From d39af139f133af86ee4418a0b97e027445dfe06f Mon Sep 17 00:00:00 2001 From: weisu Date: Sat, 16 Sep 2023 13:23:08 +0800 Subject: [PATCH 01/27] fix bug of `package` syntax of backbone --- docs/source/component/backbone.md | 12 ++- docs/source/component/component.md | 3 +- easy_rec/python/layers/backbone.py | 91 +++++++++++++------ easy_rec/python/layers/common_layers.py | 14 ++- easy_rec/python/layers/keras/blocks.py | 21 +++-- easy_rec/python/layers/keras/bst.py | 15 +-- easy_rec/python/layers/keras/din.py | 24 ++--- easy_rec/python/layers/keras/fibinet.py | 31 ++++--- easy_rec/python/layers/keras/interaction.py | 6 +- easy_rec/python/layers/keras/mask_net.py | 24 +++-- easy_rec/python/layers/keras/multi_task.py | 20 ++-- .../layers/keras/numerical_embedding.py | 28 ++++-- easy_rec/python/protos/dnn.proto | 3 +- easy_rec/python/protos/seq_encoder.proto | 2 +- 14 files changed, 193 insertions(+), 101 deletions(-) diff --git a/docs/source/component/backbone.md b/docs/source/component/backbone.md index 0af37109b..7139a46b6 100644 --- a/docs/source/component/backbone.md +++ b/docs/source/component/backbone.md @@ -947,7 +947,7 @@ MovieLens-1M数据集效果: 定义一个继承[`tf.keras.layers.Layer`](https://keras.io/api/layers/base_layer/)的组件类,至少实现两个方法:`__init__`、`call`。 ```python -def __init__(self, params, name='xxx', **kwargs): +def __init__(self, params, name='xxx', reuse=None, **kwargs): pass def call(self, inputs, training=None, **kwargs): pass @@ -971,6 +971,13 @@ def call(self, inputs, training=None, **kwargs): 【可选】如需要自定义protobuf message参数,先在`easy_rec/python/protos/layer.proto`添加参数message的定义, 再把参数注册到定义在`easy_rec/python/protos/keras_layer.proto`的`KerasLayer.params`消息体中。 +`__init__`方法的`reuse`参数表示该Layer对象的权重参数是否需要被复用。开发时需要按照可复用的逻辑来实现Layer对象,推荐严格按照keras layer的规范来实现。 +尽量在`__init__`方法中声明需要依赖的keras layer对象;仅在必要时才使用`tf.layers.*`函数,且需要传递reuse参数。 + +```{tips} +提示:实现Layer对象时尽量使用原生的 tf.keras.layers.* 对象,且全部在 __init__ 方法中预先声明好。 +``` + `call`方法用来实现主要的模块逻辑,其`inputs`参数可以是一个tenor,或者是一个tensor列表。可选的`training`参数用来标识当前是否是训练模型。 最后也是最重要的一点,新开发的Layer需要在`easy_rec.python.layers.keras.__init__.py`文件中导出才能被框架识别为组件库中的一员。例如要导出`blocks.py`文件中的`MLP`类,则需要添加:`from .blocks import MLP`。 @@ -990,8 +997,9 @@ class FM(tf.keras.layers.Layer): - 2D tensor with shape: ``(batch_size, 1)``. """ - def __init__(self, params, name='fm', **kwargs): + def __init__(self, params, name='fm', reuse=None, **kwargs): super(FM, self).__init__(name, **kwargs) + self.reuse = reuse self.use_variant = params.get_or_default('use_variant', False) def call(self, inputs, **kwargs): diff --git a/docs/source/component/component.md b/docs/source/component/component.md index f502393eb..37f4691ff 100644 --- a/docs/source/component/component.md +++ b/docs/source/component/component.md @@ -9,9 +9,10 @@ | hidden_units | list | | 各隐层单元数 | | dropout_ratio | list | | 各隐层dropout rate | | activation | str | relu | 每层的激活函数 | -| use_bias | bool | true | 是否使用偏置项 | | use_bn | bool | true | 是否使用batch normalization | | use_final_bn | bool | true | 最后一层是否使用batch normalization | +| use_bias | bool | false | 是否使用偏置项 | +| use_final_bias | bool | false | 最后一层是否使用偏置项 | | final_activation | str | relu | 最后一层的激活函数 | | initializer | str | he_uniform | 权重初始化方法,参考keras Dense layer | | use_bn_after_activation | bool | false | 是否在激活函数之后做batch norm | diff --git a/easy_rec/python/layers/backbone.py b/easy_rec/python/layers/backbone.py index 893734eba..00433a7f2 100644 --- a/easy_rec/python/layers/backbone.py +++ b/easy_rec/python/layers/backbone.py @@ -29,6 +29,8 @@ def __init__(self, config, features, input_layer, l2_reg=None): self._dag = DAG() self._name_to_blocks = {} self.loss_dict = {} + self._name_to_layer = {} + reuse = None if config.name == 'backbone' else tf.AUTO_REUSE input_feature_groups = set() for block in config.blocks: if len(block.inputs) == 0: @@ -52,6 +54,14 @@ def __init__(self, config, features, input_layer, l2_reg=None): logging.warning('input `%s` already exists in other block' % input_name) input_feature_groups.add(input_name) + else: + self.define_layers(layer, block, block.name, reuse) + + # sequential layers + for i, layer_cnf in enumerate(block.layers): + layer = layer_cnf.WhichOneof('layer') + name_i = '%s_l%d' % (block.name, i) + self.define_layers(layer, layer_cnf, name_i, reuse) num_groups = len(input_feature_groups) num_blocks = len(self._name_to_blocks) - num_groups @@ -103,6 +113,23 @@ def __init__(self, config, features, input_layer, l2_reg=None): Package.