def testTransformerAttentionLayerReference(self):
depth = 4
p = layers_with_attention.TransformerAttentionLayer.Params()
p.name = 'transformer_atten'
p.source_dim = depth
p.is_masked = False
p.num_attention_heads = 2
p.atten_tpl.params_init = py_utils.WeightInit.Gaussian(0.1, 12345)
transformer_atten_ref = layers_with_attention.TransformerAttentionLayer(
p)
(query_vec, _, aux_vecs,
aux_paddings) = self._TransformerSingleSourceInputs(depth)
ctx_ref, probs_ref = transformer_atten_ref.FPropDefaultTheta(
query_vec, aux_paddings, aux_vecs)
expected_ctx, expected_probs = self._ExpectedSingleSourceResults()
with self.session(use_gpu=True) as sess:
tf.global_variables_initializer().run()
actual_ctx_ref, actual_probs_ref = sess.run([ctx_ref, probs_ref])
tf.logging.info(np.array_repr(actual_ctx_ref))
tf.logging.info(np.array_repr(actual_probs_ref))
self.assertAllClose(expected_ctx, actual_ctx_ref)
self.assertAllClose(expected_probs, actual_probs_ref)
def testTransformerAttentionLayerDeterministicDropout(self):
with self.session(use_gpu=True) as sess:
# Needed to generate a seed pair.
py_utils.ResetStepSeed()
py_utils.GetOrCreateGlobalStep()
depth = 4
p = layers_with_attention.TransformerAttentionLayer.Params()
p.name = 'transformer_atten'
p.source_dim = depth
p.is_masked = False
p.num_attention_heads = 2
p.residual_dropout_tpl = layers.DeterministicDropoutLayer.Params()
p.residual_dropout_prob = 0.1
transformer_atten = layers_with_attention.TransformerAttentionLayer(p)
(source_vecs, source_padding, _,
_) = self._testTransformerAttentionLayerInputs(depth=depth)
ctx, probs = transformer_atten.FProp(transformer_atten.theta, source_vecs,
source_padding)
tf.global_variables_initializer().run()
actual_ctx, actual_probs = sess.run([ctx, probs])
# pylint: disable=bad-whitespace
# pyformat: disable
print(np.array_repr(actual_ctx))
expected_ctx = np.array([
[[-1.45762944, 1.5337404 , 0.34037334, -0.97208667],
[-1.35992002, -1.06530988, 1.53705895, 2.79370689]],
[[ 0.00657134, 1.12030125, -1.32564592, -1.73569465],
[-0.80793667, -0.10877949, -0.80295694, 2.25494242]],
[[ 1.76956046, -0.50777751, -1.19745886, -1.46751583],
[-1.79178905, -0.77374339, 1.31586027, 2.98173356]],
[[-0.85498607, -0.37413225, 1.25707364, -0.50043333],
[ 1.62276983, 0.50820369, -1.52967572, -2.02076197]],
[[-0.66754031, -0.68657839, -0.51643699, 1.96581018],
[-1.4816376 , 0.89419198, -0.57226259, 1.90177512]]
], dtype=np.float32)
print(np.array_repr(actual_probs))
expected_probs = np.array([
[[ 0.21387868, 0.22080734, 0. , 0. , 0.56531399],
[ 0. , 0.30584112, 0.24723588, 0.44692296, 0. ]],
[[ 0.25358215, 0.50932312, 0. , 0. , 0.23709476],
[ 0. , 0.56834149, 0.2632803 , 0.16837817, 0. ]],
[[ 0.38519409, 0.55454361, 0. , 0. , 0.06026226],
[ 0. , 0.33708778, 0.21976741, 0.4431448 , 0. ]],
[[ 0.27139962, 0.12790371, 0. , 0. , 0.60069668],
[ 0. , 0.31849149, 0.28174096, 0.39976761, 0. ]],
[[ 0.16272782, 0.15781289, 0. , 0. , 0.67945927],
[ 0. , 0.55003977, 0.26049581, 0.18946445, 0. ]]
], dtype=np.float32)
# pyformat: enable
# pylint: enable=bad-whitespace
self.assertAllClose(expected_ctx, actual_ctx, rtol=1e-05, atol=1e-05)
self.assertAllClose(expected_probs, actual_probs, rtol=1e-05, atol=1e-05)
def testTransformerAttentionLayerCase3(self):
with self.session(use_gpu=True) as sess:
depth = 4
p = layers_with_attention.TransformerAttentionLayer.Params()
p.name = 'transformer_atten'
p.source_dim = depth
p.is_masked = False
p.num_attention_heads = 2
