### What this PR does / why we need it?
This PR updates the CI runner from `linux-aarch64-a2-*` to
`linux-aarch64-a2b3-*` in various test configuration files. This change
is necessary to adapt to updates in the CI infrastructure.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
The changes are configuration updates for CI tests. The correctness will
be verified by the CI pipeline.
Signed-off-by: hfadzxy <starmoon_zhang@163.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 enhances the test_deepseek3_2_w8a8_pruning_mtp_tp2_ep E2E test
by adding both short and long prompt test cases:
- Short test: Validates basic functionality with minimal input ("Hello
")
- Long test: Validates the model can handle prompts near its maximum
context length (~163K tokens, approaching the max_position_embeddings
limit of 163,840)
Additionally, explicitly sets max_model_len=163840 to ensure the test
properly exercises the model's full context window capability.
### Does this PR introduce _any_ user-facing change?
No. This change only affects internal E2E testing infrastructure.
### How was this patch tested?
The modified test case will be executed as part of the E2E test suite
and has been validated
[here](https://github.com/vllm-project/vllm-ascend/actions/runs/21620195055/job/62308026205?pr=6499).
- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0
Signed-off-by: guozr <guozr1997@hotmail.com>
Co-authored-by: guozr <guozr1997@hotmail.com>
### What this PR does / why we need it?
Refactor MLP weight prefetch to consistency with MoE Model's prefetching
in terms of code and usage.
Environments VLLM_ASCEND_ENABLE_PREFETCH_MLP,
VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE and
VLLM_ASCEND_MLP_GATE_UP_PREFETCH_SIZE is removed, usage as following:
--additional-config '{"weight_prefetch_config": { "enabled": true,
"prefetch_ratio": {"mlp": { "gate_up": 1.0, "down": 1.0} }}}'
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: leo-pony <nengjunma@outlook.com>
### What this PR does / why we need it?
Remove kv_cache nz test case for test_mla_preprocess_nq.py. This case is
added by https://github.com/vllm-project/vllm-ascend/pull/3072 but has
not been tested on bf16 scenario. Results show that this is not
currently supported.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI passed with existing test.
- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0
Signed-off-by: whx-sjtu <2952154980@qq.com>
### What this PR does / why we need it?
Add E2E for Prefix Caching cp & Chunked Prefill cp
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: F.Liu <liufeng248@huawei.com>
Signed-off-by: Feng Liu <46866849+ader47@users.noreply.github.com>
Co-authored-by: F.Liu <liufeng248@huawei.com>
### What this PR does / why we need it?
Fix a bug in the repo and add a test case for MTP + Full Decode Only +
Qwen3Next.
The _build_dummy_attn_metadata function in NPUModelRunner seems losed a
query_star_loc.copy_to_gpu operation, which will lead to difference
between query_start_loc and query_start_loc_cpu, and they are required
to be same in MTP + Full Decode Only + Qwen3Next case.
Before this pr:
`self.query_start_loc = [0, 0, 0, 0, ... , 0]
self.query_start_loc_cpu = [0, 2, 4, 6, ... ,128]`
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: SunnyLee219 <3294305115@qq.com>
### What this PR does / why we need it?
Introduced 310P W8A8 Quantization Support: New modules and methods have
been added to enable W8A8 static quantization specifically for the
Ascend 310P platform.
Platform-Specific Quantization Configuration Loading: The system now
dynamically loads the appropriate quantization configurations
(AscendCompressedTensorsConfig, AscendModelSlimConfig) based on whether
the current hardware is an Ascend 310P device.
Implemented AscendW8A8LinearMethod310P: A dedicated linear quantization
method for 310P is provided, handling the specifics of weight and
activation quantization, including input parameter broadcasting and
weight data manipulation.
