…generation.

- Activation collection and processing now supports batched inputs [still work in progress]
- Updated existing tests to support the new shape of the activations tensor with a batch dimension
- Creating a mockGPT to more properly test lm functionality [work in progress]

…n collection. No support for saliency or other features yet.

- Activation collection and processing now supports batched inputs [still work in progress]
- Creating a mockGPT to more properly test lm functionality [work in progress]

…and NMF visualization. Tested with a batch of one input.

- Activation collection and processing now supports batched inputs [still work in progress]
- Creating a mockGPT to more properly test lm functionality [work in progress]

…and NMF visualization. Tested with a batch of one input.

- Activation collection and processing now supports batched inputs [still work in progress]
- Creating a mockGPT to more properly test lm functionality [work in progress]

# Conflicts:
#	setup.py
#	src/ecco/__init__.py
#	src/ecco/lm.py
#	tests/lm_test.py
#	tests/output_test.py

- Pytest passes locally

…els to be supported and define their key layers (embeddings for saliency, and FFNN for activations) in YAML.

- Defined a batch of initial models in model-config.yaml. The top pytorch models and two dummy models for testing purposes.
- Started writing tests in lm_test.py that act as integration tests with HF Transformers. These use tiny GPT/BERT models to ensure functionality works between ecco and the models. This is to automated tests that were previously done manually in jupyter notebooks before release.
- Switched docs from sphinx to mkdoc
- Wrote a couple of docs pages, set up a skeleton for navigation. Expanding on docstrings
- Added a 'verbose' parameter to LM to suppress printing tokens during generation.
- Removed unused MockGPT code.

…hed from layer name, to using a regex pattern. This was we can be more specific and not collect other layers by mistake (example: both attention and ffnn layers of bert contain "output.dense" layers.

- Starting documenting lm as a module. Looks messy for now. Will see how to clean it up later.
- Can now force LM to use CPU even if GPU is available. That is done by setting the "gpu" parameter to False in ecco.from_pretrained().
- __call__ now automatically moves input token to GPU if the model is on GPU.
- In _get_activations_hook, extracting the layer number is now done more precisely using regex.

- To better support new models, AutoModel is now used on everything that doesn't have "GPT2" in its model name. Stopgap for now.

…as not consistent between outputs of "generate()" and "__call__".

- generate() now produces the same shape of 'token_ids' and 'tokens' produced by __call__. dims: (batch, position). OutputSeq should likely verify the dims of inputs.
- __call__ now returns 'token_ids' without the 'input_ids' dict key. Consumers shouldn't know about the distinction.
- Added tests for nmf pipeline for both dummy bert and GPT.
- More docs

…n_ dimensions of token_ids and tokens

- created index of docs
- Added simple css to the docs template

- Adding notebook links

…the docs homepage