Add option to force use of pinv in LinUCB

Summary:
Adding an options to force pseudo-inverse to be used by default in LinUCB. This is useful for cases where the features are strongly correlated, so the design matrix is ill-conditioned.
The regular inverse is faster, it should be used when matrix inversion is the bottleneck and the design matrix is well-conditioned.

Reviewed By: zxpmirror1994

Differential Revision: D57462061

pearl/neural_networks/contextual_bandit/linear_regression.py

@@ -20,7 +20,11 @@
 
 class LinearRegression(MuSigmaCBModel):
     def __init__(
-        self, feature_dim: int, l2_reg_lambda: float = 1.0, gamma: float = 1.0
+        self,
+        feature_dim: int,
+        l2_reg_lambda: float = 1.0,
+        gamma: float = 1.0,
+        force_pinv: bool = False,
     ) -> None:
         """
         A linear regression model which can estimate both point prediction and uncertainty
@@ -41,6 +45,7 @@ def __init__(
         super(LinearRegression, self).__init__(feature_dim=feature_dim)
         self.gamma = gamma
         self.l2_reg_lambda = l2_reg_lambda
+        self.force_pinv = force_pinv
         assert (
             gamma > 0 and gamma <= 1
         ), f"gamma should be in (0, 1]. Got gamma={gamma} instead"
@@ -96,27 +101,34 @@ def append_ones(x: torch.Tensor) -> torch.Tensor:
         return result
 
     @staticmethod
-    def matrix_inv_fallback_pinv(A: torch.Tensor) -> torch.Tensor:
+    def pinv(A: torch.Tensor) -> torch.Tensor:
+        """
+        Compute the pseudo inverse of A using torch.linalg.pinv
+        """
+        # first check if A is Hermitian (symmetric A)
+        A_is_hermitian = torch.allclose(A, A.T, atol=1e-4, rtol=1e-4)
+        # applying hermitian=True saves about 50% computations
+        return torch.linalg.pinv(
+            A,
+            hermitian=A_is_hermitian,
+        ).contiguous()
+
+    def matrix_inv_fallback_pinv(self, A: torch.Tensor) -> torch.Tensor:
         """
         Try to apply regular matrix inv. If it fails, fallback to pseudo inverse
         """
+        if self.force_pinv:
+            return self.pinv(A)
         try:
-            inv_A = torch.linalg.inv(A).contiguous()
+            return torch.linalg.inv(A).contiguous()
         # pyre-ignore[16]: Module `_C` has no attribute `_LinAlgError`.
         except torch._C._LinAlgError as e:
             logger.warning(
                 "Exception raised during A inversion, falling back to pseudo-inverse",
                 e,
             )
             # switch from `inv` to `pinv`
-            # first check if A is Hermitian (symmetric A)
-            A_is_hermitian = torch.allclose(A, A.T, atol=1e-4, rtol=1e-4)
-            # applying hermitian=True saves about 50% computations
-            inv_A = torch.linalg.pinv(
-                A,
-                hermitian=A_is_hermitian,
-            ).contiguous()
-        return inv_A
+            return self.pinv(A)
 
     def _validate_train_inputs(
         self, x: torch.Tensor, y: torch.Tensor, weight: Optional[torch.Tensor]

pearl/neural_networks/contextual_bandit/neural_linear_regression.py

@@ -27,6 +27,7 @@ def __init__(
         hidden_dims: List[int],  # last one is the input dim for linear regression
         l2_reg_lambda_linear: float = 1.0,
         gamma: float = 1.0,
+        force_pinv: bool = False,
         output_activation_name: str = "linear",
         use_batch_norm: bool = False,
         use_layer_norm: bool = False,
@@ -73,6 +74,7 @@ def __init__(
             feature_dim=hidden_dims[-1],
             l2_reg_lambda=l2_reg_lambda_linear,
             gamma=gamma,
+            force_pinv=force_pinv,
         )
         self.output_activation: nn.Module = ACTIVATION_MAP[output_activation_name]()
         self.linear_layer_e2e = nn.Linear(

pearl/policy_learners/contextual_bandits/linear_bandit.py

@@ -48,6 +48,7 @@ def __init__(
         gamma: float = 1.0,
         apply_discounting_interval: float = 0.0,  # discounting will be applied after this many
         # observations (weighted) are processed. set to 0 to disable
+        force_pinv: bool = False,
         training_rounds: int = 100,
         batch_size: int = 128,
         action_representation_module: Optional[ActionRepresentationModule] = None,
@@ -60,7 +61,10 @@ def __init__(
             action_representation_module=action_representation_module,
         )
         self.model = LinearRegression(
-            feature_dim=feature_dim, l2_reg_lambda=l2_reg_lambda, gamma=gamma
+            feature_dim=feature_dim,
+            l2_reg_lambda=l2_reg_lambda,
+            gamma=gamma,
+            force_pinv=force_pinv,
         )
         self.apply_discounting_interval = apply_discounting_interval
         self.last_sum_weight_when_discounted = 0.0

pearl/policy_learners/contextual_bandits/neural_linear_bandit.py

@@ -76,6 +76,7 @@ def __init__(
         l2_reg_lambda_linear: float = 1.0,
         gamma: float = 1.0,
         apply_discounting_interval: float = 0.0,  # set to 0 to disable
+        force_pinv: bool = False,
         state_features_only: bool = True,
         loss_type: str = "mse",  # one of the LOSS_TYPES names: [mse, mae, cross_entropy]
         output_activation_name: str = "linear",
@@ -103,6 +104,7 @@ def __init__(
             hidden_dims=hidden_dims,
             l2_reg_lambda_linear=l2_reg_lambda_linear,
             gamma=gamma,
+            force_pinv=force_pinv,
             output_activation_name=output_activation_name,
             use_batch_norm=use_batch_norm,
             use_layer_norm=use_layer_norm,