autoray

import autoray as ar import numpy as np # Basic usage with automatic dispatch (inferred from array type) x_np = np.random.uniform(size=) y_np = ar.do('sqrt', x_np) print(f"Numpy sqrt: {y_np}, type: {type(y_np)}") # Using 'like' argument for explicit backend or inference # If torch is not installed, this will silently fall back to numpy behavior # For full torch functionality, ensure 'torch' is installed. x_torch_like = ar.do('random.uniform', size=(10, 10), like="torch") print(f"Array generated with 'like="torch"': {type(x_torch_like)}") # Using get_namespace for a backend-specific API (Python Array API style) try: # Attempt to get a torch namespace xp = ar.get_namespace(like="torch") # Requires 'torch' to be installed for actual torch arrays z = xp.ones((3, 4), dtype=xp.float32) result = xp.exp(z) print(f"Torch-like exp result shape: {result.shape}, type: {type(result)}") except (ImportError, TypeError): # Fallback if torch is not installed, get numpy namespace xp = ar.get_namespace(like="numpy") z = xp.ones((3, 4), dtype=xp.float32) result = xp.exp(z) print(f"Numpy-like exp result shape (fallback): {result.shape}, type: {type(result)}") # Example of a more complex operation with automatic dispatch def noised_svd(x): U, s, VH = ar.do('linalg.svd', x) sn = s + 0.1 * ar.do('random.normal', size=ar.shape(s), like=s) return ar.do('einsum', 'ij,j,jk->ik', U, sn, VH) # Use a numpy array for demonstration x_complex_op = np.random.rand(10, 10) y_complex_op = noised_svd(x_complex_op) print(f"Complex operation result shape: {y_complex_op.shape}")