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xc-llm-ascend/tests/ut/quantization/test_w4a16.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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from unittest.mock import Mock, patch
import torch
from tests.ut.base import TestBase
from vllm_ascend.quantization.methods.w4a16 import (AscendW4A16FusedMoEMethod,
pack_to_int32,
unpack_from_int32)
class TestUnpackFromInt32(TestBase):
def test_unpack_from_int32_packed_dim_1(self):
weight = torch.tensor([[305419896, -1420531520]], dtype=torch.int32)
shape = torch.Size([1, 8])
num_bits = 4
result = unpack_from_int32(weight, shape, num_bits, packed_dim=1)
self.assertEqual(result.dtype, torch.int8)
self.assertEqual(result.shape, shape)
def test_unpack_from_int32_packed_dim_0(self):
weight = torch.tensor([[305419896], [-1420531520]], dtype=torch.int32)
shape = torch.Size([8, 1])
num_bits = 4
result = unpack_from_int32(weight, shape, num_bits, packed_dim=0)
self.assertEqual(result.dtype, torch.int8)
self.assertEqual(result.shape, shape)
def test_unpack_from_int32_assertions(self):
with self.assertRaises(AssertionError):
weight = torch.tensor([[1, 2]], dtype=torch.int64)
unpack_from_int32(weight, torch.Size([8, 1]), 4)
with self.assertRaises(AssertionError):
weight = torch.tensor([[1, 2]], dtype=torch.int32)
unpack_from_int32(weight, torch.Size([8, 1]), 16)
class TestPackToInt32(TestBase):
@patch(
"vllm_ascend.quantization.methods.w4a16.torch_npu.npu_convert_weight_to_int4pack"
)
def test_pack_to_int32_int8(self, mock_npu_convert_weight_to_int4pack):
mock_npu_convert_weight_to_int4pack.return_value = torch.zeros(
(2, 4), dtype=torch.int32)
weight = torch.zeros((2, 8, 16), dtype=torch.int8)
result = pack_to_int32(weight)
self.assertEqual(result.dtype, torch.int32)
mock_npu_convert_weight_to_int4pack.assert_not_called()
self.assertEqual(result.shape, torch.Size([2, 8, 4]))
@patch(
"vllm_ascend.quantization.methods.w4a16.torch_npu.npu_convert_weight_to_int4pack"
)
def test_pack_to_int32_int32(self, mock_npu_convert_weight_to_int4pack):
def mock_convert_weight(weight):
return weight
mock_npu_convert_weight_to_int4pack.side_effect = mock_convert_weight
weight = torch.zeros((2, 8, 8), dtype=torch.int32)
result = pack_to_int32(weight)
self.assertEqual(result.dtype, torch.int32)
self.assertEqual(result.shape, weight.shape)
def test_pack_to_int32_assertion_dim(self):
with self.assertRaises(AssertionError):
weight = torch.zeros((8, 8), dtype=torch.int8)
pack_to_int32(weight)
def test_pack_to_int32_assertion_dtype(self):
with self.assertRaises(AssertionError):
weight = torch.zeros((2, 8, 8), dtype=torch.float32)
pack_to_int32(weight)
def test_pack_to_int32_assertion_divisible(self):
with self.assertRaises(AssertionError):
weight = torch.zeros((2, 8, 7), dtype=torch.int32)
pack_to_int32(weight)
with self.assertRaises(AssertionError):
weight = torch.zeros((2, 8, 7), dtype=torch.int8)
pack_to_int32(weight)
class TestAscendW4A16FusedMoEMethod(TestBase):
experts = 8
input_size = 32
output_size = 128
group_size = 32
@patch("vllm_ascend.quantization.methods.w4a16.get_ascend_config")
@patch("vllm_ascend.quantization.methods.w4a16.get_current_vllm_config")
def setUp(self, mock_get_current_vllm_config, mock_get_ascend_config):
mock_ascend_config = Mock()
mock_ascend_config.eplb_config.dynamic_eplb = False
mock_ascend_config.eplb_config.expert_map_record_path = None
mock_get_ascend_config.return_value = mock_ascend_config
mock_vllm_config = Mock()
mock_vllm_config.quant_config = Mock(quant_description={
"group_size": self.group_size,
})
mock_get_current_vllm_config.return_value = mock_vllm_config
self.quant_method = AscendW4A16FusedMoEMethod()
def test_init(self):
self.assertTrue(self.quant_method.transpose_weight)
self.assertEqual(self.quant_method.num_bits, 4)
self.assertEqual(self.quant_method.pack_factor, 8)
self.assertEqual(self.quant_method.group_size, self.group_size)
self.assertFalse(self.quant_method.dynamic_eplb)
def test_get_weight(self):
param_dict = self.quant_method.get_weight(self.experts,
self.input_size,
self.output_size,
torch.bfloat16)
self.assertEqual(param_dict["w13_weight_packed"].dtype, torch.int32)
expected_w13_shape = (self.experts, 2 * self.input_size,
self.output_size //
self.quant_method.pack_factor)
self.assertEqual(param_dict["w13_weight_packed"].shape,
expected_w13_shape)
self.assertEqual(param_dict["w2_weight_packed"].dtype, torch.int32)
expected_w2_shape = (self.experts, self.output_size,
self.input_size // self.quant_method.pack_factor)
