DLSlime Transfer Engine
Project description
Flexible & Efficient Heterogeneous Transfer Toolkit
Getting Started
DLSlime offers a set of peer-to-peer communication interfaces. For instance, consider the task of batched slice assignment from a remote tensor to a local tensor. You can accomplish this using the following APIs.
Here are some examples of DLSlime interface.
P2P Communication
RDMA RC Mode
- RDMA RC Read (Sync / Async mode)
python example/python/p2p_rdma_rc_read.py
- RDMA RC Read (Coroutine mode)
python example/python/p2p_rdma_rc_read_coroutine.py
- RDMA RC Write (Sync / Async mode)
python example/python/p2p_rdma_rc_write.py
- RDMA RC Write with immediate data (Sync / Async mode)
python example/python/p2p_rdma_rc_write_with_imm_data.py
- RDMA RC Send/Recv
python example/python/p2p_rdma_rc_send_recv.py
python example/python/p2p_rdma_rc_send_recv_gdr.py
- DLSlime torch backend
python example/python/p2p_rdma_rc_send_recv_torch.py --rank 0
python example/python/p2p_rdma_rc_send_recv_torch.py --rank 1
NVLink Mode
torchrun --nproc_per_node=2 p2p_nvlink.py
NVShmem Mode
# send
python example/python/p2p_nvshmem_ibgda_sendrecv.py --rank 0 --world-size 2
# recv
python example/python/p2p_nvshmem_ibgda_sendrecv.py --rank 1 --world-size 2
Huawei Ascend Direct Mode
See: Huawei README
[!Caution] DLSlime NVShmem transfer engine and Huawei Ascond Direct mode are in the experimental stage.
Collective Ops
Intra Node
AllGather
torchrun --nnodes 1 --master-addr 10.130.8.143 --node-rank 0 --nproc-per-node 8 --master-port 6007 example/python/all_gather_ll.py --mode intra
Inter Node
AllGather
# Node 0
torchrun --nnodes 2 --master-addr 10.130.8.143 --node-rank 0 --nproc-per-node 8 --master-port 6007 example/python/all_gather_ll.py --mode inter
# Node 1
torchrun --nnodes 2 --master-addr 10.130.8.143 --node-rank 1 --nproc-per-node 8 --master-port 6007 example/python/all_gather_ll.py --mode inter
AllGather Gemm Overlapping
# Node 0
torchrun --nnodes 2 --master-addr 10.130.8.143 --node-rank 0 --nproc-per-node 8 --master-port 6007 example/python/all_gather_gemm_overlap.py
# Node 1
torchrun --nnodes 2 --master-addr 10.130.8.143 --node-rank 1 --nproc-per-node 8 --master-port 6007 example/python/all_gather_gemm_overlap.py
[!Note] The intra- and inter- examples example above enables CUDA Graph by default. --eager-mode falls back to eager mode.
Install
pip install
pip install dlslime==0.0.1.post10
[!Note] The DLSlime pip version is built with default FLAGS (see Build from source for details).
Build from source
Python
git clone https://github.com/deeplink-org/DLSlime.git
FLAG=<ON|OFF> pip install -v --no-build-isolation -e .
CPP
git clone https://github.com/deeplink-org/DLSlime.git
mkdir -p DLSlime/build && cmake -DFLAG=<ON|OFF> ..
Build flags
The FLAG can be
| Flag | Description | Platform | default |
|---|---|---|---|
BUILD_RDMA |
Build RDMA Transfer Engine | Hetero | ON |
BUILD_PYTHON |
Build Python wrapper | Hetero | ON |
BUILD_NVLINK |
Build NVLINK Transfer Engine | GPGPU | OFF |
BUILD_NVSHMEM |
Build NVShmem Transfer Engine | NVIDIA | OFF |
BUILD_ASCEND_DIRECT |
Build Ascend direct transport | ASCEND | OFF |
BUILD_TORCH_PLUGIN |
Build DLSlime as a torch backend | Hetero | OFF |
USE_GLOO_BACKEND |
Use GLOO RDMA Send/Recv torch backend | Hetero | OFF |
BUILD_INTRA_OPS |
Use INTRA Collective OPS | GPGPU | OFF |
BUILD_INTER_OPS |
Use INTER Collective OPS (NVSHMEM) | NVIDIA | OFF |
[!Note] Please enable
USE_MECAwhen using DLSlime as a torch backend in Metax platform.
