Source code for zfit.z.zextension

#  Copyright (c) 2020 zfit
import functools
import math as _mt
from collections import defaultdict
from typing import Any, Callable

import numpy as np
import tensorflow as tf

from ..settings import ztypes
from ..util.exception import BreakingAPIChangeError
from ..util.warnings import warn_advanced_feature

[docs]def constant(value, dtype=ztypes.float, shape=None, name="Const", verify_shape=None): # TODO(tf2): remove this legacy thing below if verify_shape is not None: raise RuntimeError("'verify_shape' is not a valid argument anymore. It's always true. Please remove.") return tf.constant(value, dtype=dtype, shape=shape, name=name)
pi = np.float64(_mt.pi)
[docs]def to_complex(number, dtype=ztypes.complex): return tf.cast(number, dtype=dtype)
[docs]def to_real(x, dtype=ztypes.float): return tf.cast(x, dtype=dtype)
[docs]def abs_square(x): return tf.math.real(x * tf.math.conj(x))
[docs]def nth_pow(x, n, name=None): """Calculate the nth power of the complex Tensor x. Args: x (tf.Tensor, complex): n (int >= 0): Power name (str): No effect, for API compatibility with tf.pow """ if not n >= 0: raise ValueError("n (power) has to be >= 0. Currently, n={}".format(n)) power = to_complex(1.) for _ in range(n): power *= x return power
[docs]def unstack_x(value: Any, num: Any = None, axis: int = -1, always_list: bool = False, name: str = "unstack_x"): """Unstack a Data object and return a list of (or a single) tensors in the right order. Args: value (): num (Union[]): axis (int): always_list (bool): If True, also return a list if only one element. name (str): Returns: Union[List[tensorflow.python.framework.ops.Tensor], tensorflow.python.framework.ops.Tensor, None]: """ if isinstance(value, list): if len(value) == 1 and not always_list: value = value[0] return value try: return value.unstack_x(always_list=always_list) except AttributeError: unstacked_x = tf.unstack(value=value, num=num, axis=axis, name=name) if len(unstacked_x) == 1 and not always_list: unstacked_x = unstacked_x[0] return unstacked_x
[docs]def stack_x(values, axis: int = -1, name: str = "stack_x"): return tf.stack(values=values, axis=axis, name=name)
# random sampling
[docs]def convert_to_tensor(value, dtype=None, name=None, preferred_dtype=None): return tf.convert_to_tensor(value=value, dtype=dtype, name=name, dtype_hint=preferred_dtype)
[docs]def safe_where(condition: tf.Tensor, func: Callable, safe_func: Callable, values: tf.Tensor, value_safer: Callable = tf.ones_like) -> tf.Tensor: """Like :py:func:`tf.where` but fixes gradient `NaN` if func produces `NaN` with certain `values`. Args: condition (:py:class:`tf.Tensor`): Same argument as to :py:func:`tf.where`, a boolean :py:class:`tf.Tensor` func (Callable): Function taking `values` as argument and returning the tensor _in case condition is True_. Equivalent `x` of :py:func:`tf.where` but as function. safe_func (Callable): Function taking `values` as argument and returning the tensor _in case the condition is False_, Equivalent `y` of :py:func:`tf.where` but as function. values (:py:class:`tf.Tensor`): Values to be evaluated either by `func` or `safe_func` depending on `condition`. value_safer (Callable): Function taking `values` as arguments and returns "safe" values that won't cause troubles when given to`func` or by taking the gradient with respect to `func(value_safer(values))`. Returns: :py:class:`tf.Tensor`: """ safe_x = tf.where(condition=condition, x=values, y=value_safer(values)) result = tf.where(condition=condition, x=func(safe_x), y=safe_func(values)) return result
[docs]def run_no_nan(func, x): from import Data value_with_nans = func(x=x) if value_with_nans.dtype in (tf.complex128, tf.complex64): value_with_nans = tf.math.real(value_with_nans) + tf.math.imag(value_with_nans) # we care only about NaN or not finite_bools = tf.math.is_finite(tf.cast(value_with_nans, dtype=tf.float64)) finite_indices = tf.where(finite_bools) new_x = tf.gather_nd(params=x, indices=finite_indices) new_x = Data.from_tensor(obs=x.obs, tensor=new_x) vals_no_nan = func(x=new_x) result = tf.scatter_nd(indices=finite_indices, updates=vals_no_nan, shape=tf.shape(input=value_with_nans, out_type=finite_indices.dtype)) return result
