avoid import error when no torch installed

python/jittor/test/test_bicubic.py

@@ -10,11 +10,19 @@
 # ***************************************************************
 import unittest
 import jittor as jt
-import torch
-from torch.nn import functional as F
 import numpy as np
 
+skip_this_test = False
+try:
+    jt.dirty_fix_pytorch_runtime_error()
+    import torch
+    from torch.nn import functional as F
+except:
+    torch = None
+    skip_this_test = True
 
+
+@unittest.skipIf(skip_this_test, "No Torch found")
 class TestBicubicInterpolate(unittest.TestCase):
     # this is for testing bicubic interpolate
     def test_bicubic(self):

python/jittor/test/test_cumprod_op.py

@@ -9,11 +9,18 @@
 import unittest
 import jittor as jt
 import numpy as np
-import ctypes
-import sys
-import torch
-from torch.autograd import Variable
 
+skip_this_test = False
+try:
+    jt.dirty_fix_pytorch_runtime_error()
+    import torch
+    from torch.autograd import Variable
+except:
+    torch = None
+    skip_this_test = True
+
+
+@unittest.skipIf(skip_this_test, "No Torch found")
 class TestCumprod(unittest.TestCase):
     def test_cumprod_cpu(self):
         for i in range(1,6):

python/jittor/test/test_distributions.py

@@ -12,6 +12,14 @@ import jittor as jt
 import numpy as np
 import jittor.distributions as jd
 
+skip_this_test = False
+try:
+    jt.dirty_fix_pytorch_runtime_error()
+    import torch
+except:
+    torch = None
+    skip_this_test = True
+
 
 class TestOneHot(unittest.TestCase):
     def test_presum(self):
@@ -19,6 +27,7 @@ class TestOneHot(unittest.TestCase):
         b = jd.simple_presum(a)
         assert (b.data == [[0,1,3,6,10]]).all()
 
+    @unittest.skipIf(skip_this_test, "No Torch Found")
     def test_one_hot(self):
         a = jd.OneHotCategorical(jt.array([0.25, 0.25, 0.25, 0.25]))
         x = a.sample().numpy()
@@ -30,7 +39,7 @@ class TestOneHot(unittest.TestCase):
         assert y.shape == [2,3,4]
         probs,probs2 = np.random.uniform(0,1,(10)), np.random.uniform(0,1,(10))
         probs,probs2 = probs / probs.sum(),probs2 / probs2.sum()
-        import torch
+
         jc, jc2 = jd.OneHotCategorical(jt.array(probs)),jd.OneHotCategorical(jt.array(probs2))
         tc, tc2 = torch.distributions.OneHotCategorical(torch.tensor(probs)),torch.distributions.OneHotCategorical(torch.tensor(probs2))
         assert np.allclose(jc.entropy().data,tc.entropy().numpy())
@@ -51,8 +60,8 @@ class TestOneHot(unittest.TestCase):
         y.sync()
         assert y.shape == [2,3]
         
+    @unittest.skipIf(skip_this_test, "No Torch Found")
     def test_normal(self):
-        import torch
         for _ in range(4):
             mu = np.random.uniform(-1,1)
             sigma = np.random.uniform(0,2)
@@ -67,8 +76,8 @@ class TestOneHot(unittest.TestCase):
             tn2 = torch.distributions.Normal(mu2,sigma2)
             assert np.allclose(jd.kl_divergence(jn,jn2).data,torch.distributions.kl_divergence(tn,tn2).numpy())
 
+    @unittest.skipIf(skip_this_test, "No Torch Found")
     def test_categorical1(self):
-        import torch
         for _ in range(4):
             probs,probs2 = np.random.uniform(0,1,(10)), np.random.uniform(0,1,(10))
             probs,probs2 = probs / probs.sum(),probs2 / probs2.sum()
@@ -79,9 +88,9 @@ class TestOneHot(unittest.TestCase):
             np.testing.assert_allclose(jc.log_prob(x), tc.log_prob(torch.tensor(x)), atol=1e-5)
             assert np.allclose(jd.kl_divergence(jc,jc2),torch.distributions.kl_divergence(tc,tc2))
 
+    @unittest.skipIf(skip_this_test, "No Torch Found")
     def test_categorical2(self):
         def check(prob_shape, sample_shape):
-            import torch
             for _ in range(4):
                 probs,probs2 = np.random.uniform(0,1,prob_shape), np.random.uniform(0,1, prob_shape)
 
@@ -98,9 +107,9 @@ class TestOneHot(unittest.TestCase):
         check((2,3), (4,))
         check((3,4,5,6), (2,))
 
+    @unittest.skipIf(skip_this_test, "No Torch Found")
     def test_one_hot_categorical2(self):
         def check(prob_shape, sample_shape):
-            import torch
             for _ in range(4):
                 probs,probs2 = np.random.uniform(0,1,prob_shape), np.random.uniform(0,1, prob_shape)
 
@@ -117,8 +126,8 @@ class TestOneHot(unittest.TestCase):
         check((2,3), (4,))
         check((3,4,5,6), (2,))
             
+    @unittest.skipIf(skip_this_test, "No Torch Found")
     def test_uniform(self):
-        import torch
         for _ in range(4):
             low, low2 = np.random.randint(-1,2), np.random.randint(-1,2)
             leng, leng2 = np.random.uniform(0,2), np.random.uniform(0,2)
@@ -130,8 +139,8 @@ class TestOneHot(unittest.TestCase):
             assert np.allclose(ju.log_prob(x),tu.log_prob(torch.tensor(x)))
             assert np.allclose(jd.kl_divergence(ju,ju2),torch.distributions.kl_divergence(tu,tu2))
     
