convert-new.py : output gguf (#2635)

* convert-new.py : output gguf (WIP)

* convert-new.py : add gguf key-value pairs

* llama : add hparams.ctx_train + no longer print ftype

* convert-new.py : minor fixes

* convert-new.py : vocab-only option should work now

* llama : fix tokenizer to use llama_char_to_byte

* tests : add new ggml-vocab-llama.gguf

* convert-new.py : tensor name mapping

* convert-new.py : add map for skipping tensor serialization

* convert-new.py : convert script now works

* gguf.py : pick some of the refactoring from #2644

* convert-new.py : minor fixes

convert-llama-7b-pth-to-gguf.py

@@ -298,7 +298,7 @@ for part_name in part_names:
 
         print( name + ", shape " + str(len(data.shape)) + ", " + str(old_dtype) + " --> " + str(data.dtype))
 
-        gguf_writer.write_tensor_to_file(data)
+        gguf_writer.write_tensor_data(data)
 
 gguf_writer.close()
 

convert-new.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python
 
+import gguf
 import argparse
 import concurrent.futures
 import copy
@@ -33,6 +34,13 @@ if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'):
 
 NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
 
+ARCH=gguf.MODEL_ARCH.LLAMA
+NAMES=gguf.MODEL_TENSOR_NAMES[ARCH]
+
+#
+# data types
+#
+
 @dataclass(frozen=True)
 class UnquantizedDataType:
     name: str
@@ -44,14 +52,6 @@ DT_BF16 = UnquantizedDataType('BF16')
 
 DataType = Union[UnquantizedDataType]
 
-DATA_TYPE_TO_FTYPE: Dict[DataType, int] = {
-    DT_F32: 0,
-    DT_F16: 1,
-}
-
-FTYPE_TO_DATA_TYPE: Dict[int, DataType] = \
-    {ftype: dtype for (dtype, ftype) in DATA_TYPE_TO_FTYPE.items()}
-
 DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = {
     DT_BF16: np.dtype(np.uint16),
     DT_F16:  np.dtype(np.float16),
@@ -62,6 +62,13 @@ DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = {
 NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \
     {dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()}
 
+SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
+    'BF16': DT_BF16,
+    'F16': DT_F16,
+    'F32': DT_F32,
+    'I32': DT_I32,
+}
+
 class GGMLFileType(enum.Enum):
     AllF32    = 0
     MostlyF16 = 1  # except 1d tensors
@@ -77,48 +84,31 @@ class GGMLFileType(enum.Enum):
         else:
             raise ValueError(self)
 
-# TODO: this is LLaMA specific
-def make_tensors_list() -> List[str]:
-    ret = [
-        'tok_embeddings.weight',
-        'norm.weight',
-        'output.weight',
-    ]
-    for i in range(80):  # maximum number of layer
-        ret += [
-            f'layers.{i}.attention.wq.weight',
-            f'layers.{i}.attention.wk.weight',
-            f'layers.{i}.attention.wv.weight',
-            f'layers.{i}.attention.wo.weight',
-            f'layers.{i}.attention_norm.weight',
-            f'layers.{i}.feed_forward.w1.weight',
-            f'layers.{i}.feed_forward.w2.weight',
-            f'layers.{i}.feed_forward.w3.weight',
-            f'layers.{i}.ffn_norm.weight',
-        ]
-    return ret
-
-# TODO: this should be generalized for non-LLaMA models
-TENSORS_LIST = make_tensors_list()
-TENSORS_SET = set(TENSORS_LIST)
-
-def find_n_mult(n_ff: int, n_embd: int) -> int:
-    # hardcoded magic range
-    for n_mult in range(8192, 1, -1):
-        calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
-        if calc_ff == n_ff:
-            return n_mult
-    raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).")
 
+#
+# hparams loading
+#
 
 @dataclass
 class Params:
-    n_vocab:   int
+    n_vocab:    int
-    n_embd:    int
+    n_embd:     int
-    n_mult:    int
+    n_mult:     int
-    n_head:    int
+    n_layer:    int
-    n_layer:   int
+    n_ctx:      int
-    n_kv_head: Optional[int]  # This parameter is only used for Llama 2
+    n_ff:       int
+    n_head:     int
+    n_head_kv:  int
+    f_norm_eps: float
+
+    @staticmethod
+    def find_n_mult(n_ff: int, n_embd: int) -> int:
+        # hardcoded magic range
+        for n_mult in range(8192, 1, -1):
+            calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
+            if calc_ff == n_ff:
+                return n_mult
+        raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).")
 
     @staticmethod
     def guessed(model: 'LazyModel') -> 'Params':
@@ -137,37 +127,57 @@ class Params:
             raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
                             "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
 
-        n_head=n_embd // 128 # guessed
+        n_head = n_embd // 128 # guessed
+        n_mult = 256           # guessed
+
+        # TODO: verify this
+        n_ff = int(2 * (4 * n_embd) / 3)
+        n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult)
 
         return Params(
-            n_vocab   = n_vocab,
+            n_vocab    = n_vocab,
-            n_embd    = n_embd,
+            n_embd     = n_embd,
-            n_mult    = 256,
+            n_mult     = n_mult,
-            n_head    = n_head,
+            n_layer    = n_layer,
-            n_layer   = n_layer,
+            n_ctx      = -1,
-            n_kv_head = None,
+            n_ff       = n_ff,
+            n_head     = n_head,
+            n_head_kv  = n_head,
+            f_norm_eps = 1e-5,
         )
 
     @staticmethod
     def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
         config = json.load(open(config_path))
 
-        n_vocab   = config["vocab_size"];
+        n_vocab    = config["vocab_size"];
-        n_embd    = config["hidden_size"];
+        n_embd     = config["hidden_size"];
-        n_head    = config["num_attention_heads"];
+        n_layer    = config["num_hidden_layers"];
-        n_layer   = config["num_hidden_layers"];
+        n_ff       = config["intermediate_size"];
-        n_ff      = config["intermediate_size"];
+        n_head     = config["num_attention_heads"];
-        n_kv_head = config.get("num_key_value_heads")
+        n_head_kv  = config["num_key_value_heads"];
+        f_norm_eps = config["rms_norm_eps"];
 
-        n_mult = find_n_mult(n_ff, n_embd);
+        n_mult = Params.find_n_mult(n_ff, n_embd);
+
+        if "max_sequence_length" in config:
+            n_ctx = config["max_sequence_length"]
+        elif "max_position_embeddings" in config:
+            n_ctx = config["max_position_embeddings"]
+        else:
+            raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
+                            "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
 
         return Params(
-            n_vocab   = n_vocab,
+            n_vocab    = n_vocab,
-            n_embd    = n_embd,
+            n_embd     = n_embd,
-            n_mult    = n_mult,
+            n_mult     = n_mult,
-            n_head    = n_head,
+            n_layer    = n_layer,
-            n_layer   = n_layer,
+            n_ctx      = n_ctx,
-            n_kv_head = n_kv_head,
+            n_ff       = n_ff,
+            n_head     = n_head,
+            n_head_kv  = n_head_kv,
+            f_norm_eps = f_norm_eps,
         )
 
     # LLaMA v2 70B params.json
@@ -176,22 +186,32 @@ class Params:
     def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
         config = json.load(open(config_path))
 
