Source code for transformers4rec.torch.model.prediction_task

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import logging
from math import sqrt
from typing import Dict, Iterable, Optional, Sequence, Tuple

import torch
import torchmetrics as tm

from ..block.base import Block, BuildableBlock, SequentialBlock
from ..block.mlp import MLPBlock
from ..masking import MaskedLanguageModeling
from ..ranking_metric import AvgPrecisionAt, NDCGAt, RecallAt
from ..utils.torch_utils import LambdaModule
from .base import BlockType, PredictionTask

LOG = logging.getLogger("transformers4rec")


[docs]class BinaryClassificationPrepareBlock(BuildableBlock):
[docs] def build(self, input_size) -> SequentialBlock: return SequentialBlock( torch.nn.Linear(input_size[-1], 1, bias=False), torch.nn.Sigmoid(), LambdaModule(lambda x: torch.squeeze(x, -1)), output_size=[ None, ], )
[docs]class BinaryClassificationTask(PredictionTask): """Returns a ``PredictionTask`` for binary classification. Example usage:: # Define the input module to process the tabular input features. input_module = tr.TabularSequenceFeatures.from_schema( schema, max_sequence_length=max_sequence_length, continuous_projection=d_model, aggregation="concat", masking=None, ) # Define XLNetConfig class and set default parameters for HF XLNet config. transformer_config = tr.XLNetConfig.build( d_model=d_model, n_head=4, n_layer=2, total_seq_length=max_sequence_length ) # Define the model block including: inputs, masking, projection and transformer block. body = tr.SequentialBlock( input_module, tr.MLPBlock([64]), tr.TransformerBlock( transformer_config, masking=input_module.masking ) ) # Define a head with BinaryClassificationTask. head = tr.Head( body, tr.BinaryClassificationTask( "click", summary_type="mean", metrics=[ tm.Precision(task='binary'), tm.Recall(task='binary'), tm.Accuracy(task='binary'), tm.F1Score(task='binary') ] ), inputs=input_module, ) # Get the end-to-end Model class. model = tr.Model(head) Parameters ---------- target_name: Optional[str] = None Specifies the variable name that represents the positive and negative values. task_name: Optional[str] = None Specifies the name of the prediction task. If this parameter is not specified, a name is automatically constructed based on ``target_name`` and the Python class name of the model. task_block: Optional[BlockType] = None Specifies a module to transform the input tensor before computing predictions. loss: torch.nn.Module Specifies the loss function for the task. The default class is ``torch.nn.BCELoss``. metrics: Tuple[torch.nn.Module, ...] Specifies the metrics to calculate during training and evaluation. The default metrics are ``Precision``, ``Recall``, and ``Accuracy``. summary_type: str Summarizes a sequence into a single tensor. Accepted values are: - ``last`` -- Take the last token hidden state (like XLNet) - ``first`` -- Take the first token hidden state (like Bert) - ``mean`` -- Take the mean of all tokens hidden states - ``cls_index`` -- Supply a Tensor of classification token position (GPT/GPT-2) - ``attn`` -- Not implemented now, use multi-head attention """ DEFAULT_LOSS = torch.nn.BCELoss() DEFAULT_METRICS = ( tm.Precision(num_classes=2, task="binary"), tm.Recall(num_classes=2, task="binary"), tm.Accuracy(task="binary"), # TODO: Fix this: tm.AUC() ) def __init__( self, target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[BlockType] = None, loss=DEFAULT_LOSS, metrics=DEFAULT_METRICS, summary_type="first", ): self.target_dim = 1 super().__init__( loss=loss, metrics=metrics, target_name=target_name, task_name=task_name, summary_type=summary_type, task_block=task_block, pre=BinaryClassificationPrepareBlock(), forward_to_prediction_fn=lambda x: torch.round(x).int(), )
[docs]class RegressionPrepareBlock(BuildableBlock):
[docs] def build(self, input_size) -> SequentialBlock: return SequentialBlock( torch.nn.Linear(input_size[-1], 1), LambdaModule(lambda x: torch.squeeze(x, -1)), output_size=[ None, ], )
[docs]class RegressionTask(PredictionTask): DEFAULT_LOSS = torch.nn.MSELoss() DEFAULT_METRICS = (tm.regression.MeanSquaredError(),) def __init__( self, target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[BlockType] = None, loss=DEFAULT_LOSS, metrics=DEFAULT_METRICS, summary_type="first", ): self.target_dim = 1 super().__init__( loss=loss, metrics=metrics, target_name=target_name, task_name=task_name, summary_type=summary_type, task_block=task_block, pre=RegressionPrepareBlock(), )
