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nvidia_deeprecommender: add support for xpu devices #2085

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nvidia_deeprecommender: add support for xpu devices #2085

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5897398
nvidia_deeprecommender: fix cuda bias code
zhuyuhua-v Dec 11, 2023
b5620ff
fix format
weishi-deng Dec 11, 2023
06422fb
add xpu profile info
weishi-deng Dec 13, 2023
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31 changes: 23 additions & 8 deletions torchbenchmark/models/nvidia_deeprecommender/nvinfer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,8 @@ def getCommandLineArgs() :
help='jit-ify model before running')
parser.add_argument('--forcecuda', action='store_true',
help='force cuda use')
parser.add_argument('--forcexpu', action='store_true',
help='force xpu use')
parser.add_argument('--forcecpu', action='store_true',
help='force cpu use')
parser.add_argument('--nooutput', action='store_true',
Expand All @@ -57,7 +59,7 @@ def getCommandLineArgs() :

return args

def getBenchmarkArgs(forceCuda):
def getBenchmarkArgs(forceCuda, forceXpu):

class Args:
pass
Expand All @@ -76,7 +78,8 @@ class Args:
args.batch_size = 1
args.jit = False
args.forcecuda = forceCuda
args.forcecpu = not forceCuda
args.forcexpu = forceXpu
args.forcecpu = not (forceCuda or forceXpu)
args.nooutput = True
args.silent = True
args.profile = False
Expand All @@ -93,20 +96,26 @@ def processArgState(args) :
quit()

args.use_cuda = torch.cuda.is_available() # global flag
args.use_xpu = torch.xpu.is_available() # global flag
if not args.silent:
if args.use_cuda:
if args.use_cuda or args.use_xpu:
print('GPU is available.')
else:
print('GPU is not available.')

if args.use_cuda and args.forcecpu:
args.use_cuda = False
device = 'cpu'

if args.use_xpu and args.forcecpu:
args.use_xpu = False
device = 'cpu'

if not args.silent:
if args.use_cuda:
if args.use_cuda or args.use_xpu:
print('Running On GPU')
else:
print('Running On CUDA')
print('Running On CPU')

if args.profile:
print('Profiler Enabled')
Expand All @@ -132,13 +141,18 @@ def __init__(self, device = 'cpu', jit=False, batch_size=256, usecommandlineargs
else:
if device == "cpu":
forcecuda = False
forcexpu = False
elif device == "cuda":
forcecuda = True
forcexpu = False
elif device == "xpu":
forcecuda = False
forcexpu = True
else:
# unknown device string, quit init
return

self.args = getBenchmarkArgs(forcecuda)
self.args = getBenchmarkArgs(forcecuda, forcexpu)

args = processArgState(self.args)

Expand Down Expand Up @@ -199,6 +213,7 @@ def __init__(self, device = 'cpu', jit=False, batch_size=256, usecommandlineargs


if self.args.use_cuda: self.rencoder = self.rencoder.cuda()
if self.args.use_xpu: self.rencoder = self.rencoder.xpu()

if self.toytest == False:
self.inv_userIdMap = {v: k for k, v in self.data_layer.userIdMap.items()}
Expand All @@ -214,7 +229,7 @@ def eval(self, niter=1):
continue

for i, ((out, src), majorInd) in enumerate(self.eval_data_layer.iterate_one_epoch_eval(for_inf=True)):
inputs = Variable(src.cuda().to_dense() if self.args.use_cuda else src.to_dense())
inputs = Variable(src.to(device).to_dense())
targets_np = out.to_dense().numpy()[0, :]

out = self.rencoder(inputs)
Expand All @@ -237,7 +252,7 @@ def TimedInferenceRun(self) :
e_start_time = time.time()

if self.args.profile:
with profiler.profile(record_shapes=True, use_cuda=True) as prof:
with profiler.profile(record_shapes=True, use_cuda=self.args.use_cuda, use_xpu=self.args.use_xpu) as prof:
with profiler.record_function("Inference"):
self.eval()
else:
Expand Down
44 changes: 33 additions & 11 deletions torchbenchmark/models/nvidia_deeprecommender/nvtrain.py
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Original file line number Diff line number Diff line change
Expand Up @@ -101,6 +101,8 @@ def getTrainCommandLineArgs() :
help='disable all messages')
parser.add_argument('--forcecuda', action='store_true',
help='force cuda use')
parser.add_argument('--forcexpu', action='store_true',
help='force xpu use')
parser.add_argument('--forcecpu', action='store_true',
help='force cpu use')
parser.add_argument('--profile', action='store_true',
Expand All @@ -120,18 +122,25 @@ def processTrainArgState(args) :
quit()

args.use_cuda = torch.cuda.is_available() # global flag
args.use_xpu = torch.xpu.is_available() # global flag
if not args.silent:
if args.use_cuda:
if args.use_cuda or args.use_xpu:
print('GPU is available.')
else:
print('GPU is not available.')

