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Cuda updates

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Summary:
Updates to:
- enable cuda kernel launches on any GPU (not just the default)
- cuda and contiguous checks for all kernels
- checks to ensure all tensors are on the same device
- error reporting in the cuda kernels
- cuda tests now run on a random device not just the default

Reviewed By: jcjohnson, gkioxari

Differential Revision: D21215280

fbshipit-source-id: 1bedc9fe6c35e9e920bdc4d78ed12865b1005519
This commit is contained in:
Nikhila Ravi
2020-04-24 09:07:54 -07:00
committed by Facebook GitHub Bot
parent c9267ab7af
commit c3d636dc8c
33 changed files with 979 additions and 240 deletions
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15
tests/test_compositing.py
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@@ -3,6 +3,7 @@
import unittest
import torch
from common_testing import TestCaseMixin, get_random_cuda_device
from pytorch3d.renderer.compositing import (
alpha_composite,
norm_weighted_sum,
@@ -10,7 +11,7 @@ from pytorch3d.renderer.compositing import (
)
class TestAccumulatePoints(unittest.TestCase):
class TestAccumulatePoints(TestCaseMixin, unittest.TestCase):
# NAIVE PYTHON IMPLEMENTATIONS (USED FOR TESTING)
@staticmethod
@@ -120,7 +121,7 @@ class TestAccumulatePoints(unittest.TestCase):
self._simple_wsumnorm(norm_weighted_sum, device)
def test_cuda(self):
device = torch.device("cuda:0")
device = get_random_cuda_device()
self._simple_alphacomposite(alpha_composite, device)
self._simple_wsum(weighted_sum, device)
self._simple_wsumnorm(norm_weighted_sum, device)
@@ -142,7 +143,7 @@ class TestAccumulatePoints(unittest.TestCase):
C = 3
P = 32
for d in ["cpu", "cuda"]:
for d in ["cpu", get_random_cuda_device()]:
# TODO(gkioxari) add torch.float64 to types after double precision
# support is added to atomicAdd
for t in [torch.float32]:
@@ -181,7 +182,7 @@ class TestAccumulatePoints(unittest.TestCase):
res1 = fn1(*args1)
res2 = fn2(*args2)
self.assertTrue(torch.allclose(res1.cpu(), res2.cpu(), atol=1e-6))
self.assertClose(res1.cpu(), res2.cpu(), atol=1e-6)
if not compare_grads:
return
@@ -200,7 +201,7 @@ class TestAccumulatePoints(unittest.TestCase):
grads2 = [gradsi.grad.data.clone().cpu() for gradsi in grads2]
for i in range(0, len(grads1)):
self.assertTrue(torch.allclose(grads1[i].cpu(), grads2[i].cpu(), atol=1e-6))
self.assertClose(grads1[i].cpu(), grads2[i].cpu(), atol=1e-6)
def _simple_wsum(self, accum_func, device):
# Initialise variables
@@ -273,7 +274,7 @@ class TestAccumulatePoints(unittest.TestCase):
]
).to(device)
self.assertTrue(torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3))
self.assertClose(result.cpu(), true_result.cpu(), rtol=1e-3)
def _simple_wsumnorm(self, accum_func, device):
# Initialise variables
@@ -346,7 +347,7 @@ class TestAccumulatePoints(unittest.TestCase):
]
).to(device)
self.assertTrue(torch.allclose(result.cpu(), true_result.cpu(), rtol=1e-3))
self.assertClose(result.cpu(), true_result.cpu(), rtol=1e-3)
def _simple_alphacomposite(self, accum_func, device):
# Initialise variables