From f4948af82e4f1183a1b66ddf596308cb362f1b61 Mon Sep 17 00:00:00 2001 From: yangxudong Date: Tue, 24 Sep 2024 11:18:55 +0800 Subject: [PATCH] Add NaryDisEmbedding & Transformer Component (#485) * add transformer layer & NaryDisEmbedding --- docs/source/component/backbone.md | 57 -- docs/source/component/component.md | 174 ++++- docs/source/index.rst | 1 + easy_rec/python/compat/array_ops.py | 229 ++++++ .../python/feature_column/feature_column.py | 5 + easy_rec/python/layers/backbone.py | 83 +- easy_rec/python/layers/input_layer.py | 37 + easy_rec/python/layers/keras/__init__.py | 6 + easy_rec/python/layers/keras/activation.py | 19 + easy_rec/python/layers/keras/attention.py | 15 +- easy_rec/python/layers/keras/einsum_dense.py | 598 +++++++++++++++ easy_rec/python/layers/keras/embedding.py | 81 ++ easy_rec/python/layers/keras/mask_net.py | 5 +- .../layers/keras/multi_head_attention.py | 717 ++++++++++++++++++ .../layers/keras/numerical_embedding.py | 126 ++- easy_rec/python/layers/keras/transformer.py | 192 +++++ easy_rec/python/layers/utils.py | 17 +- easy_rec/python/loss/pairwise_loss.py | 25 +- easy_rec/python/model/multi_task_model.py | 18 +- easy_rec/python/ops/1.12/libcustom_ops.so | Bin 1378512 -> 1397296 bytes easy_rec/python/ops/1.12_pai/libcustom_ops.so | Bin 1378536 -> 1393224 bytes easy_rec/python/protos/backbone.proto | 18 +- easy_rec/python/protos/keras_layer.proto | 5 + easy_rec/python/protos/layer.proto | 9 + easy_rec/python/protos/seq_encoder.proto | 59 ++ easy_rec/python/protos/tower.proto | 8 + easy_rec/version.py | 2 +- .../dlrm_on_criteo_with_narydis.config | 586 ++++++++++++++ 28 files changed, 2955 insertions(+), 137 deletions(-) create mode 100644 easy_rec/python/compat/array_ops.py create mode 100644 easy_rec/python/layers/keras/einsum_dense.py create mode 100644 easy_rec/python/layers/keras/embedding.py create mode 100644 easy_rec/python/layers/keras/multi_head_attention.py create mode 100644 easy_rec/python/layers/keras/transformer.py create mode 100644 examples/configs/dlrm_on_criteo_with_narydis.config diff --git a/docs/source/component/backbone.md b/docs/source/component/backbone.md index d9edd2ef7..5e05ec589 100644 --- a/docs/source/component/backbone.md +++ b/docs/source/component/backbone.md @@ -1094,63 +1094,6 @@ MovieLens-1M数据集效果： - Cross Decoupling Network: [CDN](../models/cdn.md) - DLRM+SENet: [dlrm_senet_on_criteo.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/dlrm_senet_on_criteo.config) -# 组件库介绍 - -## 1.基础组件 - -| 类名 | 功能 | 说明 | 示例 | -| ----------------- | ------ | ------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | -| MLP | 多层感知机 | 可定制激活函数、initializer、Dropout、BN等 | [案例1](#wide-deep) | -| Highway | 类似残差链接 | 可用来对预训练embedding做增量微调 | [highway network](../models/highway.html) | -| Gate | 门控 | 多个输入的加权求和 | [Cross Decoupling Network](../models/cdn.html#id2) | -| PeriodicEmbedding | 周期激活函数 | 数值特征Embedding | [案例5](#dlrm-embedding) | -| AutoDisEmbedding | 自动离散化 | 数值特征Embedding | [dlrm_on_criteo_with_autodis.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/dlrm_on_criteo_with_autodis.config) | -| TextCNN | 文本卷积 | 提取文本序列的特征 | [text_cnn_on_movielens.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/text_cnn_on_movielens.config) | - -**备注**：Gate组件的第一个输入是权重向量，后面的输入拼凑成一个列表，权重向量的长度应等于列表的长度 - -## 2.特征交叉组件 - -| 类名 | 功能 | 说明 | 示例 | -| -------------- | --------------------- | ---------------- | -------------------------------------------------------------------------------------------------------------------------- | -| FM | 二阶交叉 | DeepFM模型的组件 | [案例2](#deepfm) | -| DotInteraction | 二阶内积交叉 | DLRM模型的组件 | [案例4](#dlrm) | -| Cross | bit-wise交叉 | DCN v2模型的组件 | [案例3](#dcn) | -| BiLinear | 双线性 | FiBiNet模型的组件 | [fibinet_on_movielens.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/fibinet_on_movielens.config) | -| FiBiNet | SENet & BiLinear | FiBiNet模型 | [fibinet_on_movielens.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/fibinet_on_movielens.config) | -| Attention | Dot-product attention | Transformer模型的组件 | | - -## 3.特征重要度学习组件 - -| 类名 | 功能 | 说明 | 示例 | -| --------- | ----------------- | ------------ | ----------------------------------------------------- | -| SENet | 建模特征重要度 | FiBiNet模型的组件 | [MMoE](../models/mmoe.html#id4) | -| MaskBlock | 建模特征重要度 | MaskNet模型的组件 | [Cross Decoupling Network](../models/cdn.html#id2) | -| MaskNet | 多个串行或并行的MaskBlock | MaskNet模型 | [DBMTL](../models/dbmtl.html#dbmtl-based-on-backbone) | -| PPNet | 参数个性化网络 | PPNet模型 | [PPNet](../models/ppnet.html#id2) | - -## 4. 序列特征编码组件 - -| 类名 | 功能 | 说明 | 示例 | -| ---------- | ---------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------ | -| DIN | target attention | DIN模型的组件 | [DIN_backbone.config](https://github.com/alibaba/EasyRec/blob/master/samples/model_config/din_backbone_on_taobao.config) | -| BST | transformer | BST模型的组件 | [BST_backbone.config](https://github.com/alibaba/EasyRec/blob/master/samples/model_config/bst_backbone_on_taobao.config) | -| SeqAugment | 序列数据增强 | crop, mask, reorder | [CL4SRec](../models/cl4srec.html#id2) | - -## 5. 多目标学习组件 - -| 类名 | 功能 | 说明 | 示例 | -| ---- | --------------------------- | --------- | ------------ | -| MMoE | Multiple Mixture of Experts | MMoE模型的组件 | [案例8](#mmoe) | - -## 6. 辅助损失函数组件 - -| 类名 | 功能 | 说明 | 示例 | -| ------------- | ---------- | --------- | ----------- | -| AuxiliaryLoss | 用来计算辅助损失函数 | 常用在自监督学习中 | [案例7](#id7) | - -各组件的详细参数请查看"[组件详细参数](component.md)"。 - # 如何自定义组件 在 `easy_rec/python/layers/keras` 目录下新建一个`py`文件，也可直接添加到一个已有的文件中。我们建议目标类似的组件定义在同一个文件中，减少文件数量；比如特征交叉的组件都放在`interaction.py`里。 diff --git a/docs/source/component/component.md b/docs/source/component/component.md index 731e95759..fa50ecd32 100644 --- a/docs/source/component/component.md +++ b/docs/source/component/component.md @@ -1,3 +1,62 @@ +# 组件库介绍 + +## 1.基础组件 + +| 类名 | 功能 | 说明 | 示例 | +| ----------------- | ------ | ------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | +| MLP | 多层感知机 | 可定制激活函数、initializer、Dropout、BN等 | [案例1](backbone.md#wide-deep) | +| Highway | 类似残差链接 | 可用来对预训练embedding做增量微调 | [highway network](../models/highway.html) | +| Gate | 门控 | 多个输入的加权求和 | [Cross Decoupling Network](../models/cdn.html#id2) | +| PeriodicEmbedding | 周期激活函数 | 数值特征Embedding | [案例5](backbone.md#dlrm-embedding) | +| AutoDisEmbedding | 自动离散化 | 数值特征Embedding | [dlrm_on_criteo_with_autodis.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/dlrm_on_criteo_with_autodis.config) | +| TextCNN | 文本卷积 | 提取文本序列的特征 | [text_cnn_on_movielens.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/text_cnn_on_movielens.config) | + +**备注**：Gate组件的第一个输入是权重向量，后面的输入拼凑成一个列表，权重向量的长度应等于列表的长度 + +## 2.特征交叉组件 + +| 类名 | 功能 | 说明 | 示例 | +| -------------- | ---------------- | ------------ | -------------------------------------------------------------------------------------------------------------------------- | +| FM | 二阶交叉 | DeepFM模型的组件 | [案例2](backbone.md#deepfm) | +| DotInteraction | 二阶内积交叉 | DLRM模型的组件 | [案例4](backbone.md#dlrm) | +| Cross | bit-wise交叉 | DCN v2模型的组件 | [案例3](backbone.md#dcn) | +| BiLinear | 双线性 | FiBiNet模型的组件 | [fibinet_on_movielens.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/fibinet_on_movielens.config) | +| FiBiNet | SENet & BiLinear | FiBiNet模型 | [fibinet_on_movielens.config](https://github.com/alibaba/EasyRec/tree/master/examples/configs/fibinet_on_movielens.config) | + +## 3.特征重要度学习组件 + +| 类名 | 功能 | 说明 | 示例 | +| --------- | ----------------- | ------------ | ----------------------------------------------------- | +| SENet | 建模特征重要度 | FiBiNet模型的组件 | [MMoE](../models/mmoe.html#id4) | +| MaskBlock | 建模特征重要度 | MaskNet模型的组件 | [Cross Decoupling Network](../models/cdn.html#id2) | +| MaskNet | 多个串行或并行的MaskBlock | MaskNet模型 | [DBMTL](../models/dbmtl.html#dbmtl-based-on-backbone) | +| PPNet | 参数个性化网络 | PPNet模型 | [PPNet](../models/ppnet.html#id2) | + +## 4. 序列特征编码组件 + +| 类名 | 功能 | 说明 | 示例 | +| ------------------ | --------------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------ | +| DIN | target attention | DIN模型的组件 | [DIN_backbone.config](https://github.com/alibaba/EasyRec/blob/master/samples/model_config/din_backbone_on_taobao.config) | +| BST | transformer | BST模型的组件 | [BST_backbone.config](https://github.com/alibaba/EasyRec/blob/master/samples/model_config/bst_backbone_on_taobao.config) | +| SeqAugment | 序列数据增强 | crop, mask, reorder | [CL4SRec](../models/cl4srec.html#id2) | +| Attention | Dot-product attention | Transformer模型的组件 | | +| MultiHeadAttention | Multi-head attention | Transformer模型的组件 | | +| TransformerBlock | Transformer layer | Transformer模型的组件 | | +| TransformerEncoder | Transformer encoder | Transformer模型的组件 | | +| TextEncoder | BERT 模型 | 类似BERT模型 | | + +## 5. 多目标学习组件 + +| 类名 | 功能 | 说明 | 示例 | +| ---- | --------------------------- | --------- | ----------------------- | +| MMoE | Multiple Mixture of Experts | MMoE模型的组件 | [案例8](backbone.md#mmoe) | + +## 6. 辅助损失函数组件 + +| 类名 | 功能 | 说明 | 示例 | +| ------------- | ---------- | --------- | ---------------------- | +| AuxiliaryLoss | 用来计算辅助损失函数 | 常用在自监督学习中 | [案例7](backbone.md#id7) | + # 组件详细参数 ## 1.基础组件 @@ -79,33 +138,6 @@ | senet | SENet | | protobuf message | | mlp | MLP | | protobuf message | -- Attention - -Dot-product attention layer, a.k.a. Luong-style attention. - -The calculation follows the steps: - -1. Calculate attention scores using query and key with shape (batch_size, Tq, Tv). -1. Use scores to calculate a softmax distribution with shape (batch_size, Tq, Tv). -1. Use the softmax distribution to create a linear combination of value with shape (batch_size, Tq, dim). - -| 参数 | 类型 | 默认值 | 说明 | -| ----------------------- | ------ | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| use_scale | bool | False | If True, will create a scalar variable to scale the attention scores. | -| score_mode | string | dot | Function to use to compute attention scores, one of {"dot", "concat"}. "dot" refers to the dot product between the query and key vectors. "concat" refers to the hyperbolic tangent of the concatenation of the query and key vectors. | -| dropout | float | 0.0 | Float between 0 and 1. Fraction of the units to drop for the attention scores. | -| seed | int | None | A Python integer to use as random seed incase of dropout. | -| return_attention_scores | bool | False | if True, returns the attention scores (after masking and softmax) as an additional output argument. | -| use_causal_mask | bool | False | Set to True for decoder self-attention. Adds a mask such that position i cannot attend to positions j > i. This prevents the flow of information from the future towards the past. | - -- inputs: List of the following tensors: - - query: Query tensor of shape (batch_size, Tq, dim). - - value: Value tensor of shape (batch_size, Tv, dim). - - key: Optional key tensor of shape (batch_size, Tv, dim). If not given, will use value for both key and value, which is the most common case. -- output: - - Attention outputs of shape (batch_size, Tq, dim). - - (Optional) Attention scores after masking and softmax with shape (batch_size, Tq, Tv). - ## 3.特征重要度学习组件 - SENet @@ -156,7 +188,7 @@ The calculation follows the steps: ## 4. 序列特征编码组件 -- SeqAugment +- SeqAugment (序列数据增强) | 参数 | 类型 | 默认值 | 说明 | | ------------ | ----- | --- | --------------- | @@ -180,8 +212,8 @@ The calculation follows the steps: | num_hidden_layers | int | | transformer层数 | | num_attention_heads | int | | transformer head数 | | intermediate_size | int | | transformer中间层单元数 | -| hidden_act | string | gelu | 隐藏激活函数 | -| hidden_dropout_prob | float | 0.1 | 隐藏dropout rate | +| hidden_act | string | gelu | 隐藏层激活函数 | +| hidden_dropout_prob | float | 0.1 | 隐藏层dropout rate | | attention_probs_dropout_prob | float | 0.1 | attention层dropout rate | | max_position_embeddings | int | 512 | 序列最大长度 | | use_position_embeddings | bool | true | 是否使用位置编码 | @@ -190,6 +222,88 @@ The calculation follows the steps: | target_item_position | string | head | target item的插入位置，可选：head, tail, ignore | | reserve_target_position | bool | true | 是否为target item保留一个位置 | +- Attention + +Dot-product attention layer, a.k.a. Luong-style attention. + +The calculation follows the steps: + +1. Calculate attention scores using query and key with shape (batch_size, Tq, Tv). +1. Use scores to calculate a softmax distribution with shape (batch_size, Tq, Tv). +1. Use the softmax distribution to create a linear combination of value with shape (batch_size, Tq, dim). + +| 参数 | 类型 | 默认值 | 说明 | +| ----------------------- | ------ | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| use_scale | bool | False | If True, will create a scalar variable to scale the attention scores. | +| scale_by_dim | bool | Fasle | whether to scale by dimension | +| score_mode | string | dot | Function to use to compute attention scores, one of {"dot", "concat"}. "dot" refers to the dot product between the query and key vectors. "concat" refers to the hyperbolic tangent of the concatenation of the query and key vectors. | +| dropout | float | 0.0 | Float between 0 and 1. Fraction of the units to drop for the attention scores. | +| seed | int | None | A Python integer to use as random seed incase of dropout. | +| return_attention_scores | bool | False | if True, returns the attention scores (after masking and softmax) as an additional output argument. | +| use_causal_mask | bool | False | Set to True for decoder self-attention. Adds a mask such that position i cannot attend to positions j > i. This prevents the flow of information from the future towards the past. | + +> - inputs: List of the following tensors: +> \- query: Query tensor of shape (batch_size, Tq, dim). +> \- value: Value tensor of shape (batch_size, Tv, dim). +> \- key: Optional key tensor of shape (batch_size, Tv, dim). If not given, will use value for both key and value, which is the most common case. +> - output: +> \- Attention outputs of shape (batch_size, Tq, dim). +> \- (Optional) Attention scores after masking and softmax with shape (batch_size, Tq, Tv). + +- MultiHeadAttention + +| 参数 | 类型 | 默认值 | 说明 | +| ----------------------- | ------ | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| num_heads | uint32 | 无 | Number of attention heads. | +| key_dim | uint32 | | Size of each attention head for query and key. | +| value_dim | uint32 | | Size of each attention head for value. | +| dropout | float | 0.0 | Dropout probability. | +| use_bias | bool | true | whether the dense layers use bias vectors/matrices. | +| return_attention_scores | bool | false | whether the output should be (attention_output, attention_scores) | +| use_causal_mask | bool | false | whether to apply a causal mask to prevent tokens from attending to future tokens (e.g., used in a decoder Transformer). | +| output_shape | uint32 | | The expected shape of an output tensor, besides the batch and sequence dims. If not specified, projects back to the query feature dim (the query input's last dimension). | +| kernel_initializer | string | | Initializer for dense layer kernels. | +| bias_initializer | string | | Initializer for dense layer biases. | + +- TransformerBlock + +Transformer encoder 的其中一个layer。 + +| 参数 | 类型 | 默认值 | 说明 | +| ---------------------------- | ------ | ---- | ----------------------- | +| hidden_size | int | | transformer 编码层单元数 | +| num_attention_heads | int | | transformer head数 | +| intermediate_size | int | | transformer中间层单元数 | +| hidden_act | string | relu | 隐藏层激活函数 | +| hidden_dropout_prob | float | 0.1 | 隐藏层dropout rate | +| attention_probs_dropout_prob | float | 0.0 | attention层的dropout rate | + +- TransformerEncoder + +| 参数 | 类型 | 默认值 | 说明 | +| ---------------------------- | ------ | ---- | ----------------------- | +| vocab_size | uint32 | | 词汇表大小 | +| hidden_size | uint32 | | transformer 编码层单元数 | +| num_hidden_layers | uint32 | | transformer层数 | +| num_attention_heads | uint32 | | transformer head数 | +| intermediate_size | uint32 | | transformer中间层单元数 | +| hidden_act | string | relu | 隐藏层激活函数 | +| hidden_dropout_prob | float | 0.1 | 隐藏层dropout rate | +| attention_probs_dropout_prob | float | 0.0 | attention层的dropout rate | +| max_position_embeddings | uint32 | 512 | 序列最大长度 | +| output_all_token_embeddings | bool | true | 是否输出所有token embedding | + +- TextEncoder + +BERT模型结构 + +| 参数 | 类型 | 默认值 | 说明 | +| ---------------- | ------------------ | --- | ----------------------------- | +| transformer | TransformerEncoder | 无 | transformer 子组件的配置 | +| separator | string | ' ' | 文本分隔符 | +| vocab_file | string | 无 | 词汇表文件路径，不设置时使用hash获得token id | +| default_token_id | int32 | 0 | Out of vocabulary 的token的默认id | + ## 5. 