def run_mini_ds2_benchmark(max_length,
nbands,
str_w,
batch_size,
max_iter,
skip_iter,
nfilters,
filter_width,
depth,
hidden_size,
batch_norm,
device_id,
device,
transformer,
visualize=False):
inputs, train_set, eval_set = generate_ds2_data(max_length, str_w, nbands,
batch_size, max_iter)
model_out = get_mini_ds2(inputs, nfilters, filter_width, str_w, nbands,
depth, hidden_size, batch_norm, device_id)
fprop_computation_op = ng.computation(model_out, "all")
benchmark_fprop = Benchmark(fprop_computation_op, train_set, inputs,
transformer, device)
Benchmark.print_benchmark_results(
benchmark_fprop.time(max_iter, skip_iter, 'ds2_fprop', visualize))
def run_mini_ds2_benchmark(args, **kwargs):
device_id = kwargs.get('device_id')
inputs, train_set, eval_set = generate_ds2_data(args.max_length,
args.str_w, args.nout,
args.nbands,
args.batch_size,
args.num_iterations)
model_out = get_mini_ds2(inputs, args.nfilters, args.filter_width,
args.str_w, args.nbands, args.depth,
args.hidden_size, args.batch_norm,
args.hetr_device, device_id)
if args.bprop:
with ng.metadata(device=args.hetr_device,
device_id=device_id,
parallel=ax.N):
loss = ng.ctc(model_out, ng.flatten(inputs["char_map"]),
inputs["audio_length"], inputs["trans_length"])
optimizer = GradientDescentMomentum(learning_rate=2e-5,
momentum_coef=0.99,
gradient_clip_norm=400,
nesterov=args.nesterov)
updates = optimizer(loss)
mean_cost = ng.sequential([updates, ng.mean(loss, out_axes=())])
bprop_computation_op = ng.computation(mean_cost, "all")
benchmark = Benchmark(bprop_computation_op, train_set, inputs,
args.backend, args.hetr_device)
Benchmark.print_benchmark_results(
benchmark.time(args.num_iterations,
args.skip_iter,
'ds2_bprop',
args.visualize,
preprocess=True))
else:
fprop_computation_op = ng.computation(model_out, "all")
benchmark_fprop = Benchmark(fprop_computation_op, train_set, inputs,
args.backend, args.hetr_device)
Benchmark.print_benchmark_results(
benchmark_fprop.time(args.num_iterations,
args.skip_iter,
'ds2_fprop',
args.visualize,
preprocess=True))
def run_resnet_benchmark(dataset,
num_iterations,
n_skip,
batch_size,
device_id,
transformer_type,
device,
bprop=True,
batch_norm=False,
visualize=False):
inputs, data, train_set = get_fake_data(dataset, batch_size,
num_iterations)
# Running forward propagation
model_out = get_mini_resnet(inputs,
dataset,
device_id,
batch_norm=batch_norm)
# Running back propagation
if bprop:
with ng.metadata(device_id=device_id, parallel=ax.N):
optimizer = GradientDescentMomentum(0.01, 0.9)
train_loss = ng.cross_entropy_multi(
model_out, ng.one_hot(inputs['label'], axis=ax.Y))
batch_cost = ng.sequential(
[optimizer(train_loss),
ng.mean(train_loss, out_axes=())])
batch_cost_computation_op = ng.computation(batch_cost, "all")
benchmark = Benchmark(batch_cost_computation_op, train_set, inputs,
transformer_type, device)
Benchmark.print_benchmark_results(
benchmark.time(num_iterations, n_skip, dataset + '_msra_bprop',
visualize, 'device_id'))
else:
fprop_computation_op = ng.computation(model_out, 'all')
benchmark = Benchmark(fprop_computation_op, train_set, inputs,
transformer_type, device)
Benchmark.print_benchmark_results(
benchmark.time(num_iterations, n_skip, dataset + '_msra_fprop',
visualize))