-        n_vocab = config["vocab_size"];
+        n_vocab    = config["vocab_size"];
-        n_embd  = config["dim"];
+        n_embd     = config["dim"];
-        n_head  = config["n_heads"];
+        n_layer    = config["n_layers"];
-        n_layer = config["n_layers"];
+        n_mult     = config["multiple_of"];
-        n_mult  = config["multiple_of"];
+        n_ctx      = 2048 if config["norm_eps"] == 1e-06 else 4096 # hack to determine LLaMA v1 vs v2
+        n_ff       = -1;
+        n_head     = config["n_heads"];
+        n_head_kv  = config["n_kv_heads"] if "n_kv_heads" in config else n_head;
+        f_norm_eps = config["norm_eps"];
 
         if n_vocab == -1:
             n_vocab = model["tok_embeddings.weight"].shape[0]
 
+        if n_ff == -1:
+            n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]
+
         return Params(
-            n_vocab   = n_vocab,
+            n_vocab    = n_vocab,
-            n_embd    = n_embd,
+            n_embd     = n_embd,
-            n_mult    = n_mult,
+            n_mult     = n_mult,
-            n_head    = n_head,
+            n_layer    = n_layer,
-            n_layer   = n_layer,
+            n_ctx      = n_ctx,
-            n_kv_head = None,
+            n_ff       = n_ff,
+            n_head     = n_head,
+            n_head_kv  = n_head_kv,
+            f_norm_eps = f_norm_eps,
         )
 
     @staticmethod
@@ -206,10 +226,13 @@ class Params:
         else:
             params = Params.guessed(model_plus.model)
 
-        print(f'params: n_vocab:{params.n_vocab} n_embd:{params.n_embd} n_mult:{params.n_mult} n_head:{params.n_head} n_layer:{params.n_layer}')
         return params
 
 
+#
+# vocab
+#
+
 class BpeVocab:
     def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None:
         self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
@@ -294,13 +317,17 @@ class SentencePieceVocab:
     def __repr__(self) -> str:
         return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
 
-
 Vocab = Union[BpeVocab, SentencePieceVocab]
 
 
-def permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
+#
-    if n_kv_head is not None and n_head != n_kv_head:
+# data loading
-        n_head //= n_kv_head
+# TODO: reuse (probably move to gguf.py?)
+#
+
+def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
+    if n_head_kv is not None and n_head != n_head_kv:
+        n_head //= n_head_kv
     return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
                 .swapaxes(1, 2)
                 .reshape(weights.shape))
@@ -312,7 +339,7 @@ class Tensor(metaclass=ABCMeta):
     @abstractmethod
     def astype(self, data_type: DataType) -> 'Tensor': ...
     @abstractmethod
-    def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'Tensor': ...
+    def permute(self, n_head: int, n_head_kv: int) -> 'Tensor': ...
     @abstractmethod
     def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
     @abstractmethod
@@ -350,8 +377,8 @@ class UnquantizedTensor(Tensor):
         r = self.ndarray.shape[0] // 3
         return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
 
-    def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'UnquantizedTensor':
+    def permute(self, n_head: int, n_head_kv: int) -> 'UnquantizedTensor':
-        return UnquantizedTensor(permute(self.ndarray, n_head, n_kv_head))
+        return UnquantizedTensor(permute(self.ndarray, n_head, n_head_kv))
 
 
 def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray:
@@ -374,18 +401,18 @@ GGMLCompatibleTensor = Union[UnquantizedTensor]
 
 
 class DeferredPermutedTensor(Tensor):
-    def __init__(self, base: Tensor, n_head: int, n_kv_head: Optional[int] = None) -> None:
+    def __init__(self, base: Tensor, n_head: int, n_head_kv: int) -> None:
         self.base = base
         self.n_head = n_head
         self.data_type = self.base.data_type
 
     def astype(self, data_type: DataType) -> Tensor:
-        return self.base.astype(data_type).permute(self.n_head, self.n_kv_head)
+        return self.base.astype(data_type).permute(self.n_head, self.n_head_kv)
 
     def to_ggml(self) -> GGMLCompatibleTensor:
-        return self.base.to_ggml().permute(self.n_head, self.n_kv_head)
+        return self.base.to_ggml().permute(self.n_head, self.n_head_kv)
 
-    def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor:
+    def permute(self, n_head: int, n_head_kv: int) -> Tensor:
         raise Exception("shouldn't permute twice")
 
 
@@ -481,10 +508,10 @@ def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus:
     return ModelPlus(model, paths, format, vocab)
 
 
-def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_kv_head: Optional[int] = None) -> LazyTensor:
+def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor:
     def load() -> Tensor:
-        return lazy_tensor.load().permute(n_head, n_kv_head)
+        return lazy_tensor.load().permute(n_head, n_head_kv)
-    return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_kv_head}) ' + lazy_tensor.description)
+    return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)
 
 def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor:
     def load() -> Tensor:
@@ -500,34 +527,6 @@ def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
     s[0] = s[0] // 3
     return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description)
 
-def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel:
-    out: LazyModel = {}
-    out["tok_embeddings.weight"] = model["model.embed_tokens.weight"]
-    out["norm.weight"]           = model["model.norm.weight"]
-    out["output.weight"]         = model["lm_head.weight"]
-
-    for i in itertools.count():
-        if f"model.layers.{i}.self_attn.q_proj.weight" in model:
-            out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head)
-            out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_kv_head)
-            out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
-        elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
-            out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head)
-            out[f"layers.{i}.attention.wk.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head)
-            out[f"layers.{i}.attention.wv.weight"] = part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
-        else:
-            break
-
-        out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"]
-
-        out[f"layers.{i}.feed_forward.w1.weight"] = model[f"model.layers.{i}.mlp.gate_proj.weight"]
-        out[f"layers.{i}.feed_forward.w2.weight"] = model[f"model.layers.{i}.mlp.down_proj.weight"]
-        out[f"layers.{i}.feed_forward.w3.weight"] = model[f"model.layers.{i}.mlp.up_proj.weight"]
-
-        out[f"layers.{i}.attention_norm.weight"] = model[f"model.layers.{i}.input_layernorm.weight"]
-        out[f"layers.{i}.ffn_norm.weight"]       = model[f"model.layers.{i}.post_attention_layernorm.weight"]
-    return out
-
 
 # Functionality that simulates `torch.load` but where individual tensors are
 # only loaded into memory on demand, not all at once.
@@ -621,14 +620,6 @@ def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus:
     return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None)
 
 
-SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
-    'BF16': DT_BF16,
-    'F16': DT_F16,
-    'F32': DT_F32,
-    'I32': DT_I32,
-}
-
-
 def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
     header_size, = struct.unpack('<Q', fp.read(8))
     header: Dict[str, Dict[str, Any]] = json.loads(fp.read(header_size))
@@ -678,7 +669,6 @@ def lazy_load_file(path: Path) -> ModelPlus:
 In = TypeVar('In')
 Out = TypeVar('Out')
 
-
 def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int) -> Iterable[Out]:
     '''Parallel map, but with backpressure.  If the caller doesn't call `next`
     fast enough, this will stop calling `func` at some point rather than
@@ -715,88 +705,133 @@ def check_vocab_size(params: Params, vocab: Vocab) -> None:
 
 class OutputFile:
     def __init__(self, fname_out: Path) -> None:
-        self.fout = open(fname_out, "wb")
+        self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
 
