Make the Python Wrapper more Hackable and simplify Quantization (#1010)

* Some first `Module` implementations

* Add `state_dict` and `load_state_dict` functionality

* Move modules around and create `candle.nn.Linear`

* Add `nn.Embedding` and `nn.LayerNorm`

* Add BERT implementation

* Batch q-matmul

* Automatically dequantize `QTensors` if a `Tensor` is expected

* Add Module `.to()`, `.cuda()`, `cpu()` and `.type()` functionality

* Unittests for `Module`, `Tensor` and `candle.utils`

* Add `pytorch` like slicing to `Tensor`

* Cleanup and BERT fixes

* `black` formatting + unit-test for `nn.Linear`

* Refactor slicing implementation

candle-pyo3/tests/native/test_tensor.py

@@ -0,0 +1,74 @@
+import candle
+from candle import Tensor
+
+
+def test_tensor_can_be_constructed():
+    t = Tensor(42.0)
+    assert t.values() == 42.0
+
+
+def test_tensor_can_be_constructed_from_list():
+    t = Tensor([3.0, 1, 4, 1, 5, 9, 2, 6])
+    assert t.values() == [3.0, 1, 4, 1, 5, 9, 2, 6]
+
+
+def test_tensor_can_be_constructed_from_list_of_lists():
+    t = Tensor([[3.0, 1, 4, 1], [5, 9, 2, 6]])
+    assert t.values() == [[3.0, 1, 4, 1], [5, 9, 2, 6]]
+
+
+def test_tensor_can_be_quantized():
+    t = candle.randn((16, 256))
+    for format in [
+        "q4_0",
+        "q4_1",
+        "q5_0",
+        "q5_1",
+        "q8_0",
+        "q2k",
+        "q3k",
+        "q4k",
+        "q5k",
+        "q8k",
+    ]:
+        for formatted_format in [format.upper(), format.lower()]:
+            quant_t = t.quantize(formatted_format)
+            assert quant_t.ggml_dtype.lower() == format.lower()
+            assert quant_t.shape == t.shape
+
+
+def test_tensor_can_be_indexed():
+    t = Tensor([[3.0, 1, 4, 1], [5, 9, 2, 6]])
+    assert t[0].values() == [3.0, 1.0, 4.0, 1.0]
+    assert t[1].values() == [5.0, 9.0, 2.0, 6.0]
+    assert t[-1].values() == [5.0, 9.0, 2.0, 6.0]
+    assert t[-2].values() == [3.0, 1.0, 4.0, 1.0]
+
+
+def test_tensor_can_be_sliced():
+    t = Tensor([3.0, 1, 4, 10, 5, 9, 2, 6])
+
+    assert t[0:4].values() == [3.0, 1.0, 4.0, 10.0]
+    assert t[4:8].values() == [5.0, 9.0, 2.0, 6.0]
+    assert t[-4:].values() == [5.0, 9.0, 2.0, 6.0]
+    assert t[:-4].values() == [3.0, 1.0, 4.0, 10.0]
+    assert t[-4:-2].values() == [5.0, 9.0]
+
+
+def test_tensor_can_be_sliced_2d():
+    t = Tensor([[3.0, 1, 4, 1], [5, 9, 2, 6]])
+    assert t[:, 0].values() == [3.0, 5]
+    assert t[:, 1].values() == [1.0, 9.0]
+    assert t[0, 0].values() == 3.0
+    assert t[:, -1].values() == [1.0, 6.0]
+    assert t[:, -4].values() == [3.0, 5]
+    assert t[..., 0].values() == [3.0, 5]
+
+
+def test_tensor_can_be_scliced_3d():
+    t = Tensor([[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]]])
+    assert t[:, :, 0].values() == [[1, 5], [9, 13]]
+    assert t[:, :, 0:2].values() == [[[1, 2], [5, 6]], [[9, 10], [13, 14]]]
+    assert t[:, 0, 0].values() == [1, 9]
+    assert t[..., 0].values() == [[1, 5], [9, 13]]
+    assert t[..., 0:2].values() == [[[1, 2], [5, 6]], [[9, 10], [13, 14]]]