[quantization] Add w8a16 quantization support (#4541)

### What this PR does / why we need it?
related to https://github.com/vllm-project/vllm-ascend/issues/4267

### Does this PR introduce _any_ user-facing change?
support w8a16 quantization now

### How was this patch tested?

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

### Test
tested using [aisbench](https://gitee.com/aisbench/benchmark/) with tp2
#### Precision
  | ceval | mmlu | gsm8k
-- | -- | -- | --
bf16 | 90.46 | 89.17 | 96.21
w8a16 | 89.51 | 89.29 | 95.98

#### Performance
  | input_len | output_len | concurrency | TTFT (ms) | TPOT (ms) | TPS
(Total) (tokens/s)
-- | -- | -- | -- | -- | -- | --
bf16 | 2048 | 2048 | 10 | 1911.7136 | 77.988 | 253.9866
w8a16 | 2048 | 2048 | 10 | 2128.6334 | 67.1633 | 293.9117
bf16 | 3500 | 1024 | 10 | 3076.2509 | 84.3525 | 506.949
w8a16 | 3500 | 1024 | 10 | 2685.2031 | 73.015 | 585.4717

---------

Signed-off-by: yyt <yangyit139@gmail.com>
Signed-off-by: TmacAaron <yangyit139@gmail.com>
Co-authored-by: realliujiaxu <realliujiaxu@163.com>

tests/e2e/singlecard/test_quantization.py

@@ -18,6 +18,7 @@
 from modelscope import snapshot_download  # type: ignore[import-untyped]
 
 from tests.e2e.conftest import VllmRunner
+from tests.e2e.model_utils import check_outputs_equal
 
 
 def test_qwen3_w8a8_quant():
@@ -25,10 +26,53 @@ def test_qwen3_w8a8_quant():
     example_prompts = [
         "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs."
     ]
+    vllm_target_outputs = [([
+        85, 4086, 44, 374, 264, 1550, 42747, 628, 323, 4938, 72816, 44378, 323,
+        13480, 4712, 369, 444, 10994, 82, 13, 1084, 374, 6188, 311, 387
+    ], 'vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be'
+                            )]
+
     with VllmRunner(
             snapshot_download("vllm-ascend/Qwen3-0.6B-W8A8"),
             max_model_len=8192,
             gpu_memory_utilization=0.7,
             quantization="ascend",
     ) as vllm_model:
-        vllm_model.generate_greedy(example_prompts, max_tokens)
+        vllm_quant_w8a8_outputs = vllm_model.generate_greedy(
+            example_prompts, max_tokens)
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_target_outputs,
+        outputs_1_lst=vllm_quant_w8a8_outputs,
+        name_0="vllm_target_outputs",
+        name_1="vllm_w8a16_outputs",
+    )
+
+
+def test_qwen3_dense_w8a16():
+    max_tokens = 5
+    example_prompts = [
+        "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs."
+    ]
+    vllm_target_outputs = [([
+        85, 4086, 44, 374, 264, 1550, 42747, 628, 323, 4938, 72816, 44378, 323,
+        13480, 4712, 369, 444, 10994, 82, 13, 1084, 374, 6188, 311, 387
+    ], 'vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be'
+                            )]
+
+    with VllmRunner(
+            snapshot_download("vllm-ascend/Qwen3-0.6B-W8A16"),
+            max_model_len=8192,
+            enforce_eager=False,
+            gpu_memory_utilization=0.7,
+            quantization="ascend",
+    ) as vllm_model:
+        vllm_quant_w8a16_outputs = vllm_model.generate_greedy(
+            example_prompts, max_tokens)
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_target_outputs,
+        outputs_1_lst=vllm_quant_w8a16_outputs,
+        name_0="vllm_target_outputs",
+        name_1="vllm_w8a16_outputs",
+    )