-    def write_file_header(self, params: Params, file_type: GGMLFileType) -> None:
+    def add_meta_arch(self, params: Params) -> None:
-        self.fout.write(b"ggjt"[::-1])  # magic
+        self.gguf.add_context_length      (params.n_ctx)
-        values = [
+        self.gguf.add_embedding_length    (params.n_embd)
-            1,  # file version
+        self.gguf.add_block_count         (params.n_layer)
-            params.n_vocab,
+        self.gguf.add_feed_forward_length (params.n_ff)
-            params.n_embd,
+        self.gguf.add_rope_dimension_count(params.n_embd // params.n_head)
-            params.n_mult,
+        self.gguf.add_head_count          (params.n_head)
-            params.n_head,
+        self.gguf.add_head_count_kv       (params.n_head_kv)
-            params.n_layer,
+        self.gguf.add_layer_norm_rms_eps  (params.f_norm_eps)
-            params.n_embd // params.n_head,  # rot (obsolete)
-            file_type.value,
-        ]
-        self.fout.write(struct.pack("i" * len(values), *values))
 
-    def write_tensor_header(self, name: str, shape: Sequence[int], data_type: DataType) -> None:
+    def add_meta_vocab(self, vocab: Vocab) -> None:
-        sname = name.encode('utf-8')
+        tokens = []
-        self.fout.write(struct.pack("iii", len(shape), len(sname), DATA_TYPE_TO_FTYPE[data_type]))
+        scores = []
-        self.fout.write(struct.pack("i" * len(shape), *shape[::-1]))
-        self.fout.write(sname)
-        self.fout.seek((self.fout.tell() + 31) & -32)
-
-    def write_vocab(self, vocab: Vocab) -> None:
         for text, score in vocab.all_tokens():
-            self.fout.write(struct.pack("i", len(text)))
+            tokens.append(text)
-            self.fout.write(text)
+            scores.append(score)
-            self.fout.write(struct.pack("f", score))
+
+        self.gguf.add_tokenizer_model("llama")
+        self.gguf.add_token_list(tokens)
+        self.gguf.add_token_scores(scores)
+        #self.gguf.add_token_types(toktypes) # TODO: add this
+
+        # TODO: added / special tokens
+
+    def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:
+        n_elements = 1
+        for dim in tensor.shape:
+            n_elements *= dim
+        data_type = DATA_TYPE_TO_NUMPY[tensor.data_type]
+        data_nbytes = n_elements * data_type.itemsize
+        self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes)
+
+    def write_meta(self) -> None:
+        self.gguf.write_header_to_file()
+        self.gguf.write_kv_data_to_file()
+
+    def write_tensor_info(self) -> None:
+        self.gguf.write_ti_data_to_file()
+
+    def close(self) -> None:
+        self.gguf.close()
 
     @staticmethod
-    def write_vocab_only(fname_out: Path, vocab: Vocab) -> None:
+    def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab) -> None:
-        of = OutputFile(fname_out)
-        params = Params(n_vocab=vocab.vocab_size, n_embd=0, n_mult=0, n_head=1, n_layer=0)
-        of = OutputFile(fname_out)
-        of.write_file_header(params, file_type=GGMLFileType.AllF32)
-        of.write_vocab(vocab)
-        of.fout.close()
-
-    @staticmethod
-    def write_all(fname_out: Path, params: Params, file_type: GGMLFileType, model: LazyModel, vocab: Vocab) -> None:
         check_vocab_size(params, vocab)
+
         of = OutputFile(fname_out)
-        of.write_file_header(params, file_type)
+
-        print("Writing vocab...")
+        # meta data
-        of.write_vocab(vocab)
+        of.add_meta_arch(params)
+        of.add_meta_vocab(vocab)
+        of.write_meta()
+
+        of.close()
+
+    @staticmethod
+    def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None:
+        check_vocab_size(params, vocab)
+
+        of = OutputFile(fname_out)
+
+        # meta data
+        of.add_meta_arch(params)
+        of.add_meta_vocab(vocab)
+
+        # tensor info
+        for name, lazy_tensor in model.items():
+            of.add_tensor_info(name, lazy_tensor)
+
+        of.write_meta()
+        of.write_tensor_info()
 
         def do_item(item: Tuple[str, LazyTensor]) -> NDArray:
             name, lazy_tensor = item
             return lazy_tensor.load().to_ggml().ndarray
 
+        # tensor data
         ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8)
         for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
             size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
             padi = len(str(len(model)))
             print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}")
-            of.write_tensor_header(name, lazy_tensor.shape, lazy_tensor.data_type)
+            of.gguf.write_tensor_data(ndarray)
-            ndarray.tofile(of.fout)
-        of.fout.close()
 
+        of.close()
 
 def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFileType:
-    wq_type = model["layers.0.attention.wq.weight"].data_type
+    wq_type = model[NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0)+".weight"].data_type
-    if output_type_str == "f32" or (output_type_str is None and wq_type in (DT_F32, DT_BF16)):
+
+    if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32):
         return GGMLFileType.AllF32
-    if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16):
+    if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
         return GGMLFileType.MostlyF16
+
     name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}
+
     raise Exception(f"Unexpected combination of types: {name_to_type}")
 
-
-def do_necessary_conversions(model: LazyModel, params: Params) -> LazyModel:
-    if "lm_head.weight" in model:
-        model = convert_transformers_to_orig(model, params)
-    model = filter_and_sort_tensors(model)
-
-    return model
-
-
 def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
     return {name: tensor.astype(output_type.type_for_tensor(name, tensor))
             for (name, tensor) in model.items()}
 
+def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
+    tmap = gguf.get_tensor_name_map(ARCH, params.n_layer)
+
+    out: LazyModel = {}
+    for name, lazy_tensor in model.items():
+        name_new = name
+
+        if name in tmap:
+            name_new = tmap[name]
+        elif name.endswith(".weight") and name[:-7] in tmap:
+            name_new = tmap[name[:-7]] + ".weight"
+        elif name.endswith(".bias") and name[:-5] in tmap:
+            name_new = tmap[name[:-5]] + ".bias"
+        else:
+            raise Exception(f"Unexpected tensor name: {name}")
+
+        if gguf.should_skip_tensor(ARCH, params.n_layer, name_new):
+            print(f"skipping tensor {name_new}")
+        else:
+            print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type} | {lazy_tensor.shape}")
+            out[name_new] = lazy_tensor
+
+    return out
 
 def nth_multifile_path(path: Path, n: int) -> Optional[Path]:
     '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return
@@ -847,11 +882,6 @@ def load_some_model(path: Path) -> ModelPlus:
             # Try the PyTorch patterns too, with lower priority
             globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"]
             files = [file for glob in globs for file in path.glob(glob)]
-        if not files:
-            # Try GGML too, but with lower priority, since if both a non-GGML
-            # model and a GGML model exist in the same directory, we assume the
-            # latter was converted from the former.
-            files = list(path.glob("ggml-model*.bin*"))
         if not files:
             raise Exception(f"Can't find model in directory {path}")
         if len(files) > 1:
@@ -868,12 +898,7 @@ def load_some_model(path: Path) -> ModelPlus:
     return model_plus
 
