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Refine Model Hyperparameters and Params Class

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- Updated type annotations to use `Optional` for clarity.
- Improved method names and attribute consistency.
- Removed unnecessary variables for better code readability.

Additional Notes:

- Highlighted the use of `Optional` for clearer intent.
- Ensured backward and forward compatibility.
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teleprint-me 2024-01-07 19:25:07 -05:00
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commit 15e18973da
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convert.py
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@ -182,65 +182,85 @@ GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
@dataclass
class Params:
n_vocab: int
n_embd: int
n_layer: int
n_ctx: int
n_ff: int
n_head: int
n_head_kv: int
n_experts: int | None = None
n_experts_used: int | None = None
f_norm_eps: float | None = None
n_vocab: int
n_embd: int
n_layer: int
n_ctx: int
n_ff: int
n_head: int
n_head_kv: int
f_norm_eps: Optional[float] = None
n_experts: Optional[int] = None
n_experts_used: Optional[int] = None
rope_scaling_type: gguf.RopeScalingType | None = None
f_rope_freq_base: float | None = None
f_rope_scale: float | None = None
n_orig_ctx: int | None = None
rope_finetuned: bool | None = None
rope_scaling_type: Optional[gguf.RopeScalingType] = None
f_rope_freq_base: Optional[float] = None
f_rope_scale: Optional[float] = None
n_orig_ctx: Optional[int] = None
rope_finetuned: Optional[bool] = None
ftype: GGMLFileType | None = None
ftype: Optional[GGMLFileType] = None
# path to the directory containing the model files
path_model: Path | None = None
path_model: Optional[Path] = None
@staticmethod
def guessed(model: LazyModel) -> Params:
def guessed(model: LazyModel) -> "Params":
# try transformer naming first
n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape
n_vocab, n_embd = (
model["model.embed_tokens.weight"].shape
if "model.embed_tokens.weight" in model
else model["tok_embeddings.weight"].shape
)
# try transformer naming first
if "model.layers.0.self_attn.q_proj.weight" in model:
n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
elif "model.layers.0.self_attn.W_pack.weight" in model: # next: try baichuan naming
n_layer = next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model)
n_layer = next(
i
for i in itertools.count()
if f"model.layers.{i}.self_attn.q_proj.weight" not in model
)
elif (
"model.layers.0.self_attn.W_pack.weight" in model
): # next: try baichuan naming
n_layer = next(
i
for i in itertools.count()
if f"model.layers.{i}.self_attn.W_pack.weight" not in model
)
else:
n_layer = next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
n_layer = next(
i
for i in itertools.count()
if f"layers.{i}.attention.wq.weight" not in model
)
if n_layer < 1:
raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files.")
raise Exception(
"failed to guess 'n_layer'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files."
)
n_head = n_embd // 128 # guessed
n_mult = 256 # guessed
n_head = n_embd // 128 # guessed
n_mult = 256 # guessed
# TODO: verify this
n_ff = int(2 * (4 * n_embd) / 3)
n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult)
return Params(
n_vocab = n_vocab,
n_embd = n_embd,
n_layer = n_layer,
n_ctx = -1,
n_ff = n_ff,
n_head = n_head,
n_head_kv = n_head,
f_norm_eps = 1e-5,
n_vocab=n_vocab,
n_embd=n_embd,
n_layer=n_layer,
n_ctx=-1,
n_ff=n_ff,
n_head=n_head,
n_head_kv=n_head,
f_norm_eps=1e-5,
)
@staticmethod
def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params:
def load_transformers_config(model: LazyModel, config_path: Path) -> "Params":
config = json.load(open(config_path))
rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
@ -253,20 +273,22 @@ class Params:
rope_scaling_type = gguf.RopeScalingType.LINEAR
elif typ == "yarn":
rope_scaling_type = gguf.RopeScalingType.YARN
n_orig_ctx = rope_scaling['original_max_position_embeddings']
rope_finetuned = rope_scaling['finetuned']
n_orig_ctx = rope_scaling["original_max_position_embeddings"]