tests/ut/quantization/test_w8a16.py

@@ -0,0 +1,91 @@
+import os
+from unittest.mock import MagicMock, patch
+
+import torch
+
+from tests.ut.base import TestBase
+from vllm_ascend.quantization.w8a16 import AscendW8A16LinearMethod
+
+
+class TestAscendW8A16LinearMethod(TestBase):
+
+    def setUp(self):
+        self.method = AscendW8A16LinearMethod()
+
+    def test_get_weight(self):
+        weight = self.method.get_weight(10, 20)
+        self.assertEqual(weight['weight'].dtype, torch.int8)
+        self.assertEqual(weight['weight'].shape, (20, 10))
+
+    @patch("torch_npu.npu_weight_quant_batchmatmul")
+    def test_apply_with_x_is_int8(self, mock_npu_weight_quant_batchmatmul):
+        layer = MagicMock()
+        layer.weight.data = torch.randn(128, 256)
+        layer.weight_scale.data = torch.randn(128, 1)
+        layer.weight_offset.data = torch.randn(128, 1)
+
+        x = torch.randn(32, 128)
+        bias = torch.randn(256)
+
+        expected_y_output = torch.randn(32, 256)
+        mock_npu_weight_quant_batchmatmul.return_value = expected_y_output
+
+        output = self.method.apply(layer, x, bias)
+        expected_y_output += bias
+        self.assertTrue(torch.equal(output, expected_y_output))
+
+    @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "0"})
+    @patch('torch_npu.npu_format_cast')
+    def test_process_weights_after_loading_with_nz0(self,
+                                                    mock_npu_format_cast):
+        layer = MagicMock()
+        layer.weight.data = torch.randint(-127,
+                                          128, (128, 256),
+                                          dtype=torch.int8)
+        layer.weight_scale.data = torch.randn(128, 1)
+        layer.weight_offset.data = torch.randn(128, 1)
+
+        mock_npu_format_cast.return_value = MagicMock
+        self.method.process_weights_after_loading(layer)
+
+        self.assertEqual(layer.weight_scale.data.shape, (128, ))
+        self.assertEqual(layer.weight_offset.data.shape, (128, ))
+        mock_npu_format_cast.assert_not_called()
+
+    @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "1"})
+    @patch('torch_npu.npu_format_cast')
+    def test_process_weights_after_loading_with_nz1(self,
+                                                    mock_npu_format_cast):
+        layer = MagicMock()
+
+        layer.weight.data = torch.randint(-127,
+                                          128, (128, 256),
+                                          dtype=torch.int8)
+        layer.weight_scale.data = torch.randn(128, 1)
+        layer.weight_offset.data = torch.randn(128, 1)
+
+        mock_npu_format_cast.return_value = MagicMock
+        self.method.process_weights_after_loading(layer)
+
+        self.assertEqual(layer.weight_scale.data.shape, (128, ))
+        self.assertEqual(layer.weight_offset.data.shape, (128, ))
+        mock_npu_format_cast.assert_called_once()
+
+    @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "2"})
+    @patch('torch_npu.npu_format_cast')
+    def test_process_weights_after_loading_with_nz2(self,
+                                                    mock_npu_format_cast):
+        layer = MagicMock()
+
+        layer.weight.data = torch.randint(-127,
+                                          128, (128, 256),
+                                          dtype=torch.int8)
+        layer.weight_scale.data = torch.randn(128, 1)
+        layer.weight_offset.data = torch.randn(128, 1)
+
+        mock_npu_format_cast.return_value = MagicMock
+        self.method.process_weights_after_loading(layer)
+
+        self.assertEqual(layer.weight_scale.data.shape, (128, ))
+        self.assertEqual(layer.weight_offset.data.shape, (128, ))
+        mock_npu_format_cast.assert_called_once()

vllm_ascend/quantization/utils.py

@@ -14,6 +14,7 @@ from .w8a8_dynamic import (AscendW8A8DynamicFusedMoEMethod,
                            AscendW8A8DynamicLinearMethod)
 from .w8a8_pdmix import (AscendW8A8PDMixFusedMoeMethod,
                          AscendW8A8PDMixLinearMethod)
+from .w8a16 import AscendW8A16LinearMethod
 
 ASCEND_QUANTIZATION_METHOD_MAP: Dict[str, Dict[str, Type[Any]]] = {
     "W4A16": {
@@ -36,6 +37,9 @@ ASCEND_QUANTIZATION_METHOD_MAP: Dict[str, Dict[str, Type[Any]]] = {
     "W8A8_MIX": {
         "linear": AscendW8A8PDMixLinearMethod,
         "moe": AscendW8A8PDMixFusedMoeMethod,
+    },
+    "W8A16": {
+        "linear": AscendW8A16LinearMethod,
     }
 }
 

vllm_ascend/quantization/w8a16.py

@@ -0,0 +1,89 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Any, Dict, Optional
+
+import torch
+import torch_npu
+
+from vllm_ascend.utils import maybe_trans_nz
+
+
+class AscendW8A16LinearMethod:
+    """Linear method for Ascend W8A16.
+
+    """
+
+    def __init__(self) -> None:
+        pass
+
+    @staticmethod
+    def get_weight(
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype = torch.bfloat16,
+    ) -> Dict[str, Any]:
+        params_dict = {
+            "weight": torch.empty(output_size, input_size, dtype=torch.int8)
+        }
+        return params_dict
+
+    @staticmethod
+    def get_pertensor_param(params_dtype: torch.dtype) -> Dict[str, Any]:
+        return {}
+
+    @staticmethod
+    def get_perchannel_param(
+        output_size: int,
+        params_dtype: torch.dtype,
+    ) -> Dict[str, Any]:
+        params_dict = {}
+        params_dict["weight_scale"] = torch.empty(output_size,
+                                                  1,
+                                                  dtype=params_dtype)
+        params_dict["weight_offset"] = torch.empty(output_size,
+                                                   1,
+                                                   dtype=params_dtype)
+        return params_dict
+
+    def get_pergroup_param(self,
+                           input_size: int,
+                           output_size: int,
+                           params_dtype: torch.dtype,
+                           layer_type: Optional[str] = None) -> Dict[str, Any]:
+        return {}
+
+    @staticmethod
+    def apply(
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+        tp_rank: Optional[int] = 0,
+    ) -> torch.Tensor:
+        output = torch_npu.npu_weight_quant_batchmatmul(
+            x=x,
+            weight=layer.weight,
+            antiquant_scale=layer.weight_scale,
+            antiquant_offset=layer.weight_offset,
+            bias=bias)
+        return output
+
+    def process_weights_after_loading(self, layer):
+        layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
+        layer.weight.data = maybe_trans_nz(layer.weight.data)
+        layer.weight_scale.data = torch.flatten(layer.weight_scale.data)
+        layer.weight_offset.data = torch.flatten(layer.weight_offset.data)