diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index d4441bbe9..01b58f976 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -687,6 +687,9 @@ class Model: if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1": # ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct res = "megrez" + if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5": + # ref: https://huggingface.co/deepseek-ai/DeepSeek-V3 + res = "deepseek-v3" if res is None: logger.warning("\n") @@ -3849,6 +3852,7 @@ class DeepseekModel(Model): @Model.register("DeepseekV2ForCausalLM") +@Model.register("DeepseekV3ForCausalLM") class DeepseekV2Model(Model): model_arch = gguf.MODEL_ARCH.DEEPSEEK2 @@ -3870,6 +3874,15 @@ class DeepseekV2Model(Model): self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"]) + self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"]) + + if hparams["scoring_func"] == "sigmoid": + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) + elif hparams["scoring_func"] == "softmax": + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX) + else: + raise ValueError(f"Unsupported scoring_func value: {hparams['scoring_func']}") + self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: @@ -3882,6 +3895,16 @@ class DeepseekV2Model(Model): _experts: list[dict[str, Tensor]] | None = None def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # rename e_score_correction_bias tensors + if name.endswith("e_score_correction_bias"): + name = name.replace("e_score_correction_bias", "e_score_correction.bias") + + # skip Multi-Token Prediction (MTP) layers + block_count = self.hparams["num_hidden_layers"] + match = re.match(r"model.layers.(\d+)", name) + if match and int(match.group(1)) >= block_count: + return [] + # process the experts separately if name.find("mlp.experts") != -1: n_experts = self.hparams["n_routed_experts"] diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index fea23ddb4..56edc64a7 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -107,6 +107,7 @@ models = [ {"name": "roberta-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"}, {"name": "gigachat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"}, {"name": "megrez", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"}, + {"name": "deepseek-v3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"}, ] diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index cdf79673b..9d0e7489f 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -102,6 +102,8 @@ class Keys: EXPERT_USED_COUNT = "{arch}.expert_used_count" EXPERT_SHARED_COUNT = "{arch}.expert_shared_count" EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale" + EXPERT_WEIGHTS_NORM = "{arch}.expert_weights_norm" + EXPERT_GATING_FUNC = "{arch}.expert_gating_func" POOLING_TYPE = "{arch}.pooling_type" LOGIT_SCALE = "{arch}.logit_scale" DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id" @@ -313,6 +315,7 @@ class MODEL_TENSOR(IntEnum): FFN_GATE_SHEXP = auto() FFN_DOWN_SHEXP = auto() FFN_UP_SHEXP = auto() + FFN_EXP_PROBS_B = auto() ATTN_Q_NORM = auto() ATTN_K_NORM = auto() LAYER_OUT_NORM = auto() @@ -498,6 +501,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps", MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps", MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps", + MODEL_TENSOR.FFN_EXP_PROBS_B: "blk.{bid}.exp_probs_b", MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in", MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d", @@ -1290,6 +1294,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_GATE_SHEXP, MODEL_TENSOR.FFN_DOWN_SHEXP, MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, ], MODEL_ARCH.CHATGLM : [ MODEL_TENSOR.TOKEN_EMBD, @@ -1590,6 +1595,11 @@ class GGMLQuantizationType(IntEnum): TQ2_0 = 35 +class ExpertGatingFuncType(IntEnum): + SOFTMAX = 1 + SIGMOID = 2 + + # TODO: add GGMLFileType from ggml_ftype in ggml.h diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 3023b539a..4a0a65e3c 