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[CI] Add DeepSeek-V3.2-W8A8-Pruning e2e test (#5922)

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### What this PR does / why we need it?
1. Fix DeepSeek-V3.2-W8A8-Pruning mtp
2. Add DeepSeek-V3.2-W8A8-Pruning e2e test

### How was this patch tested?

- vLLM version: v0.13.0
- vLLM main:
11b6af5280

Signed-off-by: hfadzxy <starmoon_zhang@163.com>
This commit is contained in:
zhangxinyuehfad
2026-01-16 15:49:57 +08:00
committed by GitHub
parent 69b170b8b5
commit 4f446aec4c
4 changed files with 32 additions and 2 deletions
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2
.github/workflows/_e2e_test.yaml vendored
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@@ -215,6 +215,7 @@ jobs:
if: ${{ inputs.type == 'light' }}
run: |
pytest -sv --durations=0 tests/e2e/multicard/2-cards/test_qwen3_moe.py::test_qwen3_moe_distributed_mp_tp2_ep
pytest -sv --durations=0 tests/e2e/multicard/2-cards/test_offline_inference_distributed.py::test_deepseek3_2_w8a8_pruning_mtp_tp2_ep
- name: Run vllm-project/vllm-ascend test (full)
env:
@@ -237,6 +238,7 @@ jobs:
pytest -sv --durations=0 tests/e2e/multicard/2-cards/test_offline_inference_distributed.py::test_deepseek_v2_lite_fc1_tp2
pytest -sv --durations=0 tests/e2e/multicard/2-cards/test_offline_inference_distributed.py::test_qwen3_dense_fc1_tp2
pytest -sv --durations=0 tests/e2e/multicard/2-cards/test_offline_inference_distributed.py::test_qwen3_dense_prefetch_mlp_weight_tp2
pytest -sv --durations=0 tests/e2e/multicard/2-cards/test_offline_inference_distributed.py::test_deepseek3_2_w8a8_pruning_mtp_tp2_ep
pytest -sv --durations=0 tests/e2e/multicard/2-cards/test_offline_weight_load.py
pytest -sv --durations=0 tests/e2e/multicard/2-cards/test_pipeline_parallel.py

26
tests/e2e/multicard/2-cards/test_offline_inference_distributed.py
View File

@@ -235,3 +235,29 @@ def test_qwen3_dense_prefetch_mlp_weight_tp2(model):
quantization="ascend",
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
@patch.dict(os.environ, {"HCCL_OP_EXPANSION_MODE": "AIV"})
@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "0"})
@patch.dict(os.environ, {"ASCEND_AGGREGATE_ENABLE": "1"})
@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
def test_deepseek3_2_w8a8_pruning_mtp_tp2_ep():
example_prompts = [
"Hello, my name is",
]
max_tokens = 5
with VllmRunner("vllm-ascend/DeepSeek-V3.2-W8A8-Pruning",
tensor_parallel_size=2,
quantization="ascend",
enable_expert_parallel=True,
compilation_config={
"cudagraph_capture_sizes": [3, 6, 9, 12],
"cudagraph_mode": "FULL_DECODE_ONLY"
},
speculative_config={
"num_speculative_tokens": 2,
"method": "deepseek_mtp"
},
reasoning_parser="deepseek_v3",
tokenizer_mode="deepseek_v32") as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)

2
vllm_ascend/spec_decode/eagle_proposer.py
View File

@@ -106,6 +106,8 @@ class EagleProposer(VllmEagleProposer):
self.dcp_size = self.runner.dcp_size
self.pcp_rank = self.runner.pcp_rank
self.dcp_rank = self.runner.dcp_rank
self.use_aclgraph = self.runner._use_aclgraph()
self.full_indices = range(
self.runner.max_num_tokens * self.pcp_size * self.dcp_size +

4
vllm_ascend/spec_decode/mtp_proposer.py
View File

@@ -242,11 +242,11 @@ class MtpProposer(EagleProposer):
assert self.runner is not None
# Note(qcs): We may need to refactor these check logics.
if self.use_cuda_graph and num_scheduled_tokens <= self.runner.cudagraph_batch_sizes[
if self.runner.use_aclgraph and num_scheduled_tokens <= self.runner.cudagraph_batch_sizes[
-1]:
num_input_tokens = self.vllm_config.pad_for_cudagraph(
num_scheduled_tokens)
elif self.use_cuda_graph and num_tokens <= self.runner.cudagraph_batch_sizes[
elif self.use_aclgraph and num_tokens <= self.runner.cudagraph_batch_sizes[
-1]:
# Acl graph mode, add padding to the batch size
num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)