numba_dpex.core.passes.rename_numpy_functions_pass module

class numba_dpex.core.passes.rename_numpy_functions_pass.DPPYRewriteNdarrayFunctions

Bases: numba.core.compiler_machinery.FunctionPass

pass_id = 47

run_pass(state): Runs the pass itself. Must return True/False depending on whether statement level modification took place.

class numba_dpex.core.passes.rename_numpy_functions_pass.DPPYRewriteOverloadedNumPyFunctions

Bases: numba.core.compiler_machinery.FunctionPass

pass_id = 46

run_pass(state): Runs the pass itself. Must return True/False depending on whether statement level modification took place.

class numba_dpex.core.passes.rename_numpy_functions_pass.RewriteNdarrayFunctions(state, rewrite_function_name_map={'all': (['numpy'], 'all'), 'amax': (['numpy'], 'amax'), 'amin': (['numpy'], 'amin'), 'argmax': (['numpy'], 'argmax'), 'argmin': (['numpy'], 'argmin'), 'argsort': (['numpy'], 'argsort'), 'beta': (['random'], 'beta'), 'binomial': (['random'], 'binomial'), 'chisquare': (['random'], 'chisquare'), 'cholesky': (['linalg'], 'cholesky'), 'copy': (['numpy'], 'copy'), 'cov': (['numpy'], 'cov'), 'cumprod': (['numpy'], 'cumprod'), 'cumsum': (['numpy'], 'cumsum'), 'det': (['linalg'], 'det'), 'diagonal': (['numpy'], 'diagonal'), 'dot': (['numpy'], 'dot'), 'eig': (['linalg'], 'eig'), 'eigvals': (['linalg'], 'eigvals'), 'exponential': (['random'], 'exponential'), 'full': (['numpy'], 'full'), 'full_like': (['numpy'], 'full_like'), 'gamma': (['random'], 'gamma'), 'geometric': (['random'], 'geometric'), 'gumbel': (['random'], 'gumbel'), 'hypergeometric': (['random'], 'hypergeometric'), 'laplace': (['random'], 'laplace'), 'lognormal': (['random'], 'lognormal'), 'matmul': (['numpy'], 'matmul'), 'matrix_power': (['linalg'], 'matrix_power'), 'matrix_rank': (['linalg'], 'matrix_rank'), 'max': (['numpy'], 'max'), 'mean': (['numpy'], 'mean'), 'median': (['numpy'], 'median'), 'min': (['numpy'], 'min'), 'multi_dot': (['linalg'], 'multi_dot'), 'multinomial': (['random'], 'multinomial'), 'multivariate_normal': (['random'], 'multivariate_normal'), 'nanprod': (['numpy'], 'nanprod'), 'nansum': (['numpy'], 'nansum'), 'negative_binomial': (['random'], 'negative_binomial'), 'normal': (['random'], 'normal'), 'ones_like': (['numpy'], 'ones_like'), 'partition': (['numpy'], 'partition'), 'poisson': (['random'], 'poisson'), 'prod': (['numpy'], 'prod'), 'rand': (['random'], 'rand'), 'randint': (['random'], 'randint'), 'random': (['random'], 'random'), 'random_integers': (['random'], 'random_integers'), 'random_sample': (['random'], 'random_sample'), 'ranf': (['random'], 'ranf'), 'rayleigh': (['random'], 'rayleigh'), 'repeat': (['numpy'], 'repeat'), 'sample': (['random'], 'sample'), 'sort': (['numpy'], 'sort'), 'standard_cauchy': (['random'], 'standard_cauchy'), 'standard_exponential': (['random'], 'standard_exponential'), 'standard_gamma': (['random'], 'standard_gamma'), 'standard_normal': (['random'], 'standard_normal'), 'sum': (['numpy'], 'sum'), 'take': (['numpy'], 'take'), 'trace': (['numpy'], 'trace'), 'uniform': (['random'], 'uniform'), 'vdot': (['numpy'], 'vdot'), 'weibull': (['random'], 'weibull'), 'zeros_like': (['numpy'], 'zeros_like')})

