def test_partitioned_dynamic_embedding_lookup_2D_input( self, sigma_dimension): emb_dim = 5 + sigma_dimension config = test_util.default_de_config(emb_dim, [1] * emb_dim) emb, sigma = sparse_features._partitioned_dynamic_embedding_lookup( [['input1', ''], ['input2', 'input3']], config, 5, sigma_dimension, 'feature_name1_%d' % sigma_dimension, service_address=self._kbs_address) if sigma_dimension > 0: self.assertEqual((2, 2, sigma_dimension), sigma.shape) self.assertEqual((2, 2, 5), emb.shape) self.assertAllClose( [[[1] * sigma_dimension, [0] * sigma_dimension ], [[1] * sigma_dimension, [1] * sigma_dimension]], sigma.numpy()) self.assertAllClose([[[1] * 5, [0] * 5], [[1] * 5, [1] * 5]], emb.numpy()) else: self.assertAllClose([[[1] * 5, [0] * 5], [[1] * 5, [1] * 5]], emb.numpy()) self.assertIsNone(sigma)
def test_compute_sampled_logits_grad(self): cs_config = cs_config_builder.build_candidate_sampler_config( cs_config_builder.negative_sampler(unique=True, algorithm='UNIFORM')) de_config = test_util.default_de_config(3, cs_config=cs_config) # Add a few embeddings into knowledge bank. de_ops.dynamic_embedding_update(['key1', 'key2', 'key3'], tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]), de_config, 'emb', service_address=self._kbs_address) # A simple one layer NN model. # Input data: x = [[1, 2], [3, 4]]. # Weights from input to logit output layer: W = [[1, 2, 3], [4, 5, 6]]. # Input activation at output layer i = x*W = [[9, 12, 15], [19, 26, 33]]. # Logits output therefore becomes E*i, where E are the embeddings of output # keys, i.e., E = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]. # Then the logits output becomes [[78, 186, 294], [170, 404, 638]] # # If we define the loss to be L = tf.reduced_sum(Logits), then # dL/dE = sum_by_key(i) = [[28, 38, 48], [28, 38, 48], [28, 38, 48]]. # So the expected new embeddings become # E - 0.1 * dL/dE = [[-1.8, -1.8, -1.8], [1.2, 1.2, 1.2], [4.2, 4.2, 4.2]]. weights = tf.Variable([[1, 2, 3], [4, 5, 6]], dtype=tf.float32) inputs = tf.constant([[1.0, 2.0], [3.0, 4.0]]) with tf.GradientTape() as tape: logits, _, _, _, _ = cs_ops.compute_sampled_logits( [['key1', ''], ['key2', 'key3']], tf.matmul(inputs, weights), 3, de_config, 'emb', service_address=self._kbs_address) loss = tf.reduce_sum(logits) # Applies the gradient descent. grads = tape.gradient(loss, weights) # The gradients updated by the knowledge bank. updated_embedding = de_ops.dynamic_embedding_lookup( ['key1', 'key2', 'key3'], de_config, 'emb', service_address=self._kbs_address) self.assertAllClose( updated_embedding, [[-1.8, -1.8, -1.8], [1.2, 1.2, 1.2], [4.2, 4.2, 4.2]]) # The gradients w.r.t. the weight W is calculated as # dL/dw = dL/di * di/dW = sum_by_dim(E) * x = # [12, 15, 18] * [[4, 4, 4], [6, 6, 6]] = [[48, 60, 72], [72, 90, 108]] self.assertAllClose(grads, [[48, 60, 72], [72, 90, 108]])
def test_single_feature_lookup_1D(self, sigma_dimension): emb_dim = 5 + sigma_dimension config = test_util.default_de_config(emb_dim, [1] * emb_dim) fea_embed = sparse_features.SparseFeatureEmbedding( config, {'fea': (5, sigma_dimension)}, op_name='single_feature_%d' % sigma_dimension, service_address=self._kbs_address) embed, _, _, embed_map = fea_embed.lookup(['input1', 'input2']) if sigma_dimension > 0: self.assertEqual((2, 5), embed.shape) else: self.assertAllClose([[1] * 5, [1] * 5], embed) self.assertEqual(['fea'], list(embed_map.keys())) self.assertEqual((2, 5), embed_map['fea'].shape) self.assertEqual(['fea'], list(fea_embed._variable_map.keys()))