__packages[self._config.name] = self + def define_layers(self, layer, layer_cnf, name, reuse): + if layer == 'keras_layer': + layer_obj = self.load_keras_layer(layer_cnf.keras_layer, name, reuse) + self._name_to_layer[name] = layer_obj + elif layer == 'recurrent': + for i in range(layer_cnf.recurrent.num_steps): + name_i = '%s_%d' % (name, i) + layer_obj = self.load_keras_layer(layer_cnf.recurrent.keras_layer, + name_i, reuse) + self._name_to_layer[name_i] = layer_obj + elif layer == 'repeat': + for i in range(layer_cnf.repeat.num_repeat): + name_i = '%s_%d' % (name, i) + layer_obj = self.load_keras_layer(layer_cnf.repeat.keras_layer, name_i, + reuse) + self._name_to_layer[name_i] = layer_obj + def block_input(self, config, block_outputs, training=None): inputs = [] for input_node in config.inputs: @@ -152,8 +179,9 @@ def call(self, is_training): if config.layers: # sequential layers logging.info('call sequential %d layers' % len(config.layers)) output = self.block_input(config, block_outputs, is_training) - for layer in config.layers: - output = self.call_layer(output, layer, block, is_training) + for i, layer in enumerate(config.layers): + name_i = '%s_l%d' % (block, i) + output = self.call_layer(output, layer, name_i, is_training) block_outputs[block] = output continue # just one of layer @@ -185,7 +213,7 @@ def call(self, is_training): output = merge_inputs(outputs, msg='backbone') return output - def call_keras_layer(self, layer_conf, inputs, name, training): + def load_keras_layer(self, layer_conf, name, reuse=None): layer_cls, customize = load_keras_layer(layer_conf.class_name) if layer_cls is None: raise ValueError('Invalid keras layer class name: ' + @@ -198,34 +226,39 @@ def call_keras_layer(self, layer_conf, inputs, name, training): else: pb_params = getattr(layer_conf, param_type) params = Parameter(pb_params, False, l2_reg=self._l2_reg) - layer = layer_cls(params, name=name) - kwargs = {'loss_dict': self.loss_dict} - return layer(inputs, training=training, **kwargs) - else: # internal keras layer - if param_type is None: - layer = layer_cls(name=name) - else: - assert param_type == 'st_params', 'internal keras layer only support st_params' - try: - kwargs = convert_to_dict(layer_conf.st_params) - logging.info('call %s layer with params %r' % - (layer_conf.class_name, kwargs)) - layer = layer_cls(name=name, **kwargs) - except TypeError as e: - logging.warning(e) - args = map(format_value, layer_conf.st_params.values()) - logging.info('try to call %s layer with params %r' % - (layer_conf.class_name, args)) - layer = layer_cls(*args, name=name) + layer = layer_cls(params, name=name, reuse=reuse) + return layer + elif param_type is None: # internal keras layer + layer = layer_cls(name=name) + return layer + else: + assert param_type == 'st_params', 'internal keras layer only support st_params' try: - return layer(inputs, training=training) - except TypeError: - return layer(inputs) + kwargs = convert_to_dict(layer_conf.st_params) + logging.info('call %s layer with params %r' % + (layer_conf.class_name, kwargs)) + layer = layer_cls(name=name, **kwargs) + except TypeError as e: + logging.warning(e) + args = map(format_value, layer_conf.st_params.values()) + logging.info('try to call %s layer with params %r' % + (layer_conf.class_name, args)) + layer = layer_cls(*args, name=name) + return layer + + def call_keras_layer(self, inputs, name, training): + """Call predefined Keras Layer, which can be reused.""" + layer = self._name_to_layer[name] + kwargs = {'loss_dict': self.loss_dict} + try: + return layer(inputs, training=training, **kwargs) + except TypeError: + return layer(inputs) def call_layer(self, inputs, config, name, training): layer_name = config.WhichOneof('layer') if