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convert-gptneox-hf-to-gguf.py : fixes

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klosax 2023-08-17 21:42:26 +02:00 committed by GitHub
parent fc3a523211
commit b668cd3296
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1 changed files with 13 additions and 14 deletions
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convert-gptneox-hf-to-gguf.py
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@ -89,8 +89,8 @@ if hparams["architectures"][0] != "GPTNeoXForCausalLM":
# get number of model parts
num_parts = count_model_parts(dir_model)
llm_arch = "gptneox"
gguf_writer = gguf.GGUFWriter(fname_out, arch=llm_arch)
ARCH=gguf.MODEL_ARCH.GPTNEOX
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
print("gguf: get model metadata")
@ -194,7 +194,7 @@ if Path(dir_model + "/tokenizer.json").is_file():
# TENSORS
tensor_map = gguf.get_tensor_name_map(block_count)
tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
# tensor info
print("gguf: get tensor metadata")
@ -217,6 +217,8 @@ for part_name in part_names:
if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
continue
old_dtype = data.dtype
# convert any unsupported data types to float32
if data.dtype != torch.float16 and data.dtype != torch.float32:
data = data.to(torch.float32)
@ -233,24 +235,21 @@ for part_name in part_names:
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
old_dtype = data_dtype
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if ftype == 0 and data.dtype == np.float16:
data_dtype = np.float32
if 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 ftype == 1 and data.dtype == np.float16 and n_dims == 1:
data_dtype = np.float32
if 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 ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data_dtype = np.float16
if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data_dtype))
data = data.astype(data_dtype)
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.add_tensor(name, data)