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Add EGLContext and DeviceContextManager

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Summary:
EGLContext is a utility to render with OpenGL without an attached display (that is, without a monitor).

DeviceContextManager allows us to avoid unnecessary context creations and releases. See docstrings for more info.

Reviewed By: jcjohnson

Differential Revision: D36562551

fbshipit-source-id: eb0d2a2f85555ee110e203d435a44ad243281d2c
This commit is contained in:
Krzysztof Chalupka
2022-07-22 09:43:05 -07:00
committed by Facebook GitHub Bot
parent 54c75b4114
commit 78bb6d17fa
5 changed files with 852 additions and 1 deletions
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2
tests/implicitron/test_build.py
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@@ -27,7 +27,7 @@ class TestBuild(unittest.TestCase):
root_dir = get_pytorch3d_dir() / "pytorch3d"
for module_file in root_dir.glob("**/*.py"):
if module_file.stem in ("__init__", "plotly_vis"):
if module_file.stem in ("__init__", "plotly_vis", "opengl_utils"):
continue
relative_module = str(module_file.relative_to(root_dir))[:-3]
module = "pytorch3d." + relative_module.replace("/", ".")

387
tests/test_opengl_utils.py Normal file
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@@ -0,0 +1,387 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import ctypes
import os
import sys
import threading
import unittest
import torch
os.environ["PYOPENGL_PLATFORM"] = "egl"
import pycuda._driver # noqa
from OpenGL import GL as gl # noqa
from OpenGL.raw.EGL._errors import EGLError # noqa
from pytorch3d.renderer.opengl import _can_import_egl_and_pycuda # noqa
from pytorch3d.renderer.opengl.opengl_utils import ( # noqa
_define_egl_extension,
_egl_convert_to_int_array,
_get_cuda_device,
egl,
EGLContext,
global_device_context_store,
)
from .common_testing import TestCaseMixin # noqa
MAX_EGL_HEIGHT = global_device_context_store.max_egl_height
MAX_EGL_WIDTH = global_device_context_store.max_egl_width
def _draw_square(r=1.0, g=0.0, b=1.0, **kwargs) -> torch.Tensor:
gl.glClear(gl.GL_COLOR_BUFFER_BIT)
gl.glColor3f(r, g, b)
x1, x2 = -0.5, 0.5
y1, y2 = -0.5, 0.5
gl.glRectf(x1, y1, x2, y2)
out_buffer = gl.glReadPixels(
0, 0, MAX_EGL_WIDTH, MAX_EGL_HEIGHT, gl.GL_RGB, gl.GL_UNSIGNED_BYTE
)
image = torch.frombuffer(out_buffer, dtype=torch.uint8).reshape(
MAX_EGL_HEIGHT, MAX_EGL_WIDTH, 3
)
return image
def _draw_squares_with_context(
cuda_device_id=0, result=None, thread_id=None, **kwargs
) -> None:
context = EGLContext(MAX_EGL_WIDTH, MAX_EGL_HEIGHT, cuda_device_id)
with context.active_and_locked():
images = []
for _ in range(3):
images.append(_draw_square(**kwargs).float())
if result is not None and thread_id is not None:
egl_info = context.get_context_info()
data = {"egl": egl_info, "images": images}
result[thread_id] = data
def _draw_squares_with_context_store(
cuda_device_id=0,
result=None,
thread_id=None,
verbose=False,
**kwargs,
) -> None:
device = torch.device(f"cuda:{cuda_device_id}")
context = global_device_context_store.get_egl_context(device)
if verbose:
print(f"In thread {thread_id}, device {cuda_device_id}.")
with context.active_and_locked():
images = []
for _ in range(3):
images.append(_draw_square(**kwargs).float())
if result is not None and thread_id is not None:
egl_info = context.get_context_info()
data = {"egl": egl_info, "images": images}
result[thread_id] = data
class TestDeviceContextStore(TestCaseMixin, unittest.TestCase):
def test_cuda_context(self):
cuda_context_1 = global_device_context_store.get_cuda_context(
device=torch.device("cuda:0")
)
cuda_context_2 = global_device_context_store.get_cuda_context(
device=torch.device("cuda:0")
)
cuda_context_3 = global_device_context_store.get_cuda_context(
device=torch.device("cuda:1")
)
cuda_context_4 = global_device_context_store.get_cuda_context(
device=torch.device("cuda:1")
)
self.assertIs(cuda_context_1, cuda_context_2)
self.assertIs(cuda_context_3, cuda_context_4)
self.assertIsNot(cuda_context_1, cuda_context_3)
def test_egl_context(self):
egl_context_1 = global_device_context_store.get_egl_context(
