[quantization] Ouput kv-tuples

This PR outputs kv-tuples in case `use_cache` was set.

TICO-DCO-1.0-Signed-off-by: s.malakhov <s.malakhov@partner.samsung.com>

Author: stamalakhov

test/quantization/wrapq/wrappers/llama/test_quant_model.py

@@ -109,3 +109,68 @@ def test_forward_diff(self):
         self.assertGreater(diff, 0.0)
         self.assertLess(diff, 0.4)
         self.assertEqual(fp_out.shape, q_out.shape)
+
+
+@unittest.skipUnless(has_transformers_for("llama"), skip_msg)
+class TestQuantLlamaModelWithCache(unittest.TestCase):
+    seq_len: int
+    vocab_size: int
+    hid_layers: int
+    fp_model: torch.nn.Module
+
+    @classmethod
+    def setUpClass(cls):
+        torch.manual_seed(0)
+
+        from transformers.models.llama.configuration_llama import LlamaConfig
+        from transformers.models.llama.modeling_llama import LlamaModel
+
+        cls.seq_len = 16
+        cls.vocab_size = 10000
+        cls.hid_layers = 3
+        cfg = LlamaConfig(
+            hidden_size=8,
+            num_attention_heads=2,
+            num_key_value_heads=1,
+            head_dim=4,
+            attention_bias=False,
+            attention_dropout=0.0,
+            attn_implementation="eager",
+            num_hidden_layers=cls.hid_layers,
+            max_position_embeddings=cls.seq_len,
+            use_cache=True,
+            return_dict=False,
+        )
+        cls.fp_model = LlamaModel(cfg)
+
+    def test_model_output(self):
+        qmodel = QuantLlamaModel(
+            self.fp_model, qcfg=PTQConfig(wrapper_variant="prefill")
+        )
+        self.assertIs(qmodel._mode, Mode.NO_QUANT)
+
+        qmodel.enable_calibration()
+        self.assertIs(qmodel._mode, Mode.CALIB)
+
+        x = torch.randint(
+            0,
+            self.vocab_size,
+            (
+                1,
+                self.seq_len,
+            ),
+        )
+        output = qmodel(x)
+
+        assert len(output) == 2  # last_hidden_states + past_key_values
+        past_key_values = output[1]
+        assert len(past_key_values) == self.hid_layers
+        for index in range(self.hid_layers):
+            past_key_value = past_key_values[index]
+            assert isinstance(past_key_value, tuple)
+
+            past_key = past_key_value[0]
+            assert past_key.shape[-2] == self.seq_len
+
+            past_value = past_key_value[1]
+            assert past_value.shape[-2] == self.seq_len