layer_name == 'keras_layer': - return self.call_keras_layer(config.keras_layer, inputs, name, training) + return self.call_keras_layer(inputs, name, training) if layer_name == 'lambda': conf = getattr(config, 'lambda') fn = eval(conf.expression) @@ -236,8 +269,7 @@ def call_layer(self, inputs, config, name, training): outputs = [] for i in range(n_loop): name_i = '%s_%d' % (name, i) - output = self.call_keras_layer(conf.keras_layer, inputs, name_i, - training) + output = self.call_keras_layer(inputs, name_i, training) outputs.append(output) if len(outputs) == 1: return outputs[0] @@ -254,8 +286,7 @@ def call_layer(self, inputs, config, name, training): output = inputs for i in range(conf.num_steps): name_i = '%s_%d' % (name, i) - layer = conf.keras_layer - output_i = self.call_keras_layer(layer, output, name_i, training) + output_i = self.call_keras_layer(output, name_i, training) if fixed_input_index >= 0: j = 0 for idx in range(len(output)): diff --git a/easy_rec/python/layers/common_layers.py b/easy_rec/python/layers/common_layers.py index fae4fe3fc..a5cd1e5f3 100644 --- a/easy_rec/python/layers/common_layers.py +++ b/easy_rec/python/layers/common_layers.py @@ -82,7 +82,7 @@ def layer_norm(input_tensor, name=None, reuse=None): class EnhancedInputLayer(object): """Enhance the raw input layer.""" - def __init__(self, config, input_layer, feature_dict): + def __init__(self, config, input_layer, feature_dict, reuse=None): if config.do_batch_norm and config.do_layer_norm: raise ValueError( 'can not do batch norm and layer norm for input layer at the same time' @@ -90,6 +90,7 @@ def __init__(self, config, input_layer, feature_dict): self._config = config self._input_layer = input_layer self._feature_dict = feature_dict + self._reuse = reuse def __call__(self, group, is_training, **kwargs): with tf.name_scope('input_' + group): @@ -112,18 +113,21 @@ def call(self, group, is_training): bern = tf.distributions.Bernoulli(probs=keep_prob, dtype=tf.float32) mask = bern.sample(num_features) elif do_bn: - features = tf.layers.batch_normalization(features, training=is_training) + features = tf.layers.batch_normalization( + features, training=is_training, reuse=self._reuse) elif do_ln: - features = layer_norm(features) + features = layer_norm( + features, name=group + '_features', reuse=self._reuse) do_dropout = 0.0 < self._config.dropout_rate < 1.0 if do_feature_dropout or do_ln or do_bn or do_dropout: for i in range(num_features): fea = feature_list[i] if self._config.do_batch_norm: - fea = tf.layers.batch_normalization(fea, training=is_training) + fea = tf.layers.batch_normalization( + fea, training=is_training, reuse=self._reuse) elif self._config.do_layer_norm: - fea = layer_norm(fea) + fea = layer_norm(fea, name=group + 'f_%d' % i, reuse=self._reuse) if do_dropout: fea = tf.layers.dropout( fea, self._config.dropout_rate, training=is_training) diff --git a/easy_rec/python/layers/keras/blocks.py b/easy_rec/python/layers/keras/blocks.py index 928329d16..dabe17b32 100644 --- a/easy_rec/python/layers/keras/blocks.py +++ b/easy_rec/python/layers/keras/blocks.py @@ -22,12 +22,13 @@ class MLP(tf.keras.layers.Layer): **kwargs: Extra args passed to the Keras Layer base class. """ - def __init__(self, params, name='mlp', **kwargs): + def __init__(self, params, name='mlp', reuse=None, **kwargs): super(MLP, self).__init__(name=name, **kwargs) params.check_required('hidden_units') use_bn = params.get_or_default('use_bn', True) use_final_bn = params.get_or_default('use_final_bn', True) - use_bias = params.get_or_default('use_bias', True) + use_bias = params.get_or_default('use_bias', False) + use_final_bias = params.get_or_default('use_final_bias', False) dropout_rate = list(params.get_or_default('dropout_ratio', [])) activation = params.get_or_default('activation', 'relu') initializer = params.get_or_default('initializer', 'he_uniform') @@ -40,6 +41,7 @@ def __init__(self, params, name='mlp', **kwargs): (name, units, dropout_rate, activation, use_bn, use_final_bn, final_activation, use_bias, initializer, use_bn_after_act)) assert len(units) > 0, 