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -26,6 +26,7 @@ from .constants import ( RopeScalingType, PoolingType, TokenType, + ExpertGatingFuncType, ) from .quants import quant_shape_from_byte_shape @@ -715,6 +716,12 @@ class GGUFWriter: def add_expert_weights_scale(self, value: float) -> None: self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value) + def add_expert_weights_norm(self, value: bool) -> None: + self.add_bool(Keys.LLM.EXPERT_WEIGHTS_NORM.format(arch=self.arch), value) + + def add_expert_gating_func(self, value: ExpertGatingFuncType) -> None: + self.add_uint32(Keys.LLM.EXPERT_GATING_FUNC.format(arch=self.arch), value.value) + def add_swin_norm(self, value: bool) -> None: self.add_bool(Keys.LLM.SWIN_NORM.format(arch=self.arch), value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 7009a11d4..efe2a4aa4 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -276,6 +276,10 @@ class TensorNameMap: "model.layers.{bid}.mlp.shared_expert_gate", # qwen2moe ), + MODEL_TENSOR.FFN_EXP_PROBS_B: ( + "model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3 + ), + # Feed-forward up MODEL_TENSOR.FFN_UP: ( "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox diff --git a/include/llama.h b/include/llama.h index 7b305b299..a0d5ba5dd 100644 --- a/include/llama.h +++ b/include/llama.h @@ -105,6 +105,7 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_EXAONE = 25, LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26, LLAMA_VOCAB_PRE_TYPE_MINERVA = 27, + LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28, }; enum llama_rope_type { diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index fea4b21d3..007d79f82 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -92,6 +92,8 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, { LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" }, { LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" }, + { LLM_KV_EXPERT_WEIGHTS_NORM, "%s.expert_weights_norm" }, + { LLM_KV_EXPERT_GATING_FUNC, "%s.expert_gating_func" }, { LLM_KV_POOLING_TYPE, "%s.pooling_type" }, { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, { LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" }, @@ -984,6 +986,7 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" }, { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" }, { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, + { LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" }, }, }, { @@ -1366,6 +1369,7 @@ static const std::map LLM_TENSOR_INFOS = { {LLM_TENSOR_FFN_DOWN_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, {LLM_TENSOR_FFN_GATE_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, {LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}}, + {LLM_TENSOR_FFN_EXP_PROBS_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}}, // this tensor is loaded for T5, but never used {LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}}, {LLM_TENSOR_CONV1D, {LLM_TENSOR_LAYER_INPUT, GGML_OP_IM2COL}}, diff --git a/src/llama-arch.h b/src/llama-arch.h index 10bd619a4..45e458bb9 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -96,6 +96,8 @@ enum llm_kv { LLM_KV_EXPERT_USED_COUNT, LLM_KV_EXPERT_SHARED_COUNT, LLM_KV_EXPERT_WEIGHTS_SCALE, + LLM_KV_EXPERT_WEIGHTS_NORM, + LLM_KV_EXPERT_GATING_FUNC, LLM_KV_POOLING_TYPE, LLM_KV_LOGIT_SCALE, LLM_KV_DECODER_START_TOKEN_ID, @@ -231,6 +233,7 @@ enum llm_tensor { LLM_TENSOR_FFN_DOWN_SHEXP, LLM_TENSOR_FFN_GATE_SHEXP, LLM_TENSOR_FFN_UP_SHEXP, + LLM_TENSOR_FFN_EXP_PROBS_B, LLM_TENSOR_ATTN_Q_NORM, LLM_TENSOR_ATTN_K_NORM, LLM_TENSOR_LAYER_OUT_NORM, diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp index a07e9cf00..44670d3d8 100644 --- a/src/llama-chat.cpp +++ b/src/llama-chat.cpp @@ -45,6 +45,7 @@ static const std::map LLM_CHAT_TEMPLATES = { { "vicuna-orca", LLM_CHAT_TEMPLATE_VICUNA_ORCA }, { "deepseek", LLM_CHAT_TEMPLATE_DEEPSEEK }, { "deepseek2", LLM_CHAT_TEMPLATE_DEEPSEEK_2 }, + { "deepseek3", LLM_CHAT_TEMPLATE_DEEPSEEK_3 }, { "command-r", LLM_CHAT_TEMPLATE_COMMAND_R }, { "llama3", LLM_CHAT_TEMPLATE_LLAMA_3 }, { "chatglm3", LLM_CHAT_TEMPLATE_CHATGML_3 }, @@ -148,6 +149,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) { return LLM_CHAT_TEMPLATE_MINICPM; } else if (tmpl_contains("'Assistant: ' + message['content'] + eos_token")) { return LLM_CHAT_TEMPLATE_DEEPSEEK_2; + } else if (tmpl_contains(LU8("'<｜Assistant｜>' + message['content'] + '<｜end▁of▁sentence｜>'"))) { + return LLM_CHAT_TEMPLATE_DEEPSEEK_3; } else if (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]")) { // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb // EXAONE-3.0-7.8B-Instruct @@ -453,6 +456,21 @@ int32_t llm_chat_apply_template( if (add_ass) { ss << "Assistant:"; } + } else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK_3) { + // DeepSeek-V3 + for (auto message : chat) { + std::string role(message->role); + if (role == "system") { + ss << message->content << "\n\n"; + } else if (role == "user") { + ss << LU8("<｜User｜>") << message->content; + } else if (role == "assistant") { + ss << LU8("<｜Assistant｜>") << message->content << LU8("<｜end▁of▁sentence｜>"); + } + } + if (add_ass) { + ss << LU8("<｜Assistant｜>"); + } } else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_3) { // ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb // EXAONE-3.0-7.8B-Instruct diff --git a/src/llama-chat.h b/src/llama-chat.h index 364318c27..b8e94d9ef 100644 --- a/src/llama-chat.h +++ b/src/llama-chat.h @@ -25,6 +25,7 @@ enum llm_chat_template { LLM_CHAT_TEMPLATE_VICUNA_ORCA, LLM_CHAT_TEMPLATE_DEEPSEEK, LLM_CHAT_TEMPLATE_DEEPSEEK_2, + LLM_CHAT_TEMPLATE_DEEPSEEK_3, LLM_CHAT_TEMPLATE_COMMAND_R, LLM_CHAT_TEMPLATE_LLAMA_3, LLM_CHAT_TEMPLATE_CHATGML_3, diff --git a/src/llama-hparams.h b/src/llama-hparams.h index 3a76b71a4..a29f20ec4 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -6,7 +6,13 @@ // bump if necessary #define LLAMA_MAX_LAYERS 512 -#define LLAMA_MAX_EXPERTS 160 // DeepSeekV2 +#define LLAMA_MAX_EXPERTS 256 // DeepSeekV3 + +enum llama_expert_gating_func_type { + LLAMA_EXPERT_GATING_FUNC_TYPE_NONE = 0, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX = 1, + LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID = 2, +}; struct llama_hparams_posnet { uint32_t n_embd; @@ -54,7 +60,9 @@ struct llama_hparams { uint32_t n_expert_shared = 0; uint32_t n_norm_groups = 0; - float expert_weights_scale = 0.0; + float expert_weights_scale = 0.0; + bool expert_weights_norm = false; + uint32_t expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE; float f_norm_eps; float f_norm_rms_eps; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index c356abded..405e0528f 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -66,6 +66,7 @@ const char * llm_type_name(llm_type type) { case MODEL_70B: return "70B"; case MODEL_236B: return "236B"; case MODEL_314B: return "314B"; + case MODEL_671B: return "671B"; case MODEL_SMALL: return "0.1B"; case MODEL_MEDIUM: return "0.4B"; case MODEL_LARGE: return "0.8B"; @@ -125,6 +126,14 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { } } +static const char * llama_expert_gating_func_name(llama_expert_gating_func_type type) { + switch (type) { + case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX: return "softmax"; + case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID: return "sigmoid"; + default: return "unknown"; + } +} + std::string llama_model_arch_name (const llama_model & model) { return llm_arch_name(model.arch); } @@ -933,11 +942,19 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) { ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); + ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); + if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) { + // for compatibility with existing DeepSeek V2 and V2.5 GGUFs + // that have no expert_gating_func model parameter set + hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX; + } ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul); switch (hparams.n_layer) { case 27: model.type = e_model::MODEL_16B; break; case 60: model.type = e_model::MODEL_236B; break; + case 61: model.type = e_model::MODEL_671B; break; default: model.type = e_model::MODEL_UNKNOWN; } } break; @@ -1259,6 +1276,10 @@ void llm_load_vocab(llama_model_loader & ml, llama_model & model) { tokenizer_pre == "deepseek-coder") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER; vocab.tokenizer_clean_spaces = false; + } else if ( + tokenizer_pre == "deepseek-v3") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM; + vocab.tokenizer_clean_spaces = false; } else if ( tokenizer_pre == "falcon") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON; @@ -1941,6 +1962,8 @@ void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp); LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared); LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale); + LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm); + LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((enum llama_expert_gating_func_type) hparams.expert_gating_func)); LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul); } diff --git a/src/llama-model.h b/src/llama-model.h index 01c780c41..ce038932d 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -63,6 +63,7 @@ enum llm_type { MODEL_70B, MODEL_236B, MODEL_314B, + MODEL_671B, MODEL_SMALL, MODEL_MEDIUM, MODEL_LARGE, @@ -213,6 +214,7 @@ struct llama_layer { struct ggml_tensor * ffn_down_b = nullptr; // b2 struct ggml_tensor * ffn_up_b = nullptr; // b3 struct ggml_tensor * ffn_act = nullptr; + struct ggml_tensor * ffn_exp_probs_b = nullptr; // mamba proj struct ggml_tensor * ssm_in = nullptr; diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 909e04871..3fcfcaa3f 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -382,6 +382,13 @@ struct llm_tokenizer_bpe : llm_tokenizer { "\\p{N}+", }; break; + case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM: + regex_exprs = { + "\\p{N}{1,3}", + "[一-龥぀-ゟ゠-ヿ]+", + "[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+", + }; + break; case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER: regex_exprs = { "[\r\n]", diff --git a/src/llama.cpp b/src/llama.cpp index 50e9191fa..ea78ea487 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -1857,6 +1857,7 @@ static bool llm_load_tensors( layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); } else { layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); if (n_expert == 0) { throw std::runtime_error("n_expert must be > 0"); @@ -2837,12 +2838,14 @@ static struct ggml_tensor * llm_build_moe_ffn( struct ggml_tensor * up_exps, struct ggml_tensor * gate_exps, struct ggml_tensor * down_exps, + struct ggml_tensor * exp_probs_b, int64_t n_expert, int64_t n_expert_used, llm_ffn_op_type type_op, bool norm_w, bool scale_w, float w_scale, +llama_expert_gating_func_type gating_op, const llm_build_cb & cb, int il) { int64_t n_embd = cur->ne[0]; @@ -2851,11 +2854,31 @@ static struct ggml_tensor * llm_build_moe_ffn( ggml_tensor * logits = llm_build_lora_mm(lctx, ctx, gate_inp, cur); // [n_expert, n_tokens] cb(logits, "ffn_moe_logits", il); - ggml_tensor * probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens] + ggml_tensor * probs = nullptr; + switch (gating_op) { + case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX: + { + probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens] + } break; + case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID: + { + probs = ggml_sigmoid(ctx, logits); // [n_expert, n_tokens] + } break; + default: + GGML_ABORT("fatal error"); + } cb(probs, "ffn_moe_probs", il); + // add experts selection bias - introduced in DeepSeek V3 + // leave probs unbiased as it's later used to get expert weights + ggml_tensor * selection_probs = probs; + if (exp_probs_b != nullptr) { + selection_probs = ggml_add(ctx, probs, exp_probs_b); + cb(selection_probs, "ffn_moe_probs_biased", il); + } + // select