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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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6
pytorch3d/renderer/__init__.py
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@ -64,6 +64,12 @@ from .mesh import (
TexturesUV,
TexturesVertex,
)
try:
from .opengl import EGLContext, global_device_context_store
except (ImportError, ModuleNotFoundError):
pass # opengl or pycuda.gl not available, or pytorch3_opengl not in TARGETS.
from .points import (
AlphaCompositor,
NormWeightedCompositor,

36
pytorch3d/renderer/opengl/__init__.py Normal file
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@ -0,0 +1,36 @@
# 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.
# If we can access EGL, import MeshRasterizerOpenGL.
def _can_import_egl_and_pycuda():
import os
import warnings
try:
os.environ["PYOPENGL_PLATFORM"] = "egl"
import OpenGL.EGL
except (AttributeError, ImportError, ModuleNotFoundError):
warnings.warn(
"Can't import EGL, not importing MeshRasterizerOpenGL. This might happen if"
" your Python application imported OpenGL with a non-EGL backend before"
" importing PyTorch3D, or if you don't have pyopengl installed as part"
" of your Python distribution."
)
return False
try:
import pycuda.gl
except (ImportError, ImportError, ModuleNotFoundError):
warnings.warn("Can't import pucuda.gl, not importing MeshRasterizerOpenGL.")
return False
return True
if _can_import_egl_and_pycuda():
from .opengl_utils import EGLContext, global_device_context_store
__all__ = [k for k in globals().keys() if not k.startswith("_")]

422
pytorch3d/renderer/opengl/opengl_utils.py Executable file
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@ -0,0 +1,422 @@
# 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.
# Utilities useful for OpenGL rendering.
#
# NOTE: This module MUST be imported before any other OpenGL modules in this Python
# session, unless you set PYOPENGL_PLATFORM to egl *before* importing other modules.
# Otherwise, the imports below will throw an error.
#
# This module (as well as rasterizer_opengl) will not be imported into pytorch3d if
# you do not have pycuda.gl and pyopengl installed.
import contextlib
import ctypes
import os
import threading
from typing import Any, Dict
os.environ["PYOPENGL_PLATFORM"] = "egl"
import OpenGL.EGL as egl # noqa
import pycuda.driver as cuda # noqa
from OpenGL._opaque import opaque_pointer_cls # noqa
from OpenGL.raw.EGL._errors import EGLError # noqa
# A few constants necessary to use EGL extensions, see links for details.
# https://www.khronos.org/registry/EGL/extensions/EXT/EGL_EXT_platform_device.txt
EGL_PLATFORM_DEVICE_EXT = 0x313F
# https://www.khronos.org/registry/EGL/extensions/NV/EGL_NV_device_cuda.txt
EGL_CUDA_DEVICE_NV = 0x323A
# To use EGL extensions, we need to tell OpenGL about them. For details, see
# https://developer.nvidia.com/blog/egl-eye-opengl-visualization-without-x-server/.
# To avoid garbage collection of the protos, we'll store them in a module-global list.
def _define_egl_extension(name: str, type):
if hasattr(egl, name):
return
addr = egl.eglGetProcAddress(name)
if addr is None:
raise RuntimeError(f"Cannot find EGL extension {name}.")
else:
proto = ctypes.CFUNCTYPE(type)
func = proto(addr)
setattr(egl, name, func)
return proto
_protos = []
_protos.append(_define_egl_extension("eglGetPlatformDisplayEXT", egl.EGLDisplay))
_protos.append(_define_egl_extension("eglQueryDevicesEXT", egl.EGLBoolean))
_protos.append(_define_egl_extension("eglQueryDeviceAttribEXT", egl.EGLBoolean))
_protos.append(_define_egl_extension("eglQueryDisplayAttribEXT", egl.EGLBoolean))
_protos.append(_define_egl_extension("eglQueryDeviceStringEXT", ctypes.c_char_p))
if not hasattr(egl, "EGLDeviceEXT"):
egl.EGLDeviceEXT = opaque_pointer_cls("EGLDeviceEXT")
def _egl_convert_to_int_array(egl_attributes):
"""
Convert a Python dict of EGL attributes into an array of ints (some of which are
special EGL ints.
