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More pyo3.
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@ -17,3 +17,4 @@ crate-type = ["cdylib"]
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[dependencies]
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candle = { path = "../candle-core", default-features=false }
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pyo3 = { version = "0.19.0", features = ["extension-module"] }
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half = { version = "2.3.1", features = ["num-traits"] }
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@ -1,8 +1,10 @@
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use pyo3::types::PyTuple;
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use pyo3::exceptions::{PyTypeError, PyValueError};
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use pyo3::prelude::*;
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use pyo3::types::{PyString, PyTuple};
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use ::candle::{Device::Cpu, Tensor};
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use half::{bf16, f16};
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use ::candle::{DType, Device::Cpu, Tensor};
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pub fn wrap_err(err: ::candle::Error) -> PyErr {
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PyErr::new::<PyValueError, _>(format!("{err:?}"))
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@ -23,15 +25,48 @@ impl std::ops::Deref for PyTensor {
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#[pymethods]
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impl PyTensor {
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#[new]
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// TODO: Handle arbitrary input dtype and shape.
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fn new(f: f32) -> PyResult<Self> {
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Ok(Self(Tensor::new(f, &Cpu).map_err(wrap_err)?))
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}
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fn values(&self, py: Python<'_>) -> PyResult<PyObject> {
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// TODO: Handle arbitrary shapes.
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let v = match self.0.dtype() {
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// TODO: Use the map bits to avoid enumerating the types.
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DType::U8 => self.to_scalar::<u8>().map_err(wrap_err)?.to_object(py),
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DType::U32 => self.to_scalar::<u32>().map_err(wrap_err)?.to_object(py),
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DType::F32 => self.to_scalar::<f32>().map_err(wrap_err)?.to_object(py),
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DType::F64 => self.to_scalar::<f64>().map_err(wrap_err)?.to_object(py),
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DType::BF16 => self
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.to_scalar::<bf16>()
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.map_err(wrap_err)?
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.to_f32()
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.to_object(py),
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DType::F16 => self
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.to_scalar::<f16>()
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.map_err(wrap_err)?
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.to_f32()
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.to_object(py),
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};
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Ok(v)
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}
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#[getter]
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fn shape(&self, py: Python<'_>) -> PyObject {
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PyTuple::new(py, self.0.dims()).to_object(py)
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}
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#[getter]
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fn stride(&self, py: Python<'_>) -> PyObject {
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PyTuple::new(py, self.0.stride()).to_object(py)
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}
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#[getter]
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fn dtype(&self, py: Python<'_>) -> PyObject {
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PyString::new(py, self.0.dtype().as_str()).to_object(py)
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}
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#[getter]
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fn rank(&self) -> usize {
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self.0.rank()
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@ -59,6 +94,21 @@ impl PyTensor {
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fn __radd__(&self, rhs: &PyAny) -> PyResult<Self> {
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self.__add__(rhs)
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}
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fn __mul__(&self, rhs: &PyAny) -> PyResult<Self> {
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let tensor = if let Ok(rhs) = rhs.extract::<Self>() {
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(&self.0 * &rhs.0).map_err(wrap_err)?
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} else if let Ok(rhs) = rhs.extract::<f64>() {
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(&self.0 * rhs).map_err(wrap_err)?
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} else {
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Err(PyTypeError::new_err("unsupported for mul"))?
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};
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Ok(Self(tensor))
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}
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fn __rmul__(&self, rhs: &PyAny) -> PyResult<Self> {
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self.__mul__(rhs)
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}
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}
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#[pyfunction]
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