'MLP(%s) takes at least one hidden units' % name + self.reuse = reuse num_dropout = len(dropout_rate) self._sub_layers = [] @@ -54,7 +56,7 @@ def __init__(self, params, name='mlp', **kwargs): drop_rate = dropout_rate[n] if num_dropout > n else 0.0 name = 'dnn_%d' % n self.add_rich_layer(units[-1], use_final_bn, drop_rate, final_activation, - initializer, use_bias, use_bn_after_act, name, + initializer, use_final_bias, use_bn_after_act, name, params.l2_regularizer) def add_rich_layer(self, @@ -70,7 +72,10 @@ def add_rich_layer(self, def batch_norm(x, training): return tf.layers.batch_normalization( - x, training=training, name='%s/%s/bn' % (self.name, name)) + x, + training=training, + name='%s/%s/bn' % (self.name, name), + reuse=self.reuse) act_fn = get_activation(activation) if use_bn and not use_bn_after_activation: @@ -122,13 +127,14 @@ def call(self, x, training=None, **kwargs): class Highway(tf.keras.layers.Layer): - def __init__(self, params, name='highway', **kwargs): + def __init__(self, params, name='highway', reuse=None, **kwargs): super(Highway, self).__init__(name, **kwargs) params.check_required('emb_size') self.emb_size = params.emb_size self.num_layers = params.get_or_default('num_layers', 1) self.activation = params.get_or_default('activation', 'gelu') self.dropout_rate = params.get_or_default('dropout_rate', 0.0) + self.reuse = reuse def call(self, inputs, training=None, **kwargs): from easy_rec.python.layers.common_layers import highway @@ -137,13 +143,14 @@ def call(self, inputs, training=None, **kwargs): self.emb_size, activation=self.activation, num_layers=self.num_layers, - dropout=self.dropout_rate if training else 0.0) + dropout=self.dropout_rate if training else 0.0, + reuse=self.reuse) class Gate(tf.keras.layers.Layer): """Weighted sum gate.""" - def __init__(self, params, name='gate', **kwargs): + def __init__(self, params, name='gate', reuse=None, **kwargs): super(Gate, self).__init__(name, **kwargs) self.weight_index = params.get_or_default('weight_index', 0) diff --git a/easy_rec/python/layers/keras/bst.py b/easy_rec/python/layers/keras/bst.py index 020a06d59..8c15d971d 100644 --- a/easy_rec/python/layers/keras/bst.py +++ b/easy_rec/python/layers/keras/bst.py @@ -10,9 +10,10 @@ class BST(Layer): - def __init__(self, params, name='bst', l2_reg=None, **kwargs): + def __init__(self, params, name='bst', reuse=None, **kwargs): super(BST, self).__init__(name=name, **kwargs) - self.l2_reg = l2_reg + self.reuse = reuse + self.l2_reg = params.l2_regularizer self.config = params.get_pb_config() def encode(self, seq_input, max_position): @@ -20,7 +21,7 @@ def encode(self, seq_input, max_position): seq_input, position_embedding_name=self.name + '/position_embeddings', max_position_embeddings=max_position, - reuse_position_embedding=tf.AUTO_REUSE) + reuse_position_embedding=self.reuse) n = tf.count_nonzero(seq_input, axis=-1) seq_mask = tf.cast(n > 0, tf.int32) @@ -41,7 +42,7 @@ def encode(self, seq_input, max_position): attention_probs_dropout_prob=self.config.attention_probs_dropout_prob, initializer_range=self.config.initializer_range, name=self.name + '/transformer', - reuse=tf.AUTO_REUSE) + reuse=self.reuse) # attention_fea shape: [batch_size, seq_length, hidden_size] out_fea = attention_fea[:, 0, :] # target feature print('bst output shape:', out_fea.shape) @@ -76,7 +77,8 @@ def call(self, inputs, training=None, **kwargs): seq_input, self.config.hidden_size, activation=tf.nn.relu, - kernel_regularizer=self.l2_reg) + kernel_regularizer=self.l2_reg, + reuse=self.reuse) if len(target_features) > 0: target_feature = tf.concat(target_features, axis=-1) @@ -88,7 +90,8 @@ def call(self, inputs, training=None, **kwargs): target_feature, self.config.hidden_size, activation=tf.nn.relu, - kernel_regularizer=self.l2_reg) + kernel_regularizer=self.l2_reg, + reuse=self.reuse) # target_feature: [batch_size, 1, embed_size] target_feature = tf.expand_dims(target_feature, 1) # seq_input: [batch_size, seq_len+1, embed_size] diff --git a/easy_rec/python/layers/keras/din.py b/easy_rec/python/layers/keras/din.py index cee57ac90..ca59da0d1 100644 --- a/easy_rec/python/layers/keras/din.py +++ b/easy_rec/python/layers/keras/din.py @@ -5,15 +5,17 @@ import tensorflow as tf from tensorflow.python.keras.layers import Layer -from easy_rec.python.layers import dnn +from easy_rec.python.layers.keras import MLP +from easy_rec.python.layers.utils import Parameter from easy_rec.python.utils.shape_utils import get_shape_list class DIN(Layer): - def __init__(self, params, name='din', l2_reg=None, **kwargs): + def __init__(self, params, name='din', reuse=None, **kwargs): super(DIN, self).