RGAT remove in-memory flag (mlcommons#2033)

Co-authored-by: Miro <[email protected]>

Author: pgmpablo157321

graph/R-GAT/README.md

@@ -2,7 +2,7 @@
 
 This is the reference implementation for MLPerf Inference Graph Neural Network. The reference implementation currently uses Deep Graph Library (DGL), and pytorch as the backbone of the model.
 
-**Hardware requirements:** The minimun requirements to run this benchmark are ~600GB of RAM and ~2.3TB of disk. This requires to create a memory map for the graph features and not load them to memory all at once. If you want to load all the features to ram, you will need ~3TB and can be done by using the flag `--in-memory`
+**Hardware requirements:** The minimun requirements to run this benchmark are ~600GB of RAM and ~2.3TB of disk. This requires to create a memory map for the graph features and not load them to memory all at once.
 
 ## Supported Models
 
@@ -161,7 +161,7 @@ cm run script --tags=get,dataset,igbh,_full,_calibration --outdirname=<path to d
 cd $GRAPH_FOLDER
 
 # Run the benchmark DGL
-python3 main.py --dataset igbh-dgl-tiny --dataset-path igbh/ --profile debug-dgl [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
+python3 main.py --dataset igbh-dgl-tiny --dataset-path igbh/ --profile debug-dgl [--model-path <path_to_ckpt>] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
 
@@ -171,7 +171,7 @@ python3 main.py --dataset igbh-dgl-tiny --dataset-path igbh/ --profile debug-dgl
 cd $GRAPH_FOLDER
 
 # Run the benchmark DGL
-python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
+python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full [--model-path <path_to_ckpt>] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
 ### Evaluate the accuracy
@@ -188,7 +188,7 @@ Not implemented yet
 #### Accuracy run
 Add the `--accuracy` to the command to run the benchmark
 ```bash
-python3 main.py --dataset igbh --dataset-path igbh/ --accuracy --model-path model/ [--model-path <path_to_ckpt>] [--in-memory] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
+python3 main.py --dataset igbh --dataset-path igbh/ --accuracy --model-path model/ [--model-path <path_to_ckpt>] [--device <cpu or gpu>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>] [--layout <COO, CSC or CSR>]
 ```
 
 **NOTE:** For official submissions you should submit the results of the accuracy run in a file called `accuracy.txt` with the following format:
@@ -209,7 +209,7 @@ docker run --rm -it -v $(pwd):/root rgat-cpu
 ```
 Run benchmark inside the docker container:
 ```bash
-python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device cpu [--model-path <path_to_ckpt>] [--in-memory] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
+python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device cpu [--model-path <path_to_ckpt>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
 
@@ -225,7 +225,7 @@ docker run --rm -it -v $(pwd):/workspace/root --gpus all rgat-gpu
 Go inside the root folder and run benchmark inside the docker container:
 ```bash
 cd root
-python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device gpu [--model-path <path_to_ckpt>] [--in-memory] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
+python3 main.py --dataset igbh-dgl --dataset-path igbh/ --profile rgat-dgl-full --device gpu [--model-path <path_to_ckpt>] [--dtype <fp16 or fp32>] [--scenario <SingleStream, MultiStream, Server or Offline>]
 ```
 
 **NOTE:** For official submissions, this benchmark is required to run in equal issue mode. Please make sure that the flag `rgat.*.sample_concatenate_permutation` is set to one in the [mlperf.conf](../../loadgen/mlperf.conf) file when loadgen is built.

graph/R-GAT/igbh.py

@@ -131,7 +131,7 @@ def __init__(
         self,
         path,
         dataset_size="tiny",
-        in_memory=True,
+        in_memory=False,
         use_label_2K=False,
         with_edges=True,
         layout: Literal["CSC", "CSR", "COO"] = "COO",

graph/R-GAT/main.py

@@ -118,15 +118,11 @@ def get_args():
         "--dataset-path",
         required=True,
         help="path to the dataset")
-    parser.add_argument(
-        "--in-memory",
-        action="store_true",
-        help="path to the dataset")
     parser.add_argument(
         "--layout",
         default="COO",
         choices=["CSC", "CSR", "COO"],
-        help="path to the dataset",
+        help="layout of the dataset",
     )
     parser.add_argument(
         "--profile", choices=SUPPORTED_PROFILES.keys(), help="standard profiles"
@@ -363,7 +359,6 @@ def main():
     ds = dataset_class(
         data_path=args.dataset_path,
         name=args.dataset,
-        in_memory=args.in_memory,
         layout=args.layout,
         type=args.dtype,
         **kwargs,