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convert : allow conversion of Mistral HF models (#6144)

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* Allow conversion of Mistral HF models

* Homogenize Llama, Mistral, Mixtral under the same entry.

* Fix tokenizer, permute tensors

* Use sentencepiece tokenizer, or fall back to hfft.

* convert-hf : small fix for mypy

* convert-hf : fix duplicated block_count

* convert-hf : add vocab size to metadata

---------

Co-authored-by: Jared Van Bortel <jared@nomic.ai>
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Pedro Cuenca 2024-03-29 08:15:00 +01:00 committed by GitHub
parent bfe7dafc9c
commit b75c38166c
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1 changed files with 64 additions and 4 deletions
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convert-hf-to-gguf.py
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@ -23,7 +23,7 @@ if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
from convert import LlamaHfVocab
from convert import LlamaHfVocab, permute
###### MODEL DEFINITIONS ######
@ -1052,12 +1052,72 @@ class StableLMModel(Model):
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
@Model.register("MixtralForCausalLM")
class MixtralModel(Model):
@Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
class LlamaModel(Model):
model_arch = gguf.MODEL_ARCH.LLAMA
def set_vocab(self):
self._set_vocab_sentencepiece()
try:
self. _set_vocab_sentencepiece()
except FileNotFoundError:
self._set_vocab_llama_hf()
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
# Same as super class, but permuting q_proj, k_proj
def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
n_head = self.hparams.get("num_attention_heads")
n_kv_head = self.hparams.get("num_key_value_heads")
for name, data_torch in self.get_tensors():
# we don't need these
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
continue
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
data = data_torch.numpy()
if name.endswith("q_proj.weight"):
data = permute(data, n_head, n_head)
if name.endswith("k_proj.weight"):
data = permute(data, n_head, n_kv_head)
data = data.squeeze()
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
@Model.register("GrokForCausalLM")