experts - ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_expert_used); // [n_expert_used, n_tokens] + ggml_tensor * selected_experts = ggml_top_k(ctx, selection_probs, n_expert_used); // [n_expert_used, n_tokens] cb(selected_experts->src[0], "ffn_moe_argsort", il); cb(selected_experts, "ffn_moe_topk", il); @@ -3976,9 +3999,11 @@ struct llm_build_context { model.layers[il].ffn_up_exps, model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, + nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, false, 0.0, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, cb, il); cb(cur, "ffn_moe_out", il); } @@ -4628,9 +4653,11 @@ struct llm_build_context { model.layers[il].ffn_up_exps, model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, + nullptr, n_expert, n_expert_used, LLM_FFN_GELU, true, false, 0.0, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, cb, il); cb(cur, "ffn_moe_out", il); @@ -4769,9 +4796,11 @@ struct llm_build_context { model.layers[il].ffn_up_exps, model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, + nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, false, 0.0, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, cb, il); cb(cur, "ffn_moe_out", il); @@ -6017,9 +6046,11 @@ struct llm_build_context { model.layers[il].ffn_up_exps, model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, + nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, false, 0.0, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, cb, il); cb(cur, "ffn_moe_out", il); @@ -8142,9 +8173,11 @@ struct llm_build_context { model.layers[il].ffn_up_exps, model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, + nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, false, 0.0, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, cb, il); cb(cur, "ffn_moe_out", il); @@ -8539,9 +8572,11 @@ struct llm_build_context { model.layers[il].ffn_up_exps, model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, + nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, false, 0.0, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, cb, il); cb(cur, "ffn_moe_out", il); @@ -8680,9 +8715,11 @@ struct llm_build_context { model.layers[il].ffn_up_exps, model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, + nullptr, n_expert, n_expert_used, LLM_FFN_SILU, false, false, hparams.expert_weights_scale, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, cb, il); cb(moe_out, "ffn_moe_out", il); @@ -8909,9 +8946,11 @@ struct llm_build_context { model.layers[il].ffn_up_exps, model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, - LLM_FFN_SILU, false, + LLM_FFN_SILU, hparams.expert_weights_norm, true, hparams.expert_weights_scale, + (enum llama_expert_gating_func_type) hparams.expert_gating_func, cb, il); cb(moe_out, "ffn_moe_out", il); diff --git a/src/unicode.cpp b/src/unicode.cpp index 8ed6b1a51..7aca6544b 100644 --- a/src/unicode.cpp +++ b/src/unicode.cpp @@ -667,18 +667,24 @@ std::vector unicode_regex_split(const std::string & text, const std { "\\p{N}", unicode_cpt_flags::NUMBER }, { "\\p{L}", unicode_cpt_flags::LETTER }, { "\\p{P}", unicode_cpt_flags::PUNCTUATION }, + { "\\p{M}", unicode_cpt_flags::ACCENT_MARK }, + { "\\p{S}", unicode_cpt_flags::SYMBOL }, }; static const std::map k_ucat_cpt = { { unicode_cpt_flags::NUMBER, 0xD1 }, { unicode_cpt_flags::LETTER, 0xD2 }, { unicode_cpt_flags::PUNCTUATION, 0xD3 }, + { unicode_cpt_flags::ACCENT_MARK, 0xD4 }, + { unicode_cpt_flags::SYMBOL, 0xD5 }, }; static const std::map k_ucat_map = { { unicode_cpt_flags::NUMBER, "\x30-\x39" }, // 0-9 { unicode_cpt_flags::LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z { unicode_cpt_flags::PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\} + { unicode_cpt_flags::ACCENT_MARK, "" }, // no sub-128 codepoints + { unicode_cpt_flags::SYMBOL, "\\\x24\\\x2B\x3C-\x3E\x5E\x60\\\x7C" }, // $+<=>^`| }; // compute collapsed codepoints only if needed by at least one regex