### What this PR does / why we need it?
This PR builds upon PR
https://github.com/vllm-project/vllm-ascend/pull/5011 and aims to
further enhance the npu_graph_ex_passes module. Based on prior work, we
have added graph optimization support for the add_rms_quant fused
operator in scenarios where a bias term is present—ensuring the fusion
pattern is correctly registered and matched into the computation graph.
For validation, we switched to the Qwen3-235B-A22B-W8A8 model for
SPPatternWithBias and Qwen3-32B model for SPPattern. Benchmark results
show that, compared to the unfused baseline, enabling this fusion pass
significantly improves inference throughput for W8A8 quantized models.
For more details can refer to the
RFC:https://github.com/vllm-project/vllm-ascend/issues/4715
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
```
llm = LLM(
model=model,
tensor_parallel_size=GPUs_per_dp_rank,
enforce_eager=False,
enable_expert_parallel=enable_expert_parallel,
trust_remote_code=trust_remote_code,
gpu_memory_utilization=0.98,
max_num_batched_tokens=512,
# load_format="dummy",
max_model_len=2048,
max_num_seqs=16,
quantization="ascend",
additional_config={
"refresh": True,
"enable_npugraph_ex": True
},
compilation_config={
"cudagraph_capture_sizes": [8, 16],
"cudagraph_mode": "FULL_DECODE_ONLY",
},
)
if profile_dir:
llm.start_profile()
outputs = llm.generate(prompts, sampling_params)
if profile_dir:
llm.stop_profile()
for i, output in enumerate(outputs):
if i >= 5:
break
prompt = output.prompt
generated_text = output.outputs[0].text
print(
f"DP rank {global_dp_rank}, Prompt: {prompt!r}, "
f"Generated text: {generated_text!r}"
)
```
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: cjian <2318164299@qq.com>
### What this PR does / why we need it?
[Bugfix] fix dcp_only bug and add e2e accuracy test for dcp only and pcp
only
this pr fix the bug of accuracy test when decode_parallel_size>1 and
prefill_context_parallel_size=1.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
---------
Signed-off-by: zhenwenqi2024 <zhenwenqi_2022@qq.com>
### What this PR does / why we need it?
Revert PR 5253 to fix the smoking problem
### Does this PR introduce _any_ user-facing change?
Does not.
### How was this patch tested?
It was tested in the failure case.
Signed-off-by: Rifa <865071616@qq.com>
### What this PR does / why we need it?
[P/D] Performance enhancement of Layerwise connector in TP asymmetric
scenarios
1. Session fusion: For transmission tasks at each layer, aggregate
transmission tasks with the same destination and merge them into a
single task for assignment.
2. Alltoall aggregation: For TP asymmetric scenarios, perform all
alltoall operations at once according to the block granularity for all
requests.
[RFC]: CDCP Scheduling for Disaggregated Prefilling with KV Cache
Layerwise Push Support
https://github.com/vllm-project/vllm-ascend/issues/4842
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
---------
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Co-authored-by: nwpu-zxr <zhouxuerong2@huawei.com>
Co-authored-by: wangxiaoteng <wangxiaoteng@huawei.com>
### What this PR does / why we need it?
When using the swa parameter in fia, headDim does not currently support
256, and when gemma3's headDim is equal to 256, an error will occur.
Therefore, code rollback is required, and it will be incorporated after
cann supports it.
### Does this PR introduce _any_ user-facing change?
Remove swa parameter of fia.
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
---------
Signed-off-by: nsdie <yeyifan@huawei.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
#### Overview
This PR fixes a shape mismatch bug between `expert_placement_map` and
`log2phy_expert_map` when **redundant experts** are enabled in the
vLLM-Ascend platform. The issue occurred during the initialization of
expert maps and their updates via EPLB (Expert Load Balancer)
adjustment, leading to potential tensor shape errors and incorrect
expert routing in distributed MoE deployments.
#### Key Changes
1. **Unify expert map shape calculation logic**
- Ensure the shape of `expert_placement_map` and `log2phy_expert_map`
strictly aligns with the total number of experts (including redundant
experts) during initialization.
- Update the shape adjustment logic in EPLB dynamic update process to
match the initial expert map dimensions.
2. **Add shape consistency checks**
- Add assertion statements to verify the shape consistency of the two
maps after initialization and EPLB adjustment, preventing silent shape
mismatches in subsequent operations.
#### Impact
- Resolves tensor shape errors when using redundant experts with EPLB on
Ascend platform.
- Ensures correct expert routing and load balancing for MoE models with
redundant expert configurations.
