PyO3: Add equal and __richcmp__ to candle.Tensor (#1099)

* add `equal` to tensor

* add `__richcmp__` support  for tensors and scalars

* typo

* more typos

* Add `abs` + `candle.testing`

* remove duplicated `broadcast_shape_binary_op`

* `candle.i16` => `candle.i64`

* `tensor.nelements` -> `tensor.nelement`

* Cleanup `abs`

candle-core/src/shape.rs

@@ -203,7 +203,7 @@ impl Shape {
 
     /// Check whether the two shapes are compatible for broadcast, and if it is the case return the
     /// broadcasted shape. This is to be used for binary pointwise ops.
-    pub(crate) fn broadcast_shape_binary_op(&self, rhs: &Self, op: &'static str) -> Result<Shape> {
+    pub fn broadcast_shape_binary_op(&self, rhs: &Self, op: &'static str) -> Result<Shape> {
         let lhs = self;
         let lhs_dims = lhs.dims();
         let rhs_dims = rhs.dims();

candle-pyo3/_additional_typing/__init__.py

@@ -53,3 +53,39 @@ class Tensor:
         Return a slice of a tensor.
         """
         pass
+
+    def __eq__(self, rhs: Union["Tensor", "Scalar"]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass
+
+    def __ne__(self, rhs: Union["Tensor", "Scalar"]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass
+
+    def __lt__(self, rhs: Union["Tensor", "Scalar"]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass
+
+    def __le__(self, rhs: Union["Tensor", "Scalar"]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass
+
+    def __gt__(self, rhs: Union["Tensor", "Scalar"]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass
+
+    def __ge__(self, rhs: Union["Tensor", "Scalar"]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass

candle-pyo3/py_src/candle/__init__.pyi

@@ -124,16 +124,46 @@ class Tensor:
         Add a scalar to a tensor or two tensors together.
         """
         pass
+    def __eq__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass
+    def __ge__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass
     def __getitem__(self, index: Union[Index, Tensor, Sequence[Index]]) -> "Tensor":
         """
         Return a slice of a tensor.
         """
         pass
+    def __gt__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass
+    def __le__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass
+    def __lt__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass
     def __mul__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Multiply a tensor by a scalar or one tensor by another.
         """
         pass
+    def __ne__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
+        """
+        Compare a tensor with a scalar or one tensor with another.
+        """
+        pass
     def __radd__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Add a scalar to a tensor or two tensors together.
@@ -159,6 +189,11 @@ class Tensor:
         Divide a tensor by a scalar or one tensor by another.
         """
         pass
+    def abs(self) -> Tensor:
+        """
+        Performs the `abs` operation on the tensor.
+        """
+        pass
     def argmax_keepdim(self, dim: int) -> Tensor:
         """
         Returns the indices of the maximum value(s) across the selected dimension.
@@ -308,6 +343,12 @@ class Tensor:
         ranges from `start` to `start + len`.
         """
         pass
+    @property
+    def nelement(self) -> int:
+        """
+        Gets the tensor's element count.
+        """
+        pass
     def powf(self, p: float) -> Tensor:
         """
         Performs the `pow` operation on the tensor with the given exponent.

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

@@ -0,0 +1,70 @@
+import candle
+from candle import Tensor
+
+
+_UNSIGNED_DTYPES = set([str(candle.u8), str(candle.u32)])
+
+
+def _assert_tensor_metadata(
+    actual: Tensor,
+    expected: Tensor,
+    check_device: bool = True,
+    check_dtype: bool = True,
+    check_layout: bool = True,
+    check_stride: bool = False,
+):
+    if check_device:
+        assert actual.device == expected.device, f"Device mismatch: {actual.device} != {expected.device}"
+
+    if check_dtype:
+        assert str(actual.dtype) == str(expected.dtype), f"Dtype mismatch: {actual.dtype} != {expected.dtype}"
+
+    if check_layout:
+        assert actual.shape == expected.shape, f"Shape mismatch: {actual.shape} != {expected.shape}"
+
+    if check_stride:
+        assert actual.stride == expected.stride, f"Stride mismatch: {actual.stride} != {expected.stride}"
+
+
+def assert_equal(
+    actual: Tensor,
+    expected: Tensor,
+    check_device: bool = True,
+    check_dtype: bool = True,
+    check_layout: bool = True,
+    check_stride: bool = False,
+):
+    """
+    Asserts that two tensors are exact equals.
+    """
+    _assert_tensor_metadata(actual, expected, check_device, check_dtype, check_layout, check_stride)
+    assert (actual - expected).abs().sum_all().values() == 0, f"Tensors mismatch: {actual} != {expected}"
+
+
+def assert_almost_equal(
+    actual: Tensor,
+    expected: Tensor,
+    rtol=1e-05,
+    atol=1e-08,
+    check_device: bool = True,
+    check_dtype: bool = True,
+    check_layout: bool = True,
+    check_stride: bool = False,
+):
+    """
+    Asserts, that two tensors are almost equal by performing an element wise comparison of the tensors with a tolerance.
+
+    Computes: |actual - expected| ≤ atol + rtol x |expected|
+    """
+    _assert_tensor_metadata(actual, expected, check_device, check_dtype, check_layout, check_stride)
+
+    # Secure against overflow of u32 and u8 tensors
+    if str(actual.dtype) in _UNSIGNED_DTYPES or str(expected.dtype) in _UNSIGNED_DTYPES:
+        actual = actual.to(candle.i64)
+        expected = expected.to(candle.i64)
+
+    diff = (actual - expected).abs()
+
+    threshold = (expected.abs().to_dtype(candle.f32) * rtol + atol).to(expected)
+
+    assert (diff <= threshold).sum_all().values() == actual.nelement, f"Difference between tensors was to great"

