added test_pack_uin4 and test_pack_in4

tests/torch/ptq/test_weights_compression.py

@@ -22,6 +22,10 @@
 from nncf.torch.quantization.layers import INT4SymmetricWeightsDecompressor
 from nncf.torch.quantization.layers import INT8AsymmetricWeightsDecompressor
 from nncf.torch.quantization.layers import INT8SymmetricWeightsDecompressor
+from nncf.torch.quantization.quantize_functions import pack_int4
+from nncf.torch.quantization.quantize_functions import pack_uint4
+from nncf.torch.quantization.quantize_functions import unpack_int4
+from nncf.torch.quantization.quantize_functions import unpack_uint4
 
 DATA_BASED_SENSITIVITY_METRICS = (
     SensitivityMetric.HESSIAN_INPUT_ACTIVATION,
@@ -311,3 +315,21 @@ def test_model_devices_and_precisions(use_cuda, dtype):
     assert compressed_model.state_dict()["_nncf.external_op.weights_decompressor_w._scale"].dtype == torch.float16
     # Result should be in the precision of the model
     assert result.dtype == dtype
+
+
+def test_pack_uint4():
+    w_uint8 = torch.randint(0, 15, (4, 4), dtype=torch.uint8)
+    packed_w = pack_uint4(w_uint8)
+    assert packed_w.dtype == torch.uint8
+    assert packed_w.numel() * 2 == w_uint8.numel()
+    unpacked_w = unpack_uint4(packed_w).reshape(w_uint8.shape)
+    assert torch.all(unpacked_w == w_uint8)
+
+
+def test_pack_int4():
+    w_int8 = torch.randint(-8, 7, (4, 4), dtype=torch.int8)
+    packed_w = pack_int4(w_int8)
+    assert packed_w.dtype == torch.uint8
+    assert packed_w.numel() * 2 == w_int8.numel()
+    unpacked_w = unpack_int4(packed_w).reshape(w_int8.shape)
+    assert torch.all(unpacked_w == w_int8)