def test_case_small_fastemit_clamp(self, device, fastemit_lambda):
if device == 'cuda':
numba_utils.skip_numba_cuda_test_if_unsupported(__NUMBA_MINIMUM_VERSION__)
GRAD_CLAMP = 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]],
]
]
)
labels = [[1, 2]]
fn_pt = RNNTLossNumba(blank=0, reduction='sum', fastemit_lambda=fastemit_lambda, clamp=GRAD_CLAMP)
pt_cost, pt_grads = wrap_and_call(fn_pt, acts, labels, device)
fn_np = RNNTLoss_Numpy(blank=0, fastemit_lambda=fastemit_lambda, clamp=GRAD_CLAMP)
np_cost, np_grads = wrap_and_call(fn_np, acts, labels, device)
expected_cost = 4.495666
expected_cost += expected_cost * fastemit_lambda
assert np.allclose(pt_cost, expected_cost, rtol=1e-6), "small_test costs mismatch."
assert np.allclose(pt_cost, np_cost, rtol=1e-6), "small_test costs mismatch."
assert np.allclose(pt_grads, np_grads), "small_test gradient mismatch."
def test_case_small_random(self, device):
rng = np.random.RandomState(0)
acts = rng.randn(1, 4, 3, 3)
labels = [[1, 2]]
fn_pt = RNNTLossNumba(blank=0, reduction='sum')
pt_cost, pt_grads = wrap_and_call(fn_pt, acts, labels, device)
fn_np = RNNTLoss_Numpy()
np_cost, np_grads = wrap_and_call(fn_np, acts, labels, device)
assert np.allclose(pt_cost, np_cost, rtol=1e-6), "small_random_test costs mismatch."
assert np.allclose(pt_grads, np_grads), "small_random_test gradient mismatch."
def test_case_small_random_fastemit_reg(self, device, fastemit_lambda):
if device == 'cuda':
numba_utils.skip_numba_cuda_test_if_unsupported(__NUMBA_MINIMUM_VERSION__)
rng = np.random.RandomState(0)
acts = rng.randn(1, 4, 3, 3)
labels = [[1, 2]]
fn_pt = RNNTLossNumba(blank=0, reduction='sum', fastemit_lambda=fastemit_lambda)
pt_cost, pt_grads = wrap_and_call(fn_pt, acts, labels, device)
fn_np = RNNTLoss_Numpy(fastemit_lambda=fastemit_lambda)
np_cost, np_grads = wrap_and_call(fn_np, acts, labels, device)
assert np.allclose(pt_cost, np_cost, rtol=1e-6), "small_random_test costs mismatch."
assert np.allclose(pt_grads, np_grads, atol=1e-5, rtol=1e-5), "small_random_test gradient mismatch."
def test_case_large_random(self, device):
rng = np.random.RandomState(0)
acts = rng.randn(4, 8, 11, 5)
labels = [
[1, 2, 4, 3, 2, 2, 1, 1, 1, 1],
[3, 2, 2, 3, 4, 1, 1, 1, 1, 1],
[4, 4, 1, 2, 1, 3, 4, 3, 1, 2],
[1, 1, 2, 1, 2, 3, 3, 1, 1, 1],
]
fn_pt = RNNTLossNumba(blank=0, reduction='sum')
pt_cost, pt_grads = wrap_and_call(fn_pt, acts, labels, device)
fn_np = RNNTLoss_Numpy()
np_cost, np_grads = wrap_and_call(fn_np, acts, labels, device)
assert np.allclose(pt_cost, np_cost, atol=1e-5, rtol=1e-3), "large_random_test costs mismatch."
assert np.allclose(pt_grads, np_grads, atol=1e-5, rtol=1e-3), "large_random_test gradient mismatch."
def test_case_small(self, device):
if device == 'cuda':
numba_utils.skip_numba_cuda_test_if_unsupported(__NUMBA_MINIMUM_VERSION__)
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]],
]
]
)
labels = [[1, 2]]
fn_pt = RNNTLossNumba(blank=0, reduction='sum')
pt_cost, pt_grads = wrap_and_call(fn_pt, acts, labels, device)
fn_np = RNNTLoss_Numpy()
np_cost, np_grads = wrap_and_call(fn_np, acts, labels, device)
expected_cost = 4.495666
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(pt_cost, expected_cost, rtol=1e-6), "small_test costs mismatch."
assert np.allclose(pt_grads, expected_grads), "small_test gradient mismatch."
assert np.allclose(pt_cost, np_cost, rtol=1e-6), "small_test costs mismatch."
assert np.allclose(pt_grads, np_grads), "small_test gradient mismatch."
def test_case_large_random(self, device):
if device == 'cuda':
numba_utils.skip_numba_cuda_test_if_unsupported(__NUMBA_MINIMUM_VERSION__)
rng = np.random.RandomState(0)
acts = rng.randn(4, 8, 11, 5)
labels = [
[1, 2, 4, 3, 2, 2, 1, 1, 1, 1],
[3, 2, 2, 3, 4, 1, 1, 1, 1, 1],
[4, 4, 1, 2, 1, 3, 4, 3, 1, 2],
[1, 1, 2, 1, 2, 3, 3, 1, 1, 1],
]
fn_pt = RNNTLossNumba(blank=0, reduction='sum')
pt_cost, pt_grads = wrap_and_call(fn_pt, acts, labels, device)
fn_np = RNNTLoss_Numpy()
np_cost, np_grads = wrap_and_call(fn_np, acts, labels, device)
assert np.allclose(pt_cost, np_cost, atol=1e-5, rtol=1e-3), "large_random_test costs mismatch."
assert np.allclose(pt_grads, np_grads, atol=1e-5, rtol=1e-3), "large_random_test gradient mismatch."