Extended AscendModelSlimConfig for 310P: A specialized configuration
class for 310P integrates the new W8A8 linear method for both standard
linear layers and vocabulary parallel embeddings, ensuring proper
quantization application.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
Signed-off-by: Shaoxu Cheng <2906339855@qq.com>
### What this PR does / why we need it?
As #2947 describe, we need to transpose kv cache layout after GQA kv
transfer when prefill and decode tensor parallel size are heterogeneous,
in the previous implementation, we use `npu_paged_cache_load ` +
`tranpose` + `_npu_reshape_and_cache` to do this work.
But obviously, it is not an efficient plan, the ops above need to be
called for each layer, which introduces 3 * layer_num kernel launch, and
6 * layer_num data movement between L1 Cache and HBM for one request on
decode node. Usually, decode node uses graph mode, so these op kernels
will be called between decode forward launched by an async thread in
mooncacke connector, this kernels maybe last for several decode forward
and TTFT will increase by 3~4 decode forward time.
In this PR, we implement an AscendC fused op
`transpose_kv_cache_by_block` to do this with only once kernel launch
and move data between L1 Cache and HBM only once.
After using this fused op, the time cost in transpose kv cacke layout
can be decreased to 0.24ms from 7ms in UT on 910C, and in PD
disaggregation scenario, TTFT can decrease about 90 ~ 110 ms in
qwen3-235B.
| request_num | original | fused_op|
|:----------------------:|:---------------:|:-------------------:|
| 1 | 643 ms | 578 ms |
| 128 | 1480 ms | 1368 ms |
### Does this PR introduce _any_ user-facing change?
Use fused op by default, incase the op has bug in any scenario, provide
fallback choice using env to disable it.
**DISABLE fused op by add following env**
`export VLLM_ASCEND_FUSION_OP_TRANSPOSE_KV_CACHE_BY_BLOCK=0`
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: lidenghui <lidenghui1110@gmail.com>
### What this PR does / why we need it?
Add New Output for Expert Token Count
An additional output tensor expert_token_nums is added to both operators
to meet the requirement of tracking token distribution among experts:
Tensor Name: expert_token_nums
Dimension: 1D tensor
Shape: (local_expert_num,)
Data Type: int32
Semantics: Represents the number of tokens actually received by each
expert on the current card.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: guanguan0308 <1546542263@qq.com>
Signed-off-by: guanguan0308 <162653673+guanguan0308@users.noreply.github.com>
# What this PR does / why we need it?
This PR reverts commit 8134146ab6, which
modified the DeepSeek V3.2 (W8A8) single-node nightly test
configuration. as there is no limit between tp_size and MTP.
# Does this PR introduce any user-facing change?
No. This PR only affects CI/CD test configurations and does not
introduce any user-facing changes.
# How was this patch tested?
N/A for a revert PR. The changes restore the previously known working
configuration.
- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0
Signed-off-by: guozr <guozr1997@hotmail.com>
Co-authored-by: guozr <guozr1997@hotmail.com>
### What this PR does / why we need it?
When using Mooncake on Ascend NPU, AscendDirectTransport randomly
allocates ports within range `[20000, 20000 + npu_per_node × 1000)`.
Reference:
[ascend_direct_transport.cpp#L554](https://github.com/kvcache-ai/Mooncake/blob/v0.3.7.post2/mooncake-transfer-engine/src/transport/ascend_transport/ascend_direct_transport/ascend_direct_transport.cpp#L475)
If `kv_port` overlaps with this range, users may encounter intermittent
startup failures:
```bash
zmq.error.ZMQError: Address already in use (addr='tcp://x.x.x.x:30012')
RuntimeError: KV Cache sending/receiving thread failed to start.
```
This pr fix intermittent kv_port conflict with AscendDirectTransport in
`Qwen3-235B-W8A8-EPLB.yaml`, and add Added `kv_port Configuration Guide`
section in `pd_disaggregation_mooncake_multi_node.md`.
test
Results(tests/e2e/nightly/multi_node/config/Qwen3-235B-W8A8-EPLB.yaml):
https://github.com/vllm-project/vllm-ascend/actions/runs/21540138907/job/62073265259
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
### What this PR does / why we need it?
While using the LLM Compressor quantization tool from the VLLM community
to generate quantized weights, the VLLM Ascend engine needs to be
adapted to support the compressed tensors quantization format.
