weightbridge 🌉

What?

A library to map (deep learning) model weights between different model implementations in Python.

Why?

Model weights trained using one implementation of an architecture typically cannot directly be loaded into a different implementation of the same architecture, due to:

• Different parameter and layer names.
• Different nesting of modules.
• Different parameter shapes (e.g. (8, 8) vs (64) vs (1, 8, 8)).
• Different order of dimensions (e.g. (64, 48) vs (48, 64)).
• Different deep learning frameworks (e.g. PyTorch, Tensorflow, Flax).

Adapting the weights manually is a tedious and error-prone process:

k = k.replace('downsample_layers.0.', 'stem.')
k = re.sub(r'stages.([0-9]+).([0-9]+)', r'stages.\1.blocks.\2', k)
k = re.sub(r'downsample_layers.([0-9]+).([0-9]+)', r'stages.\1.downsample.\2', k)
k = k.replace('pwconv', 'mlp.fc')
if 'grn' in k:
    k = k.replace('grn.beta', 'mlp.grn.bias')
    v = v.reshape(v.shape[-1])
k = k.replace('head.', 'head.fc.')
if v.ndim == 2 and 'head' not in k:
    model_shape = model.state_dict()[k].shape
    v = v.reshape(model_shape)

weightbridge does most of this work for you.

How?

import weightbridge
new_my_weights = weightbridge.adapt(their_weights, my_weights)

• my_weights contains the (random) untrained weights created at model initialization (e.g. as the result of model.state_dict() in PyTorch, or using model.init in Flax and Haiku).
• their_weights contains the pretrained weights (e.g. as the result of torch.load, tf.train.load_checkpoint or np.load).

The output has the same structure and weight shapes as my_weights, but with the weight values from their_weights. It can be used as drop-in for my_weights, and for example be stored back into the model using model.load_state_dict in PyTorch, or be used in model.apply in Flax and Haiku.

Installation:

pip install weightbridge

Full examples:

• examples/mamba2flax.py: Download weights for Mamba and load into custom Flax implementation. Print example text.
• examples/gpt2haiku.py: Download weights for OpenAI GPT-2 and load into custom Haiku implementation. Print example text.
• examples/convnext2timm.py: Download original weights for ConvNext and ConvNextV2 and load into timm implementation. Test on an example image of ImageNet.
• examples/vit2flax.py: Download weights for a vision transformer from torchvision and load into a custom Flax implementation. Test on an example image of ImageNet.

Additional parameters:

• {in_format|out_format}="{pytorch|tensorflow|flax|haiku|...}" when weights are adapted between different deep learning frameworks (to permute weight axes as required).
• hints=[...] to provide additional hints when ambiguous matches cannot be resolved. weightbridge prints an error when this happens, for example:

  Failed to pair the following nodes
    OUT load_prefix/encode/stage3/block6/reduce/linear/w ((262144,),)
    OUT load_prefix/encode/stage3/block6/expand/linear/w ((262144,),)
    IN  backbone.0.body.layer3.5.conv1.weight ((262144,),)
    IN  backbone.0.body.layer3.5.conv3.weight ((262144,),)
  

  We can pass hints=[("reduce", "conv1")] (consisting of some uniquely identifying substrings) to resolve the matching failure.
• cache="some-file" to store the mapping in a file and reuse it in subsequent calls. If it is not an absolute path, the file is created in the directory of the module from which weightbridge.adapt is called.
• verbose=True to print the matching steps and the final mapping between weights.

weightbridge internally uses a set of heuristics based on the weights' names and shapes to iteratively find mappings between subsets of my_weights and their_weights, until a unique pairing between all weights is found.

What does weightbridge not do?

• Model implementation: weightbridge does not implement the model, but adapts the weights once the model is implemented (athough it provides a partial sanity-check for the implementation by ensuring that a mapping between the two sets of weights is possible). When the architecture is implemented using different operations, the weights have to be adapted manually. E.g. in Transformer attention, queries, keys and values can be inferred using different operations:

  # Option 1
  x = nn.Linear(features=3 * c)(x)
  q, k, v = jnp.split(x, 3, axis=-1)
  
  # Option 2
  q = nn.Linear(features=c)(x)
  k = nn.Linear(features=c)(x)
  v = nn.Linear(features=c)(x)
  

  The corresponding weights have to be split/ concatenated manually and will not be matched by weightbridge otherwise, since it relies on a one-to-one mapping between weights.
• Hyperparameters: weightbridge does not ensure that hyperparameters like nn.LayerNorm(epsilon=1e-6) or nn.Conv(padding="SAME") are set correctly (although some hyperparameters like use_bias={True|False} will raise an exception if not set correctly).