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>
107 lines
4.8 KiB
Python
107 lines
4.8 KiB
Python
from unittest.mock import Mock, patch
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import torch
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from tests.ut.base import TestBase
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from vllm_ascend.quantization.methods.w8a8_dynamic import \
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AscendW8A8DynamicFusedMoEMethod
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class TestAscendW8A8FusedMoEMethod(TestBase):
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num_experts = 8
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hidden_size = 128
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intermediate_size = 128
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@patch("torch.distributed.get_rank")
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@patch("vllm_ascend.quantization.methods.w8a8_dynamic.get_mc2_group")
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@patch("vllm_ascend.quantization.methods.w8a8_dynamic.get_ascend_config")
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@patch("vllm_ascend.quantization.methods.w8a8_dynamic.get_ep_group")
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def setUp(self, mock_get_ep_group, mock_get_ascend_config,
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mock_get_mc2_group, mock_get_rank):
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with patch(
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'vllm_ascend.quantization.methods.w8a8_dynamic.get_current_vllm_config'
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) as mock_get_current_vllm_config:
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mock_vllm_config = Mock()
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mock_vllm_config.quant_config = Mock(
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quant_description={"group_size": 256})
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mock_vllm_config.scheduler_config = Mock(
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max_num_batched_tokens=2048,
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max_model_len=2048,
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enable_chunked_prefill=False)
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mock_get_current_vllm_config.return_value = mock_vllm_config
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mock_ep_group = Mock()
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mock_get_ep_group.return_value = mock_ep_group
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mock_ascend_config = Mock()
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mock_ascend_config.enable_chunked_prefill = False
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mock_get_ascend_config.return_value = mock_ascend_config
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mock_mc2_group = Mock(device_group=0)
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mock_get_mc2_group.return_value = mock_mc2_group
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mock_rank = Mock()
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mock_get_rank.return_value = mock_rank
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self.quant_method = AscendW8A8DynamicFusedMoEMethod()
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def test_get_weight(self):
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param_dict = self.quant_method.get_weight(self.num_experts,
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self.intermediate_size,
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self.hidden_size,
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torch.bfloat16)
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self.assertEqual(param_dict["w13_weight"].dtype, torch.int8)
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self.assertEqual(
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param_dict["w13_weight"].shape,
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(self.num_experts, 2 * self.intermediate_size, self.hidden_size))
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def test_get_dynamic_quant_param(self):
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param_dict = self.quant_method.get_dynamic_quant_param(
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self.num_experts, self.intermediate_size, self.hidden_size,
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torch.bfloat16)
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self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.bfloat16)
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self.assertEqual(param_dict["w13_weight_scale"].shape,
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(self.num_experts, 2 * self.intermediate_size, 1))
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def build_layer(self):
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layer = torch.nn.Module()
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layer.w13_weight = torch.nn.Parameter(torch.empty(
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self.num_experts,
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2 * self.intermediate_size,
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self.hidden_size,
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dtype=torch.int8),
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requires_grad=False)
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layer.w2_weight = torch.nn.Parameter(torch.empty(
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self.num_experts,
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self.hidden_size,
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self.intermediate_size,
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dtype=torch.int8),
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requires_grad=False)
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w13_weight_scale = torch.zeros(
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(self.num_experts, 2 * self.intermediate_size, 1),
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dtype=torch.float32)
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layer.w13_weight_scale = torch.nn.Parameter(w13_weight_scale,
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requires_grad=False)
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w13_weight_offset = torch.zeros(
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(self.num_experts, 2 * self.intermediate_size, 1),
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dtype=torch.float32)
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layer.w13_weight_offset = torch.nn.Parameter(w13_weight_offset,
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requires_grad=False)
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w2_weight_scale = torch.zeros((self.num_experts, self.hidden_size, 1),
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dtype=torch.float32)
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layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale,
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requires_grad=False)
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w2_weight_offset = torch.zeros((self.num_experts, self.hidden_size, 1),
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dtype=torch.float32)
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layer.w2_weight_offset = torch.nn.Parameter(w2_weight_offset,
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requires_grad=False)
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return layer
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@patch('torch_npu.npu_format_cast')
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def test_process_weights_after_loading(self, mock_npu_format_cast):
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def func_by_args(weight, num_format):
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return weight
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mock_npu_format_cast.side_effect = func_by_args
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new_layer = self.build_layer()
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self.quant_method.process_weights_after_loading(new_layer)
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mock_npu_format_cast.assert_called()
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