modeling
Model
Base class for all LLaMPPL models.
Your models should subclass this class. Minimally, you should provide an __init__
method
that calls super().__init__(self)
, and a step
method.
Source code in hfppl/modeling.py
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condition(b)
Constrain a given Boolean expression to be True
.
If the condition is False, the particle's weight is set to zero and self.finish()
is called, so that no further step
calls are made.
Parameters:
Name  Type  Description  Default 

b 
the Boolean expression whose value is constrained to be True. 
required 
Source code in hfppl/modeling.py
immutable_properties()
Return a set[str]
of properties that LLaMPPL may assume do not change during execution of step
.
This set is empty by default but can be overridden by subclasses to speed up inference.
Returns:
Name  Type  Description 

properties 
set[str]

a set of immutable property names 
Source code in hfppl/modeling.py
intervene(dist, x)
async
Force the distribution to take on the value x
, but do not condition on this result.
This is useful primarily with distributions that have side effects (e.g., modifying some state). For example, a model with the code
token_1 = await self.sample(self.stateful_lm.next_token())
await self.observe(self.stateful_lm.next_token(), token_2)
encodes a posterior inference problem, to find token_1
values that likely preceded token_2
. By contrast,
token_1 = await self.sample(stateful_lm.next_token())
await self.intervene(self.stateful_lm.next_token(), token_2)
encodes a much easier task: freely generate token_1
and then forcefeed token_2
as the following token.
Parameters:
Name  Type  Description  Default 

dist 
Distribution

the distribution on which to intervene. 
required 
x 
the value to intervene with. 
required 
Source code in hfppl/modeling.py
observe(dist, x)
async
Condition the model on the value x
being sampled from the distribution dist
.
For discrete distributions dist
, await self.observe(dist, x)
specifies the same constraint as
Parameters:
Name  Type  Description  Default 

dist 
a 
required  
x 
the value observed from 
required 
Source code in hfppl/modeling.py
sample(dist, proposal=None)
async
Extend the model with a sample from a given Distribution
, with support for autobatching.
If specified, the Distribution proposal
is used during inference to generate informed hypotheses.
Parameters:
Name  Type  Description  Default 

dist 
the 
required  
proposal 
if provided, inference algorithms will use this 
None

Returns:
Name  Type  Description 

value 
the value sampled from the distribution. 
Source code in hfppl/modeling.py
score(score)
Multiply this particle's weight by exp(score)
.
The score
method is a lowlevel way to change the target distribution.
For many use cases, it is sufficient to use sample
, observe
, condition
,
and twist
, all of which are implemented in terms of score
.
Parameters:
Name  Type  Description  Default 

score 
logarithm of the amount by which the particle's weight should be multiplied. 
required 
Source code in hfppl/modeling.py
step()
async
Defines the computation performed in each step of the model.
All subclasses should override this method.
twist(amt)
Multiply this particle's weight by exp(amt)
, but divide it back out before the next step
.
Use this method to provide heuristic guidance about whether a particle is "on the right track" without changing the ultimate target distribution.
Parameters:
Name  Type  Description  Default 

amt 
the logarithm of the amount by which to (temporarily) multiply this particle's weight. 
required 
Source code in hfppl/modeling.py
submodel(f)
Decorator to create a SubModel implementation from an async function.
For example:
@submodel
async def sample_two_tokens(self, context):
token1 = await self.sample(context.next_token())
token2 = await self.sample(context.next_token())
return token1, token2
This SubModel can then be used from another model or submodel, using the syntax await self.call(sample_two_tokens(context))
.