+    @unittest.skipIf(skip_this_test, "No Torch Found")
     def test_geometric(self):
-        import torch
         for _ in range(4):
             prob, prob2 = np.random.uniform(0,1), np.random.uniform(0,1)
             jg, jg2 = jd.Geometric(prob),jd.Geometric(prob2)

python/jittor/test/test_fold.py

@@ -10,11 +10,19 @@
 # ***************************************************************
 import unittest
 import jittor as jt
-import torch
-from torch.nn import functional as F
 import numpy as np
 
+skip_this_test = False
+try:
+    jt.dirty_fix_pytorch_runtime_error()
+    import torch
+    from torch.nn import functional as F
+except:
+    torch = None
+    skip_this_test = True
 
+
+@unittest.skipIf(skip_this_test, "No Torch Found")
 class TestFoldOp(unittest.TestCase):
     def test_fold(self):
         # test unfold first and the test fold.

python/jittor/test/test_linalg.py

@@ -8,13 +8,13 @@
 # This file is subject to the terms and conditions defined in
 # file 'LICENSE.txt', which is part of this source code package.
 # ***************************************************************
-import torch
-from torch.autograd import Variable
 import jittor as jt
 import numpy as np
 import unittest
 
 try:
+    import torch
+    from torch.autograd import Variable
     import autograd.numpy as anp
     from autograd import jacobian
 

python/jittor/test/test_random_op.py

@@ -18,12 +18,14 @@ import unittest
 from .test_reorder_tuner import simple_parser
 from .test_log import find_log_with_re
 
+skip_this_test = False
 try:
     jt.dirty_fix_pytorch_runtime_error()
     import torch
 except:
     skip_this_test = True
 
+
 class TestRandomOp(unittest.TestCase):
     @unittest.skipIf(not jt.has_cuda, "Cuda not found")
     @jt.flag_scope(use_cuda=1)
@@ -51,6 +53,7 @@ class TestRandomOp(unittest.TestCase):
         logs = find_log_with_re(raw_log, "(Jit op key (not )?found: " + "curand_random" + ".*)")
         assert len(logs)==1
 
+    @unittest.skipIf(skip_this_test, "No Torch Found")
     def test_normal(self):
         from jittor import init
         n = 10000

python/jittor/test/test_search_sorted.py

@@ -18,12 +18,9 @@ try:
     jt.dirty_fix_pytorch_runtime_error()
     import torch
     import torch.nn as tnn
-    import torchvision
-    from torch.autograd import Variable
 except:
     torch = None
     tnn = None
-    torchvision = None
     skip_this_test = True
 
 # TODO: more test

python/jittor/test/test_searchsorted_op.py

@@ -9,11 +9,16 @@
 import unittest
 import jittor as jt
 import numpy as np
-import ctypes
-import sys
-import torch
-from torch.autograd import Variable
 
+skip_this_test = False
+try:
+    jt.dirty_fix_pytorch_runtime_error()
+    import torch
+except:
+    skip_this_test = True
+
+
+@unittest.skipIf
 class TestSearchsorted(unittest.TestCase):
     def test_searchsorted_cpu(self):
         for i in range(1,3):

python/jittor/test/test_utils.py

@@ -32,7 +32,7 @@ cc_flags = f" -g -O0 -DTEST --std=c++14 -I{jittor_path}/test -I{jittor_path}/src
 
 class TestUtils(unittest.TestCase):
     def test_cache_compile(self):
-        cmd = f"cd {cache_path} && g++ {jittor_path}/src/utils/log.cc {jittor_path}/src/utils/tracer.cc {jittor_path}/src/utils/cache_compile.cc -lpthread {cc_flags} -o cache_compile && cache_path={cache_path} jittor_path={jittor_path} ./cache_compile"
+        cmd = f"cd {cache_path} && g++ {jittor_path}/src/utils/log.cc {jittor_path}/src/utils/tracer.cc {jittor_path}/src/utils/str_utils.cc {jittor_path}/src/utils/cache_compile.cc -lpthread {cc_flags} -o cache_compile && cache_path={cache_path} jittor_path={jittor_path} ./cache_compile"
         self.assertEqual(os.system(cmd), 0)
         
     def test_log(self):

python/jittor/version

@@ -1 +1 @@
-9edb1890b66321883c398591483d4d65377a848d
+939b29514b2e5cc591053aab614efd569772585d

python/jittor_utils/__init__.py

@@ -4,7 +4,7 @@
 # This file is subject to the terms and conditions defined in
 # file 'LICENSE.txt', which is part of this source code package.
 # ***************************************************************
-from multiprocessing import Pool, Value
+from multiprocessing import Pool
 import multiprocessing as mp
 import subprocess as sp
 import os

python/jittor_utils/config.py

@@ -31,7 +31,7 @@ if __name__ == "__main__":
             libext = {
                 'Linux': 'so',
                 'Darwin': 'dylib',
-                'Window': 'DLL',
+                'Windows': 'DLL',
             }[platform.system()]
             ldflags = jittor_utils.run_cmd(jittor_utils.py3_config_path + " --ldflags")
             libpaths = [l[2:] for l in ldflags.split(' ') if l.startswith("-L")]