a69ef10c3a
### 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>
63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from typing import Any, Dict, Optional, Tuple, Type
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# Registry: maps (quant_type, layer_type) -> SchemeClass
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_SCHEME_REGISTRY: Dict[Tuple[str, str], Type[Any]] = {}
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def register_scheme(quant_type: str, layer_type: str):
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"""Decorator to register a quantization scheme.
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Args:
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quant_type: Quantization type (e.g., "W8A8", "W8A8_DYNAMIC").
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layer_type: Layer type (e.g., "linear", "moe").
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Returns:
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Decorator function that registers the class.
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Example:
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@register_scheme("W8A8_DYNAMIC", "linear")
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class W8A8DynamicLinearScheme(AscendLinearScheme):
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...
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"""
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def decorator(cls: Type[Any]) -> Type[Any]:
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key = (quant_type, layer_type)
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if key in _SCHEME_REGISTRY:
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raise ValueError(
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f"Scheme already registered for {quant_type}/{layer_type}: "
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f"{_SCHEME_REGISTRY[key].__name__}")
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_SCHEME_REGISTRY[key] = cls
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return cls
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return decorator
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def get_scheme_class(quant_type: str, layer_type: str) -> Optional[Type[Any]]:
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"""Get scheme class for given quant_type and layer_type.
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Args:
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quant_type: Quantization type (e.g., "W8A8", "W8A8_DYNAMIC").
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layer_type: Layer type (e.g., "linear", "moe").
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Returns:
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The registered scheme class, or None if not found.
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"""
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return _SCHEME_REGISTRY.get((quant_type, layer_type))
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