def test_embed_single_feature_1D_input(self, sigma_dimension): emb_dim = 5 + sigma_dimension config = test_util.default_de_config(emb_dim, [1] * emb_dim) emb, vc, sigma, input_embed, variables = sparse_features.embed_single_feature( ['input1', 'input2'], config, 5, sigma_dimension, 'feature_name2_%d' % sigma_dimension, service_address=self._kbs_address) if sigma_dimension > 0: self.assertIsNotNone(variables) self.assertEqual((2, 5), emb.shape) self.assertEqual(5, vc.shape) self.assertEqual((2, 1), sigma.shape) self.assertEqual((2, 5), input_embed.shape) else: self.assertAllClose([[1] * 5, [1] * 5], emb.numpy()) self.assertIsNone(vc) self.assertIsNone(sigma) self.assertAllClose([[1] * 5, [1] * 5], input_embed) # Lookup again with given variables. Checks all values are the same. new_emb, new_vc, new_sigma, new_input_embed, variables = ( sparse_features.embed_single_feature( ['input1', 'input2'], config, 5, sigma_dimension, 'feature_name2_%d' % sigma_dimension, variables=variables, service_address=self._kbs_address)) if sigma_dimension > 0: self.assertIsNotNone(variables) self.assertAllClose(emb.numpy(), new_emb.numpy()) if vc is not None: self.assertAllClose(vc.numpy(), new_vc.numpy()) if sigma is not None: self.assertAllClose(sigma.numpy(), new_sigma.numpy()) self.assertAllClose(input_embed.numpy(), new_input_embed.numpy())
def test_brute_force_topk(self): cs_config = cs_config_builder.build_candidate_sampler_config( cs_config_builder.brute_force_topk_sampler('DOT_PRODUCT')) de_config = test_util.default_de_config(2, cs_config=cs_config) # Add a few embeddings into knowledge bank. de_ops.dynamic_embedding_update(['key1', 'key2', 'key3'], tf.constant([[2.0, 4.0], [4.0, 8.0], [8.0, 16.0]]), de_config, 'emb', service_address=self._kbs_address) keys, logits = cs_ops.top_k([[1.0, 2.0], [-1.0, -2.0]], 3, de_config, 'emb', service_address=self._kbs_address) self.assertAllEqual( keys.numpy(), [[b'key3', b'key2', b'key1'], [b'key1', b'key2', b'key3']]) self.assertAllClose(logits.numpy(), [[40, 20, 10], [-10, -20, -40]])
def test_multiple_feature_lookup_2D_without_sigma(self): config = test_util.default_de_config(5, [1] * 5) fea_embed = sparse_features.SparseFeatureEmbedding( config, { 'fea1': (5, 0), 'fea2': (5, 0) }, op_name='multiple_feature3', service_address=self._kbs_address) embed, _, _, embed_map = fea_embed.lookup({ 'fea1': [['input1', ''], ['input2', '']], 'fea2': [['input3', 'input5'], ['input4', 'input6']] }) self.assertAllClose([[1] * 10, [1] * 10], embed.numpy()) self.assertLen(embed_map.keys(), 2) self.assertIn('fea1', embed_map.keys()) self.assertIn('fea2', embed_map.keys()) self.assertEqual((2, 2, 5), embed_map['fea1'].shape) self.assertEqual((2, 2, 5), embed_map['fea2'].shape) self.assertLen(fea_embed._variable_map.keys(), 2) self.assertIn('fea1', fea_embed._variable_map.keys()) self.assertIn('fea2', fea_embed._variable_map.keys())