transformer_atten = layers_with_attention.TransformerAttentionLayer(
p)
(query_vec, _, aux_vecs,
aux_paddings) = self._testTransformerAttentionLayerInputs(
depth=depth)
ctx, probs = transformer_atten.FPropDefaultTheta(
query_vec, aux_paddings, aux_vecs)
tf.global_variables_initializer().run()
actual_ctx, actual_probs = sess.run([ctx, probs])
tf.logging.info(np.array_repr(actual_ctx))
tf.logging.info(np.array_repr(actual_probs))
# pylint: disable=bad-whitespace
# pyformat: disable
expected_ctx = [
[[-1.42420077, 1.19024372, 1.35146523, 0.85896158],
[-0.44974625, -1.00108492, 1.63387251, 1.678146]],
[[0.1134335, 1.97617495, -0.35918081, 0.26396495],
[-0.19688171, -0.71197301, 0.0659425, 2.5417304]],
[[1.58169425, 0.81259179, -0.58948535, 0.20254248],
[-0.84438968, -0.65845209, 1.45584249, 1.87587976]],
[[-1.01532316, -0.05166581, 2.07901478, 0.97540361],
[2.08563352, 0.34328598, -0.23240227, -0.19035631]],
[[-0.53881919, -0.60117185, 0.29170275, 2.6474514],
[-0.88318163, 0.37149727, -0.16098523, 2.3810885]]
]
expected_probs = [
[[0.32392544, 0., 0.27218491, 0., 0.19574419, 0., 0.20814547],
[0., 0.273045, 0., 0.43572819, 0., 0.2912268, 0.]],
[[0.24094662, 0., 0.23919827, 0., 0.26563686, 0., 0.25421822],
[0., 0.21680018, 0., 0.33962148, 0., 0.44357836, 0.]],
[[0.20083594, 0., 0.20683075, 0., 0.28931937, 0., 0.30301392],
[0., 0.24710922, 0., 0.453915, 0., 0.29897571, 0.]],
[[0.32845193, 0., 0.26491433, 0., 0.18304622, 0., 0.22358747],
[0., 0.39426237, 0., 0.19774443, 0., 0.4079932, 0.]],
[[0.23542665, 0., 0.27910906, 0., 0.30036426, 0., 0.18510005],
[0., 0.20147586, 0., 0.37759233, 0., 0.42093182, 0.]]
]
# pyformat: enable
# pylint: enable=bad-whitespace
self.assertAllClose(expected_ctx,
actual_ctx,
rtol=1e-05,
atol=1e-05)
self.assertAllClose(expected_probs,
actual_probs,
rtol=1e-05,
atol=1e-05)
def testTransformerAttentionLayerCase1(self):
with self.session(use_gpu=True) as sess:
depth = 4
p = layers_with_attention.TransformerAttentionLayer.Params()
p.name = 'transformer_atten'
p.source_dim = depth
p.is_masked = False
p.num_attention_heads = 2
transformer_atten = layers_with_attention.TransformerAttentionLayer(
p)
(source_vecs, source_padding, _,
_) = self._testTransformerAttentionLayerInputs(depth=depth)
ctx, probs = transformer_atten.FPropDefaultTheta(
source_vecs, source_padding)
tf.global_variables_initializer().run()
actual_ctx, actual_probs = sess.run([ctx, probs])
# pylint: disable=bad-whitespace
# pyformat: disable
expected_ctx = [
[[-1.47126436, 1.46579707, 0.39105844, -0.88563323],
[-1.29514003, -1.08241224, 1.49894714, 2.5935874]],
[[-0.00313053, 1.17399275, -1.28071034, -1.6311729],
[-0.77028418, -0.18855178, -0.75814998, 2.19872856]],
[[1.72851753, -0.40323859, -1.19053328, -1.39761829],
[-1.72141743, -0.78715289, 1.28404212, 2.78338313]],
[[-0.8881942, 0.33776048, 1.28791749, -0.45082122],
[1.4362365, 0.46009994, -1.45436597, -1.90602148]],
[[-0.51681399, -0.70075679, -0.48352116, 1.93754733],
[-1.44486678, 0.81801879, -1.03079689, 1.86697066]]
]
expected_probs = [[[0.21387868, 0.22080734, 0., 0., 0.56531399],
[0., 0.30584112, 0.24723588, 0.44692296, 0.]],
[[0.25358215, 0.50932312, 0., 0., 0.23709476],
[0., 0.56834149, 0.2632803, 0.16837817, 0.]],
[[0.38519409, 0.55454361, 0., 0., 0.06026226],
[0., 0.33708778, 0.21976741, 0.4431448, 0.]],
[[0.27139962, 0.12790371, 0., 0., 0.60069668],
[0., 0.31849149, 0.28174096, 0.39976761, 0.]],
[[0.16272782, 0.15781289, 0., 0., 0.67945927],
[0., 0.55003977, 0.26049581, 0.18946445, 0.]]]
