Added decaying exp scaling to step size in integral to remove oom errors

NumGI/Loss/LossDataset.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import math
 import random
 
 import sympy as sp
@@ -15,12 +16,11 @@ class LossDataset:
         DatasetTokenizer (DatasetTokenizer): DatasetTokenizer to create loss dataset from.
     """
 
-    def __init__(self, eq_dataset: DatasetTokenizer, N: int, ell_norm: int = 1):
+    def __init__(self, eq_dataset: DatasetTokenizer):
         self.eq_dataset = eq_dataset
-        self.grid_size = (100, 100, 1000)
+        self.grid_size = (-100, 100, 1000)
         self.max_integral_value = 10e10  # we can play with this value
         self.var_dict = self.create_var_dict()
-        self.loss = self.calculate_n_pairwise_loss(N, ell_norm)
 
     def create_var_dict(self):
         """Creates a dictionary of different variables and their corresponding equations.
@@ -50,7 +50,6 @@ def calculate_n_pairwise_loss(self, N, ell_norm):
         second_batch = N - first_batch
         for i in range(first_batch):
             chosen_symbols = random.choice(list(possible_symbols))
-
             possible_equations = {i[1] for i in self.var_dict[chosen_symbols]}
 
             idx_sympy_1, idx_sympy_2 = random.sample(possible_equations, 2)
@@ -76,7 +75,7 @@ def calculate_n_pairwise_loss(self, N, ell_norm):
             loss[1, i] = sol_sympy_2[1]
             loss[2, i] = torch.inf
 
-        return loss
+        self.loss = loss
 
     def compute_integral(self, sympy_eq):
         func, symbols = self.eq_dataset.sympy_to_torch(sympy_eq)
@@ -87,7 +86,10 @@ def compute_integral(self, sympy_eq):
         return result
 
     def create_discrete_grids(self, symbols):
-        grid = torch.linspace(*self.grid_size, device=self.eq_dataset.device)
+        grid_low, grid_high, num_grid = self.grid_size
+        # scale grid down with dimesion
+        num_grid = int(num_grid * math.exp(-len(symbols)))
+        grid = torch.linspace(grid_low, grid_high, num_grid, device=self.eq_dataset.device)
         grids = [grid for i in symbols]
         mesh = torch.meshgrid(grids)
         return mesh