Principle:Allenai Open instruct Tensor Caching

Knowledge Sources	PyTorch
Domains	Machine Learning, Systems Engineering, Distributed Computing
Last Updated	2026-02-07 00:00 GMT

Overview

Tensor caching is the practice of storing precomputed tensor values in a structured, index-addressable container that supports disk persistence and distributed aggregation, enabling efficient reuse of expensive computations across training iterations.

Description

In machine learning training pipelines, certain computations produce tensors that remain constant throughout training. Recomputing these tensors every step wastes GPU cycles and memory. Tensor caching addresses this by:

Structured Storage: Tensors are organized in a named dictionary, allowing multiple related values (e.g., chosen and rejected log-probabilities) to be stored and retrieved together under meaningful keys. This avoids ad-hoc tensor management scattered throughout the codebase.

Index-Based Retrieval: During training, each batch carries sample indices that map back into the full dataset. The cache supports direct indexing with these indices, returning a dictionary of sliced tensors for the current batch with minimal overhead. This is critical for data-parallel training where each process sees a different subset of indices per batch.

Disk Persistence: The cache can be saved to and loaded from disk, enabling:

• Resumption: A crashed or preempted training run can reload the cache without recomputation.
• Sharing: Multiple experiments with the same reference model and dataset can share a single cached result.
• Pipeline decoupling: The caching step can be run as a separate job from the training step.

Atomic writes (via temporary file + rename) prevent corruption from interrupted saves.

Distributed Aggregation: In multi-GPU or multi-node settings, each process computes a shard of the full tensor. The cache tensors are initialized to sentinel values (e.g., $-\mathrm{\infty }$), and an all_reduce(MAX) operation merges shards, since the sentinel is dominated by any real computed value.

Memory Management: Cache tensors reside on GPU for fast indexing during training. The memory footprint is logged to help operators monitor resource usage. For large datasets, the cache size is typically small relative to model parameters (two float32 scalars per training example).

Usage

Use tensor caching when:

• A computation produces values that are constant across all training steps (e.g., reference model log-probabilities).
• The computation is expensive enough that recomputing it every step would meaningfully increase training time.
• Training may be interrupted and resumed, requiring persistence of precomputed values.
• Multiple experiments share the same precomputed values and can benefit from cache reuse.

Theoretical Basis

Tensor caching is a form of memoization applied to the training pipeline. For a function $f$ that maps dataset indices to tensor values:

$\text{cache}[i]=f(x_i)\mathrm{\forall }i\in \{1,\dots ,N\}$

Once computed, retrieval is $O(1)$ per index (GPU tensor indexing), versus $O(T_{\text{forward}})$ per batch for recomputation, where $T_{\text{forward}}$ is the cost of a model forward pass.

For distributed aggregation with $P$ processes, each process $p$ computes a disjoint subset $S_p$ of indices:

# Initialize to sentinel
cache[i] = -inf for all i

# Each process p computes its shard
for i in S_p:
    cache[i] = f(x_i)

# Merge across processes
all_reduce(cache, op=MAX)
# Result: cache[i] = f(x_i) for all i (since f(x_i) > -inf)

The MAX reduction is correct because $f(x_i)>-\mathrm{\infty }$ for any valid computation, and each index is computed by exactly one process.

Related Pages

Implemented By

• Implementation:Allenai_Open_instruct_TensorCache