Args:
egl_attributes: A dict where keys are EGL attributes, and values are their vals.
Returns:
A c-list of length 2 * len(egl_attributes) + 1, of the form [key1, val1, ...,
keyN, valN, EGL_NONE]
"""
attributes_list = sum(([k, v] for k, v in egl_attributes.items()), []) + [
egl.EGL_NONE
]
return (egl.EGLint * len(attributes_list))(*attributes_list)
def _get_cuda_device(requested_device_id: int):
"""
Find an EGL device with a given CUDA device ID.
Args:
requested_device_id: The desired CUDA device ID, e.g. "1" for "cuda:1".
Returns:
EGL device with the desired CUDA ID.
"""
num_devices = egl.EGLint()
if (
# pyre-ignore Undefined attribute [16]
not egl.eglQueryDevicesEXT(0, None, ctypes.pointer(num_devices))
or num_devices.value < 1
):
raise RuntimeError("EGL requires a system that supports at least one device.")
devices = (egl.EGLDeviceEXT * num_devices.value)() # array of size num_devices
if (
# pyre-ignore Undefined attribute [16]
not egl.eglQueryDevicesEXT(
num_devices.value, devices, ctypes.pointer(num_devices)
)
or num_devices.value < 1
):
raise RuntimeError("EGL sees no available devices.")
if len(devices) < requested_device_id + 1:
raise ValueError(
f"Device {requested_device_id} not available. Found only {len(devices)} devices."
)
# Iterate over all the EGL devices, and check if their CUDA ID matches the request.
for device in devices:
available_device_id = egl.EGLAttrib(ctypes.c_int(-1))
# pyre-ignore Undefined attribute [16]
egl.eglQueryDeviceAttribEXT(device, EGL_CUDA_DEVICE_NV, available_device_id)
if available_device_id.contents.value == requested_device_id:
return device
raise ValueError(
f"Found {len(devices)} CUDA devices, but none with CUDA id {requested_device_id}."
)
def _get_egl_config(egl_dpy, surface_type):
"""
Get an EGL config with reasonable settings (for use with MeshRasterizerOpenGL).
Args:
egl_dpy: An EGL display constant (int).
surface_type: An EGL surface_type int.
Returns:
An EGL config object.
Throws:
ValueError if the desired config is not available or invalid.
"""
egl_config_dict = {
egl.EGL_RED_SIZE: 8,
egl.EGL_GREEN_SIZE: 8,
egl.EGL_BLUE_SIZE: 8,
egl.EGL_ALPHA_SIZE: 8,
egl.EGL_DEPTH_SIZE: 24,
egl.EGL_STENCIL_SIZE: egl.EGL_DONT_CARE,
egl.EGL_RENDERABLE_TYPE: egl.EGL_OPENGL_BIT,
egl.EGL_SURFACE_TYPE: surface_type,
}
egl_config_array = _egl_convert_to_int_array(egl_config_dict)
egl_config = egl.EGLConfig()
num_configs = egl.EGLint()
if (
not egl.eglChooseConfig(
egl_dpy,
egl_config_array,
ctypes.pointer(egl_config),
1,
ctypes.pointer(num_configs),
)
or num_configs.value == 0
):
raise ValueError("Invalid EGL config.")
return egl_config
class EGLContext:
"""
A class representing an EGL context. In short, EGL allows us to render OpenGL con-
tent in a headless mode, that is without an actual display to render to. This capa-
bility enables MeshRasterizerOpenGL to render on the GPU and then transfer the re-
sults to PyTorch3D.
"""
def __init__(self, width: int, height: int, cuda_device_id: int = 0) -> None:
"""
Args:
width: Width of the "display" to render to.
height: Height of the "display" to render to.
cuda_device_id: Device ID to render to, in the CUDA convention (note that
this might be different than EGL's device numbering).
