{"id":8721,"library":"torch-einops-utils","title":"Torch Einops Utils","description":"torch-einops-utils is a collection of personal utility functions designed to work with PyTorch and Einops, providing convenient abstractions for common tensor manipulations. It is currently at version 0.0.30 and receives frequent updates, indicating active development with rapid iteration.","status":"active","version":"0.0.30","language":"en","source_language":"en","source_url":"https://github.com/lucidrains/torch-einops-utils","tags":["pytorch","einops","deep-learning","utilities","tensor-manipulation"],"install":[{"cmd":"pip install torch-einops-utils","lang":"bash","label":"Install latest version"}],"dependencies":[{"reason":"Core deep learning framework","package":"torch","optional":false},{"reason":"Tensor rearrangement and reduction library","package":"einops","optional":false}],"imports":[{"symbol":"EinopsToAndFrom","correct":"from torch_einops_utils import EinopsToAndFrom"},{"symbol":"EinopsToNoOp","correct":"from torch_einops_utils import EinopsToNoOp"},{"symbol":"Rearrange","correct":"from torch_einops_utils import Rearrange"},{"symbol":"Reduce","correct":"from torch_einops_utils import Reduce"},{"symbol":"rearrange_many","correct":"from torch_einops_utils import rearrange_many"},{"symbol":"repeat_many","correct":"from torch_einops_utils import repeat_many"}],"quickstart":{"code":"import torch\nfrom torch import nn\nfrom torch_einops_utils import EinopsToAndFrom\n\nclass Foo(EinopsToAndFrom):\n    def __init__(self, fn: nn.Module):\n        # EinopsToAndFrom requires input pattern, output pattern, and a callable/nn.Module\n        super().__init__('b n d', 'b n d', fn)\n\n    def forward(self, x):\n        # The `fn` provided in __init__ is called within EinopsToAndFrom's forward\n        # after applying the input pattern, and before applying the output pattern.\n        # In this example, 'b n d' -> 'b n d' is a no-op rearrangement\n        # so the fn acts directly on the input shape.\n        return self.fn(x)\n\n# Example usage with a simple nn.Identity\nmodel = Foo(nn.Identity())\nx = torch.randn(1, 10, 32) # Batch, Sequence Length, Dimension\ny = model(x)\n\nprint(f\"Input shape: {x.shape}\")\nprint(f\"Output shape: {y.shape}\")\n\n# Example with a lambda function\ndummy_fn = lambda z: z * 2 # Multiply by 2\nmodel_lambda = Foo(dummy_fn)\ny_lambda = model_lambda(x)\nprint(f\"Output with lambda: {y_lambda.shape}\")\nprint(f\"First element value: {y_lambda[0,0,0]:.2f}\")","lang":"python","description":"This quickstart demonstrates how to use `EinopsToAndFrom`, one of the core utility classes. It shows how to define a custom module that wraps a function or `nn.Module` with specified Einops input and output patterns. The example uses `nn.Identity` and a simple lambda function to illustrate its application."},"warnings":[{"fix":"Always pin your `torch-einops-utils` version in `requirements.txt` or `pyproject.toml` (e.g., `torch-einops-utils==0.0.30`) and consult the GitHub commit history before upgrading to a new `0.0.x` version.","message":"The API surface can change frequently and without explicit deprecation warnings across `0.0.x` releases, as this library is a collection of personal utilities in active development.","severity":"breaking","affected_versions":"All 0.0.x versions"},{"fix":"Ensure a strong foundational understanding of `einops` rearrange and reduce patterns, as well as core `torch` tensor operations. Consult the `einops` official documentation if patterns or operations are unclear.","message":"This library heavily relies on `einops` syntax for defining tensor manipulations and assumes a strong understanding of PyTorch tensors.","severity":"gotcha","affected_versions":"All versions"},{"fix":"Review the source code for specific function behaviors if they deviate from expected generic utility patterns, or if you encounter unexpected results, as the implementations might be optimized for particular scenarios.","message":"As a 'personal utility functions' library, its design choices might be opinionated or tailored for specific use cases not broadly applicable, which can lead to unexpected behavior if used outside of its intended scope.","severity":"gotcha","affected_versions":"All versions"}],"env_vars":null,"last_verified":"2026-04-16T00:00:00.000Z","next_check":"2026-07-15T00:00:00.000Z","problems":[{"fix":"Check the exact spelling of the imported symbol. If correct, ensure your `torch-einops-utils` version is up-to-date or matches the version expected by your code. Run `pip install --upgrade torch-einops-utils` or `pip install torch-einops-utils==<expected_version>`.","cause":"The specified symbol either does not exist in your installed version of `torch-einops-utils`, is misspelled, or the library itself is not installed correctly. This is common due to frequent `0.0.x` API changes.","error":"ImportError: cannot import name 'EinopsToAndFrom' from 'torch_einops_utils'"},{"fix":"Instantiate the class with the correct positional arguments. For `EinopsToAndFrom`, this means providing `pattern_in`, `pattern_out`, and the callable `fn`: `EinopsToAndFrom('b n d', 'b n d', nn.Identity())`.","cause":"Classes like `EinopsToAndFrom` and `EinopsToNoOp` require specific `einops` patterns (e.g., `pattern_in`, `pattern_out`) and a callable function or `nn.Module` instance during initialization.","error":"TypeError: EinopsToAndFrom.__init__ missing 1 required positional argument: 'pattern_in'"}]}