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@nielstron
nielstron committed Jan 2, 2025
1 parent 67c7b21 commit f0f4694
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Showing 3 changed files with 42 additions and 37 deletions.
12 changes: 11 additions & 1 deletion uplc/ast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -135,6 +135,10 @@ def ex_mem(self) -> int:
"""The memory consumption of this element"""
raise NotImplementedError()

def literal_cost(self):
"""The memory consumption of this element if interpreted literally as size"""
raise NotImplementedError()


@dataclass(frozen=True)
class Constant(AST):
Expand Down Expand Up @@ -187,7 +191,13 @@ def valuestring(self, dialect=UPLCDialect.Plutus):
def ex_mem(self) -> int:
if self.value == 0:
return 1
return ((abs(self.value).bit_length() - 1) // 64) + 1
return ((self.value.bit_length() - 1) // 64) + 1

def literal_cost(self):
# based on https://github.com/aiken-lang/aiken/blob/620fe6b2997537aab655fefaf27b43e5c8ed1916/crates/uplc/src/machine/value.rs#L256
if self.value == 0:
return 0
return (self.value - 1) // 8 + 1

def __add__(self, other):
assert isinstance(
Expand Down
63 changes: 29 additions & 34 deletions uplc/cost_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,12 +6,11 @@
import json
import math
from pathlib import Path
from typing import Dict, Any, Union
from typing import Dict, Any, Union, List
from enum import Enum

import pycardano

from .ast import BuiltInFun
from .ast import BuiltInFun, AST

# 30000000 appears to be default (see https://github.com/aiken-lang/aiken/blob/e1d46fa8f063445da8c0372e3c031c8a11ad0b14/crates/uplc/src/machine/cost_model.rs#L3376)
DEFAULT_COST_COEFF = 30000000000
Expand Down Expand Up @@ -79,7 +78,7 @@ def exhausted(self):


class CostingFun:
def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values: List[AST]=()) -> int:
raise NotImplementedError("Abstract cost not implemented")

@classmethod
Expand All @@ -102,7 +101,7 @@ def update_from_network_config(
class ConstantCost(CostingFun):
constant: int = 0

def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.constant

@classmethod
Expand All @@ -120,7 +119,7 @@ class LinearCost(CostingFun):
intercept: int = 0
slope: int = 0

def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.intercept + self.slope * memories[0]

@classmethod
Expand Down Expand Up @@ -150,37 +149,41 @@ def update_from_network_config(

@dataclasses.dataclass
class LinearInX(Derived):
def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.model.cost(memories[0])


@dataclasses.dataclass
class LinearInY(Derived):
def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.model.cost(memories[1])

@dataclasses.dataclass
class LinearInMaxYz(Derived):
def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.model.cost(max(memories[1], memories[2]))

@dataclasses.dataclass
class LiteralInX(Derived):
def cost(self, *memories: int, values=()) -> int:
return self.model.cost(values[0].literal_cost())

@dataclasses.dataclass
class LinearInZ(Derived):
def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.model.cost(memories[2])

@dataclasses.dataclass
class AddedSizes(Derived):
def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.model.cost(sum(memories))


@dataclasses.dataclass
class SubtractedSizes(Derived):
minimum: int = 0

def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.model.cost(max(memories[0] - memories[1], self.minimum))

@classmethod
Expand All @@ -202,19 +205,19 @@ def update_from_network_config(

@dataclasses.dataclass
class MultipliedSizes(Derived):
def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.model.cost(math.prod(memories))


@dataclasses.dataclass
class MinSize(Derived):
def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.model.cost(min(memories))


@dataclasses.dataclass
class MaxSize(Derived):
def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.model.cost(max(memories))


Expand All @@ -225,7 +228,7 @@ class LinearOnDiagonal(CostingFun):
default_factory=lambda: ConstantCost(0)
)

def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
x, y = memories[0], memories[1]
if x == y:
return self.model_on_diagonal.cost(x)
Expand Down Expand Up @@ -253,7 +256,7 @@ class ConstAboveDiagonal(CostingFun):
default_factory=lambda: ConstantCost(0)
)

def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
x, y = memories[0], memories[1]
if x > y:
return self.model_above_diagonal.cost(x, y)
Expand Down Expand Up @@ -282,7 +285,7 @@ class ConstBelowDiagonal(CostingFun):
default_factory=lambda: ConstantCost(0)
)

def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
x, y = memories[0], memories[1]
if x >= y:
return self.model_above_equal_diagonal.cost(x, y)
Expand Down Expand Up @@ -310,7 +313,7 @@ class QuadraticInY(CostingFun):
c1: int = 0
c2: int = 0

def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.c0 + self.c1 * memories[1] + self.c2 * memories[1]**2

@classmethod
Expand All @@ -330,7 +333,7 @@ class QuadraticInZ(CostingFun):
c1: int = 0
c2: int = 0

def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.c0 + self.c1 * memories[2] + self.c2 * memories[2]**2

@classmethod
Expand All @@ -354,7 +357,7 @@ class QuadraticInXAndY(CostingFun):
c20: int = 0
minimum: int = 0

def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
x, y = memories[0], memories[1]
poly = self.c00 + self.c10 * x + self.c01 * y + + self.c20 * x * x + self.c11 * x * y + self.c02 * y * y
return max(poly, self.minimum)
Expand All @@ -379,20 +382,11 @@ class LiteralInYOrLinearInZ(CostingFun):
intercept: int = 0
slope: int = 0

def cost(self, *memories: int, values=[]) -> int:
# LOL good for you having implemented this extra step to pass actual values to the costing function
# because the specs say you need it
# (correct implementation based on official specifications below)
y = values[1].value
def cost(self, *memories: int, values=()) -> int:
y = values[1].literal_cost()
if y == 0:
return self.intercept + self.slope * memories[2]
return int(math.ceil((abs(y) - 1) / 8))
# NO FCK YOU and instead use this completely broken implementation because possibly some IOHK engineer
# was too lazy to implement the specs or they realized that the specs were a terrible idea
# y = memories[1]
# if y == 0:
# return self.intercept + self.slope * memories[2]
# return y
return y

@classmethod
def from_arguments(cls, arguments: Dict[str, int]):
Expand All @@ -415,7 +409,7 @@ class LinearInYAndZ(CostingFun):
slope1: int = 0
slope2: int = 0

def cost(self, *memories: int, values=[]) -> int:
def cost(self, *memories: int, values=()) -> int:
return self.intercept + self.slope1 * memories[1] + self.slope2 * memories[2]

@classmethod
Expand Down Expand Up @@ -470,6 +464,7 @@ class CekMachineCostModel:
MultipliedSizes,
MinSize,
MaxSize,
LiteralInX,
LinearInX,
LinearInY,
LinearInZ,
Expand Down
4 changes: 2 additions & 2 deletions uplc/cost_model_files/base/builtinCostModel.json
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Original file line number Diff line number Diff line change
Expand Up @@ -1051,14 +1051,14 @@
"intercept": 180194,
"slope": 159
},
"type": "linear_in_x"
"type": "literal_in_x"
},
"memory": {
"arguments": {
"intercept": 1,
"slope": 1
},
"type": "linear_in_x"
"type": "literal_in_x"
}
},
"shiftByteString": {
Expand Down

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