多任务学习组件 - MMoE diff --git a/docs/source/index.rst b/docs/source/index.rst index 238952075..7eeebba67 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -31,6 +31,7 @@ Welcome to easy_rec's documentation! component/backbone component/component component/sequence + component/custom_op .. toctree:: :maxdepth: 3 diff --git a/easy_rec/python/compat/array_ops.py b/easy_rec/python/compat/array_ops.py new file mode 100644 index 000000000..d788bc8c1 --- /dev/null +++ b/easy_rec/python/compat/array_ops.py @@ -0,0 +1,229 @@ +import numpy as np +import tensorflow as tf +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import gen_math_ops + + +def convert_to_int_tensor(tensor, name, dtype=tf.int32): + """Converts the given value to an integer Tensor.""" + tensor = ops.convert_to_tensor(tensor, name=name, preferred_dtype=dtype) + if tensor.dtype.is_integer: + tensor = gen_math_ops.cast(tensor, dtype) + else: + raise TypeError('%s must be an integer tensor; dtype=%s' % + (name, tensor.dtype)) + return tensor + + +def _with_nonzero_rank(data): + """If `data` is scalar, then add a dimension; otherwise return as-is.""" + if data.shape.ndims is not None: + if data.shape.ndims == 0: + return tf.stack([data]) + else: + return data + else: + data_shape = tf.shape(data) + data_ndims = tf.rank(data) + return tf.reshape(data, tf.concat([[1], data_shape], axis=0)[-data_ndims:]) + + +def get_positive_axis(axis, ndims): + """Validate an `axis` parameter, and normalize it to be positive. + + If `ndims` is known (i.e., not `None`), then check that `axis` is in the + range `-ndims <= axis < ndims`, and return `axis` (if `axis >= 0`) or + `axis + ndims` (otherwise). + If `ndims` is not known, and `axis` is positive, then return it as-is. + If `ndims` is not known, and `axis` is negative, then report an error. + + Args: + axis: An integer constant + ndims: An integer constant, or `None` + + Returns: + The normalized `axis` value. + + Raises: + ValueError: If `axis` is out-of-bounds, or if `axis` is negative and + `ndims is None`. + """ + if not isinstance(axis, int): + raise TypeError('axis must be an int; got %s' % type(axis).__name__) + if ndims is not None: + if 0 <= axis < ndims: + return axis + elif -ndims <= axis < 0: + return axis + ndims + else: + raise ValueError('axis=%s out of bounds: expected %s<=axis<%s' % + (axis, -ndims, ndims)) + elif axis < 0: + raise ValueError('axis may only be negative if ndims is statically known.') + return axis + + +def tile_one_dimension(data, axis, multiple): + """Tiles a single dimension of a tensor.""" + # Assumes axis is a nonnegative int. + if data.shape.ndims is not None: + multiples = [1] * data.shape.ndims + multiples[axis] = multiple + else: + ones_value = tf.ones(tf.rank(data), tf.int32) + multiples = tf.concat( + [ones_value[:axis], [multiple], ones_value[axis + 1:]], axis=0) + return tf.tile(data, multiples) + + +def _all_dimensions(x): + """Returns a 1D-tensor listing all dimensions in x.""" + # Fast path: avoid creating Rank and Range ops if ndims is known. + if isinstance(x, ops.Tensor) and x.get_shape().ndims is not None: + return constant_op.constant(np.arange(x.get_shape().ndims), dtype=tf.int32) + if (isinstance(x, sparse_tensor.SparseTensor) and + x.dense_shape.get_shape().is_fully_defined()): + r = x.dense_shape.get_shape().dims[0].value # sparse.dense_shape is 1-D. + return constant_op.constant(np.arange(r), dtype=tf.int32) + + # Otherwise, we rely on `range` and `rank` to do the right thing at runtime. + return gen_math_ops._range(0, tf.rank(x), 1) + + +# This op is intended to exactly match the semantics of numpy.repeat, with +# one exception: numpy.repeat has special (and somewhat non-intuitive) behavior +# when axis is not specified. Rather than implement that special behavior, we +# simply make `axis` be a required argument. +# +# External (OSS) `tf.repeat` feature request: +# https://github.com/tensorflow/tensorflow/issues/8246 +def repeat_with_axis(data, repeats, axis, name=None): + """Repeats elements of `data`. + + Args: + data: An `N`-dimensional tensor. + repeats: A 1-D integer tensor specifying how many times each element in + `axis` should be repeated. `len(repeats)` must equal `data.shape[axis]`. + Supports broadcasting from a scalar value. + axis: `int`. The axis along which to repeat values. Must be less than + `max(N, 1)`. + name: A name for the operation. + + Returns: + A tensor with `max(N, 1)` dimensions. Has the same shape as `data`, + except that dimension `axis` has size `sum(repeats)`. + #### Examples: + ```python + >>> repeat(['a', 'b', 'c'], repeats=[3, 0, 2], axis=0) + ['a', 'a', 'a', 'c', 'c'] + >>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=0) + [[1, 2], [1, 2], [3, 4], [3, 4], [3, 4]] + >>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=1) + [[1, 1, 2, 2, 2], [3, 3, 4, 4, 4]] + ``` + """ + if not isinstance(axis, int): + raise TypeError('axis must be an int; got %s' % type(axis).__name__) + + with ops.name_scope(name, 'Repeat', [data, repeats]): + data = ops.convert_to_tensor(data, name='data') + repeats = convert_to_int_tensor(repeats, name='repeats') + repeats.shape.with_rank_at_most(1) + + # If `data` is a scalar, then upgrade it to a vector. + data = _with_nonzero_rank(data) + data_shape = tf.shape(data) + + # If `axis` is negative, then convert it to a positive value. + axis = get_positive_axis(axis, data.shape.ndims) + + # Check data Tensor shapes. + if repeats.shape.ndims == 1: + data.shape.dims[axis].assert_is_compatible_with(repeats.shape[0]) + + # If we know that `repeats` is a scalar, then we can just tile & reshape. + if repeats.shape.ndims == 0: + expanded = tf.expand_dims(data, axis + 1) + tiled = tile_one_dimension(expanded, axis + 1, repeats) + result_shape = tf.concat([data_shape[:axis], [-1], data_shape[axis + 1:]], + axis=0) + return tf.reshape(tiled, result_shape) + + # Broadcast the `repeats` tensor so rank(repeats) == axis + 1. + if repeats.shape.ndims != axis + 1: + repeats_shape = tf.shape(repeats) + repeats_ndims = tf.rank(repeats) + broadcast_shape = tf.concat( + [data_shape[:axis + 1 - repeats_ndims], repeats_shape], axis=0) + repeats = tf.broadcast_to(repeats, broadcast_shape) + repeats.set_shape([None] * (axis + 1)) + + # Create a "sequence mask" based on `repeats`, where slices across `axis` + # contain one `True` value for each repetition. E.g., if + # `repeats = [3, 1, 2]`, then `mask = [[1, 1, 1], [1, 0, 0], [1, 1, 0]]`. + max_repeat = gen_math_ops.maximum( + 0, gen_math_ops._max(repeats, _all_dimensions(repeats))) + mask = tf.sequence_mask(repeats, max_repeat) + + # Add a new dimension around each value that needs to be repeated, and + # then tile that new dimension to match the maximum number of repetitions. + expanded = tf.expand_dims(data, axis + 1) + tiled = tile_one_dimension(expanded, axis + 1, max_repeat) + + # Use `boolean_mask` to discard the extra repeated values. This also + # flattens all dimensions up through `axis`. + masked = tf.boolean_mask(tiled, mask) + + # Reshape the output tensor to add the outer dimensions back. + if axis == 0: + result = masked + else: + result_shape = tf.concat([data_shape[:axis], [-1], data_shape[axis + 1:]], + axis=0) + result = tf.reshape(masked, result_shape) + + # Preserve shape information. + if data.shape.ndims is not None: + new_axis_size = 0 if repeats.shape[0] == 0 else None + result.set_shape(data.shape[:axis].concatenate( + [new_axis_size]).concatenate(data.shape[axis + 1:])) + + return result + + +def repeat(input, repeats, axis=None, name=None): # pylint: disable=redefined-builtin + """Repeat elements of `input`. + + Args: + input: An `N`-dimensional Tensor. + repeats: An 1-D `int` Tensor. The number of repetitions for each element. + repeats is broadcasted to fit the shape of the given axis. `len(repeats)` + must equal `input.shape[axis]` if axis is not None. + axis: An int. The axis along which to repeat values. By default (axis=None), + use the flattened input array, and return a flat output array. + name: A name for the operation. + + Returns: + A Tensor which has the same shape as `input`, except along the given axis. + If axis is None then the output array is flattened to match the flattened + input array. + #### Examples: + ```python + >>> repeat(['a', 'b', 'c'], repeats=[3, 0, 2], axis=0) + ['a', 'a', 'a', 'c', 'c'] + >>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=0) + [[1, 2], [1, 2], [3, 4], [3, 4], [3, 4]] + >>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=1) + [[1, 1, 2, 2, 2], [3, 3, 4, 4, 4]] + >>> repeat(3, repeats=4) + [3, 3, 3, 3] + >>> repeat([[1,2], [3,4]], repeats=2) + [1, 1, 2, 2, 3, 3, 4, 4] + ``` + """ + if axis is None: + input = tf.reshape(input, [-1]) + axis = 0 + return repeat_with_axis(input, repeats, axis, name) diff --git a/easy_rec/python/feature_column/feature_column.py b/easy_rec/python/feature_column/feature_column.py index 1b74ea344..8701b55fc 100644 --- a/easy_rec/python/feature_column/feature_column.py +++ b/easy_rec/python/feature_column/feature_column.py @@ -115,6 +115,7 @@ def _cmp_embed_config(a, b): embed_name: [] for embed_name in self._share_embed_names } + self._feature_vocab_size = {} for config in self._feature_configs: assert isinstance(config, FeatureConfig) try: @@ -236,6 +237,9 @@ def is_deep(self, config): WideOrDeep.DEEP, WideOrDeep.WIDE_AND_DEEP ] + def get_feature_vocab_size(self, feature): + return self._feature_vocab_size.get(feature, 1) + def _get_vocab_size(self, vocab_path): if vocab_path in self._vocab_size: return self._vocab_size[vocab_path] @@ -623,6 +627,7 @@ def _add_deep_embedding_column(self, fc, config): feature_name = config.feature_name if config.HasField('feature_name') \ else config.input_names[0] assert config.embedding_dim > 0, 'embedding_dim is not set for %s' % feature_name + self._feature_vocab_size[feature_name] = fc.num_buckets if config.embedding_name in self._deep_share_embed_columns: fc = self._add_shared_embedding_column(config.embedding_name, fc) else: diff --git a/easy_rec/python/layers/backbone.py b/easy_rec/python/layers/backbone.py index 5c503dd31..f3dab6391 100644 --- a/easy_rec/python/layers/backbone.py +++ b/easy_rec/python/layers/backbone.py @@ -8,6 +8,7 @@ from easy_rec.python.layers.common_layers import EnhancedInputLayer from easy_rec.python.layers.keras import MLP +from easy_rec.python.layers.keras import EmbeddingLayer from easy_rec.python.layers.utils import Parameter from easy_rec.python.protos import backbone_pb2 from easy_rec.python.utils.dag import DAG @@ -47,8 +48,9 @@ def __init__(self, config, features, input_layer, l2_reg=None): self.reset_input_config(None) self._block_outputs = {} self._package_input = None + self._feature_group_inputs = {} reuse = None if config.name == 'backbone' else tf.AUTO_REUSE - input_feature_groups = {} + input_feature_groups = self._feature_group_inputs for block in config.blocks: if len(block.inputs) == 0: @@ -56,7 +58,7 @@ def __init__(self, config, features, input_layer, l2_reg=None): self._dag.add_node(block.name) self._name_to_blocks[block.name] = block layer = block.WhichOneof('layer') - if layer in {'input_layer', 'raw_input'}: + if layer in {'input_layer', 'raw_input', 'embedding_layer'}: if len(block.inputs) != 1: raise ValueError('input layer `%s` takes only one input' % block.name) one_input = block.inputs[0] @@ -69,14 +71,34 @@ def __init__(self, config, features, input_layer, l2_reg=None): if not input_layer.has_group(group): raise KeyError('invalid feature group name: ' + group) if group in input_feature_groups: - logging.warning('input `%s` already exists in other block' % group) + if layer == input_layer: + logging.warning('input `%s` already exists in other block' % group) + elif layer == 'raw_input': + input_fn = input_feature_groups[group] + self._name_to_layer[block.name] = input_fn + elif layer == 'embedding_layer': + inputs, vocab, weights = input_feature_groups[group] + block.embedding_layer.vocab_size = vocab + params = Parameter.make_from_pb(block.embedding_layer) + input_fn = EmbeddingLayer(params, block.name) + self._name_to_layer[block.name] = input_fn else: if layer == 'input_layer': input_fn = EnhancedInputLayer(self._input_layer, self._features, group, reuse) - else: + input_feature_groups[group] = input_fn + elif layer == 'raw_input': input_fn = self._input_layer.get_raw_features(self._features, group) - input_feature_groups[group] = input_fn + input_feature_groups[group] = input_fn + else: # embedding_layer + inputs, vocab, weights = self._input_layer.get_bucketized_features( + self._features, group) + block.embedding_layer.vocab_size = vocab + params = Parameter.make_from_pb(block.embedding_layer) + input_fn = EmbeddingLayer(params, block.name) + input_feature_groups[group] = (inputs, vocab, weights) + logging.info('add an embedding layer %s with vocab size %d', + block.name, vocab) self._name_to_layer[block.name] = input_fn else: self.define_layers(layer, block, block.name, reuse) @@ -94,7 +116,7 @@ def __init__(self, config, features, input_layer, l2_reg=None): num_pkg_input = 0 for block in config.blocks: layer = block.WhichOneof('layer') - if layer in {'input_layer', 'raw_input'}: + if layer in {'input_layer', 'raw_input', 'embedding_layer'}: continue name = block.name if name in input_feature_groups: @@ -150,14 +172,16 @@ def __init__(self, config, features, input_layer, l2_reg=None): num_groups = len(input_feature_groups) assert num_pkg_input > 0 or num_groups > 0, 'there must be at least one input layer/feature group' - if len(config.concat_blocks) == 0: + if len(config.concat_blocks) == 0 and len(config.output_blocks) == 0: leaf = self._dag.all_leaves() logging.warning( - '%s has no `concat_blocks`, try to use all leaf blocks: %s' % - (config.name, ','.join(leaf))) + '%s has no `concat_blocks` or `output_blocks`, try to concat all leaf blocks: %s' + % (config.name, ','.join(leaf))) self._config.concat_blocks.extend(leaf) Package.__packages[self._config.name] = self + logging.info('%s layers: %s' % + (config.name, ','.join(self._name_to_layer.keys()))) def define_layers(self, layer, layer_cnf, name, reuse): if layer == 'keras_layer': @@ -239,7 +263,12 @@ def block_input(self, config, block_outputs, training=None, **kwargs): if config.merge_inputs_into_list: output = inputs else: - output = merge_inputs(inputs, config.input_concat_axis, config.name) + try: + output = merge_inputs(inputs, config.input_concat_axis, config.name) + except ValueError as e: + logging.error('merge inputs of block %s failed: %s', config.name, + e.message) + raise e if config.HasField('extra_input_fn'): fn = eval(config.extra_input_fn) @@ -282,19 +311,37 @@ def call(self, is_training, **kwargs): input_config = self.input_config input_fn.reset(input_config, is_training) block_outputs[block] = input_fn(input_config, is_training) + elif layer == 'embedding_layer': + input_fn = self._name_to_layer[block] + feature_group = config.inputs[0].feature_group_name + inputs, _, weights = self._feature_group_inputs[feature_group] + block_outputs[block] = input_fn([inputs, weights], is_training) else: inputs = self.block_input(config, block_outputs, is_training, **kwargs) output = self.call_layer(inputs, config, block, is_training, **kwargs) block_outputs[block] = output outputs = [] + for output in self._config.output_blocks: + if output in block_outputs: + temp = block_outputs[output] + outputs.append(temp) + else: + raise ValueError('No output `%s` of backbone to be concat' % output) + if outputs: + return outputs + for output in self._config.concat_blocks: if output in block_outputs: temp = block_outputs[output] outputs.append(temp) else: raise ValueError('No output `%s` of backbone to be concat' % output) - output = merge_inputs(outputs, msg='backbone') + try: + output = merge_inputs(outputs, msg='backbone') + except ValueError as e: + logging.error("merge backbone's output failed: %s", e.message) + raise e return output def load_keras_layer(self, layer_conf, name, reuse=None): @@ -352,7 +399,12 @@ def call_keras_layer(self, inputs, name, training, **kwargs): layer, customize = self._name_to_layer[name] cls = layer.