Experimental implementations#
Helpers#
mlprodict.testing.experimental_c_impl.experimental_c.code_optimisation ()
code_optimisation() -> str
Returns a string giving some insights about optimisations.
Implementation of ONNX operators#
Experimental implementations for algorithm.
Einsum#
mlprodict.testing.einsum (equation, inputs, optimize = False, runtime = ‘batch_dot’, cache = True, opset = None, decompose = True, strategy = None, verbose = None)
Proposes a new implementation of numpy.einsum. It does not allow expresion using … and expects a right member.
mlprodict.testing.einsum.einsum_fct.CachedEinsum (self, equation, runtime = ‘batch_dot’, opset = None, optimize = False, dtype = <class ‘numpy.float64’>, decompose = True, strategy = None, verbose = None, key = None)
Stores all the necessary information to cache the preprocessing of a an einsum equation.
mlprodict.testing.einsum.optimize_decompose_einsum_equation (equation, dtype, optimize = False, runtime = ‘batch_dot’, cache = True, opset = None, decompose = True, strategy = None, verbose = None)
Proposes a new implementation of numpy.einsum. It does not allow expresion using … and expects a right member.
mlprodict.testing.einsum.einsum_impl.analyse_einsum_equation (equation)
Analyses an einsum equation.
mlprodict.testing.einsum.einsum_impl.apply_sequence
mlprodict.testing.einsum.decompose_einsum_equation (equation, shapes, strategy = ‘simple’, clean = False, verbose = False)
Decomposes an equation used in numpy.einsum knowing the input shapes. It returns a sequence of operations to do to compute the results.
mlprodict.testing.experimental_c_impl.experimental_c.custom_einsum_float (equation, x, y, nthread = 0)
custom_einsum_float(equation: str, x: numpy.ndarray[numpy.float32], y: numpy.ndarray[numpy.float32], nthread: int = 0) -> numpy.ndarray[numpy.float32]
Custom C++ implementation of operator einsum with float. The function only works with contiguous arrays. It does not any explicit transposes. It does not support diagonal operator (repetition of the same letter). See python’s version
custom_einsum.
mlprodict.testing.experimental_c_impl.experimental_c.custom_einsum_double (equation, x, y, nthread = 0)
custom_einsum_double(equation: str, x: numpy.ndarray[numpy.float64], y: numpy.ndarray[numpy.float64], nthread: int = 0) -> numpy.ndarray[numpy.float64]
Custom C++ implementation of operator einsum with double. The function only works with contiguous arrays. It does not any explicit transposes. It does not support diagonal operator (repetition of the same letter). See python’s version
custom_einsum.
mlprodict.testing.einsum.einsum_benchmark (equation = ‘abc,cd->abd’, shape = 30, perm = False, runtime = ‘python’, use_tqdm = False, number = 5, repeat = 5, opset = 15)
Investigates whether or not the decomposing einsum is faster.
mlprodict.testing.einsum.numpy_diagonal (m, axis, axes)
Extracts diagonal coefficients from an array.
mlprodict.testing.einsum.numpy_extended_dot (m1, m2, axes, left, right, verbose = False)
Extended version of a matrix multiplication (numpy.dot) with two matrices m1, m2 of the same dimensions. Loops over left axes for m1 and right axes for m2, summation is done over axes. Other axes must be empty. This multiplication combines matrix multiplication (dot) and broadcasted multiplication term by term.
mlprodict.testing.einsum.numpy_extended_dot_python (m1, m2, axes, left, right, verbose = False)
Implementation of
numpy_extended_dotin pure python. This implementation is not efficient but shows how to implement this operation without numpy.einsum.
mlprodict.testing.einsum.numpy_extended_dot_matrix (m1, m2, axes, left, right, verbose = False)
Implementation of
numpy_extended_dotusing dot product, multiplication, transpose and reduction but not a custom python implementation likenumpy_extended_dot_python.
mlprodict.testing.einsum.einsum_impl_ext.numpy_extended_dot_ouput_shape (m1, m2, axes, left, right)
Computes the output shape of results produced by function
numpy_extended_dotornumpy_extended_dot_python.
Pad#
mlprodict.testing.experimental.custom_pad (arr, paddings, constant = 0, verbose = False)
Implements function pad in python, only the constant version.
ReduceSum#
mlprodict.testing.experimental_c_impl.experimental_c.custom_reducesum_rk_double (x, nthread = 0)
custom_reducesum_rk_double(x: numpy.ndarray[numpy.float64], nthread: int = 0) -> numpy.ndarray[numpy.float64]
Custom C++ implementation of operator ReduceSum with double when the reduced matrix has two dimensions and the reduced axis is the first one. x is the reduced matrix. nthread specifies the number of threads used to distribute. Negative means OMP default values.
mlprodict.testing.experimental_c_impl.experimental_c.custom_reducesum_rk_float (x, nthread = 0)
custom_reducesum_rk_float(x: numpy.ndarray[numpy.float32], nthread: int = 0) -> numpy.ndarray[numpy.float32]
Custom C++ implementation of operator ReduceSum with float when the reduced matrix has two dimensions and the reduced axis is the first one. x is the reduced matrix. nthread specifies the number of threads used to distribute. Negative means OMP default values.