Merge pull request #488 from helmholtz-analytics/enhancement/select-d…

…evice Enhancement/select device
helmholtz-analytics · Jun 16, 2020 · 42bf442 · 42bf442
2 parents fc77d90 + 01eccf1
commit 42bf442
Show file tree

Hide file tree

Showing 35 changed files with 2,194 additions and 2,389 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -1,5 +1,6 @@
 # Pending Additions
 
+- [#488](https://github.com/helmholtz-analytics/heat/pull/488) Enhancement: Rework of the test device selection.
 - [#573](https://github.com/helmholtz-analytics/heat/pull/573) Bugfix: matmul fixes: early out for 2 vectors, remainders not added if inner block is 1 for split 10 case
 - [#575](https://github.com/helmholtz-analytics/heat/pull/558) Bugfix: Binary operations use proper type casting
 - [#575](https://github.com/helmholtz-analytics/heat/pull/558) Bugfix: `where` and `cov` convert ints to floats when given as parameters

diff --git a/heat/cluster/tests/test_kmeans.py b/heat/cluster/tests/test_kmeans.py
@@ -3,20 +3,10 @@
 
 import heat as ht
 
-if os.environ.get("DEVICE") == "gpu" and ht.torch.cuda.is_available():
-    ht.use_device("gpu")
-    ht.torch.cuda.set_device(ht.torch.device(ht.get_device().torch_device))
-else:
-    ht.use_device("cpu")
-device = ht.get_device().torch_device
-ht_device = None
-if os.environ.get("DEVICE") == "lgpu" and ht.torch.cuda.is_available():
-    device = ht.gpu.torch_device
-    ht_device = ht.gpu
-    ht.torch.cuda.set_device(device)
+from ...core.tests.test_suites.basic_test import TestCase
 
 
-class TestKMeans(unittest.TestCase):
+class TestKMeans(TestCase):
     def test_clusterer(self):
         kmeans = ht.cluster.KMeans()
         self.assertTrue(ht.is_estimator(kmeans))

diff --git a/heat/cluster/tests/test_spectral.py b/heat/cluster/tests/test_spectral.py
@@ -3,20 +3,10 @@
 
 import heat as ht
 
-if os.environ.get("DEVICE") == "gpu" and ht.torch.cuda.is_available():
-    ht.use_device("gpu")
-    ht.torch.cuda.set_device(ht.torch.device(ht.get_device().torch_device))
-else:
-    ht.use_device("cpu")
-device = ht.get_device().torch_device
-ht_device = None
-if os.environ.get("DEVICE") == "lgpu" and ht.torch.cuda.is_available():
-    device = ht.gpu.torch_device
-    ht_device = ht.gpu
-    ht.torch.cuda.set_device(device)
+from ...core.tests.test_suites.basic_test import TestCase
 
 
-class TestSpectral(unittest.TestCase):
+class TestSpectral(TestCase):
     def test_clusterer(self):
         spectral = ht.cluster.Spectral()
         self.assertTrue(ht.is_estimator(spectral))

diff --git a/heat/core/linalg/tests/test_basics.py b/heat/core/linalg/tests/test_basics.py
diff --git a/heat/core/linalg/tests/test_qr.py b/heat/core/linalg/tests/test_qr.py
@@ -5,17 +5,7 @@
 import unittest
 import warnings
 
-if os.environ.get("DEVICE") == "gpu" and torch.cuda.is_available():
-    ht.use_device("gpu")
-    torch.cuda.set_device(torch.device(ht.get_device().torch_device))
-else:
-    ht.use_device("cpu")
-device = ht.get_device().torch_device
-ht_device = None
-if os.environ.get("DEVICE") == "lgpu" and torch.cuda.is_available():
-    device = ht.gpu.torch_device
-    ht_device = ht.gpu
-    torch.cuda.set_device(device)
+from ...tests.test_suites.basic_test import TestCase
 
 if os.environ.get("EXTENDED_TESTS"):
     extended_tests = True
@@ -24,122 +14,84 @@
     extended_tests = False
 
