## What this PR does / why we need it?
pick-from:https://github.com/vllm-project/vllm-ascend/pull/7452
### Problem
Embedding models produce inconsistent outputs when prefix caching is
enabled vs disabled.
### Root Cause
The attention router condition was too broad:
- All `model_runner_type == "pooling"` → `_forward_encoder_attention()`
→ uses `npu_fusion_attention`
- **But `npu_fusion_attention` does NOT support prefix caching**
- Result: Numerical mismatch when KV cache is managed by prefix caching
### Solution
Refine the router condition to check causality:
**Before**:
```
if attn_metadata.model_runner_type == "pooling":
→ npu_fusion_attention (no prefix caching support)
```
**After**:
```
if attn_metadata.model_runner_type == "pooling" and not attn_metadata.causal:
→ npu_fusion_attention (for true encoders)
else:
→ npu_fused_infer_attention_score (prefix caching support)
```
### Changes Made
1. **Fixed router condition** (`vllm_ascend/attention/attention_v1.py`
L968)
- Added `and not attn_metadata.causal` check
- Effect: Non-causal embeddings now use correct operator
2. **Simplified encoder attention**
(`vllm_ascend/attention/attention_v1.py` L864-877)
- Removed redundant causal branch (encoders never use causal mask)
- Reduced from 34 lines to 14 lines
3. **Added test** (`tests/e2e/singlecard/pooling/test_embedding.py`)
- Validates embedding outputs with/without prefix caching are consistent
## Does this PR introduce _any_ user-facing change?
### Functional Changes
✅ **Yes** - Bug fix: Embedding models now produce consistent outputs
with prefix caching
### API Changes
❌ **No** - All public APIs unchanged
### Configuration Changes
❌ **No** - No new configuration required
### Backward Compatibility
✅ **Fully compatible** - Only fixes incorrect behavior
## How was this patch tested?
### New Test
Added `test_embed_models_using_prefix_caching_correctness()`:
- Tests: `Qwen3-Embedding-0.6B`
- Validates numerical consistency between runs with/without prefix
caching
- Uses long sequences to activate prefix caching
- Tolerance: 1e-2
- vLLM version: v0.18.0
Signed-off-by: underfituu <hzhucong@163.com>
### What this PR does / why we need it?
2nd PR for https://github.com/vllm-project/vllm-ascend/issues/5712,
extend SP to VL MoE models.
### Does this PR introduce _any_ user-facing change?
remove `sp_threshold` in additional config and reuse `sp_min_token_num`
from vLLM.
### How was this patch tested?
- Model: Qwen3-VL-30B-A3B,
- TP4 DP2
- 100 reqs
- max concurrency 1
| Seq length | Mean TTFT (ms) main | Mean TTFT (ms) this PR |
|------------|---------------------|------------------------|
| 4k | 429.40 | 323.3 |
| 16k | 1297.01 | 911.74 |
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: realliujiaxu <realliujiaxu@163.com>
### What this PR does / why we need it?
Replace text-match assertions with a two-tier logprob accuracy check:
- Prefill (token 0): assert token ID is identical between eager baseline
and compiled mode, then verify logprob matches within `atol`.
- Decode (tokens 1-2): if chosen tokens match, compare logprobs
directly; if they differ, cross-lookup the baseline token in the
compiled model's top-20 distribution and assert the assigned logprob is
within `decode_atol` (defaults to 2x atol). This tolerates minor argmax
drift caused by floating-point differences while still catching
distribution divergence.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.17.0
- vLLM main:
8a680463fa
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
**Refactor: Replace npu_ring_mla with FIA in MLA prefill**
This PR refactors the MLA (Multi-Layer Attention) prefill implementation
by replacing `npu_ring_mla` with `npu_fused_infer_attention_score` (FIA)
operator, unifying the attention backend with the standard attention
implementation.
