PyO3: Better shape handling

candle-examples/Cargo.toml

@@ -21,7 +21,7 @@ half = { workspace = true, optional = true }
 image = { workspace = true }
 intel-mkl-src = { workspace = true, optional = true }
 num-traits = { workspace = true }
-pyo3 = { version = "0.19.0", features = ["auto-initialize"], optional = true }
+pyo3 = { version = "0.20.0", features = ["auto-initialize"], optional = true }
 rayon = { workspace = true }
 safetensors = { workspace = true }
 serde = { workspace = true }

candle-pyo3/Cargo.toml

@@ -17,10 +17,10 @@ crate-type = ["cdylib"]
 candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
 candle-nn = { path = "../candle-nn", version = "0.3.0" }
 half = { workspace = true }
-pyo3 = { version = "0.19.0", features = ["extension-module"] }
+pyo3 = { version = "0.20.0", features = ["extension-module"] }
 
 [build-dependencies]
-pyo3-build-config = "0.19"
+pyo3-build-config = "0.20"
 
 [features]
 default = []

candle-pyo3/py_src/candle/__init__.pyi

@@ -1,7 +1,7 @@
 # Generated content DO NOT EDIT
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
 from os import PathLike
-from candle.typing import _ArrayLike, Device, Scalar, Index
+from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
 
 class bf16(DType):
     pass
@@ -26,21 +26,21 @@ class i64(DType):
     pass
 
 @staticmethod
-def ones(shape: Sequence[int], dtype: Optional[DType] = None, device: Optional[Device] = None) -> Tensor:
+def ones(*shape: Shape, dtype: Optional[DType] = None, device: Optional[Device] = None) -> Tensor:
     """
     Creates a new tensor filled with ones.
     """
     pass
 
 @staticmethod
-def rand(shape: Sequence[int], device: Optional[Device] = None) -> Tensor:
+def rand(*shape: Shape, device: Optional[Device] = None) -> Tensor:
     """
     Creates a new tensor with random values.
     """
     pass
 
 @staticmethod
-def randn(shape: Sequence[int], device: Optional[Device] = None) -> Tensor:
+def randn(*shape: Shape, device: Optional[Device] = None) -> Tensor:
     """
     Creates a new tensor with random values from a normal distribution.
     """
@@ -67,7 +67,7 @@ class u8(DType):
     pass
 
 @staticmethod
-def zeros(shape: Sequence[int], dtype: Optional[DType] = None, device: Optional[Device] = None) -> Tensor:
+def zeros(shape: Shape, dtype: Optional[DType] = None, device: Optional[Device] = None) -> Tensor:
     """
     Creates a new tensor filled with zeros.
     """
@@ -174,7 +174,7 @@ class Tensor:
         Adds the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
         """
         pass
-    def broadcast_as(self, shape: Sequence[int]) -> Tensor:
+    def broadcast_as(self, *shape: Shape) -> Tensor:
         """
         Broadcasts the tensor to the given shape.
         """
@@ -184,7 +184,7 @@ class Tensor:
         Divides the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
         """
         pass
-    def broadcast_left(self, shape: Sequence[int]) -> Tensor:
+    def broadcast_left(self, *shape: Shape) -> Tensor:
         """
         Broadcasts the tensor to the given shape, adding new dimensions on the left.
         """
@@ -329,7 +329,7 @@ class Tensor:
         Get the `recip` of the tensor.
         """
         pass
-    def reshape(self, shape: Sequence[int]) -> Tensor:
+    def reshape(self, *shape: Shape) -> Tensor:
         """
         Reshapes the tensor to the given shape.
         """

candle-pyo3/py_src/candle/functional/__init__.pyi

@@ -1,7 +1,7 @@
 # Generated content DO NOT EDIT
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
 from os import PathLike
-from candle.typing import _ArrayLike, Device, Scalar, Index
+from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
 from candle import Tensor, DType, QTensor
 
 @staticmethod

candle-pyo3/py_src/candle/typing/__init__.py

@@ -18,3 +18,5 @@
 Scalar = Union[int, float]
 