- No breaking changes to existing functionality; compatible with
non-redundant expert deployments.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Che Ruan <cr623@ic.ac.uk>
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
Co-authored-by: Che Ruan <cr623@ic.ac.uk>
Co-authored-by: shenchuxiaofugui <1311027364@qq.com>
### What this PR does / why we need it?
This PR aims to delete redundant methods in mtp_proposer. All the
deleted methods now can be found in eagle_proposer. We also remove some
methods in eagle_proposer since they are identical to those in
vllm-eagle.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
by ci
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: Zetong Li <slippersss@126.com>
This reverts commit fb9fdcdbe4.
### What this PR does / why we need it?
this pr breaks the smoke test because of that leads the error of
aclnnNeScalar:Kernel Run failed. opType: 25, NotEqual
launch failed for NotEqual, errno:361001
<img width="1149" height="166"
alt="A6C9453D-4F0B-4256-DD80-A9C181DAB2D9"
src="https://github.com/user-attachments/assets/cab9c4b8-3fd1-4c6b-b424-474b46042726"
/>
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: zxwang <1476209578@qq.com>
### What this PR does / why we need it?
Add nightly test for triton split_rmsnorm_rope
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Angazenn <supperccell@163.com>
### What this PR does / why we need it?
- Problem: In MLA+MLAPO, KV-consumer deployments keep
fused_qkv_a_proj/q_proj weights and quant params even though MLAPO uses
the prepacked buffers, increasing memory footprint on decode nodes.
- Fix: Conditionally drop those tensors only when
`kv_transfer_config.is_kv_consumer` to reclaim memory (consistent with
the SFA behavior #4774 ).
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: Chen Chen <0109chenchen@gmail.com>
### What this PR does / why we need it?
In mooncake kvpool, `local_hostname` is not used. Instead, the local IP
is obtained directly via `get_ip()`. Therefore, remove this parameter to
avoid confusion.
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: LCAIZJ <leichao139636@163.com>
### What this PR does / why we need it?
In scenarios where models like
[Moonlight](https://modelscope.cn/models/moonshotai/Moonlight-16B-A3B-Instruct)
(using MLA but without `rope_scaling` in config.json) invoke
`AscendRotaryEmbedding`. `_cos_cache` and `_sin_cache` are not recorded
correctly.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
Signed-off-by: Debonex <719893090@qq.com>
### What this PR does / why we need it?
This fixes a bug that occurred when running `test_camem.py` in the
triton-ascend environment `NPU function error:
aclrtGetMemInfo(ACL_HBM_MEM, &device_free, &device_total)`
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
Currently, the vllm pull request
(https://github.com/vllm-project/vllm/pull/24252) is causing operator
fusion to fail. This issue was previously fixed by patching the backend.
The root cause has been identified, and the problem can be resolved with
this pull request.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: wxsIcey <1790571317@qq.com>
### What this PR does / why we need it?
Replace multiple PyTorch operations with a fused Triton kernel to
determine token indices for sampling during speculative decoding. This
reduces kernel launch overhead and memory traffic, improving overall
performance on Ascend hardware.
---------
Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>
### What this PR does / why we need it?
Fix the bug in the PCP overlay feature
1、Fix the bug related to PCP and EPLB overlap by including PCP size in
the word_size calculation.
2、In the PCP pooling scenario, a prompt has been added for setting the
cp_kv_cache_interleave_size.
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
### What this PR does / why we need it?
1. Refactor eagle and mtp function: load_model and generate_token_ids
2. Remove redundant code in mtp and eagle file
3. Refactor the UT of file
2/N of Refactor and merge mtp and eagle
Relational RFC: https://github.com/vllm-project/vllm-ascend/issues/5467
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
ut and tests
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: lilinsiman <lilinsiman@gmail.com>
### What this PR does / why we need it?
Since the _npu_ring_mla operator deteriorates in long-sequencescenarios,
the long sequence is split into shorter sequences for input to improve
performance.
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: pichangping <1337510399@qq.com>
### What this PR does / why we need it?
In the training-inference switching scenario, there is no need to resume
the model weights during KV cache resumption, as this would lead to
format mismatch.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: p00465316 <panchao13@huawei.com>
Co-authored-by: p00465316 <panchao13@huawei.com>
### What this PR does / why we need it?
Fix chunk prefill bug for long_sequence feature
When there are two requests with chunk prefill enabled in the
long-sequence scenario, if one request has only 1 token during
scheduling, it will be identified as a decode request and trigger an
error. This PR fixes the issue.