**Key changes:**
1. **Core prefill refactoring (`mla_v1.py`)**
- Replace `npu_ring_mla` with `npu_fused_infer_attention_score` in
`_forward_prefill` and `_compute_prefill_context`
- Use TND layout with `softmax_lse_flag=True` for prefill attention
- Use `npu_attention_update` to merge multiple chunk outputs with LSE
(Log-Sum-Exp)
- Change `attn_mask` from `get_final_mla_mask()` to
`get_splitfuse_attn_mask()` for FIA compatibility
2. **Data type handling**
- Add automatic float16 → bfloat16 conversion (FIA with TND layout only
supports bfloat16)
- Convert output back to original dtype after FIA computation
3. **Metadata optimization**
- Pre-calculate `actual_seq_lengths_q` in `AscendMLAPrefillMetadata`
- Pre-calculate `chunk_actual_seq_lengths_kv_list` in
`ChunkedContextMetadata`
- Move `torch.cumsum` operations from forward pass to metadata building
phase
4. **CP compatibility (`mla_cp.py`)**
- Add `_ring_mla_mask_builder` to get `npu_ring_mla`-compatible masks
for Context Parallel scenarios
- Add `chunk_actual_seq_lengths_kv_list` field to
`CPChunkedContextMetadata`
**Why we need it:**
- **Backend unification**: Aligns MLA prefill with standard attention
implementation (`attention_v1.py`)
- **Better chunked context support**: FIA + `npu_attention_update`
provides native LSE-based output merging
- **Future compatibility**: Prepares for eventual `npu_ring_mla` removal
across the codebase
### Does this PR introduce _any_ user-facing change?
**No.** This is a pure refactoring with no functional changes - same
behavior, unified backend.
---
- Related issue: #5463 (item 7)
- vLLM version: v0.14.1
Signed-off-by: lico67373 <918688502@qq.com>
### What this PR does / why we need it?
Fix the error that reports while initializing qwen3-reranker-0.6b model
with `--enable-lora`.
And add a testcase to verify the fix.
- vLLM version: v0.17.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: paulyu12 <507435917@qq.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
### What this PR does / why we need it?
Reapply the auto-detect quantization format feature (originally in
#6645, reverted in #6873) and extend it to support remote model
identifiers (e.g., `org/model-name`).
Changes:
- Reapply auto-detection of quantization method from model files
(`quant_model_description.json` for ModelSlim, `config.json` for
compressed-tensors)
- Add `get_model_file()` utility to handle file retrieval from both
local paths and remote repos (HuggingFace Hub / ModelScope)
- Update `detect_quantization_method()` to accept remote repo IDs with
optional `revision` parameter
- Update `maybe_update_config()` to work with remote model identifiers
- Add platform-level `auto_detect_quantization` support
- Add unit tests and e2e tests for both local and remote model ID
scenarios
Closes#6836
### Does this PR introduce _any_ user-facing change?
Yes. When `--quantization` is not explicitly specified, vllm-ascend will
now automatically detect the quantization format from the model files
for both local directories and remote model IDs.
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
### What this PR does / why we need it?
This PR aims to support aclgraph for model runner v2, please see RFC
#5208. The PR contains these modifications:
- adapt to newest commit of vllm main branch.
- supply a unified interface of extra forward context for both model
runner v1 and model runner v2.
- implement graph mode for main model.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
### What this PR does / why we need it?
1. For all parts of the current test module involving the millisecond
download model, add the `local_file_only` parameter to specify offline
mode; this ensures that CI will not fail due to network instability.
