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author | laurent <laurent.mazare@gmail.com> | 2023-07-02 20:42:55 +0100 |
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committer | laurent <laurent.mazare@gmail.com> | 2023-07-02 20:42:55 +0100 |
commit | fbfe74caab8835d758a1a2bb9ab1c62c9afd50d5 (patch) | |
tree | b577884df10ad2fe3ca9a401cb48520142856b1e /candle-pyo3/src/lib.rs | |
parent | eb6f7d30b6f8bae64e9958c27bc8f60f251e5c52 (diff) | |
download | candle-fbfe74caab8835d758a1a2bb9ab1c62c9afd50d5.tar.gz candle-fbfe74caab8835d758a1a2bb9ab1c62c9afd50d5.tar.bz2 candle-fbfe74caab8835d758a1a2bb9ab1c62c9afd50d5.zip |
Preliminary pyo3 support for device.
Diffstat (limited to 'candle-pyo3/src/lib.rs')
-rw-r--r-- | candle-pyo3/src/lib.rs | 47 |
1 files changed, 45 insertions, 2 deletions
diff --git a/candle-pyo3/src/lib.rs b/candle-pyo3/src/lib.rs index d5d472d5..62eb21e8 100644 --- a/candle-pyo3/src/lib.rs +++ b/candle-pyo3/src/lib.rs @@ -4,7 +4,7 @@ use pyo3::types::PyTuple; use half::{bf16, f16}; -use ::candle::{DType, Device::Cpu, Tensor, WithDType}; +use ::candle::{DType, Device, Tensor, WithDType}; pub fn wrap_err(err: ::candle::Error) -> PyErr { PyErr::new::<PyValueError, _>(format!("{err:?}")) @@ -30,7 +30,7 @@ impl<'source> FromPyObject<'source> for PyDType { use std::str::FromStr; let dtype: &str = ob.extract()?; let dtype = DType::from_str(dtype) - .map_err(|_| PyTypeError::new_err(format!("invalid dtype {dtype}")))?; + .map_err(|_| PyTypeError::new_err(format!("invalid dtype '{dtype}'")))?; Ok(Self(dtype)) } } @@ -41,6 +41,43 @@ impl ToPyObject for PyDType { } } +#[derive(Clone, Copy, Debug, PartialEq, Eq)] +enum PyDevice { + Cpu, + Cuda, +} + +impl PyDevice { + fn from_device(device: Device) -> Self { + match device { + Device::Cpu => Self::Cpu, + Device::Cuda(_) => Self::Cuda, + } + } +} + +impl<'source> FromPyObject<'source> for PyDevice { + fn extract(ob: &'source PyAny) -> PyResult<Self> { + let device: &str = ob.extract()?; + let device = match device { + "cpu" => PyDevice::Cpu, + "cuda" => PyDevice::Cuda, + _ => Err(PyTypeError::new_err(format!("invalid device '{device}'")))?, + }; + Ok(device) + } +} + +impl ToPyObject for PyDevice { + fn to_object(&self, py: Python<'_>) -> PyObject { + let str = match self { + PyDevice::Cpu => "cpu", + PyDevice::Cuda => "cuda", + }; + str.to_object(py) + } +} + trait PyWithDType: WithDType { fn to_py(&self, py: Python<'_>) -> PyObject; } @@ -83,6 +120,7 @@ impl PyTensor { #[new] // TODO: Handle arbitrary input dtype and shape. fn new(py: Python<'_>, vs: PyObject) -> PyResult<Self> { + use Device::Cpu; let tensor = if let Ok(vs) = vs.extract::<u32>(py) { Tensor::new(vs, &Cpu).map_err(wrap_err)? } else if let Ok(vs) = vs.extract::<Vec<u32>>(py) { @@ -156,6 +194,11 @@ impl PyTensor { } #[getter] + fn device(&self, py: Python<'_>) -> PyObject { + PyDevice::from_device(self.0.device()).to_object(py) + } + + #[getter] fn rank(&self) -> usize { self.0.rank() } |