vbatts/llama.cpp
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model: dbrx: convert experts to f16

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Pierrick HYMBERT 2024-04-07 18:47:37 +02:00
parent d151d8fad9
commit f062b834ed
1 changed files with 9 additions and 13 deletions
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22
convert-hf-to-gguf.py
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@ -1538,7 +1538,7 @@ class DbrxModel(Model):
# Every other model has the weight names ending in .weight,
# let's assume that is the convention which is not the case for dbrx:
# https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15
new_name = tensor_map.get_name(name if not experts else name + ".weight", try_suffixes=(".weight", ".bias"))
new_name = tensor_map.get_name(name if not experts else name + ".weight", try_suffixes=".weight")
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
@ -1546,23 +1546,19 @@ class DbrxModel(Model):
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)
# Reshape experts tensors from 2D to 3D as expected by GeLU
if experts and n_dims == 2:
data = data.reshape((self.hparams["d_model"], self.hparams["ffn_config"]["ffn_hidden_size"], self.hparams["ffn_config"]["moe_num_experts"]))
n_dims = len(data.shape)
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
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 n_dims > 1:
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)