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convert : fix big-endian conversion (#17431)

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* Fix convert_hf_to_gguf.py script on s390x

Assume converted model data is originally little-endian.
Byteswap data on s390x after reading it to put values in correct presentation
for any transformation needed, like calculating weight tensors.

Then byteswap data to little-endian before passing it to GGUFWriter while
GGUFWriter will byteswap data back to big endian if big endian output is requested.

byteswap(inplace=True) calls don't work with lazy tensor and array wrappers.
Use byteswap with copying data to workaround this behaviour.

* Make GGUFWriter accept tensors in native endianness instead of little-endian

With this change if no byteswapping is actually needed, 2 excessive byteswaps can be omitted on s390x

* Fix byteswapping in convert_hf_to_gguf.py for remote models
This commit is contained in:
Aleksei Nikiforov
2025-11-25 14:18:16 +01:00
committed by GitHub
parent 55ab25caf5
commit 05872ac885
2 changed files with 42 additions and 6 deletions
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convert_hf_to_gguf.py
+33 -2
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@@ -10061,6 +10061,25 @@ class LazyTorchTensor(gguf.LazyBase):
torch.uint8: np.uint8,
}
# only used when byteswapping data. Only correct size is needed
_dtype_byteswap_map: dict[torch.dtype, type] = {
torch.float64: np.float64,
torch.float32: np.float32,
torch.bfloat16: np.float16,
torch.float16: np.float16,
torch.int64: np.int64,
torch.uint64: np.uint64,
torch.int32: np.int32,
torch.uint32: np.uint32,
torch.int16: np.int16,
torch.uint16: np.uint16,
torch.int8: np.int8,
torch.uint8: np.uint8,
torch.bool: np.uint8,
torch.float8_e4m3fn: np.uint8,
torch.float8_e5m2: np.uint8,
}
# used for safetensors slices
# ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046
# TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734
@@ -10104,8 +10123,14 @@ class LazyTorchTensor(gguf.LazyBase):
@classmethod
def from_local_tensor(cls, t: gguf.utility.LocalTensor) -> Tensor:
def load_tensor(tensor: gguf.utility.LocalTensor) -> Tensor:
def byteswap_tensor(tensor: np.ndarray, dtype: type) -> np.ndarray:
if sys.byteorder == 'big':
# switch data back to big endian
tensor = tensor.view(dtype).byteswap(inplace=False)
return tensor
dtype = cls._dtype_str_map[tensor.dtype]
return torch.from_numpy(tensor.mmap_bytes()).view(dtype).reshape(tensor.shape)
numpy_dtype = cls._dtype_byteswap_map[dtype]
return torch.from_numpy(byteswap_tensor(tensor.mmap_bytes(), numpy_dtype)).view(dtype).reshape(tensor.shape)
dtype = cls._dtype_str_map[t.dtype]
shape = t.shape
lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(t,), func=lambda r: load_tensor(r))
@@ -10113,10 +10138,16 @@ class LazyTorchTensor(gguf.LazyBase):
@classmethod
def from_remote_tensor(cls, remote_tensor: gguf.utility.RemoteTensor):
def byteswap_tensor(tensor: np.ndarray, dtype: type) -> np.ndarray:
if sys.byteorder == 'big':
# switch data back to big endian
tensor = tensor.view(dtype).byteswap(inplace=False)
return tensor
dtype = cls._dtype_str_map[remote_tensor.dtype]
numpy_dtype = cls._dtype_byteswap_map[dtype]
shape = remote_tensor.shape
meta = cls.meta_with_dtype_and_shape(dtype, shape)
lazy = cls(meta=meta, args=(remote_tensor,), func=lambda r: torch.frombuffer(r.data(), dtype=dtype).reshape(shape))
lazy = cls(meta=meta, args=(remote_tensor,), func=lambda r: torch.from_numpy(byteswap_tensor(np.frombuffer(r.data(), dtype=numpy_dtype), numpy_dtype)).view(dtype).reshape(shape))
return cast(torch.Tensor, lazy)
@classmethod