Update proxies: outputs are not negative by default anymore; Restore …

…proxy2reward and beta values in configs but as proxy config

config/experiments/crystals/albatross.yaml

@@ -50,6 +50,11 @@ env:
       output_csv: ccrystal_val.csv
       output_pkl: ccrystal_val.pkl
 
+# Proxy
+proxy:
+  reward_function: exponential
+  beta: 8
+
 # GFlowNet hyperparameters
 gflownet:
   random_action_prob: 0.1

config/experiments/crystals/albatross_sg_first.yaml

@@ -52,6 +52,11 @@ env:
       output_csv: ccrystal_val.csv
       output_pkl: ccrystal_val.pkl
 
+# Proxy
+proxy:
+  reward_function: exponential
+  beta: 8
+
 # GFlowNet hyperparameters
 gflownet:
   random_action_prob: 0.1

config/experiments/crystals/lattice_parameters.yaml

@@ -12,6 +12,11 @@ env:
   buffer:
     replay_capacity: 1000
 
+# Proxy
+proxy:
+  reward_function: exponential
+  beta: 0.3
+
 # GFlowNet hyperparameters
 gflownet:
   random_action_prob: 0.1

config/experiments/crystals/pigeon.yaml

@@ -54,6 +54,11 @@ env:
       output_csv: ccrystal_val.csv
       output_pkl: ccrystal_val.pkl
 
+# Proxy
+proxy:
+  reward_function: exponential
+  beta: 8
+
 # GFlowNet hyperparameters
 gflownet:
   random_action_prob: 0.1

config/experiments/neurips23/crystal-comp-sg-lp.yaml

@@ -17,6 +17,11 @@ env:
   composition_kwargs:
     elements: 89
 
+# Proxy
+proxy:
+  reward_function: exponential
+  beta: 1
+
 # GFlowNet hyperparameters
 gflownet:
   random_action_prob: 0.1

config/experiments/simple_tetris.yaml

@@ -20,6 +20,10 @@ env:
       output_pkl: simple_tetris_val.pkl
       n: 100
 
+proxy:
+  reward_function: exponential
+  beta: 10
+
 gflownet:
   random_action_prob: 0.3
   optimizer:
@@ -42,4 +46,4 @@ device: cpu
 logger:
   do:
     online: True
-  project_name: simple_tetris
+  project_name: simple_tetris

config/experiments/tree.yaml

@@ -19,6 +19,11 @@ env:
   buffer:
     replay_capacity: 100
 
+# Proxy
+proxy:
+  reward_function: exponential
+  beta: 32
+
 # GFlowNet hyperparameters
 gflownet:
   random_action_prob: 0.1

config/experiments/workshop23/discrete-matbench.yaml

@@ -21,6 +21,11 @@ env:
   buffer:
     replay_capacity: 0
 
+# Proxy
+proxy:
+  reward_function: exponential
+  beta: 1
+
 # GFlowNet hyperparameters
 gflownet:
   random_action_prob: 0.1

config/proxy/base.yaml

@@ -1,14 +1,14 @@
 _target_: gflownet.proxy.base.Proxy
 
 # Reward function: string identifier of the proxy-to-reward function:
-# - identity
-# - absolute (default)
+# - identity (default)
+# - absolute
 # - power
 # - exponential
 # - shift
 # - product
 # Alternatively, it can be a callable of the function itself.
-reward_function: absolute
+reward_function: identity
 # A callable of the proxy-to-logreward function. 
 # None by default, which takes the log of the proxy-to-reward function
 logreward_function: null

gflownet/proxy/base.py

@@ -24,7 +24,7 @@ def __init__(
         self,
         device,
         float_precision,
-        reward_function: Optional[Union[Callable, str]] = "absolute",
+        reward_function: Optional[Union[Callable, str]] = "identity",
         logreward_function: Optional[Callable] = None,
         reward_function_kwargs: Optional[dict] = {},
         reward_min: float = 0.0,

gflownet/proxy/corners.py

@@ -41,8 +41,7 @@ def min(self):
 
     def __call__(self, states: TensorType["batch", "state_dim"]) -> TensorType["batch"]:
         return (
-            -1.0
-            * self.mulnormal_norm
+            self.mulnormal_norm
             * torch.exp(
                 -0.5
                 * (

gflownet/proxy/scrabble.py

@@ -93,7 +93,7 @@ def __call__(
                 ):
                     scores.append(0.0)
                 else:
-                    scores.append(-1.0 * self._sum_scores(sample))
+                    scores.append(self._sum_scores(sample))
             return tfloat(scores, device=self.device, float_type=self.float)
         else:
             raise NotImplementedError(

gflownet/proxy/tetris.py

@@ -17,9 +17,9 @@ def setup(self, env=None):
     @property
     def norm(self):
         if self.normalize:
-            return -(self.height * self.width)
+            return (self.height * self.width)
         else:
-            return -1.0
+            return 1.0
 
     def __call__(self, states: TensorType["batch", "state_dim"]) -> TensorType["batch"]:
         if states.dim() == 2:

gflownet/proxy/torus.py

@@ -19,10 +19,10 @@ def setup(self, env=None):
     def min(self):
         if not hasattr(self, "_min"):
             if self.normalize:
-                self._min = torch.tensor(-1.0, device=self.device, dtype=self.float)
+                self._min = torch.tensor(0.0, device=self.device, dtype=self.float)
             else:
                 self._min = torch.tensor(
-                    -((self.n_dim * 2) ** 3), device=self.device, dtype=self.float
+                    ((self.n_dim * 2) ** 3), device=self.device, dtype=self.float
                 )
         return self._min
 
@@ -31,10 +31,10 @@ def norm(self):
         if not hasattr(self, "_norm"):
             if self.normalize:
                 self._norm = torch.tensor(
-                    -((self.n_dim * 2) ** 3), device=self.device, dtype=self.float
+                    ((self.n_dim * 2) ** 3), device=self.device, dtype=self.float
                 )
             else:
-                self._norm = torch.tensor(-1.0, device=self.device, dtype=self.float)
+                self._norm = torch.tensor(1.0, device=self.device, dtype=self.float)
         return self._norm
 
     def __call__(self, states: TensorType["batch", "state_dim"]) -> TensorType["batch"]:

gflownet/proxy/uniform.py

@@ -13,10 +13,10 @@ def __init__(self, **kwargs):
     def __call__(
         self, states: Union[List, TensorType["batch", "state_dim"]]
     ) -> TensorType["batch"]:
-        return -1.0 * torch.ones(len(states), device=self.device, dtype=self.float)
+        return torch.ones(len(states), device=self.device, dtype=self.float)
 
     @property
     def min(self):
         if not hasattr(self, "_min"):
-            self._min = torch.tensor(-1.0, device=self.device, dtype=self.float)
+            self._min = torch.tensor(1.0, device=self.device, dtype=self.float)
         return self._min