# pyformat: enable
# pylint: enable=bad-whitespace
self.assertAllClose(expected_ctx,
actual_ctx,
rtol=1e-05,
atol=1e-05)
self.assertAllClose(expected_probs,
actual_probs,
rtol=1e-05,
atol=1e-05)
def testTransformerAttentionLayerCase2(self):
with self.session(use_gpu=True) as sess:
depth = 4
p = layers_with_attention.TransformerAttentionLayer.Params()
p.name = 'transformer_atten'
p.source_dim = depth
p.is_masked = True
p.num_attention_heads = 2
transformer_atten = layers_with_attention.TransformerAttentionLayer(
p)
(source_vecs, source_padding, _,
_) = self._testTransformerAttentionLayerInputs(depth=depth)
ctx, probs = transformer_atten.FPropDefaultTheta(
source_vecs, source_padding)
tf.global_variables_initializer().run()
actual_ctx, actual_probs = sess.run([ctx, probs])
tf.logging.info(np.array_repr(actual_ctx))
tf.logging.info(np.array_repr(actual_probs))
# pylint: disable=bad-whitespace
# pyformat: disable
expected_ctx = [
[[-0.14429152, 1.15510106, 1.11930299, -1.19245839],
[-0.69580591, -0.47006619, 0.82592297, 0.69593251]],
[[0.24164687, 0.53328454, -1.02119482, -1.49412084],
[-0.82601064, 0.024203, -1.11880171, 1.80784416]],
[[1.7644347, -0.53346401, -1.1461122, -1.42797422],
[-0.95326459, 0.39580142, 0.39262164, 0.67513674]],
[[-0.28252155, -0.95237327, 2.08757687, -0.21231559],
[1.4362365, 0.46009994, -1.45436597, -1.90602148]],
[[-0.51681399, -0.70075679, -0.48352116, 1.93754733],
[-1.44486678, 0.81801879, -1.03079689, 1.86697066]]
]
expected_probs = [[[1., 0., 0., 0., 0.], [0.2, 0.2, 0.2, 0.2,
0.2]],
[[0.3966811, 0.60331887, 0., 0., 0.],
[0., 1., 0., 0., 0.]],
[[0.41050252, 0.58949745, 0., 0., 0.],
[0., 0.5245893, 0.4754107, 0., 0.]],
[[0.58882225, 0.41117775, 0., 0., 0.],
[0., 0.31849149, 0.28174096, 0.39976761, 0.]],
[[0.16272782, 0.15781289, 0., 0., 0.67945927],
[0., 0.55003977, 0.26049581, 0.18946445, 0.]]]
# pyformat: enable
# pylint: enable=bad-whitespace
self.assertAllClose(expected_ctx, actual_ctx)
self.assertAllClose(expected_probs, actual_probs)
def testTransformerAttentionLayerStepByStep(self):
with self.session(use_gpu=True) as sess:
depth = 4
p = layers_with_attention.TransformerAttentionLayer.Params()
p.name = 'transformer_atten'
p.source_dim = depth
p.is_masked = True
p.num_attention_heads = 2
x_atten = layers_with_attention.TransformerAttentionLayer(p)
(source_vecs, _, _,
_) = self._testTransformerAttentionLayerInputs(depth=depth)
source_padding = tf.zeros([5, 2])
ctx1, probs1 = x_atten.FPropDefaultTheta(source_vecs,
source_padding)
ctx2 = []
probs2 = []
cached_source_vecs = tf.zeros([0, 2, 4])
cached_source_contexts = tf.zeros([0, 2, 4])
prefix_states = py_utils.NestedMap(key=cached_source_vecs,
value=cached_source_contexts)
for i in range(5):
ctx, probs, prefix_states = x_atten.ExtendStep(
x_atten.theta, source_vecs[i, :, :], prefix_states)
probs_pad = tf.zeros([2, 5 - i - 1])
padded_probs = tf.concat([probs, probs_pad], 1)
ctx2.append(ctx)
probs2.append(padded_probs)
ctx2 = tf.stack(ctx2)
probs2 = tf.stack(probs2)
tf.global_variables_initializer().run()
ctx1_v, probs1_v, ctx2_v, probs2_v = sess.run(
[ctx1, probs1, ctx2, probs2])
tf.logging.info(np.array_repr(ctx1_v))
tf.logging.info(np.array_repr(probs1_v))
tf.logging.info(np.array_repr(ctx2_v))
tf.logging.info(np.array_repr(probs2_v))
self.assertAllClose(ctx1_v, ctx2_v)
self.assertAllClose(probs1_v, probs2_v)
