[CPU] Support SHM based inference_all_reduce in TorchBackend#5391
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tjruwase merged 10 commits intodeepspeedai:masterfrom Apr 17, 2024
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[CPU] Support SHM based inference_all_reduce in TorchBackend#5391tjruwase merged 10 commits intodeepspeedai:masterfrom
tjruwase merged 10 commits intodeepspeedai:masterfrom
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Hi @loadams the formatting error had been fixed, thanks! |
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…edai#5391) This PR adds SHM based `inference_all_reduce` kernel to `TorchBackend` communication backend. When inference on CPU server, this path replaces default `torch.distributed.all_reduce` which eventurally use gloo backend. This PR will improve inference performance with AutoTP when only stock PyTorch is installed without Intel Extension for PyTorch. Compared with gloo backend. SHM based inference_all_reduce kernel is a more directed path and perform much better on single node. | message size | gloo all_reduce(ms) | SHM all_reduce(ms) | | --- | --- | --- | | 32MB | 30.7 | 0.65 | | 64KB | 0.23 | 0.028 | In text generation of bloom-3b with AutoTP, average token latency improved 1.45x with this PR on 2S Xeon node. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
umchand
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…edai#5391) This PR adds SHM based `inference_all_reduce` kernel to `TorchBackend` communication backend. When inference on CPU server, this path replaces default `torch.distributed.all_reduce` which eventurally use gloo backend. This PR will improve inference performance with AutoTP when only stock PyTorch is installed without Intel Extension for PyTorch. Compared with gloo backend. SHM based inference_all_reduce kernel is a more directed path and perform much better on single node. | message size | gloo all_reduce(ms) | SHM all_reduce(ms) | | --- | --- | --- | | 32MB | 30.7 | 0.65 | | 64KB | 0.23 | 0.028 | In text generation of bloom-3b with AutoTP, average token latency improved 1.45x with this PR on 2S Xeon node. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
dbyoung18
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…edai#5391) This PR adds SHM based `inference_all_reduce` kernel to `TorchBackend` communication backend. When inference on CPU server, this path replaces default `torch.distributed.all_reduce` which eventurally use gloo backend. This PR will improve inference performance with AutoTP when only stock PyTorch is installed without Intel Extension for PyTorch. Compared with gloo backend. SHM based inference_all_reduce kernel is a more directed path and perform much better on single node. | message size | gloo all_reduce(ms) | SHM all_reduce(ms) | | --- | --- | --- | | 32MB | 30.7 | 0.65 | | 64KB | 0.23 | 0.028 | In text generation of bloom-3b with AutoTP, average token latency improved 1.45x with this PR on 2S Xeon node. --------- Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
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This PR adds SHM based
inference_all_reducekernel toTorchBackendcommunication backend. When inference on CPU server, this path replaces defaulttorch.distributed.all_reducewhich eventurally use gloo backend. This PR will improve inference performance with AutoTP when only stock PyTorch is installed without Intel Extension for PyTorch.Compared with gloo backend. SHM based inference_all_reduce kernel is a more directed path and perform much better on single node.
In text generation of bloom-3b with AutoTP, average token latency improved 1.45x with this PR on 2S Xeon node.