Support for SP+GEMS

benchmarks/gems_master_with_spatial_parallelism/README.md

@@ -0,0 +1,70 @@
+# GEMS-MASTER + SP
+
+GEMS improves performance by efficiently utilizing memory, whereas SP is used to train high-resolution images. GEMS+SP enables training high-resolution images and enhances performance by integrating GEMS which allows training model with larger batch size than the maximum feasible batch size due to GEMS.
+
+
+## Run GEMS-MASTER + SP:
+
+#### Generic command:
+```bash
+$MV2_HOME/bin/mpirun_rsh --export-all -np ${np} --hostfile ${HOSTFILE} MV2_USE_GDRCOPY=0 MV2_ENABLE_AFFINITY=0 MV2_USE_CUDA=1 LD_PRELOAD=$MV2_HOME/lib/libmpi.so python ${gems_sp_model_script} --split-size ${split_size} --batch-size ${batch_size} --times ${times}
+
+```
+#### Examples
+
+- Example to run AmoebaNet MASTER+SP model for 1024 * 1024 image size with 5 model split size(i.e. # of partitions for MP), model replication factor (η = 2) and batch size for each model replica as 1 (i.e. effective batch size (EBS) = η × BS = 2).
+
+```bash
+$MV2_HOME/bin/mpirun_rsh --export-all -np ${np} --hostfile ${HOSTFILE} MV2_USE_GDRCOPY=0 MV2_ENABLE_AFFINITY=0 MV2_USE_CUDA=1 LD_PRELOAD=$MV2_HOME/lib/libmpi.so python benchmarks/gems_master_with_spatial_parallelism/benchmark_amoebanet_gems_master_with_sp.py --split-size 5 --batch-size 1 --image-size 1024 --times 2
+
+```
+- Similarly, we can run benchmark for ResNet MASTER model.
+Below is example to run ResNet MASTER+SP model for 2048 * 2048 image size with 5 model split size(i.e. # of partitions for MP), model replication factor (η = 4) and batch size for each model replica as 1 (i.e. effective batch size (EBS) = η × BS = 4).
+```bash
+$MV2_HOME/bin/mpirun_rsh --export-all -np $np --hostfile ${HOSTFILE} MV2_USE_GDRCOPY=0 MV2_ENABLE_AFFINITY=0 MV2_USE_CUDA=1 LD_PRELOAD=$MV2_HOME/lib/libmpi.so python benchmarks/gems_master_model/benchmark_resnet_gems_master_with_sp.py --split-size 5 --image-size 2048 --batch-size 1 --times 4
+
+```
+Below are the available configuration options :
+
+<pre>
+usage: benchmark_amoebanet_sp.py [-h] [-v] [--batch-size BATCH_SIZE] [--parts PARTS] [--split-size SPLIT_SIZE] [--num-spatial-parts NUM_SPATIAL_PARTS]
+                        [--spatial-size SPATIAL_SIZE] [--times TIMES] [--image-size IMAGE_SIZE] [--num-epochs NUM_EPOCHS] [--num-layers NUM_LAYERS]
+                        [--num-filters NUM_FILTERS] [--balance BALANCE] [--halo-D2] [--fused-layers FUSED_LAYERS] [--local-DP LOCAL_DP] [--slice-method SLICE_METHOD]
+                        [--app APP] [--datapath DATAPATH]
+
+SP-MP-DP Configuration Script
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -v, --verbose         Prints performance numbers or logs (default: False)
+  --batch-size BATCH_SIZE
+                        input batch size (default: 32)
+  --parts PARTS         Number of parts for MP (default: 1)
+  --split-size SPLIT_SIZE
+                        Number of process for MP (default: 2)
+  --num-spatial-parts NUM_SPATIAL_PARTS
+                        Number of partitions in spatial parallelism (default: 4)
+  --spatial-size SPATIAL_SIZE
+                        Number splits for spatial parallelism (default: 1)
+  --times TIMES         Number of times to repeat MASTER 1: 2 repications, 2: 4 replications (default: 1)
+  --image-size IMAGE_SIZE
+                        Image size for synthetic benchmark (default: 32)
+  --num-epochs NUM_EPOCHS
+                        Number of epochs (default: 1)
+  --num-layers NUM_LAYERS
+                        Number of layers in amoebanet (default: 18)
+  --num-filters NUM_FILTERS
+                        Number of layers in amoebanet (default: 416)
+  --balance BALANCE     length of list equals to number of partitions and sum should be equal to num layers (default: None)
+  --halo-D2             Enable design2 (do halo exhange on few convs) for spatial conv. (default: False)
+  --fused-layers FUSED_LAYERS
+                        When D2 design is enables for halo exchange, number of blocks to fuse in ResNet model (default: 1)
+  --local-DP LOCAL_DP   LBANN intergration of SP with MP. MP can apply data parallelism. 1: only one GPU for a given split, 2: two gpus for a given split (uses DP)
+                        (default: 1)
+  --slice-method SLICE_METHOD
+                        Slice method (square, vertical, and horizontal) in Spatial parallelism (default: square)
+  --app APP             Application type (1.medical, 2.cifar, and synthetic) in Spatial parallelism (default: 3)
+  --datapath DATAPATH   local Dataset path (default: ./train)
+  </pre>
+
+  *Note:"--times" is GEMS specific parameter and certain parameters such as "--num-spatial-parts", "--slice-method", "--halo-D2" would not be required by GEMS.*