 
-def filter_and_sort_tensors(model: LazyModel) -> LazyModel:
-    return {name: model[name] for name in TENSORS_LIST if name in model}
-
-
 def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, SentencePieceVocab]:
-    print(f"vocabtype: {vocabtype}")
     # Be extra-friendly and accept either a file or a directory.  Also, if it's
     # a directory, it might be the model directory, and tokenizer.model might
     # be in the parent of that.
@@ -892,8 +917,10 @@ def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, Sentence
             raise FileNotFoundError(
                 f"Could not find tokenizer.model in {path} or its parent; "
                 "if it's in another directory, pass the directory as --vocab-dir")
+
+    print(f"Loading vocab file '{path}', type '{vocabtype}'")
+
     added_tokens_path = path.parent / "added_tokens.json"
-    print(f"Loading vocab file {path}")
     if vocabtype == "bpe":
         return BpeVocab(path, added_tokens_path if added_tokens_path.exists() else None)
     elif vocabtype == "spm":
@@ -933,38 +960,52 @@ def main(args_in: Optional[List[str]] = None) -> None:
     parser.add_argument("--vocab-dir",   type=Path,              help="directory containing tokenizer.model, if separate from model file")
     parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
     parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
-    parser.add_argument("--vocabtype",   choices=["spm", "bpe"], help="vocab format (default: spm)")
+    parser.add_argument("--vocabtype",   choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
+    parser.add_argument("--ctx",         type=int,               help="model training context (default: based on input)")
     args = parser.parse_args(args_in)
 
-    vocab: Vocab
     if args.dump_single:
         model_plus = lazy_load_file(args.model)
         do_dump_model(model_plus)
-    elif args.vocab_only:
+
+    model_plus = load_some_model(args.model)
+
+    params = Params.load(model_plus)
+    if params.n_ctx == -1:
+        if args.ctx is None:
+            raise Exception("The model doesn't have a context size, and you didn't specify one with --ctx\n"
+                            "Please specify one with --ctx:\n"
+                            " - LLaMA v1: --ctx 2048\n"
+                            " - LLaMA v2: --ctx 4096\n")
+        params.n_ctx = args.ctx
+
+    print(f"params = {params}")
+
+    vocab: Vocab
+    if args.vocab_only:
         vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)
         assert args.outfile, "need --outfile if using --vocab-only"
         outfile = args.outfile
-        OutputFile.write_vocab_only(outfile, vocab)
+        OutputFile.write_vocab_only(outfile, params, vocab)
         print(f"Wrote {outfile}")
     else:
-        model_plus = load_some_model(args.model)
         if args.dump:
             do_dump_model(model_plus)
             return
+
         if model_plus.vocab is not None and args.vocab_dir is None:
             vocab = model_plus.vocab
         else:
             vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
             vocab = load_vocab(vocab_dir, args.vocabtype)
 
-        params      = Params.load(model_plus)
         model       = model_plus.model
-        model       = do_necessary_conversions(model, params)
+        model       = convert_model_names(model, params)
         output_type = pick_output_type(model, args.outtype)
         model       = convert_to_output_type(model, output_type)
         outfile     = args.outfile or default_outfile(model_plus.paths, output_type)
 
-        OutputFile.write_all(outfile, params, output_type, model, vocab)
+        OutputFile.write_all(outfile, params, model, vocab)
         print(f"Wrote {outfile}")
 
 

gguf.py

@@ -8,7 +8,7 @@ import sys
 import struct
 import numpy as np
 
-from enum import IntEnum
+from enum import IntEnum, auto
 from typing import Any, IO, List
 
 #
@@ -33,24 +33,24 @@ KEY_GENERAL_SOURCE_URL           = "general.source.url"
 KEY_GENERAL_SOURCE_HF_REPO       = "general.source.hugginface.repository"
 
 # LLM
-KEY_LLM_CONTEXT_LENGTH           = "{llm}.context_length"
+KEY_LLM_CONTEXT_LENGTH           = "{arch}.context_length"
-KEY_LLM_EMBEDDING_LENGTH         = "{llm}.embedding_length"
+KEY_LLM_EMBEDDING_LENGTH         = "{arch}.embedding_length"
-KEY_LLM_BLOCK_COUNT              = "{llm}.block_count"
+KEY_LLM_BLOCK_COUNT              = "{arch}.block_count"
-KEY_LLM_FEED_FORWARD_LENGTH      = "{llm}.feed_forward_length"
+KEY_LLM_FEED_FORWARD_LENGTH      = "{arch}.feed_forward_length"
-KEY_LLM_USE_PARALLEL_RESIDUAL    = "{llm}.use_parallel_residual"
+KEY_LLM_USE_PARALLEL_RESIDUAL    = "{arch}.use_parallel_residual"
-KEY_LLM_TENSOR_DATA_LAYOUT       = "{llm}.tensor_data_layout"
+KEY_LLM_TENSOR_DATA_LAYOUT       = "{arch}.tensor_data_layout"
 
 # attention
-KEY_ATTENTION_HEAD_COUNT         = "{llm}.attention.head_count"
+KEY_ATTENTION_HEAD_COUNT         = "{arch}.attention.head_count"
-KEY_ATTENTION_HEAD_COUNT_KV      = "{llm}.attention.head_count_kv"
+KEY_ATTENTION_HEAD_COUNT_KV      = "{arch}.attention.head_count_kv"
-KEY_ATTENTION_MAX_ALIBI_BIAS     = "{llm}.attention.max_alibi_bias"
+KEY_ATTENTION_MAX_ALIBI_BIAS     = "{arch}.attention.max_alibi_bias"
-KEY_ATTENTION_CLAMP_KQV          = "{llm}.attention.clamp_kqv"
+KEY_ATTENTION_CLAMP_KQV          = "{arch}.attention.clamp_kqv"
-KEY_ATTENTION_LAYERNORM_EPS      = "{llm}.attention.layer_norm_epsilon"
+KEY_ATTENTION_LAYERNORM_EPS      = "{arch}.attention.layer_norm_epsilon"
-KEY_ATTENTION_LAYERNORM_RMS_EPS  = "{llm}.attention.layer_norm_rms_epsilon"
+KEY_ATTENTION_LAYERNORM_RMS_EPS  = "{arch}.attention.layer_norm_rms_epsilon"
 
 # RoPE
-KEY_ROPE_DIMENSION_COUNT         = "{llm}.rope.dimension_count"
+KEY_ROPE_DIMENSION_COUNT         = "{arch}.rope.dimension_count"
-KEY_ROPE_SCALE                   = "{llm}.rope.scale"
+KEY_ROPE_SCALE                   = "{arch}.rope.scale"
 
 # tokenization
 KEY_TOKENIZER_MODEL      = "tokenizer.ggml.model"
@@ -70,34 +70,137 @@ KEY_TOKENIZER_RWKV       = "tokenizer.rwkv.world"
 # recommended mapping of model tensor names for storage in gguf
 #
 