"""
# Lock used to prevent multiple threads from rendering on the same device
# at the same time, creating/destroying contexts at the same time, etc.
self.lock = threading.Lock()
self.cuda_device_id = cuda_device_id
self.device = _get_cuda_device(self.cuda_device_id)
self.width = width
self.height = height
self.dpy = egl.eglGetPlatformDisplayEXT(
EGL_PLATFORM_DEVICE_EXT, self.device, None
)
major, minor = egl.EGLint(), egl.EGLint()
# Initialize EGL components: the display, surface, and context
egl.eglInitialize(self.dpy, ctypes.pointer(major), ctypes.pointer(minor))
config = _get_egl_config(self.dpy, egl.EGL_PBUFFER_BIT)
pb_surf_attribs = _egl_convert_to_int_array(
{
egl.EGL_WIDTH: width,
egl.EGL_HEIGHT: height,
}
)
self.surface = egl.eglCreatePbufferSurface(self.dpy, config, pb_surf_attribs)
if self.surface == egl.EGL_NO_SURFACE:
raise RuntimeError("Failed to create an EGL surface.")
if not egl.eglBindAPI(egl.EGL_OPENGL_API):
raise RuntimeError("Failed to bind EGL to the OpenGL API.")
self.context = egl.eglCreateContext(self.dpy, config, egl.EGL_NO_CONTEXT, None)
if self.context == egl.EGL_NO_CONTEXT:
raise RuntimeError("Failed to create an EGL context.")
@contextlib.contextmanager
def active_and_locked(self):
"""
A context manager used to make sure a given EGL context is only current in
a single thread at a single time. It is recommended to ALWAYS use EGL within
a `with context.active_and_locked():` context.
Throws:
EGLError when the context cannot be made current or make non-current.
"""
self.lock.acquire()
egl.eglMakeCurrent(self.dpy, self.surface, self.surface, self.context)
yield
egl.eglMakeCurrent(
self.dpy, egl.EGL_NO_SURFACE, egl.EGL_NO_SURFACE, egl.EGL_NO_CONTEXT
)
self.lock.release()
def get_context_info(self) -> Dict[str, Any]:
"""
Return context info. Useful for debugging.
Returns:
A dict of keys and ints, representing the context's display, surface,
the context itself, and the current thread.
"""
return {
"dpy": self.dpy,
"surface": self.surface,
"context": self.context,
"thread": threading.get_ident(),
}
def release(self):
"""
Release the context's resources.
"""
self.lock.acquire()
try:
if self.surface:
egl.eglDestroySurface(self.dpy, self.surface)
if self.context and self.dpy:
egl.eglDestroyContext(self.dpy, self.context)
egl.eglMakeCurrent(
self.dpy, egl.EGL_NO_SURFACE, egl.EGL_NO_SURFACE, egl.EGL_NO_CONTEXT
)
if self.dpy:
egl.eglTerminate(self.dpy)
except EGLError as err:
print(
f"EGL could not release context on device cuda:{self.cuda_device_id}."
" This can happen if you created two contexts on the same device."
" Instead, you can use DeviceContextStore to use a single context"
" per device, and EGLContext.make_(in)active_in_current_thread to"
" (in)activate the context as needed."
)
raise err
egl.eglReleaseThread()
self.lock.release()
class _DeviceContextStore:
"""
DeviceContextStore provides thread-safe storage for EGL and pycuda contexts. It
should not be used directly. opengl_utils instantiates a module-global variable
called opengl_utils.global_device_context_store. MeshRasterizerOpenGL uses this
store to avoid unnecessary context creation and destruction.