[docs]class NextItemPredictionTask(PredictionTask): """This block performs item prediction task for session and sequential-based models. It requires a body containing a masking schema to use for training and target generation. For the supported masking schemes, please refers to: https://nvidia-merlin.github.io/Transformers4Rec/main/model_definition.html#sequence-masking Parameters ---------- loss: torch.nn.Module Loss function to use. Defaults to NLLLos. metrics: Iterable[torchmetrics.Metric] List of ranking metrics to use for evaluation. task_block: Module to transform input tensor before computing predictions. task_name: str, optional Name of the prediction task, if not provided a name will be automatically constructed based on the target-name & class-name. weight_tying: bool The item id embedding table weights are shared with the prediction network layer. softmax_temperature: float Softmax temperature, used to reduce model overconfidence, so that softmax(logits / T). Value 1.0 reduces to regular softmax. padding_idx: int pad token id. target_dim: int vocabulary size of item ids sampled_softmax: Optional[bool] Enables sampled softmax. By default False max_n_samples: Optional[int] Number of samples for sampled softmax. By default 100 """ DEFAULT_METRICS = ( # default metrics suppose labels are int encoded NDCGAt(top_ks=[10, 20], labels_onehot=True), AvgPrecisionAt(top_ks=[10, 20], labels_onehot=True), RecallAt(top_ks=[10, 20], labels_onehot=True), ) def __init__( self, loss: torch.nn.Module = torch.nn.CrossEntropyLoss(), metrics: Iterable[tm.Metric] = DEFAULT_METRICS, task_block: Optional[BlockType] = None, task_name: str = "next-item", weight_tying: bool = False, softmax_temperature: float = 1, padding_idx: int = 0, target_dim: int = None, sampled_softmax: Optional[bool] = False, max_n_samples: Optional[int] = 100, ): super().__init__(loss=loss, metrics=metrics, task_block=task_block, task_name=task_name) self.softmax_temperature = softmax_temperature self.weight_tying = weight_tying self.padding_idx = padding_idx self.target_dim = target_dim self.sampled_softmax = sampled_softmax self.max_n_samples = max_n_samples self.item_embedding_table = None self.masking = None
[docs] def build(self, body, input_size, device=None, inputs=None, task_block=None, pre=None): """Build method, this is called by the `Head`.""" if not len(input_size) == 3 or isinstance(input_size, dict): raise ValueError( "NextItemPredictionTask needs a 3-dim vector as input, found:" f"{input_size}" ) # Retrieve the embedding module to get the name of itemid col and its related table if not inputs: inputs = body.inputs if not getattr(inputs, "item_id", None): raise ValueError( "For Item Prediction task a categorical_module " "including an item_id column is required." ) self.embeddings = inputs.categorical_module if not self.target_dim: self.target_dim = self.embeddings.item_embedding_table.num_embeddings if self.weight_tying: self.item_embedding_table = self.embeddings.item_embedding_table item_dim = self.item_embedding_table.weight.shape[1] if input_size[-1] != item_dim and not task_block: LOG.warning( f"Projecting inputs of NextItemPredictionTask to'{item_dim}' " f"As weight tying requires the input dimension '{input_size[-1]}' " f"to be equal to the item-id embedding dimension '{item_dim}'" ) # project input tensors to same dimension as item-id embeddings task_block = MLPBlock([item_dim], activation=None) # Retrieve the masking from the input block self.masking = inputs.masking if not self.masking: raise ValueError( "The input block should contain a masking schema for training and evaluation" ) self.padding_idx = self.masking.padding_idx pre = NextItemPredictionPrepareBlock( target_dim=self.target_dim, weight_tying=self.weight_tying, item_embedding_table=self.item_embedding_table, softmax_temperature=self.softmax_temperature, sampled_softmax=self.sampled_softmax, max_n_samples=self.max_n_samples, min_id=self.padding_idx + 1, ) super().build( body, input_size, device=device, inputs=inputs, task_block=task_block, pre=pre )