2. Install modelscope from a fixed commit until it next release
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
check if the env or arg `local_files_only` works
1) set the env:
```shell
export HF_HUB_OFFLINE=1
```
2) run the script
```python
from transformers import PretrainedConfig
import huggingface_hub
from modelscope.utils.hf_util import patch_hub
patch_hub()
model="Qwen/Qwen3-0.6B"
kwargs = {}
config_dict, _ = PretrainedConfig.get_config_dict(
model,
trust_remote_code=True,
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
**kwargs,
)
print(config_dict)
```
it works well:
```shell
2026-03-06 06:40:12,546 - modelscope - WARNING - We can not confirm the cached file is for revision: master
The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
{'architectures': ['Qwen3ForCausalLM'], 'attention_bias': False, 'attention_dropout': 0.0, 'bos_token_id': 151643, 'eos_token_id': 151645, 'head_dim': 128, 'hidden_act': 'silu', 'hidden_size': 1024, 'initializer_range': 0.02, 'intermediate_size': 3072, 'max_position_embeddings': 40960, 'max_window_layers': 28, 'model_type': 'qwen3', 'num_attention_heads': 16, 'num_hidden_layers': 28, 'num_key_value_heads': 8, 'rms_norm_eps': 1e-06, 'rope_scaling': None, 'rope_theta': 1000000, 'sliding_window': None, 'tie_word_embeddings': True, 'torch_dtype': 'bfloat16', 'transformers_version': '4.51.0', 'use_cache': True, 'use_sliding_window': False, 'vocab_size': 151936, '_commit_hash': None}
```
3) test the model repo does not cached locally when the env
`HF_HUB_OFFLINE`==True
```python
from transformers import PretrainedConfig
import huggingface_hub
from modelscope.utils.hf_util import patch_hub
patch_hub()
model="FireRedTeam/FireRed-OCR"
kwargs = {}
config_dict, _ = PretrainedConfig.get_config_dict(
model,
trust_remote_code=True,
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
**kwargs,
)
print(config_dict)
```
and the result is as expected:
```shell
File "/workspace/demo.py", line 12, in <module>
config_dict, _ = PretrainedConfig.get_config_dict(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/utils/hf_util/patcher.py", line 189, in patch_get_config_dict
model_dir = get_model_dir(pretrained_model_name_or_path,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/utils/hf_util/patcher.py", line 164, in get_model_dir
model_dir = snapshot_download(
^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/hub/snapshot_download.py", line 137, in snapshot_download
return _snapshot_download(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/hub/snapshot_download.py", line 283, in _snapshot_download
raise ValueError(
ValueError: Cannot find the requested files in the cached path and outgoing traffic has been disabled. To enable look-ups and downloads online, set 'local_files_only' to False
```
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
The merged graph of draft in `FULL` mode is broken now.
This pr solves it.
Also, `actual_seq_lengths_q` in `model_runner` is found redundant, so,
it is removed.
It depends on https://github.com/vllm-project/vllm-ascend/pull/7144 and
https://github.com/vllm-project/vllm-ascend/pull/7148.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
Test code is shown as below:
```python
prompts = [
"1.Who are you?",
"2. Who are you?",
]
sampling_params = SamplingParams(temperature=0.0, top_p=0.95, top_k=40, max_tokens=200)
llm = LLM(
model="/home/some-model/Meta-Llama-3.1-8B-Instruct",
tensor_parallel_size=1,
max_num_seqs=32,
# enforce_eager=True,
disable_log_stats=False,
distributed_executor_backend="mp",
gpu_memory_utilization=0.7,
async_scheduling=True,
speculative_config={
"enforce_eager": True,
"model": "/home/some-model/EAGLE3-LLaMA3.1-Instruct-8B",
"disable_padded_drafter_batch": False,
"method": "eagle3",
"num_speculative_tokens": 3,
},
compilation_config={
"cudagraph_mode": "FULL",
"cudagraph_num_of_warmups": 1,
},
max_model_len=4096,
enable_prefix_caching=False,
)
outputs = llm.generate(prompts, sampling_params)
```
The result before:
```text
File "/vllm-workspace/vllm-ascend/vllm_ascend/attention/attention_v1.py", line 575, in full_graph_fia
graph_params.events[num_tokens].append(event)
~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
KeyError: 132
```
The result after:
```text
--------------------------------------------------
total_num_output_tokens: 400
num_drafts: 242
num_draft_tokens: 726
num_accepted_tokens: 156
mean acceptance length: 1.64
--------------------------------------------------
acceptance at token 0: 0.42
acceptance at token 1: 0.16
acceptance at token 2: 0.07
```
We also test `FULL_DECODE_ONLY` mode.
