dlrm_s_pytorch.py profile api arguments mismatch

$ python -m torchbenchmark.models.dlrm.dlrm_s_pytorch --mini-batch-size=2 --data-size=6 --debug-mode

/opt/py3.10/lib/python3.10/runpy.py:126: RuntimeWarning: 'torchbenchmark.models.dlrm.dlrm_s_pytorch' found in sys.modules after import of package 'torchbenchmark.models.dlrm', but prior to execution of 'torchbenchmark.models.dlrm.dlrm_s_pytorch'; this may result in unpredictable behaviour
  warn(RuntimeWarning(msg))
Using CPU...
model arch:
mlp top arch 3 layers, with input to output dimensions:
[8 4 2 1]
# of interactions
8
mlp bot arch 2 layers, with input to output dimensions:
[4 3 2]
# of features (sparse and dense)
4
dense feature size
4
sparse feature size
2
# of embeddings (= # of sparse features) 3, with dimensions 2x:
[4 3 2]
data (inputs and targets):
mini-batch: 0
[[0.69647 0.28614 0.22685 0.55131]
 [0.71947 0.42311 0.98076 0.68483]]
[[1, 2], [1, 1], [1, 1]]
[[1, 0, 1], [0, 1], [1, 0]]
[[0.36179]
 [0.22826]]
mini-batch: 1
[[0.29371 0.63098 0.0921  0.4337 ]
 [0.43086 0.49369 0.42583 0.31226]]
[[1, 2], [1, 2], [1, 1]]
[[3, 0, 2], [1, 1, 2], [1, 1]]
[[0.60306]
 [0.54507]]
mini-batch: 2
[[0.34276 0.30412 0.41702 0.6813 ]
 [0.87546 0.51042 0.66931 0.58594]]
[[2, 1], [1, 2], [1, 1]]
[[2, 3, 2], [0, 0, 2], [1, 1]]
[[0.55679]
 [0.15896]]
initial parameters (weights and bias):
[[-0.34693  0.19553]
 [-0.18123  0.19197]
 [ 0.05438 -0.11105]
 [ 0.42513  0.34167]]
[[-0.16466 -0.52702]
 [-0.22543 -0.11757]
 [ 0.23667  0.57199]]
[[-0.20377  0.3713 ]
 [ 0.13177  0.27111]]
[[-0.16825 -0.58044 -0.39152 -0.64812]
 [ 1.11561  0.0879   0.61481 -0.67743]
 [ 0.09677  0.62959 -0.17907  0.55115]]
[0. 0. 0.]
[[-0.68594 -0.86234  0.23995]
 [-0.23981  0.40607 -1.25093]]
[0. 0.]
[[ 0.29078  1.06075 -0.01005  0.01394  0.0733  -0.76015  0.17397 -0.65541]
 [-0.1746   0.5074  -0.30015  0.20463  0.41345  0.1138  -0.55969 -0.13573]
 [ 0.79993 -0.82672 -0.11259 -0.2254   0.04929  0.30546  0.65675 -0.11032]
 [ 0.33164  0.20402  0.19365 -0.23022 -0.40715 -0.44909 -0.30881  0.13133]]
[0. 0. 0. 0.]
[[ 0.43933  0.18675 -0.31694  1.04268]
 [ 0.87692 -0.20438 -0.47541  0.07518]]
[0. 0.]
[[1.03474 0.2717 ]]
[0.]
time/loss/accuracy (if enabled):
Traceback (most recent call last):
  File "/opt/py3.10/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/opt/py3.10/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/workspace/torch/benchmark/torchbenchmark/models/dlrm/dlrm_s_pytorch.py", line 970, in <module>
    with torch.autograd.profiler.profile(args.enable_profiling, use_gpu) as prof:
TypeError: profile.__init__() takes from 1 to 2 positional arguments but 3 were given

with torch.autograd.profiler.profile(args.enable_profiling, use_gpu) as prof:

should be changed to

with torch.autograd.profiler.profile(args.enable_profiling, use_cuda=use_gpu) as prof:

https://pytorch.org/docs/stable/autograd.html#profiler

Open torchbenchmark/models/dlrm/dlrm_s_pytorch.py near line 970 and compare the profiler call with the current PyTorch profiler API. Apply the issue's argument change, then rerun the provided module command with --mini-batch-size=2, --data-size=6, and --debug-mode. Done means the command no longer fails with the positional-argument TypeError.