derivativeGP gpu support (#444)

Summary:

Add gpu support for derivative GP.

I noticed that this model isn’t actually like a normal model that can show up in a live experiment with a config, but we should still make it work for GPU. I did most of that but it did require some pretty arcane shenanigans with overriding GPyTorch’s underlying handling of train_inputs. This in turn made me do some arcane mypy stuff.

Differential Revision: D65515631

aepsych/kernels/pairwisekernel.py

@@ -16,12 +16,12 @@ class PairwiseKernel(Kernel):
     """
 
     def __init__(
-          self, latent_kernel: Kernel, is_partial_obs: bool=False, **kwargs
+        self, latent_kernel: Kernel, is_partial_obs: bool = False, **kwargs
     ) -> None:
         """
-         Args:
-            latent_kernel (Kernel): The underlying kernel used to compute the covariance for the GP.
-            is_partial_obs (bool): If the kernel should handle partial observations. Defaults to False.
+        Args:
+           latent_kernel (Kernel): The underlying kernel used to compute the covariance for the GP.
+           is_partial_obs (bool): If the kernel should handle partial observations. Defaults to False.
         """
         super(PairwiseKernel, self).__init__(**kwargs)
 
@@ -40,11 +40,11 @@ def forward(
             x1 (torch.Tensor): A `b x n x d` or `n x d` tensor, where `d = 2k` and `k` is the dimension of the latent space.
             x2 (torch.Tensor): A `b x m x d` or `m x d` tensor, where `d = 2k` and `k` is the dimension of the latent space.
             diag (bool): Should the Kernel compute the whole covariance matrix or just the diagonal? Defaults to False.
-            
+
 
         Returns:
             torch.Tensor (or :class:`gpytorch.lazy.LazyTensor`) : A `b x n x m` or `n x m` tensor representing
-            the covariance matrix between `x1` and `x2`. 
+            the covariance matrix between `x1` and `x2`.
             The exact size depends on the kernel's evaluation mode:
             * `full_covar`: `n x m` or `b x n x m`
             * `diag`: `n` or `b x n`

aepsych/kernels/rbf_partial_grad.py

@@ -31,14 +31,14 @@ def forward(
         self, x1: torch.Tensor, x2: torch.Tensor, diag: bool = False, **params: Any
     ) -> torch.Tensor:
         """Computes the covariance matrix between x1 and x2 based on the RBF
-        
+
         Args:
             x1 (torch.Tensor): A `b x n x d` or `n x d` tensor, where `d = 2k` and `k` is the dimension of the latent space.
             x2 (torch.Tensor): A `b x m x d` or `m x d` tensor, where `d = 2k` and `k` is the dimension of the latent space.
             diag (bool): Should the Kernel compute the whole covariance matrix (False) or just the diagonal (True)? Defaults to False.
 
-        
-        
+
+
         Returns:
             torch.Tensor: A `b x n x m` or `n x m` tensor representing the covariance matrix between `x1` and `x2`.
             The exact size depends on the kernel's evaluation mode:

aepsych/models/base.py

@@ -116,7 +116,7 @@ class AEPsychMixin(GPyTorchModel):
 
     extremum_solver = "Nelder-Mead"
     outcome_types: List[str] = []
-    train_inputs: Optional[Tuple[torch.Tensor]]
+    train_inputs: Optional[Tuple[torch.Tensor, ...]]
     train_targets: Optional[torch.Tensor]
 
     @property
@@ -393,7 +393,7 @@ def p_below_threshold(
 
 
 class AEPsychModelDeviceMixin(AEPsychMixin):
-    _train_inputs: Optional[Tuple[torch.Tensor]]
+    _train_inputs: Optional[Tuple[torch.Tensor, ...]]
     _train_targets: Optional[torch.Tensor]
 
     def set_train_data(self, inputs=None, targets=None, strict=False):
@@ -423,13 +423,17 @@ def device(self) -> torch.device:
             return torch.device("cpu")
 
     @property
-    def train_inputs(self) -> Optional[Tuple[torch.Tensor]]:
+    def train_inputs(self) -> Optional[Tuple[torch.Tensor, ...]]:
         if self._train_inputs is None:
             return None
 
         # makes sure the tensors are on the right device, move in place
+        _train_inputs = []
         for input in self._train_inputs:
-            input.to(self.device)
+            _train_inputs.append(input.to(self.device))
+
+        _tuple_inputs: Tuple[torch.Tensor, ...] = tuple(_train_inputs)
+        self._train_inputs = _tuple_inputs
 
         return self._train_inputs
 

aepsych/models/derivative_gp.py

@@ -23,7 +23,9 @@
 from gpytorch.variational import CholeskyVariationalDistribution, VariationalStrategy
 
 
-class MixedDerivativeVariationalGP(gpytorch.models.ApproximateGP, AEPsychModelDeviceMixin, GPyTorchModel):
+class MixedDerivativeVariationalGP(
+    gpytorch.models.ApproximateGP, AEPsychModelDeviceMixin, GPyTorchModel
+):
     """A variational GP with mixed derivative observations.
 
     For more on GPs with derivative observations, see e.g. Riihimaki & Vehtari 2010.

tests_gpu/models/test_derivative_gp.py

@@ -0,0 +1,39 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+from aepsych import Config, SequentialStrategy
+from aepsych.models.derivative_gp import MixedDerivativeVariationalGP
+from botorch.fit import fit_gpytorch_mll
+from botorch.utils.testing import BotorchTestCase
+from gpytorch.likelihoods import BernoulliLikelihood
+from gpytorch.mlls.variational_elbo import VariationalELBO
+
+
+class TestDerivativeGP(BotorchTestCase):
+    def test_MixedDerivativeVariationalGP_gpu(self):
+        train_x = torch.cat(
+            (torch.tensor([1.0, 2.0, 3.0, 4.0]).unsqueeze(1), torch.zeros(4, 1)), dim=1
+        )
+        train_y = torch.tensor([1.0, 2.0, 3.0, 4.0])
+        m = MixedDerivativeVariationalGP(
+            train_x=train_x,
+            train_y=train_y,
+            inducing_points=train_x,
+            fixed_prior_mean=0.5,
+        ).cuda()
+
+        self.assertEqual(m.mean_module.constant.item(), 0.5)
+        self.assertEqual(
+            m.covar_module.base_kernel.raw_lengthscale.shape, torch.Size([1, 1])
+        )
+        mll = VariationalELBO(
+            likelihood=BernoulliLikelihood(), model=m, num_data=train_y.numel()
+        ).cuda()
+        mll = fit_gpytorch_mll(mll)
+        test_x = torch.tensor([[1.0, 0], [3.0, 1.0]]).cuda()
+        m(test_x)

tests_gpu/test_strategy.py

@@ -27,6 +27,5 @@ def test_gpu_no_model_generator_warn(self):
             )
 
 
-
 if __name__ == "__main__":
     unittest.main()