__init__(name=name, **kwargs) - self.l2_reg = l2_reg + self.reuse = reuse + self.l2_reg = params.l2_regularizer self.config = params.get_pb_config() def call(self, inputs, training=None, **kwargs): @@ -40,14 +42,14 @@ def call(self, inputs, training=None, **kwargs): queries = tf.tile(tf.expand_dims(query, 1), [1, max_seq_len, 1]) din_all = tf.concat([queries, keys, queries - keys, queries * keys], axis=-1) - din_layer = dnn.DNN( - self.config.attention_dnn, - self.l2_reg, - self.name + '/din_attention', - training, - last_layer_no_activation=True, - last_layer_no_batch_norm=True) - output = din_layer(din_all) # [B, L, 1] + + self.config.attention_dnn.use_final_bn = False + self.config.attention_dnn.use_final_bias = True + self.config.attention_dnn.final_activation = 'linear' + params = Parameter.make_from_pb(self.config.attention_dnn) + params.l2_regularizer = self.l2_reg + din_layer = MLP(params, name=self.name + '/din_attention', reuse=self.reuse) + output = din_layer(din_all, training) # [B, L, 1] scores = tf.transpose(output, [0, 2, 1]) # [B, 1, L] seq_len = seq_features[0][1] diff --git a/easy_rec/python/layers/keras/fibinet.py b/easy_rec/python/layers/keras/fibinet.py index 98cdb3179..aa9ee9bfe 100644 --- a/easy_rec/python/layers/keras/fibinet.py +++ b/easy_rec/python/layers/keras/fibinet.py @@ -30,9 +30,10 @@ class SENet(tf.keras.layers.Layer): Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction """ - def __init__(self, params, name='SENet', **kwargs): + def __init__(self, params, name='SENet', reuse=None, **kwargs): super(SENet, self).__init__(name, **kwargs) self.config = params.get_pb_config() + self.reuse = reuse def call(self, inputs, **kwargs): g = self.config.num_squeeze_group @@ -61,17 +62,18 @@ def call(self, inputs, **kwargs): r = self.config.reduction_ratio reduction_size = max(1, field_size * g * 2 // r) - initializer = tf.glorot_normal_initializer() a1 = tf.layers.dense( z, reduction_size, - kernel_initializer=initializer, + kernel_initializer=tf.initializers.variance_scaling(), activation=tf.nn.relu, + reuse=self.reuse, name='%s/W1' % self.name) weights = tf.layers.dense( a1, sum(feature_size_list), - kernel_initializer=initializer, + kernel_initializer=tf.glorot_normal_initializer(), + reuse=self.reuse, name='%s/W2' % self.name) # Re-weight @@ -84,7 +86,8 @@ def call(self, inputs, **kwargs): # Layer Normalization if self.config.use_output_layer_norm: - output = layer_norm(output) + output = layer_norm( + output, name=self.name + '/ln_output', reuse=self.reuse) return output @@ -118,8 +121,9 @@ class BiLinear(tf.keras.layers.Layer): Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction """ - def __init__(self, params, name='bilinear', **kwargs): + def __init__(self, params, name='bilinear', reuse=None, **kwargs): super(BiLinear, self).__init__(name, **kwargs) + self.reuse = reuse params.check_required(['num_output_units']) bilinear_plus = params.get_or_default('use_plus', True) self.bilinear_type = params.get_or_default('type', 'interaction').lower() @@ -199,7 +203,10 @@ def call(self, inputs, **kwargs): ] output = tf.layers.dense( - tf.concat(p, axis=-1), self.output_size, kernel_initializer=initializer) + tf.concat(p, axis=-1), + self.output_size, + kernel_initializer=initializer, + reuse=self.reuse) return output @@ -211,13 +218,14 @@ class FiBiNet(tf.keras.layers.Layer): Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction """ - def __init__(self, params, name='fibinet', **kwargs): + def __init__(self, params, name='fibinet', reuse=None, **kwargs): super(FiBiNet, self).