5 Commits

Author SHA1 Message Date
wangxiyuan
8a7154001e [0.11.0]Chery pick pta upgrade change (#3940)
This PR cherry-pick two commit from main to upgrade torch-npu to 2.7.1
official release

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Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
2025-10-31 22:14:26 +08:00
huangdong2022
3a53bbc508 [Feat]Qwen3 Moe supports npu_add_rms_norm_quant op by default, update op with bias, resolve conflict with weight prefetch (#3465)
### What this PR does / why we need it?
1.qwen3 moe uses add_rms_norm_quant op instead of 'add_rms_norm op and
quant op' during quantization scene.
2.torch_npu.add_rms_norm_quant op fixed accuracy while model weights is
quantized by anti_method m4, m4 quantization is asymmetric outlier
suppression method, it will generate none-zero norm bias,
add_rms_norm_quant op updated to add this parameter to calculate.
3. add torch-npu check

### Does this PR introduce _any_ user-facing change?
new feature works if torch_npu version >= torch_npu-2.7.1.dev20250919

### How was this patch tested?
1.no special parameters to set, no new envs to set. new feature works if
torch_npu version >= torch_npu-2.7.1.dev20250919
2.use qwen3 moe quantization model to test ,such as
Qwen3-235B-A22B-W8A8, Qwen3-30B-A3B-W8A8,
Qwen3-235B-A22B-Instruct-2507-m4 (anti_method m4)

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

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Signed-off-by: h30027576 <huangdong51@huawei.com>
2025-10-17 09:30:51 +08:00
yuzhup
78777237a9 [2/N][Feat] Attention and MoE weight prefetch in Qwen3MoE models (#3203)
### What this PR does / why we need it?

- Refacotr and integrate a unified `WeightPrefetchMethod`
- Integrate `gate_up_proj.weight` in quantized Attention modules
- Prefetching these weights ahead of matmul-like operators imporves
performance by reducing L2 cache transfer latency

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

Add a new config in `--additional-config` for configuration:
```json
{
    "weight_prefetch_config": {
        "enabled": True,
        "prefetch_ratio": {
            "moe": {
                "gate_up": 0.8
            },
        },
    },
}
```
This feature is enabled by default, and can be disabled through this
configuration

### How was this patch tested?


- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: yuzhup <15705211260@163.com>
2025-10-14 20:16:33 +08:00
Ruri
866f5e7283 [Bugfix] Fix weight prefetching AssertionError in W8A8 MTP scene (#3361)
### What this PR does / why we need it?

- Fix `AssertionError` of `weight_prefetch_method` in W8A8 MTP scene
- Remove hard-code key
(https://github.com/vllm-project/vllm-ascend/pull/3146#discussion_r2416644010)

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

None

### How was this patch tested?
`weight_prefetch_method is None` (tested on DeepSeek-R1-w8a8mix_MTP)

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
2025-10-11 09:24:02 +08:00
Ruri
ff37575936 [1/N][Feat] Add weight prefetch feature for Attention layers (#3146)
### What this PR does / why we need it?

- Refacotr and integrate a unified `WeightPrefetchMethod`
- Integrate `qkv_proj.weight` and `o_proj.weight` in quantized Attention
modules
- Prefetching these weights ahead of matmul-like operators imporves
performance by reducing L2 cache transfer latency

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

Add a new config in `--additional-config` for configuration:
```json
{
    "weight_prefetch_config": {
        "enabled": false,
        "prefetch_ratio": {
            "attn": {
                "qkv": 1.0,
                "o": 1.0,
            },
        },
    },
}
```
This feature is enabled by default, and can be disabled through this
configuration

### How was this patch tested?


- vLLM version: v0.11.0

---------

Signed-off-by: yuzhup <15705211260@163.com>
Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
Co-authored-by: yuzhup <15705211260@163.com>
2025-10-09 20:38:39 +08:00