# Distributed Data Parallel Benchmark This tool is used to measure distributed training iteration time. This is helpful for evaluating the performance impact of code changes to `torch.nn.parallel.DistributedDataParallel`, `torch.distributed`, or anything in between. It optionally produces a JSON file with all measurements, allowing for an easy A/B comparison of code, configuration, or environment. This comparison can be produced by `diff.py`. ## Requirements This benchmark depends on PyTorch and torchvision. ## How to run Run as many copies of this script as you have model replicas. If you launch a single task per machine with multiple GPUs, consider using [`torch.distributed.launch`][launch] to spawn multiple processes per machine. [launch]: https://pytorch.org/docs/stable/distributed.html#launch-utility Example output (only on rank 0): ``` ----------------------------------- PyTorch distributed benchmark suite ----------------------------------- * PyTorch version: 1.4.0a0+05140f0 * CUDA version: 10.0 * Distributed backend: nccl --- nvidia-smi topo -m --- GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 mlx5_2 mlx5_0 mlx5_3 mlx5_1 CPU Affinity GPU0 X NV1 NV1 NV2 NV2 SYS SYS SYS SYS PIX SYS PHB 0-19,40-59 GPU1 NV1 X NV2 NV1 SYS NV2 SYS SYS SYS PIX SYS PHB 0-19,40-59 GPU2 NV1 NV2 X NV2 SYS SYS NV1 SYS SYS PHB SYS PIX 0-19,40-59 GPU3 NV2 NV1 NV2 X SYS SYS SYS NV1 SYS PHB SYS PIX 0-19,40-59 GPU4 NV2 SYS SYS SYS X NV1 NV1 NV2 PIX SYS PHB SYS 0-19,40-59 GPU5 SYS NV2 SYS SYS NV1 X NV2 NV1 PIX SYS PHB SYS 0-19,40-59 GPU6 SYS SYS NV1 SYS NV1 NV2 X NV2 PHB SYS PIX SYS 0-19,40-59 GPU7 SYS SYS SYS NV1 NV2 NV1 NV2 X PHB SYS PIX SYS 0-19,40-59 mlx5_2 SYS SYS SYS SYS PIX PIX PHB PHB X SYS PHB SYS mlx5_0 PIX PIX PHB PHB SYS SYS SYS SYS SYS X SYS PHB mlx5_3 SYS SYS SYS SYS PHB PHB PIX PIX PHB SYS X SYS mlx5_1 PHB PHB PIX PIX SYS SYS SYS SYS SYS PHB SYS X Legend: X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe switches (without traversing the PCIe Host Bridge) PIX = Connection traversing a single PCIe switch NV# = Connection traversing a bonded set of # NVLinks -------------------------- Benchmark: resnet50 with batch size 32 sec/iter ex/sec sec/iter ex/sec sec/iter ex/sec sec/iter ex/sec 1 GPUs -- no ddp: p50: 0.097s 329/s p75: 0.097s 329/s p90: 0.097s 329/s p95: 0.097s 329/s 1 GPUs -- 1M/1G: p50: 0.100s 319/s p75: 0.100s 318/s p90: 0.100s 318/s p95: 0.100s 318/s 2 GPUs -- 1M/2G: p50: 0.103s 310/s p75: 0.103s 310/s p90: 0.103s 310/s p95: 0.103s 309/s 4 GPUs -- 1M/4G: p50: 0.103s 310/s p75: 0.103s 310/s p90: 0.103s 310/s p95: 0.103s 310/s 8 GPUs -- 1M/8G: p50: 0.104s 307/s p75: 0.104s 307/s p90: 0.104s 306/s p95: 0.104s 306/s 16 GPUs -- 2M/8G: p50: 0.104s 306/s p75: 0.104s 306/s p90: 0.104s 306/s p95: 0.104s 306/s Benchmark: resnet101 with batch size 32 sec/iter ex/sec sec/iter ex/sec sec/iter ex/sec sec/iter ex/sec 1 GPUs -- no ddp: p50: 0.162s 197/s p75: 0.162s 197/s p90: 0.162s 197/s p95: 0.162s 197/s 1 GPUs -- 1M/1G: p50: 0.171s 187/s p75: 0.171s 186/s p90: 0.171s 186/s p95: 0.172s 185/s 2 GPUs -- 1M/2G: p50: 0.176s 182/s p75: 0.176s 181/s p90: 0.176s 181/s p95: 0.176s 181/s 4 GPUs -- 1M/4G: p50: 0.176s 182/s p75: 0.176s 181/s p90: 0.176s 181/s p95: 0.176s 181/s 8 GPUs -- 1M/8G: p50: 0.179s 179/s p75: 0.179s 178/s p90: 0.180s 178/s p95: 0.180s 177/s 16 GPUs -- 2M/8G: p50: 0.179s 178/s p75: 0.180s 177/s p90: 0.183s 174/s p95: 0.188s 170/s Benchmark: resnext50_32x4d