model : add grok-2 support

convert_hf_to_gguf.py

@@ -735,6 +735,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c":
             # ref: https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
             res = "qwen2"
+        if chkhsh == "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273":
+            # ref: https://huggingface.co/alvarobartt/grok-2-tokenizer
+            res = "grok-2"
         if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
             # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
             res = "llama-bpe"
@@ -2682,57 +2685,105 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         yield (new_name, data_torch)
 
 
-@ModelBase.register("GrokForCausalLM")
+@ModelBase.register("GrokForCausalLM", "Grok1ForCausalLM")
 class GrokModel(TextModel):
     model_arch = gguf.MODEL_ARCH.GROK
 
     def set_vocab(self):
-        self._set_vocab_sentencepiece()
+        if (self.dir_model / 'tokenizer.model').is_file():
+            self._set_vocab_sentencepiece()
+            return
+
+        if not (self.dir_model / 'tokenizer.json').is_file() or not (self.dir_model / 'chat_template.jinja').is_file():
+            logger.error('Error: Missing vocab and chat template, download files from https://huggingface.co/alvarobartt/grok-2-tokenizer')
+            sys.exit(1)
+
+        self._set_vocab_gpt2()
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
-    _experts: list[dict[str, Tensor]] | None = None
+        self.gguf_writer.add_attn_logit_softcapping(self.hparams.get("attn_logit_softcapping", 30.0))
+        self.gguf_writer.add_router_logit_softcapping(self.hparams.get("router_logit_softcapping", 30.0))
+        if (final_logit_softcap := self.hparams.get("final_logit_softcapping")):
+            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
+
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+
+        # Treat "original" as "yarn", seems to have been a mistake
+        if self.hparams.get("rope_type") in ("yarn", "original"):
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+            self.gguf_writer.add_rope_scaling_factor(self.hparams["scaling_factor"])
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["original_max_position_embeddings"])
+
+        if temp_len := self.hparams.get("attn_temperature_len"):
+            self.gguf_writer.add_attn_temperature_length(temp_len)
+
+        self.gguf_writer.add_attn_output_scale(self.hparams.get("attn_output_multiplier", rope_dim**-0.5))
+        self.gguf_writer.add_embedding_scale(self.hparams["embedding_multiplier_scale"])
+        self.gguf_writer.add_logit_scale(self.hparams["output_multiplier_scale"])
+
+    _experts: list[dict[str, list[Tensor]]] | None = None
+    _cur_expert = ""
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        tensors: list[tuple[str, Tensor]] = []
+        is_expert = ".moe." in name or ".block_sparse_moe.experts." in name
+
+        if not is_expert:
+            tensors.append((self.map_tensor_name(name), data_torch))
+
         # process the experts separately
-        if name.find(".moe.") != -1:
+        if is_expert or self._cur_expert:
             n_experts = self.hparams["num_local_experts"]
 
             assert bid is not None
 
             if self._experts is None:
                 self._experts = [{} for _ in range(self.block_count)]
 
-            self._experts[bid][name] = data_torch
+            # concatenate split tensors
+            if name in self._experts[bid]:
+                self._cur_expert = name
+                self._experts[bid][name].append(data_torch)
+                return []
+            elif is_expert:
+                self._cur_expert = name
+                self._experts[bid][name] = [data_torch]
+                return []
+            else:
+                self._cur_expert = ""
 
-            if len(self._experts[bid]) >= n_experts * 3:
-                tensors: list[tuple[str, Tensor]] = []
+            for bid in range(self.block_count):
+                if len(self._experts[bid]) >= n_experts * 3:
+                    # merge the experts into a single 3d tensor
+                    for wid in [("linear", "w1", 0), ("linear_1", "w2", 1), ("linear_v", "w3", 0)]:
+                        datas: list[Tensor] = []
 
-                # merge the experts into a single 3d tensor
-                for wid in ["linear", "linear_1", "linear_v"]:
-                    datas: list[Tensor] = []
+                        for xid in range(n_experts):
+                            ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid[0]}.weight"
+                            if ename not in self._experts[bid]:
+                                ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid[1]}.weight"
+                            tensor_list = self._experts[bid][ename]
+                            datas.append(torch.cat(tensor_list, dim=wid[2]) if len(tensor_list) > 1 else tensor_list[0])
+                            del self._experts[bid][ename]
 
-                    for xid in range(n_experts):
-                        ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
+                        data_torch = torch.stack(datas, dim=0)
 