torch.device("cuda:0")
)
egl_context_2 = global_device_context_store.get_egl_context(
torch.device("cuda:0")
)
egl_context_3 = global_device_context_store.get_egl_context(
torch.device("cuda:1")
)
egl_context_4 = global_device_context_store.get_egl_context(
torch.device("cuda:1")
)
self.assertIs(egl_context_1, egl_context_2)
self.assertIs(egl_context_3, egl_context_4)
self.assertIsNot(egl_context_1, egl_context_3)
class TestUtils(TestCaseMixin, unittest.TestCase):
def test_load_extensions(self):
# This should work
_define_egl_extension("eglGetPlatformDisplayEXT", egl.EGLDisplay)
# And this shouldn't (wrong extension)
with self.assertRaisesRegex(RuntimeError, "Cannot find EGL extension"):
_define_egl_extension("eglFakeExtensionEXT", egl.EGLBoolean)
def test_get_cuda_device(self):
# This should work
device = _get_cuda_device(0)
self.assertIsNotNone(device)
with self.assertRaisesRegex(ValueError, "Device 10000 not available"):
_get_cuda_device(10000)
def test_egl_convert_to_int_array(self):
egl_attributes = {egl.EGL_RED_SIZE: 8}
attribute_array = _egl_convert_to_int_array(egl_attributes)
self.assertEqual(attribute_array._type_, ctypes.c_int)
self.assertEqual(attribute_array._length_, 3)
self.assertEqual(attribute_array[0], egl.EGL_RED_SIZE)
self.assertEqual(attribute_array[1], 8)
self.assertEqual(attribute_array[2], egl.EGL_NONE)
class TestOpenGLSingleThreaded(TestCaseMixin, unittest.TestCase):
def test_draw_square(self):
context = EGLContext(width=MAX_EGL_WIDTH, height=MAX_EGL_HEIGHT)
with context.active_and_locked():
rendering_result = _draw_square().float()
expected_result = torch.zeros(
(MAX_EGL_WIDTH, MAX_EGL_HEIGHT, 3), dtype=torch.float
)
start_px = int(MAX_EGL_WIDTH / 4)
end_px = int(MAX_EGL_WIDTH * 3 / 4)
expected_result[start_px:end_px, start_px:end_px, 0] = 255.0
expected_result[start_px:end_px, start_px:end_px, 2] = 255.0
self.assertTrue(torch.all(expected_result == rendering_result))
def test_render_two_squares(self):
# Check that drawing twice doesn't overwrite the initial buffer.
context = EGLContext(width=MAX_EGL_WIDTH, height=MAX_EGL_HEIGHT)
with context.active_and_locked():
red_square = _draw_square(r=1.0, g=0.0, b=0.0)
blue_square = _draw_square(r=0.0, g=0.0, b=1.0)
start_px = int(MAX_EGL_WIDTH / 4)
end_px = int(MAX_EGL_WIDTH * 3 / 4)
self.assertTrue(
torch.all(
red_square[start_px:end_px, start_px:end_px]
== torch.tensor([255, 0, 0])
)
)
self.assertTrue(
torch.all(
blue_square[start_px:end_px, start_px:end_px]
== torch.tensor([0, 0, 255])
)
)
class TestOpenGLMultiThreaded(TestCaseMixin, unittest.TestCase):
def test_multiple_renders_single_gpu_single_context(self):
_draw_squares_with_context()
def test_multiple_renders_single_gpu_context_store(self):
_draw_squares_with_context_store()
def test_render_two_threads_single_gpu(self):
self._render_two_threads_single_gpu(_draw_squares_with_context)
def test_render_two_threads_single_gpu_context_store(self):
self._render_two_threads_single_gpu(_draw_squares_with_context_store)
def test_render_two_threads_two_gpus(self):
self._render_two_threads_two_gpus(_draw_squares_with_context)
def test_render_two_threads_two_gpus_context_store(self):
self._render_two_threads_two_gpus(_draw_squares_with_context_store)
def _render_two_threads_single_gpu(self, draw_fn):
result = [None] * 2
thread1 = threading.Thread(
target=draw_fn,
kwargs={
"cuda_device_id": 0,
"result": result,
"thread_id": 0,
"r": 1.0,
"g": 0.0,
"b": 0.0,
},
)
thread2 = threading.Thread(
target=draw_fn,
kwargs={
"cuda_device_id": 0,
"result": result,
"thread_id": 1,
"r": 0.0,
"g": 1.0,
"b": 0.0,
},
)
thread1.start()
thread2.start()
thread1.join()
thread2.join()
start_px = int(MAX_EGL_WIDTH / 4)
end_px = int(MAX_EGL_WIDTH * 3 / 4)
red_squares = torch.stack(result[0]["images"], dim=0)[
:, start_px:end_px, start_px:end_px
]
green_squares = torch.stack(result[1]["images"], dim=0)[
:, start_px:end_px, start_px:end_px
]
self.assertTrue(torch.all(red_squares == torch.tensor([255.0, 0.0, 0.0])))
self.assertTrue(torch.all(green_squares == torch.tensor([0.0, 255.0, 0.0])))
def _render_two_threads_two_gpus(self, draw_fn):
