim"]) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + + def index_tensors(self, remote_hf_model_id: str | None = None): + hparams = {**self.hparams, **self.hparams.get("text_config", {})} + self._n_main_layers = hparams.get("num_hidden_layers") + type(self)._n_main_layers = self._n_main_layers + return super().index_tensors(remote_hf_model_id=remote_hf_model_id) + + @classmethod + def filter_tensors(cls, item): + if (titem := super().filter_tensors(item)) is None: + return None + name, gen = titem + + if cls._n_main_layers is not None: + is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers + if is_mtp and cls.no_mtp: + return None + if cls.mtp_only and not is_mtp and name not in ( + "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight", + ): + return None + + return name, gen + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if name.endswith(".bias"): + if torch.any(data_torch != 0): + raise ValueError(f"Bias tensor {name!r} is not zero.") + logger.debug(f"Skipping bias tensor {name!r}.") + return + + if (m := self._expert_tensor_re.fullmatch(name)) is not None: + n_experts = self.hparams["num_experts"] + layer_idx = int(m.group(1)) + assert bid is None or bid == layer_idx + + self._experts[layer_idx][name] = data_torch + + expected = { + f"model.layers.{layer_idx}.mlp.experts.{xid}.{w_name}.weight" + for xid in range(n_experts) + for w_name in ("down_proj", "gate_proj", "up_proj") + } + if expected.issubset(self._experts[layer_idx]): + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{layer_idx}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[layer_idx][ename]) + del self._experts[layer_idx][ename] + + data_torch = torch.stack(datas, dim=0) + merged_name = f"model.layers.{layer_idx}.mlp.experts.{w_name}.weight" + + yield from super().modify_tensors(data_torch, merged_name, layer_idx) + return + + yield from super().modify_tensors(data_torch, name, bid) + + def prepare_tensors(self): + super().prepare_tensors() + + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index b4c8a7cf00..91c006278f 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -100,6 +100,7 @@ models = [ {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, {"name": "tiny_aya", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereLabs/tiny-aya-base", }, + {"name": "cohere2moe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0", }, {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", }, {"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", }, {"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", }, diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 4b6dfea64d..463963f2ac 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -457,6 +457,7 @@ class MODEL_ARCH(IntEnum): XVERSE = auto() COMMAND_R = auto() COHERE2 = auto() + COHERE2MOE = auto() DBRX = auto() OLMO = auto() OLMO2 = auto() @@ -1012,6 +1013,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.XVERSE: "xverse", MODEL_ARCH.COMMAND_R: "command-r", MODEL_ARCH.COHERE2: "cohere2", + MODEL_ARCH.COHERE2MOE: "cohere2moe", MODEL_ARCH.DBRX: "dbrx", MODEL_ARCH.OLMO: "olmo", MODEL_ARCH.OLMO2: "olmo2", @@ -2872,6 +2874,33 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.COHERE2MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_GATE_UP_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + ], MODEL_ARCH.DBRX: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 9f93d5bc7c..4a52d97729 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -66,6 +66,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_XVERSE, "xverse" }, { LLM_ARCH_COMMAND_R, "command-r" }, { LLM_ARCH_COHERE2, "cohere2" }, + { LLM_ARCH_COHERE2MOE, "cohere2moe" }, { LLM_ARCH_DBRX, "dbrx" }, { LLM_ARCH_OLMO, "olmo" }, { LLM_ARCH_OLMO2, "olmo2" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index c5245fb589..989da06d8d 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -71,6 +71,7 @@ enum llm_arch { LLM_ARCH_XVERSE, LLM_ARCH_COMMAND_R, LLM_ARCH_COHERE2, + LLM_ARCH_COHERE2MOE, LLM_ARCH_DBRX, LLM_ARCH_OLMO, LLM_ARCH_OLMO2, diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp index 67d4a9df0f..a3928523ba 100644 --- a/src/llama-model-saver.cpp +++ b/src/llama-model-saver.cpp @@ -18,6 +18,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) { case LLM_ARCH_GEMMA3: case LLM_ARCH_GEMMA3N: case LLM_ARCH_COHERE2: + case LLM_ARCH_COHERE2MOE: case LLM_ARCH_OLMO2: case LLM_ARCH_BITNET: case LLM_ARCH_T5: diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 7281ed79f1..c528755339 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -157,6 +157,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_command_r(params); case LLM_ARCH_COHERE2: return new llama_model_cohere2(params); + case LLM_ARCH_COHERE2MOE: + return new llama_model_cohere2moe(params); case LLM_ARCH_DBRX: return new llama_model_dbrx(params); case LLM_ARCH_OLMO: @@ -1467,9 +1469,12 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) { } ml.done_getting_tensors(); + // Tied NVFP4 output is valid when no separate LM-head scale tensors are present. + // If sidecar scales exist, the output weight must be an actual output tensor. GGML_ASSERT(!