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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for moe.py."""
import numpy as np
import tensorflow as tf, tf_keras
from official.nlp.modeling.layers import moe
def small_config():
"""Creates a small model config that can be used by all tests."""
config = {}
config['d_ff'] = 32
config['output_dropout'] = 0.1
config['num_experts'] = 2
config['expert_d_ff'] = 33
config['expert_dropout_rate'] = 0.1
config['jitter_noise'] = 0.1
config['train_capacity_factor'] = 1.0
config['eval_capacity_factor'] = 1.0
config['examples_per_group'] = 2.0
config['backbone_d_ff'] = 13
return config
def make_input_ones(batch_size: int = 4,
seq_length: int = 10,
hidden_dim: int = 7) -> tf.Tensor:
return tf.ones((batch_size, seq_length, hidden_dim))
def make_experts_input_ones(num_groups: int = 1,
num_experts: int = 2,
expert_capacity: int = 5,
hidden_dim: int = 7) -> tf.Tensor:
return tf.ones((num_groups, num_experts, expert_capacity, hidden_dim))
class MoeTest(tf.test.TestCase):
def tearDown(self):
super().tearDown()
tf_keras.mixed_precision.set_global_policy('float32')
def test_router_z_loss_dtype(self):
x = tf.constant([[[10.0, 5.0]]], dtype=tf.float32)
y = moe._router_z_loss(x)
expected = (5 + np.log(np.exp(5) + 1))**2
self.assertAllClose(expected, y, atol=1e-7)
self.assertDTypeEqual(y, tf.float32)
def test_router_z_loss_shape(self):
x = make_input_ones(2, 5, 7)
y = moe._router_z_loss(x)
expected = (np.log(7) + 1)**2
self.assertAllClose(expected, y, atol=1e-7)
def test_experts_choose_masked_router_dtype_shape(self):
tf_keras.mixed_precision.set_global_policy('mixed_bfloat16')
num_groups = 2
tokens_per_group = 3
hidden_dim = tokens_per_group
num_experts = tokens_per_group
expert_capacity = 2
x = np.zeros([num_groups, tokens_per_group, hidden_dim])
x[0, 0, 0] += 1
x[0, :2, :2] += 1
x[1, 1:, 1:] += 1
x[1, -1, -1] += 1
router = moe.ExpertsChooseMaskedRouter(
num_experts=num_experts,
jitter_noise=0.1,
use_bias=True,
kernel_initializer=tf_keras.initializers.get('identity'),
bias_initializer=tf_keras.initializers.get('ones'))
router_mask = router(x, expert_capacity=expert_capacity, training=False)
self.assertDTypeEqual(router_mask.dispatch_mask, tf.bfloat16)
self.assertDTypeEqual(router_mask.combine_array, tf.bfloat16)
expect_shape = [num_groups, tokens_per_group, num_experts, expert_capacity]
self.assertEqual(expect_shape, router_mask.dispatch_mask.shape)
self.assertEqual(expect_shape, router_mask.combine_array.shape)
# top_k call may not be sorted, so can't compare the output directly
# Check that the output contains only 0s and 1s
out_dm = router_mask.dispatch_mask.numpy()
self.assertSetEqual({0, 1}, set(out_dm.flatten().astype(np.int32)))
# Check that the right tokens for selected
out_dm_indices = np.dot(
out_dm.transpose((0, 2, 3, 1)), np.arange(tokens_per_group))
# Shape [num_groups, num_experts, expert_capacity]
self.assertSetEqual({0, 1}, set(out_dm_indices[0, 0, :].astype(np.int32)))
self.assertSetEqual({1, 2}, set(out_dm_indices[0, 1, :].astype(np.int32)))
self.assertSetEqual({1, 2}, set(out_dm_indices[0, 2, :].astype(np.int32)))
self.assertSetEqual({0, 1}, set(out_dm_indices[1, 0, :].astype(np.int32)))
self.assertSetEqual({0, 1}, set(out_dm_indices[1, 1, :].astype(np.int32)))
self.assertSetEqual({1, 2}, set(out_dm_indices[1, 2, :].astype(np.int32)))
out_ca = router_mask.combine_array.numpy()
out_ca = np.dot(out_ca, np.ones((expert_capacity,)))
expected_combine_array = np.array([[[0.66, 0.0, 0.0], [0.42, 0.42, 0.16],
[0.0, 0.33, 0.33]],
[[0.33, 0.33, 0.0], [0.16, 0.42, 0.42],
[0.0, 0.0, 0.66]]])
self.assertAllClose(expected_combine_array, out_ca, atol=1e-2)
def test_feed_forward_shape_and_vars(self):
config = small_config()
layer = moe.FeedForward(
d_ff=config['d_ff'], output_dropout=config['output_dropout'])
inputs = make_input_ones()
outputs = layer(inputs)