Benchmark
GDRDMA P2P Read/Write
- Platform: NVIDIA ConnectX-7 HHHL Adapter Card; 200GbE (default mode) / NDR200 IB; Dual-port QSFP112; PCIe 5.0 x16 with x16 PCIe extension option; RoCE v2.
#BS=1, #Concurrency=1
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 1 --node-rank 1 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 1 --num-iteration 100 --num-concurrency 1
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 1 --node-rank 0 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 1 --num-iteration 100 --num-concurrency 1
| Transfer Engine | #Channels | Message Size (bytes) | Batch Size | Num Concurrency | Avg Latency(ms) | Bandwidth(MB/s) |
|---|---|---|---|---|---|---|
| dlslime | 1 | 2,048 | 1 | 1 | 0.039 | 52 |
| dlslime | 1 | 4,096 | 1 | 1 | 0.037 | 111 |
| dlslime | 1 | 8,192 | 1 | 1 | 0.038 | 216 |
| dlslime | 1 | 16,384 | 1 | 1 | 0.037 | 442 |
| dlslime | 1 | 32,768 | 1 | 1 | 0.039 | 836 |
| dlslime | 1 | 65,536 | 1 | 1 | 0.039 | 1689 |
| dlslime | 1 | 131,072 | 1 | 1 | 0.041 | 3195 |
| dlslime | 1 | 262,144 | 1 | 1 | 0.043 | 6059 |
| dlslime | 1 | 524,288 | 1 | 1 | 0.049 | 10689 |
| dlslime | 1 | 1,048,576 | 1 | 1 | 0.062 | 17012 |
| dlslime | 1 | 2,097,152 | 1 | 1 | 0.083 | 25154 |
| dlslime | 1 | 4,194,304 | 1 | 1 | 0.127 | 33112 |
| dlslime | 1 | 8,388,608 | 1 | 1 | 0.211 | 39797 |
| dlslime | 1 | 16,777,216 | 1 | 1 | 0.382 | 43893 |
| dlslime | 1 | 33,554,432 | 1 | 1 | 0.726 | 46244 |
| dlslime | 1 | 67,108,864 | 1 | 1 | 1.412 | 47518 |
| dlslime | 1 | 134,217,728 | 1 | 1 | 2.783 | 48235 |
#BS=64, #Concurrency=1
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 1 --node-rank 1 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 64 --num-iteration 100 --num-concurrency 1
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 1 --node-rank 0 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 64 --num-iteration 100 --num-concurrency 1
| Transfer Engine | #Channels | Message Size (bytes) | Batch Size | Num Concurrency | Avg Latency(ms) | Bandwidth(MB/s) |
|---|---|---|---|---|---|---|
| dlslime | 1 | 2,048 | 64 | 1 | 0.084 | 1562 |
| dlslime | 1 | 4,096 | 64 | 1 | 0.082 | 3213 |
| dlslime | 1 | 8,192 | 64 | 1 | 0.086 | 6095 |
| dlslime | 1 | 16,384 | 64 | 1 | 0.093 | 11249 |
| dlslime | 1 | 32,768 | 64 | 1 | 0.115 | 18193 |
| dlslime | 1 | 65,536 | 64 | 1 | 0.158 | 26542 |
| dlslime | 1 | 131,072 | 64 | 1 | 0.243 | 34498 |
| dlslime | 1 | 262,144 | 64 | 1 | 0.414 | 40549 |
| dlslime | 1 | 524,288 | 64 | 1 | 0.758 | 44248 |
| dlslime | 1 | 1,048,576 | 64 | 1 | 1.443 | 46510 |
| dlslime | 1 | 2,097,152 | 64 | 1 | 2.809 | 47782 |
| dlslime | 1 | 4,194,304 | 64 | 1 | 5.555 | 48327 |
| dlslime | 1 | 8,388,608 | 64 | 1 | 11.041 | 48624 |