The result is:
```text
--------------------------------------------------
total_num_output_tokens: 400
num_drafts: 244
num_draft_tokens: 732
num_accepted_tokens: 155
mean acceptance length: 1.64
--------------------------------------------------
acceptance at token 0: 0.42
acceptance at token 1: 0.16
acceptance at token 2: 0.06
```
- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
Signed-off-by: drslark <slarksblood@qq.com>
### What this PR does / why we need it?
Fix the LoRA e2e test accuracy issue that introduced by the upstream PR
https://github.com/vllm-project/vllm/pull/32005
### How was this patch tested?
pytest -sv tests/e2e/singlecard/test_llama32_lora.py
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: paulyu12 <507435917@qq.com>
Signed-off-by: yupeng <507435917@qq.com>
### What this PR does / why we need it?
This PR optimizes the `split_qkv_rmsnorm_rope` operator by introducing a
new Triton kernel, `split_qkv_rmsnorm_rope_prefill_kernel`, for the
prefill stage (i.e., large batch sizes). The implementation now
dynamically selects between the existing decode kernel and the new
prefill kernel based on the batch size, which improves performance for
large batch scenarios.
Additionally, the RoPE implementation is updated to support partial
rotation dimensions (`rope_dim`), making the operator more flexible.
### Does this PR introduce _any_ user-facing change?
No. This is a performance optimization and is not expected to introduce
any user-facing changes.
### How was this patch tested?
CI should pass with existing tests. The new prefill path is triggered
when the batch size is larger than the number of available vector cores.
The partial RoPE feature can be tested by passing the `rope_dim`
argument.
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: guzhiyong <guzhiyong5@h-partners.com>
Signed-off-by: frank <2547457096@qq.com>
Co-authored-by: guzhiyong <guzhiyong5@h-partners.com>
### What this PR does / why we need it?
Add muls_add triton kernel with related fusion pass. What's more, this
PR refactors `AscendCompilationConfig` and delete `NpugraphExConfig`.
### Does this PR introduce _any_ user-facing change?
None
### How was this patch tested?
CI passed with new added test.
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
---------
Signed-off-by: whx-sjtu <2952154980@qq.com>
## 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>
### What this PR does / why we need it?
New FIA operator requires queryT equal to the last element of
actualSequenceLengthQ.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Passed existing test (test_mtp_eagle_correctness.py).
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: Wangbingjie <wangbj1207@126.com>
Signed-off-by: Wangbingjie <w30061490@china.huawei.com>
Co-authored-by: Wangbingjie <w30061490@china.huawei.com>
### What this PR does / why we need it?
Integrating inductor pass and npugraph ex pass, see RFC:
https://github.com/vllm-project/vllm-ascend/issues/6347
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
all tests passed.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
### What this PR does / why we need it?
Fix the issue where, in graph mode, the fused `AddRMSNormQuant` operator
does not take effect when there is no bias.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: ZYang6263 <zy626375@gmail.com>
### What this PR does / why we need it?
This pull request enables the `npugraph_ex` backend by default to
improve performance on Ascend NPUs, as proposed in the
[RFC](https://github.com/vllm-project/vllm-ascend/issues/6214).
### Does this PR introduce _any_ user-facing change?
Yes. `npugraph_ex` is now enabled by default. Users can disable it by
setting `enable: false` in the `npugraph_ex_config` section of the
`additional_config`.
### How was this patch tested?
CI passed. The changes are covered by existing and new E2E tests
(`test_aclgraph_accuracy.py`) and unit tests (`test_ascend_config.py`)
that have been updated to reflect the new default behavior. The tests
verify correctness and consistency with `npugraph_ex` enabled and
disabled, as well as with the new static kernel option.
Signed-off-by: huyuanquan1 <huyuanquan1@huawei.com>
Co-authored-by: huyuanquan1 <huyuanquan1@huawei.com>
### What this PR does / why we need it?
This PR extends original `rope_triton_forward` and
`split_qkv_rmsnorm_rope` to support `cos_sin_cache` && `positions` as
inputs. This fully aligns to vLLM RoPE api interface. Compared with
earlier implementation for RoPE, the benefits are:
1. avoiding pre-computation of `cos` `sin` before model execution, which
helps to remove redundant codes.