Bases: object

run()

class numba_dpex.core.passes.rename_numpy_functions_pass.RewriteNumPyOverloadedFunctions(state, rewrite_function_name_map={'all': (['numpy'], 'all'), 'amax': (['numpy'], 'amax'), 'amin': (['numpy'], 'amin'), 'argmax': (['numpy'], 'argmax'), 'argmin': (['numpy'], 'argmin'), 'argsort': (['numpy'], 'argsort'), 'beta': (['random'], 'beta'), 'binomial': (['random'], 'binomial'), 'chisquare': (['random'], 'chisquare'), 'cholesky': (['linalg'], 'cholesky'), 'copy': (['numpy'], 'copy'), 'cov': (['numpy'], 'cov'), 'cumprod': (['numpy'], 'cumprod'), 'cumsum': (['numpy'], 'cumsum'), 'det': (['linalg'], 'det'), 'diagonal': (['numpy'], 'diagonal'), 'dot': (['numpy'], 'dot'), 'eig': (['linalg'], 'eig'), 'eigvals': (['linalg'], 'eigvals'), 'exponential': (['random'], 'exponential'), 'full': (['numpy'], 'full'), 'full_like': (['numpy'], 'full_like'), 'gamma': (['random'], 'gamma'), 'geometric': (['random'], 'geometric'), 'gumbel': (['random'], 'gumbel'), 'hypergeometric': (['random'], 'hypergeometric'), 'laplace': (['random'], 'laplace'), 'lognormal': (['random'], 'lognormal'), 'matmul': (['numpy'], 'matmul'), 'matrix_power': (['linalg'], 'matrix_power'), 'matrix_rank': (['linalg'], 'matrix_rank'), 'max': (['numpy'], 'max'), 'mean': (['numpy'], 'mean'), 'median': (['numpy'], 'median'), 'min': (['numpy'], 'min'), 'multi_dot': (['linalg'], 'multi_dot'), 'multinomial': (['random'], 'multinomial'), 'multivariate_normal': (['random'], 'multivariate_normal'), 'nanprod': (['numpy'], 'nanprod'), 'nansum': (['numpy'], 'nansum'), 'negative_binomial': (['random'], 'negative_binomial'), 'normal': (['random'], 'normal'), 'ones_like': (['numpy'], 'ones_like'), 'partition': (['numpy'], 'partition'), 'poisson': (['random'], 'poisson'), 'prod': (['numpy'], 'prod'), 'rand': (['random'], 'rand'), 'randint': (['random'], 'randint'), 'random': (['random'], 'random'), 'random_integers': (['random'], 'random_integers'), 'random_sample': (['random'], 'random_sample'), 'ranf': (['random'], 'ranf'), 'rayleigh': (['random'], 'rayleigh'), 'repeat': (['numpy'], 'repeat'), 'sample': (['random'], 'sample'), 'sort': (['numpy'], 'sort'), 'standard_cauchy': (['random'], 'standard_cauchy'), 'standard_exponential': (['random'], 'standard_exponential'), 'standard_gamma': (['random'], 'standard_gamma'), 'standard_normal': (['random'], 'standard_normal'), 'sum': (['numpy'], 'sum'), 'take': (['numpy'], 'take'), 'trace': (['numpy'], 'trace'), 'uniform': (['random'], 'uniform'), 'vdot': (['numpy'], 'vdot'), 'weibull': (['random'], 'weibull'), 'zeros_like': (['numpy'], 'zeros_like')})

Bases: object

run()

This function rewrites the name of NumPy functions that exist in self.function_name_map e.g np.sum(a) would produce the following:

np.sum() –> numba_dpex.dpnp.sum()

$2load_global.0 = global(np: <module ‘numpy’ from ‘numpy/__init__.py’>) [‘$2load_global.0’] $4load_method.1 = getattr(value=$2load_global.0, attr=sum) [‘$2load_global.0’, ‘$4load_method.1’] $8call_method.3 = call $4load_method.1(a, func=$4load_method.1, args=[Var(a, test_rewrite.py:7)],

kws=(), vararg=None) [‘$4load_method.1’, ‘$8call_method.3’, ‘a’]

$dppy_replaced_var.0 = global(numba_dpex: <module ‘numba_dpex’ from ‘numba_dpex/__init__.py’>) [‘$dppy_replaced_var.0’] $dpnp_var.1 = getattr(value=$dppy_replaced_var.0, attr=dpnp) [‘$dpnp_var.1’, ‘$dppy_replaced_var.0’] $4load_method.1 = getattr(value=$dpnp_var.1, attr=sum) [‘$4load_method.1’, ‘$dpnp_var.1’] $8call_method.3 = call $4load_method.1(a, func=$4load_method.1, args=[Var(a, test_rewrite.py:7)],

kws=(), vararg=None) [‘$4load_method.1’, ‘$8call_method.3’, ‘a’]

numba_dpex.core.passes.rename_numpy_functions_pass.get_dpnp_func_typ(func)