 
-class TestQR(unittest.TestCase):
+class TestQR(TestCase):
     @unittest.skipIf(not extended_tests, "extended tests")
     def test_qr_sp0_ext(self):
-        st_whole = torch.randn(70, 70, device=device)
+        st_whole = torch.randn(70, 70, device=self.device.torch_device)
         sp = 0
         for m in range(50, st_whole.shape[0] + 1, 1):
             for n in range(50, st_whole.shape[1] + 1, 1):
                 for t in range(1, 3):
                     st = st_whole[:m, :n].clone()
-                    a_comp = ht.array(st, split=0, device=ht_device)
-                    a = ht.array(st, split=sp, device=ht_device)
+                    a_comp = ht.array(st, split=0)
+                    a = ht.array(st, split=sp)
                     qr = a.qr(tiles_per_proc=t)
                     self.assertTrue(ht.allclose(a_comp, qr.Q @ qr.R, rtol=1e-5, atol=1e-5))
-                    self.assertTrue(
-                        ht.allclose(
-                            qr.Q.T @ qr.Q, ht.eye(m, device=ht_device), rtol=1e-5, atol=1e-5
-                        )
-                    )
-                    self.assertTrue(
-                        ht.allclose(
-                            ht.eye(m, device=ht_device), qr.Q @ qr.Q.T, rtol=1e-5, atol=1e-5
-                        )
-                    )
+                    self.assertTrue(ht.allclose(qr.Q.T @ qr.Q, ht.eye(m), rtol=1e-5, atol=1e-5))
+                    self.assertTrue(ht.allclose(ht.eye(m), qr.Q @ qr.Q.T, rtol=1e-5, atol=1e-5))
 
     @unittest.skipIf(not extended_tests, "extended tests")
     def test_qr_sp1_ext(self):
-        st_whole = torch.randn(70, 70, device=device)
+        st_whole = torch.randn(70, 70, device=self.device.torch_device)
         sp = 1
         for m in range(50, st_whole.shape[0] + 1, 1):
             for n in range(50, st_whole.shape[1] + 1, 1):
                 for t in range(1, 3):
                     st = st_whole[:m, :n].clone()
-                    a_comp = ht.array(st, split=0, device=ht_device)
-                    a = ht.array(st, split=sp, device=ht_device)
+                    a_comp = ht.array(st, split=0)
+                    a = ht.array(st, split=sp)
                     qr = a.qr(tiles_per_proc=t)
                     self.assertTrue(ht.allclose(a_comp, qr.Q @ qr.R, rtol=1e-5, atol=1e-5))
-                    self.assertTrue(
-                        ht.allclose(
-                            qr.Q.T @ qr.Q, ht.eye(m, device=ht_device), rtol=1e-5, atol=1e-5
-                        )
-                    )
-                    self.assertTrue(
-                        ht.allclose(
-                            ht.eye(m, device=ht_device), qr.Q @ qr.Q.T, rtol=1e-5, atol=1e-5
-                        )
-                    )
+                    self.assertTrue(ht.allclose(qr.Q.T @ qr.Q, ht.eye(m), rtol=1e-5, atol=1e-5))
+                    self.assertTrue(ht.allclose(ht.eye(m), qr.Q @ qr.Q.T, rtol=1e-5, atol=1e-5))
 
     def test_qr(self):
         m, n = 20, 40
-        st = torch.randn(m, n, device=device, dtype=torch.float)
-        a_comp = ht.array(st, split=0, device=ht_device)
+        st = torch.randn(m, n, device=self.device.torch_device, dtype=torch.float)
+        a_comp = ht.array(st, split=0)
         for t in range(1, 3):
             for sp in range(2):
-                a = ht.array(st, split=sp, device=ht_device, dtype=torch.float)
+                a = ht.array(st, split=sp, dtype=torch.float)
                 qr = a.qr(tiles_per_proc=t)
                 self.assertTrue(ht.allclose((a_comp - (qr.Q @ qr.R)), 0, rtol=1e-5, atol=1e-5))
-                self.assertTrue(
-                    ht.allclose(qr.Q.T @ qr.Q, ht.eye(m, device=ht_device), rtol=1e-5, atol=1e-5)
-                )
-                self.assertTrue(
-                    ht.allclose(ht.eye(m, device=ht_device), qr.Q @ qr.Q.T, rtol=1e-5, atol=1e-5)
-                )
+                self.assertTrue(ht.allclose(qr.Q.T @ qr.Q, ht.eye(m), rtol=1e-5, atol=1e-5))
+                self.assertTrue(ht.allclose(ht.eye(m), qr.Q @ qr.Q.T, rtol=1e-5, atol=1e-5))
         m, n = 40, 40
-        st1 = torch.randn(m, n, device=device)
-        a_comp1 = ht.array(st1, split=0, device=ht_device)
+        st1 = torch.randn(m, n, device=self.device.torch_device)
+        a_comp1 = ht.array(st1, split=0)
         for t in range(1, 3):
             for sp in range(2):
-                a1 = ht.array(st1, split=sp, device=ht_device)
+                a1 = ht.array(st1, split=sp)
                 qr1 = a1.qr(tiles_per_proc=t)
                 self.assertTrue(ht.allclose((a_comp1 - (qr1.Q @ qr1.R)), 0, rtol=1e-5, atol=1e-5))
-                self.assertTrue(
-                    ht.allclose(qr1.Q.T @ qr1.Q, ht.eye(m, device=ht_device), rtol=1e-5, atol=1e-5)
-                )
-                self.assertTrue(
-                    ht.allclose(ht.eye(m, device=ht_device), qr1.Q @ qr1.Q.T, rtol=1e-5, atol=1e-5)
-                )
+                self.assertTrue(ht.allclose(qr1.Q.T @ qr1.Q, ht.eye(m), rtol=1e-5, atol=1e-5))
+                self.assertTrue(ht.allclose(ht.eye(m), qr1.Q @ qr1.Q.T, rtol=1e-5, atol=1e-5))
         m, n = 40, 20
-        st2 = torch.randn(m, n, dtype=torch.double, device=device)
-        a_comp2 = ht.array(st2, split=0, dtype=ht.double, device=ht_device)
+        st2 = torch.randn(m, n, dtype=torch.double, device=self.device.torch_device)
+        a_comp2 = ht.array(st2, split=0, dtype=ht.double)
         for t in range(1, 3):
             for sp in range(2):
-                a2 = ht.array(st2, split=sp, device=ht_device)
+                a2 = ht.array(st2, split=sp)
                 qr2 = a2.qr(tiles_per_proc=t)
                 self.assertTrue(ht.allclose(a_comp2, qr2.Q @ qr2.R, rtol=1e-5, atol=1e-5))
                 self.assertTrue(
-                    ht.allclose(
-                        qr2.Q.T @ qr2.Q,
-                        ht.eye(m, dtype=ht.double, device=ht_device),
-                        rtol=1e-5,
-                        atol=1e-5,
-                    )
+                    ht.allclose(qr2.Q.T @ qr2.Q, ht.eye(m, dtype=ht.double), rtol=1e-5, atol=1e-5)
                 )
                 self.assertTrue(
-                    ht.allclose(
-                        ht.eye(m, dtype=ht.double, device=ht_device),
-                        qr2.Q @ qr2.Q.T,
-                        rtol=1e-5,
-                        atol=1e-5,
-                    )
+                    ht.allclose(ht.eye(m, dtype=ht.double), qr2.Q @ qr2.Q.T, rtol=1e-5, atol=1e-5)
                 )
                 # test if calc R alone works
                 qr = ht.qr(a2, calc_q=False, overwrite_a=True)
                 self.assertTrue(qr.Q is None)
 