 Index = Union[int, slice, None, "Ellipsis"]
+
+Shape = Union[int, Sequence[int]]

candle-pyo3/py_src/candle/utils/__init__.pyi

@@ -1,7 +1,7 @@
 # Generated content DO NOT EDIT
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
 from os import PathLike
-from candle.typing import _ArrayLike, Device, Scalar, Index
+from candle.typing import _ArrayLike, Device, Scalar, Index, Shape
 from candle import Tensor, DType, QTensor
 
 @staticmethod

candle-pyo3/src/lib.rs

@@ -10,26 +10,13 @@ use half::{bf16, f16};
 
 use ::candle::{quantized::QTensor, DType, Device, Tensor, WithDType};
 
+mod shape;
+use shape::{PyRelativeShape, PyShape};
+
 pub fn wrap_err(err: ::candle::Error) -> PyErr {
     PyErr::new::<PyValueError, _>(format!("{err:?}"))
 }
 
-#[derive(Clone, Debug)]
-struct PyShape(Vec<usize>);
-
-impl<'source> pyo3::FromPyObject<'source> for PyShape {
-    fn extract(ob: &'source PyAny) -> PyResult<Self> {
-        let dims: Vec<usize> = pyo3::FromPyObject::extract(ob)?;
-        Ok(PyShape(dims))
-    }
-}
-
-impl From<PyShape> for ::candle::Shape {
-    fn from(val: PyShape) -> Self {
-        val.0.into()
-    }
-}
-
 #[derive(Clone, Debug)]
 #[pyclass(name = "Tensor")]
 /// A `candle` tensor.
@@ -654,25 +641,37 @@ impl PyTensor {
         Ok(Self(tensor))
     }
 
-    #[pyo3(text_signature = "(self, shape:Sequence[int])")]
+    #[pyo3(signature=(*shape), text_signature = "(self, *shape:Shape)")]
     /// Reshapes the tensor to the given shape.
     /// &RETURNS&: Tensor
-    fn reshape(&self, shape: PyShape) -> PyResult<Self> {
-        Ok(PyTensor(self.0.reshape(shape).map_err(wrap_err)?))
+    fn reshape(&self, shape: PyRelativeShape) -> PyResult<Self> {
+        Ok(PyTensor(
+            self.0
+                .reshape(shape.to_absolute(&self.0)?)
+                .map_err(wrap_err)?,
+        ))
     }
 
-    #[pyo3(text_signature = "(self, shape:Sequence[int])")]
+    #[pyo3(signature=(*shape), text_signature = "(self, *shape:Shape)")]
     /// Broadcasts the tensor to the given shape.
     /// &RETURNS&: Tensor
-    fn broadcast_as(&self, shape: PyShape) -> PyResult<Self> {
-        Ok(PyTensor(self.0.broadcast_as(shape).map_err(wrap_err)?))
+    fn broadcast_as(&self, shape: PyRelativeShape) -> PyResult<Self> {
+        Ok(PyTensor(
+            self.0
+                .broadcast_as(shape.to_absolute(&self.0)?)
+                .map_err(wrap_err)?,
+        ))
     }
 
-    #[pyo3(text_signature = "(self, shape:Sequence[int])")]
+    #[pyo3(signature=(*shape), text_signature = "(self, *shape:Shape)")]
     /// Broadcasts the tensor to the given shape, adding new dimensions on the left.
     /// &RETURNS&: Tensor
-    fn broadcast_left(&self, shape: PyShape) -> PyResult<Self> {
-        Ok(PyTensor(self.0.broadcast_left(shape).map_err(wrap_err)?))
+    fn broadcast_left(&self, shape: PyRelativeShape) -> PyResult<Self> {
+        Ok(PyTensor(
+            self.0
+                .broadcast_left(shape.to_absolute(&self.0)?)
+                .map_err(wrap_err)?,
+        ))
     }
 