Closes: https://github.com/vllm-project/vllm-ascend/issues/5445
- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: LookAround <lixushi@huawei.com>
### What this PR does / why we need it?
kvpool decode save kvcache
now only support mla
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: baxingpiaochong <771405853@qq.com>
Co-authored-by: Chao Lei <leichao139636@163.com>
…w8a8 while main model uses w8a8
### What this PR does / why we need it?
Disable dispatch_gmm_combine_decode operator when mtp drafter model uses
non-w8a8 while main model uses w8a8, or drafter model is eagle series.
More info about this operator, please refer to RFC: issue
https://github.com/vllm-project/vllm-ascend/issues/5476
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangqiankun <wangqiankun13@huawei.com>
### What this PR does / why we need it?
Since the [PR](https://github.com/vllm-project/vllm/pull/28988) for PCP
modifications to `GPUModelRunner` has not yet been merged into vLLM,
this PR temporarily requires adjustments to certain buffer sizes. These
changes can be reverted once the original
[PR](https://github.com/vllm-project/vllm/pull/28988) is merged.
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.13.0
- vLLM main:
5326c89803
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
PR #4892 was revert in #4981, we recover it now. For the potential bug
break deepseek3.2 in PD case, we will find it out and fix it.
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
---------
Signed-off-by: lidenghui <lidenghui1110@gmail.com>
### What this PR does / why we need it?
This PR adds multi-stream for GQA to enable computation-communication
overlap. For chunked prefill, we reduce TTFT by approximately 4%.
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
---------
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
Previously, it was necessary to set the environment variables
HCCL_INTRA_PCIE_ENABLE=1 and HCCL_INTRA_ROCE_ENABLE=0. This PR enables
hierarchical MC2 operations on A2 by default.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
7157596103
Signed-off-by: hwhaokun <haokun0405@163.com>
### What this PR does / why we need it?
Supported to use full-graph with Qwen3-Next-MTP.
In detail, we adatpted `AscendAttentionState.ChunkedPrefill` in main
model, and also adapted `AscendAttentionState.ChunkedPrefill` in mtp
model.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
We changed the test of Qwen3-Next-MTP in
`tests/e2e/multicard/test_qwen3_next.py` to make it a test of
`FULL_DECODE_ONLY`. Then run `pytest -s
tests/e2e/multicard/test_qwen3_next.py::test_qwen3_next_distributed_mp_eager_mtp_similarity_tp4`.
And this test passed.
```text
.
================================================================================================================================= warnings summary =================================================================================================================================
<frozen importlib._bootstrap>:241
<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyPacked has no __module__ attribute
<frozen importlib._bootstrap>:241
<frozen importlib._bootstrap>:241: DeprecationWarning: builtin type SwigPyObject has no __module__ attribute
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
==================================================================================================================== 1 passed, 2 warnings in 271.89s (0:04:31) =====================================================================================================================
```
- vLLM version: v0.13.0
- vLLM main:
5326c89803
Signed-off-by: drslark <slarksblood@qq.com>
since we support self-defined pass manager now, it's no need to override
the pass config. Let's clean up it.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
Currently, when the MooncakeConnector interacts via ZeroMQ, it throws
the following exception upon send/receive failure:
**Issue 1:** The currently used `zmq.REQ` socket follows a strict
request-reply pattern, requiring an alternating sequence of send →
receive → send → receive... If either a send() or receive() operation
fails, the ZeroMQ socket becomes unusable.
**Solution:** When a send() or receive() exception occurs, close and
delete the ZeroMQ socket, and recreate it upon next use.
**Issue 2:** In `_handle_request`, if `_send_done_recv_signal` raises an
exception, the exception is thrown immediately and subsequent code is
not executed, causing the decode logic to fail to properly release the
request.
**Solution:** Move the call to `_send_done_recv_signal` to the end of
the function.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
Signed-off-by: LCAIZJ <leichao139636@163.com>
### What this PR does / why we need it?
This PR builds upon PR #5011 and aims to further enhance the
npu_graph_ex_passes module. Based on prior work, we have added graph
optimization support for the add_rms_quant fused operator in scenarios
where a bias term is present—ensuring the fusion pattern is correctly
registered and matched into the computation graph.
For validation, we switched to the Qwen3-235B-A22B-W8A8 model. Benchmark
results show that, compared to the unfused baseline, enabling this
fusion pass significantly improves inference throughput for W8A8
quantized models.