         m, n = 40, 20
-        st = torch.randn(m, n, device=device)
-        a_comp = ht.array(st, split=None, device=ht_device)
-        a = ht.array(st, split=None, device=ht_device)
+        st = torch.randn(m, n, device=self.device.torch_device)
+        a_comp = ht.array(st, split=None)
+        a = ht.array(st, split=None)
         qr = a.qr()
         self.assertTrue(ht.allclose(a_comp, qr.Q @ qr.R, rtol=1e-5, atol=1e-5))
-        self.assertTrue(
-            ht.allclose(qr.Q.T @ qr.Q, ht.eye(m, device=ht_device), rtol=1e-5, atol=1e-5)
-        )
-        self.assertTrue(
-            ht.allclose(ht.eye(m, device=ht_device), qr.Q @ qr.Q.T, rtol=1e-5, atol=1e-5)
-        )
+        self.assertTrue(ht.allclose(qr.Q.T @ qr.Q, ht.eye(m), rtol=1e-5, atol=1e-5))
+        self.assertTrue(ht.allclose(ht.eye(m), qr.Q @ qr.Q.T, rtol=1e-5, atol=1e-5))
 
         # raises
         with self.assertRaises(TypeError):

diff --git a/heat/core/linalg/tests/test_solver.py b/heat/core/linalg/tests/test_solver.py
@@ -4,20 +4,10 @@
 import heat as ht
 import numpy as np
 
-if os.environ.get("DEVICE") == "gpu" and torch.cuda.is_available():
-    ht.use_device("gpu")
-    torch.cuda.set_device(torch.device(ht.get_device().torch_device))
-else:
-    ht.use_device("cpu")
-device = ht.get_device().torch_device
-ht_device = None
-if os.environ.get("DEVICE") == "lgpu" and torch.cuda.is_available():
-    device = ht.gpu.torch_device
-    ht_device = ht.gpu
-    torch.cuda.set_device(device)
+from ...tests.test_suites.basic_test import TestCase
 
 
-class TestSolver(unittest.TestCase):
+class TestSolver(TestCase):
     def test_cg(self):
         size = ht.communication.MPI_WORLD.size * 3
         b = ht.arange(1, size + 1, dtype=ht.float32, split=0)

diff --git a/heat/core/operations.py b/heat/core/operations.py
@@ -53,7 +53,7 @@ def __binary_op(operation, t1, t2):
             output_device = None
             output_comm = MPI_WORLD
         elif isinstance(t2, dndarray.DNDarray):
-            t1.gpu() if t2.device.device_type == "gpu" else t1.cpu()
+            t1 = t1.gpu() if t2.device.device_type == "gpu" else t1.cpu()
 
             output_shape = t2.shape
             output_split = t2.split