| dlslime | 1 | 16,777,216 | 64 | 1 | 22.003 | 48798 |
| dlslime | 1 | 33,554,432 | 64 | 1 | 43.941 | 48872 |
| dlslime | 1 | 67,108,864 | 64 | 1 | 87.809 | 48912 |
| dlslime | 1 | 134,217,728 | 64 | 1 | 175.512 | 48942 |
#BS=64, #Concurrency=8
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 1 --node-rank 1 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 64 --num-iteration 100 --num-concurrency 8
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 1 --node-rank 0 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 64 --num-iteration 100 --num-concurrency 8
| Transfer Engine | #Channels | Message Size (bytes) | Batch Size | Num Concurrency | Avg Latency(ms) | Bandwidth(MB/s) |
|---|---|---|---|---|---|---|
| dlslime | 1 | 2,048 | 64 | 8 | 0.037 | 3519 |
| dlslime | 1 | 4,096 | 64 | 8 | 0.038 | 6948 |
| dlslime | 1 | 8,192 | 64 | 8 | 0.038 | 13758 |
| dlslime | 1 | 16,384 | 64 | 8 | 0.04 | 26416 |
| dlslime | 1 | 32,768 | 64 | 8 | 0.057 | 36997 |
| dlslime | 1 | 65,536 | 64 | 8 | 0.098 | 42618 |
| dlslime | 1 | 131,072 | 64 | 8 | 0.184 | 45602 |
| dlslime | 1 | 262,144 | 64 | 8 | 0.356 | 47148 |
| dlslime | 1 | 524,288 | 64 | 8 | 0.699 | 47975 |
| dlslime | 1 | 1,048,576 | 64 | 8 | 1.384 | 48478 |
| dlslime | 1 | 2,097,152 | 64 | 8 | 2.755 | 48709 |
| dlslime | 1 | 4,194,304 | 64 | 8 | 5.498 | 48823 |
| dlslime | 1 | 8,388,608 | 64 | 8 | 10.982 | 48884 |
| dlslime | 1 | 16,777,216 | 64 | 8 | 21.954 | 48908 |
| dlslime | 1 | 33,554,432 | 64 | 8 | 43.895 | 48923 |
| dlslime | 1 | 67,108,864 | 64 | 8 | 87.766 | 48936 |
| dlslime | 1 | 134,217,728 | 64 | 8 | 175.517 | 48940 |
GDRDMA Aggregated Bandwidth
#BS=1, #Concurrency=1
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 8 --node-rank 1 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 1 --num-iteration 100 --num-concurrency 1
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 8 --node-rank 0 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 1 --num-iteration 100 --num-concurrency 1
| Transfer Engine | #Channels | Message Size (bytes) | Batch Size | Num Concurrency | Avg Latency(ms) | Bandwidth(MB/s) |
|---|---|---|---|---|---|---|
| dlslime | 8 | 2,048 | 1 | 1 | 0.051 | 157 |
| dlslime | 8 | 4,096 | 1 | 1 | 0.042 | 768 |
| dlslime | 8 | 8,192 | 1 | 1 | 0.04 | 1576 |
| dlslime | 8 | 16,384 | 1 | 1 | 0.054 | 2929 |
| dlslime | 8 | 32,768 | 1 | 1 | 0.051 | 5713 |
| dlslime | 8 | 65,536 | 1 | 1 | 0.052 | 11547 |
| dlslime | 8 | 131,072 | 1 | 1 | 0.055 | 22039 |
| dlslime | 8 | 262,144 | 1 | 1 | 0.058 | 42313 |
| dlslime | 8 | 524,288 | 1 | 1 | 0.064 | 74753 |
| dlslime | 8 | 1,048,576 | 1 | 1 | 0.072 | 127489 |
| dlslime | 8 | 2,097,152 | 1 | 1 | 0.101 | 184823 |
