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remove .rotary_pos_emb.inv_freq and unuse code for chatglm3 model
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Signed-off-by: XingXing Qiao <[email protected]>
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xingxingqiao committed May 15, 2024
1 parent 398fecb commit cb324f4
Showing 1 changed file with 8 additions and 75 deletions.
83 changes: 8 additions & 75 deletions convert-hf-to-gguf.py
Original file line number Diff line number Diff line change
Expand Up @@ -2468,85 +2468,18 @@ def set_gguf_parameters(self):
self.gguf_writer.add_rope_dimension_count(64)
self.gguf_writer.add_add_bos_token(False)

def write_tensors(self):
block_count = self.hparams["num_layers"]
tensors = dict(self.get_tensors())
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
has_lm_head = True
n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads"))
n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))

for name, data_torch in tensors.items():
if name.endswith(".rotary_pos_emb.inv_freq"):
continue

if "lm_head.weight" not in tensors.keys() and "output.weight" not in tensors.keys():
has_lm_head = False
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name.endswith(".rotary_pos_emb.inv_freq"):
return []

name = re.sub(r'transformer\.', '', name)
del bid # unused

old_dtype = data_torch.dtype
name = re.sub(r'transformer\.', '', name)

# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
if name == "word_embeddings.weight":
assert self.tensor_names is not None

data = data_torch.squeeze().numpy()

if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
# Map bloom-style qkv_linear to gpt-style qkv_linear
# bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa
# gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa
qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed))
data = np.concatenate(
(
qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
qkv_weights[:, 2, :, :].reshape((-1, n_embed)),
),
axis=0,
)
print("re-format attention.linear_qkv.weight")
elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
qkv_bias = data.reshape((n_head, 3, n_embed // n_head))
data = np.concatenate(
(
qkv_bias[:, 0, :].reshape((n_embed,)),
qkv_bias[:, 1, :].reshape((n_embed,)),
qkv_bias[:, 2, :].reshape((n_embed,)),
),
axis=0,
)
print("re-format attention.linear_qkv.bias")

# 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}, shape = {data.shape}, {old_dtype} --> {data.dtype}")

self.gguf_writer.add_tensor(new_name, data)

if not has_lm_head and name == "word_embeddings.weight":
self.gguf_writer.add_tensor("output.weight", data)
print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
return [(self.map_tensor_name(name), data_torch)]


###### CONVERSION LOGIC ######
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