-                    data_torch = torch.stack(datas, dim=0)
+                        merged_name = f"transformer.decoder_layer.{bid}.moe.{wid[0]}.weight"
 
-                    merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight"
-
-                    new_name = self.map_tensor_name(merged_name)
+                        new_name = self.map_tensor_name(merged_name)
 
-                    tensors.append((new_name, data_torch))
-                return tensors
-            else:
-                return []
+                        yield (new_name, data_torch)
 
-        return [(self.map_tensor_name(name), data_torch)]
+        yield from tensors
 
 
 @ModelBase.register("DbrxForCausalLM")

convert_hf_to_gguf_update.py

@@ -158,6 +158,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-34B-Base", "chkhsh": "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b"},
     {"name": "kimi-k2",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base",   "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
     {"name": "qwen2",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
+    {"name": "grok-2",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/alvarobartt/grok-2-tokenizer", "chkhsh": "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273"},
 ]
 
 

gguf-py/gguf/constants.py

@@ -110,6 +110,7 @@ class LLM:
         LOGIT_SCALE                       = "{arch}.logit_scale"
         DECODER_START_TOKEN_ID            = "{arch}.decoder_start_token_id"
         ATTN_LOGIT_SOFTCAPPING            = "{arch}.attn_logit_softcapping"
+        ROUTER_LOGIT_SOFTCAPPING          = "{arch}.router_logit_softcapping"
         FINAL_LOGIT_SOFTCAPPING           = "{arch}.final_logit_softcapping"
         SWIN_NORM                         = "{arch}.swin_norm"
         RESCALE_EVERY_N_LAYERS            = "{arch}.rescale_every_n_layers"
@@ -145,6 +146,8 @@ class Attention:
         REL_BUCKETS_COUNT            = "{arch}.attention.relative_buckets_count"
         SLIDING_WINDOW               = "{arch}.attention.sliding_window"
         SCALE                        = "{arch}.attention.scale"
+        OUTPUT_SCALE                 = "{arch}.attention.output_scale"
+        TEMPERATURE_LENGTH           = "{arch}.attention.temperature_length"
         KEY_LENGTH_MLA               = "{arch}.attention.key_length_mla"
         VALUE_LENGTH_MLA             = "{arch}.attention.value_length_mla"
         SHARED_KV_LAYERS             = "{arch}.attention.shared_kv_layers"
@@ -1110,6 +1113,7 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_GATE_EXP,
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_POST_NORM,
         MODEL_TENSOR.LAYER_OUT_NORM,
     ],
     MODEL_ARCH.GPTNEOX: [

gguf-py/gguf/gguf_writer.py

@@ -730,6 +730,9 @@ def add_logit_scale(self, value: float) -> None:
     def add_attn_logit_softcapping(self, value: float) -> None:
         self.add_float32(Keys.LLM.ATTN_LOGIT_SOFTCAPPING.format(arch=self.arch), value)
 
+    def add_router_logit_softcapping(self, value: float) -> None:
+        self.add_float32(Keys.LLM.ROUTER_LOGIT_SOFTCAPPING.format(arch=self.arch), value)
+
     def add_final_logit_softcapping(self, value: float) -> None:
         self.add_float32(Keys.LLM.FINAL_LOGIT_SOFTCAPPING.format(arch=self.arch), value)
 
@@ -826,6 +829,12 @@ def add_sliding_window(self, value: int) -> None:
     def add_attention_scale(self, value: float) -> None:
         self.add_float32(Keys.Attention.SCALE.format(arch=self.arch), value)
 
+    def add_attn_output_scale(self, value: float) -> None:
+        self.add_float32(Keys.Attention.OUTPUT_SCALE.format(arch=self.arch), value)
+
+    def add_attn_temperature_length(self, value: int) -> None:
+        self.add_uint32(Keys.Attention.TEMPERATURE_LENGTH.format(arch=self.arch), value)
+
     def add_pooling_type(self, value: PoolingType) -> None:
         self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
 

gguf-py/gguf/tensor_mapping.py

@@ -135,6 +135,7 @@ class TensorNameMap:
             "model.layers.{bid}.norm",                              # mamba-qbert
             "backbone.layers.{bid}.norm",                           # mamba
             "transformer.decoder_layer.{bid}.rms_norm",             # Grok
+            "model.layers.{bid}.pre_attn_norm",                     # grok-2
             "transformer.blocks.{bid}.norm_attn_norm.norm_1",       # dbrx
             "encoder.layers.{bid}.input_layernorm",                 # chatglm
             "transformer.layers.{bid}.attn_norm",                   # openelm
@@ -272,6 +273,7 @@ class TensorNameMap:
             "transformer.layer.{bid}.sa_layer_norm",           # distillbert
             "encoder.layers.{bid}.norm1",                      # nomic-bert
             "transformer.decoder_layer.{bid}.rms_norm_1",      # Grok
+            "model.layers.{bid}.post_attn_norm",               # grok-2
             "transformer.blocks.{bid}.norm_attn_norm.norm_2",  # dbrx
         ),
 