| dlslime | 8 | 4,194,304 | 1 | 1 | 0.149 | 246861 |
| dlslime | 8 | 8,388,608 | 1 | 1 | 0.237 | 299510 |
| dlslime | 8 | 16,777,216 | 1 | 1 | 0.403 | 340252 |
| dlslime | 8 | 33,554,432 | 1 | 1 | 0.743 | 364918 |
| dlslime | 8 | 67,108,864 | 1 | 1 | 1.423 | 378620 |
| dlslime | 8 | 134,217,728 | 1 | 1 | 2.79 | 384630 |
#BS=64, #Concurrency=1
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 8 --node-rank 1 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 64 --num-iteration 100 --num-concurrency 1
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 8 --node-rank 0 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 64 --num-iteration 100 --num-concurrency 1
| Transfer Engine | #Channels | Message Size (bytes) | Batch Size | Num Concurrency | Avg Latency(ms) | Bandwidth(MB/s) |
|---|---|---|---|---|---|---|
| dlslime | 8 | 2,048 | 64 | 1 | 0.091 | 11690 |
| dlslime | 8 | 4,096 | 64 | 1 | 0.081 | 24403 |
| dlslime | 8 | 8,192 | 64 | 1 | 0.091 | 45926 |
| dlslime | 8 | 16,384 | 64 | 1 | 0.098 | 84092 |
| dlslime | 8 | 32,768 | 64 | 1 | 0.117 | 138696 |
| dlslime | 8 | 65,536 | 64 | 1 | 0.16 | 206866 |
| dlslime | 8 | 131,072 | 64 | 1 | 0.241 | 273976 |
| dlslime | 8 | 262,144 | 64 | 1 | 0.415 | 320008 |
| dlslime | 8 | 524,288 | 64 | 1 | 0.757 | 353714 |
| dlslime | 8 | 1,048,576 | 64 | 1 | 1.439 | 372217 |
| dlslime | 8 | 2,097,152 | 64 | 1 | 2.819 | 381397 |
| dlslime | 8 | 4,194,304 | 64 | 1 | 5.555 | 386489 |
| dlslime | 8 | 8,388,608 | 64 | 1 | 11.044 | 388927 |
| dlslime | 8 | 16,777,216 | 64 | 1 | 22.009 | 390278 |
| dlslime | 8 | 33,554,432 | 64 | 1 | 43.951 | 390978 |
| dlslime | 8 | 67,108,864 | 64 | 1 | 87.804 | 391370 |
| dlslime | 8 | 134,217,728 | 64 | 1 | 175.508 | 391588 |
#BS=64, #Concurrency=8
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 8 --node-rank 1 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 64 --num-iteration 100 --num-concurrency 8
torchrun --master-addr 10.130.8.145 --master-port 6006 --nnodes 2 --nproc-per-node 8 --node-rank 0 bench/python/agg_transfer_bench_spmd.py --qp-num 8 --transfer-engine dlslime --batch-size 64 --num-iteration 100 --num-concurrency 8
| Transfer Engine | #Channels | Message Size (bytes) | Batch Size | Num Concurrency | Avg Latency(ms) | Bandwidth(MB/s) |
|---|---|---|---|---|---|---|
| dlslime | 8 | 2,048 | 64 | 8 | 0.036 | 28494 |
| dlslime | 8 | 4,096 | 64 | 8 | 0.038 | 50860 |
| dlslime | 8 | 8,192 | 64 | 8 | 0.048 | 104545 |
| dlslime | 8 | 16,384 | 64 | 8 | 0.041 | 207051 |
| dlslime | 8 | 32,768 | 64 | 8 | 0.056 | 297354 |
| dlslime | 8 | 65,536 | 64 | 8 | 0.099 | 337571 |
| dlslime | 8 | 131,072 | 64 | 8 | 0.185 | 363003 |
| dlslime | 8 | 262,144 | 64 | 8 | 0.356 | 376743 |