__class__.__name__ if customize: - output = layer(inputs, training=training, **kwargs) + try: + output = layer(inputs, training=training, **kwargs) + except Exception as e: + logging.error('call keras layer %s (%s) failed: %s' % + (name, cls, e.message)) + raise e else: try: output = layer(inputs, training=training) @@ -433,7 +485,10 @@ def __init__(self, config, features, input_layer, l2_reg=None): main_pkg = backbone_pb2.BlockPackage() main_pkg.name = 'backbone' main_pkg.blocks.MergeFrom(config.blocks) - main_pkg.concat_blocks.extend(config.concat_blocks) + if config.concat_blocks: + main_pkg.concat_blocks.extend(config.concat_blocks) + if config.output_blocks: + main_pkg.output_blocks.extend(config.output_blocks) self._main_pkg = Package(main_pkg, features, input_layer, l2_reg) for pkg in config.packages: Package(pkg, features, input_layer, l2_reg) @@ -445,6 +500,8 @@ def __call__(self, is_training, **kwargs): params = Parameter.make_from_pb(self._config.top_mlp) params.l2_regularizer = self._l2_reg final_mlp = MLP(params, name='backbone_top_mlp') + if type(output) in (list, tuple): + output = tf.concat(output, axis=-1) output = final_mlp(output, training=is_training, **kwargs) return output diff --git a/easy_rec/python/layers/input_layer.py b/easy_rec/python/layers/input_layer.py index 088fb79e3..bf68f5ec4 100644 --- a/easy_rec/python/layers/input_layer.py +++ b/easy_rec/python/layers/input_layer.py @@ -1,5 +1,6 @@ # -*- encoding: utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. +import logging import os from collections import OrderedDict @@ -205,6 +206,42 @@ def get_raw_features(self, features, group_name): feature_group = self._feature_groups[group_name] return [features[x] for x in feature_group.feature_names] + def get_bucketized_features(self, features, group_name): + """Get features by group_name. + + Args: + features: input tensor dict + group_name: feature_group name + + Return: + features: all raw features in list, added feature offset + """ + assert group_name in self._feature_groups, 'invalid group_name[%s], list: %s' % ( + group_name, ','.join([x for x in self._feature_groups])) + feature_group = self._feature_groups[group_name] + offset = 0 + values = [] + weights = [] + for feature in feature_group.feature_names: + vocab = self._fc_parser.get_feature_vocab_size(feature) + logging.info('vocab size of feature %s is %d' % (feature, vocab)) + weights.append(None) + if tf.is_numeric_tensor(features[feature]): + # suppose feature already have be bucketized + value = tf.to_int64(features[feature]) + elif isinstance(features[feature], tf.SparseTensor): + # TagFeature + dense = tf.sparse.to_dense(features[feature], default_value='') + value = tf.string_to_hash_bucket_fast(dense, vocab) + if (feature + '_w') in features: + weights[-1] = features[feature + '_w'] # SparseTensor + logging.info('feature %s has weight %s', feature, feature + '_w') + else: # IdFeature + value = tf.string_to_hash_bucket_fast(features[feature], vocab) + values.append(value + offset) + offset += vocab + return values, offset, weights + def __call__(self, features, group_name, is_combine=True, is_dict=False): """Get features by group_name. diff --git a/easy_rec/python/layers/keras/__init__.py b/easy_rec/python/layers/keras/__init__.py index ffbd5c8c3..ad0bf8528 100644 --- a/easy_rec/python/layers/keras/__init__.py +++ b/easy_rec/python/layers/keras/__init__.py @@ -12,6 +12,7 @@ from .custom_ops import TextNormalize from .data_augment import SeqAugment from .din import DIN +from .embedding import EmbeddingLayer from .fibinet import BiLinear from .fibinet import FiBiNet from .fibinet import SENet @@ -20,7 +21,12 @@ from .interaction import DotInteraction from .mask_net import MaskBlock from .mask_net import MaskNet +from .multi_head_attention import MultiHeadAttention from .multi_task import MMoE from .numerical_embedding import AutoDisEmbedding +from .numerical_embedding import NaryDisEmbedding from .numerical_embedding import PeriodicEmbedding from .ppnet import PPNet +from .transformer import TextEncoder +from .transformer import TransformerBlock +from .transformer import TransformerEncoder diff --git a/easy_rec/python/layers/keras/activation.py b/easy_rec/python/layers/keras/activation.py index b2a8708c8..532d5ca8f 100644 --- a/easy_rec/python/layers/keras/activation.py +++ b/easy_rec/python/layers/keras/activation.py @@ -80,6 +80,25 @@ def get_config(self,): return dict(list(base_config.items()) + list(config.items())) +class MaskedSoftmax(Layer): + + def __init__(self, axis=-1, **kwargs): + super(MaskedSoftmax, self).__init__(**kwargs) + self.axis = axis + + def call(self, inputs, mask=None): + if mask is not None: + adder = (1.0 - tf.cast(mask, inputs.dtype)) * -1e9 + inputs += adder + # Calculate softmax + if isinstance(self.axis, (tuple, list)): + if len(self.axis) > 1: + raise ValueError('MaskedSoftmax not support multiple axis') + else: + return tf.nn.softmax(inputs, axis=self.axis[0]) + return tf.nn.softmax(inputs, axis=self.axis) + + def activation_layer(activation): if activation in ('dice', 'Dice'): act_layer = Dice() diff --git a/easy_rec/python/layers/keras/attention.py b/easy_rec/python/layers/keras/attention.py index cf686356f..4831ccae8 100644 --- a/easy_rec/python/layers/keras/attention.py +++ b/easy_rec/python/layers/keras/attention.py @@ -79,10 +79,14 @@ def __init__(self, params, name='attention', reuse=None, **kwargs): self.seed = params.get_or_default('seed', None) self.scale = None self.concat_score_weight = None - self.return_attention_scores = params.get_or_default( + self._return_attention_scores = params.get_or_default( 'return_attention_scores', False) self.use_causal_mask = params.get_or_default('use_causal_mask', False) + @property + def return_attention_scores(self): + return self._return_attention_scores + def build(self, input_shape): self._validate_inputs(input_shape) if self.use_scale: @@ -199,12 +203,7 @@ def _calculate_score_mask(self, scores, v_mask, use_causal_mask): # or None if the value mask is not provided. return v_mask - def call( - self, - inputs, - mask=None, - training=False, - ): + def call(self, inputs, mask=None, training=False, **kwargs): self._validate_inputs(inputs=inputs, mask=mask) q = inputs[0] v = inputs[1] @@ -220,7 +219,7 @@ def call( # Mask of shape [batch_size, Tq, 1]. q_mask = tf.expand_dims(q_mask, axis=-1) result *= tf.cast(q_mask, dtype=result.dtype) - if self.return_attention_scores: + if self._return_attention_scores: return result, attention_scores return result diff --git a/easy_rec/python/layers/keras/einsum_dense.py b/easy_rec/python/layers/keras/einsum_dense.py new file mode 100644 index 000000000..7531644dc --- /dev/null +++ b/easy_rec/python/layers/keras/einsum_dense.py @@ -0,0 +1,598 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import re +import string + +import tensorflow as tf +from tensorflow.python.keras import activations +from tensorflow.python.keras import constraints +from tensorflow.python.keras import initializers +from tensorflow.python.keras import regularizers +from tensorflow.python.keras.layers import Layer + + +class EinsumDense(Layer): + """A layer that uses `einsum` as the backing computation. + + This layer can perform einsum calculations of arbitrary dimensionality. + + Args: + equation: An equation describing the einsum to perform. + This equation must be a valid einsum string of the form + `ab,bc->ac`, `...ab,bc->...ac`, or + `ab...,bc->ac...` where 'ab', 'bc', and 'ac' can be any valid einsum + axis expression sequence. + output_shape: The expected shape of the output tensor + (excluding the batch dimension and any dimensions + represented by ellipses). You can specify `None` for any dimension + that is unknown or can be inferred from the input shape. + activation: Activation function to use. If you don't specify anything, + no activation is applied + (that is, a "linear" activation: `a(x) = x`). + bias_axes: A string containing the output dimension(s) + to apply a bias to. Each character in the `bias_axes` string + should correspond to a character in the output portion + of the `equation` string. + kernel_initializer: Initializer for the `kernel` weights matrix. + bias_initializer: Initializer for the bias vector. + kernel_regularizer: Regularizer function applied to the `kernel` weights + matrix. + bias_regularizer: Regularizer function applied to the bias vector. + kernel_constraint: Constraint function applied to the `kernel` weights + matrix. + bias_constraint: Constraint function applied to the bias vector. + lora_rank: Optional integer. If set, the layer's forward pass + will implement LoRA (Low-Rank Adaptation) + with the provided rank. LoRA sets the layer's kernel + to non-trainable and replaces it with a delta over the + original kernel, obtained via multiplying two lower-rank + trainable matrices + (the factorization happens on the last dimension). + This can be useful to reduce the + computation cost of fine-tuning large dense layers. + You can also enable LoRA on an existing + `EinsumDense` layer by calling `layer.enable_lora(rank)`. + **kwargs: Base layer keyword arguments, such as `name` and `dtype`. + + Examples: + **Biased dense layer with einsums** + + This example shows how to instantiate a standard Keras dense layer using + einsum operations. This example is equivalent to + `keras.layers.Dense(64, use_bias=True)`. + + >>> layer = tf.keras.layers.EinsumDense("ab,bc->ac", + ... output_shape=64, + ... bias_axes="c") + >>> input_tensor = tf.keras.Input(shape=[32]) + >>> output_tensor = layer(input_tensor) + >>> output_tensor.shape + (None, 64) + + **Applying a dense layer to a sequence** + + This example shows how to instantiate a layer that applies the same dense + operation to every element in a sequence. Here, the `output_shape` has two + values (since there are two non-batch dimensions in the output); the first + dimension in the `output_shape` is `None`, because the sequence dimension + `b` has an unknown shape. + + >>> layer = tf.keras.layers.EinsumDense("abc,cd->abd", + ... output_shape=(None, 64), + ... bias_axes="d") + >>> input_tensor = tf.keras.Input(shape=[32, 128]) + >>> output_tensor = layer(input_tensor) + >>> output_tensor.shape + (None, 32, 64) + + **Applying a dense layer to a sequence using ellipses** + + This example shows how to instantiate a layer that applies the same dense + operation to every element in a sequence, but uses the ellipsis notation + instead of specifying the batch and sequence dimensions. + + Because we are using ellipsis notation and have specified only one axis, the + `output_shape` arg is a single value. When instantiated in this way, the + layer can handle any number of sequence dimensions - including the case + where no sequence dimension exists. + + >>> layer = tf.keras.layers.EinsumDense("...x,xy->...y", + ... output_shape=64, + ... bias_axes="y") + >>> input_tensor = tf.keras.Input(shape=[32, 128]) + >>> output_tensor = layer(input_tensor) + >>> output_tensor.shape + (None, 32, 64) + """ + + def __init__(self, + equation, + output_shape, + activation=None, + bias_axes=None, + kernel_initializer='glorot_uniform', + bias_initializer='zeros', + kernel_regularizer=None, + bias_regularizer=None, + kernel_constraint=None, + bias_constraint=None, + lora_rank=None, + **kwargs): + super(EinsumDense, self).__init__(**kwargs) + self.equation = equation + if isinstance(output_shape, int): + self.partial_output_shape = (output_shape,) + else: + self.partial_output_shape = tuple(output_shape) + self.bias_axes = bias_axes + self.activation = activations.get(activation) + self.kernel_initializer = initializers.get(kernel_initializer) + self.bias_initializer = initializers.get(bias_initializer) + self.kernel_regularizer = regularizers.get(kernel_regularizer) + self.bias_regularizer = regularizers.get(bias_regularizer) + self.kernel_constraint = constraints.get(kernel_constraint) + self.bias_constraint = constraints.get(bias_constraint) + self.lora_rank = lora_rank + self.lora_enabled = False + + def build(self, input_shape): + shape_data = _analyze_einsum_string( + self.equation, + self.bias_axes, + input_shape, + self.partial_output_shape, + ) + kernel_shape, bias_shape, full_output_shape = shape_data + for i in range(len(kernel_shape)): + dim = kernel_shape[i] + if isinstance(dim, tf.Dimension): + kernel_shape[i] = dim.value + for i in range(len(bias_shape)): + dim = bias_shape[i] + if isinstance(dim, tf.Dimension): + bias_shape[i] = dim.value + for i in range(len(full_output_shape)): + dim = full_output_shape[i] + if isinstance(dim, tf.Dimension): + full_output_shape[i] = dim.value + self.full_output_shape = tuple(full_output_shape) + self._kernel = self.add_weight( + name='kernel', + shape=tuple(kernel_shape), + initializer=self.kernel_initializer, + regularizer=self.kernel_regularizer, + constraint=self.kernel_constraint, + dtype=self.dtype, + trainable=True, + ) + if bias_shape is not None: + self.bias = self.add_weight( + name='bias', + shape=tuple(bias_shape), + initializer=self.bias_initializer, + regularizer=self.bias_regularizer, + constraint=self.bias_constraint, + dtype=self.dtype, + trainable=True, + ) + else: + self.bias = None + self.built = True + if self.lora_rank: + self.enable_lora(self.lora_rank) + + @property + def kernel(self): + if not self.built: + raise AttributeError( + 'You must build the layer before accessing `kernel`.') + if self.lora_enabled: + return self._kernel + tf.matmul(self.lora_kernel_a, self.lora_kernel_b) + return self._kernel + + def compute_output_shape(self, _): + return self.full_output_shape + + def call(self, inputs, training=None): + x = tf.einsum(self.equation, inputs, self.kernel) + if self.bias is not None: + x += self.bias + if self.activation is not None: + x = self.activation(x) + return x + + def enable_lora(self, + rank, + a_initializer='he_uniform', + b_initializer='zeros'): + if self.kernel_constraint: + raise ValueError('Lora is incompatible with kernel constraints. ' + 'In order to enable lora on this layer, remove the ' + '`kernel_constraint` argument.') + if not self.built: + raise ValueError("Cannot enable lora on a layer that isn't yet built.") + if self.lora_enabled: + raise ValueError('lora is already enabled. ' + 'This can only be done once per layer.') + self._tracker.unlock() + self.lora_kernel_a = self.add_weight( + name='lora_kernel_a', + shape=(self.kernel.shape[:-1] + (rank,)), + initializer=initializers.get(a_initializer), + regularizer=self.kernel_regularizer, + ) + self.lora_kernel_b = self.add_weight( + name='lora_kernel_b', + shape=(rank, self.kernel.shape[-1]), + initializer=initializers.get(b_initializer), + regularizer=self.kernel_regularizer, + ) + self._kernel.trainable = False + self._tracker.lock() + self.lora_enabled = True + self.lora_rank = rank + + def save_own_variables(self, store): + # Do nothing if the layer isn't yet built + if not self.built: + return + # The keys of the `store` will be saved as determined because the + # default ordering will change after quantization + kernel_value, kernel_scale = self._get_kernel_with_merged_lora() + target_variables = [kernel_value] + if self.bias is not None: + target_variables.append(self.bias) + for i, variable in enumerate(target_variables): + store[str(i)] = variable + + def load_own_variables(self, store): + if not self.lora_enabled: + self._check_load_own_variables(store) + # Do nothing if the layer isn't yet built + if not self.built: + return + # The keys of the `store` will be saved as determined because the + # default ordering will change after quantization + target_variables = [self._kernel] + if self.bias is not None: + target_variables.append(self.bias) + for i, variable in enumerate(target_variables): + variable.assign(store[str(i)]) + if self.lora_enabled: + self.lora_kernel_a.assign(tf.zeros(self.lora_kernel_a.shape)) + self.lora_kernel_b.assign(tf.zeros(self.lora_kernel_b.shape)) + + def get_config(self): + base_config = super(EinsumDense, self).get_config() + config = { + 'output_shape': + self.partial_output_shape, + 'equation': + self.equation, + 'activation': + activations.serialize(self.activation), + 'bias_axes': + self.bias_axes, + 'kernel_initializer': + initializers.serialize(self.kernel_initializer), + 'bias_initializer': + initializers.serialize(self.bias_initializer), + 'kernel_regularizer': + regularizers.serialize(self.kernel_regularizer), + 'bias_regularizer': + regularizers.serialize(self.bias_regularizer), + 'activity_regularizer': + regularizers.serialize(self.activity_regularizer), + 'kernel_constraint': + constraints.serialize(self.kernel_constraint), + 'bias_constraint': + constraints.serialize(self.bias_constraint), + } + if self.lora_rank: + config['lora_rank'] = self.lora_rank + config.update(base_config) + return config + + def _check_load_own_variables(self, store): + all_vars = self._trainable_variables + self._non_trainable_variables + if len(store.keys()) != len(all_vars): + if len(all_vars) == 0 and not self.built: + raise ValueError( + "Layer '{name}' was never built " + "and thus it doesn't have any variables. " + 'However the weights file lists {num_keys} ' + 'variables for this layer.