def _warn_if_nan(name, value):
if torch.is_tensor(value):
value = value.item()
if torch_isnan(value):
warnings.warn("Encountered NAN log_prob_sum at site '{}'".format(name))
if torch_isinf(value) and value > 0:
warnings.warn("Encountered +inf log_prob_sum at site '{}'".format(name))
def __call__(self, inputs, s, dim=0, keepdim=False):
"""Updates the moving average, and returns :code:`inputs.log()`."""
self.n += 1
if torch_isnan(self.ewma) or torch_isinf(self.ewma):
ewma = inputs
else:
ewma = inputs * (1.0 - self.alpha) / (
1 - self.alpha**self.n) + torch.exp(self.s - s) * self.ewma * (
self.alpha - self.alpha**self.n) / (1 - self.alpha**self.n)
self.ewma = ewma.detach()
self.s = s.detach()
return _ewma_log_fn(inputs, ewma)
def initial_trace(self):
"""
Find a valid trace to initiate the MCMC sampler. This is also used as a
prototype trace to inter-convert between Pyro's trace object and dict
object used by the integrator.
"""
if self._initial_trace:
return self._initial_trace
trace = poutine.trace(self.model).get_trace(*self._args,
**self._kwargs)
for i in range(self._max_tries_initial_trace):
trace_log_prob_sum = self._compute_trace_log_prob(trace)
if not torch_isnan(trace_log_prob_sum) and not torch_isinf(
trace_log_prob_sum):
self._initial_trace = trace
return trace
trace = poutine.trace(self.model).get_trace(
self._args, self._kwargs)
raise ValueError(
"Model specification seems incorrect - cannot find a valid trace.")
def _validate_trace(self, trace):
trace_log_prob_sum = trace.log_prob_sum()
if torch_isnan(trace_log_prob_sum) or torch_isinf(trace_log_prob_sum):
raise ValueError(
"Model specification incorrect - trace log pdf is NaN or Inf.")
def _validate_trace(self, trace):
trace_log_prob_sum = trace.log_prob_sum()
if torch_isnan(trace_log_prob_sum) or torch_isinf(trace_log_prob_sum):
raise ValueError("Model specification incorrect - trace log pdf is NaN or Inf.")