vbatts/llama.cpp
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fix convert scripts

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This commit is contained in:
Cebtenzzre 2023-09-20 22:25:21 -04:00
parent 904d4edfa1
commit 56abb9a406
2 changed files with 15 additions and 13 deletions
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3
convert-baichuan-hf-to-gguf.py
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@ -154,7 +154,8 @@ gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]:
if "type" in hparams["rope_scaling"]:
if hparams["rope_scaling"]["type"] == "linear":
gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"])
gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"])
# TOKENIZATION

25
convert.py
View file

@ -205,13 +205,13 @@ class Params:
rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
rope_scaling = config.get("rope_scaling")
if rope_scaling is not None and typ := rope_scaling.get("type"):
if rope_scaling is not None and (typ := rope_scaling.get("type")):
rope_factor = rope_scaling.get("factor")
f_rope_scale = rope_factor
if typ == "linear":
rope_scaling_type = RopeScalingType.LINEAR
rope_scaling_type = gguf.RopeScalingType.LINEAR
elif typ == "yarn":
rope_scaling_type = RopeScalingType.YARN
rope_scaling_type = gguf.RopeScalingType.YARN
n_orig_ctx = rope_scaling['original_max_position_embeddings']
rope_finetuned = rope_scaling['finetuned']
else:
@ -231,10 +231,10 @@ class Params:
n_layer = config["num_hidden_layers"],
n_ctx = n_ctx,
n_ff = config["intermediate_size"],
n_head = config["num_attention_heads"],
n_head_kv = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head,
n_head = (n_head := config["num_attention_heads"]),
n_head_kv = config.get("num_key_value_heads", n_head),
f_norm_eps = config["rms_norm_eps"],
f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None,
f_rope_freq_base = config.get("rope_theta"),
f_rope_scale = f_rope_scale,
n_orig_ctx = n_orig_ctx,
rope_finetuned = rope_finetuned,
@ -247,7 +247,7 @@ class Params:
config = json.load(open(config_path))
# hack to determine LLaMA v1 vs v2 vs CodeLlama
if f_rope_freq_base == 1000000:
if config.get("rope_theta") == 1000000:
# CodeLlama
n_ctx = 16384
elif config["norm_eps"] == 1e-05:
@ -263,10 +263,10 @@ class Params:
n_layer = config["n_layers"],
n_ctx = n_ctx,
n_ff = model["layers.0.feed_forward.w1.weight"].shape[0],
n_head = config["n_heads"],
n_head_kv = config["n_kv_heads"] if "n_kv_heads" in config else n_head,
n_head = (n_head := config["n_heads"]),
n_head_kv = config.get("n_kv_heads", n_head),
f_norm_eps = config["norm_eps"],
f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None,
f_rope_freq_base = config.get("rope_theta"),
)
@staticmethod
@ -834,14 +834,15 @@ class OutputFile:
self.gguf.add_rope_freq_base(params.f_rope_freq_base)
if params.rope_scaling_type:
assert params.f_rope_scale is not None
self.gguf.add_rope_scaling_type(params.rope_scaling_type)
self.gguf.add_rope_scaling_factor(params.f_rope_scale)
if params.n_orig_ctx is not None:
self.gguf.add_rope_original_context_length(params.n_orig_ctx)
self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx)
if params.rope_finetuned is not None:
self.gguf.add_rope_finetuned(params.rope_finetuned)
self.gguf.add_rope_scaling_finetuned(params.rope_finetuned)
if params.ftype is not None:
self.gguf.add_file_type(params.ftype)