# Contrary to _render_two_threads_two_gpus, this renders in two separate threads
# but on a different GPU each. This means using different EGL contexts and is a
# much less risky endeavour.
result = [None] * 2
thread1 = threading.Thread(
target=draw_fn,
kwargs={
"cuda_device_id": 0,
"result": result,
"thread_id": 0,
"r": 1.0,
"g": 0.0,
"b": 0.0,
},
)
thread2 = threading.Thread(
target=draw_fn,
kwargs={
"cuda_device_id": 1,
"result": result,
"thread_id": 1,
"r": 0.0,
"g": 1.0,
"b": 0.0,
},
)
thread1.start()
thread2.start()
thread1.join()
thread2.join()
self.assertNotEqual(
result[0]["egl"]["context"].address, result[1]["egl"]["context"].address
)
start_px = int(MAX_EGL_WIDTH / 4)
end_px = int(MAX_EGL_WIDTH * 3 / 4)
red_squares = torch.stack(result[0]["images"], dim=0)[
:, start_px:end_px, start_px:end_px
]
green_squares = torch.stack(result[1]["images"], dim=0)[
:, start_px:end_px, start_px:end_px
]
self.assertTrue(torch.all(red_squares == torch.tensor([255.0, 0.0, 0.0])))
self.assertTrue(torch.all(green_squares == torch.tensor([0.0, 255.0, 0.0])))
def test_render_multi_thread_multi_gpu(self):
# Multiple threads using up multiple GPUs; more threads than GPUs.
# This is certainly not encouraged in practice, but shouldn't fail. Note that
# the context store will only allow one rendering at a time to occur on a
# single GPU, even across threads.
n_gpus = torch.cuda.device_count()
n_threads = 10
kwargs = {
"r": 1.0,
"g": 0.0,
"b": 0.0,
"verbose": True,
}
threads = []
for thread_id in range(n_threads):
kwargs.update(
{"cuda_device_id": thread_id % n_gpus, "thread_id": thread_id}
)
threads.append(
threading.Thread(
target=_draw_squares_with_context_store, kwargs=dict(kwargs)
)
)
for thread in threads:
thread.start()
for thread in threads:
thread.join()
class TestOpenGLUtils(TestCaseMixin, unittest.TestCase):
def test_device_context_store(self):
# Most of DCS's functionality is tested in the tests above, test the remainder.
device = torch.device("cuda:0")
global_device_context_store.set_context_data(device, 123)
self.assertEqual(global_device_context_store.get_context_data(device), 123)
self.assertEqual(
global_device_context_store.get_context_data(torch.device("cuda:1")), None
)
# Check that contexts in store can be manually released (although that's a very
# bad idea! Don't do it manually!)
egl_ctx = global_device_context_store.get_egl_context(device)
cuda_ctx = global_device_context_store.get_cuda_context(device)
egl_ctx.release()
cuda_ctx.detach()
# Reset the contexts (just for testing! never do this manually!). Then, check
# that first running DeviceContextStore.release() will cause subsequent releases
# to fail (because we already released all the contexts).
global_device_context_store._cuda_contexts = {}
global_device_context_store._egl_contexts = {}
egl_ctx = global_device_context_store.get_egl_context(device)
cuda_ctx = global_device_context_store.get_cuda_context(device)
global_device_context_store.release()
with self.assertRaisesRegex(EGLError, "EGL_NOT_INITIALIZED"):
egl_ctx.release()
with self.assertRaisesRegex(pycuda._driver.LogicError, "cannot detach"):
cuda_ctx.detach()
def test_no_egl_error(self):
# Remove EGL, import OpenGL with the wrong backend. This should make it
# impossible to import OpenGL.EGL.
del os.environ["PYOPENGL_PLATFORM"]
modules = list(sys.modules)
for m in modules:
if "OpenGL" in m:
del sys.modules[m]
import OpenGL.GL # noqa
self.assertFalse(_can_import_egl_and_pycuda())
# Import OpenGL back with the right backend. This should get things on track.
modules = list(sys.modules)
for m in modules:
if "OpenGL" in m:
del sys.modules[m]
os.environ["PYOPENGL_PLATFORM"] = "egl"
self.assertTrue(_can_import_egl_and_pycuda())
def test_egl_release_error(self):
# Creating two contexts on the same device will lead to trouble (that's one of
# the reasons behind DeviceContextStore). You can release one of them,
# but you cannot release the same EGL resources twice!
ctx1 = EGLContext(width=100, height=100)
ctx2 = EGLContext(width=100, height=100)
ctx1.release()
with self.assertRaisesRegex(EGLError, "EGL_NOT_INITIALIZED"):
ctx2.release()