-def get_tensor_name_map(n_blocks : int):
+class MODEL_ARCH(IntEnum):
+    LLAMA   = auto()
+    FALCON  = auto()
+    GPT2    = auto()
+    GPTJ    = auto()
+    GPTNEOX = auto()
+    MPT     = auto()
+
+class MODEL_TENSOR(IntEnum):
+    TOKEN_EMBD        = auto()
+    POS_EMBD          = auto()
+    OUTPUT            = auto()
+    OUTPUT_NORM       = auto()
+    ROPE_FREQS        = auto()
+    ATTN_Q            = auto()
+    ATTN_K            = auto()
+    ATTN_V            = auto()
+    ATTN_QKV          = auto()
+    ATTN_OUT          = auto()
+    ATTN_NORM         = auto()
+    ATTN_NORM_2       = auto()
+    ATTN_ROT_EMBD     = auto()
+    FFN_GATE          = auto()
+    FFN_DOWN          = auto()
+    FFN_UP            = auto()
+    FFN_NORM          = auto()
+
+MODEL_ARCH_NAMES = {
+    MODEL_ARCH.LLAMA   : "llama",
+    MODEL_ARCH.FALCON  : "falcon",
+    MODEL_ARCH.GPT2    : "gpt2",
+    MODEL_ARCH.GPTJ    : "gptj",
+    MODEL_ARCH.GPTNEOX : "gptneox",
+    MODEL_ARCH.MPT     : "mpt",
+    }
+
+MODEL_TENSOR_NAMES = {
+    MODEL_ARCH.LLAMA  : {
+        MODEL_TENSOR.TOKEN_EMBD        : "token_embd",
+        MODEL_TENSOR.OUTPUT_NORM       : "output_norm",
+        MODEL_TENSOR.OUTPUT            : "output",
+        MODEL_TENSOR.ROPE_FREQS        : "rope_freqs",
+        MODEL_TENSOR.ATTN_NORM         : "blk.{bid}.attn_norm",
+        MODEL_TENSOR.ATTN_Q            : "blk.{bid}.attn_q",
+        MODEL_TENSOR.ATTN_K            : "blk.{bid}.attn_k",
+        MODEL_TENSOR.ATTN_V            : "blk.{bid}.attn_v",
+        MODEL_TENSOR.ATTN_OUT          : "blk.{bid}.attn_output",
+        MODEL_TENSOR.ATTN_ROT_EMBD     : "blk.{bid}.attn_rot_embd",
+        MODEL_TENSOR.FFN_NORM          : "blk.{bid}.ffn_norm",
+        MODEL_TENSOR.FFN_GATE          : "blk.{bid}.ffn_gate",
+        MODEL_TENSOR.FFN_DOWN          : "blk.{bid}.ffn_down",
+        MODEL_TENSOR.FFN_UP            : "blk.{bid}.ffn_up",
+        },
+    MODEL_ARCH.FALCON : {
+        MODEL_TENSOR.TOKEN_EMBD  : "token_embd",
+        MODEL_TENSOR.OUTPUT_NORM : "output_norm",
+        MODEL_TENSOR.OUTPUT      : "output",
+        MODEL_TENSOR.ATTN_NORM   : "blk.{bid}.attn_norm",
+        MODEL_TENSOR.ATTN_NORM_2 : "blk.{bid}.attn_norm_2",
+        MODEL_TENSOR.ATTN_QKV    : "blk.{bid}.attn_qkv",
+        MODEL_TENSOR.ATTN_OUT    : "blk.{bid}.attn_output",
+        MODEL_TENSOR.FFN_DOWN    : "blk.{bid}.ffn_down",
+        MODEL_TENSOR.FFN_UP      : "blk.{bid}.ffn_up",
+        },
+    MODEL_ARCH.GPT2 : {
+        # TODO
+        },
+    # TODO
+    }
+
+# tensors that will not be serialized
+MODEL_TENSOR_SKIP = {
+    MODEL_ARCH.LLAMA : [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+        ],
+    }
+
+def should_skip_tensor(arch : MODEL_ARCH, n_blocks : int, name : str) -> bool:
+    for skip in MODEL_TENSOR_SKIP.get(arch, []):
+        for i in range(n_blocks):
+            if name == MODEL_TENSOR_NAMES[arch][skip].format(bid=i):
+                return True
+
+    return False
+
+def get_tensor_name_map(arch : MODEL_ARCH, n_blocks : int) -> dict:
     tensor_map = {}
+
     # Token embeddings
-    mapped_to = "token_embd"
+    mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.TOKEN_EMBD, None)
+
     tensor_map["gpt_neox.embed_in"]           = mapped_to # gptneox
     tensor_map["transformer.wte"]             = mapped_to # gpt2 mpt
     tensor_map["transformer.word_embeddings"] = mapped_to # falcon
     tensor_map["model.embed_tokens"]          = mapped_to # llama-hf
     tensor_map["tok_embeddings"]              = mapped_to # llama-pth
+
     # Position embeddings
-    mapped_to = "pos_embd"
+    mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.POS_EMBD, None)
+
     tensor_map["transformer.wpe"] = mapped_to # gpt2
+
+    # Output
+    mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT, None)
+
+    tensor_map["embed_out"] = mapped_to # gptneox
+    tensor_map["lm_head"]   = mapped_to # gpt2 mpt falcon llama-hf
+    tensor_map["output"]    = mapped_to # llama-pth
+
     # Output norm
-    mapped_to = "output_norm"
+    mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT_NORM, None)
+
     tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox
     tensor_map["transformer.ln_f"]          = mapped_to # gpt2 falcon
     tensor_map["transformer.norm_f"]        = mapped_to # mpt
     tensor_map["model.norm"]                = mapped_to # llama-hf
     tensor_map["norm"]                      = mapped_to # llama-pth
-    # Output
+
-    mapped_to = "output"
+    # Rope frequencies
-    tensor_map["embed_out"] = mapped_to # gptneox
+    mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ROPE_FREQS, None)
-    tensor_map["lm_head"]   = mapped_to # gpt2 mpt falcon llama-hf
+
-    tensor_map["output"]    = mapped_to # llama-pth
+    tensor_map["rope.freqs"] = mapped_to # llama-pth
-    # Attention and fee-forward layer blocks
+
+    # Attention and feed-forward blocks
     for i in range(0,n_blocks):
         # Attention norm
-        mapped_to = "blk."+str(i)+".attn_norm"
+        # TODO: is there are simpler way to write these 2 lines in Python?
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to else None
+
         tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox
         tensor_map["transformer.h."+str(i)+".ln_1"]              = mapped_to # gpt2
         tensor_map["transformer.blocks."+str(i)+".norm_1"]       = mapped_to # mpt
@@ -105,56 +208,93 @@ def get_tensor_name_map(n_blocks : int):
         tensor_map["transformer.h."+str(i)+".ln_attn"]           = mapped_to # falcon40b
         tensor_map["model.layers."+str(i)+".input_layernorm"]    = mapped_to # llama-hf
         tensor_map["layers."+str(i)+".attention_norm"]           = mapped_to # llama-pth
+
         # Attention norm 2
-        mapped_to = "blk."+str(i)+".attn_norm_2"
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM_2, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
+
         tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b
+
         # Attention query-key-value
-        mapped_to = "blk."+str(i)+".attn_qkv"
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_QKV, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
+
         tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"]    = mapped_to # gptneox
         tensor_map["transformer.h."+str(i)+".attn.c_attn"]                    = mapped_to # gpt2
         tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"]                 = mapped_to # mpt
         tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to # falcon
+
         # Attention query
-        mapped_to = "blk."+str(i)+".attn_q"
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_Q, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
+
         tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf
         tensor_map["layers."+str(i)+".attention.wq"]           = mapped_to # llama-pth
+
         # Attention key
-        mapped_to = "blk."+str(i)+".attn_k"
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_K, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
+
         tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf
         tensor_map["layers."+str(i)+".attention.wk"]           = mapped_to # llama-pth
+
         # Attention value
-        mapped_to = "blk."+str(i)+".attn_v"
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_V, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
+
         tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf
         tensor_map["layers."+str(i)+".attention.wv"]           = mapped_to # llama-pth
+
         # Attention output
-        mapped_to = "blk."+str(i)+".attn_output"
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_OUT, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
+
         tensor_map["gpt_neox.layers."+str(i)+".attention.dense"]    = mapped_to # gptneox
         tensor_map["transformer.h."+str(i)+".attn.c_proj"]          = mapped_to # gpt2
         tensor_map["transformer.blocks."+str(i)+".attn.out_proj"]   = mapped_to # mpt
         tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to # falcon
         tensor_map["model.layers."+str(i)+".self_attn.o_proj"]      = mapped_to # llama-hf
         tensor_map["layers."+str(i)+".attention.wo"]                = mapped_to # llama-pth
+
+        # Rotary embeddings
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_ROT_EMBD, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
+
+        tensor_map["model.layers."+str(i)+".self_attn.rotary_emb.inv_freq"]  = mapped_to # llama-hf
+        tensor_map["layers."+str(i)+".attention.inner_attention.rope.freqs"] = mapped_to # llama-pth
+
         # Feed-forward norm
-        mapped_to = "blk."+str(i)+".ffn_norm"
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_NORM, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
+
         tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox
         tensor_map["transformer.h."+str(i)+".ln_2"]                       = mapped_to # gpt2
         tensor_map["transformer.blocks."+str(i)+".norm_2"]                = mapped_to # mpt
         tensor_map["model.layers."+str(i)+".post_attention_layernorm"]    = mapped_to # llama-hf
         tensor_map["layers."+str(i)+".ffn_norm"]                          = mapped_to # llama-pth
+
         # Feed-forward up
-        mapped_to = "blk."+str(i)+".ffn_up"
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_UP, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
+
         tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox
         tensor_map["transformer.h."+str(i)+".mlp.c_fc"]            = mapped_to # gpt2
         tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"]    = mapped_to # mpt
         tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"]   = mapped_to # falcon
         tensor_map["model.layers."+str(i)+".mlp.up_proj"]          = mapped_to # llama-hf
         tensor_map["layers."+str(i)+".feed_forward.w3"]            = mapped_to # llama-pth
+
         # Feed-forward gate
-        mapped_to = "blk."+str(i)+".ffn_gate"
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_GATE, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
+
         tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf
         tensor_map["layers."+str(i)+".feed_forward.w1"]     = mapped_to # llama-pth
+
         # Feed-forward down
-        mapped_to = "blk."+str(i)+".ffn_down"
+        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_DOWN, None)
+        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
+
         tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # gptneox
         tensor_map["transformer.h."+str(i)+".mlp.c_proj"]          = mapped_to # gpt2
         tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"]  = mapped_to # mpt
@@ -203,14 +343,16 @@ class GGUFValueType(IntEnum):
 
