From: Nikita Shulga <nshulga@fb.com>
Date: Mon, 13 Sep 2021 19:46:11 +0000 (-0700)
Subject: Pin SciPy to 1.6.3 on Mac (take 2) (#64922)
X-Git-Tag: accepted/tizen/8.0/unified/20231005.095509~257
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=af984c78a975400ffd8c475a7e00abb25e590bb1;p=platform%2Fupstream%2Fpytorch.git

Pin SciPy to 1.6.3 on Mac (take 2) (#64922)

Summary:
It's already pinned by via docker install on Linux

`scipy.stats.`[`poission`|`geom`|`binom`] returns quite different results between 1.6.x and 1.7+ versions of SciPy, which results in several distributions tests failing accuracy thresholds

Reland of https://github.com/pytorch/pytorch/pull/64844 but limited to just Mac platform
Followup PR for Windows are coming as well

Pull Request resolved: https://github.com/pytorch/pytorch/pull/64922

Reviewed By: janeyx99

Differential Revision: D30901257

Pulled By: malfet

fbshipit-source-id: 0543e7bae9d3bbeb8b6be7b3ecf605880f97665f
---

diff --git a/.jenkins/pytorch/macos-test.sh b/.jenkins/pytorch/macos-test.sh
index 71d6773..d7e223f 100755
--- a/.jenkins/pytorch/macos-test.sh
+++ b/.jenkins/pytorch/macos-test.sh
@@ -7,7 +7,7 @@ source "$(dirname "${BASH_SOURCE[0]}")/macos-common.sh"
 export PYTORCH_TEST_SKIP_NOARCH=1
 
 conda install -y six
-pip install -q hypothesis "expecttest==0.1.3" "librosa>=0.6.2" "numba<=0.49.1" psutil
+pip install -q hypothesis "expecttest==0.1.3" "librosa>=0.6.2" "numba<=0.49.1" psutil "scipy==1.6.3"
 
 # TODO move this to docker
 pip install unittest-xml-reporting pytest
diff --git a/test/distributions/test_distributions.py b/test/distributions/test_distributions.py
index 268deb5..ee5abb4 100644
--- a/test/distributions/test_distributions.py
+++ b/test/distributions/test_distributions.py
@@ -20,6 +20,10 @@ change. This file contains two types of randomized tests:
    it's fine to increment the seed of the failing test (but you shouldn't need
    to increment it more than once; otherwise something is probably actually
    wrong).
+
+3. `test_geometric_sample`, `test_binomial_sample` and `test_poisson_sample`
+   are validated against `scipy.stats.` which are not guaranteed to be identical
+   across different versions of scipy (namely, they yield invalid results in 1.7+)
 """
 
 import math
@@ -38,7 +42,7 @@ torch.set_default_dtype(torch.double)
 from torch._six import inf
 from torch.testing._internal.common_utils import \
     (TestCase, run_tests, set_rng_seed, TEST_WITH_UBSAN, load_tests,
-     gradcheck, IS_MACOS, IS_WINDOWS)
+     gradcheck, IS_WINDOWS)
 from torch.testing._internal.common_cuda import TEST_CUDA
 from torch.autograd import grad
 from torch.autograd.functional import jacobian
@@ -1035,7 +1039,6 @@ class TestDistributions(TestCase):
         self.assertEqual(float(Geometric(s).entropy()), scipy.stats.geom(s, loc=-1).entropy().item(), atol=1e-3, rtol=0)
 
     @unittest.skipIf(IS_WINDOWS, "See https://github.com/pytorch/pytorch/issues/64595")
-    @unittest.skipIf(IS_MACOS, "See https://github.com/pytorch/pytorch/issues/60347")
     @unittest.skipIf(not TEST_NUMPY, "NumPy not found")
     def test_geometric_sample(self):
         set_rng_seed(0)  # see Note [Randomized statistical tests]
@@ -1053,7 +1056,6 @@ class TestDistributions(TestCase):
         self.assertRaises(NotImplementedError, Binomial(10, p).entropy)
 
     @unittest.skipIf(IS_WINDOWS, "See https://github.com/pytorch/pytorch/issues/64595")
-    @unittest.skipIf(IS_MACOS, "See https://github.com/pytorch/pytorch/issues/60347")
     @unittest.skipIf(not TEST_NUMPY, "NumPy not found")
     def test_binomial_sample(self):
         set_rng_seed(0)  # see Note [Randomized statistical tests]
@@ -1347,7 +1349,6 @@ class TestDistributions(TestCase):
         torch.testing.assert_allclose(rate_zero.grad, torch.inf)
 
     @unittest.skipIf(IS_WINDOWS, "See https://github.com/pytorch/pytorch/issues/64595")
-    @unittest.skipIf(IS_MACOS, "See https://github.com/pytorch/pytorch/issues/60347")
     @unittest.skipIf(not TEST_NUMPY, "Numpy not found")
     def test_poisson_sample(self):
         set_rng_seed(1)  # see Note [Randomized statistical tests]