Hunyuan tokenizer

convert_hf_to_gguf.py

@@ -346,6 +346,8 @@
                         data_qtype = gguf.GGMLQuantizationType.BF16
                     elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0:
                         data_qtype = gguf.GGMLQuantizationType.Q8_0
+                    elif self.ftype == gguf.LlamaFileType.MOSTLY_Q4_0:
+                        data_qtype = gguf.GGMLQuantizationType.Q4_0
                     elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ1_0:
                         data_qtype = gguf.GGMLQuantizationType.TQ1_0
                     elif self.ftype == gguf.LlamaFileType.MOSTLY_TQ2_0:
@@ -607,7 +609,7 @@
 
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
         assert max(tokenizer.vocab.values()) < vocab_size
 
         tokpre = self.get_vocab_base_pre(tokenizer)
 
@@ -616,7 +618,7 @@
 
         added_tokens_decoder = tokenizer.added_tokens_decoder
 
         for i in range(vocab_size):
             if i not in reverse_vocab:
                 tokens.append(f"[PAD{i}]")
                 toktypes.append(gguf.TokenType.UNUSED)
@@ -842,14 +844,14 @@
     def _set_vocab_none(self) -> None:
         self.gguf_writer.add_tokenizer_model("none")
 
-    def _set_vocab_gpt2(self) -> None:
+    def _set_vocab_gpt2(self, load_merges=True) -> 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 = gguf.SpecialVocab(self.dir_model, load_merges)
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def _set_vocab_qwen(self):
@@ -6394,24 +6396,27 @@
 
 
 @ModelBase.register("HunYuanMoEV1ForCausalLM")
-class HunYuanMoEModel(LlamaModel):
+class HunYuanMoEModel(TextModel):
     model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE
-    undo_permute = False
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
+        # For handling tied embeddings
+        self._tok_embd = None
 
     def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
+        """
+        A self-contained vocab implementation for the HunYuan tiktoken-based tokenizer.
+        This method correctly generates tokens, types, and the required "fake" merges
+        to satisfy the llama.cpp GGUF loader.
+        """
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
 
-        # merge logic is copied from QwenModel, maybe incorrect
+        # 1. Get the pre-tokenizer identifier hash
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        # 2. Reverse-engineer the merges list from mergeable_ranks
         merges = []
         vocab = {}
         mergeable_ranks = tokenizer.mergeable_ranks
@@ -6420,97 +6425,107 @@
             if len(token) == 1:
                 continue
             merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
-            if len(merged) == 2:
+            if len(merged) == 2: #todo this is an assert in Qwen, why?
                 merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
-        self.gguf_writer.add_token_merges(merges)
-
-        reverse_vocab = tokenizer.decoder
-        assert max(reverse_vocab.keys()) < tokenizer.vocab_size
-
-        tokpre = self.get_vocab_base_pre(tokenizer)
-        added_vocab = tokenizer.get_added_vocab()
-
-        added_tokens_decoder = tokenizer.added_tokens_decoder
 
-        for i in range(tokenizer.vocab_size):
+        # 3. Generate the tokens and toktypes lists
+        vocab_size = self.hparams["vocab_size"]
+        special_tokens = tokenizer.special_tokens
+        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
+        tokens: list[str] = []
+        toktypes: list[int] = []
+        for i in range(vocab_size):
             if i not in reverse_vocab:
                 tokens.append(f"[PAD{i}]")
                 toktypes.append(gguf.TokenType.UNUSED)
             else:
-                token: str = reverse_vocab[i]
-                if token in added_vocab:
-                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
-                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
-                    if not added_tokens_decoder[i].normalized:
-                        previous_token = token
-                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))
-                        if previous_token != token:
-                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
-
-                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        # NOTE: this was added for Gemma.
-                        # Encoding and decoding the tokens above isn't sufficient for this case.
-                        token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ")  # pre-normalize user-defined spaces
-                        toktypes.append(gguf.TokenType.USER_DEFINED)
+                token = reverse_vocab[i]
+                tokens.append(token)
+                if i in special_tokens.values():
+                    toktypes.append(gguf.TokenType.CONTROL)
                 else:
                     toktypes.append(gguf.TokenType.NORMAL)
-                tokens.append(token)
 
-        return tokens, toktypes, tokpre
+        # 4. Write all vocab-related fields to the GGUF writer
+        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)
+        self.gguf_writer.add_token_merges(merges)
+
+        # 5. Add special tokens and chat templates
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+        special_vocab.add_to_gguf(self.gguf_writer)
+        # FIX for BOS token: Manually set the correct BOS token ID.
+        self.gguf_writer.add_bos_token_id(127959) # <|bos|>
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
+        hparams = self.hparams
 
-        self.gguf_writer.add_expert_count(self.hparams["num_experts"])
-        self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["intermediate_size"])
+        self.gguf_writer.add_expert_count(hparams["num_experts"])
+        self.gguf_writer.add_expert_shared_feed_forward_length(hparams["intermediate_size"])
 
-        moe_intermediate_size = self.hparams["moe_intermediate_size"]
+        moe_intermediate_size = hparams["moe_intermediate_size"]
         assert all(n == moe_intermediate_size[0] for n in moe_intermediate_size)
         self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size[0])
 