[docs] def forward( self, inputs: torch.Tensor, targets=None, training=False, testing=False, top_k=None, **kwargs, ): if isinstance(inputs, (tuple, list)): inputs = inputs[0] x = inputs.float() if self.task_block: x = self.task_block(x) # type: ignore # Retrieve labels from masking if training or testing: labels = self.masking.masked_targets # type: ignore trg_flat = labels.flatten() non_pad_mask = trg_flat != self.padding_idx labels_all = torch.masked_select(trg_flat, non_pad_mask).long() # remove padded items, keep only masked positions x = self.remove_pad_3d(x, non_pad_mask) y = labels_all x, y = self.pre(x, targets=y, training=training, testing=testing) # type: ignore loss = self.loss(x, y) return { "loss": loss, "labels": y, "predictions": x, } else: # Get the hidden position to use for predicting the next item labels = self.embeddings.item_seq non_pad_mask = labels != self.padding_idx rows_ids = torch.arange(labels.size(0), dtype=torch.long, device=labels.device) if isinstance(self.masking, MaskedLanguageModeling): last_item_sessions = non_pad_mask.sum(dim=1) else: last_item_sessions = non_pad_mask.sum(dim=1) - 1 x = x[rows_ids, last_item_sessions] # Compute predictions probs x, _ = self.pre(x) # type: ignore if top_k is None: return x else: preds_sorted_item_scores, preds_sorted_item_ids = torch.topk(x, k=top_k, dim=-1) return preds_sorted_item_scores, preds_sorted_item_ids
[docs] def remove_pad_3d(self, inp_tensor, non_pad_mask): # inp_tensor: (n_batch x seqlen x emb_dim) inp_tensor = inp_tensor.flatten(end_dim=1) inp_tensor_fl = torch.masked_select( inp_tensor, non_pad_mask.unsqueeze(1).expand_as(inp_tensor) ) out_tensor = inp_tensor_fl.view(-1, inp_tensor.size(1)) return out_tensor
[docs] def calculate_metrics(self, predictions, targets) -> Dict[str, torch.Tensor]: # type: ignore if isinstance(targets, dict) and self.target_name: targets = targets[self.target_name] outputs = {} predictions = self.forward_to_prediction_fn(predictions) for metric in self.metrics: outputs[self.metric_name(metric)] = metric(predictions, targets) return outputs
[docs] def compute_metrics(self): metrics = { self.metric_name(metric): metric.compute() for metric in self.metrics if getattr(metric, "top_ks", None) } # Explode metrics for each cut-off # TODO make result generic: # To accept a mix of ranking metrics and others not requiring top_ks ? topks = {self.metric_name(metric): metric.top_ks for metric in self.metrics} results = {} for name, metric in metrics.items(): for measure, k in zip(metric, topks[name]): results[f"{name}_{k}"] = measure return results
[docs]class NextItemPredictionPrepareBlock(BuildableBlock): def __init__( self, target_dim: int, weight_tying: bool = False, item_embedding_table: Optional[torch.nn.Module] = None, softmax_temperature: float = 0, sampled_softmax: Optional[bool] = False, max_n_samples: Optional[int] = 100, min_id: Optional[int] = 0, ): super().__init__() self.target_dim = target_dim self.weight_tying = weight_tying self.item_embedding_table = item_embedding_table self.softmax_temperature = softmax_temperature self.sampled_softmax = sampled_softmax self.max_n_samples = max_n_samples self.min_id = min_id
[docs] def build(self, input_size) -> Block: return Block( _NextItemPredictionTask( input_size, self.target_dim, self.weight_tying, self.item_embedding_table, self.softmax_temperature, self.sampled_softmax, self.max_n_samples, self.min_id, ), [-1, self.target_dim], )
class _NextItemPredictionTask(torch.nn.Module): """Predict the interacted item-id probabilities. - During inference, the task consists of predicting the next item. - During training, the class supports the following Language modeling tasks: Causal LM, Masked LM, Permutation LM and Replacement Token Detection Parameters: ----------- input_size: int Input size of this module. target_dim: int Dimension of the target. weight_tying: bool The item id embedding table weights are shared with the prediction network layer. item_embedding_table: torch.nn.Module Module that's used to store the embedding table for the item. softmax_temperature: float Softmax temperature, used to reduce model overconfidence, so that softmax(logits / T). Value 1.0 reduces to regular softmax. sampled_softmax: Optional[bool] Enables sampled softmax. By default False max_n_samples: Optional[int] Number of samples for sampled softmax. By default 100 min_id : Optional[int] The minimum value of the range for the log-uniform sampling. By default 0. """ def __init__( self, input_size: Sequence, target_dim: int, weight_tying: bool = False, item_embedding_table: Optional[torch.nn.Module] = None, softmax_temperature: float = 0, sampled_softmax: Optional[bool] = False, max_n_samples: Optional[int] = 100, min_id: Optional[int] = 0, ): super().