__init__(name, **kwargs) + self.reuse = reuse self._config = params.get_pb_config() if self._config.HasField('mlp'): p = Parameter.make_from_pb(self._config.mlp) p.l2_regularizer = params.l2_regularizer - self.final_mlp = MLP(p, name=name) + self.final_mlp = MLP(p, name=name, reuse=reuse) else: self.final_mlp = None @@ -225,13 +233,14 @@ def call(self, inputs, training=None, **kwargs): feature_list = [] params = Parameter.make_from_pb(self._config.senet) - senet = SENet(params, name='%s/senet' % self.name) + senet = SENet(params, name='%s/senet' % self.name, reuse=self.reuse) senet_output = senet(inputs) feature_list.append(senet_output) if self._config.HasField('bilinear'): params = Parameter.make_from_pb(self._config.bilinear) - bilinear = BiLinear(params, name='%s/bilinear' % self.name) + bilinear = BiLinear( + params, name='%s/bilinear' % self.name, reuse=self.reuse) bilinear_output = bilinear(inputs) feature_list.append(bilinear_output) diff --git a/easy_rec/python/layers/keras/interaction.py b/easy_rec/python/layers/keras/interaction.py index 55f56f7a1..f3426d02a 100644 --- a/easy_rec/python/layers/keras/interaction.py +++ b/easy_rec/python/layers/keras/interaction.py @@ -17,7 +17,7 @@ class FM(tf.keras.layers.Layer): - 2D tensor with shape: ``(batch_size, 1)``. """ - def __init__(self, params, name='fm', **kwargs): + def __init__(self, params, name='fm', reuse=None, **kwargs): super(FM, self).__init__(name, **kwargs) self.use_variant = params.get_or_default('use_variant', False) @@ -66,7 +66,7 @@ class DotInteraction(tf.keras.layers.Layer): name: String name of the layer. """ - def __init__(self, params, name=None, **kwargs): + def __init__(self, params, name=None, reuse=None, **kwargs): self._self_interaction = params.get_or_default('self_interaction', False) self._skip_gather = params.get_or_default('skip_gather', False) super(DotInteraction, self).__init__(name=name, **kwargs) @@ -187,7 +187,7 @@ class Cross(tf.keras.layers.Layer): Output shape: A single (batch_size, `input_dim`) dimensional output. """ - def __init__(self, params, **kwargs): + def __init__(self, params, reuse=None, **kwargs): super(Cross, self).__init__(**kwargs) self._projection_dim = params.get_or_default('projection_dim', None) self._diag_scale = params.get_or_default('diag_scale', 0.0) diff --git a/easy_rec/python/layers/keras/mask_net.py b/easy_rec/python/layers/keras/mask_net.py index 6ef740b47..507a0020d 100644 --- a/easy_rec/python/layers/keras/mask_net.py +++ b/easy_rec/python/layers/keras/mask_net.py @@ -43,11 +43,14 @@ def call(self, inputs, **kwargs): 'Need one of reduction factor or aggregation size for MaskBlock.') if self.config.input_layer_norm: - input_name = net.name.replace(':', '_') - net = layer_norm(net, reuse=tf.AUTO_REUSE, name='ln_' + input_name) + idx = self.name.rfind('_') + if idx > 0 and self.name[idx + 1:].isdigit(): + input_name = self.name[:idx] + else: + input_name = self.name + net = layer_norm(net, reuse=tf.AUTO_REUSE, name=input_name + '/input_ln') - # initializer = tf.initializers.variance_scaling() - initializer = tf.glorot_uniform_initializer() + initializer = tf.initializers.variance_scaling() if self._projection_dim is None: mask = tf.layers.dense( @@ -62,7 +65,7 @@ def call(self, inputs, **kwargs): u = tf.layers.dense( mask_input, self._projection_dim, - kernel_initializer=initializer, + kernel_initializer=tf.glorot_normal_initializer(), kernel_regularizer=self.l2_reg, use_bias=False, name='%s/prj_u' % self.name, @@ -97,14 +100,15 @@ class MaskNet(tf.keras.layers.Layer): Refer: https://arxiv.org/pdf/2102.07619.pdf """ - def __init__(self, params, name='mask_net', **kwargs): + def __init__(self, params, name='mask_net', reuse=None, **kwargs): super(MaskNet, self).