vbatts/llama.cpp
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convert-hf: add missing ftype

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Francis Couture-Harpin 2024-05-11 23:18:30 -04:00
parent d7e199e444
commit 2b1e5ea37b
2 changed files with 10 additions and 1 deletions
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4
convert-hf-to-gguf.py
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@ -1202,6 +1202,7 @@ class StableLMModel(Model):
self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
self.gguf_writer.add_file_type(self.ftype)
_q_norms: list[dict[str, Tensor]] | None = None
_k_norms: list[dict[str, Tensor]] | None = None
@ -1578,6 +1579,7 @@ class QwenModel(Model):
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype)
@Model.register("Qwen2ForCausalLM")
@ -1815,6 +1817,7 @@ class PlamoModel(Model):
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong
self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
self.gguf_writer.add_file_type(self.ftype)
def shuffle_attn_q_weight(self, data_torch):
assert data_torch.size() == (5120, 5120)
@ -1994,6 +1997,7 @@ in chat mode so that the conversation can end normally.")
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
self.gguf_writer.add_file_type(self.ftype)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
num_heads = self.hparams["num_attention_heads"]

7
gguf-py/gguf/lazy.py
View file

@ -174,7 +174,12 @@ class LazyBase(ABC, metaclass=LazyMeta):
while _t._data is None:
lt = _t._lazy.popleft()
if lt._data is not None:
raise ValueError(f"{lt} did not belong in the lazy queue")
# Lazy tensor did not belong in the lazy queue.
# Weirdly only happens with Bloom models...
# likely because tensors aren't unique in the queue.
# The final output is still the same as in eager mode,
# so it's safe to ignore this.
continue
assert lt._func is not None
lt._args = cls._recurse_apply(lt._args, already_eager_to_eager)
lt._data = lt._func(lt._args)