llama : implement YaRN RoPE scaling (#2268)
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com> Co-authored-by: Jeffrey Quesnelle <jquesnelle@gmail.com>
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cebtenzzre
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15 changed files with 763 additions and 257 deletions
97
convert.py
97
convert.py
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@ -151,8 +151,11 @@ class Params:
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n_head_kv: int
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f_norm_eps: float
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rope_scaling_type: gguf.RopeScalingType | None = None
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f_rope_freq_base: float | None = None
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f_rope_scale: float | None = None
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n_orig_ctx: int | None = None
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rope_finetuned: bool | None = None
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ftype: GGMLFileType | None = None
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@ -198,20 +201,20 @@ class Params:
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def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
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config = json.load(open(config_path))
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n_vocab = config["vocab_size"]
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n_embd = config["hidden_size"]
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n_layer = config["num_hidden_layers"]
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n_ff = config["intermediate_size"]
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n_head = config["num_attention_heads"]
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n_head_kv = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head
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f_norm_eps = config["rms_norm_eps"]
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f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
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rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
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rope_scaling = config.get("rope_scaling")
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if isinstance(rope_scaling, dict) and rope_scaling.get("type") == "linear":
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f_rope_scale = config["rope_scaling"].get("factor")
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else:
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f_rope_scale = None
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if rope_scaling is not None and (typ := rope_scaling.get("type")):
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rope_factor = rope_scaling.get("factor")
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f_rope_scale = rope_factor
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if typ == "linear":
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rope_scaling_type = gguf.RopeScalingType.LINEAR
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elif typ == "yarn":
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rope_scaling_type = gguf.RopeScalingType.YARN
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n_orig_ctx = rope_scaling['original_max_position_embeddings']
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rope_finetuned = rope_scaling['finetuned']
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else:
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raise NotImplementedError(f'Unknown rope scaling type: {typ}')
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if "max_sequence_length" in config:
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n_ctx = config["max_sequence_length"]
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@ -222,16 +225,19 @@ class Params:
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"Suggestion: provide 'config.json' of the model in the same directory containing model files.")
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return Params(
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n_vocab = n_vocab,
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n_embd = n_embd,
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n_layer = n_layer,
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n_ctx = n_ctx,
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n_ff = n_ff,
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n_head = n_head,
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n_head_kv = n_head_kv,
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f_norm_eps = f_norm_eps,
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f_rope_freq_base = f_rope_freq_base,
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f_rope_scale = f_rope_scale,
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n_vocab = config["vocab_size"],
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n_embd = config["hidden_size"],
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n_layer = config["num_hidden_layers"],
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n_ctx = n_ctx,
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n_ff = config["intermediate_size"],
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n_head = (n_head := config["num_attention_heads"]),
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n_head_kv = config.get("num_key_value_heads", n_head),
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f_norm_eps = config["rms_norm_eps"],
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f_rope_freq_base = config.get("rope_theta"),
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rope_scaling_type = rope_scaling_type,
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f_rope_scale = f_rope_scale,
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n_orig_ctx = n_orig_ctx,
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rope_finetuned = rope_finetuned,
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)
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# LLaMA v2 70B params.json
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@ -240,17 +246,8 @@ class Params:
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def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
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config = json.load(open(config_path))
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n_vocab = config["vocab_size"] if "vocab_size" in config else -1
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n_embd = config["dim"]
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n_layer = config["n_layers"]
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n_ff = -1
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n_head = config["n_heads"]
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n_head_kv = config["n_kv_heads"] if "n_kv_heads" in config else n_head
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f_norm_eps = config["norm_eps"]
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f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
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# hack to determine LLaMA v1 vs v2 vs CodeLlama
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if f_rope_freq_base == 1000000:
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if config.get("rope_theta") == 1000000:
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# CodeLlama
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n_ctx = 16384
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elif config["norm_eps"] == 1e-05:
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@ -260,22 +257,16 @@ class Params:
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# LLaMA v1
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n_ctx = 2048
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if n_vocab == -1:
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n_vocab = model["tok_embeddings.weight"].shape[0]
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if n_ff == -1:
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n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]
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return Params(
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n_vocab = n_vocab,
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n_embd = n_embd,
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n_layer = n_layer,
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n_vocab = config.get("vocab_size", model["tok_embeddings.weight"].shape[0]),
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n_embd = config["dim"],
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n_layer = config["n_layers"],
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n_ctx = n_ctx,
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n_ff = n_ff,
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n_head = n_head,
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n_head_kv = n_head_kv,
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f_norm_eps = f_norm_eps,
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f_rope_freq_base = f_rope_freq_base,
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n_ff = model["layers.0.feed_forward.w1.weight"].shape[0],
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n_head = (n_head := config["n_heads"]),
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n_head_kv = config.get("n_kv_heads", n_head),
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f_norm_eps = config["norm_eps"],
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f_rope_freq_base = config.get("rope_theta"),
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)
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@staticmethod
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@ -831,8 +822,16 @@ class OutputFile:
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if params.f_rope_freq_base is not None:
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self.gguf.add_rope_freq_base(params.f_rope_freq_base)
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if params.f_rope_scale is not None:
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self.gguf.add_rope_scale_linear(params.f_rope_scale)
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if params.rope_scaling_type:
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assert params.f_rope_scale is not None
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self.gguf.add_rope_scaling_type(params.rope_scaling_type)
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self.gguf.add_rope_scaling_factor(params.f_rope_scale)
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if params.n_orig_ctx is not None:
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self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx)
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if params.rope_finetuned is not None:
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self.gguf.add_rope_scaling_finetuned(params.rope_finetuned)
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if params.ftype is not None:
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self.gguf.add_file_type(params.ftype)
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