Preliminary pyo3 support for device.

candle-pyo3/Cargo.toml

@@ -18,3 +18,7 @@ crate-type = ["cdylib"]
 candle = { path = "../candle-core", default-features=false }
 pyo3 = { version = "0.19.0", features = ["extension-module"] }
 half = { version = "2.3.1", features = ["num-traits"] }
+
+[features]
+default = ["cuda"]
+cuda = ["candle/cuda"]

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) {
@@ -155,6 +193,11 @@ impl PyTensor {
         PyDType(self.0.dtype()).to_object(py)
     }
 
+    #[getter]
+    fn device(&self, py: Python<'_>) -> PyObject {
+        PyDevice::from_device(self.0.device()).to_object(py)
+    }
+
     #[getter]
     fn rank(&self) -> usize {
         self.0.rank()

candle-pyo3/test.py

@@ -2,12 +2,14 @@ import candle
 
 t = candle.Tensor(42.0)
 print(t)
-print("shape", t.shape, t.rank)
+print(t.shape, t.rank, t.device)
 print(t + t)
 
 t = candle.Tensor([3.0, 1, 4, 1, 5, 9, 2, 6])
 print(t)
 print(t+t)
+
 t = t.reshape([2, 4])
 print(t.matmul(t.t()))
+
 print(t.to_dtype("u8"))