convert-hf : allow unusual model part names
For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer.
This commit is contained in:
Francis Couture-Harpin
parent
56f60f5d69
commit
3870164f47
1 changed files with 30 additions and 41 deletions
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@ -49,9 +49,8 @@ class Model(Protocol):
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is_big_endian: bool
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endianess: gguf.GGUFEndian
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use_temp_file: bool
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part_names: list[str]
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is_safetensors: bool
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num_parts: int
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part_names: Iterable[str]
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hparams: dict[str, Any]
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gguf_writer: gguf.GGUFWriter
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block_count: int
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@ -67,9 +66,10 @@ class Model(Protocol):
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self.is_big_endian = is_big_endian
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self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
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self.use_temp_file = use_temp_file
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self.is_safetensors = self._is_model_safetensors()
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self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin")
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self.part_names = self._get_part_names()
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self.part_names = Model.get_model_part_names(self.dir_model, ".safetensors")
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self.is_safetensors = len(self.part_names) > 0
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if not self.is_safetensors:
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self.part_names = Model.get_model_part_names(self.dir_model, ".bin")
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self.hparams = Model.load_hparams(self.dir_model)
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self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
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self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
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@ -109,7 +109,7 @@ class Model(Protocol):
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sys.exit()
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if "{bid}" in name:
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assert bid is not None
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name = name.format(bid)
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name = name.format(bid=bid)
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return name + suffix
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def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
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@ -228,13 +228,13 @@ class Model(Protocol):
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self.gguf_writer.close()
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@staticmethod
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def count_model_parts(dir_model: Path, prefix: str) -> int:
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num_parts = 0
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def get_model_part_names(dir_model: Path, suffix: str) -> list[str]:
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part_names: list[str] = []
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for filename in os.listdir(dir_model):
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if filename.endswith(prefix):
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num_parts += 1
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if filename.endswith(suffix):
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part_names.append(filename)
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return num_parts
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return part_names
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@staticmethod
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def load_hparams(dir_model):
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@ -258,19 +258,6 @@ class Model(Protocol):
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except KeyError:
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raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
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def _is_model_safetensors(self) -> bool:
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return Model.count_model_parts(self.dir_model, ".safetensors") > 0
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def _get_part_names(self) -> Iterable[str]:
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if self.is_safetensors:
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if self.num_parts == 1: # there's only one .safetensors file
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return ("model.safetensors",)
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return (f"model-{n:05}-of-{self.num_parts:05}.safetensors" for n in range(1, self.num_parts + 1))
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if self.num_parts == 1: # there's only one .bin file
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return ("pytorch_model.bin",)
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return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
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# used for GPT-2 BPE and WordPiece vocabs
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def get_vocab_base(self) -> tuple[list[str], list[int], str]:
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tokens: list[str] = []
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@ -446,7 +433,7 @@ class Model(Protocol):
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raise FileNotFoundError(f"File not found: {tokenizer_path}")
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tokenizer = SentencePieceProcessor()
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tokenizer.LoadFromFile(tokenizer_path)
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tokenizer.LoadFromFile(str(tokenizer_path))
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vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
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@ -1120,7 +1107,7 @@ class StableLMModel(Model):
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ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight"
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datas.append(norms[ename])
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del norms[ename]
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data_torch = torch.cat(datas, dim=0)
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight"
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new_name = self.map_tensor_name(merged_name)
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@ -1204,7 +1191,7 @@ class LlamaModel(Model):
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assert bid is not None
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if self._experts is None:
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self._experts = [{} for _ in range(n_experts)]
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self._experts = [{} for _ in range(self.block_count)]
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self._experts[bid][name] = data_torch
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@ -1220,7 +1207,7 @@ class LlamaModel(Model):
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datas.append(self._experts[bid][ename])
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del self._experts[bid][ename]
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data_torch = torch.cat(datas, dim=0)
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
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@ -1267,7 +1254,7 @@ class GrokModel(Model):
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assert bid is not None
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if self._experts is None:
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self._experts = [{} for _ in range(n_experts)]
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self._experts = [{} for _ in range(self.block_count)]
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self._experts[bid][name] = data_torch
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@ -1283,7 +1270,7 @@ class GrokModel(Model):
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datas.append(self._experts[bid][ename])
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del self._experts[bid][ename]
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data_torch = torch.cat(datas, dim=0)
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight"
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@ -1484,7 +1471,7 @@ class Qwen2MoeModel(Model):
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assert bid is not None
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if self._experts is None:
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self._experts = [{} for _ in range(n_experts)]
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self._experts = [{} for _ in range(self.block_count)]
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self._experts[bid][name] = data_torch
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@ -1500,7 +1487,7 @@ class Qwen2MoeModel(Model):
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datas.append(self._experts[bid][ename])
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del self._experts[bid][ename]
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data_torch = torch.cat(datas, dim=0)
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
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@ -1604,7 +1591,7 @@ class Phi3MiniModel(Model):
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sys.exit(1)
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tokenizer = SentencePieceProcessor()
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tokenizer.LoadFromFile(tokenizer_path)
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tokenizer.LoadFromFile(str(tokenizer_path))
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vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
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@ -1786,7 +1773,7 @@ class InternLM2Model(Model):
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add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
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tokenizer = SentencePieceProcessor()
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tokenizer.LoadFromFile(tokenizer_path)
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tokenizer.LoadFromFile(str(tokenizer_path))
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tokenizer.serialized_model_proto
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vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
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@ -2171,13 +2158,15 @@ class MambaModel(Model):
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def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool:
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del n_dims # unused
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return new_name in (self.format_tensor_name(n, bid, ".weight" if name.endswith(".weight") else "") for n in [
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gguf.MODEL_TENSOR.SSM_CONV1D,
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gguf.MODEL_TENSOR.SSM_X,
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gguf.MODEL_TENSOR.SSM_DT,
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gguf.MODEL_TENSOR.SSM_A,
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gguf.MODEL_TENSOR.SSM_D,
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])
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return bid is not None and new_name in (
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self.format_tensor_name(n, bid, ".weight" if name.endswith(".weight") else "") for n in [
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gguf.MODEL_TENSOR.SSM_CONV1D,
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gguf.MODEL_TENSOR.SSM_X,
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gguf.MODEL_TENSOR.SSM_DT,
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gguf.MODEL_TENSOR.SSM_A,
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gguf.MODEL_TENSOR.SSM_D,
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]
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)
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@Model.register("CohereForCausalLM")
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