2. allowing eagle3 draft model to have different rope parameters with
main model (see #6612 ). This help to recover accept rate && accuracy in
that case.
In addition, this kernel change only introduces very small performance
degradation. Those `index_select` or `chunk` operations are now changed
into simple memory access in triton kernel (For example,
https://github.com/vllm-project/vllm-ascend/pull/5450/changes#diff-a4c2d3071530df193b98f9bf38553874bc4d47571336711f116c26d019cfbb6aR77-R81).
**Highlights**
- **RoPE Cache Unification**: Replaced separate _sin and _cos global
tensors with a unified cos_sin_cache and explicit positions tensor for
Rotary Positional Embeddings (RoPE), streamlining data handling.
- **Triton Kernel Integration**: Updated Triton kernels
(split_qkv_rmsnorm_rope_kernel, _triton_rope) to directly consume the
cos_sin_cache and positions for more efficient and integrated RoPE
calculations.
- **Custom Operation Registration**: Registered `rope_forward_oot` as a
new custom operation, allowing its use in fused compilation passes and
providing a dedicated entry point for the new RoPE implementation.
- **Refactored RoPE Forward Pass**: Modified the rope_forward_oot
function to accept the new cos_sin_cache and positions arguments,
enabling a more flexible and integrated RoPE application within the
system.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
5326c89803
Additional test on Qwen3-235b accuracy:
| Aime2024 | GSM8K | Livecodebench |
| -------- | -------- | -------- |
| 83.33 | 96.26 | 70.23 |
---------
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
This PR upgrades the core vLLM dependency to a newer version from the
main branch (`13397841ab469cecf1ed425c3f52a9ffc38139b5`). This is
necessary to keep our project up-to-date with the latest features and
fixes from upstream vLLM.
1.
ac32e66cf9
pass file is moved.
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: wxsIcey <1790571317@qq.com>
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
Co-authored-by: wxsIcey <1790571317@qq.com>
### What this PR does / why we need it?
This PR adds an end-to-end test case to verify the correctness of base
model inference when LoRA is enabled. This is to ensure that after a
LoRA base model request issue was fixed, the functionality remains
correct and does not regress. The new test case calls `do_sample` with
`lora_id=0` to target the base model and asserts the output against
expected SQL queries.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with the new test case. The test can be run with:
```bash
pytest -sv tests/e2e/singlecard/test_llama32_lora.py
Signed-off-by: paulyu12 <507435917@qq.com>
### What this PR does / why we need it?
When running the Qwen3-0.6B model using the npugraph_ex backend, the
last few characters of the generated results changed. We have modified
the relevant test cases to ensure the CI runs smoothly.
### 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: chencangtao <chencangtao@huawei.com>
Co-authored-by: chencangtao <chencangtao@huawei.com>
### What this PR does / why we need it?
This PR reverts "[ModelRunner] Revert [Fix] Pads query_start_loc to
satisfy FIA/TND constraint #6459 (commit
5b0a6bcfe9)" and fixes a check in
`model_runner_v1`.
**A key change is that we remove the strict assertion in the latest
commit, as it turns out MLA + PIECEWISE will slice during computing,
leaving our assertion uncalled for and will only cause false alarm.**
This handles both uniform and mixed batches (by inserting a dummy
request for mixed batches), consolidates ad-hoc padding into a single
helper, copies the updated buffer to the device, which prevents kernel
mismatches or failures and ensure correct shapes for FIA/TND execution
in full graph modes.
We currently place this helper in `execute_model`. My original design
was to include it in `_prepare_inputs`, but that doesn’t work because it
must run after padding. While I’d prefer to minimize the impact and
reuse as much of the base class as possible in the future, it doesn’t
seem achievable at the moment.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Test cases added.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
This PR upgrades the vLLM dependency from `v0.14.1` to `v0.15.0`. This
involves:
- Updating the `VLLM_TAG` in all `Dockerfile`.
- Updating the vLLM version in `docs/source/conf.py`.
- Removing conditional code paths specific to `v0.14.1` across the
codebase, which simplifies maintenance.
