[Feat.][310P]: weightNZ feature with quant or unquant. (#6705)

NZ Format Support for Linear Layers: Implemented support for the NZ
(N-dimensional Z-order) format for linear layer weights on Ascend 310P,
enhancing performance for both quantized and unquantized layers.
Unquantized Linear Method for Ascend 310P: Introduced
AscendUnquantizedLinearMethod310 to specifically handle and apply NZ
format casting to unquantized linear layer weights during the loading
process.
MRotaryEmbedding Integration: Extended Rotary Embedding support by
adding AscendMRotaryEmbedding310 to provide an Ascend-specific
implementation for MRotaryEmbedding.
Quantization Method Updates: Updated the w8a8_static quantization method
to directly transpose weights and apply NZ format casting, ensuring
consistency with the new format.
- vLLM version: v0.15.0
- vLLM main:
9562912cea

---------

Signed-off-by: Tflowers-0129 <2906339855@qq.com>

tests/ut/_310p/quantization/test_modelslim_config_310.py

@@ -21,8 +21,8 @@ from vllm.model_executor.layers.linear import LinearBase
 
 from tests.ut.base import TestBase
 from vllm_ascend._310p.fused_moe.fused_moe import AscendUnquantizedFusedMoEMethod310
+from vllm_ascend._310p.ops.linear import AscendUnquantizedLinearMethod310
 from vllm_ascend._310p.quantization.modelslim_config import AscendModelSlimConfig310
-from vllm_ascend.ops.linear import AscendUnquantizedLinearMethod
 
 
 class TestAscendModelSlimConfig310(TestBase):
@@ -50,7 +50,7 @@ class TestAscendModelSlimConfig310(TestBase):
             patch.object(self.ascend_config, "is_layer_skipped_ascend", return_value=True),
         ):
             method = self.ascend_config.get_quant_method(linear_layer, ".attn")
-            self.assertIsInstance(method, AscendUnquantizedLinearMethod)
+            self.assertIsInstance(method, AscendUnquantizedLinearMethod310)
 
         # Test quantized layer
         mock_scheme = MagicMock()

tests/ut/_310p/quantization/test_w8a8_static_310.py

@@ -44,6 +44,7 @@ class TestAscendW8A8LinearMethod310(TestBase):
         self.assertEqual(params["deq_scale"].dtype, torch.int64)
         self.assertEqual(params["weight_scale"].dtype, torch.float16)
         self.assertEqual(params["weight_offset"].dtype, torch.float16)
+
         self.assertEqual(params["quant_bias"].shape, (10,))
         self.assertEqual(params["deq_scale"].shape, (10,))
         self.assertEqual(params["weight_scale"].shape, (10, 1))
@@ -71,12 +72,23 @@ class TestAscendW8A8LinearMethod310(TestBase):
         output = self.method.apply(layer, x, tp_rank=0)
 
         mock_quantize.assert_called_with(
-            x, layer.aclnn_input_scale, layer.aclnn_input_scale_reciprocal, layer.aclnn_input_offset
+            x,
+            layer.aclnn_input_scale,
+            layer.aclnn_input_scale_reciprocal,
+            layer.aclnn_input_offset,
         )
-        mock_npu_quant_matmul.assert_called_with(
-            expect_x_output, layer.weight, layer.deq_scale, bias=layer.quant_bias, output_dtype=layer.params_dtype
-        )
-        # The bias is added by the linear layer's forward pass, not the quant method.
+        mock_npu_quant_matmul.assert_called_once()
+        (args, kwargs) = mock_npu_quant_matmul.call_args
+
+        # positional args
+        self.assertTrue(torch.equal(args[0], expect_x_output))
+        self.assertTrue(torch.equal(args[1], layer.weight.data))
+        self.assertTrue(torch.equal(args[2], layer.deq_scale))
+
+        # kwargs
+        self.assertTrue(torch.equal(kwargs["bias"], layer.quant_bias))
+        self.assertEqual(kwargs["output_dtype"], layer.params_dtype)
+
         self.assertTrue(torch.equal(output, expected_y_output))
 
     @patch("torch.ops.vllm.quantize")
@@ -98,8 +110,41 @@ class TestAscendW8A8LinearMethod310(TestBase):
         output = self.method.apply(layer, x, tp_rank=0)
 
         mock_quantize.assert_not_called()
-        mock_npu_quant_matmul.assert_called_with(
-            x, layer.weight, layer.deq_scale, bias=layer.quant_bias, output_dtype=layer.params_dtype
-        )
-        # The bias is added by the linear layer's forward pass, not the quant method.
+        mock_npu_quant_matmul.assert_called_once()
+        (args, kwargs) = mock_npu_quant_matmul.call_args
+
+        self.assertTrue(torch.equal(args[0], x))
+        self.assertTrue(torch.equal(args[1], layer.weight.data))
+        self.assertTrue(torch.equal(args[2], layer.deq_scale))
+
+        self.assertTrue(torch.equal(kwargs["bias"], layer.quant_bias))
+        self.assertEqual(kwargs["output_dtype"], layer.params_dtype)
+
         self.assertTrue(torch.equal(output, expected_y_output))
+
+    @patch("torch_npu.npu_format_cast")
+    def test_process_weights_after_loading_calls_nz_format_cast_310p(self, mock_npu_format_cast):
+        mock_npu_format_cast.side_effect = lambda x, fmt: x
+
+        layer = MagicMock()
+
+        # Attributes used by process_weights_after_loading()
+        layer.weight = MagicMock()
+        layer.input_scale = MagicMock()
+        layer.input_offset = MagicMock()
+        layer.weight_scale = MagicMock()
+        layer.weight_offset = MagicMock()
+        layer.w2_weight_offset = MagicMock()
+
+        layer.weight.data = torch.randint(-127, 128, (128, 256), dtype=torch.int8)
+        layer.input_scale.data = torch.tensor([0.1], dtype=torch.float16)
+        layer.input_offset.data = torch.tensor([0], dtype=torch.int8)
+
+        layer.weight_scale.data = torch.randn(128, 1, dtype=torch.bfloat16)
+        layer.weight_offset.data = torch.randn(128, 1, dtype=torch.bfloat16)
+        # w2_weight_offset is reshaped to (N, -1); any (N, 1) is fine
+        layer.w2_weight_offset.data = torch.randn(128, 1, dtype=torch.bfloat16)
+
+        self.method.process_weights_after_loading(layer)
+
+        mock_npu_format_cast.assert_called_once()