# Unable to find good initial values in toy Pyro bayesian pcfg model

Hi folks,

I’m new to pyro, but it seems great - I’m trying to fit a very simple toy model of a bayesian pcfg (probabilistic context free grammar), but I’m getting a warning re: unable to find start values.

The model code is here:

``````import torch
import pyro
import pyro.distributions as dist
from pyro.infer import MCMC, NUTS
from pyro import poutine
from torch.distributions.utils import lazy_property
import math

# Define grammar rules and their probabilities
grammar = {
"S": [(0.8, ("NP", "VP")), (0.2, ("VP",))],
"NP": [(0.6, ("Det", "N")), (0.4, ("N",))],
"VP": [(0.7, ("V", "NP")), (0.3, ("V",))],
"Det": [(0.6, ("the",)), (0.4, ("a",))],
"N": [(0.5, ("dog",)), (0.5, ("cat",))],
"V": [(1.0, ("chased",))]
}

# Functions to generate rules from the grammar
def generate_rules(grammar):
rules = []
for lhs, rhs_list in grammar.items():
for probability, rhs in rhs_list:
rules.append((lhs, rhs, probability))
return rules
rules = generate_rules(grammar)

def logsumexp(x, y):
m = max(x, y)
return m + math.log(math.exp(x - m) + math.exp(y - m))

def cky_parse(sentence, rules):
n = len(sentence)
chart = [[{} for _ in range(n)] for _ in range(n)]

# Fill the chart with terminal rules
for i, word in enumerate(sentence):
for rule in rules:
lhs, rhs, log_probability = rule
if rhs == (word,):
chart[i][i][lhs] = log_probability

# Apply nonterminal rules
for span in range(2, n + 1):
for start in range(n - span + 1):
end = start + span - 1
for split in range(start, end):
for rule in rules:
lhs, rhs, log_probability = rule
if len(rhs) == 2:
lhs1, lhs2 = rhs
log_prob = chart[start][split].get(lhs1, float('-inf')) + chart[split + 1][end].get(lhs2, float('-inf')) + log_probability
chart[start][end][lhs] = logsumexp(chart[start][end].get(lhs, float('-inf')), log_prob)

return chart

def pcfg_pyro_model(sentence, rules, updated_grammar):
# Parse the sentence with the updated grammar
chart = cky_parse(sentence, generate_rules(updated_grammar))

# Target variable: the probability of generating the sentence
target = pyro.sample("target", dist.Exponential(1.0))
target_obs = chart[0][-1].get("S", 0)
pyro.factor("obs", math.log(target_obs) - math.log(target))
def run_pcfg_inference(sentence, rules):
# Initialize the rule probabilities using a ParamStoreDict
param_store = pyro.get_param_store()
for i, rule in enumerate(rules):
param_name = f"rule_prob_{i}"
if param_name not in param_store:
param_store[param_name] = pyro.param(param_name, torch.tensor(rule[2]), constraint=dist.constraints.positive)

# Update the grammar with the sampled rule probabilities
updated_grammar = {nt: [(pyro.param(f"rule_prob_{i}"), r[1]) for i, r in enumerate(rules) if r[0] == nt] for nt in
grammar.keys()}

# Run inference using NUTS and MCMC
nuts_kernel = NUTS(lambda: pcfg_pyro_model(sentence, rules, updated_grammar))
mcmc = MCMC(nuts_kernel, num_samples=1000, warmup_steps=500)
mcmc.run()

# Inspect the results
mcmc.summary()

# Example sentence to parse
sentence = ["the", "dog", "chased"]

# Perform inference
run_pcfg_inference(sentence, rules)

``````

And the error I get is this:

``````Warmup:   0%|          | 0/1500 [00:00, ?it/s]Traceback (most recent call last):
File "G:\My Drive\MIT\Research\venv\lib\site-packages\pyro\poutine\messenger.py", line 12, in _context_wrap
return fn(*args, **kwargs)
File "G:\My Drive\MIT\Research\venv\lib\site-packages\pyro\infer\mcmc\api.py", line 563, in run
for x, chain_id in self.sampler.run(*args, **kwargs):
File "G:\My Drive\MIT\Research\venv\lib\site-packages\pyro\infer\mcmc\api.py", line 223, in run
for sample in _gen_samples(
File "G:\My Drive\MIT\Research\venv\lib\site-packages\pyro\infer\mcmc\api.py", line 144, in _gen_samples
kernel.setup(warmup_steps, *args, **kwargs)
File "G:\My Drive\MIT\Research\venv\lib\site-packages\pyro\infer\mcmc\hmc.py", line 345, in setup
self._initialize_model_properties(args, kwargs)
File "G:\My Drive\MIT\Research\venv\lib\site-packages\pyro\infer\mcmc\hmc.py", line 279, in _initialize_model_properties
init_params, potential_fn, transforms, trace = initialize_model(
File "G:\My Drive\MIT\Research\venv\lib\site-packages\pyro\infer\mcmc\util.py", line 468, in initialize_model
initial_params = _find_valid_initial_params(
File "G:\My Drive\MIT\Research\venv\lib\site-packages\pyro\infer\mcmc\util.py", line 365, in _find_valid_initial_params
raise ValueError(
ValueError: Model specification seems incorrect - cannot find valid initial params.

Process finished with exit code 1
``````

It seems like the model compiles okay, but there’s something about start values that it’s not liking. Any help would be very much appreciated!

Hi @cbreiss, you can use init_to_value to specify initial ones; - this can help you debug the issue. I suspect that there are numerical issues happen that lead to invalid joint density (you can add many print statements for debugging).

Hi @fehiepsi, thanks so much for your response. Looking at the documentation for that function, it seems to be an argument to a guide class, which I’m not using, since I’m doing mcmc. Does that mean that you think I should redo the model with variational inference, or am I misunderstanding what you meant?

Thanks again,
Canan

i think there may be an issue with your model. what observation likelihood is this supposed to encode? it would seem that this pushes `target` to infinity, which could explain your issue

yes, you can use it as `init_strategy` in NUTS