self.assertEqual(param_dict["w2_weight_packed"].shape,
expected_w2_shape)
def test_get_dynamic_quant_param(self):
param_dict = self.quant_method.get_dynamic_quant_param(
self.experts, self.input_size, self.output_size, torch.bfloat16)
self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.bfloat16)
expected_w13_scale_shape = (self.experts, 2 * self.input_size,
self.output_size // self.group_size)
self.assertEqual(param_dict["w13_weight_scale"].shape,
expected_w13_scale_shape)
self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.bfloat16)
expected_w2_scale_shape = (self.experts, self.output_size,
self.input_size // self.group_size)
self.assertEqual(param_dict["w2_weight_scale"].shape,
expected_w2_scale_shape)
self.assertEqual(param_dict["w13_weight_shape"].dtype, torch.int32)
self.assertEqual(param_dict["w13_weight_shape"].shape,
(self.experts, 2))
self.assertEqual(param_dict["w2_weight_shape"].dtype, torch.int32)
self.assertEqual(param_dict["w2_weight_shape"].shape,
(self.experts, 2))
self.assertEqual(param_dict["w13_weight_offset"].dtype, torch.bfloat16)
self.assertEqual(param_dict["w13_weight_offset"].shape,
expected_w13_scale_shape)
self.assertEqual(param_dict["w2_weight_offset"].dtype, torch.bfloat16)
self.assertEqual(param_dict["w2_weight_offset"].shape,
expected_w2_scale_shape)
def build_layer(self):
"""Build a mock layer for testing"""
layer = torch.nn.Module()
w13_shape = (self.experts, 2 * self.input_size,
self.output_size // self.quant_method.pack_factor)
w2_shape = (self.experts, self.output_size,
self.input_size // self.quant_method.pack_factor)
layer.w13_weight_packed = torch.nn.Parameter(torch.randint(
-100, 100, w13_shape, dtype=torch.int32),
requires_grad=False)
layer.w2_weight_packed = torch.nn.Parameter(torch.randint(
-100, 100, w2_shape, dtype=torch.int32),
requires_grad=False)
w13_scale_shape = (self.experts, 2 * self.input_size,
self.output_size // self.group_size)
w2_scale_shape = (self.experts, self.output_size,
self.input_size // self.group_size)
layer.w13_weight_scale = torch.nn.Parameter(torch.ones(
w13_scale_shape, dtype=torch.bfloat16),
requires_grad=False)
layer.w2_weight_scale = torch.nn.Parameter(torch.ones(
w2_scale_shape, dtype=torch.bfloat16),
requires_grad=False)
layer.w13_weight_offset = torch.nn.Parameter(torch.zeros(
w13_scale_shape, dtype=torch.bfloat16),
requires_grad=False)
layer.w2_weight_offset = torch.nn.Parameter(torch.zeros(
w2_scale_shape, dtype=torch.bfloat16),
requires_grad=False)
layer.w13_weight_shape = torch.nn.Parameter(torch.tensor(
[[2 * self.input_size, self.output_size]] * self.experts,
dtype=torch.int32),
requires_grad=False)
layer.w2_weight_shape = torch.nn.Parameter(torch.tensor(
[[self.output_size, self.input_size]] * self.experts,
dtype=torch.int32),
requires_grad=False)
return layer
@patch(
"vllm_ascend.quantization.methods.w4a16.torch_npu.npu_convert_weight_to_int4pack"
)
def test_process_weights_after_loading_with_transpose(
self, mock_npu_convert_weight_to_int4pack):
def mock_convert_weight(weight):
new_shape = list(weight.shape)
new_shape[-1] = new_shape[-1] // 8
return torch.zeros(new_shape, dtype=torch.int32)
mock_npu_convert_weight_to_int4pack.side_effect = mock_convert_weight
layer = self.build_layer()
self.quant_method.transpose_weight = True
self.quant_method.process_weights_after_loading(layer)
self.assertEqual(layer.w13_weight_packed.data.shape,
torch.Size([8, 128, 8]))
self.assertEqual(layer.w2_weight_packed.data.shape,
torch.Size([8, 32, 16]))
self.assertEqual(layer.w13_weight_scale.data.shape,
torch.Size([8, 4, 64]))
self.assertEqual(layer.w2_weight_scale.data.shape,
torch.Size([8, 1, 128]))
self.assertEqual(layer.w13_weight_offset.data.shape,
torch.Size([8, 4, 64]))
self.assertEqual(layer.w2_weight_offset.data.shape,
torch.Size([8, 1, 128]))
self.assertTrue(layer.w13_weight_scale.data.is_contiguous())
self.assertTrue(layer.w2_weight_scale.data.is_contiguous())
self.assertTrue(layer.w13_weight_offset.data.is_contiguous())
self.assertTrue(layer.w2_weight_offset.data.is_contiguous())
def test_process_weights_after_loading_without_transpose(self):
layer = self.build_layer()
self.quant_method.transpose_weight = False
original_w13_data = layer.w13_weight_packed.data.clone()
original_w2_data = layer.w2_weight_packed.data.clone()
self.quant_method.process_weights_after_loading(layer)
self.assertTrue(
torch.equal(layer.w13_weight_packed.data, original_w13_data))
self.assertTrue(
torch.equal(layer.w2_weight_packed.data, original_w2_data))
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