Source code for transformers4rec.torch.features.sequence

# Copyright (c) 2021, NVIDIA CORPORATION.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Dict, List, Optional, Tuple, Union

import torch
from merlin.models.utils.doc_utils import docstring_parameter
from merlin.schema import Tags, TagsType

from merlin_standard_lib import Schema

from ..block.base import BlockOrModule, BuildableBlock, SequentialBlock
from ..block.mlp import MLPBlock
from ..masking import MaskSequence, masking_registry
from ..tabular.base import (
from . import embedding
from .tabular import TABULAR_FEATURES_PARAMS_DOCSTRING, TabularFeatures

[docs]@docstring_parameter( tabular_module_parameters=TABULAR_MODULE_PARAMS_DOCSTRING, embedding_features_parameters=embedding.EMBEDDING_FEATURES_PARAMS_DOCSTRING, ) class SequenceEmbeddingFeatures(embedding.EmbeddingFeatures): """Input block for embedding-lookups for categorical features. This module produces 3-D tensors, this is useful for sequential models like transformers. Parameters ---------- {embedding_features_parameters} padding_idx: int The symbol to use for padding. {tabular_module_parameters} """ def __init__( self, feature_config: Dict[str, embedding.FeatureConfig], item_id: Optional[str] = None, padding_idx: int = 0, pre: Optional[TabularTransformationType] = None, post: Optional[TabularTransformationType] = None, aggregation: Optional[TabularAggregationType] = None, schema: Optional[Schema] = None, ): self.padding_idx = padding_idx super(SequenceEmbeddingFeatures, self).__init__( feature_config=feature_config, item_id=item_id, pre=pre, post=post, aggregation=aggregation, schema=schema, )
[docs] def table_to_embedding_module(self, table: embedding.TableConfig) -> torch.nn.Embedding: embedding_table = torch.nn.Embedding( table.vocabulary_size, table.dim, padding_idx=self.padding_idx ) if table.initializer is not None: table.initializer(embedding_table.weight) return embedding_table
[docs] def forward_output_size(self, input_sizes): sizes = {} for fname, fconfig in self.feature_config.items(): fshape = input_sizes[fname] sizes[fname] = torch.Size(list(fshape) + [fconfig.table.dim]) return sizes
[docs]@docstring_parameter( tabular_module_parameters=TABULAR_MODULE_PARAMS_DOCSTRING, tabular_features_parameters=TABULAR_FEATURES_PARAMS_DOCSTRING, ) class TabularSequenceFeatures(TabularFeatures): """Input module that combines different types of features to a sequence: continuous, categorical & text. Parameters ---------- {tabular_features_parameters} projection_module: BlockOrModule, optional Module that's used to project the output of this module, typically done by an MLPBlock. masking: MaskSequence, optional Masking to apply to the inputs. {tabular_module_parameters} """ EMBEDDING_MODULE_CLASS = SequenceEmbeddingFeatures def __init__( self, continuous_module: Optional[TabularModule] = None, categorical_module: Optional[TabularModule] = None, pretrained_embedding_module: Optional[TabularModule] = None, projection_module: Optional[BlockOrModule] = None, masking: Optional[MaskSequence] = None, pre: Optional[TabularTransformationType] = None, post: Optional[TabularTransformationType] = None, aggregation: Optional[TabularAggregationType] = None, schema: Optional[Schema] = None, **kwargs ): super().__init__( continuous_module, categorical_module, pretrained_embedding_module, pre=pre, post=post, aggregation=aggregation, schema=schema, **kwargs ) self.projection_module = projection_module self.set_masking(masking)
[docs] @classmethod def from_schema( # type: ignore cls, schema: Schema, continuous_tags: Optional[Union[TagsType, Tuple[Tags]]] = (Tags.CONTINUOUS,), categorical_tags: Optional[Union[TagsType, Tuple[Tags]]] = (Tags.CATEGORICAL,), pretrained_embeddings_tags: Optional[Union[TagsType, Tuple[Tags]]] = (Tags.EMBEDDING,), aggregation: Optional[str] = None, automatic_build: bool = True, max_sequence_length: Optional[int] = None, continuous_projection: Optional[Union[List[int], int]] = None, continuous_soft_embeddings: bool = False, projection: Optional[Union[torch.nn.Module, BuildableBlock]] = None, d_output: Optional[int] = None, masking: Optional[Union[str, MaskSequence]] = None, **kwargs ) -> "TabularSequenceFeatures": """Instantiates ``TabularFeatures`` from a ``DatasetSchema`` Parameters ---------- schema : DatasetSchema Dataset schema continuous_tags : Optional[Union[TagsType, Tuple[Tags]]], optional Tags to