__init__() self.input_size = input_size self.target_dim = target_dim self.weight_tying = weight_tying self.item_embedding_table = item_embedding_table self.softmax_temperature = softmax_temperature self.sampled_softmax = sampled_softmax if not self.weight_tying: self.output_layer = torch.nn.Parameter(torch.empty(self.target_dim, input_size[-1])) torch.nn.init.kaiming_uniform_(self.output_layer, a=sqrt(5)) if self.sampled_softmax: self.sampler = LogUniformSampler( max_n_samples=max_n_samples, max_id=target_dim, min_id=min_id, unique_sampling=True, ) def forward( self, inputs: torch.Tensor, targets: Optional[torch.Tensor] = None, training=False, testing=False, **kwargs, ) -> Tuple[torch.Tensor, torch.Tensor]: if self.weight_tying: output_weights = self.item_embedding_table.weight else: output_weights = self.output_layer if self.sampled_softmax and training: logits, targets = self.sampled(inputs, targets, output_weights) else: logits = inputs @ output_weights.t() if self.softmax_temperature: # Softmax temperature to reduce model overconfidence # and better calibrate probs and accuracy logits = torch.div(logits, self.softmax_temperature) return logits, targets def sampled(self, inputs, targets, output_weights): """Returns logits using sampled softmax""" neg_samples, targets_probs, samples_probs = self.sampler.sample(targets) positive_weights = output_weights[targets] negative_weights = output_weights[neg_samples] positive_scores = (inputs * positive_weights).sum(dim=-1, keepdim=True) negative_scores = inputs @ negative_weights.t() # logQ correction, to not overpenalize popular items for being sampled # more often as negatives epsilon = 1e-16 positive_scores -= torch.unsqueeze(torch.log(targets_probs + epsilon), dim=-1) negative_scores -= torch.unsqueeze(torch.log(samples_probs + epsilon), dim=0) # Remove accidental matches accidental_hits = torch.unsqueeze(targets, -1) == torch.unsqueeze(neg_samples, 0) negative_scores[accidental_hits] = torch.finfo(torch.float16).min / 100.0 logits = torch.cat([positive_scores, negative_scores], axis=1) new_targets = torch.zeros(logits.shape[0], dtype=torch.int64, device=targets.device) return logits, new_targets def _get_name(self) -> str: return "NextItemPredictionTask"
[docs]class LogUniformSampler(torch.nn.Module): def __init__( self, max_n_samples: int, max_id: int, min_id: Optional[int] = 0, unique_sampling: bool = True, n_samples_multiplier_before_unique: int = 2, ): """LogUniformSampler samples negative samples based on a log-uniform distribution. `P(class) = (log(class + 2) - log(class + 1)) / log(max_id + 1)` This implementation is based on to: https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/utils/log_uniform_sampler.py TensorFlow Reference: https://github.com/tensorflow/tensorflow/blob/r1.10/tensorflow/python/ops/candidate_sampling_ops.py LogUniformSampler assumes item ids are sorted decreasingly by their frequency. if `unique_sampling==True`, then only unique sampled items will be returned. The actual # samples will vary from run to run if `unique_sampling==True`, as sampling without replacement (`torch.multinomial(..., replacement=False)`) is slow, so we use `torch.multinomial(..., replacement=True).unique()` which doesn't guarantee the same number of unique sampled items. You can try to increase n_samples_multiplier_before_unique to increase the chances to have more unique samples in that case. Parameters ---------- max_n_samples : int The maximum desired number of negative samples. The number of samples might be smaller than that if `unique_sampling==True`, as explained above. max_id : int The maximum value of the range for the log-uniform distribution. min_id : Optional[int] The minimum value of the range for the log-uniform sampling. By default 0. unique_sampling : bool Whether to return unique samples. By default True n_samples_multiplier_before_unique : int If unique_sampling=True, it is not guaranteed that the number of returned samples will be equal to max_n_samples, as explained above. You can increase n_samples_multiplier_before_unique to maximize chances that a larger number of unique samples is returned. """ super().