For more details can refer to the
RFC:https://github.com/vllm-project/vllm-ascend/issues/4715
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
```
llm = LLM(
model=model,
tensor_parallel_size=GPUs_per_dp_rank,
enforce_eager=False,
enable_expert_parallel=enable_expert_parallel,
trust_remote_code=trust_remote_code,
gpu_memory_utilization=0.98,
max_num_batched_tokens=512,
# load_format="dummy",
max_model_len=2048,
max_num_seqs=16,
quantization="ascend",
additional_config={
"refresh": True,
"enable_npugraph_ex": True
},
compilation_config={
"cudagraph_capture_sizes": [8, 16],
"cudagraph_mode": "FULL_DECODE_ONLY",
},
)
if profile_dir:
llm.start_profile()
outputs = llm.generate(prompts, sampling_params)
if profile_dir:
llm.stop_profile()
for i, output in enumerate(outputs):
if i >= 5:
break
prompt = output.prompt
generated_text = output.outputs[0].text
print(
f"DP rank {global_dp_rank}, Prompt: {prompt!r}, "
f"Generated text: {generated_text!r}"
)
```
- vLLM version: v0.13.0
- vLLM main:
5326c89803
Signed-off-by: cjian <2318164299@qq.com>
### What this PR does / why we need it?
Add LongCat-Flash support.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
CI passed
- vLLM version: v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: chuyuelin <923822139@qq.com>
Co-authored-by: chuyuelin <chuyuelin1@huawei.com>
### What this PR does / why we need it?
In the current process of implementing attention updates, the FIA
operator shares a single workspace among different layers within the
same computation graph. To enable memory reuse, we adopt the
weak_ref_tensor mechanism. However, this approach may lead to precision
anomalies in certain scenarios. To address this issue, different layers
in the same computation graph are assigned independent workspaces.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
Signed-off-by: WithHades <244036962@qq.com>
### What this PR does / why we need it?
Improve the performance of Layerwise Connector, mainly includes the
following points:
1. Use event synchronize to replace stream synchronize.
2. Access metaserver when scheduling.
3. Transfer kvcache each Chunk prefill segmentation.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
By CI.
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
---------
Signed-off-by: nwpu-zxr <zhouxuerong2@huawei.com>
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
Co-authored-by: wangxiaoteng <wangxiaoteng@huawei.com>
Currently in the Fused MoE module, functions of classes like
MoECommMethod and MoETokenDispatcher output data in dictionary or tuple
format, which hampers code maintainability, readability, and
extensibility. This PR introduces dataclasses for these key output types
to address these issues.
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
By converting the KV cache from ND to NZ format when the decode node
receives it, this PR ensures that the KV NZ feature works correctly
during the decoding phase in disagg-prefill scenario.
- vLLM version: v0.11.0
- vLLM main:
83f478bb19
---------
Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>
Co-authored-by: ghphotoframe <854746559@qq.com>
Co-authored-by: alex101-ops <alex1015718386@gmail.com>
### What this PR does / why we need it?
1. This PR is proposed to support complicated pcp/dcp parallelisms in
Prefill and Decode nodes in Mooncake, such as Prefill: TP8/PCP2DCP8 and
Decode: TP8/DCP4/DP2, which is not supported now. We establish the link
mappings to transfer KVCache between prefill and decode nodes. The main
function is realized in Function of `_get_kv_split_metadata` in
Mooncake_connector.py
2. After a prefill rank is pulled KVCache by a decode rank, the decode
rank will send `DONE_RECVING_MSG` to the prefill rank and the prefill
rank will free its KVCache blocks. If a prefill rank is pulled KVCache
more than one time by several decode ranks and it surely could happen in
complicated pcp/dcp parallelisms, it will cause the prefill rank free
its KVCache blocks for several times, which could cause memory issue.
This PR solve this issue by counting the times of prefill rank would be
pulled KVCache and in the last time, it will free the prefill rank
KVCache blocks. The related code is in Function of `run_busy_loop` in
Mooncake_connector.py
3. If a prefill rank is not pulled KVCache by any decode ranks, the
first rank in decode node will send "DONE_RECVING_MSG" to free its
blocks. The related code is in Function of
`_send_done_signal_to_free_remote_port` in Mooncake_connector.py
### How was this patch tested?
This PR is tested in many pcp/dcp parallelisms, and the accuracy are all
correct.
MLA model:
Prefill node: TP8/DP2, Decode node: TP8/DP2
Prefill node: TP8/PCP2/DCP8, Decode node: TP8/DP2
Prefill node: TP8/PCP2/DCP8, Decode node: TP8/DCP4/DP2
Prefill node: TP8/PCP2/DCP4, Decode node: TP4/DCP2/DP4
Prefill node: TP8/PCP2/DCP2, Decode node: TP4/DCP4/DP4
Prefill node: TP8/PCP2, Decode node: TP4/DCP2
GQA model:
Prefill node: TP8/DP2, Decode node: TP8/DP2
Prefill node: TP8/PCP2/DCP2, Decode node: TP8/DP2
Prefill node: TP8/PCP2/DCP2, Decode node: TP8/DCP2/DP2
Prefill node: TP8/PCP2/DCP2, Decode node: TP4/DP4
Prefill node: TP16/DCP2/PCP1, Decode node: TP8/DCP2/DP2
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
- Co-author by: Daishixun dsxtsteven@sina.com
---------
Signed-off-by: wangxiaochao <w00642655@china.huawei.com>
Co-authored-by: wangxiaochao <w00642655@china.huawei.com>
Co-authored-by: Jade Zheng <zheng.shoujian@outlook.com>
### What this PR does / why we need it?
We should transfer the mm_embed to the dtype of input_embed before
performing the in-place assignment
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: wangli <wangli858794774@gmail.com>
### What this PR does / why we need it?
Refactor pcp& dcp related code. we use pcp_manager class to Unifiy
Manage pcp & dcp . as we do this , many code can be deleted from
model_runner, and can avoid break pcp & dcp by other developments.
RFC：https://github.com/vllm-project/vllm-ascend/issues/5449
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: zhenwenqi2024 <zhenwenqi_2022@qq.com>
Co-authored-by: zzzzwwjj <34335947+zzzzwwjj@users.noreply.github.com>
### What this PR does / why we need it?
The float kernel of MOE_init_routing_v2 in the dispatch allgather
operation does not support tensor format for active_expert_range; it
only supports int.
PR5311 To unify the variables `local_num_experts` and
`self.local_num_experts`, `self.local_num_experts` was used
consistently, which led to the subsequent integer type parameter being
converted to a tensor type.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
gsm8k | exact_match,strict-match: ground_truth=0.89 | measured=0.8939 |
success=✅
gsm8k | exact_match,flexible-extract: ground_truth=0.85 | measured=0.856
| success=✅
ceval-valid | acc,none: ground_truth=0.84 | measured=0.8373 | success=✅
Model Parameters:
{'pretrained': 'Qwen/Qwen3-30B-A3B', 'tensor_parallel_size': 2, 'dtype':
'auto', 'trust_remote_code': False, 'max_model_len': 4096,
'gpu_memory_utilization': 0.6, 'enable_expert_parallel': True}
- vLLM version: v0.13.0
- vLLM main:
45c1ca1ca1
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
1. What this PR does / why we need it?
This PR supports the moe_gating_top_k operator, which enables
post-positioned renormalization (renorm) on the basis of softmax.
2. Does this PR introduce any user-facing change?
No user-facing changes are required.
3. How was this patch tested?
This patch was tested with the test_npu_moe_gating_top_k test case.
vLLM version: release/v0.13.0
vLLM main:
ad32e3e19c
---------
Signed-off-by: ZCG12345 <2097562023@qq.com>
Signed-off-by: zzzzwwjj <34335947+zzzzwwjj@users.noreply.github.com>
Co-authored-by: zzzzwwjj <34335947+zzzzwwjj@users.noreply.github.com>
### What this PR does / why we need it?
Refactor the `capture_model` method in model_runner to directly reuse
the method from vLLM.
Currently, most of the logic in the capture_model method is similar to
that in the vllm code. Directly using the vllm method can reduce the
maintenance cost of the vllm-ascend code. Modify as follows:
1、refactor capture_model function, directly inheriting community methods
2、refactor initialize_aclgraph_capture function, move to
initialize_attn_backend
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
### What this PR does / why we need it?
- Fixes vllm break:
1. [[BugFix] register quant scale tensors as buffer #31395]
(https://github.com/vllm-project/vllm/pull/31395)
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
5326c89803
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
### What this PR does / why we need it?
This PR aims to refactor eagle-related modules in vllm-ascend.
This is the starting PR of eagle refactoring. Provided with vllm-eagle,
ascend-eagle and ascend-mtp, we first let ascend-mtp inherit from
ascend-eagle and let ascend-eagle inherit from vllm-eagle. As a
initialization, we just delete `__init__` in mtp_proposer and simplify
the corresponding logic in eagle_proposer.
Based on "vllm-eagle <----- ascend-eagle <----- ascend-mtp", our target
is to gradually delete ascend-mtp and enable ascend-eagle to converge to
vllm-eagle. So the main workspace is eagle_proposer. In this way, we
hope that contributors can concurrently refactor eagle.
Incoming changes:
1. delete common methods in vllm-eagle & ascend-eagle & ascend-mtp
2. delete `load_model` in mtp_proposer
3. delete `dummy_run` and `propose` in mtp_proposer
4. ......
RFC: #5467
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
by ci
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Zetong Li <slippersss@126.com>