self.assertAllEqual(tf.shape(inputs), tf.shape(outputs))
var_names = sorted([v.name for v in layer.trainable_variables])
self.assertAllEqual([
'feed_forward/intermediate/bias:0',
'feed_forward/intermediate/kernel:0', 'feed_forward/output/bias:0',
'feed_forward/output/kernel:0'
], var_names)
def test_feed_forward_manual(self):
config = small_config()
layer = moe.FeedForward(
d_ff=config['d_ff'],
output_dropout=config['output_dropout'],
activation=tf_keras.activations.relu,
kernel_initializer=tf_keras.initializers.get('ones'),
bias_initializer=tf_keras.initializers.get('ones'))
inputs = make_input_ones(1, 2, 3)
outputs = layer(inputs, training=False)
manual_outputs = tf.constant([[[129.0, 129.0, 129.0], [129.0, 129.0,
129.0]]])
self.assertAllClose(manual_outputs, outputs, atol=1e-7)
def test_feed_forward_experts_shape_and_vars(self):
config = small_config()
layer = moe.FeedForwardExperts(
num_experts=config['num_experts'],
d_ff=config['expert_d_ff'],
output_dropout=config['expert_dropout_rate'])
inputs = make_experts_input_ones()
outputs = layer(inputs)
self.assertAllEqual(tf.shape(inputs), tf.shape(outputs))
var_names = sorted([v.name for v in layer.trainable_variables])
self.assertAllEqual([
'experts/intermediate/bias:0', 'experts/intermediate/kernel:0',
'experts/output/bias:0', 'experts/output/kernel:0'
], var_names)
def test_feed_forward_experts_manual(self):
config = small_config()
layer = moe.FeedForwardExperts(
num_experts=1,
d_ff=config['expert_d_ff'],
output_dropout=config['expert_dropout_rate'],
activation=tf_keras.activations.relu,
kernel_initializer=tf_keras.initializers.get('ones'),
bias_initializer=tf_keras.initializers.get('ones'))
inputs = make_experts_input_ones(1, 1, 2, 3)
outputs = layer(inputs, training=False)
manual_outputs = tf.constant([[[[133.0, 133.0, 133.0],
[133.0, 133.0, 133.0]]]])
self.assertAllClose(manual_outputs, outputs, atol=1e-7)
def test_moe_layer(self):
config = small_config()
experts = moe.FeedForwardExperts(
num_experts=config['num_experts'],
d_ff=config['expert_d_ff'],
output_dropout=config['expert_dropout_rate'])
router = moe.ExpertsChooseMaskedRouter(
config['num_experts'], jitter_noise=config['jitter_noise'])
moe_layer = moe.MoeLayer(
experts,
router,
train_capacity_factor=config['train_capacity_factor'],
eval_capacity_factor=config['eval_capacity_factor'],
examples_per_group=config['examples_per_group'])
inputs = make_input_ones()
outputs = moe_layer(inputs, training=True)
self.assertAllEqual(tf.shape(inputs), tf.shape(outputs))
var_names = sorted([v.name for v in moe_layer.trainable_variables])
self.assertAllEqual([
'moe/experts/intermediate/bias:0', 'moe/experts/intermediate/kernel:0',
'moe/experts/output/bias:0', 'moe/experts/output/kernel:0',
'moe/router/router_weights/bias:0', 'moe/router/router_weights/kernel:0'
], var_names)
self.assertLen(moe_layer.losses, 1)
metrics = [metric.name for metric in moe_layer.metrics]
self.assertSetEqual(
{
'router_z_loss', 'unscaled_router_z_loss', 'load_balancing_loss',
'fraction_tokens_left_behind', 'router_confidence', 'expert_usage'
}, set(metrics))
def test_moe_layer_with_backbone(self):
config = small_config()
experts = moe.FeedForwardExperts(
num_experts=config['num_experts'],
d_ff=config['expert_d_ff'],
output_dropout=config['expert_dropout_rate'])
router = moe.ExpertsChooseMaskedRouter(
config['num_experts'], jitter_noise=config['jitter_noise'])
moe_layer = moe.MoeLayer(
experts,
router,
train_capacity_factor=config['train_capacity_factor'],
eval_capacity_factor=config['eval_capacity_factor'],
examples_per_group=config['examples_per_group'])
layer = moe.MoeLayerWithBackbone(moe_layer, config['backbone_d_ff'])
inputs = make_input_ones()
outputs = layer(inputs)
self.assertAllEqual(tf.shape(inputs), tf.shape(outputs))
if __name__ == '__main__':
tf.test.main()
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