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test for both cpu and gpu
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@HawkAaron
HawkAaron committed May 29, 2018
1 parent b7a5b48 commit 5e16b3d
Showing 1 changed file with 56 additions and 78 deletions.
134 changes: 56 additions & 78 deletions pytorch_binding/test/test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,14 +18,10 @@
from transducer_np import RNNTLoss as rnntloss

parser = argparse.ArgumentParser(description='MXNet RNN Transducer Test.')
parser.add_argument('B', type=int, default=1, help='batch size')
parser.add_argument('T', type=int, default=300, help='time step')
parser.add_argument('U', type=int, default=100, help='prediction step')
parser.add_argument('V', type=int, default=60, help='vocab size')
parser.add_argument('--np', default=False, action='store_true', help='numpy loss')
args = parser.parse_args()

fn = rnntloss() if args.np else RNNTLoss()
fn = rnntloss() if args.np else RNNTLoss(size_average=False)

def wrap_and_call(acts, labels):
acts = torch.FloatTensor(acts)
Expand All @@ -38,48 +34,47 @@ def wrap_and_call(acts, labels):
labels = autograd.Variable(torch.IntTensor(labels))
lengths = autograd.Variable(torch.IntTensor(lengths))
label_lengths = autograd.Variable(torch.IntTensor(label_lengths))
if use_cuda:
labels = labels.cuda()
lengths = lengths.cuda()
label_lengths = label_lengths.cuda()

log_probs = nn.functional.log_softmax(acts, dim=3)
def grad_hook(grad):
log_probs.saved_grad = grad.clone()
log_probs.register_hook(grad_hook)

costs = fn(log_probs, labels, lengths, label_lengths)
cost = torch.sum(costs)
cost.backward()
grads = log_probs.saved_grad
if use_cuda:
costs = costs.cpu()
grads = grads.cpu()
for i, a in enumerate(acts.grad.data.numpy().reshape(-1)):
if i % 6 == 0: print(end='\n')
print('{:.6f}, '.format(a), end='')
print()
# print(acts.grad.data.numpy())
return costs.data.numpy(), grads.data.numpy()
print(repr(acts.grad.data.cpu().numpy()))
return costs.data.cpu().numpy(), acts.grad.data.cpu().numpy()


def small_test():
acts = np.array([[[0.1, 0.6, 0.1, 0.1, 0.1],
acts = np.array([[[[0.1, 0.6, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.6, 0.1, 0.1],
[0.1, 0.1, 0.2, 0.8, 0.1]],
[[0.1, 0.6, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.2, 0.1, 0.1],
[0.7, 0.1, 0.2, 0.1, 0.1]]])
[0.7, 0.1, 0.2, 0.1, 0.1]]]])
labels = [[1, 2]]

acts = acts[None, ...]

cost, grads = wrap_and_call(acts, labels)
expected_cost = 4.495666
expected_grads = np.array([[[-0.308198071906, -0.6918019280939998, 0.0, 0.0, 0.0],
[-0.308198071906, 0.0, -0.3836038561880001, 0.0, 0.0],
[-0.3836038561880001, 0.0, 0.0, 0.0, 0.0]],
[[0.0, -0.308198071906, 0.0, 0.0, 0.0],
[0.0, 0.0, -0.6163961438119995, 0.0, 0.0],
[-0.9999999999999991, 0.0, 0.0, 0.0, 0.0]]])
expected_grads = np.array([[[[-0.13116688, -0.3999269 , 0.17703125, 0.17703125,
0.17703125],
[-0.18572757, 0.12247056, -0.18168412, 0.12247056,
0.12247056],
[-0.32091254, 0.06269141, 0.06928472, 0.12624499,
0.06269141]],

[[ 0.05456069, -0.21824276, 0.05456069, 0.05456069,
0.05456069],
[ 0.12073959, 0.12073959, -0.48295835, 0.12073959,
0.12073959],
[-0.6925882 , 0.16871116, 0.18645467, 0.16871116,
0.16871116]]]])
assert np.allclose(cost, expected_cost, rtol=1e-6), \
"small_test costs mismatch."
print(grads) # TODO change this exptected grad to actis.
assert np.allclose(grads, expected_grads), \
"small_test gradient mismatch."

