infinite-tensor 0.2.3

A Python library for performing operations on tensors with infinite dimensions.

Infinite Tensors

A Python library for performing operations on theoretically infinite tensors using a sliding window approach. This library enables processing of large tensors without loading the entire tensor into memory.

Installation

Install using pip:

pip install infinite-tensor

What is an Infinite Tensor?

An Infinite Tensor represents an immutable tensor with one or more unbounded dimensions, defined by a deterministic function f. Instead of loading all data into memory at once, it:

• Loads only the parts you need, when you need them
• Processes data in manageable windows
• Caches results so repeated reads are instant

Internally, when you index a region, the system identifies which output windows intersect that region, invokes f on each, and sums the outputs together. Results are cached, and no data outside the requested region is generated.

Key Concepts

1. Windows: Define how your processing function sees the data

   • Fixed size (e.g., 64x64 pixels)
   • Outputs in overlapping regions are added together
   • Defined by size, stride, and offset

2. Infinite Tensors: Immutable tensors with infinite dimensions

   • Some dimensions can be None (infinite), others must be finite
   • Defined by a deterministic function f operating on the "output window"
   • Can depend on other infinite tensors

Getting Started

1. Creating an Infinite Tensor

Always create tensors through a TileStore:

import uuid
import torch
from infinite_tensor import TensorWindow, MemoryTileStore

# Create a tile store (in-memory)
tile_store = MemoryTileStore()

# Define how each window is generated; must match the window's shape
def your_processing_function(ctx):
    # ctx is the window index (e.g., (wy, wx) for 2D). It is NOT pixel coordinates.
    return torch.ones(512, 512)

# Define the output window seen by your function
window = TensorWindow((512, 512))

# Create an infinite tensor (2D infinite)
tensor = tile_store.get_or_create(
    "my_infinite_tensor",
    shape=(None, None),         # None means infinite dimension
    f=your_processing_function, # A function that takes the index of the current output window as input: e.g (0, 0)
    output_window=window,
    tile_size=512,              # internal tile size (optional)
)

2. Using the Tensor

Just slice it like a normal tensor (computed on-demand)

result = tensor[0:1024, 0:1024]

Advanced Features

1. Caching Methods

InfiniteTensor supports two caching strategies via cache_method:

• cache_method='indirect' (default): Window outputs are accumulated into tiles. Best for persistent storage (e.g., HDF5TileStore) since it uses the least disk space. Use tile_size to indicate the size of the tiles.

• cache_method='direct': Window outputs are cached directly with LRU eviction. Best when you want to limit memory usage and don't need persistent storage. Use cache_limit to set the max cache size in bytes (default: 10MB), or None for unlimited. tile_size is ignored with this method.

2. Dependency Chaining

Create processing pipelines by making one infinite tensor depend on another.

In this case, f is called like f(ctx, *args_sliced), where ctx is the output window index, and args_sliced are the upstream tensors (args) sliced by args_windows.

import torch
from infinite_tensor import TensorWindow, MemoryTileStore

tile_store = MemoryTileStore()

def zeros_tensor_func(ctx):
    return torch.zeros(10, 512, 512)  # (C, H, W)

base_window = TensorWindow((10, 512, 512))
base = tile_store.get_or_create("my_tensor", (10, None, None), zeros_tensor_func, base_window)

# Define an offset window for the dependent tensor
offset_window = TensorWindow((10, 512, 512), offset=(0, -256, -256))

# The function receives the upstream window directly (already sliced)
def inc_func(ctx, prev):
    return prev + 1

dep = tile_store.get_or_create(
    "my_second_tensor",
    (10, None, None),
    inc_func,
    offset_window,
    args=(base,),
    args_windows=(offset_window,),
)

out = dep[:, 0:512, 0:512]  # ones

Note: Manually slicing dependencies inside f is not recommended, as it prevents the use of batching, and future versions may introduce automatic memory management utilizing this future.

3. Batching

Optionally, f can take in a list of tensors, instead of one at a time. The max size of the list is given by batch_size. Here is the same example as above but with batching:

import torch
from infinite_tensor import TensorWindow, MemoryTileStore

tile_store = MemoryTileStore()

def zeros_tensor_func(ctx):
    return torch.zeros(10, 512, 512)  # (C, H, W)

base_window = TensorWindow((10, 512, 512))
base = tile_store.get_or_create("my_tensor", (10, None, None), zeros_tensor_func, base_window)

# Define an offset window for the dependent tensor
offset_window = TensorWindow((10, 512, 512), offset=(0, -256, -256))

# The function receives the upstream window directly (already sliced)
# now prev is a list of up to 4 tensors
def inc_func(ctx, prev):
    # return a list of the same size
    prev_stack = torch.stack(prev)
    return [p for p in (prev_stack + 1)]

dep = tile_store.get_or_create(
    "my_second_tensor",
    (10, None, None),
    inc_func,
    offset_window,
    args=(base,),
    args_windows=(offset_window,),
    batch_size=4
)

out = dep[:, 0:512, 0:512]  # ones

Important Notes

1. Deterministic f: Your function must be deterministic—results are cached assuming f is pure.
2. Create via TileStore: Construct tensors with tile_store.get_or_create(...). Direct construction of InfiniteTensor is not supported.
3. Avoid manual slicing: Do not manually slice dependencies. Use args/args_windows so the framework manages slicing and dependencies.
4. CPU Only: Outputs and inputs to f are always on the CPU. Returning tensors on other devices will raise errors.
5. Window Size: Your function must return exactly the size specified in TensorWindow.
6. Finite Dimensions: Non-infinite dimensions must fit in memory.

Example

Check out examples/blur.py for a complete example showing how to:

• Process images larger than memory
• Handle boundaries correctly
• Chain multiple processing steps

License

MIT License - See LICENSE file for details.