vbatts/llama.cpp
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convert-*.py: prepare_tensors_for_writing() --> prepare_tensors()

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> Especially since it can be used for other purposes than "for writing", like preparing the tensors to then count and sum all their sizes.

Co-authored-by: compilade <git@compilade.net>
This commit is contained in:
brian khuu 2024-07-11 21:14:04 +10:00
parent 64707b625c
commit 04c4fffdcc
1 changed files with 15 additions and 15 deletions
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30
convert_hf_to_gguf.py
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@ -293,7 +293,7 @@ class Model:
return False
def prepare_tensors_for_writing(self):
def prepare_tensors(self):
max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
for name, data_torch in self.get_tensors():
@ -398,7 +398,7 @@ class Model:
self.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
def write(self):
self.prepare_tensors_for_writing()
self.prepare_tensors()
self.prepare_key_value_store()
self.gguf_writer.write_header_to_file()
self.gguf_writer.write_kv_data_to_file()
@ -411,7 +411,7 @@ class Model:
if self.metadata.uuid is None:
# Required tensor data least for uuid generation if in vocab_only mode
self.prepare_tensors_for_writing()
self.prepare_tensors()
self.prepare_key_value_store()
self.gguf_writer.write_header_to_file()
@ -1441,8 +1441,8 @@ class StableLMModel(Model):
return [(new_name, data_torch)]
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
def prepare_tensors(self):
super().prepare_tensors()
if self._q_norms is not None or self._k_norms is not None:
# flatten two `list[dict[str, Tensor]]` into a single `list[str]`
@ -1558,8 +1558,8 @@ class LlamaModel(Model):
return [(self.map_tensor_name(name), data_torch)]
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
def prepare_tensors(self):
super().prepare_tensors()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
@ -1882,8 +1882,8 @@ class Qwen2MoeModel(Model):
return [(self.map_tensor_name(name), data_torch)]
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
def prepare_tensors(self):
super().prepare_tensors()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
@ -2950,8 +2950,8 @@ class ArcticModel(Model):
return [(self.map_tensor_name(name), data_torch)]
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
def prepare_tensors(self):
super().prepare_tensors()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
@ -3029,8 +3029,8 @@ class DeepseekV2Model(Model):
return [(self.map_tensor_name(name), data_torch)]
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
def prepare_tensors(self):
super().prepare_tensors()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
@ -3266,8 +3266,8 @@ class JaisModel(Model):
return tensors
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
def prepare_tensors(self):
super().prepare_tensors()
self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)