from __future__ import annotations import logging from collections.abc import Iterable, Iterator from contextlib import nullcontext from functools import partial from typing import Any import torch import tqdm from torch import Tensor, nn from sentence_transformers.losses.CachedMultipleNegativesRankingLoss import RandContext, _backward_hook from sentence_transformers.sparse_encoder.losses.SpladeLoss import SpladeLoss from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder logger = logging.getLogger(__name__) class CachedSpladeLoss(SpladeLoss): def __init__( self, model: SparseEncoder, loss: nn.Module, document_regularizer_weight: float, query_regularizer_weight: float | None = None, document_regularizer: nn.Module | None = None, query_regularizer: nn.Module | None = None, document_regularizer_threshold: int | None = None, query_regularizer_threshold: int | None = None, use_document_regularizer_only: bool = False, mini_batch_size: int = 32, show_progress_bar: bool = False, ): """ Cached version of :class:`SpladeLoss` that uses the GradCache technique to allow for much larger effective batch sizes without additional GPU memory usage. By performing the GradCache mini-batch embedding at the SpladeLoss level, both the base loss and regularizers still receive pre-computed embeddings via ``compute_loss_from_embeddings()`` — no changes to base losses or regularizers are needed. In detail, the GradCache technique works as follows: (1) A quick embedding step without gradients/computation graphs to get all embeddings in mini-batches; (2) Calculate the combined loss (base + regularizers), backward up to the embeddings and cache the gradients w.r.t. the embeddings; (3) A 2nd embedding step with gradients/computation graphs and connect the cached gradients into the backward chain. Args: model: SparseEncoder model loss: The principal loss function to use can be any of the SparseEncoder losses except CSR related losses and flops loss. Must have a ``compute_loss_from_embeddings`` method. document_regularizer_weight: Weight for the document regularization term. This term encourages sparsity in the document embeddings. In some papers, this parameter is referred to as "lambda_d" (document) or "lambda_c" (corpus). query_regularizer_weight: Weight for the query regularization term. This term encourages sparsity in the query embeddings. If None, no query regularization will be applied. In some papers, this parameter is referred to as "lambda_q" (query). document_regularizer: Optional regularizer to use specifically for document regularization instead of the default FlopsLoss. query_regularizer: Optional regularizer to use specifically for query regularization instead of the default FlopsLoss. document_regularizer_threshold: Optional threshold for the number of non-zero (active) elements in the document embeddings to be considered in the FlopsLoss. query_regularizer_threshold: Optional threshold for the number of non-zero (active) elements in the query embeddings to be considered in the FlopsLoss. use_document_regularizer_only: If True, all input embeddings are treated as documents and regularized together with document_regularizer_weight. mini_batch_size: Mini-batch size for the forward pass, this denotes how much memory is actually used during training and evaluation. The larger the mini-batch size, the more memory efficient the training is, but the slower the training will be. It's recommended to set it as high as your GPU memory allows. The default value is 32. show_progress_bar: If True, a progress bar for the mini-batches is shown during training. References: - Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup: https://huggingface.co/papers/2101.06983 - From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective: https://huggingface.co/papers/2205.04733 Requirements: 1. Input requirements depend on the chosen loss 2. Should be used with large ``per_device_train_batch_size`` and low ``mini_batch_size`` for superior performance, but slower training time than :class:`SpladeLoss`. Example: :: from datasets import Dataset from sentence_transformers.sparse_encoder import SparseEncoder, SparseEncoderTrainer, losses model = SparseEncoder("distilbert/distilbert-base-uncased") train_dataset = Dataset.from_dict( { "anchor": ["It's nice weather outside today.", "He drove to work."], "positive": ["It's so sunny.", "He took the car to the office."], } ) loss = losses.CachedSpladeLoss( model=model, loss=losses.SparseMultipleNegativesRankingLoss(model), document_regularizer_weight=3e-5, query_regularizer_weight=5e-5, mini_batch_size=32, ) trainer = SparseEncoderTrainer(model=model, train_dataset=train_dataset, loss=loss) trainer.train() """ super().__init__( model=model, loss=loss, document_regularizer_weight=document_regularizer_weight, query_regularizer_weight=query_regularizer_weight, document_regularizer=document_regularizer, query_regularizer=query_regularizer, document_regularizer_threshold=document_regularizer_threshold, query_regularizer_threshold=query_regularizer_threshold, use_document_regularizer_only=use_document_regularizer_only, ) self.mini_batch_size = mini_batch_size self.show_progress_bar = show_progress_bar self.cache: list[list[Tensor]] | None = None self.random_states: list[list[RandContext]] | None = None def embed_minibatch( self, sentence_feature: dict[str, Tensor], begin: int, end: int, with_grad: bool, copy_random_state: bool, random_state: RandContext | None = None, ) -> tuple[Tensor, RandContext | None]: """Embed a mini-batch of sentences.""" grad_context = nullcontext if with_grad else torch.no_grad random_state_context = nullcontext() if random_state is None else random_state sentence_feature_minibatch = { key: value[begin:end] if isinstance(value, torch.Tensor) else value for key, value in sentence_feature.items() } with random_state_context: with grad_context(): random_state = RandContext(*sentence_feature_minibatch.values()) if copy_random_state else None reps = self.model(sentence_feature_minibatch)["sentence_embedding"] return reps, random_state def embed_minibatch_iter( self, sentence_feature: dict[str, Tensor], with_grad: bool, copy_random_state: bool, random_states: list[RandContext] | None = None, ) -> Iterator[tuple[Tensor, RandContext | None]]: """Iterate over mini-batches of sentences for embedding.""" input_ids: Tensor = sentence_feature["input_ids"] batch_size = input_ids.shape[0] for i, begin in enumerate( tqdm.trange( 0, batch_size, self.mini_batch_size, desc="Embed mini-batches", disable=not self.show_progress_bar, ) ): end = begin + self.mini_batch_size reps, random_state = self.embed_minibatch( sentence_feature=sentence_feature, begin=begin, end=end, with_grad=with_grad, copy_random_state=copy_random_state, random_state=None if random_states is None else random_states[i], ) yield reps, random_state def calculate_loss_and_cache_gradients(self, reps: list[list[Tensor]], labels: Tensor | None) -> Tensor: """Calculate the combined loss (base + regularizers) and cache gradients w.r.t. the embeddings.""" loss = self._compute_total_loss(reps, labels, with_backward=True) loss = loss.detach().requires_grad_() self.cache = [[r.grad for r in rs] for rs in reps] return loss def _compute_total_loss( self, reps: list[list[Tensor]], labels: Tensor | None, with_backward: bool = False ) -> Tensor: """Compute total loss from base loss + regularizers on mini-batch reps.""" embeddings = [torch.cat(r) for r in reps] # Base loss base_loss = self.loss.compute_loss_from_embeddings(embeddings, labels) if isinstance(base_loss, dict): total_loss = sum(base_loss.values()) else: total_loss = base_loss self._base_loss_value = total_loss.detach().item() # Document regularizer if self.use_document_regularizer_only: document_emb = torch.cat(embeddings) else: document_emb = torch.cat(embeddings[1:]) doc_reg_loss = self.document_regularizer.compute_loss_from_embeddings(document_emb) weighted_doc_reg = doc_reg_loss * self.document_regularizer_weight self._doc_reg_value = weighted_doc_reg.detach().item() total_loss = total_loss + weighted_doc_reg # Query regularizer if self.query_regularizer_weight is not None: query_reg_loss = self.query_regularizer.compute_loss_from_embeddings(embeddings[0]) weighted_query_reg = query_reg_loss * self.query_regularizer_weight self._query_reg_value = weighted_query_reg.detach().item() total_loss = total_loss + weighted_query_reg else: self._query_reg_value = None if with_backward: total_loss.backward() total_loss = total_loss.detach() return total_loss def forward( self, sentence_features: Iterable[dict[str, Tensor]], labels: Tensor | None = None ) -> dict[str, Tensor] | Tensor: sentence_features = list(sentence_features) # Step (1): Embed all mini-batches without gradients to get all embeddings reps = [] self.random_states = [] for sentence_feature in sentence_features: reps_mbs = [] random_state_mbs = [] for reps_mb, random_state in self.embed_minibatch_iter( sentence_feature=sentence_feature, with_grad=False, copy_random_state=True, ): reps_mbs.append(reps_mb.detach().requires_grad_()) random_state_mbs.append(random_state) reps.append(reps_mbs) self.random_states.append(random_state_mbs) if torch.is_grad_enabled(): # Step (2): Calculate the combined loss, backward to embeddings and cache gradients loss = self.calculate_loss_and_cache_gradients(reps, labels) # Step (3): Register backward hook to chain cached gradients back through the model loss.register_hook(partial(_backward_hook, sentence_features=sentence_features, loss_obj=self)) # Build dict for loss component logging while preserving gradient flow through `loss`. # The trainer sums all dict values for backward, so we put the gradient-carrying tensor # in "base_loss" and use detached tensors for regularizer entries. device = loss.device result = {} non_base_sum = torch.tensor(0.0, device=device) doc_reg_detached = torch.tensor(self._doc_reg_value, device=device) result["document_regularizer_loss"] = doc_reg_detached non_base_sum = non_base_sum + doc_reg_detached if self._query_reg_value is not None: query_reg_detached = torch.tensor(self._query_reg_value, device=device) result["query_regularizer_loss"] = query_reg_detached non_base_sum = non_base_sum + query_reg_detached # base_loss = loss - regularizers, so trainer's sum(values) = loss (exact gradient flow) result["base_loss"] = loss - non_base_sum return result else: # Eval mode: no caching needed, compute losses directly embeddings = [torch.cat(r) for r in reps] losses = {} base_loss = self.loss.compute_loss_from_embeddings(embeddings, labels) if isinstance(base_loss, dict): losses.update(base_loss) else: losses["base_loss"] = base_loss if self.use_document_regularizer_only: document_emb = torch.cat(embeddings) else: document_emb = torch.cat(embeddings[1:]) doc_reg_loss = self.document_regularizer.compute_loss_from_embeddings(document_emb) losses["document_regularizer_loss"] = doc_reg_loss * self.document_regularizer_weight if self.query_regularizer_weight is not None: query_reg_loss = self.query_regularizer.compute_loss_from_embeddings(embeddings[0]) losses["query_regularizer_loss"] = query_reg_loss * self.query_regularizer_weight return losses def get_config_dict(self) -> dict[str, Any]: config = super().get_config_dict() config["mini_batch_size"] = self.mini_batch_size return config @property def citation(self) -> str: return """ @misc{gao2021scaling, title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, year={2021}, eprint={2101.06983}, archivePrefix={arXiv}, primaryClass={cs.LG} } @misc{formal2022distillationhardnegativesampling, title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and St\\'ephane Clinchant}, year={2022}, eprint={2205.04733}, archivePrefix={arXiv}, primaryClass={cs.IR}, } """