if args.use_cuda and args.forcecpu:
args.use_cuda = False
device = 'cpu'
if args.use_xpu and args.forcecpu:
args.use_xpu = False
device = 'cpu'

if not args.silent:
if args.use_cuda:
print('Running On CUDA')
elif args.use_xpu:
print('Running On XPU')
else:
print('Running On CPU')

Expand Down Expand Up @@ -164,13 +173,13 @@ def log_var_and_grad_summaries(logger, layers, global_step, prefix, log_histogra
logger.histo_summary(tag="Gradients/{}_{}".format(prefix, ind), values=w.grad.data.cpu().numpy(),
step=global_step)

def DoTrainEval(encoder, evaluation_data_layer, use_cuda):
def DoTrainEval(encoder, evaluation_data_layer, device):
encoder.eval()
denom = 0.0
total_epoch_loss = 0.0
for i, (eval, src) in enumerate(evaluation_data_layer.iterate_one_epoch_eval()):
inputs = Variable(src.cuda().to_dense() if use_cuda else src.to_dense())
targets = Variable(eval.cuda().to_dense() if use_cuda else eval.to_dense())
inputs = Variable(src.to(device).to_dense())
targets = Variable(eval.to(device).to_dense())
outputs = encoder(inputs)
loss, num_ratings = model.MSEloss(outputs, targets)
total_epoch_loss += loss.item()
Expand Down Expand Up @@ -201,14 +210,24 @@ def TrainInit(self, device="cpu", jit=False, batch_size=256, processCommandLine

if device == "cpu":
forcecuda = False
forcecpu = True
forcexpu = False
elif device == "cuda":
forcecuda = True
forcecpu = False
forcexpu = False
elif device == 'xpu':
forcecuda = False
forcecpu = False
forcexpu = True
else:
# unknown device string, quit init
print('warning: skip by unknown device:', device)
return

self.args.forcecuda = forcecuda
self.args.forcecpu = not forcecuda
self.args.forcecpu = forcecpu
self.args.forcexpu = forcexpu

self.args = processTrainArgState(self.args)

Expand Down Expand Up @@ -274,15 +293,18 @@ def TrainInit(self, device="cpu", jit=False, batch_size=256, processCommandLine
gpu_ids = [int(g) for g in self.args.gpu_ids.split(',')]
if not self.args.silent:
print('Using GPUs: {}'.format(gpu_ids))

if len(gpu_ids)>1:
self.rencoder = nn.DataParallel(self.rencoder,
device_ids=gpu_ids)

self.rencoder = self.rencoder.cuda()
self.toyinputs = self.toyinputs.to(device)


if self.args.use_xpu:
self.rencoder = self.rencoder.xpu()
self.toyinputs = self.toyinputs.to('xpu')

if self.args.optimizer == "adam":
self.optimizer = optim.Adam(self.rencoder.parameters(),
lr=self.args.lr,
Expand Down Expand Up @@ -326,7 +348,7 @@ def DoTrain(self):

for i, mb in enumerate(self.data_layer.iterate_one_epoch()):

inputs = Variable(mb.cuda().to_dense() if self.args.use_cuda else mb.to_dense())
inputs = Variable(mb.to(device).to_dense())

self.optimizer.zero_grad()

Expand Down Expand Up @@ -404,7 +426,7 @@ def train(self, niter=1) :
self.logger.scalar_summary("Training_RMSE_per_epoch", sqrt(self.total_epoch_loss/self.denom), self.epoch)
self.logger.scalar_summary("Epoch_time", e_end_time - e_start_time, self.epoch)
if self.epoch % self.args.save_every == 0 or self.epoch == self.args.num_epochs - 1:
eval_loss = DoTrainEval(self.rencoder, self.eval_data_layer, self.args.use_cuda)
eval_loss = DoTrainEval(self.rencoder, self.eval_data_layer, device)
print('Epoch {} EVALUATION LOSS: {}'.format(self.epoch, eval_loss))

self.logger.scalar_summary("EVALUATION_RMSE", eval_loss, self.epoch)
Expand All @@ -417,13 +439,13 @@ def train(self, niter=1) :

# save to onnx
dummy_input = Variable(torch.randn(self.params['batch_size'], self.data_layer.vector_dim).type(torch.float))
torch.onnx.export(self.rencoder.float(), dummy_input.cuda() if self.args.use_cuda else dummy_input,
torch.onnx.export(self.rencoder.float(), dummy_input.to(device),
self.model_checkpoint + ".onnx", verbose=True)
print("ONNX model saved to {}!".format(self.model_checkpoint + ".onnx"))

def TimedTrainingRun(self):
if self.args.profile:
with profiler.profile(record_shapes=True, use_cuda=self.args.use_cuda) as prof:
with profiler.profile(record_shapes=True, use_cuda=self.args.use_cuda, use_xpu=self.args.use_xpu) as prof:
with profiler.record_function("training_epoch"):
self.train(self.args.num_epochs)
else:
Expand Down
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