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-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))