Krzysztof Chalupka cb49550486 Add MeshRasterizerOpenGL
Summary:
Adding MeshRasterizerOpenGL, a faster alternative to MeshRasterizer. The new rasterizer follows the ideas from "Differentiable Surface Rendering via non-Differentiable Sampling".

The new rasterizer 20x faster on a 2M face mesh (try pose optimization on Nefertiti from https://www.cs.cmu.edu/~kmcrane/Projects/ModelRepository/!). The larger the mesh, the larger the speedup.

There are two main disadvantages:
* The new rasterizer works with an OpenGL backend, so requires pycuda.gl and pyopengl installed (though we avoided writing any C++ code, everything is in Python!)
* The new rasterizer is non-differentiable. However, you can still differentiate the rendering function if you use if with the new SplatterPhongShader which we recently added to PyTorch3D (see the original paper cited above).

Reviewed By: patricklabatut, jcjohnson

Differential Revision: D37698816

fbshipit-source-id: 54d120639d3cb001f096237807e54aced0acda25
2022-07-22 15:52:50 -07:00

449 lines
16 KiB
Python
Executable File

# 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)
try:
yield
finally:
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
def _torch_to_opengl(torch_tensor, cuda_context, cuda_buffer):
# CUDA access to the OpenGL buffer is only allowed within a map-unmap block.
cuda_context.push()
mapping_obj = cuda_buffer.map()
# data_ptr points to the OpenGL shader storage buffer memory.
data_ptr, sz = mapping_obj.device_ptr_and_size()
# Copy the torch tensor to the OpenGL buffer directly on device.
cuda_copy = cuda.Memcpy2D()
cuda_copy.set_src_device(torch_tensor.data_ptr())
cuda_copy.set_dst_device(data_ptr)
cuda_copy.width_in_bytes = cuda_copy.src_pitch = cuda_copy.dst_ptch = (
torch_tensor.shape[1] * 4
)
cuda_copy.height = torch_tensor.shape[0]
cuda_copy(False)
# Unmap and pop the cuda context to make sure OpenGL won't interfere with
# PyTorch ops down the line.
mapping_obj.unmap()
cuda_context.pop()
# Initialize a global _DeviceContextStore. Almost always we will only need a single one.
global_device_context_store = _DeviceContextStore()