candle-pyo3/src/lib.rs

@@ -1,8 +1,11 @@
 #![allow(clippy::redundant_closure_call)]
 use pyo3::exceptions::{PyTypeError, PyValueError};
 use pyo3::prelude::*;
+use pyo3::pyclass::CompareOp;
 use pyo3::types::{IntoPyDict, PyDict, PyTuple};
 use pyo3::ToPyObject;
+use std::collections::hash_map::DefaultHasher;
+use std::hash::{Hash, Hasher};
 use std::os::raw::c_long;
 use std::sync::Arc;
 
@@ -132,9 +135,10 @@ macro_rules! pydtype {
         }
     };
 }
+
+pydtype!(i64, |v| v);
 pydtype!(u8, |v| v);
 pydtype!(u32, |v| v);
-pydtype!(i64, |v| v);
 pydtype!(f16, f32::from);
 pydtype!(bf16, f32::from);
 pydtype!(f32, |v| v);
@@ -317,6 +321,13 @@ impl PyTensor {
         PyTuple::new(py, self.0.dims()).to_object(py)
     }
 
+    #[getter]
+    /// Gets the tensor's element count.
+    /// &RETURNS&: int
+    fn nelement(&self) -> usize {
+        self.0.elem_count()
+    }
+
     #[getter]
     /// Gets the tensor's strides.
     /// &RETURNS&: Tuple[int]
@@ -353,6 +364,12 @@ impl PyTensor {
         self.__repr__()
     }
 
+    /// Performs the `abs` operation on the tensor.
+    /// &RETURNS&: Tensor
+    fn abs(&self) -> PyResult<Self> {
+        Ok(PyTensor(self.0.abs().map_err(wrap_err)?))
+    }
+
     /// Performs the `sin` operation on the tensor.
     /// &RETURNS&: Tensor
     fn sin(&self) -> PyResult<Self> {
@@ -670,6 +687,58 @@ impl PyTensor {
         };
         Ok(Self(tensor))
     }
+    /// Rich-compare two tensors.
+    /// &RETURNS&: Tensor
+    fn __richcmp__(&self, rhs: &PyAny, op: CompareOp) -> PyResult<Self> {
+        let compare = |lhs: &Tensor, rhs: &Tensor| {
+            let t = match op {
+                CompareOp::Eq => lhs.eq(rhs),
+                CompareOp::Ne => lhs.ne(rhs),
+                CompareOp::Lt => lhs.lt(rhs),
+                CompareOp::Le => lhs.le(rhs),
+                CompareOp::Gt => lhs.gt(rhs),
+                CompareOp::Ge => lhs.ge(rhs),
+            };
+            Ok(PyTensor(t.map_err(wrap_err)?))
+        };
+        if let Ok(rhs) = rhs.extract::<PyTensor>() {
+            if self.0.shape() == rhs.0.shape() {
+                compare(&self.0, &rhs.0)
+            } else {
+                // We broadcast manually here because `candle.cmp` does not support automatic broadcasting
+                let broadcast_shape = self
+                    .0
+                    .shape()
+                    .broadcast_shape_binary_op(rhs.0.shape(), "cmp")
+                    .map_err(wrap_err)?;
+                let broadcasted_lhs = self.0.broadcast_as(&broadcast_shape).map_err(wrap_err)?;
+                let broadcasted_rhs = rhs.0.broadcast_as(&broadcast_shape).map_err(wrap_err)?;
+
+                compare(&broadcasted_lhs, &broadcasted_rhs)
+            }
+        } else if let Ok(rhs) = rhs.extract::<f64>() {
+            let scalar_tensor = Tensor::new(rhs, self.0.device())
+                .map_err(wrap_err)?
+                .to_dtype(self.0.dtype())
+                .map_err(wrap_err)?
+                .broadcast_as(self.0.shape())
+                .map_err(wrap_err)?;
+
+            compare(&self.0, &scalar_tensor)
+        } else {
+            return Err(PyTypeError::new_err("unsupported rhs for __richcmp__"));
+        }
+    }
+
+    fn __hash__(&self) -> u64 {
+        // we have overridden __richcmp__ => py03 wants us to also override __hash__
+        // we simply hash the address of the tensor
+        let mut hasher = DefaultHasher::new();
+        let pointer = &self.0 as *const Tensor;
+        let address = pointer as usize;
+        address.hash(&mut hasher);
+        hasher.finish()
+    }
 
