diff options
Diffstat (limited to 'convert.py')
| -rwxr-xr-x | convert.py | 74 |
1 files changed, 57 insertions, 17 deletions
@@ -42,6 +42,7 @@ NDArray: TypeAlias = 'np.ndarray[Any, Any]' ARCH = gguf.MODEL_ARCH.LLAMA DEFAULT_CONCURRENCY = 8 + # # data types # @@ -62,10 +63,10 @@ class UnquantizedDataType(DataType): pass -DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0']) -DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0']) -DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = []) -DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0']) +DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0']) +DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0']) +DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = []) +DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0']) @dataclass(frozen=True) @@ -151,14 +152,16 @@ 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 - f_norm_eps: float + 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 rope_scaling_type: gguf.RopeScalingType | None = None f_rope_freq_base: float | None = None @@ -233,6 +236,13 @@ class Params: 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_used = None + + if "num_local_experts" in config: + n_experts = config["num_local_experts"] + n_experts_used = config["num_experts_per_tok"] + return Params( n_vocab = config["vocab_size"], n_embd = config["hidden_size"], @@ -241,6 +251,8 @@ class Params: 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, @@ -255,8 +267,15 @@ class Params: def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params: config = json.load(open(config_path)) + n_experts = None + n_experts_used = None + f_rope_freq_base = None + # hack to determine LLaMA v1 vs v2 vs CodeLlama - if config.get("rope_theta") == 1000000: + if config.get("moe"): + # Mixtral + n_ctx = 32768 + elif config.get("rope_theta") == 1000000: # CodeLlama n_ctx = 16384 elif config["norm_eps"] == 1e-05: @@ -266,16 +285,27 @@ class Params: # LLaMA v1 n_ctx = 2048 + if "layers.0.feed_forward.w1.weight" in model: + n_ff = model["layers.0.feed_forward.w1.weight"].shape[0] + + 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_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 = model["layers.0.feed_forward.w1.weight"].shape[0], + 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 = config.get("rope_theta", f_rope_freq_base), ) @staticmethod @@ -832,7 +862,17 @@ class OutputFile: self.gguf.add_rope_dimension_count(params.n_embd // params.n_head) self.gguf.add_head_count (params.n_head) self.gguf.add_head_count_kv (params.n_head_kv) - self.gguf.add_layer_norm_rms_eps (params.f_norm_eps) + + if params.n_experts: + self.gguf.add_expert_count(params.n_experts) + + if params.n_experts_used: + self.gguf.add_expert_used_count(params.n_experts_used) + + if params.f_norm_eps: + self.gguf.add_layer_norm_rms_eps(params.f_norm_eps) + else: + raise ValueError('f_norm_eps is None') if params.f_rope_freq_base is not None: self.gguf.add_rope_freq_base(params.f_rope_freq_base) @@ -956,7 +996,7 @@ class OutputFile: def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType: - wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) +".weight"].data_type + wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) + ".weight"].data_type if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32): return GGMLFileType.AllF32 |