     #[pyo3(text_signature = "(self, dim:int)")]
@@ -885,21 +884,21 @@ impl PyTensor {
         }
 
         if let Some(kwargs) = kwargs {
-            if let Some(any) = kwargs.get_item("dtype") {
+            if let Ok(Some(any)) = kwargs.get_item("dtype") {
                 handle_duplicates(
                     &mut dtype,
                     any.extract::<PyDType>(),
                     "cannot specify multiple dtypes",
                 )?;
             }
-            if let Some(any) = kwargs.get_item("device") {
+            if let Ok(Some(any)) = kwargs.get_item("device") {
                 handle_duplicates(
                     &mut device,
                     any.extract::<PyDevice>(),
                     "cannot specify multiple devices",
                 )?;
             }
-            if let Some(any) = kwargs.get_item("other") {
+            if let Ok(Some(any)) = kwargs.get_item("other") {
                 handle_duplicates(
                     &mut other,
                     any.extract::<PyTensor>(),
@@ -1019,27 +1018,27 @@ fn tensor(py: Python<'_>, data: PyObject) -> PyResult<PyTensor> {
 }
 
 #[pyfunction]
-#[pyo3(signature = (shape, *, device=None), text_signature = "(shape:Sequence[int], device:Optional[Device]=None)")]
+#[pyo3(signature = (*shape,device=None), text_signature = "(*shape:Shape, device:Optional[Device]=None)")]
 /// Creates a new tensor with random values.
 /// &RETURNS&: Tensor
 fn rand(_py: Python<'_>, shape: PyShape, device: Option<PyDevice>) -> PyResult<PyTensor> {
     let device = device.unwrap_or(PyDevice::Cpu).as_device()?;
-    let tensor = Tensor::rand(0f32, 1f32, shape.0, &device).map_err(wrap_err)?;
+    let tensor = Tensor::rand(0f32, 1f32, shape, &device).map_err(wrap_err)?;
     Ok(PyTensor(tensor))
 }
 
 #[pyfunction]
-#[pyo3(signature = (shape, *, device=None), text_signature = "(shape:Sequence[int], device:Optional[Device]=None)")]
+#[pyo3(signature = (*shape,device=None), text_signature = "(*shape:Shape, device:Optional[Device]=None)")]
 /// Creates a new tensor with random values from a normal distribution.
 /// &RETURNS&: Tensor
 fn randn(_py: Python<'_>, shape: PyShape, device: Option<PyDevice>) -> PyResult<PyTensor> {
     let device = device.unwrap_or(PyDevice::Cpu).as_device()?;
-    let tensor = Tensor::randn(0f32, 1f32, shape.0, &device).map_err(wrap_err)?;
+    let tensor = Tensor::randn(0f32, 1f32, shape, &device).map_err(wrap_err)?;
     Ok(PyTensor(tensor))
 }
 
 #[pyfunction]
-#[pyo3(signature = (shape, *, dtype=None, device=None),text_signature = "(shape:Sequence[int], dtype:Optional[DType]=None, device:Optional[Device]=None)")]
+#[pyo3(signature = (*shape, dtype=None, device=None),text_signature = "(*shape:Shape, dtype:Optional[DType]=None, device:Optional[Device]=None)")]
 /// Creates a new tensor filled with ones.
 /// &RETURNS&: Tensor
 fn ones(
@@ -1053,12 +1052,12 @@ fn ones(
         Some(dtype) => PyDType::from_pyobject(dtype, py)?.0,
     };
     let device = device.unwrap_or(PyDevice::Cpu).as_device()?;
-    let tensor = Tensor::ones(shape.0, dtype, &device).map_err(wrap_err)?;
+    let tensor = Tensor::ones(shape, dtype, &device).map_err(wrap_err)?;
     Ok(PyTensor(tensor))
 }
 
 #[pyfunction]
-#[pyo3(signature = (shape, *, dtype=None, device=None), text_signature = "(shape:Sequence[int], dtype:Optional[DType]=None, device:Optional[Device]=None)")]
+#[pyo3(signature = (*shape, dtype=None, device=None), text_signature = "(shape:Shape, dtype:Optional[DType]=None, device:Optional[Device]=None)")]
 /// Creates a new tensor filled with zeros.
 /// &RETURNS&: Tensor
 fn zeros(
@@ -1072,7 +1071,7 @@ fn zeros(
         Some(dtype) => PyDType::from_pyobject(dtype, py)?.0,
     };
     let device = device.unwrap_or(PyDevice::Cpu).as_device()?;
-    let tensor = Tensor::zeros(shape.0, dtype, &device).map_err(wrap_err)?;
+    let tensor = Tensor::zeros(shape, dtype, &device).map_err(wrap_err)?;
     Ok(PyTensor(tensor))
 }
 