\n' + 'In most cases, this error indicates that either:\n\n' + '1. The layer is owned by a parent layer that ' + 'implements a `build()` method, but calling the ' + "parent's `build()` method did NOT create the state of " + "the child layer '{name}'. A `build()` method " + 'must create ALL state for the layer, including ' + 'the state of any children layers.\n\n' + '2. You need to implement ' + 'the `def build_from_config(self, config)` method ' + "on layer '{name}', to specify how to rebuild " + 'it during loading. ' + 'In this case, you might also want to implement the ' + 'method that generates the build config at saving time, ' + '`def get_build_config(self)`. ' + 'The method `build_from_config()` is meant ' + 'to create the state ' + 'of the layer (i.e. its variables) upon deserialization.'.format( + name=self.name, num_keys=len(store.keys()))) + raise ValueError( + "Layer '{name}' expected {num_var} variables, but received " + '{num_key} variables during loading. ' + 'Expected: {names}'.format( + name=self.name, + num_var=len(store.keys()), + num_key=len(store.keys()), + names=[v.name for v in all_vars])) + + def _get_kernel_with_merged_lora(self): + kernel_value = self.kernel + kernel_scale = None + return kernel_value, kernel_scale + + +def _analyze_einsum_string(equation, bias_axes, input_shape, output_shape): + """Analyzes an einsum string to determine the required weight shape.""" + dot_replaced_string = re.sub(r'\.\.\.', '0', equation) + + # This is the case where no ellipses are present in the string. + split_string = re.match('([a-zA-Z]+),([a-zA-Z]+)->([a-zA-Z]+)', + dot_replaced_string) + if split_string: + return _analyze_split_string(split_string, bias_axes, input_shape, + output_shape) + + # This is the case where ellipses are present on the left. + split_string = re.match('0([a-zA-Z]+),([a-zA-Z]+)->0([a-zA-Z]+)', + dot_replaced_string) + if split_string: + return _analyze_split_string( + split_string, bias_axes, input_shape, output_shape, left_elided=True) + + # This is the case where ellipses are present on the right. + split_string = re.match('([a-zA-Z]{2,})0,([a-zA-Z]+)->([a-zA-Z]+)0', + dot_replaced_string) + if split_string: + return _analyze_split_string(split_string, bias_axes, input_shape, + output_shape) + + raise ValueError( + "Invalid einsum equation '{equation}'. Equations must be in the form " + '[X],[Y]->[Z], ...[X],[Y]->...[Z], or [X]...,[Y]->[Z]....'.format( + equation=equation)) + + +def _analyze_split_string(split_string, + bias_axes, + input_shape, + output_shape, + left_elided=False): + """Analyze an pre-split einsum string to find the weight shape.""" + input_spec = split_string.group(1) + weight_spec = split_string.group(2) + output_spec = split_string.group(3) + elided = len(input_shape) - len(input_spec) + if isinstance(output_shape, int): + output_shape = [output_shape] + else: + output_shape = list(output_shape) + + output_shape.insert(0, input_shape[0]) + + if elided > 0 and left_elided: + for i in range(1, elided): + # We already inserted the 0th input dimension at dim 0, so we need + # to start at location 1 here. + output_shape.insert(1, input_shape[i]) + elif elided > 0 and not left_elided: + for i in range(len(input_shape) - elided, len(input_shape)): + output_shape.append(input_shape[i]) + + if left_elided: + # If we have beginning dimensions elided, we need to use negative + # indexing to determine where in the input dimension our values are. + input_dim_map = { + dim: (i + elided) - len(input_shape) for i, dim in enumerate(input_spec) + } + # Because we've constructed the full output shape already, we don't need + # to do negative indexing. + output_dim_map = {dim: (i + elided) for i, dim in enumerate(output_spec)} + else: + input_dim_map = {dim: i for i, dim in enumerate(input_spec)} + output_dim_map = {dim: i for i, dim in enumerate(output_spec)} + + for dim in input_spec: + input_shape_at_dim = input_shape[input_dim_map[dim]] + if dim in output_dim_map: + output_shape_at_dim = output_shape[output_dim_map[dim]] + if (output_shape_at_dim is not None and + output_shape_at_dim != input_shape_at_dim): + raise ValueError( + 'Input shape and output shape do not match at shared ' + "dimension '{dim}'. Input shape is {input_shape_at_dim}, " + 'and output shape is {output_shape}.'.format( + dim=dim, + input_shape_at_dim=input_shape_at_dim, + output_shape=output_shape[output_dim_map[dim]])) + + for dim in output_spec: + if dim not in input_spec and dim not in weight_spec: + raise ValueError( + "Dimension '{dim}' was specified in the output " + "'{output_spec}' but has no corresponding dim in the input " + "spec '{input_spec}' or weight spec '{output_spec}'".format( + dim=dim, output_spec=output_spec, input_spec=input_spec)) + + weight_shape = [] + for dim in weight_spec: + if dim in input_dim_map: + weight_shape.append(input_shape[input_dim_map[dim]]) + elif dim in output_dim_map: + weight_shape.append(output_shape[output_dim_map[dim]]) + else: + raise ValueError( + "Weight dimension '{dim}' did not have a match in either " + "the input spec '{input_spec}' or the output " + "spec '{output_spec}'. For this layer, the weight must " + 'be fully specified.'.format( + dim=dim, input_spec=input_spec, output_spec=output_spec)) + + if bias_axes is not None: + num_left_elided = elided if left_elided else 0 + idx_map = { + char: output_shape[i + num_left_elided] + for i, char in enumerate(output_spec) + } + + for char in bias_axes: + if char not in output_spec: + raise ValueError( + "Bias dimension '{char}' was requested, but is not part " + "of the output spec '{output_spec}'".format( + char=char, output_spec=output_spec)) + + first_bias_location = min([output_spec.find(char) for char in bias_axes]) + bias_output_spec = output_spec[first_bias_location:] + + bias_shape = [ + idx_map[char] if char in bias_axes else 1 for char in bias_output_spec + ] + + if not left_elided: + for _ in range(elided): + bias_shape.append(1) + else: + bias_shape = None + + return weight_shape, bias_shape, output_shape + + +def _analyze_quantization_info(equation, input_shape): + + def get_specs(equation, input_shape): + possible_labels = string.ascii_letters + dot_replaced_string = re.sub(r'\.\.\.', '0', equation) + + # This is the case where no ellipses are present in the string. + split_string = re.match('([a-zA-Z]+),([a-zA-Z]+)->([a-zA-Z]+)', + dot_replaced_string) + if split_string is not None: + input_spec = split_string.group(1) + weight_spec = split_string.group(2) + output_spec = split_string.group(3) + return input_spec, weight_spec, output_spec + + # This is the case where ellipses are present on the left. + split_string = re.match('0([a-zA-Z]+),([a-zA-Z]+)->0([a-zA-Z]+)', + dot_replaced_string) + if split_string is not None: + input_spec = split_string.group(1) + weight_spec = split_string.group(2) + output_spec = split_string.group(3) + elided = len(input_shape) - len(input_spec) + possible_labels = sorted( + set(possible_labels) - set(input_spec) - set(weight_spec) - + set(output_spec)) + # Pad labels on the left to `input_spec` and `output_spec` + for i in range(elided): + input_spec = possible_labels[i] + input_spec + output_spec = possible_labels[i] + output_spec + return input_spec, weight_spec, output_spec + + # This is the case where ellipses are present on the right. + split_string = re.match('([a-zA-Z]{2,})0,([a-zA-Z]+)->([a-zA-Z]+)0', + dot_replaced_string) + if split_string is not None: + input_spec = split_string.group(1) + weight_spec = split_string.group(2) + output_spec = split_string.group(3) + elided = len(input_shape) - len(input_spec) + possible_labels = sorted( + set(possible_labels) - set(input_spec) - set(weight_spec) - + set(output_spec)) + # Pad labels on the right to `input_spec` and `output_spec` + for i in range(elided): + input_spec = input_spec + possible_labels[i] + output_spec = output_spec + possible_labels[i] + return input_spec, weight_spec, output_spec + + raise ValueError( + "Invalid einsum equation '{equation}'. Equations must be in the " + 'form [X],[Y]->[Z], ...[X],[Y]->...[Z], or [X]...,[Y]->[Z]....'.format( + equation=equation)) + + input_spec, weight_spec, output_spec = get_specs(equation, input_shape) + + # Determine the axes that should be reduced by the quantizer + input_reduced_axes = [] + weight_reduced_axes = [] + for i, label in enumerate(input_spec): + index = output_spec.find(label) + if index == -1: + input_reduced_axes.append(i) + for i, label in enumerate(weight_spec): + index = output_spec.find(label) + if index == -1: + weight_reduced_axes.append(i) + + # Determine the axes of `ops.expand_dims` + input_expand_axes = [] + weight_expand_axes = [] + for i, label in enumerate(output_spec): + index_input = input_spec.find(label) + index_weight = weight_spec.find(label) + if index_input == -1: + input_expand_axes.append(i) + if index_weight == -1: + weight_expand_axes.append(i) + + # Determine the axes of `ops.transpose` + input_transpose_axes = [] + weight_transpose_axes = [] + for i, label in enumerate(output_spec): + index_input = input_spec.find(label) + index_weight = weight_spec.find(label) + if index_input != -1: + input_transpose_axes.append(index_input) + if index_weight != -1: + weight_transpose_axes.append(index_weight) + # Postprocess the information: + # 1. Add dummy axes (1) to transpose_axes + # 2. Add axis to squeeze_axes if 1. failed + input_squeeze_axes = [] + weight_squeeze_axes = [] + for ori_index in input_reduced_axes: + try: + index = input_expand_axes.pop(0) + except IndexError: + input_squeeze_axes.append(ori_index) + input_transpose_axes.insert(index, ori_index) + for ori_index in weight_reduced_axes: + try: + index = weight_expand_axes.pop(0) + except IndexError: + weight_squeeze_axes.append(ori_index) + weight_transpose_axes.insert(index, ori_index) + # Prepare equation for `einsum_with_inputs_gradient` + custom_gradient_equation = '{output_spec},{weight_spec}->{input_spec}'.format( + output_spec=output_spec, input_spec=input_spec, weight_spec=weight_spec) + weight_reverse_transpose_axes = [ + i for (_, i) in sorted((v, i) + for (i, v) in enumerate(weight_transpose_axes)) + ] + return ( + input_reduced_axes, + weight_reduced_axes, + input_transpose_axes, + weight_transpose_axes, + input_expand_axes, + weight_expand_axes, + input_squeeze_axes, + weight_squeeze_axes, + custom_gradient_equation, + weight_reverse_transpose_axes, + ) diff --git a/easy_rec/python/layers/keras/embedding.py b/easy_rec/python/layers/keras/embedding.py new file mode 100644 index 000000000..77b513951 --- /dev/null +++ b/easy_rec/python/layers/keras/embedding.py @@ -0,0 +1,81 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +"""Fused embedding layer.""" +import tensorflow as tf +from tensorflow.python.keras.layers import Embedding +from tensorflow.python.keras.layers import Layer + + +def _combine(embeddings, weights, comb_fn): + # embeddings shape: [B, N, D] + if callable(comb_fn): + return comb_fn(embeddings, axis=1) + if weights is None: + return tf.reduce_mean(embeddings, axis=1) + if isinstance(weights, tf.SparseTensor): + if weights.dtype == tf.string: + weights = tf.sparse.to_dense(weights, default_value='0') + weights = tf.string_to_number(weights) + else: + weights = tf.sparse.to_dense(weights, default_value=0.0) + sum_weights = tf.reduce_sum(weights, axis=1, keepdims=True) + weights = tf.expand_dims(weights / sum_weights, axis=-1) + return tf.reduce_sum(embeddings * weights, axis=1) + + +class EmbeddingLayer(Layer): + + def __init__(self, params, name='embedding_layer', reuse=None, **kwargs): + super(EmbeddingLayer, self).