def test_case_big_tensor(self, device):
if device == 'cuda':
numba_utils.skip_numba_cuda_test_if_unsupported(__NUMBA_MINIMUM_VERSION__)
# minibatch x T x U x alphabet_size
activations = [
[
[
[0.06535690384862791, 0.7875301411923206, 0.08159176605666074],
[0.5297155426466327, 0.7506749639230854, 0.7541348379087998],
[0.6097641124736383, 0.8681404965673826, 0.6225318186056529],
],
[
[0.6685222872103057, 0.8580392805336061, 0.16453892311765583],
[0.989779515236694, 0.944298460961015, 0.6031678586829663],
[0.9467833543605416, 0.666202507295747, 0.28688179752461884],
],
[
[0.09418426230195986, 0.3666735970751962, 0.736168049462793],
[0.1666804425271342, 0.7141542198635192, 0.3993997272216727],
[0.5359823524146038, 0.29182076440286386, 0.6126422611507932],
],
[
[0.3242405528768486, 0.8007644367291621, 0.5241057606558068],
[0.779194617063042, 0.18331417220174862, 0.113745182072432],
[0.24022162381327106, 0.3394695622533106, 0.1341595066017014],
],
],
[
[
[0.5055615569388828, 0.051597282072282646, 0.6402903936686337],
[0.43073311517251, 0.8294731834714112, 0.1774668847323424],
[0.3207001991262245, 0.04288308912457006, 0.30280282975568984],
],
[
[0.6751777088333762, 0.569537369330242, 0.5584738347504452],
[0.08313242153985256, 0.06016544344162322, 0.10795752845152584],
[0.7486153608562472, 0.943918041459349, 0.4863558118797222],
],
[
[0.4181986264486809, 0.6524078485043804, 0.024242983423721887],
[0.13458171554507403, 0.3663418070512402, 0.2958297395361563],
[0.9236695822497084, 0.6899291482654177, 0.7418981733448822],
],
[
[0.25000547599982104, 0.6034295486281007, 0.9872887878887768],
[0.5926057265215715, 0.8846724004467684, 0.5434495396894328],
[0.6607698886038497, 0.3771277082495921, 0.3580209022231813],
],
],
]
expected_costs = [4.2806528590890736, 3.9384369822503591]
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],
],
[
[-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],
],
[
[-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],
],
[
[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],
],
],
[
[
[-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],
],
[
[-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],
],
[
[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], [1, 1]]
fn_pt = RNNTLossNumba(blank=0, reduction='sum')
pt_costs, pt_grads = wrap_and_call(fn_pt, activations, labels, device)
fn_np = RNNTLoss_Numpy()
np_costs, np_grads = wrap_and_call(fn_np, activations, labels, device)
assert np.allclose(pt_costs, sum(expected_costs)), "big_test average costs mismatch."
assert np.allclose(pt_grads, expected_grads, rtol=1e-3), "big_test grads for average cost mismatch."
assert np.allclose(pt_costs, np_costs), "big_test average costs mismatch."
assert np.allclose(pt_grads, np_grads, rtol=1e-3), "big_test grads for average cost mismatch."
def test_case_small_random_accumulated(self, device):
if device == 'cuda':
numba_utils.skip_numba_cuda_test_if_unsupported(
__NUMBA_MINIMUM_VERSION__)
torch.manual_seed(0)
base_layer = torch.randn(3, 5, requires_grad=True)
mid1 = torch.randn(1, 4, 3, 3, requires_grad=True)
labels1 = [[1, 3]]
mid2 = torch.randn(1, 6, 5, 3, requires_grad=True)
labels2 = [[1, 2, 3, 4]]
def zero_grad():
if base_layer.grad is not None:
base_layer.grad = None
if mid1.grad is not None:
mid1.grad = None
if mid2.grad is not None:
mid2.grad = None
fn_pt = RNNTLossNumba(blank=0, reduction='sum')
fn_np = RNNTLoss_Numpy()
# run 1
acts1 = torch.matmul(mid1, base_layer) # [1, 4, 3, 5]
pt_cost1, _ = wrap_and_call(fn_pt, acts1, labels1, device)
pt_grads1 = base_layer.grad.clone().cpu().numpy()
zero_grad()
acts1 = torch.matmul(mid1, base_layer) # [1, 4, 3, 5]
np_cost1, _ = wrap_and_call(fn_np, acts1, labels1, device)
np_grads1 = base_layer.grad.clone().cpu().numpy()
zero_grad()
assert np.allclose(pt_grads1, np_grads1, atol=1e-6)
# run 2
acts2 = torch.matmul(mid2, base_layer) # [1, 4, 3, 5]
pt_cost2, _ = wrap_and_call(fn_pt, acts2, labels2, device)
pt_grads2 = base_layer.grad.clone().cpu().numpy()
zero_grad()
acts2 = torch.matmul(mid2, base_layer) # [1, 4, 3, 5]
np_cost2, _ = wrap_and_call(fn_np, acts2, labels2, device)
np_grads2 = base_layer.grad.clone().cpu().numpy()
zero_grad()
assert np.allclose(pt_grads2, np_grads2, atol=1e-6)
# run 1 + 2
acts1 = torch.matmul(mid1, base_layer) # [1, 4, 3, 5]
pt_cost1, _ = wrap_and_call(fn_pt, acts1, labels1, device)
acts2 = torch.matmul(mid2, base_layer) # [1, 6, 5, 5]
pt_cost2, _ = wrap_and_call(fn_pt, acts2, labels2, device)
pt_grads1_p_2 = base_layer.grad.clone().cpu().numpy()
assert np.allclose(pt_grads1_p_2, np_grads1 + np_grads2, atol=1e-6)