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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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33
tests/test_rasterize_meshes.py
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@@ -4,7 +4,7 @@ import functools
import unittest
import torch
from common_testing import TestCaseMixin
from common_testing import TestCaseMixin, get_random_cuda_device
from pytorch3d import _C
from pytorch3d.renderer.mesh.rasterize_meshes import (
rasterize_meshes,
@@ -32,7 +32,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
self._test_back_face_culling(rasterize_meshes, device, bin_size=0)
def test_simple_cuda_naive(self):
device = torch.device("cuda:0")
device = get_random_cuda_device()
self._simple_triangle_raster(rasterize_meshes, device, bin_size=0)
self._simple_blurry_raster(rasterize_meshes, device, bin_size=0)
self._test_behind_camera(rasterize_meshes, device, bin_size=0)
@@ -40,7 +40,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
self._test_back_face_culling(rasterize_meshes, device, bin_size=0)
def test_simple_cuda_binned(self):
device = torch.device("cuda:0")
device = get_random_cuda_device()
self._simple_triangle_raster(rasterize_meshes, device, bin_size=5)
self._simple_blurry_raster(rasterize_meshes, device, bin_size=5)
self._test_behind_camera(rasterize_meshes, device, bin_size=5)
@@ -54,7 +54,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
blur_radius = 0.1 ** 2
faces_per_pixel = 3
for d in ["cpu", "cuda"]:
for d in ["cpu", get_random_cuda_device()]:
device = torch.device(d)
compare_grads = True
# Mesh with a single face.
@@ -164,7 +164,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
verts1.requires_grad = True
meshes_cpu = Meshes(verts=[verts1], faces=[faces1])
device = torch.device("cuda:0")
device = get_random_cuda_device()
meshes_cuda = ico_sphere(0, device)
verts2, faces2 = meshes_cuda.get_mesh_verts_faces(0)
verts2.requires_grad = True
@@ -186,7 +186,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
return self._test_coarse_rasterize(torch.device("cpu"))
def test_coarse_cuda(self):
return self._test_coarse_rasterize(torch.device("cuda:0"))
return self._test_coarse_rasterize(get_random_cuda_device())
def test_cpp_vs_cuda_naive_vs_cuda_binned(self):
# Make sure that the backward pass runs for all pathways
@@ -221,7 +221,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
grad1 = verts.grad.data.cpu().clone()
# Option II: CUDA, naive
device = torch.device("cuda:0")
device = get_random_cuda_device()
meshes = ico_sphere(0, device)
verts, faces = meshes.get_mesh_verts_faces(0)
verts.requires_grad = True
@@ -229,9 +229,9 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
args = (meshes, image_size, radius, faces_per_pixel, 0, 0)
idx2, zbuf2, bary2, dist2 = rasterize_meshes(*args)
grad_zbuf = grad_zbuf.cuda()
grad_dist = grad_dist.cuda()
grad_bary = grad_bary.cuda()
grad_zbuf = grad_zbuf.to(device)
grad_dist = grad_dist.to(device)
grad_bary = grad_bary.to(device)
loss = (
(zbuf2 * grad_zbuf).sum()
+ (dist2 * grad_dist).sum()
@@ -244,7 +244,6 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
grad2 = verts.grad.data.cpu().clone()
# Option III: CUDA, binned
device = torch.device("cuda:0")
meshes = ico_sphere(0, device)
verts, faces = meshes.get_mesh_verts_faces(0)
verts.requires_grad = True
@@ -302,7 +301,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
bin_size,
max_faces_per_bin,
)
device = torch.device("cuda:0")
device = get_random_cuda_device()
meshes = meshes.clone().to(device)
faces = meshes.faces_packed()
@@ -356,8 +355,9 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
verts1, faces1 = meshes.get_mesh_verts_faces(0)
verts1.requires_grad = True
meshes1 = Meshes(verts=[verts1], faces=[faces1])
verts2 = verts1.detach().cuda().requires_grad_(True)
faces2 = faces1.detach().clone().cuda()
device = get_random_cuda_device()
verts2 = verts1.detach().to(device).requires_grad_(True)
faces2 = faces1.detach().clone().to(device)
meshes2 = Meshes(verts=[verts2], faces=[faces2])
kwargs = {"image_size": 64, "perspective_correct": True}
@@ -367,7 +367,8 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
def test_cuda_naive_vs_binned_perspective_correct(self):
meshes = ico_sphere(2, device=torch.device("cuda"))
device = get_random_cuda_device()
meshes = ico_sphere(2, device=device)
verts1, faces1 = meshes.get_mesh_verts_faces(0)
verts1.requires_grad = True
meshes1 = Meshes(verts=[verts1], faces=[faces1])
@@ -1029,7 +1030,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
max_faces_per_bin: int,
):
meshes = ico_sphere(ico_level, torch.device("cuda:0"))
meshes = ico_sphere(ico_level, get_random_cuda_device())
meshes_batch = meshes.extend(num_meshes)
torch.cuda.synchronize()