__init__(name=name, **kwargs) + params.check_required(['vocab_size', 'embedding_dim']) + vocab_size = int(params.vocab_size) + combiner = params.get_or_default('combiner', 'weight') + if combiner == 'mean': + self.combine_fn = tf.reduce_mean + elif combiner == 'sum': + self.combine_fn = tf.reduce_sum + elif combiner == 'max': + self.combine_fn = tf.reduce_max + elif combiner == 'min': + self.combine_fn = tf.reduce_min + elif combiner == 'weight': + self.combine_fn = 'weight' + else: + raise ValueError('unsupported embedding combiner: ' + combiner) + self.embed_dim = int(params.embedding_dim) + self.embedding = Embedding(vocab_size, self.embed_dim) + self.do_concat = params.get_or_default('concat', True) + + def call(self, inputs, training=None, **kwargs): + inputs, weights = inputs + # 将多个特征的输入合并为一个索引 tensor + flat_inputs = [tf.reshape(input_field, [-1]) for input_field in inputs] + all_indices = tf.concat(flat_inputs, axis=0) + # 从共享的嵌入表中进行一次 embedding lookup + all_embeddings = self.embedding(all_indices) + is_multi = [] + # 计算每个特征的嵌入 + split_sizes = [] + for input_field in inputs: + assert input_field.shape.ndims <= 2, 'dims of embedding layer input must be <= 2' + input_shape = tf.shape(input_field) + size = input_shape[0] + if input_field.shape.ndims > 1: + size *= input_shape[-1] + is_multi.append(True) + else: + is_multi.append(False) + split_sizes.append(size) + embeddings = tf.split(all_embeddings, split_sizes, axis=0) + for i in range(len(embeddings)): + if is_multi[i]: + batch_size = tf.shape(inputs[i])[0] + embeddings[i] = tf.cond( + tf.equal(tf.size(embeddings[i]), 0), + lambda: tf.zeros([batch_size, self.embed_dim]), lambda: _combine( + tf.reshape(embeddings[i], [batch_size, -1, self.embed_dim]), + weights[i], self.combine_fn)) + if self.do_concat: + embeddings = tf.concat(embeddings, axis=-1) + print('Embedding layer:', self.name, embeddings) + return embeddings diff --git a/easy_rec/python/layers/keras/mask_net.py b/easy_rec/python/layers/keras/mask_net.py index 08ea698c0..67176851c 100644 --- a/easy_rec/python/layers/keras/mask_net.py +++ b/easy_rec/python/layers/keras/mask_net.py @@ -1,5 +1,7 @@ # -*- encoding:utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. +import logging + import tensorflow as tf from tensorflow.python.keras.layers import Activation from tensorflow.python.keras.layers import Dense @@ -55,6 +57,7 @@ def build(self, input_shape): name='aggregation') self.weight_layer = Dense(input_dim, name='weights') if self._projection_dim is not None: + logging.info('%s project dim is %d', self.name, self._projection_dim) self.project_layer = Dense( self._projection_dim, kernel_regularizer=self.l2_reg, @@ -78,7 +81,7 @@ def build(self, input_shape): self.output_layer_norm = LayerNormalization(name='output_ln') super(MaskBlock, self).build(input_shape) - def call(self, inputs, **kwargs): + def call(self, inputs, training=None, **kwargs): if type(inputs) in (tuple, list): net, mask_input = inputs[:2] else: diff --git a/easy_rec/python/layers/keras/multi_head_attention.py b/easy_rec/python/layers/keras/multi_head_attention.py new file mode 100644 index 000000000..a5ca0b40d --- /dev/null +++ b/easy_rec/python/layers/keras/multi_head_attention.py @@ -0,0 +1,717 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import math +import string + +import numpy as np +import tensorflow as tf +from tensorflow.python.keras import constraints +from tensorflow.python.keras import initializers +from tensorflow.python.keras import regularizers +from tensorflow.python.keras.layers import Dropout +from tensorflow.python.keras.layers import Layer +from tensorflow.python.keras.layers import Softmax + +from easy_rec.python.layers.keras.activation import MaskedSoftmax +from easy_rec.python.layers.keras.einsum_dense import EinsumDense + + +class MultiHeadAttention(Layer): + """MultiHeadAttention layer. + + This is an implementation of multi-headed attention as described in the + paper "Attention is all you Need" + [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762). + If `query`, `key,` `value` are the same, then + this is self-attention. Each time step in `query` attends to the + corresponding sequence in `key`, and returns a fixed-width vector. + + This layer first projects `query`, `key` and `value`. These are + (effectively) a list of tensors of length `num_attention_heads`, where the + corresponding shapes are `(batch_size, , key_dim)`, + `(batch_size, , key_dim)`, + `(batch_size, , value_dim)`. + + Then, the query and key tensors are dot-producted and scaled. These are + softmaxed to obtain attention probabilities. The value tensors are then + interpolated by these probabilities, then concatenated back to a single + tensor. + + Finally, the result tensor with the last dimension as `value_dim` can take + a linear projection and return. + + Args: + num_heads: Number of attention heads. + key_dim: Size of each attention head for query and key. + value_dim: Size of each attention head for value. + dropout: Dropout probability. + use_bias: Boolean, whether the dense layers use bias vectors/matrices. + output_shape: The expected shape of an output tensor, besides the batch + and sequence dims. If not specified, projects back to the query + feature dim (the query input's last dimension). + attention_axes: axes over which the attention is applied. `None` means + attention over all axes, but batch, heads, and features. + kernel_initializer: Initializer for dense layer kernels. + bias_initializer: Initializer for dense layer biases. + kernel_regularizer: Regularizer for dense layer kernels. + bias_regularizer: Regularizer for dense layer biases. + activity_regularizer: Regularizer for dense layer activity. + kernel_constraint: Constraint for dense layer kernels. + bias_constraint: Constraint for dense layer kernels. + use_causal_mask: A boolean to indicate whether to apply a causal mask to + prevent tokens from attending to future tokens (e.g., used in a + decoder Transformer). + return_attention_scores: A boolean to indicate whether the output should + be `(attention_output, attention_scores)` if `True`, or + `attention_output` if `False`. Defaults to `False`. + + Call arguments: + query: Query tensor of shape `(B, T, dim)`, where `B` is the batch size, + `T` is the target sequence length, and dim is the feature dimension. + value: Value tensor of shape `(B, S, dim)`, where `B` is the batch size, + `S` is the source sequence length, and dim is the feature dimension. + key: Optional key tensor of shape `(B, S, dim)`. If not given, will + use `value` for both `key` and `value`, which is the most common + case. + attention_mask: a boolean mask of shape `(B, T, S)`, that prevents + attention to certain positions. The boolean mask specifies which + query elements can attend to which key elements, 1 indicates + attention and 0 indicates no attention. Broadcasting can happen for + the missing batch dimensions and the head dimension. + training: Python boolean indicating whether the layer should behave in + training mode (adding dropout) or in inference mode (no dropout). + Will go with either using the training mode of the parent + layer/model, or `False` (inference) if there is no parent layer. + + Returns: + attention_output: The result of the computation, of shape `(B, T, E)`, + where `T` is for target sequence shapes and `E` is the query input + last dimension if `output_shape` is `None`. Otherwise, the + multi-head outputs are projected to the shape specified by + `output_shape`. + attention_scores: (Optional) multi-head attention coefficients over + attention axes. + """ + + def __init__(self, params, name='multi_head_attention', reuse=None, **kwargs): + super(MultiHeadAttention, self).__init__(name=name, **kwargs) + self.supports_masking = True + self._num_heads = params.num_heads + self._key_dim = params.key_dim + # Cache 1.0 / math.sqrt(self._key_dim). + self._inverse_sqrt_key_dim = None + value_dim = params.get_or_default('value_dim', None) + self._value_dim = value_dim if value_dim else self._key_dim + self._dropout = params.get_or_default('dropout', 0.0) + self._use_bias = params.get_or_default('use_bias', True) + self._output_shape = params.get_or_default('output_shape', None) + self._kernel_initializer = initializers.get( + params.get_or_default('kernel_initializer', 'glorot_uniform')) + self._bias_initializer = initializers.get( + params.get_or_default('bias_initializer', 'zeros')) + self._kernel_regularizer = regularizers.get( + params.get_or_default('kernel_regularizer', None)) + self._bias_regularizer = regularizers.get( + params.get_or_default('bias_regularizer', None)) + self._activity_regularizer = regularizers.get( + params.get_or_default('activity_regularizer', None)) + self._kernel_constraint = constraints.get( + params.get_or_default('kernel_constraint', None)) + self._bias_constraint = constraints.get( + params.get_or_default('bias_constraint', None)) + self._attention_axes = params.get_or_default('attention_axes', None) + self._use_causal_mask = params.get_or_default('use_causal_mask', False) + self._return_attention_scores = params.get_or_default( + 'return_attention_scores', False) + + @property + def num_heads(self): + return self._num_heads + + @property + def key_dim(self): + return self._key_dim + + @property + def value_dim(self): + return self._value_dim + + @property + def dropout(self): + return self._dropout + + @property + def use_bias(self): + return self._use_bias + + @property + def output_shape(self): + return self._output_shape + + @property + def attention_axes(self): + return self._attention_axes + + def get_config(self): + base_config = super(MultiHeadAttention, self).get_config() + config = { + 'num_heads': + self._num_heads, + 'key_dim': + self._key_dim, + 'value_dim': + self._value_dim, + 'dropout': + self._dropout, + 'use_bias': + self._use_bias, + 'output_shape': + self._output_shape, + 'attention_axes': + self._attention_axes, + 'kernel_initializer': + initializers.serialize(self._kernel_initializer), + 'bias_initializer': + initializers.serialize(self._bias_initializer), + 'kernel_regularizer': + regularizers.serialize(self._kernel_regularizer), + 'bias_regularizer': + regularizers.serialize(self._bias_regularizer), + 'activity_regularizer': + regularizers.serialize(self._activity_regularizer), + 'kernel_constraint': + constraints.serialize(self._kernel_constraint), + 'bias_constraint': + constraints.serialize(self._bias_constraint), + } + config.update(base_config) + return config + + def build(self, input_shape): + """Builds layers and variables.""" + if len(input_shape) == 3: + query_shape, value_shape, key_shape = input_shape + elif len(input_shape) == 2: + query_shape, value_shape = input_shape + key_shape = None + else: + raise ValueError('invalid input shape of MultiHeadAttention') + + key_shape = value_shape if key_shape is None else key_shape + query_rank = len(query_shape) + value_rank = len(value_shape) + key_rank = len(key_shape) + einsum_equation, bias_axes, output_rank = _build_proj_equation( + query_rank - 1, bound_dims=1, output_dims=2) + self._query_dense = EinsumDense( + einsum_equation, + output_shape=_get_output_shape(output_rank - 1, + [self._num_heads, self._key_dim]), + bias_axes=bias_axes if self._use_bias else None, + name='query', + **self._get_common_kwargs_for_sublayer()) + self._query_dense.build(query_shape) + einsum_equation, bias_axes, output_rank = _build_proj_equation( + key_rank - 1, bound_dims=1, output_dims=2) + self._key_dense = EinsumDense( + einsum_equation, + output_shape=_get_output_shape(output_rank - 1, + [self._num_heads, self._key_dim]), + bias_axes=bias_axes if self._use_bias else None, + name='key', + **self._get_common_kwargs_for_sublayer()) + self._key_dense.build(key_shape) + einsum_equation, bias_axes, output_rank = _build_proj_equation( + value_rank - 1, bound_dims=1, output_dims=2) + self._value_dense = EinsumDense( + einsum_equation, + output_shape=_get_output_shape(output_rank - 1, + [self._num_heads, self._value_dim]), + bias_axes=bias_axes if self._use_bias else None, + name='value', + **self._get_common_kwargs_for_sublayer()) + self._value_dense.build(value_shape) + # Builds the attention computations for multi-head dot product + # attention. These computations could be wrapped into the keras + # attention layer once it supports multi-head einsum computations. + self._build_attention(output_rank) + self._output_dense = self._make_output_dense( + query_shape, + self._get_common_kwargs_for_sublayer(), + 'attention_output', + ) + output_dense_input_shape = list( + self._query_dense.compute_output_shape(query_shape)) + output_dense_input_shape[-1] = self._value_dim + self._output_dense.build(tuple(output_dense_input_shape)) + self.built = True + print('MultiHeadAttention (%s) built' % self.name) + + @property + def query_dense(self): + return self._query_dense + + @property + def key_dense(self): + return self._key_dense + + @property + def value_dense(self): + return self._value_dense + + @property + def output_dense(self): + return self._output_dense + + def _get_common_kwargs_for_sublayer(self): + common_kwargs = dict( + kernel_regularizer=self._kernel_regularizer, + bias_regularizer=self._bias_regularizer, + activity_regularizer=self._activity_regularizer, + kernel_constraint=self._kernel_constraint, + bias_constraint=self._bias_constraint, + dtype=tf.float32, + ) + # Create new clone of kernel/bias initializer, so that we don't reuse + # the initializer instance, which could lead to same init value since + # initializer is stateless. + kernel_initializer = self._kernel_initializer.__class__.from_config( + self._kernel_initializer.get_config()) + bias_initializer = self._bias_initializer.__class__.from_config( + self._bias_initializer.get_config()) + common_kwargs['kernel_initializer'] = kernel_initializer + common_kwargs['bias_initializer'] = bias_initializer + return common_kwargs + + def _make_output_dense(self, query_shape, common_kwargs, name=None): + """Builds the output projection matrix. + + Args: + query_shape: query tensor shape + common_kwargs: Common keyword arguments for einsum layer. + name: Name for the projection layer. + + Returns: + Projection layer. + """ + query_rank = len(query_shape) + if self._output_shape: + if hasattr(self._output_shape, '__len__'): + output_shape = self._output_shape + else: + output_shape = [self._output_shape] + else: + output_shape = [query_shape[-1]] + einsum_equation, bias_axes, output_rank = _build_proj_equation( + query_rank - 1, bound_dims=2, output_dims=len(output_shape)) + return EinsumDense( + einsum_equation, + output_shape=_get_output_shape(output_rank - 1, output_shape), + bias_axes=bias_axes if self._use_bias else None, + name=name, + **common_kwargs) + + def _build_attention(self, rank): + """Builds multi-head dot-product attention computations. + + This function builds attributes necessary for `_compute_attention` to + customize attention computation to replace the default dot-product + attention. + + Args: + rank: the rank of query, key, value tensors. + """ + if self._attention_axes is None: + self._attention_axes = tuple(range(1, rank - 2)) + else: + self._attention_axes = tuple(self._attention_axes) + ( + self._dot_product_equation, + self._combine_equation, + attn_scores_rank, + ) = _build_attention_equation( + rank, attn_axes=self._attention_axes) + norm_axes = tuple( + range(attn_scores_rank - len(self._attention_axes), attn_scores_rank)) + self._softmax = Softmax( + axis=norm_axes) if tf.