AltLuv commited on
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End of training

Browse files
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scheduler/scheduler_config.json ADDED
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+ "_config_class_name": "SDEPolynomialConfig",
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scheduler/scheduler_config.py ADDED
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+ import jax.numpy as jnp
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+ import jax
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+ import torch
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+ from dataclasses import dataclass
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+ import sympy
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+ import sympy as sp
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+ from sympy import Matrix, Symbol
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+ import math
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+ from sde_redefined_param import SDEDimension
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+ @dataclass
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+ class SDEPolynomialConfig:
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+ name = "Custom"
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+
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+ initial_variable_value = 0
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+ max_variable_value = 1# math.inf
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+ min_sample_value = 1e-6
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+
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+ variable = Symbol('t', nonnegative=True, real=True, domain=sympy.Interval(initial_variable_value, max_variable_value, left_open=False, right_open=False))
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+ drift_dimension = SDEDimension.SCALAR
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+ diffusion_dimension = SDEDimension.SCALAR
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+ diffusion_matrix_dimension = SDEDimension.SCALAR
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+
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+ drift_degree = 20
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+ diffusion_degree = 20
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+
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+ drift_parameters = Matrix([sympy.symbols(f"f:{drift_degree}", real=True, nonzero=True)])
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+
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+ diffusion_parameters = Matrix([sympy.symbols(f"l:{diffusion_degree}", real=True, nonzero=True)])
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+
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+
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+ @property
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+ def drift(self):
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+ transformed_variable = self.variable
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+ return -sympy.Abs(sum(sympy.HadamardProduct(Matrix([[transformed_variable**i for i in range(1,self.drift_degree+1)]]), self.drift_parameters).doit()))
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+
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+
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+ @property
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+ def diffusion(self):
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+ return self.variable**(sum(sympy.HadamardProduct(Matrix([[self.variable**i for i in range(0,self.diffusion_degree)]]),self.diffusion_parameters.applyfunc(lambda x: x**2)).doit()))
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+
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+ # TODO (KLAUS) : in the SDE SAMPLING CHANGING Q impacts how we sample z ~ N(0, Q*(delta t))
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+ diffusion_matrix = 1
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+
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+
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+ module = 'jax'
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+
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+ drift_integral_form=True
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+ diffusion_integral_form=True
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+ diffusion_integral_decomposition = 'cholesky' # ldl
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+
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+
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+
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+ target = "epsilon" # x0
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+ non_symbolic_parameters = {'drift': torch.ones(drift_degree), 'diffusion': torch.ones(diffusion_degree)}
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