diff options
| -rw-r--r--[-rwxr-xr-x] | convert.py | 96 |
1 files changed, 52 insertions, 44 deletions
diff --git a/convert.py b/convert.py index ac99579..ab6a4e1 100755..100644 --- a/convert.py +++ b/convert.py @@ -133,7 +133,7 @@ TENSORS_SET = set(TENSORS_LIST) def find_n_mult(n_ff: int, n_embd: int) -> int: # hardcoded magic range - for n_mult in range(256, 1, -1): + for n_mult in range(8192, 1, -1): calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult if calc_ff == n_ff: return n_mult @@ -141,11 +141,12 @@ def find_n_mult(n_ff: int, n_embd: int) -> int: @dataclass class Params: - n_vocab: int - n_embd: int - n_mult: int - n_head: int - n_layer: int + n_vocab: int + n_embd: int + n_mult: int + n_head: int + n_layer: int + n_kv_head: Optional[int] # This parameter is only used for Llama 2 @staticmethod def guessed(model: 'LazyModel') -> 'Params': @@ -167,11 +168,12 @@ class Params: n_head=n_embd // 128 # guessed return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_mult = 256, - n_head = n_head, - n_layer = n_layer, + n_vocab = n_vocab, + n_embd = n_embd, + n_mult = 256, + n_head = n_head, + n_layer = n_layer, + n_kv_head = None, ) @staticmethod @@ -183,15 +185,17 @@ class Params: n_head = config["num_attention_heads"]; n_layer = config["num_hidden_layers"]; n_ff = config["intermediate_size"]; + n_kv_head = config.get("num_key_value_heads") n_mult = find_n_mult(n_ff, n_embd); return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_mult = n_mult, - n_head = n_head, - n_layer = n_layer, + n_vocab = n_vocab, + n_embd = n_embd, + n_mult = n_mult, + n_head = n_head, + n_layer = n_layer, + n_kv_head = n_kv_head, ) # LLaMA v2 70B params.json @@ -200,21 +204,22 @@ class Params: def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params': config = json.load(open(config_path)) - n_vocab = config["vocab_size"]; - n_embd = config["dim"]; - n_head = config["n_heads"]; - n_layer = config["n_layers"]; - n_mult = config["multiple_of"]; + n_vocab = config["vocab_size"]; + n_embd = config["dim"]; + n_head = config["n_heads"]; + n_layer = config["n_layers"]; + n_mult = config["multiple_of"]; if n_vocab == -1: n_vocab = model["tok_embeddings.weight"].shape[0] return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_mult = n_mult, - n_head = n_head, - n_layer = n_layer, + n_vocab = n_vocab, + n_embd = n_embd, + n_mult = n_mult, + n_head = n_head, + n_layer = n_layer, + n_kv_head = None, ) @staticmethod @@ -317,10 +322,12 @@ class GGMLVocab: Vocab = Union[SentencePieceVocab, GGMLVocab] -def permute(weights: NDArray, n_head: int) -> NDArray: +def permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray: + if n_kv_head is not None and n_head != n_kv_head: + n_head //= n_kv_head return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) - .swapaxes(1, 2) - .reshape(weights.shape)) + .swapaxes(1, 2) + .reshape(weights.shape)) def dequantize_q4(qvalues_pack32: NDArray, scales: NDArray, addends: Optional[NDArray], g_idx: Optional[NDArray]) -> NDArray: @@ -368,7 +375,7 @@ class Tensor(metaclass=ABCMeta): @abstractmethod def astype(self, data_type: DataType) -> 'Tensor': ... @abstractmethod - def permute(self, n_head: int) -> 'Tensor': ... + def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'Tensor': ... @abstractmethod def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ... @abstractmethod @@ -406,8 +413,8 @@ class UnquantizedTensor(Tensor): r = self.ndarray.shape[0] // 3 return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...]) - def permute(self, n_head: int) -> 'UnquantizedTensor': - return UnquantizedTensor(permute(self.ndarray, n_head)) + def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'UnquantizedTensor': + return UnquantizedTensor(permute(self.ndarray, n_head, n_kv_head)) def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray: @@ -455,26 +462,27 @@ class GGMLQuantizedTensor(Tensor): def to_ggml(self) -> 'GGMLQuantizedTensor': return self - def permute(self, n_head: int) -> 'GGMLQuantizedTensor': - return GGMLQuantizedTensor(permute(self.ndarray, n_head), self.shape, self.data_type) + def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'GGMLQuantizedTensor': + return GGMLQuantizedTensor(permute(self.ndarray, n_head, n_kv_head), self.shape, self.data_type) GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor] class DeferredPermutedTensor(Tensor): - def __init__(self, base: Tensor, n_head: int) -> None: + def __init__(self, base: Tensor, n_head: int, n_kv_head: Optional[int] = None) -> None: self.base = base self.n_head = n_head + self.n_kv_head = n_kv_head self.data_type = self.base.data_type def astype(self, data_type: DataType) -> Tensor: - return self.base.astype(data_type).permute(self.n_head) + return self.base.astype(data_type).permute(self.n_head, self.n_kv_head) def to_ggml(self) -> GGMLCompatibleTensor: - return self.base.to_ggml().permute(self.n_head) + return self.base.to_ggml().permute(self.n_head, self.n_kv_head) - def permute(self, n_head: int) -> Tensor: + def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor: raise Exception("shouldn't permute twice") @@ -566,8 +574,8 @@ class GPTQForLLaMaQuantizedTensor(Tensor): ret.data_type = QuantizedDataType(groupsize=new_groupsize, have_addends=True, have_g_idx=False) return ret - def permute(self, n_head: int) -> Tensor: - return DeferredPermutedTensor(self, n_head) + def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor: + return DeferredPermutedTensor(self, n_head, n_kv_head) def to_ggml(self) -> GGMLQuantizedTensor: # The output format looks like this: @@ -698,10 +706,10 @@ def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus: return ModelPlus(model, paths, format, vocab) -def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor: +def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_kv_head: Optional[int] = None) -> LazyTensor: def load() -> Tensor: - return lazy_tensor.load().permute(n_head) - return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) + return lazy_tensor.load().permute(n_head, n_kv_head) + return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_kv_head}) ' + lazy_tensor.description) def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor: def load() -> Tensor: @@ -726,7 +734,7 @@ def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel: for i in itertools.count(): if f"model.layers.{i}.self_attn.q_proj.weight" in model: out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head) - out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head) + out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_kv_head) out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] elif f"model.layers.{i}.self_attn.W_pack.weight" in model: out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head) |