- Fix `TypeError: MMEncoderAttention.__init__() got an unexpected
keyword argument 'multimodal_config'` due to
https://github.com/vllm-project/vllm/pull/31972.
- Fix `_shared_experts: 'NoneType' object is not callable` due to
https://github.com/vllm-project/vllm/pull/32082 by
https://github.com/vllm-project/vllm-ascend/pull/6335.
- Fix `ReshapeAndCacheOperation setup failed!` due to
https://github.com/vllm-project/vllm/pull/25954 by overriding attention
metadata slots.
This upgrade is necessary to keep the project aligned with the latest
features, bug fixes, and API changes in the vLLM project.
### Does this PR introduce _any_ user-facing change?
No, this is an internal dependency update and does not introduce any
user-facing changes.
### How was this patch tested?
CI is expected to pass with these changes, ensuring that all existing
tests are successful with the new vLLM version.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
co-authored-by: shen-shanshan <467638484@qq.com>
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This PR removes the custom `ProfileExecuteDuration` utility and its
usages across the codebase. This utility was used for profiling
execution duration of different stages in the inference process. It is
replaced by the standard `vllm.v1.utils.record_function_or_nullcontext`,
which integrates with PyTorch's profiler.
This change simplifies the code by removing a custom implementation in
favor of an upstream utility, improving maintainability. Associated
documentation and tests for `ProfileExecuteDuration` are also removed.
### Does this PR introduce _any_ user-facing change?
`VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE` env is removed now.
### How was this patch tested?
CI passed. The changes are a cleanup and replacement with a standard
utility. Existing tests cover the functionality. The removed feature had
its own tests which are also removed.
Related RFC: #5304
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This reverts commit 56f5d3bd49.
### What this PR does / why we need it?
The patch https://github.com/vllm-project/vllm-ascend/pull/6357 which
break the functionality availability in the spec_decode scenario, let's
revert and make CI happy first
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
This handles both uniform and mixed batches (by inserting a dummy
request for mixed batches), consolidates ad-hoc padding into a single
helper, copies the updated buffer to the device, and asserts the layout
constraint before building the attention metadata. Together, these
changes prevent kernel mismatches or failures and ensure correct shapes
for FIA/TND execution in full graph modes.
We currently place this helper in `execute_model`. My original design
was to include it in `_prepare_inputs`, but that doesn’t work because it
must run after padding. While I’d prefer to minimize the impact and
reuse as much of the base class as possible in the future, it doesn’t
seem achievable at the moment.
### Does this PR introduce _any_ user-facing change?
None.
### How was this patch tested?
Test cases added.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
Added an E2E test case for the scenario of enabling a static kernel for
npugraph_ex, monitoring its compilation and unloading process.
Also fixed the previously existing spelling errors
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: chencangtao <chencangtao@huawei.com>
Co-authored-by: chencangtao <chencangtao@huawei.com>
### What this PR does / why we need it?
We found the custom passes of NPUGraphEX have implemented fusion
operator features, which still require E2E test case validation and
guard. This PR implements E2E test cases for the AddRMSNormQuant and
SplitQKVNormRope operator fusions under NPUGraphEX that are already in
the codebase.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: cjian <2318164299@qq.com>
### What this PR does / why we need it?
This patch purpose to add the `update_max_model_len` interface.
- vLLM version: v0.14.0
- vLLM main:
d68209402d
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
This patch add new runner labels for the HK region, and e2e single-card
testing has been migrated to this runner.
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
#5790 changes default addrmsnormBias operator if custom ops is enabled.
This PR modifies AddRmsNormQuant pass to align with addrmsnormBias.
