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[MLX] [bugfix] Preserve dtype of array when converting to torch #1349

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[MLX] [bugfix] Preserve dtype of array when converting to torch #1349

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e9a573a
[MLX] Preserve dtype of array when converting to torch
neilmehta24 Dec 20, 2024
40f6ad1
Fix style and run pre-commit check
neilmehta24 Dec 20, 2024
a1056a5
Improve test coverage
neilmehta24 Dec 22, 2024
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6 changes: 3 additions & 3 deletions outlines/processors/base_logits_processor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,9 +110,9 @@ def _to_torch(tensor_like: Array) -> torch.Tensor:
import mlx.core as mx

# https://ml-explore.github.io/mlx/build/html/usage/numpy.html#pytorch
return torch.from_dlpack(
np.array(tensor_like.astype(mx.float32), copy=False)
)
if tensor_like.dtype == mx.bfloat16:
tensor_like = tensor_like.astype(mx.float32)
return torch.from_dlpack(np.array(tensor_like, copy=False))

elif is_jax_array_type(type(tensor_like)):
import jax
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8 changes: 7 additions & 1 deletion tests/processors/test_base_processor.py
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Expand Up @@ -18,6 +18,7 @@
import mlx.core as mx

arrays["mlx"] = mx.array([[1, 2], [3, 4]], dtype=mx.float32)
arrays["mlx_bfloat16"] = mx.array([[1, 2], [3, 4]], dtype=mx.bfloat16)
except ImportError:
pass

Expand Down Expand Up @@ -59,7 +60,12 @@ def test_from_torch(array_type, processor):
torch_tensor = torch.tensor([[1, 2], [3, 4]], dtype=torch.float32)
data = processor._from_torch(torch_tensor, type(arrays[array_type]))
assert isinstance(data, type(arrays[array_type]))
assert np.allclose(data, arrays[array_type])
if array_type == "mlx_bfloat16":
# For bfloat16, we expect the output to be float32 due to the conversion
assert data.dtype == mx.float32
assert np.allclose(np.array(data), np.array([[1, 2], [3, 4]], dtype=np.float32))
else:
assert np.allclose(data, arrays[array_type])


@pytest.mark.parametrize("array_type", arrays.keys())
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