__init__(name, **kwargs) + self.reuse = reuse self.params = params self.config = params.get_pb_config() if self.config.HasField('mlp'): p = Parameter.make_from_pb(self.config.mlp) p.l2_regularizer = params.l2_regularizer - self.mlp = MLP(p, name='%s/mlp' % name) + self.mlp = MLP(p, name='%s/mlp' % name, reuse=reuse) else: self.mlp = None @@ -114,7 +118,8 @@ def call(self, inputs, training=None, **kwargs): for i, block_conf in enumerate(self.config.mask_blocks): params = Parameter.make_from_pb(block_conf) params.l2_regularizer = self.params.l2_regularizer - mask_layer = MaskBlock(params, name='%s/block_%d' % (self.name, i)) + mask_layer = MaskBlock( + params, name='%s/block_%d' % (self.name, i), reuse=self.reuse) mask_outputs.append(mask_layer((inputs, inputs))) all_mask_outputs = tf.concat(mask_outputs, axis=1) @@ -128,7 +133,8 @@ def call(self, inputs, training=None, **kwargs): for i, block_conf in enumerate(self.config.mask_blocks): params = Parameter.make_from_pb(block_conf) params.l2_regularizer = self.params.l2_regularizer - mask_layer = MaskBlock(params, name='%s/block_%d' % (self.name, i)) + mask_layer = MaskBlock( + params, name='%s/block_%d' % (self.name, i), reuse=self.reuse) net = mask_layer((net, inputs)) if self.mlp is not None: diff --git a/easy_rec/python/layers/keras/multi_task.py b/easy_rec/python/layers/keras/multi_task.py index ca865e5a7..36abf0473 100644 --- a/easy_rec/python/layers/keras/multi_task.py +++ b/easy_rec/python/layers/keras/multi_task.py @@ -7,24 +7,29 @@ from easy_rec.python.layers.keras.blocks import MLP -def gate_fn(inputs, units, name, l2_reg): - dense = tf.keras.layers.Dense( - units, kernel_regularizer=l2_reg, name='%s/dense' % name) - weights = dense(inputs) +def gate_fn(inputs, units, name, l2_reg, reuse): + weights = tf.layers.dense( + inputs, + units, + kernel_regularizer=l2_reg, + name='%s/dense' % name, + reuse=reuse) return tf.nn.softmax(weights, axis=1) class MMoE(tf.keras.layers.Layer): """Multi-gate Mixture-of-Experts model.""" - def __init__(self, params, name='MMoE', **kwargs): + def __init__(self, params, name='MMoE', reuse=None, **kwargs): super(MMoE, self).__init__(name, **kwargs) params.check_required(['num_expert', 'num_task', 'expert_mlp']) + self._reuse = reuse self._num_expert = params.num_expert self._num_task = params.num_task expert_params = params.expert_mlp self._experts = [ - MLP(expert_params, 'expert_%d' % i) for i in range(self._num_expert) + MLP(expert_params, 'expert_%d' % i, reuse=reuse) + for i in range(self._num_expert) ] self._l2_reg = params.l2_regularizer @@ -42,7 +47,8 @@ def __call__(self, inputs, **kwargs): inputs, self._num_expert, name='gate_%d' % task_id, - l2_reg=self._l2_reg) + l2_reg=self._l2_reg, + reuse=self._reuse) gate = tf.expand_dims(gate, -1) task_input = tf.multiply(experts_fea, gate) task_input = tf.reduce_sum(task_input, axis=1) diff --git a/easy_rec/python/layers/keras/numerical_embedding.py b/easy_rec/python/layers/keras/numerical_embedding.py index e83c63a7e..5fa32904e 100644 --- a/easy_rec/python/layers/keras/numerical_embedding.py +++ b/easy_rec/python/layers/keras/numerical_embedding.py @@ -39,7 +39,13 @@ class NLinear(object): assert m(x).shape == (batch_size, n_features, d_embedding_out) """ - def __init__(self, n_tokens, d_in, d_out, bias=True, scope='nd_linear'): + def __init__(self, + n_tokens, + d_in, + d_out, + bias=True, + scope='nd_linear', + reuse=None): """Init with input shapes. Args: @@ -48,8 +54,9 @@ def __init__(self, n_tokens, d_in, d_out, bias=True, scope='nd_linear'): d_out: the output dimension bias: indicates if the underlying linear layers have biases scope: variable scope name + reuse: whether to reuse variables """ - with tf.variable_scope(scope): + with tf.variable_scope(scope, reuse=reuse): self.weight = tf.get_variable( 'weights', [1, n_tokens, d_in, d_out], dtype=tf.float32) if bias: @@ -99,8 +106,9 @@ class PeriodicEmbedding(tf.keras.layers.Layer): output_tensor_list: whether to output the list of embedding """ - def __init__(self, params, name='periodic_embedding', **kwargs): + def __init__(self, params, name='periodic_embedding', reuse=None, **kwargs): super(PeriodicEmbedding, self).