__version__ >= '2.0' else MaskedSoftmax( + axis=norm_axes) + self._dropout_layer = Dropout(rate=self._dropout) + self._inverse_sqrt_key_dim = 1.0 / math.sqrt(float(self._key_dim)) + + def _masked_softmax(self, attention_scores, attention_mask=None): + # Normalize the attention scores to probabilities. + # attention_scores = [B, N, T, S] + if attention_mask is not None: + # The expand dim happens starting from the `num_heads` dimension, + # (, num_heads, ) + mask_expansion_axis = -len(self._attention_axes) * 2 - 1 + for _ in range(len(attention_scores.shape) - len(attention_mask.shape)): + attention_mask = tf.expand_dims( + attention_mask, axis=mask_expansion_axis) + return self._softmax(attention_scores, mask=attention_mask) + + def _compute_attention(self, + query, + key, + value, + attention_mask=None, + training=None): + """Applies Dot-product attention with query, key, value tensors. + + This function defines the computation inside `call` with projected + multi-head Q, K, V inputs. Users can override this function for + customized attention implementation. + + Args: + query: Projected query tensor of shape `(B, T, N, key_dim)`. + key: Projected key tensor of shape `(B, S, N, key_dim)`. + value: Projected value tensor of shape `(B, S, N, value_dim)`. + attention_mask: a boolean mask of shape `(B, T, S)`, that prevents + attention to certain positions. It is generally not needed if + the `query` and `value` (and/or `key`) are masked. + training: Python boolean indicating whether the layer should behave + in training mode (adding dropout) or in inference mode (doing + nothing). + + Returns: + attention_output: Multi-headed outputs of attention computation. + attention_scores: Multi-headed attention weights. + """ + # Note: Applying scalar multiply at the smaller end of einsum improves + # XLA performance, but may introduce slight numeric differences in + # the Transformer attention head. + query = tf.multiply(query, tf.cast(self._inverse_sqrt_key_dim, query.dtype)) + + # Take the dot product between "query" and "key" to get the raw + # attention scores. + attention_scores = tf.einsum(self._dot_product_equation, key, query) + + attention_scores = self._masked_softmax(attention_scores, attention_mask) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + if self.dropout: + final_attn_scores = self._dropout_layer( + attention_scores, training=training) + else: + final_attn_scores = attention_scores + + # `context_layer` = [B, T, N, H] + attention_output = tf.einsum(self._combine_equation, final_attn_scores, + value) + return attention_output, attention_scores + + def call(self, inputs, mask=None, training=None, **kwargs): + assert isinstance( + inputs, (tuple, list)), 'inputs of MultiHeadAttention must be a list' + query, value, key = (list(inputs) + [None] * 2)[:3] + if key is None: + key = value + if mask is None: + masks = [None] * 4 + elif type(mask) in (list, tuple): + masks = (list(mask) + [None] * 4)[:4] + else: + masks = ([mask] + [None] * 3)[:4] + query_mask, value_mask, key_mask, attention_mask = masks + if attention_mask is None and value_mask is None: + value_mask = query_mask + attention_mask = self._compute_attention_mask( + query, + value, + query_mask=query_mask, + value_mask=value_mask, + key_mask=key_mask, + attention_mask=attention_mask, + use_causal_mask=self._use_causal_mask, + ) + + # N = `num_attention_heads` + # H = `size_per_head` + # `query` = [B, T, N ,H] + query = self._query_dense(query) + + # `key` = [B, S, N, H] + key = self._key_dense(key) + + # `value` = [B, S, N, H] + value = self._value_dense(value) + attention_output, attention_scores = self._compute_attention( + query, key, value, attention_mask, training) + attention_output = self._output_dense(attention_output) + if self._return_attention_scores: + return attention_output, attention_scores + return attention_output + + def _compute_attention_mask( + self, + query, + value, + query_mask=None, + value_mask=None, + key_mask=None, + attention_mask=None, + use_causal_mask=False, + ): + """Computes the attention mask, using the Keras masks of the inputs. + + * The `query`'s mask is reshaped from [B, T] to [B, T, 1]. + * The `value`'s mask is reshaped from [B, S] to [B, 1, S]. + * The `key`'s mask is reshaped from [B, S] to [B, 1, S]. The `key`'s + mask is ignored if `key` is `None` or if `key is value`. + * If `use_causal_mask=True`, then the causal mask is computed. Its shape + is [1, T, S]. + + All defined masks are merged using a logical AND operation (`&`). + + In general, if the `query` and `value` are masked, then there is no need + to define the `attention_mask`. + + Args: + query: Projected query tensor of shape `(B, T, N, key_dim)`. + value: Projected value tensor of shape `(B, T, N, value_dim)`. + attention_mask: a boolean mask of shape `(B, T, S)`, that prevents + attention to certain positions. + use_causal_mask: A boolean to indicate whether to apply a causal + mask to prevent tokens from attending to future tokens (e.g., + used in a decoder Transformer). + + Returns: + attention_mask: a boolean mask of shape `(B, T, S)`, that prevents + attention to certain positions, based on the Keras masks of the + `query`, `key`, `value`, and `attention_mask` tensors, and the + causal mask if `use_causal_mask=True`. + """ + auto_mask = None + if query_mask is not None: + query_mask = tf.cast(query_mask, tf.bool) # defensive casting + # B = batch size, T = max query length + auto_mask = tf.expand_dims(query_mask, -1) # shape is [B, T, 1] + if value_mask is not None: + value_mask = tf.cast(value_mask, tf.bool) # defensive casting + # B = batch size, S == max value length + mask = tf.expand_dims(value_mask, -2) # shape is [B, 1, S] + auto_mask = mask if auto_mask is None else auto_mask & mask + if key_mask is not None: + key_mask = tf.cast(key_mask, tf.bool) # defensive casting + # B == batch size, S == max key length == max value length + mask = tf.expand_dims(key_mask, -2) # shape is [B, 1, S] + auto_mask = mask if auto_mask is None else auto_mask & mask + if use_causal_mask: + # the shape of the causal mask is [1, T, S] + mask = self._compute_causal_mask(query, value) + auto_mask = mask if auto_mask is None else auto_mask & mask + if auto_mask is not None: + # merge attention_mask & automatic mask, to shape [B, T, S] + attention_mask = ( + auto_mask if attention_mask is None else + tf.cast(attention_mask, tf.bool) & auto_mask) + return attention_mask + + def _compute_causal_mask(self, query, value=None): + """Computes a causal mask (e.g., for masked self-attention layers). + + For example, if query and value both contain sequences of length 4, + this function returns a boolean tensor equal to: + + ``` + [[[True, False, False, False], + [True, True, False, False], + [True, True, True, False], + [True, True, True, True]]] + ``` + + Args: + query: query tensor of shape `(B, T, ...)`. + value: value tensor of shape `(B, S, ...)` (optional, defaults to + query). + + Returns: + mask: a boolean tensor of shape `(1, T, S)` containing a lower + triangular matrix of shape `(T, S)`. + """ + q_seq_length = tf.shape(query)[1] + v_seq_length = q_seq_length if value is None else tf.shape(value)[1] + ones_mask = tf.ones((1, q_seq_length, v_seq_length), dtype='int32') + row_index = tf.cumsum(ones_mask, axis=-2) + col_index = tf.cumsum(ones_mask, axis=-1) + return tf.greater_equal(row_index, col_index) + + def compute_output_shape(self, input_shape): + if len(input_shape) == 3: + query_shape, value_shape, key_shape = input_shape + elif len(input_shape) == 2: + query_shape, value_shape = input_shape + key_shape = None + else: + raise ValueError('invalid input shape of MultiHeadAttention') + if key_shape is None: + key_shape = value_shape + + if query_shape[-1] != value_shape[-1]: + raise ValueError( + 'The last dimension of `query_shape` and `value_shape` ' + 'must be equal, but are {query_last_dim}, {value_last_dim}. ' + 'Received: query_shape={query_shape}, value_shape={value_shape}' + .format( + query_shape=query_shape, + value_shape=value_shape, + query_last_dim=query_shape[-1], + value_last_dim=value_shape[-1])) + + if value_shape[1:-1] != key_shape[1:-1]: + raise ValueError( + 'All dimensions of `value` and `key`, except the last one, ' + 'must be equal. Received: value_shape={value_shape} and ' + 'key_shape={key_shape}'.format( + key_shape=key_shape, value_shape=value_shape)) + + if self._output_shape: + if hasattr(self._output_dense, '__len__'): + return query_shape[:-1] + self._output_shape + else: + return query_shape[:-1] + [self._output_shape] + + return query_shape + + +def _index_to_einsum_variable(i): + """Coverts an index to a einsum variable name. + + We simply map indices to lowercase characters, e.g. 0 -> 'a', 1 -> 'b'. + """ + return string.ascii_lowercase[i] + + +def _build_attention_equation(rank, attn_axes): + """Builds einsum equations for the attention computation. + + Query, key, value inputs after projection are expected to have the shape as: + `(bs, , , num_heads, channels)`. + `bs` and `` are treated as ``. + + The attention operations can be generalized: + 1. Query-key dot product: + (, , num_heads, channels), + (, , num_heads, channels) -> + (, num_heads, , ) + 2. Combination: + (, num_heads, , ), + (, , num_heads, channels) -> (, , num_heads, channels) + + Args: + rank: Rank of query, key, value tensors. + attn_axes: List/tuple of axes, `[-1, rank)`, + that attention will be applied to. + + Returns: + Einsum equations. + """ + target_notation = '' + for i in range(rank): + target_notation += _index_to_einsum_variable(i) + # `batch_dims` includes the head dim. + batch_dims = tuple(np.delete(range(rank), attn_axes + (rank - 1,))) + letter_offset = rank + source_notation = '' + for i in range(rank): + if i in batch_dims or i == rank - 1: + source_notation += target_notation[i] + else: + source_notation += _index_to_einsum_variable(letter_offset) + letter_offset += 1 + + product_notation = ''.join([target_notation[i] for i in batch_dims] + + [target_notation[i] for i in attn_axes] + + [source_notation[i] for i in attn_axes]) + dot_product_equation = '%s,%s->%s' % ( + source_notation, + target_notation, + product_notation, + ) + attn_scores_rank = len(product_notation) + combine_equation = '%s,%s->%s' % ( + product_notation, + source_notation, + target_notation, + ) + return dot_product_equation, combine_equation, attn_scores_rank + + +def _build_proj_equation(free_dims, bound_dims, output_dims): + """Builds an einsum equation for projections inside multi-head attention.""" + input_str = '' + kernel_str = '' + output_str = '' + bias_axes = '' + letter_offset = 0 + for i in range(free_dims): + char = _index_to_einsum_variable(i + letter_offset) + input_str += char + output_str += char + + letter_offset += free_dims + for i in range(bound_dims): + char = _index_to_einsum_variable(i + letter_offset) + input_str += char + kernel_str += char + + letter_offset += bound_dims + for i in range(output_dims): + char = _index_to_einsum_variable(i + letter_offset) + kernel_str += char + output_str += char + bias_axes += char + equation = '{input_str},{kernel_str}->{output_str}'.format( + input_str=input_str, kernel_str=kernel_str, output_str=output_str) + return equation, bias_axes, len(output_str) + + +def _get_output_shape(output_rank, known_last_dims): + return [None] * (output_rank - len(known_last_dims)) + list(known_last_dims) + + # def __init__(self, params, name='multi_head_attention', reuse=None, **kwargs): + # super(MultiHeadAttention, self).__init__(name=name, **kwargs) + # self.num_heads = params.num_attention_heads + # self.d_model = params.hidden_size + # if self.d_model % self.num_heads != 0: + # raise ValueError( + # 'The hidden size (%d) is not a multiple of the number of attention ' + # 'heads (%d)' % (self.d_model, self.num_heads)) + # self.depth = self.d_model // self.num_heads + # self.wq = Dense(self.d_model) + # self.wk = Dense(self.d_model) + # self.wv = Dense(self.d_model) + # self.dense = Dense(self.d_model) + # att_params = Parameter.make_from_pb(params.attention) + # self.attention = Attention(att_params, 'scaled_dot_product_attention') + # + # # def split_heads(self, x, batch_size): + # # x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth)) + # # return tf.transpose(x, perm=[0, 2, 1, 3]) + # + # def call(self, inputs, training=None, **kwargs): + # q, v, k, mask = inputs + # batch_size = tf.shape(q)[0] + # + # q = self.wq(q) + # k = self.wk(k) + # v = self.wv(v) + # + # # q = self.split_heads(q, batch_size) + # # k = self.split_heads(k, batch_size) + # # v = self.split_heads(v, batch_size) + # + # attn = self.attention([q, v, k], mask=[mask, mask], training=training) + # return_attn_score = self.attention.return_attention_scores + # attention, attention_scores = attn if return_attn_score else attn, None + # + # # attention = tf.transpose(attention, perm=[0, 2, 1, 3]) + # # attention = tf.reshape(attention, (batch_size, -1, self.d_model)) + # output = self.dense(attention) + # if return_attn_score: + # return output, attention_scores + # return output diff --git a/easy_rec/python/layers/keras/numerical_embedding.py b/easy_rec/python/layers/keras/numerical_embedding.py index a5205dfed..1b37bcf83 100644 --- a/easy_rec/python/layers/keras/numerical_embedding.py +++ b/easy_rec/python/layers/keras/numerical_embedding.py @@ -1,11 +1,41 @@ # -*- encoding:utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. +import logging import math +import os import tensorflow as tf +from tensorflow.python.keras.layers import Layer +from easy_rec.python.compat.array_ops import repeat from easy_rec.python.utils.activation import get_activation +curr_dir, _ = os.path.split(__file__) +parent_dir = os.path.dirname(curr_dir) +ops_idr = os.path.dirname(parent_dir) +ops_dir = os.path.join(ops_idr, 'ops') +if 'PAI' in tf.__version__: + ops_dir = os.path.join(ops_dir, '1.12_pai') +elif tf.__version__.startswith('1.12'): + ops_dir = os.path.join(ops_dir, '1.12') +elif tf.__version__.startswith('1.15'): + if 'IS_ON_PAI' in os.environ: + ops_dir = os.path.join(ops_dir, 'DeepRec') + else: + ops_dir = os.path.join(ops_dir, '1.15') +elif tf.__version__.startswith('2.12'): + ops_dir = os.path.join(ops_dir, '2.12') + +logging.info('ops_dir is %s' % ops_dir) +custom_op_path = os.path.join(ops_dir, 'libcustom_ops.so') +try: + custom_ops = tf.load_op_library(custom_op_path) + logging.info('load custom op from %s succeed' % custom_op_path) +except Exception as ex: + logging.warning('load custom op from %s failed: %s' % + (custom_op_path, str(ex))) + custom_ops = None + if tf.__version__ >= '2.0': tf = tf.compat.v1 @@ -84,7 +114,7 @@ def __call__(self, x, *args, **kwargs): return x -class PeriodicEmbedding(tf.keras.layers.Layer): +class PeriodicEmbedding(Layer): """Periodic embeddings for numerical features described in [1]. References: @@ -157,7 +187,7 @@ def call(self, inputs, **kwargs): return output -class AutoDisEmbedding(tf.keras.layers.Layer): +class AutoDisEmbedding(Layer): """An Embedding Learning Framework for Numerical Features in CTR Prediction. Refer: https://arxiv.org/pdf/2012.08986v2.pdf @@ -210,3 +240,95 @@ def call(self, inputs, **kwargs): if self.output_3d_tensor: return output, emb return output + + +class NaryDisEmbedding(Layer): + """Numerical Feature Representation with Hybrid 𝑁 -ary Encoding, CIKM 2022.. + + Refer: https://dl.acm.org/doi/pdf/10.1145/3511808.3557090 + """ + + def __init__(self, params, name='nary_dis_embedding', reuse=None, **kwargs): + super(NaryDisEmbedding, self).__init__(name=name, **kwargs) + self.reuse = reuse + self.nary_carry = custom_ops.nary_carry + params.check_required(['embedding_dim', 'carries']) + self.emb_dim = int(params.embedding_dim) + self.carries = params.get_or_default('carries', [2, 9]) + self.lengths = list(map(self.max_length, self.carries)) + self.vocab_size = int(sum(self.lengths)) + self.multiplier = params.get_or_default('multiplier', 1.0) + self.intra_ary_pooling = params.get_or_default('intra_ary_pooling', 'sum') + self.inter_ary_pooling = params.get_or_default('inter_ary_pooling', + 'concat') + self.output_3d_tensor = params.get_or_default('output_3d_tensor', False) + logging.info( + '{} carries: {}, lengths: {}, vocab_size: {}, intra_ary: {}, inter_ary: {}, multiplier: {}' + .format(self.name, ','.join(map(str, self.carries)), + ','.join(map(str, self.lengths)), self.vocab_size, + self.intra_ary_pooling, self.inter_ary_pooling, + self.multiplier)) + + @staticmethod + def max_length(carry): + bits = math.log(4294967295, carry) + return (math.floor(bits) + 1) * carry + + def build(self, input_shape): + assert isinstance(input_shape, + tf.TensorShape), 'NaryDisEmbedding only takes 1 input' + self.num_features = int(input_shape[-1]) + logging.info('%s has %d input features', self.name, self.num_features) + vocab_size = self.num_features * self.vocab_size + self.embedding_table = self.add_weight( + 'embed_table', + shape=[vocab_size, self.emb_dim], + initializer='he_uniform', + dtype=tf.float32, + trainable=True) + super(NaryDisEmbedding, self).build(input_shape) + + def call(self, inputs, **kwargs): + if inputs.shape.ndims != 2: + raise ValueError('inputs of NaryDisEmbedding must have 2 dimensions.') + if self.multiplier != 1.0: + inputs *= self.multiplier + inputs = tf.to_int32(inputs) + offset, emb_indices, emb_splits = 0, [], [] + for carry, length in zip(self.carries, self.lengths): + values, splits = self.nary_carry(inputs, carry=carry, offset=offset) + offset += length + emb_indices.append(values) + emb_splits.append(splits) + indices = tf.concat(emb_indices, axis=0) + splits = tf.concat(emb_splits, axis=0) + # embedding shape: [B*N*C, D] + embedding = tf.nn.embedding_lookup(self.embedding_table, indices) + + total_length = tf.size(splits) + if self.intra_ary_pooling == 'sum': + segment_ids = repeat(tf.range(total_length), repeats=splits) + embedding = tf.math.segment_sum(embedding, segment_ids) + elif self.intra_ary_pooling == 'mean': + segment_ids = repeat(tf.range(total_length), repeats=splits) + embedding = tf.math.segment_mean(embedding, segment_ids) + else: + raise ValueError('Unsupported intra ary pooling method %s' % + self.intra_ary_pooling) + + if self.inter_ary_pooling == 'concat': + embeddings = tf.split(embedding, len(self.carries)) + embedding = tf.concat(embeddings, axis=-1) # [B*N, C*D] + else: + raise ValueError('Unsupported inter ary pooling method %s' % + self.inter_ary_pooling) + if self.output_3d_tensor: + embedding = tf.reshape( + embedding, [-1, self.num_features, + len(self.carries) * self.emb_dim]) # [B, N, C*D] + else: + embedding = tf.reshape( + embedding, [-1, self.num_features * len(self.carries) * self.emb_dim + ]) # [B, N*C*D] + print('NaryDisEmbedding:', embedding) + return embedding diff --git a/easy_rec/python/layers/keras/transformer.py b/easy_rec/python/layers/keras/transformer.py new file mode 100644 index 000000000..d71a02831 --- /dev/null +++ b/easy_rec/python/layers/keras/transformer.py @@ -0,0 +1,192 @@ +# -*- encoding:utf-8 -*- +# Copyright (c) Alibaba, Inc. and its affiliates. +import logging + +import numpy as np +import tensorflow as tf +from tensorflow.python.keras.layers import Dense +from tensorflow.python.keras.layers import Dropout +from tensorflow.python.keras.layers import Embedding +from tensorflow.python.keras.layers import Layer + +from easy_rec.python.layers.keras import MultiHeadAttention +from easy_rec.python.layers.keras.layer_norm import LayerNormalization +from easy_rec.python.layers.utils import Parameter +from easy_rec.python.protos import seq_encoder_pb2 + + +class TransformerBlock(Layer): + """A transformer block combines multi-head attention and feed-forward networks with layer normalization and dropout. + + Purpose: Combines attention and feed-forward layers with residual connections and normalization. + Components: Multi-head attention, feed-forward network, dropout, and layer normalization. + Output: Enhanced representation after applying attention and feed-forward layers. + """ + + def __init__(self, params, name='transformer_block', reuse=None, **kwargs): + super(TransformerBlock, self).