@@ -306,6 +308,7 @@ class TensorNameMap:
             "h.{bid}.ln_2",                                                  # gpt2
             "model.layers.{bid}.ffn_norm",                                   # internlm2
             "transformer.decoder_layer.{bid}.rms_norm_2",                    # Grok
+            "model.layers.{bid}.pre_moe_norm",                               # grok-2
             "encoder.layers.{bid}.post_attention_layernorm",                 # chatglm
             "transformer.layers.{bid}.ffn_norm",                             # openelm
             "model.layers.{bid}.pre_ff_layernorm",                           # jamba granite-hybrid
@@ -325,10 +328,11 @@ class TensorNameMap:
 
         # Post feed-forward norm
         MODEL_TENSOR.FFN_POST_NORM: (
-            "model.layers.{bid}.post_feedforward_layernorm", # gemma2 olmo2
-            "model.layers.{bid}.post_mlp_layernorm", # glm-4-0414
+            "model.layers.{bid}.post_feedforward_layernorm",  # gemma2 olmo2
+            "model.layers.{bid}.post_mlp_layernorm",          # glm-4-0414
             "model.layers.layers.{bid}.post_mlp_norm.weight", # plamo2
             "model.layers.{bid}.feed_forward.up_proj",
+            "model.layers.{bid}.post_moe_norm",               # grok-2
         ),
 
         MODEL_TENSOR.FFN_GATE_INP: (

src/llama-arch.cpp

@@ -137,6 +137,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_LOGIT_SCALE,                       "%s.logit_scale"                       },
     { LLM_KV_DECODER_START_TOKEN_ID,            "%s.decoder_start_token_id"            },
     { LLM_KV_ATTN_LOGIT_SOFTCAPPING,            "%s.attn_logit_softcapping"            },
+    { LLM_KV_ROUTER_LOGIT_SOFTCAPPING,          "%s.router_logit_softcapping"          },
     { LLM_KV_FINAL_LOGIT_SOFTCAPPING,           "%s.final_logit_softcapping"           },
     { LLM_KV_SWIN_NORM,                         "%s.swin_norm"                         },
     { LLM_KV_RESCALE_EVERY_N_LAYERS,            "%s.rescale_every_n_layers"            },
@@ -167,6 +168,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,       "%s.attention.relative_buckets_count"       },
     { LLM_KV_ATTENTION_SLIDING_WINDOW,               "%s.attention.sliding_window"               },
     { LLM_KV_ATTENTION_SCALE,                        "%s.attention.scale"                        },
+    { LLM_KV_ATTENTION_OUTPUT_SCALE,                 "%s.attention.output_scale"                 },
+    { LLM_KV_ATTENTION_TEMPERATURE_LENGTH,           "%s.attention.temperature_length"           },
     { LLM_KV_ATTENTION_KEY_LENGTH_MLA,               "%s.attention.key_length_mla"               },
     { LLM_KV_ATTENTION_VALUE_LENGTH_MLA,             "%s.attention.value_length_mla"             },
 
@@ -395,12 +398,16 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
             { 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_EXP,    "blk.%d.ffn_gate.%d" },
             { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
             { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
             { 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_POST_NORM,   "blk.%d.post_ffw_norm" },
             { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
             { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
         },

src/llama-arch.h

@@ -141,6 +141,7 @@ enum llm_kv {
     LLM_KV_LOGIT_SCALE,
     LLM_KV_DECODER_START_TOKEN_ID,
     LLM_KV_ATTN_LOGIT_SOFTCAPPING,
+    LLM_KV_ROUTER_LOGIT_SOFTCAPPING,
     LLM_KV_FINAL_LOGIT_SOFTCAPPING,
     LLM_KV_SWIN_NORM,
     LLM_KV_RESCALE_EVERY_N_LAYERS,
@@ -171,6 +172,8 @@ enum llm_kv {
     LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
     LLM_KV_ATTENTION_SLIDING_WINDOW,
     LLM_KV_ATTENTION_SCALE,
+    LLM_KV_ATTENTION_OUTPUT_SCALE,
+    LLM_KV_ATTENTION_TEMPERATURE_LENGTH,
     LLM_KV_ATTENTION_KEY_LENGTH_MLA,
     LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
 

