Refines test_torch.py generic device testing (#26244)

Mike Ruberry · facebook-github-bot · commit b6b2b4c18f5a · 2019-09-15T03:35:23.000-07:00
Summary: - Adds SkipCUDAIfRocm and skipCPUIfNoMkl decorators, ports corresponding tests - Changes "SkipIf" input semantics for consistency - Removes torchtest, which has been replaced with this new generic framework - Refactors some common parts out of CUDA tests to TestTorchDeviceType - Ensures all MAGMA tests run on default stream by putting the skipCUDANonDefaultStreamIf in the skipCUDAIfNoMagma decorator. Pull Request resolved: #26244 Differential Revision: D17389060 Pulled By: mruberry fbshipit-source-id: 1375774f24c2266049e6d4b899e7300ddf32eac8
diff --git a/test/common_device_type.py b/test/common_device_type.py
@@ -2,7 +2,8 @@
 from functools import wraps
 import unittest
 import torch
-from common_utils import TestCase
+from common_utils import TestCase, TEST_WITH_ROCM, TEST_MKL, \
+    skipCUDANonDefaultStreamIf
 
 # Note: Generic Device-Type Testing
 #
@@ -109,7 +110,7 @@ class CUDATestBase(DeviceTypeTestBase):
     def setUpClass(cls):
         # has_magma shows up after cuda is initialized
         torch.ones(1).cuda()
-        cls.has_magma = torch.cuda.has_magma
+        cls.no_magma = not torch.cuda.has_magma
 
 
 # Adds available device-type-specific test base classes
@@ -189,7 +190,7 @@ def __call__(self, fn):
         @wraps(fn)
         def dep_fn(slf, device, *args, **kwargs):
             if self.device_type is None or self.device_type == slf.device_type:
-                if not self.dep or (isinstance(self.dep, str) and not getattr(slf, self.dep, False)):
+                if (isinstance(self.dep, str) and getattr(slf, self.dep, True)) or (isinstance(self.dep, bool) and self.dep):
                     raise unittest.SkipTest(self.reason)
 
             return fn(slf, device, *args, **kwargs)
@@ -212,9 +213,19 @@ def __init__(self, dep, reason):
 
 # Specifies LAPACK as a CPU dependency.
 def skipCPUIfNoLapack(fn):
-    return skipCPUIf(torch._C.has_lapack, "PyTorch compiled without Lapack")(fn)
+    return skipCPUIf(not torch._C.has_lapack, "PyTorch compiled without Lapack")(fn)
+
+
+# Specifies MKL as a CPU dependency.
+def skipCPUIfNoMkl(fn):
+    return skipCPUIf(not TEST_MKL, "PyTorch is built without MKL support")(fn)
 
 
 # Specifies MAGMA as a CUDA dependency.
 def skipCUDAIfNoMagma(fn):
-    return skipCUDAIf('has_magma', "no MAGMA library detected")(fn)
+    return skipCUDAIf('no_magma', "no MAGMA library detected")(skipCUDANonDefaultStreamIf(True)(fn))
+
+
+# Skips this test when the CUDA device type is actually ROCm.
+def skipCUDAIfRocm(fn):
+    return skipCUDAIf(TEST_WITH_ROCM, "test doesn't currently work on the ROCm stack")(fn)
diff --git a/test/common_utils.py b/test/common_utils.py
@@ -208,60 +208,11 @@ def wrapper(*args, **kwargs):
             fn(*args, **kwargs)
     return wrapper
 
-
-
 def _test_function(fn, device):
     def run_test_function(self):
         return fn(self, device)
     return run_test_function
 
-
-class torchtest():
-    """Allows to generate and run per-device unittests.
-
-    This decorator class allows to generate and run per-device unittest.
-
-    Example:
-
-    class _TestTorchMixin(torchtest):
-
-        @torchtest.for_all_device_types()
-        def test_zeros_like(self, device):
-            expected = torch.zeros((100, 100,), device=device)
-
-    Will execute:
-
-        test_zeros_like (__main__.TestTorch) ... skipped 'Look at test_zeros_like_cpu, test_zeros_like_cuda results.'
-        test_zeros_like_cpu (__main__.TestTorch) ... ok
-        test_zeros_like_cuda (__main__.TestTorch) ... ok
-
-    To work properly, test class should be inherited from `torchtest`.
-    for_all_device_types decorator does not guarantee proper functionality in
-    combination with other decorators.
-
-    Please do not extend this decorator to support other cases (such as dtype,
-    layouts, etc) without consulting with bigger group. Devices is the special
-    case as build flags control additions/removals (see
-    https://github.com/pytorch/pytorch/pull/23824 for the reference).
-    """
-    @classmethod
-    def for_all_device_types(cls):
-        def wrapper(fn):
-            test_names = []
-
-            for device in torch.testing.get_all_device_types():
-                test_name = fn.__name__ + '_' + device
-                assert not hasattr(cls, test_name), "Duplicated test name: " + test_name
-                setattr(cls, test_name, _test_function(fn, device))
-                test_names.append(test_name)
-
-            @wraps(fn)
-            def empty_test(*args, **kwargs):
-                raise unittest.SkipTest("Look at {} results.".format(", ".join(test_names)))
-            return empty_test
-        return wrapper
-
-
 def skipIfNoLapack(fn):
     @wraps(fn)
     def wrapper(*args, **kwargs):
@@ -271,7 +222,6 @@ def wrapper(*args, **kwargs):
             fn(*args, **kwargs)
     return wrapper
 
-
 def skipIfNotRegistered(op_name, message):
     """Wraps the decorator to hide the import of the `core`.
 
diff --git a/test/test_cuda.py b/test/test_cuda.py
@@ -2176,9 +2176,7 @@ def _select_broadcastable_dims(dims_full=None):
         return _TestTorchMixin._select_broadcastable_dims(dims_full)
 
     @skipIfRocm
-    def test_fft_ifft_rfft_irfft(self):
-        _TestTorchMixin._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))
-
+    def test_fft_ifft_rfft_irfft_plan_cache(self):
         @contextmanager
         def plan_cache_max_size(n, device=None):
             if device is None:
@@ -2246,15 +2244,8 @@ def plan_cache_max_size(n, device=None):
                             self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 10)  # default is cuda:0
                         self.assertEqual(torch.backends.cuda.cufft_plan_cache.max_size, 11)  # default is cuda:1
 
-    # passes on ROCm w/ python 2.7, fails w/ python 3.6
-    @skipIfRocm
-    def test_stft(self):
-        _TestTorchMixin._test_stft(self, device=torch.device('cuda'))
-
-    def test_multinomial(self):
-        _TestTorchMixin._test_multinomial(self, torch.cuda.FloatTensor)
-
-        # Test two corner cases from older PyTorch (Issue #4858)
+    # Tests two corner cases from older PyTorch (Issue #4858).
+    def test_multinomial_corner_cases(self):
         freqs = torch.cuda.FloatTensor([
             0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
             0.03178183361887932, 0.027680952101945877, 0.033176131546497345,
@@ -2573,8 +2564,7 @@ def test_nvtx(self):
         torch.cuda.nvtx.mark("bar")
         torch.cuda.nvtx.range_pop()
 
-    def test_bincount_cuda(self):
-        _TestTorchMixin._test_bincount(self, device='cuda')
+    def test_bincount_compare(self):
         # ensure CUDA code coverage
         input_size = (5000,)
         w = torch.randn(input_size, device='cuda')
diff --git a/test/test_torch.py b/test/test_torch.py
