Change SVD type in pod.py (#449)

* Change SVD type in pod.py

pina/model/layers/pod.py

@@ -4,7 +4,7 @@ from abc import ABCMeta, abstractmethod
 import torch
 from .stride import Stride
 from .utils_convolution import optimizing
-
+import warnings
 
 class PODBlock(torch.nn.Module):
     """
@@ -85,7 +85,7 @@ class PODBlock(torch.nn.Module):
         """
         return self.__scale_coefficients
 
-    def fit(self, X):
+    def fit(self, X, randomized=True):
         """
         Set the POD basis by performing the singular value decomposition of the
         given tensor. If `self.scale_coefficients` is True, the coefficients
@@ -93,7 +93,7 @@ class PODBlock(torch.nn.Module):
 
         :param torch.Tensor X: The tensor to be reduced.
         """
-        self._fit_pod(X)
+        self._fit_pod(X, randomized)
 
         if self.__scale_coefficients:
             self._fit_scaler(torch.matmul(self._basis, X.T))
@@ -112,16 +112,24 @@ class PODBlock(torch.nn.Module):
             "mean": torch.mean(coeffs, dim=1),
         }
 
-    def _fit_pod(self, X):
+    def _fit_pod(self, X, randomized):
         """
         Private method that computes the POD basis of the given tensor and stores it in the private member `_basis`.
 
         :param torch.Tensor X: The tensor to be reduced.
         """
         if X.device.type == "mps":  #  svd_lowrank not arailable for mps
+            warnings.warn(
+                "svd_lowrank not available for mps, using svd instead."
+                "This may slow down computations.", ResourceWarning
+            )
             self._basis = torch.svd(X.T)[0].T
         else:
-            self._basis = torch.svd_lowrank(X.T, q=X.shape[0])[0].T
+            if randomized:
+                warnings.warn("Considering a randomized algorithm to compute the POD basis")
+                self._basis = torch.svd_lowrank(X.T, q=X.shape[0])[0].T
+            else:
+                self._basis = torch.svd(X.T)[0].T
 
     def forward(self, X):
         """

tests/test_layers/test_pod.py

@@ -25,9 +25,10 @@ def test_fit(rank, scale):
 
 @pytest.mark.parametrize("scale", [True, False])
 @pytest.mark.parametrize("rank", [1, 2, 10])
-def test_fit(rank, scale):
+@pytest.mark.parametrize("randomized", [True, False])
+def test_fit(rank, scale, randomized):
     pod = PODBlock(rank, scale)
-    pod.fit(toy_snapshots)
+    pod.fit(toy_snapshots, randomized)
     n_snap = toy_snapshots.shape[0]
     dof = toy_snapshots.shape[1]
     assert pod.basis.shape == (rank, dof)
@@ -65,18 +66,20 @@ def test_forward():
 
 @pytest.mark.parametrize("scale", [True, False])
 @pytest.mark.parametrize("rank", [1, 2, 10])
-def test_expand(rank, scale):
+@pytest.mark.parametrize("randomized", [True, False])
+def test_expand(rank, scale, randomized):
     pod = PODBlock(rank, scale)
-    pod.fit(toy_snapshots)
+    pod.fit(toy_snapshots, randomized)
     c = pod(toy_snapshots)
     torch.testing.assert_close(pod.expand(c), toy_snapshots)
     torch.testing.assert_close(pod.expand(c[0]), toy_snapshots[0].unsqueeze(0))
 
 @pytest.mark.parametrize("scale", [True, False])
 @pytest.mark.parametrize("rank", [1, 2, 10])
-def test_reduce_expand(rank, scale):
+@pytest.mark.parametrize("randomized", [True, False])
+def test_reduce_expand(rank, scale, randomized):
     pod = PODBlock(rank, scale)
-    pod.fit(toy_snapshots)
+    pod.fit(toy_snapshots, randomized)
     torch.testing.assert_close(
         pod.expand(pod.reduce(toy_snapshots)),
         toy_snapshots)