 
 class GGUFWriter:
-    def __init__(self, fout: IO):
+    def __init__(self, path: str, arch: str):
-        self.fout = fout
+        self.fout = open(path, "wb")
+        self.arch = arch
         self.offset_tensor = 0
         self.data_alignment = GGUF_DEFAULT_ALIGNMENT
         self.kv_data = b""
         self.kv_data_count = 0
         self.ti_data = b""
         self.ti_data_count = 0
+        self.add_architecture()
 
     def write_header_to_file(self):
         self.fout.write(struct.pack("<I", GGUF_MAGIC))
@@ -228,11 +370,6 @@ class GGUFWriter:
         self.fout.write(self.ti_data)
         self.flush()
 
-    @classmethod
-    def open(cls, path: str) -> "GGUFWriter":
-        f = open(path, "wb")
-        return cls(f)
-
     def add_key(self, key: str):
         self.add_val(key, GGUFValueType.STRING, add_vtype=False)
 
@@ -269,7 +406,8 @@ class GGUFWriter:
         self.add_val(val, GGUFValueType.BOOL)
 
     def add_string(self, key: str, val: str):
-        if len(val) == 0: return
+        if len(val) == 0:
+            return
         self.add_key(key)
         self.add_val(val, GGUFValueType.STRING)
 
@@ -323,6 +461,8 @@ class GGUFWriter:
         return ((x + n - 1) // n) * n
 
     def add_tensor_info(self, name: str, tensor_shape: np.ndarray, tensor_dtype: np.dtype, tensor_nbytes: int):
+        assert tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
+
         encoded_name = name.encode("utf8")
         self.ti_data += struct.pack("<I", len(encoded_name))
         self.ti_data += encoded_name
@@ -331,14 +471,13 @@ class GGUFWriter:
         for i in range(n_dims):
             self.ti_data += struct.pack("<I", tensor_shape[n_dims - 1 - i])
 
-        assert tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
         dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
         self.ti_data += struct.pack("<I", dtype)
         self.ti_data += struct.pack("<Q", self.offset_tensor)
         self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
         self.ti_data_count += 1
 
-    def write_tensor_to_file(self, tensor: np.ndarray):
+    def write_tensor_data(self, tensor: np.ndarray):
         pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
         if pad != 0:
             self.fout.write(bytes([0] * pad))
@@ -355,15 +494,14 @@ class GGUFWriter:
     def close(self):
         self.fout.close()
 
-    def add_architecture(self, architecture: str):
+    def add_architecture(self):
-        self.add_string(KEY_GENERAL_ARCHITECTURE,
+        self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch)
-                        architecture)
 
     def add_author(self, author: str):
         self.add_string(KEY_GENERAL_AUTHOR, author)
 
     def add_tensor_data_layout(self, layout: str):
-        self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT , layout)
+        self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
 
     def add_url(self, url: str):
         self.add_string(KEY_GENERAL_URL, url)
@@ -391,60 +529,60 @@ class GGUFWriter:
         self.data_alignment = alignment
         self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment)
 
-    def add_context_length(self, llm: str, length: int):
+    def add_context_length(self, length: int):
         self.add_uint32(
-            KEY_LLM_CONTEXT_LENGTH.format(llm=llm), length)
+            KEY_LLM_CONTEXT_LENGTH.format(arch=self.arch), length)
 
-    def add_embedding_length(self, llm: str, length: int):
+    def add_embedding_length(self, length: int):
         self.add_uint32(
-            KEY_LLM_EMBEDDING_LENGTH.format(llm=llm), length)
+            KEY_LLM_EMBEDDING_LENGTH.format(arch=self.arch), length)
 
-    def add_block_count(self, llm: str, length: int):
+    def add_block_count(self, length: int):
         self.add_uint32(
-            KEY_LLM_BLOCK_COUNT.format(llm=llm), length)
+            KEY_LLM_BLOCK_COUNT.format(arch=self.arch), length)
 
-    def add_feed_forward_length(self, llm: str, length: int):
+    def add_feed_forward_length(self, length: int):
         self.add_uint32(
-            KEY_LLM_FEED_FORWARD_LENGTH.format(llm=llm), length)
+            KEY_LLM_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
 
-    def add_parallel_residual(self, llm: str, use: bool):
+    def add_parallel_residual(self, use: bool):
         self.add_bool(
-            KEY_LLM_USE_PARALLEL_RESIDUAL.format(llm=llm), use)
+            KEY_LLM_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
 
-    def add_tensor_data_layout(self, llm: str, layout: str):
+    def add_tensor_data_layout(self, layout: str):
         self.add_string(
-            KEY_LLM_TENSOR_DATA_LAYOUT.format(llm=llm), layout)
+            KEY_LLM_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
 
-    def add_head_count(self, llm: str, count: int):
+    def add_head_count(self, count: int):
         self.add_uint32(
-            KEY_ATTENTION_HEAD_COUNT.format(llm=llm), count)
+            KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count)
 
-    def add_head_count_kv(self, llm: str, count: int):
+    def add_head_count_kv(self, count: int):
         self.add_uint32(
-            KEY_ATTENTION_HEAD_COUNT_KV.format(llm=llm), count)
+            KEY_ATTENTION_HEAD_COUNT_KV.format(arch=self.arch), count)
 
