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9 Commits

Author SHA1 Message Date
tanhaoan333
15f6564976 [Model]Add Qwen3-Omni quantization Ascend NPU adaptation and optimization (#6828) 
### What this PR does / why we need it?
This pull request is for quantization adaptation of Qwen3Omni, and it
achieves operator-level optimization and AUT (Auto-Quantization Tuning)
component optimization through patch-based modifications.
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
83b47f67b1

---------

Signed-off-by: tanhaoan333 <tanhaoan@huawei.com>
 2026-03-03 00:07:23 +08:00
Canlin Guo
e4458b2d2b [Main2Main] Upgrade vLLM to 0226 (#6813) 
### What this PR does / why we need it?

Breaking:
1. https://github.com/vllm-project/vllm/pull/33452
2. https://github.com/vllm-project/vllm/pull/33451
3. https://github.com/vllm-project/vllm/pull/32567
4. https://github.com/vllm-project/vllm/pull/32344

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
83b47f67b1

---------

Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: gcanlin <canlinguosdu@gmail.com>
Co-authored-by: MrZ20 <2609716663@qq.com>
 2026-02-27 16:05:21 +08:00
Cao Yi
3953dcf784 [Feature][Quant] Auto-detect quantization format from model files (#6645) 
## Summary

- Add automatic quantization format detection, eliminating the need to
manually specify `--quantization` when serving quantized models.
- The detection inspects only lightweight JSON files
(`quant_model_description.json` and `config.json`) at engine
initialization time, with no `.safetensors` reads.
- User-explicit `--quantization` flags are always respected;
auto-detection only applies when the flag is omitted.

## Details

**Detection priority:**
1. `quant_model_description.json` exists → `quantization="ascend"`
(ModelSlim)
2. `config.json` contains `"quant_method": "compressed-tensors"` →
`quantization="compressed-tensors"` (LLM-Compressor)
3. Neither → default float behavior

**Technical approach:**
Hooked into `NPUPlatform.check_and_update_config()` to run detection
after `VllmConfig.__post_init__`. Since `quant_config` is already `None`
at that point, we explicitly recreate it via
`VllmConfig._get_quantization_config()` to trigger the full quantization
initialization pipeline.

## Files Changed

| File | Description |
|------|-------------|
| `vllm_ascend/quantization/utils.py` | Added
`detect_quantization_method()` and `maybe_auto_detect_quantization()` |
| `vllm_ascend/platform.py` | Integrated auto-detection in
`check_and_update_config()` |
| `vllm_ascend/quantization/modelslim_config.py` | Improved error
handling for weight loading |
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd

---------

Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
 2026-02-26 10:59:25 +08:00
LoganJane
ed051737e9 [Bugfix] Support Kimi-K2.5 models (#6755) 
### What this PR does / why we need it?
This PR supports the Kimi-K2.5 models on the NPU of bf16 and w4a8
weights.
The corresponding PR in the vllm community has been merged:
https://github.com/vllm-project/vllm/pull/34501

### Does this PR introduce _any_ user-facing change?
- No.

### How was this patch tested?
We test the Kimi-K2.5 weights. The weights path:
https://modelscope.cn/models/Eco-Tech/Kimi-K2.5-W4A8
Successfully ran on 910B NPU using vllm-ascend by the w4a8 weights.

- vLLM version: v0.15.0
- vLLM main:
9562912cea

---------

Signed-off-by: LoganJane <LoganJane73@hotmail.com>
 2026-02-25 14:51:46 +08:00
yydyzr
ff3a50d011 [Model] GLM5 adaptation (#6642) 
### What this PR does / why we need it?
GLM5 adaptation
1. use torch_npu.npu_lightning_indexer for GLM5
2. forbid eagle proposer when fullgraph mode is enabled because of bugs
3. add quatization config for GLM5
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
by ci
- vLLM main:
978a37c823

---------

Signed-off-by: yydyzr <liuyuncong1@huawei.com>
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
Co-authored-by: shenchuxiaofugui <1311027364@qq.com>
 2026-02-11 22:22:22 +08:00
SILONG ZENG
99aedaff63 [Lint]Style: Convert vllm-ascend/ to ruff format(Batch #7) (#6023) 
### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
|` vllm_ascend/quantization/compressed_tensors/compressed_tensors.py`|
|` vllm_ascend/quantization/quant_config.py`|
|` vllm_ascend/quantization/utils.py`|
|` vllm_ascend/quantization/w4a16.py`|
|` vllm_ascend/quantization/w4a4_flatquant_dynamic.py`|
|` vllm_ascend/quantization/w4a8_dynamic.py`|
|` vllm_ascend/quantization/w8a16.py`|
|` vllm_ascend/quantization/w8a8.py`|
|` vllm_ascend/quantization/w8a8_dynamic.py`|
|` vllm_ascend/quantization/w8a8_pdmix.py`|
|` vllm_ascend/quantization/w8a8mxfp8.py`|
|` vllm_ascend/sample/rejection_sampler.py`|
|` vllm_ascend/sample/sampler.py`|
|` vllm_ascend/worker/block_table.py`|

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
2c24bc6996

Signed-off-by: MrZ20 <2609716663@qq.com>
 2026-02-06 14:56:53 +08:00
meihanc
922e5c163b [main2main] upgrade vllm main 0202 (#6560) 
### What this PR does / why we need it?
1. Fix `TypeError: FusedMoEParallelConfig.__init__() missing 1 required
positional argument: 'is_sequence_parallel'` due to
https://github.com/vllm-project/vllm/pull/32567
2. Fix ` TypeError: '>' not supported between instances of 'MagicMock'
and 'int'` due to https://github.com/vllm-project/vllm/pull/33035
3. Fix `TypeError: Can't instantiate abstract class AscendMLAImpl with
abstract methods forward_mha, forward_mqa` and AttributeError: 'bool'
object has no attribute 'process_weights_after_loading' due to
https://github.com/vllm-project/vllm/pull/33284
4. Fix `'AscendSharedFusedMoE' object has no attribute
'_routed_input_transform'`due to
https://github.com/vllm-project/vllm/pull/32790
5. Fix `NPUModelRunner._dummy_run() got an unexpected keyword argument
'num_active_loras'` due to
https://github.com/vllm-project/vllm/pull/32005
6. Fix the problem caused by` 'tuple' object has no attribute 'job_id'`
due to https://github.com/vllm-project/vllm/pull/27492
7. Fix the problem that all_moe_layers is not equal to vllm.moe_forward,
vllm.moe_forward_shared due to
https://github.com/vllm-project/vllm/pull/33184
8. Add patch to fix the problem "got multiple values for keyword
argument 'add_special_tokens'" due to
https://github.com/vllm-project/vllm/pull/32863
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
 2026-02-05 19:31:17 +08:00
dsxsteven
a80e524fbc [Quant] GLM4.7-Flash Support W8A8 (#6492) 
### What this PR does / why we need it?
support W8A8 quant for model GLM4.7-flash
### Does this PR introduce _any_ user-facing change?
Yes
### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0

Signed-off-by: dsxsteven <dsxsteven@sina.com>
Co-authored-by: SlightwindSec <slightwindsec@gmail.com>
 2026-02-03 19:49:58 +08:00
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