with batch size 32 sec/iter ex/sec sec/iter ex/sec sec/iter ex/sec sec/iter ex/sec 1 GPUs -- no ddp: p50: 0.145s 220/s p75: 0.145s 220/s p90: 0.145s 220/s p95: 0.145s 220/s 1 GPUs -- 1M/1G: p50: 0.147s 217/s p75: 0.147s 217/s p90: 0.148s 216/s p95: 0.148s 216/s 2 GPUs -- 1M/2G: p50: 0.153s 209/s p75: 0.153s 209/s p90: 0.153s 209/s p95: 0.153s 209/s 4 GPUs -- 1M/4G: p50: 0.153s 208/s p75: 0.153s 208/s p90: 0.154s 208/s p95: 0.154s 208/s 8 GPUs -- 1M/8G: p50: 0.157s 204/s p75: 0.157s 204/s p90: 0.157s 203/s p95: 0.157s 203/s 16 GPUs -- 2M/8G: p50: 0.157s 203/s p75: 0.157s 203/s p90: 0.158s 203/s p95: 0.158s 202/s Benchmark: resnext101_32x8d with batch size 32 sec/iter ex/sec sec/iter ex/sec sec/iter ex/sec sec/iter ex/sec 1 GPUs -- no ddp: p50: 0.415s 77/s p75: 0.415s 77/s p90: 0.416s 76/s p95: 0.417s 76/s 1 GPUs -- 1M/1G: p50: 0.425s 75/s p75: 0.426s 75/s p90: 0.426s 75/s p95: 0.426s 75/s 2 GPUs -- 1M/2G: p50: 0.438s 73/s p75: 0.439s 72/s p90: 0.439s 72/s p95: 0.439s 72/s 4 GPUs -- 1M/4G: p50: 0.439s 72/s p75: 0.439s 72/s p90: 0.440s 72/s p95: 0.440s 72/s 8 GPUs -- 1M/8G: p50: 0.447s 71/s p75: 0.447s 71/s p90: 0.448s 71/s p95: 0.448s 71/s 16 GPUs -- 2M/8G: p50: 0.450s 71/s p75: 0.451s 70/s p90: 0.451s 70/s p95: 0.451s 70/s ``` ## How to diff Run the benchmark with the `--json PATH_TO_REPORT_FILE` argument to produce the JSON file that the diff script can consume. Then, run the diff script as follows: ``` $ python3 diff.py PATH_TO_BASELINE_FILE PATH_TO_TEST_FILE baseline test -------------------- -------------------- bucket_size: 25 vs 1 cuda_version: 10.0 vs 10.0 distributed_backend: nccl vs nccl pytorch_version: 1.4.0a0+05140f0 vs 1.4.0a0+05140f0 Benchmark: resnet50 with batch size 32 sec/iter ex/sec diff sec/iter ex/sec diff 1 GPUs: p75: 0.101s 317/s -0.3% p95: 0.101s 317/s -0.4% 2 GPUs: p75: 0.104s 306/s -1.0% p95: 0.104s 306/s -1.0% 4 GPUs: p75: 0.105s 305/s -1.6% p95: 0.105s 304/s -1.8% 8 GPUs: p75: 0.107s 299/s -2.6% p95: 0.107s 298/s -2.7% 16 GPUs: p75: 0.108s 294/s -3.8% p95: 0.122s 262/s -16.4% Benchmark: resnet101 with batch size 32 sec/iter ex/sec diff sec/iter ex/sec diff 1 GPUs: p75: 0.172s 185/s -1.2% p95: 0.172s 185/s -1.3% 2 GPUs: p75: 0.179s 178/s -2.1% p95: 0.179s 178/s -2.0% 4 GPUs: p75: 0.180s 177/s -2.6% p95: 0.180s 177/s -2.6% 8 GPUs: p75: 0.184s 173/s -3.5% p95: 0.184s 173/s -3.5% 16 GPUs: p75: 0.187s 170/s -0.1% p95: 0.204s 157/s -7.9% Benchmark: resnext50_32x4d with batch size 32 sec/iter ex/sec diff sec/iter ex/sec diff 1 GPUs: p75: 0.149s 214/s -1.0% p95: 0.149s 214/s -0.9% 2 GPUs: p75: 0.156s 205/s -1.5% p95: 0.156s 205/s -1.6% 4 GPUs: p75: 0.156s 204/s -1.6% p95: 0.157s 204/s -1.8% 8 GPUs: p75: 0.159s 200/s -1.5% p95: 0.159s 200/s -1.5% 16 GPUs: p75: 0.161s 198/s -1.9% p95: 0.162s 197/s -2.3% Benchmark: resnext101_32x8d with batch size 32 sec/iter ex/sec diff sec/iter ex/sec diff 1 GPUs: p75: 0.427s 74/s -0.8% p95: 0.428s 74/s -0.7% 2 GPUs: p75: 0.444s 72/s -1.3% p95: 0.445s 71/s -0.7% 4 GPUs: p75: 0.444s 72/s -1.1% p95: 0.445s 71/s -0.8% 8 GPUs: p75: 0.452s 70/s -1.3% p95: 0.452s 70/s -1.3% 16 GPUs: p75: 0.455s 70/s -0.7% p95: 0.456s 70/s -0.6% ``` This compares throughput between `bucket_cap_mb=25` (the default) and `bucket_cap_mb=1` on 8 DGX machines with V100 GPUs. It confirms that even for a relatively small model on machines with a very fast interconnect (4x 100Gb InfiniBand per machine), it still pays off to batch allreduce calls.