support glm-4-9b-chat

Signed-off-by: XingXing Qiao <qiaoxx@dingdao.com>

convert-hf-to-gguf.py

@@ -483,6 +483,9 @@ class Model:
         if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
             # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
             res = "jina-v2-code"
+        if chkhsh == "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b":
+            # ref: https://huggingface.co/THUDM/glm-4-9b-chat
+            res = "chatglm-bpe"
 
         if res is None:
             logger.warning("\n")
@@ -2729,7 +2732,7 @@ class DeepseekV2Model(Model):
 class ChatGLMModel(Model):
     model_arch = gguf.MODEL_ARCH.CHATGLM
 
-    def set_vocab(self):
+    def set_vocab_chatglm3(self):
         dir_model = self.dir_model
         hparams = self.hparams
         tokens: list[bytearray] = []
@@ -2789,6 +2792,95 @@ class ChatGLMModel(Model):
         special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
         special_vocab.add_to_gguf(self.gguf_writer)
 
+    @staticmethod
+    def token_bytes_to_string(b):
+        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
+        byte_encoder = bytes_to_unicode()
+        return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
+
+    @staticmethod
+    def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: int | None = None) -> list[bytes]:
+        parts = [bytes([b]) for b in token]
+        while True:
+            min_idx = None
+            min_rank = None
+            for i, pair in enumerate(zip(parts[:-1], parts[1:])):
+                rank = mergeable_ranks.get(pair[0] + pair[1])
+                if rank is not None and (min_rank is None or rank < min_rank):
+                    min_idx = i
+                    min_rank = rank
+            if min_rank is None or (max_rank is not None and min_rank >= max_rank):
+                break
+            assert min_idx is not None
+            parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
+        return parts
+
+    def set_vocab(self):
+        if "THUDM/chatglm3-6b" in self.hparams.get("_name_or_path", ""):
+            self.set_vocab_chatglm3()
+            return
+
+        dir_model = self.dir_model
+        hparams = self.hparams
+        tokens: list[str] = []
+        toktypes: list[int] = []
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
+        vocab_size = hparams["padded_vocab_size"]
+        assert max(tokenizer.get_vocab().values()) < vocab_size
+
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        merges = []
+        vocab = {}
+        mergeable_ranks = tokenizer.mergeable_ranks
+        for token, rank in mergeable_ranks.items():
+            vocab[ChatGLMModel.token_bytes_to_string(token)] = rank
+            if len(token) == 1:
+                continue
+            merged = ChatGLMModel.bpe(mergeable_ranks, token, max_rank=rank)
+            assert len(merged) >= 2 and len(merged) <= 7
+            merges.append(' '.join(map(ChatGLMModel.token_bytes_to_string, merged)))
+
+        # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
+        added_vocab = tokenizer.get_added_vocab()
+        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.USER_DEFINED)
+            elif reverse_vocab[i] in added_vocab:
+                tokens.append(reverse_vocab[i])
+                if tokenizer.added_tokens_decoder[i].special:
+                    toktypes.append(gguf.TokenType.CONTROL)
+                else:
+                    toktypes.append(gguf.TokenType.USER_DEFINED)
+            else:
+                tokens.append(reverse_vocab[i])
+                toktypes.append(gguf.TokenType.NORMAL)
+
+        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(dir_model, load_merges=False)
+        special_vocab.chat_template = "ChatGLM4"
+        special_vocab.merges = merges
+        # only add special tokens when they were not already loaded from config.json
+
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])
+
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|user|>"])
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|observation|>"])
+        special_vocab._set_special_token("eot", 151336)
+        # this one is usually not in config.json anyway
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])
+        special_vocab.add_to_gguf(self.gguf_writer)
+
     def set_gguf_parameters(self):
         self.gguf_writer.add_name(self.dir_model.name)
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))

examples/server/public/index-new.html

@@ -717,6 +717,8 @@ return html`
               <option value="vicuna">Tess</option>
               <option value="yi34b">Yi-6/9/34B-Chat</option>
               <option value="zephyr">Zephyr</option>
+              <option value="chatglm3">ChatGLM3-6B</option>
+              <option value="chatglm4">ChatGLM4-9B</option>
               <option value=""></option>
             </optgroup>
           </select>