The EGL/CUDA contexts are not meant to be created and destroyed all the time,
and having multiple on a single device can be troublesome. Intended use is entirely
transparent to the user:
```
rasterizer1 = MeshRasterizerOpenGL(...some args...)
mesh1 = load_mesh_on_cuda_0()
# Now rasterizer1 will request EGL/CUDA contexts from global_device_context_store
# on cuda:0, and since there aren't any, the store will create new ones.
rasterizer1.rasterize(mesh1)
# rasterizer2 also needs EGL & CUDA contexts. But global_context_store already has
# them for cuda:0. Instead of creating new contexts, the store will tell rasterizer2
# to use them.
rasterizer2 = MeshRasterizerOpenGL(dcs)
rasterize2.rasterize(mesh1)
# When rasterizer1 needs to render on cuda:1, the store will create new contexts.
mesh2 = load_mesh_on_cuda_1()
rasterizer1.rasterize(mesh2)
```
"""
def __init__(self):
cuda.init()
# pycuda contexts, at most one per device.
self._cuda_contexts = {}
# EGL contexts, at most one per device.
self._egl_contexts = {}
# Any extra per-device data (e.g. precompiled GL objects).
self._context_data = {}
# Lock for DeviceContextStore used in multithreaded multidevice scenarios.
self._lock = threading.Lock()
# All EGL contexts created by this store will have this resolution.
self.max_egl_width = 2048
self.max_egl_height = 2048
def get_cuda_context(self, device):
"""
Return a pycuda's CUDA context on a given CUDA device. If we have not created
such a context yet, create a new one and store it in a dict. The context is
popped (you need to call context.push() to start using it). This function
is thread-safe.
Args:
device: A torch.device.
Returns: A pycuda context corresponding to the given device.
"""
cuda_device_id = device.index
with self._lock:
if cuda_device_id not in self._cuda_contexts:
self._cuda_contexts[cuda_device_id] = _init_cuda_context(cuda_device_id)
self._cuda_contexts[cuda_device_id].pop()
return self._cuda_contexts[cuda_device_id]
def get_egl_context(self, device):
"""
Return an EGL context on a given CUDA device. If we have not created such a
context yet, create a new one and store it in a dict. The context if not current
(you should use the `with egl_context.active_and_locked:` context manager when
you need it to be current). This function is thread-safe.
Args:
device: A torch.device.
Returns: An EGLContext on the requested device. The context will have size
self.max_egl_width and self.max_egl_height.
"""
cuda_device_id = device.index
with self._lock:
egl_context = self._egl_contexts.get(cuda_device_id, None)
if egl_context is None:
self._egl_contexts[cuda_device_id] = EGLContext(
self.max_egl_width, self.max_egl_height, cuda_device_id
)
return self._egl_contexts[cuda_device_id]
def set_context_data(self, device, value):
"""
Set arbitrary data in a per-device dict.
This function is intended for storing precompiled OpenGL objects separately for
EGL contexts on different devices. Each such context needs a separate compiled
OpenGL program, but (in case e.g. of MeshRasterizerOpenGL) there's no need to
re-compile it each time we move the rasterizer to the same device repeatedly,
as it happens when using DataParallel.
Args:
device: A torch.device
value: An arbitrary Python object.
"""
cuda_device_id = device.index
self._context_data[cuda_device_id] = value
def get_context_data(self, device):
"""
Get arbitrary data in a per-device dict. See set_context_data for more detail.
Args:
device: A torch.device
Returns:
The most recent object stored using set_context_data.
"""
cuda_device_id = device.index
return self._context_data.get(cuda_device_id, None)
def release(self):
"""
Release all CUDA and EGL contexts.
"""
for context in self._cuda_contexts.values():
context.detach()
for context in self._egl_contexts.values():
context.release()
def _init_cuda_context(device_id: int = 0):
"""
Initialize a pycuda context on a chosen device.
Args:
device_id: int, specifies which GPU to use.
Returns:
A pycuda Context.
"""
# pyre-ignore Undefined attribute [16]
device = cuda.Device(device_id)
cuda_context = device.make_context()
return cuda_context
# Initialize a global _DeviceContextStore. Almost always we will only need a single one.
global_device_context_store = _DeviceContextStore()

2
tests/implicitron/test_build.py
View File

@ -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()