---------
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
This PR is to replace addRmsNorm and Add With addRmsNormBias. This way
can lead to a more effecient result.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Full Test Pass
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: Chen_HaoWen <chenhaowen12@huawei.com>
Co-authored-by: Chen_HaoWen <chenhaowen12@huawei.com>
### What this PR does / why we need it?
The test case
`tests/e2e/singlecard/spec_decode/test_v1_spec_decode.py::test_llama_qwen_eagle_acceptance`
fails occasionally, such result seems not stable with method `eagle`,
for example:
[tests/e2e/singlecard/spec_decode/test_v1_spec_decode.py::test_llama_qwen_eagle_acceptance](https://github.com/vllm-project/vllm-ascend/actions/runs/21249578476/job/61147453980?pr=6151)
This PR skips the `eagle` tests to keep CI success
- vLLM version: v0.14.0
- vLLM main:
d68209402d
Signed-off-by: wjunLu <wjunlu217@gmail.com>
### What this PR does / why we need it?
Re-open `tests/e2e/singlecard/test_aclgraph_accuracy.py` and update its
golden results to match PTA 2.9.0
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: wjunLu <wjunlu217@gmail.com>
### What this PR does / why we need it?
Upgrade PTA to 2.9.0
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: wjunLu <wjunlu217@gmail.com>
According to the official documentation, the parameter
"draft_tensor_parallel_size": 1 is supposed to be applied to the Eagle3
model. However, based on actual debugging, it was found that the number
of tensor parallelisms (tp) of the Eagle model is consistent with that
of the target model. The setting of tp for the draft model did not take
effect as expected.
**Note:** This feature has not been superimposed and tested with `sp`
and `dp`. It will be adapted later
No
```python
from vllm import LLM, SamplingParams
def main():
prompts = [
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(
model="meta-llama/Llama-3.1-8B-Instruct",
tensor_parallel_size=4,
gpu_memory_utilization=0.9,
enforce_eager=True,
speculative_config={
"method": "eagle3",
"model": "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
"draft_tensor_parallel_size": 1,
"num_speculative_tokens": 3,
},
)
outputs = llm.generate(prompts, sampling_params)
print(f"Outputs: {outputs}")
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
Fixesvllm-project/vllm#31345
### What this PR does / why we need it?
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: zhaomingyu <zhaomingyu13@h-partners.com>
Co-authored-by: drslark <slarksblood@qq.com>
### What this PR does / why we need it?
This is a part of
https://github.com/vllm-project/vllm-ascend/issues/4715#issue-3694310762
1. refactor the npugraph_ex config，modified the default configuration of
the static kernel, new default value of static kernel is false
2. support online-infer with static kernel
3. fixed the issue where manually modifying FX graphs caused an abnormal
model return type, and removed the related redundant code.
### Does this PR introduce _any_ user-facing change?
yes，the new config of npugraph_ex is as follow:
```
additional_config={
"npugraph_ex_config": {
"enable": True,
"enable_static_kernel": False
}
}
```
### How was this patch tested?
```
vllm serve /data/DeepSeek-V3.1-Terminus-w4a8 \
--host 0.0.0.0 \
--port 8004 \
--data-parallel-size 4 \
--tensor-parallel-size 4 \
--quantization ascend \
--seed 1024 \
--served-model-name deepseek_v3 \
--enable-expert-parallel \
--max-num-seqs 48 \
--max-model-len 40000 \
--async-scheduling \
--max-num-batched-tokens 9000 \
--trust-remote-code \
--no-enable-prefix-caching \
--speculative-config '{"num_speculative_tokens": 3, "method":"deepseek_mtp","disable_padded_drafter_batch": false}' \
--gpu-memory-utilization 0.9 \
--compilation-config '{"cudagraph_capture_sizes":[4,32,64,112,160,176,192], "cudagraph_mode": "FULL_DECODE_ONLY"}' \
--additional-config \
'{"enable_shared_expert_dp": true,"multistream_overlap_shared_expert": true,"npugraph_ex_config":{"enable":true}}'
```
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: chencangtao <chencangtao@huawei.com>
Signed-off-by: ChenCangtao <50493711+ChenCangtao@users.noreply.github.com>
Co-authored-by: chencangtao <chencangtao@huawei.com>
### What this PR does / why we need it?
Upgrade vllm commit to releases/v0.14.0
- Re-open cases in `tests/e2e/singlecard/pooling/test_scoring.py`, since
the errors before have been fixed by
https://github.com/vllm-project/vllm/pull/32243
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
11b6af5280
Signed-off-by: wjunLu <wjunlu217@gmail.com>
