torch.linalg.solve for tinygrad
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tinysolve
torch.linalg.solve for tinygrad: solve A @ x = B for x.
from tinygrad import Tensor
from tinysolve import solve
A = Tensor.randn(2000, 16, 16) + 16 * Tensor.eye(16)
B = Tensor.randn(2000, 16, 4)
x = solve(A, B) # (2000, 16, 4)
# differentiable (higher-order too)
gA, gB = solve(A, B).sum().gradient(A, B)
Install
uv add tinysolve # or: pip install tinysolve
Development
uv run pytest # tests
uv run pytest tests/benchmark.py # benchmarks
uv run ruff check . && uv run ty check
License
MIT
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