examples/server/public/prompt-formats.js

@@ -327,5 +327,41 @@ export const promptFormats = {
   userMsgSuffix: "",
 
   stops: ""
+  },
+
+  // ----------------------------
+
+  "chatglm3": {
+    template: `[gMASK]sop<|system|>\n {{prompt}}{{history}}<|{{char}}|>`,
+
+    historyTemplate: `<|{{name}}|>\n {{message}}`,
+
+    char: "assistant",
+    charMsgPrefix: "",
+    charMsgSuffix: "",
+  
+    user: "user",
+    userMsgPrefix: "",
+    userMsgSuffix: "",
+
+    stops: ""
+  },
+
+  // ----------------------------
+
+  "chatglm4": {
+    template: `[gMASK]<sop><|system|>\n{{prompt}}{{history}}<|{{char}}|>`,
+  
+    historyTemplate: `<|{{name}}|>\n{{message}}`,
+  
+    char: "assistant",
+    charMsgPrefix: "",
+    charMsgSuffix: "",
+  
+    user: "user",
+    userMsgPrefix: "",
+    userMsgSuffix: "",
+  
+    stops: ""
   }
-  };
+};

llama.cpp

@@ -4730,6 +4730,7 @@ static void llm_load_hparams(
             ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
             switch (hparams.n_layer) {
                 case 28: model.type = e_model::MODEL_7B; break;
+                case 40: model.type = e_model::MODEL_8B; break;
                 default: model.type = e_model::MODEL_UNKNOWN;
             }
         } break;
@@ -4922,6 +4923,9 @@ static void llm_load_vocab(
             } else if (
                 tokenizer_pre == "poro-chat") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO;
+            } else if (
+                tokenizer_pre == "chatglm-bpe") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
@@ -13369,6 +13373,7 @@ struct llm_tokenizer_bpe {
                 break;
             case LLAMA_VOCAB_PRE_TYPE_DBRX:
             case LLAMA_VOCAB_PRE_TYPE_SMAUG:
+            case LLAMA_VOCAB_PRE_TYPE_CHATGLM4:
                 regex_exprs = {
                     // same as llama3
                     "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
@@ -18914,6 +18919,15 @@ static int32_t llama_chat_apply_template_internal(
         if (add_ass) {
             ss << "<|assistant|>";
         }
+    } else if (tmpl.find("ChatGLM4") != std::string::npos) {
+        ss << "[gMASK]" << "<sop>";
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|" << role << "|>" << "\n" << message->content;
+        }
+        if (add_ass) {
+            ss << "<|assistant|>";
+        }
     } else {
         // template not supported
         return -1;

llama.h

@@ -87,6 +87,7 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_DBRX           = 13,
         LLAMA_VOCAB_PRE_TYPE_SMAUG          = 14,
         LLAMA_VOCAB_PRE_TYPE_PORO           = 15,
+        LLAMA_VOCAB_PRE_TYPE_CHATGLM4       = 16,
     };
 
     // note: these values should be synchronized with ggml_rope

tests/test-chat-template.cpp

@@ -59,6 +59,8 @@ int main(void) {
         "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}",
         // ChatGLM3
         "{% for message in messages %}{% if loop.first %}[gMASK]sop<|{{ message['role'] }}|>\n {{ message['content'] }}{% else %}<|{{ message['role'] }}|>\n {{ message['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
+        // ChatGLM4
+        "ChatGLM4",
     };
     std::vector<std::string> expected_output = {
         // teknium/OpenHermes-2.5-Mistral-7B
@@ -97,6 +99,8 @@ int main(void) {
         "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n   I am an assistant   <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n",
         // ChatGLM3
         "[gMASK]sop<|system|>\n You are a helpful assistant<|user|>\n Hello<|assistant|>\n Hi there<|user|>\n Who are you<|assistant|>\n    I am an assistant   <|user|>\n Another question<|assistant|>",
+        // ChatGLM4
+        "[gMASK]<sop><|system|>\nYou are a helpful assistant<|user|>\nHello<|assistant|>\nHi there<|user|>\nWho are you<|assistant|>\n   I am an assistant   <|user|>\nAnother question<|assistant|>",
     };
     std::vector<char> formatted_chat(1024);
     int32_t res;