1. Support Moe model W4A8 dynamic weight.
- vLLM version: v0.13.0
- vLLM main:
bde38c11df
---------
Signed-off-by: LHXuuu <scut_xlh@163.com>
Signed-off-by: menogrey <1299267905@qq.com>
Co-authored-by: menogrey <1299267905@qq.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 fixes the single-node nightly test for DeepSeek V3.2 (W8A8)
model to ensure CI stability. The changes include:
1. Simplified nightly test matrix (nightly_test_a3.yaml):
- Temporarily reduced to only run deepseek3_2-w8a8 test case for
debugging
- Changed trigger from schedule/workflow_dispatch to support
push/pull_request for faster iteration
2. Updated DeepSeek V3.2 test configuration
(test_deepseek_v3_2_w8a8.py):
- Adjusted cudagraph_capture_sizes from [3, 6, 9, 12] to [8, 16, 24, 32]
for better performance
- Increased max-num-seqs from 4 to 8
- Increased gpu-memory-utilization from 0.92 to 0.98
- Increased num_speculative_tokens from 2 to 3
3. Added PR checkout step (_e2e_nightly_single_node.yaml):
- Added ability to checkout a specific PR (#6241) for testing
# Does this PR introduce any user-facing change?
No. This PR only affects CI/CD test configurations and does not
introduce any user-facing changes.
# How was this patch tested?
Mock nightly test has passed, see
[here](https://github.com/vllm-project/vllm-ascend/actions/runs/21574655952/job/62159656622?pr=6241).
<img width="1053" height="714" alt="a2f2ee359febb13e1f6330b1bd3c116b"
src="https://github.com/user-attachments/assets/3262ad0f-adec-4c71-871f-d9cf2db06fbc"
/>
- vLLM version: v0.14.1
- vLLM main:
d68209402d
---------
Signed-off-by: guozr <guozr1997@hotmail.com>
Co-authored-by: guozr <guozr1997@hotmail.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?
Since the PR (https://github.com/vllm-project/vllm/pull/32118) has
modified the criteria for judging Prefill and Decode requests in vLLM,
PCPManager needs to synchronize with this standard. As PCPManager
involves multiple calculations of PD request counts, this PR attempts to
consolidate the related logic and update the PD request count once per
batch.
### How was this patch tested?
```bash
pytest tests/e2e/multicard/4-cards/long_sequence/test_mtp.py
```
- vLLM version: v0.13.0
- vLLM main:
11b6af5280
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.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?
Enable the tests that were skipped due to an outdated driver version:
- tests/e2e/multicard/4-cards/long_sequence/test_accuracy.py
- tests/e2e/multicard/4-cards/long_sequence/test_basic.py
- tests/e2e/multicard/4-cards/long_sequence/test_chunked_prefill.py
and some cases in
- tests/e2e/multicard/2-cards/spec_decode/test_spec_decode.py
- tests/e2e/multicard/2-cards/test_external_launcher.py
- tests/e2e/multicard/2-cards/test_offline_weight_load.py
- tests/e2e/multicard/2-cards/test_quantization.py
- tests/e2e/multicard/4-cards/test_data_parallel_tp2.py
TODO:
- tests/e2e/multicard/4-cards/spec_decode/test_mtp_qwen3_next.py
- tests/e2e/multicard/4-cards/long_sequence/test_mtp.py
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.0
- vLLM main:
d68209402d
Signed-off-by: wjunLu <wjunlu217@gmail.com>
### What this PR does / why we need it?
PR #5672 attempted to remove the -1 padding for duplicate tokens in the
decode slot_mapping when adapting PCP for MLAPO, and adopted a simpler
slicing approach. However, in the single-ops logic and mixed PD batches,
the decode slot_mapping did not eliminate the -1 and also shared the
slicing method, resulting in incorrect slot_mapping. This PR resolves
this issue, and the logic will be further consolidated in subsequent
refactoring PRs.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
Fix the **import error** of qwen3-next nightly test.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: InSec <1790766300@qq.com>
### What this PR does / why we need it?
Add e2e test case for apply_top_k_top_p_custom kernel and eliminate
chinese comments.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
pytest passed.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
### What this PR does / why we need it?
The precision issue arose because the kv cache of the p-node had not
been fetched for an extended period(>6min) and was forcibly freed. To
avoid this problem, the batch size was reduced and the timeout period
has also been extended.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: dsxsteven <dsxsteven@sina.com>
### What this PR does / why we need it?
Qwen3-Next nightly test fix. Temporarily avoid the accuracy issue in the
**full graph** mode.
### Does this PR introduce _any_ user-facing change?
N/A
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
d68209402d
Signed-off-by: InSec <1790766300@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?
This PR enables FLASHCOMM1 communication optimization with layer
sharding for DeepSeek-V3.2 W8A8 model testing to
validate PR #5702. The changes include:
1. Enable FLASHCOMM1: Set VLLM_ASCEND_ENABLE_FLASHCOMM1=1
improves performance for distributed inference
2. Add layer sharding: Configure layer_sharding: ["q_b_proj", "o_proj"]
4. Update baselines: Adjust performance baselines to reflect the
improvements from FLASHCOMM1 and layer sharding
### Does this PR introduce _any_ user-facing change?
No. This is a CI/test-only change that enables new communication
optimization features for testing purposes.
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: guozr <guozr1997@hotmail.com>
Co-authored-by: guozr <guozr1997@hotmail.com>
### What this PR does / why we need it?
This PR adds mooncake common method to conftest, we need it to add more
test cases later
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running a test
- vLLM version: v0.14.0
- vLLM main:
d68209402d
Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.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?
Add nightly ci test for deepseek v3.1
- vLLM version: release/v0.13.0
- vLLM main:
bc0a5a0c08
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
PCP/DCP splits the kv-cache onto different cards. After introducing the
parameter cp-kv-cache-interleave-size, the first size tokens will be
cached at Card 0, and so on.
However, if there are too few tokens, some cards will not store the
key-value pairs, resulting in values of 0, corrupted values, and
precision issues. Currently, additional operations are introduced to
avoid this precision problem.
After we integrate FIA operator in mla_cp._forward_decode and CANN
updates to 8.5.0, we now can remove these additional operations.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
passed all CI by CANN 8.5.0
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
Signed-off-by: dsxsteven <dsxsteven@sina.com>
Signed-off-by: dsxsteven <36877507+dsxsteven@users.noreply.github.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?
Drop vLLM 0.13.0 support, upgrade to 0.14.0
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
Install clang in dokerfile for triton ascend
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: Meihan-chen <jcccx.cmh@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>
### What this PR does / why we need it?
This patch purpose to optimize the lint check term. The main idea is to
reduce unnecessary installation time.
1. The installation of vllm is not must, only append the path of vllm
src to the `PATHONPATH` is effective
2. This installation of `requirements-dev.txt` is not must, we have a
pre-built image `quay.io/ascend-ci/vllm-ascend:lint` with all the
requirements installed in advance.
**NOTE**: the conditions for triggering image builds are: 1).Daily
scheduled build; 2) Build when requirements are modified; 3) Manual
build. This ensures that the dependencies in our image are up-to-date to
the greatest extent possible.
3. The `mypy` was separated from the `pre-commit` hook for performance
reasons; we found that integrating `mypy` into the `pre-commit` hook
resulted in poor performance.
4. Reduce the CPU core consumption from 16 -> 8
### Does this PR introduce _any_ user-facing change?
The end-to-end lint time was optimized from 20min/per PR to 8min/per PR
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
---------
Signed-off-by: wangli <wangli858794774@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>