__init__(name=name, **kwargs) + d_model = params.hidden_size + num_heads = params.num_attention_heads + mha_cfg = seq_encoder_pb2.MultiHeadAttention() + mha_cfg.num_heads = num_heads + mha_cfg.key_dim = d_model // num_heads + mha_cfg.dropout = params.get_or_default('attention_probs_dropout_prob', 0.0) + mha_cfg.return_attention_scores = False + args = Parameter.make_from_pb(mha_cfg) + self.mha = MultiHeadAttention(args, 'multi_head_attn') + dropout_rate = params.get_or_default('hidden_dropout_prob', 0.1) + ffn_units = params.get_or_default('intermediate_size', d_model) + ffn_act = params.get_or_default('hidden_act', 'relu') + self.ffn_dense1 = Dense(ffn_units, activation=ffn_act) + self.ffn_dense2 = Dense(d_model) + if tf.__version__ >= '2.0': + self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6) + self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6) + else: + self.layer_norm1 = LayerNormalization(epsilon=1e-6) + self.layer_norm2 = LayerNormalization(epsilon=1e-6) + self.dropout1 = Dropout(dropout_rate) + self.dropout2 = Dropout(dropout_rate) + + def call(self, inputs, training=None, **kwargs): + x, mask = inputs + attn_output = self.mha([x, x, x], mask=mask, training=training) + attn_output = self.dropout1(attn_output, training=training) + out1 = self.layer_norm1(x + attn_output) + ffn_mid = self.ffn_dense1(out1) + ffn_output = self.ffn_dense2(ffn_mid) + ffn_output = self.dropout2(ffn_output, training=training) + out2 = self.layer_norm2(out1 + ffn_output) + return out2 + + +# Positional Encoding, https://www.tensorflow.org/text/tutorials/transformer +def positional_encoding(length, depth): + depth = depth / 2 + positions = np.arange(length)[:, np.newaxis] # (seq, 1) + depths = np.arange(depth)[np.newaxis, :] / depth # (1, depth) + angle_rates = 1 / (10000**depths) # (1, depth) + angle_rads = positions * angle_rates # (pos, depth) + pos_encoding = np.concatenate( + [np.sin(angle_rads), np.cos(angle_rads)], axis=-1) + return tf.cast(pos_encoding, dtype=tf.float32) + + +class PositionalEmbedding(Layer): + + def __init__(self, vocab_size, d_model, max_position, name='pos_embedding'): + super(PositionalEmbedding, self).__init__(name=name) + self.d_model = d_model + self.embedding = Embedding(vocab_size, d_model) + self.pos_encoding = positional_encoding(length=max_position, depth=d_model) + + def call(self, x, training=None): + length = tf.shape(x)[1] + x = self.embedding(x) + # This factor sets the relative scale of the embedding and positional_encoding. + x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32)) + x = x + self.pos_encoding[tf.newaxis, :length, :] + return x + + +class TransformerEncoder(Layer): + """The encoder consists of a stack of encoder layers. + + It converts the input sequence into a set of embeddings enriched with positional information. + Purpose: Encodes the input sequence into a set of embeddings. + Components: Embedding layer, positional encoding, and a stack of transformer blocks. + Output: Encoded representation of the input sequence. + """ + + def __init__(self, params, name='transformer_encoder', reuse=None, **kwargs): + super(TransformerEncoder, self).__init__(name=name, **kwargs) + d_model = params.hidden_size + dropout_rate = params.get_or_default('hidden_dropout_prob', 0.1) + max_position = params.get_or_default('max_position_embeddings', 512) + num_layers = params.get_or_default('num_hidden_layers', 1) + vocab_size = params.vocab_size + logging.info('vocab size of TransformerEncoder(%s) is %d', name, vocab_size) + self.output_all = params.get_or_default('output_all_token_embeddings', True) + self.pos_encoding = PositionalEmbedding(vocab_size, d_model, max_position) + self.dropout = Dropout(dropout_rate) + self.enc_layers = [ + TransformerBlock(params, 'layer_%d' % i) for i in range(num_layers) + ] + self._vocab_size = vocab_size + self._max_position = max_position + + @property + def vocab_size(self): + return self._vocab_size + + @property + def max_position(self): + return self._max_position + + def call(self, inputs, training=None, **kwargs): + x, mask = inputs + # `x` is token-IDs shape: (batch, seq_len) + x = self.pos_encoding(x) # Shape `(batch_size, seq_len, d_model)`. + x = self.dropout(x, training=training) + for block in self.enc_layers: + x = block([x, mask], training) + # x Shape `(batch_size, seq_len, d_model)`. + return x if self.output_all else x[:, 0, :] + + +class TextEncoder(Layer): + + def __init__(self, params, name='text_encoder', reuse=None, **kwargs): + super(TextEncoder, self).__init__(name=name, **kwargs) + self.separator = params.get_or_default('separator', ' ') + self.cls_token = '[CLS]' + self.separator + self.sep_token = self.separator + '[SEP]' + self.separator + params.transformer.output_all_token_embeddings = False + trans_params = Parameter.make_from_pb(params.transformer) + vocab_file = params.get_or_default('vocab_file', None) + self.vocab = None + self.default_token_id = params.get_or_default('default_token_id', 0) + if vocab_file is not None: + self.vocab = tf.feature_column.categorical_column_with_vocabulary_file( + 'tokens', + vocabulary_file=vocab_file, + default_value=self.default_token_id) + logging.info('vocab file of TextEncoder(%s) is %s', name, vocab_file) + trans_params.vocab_size = self.vocab.vocabulary_size + self.encoder = TransformerEncoder(trans_params, name='transformer') + + def call(self, inputs, training=None, **kwargs): + if type(inputs) not in (tuple, list): + inputs = [inputs] + inputs = [tf.squeeze(text) for text in inputs] + batch_size = tf.shape(inputs[0]) + cls = tf.fill(batch_size, self.cls_token) + sep = tf.fill(batch_size, self.sep_token) + sentences = [cls] + for sentence in inputs: + sentences.append(sentence) + sentences.append(sep) + text = tf.strings.join(sentences) + tokens = tf.strings.split(text, self.separator) + if self.vocab is not None: + features = {'tokens': tokens} + token_ids = self.vocab._transform_feature(features) + token_ids = tf.sparse.to_dense( + token_ids, default_value=self.default_token_id, name='token_ids') + length = tf.shape(token_ids)[-1] + token_ids = tf.cond( + tf.less_equal(length, self.encoder.max_position), lambda: token_ids, + lambda: tf.slice(token_ids, [0, 0], [-1, self.encoder.max_position])) + mask = tf.not_equal(token_ids, self.default_token_id, name='mask') + else: + tokens = tf.sparse.to_dense(tokens, default_value='') + length = tf.shape(tokens)[-1] + tokens = tf.cond( + tf.less_equal(length, self.encoder.max_position), lambda: tokens, + lambda: tf.slice(tokens, [0, 0], [-1, self.encoder.max_position])) + token_ids = tf.string_to_hash_bucket_fast( + tokens, self.encoder.vocab_size, name='token_ids') + mask = tf.not_equal(tokens, '', name='mask') + + encoding = self.encoder([token_ids, mask], training=training) + return encoding diff --git a/easy_rec/python/layers/utils.py b/easy_rec/python/layers/utils.py index 8d1094465..7eb86b791 100644 --- a/easy_rec/python/layers/utils.py +++ b/easy_rec/python/layers/utils.py @@ -163,6 +163,10 @@ def mark_input_src(name, src_desc): def is_proto_message(pb_obj, field): + if not hasattr(pb_obj, 'DESCRIPTOR'): + return False + if field not in pb_obj.DESCRIPTOR.fields_by_name: + return False field_type = pb_obj.DESCRIPTOR.fields_by_name[field].type return field_type == FieldDescriptor.TYPE_MESSAGE @@ -199,7 +203,6 @@ def __getattr__(self, key): return Parameter(value, True, self._l2_reg) else: return value - value = getattr(self.params, key) if is_proto_message(self.params, key): return Parameter(value, False, self._l2_reg) @@ -220,12 +223,14 @@ def get_or_default(self, key, def_val): return def_val else: # pb message value = getattr(self.params, key, def_val) - if hasattr(value, '__len__'): - if len(value) > 0: + if hasattr(value, '__len__'): # repeated + return value if len(value) > 0 else def_val + try: + if self.params.HasField(key): return value - elif self.params.HasField(key): - return value - return def_val + except ValueError: + pass + return def_val # maybe not equal to the default value of msg field def check_required(self, keys): if not self.is_struct: diff --git a/easy_rec/python/loss/pairwise_loss.py b/easy_rec/python/loss/pairwise_loss.py index 5b48f8dff..604f1ce2e 100644 --- a/easy_rec/python/loss/pairwise_loss.py +++ b/easy_rec/python/loss/pairwise_loss.py @@ -42,7 +42,7 @@ def pairwise_loss(labels, pairwise_logits = tf.math.subtract( tf.expand_dims(logits, -1), tf.expand_dims(logits, 0)) - margin pairwise_mask = tf.greater( - tf.expand_dims(labels, -1) - tf.expand_dims(labels, 0), 0) + tf.expand_dims(labels, -1), tf.expand_dims(labels, 0)) if session_ids is not None: logging.info('[%s] use session ids' % loss_name) group_equal = tf.equal( @@ -91,7 +91,7 @@ def pairwise_focal_loss(labels, pairwise_logits = tf.expand_dims(logits, -1) - tf.expand_dims(logits, 0) pairwise_mask = tf.greater( - tf.expand_dims(labels, -1) - tf.expand_dims(labels, 0), 0) + tf.expand_dims(labels, -1), tf.expand_dims(labels, 0)) if hinge_margin is not None: hinge_mask = tf.less(pairwise_logits, hinge_margin) pairwise_mask = tf.logical_and(pairwise_mask, hinge_mask) @@ -134,7 +134,7 @@ def pairwise_logistic_loss(labels, ohem_ratio=1.0, use_label_margin=False, name=''): - r"""Computes pairwise logistic loss between `labels` and `logits`. + r"""Computes pairwise logistic loss between `labels` and `logits`, equivalent to RankNet loss. Definition: $$ @@ -146,7 +146,8 @@ def pairwise_logistic_loss(labels, labels: A `Tensor` of the same shape as `logits` representing graded relevance. logits: A `Tensor` with shape [batch_size]. - session_ids: a `Tensor` with shape [batch_size]. Session ids of each sample, used to max GAUC metric. e.g. user_id + session_ids: a `Tensor` with shape [batch_size]. Session ids of each + sample, used to max GAUC metric. e.g. user_id temperature: (Optional) The temperature to use for scaling the logits. hinge_margin: the margin between positive and negative logits weights: A scalar, a `Tensor` with shape [batch_size] for each sample @@ -163,23 +164,23 @@ def pairwise_logistic_loss(labels, logits /= temperature if use_label_margin: labels /= temperature + pairwise_logits = tf.math.subtract( tf.expand_dims(logits, -1), tf.expand_dims(logits, 0)) - pairwise_labels = tf.math.subtract( - tf.expand_dims(labels, -1), tf.expand_dims(labels, 0)) + if use_label_margin: + pairwise_logits -= tf.math.subtract( + tf.expand_dims(labels, -1), tf.expand_dims(labels, 0)) + elif hinge_margin is not None: + pairwise_logits -= hinge_margin - pairwise_mask = tf.greater(pairwise_labels, 0) - if hinge_margin is not None: - hinge_mask = tf.less(pairwise_logits, hinge_margin) - pairwise_mask = tf.logical_and(pairwise_mask, hinge_mask) + pairwise_mask = tf.greater( + tf.expand_dims(labels, -1), tf.expand_dims(labels, 0)) if session_ids is not None: logging.info('[%s] use session ids' % loss_name) group_equal = tf.equal( tf.expand_dims(session_ids, -1), tf.expand_dims(session_ids, 0)) pairwise_mask = tf.logical_and(pairwise_mask, group_equal) - if use_label_margin: - pairwise_logits -= pairwise_labels pairwise_logits = tf.boolean_mask(pairwise_logits, pairwise_mask) num_pair = tf.size(pairwise_logits) tf.summary.scalar('loss/%s_num_of_pairs' % loss_name, num_pair) diff --git a/easy_rec/python/model/multi_task_model.py b/easy_rec/python/model/multi_task_model.py index 6361138bf..d792d3131 100644 --- a/easy_rec/python/model/multi_task_model.py +++ b/easy_rec/python/model/multi_task_model.py @@ -4,7 +4,6 @@ from collections import OrderedDict import tensorflow as tf -from google.protobuf import struct_pb2 from tensorflow.python.keras.layers import Dense from easy_rec.python.builders import loss_builder @@ -12,6 +11,7 @@ from easy_rec.python.layers.keras.attention import Attention from easy_rec.python.layers.utils import Parameter from easy_rec.python.model.rank_model import RankModel +from easy_rec.python.protos import seq_encoder_pb2 from easy_rec.python.protos import tower_pb2 from easy_rec.python.protos.easy_rec_model_pb2 import EasyRecModel from easy_rec.python.protos.loss_pb2 import LossType @@ -103,9 +103,9 @@ def build_predict_graph(self): queries = tf.stack([Dense(dim)(x) for x in tower_inputs], axis=1) keys = tf.stack([Dense(dim)(x) for x in tower_inputs], axis=1) values = tf.stack([Dense(dim)(x) for x in tower_inputs], axis=1) - st_params = struct_pb2.Struct() - st_params.update({'scale_by_dim': True}) - params = Parameter(st_params, True) + attn_cfg = seq_encoder_pb2.Attention() + attn_cfg.use_scale = True + params = Parameter.make_from_pb(attn_cfg) attention_layer = Attention(params, name='AITM_%s' % tower_name) result = attention_layer([queries, values, keys]) relation_fea = result[:, 0, :] @@ -239,6 +239,16 @@ def build_loss_graph(self): task_tower_cfg.in_task_space_weight * in_task_space + task_tower_cfg.out_task_space_weight * (1 - in_task_space)) + if task_tower_cfg.HasField('task_space_indicator_name') and \ + task_tower_cfg.HasField('task_space_indicator_value'): + in_task_space = tf.to_float( + tf.equal( + self._feature_dict[task_tower_cfg.task_space_indicator_name], + task_tower_cfg.task_space_indicator_value)) + loss_weight = loss_weight * ( + task_tower_cfg.in_task_space_weight * in_task_space + + task_tower_cfg.out_task_space_weight * (1 - in_task_space)) + task_loss_weight = task_loss_weights[tower_name] loss_dict = {} losses = task_tower_cfg.losses diff --git a/easy_rec/python/ops/1.12/libcustom_ops.so b/easy_rec/python/ops/1.12/libcustom_ops.so index ee2e093224ca96293259de7b7828f924e71b6ca4..9014d3c9b1e3037f4d09e6df16e9d9e321248032 100755 GIT binary patch delta 183256 zcmaH!2YeIt|Nk$ANSO^#AnX7|3kU?XSQ#4D*MJ2gf>sm>yC5SXqbw7aw$w@>YU-}I 