def _test_variable_update(self,
test_name,
num_workers,
num_ps,
params,
num_controllers=0):
"""Tests variables are updated correctly when the given params are used."""
output_dir_path = os.path.join(test_name, 'variable_update')
logs = _spawn_benchmark_processes(output_dir_path, num_workers, num_ps,
num_controllers, params)
actual_losses = []
for worker_logs in logs:
outputs = test_util.get_training_outputs_from_logs(
worker_logs, params.print_training_accuracy)
actual_losses.append([x.loss for x in outputs])
inputs = test_util.get_fake_var_update_inputs()
expected_losses = test_util.TestCNNModel().manually_compute_losses(
inputs, num_workers, params)
if params.variable_update == 'distributed_all_reduce':
# In distributed all reduce, each step, the controller outputs the average
# of the loss from each worker. So we modify expected losses accordingly.
# E.g, we change [[1, 2], [4, 5]] to [[2.5, 3.5]]
expected_losses = [[
sum(losses) / num_workers for losses in zip(*expected_losses)
]]
rtol = 3e-2 if params.use_fp16 else 1e-5
for worker_actual_losses, worker_expected_losses in zip(
actual_losses, expected_losses):
self.assertAllClose(
worker_actual_losses[:len(worker_expected_losses)],
worker_expected_losses,
rtol=rtol,
atol=0.)
def run_with_test_model(params):
"""Runs tf_cnn_benchmarks with a test model."""
model = test_util.TestCNNModel()
inputs = test_util.get_fake_var_update_inputs()
with test_util.monkey_patch(benchmark_cnn,
LOSS_AND_ACCURACY_DIGITS_TO_SHOW=15):
bench = benchmark_cnn.BenchmarkCNN(params, dataset=test_util.TestDataSet(),
model=model)
# The test model does not use labels when computing loss, so the label
# values do not matter as long as it's the right shape.
labels = np.array([1] * inputs.shape[0])
bench.image_preprocessor.set_fake_data(inputs, labels)
bench.run()
def _test_variable_update(self, params):
"""Tests variables are updated correctly when the given params are used.
A BenchmarkCNN is created with a TestCNNModel, and is run with some scalar
images. The losses are then compared with the losses obtained with
TestCNNModel().manually_compute_losses()
Args:
params: a Params tuple used to create BenchmarkCNN.
"""
inputs = test_util.get_fake_var_update_inputs()
actual_losses = self._get_benchmark_cnn_losses(inputs, params)
expected_losses, = test_util.TestCNNModel().manually_compute_losses(
inputs, 1, params)
rtol = 3e-2 if params.use_fp16 else 1e-5
self.assertAllClose(actual_losses[:len(expected_losses)], expected_losses,
rtol=rtol, atol=0.)
def _get_benchmark_cnn_losses(self, inputs, params):
"""Returns the losses of BenchmarkCNN on the given inputs and params."""
logs = []
model = test_util.TestCNNModel()
with test_util.monkey_patch(benchmark_cnn,
log_fn=test_util.print_and_add_to_list(logs),
LOSS_AND_ACCURACY_DIGITS_TO_SHOW=15):
bench = benchmark_cnn.BenchmarkCNN(
params, dataset=test_util.TestDataSet(), model=model)
# The test model does not use labels when computing loss, so the label
# values do not matter as long as it's the right shape.
labels = np.array([1] * inputs.shape[0])
bench.image_preprocessor.set_fake_data(inputs, labels)
bench.run()
outputs = test_util.get_training_outputs_from_logs(
logs, params.print_training_accuracy)
return [x.loss for x in outputs]