__init__(name, **kwargs) + self.reuse = reuse params.check_required(['embedding_dim', 'sigma']) self.embedding_dim = int(params.embedding_dim) if self.embedding_dim % 2: @@ -118,7 +126,7 @@ def call(self, inputs, **kwargs): num_features = int(inputs.shape[-1]) emb_dim = self.embedding_dim // 2 - with tf.variable_scope(self.name): + with tf.variable_scope(self.name, reuse=self.reuse): c = tf.get_variable( 'coefficients', shape=[1, num_features, emb_dim], @@ -130,7 +138,12 @@ def call(self, inputs, **kwargs): dim = self.embedding_dim if self.add_linear_layer: - linear = NLinear(num_features, dim, dim) + linear = NLinear( + num_features, + dim, + dim, + scope='%s_nd_linear' % self.name, + reuse=self.reuse) emb = linear(emb) act = get_activation(self.linear_activation) if callable(act): @@ -150,8 +163,9 @@ class AutoDisEmbedding(tf.keras.layers.Layer): Refer: https://arxiv.org/pdf/2012.08986v2.pdf """ - def __init__(self, params, name='auto_dis_embedding', **kwargs): + def __init__(self, params, name='auto_dis_embedding', reuse=None, **kwargs): super(AutoDisEmbedding, self).__init__(name, **kwargs) + self.reuse = reuse params.check_required(['embedding_dim', 'num_bins', 'temperature']) self.emb_dim = int(params.embedding_dim) self.num_bins = int(params.num_bins) @@ -165,7 +179,7 @@ def call(self, inputs, **kwargs): raise ValueError('inputs of AutoDisEmbedding must have 2 dimensions.') num_features = int(inputs.shape[-1]) - with tf.variable_scope(self.name): + with tf.variable_scope(self.name, reuse=self.reuse): meta_emb = tf.get_variable( 'meta_embedding', shape=[num_features, self.num_bins, self.emb_dim]) w = tf.get_variable('project_w', shape=[1, num_features, self.num_bins]) diff --git a/easy_rec/python/protos/dnn.proto b/easy_rec/python/protos/dnn.proto index ff40f0fe4..4a9c1743a 100644 --- a/easy_rec/python/protos/dnn.proto +++ b/easy_rec/python/protos/dnn.proto @@ -24,8 +24,9 @@ message MLP { optional bool use_bn = 4 [default = true]; optional bool use_final_bn = 5 [default = true]; optional string final_activation = 6 [default = 'relu']; - optional bool use_bias = 7 [default = true]; + optional bool use_bias = 7 [default = false]; // kernel_initializer optional string initializer = 8 [default = 'he_uniform']; optional bool use_bn_after_activation = 9; + optional bool use_final_bias = 10 [default = false]; } diff --git a/easy_rec/python/protos/seq_encoder.proto b/easy_rec/python/protos/seq_encoder.proto index 2b845a429..171cdb80a 100644 --- a/easy_rec/python/protos/seq_encoder.proto +++ b/easy_rec/python/protos/seq_encoder.proto @@ -29,7 +29,7 @@ message BSTEncoder { message DINEncoder { // din attention layer - required DNN attention_dnn = 1; + required MLP attention_dnn = 1; // whether to keep target item feature required bool need_target_feature = 2 [default = true]; // option: softmax, sigmoid From 8ee5af289c3e55f839559a86d95f1ae14fdff9da Mon Sep 17 00:00:00 2001 From: weisu Date: Sat, 16 Sep 2023 13:46:32 +0800 Subject: [PATCH 02/27] fix bug of `package` syntax of backbone --- docs/images/models/dcn_v2.jpg | Bin 0 -> 303108 bytes docs/images/models/dcn_v2_cross.jpg | Bin 0 -> 114676 bytes docs/source/models/dcn.md | 5 ++--- 3 files changed, 2 insertions(+), 3 deletions(-) create mode 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docs/images/models/fibinet.jpg | Bin 0 -> 241096 bytes docs/images/models/masknet.jpg | Bin 0 -> 299813 bytes docs/source/component/backbone.md | 7 +- docs/source/models/bst.md | 148 ++++++++- docs/source/models/din.md | 117 ++++++- docs/source/models/mmoe.md | 12 +- easy_rec/python/layers/backbone.py | 9 +- easy_rec/python/layers/common_layers.py | 82 +++-- easy_rec/python/layers/keras/bst.py | 6 +- easy_rec/python/protos/seq_encoder.proto | 2 + easy_rec/python/test/train_eval_test.py | 10 + .../bst_backbone_on_taobao.config | 313 ++++++++++++++++++ .../din_backbone_on_taobao.config | 311 +++++++++++++++++ 13 files changed, 967 insertions(+), 50 deletions(-) create mode 100644 docs/images/models/fibinet.jpg create mode 100644 docs/images/models/masknet.jpg create mode 100644 samples/model_config/bst_backbone_on_taobao.config create mode 100644 samples/model_config/din_backbone_on_taobao.config diff --git a/docs/images/models/fibinet.jpg b/docs/images/models/fibinet.jpg new file mode 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