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convert.py : Update to support 70B HF format model files (#2427)

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* convert.py : fix llama 2 70b conversion from Huggingface
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mj-shifu 2023-07-27 22:39:17 +02:00 committed by GitHub
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1 changed files with 52 additions and 44 deletions
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@ -133,7 +133,7 @@ TENSORS_SET = set(TENSORS_LIST)
def find_n_mult(n_ff: int, n_embd: int) -> int: def find_n_mult(n_ff: int, n_embd: int) -> int:
# hardcoded magic range # 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 calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
if calc_ff == n_ff: if calc_ff == n_ff:
return n_mult return n_mult
@ -141,11 +141,12 @@ def find_n_mult(n_ff: int, n_embd: int) -> int:
@dataclass @dataclass
class Params: class Params:
n_vocab: int n_vocab: int
n_embd: int n_embd: int
n_mult: int n_mult: int
n_head: int n_head: int
n_layer: int n_layer: int
n_kv_head: Optional[int] # This parameter is only used for Llama 2
@staticmethod @staticmethod
def guessed(model: 'LazyModel') -> 'Params': def guessed(model: 'LazyModel') -> 'Params':
@ -167,11 +168,12 @@ class Params:
n_head=n_embd // 128 # guessed n_head=n_embd // 128 # guessed
return Params( return Params(
n_vocab = n_vocab, n_vocab = n_vocab,
n_embd = n_embd, n_embd = n_embd,
n_mult = 256, n_mult = 256,
n_head = n_head, n_head = n_head,
n_layer = n_layer, n_layer = n_layer,
n_kv_head = None,
) )
@staticmethod @staticmethod
@ -183,15 +185,17 @@ class Params:
n_head = config["num_attention_heads"]; n_head = config["num_attention_heads"];
n_layer = config["num_hidden_layers"]; n_layer = config["num_hidden_layers"];
n_ff = config["intermediate_size"]; n_ff = config["intermediate_size"];
n_kv_head = config.get("num_key_value_heads")
n_mult = find_n_mult(n_ff, n_embd); n_mult = find_n_mult(n_ff, n_embd);
return Params( return Params(
n_vocab = n_vocab, n_vocab = n_vocab,
n_embd = n_embd, n_embd = n_embd,
n_mult = n_mult, n_mult = n_mult,
n_head = n_head, n_head = n_head,
n_layer = n_layer, n_layer = n_layer,
n_kv_head = n_kv_head,
) )
# LLaMA v2 70B params.json # LLaMA v2 70B params.json
@ -200,21 +204,22 @@ class Params:
def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params': def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
config = json.load(open(config_path)) config = json.load(open(config_path))
n_vocab = config["vocab_size"]; n_vocab = config["vocab_size"];
n_embd = config["dim"]; n_embd = config["dim"];
n_head = config["n_heads"]; n_head = config["n_heads"];
n_layer = config["n_layers"]; n_layer = config["n_layers"];
n_mult = config["multiple_of"]; n_mult = config["multiple_of"];
if n_vocab == -1: if n_vocab == -1:
n_vocab = model["tok_embeddings.weight"].shape[0] n_vocab = model["tok_embeddings.weight"].shape[0]
return Params( return Params(
n_vocab = n_vocab, n_vocab = n_vocab,
n_embd = n_embd, n_embd = n_embd,
n_mult = n_mult, n_mult = n_mult,
n_head = n_head, n_head = n_head,
n_layer = n_layer, n_layer = n_layer,
n_kv_head = None,
) )
@staticmethod @staticmethod
@ -317,10 +322,12 @@ class GGMLVocab:
Vocab = Union[SentencePieceVocab, 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:]) return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2) .swapaxes(1, 2)
.reshape(weights.shape)) .reshape(weights.shape))
def dequantize_q4(qvalues_pack32: NDArray, scales: NDArray, addends: Optional[NDArray], g_idx: Optional[NDArray]) -> NDArray: 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 @abstractmethod
def astype(self, data_type: DataType) -> 'Tensor': ... def astype(self, data_type: DataType) -> 'Tensor': ...
@abstractmethod @abstractmethod
def permute(self, n_head: int) -> 'Tensor': ... def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'Tensor': ...
@abstractmethod @abstractmethod
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ... def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
@abstractmethod @abstractmethod
@ -406,8 +413,8 @@ class UnquantizedTensor(Tensor):
r = self.ndarray.shape[0] // 3 r = self.ndarray.shape[0] // 3
return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...]) return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
def permute(self, n_head: int) -> 'UnquantizedTensor': def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'UnquantizedTensor':
return UnquantizedTensor(permute(self.ndarray, n_head)) return UnquantizedTensor(permute(self.ndarray, n_head, n_kv_head))
def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray: 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': def to_ggml(self) -> 'GGMLQuantizedTensor':
return self return self
def permute(self, n_head: int) -> 'GGMLQuantizedTensor': def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> 'GGMLQuantizedTensor':
return GGMLQuantizedTensor(permute(self.ndarray, n_head), self.shape, self.data_type) return GGMLQuantizedTensor(permute(self.ndarray, n_head, n_kv_head), self.shape, self.data_type)
GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor] GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor]
class DeferredPermutedTensor(Tensor): 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.base = base
self.n_head = n_head self.n_head = n_head
self.n_kv_head = n_kv_head
self.data_type = self.base.data_type self.data_type = self.base.data_type
def astype(self, data_type: DataType) -> Tensor: 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: 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") 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) ret.data_type = QuantizedDataType(groupsize=new_groupsize, have_addends=True, have_g_idx=False)
return ret return ret
def permute(self, n_head: int) -> Tensor: def permute(self, n_head: int, n_kv_head: Optional[int] = None) -> Tensor:
return DeferredPermutedTensor(self, n_head) return DeferredPermutedTensor(self, n_head, n_kv_head)
def to_ggml(self) -> GGMLQuantizedTensor: def to_ggml(self) -> GGMLQuantizedTensor:
# The output format looks like this: # 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) 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: def load() -> Tensor:
return lazy_tensor.load().permute(n_head) return lazy_tensor.load().permute(n_head, n_kv_head)
return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) 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 permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor:
def load() -> Tensor: def load() -> Tensor:
@ -726,7 +734,7 @@ def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel:
for i in itertools.count(): for i in itertools.count():
if f"model.layers.{i}.self_attn.q_proj.weight" in model: 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.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"] 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: 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) out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head)