src/llama-chat.cpp

@@ -70,6 +70,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "hunyuan-dense",     LLM_CHAT_TEMPLATE_HUNYUAN_DENSE     },
     { "kimi-k2",           LLM_CHAT_TEMPLATE_KIMI_K2           },
     { "seed_oss",          LLM_CHAT_TEMPLATE_SEED_OSS          },
+    { "grok-2",            LLM_CHAT_TEMPLATE_GROK_2            },
 };
 
 llm_chat_template llm_chat_template_from_str(const std::string & name) {
@@ -204,6 +205,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_KIMI_K2;
     } else if (tmpl_contains("<seed:bos>")) {
         return LLM_CHAT_TEMPLATE_SEED_OSS;
+    } else if (tmpl_contains("'Assistant: '  + message['content'] + '<|separator|>")) {
+        return LLM_CHAT_TEMPLATE_GROK_2;
     }
     return LLM_CHAT_TEMPLATE_UNKNOWN;
 }
@@ -763,6 +766,20 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "<seed:bos>assistant\n";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_GROK_2) {
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << "System: " << trim(message->content) << "<|separator|>\n\n";
+            } else if (role == "user") {
+                ss << "Human: " << trim(message->content) << "<|separator|>\n\n";
+            } else if (role == "assistant") {
+                ss << "Assistant: " << message->content << "<|separator|>\n\n";
+            }
+        }
+        if (add_ass) {
+            ss << "Assistant:";
+        }
     } else {
         // template not supported
         return -1;

src/llama-chat.h

@@ -50,6 +50,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_HUNYUAN_DENSE,
     LLM_CHAT_TEMPLATE_KIMI_K2,
     LLM_CHAT_TEMPLATE_SEED_OSS,
+    LLM_CHAT_TEMPLATE_GROK_2,
     LLM_CHAT_TEMPLATE_UNKNOWN,
 };
 

src/llama-context.cpp

@@ -69,6 +69,10 @@ llama_context::llama_context(
         cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
     }
 
+    if (model.arch == LLM_ARCH_GROK && params.yarn_beta_fast == 32.0f) {
+        cparams.yarn_beta_fast = 8.0f;
+    }
+
     cparams.yarn_attn_factor *= hparams.rope_attn_factor;
 
     if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {

src/llama-graph.cpp

@@ -1290,14 +1290,14 @@ ggml_tensor * llm_graph_context::build_attn_mha(
 
         if (arch == LLM_ARCH_GROK) {
             // need to do the following:
-            // multiply by attn_output_multiplyer of 0.08838834764831845
+            // multiply by attn_output_multiplier
             // and then :
             // kq = 30 * tanh(kq / 30)
             // before the softmax below
 
-            kq = ggml_tanh(ctx0, ggml_scale(ctx0, kq, 0.08838834764831845f/30.0f));
+            kq = ggml_tanh(ctx0, ggml_scale(ctx0, kq, hparams.f_attn_out_scale / hparams.f_attn_logit_softcapping));
             cb(kq, "kq_tanh", il);
-            kq = ggml_scale(ctx0, kq, 30);
+            kq = ggml_scale(ctx0, kq, hparams.f_attn_logit_softcapping);
             cb(kq, "kq_scaled", il);
         }
 

src/llama-hparams.h

@@ -81,8 +81,9 @@ struct llama_hparams {
     float f_norm_rms_eps;
     float f_norm_group_eps;
 
-    float f_attn_logit_softcapping  = 50.0f;
-    float f_final_logit_softcapping = 30.0f;
+    float f_attn_logit_softcapping   = 50.0f;
+    float f_router_logit_softcapping = 30.0f;
+    float f_final_logit_softcapping  = 30.0f;
 
     // for RWKV
     uint32_t rescale_every_n_layers = 0;
@@ -135,6 +136,10 @@ struct llama_hparams {
     float f_embedding_scale = 0.0f;
     float f_attention_scale = 0.0f;
 
+    // grok-2
+    float    f_attn_out_scale = 0.0f;
+    uint32_t attn_temp_length = 0;
+
     bool causal_attn   = true;
     bool use_alibi     = false;
     bool attn_soft_cap = false;