{"library":"openequivariance","title":"OpenEquivariance","description":"A fast GPU JIT kernel generator for the Clebsch-Gordon Tensor Product, currently at version 0.6.6. Released about monthly. Supports PyTorch and JAX backends.","language":"python","status":"active","last_verified":"Sat May 09","install":{"commands":["pip install openequivariance"],"cli":null},"imports":["from openequivariance import oe","from openequivariance import TPProblem"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import torch\nfrom openequivariance import oe\n\nirreps_in1 = [(1, (1, 1)), (1, (1, 2)), (1, (2, 1)), (1, (2, 2))]\nirreps_in2 = [(1, (1, 1)), (1, (1, 2)), (1, (2, 1)), (1, (2, 2))]\nirreps_out = [(1, (1, 1)), (1, (1, 2)), (1, (2, 1)), (1, (2, 2))]\ninstruction = [(0, 0, 0, \"uvu\", True), (1, 1, 1, \"uvu\", True)]\n\n# Create TP problem\ntp = TPProblem(irreps_in1, irreps_in2, irreps_out, instruction, dtype=torch.float32, device='cpu')\n\n# Input tensors\nN = 8\nx1 = torch.randn(N, 16)\nx2 = torch.randn(N, 16)\n\n# Forward pass\nresult = tp(x1, x2)\nprint(result.shape)","lang":"python","description":"Minimal tensor product using Torch backend on CPU.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}