beanmachine.ppl.compiler.special_function_caller module

class beanmachine.ppl.compiler.special_function_caller.KnownFunction(receiver: beanmachine.ppl.compiler.bmg_nodes.BMGNode, function: Callable)

Bases: object

function: Callable

receiver: beanmachine.ppl.compiler.bmg_nodes.BMGNode

class beanmachine.ppl.compiler.special_function_caller.SpecialFunctionCaller(bmg: beanmachine.ppl.compiler.bm_graph_builder.BMGraphBuilder)

Bases: object

bind_torch_instance_function(receiver: beanmachine.ppl.compiler.bmg_nodes.BMGNode, name: str) → beanmachine.ppl.compiler.special_function_caller.KnownFunction

distribution_to_node(distribution: torch.distributions.distribution.Distribution) → beanmachine.ppl.compiler.bmg_nodes.DistributionNode

do_special_call_always_stochastic(func: Callable, args: List[Any], kwargs: Dict[str, Any] = {}) → beanmachine.ppl.compiler.bmg_nodes.BMGNode

do_special_call_maybe_stochastic(func: Any, args: List[Any], kwargs: Dict[str, Any] = {}) → Any

is_special_function(func: Callable, args: List[Any] = [], kwargs: Dict[str, Any] = {}) → bool

is_special_tensor_bound_instance_method(f: Callable) → bool

beanmachine.ppl.compiler.special_function_caller.canonicalize_function(function: Any, arguments: List[Any]) → Tuple[Callable, List[Any]]

beanmachine.ppl.compiler.special_function_caller.only_ordinary_arguments(args, kwargs) → bool