[Feat] Unquantized linear nz support (#2619)

### What this PR does / why we need it?
Currently, when executing to the Linear layer of the model in
vLLM-Ascend, the weights input format is ND in unquantized case and
skipped ascend case, which is slower than FRACTAL_NZ.
This PR supplements the execution logic for Linear layer. When
VLLM_ASCEND_ENABLE_MLP_OPTIMIZE=1 and CANN version is 8.3, the weights
of the Linear layer will be converted to FRACTAL_NZ, in both unquantized
case and skipped ascend case.

- vLLM version: main
- vLLM main:
267c80d31f

Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>

tests/ut/ops/test_linear.py

@@ -5,11 +5,13 @@ from unittest.mock import MagicMock, patch
 
 import torch
 
+from tests.ut.base import TestBase
 from vllm_ascend import ascend_config
 from vllm_ascend.distributed import parallel_state
 from vllm_ascend.ops.linear import (AscendColumnParallelLinear,
                                     AscendMergedColumnParallelLinear,
-                                    AscendRowParallelLinear)
+                                    AscendRowParallelLinear,
+                                    AscendUnquantizedLinearMethod)
 
 
 class BaseLinearTest(unittest.TestCase):
@@ -46,6 +48,81 @@ class BaseLinearTest(unittest.TestCase):
             p.stop()
 
 
+class TestAscendUnquantizedLinearMethod(TestBase):
+
+    def setUp(self):
+        self.method = AscendUnquantizedLinearMethod()
+
+    @mock.patch("torch_npu.npu_format_cast")
+    @mock.patch("torch.version")
+    def test_process_weights_after_loading_is_cann_8_3(self, mock_version,
+                                                       mock_format_cast):
+        layer = mock.MagicMock()
+
+        mock_version.cann = "8.3.RC1"
+        self.method.process_weights_after_loading(layer)
+        mock_format_cast.assert_called_once()
+
+    @mock.patch("torch.version")
+    def test_process_weights_after_loading_not_cann_8_3(self, mock_version):
+        layer = mock.MagicMock()
+
+        mock_version.cann = "8.2.RC1"
+        # Should not raise exception
+        self.method.process_weights_after_loading(layer)
+
+    @mock.patch("torch.matmul")
+    @mock.patch("torch.version")
+    def test_apply_with_bias_is_cann_8_3(self, mock_version, mock_npu_matmul):
+        layer = mock.MagicMock()
+        layer.weight = torch.randn(128, 256)
+
+        x = torch.randn(32, 128)
+        bias = torch.randn(256)
+
+        expected_y_output = torch.randn(32, 256)
+        mock_npu_matmul.return_value = expected_y_output
+
+        mock_version.cann = "8.3.RC1"
+        output = self.method.apply(layer, x, bias)
+
+        expected_y_output += bias
+        self.assertTrue(torch.equal(output, expected_y_output))
+
+    @mock.patch("torch.matmul")
+    @mock.patch("torch.version")
+    def test_apply_without_bias_is_cann_8_3(self, mock_version,
+                                            mock_npu_matmul):
+        layer = mock.MagicMock()
+        layer.weight = torch.randn(128, 256)
+
+        x = torch.randn(32, 128)
+
+        expected_y_output = torch.randn(32, 256)
+        mock_npu_matmul.return_value = expected_y_output
+
+        mock_version.cann = "8.3.RC1"
+        output = self.method.apply(layer, x)
+
+        self.assertTrue(torch.equal(output, expected_y_output))
+
+    @mock.patch("torch.nn.functional.linear")
+    @mock.patch("torch.version")
+    def test_apply_not_cann_8_3(self, mock_version, mock_npu_linear):
+        layer = mock.MagicMock()
+        layer.weight = torch.randn(128, 256)
+
+        x = torch.randn(32, 128)
+
+        expected_y_output = torch.randn(32, 256)
+        mock_npu_linear.return_value = expected_y_output
+
+        mock_version.cann = "8.2.RC1"
+        output = self.method.apply(layer, x)
+
+        self.assertTrue(torch.equal(output, expected_y_output))
+
+
 class TestAscendRowParallelLinear(BaseLinearTest):
 
     def test_mlp_optimize(self):

tests/ut/quantization/test_quant_config.py

@@ -4,10 +4,10 @@ import torch
 from vllm.attention.layer import Attention
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
-from vllm.model_executor.layers.linear import (LinearBase,
-                                               UnquantizedLinearMethod)
+from vllm.model_executor.layers.linear import LinearBase
 
 from tests.ut.base import TestBase
+from vllm_ascend.ops.linear import AscendUnquantizedLinearMethod
 from vllm_ascend.quantization.quant_config import (AscendKVCacheMethod,
                                                    AscendQuantConfig)
 from vllm_ascend.utils import ASCEND_QUANTIZATION_METHOD
@@ -79,7 +79,7 @@ class TestAscendQuantConfig(TestBase):
                           'is_layer_skipped_ascend',
                           return_value=True):
             method = self.ascend_config.get_quant_method(linear_layer, ".attn")
-            self.assertIsInstance(method, UnquantizedLinearMethod)
+            self.assertIsInstance(method, AscendUnquantizedLinearMethod)
 
         # Test quantized layer
         with patch.object(self.ascend_config, 'is_layer_skipped_ascend', return_value=False), \