fisher-torch 0.1.0

Extract simplex arrays from PyTorch model outputs

fisher-torch

Extract simplex arrays from PyTorch model outputs.

Part of the Fisher stack — built on fisher-simplex for simplex geometry and invariants.

Install

pip install fisher-torch

With transformers support:

pip install fisher-torch[transformers]

Quick start

import torch
from fisher_torch import extract_predictions, capture_forward
from fisher_torch import SamplingPolicy

# From raw logits
logits = torch.randn(10, 50000)  # (seq_len, vocab_size)
simplex = extract_predictions(logits, top_k=50)
# simplex.shape == (10, 51)  — top-50 + remainder bin, valid simplex

# From a HuggingFace model
policy = SamplingPolicy(top_k=50, final_token_only=True)
result = capture_forward(model, input_ids, predictions=True, policy=policy)
# result.predictions is a float64 numpy simplex array
# result.projection_spec describes the projection geometry

Modules

Module	Purpose
extractors	Stateless functions: logits, attention, hidden states, routing → simplex arrays
capture	Single forward pass orchestrator (capture_forward)
convert	Tensor ↔ numpy simplex array conversion
sampling	SamplingPolicy — controls which layers, heads, positions to extract
utils	Numerically stable softmax, top-k simplex projection