143e1f46d0
### What this PR does / why we need it? Support shared expert DP for deepseek_mtp feature. `shared_expert_dp` requires `SP==True`, with corresponding parameter restrictions. Previously, due to the coupling between `shared_expert_dp` and torchair, and the removal of `deepseek_mtp` in vllm_ascend, shared expert dp of deepseek_mtp was temporarily removed. Currently, by performing the `reduce_scatter` on the input of deepssek_mtp in `mtp_proposer.py`, we ensure that it matches the dimensions of `input_embedding`, and then perform the `all_gather` on the output of mtp. ### How was this patch tested? baseline: <img width="1184" height="692" alt="image" src="https://github.com/user-attachments/assets/9680d53a-7b1d-481a-accc-b8f3dae2b9e3" /> enable shared_expert_dp and multistream_overlap_shared_expert: <img width="1167" height="687" alt="image" src="https://github.com/user-attachments/assets/2531d06b-dfda-4e24-8628-6f4b0f677ddc" /> TPOT: 48ms -> 45.4ms Average TPS per rank: 117.6 -> 126.1 - vLLM version: v0.11.2 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2 --------- Signed-off-by: chenmenglong <chenmenglong1@huawei.com> Signed-off-by: zengran <zengran2@huawei.com> Co-authored-by: zengran <zengran2@huawei.com>
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
import os
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import pytest
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from vllm import SamplingParams
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from tests.e2e.conftest import VllmRunner
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from tests.e2e.model_utils import check_outputs_equal
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MODELS = [
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"vllm-ascend/DeepSeek-V2-Lite",
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]
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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@pytest.mark.parametrize("model", MODELS)
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def test_models_with_enable_shared_expert_dp(model: str) -> None:
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if 'HCCL_OP_EXPANSION_MODE' in os.environ:
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del os.environ['HCCL_OP_EXPANSION_MODE']
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prompts = [
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"Hello, my name is", "The capital of the United States is",
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"The capital of France is", "The future of AI is"
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]
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sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
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with VllmRunner(
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model,
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max_model_len=1024,
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enforce_eager=True,
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tensor_parallel_size=2,
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enable_expert_parallel=True,
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) as runner:
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vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
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os.environ["VLLM_ASCEND_ENABLE_FLASHCOMM1"] = "1"
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with VllmRunner(
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model,
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max_model_len=1024,
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enforce_eager=True,
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tensor_parallel_size=2,
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enable_expert_parallel=True,
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additional_config={
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"enable_shared_expert_dp": True,
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},
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) as runner:
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shared_expert_dp_eager_outputs = runner.model.generate(
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prompts, sampling_params)
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with VllmRunner(
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model,
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max_model_len=1024,
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tensor_parallel_size=2,
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enforce_eager=False,
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compilation_config={
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"cudagraph_capture_sizes": [1, 4, 8, 16],
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"cudagraph_mode": "FULL_DECODE_ONLY",
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},
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additional_config={
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"enable_shared_expert_dp": True,
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},
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) as runner:
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shared_expert_dp_aclgraph_outputs = runner.model.generate(
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prompts, sampling_params)
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vllm_eager_outputs_list = []
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for output in vllm_eager_outputs:
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vllm_eager_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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shared_expert_dp_eager_outputs_list = []
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for output in shared_expert_dp_eager_outputs:
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shared_expert_dp_eager_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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shared_expert_dp_aclgraph_outputs_list = []
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for output in shared_expert_dp_aclgraph_outputs:
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shared_expert_dp_aclgraph_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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check_outputs_equal(
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outputs_0_lst=vllm_eager_outputs_list,
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outputs_1_lst=shared_expert_dp_eager_outputs_list,
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name_0="vllm_eager_outputs",
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name_1="shared_expert_dp_eager_outputs",
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)
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check_outputs_equal(
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outputs_0_lst=vllm_eager_outputs_list,
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outputs_1_lst=shared_expert_dp_aclgraph_outputs_list,
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name_0="vllm_eager_outputs",
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name_1="shared_expert_dp_aclgraph_outputs",
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)
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