def test_embed_single_feature_2D_input(self, sigma_dimension): emb_dim = 5 + sigma_dimension config = test_util.default_de_config(emb_dim, [1] * emb_dim) emb, vc, sigma, input_embed, var = sparse_features.embed_single_feature( [['input1', ''], ['input2', 'input3']], config, 5, sigma_dimension, 'feature_name3_%d' % sigma_dimension, service_address=self._kbs_address) if sigma_dimension > 0: self.assertIsNotNone(var) self.assertEqual((2, 5), emb.shape) self.assertEqual(5, vc.shape) self.assertEqual((2, 2), sigma.shape) self.assertEqual((2, 2, 5), input_embed.shape) else: self.assertAllClose([[1] * 5, [1] * 5], emb) self.assertIsNone(vc) self.assertIsNone(sigma) self.assertEqual((2, 2, 5), input_embed.shape)
def setUp(self): super(DynamicEmbeddingNeighborCacheTest, self).setUp() self._config = test_util.default_de_config(2) self._service_server = test_util.start_kbs_server()
def setUp(self): super(DynamicMemoryOpsTest, self).setUp() self._config = test_util.default_de_config(2) self._service_server = test_util.start_kbs_server() self._kbs_address = 'localhost:%d' % self._service_server.port() context.clear_all_collection()
def setUp(self): super(FeatureEmbeddingTest, self).setUp() self._config = test_util.default_de_config(2) self._service_server = test_util.start_kbs_server() self._kbs_address = 'localhost:%d' % self._service_server.port()
def test_compute_sampled_logits(self): cs_config = cs_config_builder.build_candidate_sampler_config( cs_config_builder.negative_sampler(unique=True, algorithm='UNIFORM')) de_config = test_util.default_de_config(3, cs_config=cs_config) # Add a few embeddings into knowledge bank. de_ops.dynamic_embedding_update(['key1', 'key2', 'key3'], tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]), de_config, 'emb', service_address=self._kbs_address) # Sample logits. logits, labels, keys, mask, weights = cs_ops.compute_sampled_logits( [['key1', ''], ['key2', 'key3']], tf.constant([[2.0, 4.0, 1], [-2.0, -4.0, 1]]), 3, de_config, 'emb', service_address=self._kbs_address) # Expected results: # - Example one returns one positive key {'key2'} and two negative keys # {'key2', 'key3'}. # - Example two returns two positive keys {'key2', 'key3'} and one # positive key {'key1'}. expected_weights = { b'key1': [1, 2, 3], b'key2': [4, 5, 6], b'key3': [7, 8, 9] } expected_labels = [{ b'key1': 1, b'key2': 0, b'key3': 0 }, { b'key1': 0, b'key2': 1, b'key3': 1 }] # Logit for example one: # - 'key1': [2, 4, 1] * [1, 2, 3] = 13 # - 'key2': [2, 4, 1] * [4, 5, 6] = 34 # - 'key3': [2, 4, 1] * [7, 8, 9] = 55 # Logit for example two: # - 'key1': [-2, -4, 1] * [1, 2, 3] = -7 # - 'key2': [-2, -4, 1] * [4, 5, 6] = -22 # - 'key3': [-2, -4, 1] * [7, 8, 9] = -37 expected_logits = [{ b'key1': 13, b'key2': 34, b'key3': 55 }, { b'key1': -7, b'key2': -22, b'key3': -37 }] # Check keys and weights. for b in range(2): self.assertEqual(1, mask.numpy()[b]) for key in {b'key1', b'key2', b'key3'}: self.assertIn(key, keys.numpy()[b]) for i in range(3): key = keys.numpy()[b][i] self.assertAllClose(expected_weights[key], weights.numpy()[b][i]) self.assertAllClose(expected_labels[b][key], labels.numpy()[b][i]) self.assertAllClose(expected_logits[b][key], logits.numpy()[b][i])