filter the continuous features, by default Tags.CONTINUOUS categorical_tags : Optional[Union[TagsType, Tuple[Tags]]], optional Tags to filter the categorical features, by default Tags.CATEGORICAL aggregation : Optional[str], optional Feature aggregation option, by default None automatic_build : bool, optional Automatically infers input size from features, by default True max_sequence_length : Optional[int], optional Maximum sequence length for list features by default None continuous_projection : Optional[Union[List[int], int]], optional If set, concatenate all numerical features and project them by a number of MLP layers. The argument accepts a list with the dimensions of the MLP layers, by default None continuous_soft_embeddings : bool Indicates if the soft one-hot encoding technique must be used to represent continuous features, by default False projection: Optional[Union[torch.nn.Module, BuildableBlock]], optional If set, project the aggregated embeddings vectors into hidden dimension vector space, by default None d_output: Optional[int], optional If set, init a MLPBlock as projection module to project embeddings vectors, by default None masking: Optional[Union[str, MaskSequence]], optional If set, Apply masking to the input embeddings and compute masked labels, It requires a categorical_module including an item_id column, by default None Returns ------- TabularFeatures Returns ``TabularFeatures`` from a dataset schema """ output: TabularSequenceFeatures = super().from_schema( # type: ignore schema=schema, continuous_tags=continuous_tags, categorical_tags=categorical_tags, pretrained_embeddings_tags=pretrained_embeddings_tags, aggregation=aggregation, automatic_build=automatic_build, max_sequence_length=max_sequence_length, continuous_projection=continuous_projection, continuous_soft_embeddings=continuous_soft_embeddings, **kwargs ) if d_output and projection: raise ValueError("You cannot specify both d_output and projection at the same time") if (projection or masking or d_output) and not aggregation: # TODO: print warning here for clarity output.aggregation = "concat" # type: ignore hidden_size = output.output_size() if d_output and not projection: projection = MLPBlock([d_output]) if projection and hasattr(projection, "build"): projection = # type: ignore if projection: output.projection_module = projection # type: ignore hidden_size = projection.output_size() # type: ignore if isinstance(masking, str): masking = masking_registry.parse(masking)( hidden_size=output.output_size()[-1], **kwargs ) if masking and not getattr(output, "item_id", None): raise ValueError("For masking a categorical_module is required including an item_id.") output.set_masking(masking) # type: ignore return output
@property def masking(self): return self._masking
[docs] def set_masking(self, value): self._masking = value
@property def item_id(self) -> Optional[str]: if "categorical_module" in self.to_merge: return getattr(self.to_merge["categorical_module"], "item_id", None) return None @property def item_embedding_table(self) -> Optional[torch.nn.Module]: if "categorical_module" in self.to_merge: return getattr(self.to_merge["categorical_module"], "item_embedding_table", None) return None
[docs] def forward(self, inputs, training=False, testing=False, **kwargs): outputs = super(TabularSequenceFeatures, self).forward(inputs) if self.masking or self.projection_module: outputs = self.aggregation(outputs) if self.projection_module: outputs = self.projection_module(outputs) if self.masking: outputs = self.masking( outputs, item_ids=self.to_merge["categorical_module"].item_seq, training=training, testing=testing, ) return outputs
[docs] def project_continuous_features(self, dimensions): if isinstance(dimensions, int): dimensions = [dimensions] continuous = self.to_merge["continuous_module"] continuous.aggregation = "concat" continuous = SequentialBlock( continuous, MLPBlock(dimensions), AsTabular("continuous_projection") ) self.to_merge["continuous_module"] = continuous return self
[docs] def forward_output_size(self, input_size): output_sizes = {} for in_layer in self.merge_values: output_sizes.update(in_layer.forward_output_size(input_size)) output_sizes = self._check_post_output_size(output_sizes) if self.projection_module: output_sizes = self.projection_module.output_size() return output_sizes
TabularFeaturesType = Union[TabularSequenceFeatures, TabularFeatures]