-    def add_max_alibi_bias(self, llm: str, bias: float):
+    def add_max_alibi_bias(self, bias: float):
         self.add_float32(
-            KEY_ATTENTION_MAX_ALIBI_BIAS.format(llm=llm), bias)
+            KEY_ATTENTION_MAX_ALIBI_BIAS.format(arch=self.arch), bias)
 
-    def add_clamp_kqv(self, llm: str, value: float):
+    def add_clamp_kqv(self, value: float):
         self.add_float32(
-            KEY_ATTENTION_CLAMP_KQV.format(llm=llm), value)
+            KEY_ATTENTION_CLAMP_KQV.format(arch=self.arch), value)
 
-    def add_layer_norm_eps(self, llm: str, value: float):
+    def add_layer_norm_eps(self, value: float):
         self.add_float32(
-            KEY_ATTENTION_LAYERNORM_EPS.format(llm=llm), value)
+            KEY_ATTENTION_LAYERNORM_EPS.format(arch=self.arch), value)
 
-    def add_layer_norm_rms_eps(self, llm: str, value: float):
+    def add_layer_norm_rms_eps(self, value: float):
         self.add_float32(
-            KEY_ATTENTION_LAYERNORM_RMS_EPS.format(llm=llm), value)
+            KEY_ATTENTION_LAYERNORM_RMS_EPS.format(arch=self.arch), value)
 
-    def add_rope_dimension_count(self, llm: str, count: int):
+    def add_rope_dimension_count(self, count: int):
         self.add_uint32(
-            KEY_ROPE_DIMENSION_COUNT.format(llm=llm), count)
+            KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count)
 
-    def add_rope_scale(self, llm: str, value:  float):
+    def add_rope_scale(self, value:  float):
-        self.add_float32(KEY_ROPE_SCALE.format(llm=llm), value)
+        self.add_float32(KEY_ROPE_SCALE.format(arch=self.arch), value)
 
     def add_tokenizer_model(self, model: str):
         self.add_string(KEY_TOKENIZER_MODEL, model)
@@ -479,9 +617,8 @@ class GGUFWriter:
 # Example usage:
 if __name__ == "__main__":
     # Example usage with a file
-    gguf_writer = GGUFWriter.open("example.gguf")
+    gguf_writer = GGUFWriter("example.gguf", "llama")
 
-    gguf_writer.add_architecture("llama")
     gguf_writer.add_uint32("answer", 42)  # Write a 32-bit integer
     gguf_writer.add_float32("answer_in_float", 42.0)  # Write a 32-bit float
     gguf_writer.add_custom_alignment(64)
@@ -493,7 +630,7 @@ if __name__ == "__main__":
     gguf_writer.write_header_to_file()
     gguf_writer.write_kv_data_to_file()
     gguf_writer.write_ti_data_to_file()
-    gguf_writer.write_tensor_to_file(tensor1)
+    gguf_writer.write_tensor_data(tensor1)
-    gguf_writer.write_tensor_to_file(tensor2)
+    gguf_writer.write_tensor_data(tensor2)
 
     gguf_writer.close()

llama.cpp

@@ -676,22 +676,21 @@ static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_PER_CONTEXT()
 
 // default hparams (LLaMA 7B)
 struct llama_hparams {
-    uint32_t n_vocab   = 32000;
+    uint32_t n_vocab     = 32000;
-    uint32_t n_ctx     = 512;
+    uint32_t n_ctx_train = 2048;  // the context size used during training
-    uint32_t n_embd    = 4096;
+    uint32_t n_ctx       = 512;   // the context size used during inference
-    uint32_t n_head    = 32;
+    uint32_t n_embd      = 4096;
-    uint32_t n_head_kv = 32;
+    uint32_t n_head      = 32;
-    uint32_t n_layer   = 32;
+    uint32_t n_head_kv   = 32;
-    uint32_t n_rot     = 64;
+    uint32_t n_layer     = 32;
-    uint32_t n_ff      = 11008;
+    uint32_t n_rot       = 64;
+    uint32_t n_ff        = 11008;
 
     float f_norm_rms_eps = 1e-5;
 
     float rope_freq_base  = 10000.0f;
     float rope_freq_scale = 1.0f;
 
-    enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
-
     bool operator!=(const llama_hparams & other) const {
         return static_cast<bool>(memcmp(this, &other, sizeof(llama_hparams))); // NOLINT
     }
@@ -1023,7 +1022,8 @@ struct llama_model_loader {
     int n_kv      = 0;
     int n_tensors = 0;
     int n_created = 0;
-    size_t n_tot_elements = 0;
+
+    int64_t n_elements = 0;
 
     bool use_mmap = false;
 
@@ -1051,9 +1051,9 @@ struct llama_model_loader {
         for (int i = 0; i < n_tensors; i++) {
             const char * name = gguf_get_tensor_name(ctx_gguf, i);
             struct ggml_tensor * t = ggml_get_tensor(ctx_meta, name);
-            n_tot_elements += ggml_nelements(t);
+            n_elements += ggml_nelements(t);
         }
-        
+
         // print meta data
         // TODO: make optional
         {
@@ -1123,6 +1123,10 @@ struct llama_model_loader {
     struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
         struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str());
 
+        if (cur == NULL) {
+            throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
+        }
+
         {
             bool is_ok = true;
             for (size_t i = 0; i < ne.size(); ++i) {
@@ -1332,7 +1336,7 @@ static void llama_model_load_internal(
         }
 
         GGUF_GET(hparams.n_vocab,        gguf_get_arr_n,   GGUF_TYPE_ARRAY,   true, "tokenizer.ggml.tokens");
-        GGUF_GET(hparams.n_ctx,          gguf_get_val_u32, GGUF_TYPE_UINT32,  true, "llama.context_length");
+        GGUF_GET(hparams.n_ctx_train,    gguf_get_val_u32, GGUF_TYPE_UINT32,  true, "llama.context_length");
         GGUF_GET(hparams.n_embd,         gguf_get_val_u32, GGUF_TYPE_UINT32,  true, "llama.embedding_length");
         GGUF_GET(hparams.n_ff,           gguf_get_val_u32, GGUF_TYPE_UINT32,  true, "llama.feed_forward_length");
         GGUF_GET(hparams.n_head,         gguf_get_val_u32, GGUF_TYPE_UINT32,  true, "llama.attention.head_count");
@@ -1406,22 +1410,24 @@ static void llama_model_load_internal(
     }
 
