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xc-llm-ascend/tests/ut/quantization/test_w8a8.py
Cao Yi a69ef10c3a [Refactor] Quantization Module Refactor (#5738) 
### Summary

This PR refactors the `vllm_ascend/quantization` module to improve code
organization, maintainability, and extensibility. The refactoring
introduces a clear separation of concerns with a registry-based scheme
discovery pattern, abstract base classes for quantization schemes, and
dedicated wrapper classes.

### Key Changes

#### 1. **Modular Directory Structure**

| Before | After |
|--------|-------|
| Flat file structure with mixed responsibilities | Organized into
`methods/` subpackage for schemes |
| Single `quant_config.py` (600+ lines) | Separate config files:
`modelslim_config.py`, `compressed_tensors_config.py` |
| `utils.py` with scheme lookup logic | `methods/registry.py` with
decorator-based registration |

#### 2. **Registry-Based Scheme Discovery**

Replaced hardcoded `ASCEND_QUANTIZATION_METHOD_MAP` dictionary with a
decorator-based registry pattern:

```python
# Before: Manual dictionary mapping
ASCEND_QUANTIZATION_METHOD_MAP = {
    "W8A8_DYNAMIC": {"linear": AscendW8A8DynamicLinearMethod, ...},
    ...
}

# After: Decorator-based registration
@register_scheme("W8A8_DYNAMIC", "linear")
class AscendW8A8DynamicLinearMethod(AscendLinearScheme):
    ...
```

#### 3. **Abstract Base Classes**

Introduced three abstract base classes in `methods/base.py`:
- `AscendLinearScheme` - Base for linear layer quantization
- `AscendMoEScheme` - Base for MoE layer quantization  
- `AscendAttentionScheme` - Base for attention layer quantization

#### 4. **Separated Config and Wrapper Classes**

- **Config classes** (`AscendModelSlimConfig`,
`AscendCompressedTensorsConfig`): Handle config parsing and scheme
selection
- **Wrapper classes** (`AscendLinearMethod`, `AscendFusedMoEMethod`,
etc.): Implement vLLM interfaces and delegate to schemes

#### 5. **Cleaner Public API**

```python
# New clean module interface
from vllm_ascend.quantization import (
    AscendModelSlimConfig,
    AscendCompressedTensorsConfig,
)
from vllm_ascend.quantization.methods import get_scheme_class
```

### Architecture Diagram

```mermaid
classDiagram
    direction TB
    
    class QuantizationConfig {
        <<vLLM Interface>>
        +get_quant_method()
    }
    
    class AscendModelSlimConfig {
        +quant_description
        +get_quant_method()
        -create_scheme_for_layer()
    }
    
    class AscendCompressedTensorsConfig {
        +target_scheme_map
        +get_quant_method()
        -_get_scheme_from_parts()
    }
    
    class AscendLinearMethod {
        <<Wrapper>>
        +quant_method: AscendLinearScheme
        +create_weights()
        +apply()
    }
    
    class AscendFusedMoEMethod {
        <<Wrapper>>
        +quant_method: AscendMoEScheme
        +create_weights()
        +apply()
    }
    
    class AscendLinearScheme {
        <<Abstract>>
        +get_weight()*
        +apply()*
        +get_pertensor_param()
        +get_perchannel_param()
    }
    
    class AscendMoEScheme {
        <<Abstract>>
        +get_weight()*
        +get_dynamic_quant_param()*
        +apply()*
    }
    
    class W8A8DynamicLinear {
        +get_weight()
        +apply()
    }
    
    class W8A8DynamicMoE {
        +get_weight()
        +apply()
    }
    
    QuantizationConfig <|-- AscendModelSlimConfig
    QuantizationConfig <|-- AscendCompressedTensorsConfig
    
    AscendModelSlimConfig ..> AscendLinearMethod : creates
    AscendModelSlimConfig ..> AscendFusedMoEMethod : creates
    AscendCompressedTensorsConfig ..> AscendLinearMethod : creates
    AscendCompressedTensorsConfig ..> AscendFusedMoEMethod : creates
    
    AscendLinearMethod o-- AscendLinearScheme : delegates to
    AscendFusedMoEMethod o-- AscendMoEScheme : delegates to
    
    AscendLinearScheme <|-- W8A8DynamicLinear
    AscendMoEScheme <|-- W8A8DynamicMoE
```

### Scheme Registration Flow

```mermaid
sequenceDiagram
    participant Module as Scheme Module
    participant Registry as _SCHEME_REGISTRY
    participant Config as QuantConfig
    participant Wrapper as Wrapper Class
    
    Note over Module: At import time
    Module->>Registry: @register_scheme("W8A8_DYNAMIC", "linear")
    Registry->>Registry: Store (quant_type, layer_type) -> Class
    
    Note over Config: At runtime
    Config->>Config: Determine quant_type from description
    Config->>Registry: get_scheme_class(quant_type, layer_type)
    Registry-->>Config: Return scheme class
    Config->>Config: scheme = scheme_cls()
    Config->>Wrapper: Create wrapper with scheme
    Wrapper-->>Config: Return wrapper instance
```

### File Changes Summary

| Original Files | Refactored Files |
|----------------|------------------|
| `__init__.py` (empty) | `__init__.py` (exports public API) |
| `quant_config.py` | `modelslim_config.py` + `wrappers.py` |
| `compressed_tensors/` | `compressed_tensors_config.py` |
| `utils.py` | `methods/registry.py` |
| `w8a8_dynamic.py` | `methods/w8a8_dynamic.py` |
| `w8a8.py` | `methods/w8a8_static.py` |
| `w4a4_flatquant_dynamic.py` | `methods/w4a4_flatquant.py` |
| ... | `methods/base.py` (new) |

