rework convert.py to read hyper-parameters from config.json (ggergano…

…v#1958)

* Read hyper-parameters from HuggingFace-transformer config.json, if they exist, and fall back to guessing, like before otherwise.
  This allows converting open_llama 3B and other non-standard model designs.

convert.py

@@ -130,28 +130,76 @@ def make_tensors_list() -> List[str]:
 TENSORS_SET = set(TENSORS_LIST)
 
 
+def find_n_mult(n_ff: int, n_embd: int) -> int:
+    # hardcoded magic range
+    for n_mult in range(256, 1, -1):
+        calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
+        if calc_ff == n_ff:
+            return n_mult
+    return 1
+
 @dataclass
 class Params:
     n_vocab: int
     n_embd: int
     n_mult: int
     n_head: int
     n_layer: int
-    file_type: GGMLFileType
 
     @staticmethod
-    def guessed(model: 'LazyModel', file_type: GGMLFileType) -> 'Params':
-        n_vocab, n_embd = model["tok_embeddings.weight"].shape
+    def guessed(model: 'LazyModel') -> 'Params':
+        # try transformer naming first
+        n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape
+
+        # try transformer naming first
+        if "model.layers.0.self_attn.q_proj.weight" in model:
+            n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
+        else:
+            n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
+
+        n_head=n_embd // 128 # guessed
 
         return Params(
             n_vocab=n_vocab,
             n_embd=n_embd,
             n_mult=256,
-            n_head=n_embd // 128,
-            n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model),
-            file_type=file_type,
+            n_head=n_head,
+            n_layer=n_layer,
         )
 
+    @staticmethod
+    def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
+        config = json.load(open(config_path))
+
+        n_vocab = config["vocab_size"];
+        n_embd = config["hidden_size"];
+        n_head = config["num_attention_heads"];
+        n_layer = config["num_hidden_layers"];
+        n_ff = config["intermediate_size"];
+
+        n_mult = find_n_mult(n_ff, n_embd);
+
+        return Params(
+            n_vocab=n_vocab,
+            n_embd=n_embd,
+            n_mult=n_mult,
+            n_head=n_head,
+            n_layer=n_layer,
+        )
+
+    @staticmethod
+    def load(model_plus: 'ModelPlus') -> 'Params':
+        orig_config_path = model_plus.paths[0].parent / "params.json"
+        hf_transformer_config_path = model_plus.paths[0].parent / "config.json"
+
+        if hf_transformer_config_path.exists():
+            params = Params.loadHFTransformerJson(model_plus.model, hf_transformer_config_path)
+        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
+
 
 class SentencePieceVocab:
     def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None:
@@ -595,18 +643,17 @@ def load() -> Tensor:
     return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
 
 
-def convert_transformers_to_orig(model: LazyModel) -> LazyModel:
+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"]
 
-    n_head = model["model.layers.0.self_attn.q_proj.weight"].shape[1] // 128
     for i in itertools.count():
         if f"model.layers.{i}.self_attn.q_proj.weight" not in model:
             break
-        out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], n_head)
-        out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], n_head)
+        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)
         out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
         out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"]
 
@@ -920,7 +967,7 @@ class OutputFile:
     def __init__(self, fname_out: Path) -> None:
         self.fout = open(fname_out, "wb")
 
-    def write_file_header(self, params: Params) -> None:
+    def write_file_header(self, params: Params, file_type: GGMLFileType) -> None:
         self.fout.write(b"ggjt"[::-1])  # magic
         values = [
             1,  # file version
@@ -930,7 +977,7 @@ def write_file_header(self, params: Params) -> None:
             params.n_head,
             params.n_layer,
             params.n_embd // params.n_head,  # rot (obsolete)
-            params.file_type.value,
+            file_type.value,
         ]
         self.fout.write(struct.pack("i" * len(values), *values))
 
@@ -958,10 +1005,10 @@ def write_vocab_only(fname_out: Path, vocab: Vocab) -> None:
         of.fout.close()
 
     @staticmethod
-    def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None:
+    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)
+        of.write_file_header(params, file_type)
         print("Writing vocab...")
         of.write_vocab(vocab)
 
@@ -997,11 +1044,11 @@ def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFi
     raise Exception(f"Unexpected combination of types: {name_to_type}")
 
 
-def do_necessary_conversions(model: LazyModel) -> LazyModel:
+def do_necessary_conversions(model: LazyModel, params: Params) -> LazyModel:
     model = handle_quantization(model)
 
     if "lm_head.weight" in model:
-        model = convert_transformers_to_orig(model)
+        model = convert_transformers_to_orig(model, params)
     model = filter_and_sort_tensors(model)
 
     return model
@@ -1107,14 +1154,14 @@ def load_vocab(path: Path) -> SentencePieceVocab:
     return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None)
 
 
-def default_outfile(model_paths: List[Path], params: Params) -> Path:
+def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path:
     namestr = {
         GGMLFileType.AllF32: "f32",
         GGMLFileType.MostlyF16: "f16",
         GGMLFileType.MostlyQ4_0: "q4_0",
         GGMLFileType.MostlyQ4_1: "q4_1",
         GGMLFileType.PerLayerIsQ4_1: "q4_1",
-    }[params.file_type]
+    }[file_type]
     ret = model_paths[0].parent / f"ggml-model-{namestr}.bin"
     if ret in model_paths:
         sys.stderr.write(
@@ -1164,13 +1211,13 @@ def main(args_in: Optional[List[str]] = None) -> None:
         else:
             vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
             vocab = load_vocab(vocab_dir)
+        params = Params.load(model_plus)
         model = model_plus.model
-        model = do_necessary_conversions(model)
+        model = do_necessary_conversions(model, params)
         output_type = pick_output_type(model, args.outtype)
         model = convert_to_output_type(model, output_type)
-        params = Params.guessed(model, output_type)
-        outfile = args.outfile or default_outfile(model_plus.paths, params)
-        OutputFile.write_all(outfile, params, model, vocab)
+        outfile = args.outfile or default_outfile(model_plus.paths, output_type)
+        OutputFile.write_all(outfile, params, output_type, model, vocab)
         print(f"Wrote {outfile}")