[Core] Support data parallel for vllm-v0 engine #339
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qsunnyy
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Signed-off-by: qsunnyy <qybottle@163.com> Signed-off-by: q00832892 <qiaoyang19@huawei.com>
Signed-off-by: qsunnyy <qybottle@163.com> Signed-off-by: q00832892 <qiaoyang19@huawei.com>
Signed-off-by: q00832892 <qiaoyang19@huawei.com>
Signed-off-by: q00832892 <qiaoyang19@huawei.com>
Signed-off-by: q00832892 <qiaoyang19@huawei.com>
MengqingCao
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Mar 18, 2025
| from vllm.distributed.parallel_state import ( | ||
| destroy_distributed_environment, destroy_model_parallel) | ||
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| import vllm_ascend # noqa |
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I think there is no need to import vllm-ascend
| from . import config # noqa | ||
| from . import forward_context # noqa | ||
| from .distributed import utils # noqa | ||
| from .engine import llm_engine # noqa |
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these patches seems no related to model registion, if we have to do so, let's do patch in vllm_ascend/patch
| backend_class._set_sequence_number_for_group() | ||
| pg._register_backend(device, backend_type, backend_class) | ||
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| elif backend == "nccl": |
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Let's remove the cuda related code here
| LLMEngine.__init__ = new_init | ||
| LLMEngine.has_unfinished_requests = new_has_unfinished_requests | ||
| LLMEngine.has_unfinished_requests_dp = new_has_unfinished_requests_dp | ||
| print("[Success] Custom LLMEngine patch applied!") |
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why do we need to patch engine? I don't think it's a good idea to patch engine
| dtype=torch.int32) | ||
| from vllm.distributed.parallel_state import get_dp_group | ||
| dist.all_reduce(num_tokens_tensor, group=get_dp_group().device_group) | ||
| cu_tokens_across_dp_npu = torch.cumsum(num_tokens_tensor, dim=0) |
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why gpu could use cu_tokens_across_dp_cpu tensor, but we need to use a npu tensor? same question for process group.
| # we use synchronous scheduling right now, | ||
| # adding a sync point here should not affect | ||
| # scheduling of the next batch | ||
| torch.cuda.synchronize() |
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Suggested change
| torch.cuda.synchronize() | |
| torch.npu.synchronize() |
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Set up data parallel communication, compatible with TP&EP.
Example commands to use data parallel: python examples/offline_inference_data_parallel.py