### Benefits

1. **Extensibility**: Adding new quantization schemes only requires
implementing the base class and adding `@register_scheme` decorator
2. **Maintainability**: Clear separation between config parsing, wrapper
logic, and scheme implementation
3. **Testability**: Abstract base classes enable easier unit testing and
mocking
4. **Discoverability**: Registry pattern makes it easy to list all
supported schemes
5. **Reduced Coupling**: Config classes no longer need to know about all
scheme implementations

___

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
2026-01-23 14:13:47 +08:00

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import os
from unittest.mock import MagicMock, patch
import torch
from tests.ut.base import TestBase
from vllm_ascend.quantization.methods.w8a8_static import AscendW8A8LinearMethod
from vllm_ascend.utils import AscendDeviceType
class TestAscendW8A8LinearMethod(TestBase):
def setUp(self):
self.method = AscendW8A8LinearMethod()
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))
def test_get_pertensor_param(self):
params = self.method.get_pertensor_param(torch.bfloat16)
self.assertEqual(params['input_scale'].dtype, torch.bfloat16)
self.assertEqual(params['input_offset'].dtype, torch.int8)
self.assertEqual(params['input_scale'].shape, (1, ))
self.assertEqual(params['input_offset'].shape, (1, ))
def test_get_perchannel_param(self):
params = self.method.get_perchannel_param(10, torch.bfloat16)
self.assertEqual(params['quant_bias'].dtype, torch.int32)
self.assertEqual(params['deq_scale'].dtype, torch.float32)
self.assertEqual(params['weight_scale'].dtype, torch.bfloat16)
self.assertEqual(params['weight_offset'].dtype, torch.bfloat16)
self.assertEqual(params['quant_bias'].shape, (10, ))
self.assertEqual(params['deq_scale'].shape, (10, ))
self.assertEqual(params['weight_scale'].shape, (10, 1))
self.assertEqual(params['weight_offset'].shape, (10, 1))
@patch(
"vllm_ascend.quantization.methods.w8a8_static.get_weight_prefetch_method"
)
@patch("torch.ops.vllm.quantize")
@patch("torch_npu.npu_quant_matmul")
def test_apply_with_x_not_int8(self, mock_npu_quant_matmul, mock_quantize,
mock_get_weight_prefetch_method):
layer = MagicMock()
layer.aclnn_input_scale = 0.1
layer.aclnn_input_offset = 0.2
layer.weight = torch.randn(128, 256)
layer.deq_scale = 0.3
mock_get_weight_prefetch_method.return_value = MagicMock()
x = torch.randn(32, 128)
bias = torch.randn(256)
mock_quantize.return_value = torch.randint(-128,
127,
x.shape,
dtype=torch.int8)
expected_y_output = torch.randn(32, 256)
mock_npu_quant_matmul.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("torch_npu.npu_quant_matmul")
def test_apply_with_x_is_int8(self, mock_npu_quant_matmul):
layer = MagicMock()
layer.aclnn_input_scale = 0.1
layer.aclnn_input_offset = 0.2
layer.weight = torch.randn(128, 256)
layer.deq_scale = 0.3
x = torch.randint(-128, 127, (32, 128), dtype=torch.int8)
bias = torch.randn(256)
expected_y_output = torch.randn(32, 256)
mock_npu_quant_matmul.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('vllm_ascend.utils.get_ascend_device_type',
return_value=AscendDeviceType._310P)
@patch("torch_npu.npu_quant_matmul")
def test_apply_with_x_is_310p(self, mock_npu_quant_matmul,
mock_soc_version):
layer = MagicMock()
layer.aclnn_input_scale = 0.1
layer.aclnn_input_offset = 0.2
layer.weight = torch.randn(128, 256)
layer.deq_scale = 0.3
x = torch.randint(-128, 127, (32, 128), dtype=torch.int8)
bias = torch.randn(256)
expected_y_output = torch.randn(32, 256)
mock_npu_quant_matmul.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.input_scale.data = torch.tensor([0.1])
layer.input_offset.data = torch.tensor([0])
layer.deq_scale = torch.tensor([0.5])
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)
expected_offset = torch.tensor([0]).repeat(256).to(torch.int8)
self.assertTrue(
torch.equal(layer.aclnn_input_offset.data, expected_offset))
self.assertFalse(layer.aclnn_input_offset.requires_grad)
self.assertFalse(layer.deq_scale.requires_grad)
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.input_scale.data = torch.tensor([0.1])
layer.input_offset.data = torch.tensor([0])
layer.deq_scale = torch.tensor([0.5])
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)
expected_offset = torch.tensor([0]).repeat(256).to(torch.int8)
self.assertTrue(
torch.equal(layer.aclnn_input_offset.data, expected_offset))
self.assertFalse(layer.aclnn_input_offset.requires_grad)
self.assertFalse(layer.deq_scale.requires_grad)
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.input_scale.data = torch.tensor([0.1])
layer.input_offset.data = torch.tensor([0])
layer.deq_scale = torch.tensor([0.5])
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)
expected_offset = torch.tensor([0]).repeat(256).to(torch.int8)
self.assertTrue(
torch.equal(layer.aclnn_input_offset.data, expected_offset))
self.assertFalse(layer.aclnn_input_offset.requires_grad)
self.assertFalse(layer.deq_scale.requires_grad)
self.assertEqual(layer.weight_scale.data.shape, (128, ))
self.assertEqual(layer.weight_offset.data.shape, (128, ))
mock_npu_format_cast.assert_called_once()
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