diff options
Diffstat (limited to 'convert.py')
| -rw-r--r-- | convert.py | 41 |
1 files changed, 36 insertions, 5 deletions
@@ -136,7 +136,7 @@ def find_n_mult(n_ff: int, n_embd: int) -> int: calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult if calc_ff == n_ff: return n_mult - return 1 + raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).") @dataclass class Params: @@ -321,6 +321,10 @@ class Tensor(metaclass=ABCMeta): @abstractmethod def permute(self, n_head: int) -> 'Tensor': ... @abstractmethod + def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ... + @abstractmethod + def part(self, n_part: int) -> 'UnquantizedTensor': ... + @abstractmethod def to_ggml(self) -> 'GGMLCompatibleTensor': ... @@ -345,6 +349,14 @@ class UnquantizedTensor(Tensor): def to_ggml(self) -> 'UnquantizedTensor': return self + def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': + r = self.ndarray.shape[0] // 3 + return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head)) + + def part(self, n_part: int) -> 'UnquantizedTensor': + 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)) @@ -642,6 +654,19 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor: return lazy_tensor.load().permute(n_head) return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) +def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor: + def load() -> Tensor: + return lazy_tensor.load().permute_part(n_part, n_head) + s = lazy_tensor.shape.copy() + s[0] = s[0] // 3 + return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) + +def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor: + def load() -> Tensor: + return lazy_tensor.load().part(n_part) + s = lazy_tensor.shape.copy() + s[0] = s[0] // 3 + return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description) def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel: out: LazyModel = {} @@ -650,11 +675,17 @@ def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel: out["output.weight"] = model["lm_head.weight"] for i in itertools.count(): - if f"model.layers.{i}.self_attn.q_proj.weight" not 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.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_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) + out[f"layers.{i}.attention.wk.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head) + out[f"layers.{i}.attention.wv.weight"] = part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 2) + else: break - 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.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] + out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"] out[f"layers.{i}.feed_forward.w1.weight"] = model[f"model.layers.{i}.mlp.gate_proj.weight"] |