     #[pyo3(signature=(*shape), text_signature = "(self, *shape:Shape)")]
     /// Reshapes the tensor to the given shape.
@@ -1503,7 +1572,7 @@ fn candle(py: Python<'_>, m: &PyModule) -> PyResult<()> {
     m.add_class::<PyDType>()?;
     m.add("u8", PyDType(DType::U8))?;
     m.add("u32", PyDType(DType::U32))?;
-    m.add("i16", PyDType(DType::I64))?;
+    m.add("i64", PyDType(DType::I64))?;
     m.add("bf16", PyDType(DType::BF16))?;
     m.add("f16", PyDType(DType::F16))?;
     m.add("f32", PyDType(DType::F32))?;

candle-pyo3/tests/bindings/test_testing.py

@@ -0,0 +1,33 @@
+import candle
+from candle import Tensor
+from candle.testing import assert_equal, assert_almost_equal
+import pytest
+
+
+@pytest.mark.parametrize("dtype", [candle.f32, candle.f64, candle.f16, candle.u32, candle.u8, candle.i64])
+def test_assert_equal_asserts_correctly(dtype: candle.DType):
+    a = Tensor([1, 2, 3]).to(dtype)
+    b = Tensor([1, 2, 3]).to(dtype)
+    assert_equal(a, b)
+
+    with pytest.raises(AssertionError):
+        assert_equal(a, b + 1)
+
+
+@pytest.mark.parametrize("dtype", [candle.f32, candle.f64, candle.f16, candle.u32, candle.u8, candle.i64])
+def test_assert_almost_equal_asserts_correctly(dtype: candle.DType):
+    a = Tensor([1, 2, 3]).to(dtype)
+    b = Tensor([1, 2, 3]).to(dtype)
+    assert_almost_equal(a, b)
+
+    with pytest.raises(AssertionError):
+        assert_almost_equal(a, b + 1)
+
+    assert_almost_equal(a, b + 1, atol=20)
+    assert_almost_equal(a, b + 1, rtol=20)
+
+    with pytest.raises(AssertionError):
+        assert_almost_equal(a, b + 1, atol=0.9)
+
+    with pytest.raises(AssertionError):
+        assert_almost_equal(a, b + 1, rtol=0.1)

candle-pyo3/tests/native/test_tensor.py

@@ -1,6 +1,7 @@
 import candle
 from candle import Tensor
 from candle.utils import cuda_is_available
+from candle.testing import assert_equal
 import pytest
 
 
@@ -77,6 +78,78 @@ def test_tensor_can_be_scliced_3d():
     assert t[..., 0:2].values() == [[[1, 2], [5, 6]], [[9, 10], [13, 14]]]
 
 
+def assert_bool(t: Tensor, expected: bool):
+    assert t.shape == ()
+    assert str(t.dtype) == str(candle.u8)
+    assert bool(t.values()) == expected
+
+
+def test_tensor_supports_equality_opperations_with_scalars():
+    t = Tensor(42.0)
+
+    assert_bool(t == 42.0, True)
+    assert_bool(t == 43.0, False)
+
+    assert_bool(t != 42.0, False)
+    assert_bool(t != 43.0, True)
+
+    assert_bool(t > 41.0, True)
+    assert_bool(t > 42.0, False)
+
+    assert_bool(t >= 41.0, True)
+    assert_bool(t >= 42.0, True)
+
+    assert_bool(t < 43.0, True)
+    assert_bool(t < 42.0, False)
+
+    assert_bool(t <= 43.0, True)
+    assert_bool(t <= 42.0, True)
+
+
+def test_tensor_supports_equality_opperations_with_tensors():
+    t = Tensor(42.0)
+    same = Tensor(42.0)
+    other = Tensor(43.0)
+
+    assert_bool(t == same, True)
+    assert_bool(t == other, False)
+
+    assert_bool(t != same, False)
+    assert_bool(t != other, True)
+
+    assert_bool(t > same, False)
+    assert_bool(t > other, False)
+
+    assert_bool(t >= same, True)
+    assert_bool(t >= other, False)
+
+    assert_bool(t < same, False)
+    assert_bool(t < other, True)
+
+    assert_bool(t <= same, True)
+    assert_bool(t <= other, True)
+
+
+def test_tensor_equality_opperations_can_broadcast():
+    # Create a decoder attention mask as a test case
+    # e.g.
+    # [[1,0,0]
+    #  [1,1,0]
+    #  [1,1,1]]
+    mask_cond = candle.Tensor([0, 1, 2])
+    mask = mask_cond < (mask_cond + 1).reshape((3, 1))
+    assert mask.shape == (3, 3)
+    assert_equal(mask, Tensor([[1, 0, 0], [1, 1, 0], [1, 1, 1]]).to_dtype(candle.u8))
+
+
+def test_tensor_can_be_hashed():
+    t = Tensor(42.0)
+    other = Tensor(42.0)
+    # Hash should represent a unique tensor
+    assert hash(t) != hash(other)
+    assert hash(t) == hash(t)
+
+
 def test_tensor_can_be_expanded_with_none():
     t = candle.rand((12, 12))