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

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brian khuu 2024-07-10 20:18:40 +10:00
parent fdc5a3fc80
commit 2a976e1211
1 changed files with 15 additions and 15 deletions
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30
convert_hf_to_gguf.py
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@ -272,7 +272,7 @@ class Model:
return False
def write_tensors(self):
def prepare_tensors_for_writing(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():
@ -377,7 +377,7 @@ class Model:
self.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
def write(self):
self.write_tensors()
self.prepare_tensors_for_writing()
self.prepare_key_value_store()
self.gguf_writer.write_header_to_file(self.fname_out)
self.gguf_writer.write_kv_data_to_file()
@ -390,7 +390,7 @@ class Model:
if self.metadata.uuid is None:
# Required tensor data least for uuid generation if in vocab_only mode
self.write_tensors()
self.prepare_tensors_for_writing()
self.prepare_key_value_store()
self.gguf_writer.write_header_to_file(self.fname_out)
@ -1445,8 +1445,8 @@ class StableLMModel(Model):
return [(new_name, data_torch)]
def write_tensors(self):
super().write_tensors()
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
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]`
@ -1562,8 +1562,8 @@ class LlamaModel(Model):
return [(self.map_tensor_name(name), data_torch)]
def write_tensors(self):
super().write_tensors()
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
@ -1886,8 +1886,8 @@ class Qwen2MoeModel(Model):
return [(self.map_tensor_name(name), data_torch)]
def write_tensors(self):
super().write_tensors()
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
@ -2956,8 +2956,8 @@ class ArcticModel(Model):
return [(self.map_tensor_name(name), data_torch)]
def write_tensors(self):
super().write_tensors()
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
@ -3035,8 +3035,8 @@ class DeepseekV2Model(Model):
return [(self.map_tensor_name(name), data_torch)]
def write_tensors(self):
super().write_tensors()
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
@ -3274,8 +3274,8 @@ class JaisModel(Model):
return tensors
def write_tensors(self):
super().write_tensors()
def prepare_tensors_for_writing(self):
super().prepare_tensors_for_writing()
self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)