     {
-        LLAMA_LOG_INFO("%s: format     = %s\n",   __func__, llama_file_version_name(ml->file_version));
+        LLAMA_LOG_INFO("%s: format      = %s\n",    __func__, llama_file_version_name(ml->file_version));
-        LLAMA_LOG_INFO("%s: n_vocab    = %u\n",   __func__, hparams.n_vocab);
+        LLAMA_LOG_INFO("%s: n_vocab     = %u\n",    __func__, hparams.n_vocab);
-        LLAMA_LOG_INFO("%s: n_ctx      = %u\n",   __func__, hparams.n_ctx);
+        LLAMA_LOG_INFO("%s: n_ctx_train = %u\n",    __func__, hparams.n_ctx_train);
-        LLAMA_LOG_INFO("%s: n_embd     = %u\n",   __func__, hparams.n_embd);
+        LLAMA_LOG_INFO("%s: n_ctx       = %u\n",    __func__, hparams.n_ctx);
-        LLAMA_LOG_INFO("%s: n_head     = %u\n",   __func__, hparams.n_head);
+        LLAMA_LOG_INFO("%s: n_embd      = %u\n",    __func__, hparams.n_embd);
-        LLAMA_LOG_INFO("%s: n_head_kv  = %u\n",   __func__, hparams.n_head_kv);
+        LLAMA_LOG_INFO("%s: n_head      = %u\n",    __func__, hparams.n_head);
-        LLAMA_LOG_INFO("%s: n_layer    = %u\n",   __func__, hparams.n_layer);
+        LLAMA_LOG_INFO("%s: n_head_kv   = %u\n",    __func__, hparams.n_head_kv);
-        LLAMA_LOG_INFO("%s: n_rot      = %u\n",   __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
+        LLAMA_LOG_INFO("%s: n_layer     = %u\n",    __func__, hparams.n_layer);
-        LLAMA_LOG_INFO("%s: n_gqa      = %u\n",   __func__, hparams.n_gqa());
+        LLAMA_LOG_INFO("%s: n_rot       = %u\n",    __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
-        LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
+        LLAMA_LOG_INFO("%s: n_gqa       = %u\n",    __func__, hparams.n_gqa());
-        LLAMA_LOG_INFO("%s: n_ff       = %u\n",   __func__, hparams.n_ff);
+        LLAMA_LOG_INFO("%s: f_norm_eps  = %.1e\n",  __func__, hparams.f_norm_rms_eps);
-        LLAMA_LOG_INFO("%s: freq_base  = %.1f\n", __func__, hparams.rope_freq_base);
+        LLAMA_LOG_INFO("%s: n_ff        = %u\n",    __func__, hparams.n_ff);
-        LLAMA_LOG_INFO("%s: freq_scale = %g\n",   __func__, hparams.rope_freq_scale);
+        LLAMA_LOG_INFO("%s: freq_base   = %.1f\n",  __func__, hparams.rope_freq_base);
-        LLAMA_LOG_INFO("%s: ftype      = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
+        LLAMA_LOG_INFO("%s: freq_scale  = %g\n",    __func__, hparams.rope_freq_scale);
-        LLAMA_LOG_INFO("%s: model size = %.2f B\n",   __func__, ml->n_tot_elements*1e-9);
+        LLAMA_LOG_INFO("%s: model type  = %s\n",    __func__, llama_model_type_name(model.type));
+        LLAMA_LOG_INFO("%s: model size  = %.2fB\n", __func__, ml->n_elements*1e-9);
 
+        // TODO: print number of tensors for each quantization
     }
 
     if (vocab_only) {
@@ -2310,6 +2316,18 @@ static uint8_t llama_byte_to_char(const llama_vocab & vocab, uint8_t byte) {
     return false;
 }
 
+static uint8_t llama_char_to_byte(const llama_vocab & vocab, uint8_t ch) {
+    if (llama_vocab_type(vocab) == "spm") {
+        return ch + 3;
+    }
+
+    if (llama_vocab_type(vocab) == "bpe") {
+        return ch - 32;
+    }
+
+    return false;
+}
+
 static std::string llama_escape_whitespace(const std::string& text) {
     std::string result;
     bool escaping = false;
@@ -2446,7 +2464,7 @@ private:
         if (p == rev_merge.end()) {
             // output any symbols that did not form tokens as bytes.
             for (int j = 0; j < (int)symbol.n; ++j) {
-                llama_vocab::id token_id = llama_byte_to_char(vocab_, symbol.text[j]);
+                llama_vocab::id token_id = llama_char_to_byte(vocab_, symbol.text[j]);
                 output.push_back(token_id);
             }
             return;
@@ -3373,7 +3391,6 @@ static void llama_convert_tensor_internal(struct ggml_tensor * tensor, std::vect
 static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
     ggml_type quantized_type;
     llama_ftype ftype = params->ftype;
-    int nthread = params->nthread;
 
     switch (params->ftype) {
         case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
@@ -3399,6 +3416,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
     }
 
+    int nthread = params->nthread;
+
     if (nthread <= 0) {
         nthread = std::thread::hardware_concurrency();
     }
@@ -3669,6 +3688,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
     }
 }
 
+// TODO: after the GGUF PR, this likely won't work and needs to be updated
 int llama_apply_lora_from_file_internal(const struct llama_model & model, const char * path_lora, const char * path_base_model, int n_threads) {
     LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
 
@@ -4876,8 +4896,8 @@ int llama_token_to_str_with_model(const struct llama_model * model, llama_token
     return 0;
 }
 
-int llama_token_to_str(const struct llama_context * ctx, llama_token token, char * str, int length) {
+int llama_token_to_str(const struct llama_context * ctx, llama_token token, char * buf, int length) {
-    return llama_token_to_str_with_model(&ctx->model, token, str, length);
+    return llama_token_to_str_with_model(&ctx->model, token, buf, length);
 }
 
 std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
@@ -4894,13 +4914,13 @@ std::string llama_token_to_str(const struct llama_context * ctx, llama_token tok
     return std::string(result.data(), result.size());
 }
 
-int llama_token_to_str_bpe(const struct llama_context * ctx, llama_token token, char * str, int length) {
+int llama_token_to_str_bpe(const struct llama_context * ctx, llama_token token, char * buf, int length) {
     if (0 <= token && token < llama_n_vocab_from_model(&ctx->model)) {
         std::string result = ctx->model.vocab.id_to_token[token].tok;
         if (length < (int) result.length()) {
             return -result.length();
         }
-        memcpy(str, result.c_str(), result.length());
+        memcpy(buf, result.c_str(), result.length());
         return result.length();
     }
     return 0;

tests/CMakeLists.txt

@@ -26,10 +26,10 @@ llama_build_and_test_executable(test-quantize-fns.cpp)
 llama_build_and_test_executable(test-quantize-perf.cpp)
 llama_build_and_test_executable(test-sampling.cpp)
 llama_build_executable(test-tokenizer-0.cpp)
-llama_test_executable(test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.bin)
+llama_test_executable(test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
 llama_build_executable(test-tokenizer-1.cpp)
-llama_test_executable(test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.bin)
+llama_test_executable(test-tokenizer-1.llama  test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
-llama_test_executable(test-tokenizer-1.aquila test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.bin)
+#llama_test_executable(test-tokenizer-1.aquila test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
 llama_build_and_test_executable(test-grammar-parser.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/grammar-parser.cpp)
 llama_build_and_test_executable(test-grad0.cpp) # SLOW
 # llama_build_and_test_executable(test-opt.cpp) # SLOW

tests/test-tokenizer-0.cpp

@@ -89,6 +89,8 @@ int main(int argc, char **argv) {
         return 2;
     }
 
+    bool success = true;
+
     for (const auto & test_kv : k_tests()) {
         std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, true);
         fprintf(stderr, "%s : '%s' tokenized to '%s'\n",
@@ -103,7 +105,8 @@ int main(int argc, char **argv) {
         }
 
         if (!correct) {
-            fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
+            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
+            fprintf(stderr, "%s : detokenized to: '%s'\n", __func__, unescape_whitespace(ctx, test_kv.second).c_str());
             fprintf(stderr, "%s : expected tokens: ", __func__);
             for (const auto & t : test_kv.second) {
                 fprintf(stderr, "%6d, ", t);
@@ -115,9 +118,7 @@ int main(int argc, char **argv) {
             }
             fprintf(stderr, "\n");
 
-            llama_free_model(model);
+            success = false;
-            llama_free(ctx);
-            return 3;
         }
     }
 
@@ -126,5 +127,5 @@ int main(int argc, char **argv) {
 
     llama_backend_free();
 
-    return 0;
+    return success ? 0 : 3;
 }