[docs]class FunctionWrapperRegistry: all_wrapped_functions = [] registries = [] allow_jit = True _DEFAULT_DO_JIT_TYPES = defaultdict(lambda: True) _DEFAULT_DO_JIT_TYPES.update({ None: True, 'model': False, 'loss': True, 'sample': True, 'model_sampling': True, 'zfit_tensor': True, 'tensor': True, }) do_jit_types = _DEFAULT_DO_JIT_TYPES.copy()
[docs] @classmethod def all_wrapped_functions_registered(cls): return all((func.zfit_graph_cache_registered for func in cls.all_wrapped_functions))
def __init__(self, wraps=None, **kwargs_user) -> None: """`tf.function`-like decorator with additional cache-invalidation functionality. Args: **kwargs_user: arguments to `tf.function` """ super().__init__() self._initial_user_kwargs = kwargs_user self.registries.append(self) self.wrapped_func = None if not wraps in self.do_jit_types: # raise RuntimeError(f"Currently custom 'wraps' category ({wraps}) not allowed, set explicitly in `do_jit_types`") self.do_jit_types[wraps] = True self.wraps = wraps self.function_cache = defaultdict(list) self.reset(**self._initial_user_kwargs) self.currently_traced = set() @property def do_jit(self): return self.do_jit_types[self.wraps] and self.allow_jit
[docs] def reset(self, **kwargs_user): kwargs = dict(autograph=False, experimental_relax_shapes=True) kwargs.update(self._initial_user_kwargs) kwargs.update(kwargs_user) self.tf_function = tf.function(**kwargs) for cache in self.function_cache.values(): cache.clear()
def __call__(self, func): wrapped_func = self.tf_function(func) cache = self.function_cache[func] from zfit.util.cache import FunctionCacheHolder def concrete_func(*args, **kwargs): if not self.do_jit or func in self.currently_traced: return func(*args, **kwargs) assert self.all_wrapped_functions_registered() self.currently_traced.add(func) nonlocal wrapped_func function_holder = FunctionCacheHolder(func, wrapped_func, args, kwargs) if function_holder in cache: func_holder_index = cache.index(function_holder) func_holder_cached = cache[func_holder_index] if func_holder_cached.is_valid: function_holder = func_holder_cached else: wrapped_func = self.tf_function(func) # update nonlocal wrapped function function_holder = FunctionCacheHolder(func, wrapped_func, args, kwargs) cache[func_holder_index] = function_holder else: cache.append(function_holder) func_to_run = function_holder.wrapped_func try: result = func_to_run(*args, **kwargs) finally: self.currently_traced.remove(func) return result concrete_func.zfit_graph_cache_registered = False return concrete_func
[docs]def function_factory(func=None, **kwargs): if callable(func): wrapper = FunctionWrapperRegistry() return wrapper(func) elif func: raise ValueError("All argument have to be key-word only. `func` must not be used") else: return FunctionWrapperRegistry(**kwargs)
tf_function = function_factory # legacy, remove 0.6
[docs]def function_tf_input(*_, **__): raise BreakingAPIChangeError("This function has been removed. Use `z.function(wraps='zfit_tensor') or your" "own category")
# legacy, remove 0.6
[docs]def function_sampling(*_, **__): raise BreakingAPIChangeError("This function has been removed. Use `z.function(wraps='zfit_sampling') or your" "own category")
@functools.wraps(tf.py_function) def py_function(func, inp, Tout, name=None): from .. import settings if not settings.options['numerical_grad']: warn_advanced_feature("Using py_function without numerical gradients. If the Python code does not contain any" " parametrization by `zfit.Parameter` or similar, this can work out. Otherwise, in case" " it depends on those, you may want to set ``.", identifier="py_func_autograd") return tf.py_function(func=func, inp=inp, Tout=Tout, name=name)