__init__() if max_id <= 0: raise ValueError("max_id must be a positive integer.") if max_n_samples <= 0: raise ValueError("n_sample must be a positive integer.") self.max_id = max_id self.unique_sampling = unique_sampling self.max_n_samples = max_n_samples self.n_sample = max_n_samples if self.unique_sampling: self.n_sample = int(self.n_sample * n_samples_multiplier_before_unique) with torch.no_grad(): dist = self.get_log_uniform_distr(max_id, min_id) self.register_buffer("dist", dist) unique_sampling_dist = self.get_unique_sampling_distr(dist, self.n_sample) self.register_buffer("unique_sampling_dist", unique_sampling_dist)
[docs] def get_log_uniform_distr(self, max_id: int, min_id: int = 0) -> torch.Tensor: """Approximates the items frequency distribution with log-uniform probability distribution with P(class) = (log(class + 2) - log(class + 1)) / log(max_id + 1). It assumes item ids are sorted decreasingly by their frequency. Parameters ---------- max_id : int Maximum discrete value for sampling (e.g. cardinality of the item id) Returns ------- torch.Tensor Returns the log uniform probability distribution """ log_indices = torch.arange(1.0, max_id - min_id + 2.0, 1.0).log_() probs = (log_indices[1:] - log_indices[:-1]) / log_indices[-1] if min_id > 0: probs = torch.cat([torch.zeros([min_id], dtype=probs.dtype), probs], axis=0) return probs
[docs] def get_unique_sampling_distr(self, dist, n_sample): """Returns the probability that each item is sampled at least once given the specified number of trials. This is meant to be used when self.unique_sampling == True. That probability can be approximated by by 1 - (1 - p)^n and we use a numerically stable version: -expm1(num_tries * log1p(-p)) """ return (-(-dist.double().log1p_() * n_sample).expm1_()).float()
[docs] def sample(self, labels: torch.Tensor): """Sample negative samples and calculate their probabilities. If `unique_sampling==True`, then only unique sampled items will be returned. The actual # samples will vary from run to run if `unique_sampling==True`, as sampling without replacement (`torch.multinomial(..., replacement=False)`) is slow, so we use `torch.multinomial(..., replacement=True).unique()` which doesn't guarantee the same number of unique sampled items. You can try to increase n_samples_multiplier_before_unique to increase the chances to have more unique samples in that case. Parameters ---------- labels : torch.Tensor, dtype=torch.long, shape=(batch_size,) The input labels for which negative samples should be generated. Returns ------- neg_samples : torch.Tensor, dtype=torch.long, shape=(n_samples,) The unique negative samples drawn from the log-uniform distribution. true_probs : torch.Tensor, dtype=torch.float32, shape=(batch_size,) The probabilities of the input labels according to the log-uniform distribution (depends on self.unique_sampling choice). samp_log_probs : torch.Tensor, dtype=torch.float32, shape=(n_samples,) The probabilities of the sampled negatives according to the log-uniform distribution (depends on self.unique_sampling choice). """ if not torch.is_tensor(labels): raise TypeError("Labels must be a torch.Tensor.") if labels.dtype != torch.long: raise ValueError("Labels must be a tensor of dtype long.") if labels.dim() > 2 or (labels.dim() == 2 and min(labels.shape) > 1): raise ValueError( "Labels must be a 1-dimensional tensor or a 2-dimensional tensor" "with one of the dimensions equal to 1." ) if labels.size(0) == 0: raise ValueError("Labels must not be an empty tensor.") if (labels < 0).any() or (labels > self.max_id).any(): raise ValueError("All label values must be within the range [0, max_id].") n_tries = self.n_sample with torch.no_grad(): neg_samples = torch.multinomial(self.dist, n_tries, replacement=True).unique()[ : self.max_n_samples ] device = labels.device neg_samples = neg_samples.to(device) if self.unique_sampling: dist = self.unique_sampling_dist else: dist = self.dist true_probs = dist[labels] samples_probs = dist[neg_samples] return neg_samples, true_probs, samples_probs
[docs] def forward(self, labels): return self.sample(labels)