rope_finetuned = rope_scaling["finetuned"]
else:
raise NotImplementedError(f'Unknown rope scaling type: {typ}')
raise NotImplementedError(f"Unknown rope scaling type: {typ}")
if "max_sequence_length" in config:
n_ctx = config["max_sequence_length"]
elif "max_position_embeddings" in config:
n_ctx = config["max_position_embeddings"]
else:
raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files.")
raise Exception(
"failed to guess 'n_ctx'. This model is unknown or unsupported.\n"
"Suggestion: provide 'config.json' of the model in the same directory containing model files."
)
n_experts = None
n_experts = None
n_experts_used = None
if "num_local_experts" in config:
@ -274,30 +296,30 @@ class Params:
n_experts_used = config["num_experts_per_tok"]
return Params(
n_vocab = config["vocab_size"],
n_embd = config["hidden_size"],
n_layer = config["num_hidden_layers"],
n_ctx = n_ctx,
n_ff = config["intermediate_size"],
n_head = (n_head := config["num_attention_heads"]),
n_head_kv = config.get("num_key_value_heads", n_head),
n_experts = n_experts,
n_experts_used = n_experts_used,
f_norm_eps = config["rms_norm_eps"],
f_rope_freq_base = config.get("rope_theta"),
rope_scaling_type = rope_scaling_type,
f_rope_scale = f_rope_scale,
n_orig_ctx = n_orig_ctx,
rope_finetuned = rope_finetuned,
n_vocab=config["vocab_size"],
n_embd=config["hidden_size"],
n_layer=config["num_hidden_layers"],
n_ctx=n_ctx,
n_ff=config["intermediate_size"],
n_head=(n_head := config["num_attention_heads"]),
n_head_kv=config.get("num_key_value_heads", n_head),
n_experts=n_experts,
n_experts_used=n_experts_used,
f_norm_eps=config["rms_norm_eps"],
f_rope_freq_base=config.get("rope_theta"),
rope_scaling_type=rope_scaling_type,
f_rope_scale=f_rope_scale,
n_orig_ctx=n_orig_ctx,
rope_finetuned=rope_finetuned,
)
# LLaMA v2 70B params.json
# {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1}
@staticmethod
def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params:
def load_torch_params(model: LazyModel, config_path: Path) -> "Params":
config = json.load(open(config_path))
n_experts = None
n_experts = None
n_experts_used = None
f_rope_freq_base = None
@ -320,37 +342,37 @@ class Params:
if config.get("moe"):
n_ff = model["layers.0.feed_forward.experts.0.w1.weight"].shape[0]
n_experts = config["moe"]["num_experts"]
n_experts = config["moe"]["num_experts"]
n_experts_used = config["moe"]["num_experts_per_tok"]
f_rope_freq_base = 1e6
return Params(
n_vocab = model["tok_embeddings.weight"].shape[0],
n_embd = config["dim"],
n_layer = config["n_layers"],
n_ctx = n_ctx,
n_ff = n_ff,
n_head = (n_head := config["n_heads"]),
n_head_kv = config.get("n_kv_heads", n_head),
n_experts = n_experts,
n_experts_used = n_experts_used,
f_norm_eps = config["norm_eps"],
f_rope_freq_base = config.get("rope_theta", f_rope_freq_base),
n_vocab=config.get("vocab_size", model["tok_embeddings.weight"].shape[0]),
n_embd=config["dim"],
n_layer=config["n_layers"],
n_ctx=n_ctx,
n_ff=n_ff,
n_head=(n_head := config["n_heads"]),
n_head_kv=config.get("n_kv_heads", n_head),
n_experts=n_experts,
n_experts_used=n_experts_used,
f_norm_eps=config["norm_eps"],
f_rope_freq_base=config.get("rope_theta", f_rope_freq_base),
)
@staticmethod
def load(model_plus: ModelPlus) -> Params:
hf_config_path = model_plus.paths[0].parent / "config.json"
def load(model_plus: ModelPlus) -> "Params":
hf_config_path = model_plus.paths[0].parent / "config.json"
orig_config_path = model_plus.paths[0].parent / "params.json"
if hf_config_path.exists():
params = Params.loadHFTransformerJson(model_plus.model, hf_config_path)
params = Params.load_transformers_config(model_plus.model, hf_config_path)
elif orig_config_path.exists():
params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path)
elif model_plus.format != 'none':
params = Params.load_torch_params(model_plus.model, orig_config_path)
elif model_plus.format != "none":
params = Params.guessed(model_plus.model)
else:
raise ValueError('Cannot guess params when model format is none')
raise ValueError("Cannot guess params when model format is none")
params.path_model = model_plus.paths[0].parent