ver4a/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

convert : fix null head_dim AutoConfig regression (#14248)

Browse source
This commit is contained in:
Sigbjørn Skjæret 2025-06-18 09:52:07 +02:00 • committed by GitHub
parent d03172cc79
commit 3865cff4f5
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
1 changed files with 16 additions and 22 deletions
Download patch file Download diff file Expand all files Collapse all files

38
convert_hf_to_gguf.py
View file

@ -556,11 +556,8 @@ class TextModel(ModelBase):
logger.info(f"gguf: experts used count = {n_experts_used}")
if (head_dim := self.hparams.get("head_dim")) is not None:
# Workaround for incorrect AutoConfig value for DeepSeekV3 (is set correctly in DeepSeekV2Model class)
# https://github.com/huggingface/transformers/blob/19224c3642705c5b6988c9f5f4251f83323d05ae/src/transformers/models/deepseek_v3/configuration_deepseek_v3.py#L210
if self.hparams.get("model_type") != "deepseek_v3":
self.gguf_writer.add_key_length(head_dim)
self.gguf_writer.add_value_length(head_dim)
self.gguf_writer.add_key_length(head_dim)
self.gguf_writer.add_value_length(head_dim)
self.gguf_writer.add_file_type(self.ftype)
logger.info(f"gguf: file type = {self.ftype}")
@ -1901,9 +1898,7 @@ class LlamaModel(TextModel):
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
else:
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
@ -1985,7 +1980,8 @@ class LlamaModel(TextModel):
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
if (dim := self.hparams.get("head_dim")) is None:
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_scaling.get("factor", 8.0)
@ -2321,9 +2317,7 @@ class DeciModel(TextModel):
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
else:
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
@ -2363,7 +2357,8 @@ class DeciModel(TextModel):
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
if (dim := self.hparams.get("head_dim")) is None:
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_scaling.get("factor", 8.0)
@ -3681,9 +3676,7 @@ class InternLM3Model(TextModel):
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
else:
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
@ -5098,9 +5091,7 @@ class DeepseekModel(TextModel):
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
else:
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
@ -5990,7 +5981,8 @@ class ExaoneModel(TextModel):
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
if (dim := self.hparams.get("head_dim")) is None:
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_scaling.get("factor", 8.0)
@ -6102,7 +6094,8 @@ class BailingMoeModel(TextModel):
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
rope_dim = hparams.get("head_dim") or hparams["hidden_size"] // hparams["num_attention_heads"]
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
rope_scaling = self.hparams.get("rope_scaling") or {}
@ -6134,7 +6127,8 @@ class BailingMoeModel(TextModel):
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads")
n_embd = self.hparams["hidden_size"]
head_dim = self.hparams.get("head_dim") or n_embd // n_head
if (head_dim := self.hparams.get("head_dim")) is None:
head_dim = n_embd // n_head
output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)