Expand Down Expand Up @@ -121,38 +116,38 @@ def big_test():
[0.6607698886038497, 0.3771277082495921, 0.3580209022231813]]]]

expected_costs = [4.2806528590890736, 3.9384369822503591]
expected_grads = [
[[[-0.4322264564338117, -0.5677735435661883, 0.0],
[-0.36565009313836844, 0.0, -0.20212345042782007],
[-0.20212345042782007, 0.0, 0.0]],
expected_grads = [[[[-1.86843902e-01, -6.25548810e-02, 2.49398798e-01],
[-2.03376666e-01, 2.02399328e-01, 9.77333169e-04],
[-1.41016081e-01, 7.91234672e-02, 6.18926100e-02]],

[[-0.16521672442463506, -0.2670097320091765, 0.0],
[-0.3943653886107811, 0.0, -0.2382944365367636],
[-0.44041788696458367, 0.0, 0.0]],
[[-1.15517676e-02, -8.12802389e-02, 9.28319991e-02],
[-1.54257029e-01, 2.29432687e-01, -7.51756504e-02],
[-2.46593088e-01, 1.46404594e-01, 1.00188486e-01]],

[[-0.052129794015740985, -0.11308693040889405, 0.0],
[-0.18313786985332664, 0.0, -0.3243144491663483],
[-0.7647323361309323, 0.0, 0.0]],
[[-1.29182907e-02, -6.15932420e-02, 7.45115355e-02],
[-5.59857301e-02, 2.19830811e-01, -1.63845062e-01],
[-4.97626871e-01, 2.09239945e-01, 2.88386941e-01]],

[[0.0, -0.052129794015740985, 0.0],
[0.0, 0.0, -0.23526766386906767],
[-1.0, 0.0, 0.0]]],
[[ 1.36048580e-02, -3.02196294e-02, 1.66147724e-02],
[ 1.13924511e-01, 6.27811998e-02, -1.76705718e-01],
[-6.67078257e-01, 3.67658824e-01, 2.99419403e-01]]],

[[[-0.7161424128232795, -0.2838575871767207, 0.0],
[-0.18382932237365335, -0.10002826480306751, 0.0],
[-0.10002826480306751, 0.0, 0.0]],

[[-0.41121794618117213, -0.3049244666421072, 0.0],
[-0.3295759402552584, -0.15917784876050195, 0.0],
[-0.2592061135635692, 0.0, 0.0]],
[[[-3.56343776e-01, -5.53474613e-02, 4.11691219e-01],
[-9.69219357e-02, 2.94591039e-02, 6.74628317e-02],
[-6.35175705e-02, 2.76544970e-02, 3.58630717e-02]],

[[-0.11607642141651396, -0.29514152476465827, 0.0],
[-0.2865333615432337, -0.3381841034766833, 0.0],
[-0.5973902170402529, 0.0, 0.0]],
[[-1.54499024e-01, -7.39420280e-02, 2.28441030e-01],
[-1.66789949e-01, -8.78955179e-05, 1.66877866e-01],
[-1.72369644e-01, 1.05565332e-01, 6.68043196e-02]],

[[0.0, -0.11607642141651396, 0.0],
[0.0, -0.4026097829597475, 0.0],
[-1.0, 0.0, 0.0]]]]
[[ 2.38748826e-02, -1.18255816e-01, 9.43809375e-02],
[-1.04707085e-01, -1.08934477e-01, 2.13641584e-01],
[-3.69844258e-01, 1.80118099e-01, 1.89726159e-01]],

[[ 2.57137045e-02, -7.94617534e-02, 5.37480488e-02],
[ 1.22328237e-01, -2.38788679e-01, 1.16460443e-01],
[-5.98686993e-01, 3.02203178e-01, 2.96483815e-01]]]]

activations = np.array(activations)
labels = [[1, 2],
Expand All @@ -163,33 +158,16 @@ def big_test():
assert np.allclose(costs, sum(expected_costs)), \
"big_test average costs mismatch."

assert np.allclose(grads, expected_grads), \
assert np.allclose(grads, expected_grads, rtol=1e-3), \
"big_test grads for average cost mismatch."

def time_test(blank=0):
batch_size = args.B
vocab_size = args.V
input_len = args.T
output_len = args.U
acts = np.random.rand(batch_size, input_len, output_len + 1, vocab_size)
labels = np.random.randint(1, vocab_size, (batch_size, output_len))

start = time.time()
iters = 10
for _ in range(iters):
wrap_and_call(acts, labels)
end = time.time()

print("Time per iteration: {:.3f}(s)".format((end-start)/iters))


if __name__ == "__main__":
use_cuda = False
# small_test()
small_test()
big_test()
# print("CPU Tests passed!")
# if torch.cuda.is_available():
# use_cuda = True
# small_test()
# print("GPU Tests passed!")
# time_test()
print("CPU Tests passed!")
if torch.cuda.is_available():
use_cuda = True
small_test()
big_test()
print("GPU Tests passed!")

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