(output && tok_embd && strcmp(output->name, tok_embd->name) == 0 && - output->type == GGML_TYPE_NVFP4)); + output->type == GGML_TYPE_NVFP4 && + (output_s || output_in_s))); // populate tensors_by_name for (auto & [_, ctx_ptr] : ml.ctx_map) { for (auto * cur = ggml_get_first_tensor(ctx_ptr.get()); cur != NULL; cur = ggml_get_next_tensor(ctx_ptr.get(), cur)) { @@ -1844,6 +1849,7 @@ void llama_model::print_info() const { } if (arch == LLM_ARCH_MELLUM || + arch == LLM_ARCH_COHERE2MOE || arch == LLM_ARCH_QWEN3MOE || arch == LLM_ARCH_OPENAI_MOE || arch == LLM_ARCH_QWEN3VLMOE || @@ -2389,6 +2395,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_XVERSE: case LLM_ARCH_COMMAND_R: case LLM_ARCH_COHERE2: + case LLM_ARCH_COHERE2MOE: case LLM_ARCH_OLMO: case LLM_ARCH_ARCTIC: case LLM_ARCH_DEEPSEEK: diff --git a/src/models/cohere2.cpp b/src/models/cohere2.cpp index 61a5945a19..e2b3662560 100644 --- a/src/models/cohere2.cpp +++ b/src/models/cohere2.cpp @@ -122,9 +122,9 @@ llama_model_cohere2::graph::graph(const llama_model & model, const llm_graph_par // feed-forward network { cur = build_ffn(ffn_inp, - model.layers[il].ffn_up, NULL, NULL, - model.layers[il].ffn_gate, NULL, NULL, - model.layers[il].ffn_down, NULL, NULL, + model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s, + model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s, + model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il); cb(cur, "ffn_out", il); } diff --git a/src/models/cohere2moe.cpp b/src/models/cohere2moe.cpp new file mode 100644 index 0000000000..499c73a1c4 --- /dev/null +++ b/src/models/cohere2moe.cpp @@ -0,0 +1,443 @@ +#include "models.h" + +void llama_model_cohere2moe::load_arch_hparams(llama_model_loader & ml) { + const bool found_norm = ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps, false); + const bool found_norm_rms = ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false); + if (!found_norm && !found_norm_rms) { + throw std::runtime_error("missing Cohere2 MoE norm epsilon"); + } + if (!found_norm_rms) { + hparams.f_norm_rms_eps = 0.0f; + } + + ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); + ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false); + ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); + + ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false); + GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer"); + + if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) { + hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID; + } + + hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; + uint32_t swa_period = 4; + if (ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false)) { + hparams.set_swa_pattern(swa_period, true); + } else { + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl, 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); + + switch (hparams.n_layer()) { + case 49: type = LLM_TYPE_30B_A3B; break; + default: type = LLM_TYPE_UNKNOWN; + } +} + +void llama_model_cohere2moe::load_arch_tensors(llama_model_loader & ml) { + LLAMA_LOAD_LOCALS; + + const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr); + // Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP + // tensors live in a separate file. Mark MTP tensors NOT_REQUIRED so the + // trunk loads cleanly. + const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight"; + const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr); + const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0; + const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0; + + 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); + } + + if (n_expert == 0) { + throw std::runtime_error("n_expert must be > 0 for Cohere2Moe"); + } + if (n_expert_used == 0) { + throw std::runtime_error("n_expert_used must be > 0 for Cohere2Moe"); + } + + auto load_block_trunk = [&](int i, int flags) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags); + + create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, flags); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags); + + if (static_cast(i) < hparams.n_layer_dense_lead) { + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, flags); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, flags); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, flags); + } else { + const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff; + + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags); + create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags); + + if (hparams.n_expert_shared > 0) { + const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp * hparams.n_expert_shared; + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags); + } + } + }; + + auto load_block_mtp = [&](int i, int flags) { + auto & layer = layers[i]; + + // MTP block looks like a full-attention Cohere2 MoE decoder block. + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, flags); + + create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, flags); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags); + + const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff; + + // Routed experts + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags); + create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, flags); + + if (hparams.n_expert_shared > 0) { + const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp * hparams.n_expert_shared; + + // Shared experts + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags); + } + + // NextN-specific tensors that define the MTP block. + layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags); + layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags); + layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags); + layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED); + layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED); + layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, TENSOR_NOT_REQUIRED); + }; + + for (int i = 0; i < n_layer; ++i) { + load_block_trunk(i, trunk_flags); + } + // MTP/NextN layers are loaded as extra decoder blocks. + for (int i = n_layer; i < n_layer_all; ++i) { + load_block_mtp(i, mtp_flags); + } +} + +std::unique_ptr llama_model_cohere2moe::build_arch_graph(const llm_graph_params & params) const { + if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) { + return std::make_unique(*this, params); + } + return std::make_unique(*this, params); +} + +llama_model_cohere2moe::graph::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); + + const llm_norm_type cohere2moe_norm_type = hparams.f_norm_rms_eps == 0.0f ? LLM_NORM : LLM_NORM_RMS; + const float f_logit_scale = hparams.f_logit_scale; + ggml_tensor * cur; + ggml_tensor * inpL = build_inp_embd(model.tok_embd); + ggml_tensor * inp_pos = build_inp_pos(); + + auto * inp_attn = build_attn_inp_kv_iswa(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + // MTP/NextN layers are loaded as extra decoder blocks but not executed in the main pass. + for (int il = 0; il < n_layer; ++il) { + const bool is_swa = hparams.is_swa(il); + // Dense-prefix full-attention layers use RoPE; later layers follow the SWA pattern. + const bool force_rope = static_cast(il) < hparams.n_layer_dense_lead; + + cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, cohere2moe_norm_type, il); + cb(cur, "attn_norm", il); + + ggml_tensor * ffn_inp = cur; + + { + const auto & layer = model.layers[il]; + + auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur, + n_embd_head, n_head, n_head_kv, il); + + if (is_swa || force_rope) { + ggml_tensor * rope_factors = model.get_rope_factors(cparams, 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, + layer.wo, layer.wo_b, layer.wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, + 1.0f / sqrtf(float(n_embd_head)), il); + } + + if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); + ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids); + } + + ggml_tensor * attn_out = cur; + + const auto & layer = model.layers[il]; + + if (layer.ffn_gate_inp == nullptr) { + cur = build_ffn(ffn_inp, + layer.ffn_up, nullptr, layer.ffn_up_s, + layer.ffn_gate, nullptr, layer.ffn_gate_s, + layer.ffn_down, nullptr, layer.ffn_down_s, + nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } else { + cur = build_moe_ffn(ffn_inp, + layer.ffn_gate_inp, + layer.ffn_up_exps, + layer.ffn_gate_exps, + layer.ffn_down_exps, + nullptr, + n_expert, n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il, + nullptr, layer.ffn_gate_up_exps, + layer.ffn_up_exps_s, + layer.ffn_gate_exps_s, + layer.ffn_down_exps_s); + cb(cur, "ffn_moe_out", il); + + if (layer.ffn_up_shexp) { + ggml_tensor * ffn_shexp = build_ffn(ffn_inp, + layer.ffn_up_shexp, nullptr, layer.ffn_up_shexp_s, + layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s, + layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s, + nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, cur, ffn_shexp); + cur = ggml_scale(ctx0, cur, 0.5f); + cb(cur, "ffn_out", il); + } + } + + cur = ggml_add(ctx0, cur, inpL); + cur = ggml_add(ctx0, cur, attn_out); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + inpL = cur; + } + + cur = inpL; + cur = build_norm(cur, model.output_norm, nullptr, cohere2moe_norm_type, -1); + + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + if (!cparams.embeddings_nextn_masked && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + } + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + cur = build_lora_mm(model.output, cur); + + if (f_logit_scale) { + cur = ggml_scale(ctx0, cur, f_logit_scale); + } + + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} + +llama_model_cohere2moe::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { + GGML_ASSERT(hparams.n_layer_nextn > 0 && "COHERE2MOE MTP requires n_layer_nextn > 0"); + GGML_ASSERT(hparams.n_layer_nextn == 1 && "COHERE2MOE MTP currently only supports a single MTP block"); + + 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); + + const int il = hparams.n_layer(); + const auto & layer = model.layers[il]; + GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj"); + GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm"); + GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm"); + GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp"); + + const llm_norm_type cohere2moe_norm_type = hparams.f_norm_rms_eps == 0.0f ? LLM_NORM : LLM_NORM_RMS; + + // TODO: extract in a common llm_graph_context::build_inp_embd_h() + auto inp = std::make_unique(hparams.n_embd); + + inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + ggml_set_input(inp->tokens); + + inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens); + ggml_set_input(inp->embd); + + // TODO: make static using `ggml_build_forward_select()` + // see llm_graph_context::build_inp_embd() for reference + ggml_tensor * tok_embd; + if (ubatch.token) { + ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd; + tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens); + } else { + tok_embd = inp->embd; + } + cb(tok_embd, "mtp_tok_embd", il); + + inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens); + ggml_set_input(inp->h); + ggml_set_name(inp->h, "mtp_h_input"); + + ggml_tensor * h_embd = inp->h; + + res->add_input(std::move(inp)); + + ggml_tensor * inp_pos = build_inp_pos(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + auto * inp_attn = build_attn_inp_kv_iswa(); + + ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, cohere2moe_norm_type, il); + cb(h_norm, "mtp_hnorm", il); + + ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, cohere2moe_norm_type, il); + cb(e_norm, "mtp_enorm", il); + + ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0); + cb(concat, "mtp_concat", il); + + ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s); + cb(cur, "mtp_eh_proj", il); + + ggml_tensor * inpL = cur; + + cur = build_norm(cur, layer.attn_norm, nullptr, cohere2moe_norm_type, il); + cb(cur, "mtp_attn_norm", il); + ggml_tensor * ffn_inp = cur; + + auto [Qcur, Kcur, Vcur] = build_qkv(layer, cur, n_embd_head, n_head, n_head_kv, il); + ggml_tensor * rope_factors = model.get_rope_factors(cparams, 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, "mtp_Qcur", il); + cb(Kcur, "mtp_Kcur", il); + cb(Vcur, "mtp_Vcur", il); + + cur = build_attn(inp_attn, + layer.wo, layer.wo_b, layer.wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, + 1.0f / sqrtf(float(n_embd_head)), il); + cb(cur, "mtp_attn_out", il); + + ggml_tensor * attn_out = cur; + + cur = build_moe_ffn(ffn_inp, + layer.ffn_gate_inp, + layer.ffn_up_exps, + layer.ffn_gate_exps, + layer.ffn_down_exps, + nullptr, + n_expert, n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il, + nullptr, layer.ffn_gate_up_exps, + layer.ffn_up_exps_s, + layer.ffn_gate_exps_s, + layer.ffn_down_exps_s); + cb(cur, "mtp_ffn_moe_out", il); + + if (layer.ffn_up_shexp) { + ggml_tensor * ffn_shexp = build_ffn(ffn_inp, + layer.ffn_up_shexp, nullptr, layer.ffn_up_shexp_s, + layer.ffn_gate_shexp, nullptr, layer.ffn_gate_shexp_s, + layer.ffn_down_shexp, nullptr, layer.ffn_down_shexp_s, + nullptr, LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "mtp_ffn_shexp", il); + + cur = ggml_add(ctx0, cur, ffn_shexp); + cur = ggml_scale(ctx0, cur, 0.5f); + cb(cur, "mtp_ffn_out", il); + } + + cur = ggml_add(ctx0, cur, inpL); + cur = ggml_add(ctx0, cur, attn_out); + cb(cur, "mtp_post_ffn", il); + + ggml_tensor * head_norm_w = layer.nextn.shared_head_norm + ? layer.nextn.shared_head_norm + : model.output_norm; + GGML_ASSERT(head_norm_w && "COHERE2MOE MTP: missing both nextn.shared_head_norm and output_norm"); + cur = build_norm(cur, head_norm_w, nullptr, cohere2moe_norm_type, -1); + + cb(cur, "h_nextn", -1); + res->t_h_nextn = cur; + + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + cb(cur, "mtp_shared_head_norm", -1); + + ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output; + GGML_ASSERT(head_w && "COHERE2MOE MTP: missing LM head (nextn.shared_head_head or model.output)"); + cur = build_lora_mm(head_w, cur, layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : nullptr); + + if (hparams.f_logit_scale) { + cur = ggml_scale(ctx0, cur, hparams.f_logit_scale); + } + + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/models.h b/src/models/models.h index ee3aff07b9..2ac8415a36 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -937,6 +937,23 @@ struct llama_model_cohere2 : public llama_model_base { }; +struct llama_model_cohere2moe : public llama_model_base { + llama_model_cohere2moe(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; + + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + }; + + struct graph_mtp : public llm_graph_context { + graph_mtp(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_dbrx : public llama_model_base { llama_model_dbrx(const struct llama_model_params & params) : llama_model_base(params) {} void load_arch_hparams(llama_model_loader & ml) override; diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp index 4d06274ef1..524971ae4b 100644 --- a/tests/test-llama-archs.cpp +++ b/tests/test-llama-archs.cpp @@ -185,7 +185,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) { ms.add_kv(LLM_KV_ROPE_FREQ_BASE_SWA, 10000.0f); // SWA pattern: every 5th layer is full attention (matches E2B layer_types) ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(5)); - } else if (arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35) { + } else if (arch == LLM_ARCH_COHERE2MOE || arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35) { std::vector pattern; pattern.reserve(n_layer); for (uint32_t il = 0; il < n_layer; il++) { @@ -322,6 +322,7 @@ static std::vector get_logits( static bool moe_mandatory(const llm_arch arch) { switch (arch) { case LLM_ARCH_LLAMA4: + case LLM_ARCH_COHERE2MOE: case LLM_ARCH_GROK: case LLM_ARCH_QWEN2MOE: case LLM_ARCH_QWEN3MOE: