### What this PR does / why we need it? **Problem Description:** The existing implementation for the w4a8-dynamic linear method only supports the old quantization format from msmodelslim. When attempting to load models quantized with the new version, vLLM encounters errors due to mismatched tensor shapes and unprocessed quantization parameters. Relavant issues: - https://github.com/vllm-project/vllm-ascend/issues/3192 - https://github.com/vllm-project/vllm-ascend/issues/3152 **Proposed Changes:** 1. Add support for w4a8 dynamic(new format) in AscendW4A8DynamicLinearMethod and TorchairAscendW4A8DynamicLinearMethod 2. Add unit tests and e2e tests for w4a8 dynamic new and old format models <details> <summary><b>details</b></summary> 1. **Support for new w4a8-dynamic format:** * Detects quantization format by reading the "version" field in quant_description to ensure backward compatibility. * Handles the new pre-packed weight format (`2x int4` in an `int8`), which has a halved dimension. It tells the vLLM loader how to unpack it using `_packed_dim` and `_packed_factor`. * Supports the new `scale_bias` parameter, setting its shape based on the layer type, as required by msmodelslim. For api consistency and future use, the `layer_type` parameter was also added to other quantization methods. * Updates the weight processing logic: new format weights are handled with `.view(torch.int32)` since they're pre-packed, while old ones are processed with `npu_convert_weight_to_int4pack`. 2. **New unit and E2E tests:** * Added unit tests that verify the logic for both the old and new formats. * Split the distributed E2E test to confirm that both old and new format models work correctly. </details> Theoretically, these changes will provide support for all common new version w4a8(dynamic) models from msmodelslim. ### Does this PR introduce _any_ user-facing change? no ### How was this patch tested? I implement relevant unit tests and e2e tests and test the changes with following commands: ```bash # unit tests python -m pytest tests/ut/quantization/test_w4a8_dynamic.py tests/ut/torchair/quantization/test_torchair_w4a8_dynamic.py -v # e2e tests pytest tests/e2e/singlecard/test_quantization.py -v -s pytest tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC_new_version -v -s pytest tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC_old_version -v -s pytest tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_W4A8DYNAMIC -v -s ``` I also tested Hunyuan-1.8B-Instruct quantized with the new w4a8-dynamic format: ``` vllm serve ./models/Hunyuan-1.8B-Instruct-quantized --gpu-memory-utilization 0.96 --quantization ascend --max-model-len 9600 --seed 0 --max-num-batched-tokens 16384 ``` All tests mentioned passed locally. **NOTE: I use quantization model from my own repo in test_offline_inference_distributed.py**. Here is the description: [Anionex/Qwen3-1.7B-W4A8-V1](https://modelscope.cn/models/Anionex/Qwen3-1.7B-W4A8-V1/summary) (including quantization steps).This should be replaced by a model in vllm-ascend ci modelscope repo. Thanks for reading! - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 --------- Signed-off-by: Anionex <1005128408@qq.com>
199 lines
7.9 KiB
YAML
199 lines
7.9 KiB
YAML
name: 'e2e test'
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on:
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workflow_call:
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inputs:
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vllm:
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required: true
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type: string
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runner:
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required: true
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type: string
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image:
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required: true
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type: string
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type:
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required: true
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type: string
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jobs:
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e2e:
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name: singlecard
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runs-on: ${{ inputs.runner }}-1
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container:
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image: ${{ inputs.image }}
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env:
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VLLM_LOGGING_LEVEL: ERROR
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VLLM_USE_MODELSCOPE: True
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steps:
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- name: Check npu and CANN info
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run: |
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npu-smi info
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cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info
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- name: Config mirrors
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run: |
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sed -Ei 's@(ports|archive).ubuntu.com@cache-service.nginx-pypi-cache.svc.cluster.local:8081@g' /etc/apt/sources.list
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pip config set global.index-url http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple
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pip config set global.trusted-host cache-service.nginx-pypi-cache.svc.cluster.local
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apt-get update -y
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apt install git -y
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- name: Checkout vllm-project/vllm-ascend repo
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uses: actions/checkout@v4
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- name: Install system dependencies
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run: |
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apt-get -y install `cat packages.txt`
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apt-get -y install gcc g++ cmake libnuma-dev
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- name: Checkout vllm-project/vllm repo
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uses: actions/checkout@v4
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with:
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repository: vllm-project/vllm
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ref: ${{ inputs.vllm }}
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path: ./vllm-empty
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fetch-depth: 1
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- name: Install vllm-project/vllm from source
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working-directory: ./vllm-empty
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run: |
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VLLM_TARGET_DEVICE=empty pip install -e .
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- name: Install vllm-project/vllm-ascend
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env:
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PIP_EXTRA_INDEX_URL: https://mirrors.huaweicloud.com/ascend/repos/pypi
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run: |
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pip install -r requirements-dev.txt
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pip install -v -e .
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- name: Run vllm-project/vllm-ascend test
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env:
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VLLM_WORKER_MULTIPROC_METHOD: spawn
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VLLM_USE_MODELSCOPE: True
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PYTORCH_NPU_ALLOC_CONF: max_split_size_mb:256
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if: ${{ inputs.type == 'light' }}
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run: |
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pytest -sv tests/e2e/singlecard/test_aclgraph.py
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pytest -sv tests/e2e/singlecard/test_quantization.py
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pytest -sv tests/e2e/singlecard/test_vlm.py::test_multimodal_vl
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- name: Run e2e test
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env:
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VLLM_WORKER_MULTIPROC_METHOD: spawn
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VLLM_USE_MODELSCOPE: True
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PYTORCH_NPU_ALLOC_CONF: max_split_size_mb:256
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if: ${{ inputs.type == 'full' }}
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run: |
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# We found that if running aclgraph tests in batch, it will cause AclmdlRICaptureBegin error. So we run
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# the test separately.
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pytest -sv tests/e2e/singlecard/test_aclgraph.py
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pytest -sv tests/e2e/singlecard/test_ascend_scheduler.py
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pytest -sv tests/e2e/singlecard/test_bge_model.py
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pytest -sv tests/e2e/singlecard/test_camem.py
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pytest -sv tests/e2e/singlecard/test_chunked.py
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pytest -sv tests/e2e/singlecard/test_embedding.py
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pytest -sv tests/e2e/singlecard/test_embedding_aclgraph.py
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pytest -sv tests/e2e/singlecard/test_guided_decoding.py
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pytest -sv tests/e2e/singlecard/test_ilama_lora.py
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pytest -sv tests/e2e/singlecard/test_profile_execute_duration.py
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pytest -sv tests/e2e/singlecard/test_quantization.py
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pytest -sv tests/e2e/singlecard/test_sampler.py
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pytest -sv tests/e2e/singlecard/test_vlm.py
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# ------------------------------------ v1 spec decode test ------------------------------------ #
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pytest -sv tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py
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pytest -sv tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_torchair_correctness.py
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# Fix me: OOM error
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#pytest -sv tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py
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pytest -sv tests/e2e/singlecard/ops/
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e2e-2-cards:
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name: multicard
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runs-on: ${{ inputs.runner }}-2
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container:
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image: ${{ inputs.image }}
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env:
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VLLM_LOGGING_LEVEL: ERROR
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VLLM_USE_MODELSCOPE: True
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steps:
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- name: Check npu and CANN info
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run: |
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npu-smi info
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cat /usr/local/Ascend/ascend-toolkit/latest/"$(uname -i)"-linux/ascend_toolkit_install.info
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- name: Config mirrors
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run: |
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sed -Ei 's@(ports|archive).ubuntu.com@cache-service.nginx-pypi-cache.svc.cluster.local:8081@g' /etc/apt/sources.list
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pip config set global.index-url http://cache-service.nginx-pypi-cache.svc.cluster.local/pypi/simple
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pip config set global.trusted-host cache-service.nginx-pypi-cache.svc.cluster.local
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apt-get update -y
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apt install git -y
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- name: Checkout vllm-project/vllm-ascend repo
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uses: actions/checkout@v4
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- name: Install system dependencies
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run: |
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apt-get -y install `cat packages.txt`
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apt-get -y install gcc g++ cmake libnuma-dev
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- name: Checkout vllm-project/vllm repo
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uses: actions/checkout@v4
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with:
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repository: vllm-project/vllm
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ref: ${{ inputs.vllm }}
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path: ./vllm-empty
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fetch-depth: 1
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- name: Install vllm-project/vllm from source
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working-directory: ./vllm-empty
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run: |
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VLLM_TARGET_DEVICE=empty pip install -e .
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- name: Install vllm-project/vllm-ascend
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env:
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PIP_EXTRA_INDEX_URL: https://mirrors.huaweicloud.com/ascend/repos/pypi
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run: |
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pip install -r requirements-dev.txt
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pip install -v -e .
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- name: Run vllm-project/vllm-ascend test (light)
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env:
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VLLM_WORKER_MULTIPROC_METHOD: spawn
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VLLM_USE_MODELSCOPE: True
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if: ${{ inputs.type == 'light' }}
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run: |
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pytest -sv tests/e2e/multicard/test_qwen3_moe.py::test_models_distributed_Qwen3_MOE_TP2_WITH_EP
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- name: Run vllm-project/vllm-ascend test (full)
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env:
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VLLM_WORKER_MULTIPROC_METHOD: spawn
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VLLM_USE_MODELSCOPE: True
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if: ${{ inputs.type == 'full' }}
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run: |
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pytest -sv tests/e2e/multicard/test_data_parallel.py
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pytest -sv tests/e2e/multicard/test_expert_parallel.py
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pytest -sv tests/e2e/multicard/test_external_launcher.py
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pytest -sv tests/e2e/multicard/test_single_request_aclgraph.py
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pytest -sv tests/e2e/multicard/test_fused_moe_allgather_ep.py
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pytest -sv tests/e2e/multicard/test_ilama_lora_tp2.py
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# To avoid oom, we need to run the test in a single process.
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pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_QwQ
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pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_multistream_moe
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pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W8A8
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pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC_new_version
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pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen3_W4A8DYNAMIC_old_version
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pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_W4A8DYNAMIC
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pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_sp_for_qwen3_moe
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pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen_Dense_with_flashcomm_v1
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pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_Qwen_Dense_with_prefetch_mlp_weight
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pytest -sv tests/e2e/multicard/test_pipeline_parallel.py
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pytest -sv tests/e2e/multicard/test_prefix_caching.py
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pytest -sv tests/e2e/multicard/test_qwen3_moe.py
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pytest -sv tests/e2e/multicard/test_torchair_graph_mode.py
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