-        moe_topk = self.hparams["moe_topk"]
+        moe_topk = hparams["moe_topk"]
         assert all(topk == moe_topk[0] for topk in moe_topk)
         self.gguf_writer.add_expert_used_count(moe_topk[0])
 
+        moe_shared_expert = hparams["num_shared_expert"]
+        assert all(n == moe_shared_expert[0] for n in moe_shared_expert)
+        self.gguf_writer.add_expert_shared_count(moe_shared_expert[0])
+
+        self.gguf_writer.add_qk_norm(hparams.get("use_qk_norm", True))
+
+        # Rope
+        rope_scaling = hparams.get("rope_scaling", {})
+        if rope_scaling.get("type") == "dynamic":
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["max_position_embeddings"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # process the experts separately
+        if name == "model.embed_tokens.weight":
+            self._tok_embd = data_torch.clone()
+        if name == "lm_head.weight":
+            if self.hparams.get("tie_word_embeddings", False):
+                logger.info("Skipping tied output layer 'lm_head.weight'")
+                return []
         if name.find("mlp.experts") != -1:
             n_experts = self.hparams["num_experts"]
             assert bid is not None
-
-            tensors: list[tuple[str, Tensor]] = []
-
             if self._experts is None:
                 self._experts = [{} for _ in range(self.block_count)]
-
             self._experts[bid][name] = data_torch
-
             if len(self._experts[bid]) >= n_experts * 3:
-                # merge the experts into a single 3d tensor
+                tensors: list[tuple[str, Tensor]] = []
                 for w_name in ["down_proj", "gate_proj", "up_proj"]:
                     datas: list[Tensor] = []
-
                     for xid in range(n_experts):
                         ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                         datas.append(self._experts[bid][ename])
                         del self._experts[bid][ename]
-
                     data_torch = torch.stack(datas, dim=0)
-
                     merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
                     new_name = self.map_tensor_name(merged_name)
-
                     tensors.append((new_name, data_torch))
-
                 return tensors
             else:
                 return []
-
         return [(self.map_tensor_name(name), data_torch)]
 
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
 ###### CONVERSION LOGIC ######
 
 
@@ -6600,7 +6615,7 @@
         help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
     )
     parser.add_argument(
-        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "tq1_0", "tq2_0", "auto"], default="f16",
+        "--outtype", type=str, choices=["f32", "f16", "bf16", "q4_0", "q8_0", "tq1_0", "tq2_0", "auto"], default="f16",
         help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, tq1_0 or tq2_0 for ternary, and auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
     )
     parser.add_argument(
@@ -6732,6 +6747,7 @@
         "f32": gguf.LlamaFileType.ALL_F32,
         "f16": gguf.LlamaFileType.MOSTLY_F16,
         "bf16": gguf.LlamaFileType.MOSTLY_BF16,
+        "q4_0": gguf.LlamaFileType.MOSTLY_Q4_0,
         "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
         "tq1_0": gguf.LlamaFileType.MOSTLY_TQ1_0,
         "tq2_0": gguf.LlamaFileType.MOSTLY_TQ2_0,

gguf-py/gguf/constants.py

@@ -148,6 +148,7 @@ class Attention:
         VALUE_LENGTH_MLA             = "{arch}.attention.value_length_mla"
         SHARED_KV_LAYERS             = "{arch}.attention.shared_kv_layers"
         SLIDING_WINDOW_PATTERN       = "{arch}.attention.sliding_window_pattern"
+        QK_NORM                      = "{arch}.attention.qk_norm"
 
     class Rope:
         DIMENSION_COUNT         = "{arch}.rope.dimension_count"

gguf-py/gguf/gguf_writer.py

@@ -792,6 +792,9 @@ def add_group_norm_groups(self, value: int) -> None:
     def add_causal_attention(self, value: bool) -> None:
         self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
 
+    def add_qk_norm(self, value: bool) -> None:
+        self.add_bool(Keys.Attention.QK_NORM.format(arch=self.arch), value)
+
     def add_q_lora_rank(self, length: int) -> None:
         self.add_uint32(Keys.Attention.Q_LORA_RANK.format(arch=self.arch), length)
 

src/llama-arch.cpp

@@ -1672,7 +1672,6 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
             { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
             { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { 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_SHEXP,  "blk.%d.ffn_gate_shexp" },

src/llama-graph.cpp

@@ -705,13 +705,6 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
             ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
     cb(weights, "ffn_moe_weights", il);
 
-    if (arch == LLM_ARCH_HUNYUAN_MOE) {
-        weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_expert_used, n_tokens]
-        weights = ggml_div(ctx0, weights, ggml_sum_rows(ctx0, weights)); // [1, n_tokens]
-        weights = ggml_reshape_3d(ctx0, weights, 1, n_expert_used, n_tokens); // [1, n_expert_used, n_tokens]
-        cb(weights, "ffn_moe_weights_scaled", il);
-    }
-
     if (norm_w) {
         weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens);
 

src/llama-model.cpp

@@ -14437,8 +14437,10 @@ struct llm_build_hunyuan_moe : public llm_graph_context {
                     model.layers[il].ffn_down_exps,
                     nullptr,
                     n_expert, n_expert_used,
-                    LLM_FFN_SILU, false,
-                    false, 0.0,
+                    LLM_FFN_SILU,
+                    true, // norm_topk_prob
+                    false,
+                    0.0,
                     LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
                     il);
             cb(cur_moe, "ffn_moe_out", il);