| dlslime | 8 | 524,288 | 64 | 8 | 0.701 | 383701 |
| dlslime | 8 | 1,048,576 | 64 | 8 | 1.386 | 387629 |
| dlslime | 8 | 2,097,152 | 64 | 8 | 2.757 | 389493 |
| dlslime | 8 | 4,194,304 | 64 | 8 | 5.5 | 390523 |
| dlslime | 8 | 8,388,608 | 64 | 8 | 10.984 | 391043 |
| dlslime | 8 | 16,777,216 | 64 | 8 | 21.955 | 391291 |
| dlslime | 8 | 33,554,432 | 64 | 8 | 43.891 | 391407 |
| dlslime | 8 | 67,108,864 | 64 | 8 | 87.771 | 391480 |
| dlslime | 8 | 134,217,728 | 64 | 8 | 175.518 | 391530 |
GDRDMA P2P Send/Recv
SLIME_QP_NUM=2 python bench/python/dlslime_torch_dist_sendrecv_bench.py --mode send --use-gpu --iterations 100
SLIME_QP_NUM=2 python bench/python/dlslime_torch_dist_sendrecv_bench.py --mode recv --use-gpu --iterations 100
| Message Size (bytes) | Avg Latency | Bandwidth | Device |
|---|---|---|---|
| 1,024 | 0.027 ms | 37.65 MB/s | GPU |
| 2,048 | 0.028 ms | 72.17 MB/s | GPU |
| 4,096 | 0.028 ms | 144.81 MB/s | GPU |
| 8,192 | 0.028 ms | 295.98 MB/s | GPU |
| 16,384 | 0.029 ms | 564.15 MB/s | GPU |
| 32,768 | 0.031 ms | 1069.90 MB/s | GPU |
| 65,536 | 0.031 ms | 2083.20 MB/s | GPU |
| 131,072 | 0.032 ms | 4038.17 MB/s | GPU |
| 262,144 | 0.036 ms | 7299.42 MB/s | GPU |
| 524,288 | 0.042 ms | 12495.87 MB/s | GPU |
| 1,048,576 | 0.053 ms | 19961.18 MB/s | GPU |
| 2,097,152 | 0.075 ms | 27924.99 MB/s | GPU |
| 4,194,304 | 0.117 ms | 35716.55 MB/s | GPU |
| 8,388,608 | 0.212 ms | 39637.66 MB/s | GPU |
| 16,777,216 | 0.387 ms | 43386.08 MB/s | GPU |
| 33,554,432 | 0.871 ms | 38532.98 MB/s | GPU |
| 67,108,864 | 1.665 ms | 40298.91 MB/s | GPU |
| 134,217,728 | 3.159 ms | 42487.69 MB/s | GPU |
| 268,435,456 | 5.643 ms | 47572.53 MB/s | GPU |
| 536,870,912 | 11.137 ms | 48204.20 MB/s | GPU |
Heterogeneous Interconnection
- hardware configs
| Device | NIC Model | Bandwidth | PCIe Version | PCIe Lanes |
|---|---|---|---|---|
| A | Mellanox ConnectX-7 Lx (MT4129) | 400 Gbps | PCIe 5.0 | x16 |
| B | Mellanox ConnectX-7 Lx (MT4129) | 400 Gbps | PCIe 5.0 | x8 |
| C | Mellanox ConnectX-7 Lx (MT4129) | 200 Gbps | PCIe 5.0 | x16 |
| D | Mellanox ConnectX-7 Lx (MT4129) | 400 Gbps | PCIe 5.0 | x16 |
-
experiments configs
- Message Size = 128 MB
- RDMA RC Read(single NIC)
- Under affinity scenario
- RDMA with GPU Direct
-
Interconnect bandwidth matrix：(MB/s, demonstrates attainment of the theoretical bound).
| Throughput (MB/s) | A | B | C | D |
|---|---|---|---|---|
| A | 48967.45 | 28686.29 | 24524.29 | 27676.57 |
| B | 28915.72 | 28275.85 | 23472.29 | 27234.60 |
| C | 24496.14 | 24496.51 | 24513.57 | 24493.89 |
| D | 29317.66 | 28683.25 | 24515.30 | 27491.33 |
detailed results: bench
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