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zTF)@ednNa*(RxNb+rjP3fz8!YOrTe?C;1Mr$~DsW{0TJI?C6!;w&sBPNtO*gX-@W{ Qa8EnQHXj$oquuoX0XJ~&Z~y=R diff --git a/easy_rec/python/protos/backbone.proto b/easy_rec/python/protos/backbone.proto index 8bf5fd7cc..b86da78ec 100644 --- a/easy_rec/python/protos/backbone.proto +++ b/easy_rec/python/protos/backbone.proto @@ -20,6 +20,13 @@ message InputLayer { message RawInputLayer { } +message EmbeddingLayer { + required uint32 embedding_dim = 1; + optional uint32 vocab_size = 2; + optional string combiner = 3 [default = 'weight']; + optional bool concat = 4 [default = true]; +} + message Lambda { required string expression = 1; } @@ -82,6 +89,7 @@ message Block { RecurrentLayer recurrent = 104; RepeatLayer repeat = 105; RawInputLayer raw_input = 106; + EmbeddingLayer embedding_layer = 107; } } @@ -91,8 +99,10 @@ message BlockPackage { required string name = 1; // a few blocks generating a DAG repeated Block blocks = 2; - // the names of output blocks + // the names of output blocks, will be merge into a tensor repeated string concat_blocks = 3; + // the names of output blocks, return as a list or single tensor + repeated string output_blocks = 4; } message BackboneTower { @@ -100,8 +110,10 @@ message BackboneTower { repeated BlockPackage packages = 1; // a few blocks generating a DAG repeated Block blocks = 2; - // the names of output blocks + // the names of output blocks, will be merge into a tensor repeated string concat_blocks = 3; + // the names of output blocks, return as a list or single tensor + repeated string output_blocks = 4; // optional top mlp layer - optional MLP top_mlp = 4; + optional MLP top_mlp = 5; } diff --git a/easy_rec/python/protos/keras_layer.proto b/easy_rec/python/protos/keras_layer.proto index d9b8b2e11..8a12207a4 100644 --- a/easy_rec/python/protos/keras_layer.proto +++ b/easy_rec/python/protos/keras_layer.proto @@ -13,6 +13,7 @@ message KerasLayer { google.protobuf.Struct st_params = 2; PeriodicEmbedding periodic_embedding = 3; AutoDisEmbedding auto_dis_embedding = 4; + NaryDisEmbedding nary_dis_embedding = 21; FM fm = 5; MaskBlock mask_block = 6; MaskNet masknet = 7; @@ -29,5 +30,9 @@ message KerasLayer { HighWayTower highway = 18; OverlapFeature overlap = 19; MappedDotProduct dot_product = 20; + Attention attention = 22; + MultiHeadAttention multi_head_attention = 23; + Transformer transformer = 24; + TextEncoder text_encoder = 25; } } diff --git a/easy_rec/python/protos/layer.proto b/easy_rec/python/protos/layer.proto index 63f32a85a..a76dfce34 100644 --- a/easy_rec/python/protos/layer.proto +++ b/easy_rec/python/protos/layer.proto @@ -30,6 +30,15 @@ message AutoDisEmbedding { optional bool output_tensor_list = 6; } +message NaryDisEmbedding { + required uint32 embedding_dim = 1; + repeated uint32 carries = 2; + optional float multiplier = 3 [default = 1.0]; + optional string intra_ary_pooling = 4 [default = 'sum']; + optional string inter_ary_pooling = 5 [default = 'concat']; + optional bool output_3d_tensor = 6 [default = false]; +} + message SENet { required uint32 reduction_ratio = 1 [default = 4]; optional uint32 num_squeeze_group = 2 [default = 2]; diff --git a/easy_rec/python/protos/seq_encoder.proto b/easy_rec/python/protos/seq_encoder.proto index 54ebd306f..2edf71fa3 100644 --- a/easy_rec/python/protos/seq_encoder.proto +++ b/easy_rec/python/protos/seq_encoder.proto @@ -4,6 +4,65 @@ package protos; import "easy_rec/python/protos/dnn.proto"; +message Attention { + optional bool use_scale = 1 [default = false]; + optional bool scale_by_dim = 2 [default = false]; + optional string score_mode = 3 [default = 'dot']; + optional float dropout = 4 [default = 0.0]; + optional int32 seed = 5; + optional bool return_attention_scores = 6 [default = false]; + optional bool use_causal_mask = 7 [default = false]; +} + +message MultiHeadAttention { + required uint32 num_heads = 1; + required uint32 key_dim = 2; + optional uint32 value_dim = 3; + optional float dropout = 4 [default = 0.0]; + optional bool use_bias = 5 [default = true]; + optional bool return_attention_scores = 6 [default = false]; + optional bool use_causal_mask = 7 [default = false]; + // The expected shape of an output tensor, besides the batch + // and sequence dims. If not specified, projects back to the query + // feature dim (the query input's last dimension). + optional uint32 output_shape = 8; + // axes over which the attention is applied. + repeated int32 attention_axes = 9; + optional string kernel_initializer = 10 [default = 'glorot_uniform']; + optional string bias_initializer = 11 [default = 'zeros']; +} + +message Transformer { + // Size of the encoder layers and the pooler layer + required uint32 hidden_size = 1; + // Number of hidden layers in the Transformer encoder + required uint32 num_hidden_layers = 2; + // Number of attention heads for each attention layer in the Transformer encoder + required uint32 num_attention_heads = 3; + // The size of the "intermediate" (i.e. feed-forward) layer in the Transformer encoder + required uint32 intermediate_size = 4; + // The non-linear activation function (function or string) in the encoder and pooler. + required string hidden_act = 5 [default = 'relu']; + // The dropout probability for all fully connected layers in the embeddings, encoder, and pooler + required float hidden_dropout_prob = 6 [default = 0.1]; + required uint32 vocab_size = 7; + // The maximum sequence length that this model might ever be used with + required uint32 max_position_embeddings = 8 [default = 512]; + // Whether to add position embeddings for the position of each token in the text sequence + required bool use_position_embeddings = 9 [default = false]; + // Whether to output all token embedding, if set to false, then only output the first token embedding + required bool output_all_token_embeddings = 10 [default = true]; + // The dropout ratio for the attention probabilities + optional float attention_probs_dropout_prob = 11 [default = 0.0]; +} + +message TextEncoder { + required Transformer transformer = 1; + required string separator = 2 [default = ' ']; + optional string vocab_file = 3; + optional int32 default_token_id = 4 [default = 0]; +} + message BSTEncoder { // Size of the encoder layers and the pooler layer required uint32 hidden_size = 1; diff --git a/easy_rec/python/protos/tower.proto b/easy_rec/python/protos/tower.proto index 14cf64c63..fcaedafcd 100644 --- a/easy_rec/python/protos/tower.proto +++ b/easy_rec/python/protos/tower.proto @@ -36,6 +36,10 @@ message TaskTower { repeated Loss losses = 13; // whether to use sample weight in this tower required bool use_sample_weight = 14 [default = true]; + // field name for indicating the sample space for this task + optional string task_space_indicator_name = 15; + // field value for indicating the sample space for this task + optional string task_space_indicator_value = 16; }; @@ -76,4 +80,8 @@ message BayesTaskTower { optional bool use_ait_module = 17 [default = false]; // set this when the dimensions of last layer of towers are not equal optional uint32 ait_project_dim = 18; + // field name for indicating the sample space for this task + optional string task_space_indicator_name = 19; + // field value for indicating the sample space for this task + optional string task_space_indicator_value = 20; }; diff --git a/easy_rec/version.py b/easy_rec/version.py index 2a148c007..f6722ee1a 100644 --- a/easy_rec/version.py +++ b/easy_rec/version.py @@ -1,4 +1,4 @@ # -*- encoding:utf-8 -*- # Copyright (c) Alibaba, Inc. and its affiliates. -__version__ = '0.8.2' +__version__ = '0.8.3' diff --git a/examples/configs/dlrm_on_criteo_with_narydis.config b/examples/configs/dlrm_on_criteo_with_narydis.config new file mode 100644 index 000000000..469734c23 --- /dev/null +++ b/examples/configs/dlrm_on_criteo_with_narydis.config @@ -0,0 +1,586 @@ +train_input_path: "examples/data/criteo/criteo_train_data" +eval_input_path: "examples/data/criteo/criteo_test_data" +model_dir: "examples/ckpt/dlrm_narydis_criteo" + +train_config { + log_step_count_steps: 500 + optimizer_config: { + adam_optimizer: { + learning_rate: { + exponential_decay_learning_rate { + initial_learning_rate: 0.001 + decay_steps: 1000 + decay_factor: 0.5 + min_learning_rate: 0.00001 + } + } + } + use_moving_average: false + } + save_checkpoints_steps: 20000 + sync_replicas: True +} + +eval_config { + metrics_set: { + auc {} + } +} + +data_config { + separator: "\t" + input_fields: { + input_name: "label" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F1" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F2" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F3" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F4" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F5" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F6" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F7" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F8" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F9" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F10" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F11" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F12" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "F13" + input_type: FLOAT + default_val:"0" + } + input_fields: { + input_name: "C1" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C2" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C3" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C4" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C5" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C6" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C7" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C8" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C9" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C10" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C11" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C12" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C13" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C14" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C15" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C16" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C17" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C18" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C19" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C20" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C21" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C22" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C23" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C24" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C25" + input_type: STRING + default_val:"" + } + input_fields: { + input_name: "C26" + input_type: STRING + default_val:"" + } + label_fields: "label" + + batch_size: 4096 + num_epochs: 1 + prefetch_size: 32 + input_type: CSVInput +} + +feature_config: { + features: { + input_names: "F1" + feature_type: RawFeature + min_val:0.0 + max_val: 5775.0 + } + features: { + input_names: "F2" + feature_type: RawFeature + min_val: -3.0 + max_val: 257675.0 + } + features: { + input_names: "F3" + feature_type: RawFeature + min_val: 0.0 + max_val: 65535.0 + } + features: { + input_names: "F4" + feature_type: RawFeature + min_val: 0.0 + max_val: 969.0 + } + features: { + input_names: "F5" + feature_type: RawFeature + min_val: 0.0 + max_val: 23159456.0 + } + features: { + input_names: "F6" + feature_type: RawFeature + min_val: 0.0 + max_val: 431037.0 + } + features: { + input_names: "F7" + feature_type: RawFeature + min_val: 0.0 + max_val: 56311.0 + } + features: { + input_names: "F8" + feature_type: RawFeature + min_val: 0.0 + max_val: 6047.0 + } + features: { + input_names: "F9" + feature_type: RawFeature + min_val: 0.0 + max_val: 29019.0 + } + features: { + input_names: "F10" + feature_type: RawFeature + min_val: 0.0 + max_val: 46.0 + } + features: { + input_names: "F11" + feature_type: RawFeature + min_val: 0.0 + max_val: 231.0 + } + features: { + input_names: "F12" + feature_type: RawFeature + min_val: 0.0 + max_val: 4008.0 + } + features: { + input_names: "F13" + feature_type: RawFeature + min_val: 0.0 + max_val: 7393.0 + } + features: { + input_names: "C1" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C2" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C3" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C4" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C5" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C6" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C7" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C8" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C9" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C10" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C11" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C12" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C13" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C14" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C15" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C16" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C17" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C18" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C19" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C20" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C21" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C22" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C23" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C24" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + }features: { + input_names: "C25" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } + features: { + input_names: "C26" + hash_bucket_size: 1000000 + feature_type: IdFeature + embedding_dim: 16 + } +} +model_config: { + model_name: 'DLRM with autodis' + model_class: 'RankModel' + feature_groups: { + group_name: "dense" + feature_names: "F1" + feature_names: "F2" + feature_names: "F3" + feature_names: "F4" + feature_names: "F5" + feature_names: "F6" + feature_names: "F7" + feature_names: "F8" + feature_names: "F9" + feature_names: "F10" + feature_names: "F11" + feature_names: "F12" + feature_names: "F13" + wide_deep:DEEP + } + feature_groups: { + group_name: "sparse" + feature_names: "C1" + feature_names: "C2" + feature_names: "C3" + feature_names: "C4" + feature_names: "C5" + feature_names: "C6" + feature_names: "C7" + feature_names: "C8" + feature_names: "C9" + feature_names: "C10" + feature_names: "C11" + feature_names: "C12" + feature_names: "C13" + feature_names: "C14" + feature_names: "C15" + feature_names: "C16" + feature_names: "C17" + feature_names: "C18" + feature_names: "C19" + feature_names: "C20" + feature_names: "C21" + feature_names: "C22" + feature_names: "C23" + feature_names: "C24" + feature_names: "C25" + feature_names: "C26" + wide_deep:DEEP + } + backbone { + blocks { + name: 'num_emb' + inputs { + feature_group_name: 'dense' + } + keras_layer { + class_name: 'NaryDisEmbedding' + nary_dis_embedding { + embedding_dim: 8 + carries: [2, 9] + multiplier: 1e6 + } + } + } + blocks { + name: 'sparse' + inputs { + feature_group_name: 'sparse' + } + input_layer { + output_2d_tensor_and_feature_list: true + } + } + blocks { + name: 'dot' + inputs { + block_name: 'num_emb' + input_slice: '[1]' + } + inputs { + block_name: 'sparse' + input_slice: '[1]' + } + keras_layer { + class_name: 'DotInteraction' + } + } + blocks { + name: 'sparse_2d' + inputs { + block_name: 'sparse' + input_slice: '[0]' + } + } + blocks { + name: 'num_emb_2d' + inputs { + block_name: 'num_emb' + input_slice: '[0]' + } + } + concat_blocks: ['num_emb_2d', 'dot', 'sparse_2d'] + top_mlp { + hidden_units: [256, 128, 64] + } + } + model_params { + l2_regularization: 1e-5 + } + embedding_regularization: 1e-5 +}