Optimizing Einsum Calculations in PyTorch with torch.backends.opt_einsum.enabled

Context

• torch.einsum can leverage the opt_einsum library (if available) to optimize the order of computations for better performance, especially when dealing with three or more input tensors.
• PyTorch offers torch.einsum for performing efficient Einstein summation operations on tensors.

torch.backends.opt_einsum.enabled

• By default, torch.backends.opt_einsum.enabled is set to True, meaning opt_einsum is automatically used when applicable.
• This attribute is a boolean flag that controls whether torch.einsum uses opt_einsum for optimization.

Reasons to Disable opt_einsum

• In some rare cases, the default optimization strategy might not be the most efficient. You can disable opt_einsum and experiment with different contraction orders manually.
• If opt_einsum is not installed, set enabled to False to avoid errors.

How to Disable opt_einsum

import torch

torch.backends.opt_einsum.enabled = False

Additional Notes

• Disabling opt_einsum might be necessary for debugging purposes if you suspect issues with the optimization process.
• Even with opt_einsum disabled, torch.einsum still performs basic optimizations.

• Disable it only if you encounter issues or have specific reasons to experiment with alternative contraction orders.
• Keep it enabled by default for most use cases.
• torch.backends.opt_einsum.enabled is a convenient way to control whether torch.einsum utilizes opt_einsum for performance optimization.

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Example 1: Using opt_einsum (default behavior)

import torch

# Assuming opt_einsum is installed

A = torch.randn(3, 4)
B = torch.randn(4, 5)
C = torch.randn(5, 2)

# Optimized einsum using opt_einsum (if available)
result = torch.einsum("ij,jk,kl->il", A, B, C)
print(result.shape)  # Output: torch.Size([3, 2])

This code performs an Einstein summation with three tensors (A, B, and C) using the default behavior (assuming opt_einsum is installed). torch.einsum will automatically leverage opt_einsum to find the potentially most efficient contraction order.

Example 2: Disabling opt_einsum

import torch

A = torch.randn(3, 4)
B = torch.randn(4, 5)
C = torch.randn(5, 2)

# Disable opt_einsum for this specific calculation
torch.backends.opt_einsum.enabled = False

result = torch.einsum("ij,jk,kl->il", A, B, C)
print(result.shape)  # Output: torch.Size([3, 2])

# Re-enable opt_einsum for future calculations
torch.backends.opt_einsum.enabled = True

This code demonstrates disabling opt_einsum for a single torch.einsum operation. This might be useful for debugging purposes or if you have a specific reason to try different contraction orders manually. Remember to re-enable opt_einsum afterwards for optimal performance in other calculations.

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Manually Specifying Contraction Order

• Instead of relying on opt_einsum, you can explicitly define the order of tensor contractions within the einsum equation string. This gives you granular control over the computation. However, finding the optimal order can be challenging for complex expressions.

Example

import torch

A = torch.randn(3, 4)
B = torch.randn(4, 5)
C = torch.randn(5, 2)

# Manually specify contraction order (might not be optimal)
result = torch.einsum("ik,kj->ij", A, B)
print(result.shape)  # Output: torch.Size([3, 3])

Using Libraries with Different Optimization Strategies

• These libraries might provide different optimization strategies or offer more control over tensor contraction. However, they come with their own learning curve and potential compatibility issues with PyTorch code.
• If opt_einsum doesn't suit your needs, consider alternative libraries like:
  • cupy (if you're using GPUs)
  • jax (supports various backends)

Customizing opt_einsum Behavior (Advanced)

• If you're comfortable with advanced usage, you can potentially modify the underlying opt_einsum library (if installed) to define custom optimization strategies. This requires significant expertise in the library's codebase and is not recommended for most users.

• Customizing opt_einsum behavior is for very advanced users with deep understanding of the library's internals.
• Explore alternative libraries if opt_einsum is not available or you require different optimization options, but be prepared for learning a new library and potential integration challenges.
• Consider manual contraction order specification only for debugging or specific use cases where the default optimization might be suboptimal.
• In most cases, leaving torch.backends.opt_einsum.enabled to True is recommended as it leverages opt_einsum for potentially better performance.