candle-pyo3/src/shape.rs

@@ -0,0 +1,89 @@
+use ::candle::Tensor;
+use pyo3::prelude::*;
+
+#[derive(Clone, Debug)]
+/// Represents an absolute shape e.g. (1, 2, 3)
+pub struct PyShape(Vec<usize>);
+
+impl<'source> pyo3::FromPyObject<'source> for PyShape {
+    fn extract(ob: &'source PyAny) -> PyResult<Self> {
+        if ob.is_none() {
+            return Err(PyErr::new::<pyo3::exceptions::PyValueError, _>(
+                "Shape cannot be None",
+            ));
+        }
+
+        let tuple = ob.downcast::<pyo3::types::PyTuple>()?;
+        if tuple.len() == 1 {
+            let first_element = tuple.get_item(0)?;
+            let dims: Vec<usize> = pyo3::FromPyObject::extract(first_element)?;
+            Ok(PyShape(dims))
+        } else {
+            let dims: Vec<usize> = pyo3::FromPyObject::extract(tuple)?;
+            Ok(PyShape(dims))
+        }
+    }
+}
+
+impl From<PyShape> for ::candle::Shape {
+    fn from(val: PyShape) -> Self {
+        val.0.into()
+    }
+}
+
+#[derive(Clone, Debug)]
+/// Represents a relative shape e.g. (1, -1, 3)
+pub struct PyRelativeShape(Vec<isize>);
+
+impl<'source> pyo3::FromPyObject<'source> for PyRelativeShape {
+    fn extract(ob: &'source PyAny) -> PyResult<Self> {
+        if ob.is_none() {
+            return Err(PyErr::new::<pyo3::exceptions::PyValueError, _>(
+                "Shape cannot be None",
+            ));
+        }
+
+        let tuple = ob.downcast::<pyo3::types::PyTuple>()?;
+        if tuple.len() == 1 {
+            let first_element = tuple.get_item(0)?;
+            let dims: Vec<isize> = pyo3::FromPyObject::extract(first_element)?;
+            Ok(PyRelativeShape(dims))
+        } else {
+            let dims: Vec<isize> = pyo3::FromPyObject::extract(tuple)?;
+            Ok(PyRelativeShape(dims))
+        }
+    }
+}
+
+impl PyRelativeShape {
+    /// Returns `true` if the shape is absolute e.g. (1, 2, 3)
+    pub fn is_absolute(&self) -> bool {
+        self.0.iter().all(|x| *x > 0)
+    }
+
+    /// Convert a relative shape to an absolute shape e.g. (1, -1) -> (1, 12)
+    pub fn to_absolute(&self, t: &Tensor) -> PyResult<PyShape> {
+        if self.is_absolute() {
+            return Ok(PyShape(
+                self.0.iter().map(|x| *x as usize).collect::<Vec<usize>>(),
+            ));
+        }
+
+        let mut elements = t.elem_count();
+        let mut new_dims: Vec<usize> = vec![];
+        for dim in self.0.iter() {
+            if *dim > 0 {
+                new_dims.push(*dim as usize);
+                elements /= *dim as usize;
+            } else if *dim == -1 {
+                new_dims.push(elements);
+            } else {
+                return Err(PyErr::new::<pyo3::exceptions::PyValueError, _>(format!(
+                    "Invalid dimension in shape: {}",
+                    dim
+                )));
+            }
+        }
+        Ok(PyShape(new_dims))
+    }
+}

candle-pyo3/stub.py

@@ -13,7 +13,7 @@
 TYPING = """from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
 from os import PathLike
 """
-CANDLE_SPECIFIC_TYPING = "from candle.typing import _ArrayLike, Device, Scalar, Index\n"
+CANDLE_SPECIFIC_TYPING = "from candle.typing import _ArrayLike, Device, Scalar, Index, Shape\n"
 CANDLE_TENSOR_IMPORTS = "from candle import Tensor,DType,QTensor\n"
 RETURN_TYPE_MARKER = "&RETURNS&: "
 ADDITIONAL_TYPEHINTS = {}