tensorcontainer 0.8.0.dev20250901151822

TensorDict-like functionality for PyTorch with PyTree compatibility and torch.compile support

Tensor Container

Tensor containers for PyTorch with PyTree compatibility and torch.compile optimization

Tensor Container provides efficient, type-safe tensor container implementations for PyTorch workflows. It includes PyTree integration and torch.compile optimization for batched tensor operations.

The library includes tensor containers (dict, dataclass) and distributions (torch.distributions equivalent).

What is TensorContainer?

TensorContainer transforms how you work with structured tensor data in PyTorch by providing tensor-like operations for entire data structures. Instead of manually managing individual tensors, TensorContainer lets you treat complex data as unified entities that behave just like regular tensors.

Core Benefits

• Unified Operations: Apply tensor operations like view(), permute(), detach(), and device transfers to entire data structures
• Drop-in Compatibility: Seamless integration with existing PyTorch workflows and torch.compile
• Zero Boilerplate: Eliminate manual parameter handling and type-specific operations
• Type Safety: Full IDE support with static typing and autocomplete

data = TensorDict(
   {"a": torch.rand(24), "b": torch.rand(24)}, 
   shape=(24,), 
   device="cpu"
)

# Single operation transforms entire structure
data = data.view(2, 3, 4).permute(1, 0, 2).to('cuda').detach()

Key Features

• ⚡ JIT Compilation: Designed for torch.compile with fullgraph=True, minimizing graph breaks and maximizing performance
• 📐 Batch/Event Semantics: Clear distinction between batch dimensions (consistent across tensors) and event dimensions (tensor-specific)
• 🔄 Device Management: Move entire structures between CPU/GPU with single operations and flexible device compatibility
• 🔒 Type Safety: Full IDE support with static typing and autocomplete
• 🏗️ Multiple Container Types: Three specialized containers for different use cases:
  • TensorDict for dynamic, dictionary-style data collections
  • TensorDataClass for type-safe, dataclass-based structures
  • TensorDistribution for probabilistic modeling with 40+ probability distributions
• 🔧 Advanced Operations: Full PyTorch tensor operations support including view(), permute(), stack(), cat(), and more
• 🎯 Advanced Indexing: Complete PyTorch indexing semantics with boolean masks, tensor indices, and ellipsis support
• 📊 Shape Validation: Automatic verification of tensor compatibility with detailed error messages
• 🌳 Nested Structure Support: Create nested structure with different TensorContainers

Table of Contents

• What is TensorContainer?
• Installation
• Quick Start
• Features
• API Overview
• torch.compile Compatibility
• Examples
• Contributing
• Documentation
• License
• Authors
• Contact and Support

Installation

Using pip

pip install tensorcontainer

Requirements

• Python 3.9+
• PyTorch 2.6+

Quick Start

TensorContainer transforms how you work with structured tensor data. Instead of managing individual tensors, you can treat entire data structures as unified entities that behave like regular tensors.

# Single operation transforms entire structure
data = data.view(2, 3, 4).permute(1, 0, 2).to('cuda').detach()

1. TensorDict: Dynamic Data Collections

Perfect for reinforcement learning data and dynamic collections:

import torch
from tensorcontainer import TensorDict

# Create a container for RL training data
data = TensorDict({
    'observations': torch.randn(32, 128),
    'actions': torch.randn(32, 4),
    'rewards': torch.randn(32, 1)
}, shape=(32,))

# Dictionary-like access with tensor operations
obs = data['observations']
data['advantages'] = torch.randn(32, 1)  # Add new fields dynamically

# Batch operations work seamlessly
batch = torch.stack([data, data])  # Shape: (2, 32)

2. TensorDataClass: Type-Safe Structures

Ideal for model inputs and structured data with compile-time safety:

import torch
from tensorcontainer import TensorDataClass

class ModelInput(TensorDataClass):
    features: torch.Tensor
    labels: torch.Tensor

# Create with full type safety and IDE support
batch = ModelInput(
    features=torch.randn(32, 64, 784),
    labels=torch.randint(0, 10, (32, 64)),
    shape=(32, 64)
)

# Unified operations on entire structure - reshape all tensors at once
batch = batch.view(2048)

# Type-safe access with autocomplete works on reshaped data too
loss = torch.nn.functional.cross_entropy(batch.features, batch.labels)

3. TensorDistribution: Probabilistic Modeling

Streamline probabilistic computations in reinforcement learning and generative models:

import torch
from tensorcontainer.tensor_distribution import TensorNormal

normal = TensorNormal(
    loc=torch.zeros(100, 4),
    scale=torch.ones(100, 4)  
)

# With torch.distributions we need to extract the parameters, detach them
# and create a new Normal distribution. With TensorDistribution we just call
# .detach() on the distribution. We can also apply other tensor operations,
# such as .view()!
detached_normal = normal.detach()

Documentation

The project includes comprehensive documentation:

• docs/user_guide/overview.md: Complete user guide with examples and best practices
• docs/developer_guide/compatibility.md: Python version compatibility guide and best practices
• docs/developer_guide/testing.md: Testing philosophy, standards, and guidelines
• Source Code Documentation: Extensive docstrings and type annotations throughout the codebase
• Test Coverage: 643+ tests covering all major functionality with 86% code coverage

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors

• Tim Joseph - mctigger

Contact and Support

• Issues: Report bugs and request features on GitHub Issues
• Discussions: Join conversations on GitHub Discussions
• Email: For direct inquiries, contact [e-mail]

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Tensor Container is an academic research project for learning PyTorch internals and tensor container patterns. For production applications, we strongly recommend using the official torch/tensordict library, which is actively maintained by the PyTorch team.