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blender/intern/cycles/device/device_optix.cpp
Patrick Mours 03cdfc2ff6 Fix potential access to deleted memory in OptiX kernel loading code 
Calling "OptiXDevice::load_kernels" multiple times would call "optixPipelineDestroy" on a pipeline
pointer that may have already been deleted previously (since the PIP_SHADER_EVAL pipeline is only
created conditionally).
This change also avoids a CUDA kernel reload every time this is called. The CUDA kernels are
precompiled and don't change, so there is no need to reload them every time.
2019-11-25 18:36:55 +01:00

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/*
* Copyright 2019, NVIDIA Corporation.
* Copyright 2019, Blender Foundation.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifdef WITH_OPTIX
# include "device/device.h"
# include "device/device_intern.h"
# include "device/device_denoising.h"
# include "bvh/bvh.h"
# include "render/scene.h"
# include "render/mesh.h"
# include "render/object.h"
# include "render/buffers.h"
# include "util/util_md5.h"
# include "util/util_path.h"
# include "util/util_time.h"
# include "util/util_debug.h"
# include "util/util_logging.h"
# undef _WIN32_WINNT // Need minimum API support for Windows 7
# define _WIN32_WINNT _WIN32_WINNT_WIN7
# ifdef WITH_CUDA_DYNLOAD
# include <cuew.h>
// Do not use CUDA SDK headers when using CUEW
# define OPTIX_DONT_INCLUDE_CUDA
# endif
# include <optix_stubs.h>
# include <optix_function_table_definition.h>
CCL_NAMESPACE_BEGIN
/* Make sure this stays in sync with kernel_globals.h */
struct ShaderParams {
uint4 *input;
float4 *output;
int type;
int filter;
int sx;
int offset;
int sample;
};
struct KernelParams {
WorkTile tile;
KernelData data;
ShaderParams shader;
# define KERNEL_TEX(type, name) const type *name;
# include "kernel/kernel_textures.h"
# undef KERNEL_TEX
};
# define check_result_cuda(stmt) \
{ \
CUresult res = stmt; \
if (res != CUDA_SUCCESS) { \
const char *name; \
cuGetErrorName(res, &name); \
set_error(string_printf("OptiX CUDA error %s in %s, line %d", name, #stmt, __LINE__)); \
return; \
} \
} \
(void)0
# define check_result_cuda_ret(stmt) \
{ \
CUresult res = stmt; \
if (res != CUDA_SUCCESS) { \
const char *name; \
cuGetErrorName(res, &name); \
set_error(string_printf("OptiX CUDA error %s in %s, line %d", name, #stmt, __LINE__)); \
return false; \
} \
} \
(void)0
# define check_result_optix(stmt) \
{ \
enum OptixResult res = stmt; \
if (res != OPTIX_SUCCESS) { \
const char *name = optixGetErrorName(res); \
set_error(string_printf("OptiX error %s in %s, line %d", name, #stmt, __LINE__)); \
return; \
} \
} \
(void)0
# define check_result_optix_ret(stmt) \
{ \
enum OptixResult res = stmt; \
if (res != OPTIX_SUCCESS) { \
const char *name = optixGetErrorName(res); \
set_error(string_printf("OptiX error %s in %s, line %d", name, #stmt, __LINE__)); \
return false; \
} \
} \
(void)0
class OptiXDevice : public Device {
// List of OptiX program groups
enum {
PG_RGEN,
PG_MISS,
PG_HITD, // Default hit group
PG_HITL, // __BVH_LOCAL__ hit group
PG_HITS, // __SHADOW_RECORD_ALL__ hit group
# ifdef WITH_CYCLES_DEBUG
PG_EXCP,
# endif
PG_BAKE, // kernel_bake_evaluate
PG_DISP, // kernel_displace_evaluate
PG_BACK, // kernel_background_evaluate
NUM_PROGRAM_GROUPS
};
// List of OptiX pipelines
enum { PIP_PATH_TRACE, PIP_SHADER_EVAL, NUM_PIPELINES };
// A single shader binding table entry
struct SbtRecord {
char header[OPTIX_SBT_RECORD_HEADER_SIZE];
};
// Information stored about CUDA memory allocations
struct CUDAMem {
bool free_map_host = false;
CUarray array = NULL;
CUtexObject texobject = 0;
bool use_mapped_host = false;
};
// Helper class to manage current CUDA context
struct CUDAContextScope {
CUDAContextScope(CUcontext ctx)
{
cuCtxPushCurrent(ctx);
}
~CUDAContextScope()
{
cuCtxPopCurrent(NULL);
}
};
// Use a pool with multiple threads to support launches with multiple CUDA streams
TaskPool task_pool;
// CUDA/OptiX context handles
CUdevice cuda_device = 0;
CUcontext cuda_context = NULL;
vector<CUstream> cuda_stream;
OptixDeviceContext context = NULL;
// Need CUDA kernel module for some utility functions
CUmodule cuda_module = NULL;
CUmodule cuda_filter_module = NULL;
// All necessary OptiX kernels are in one module
OptixModule optix_module = NULL;
OptixPipeline pipelines[NUM_PIPELINES] = {};
bool motion_blur = false;
bool need_texture_info = false;
device_vector<SbtRecord> sbt_data;
device_vector<TextureInfo> texture_info;
device_only_memory<KernelParams> launch_params;
vector<device_only_memory<uint8_t>> blas;
OptixTraversableHandle tlas_handle = 0;
// TODO(pmours): This is copied from device_cuda.cpp, so move to common code eventually
int can_map_host = 0;
size_t map_host_used = 0;
size_t map_host_limit = 0;
size_t device_working_headroom = 32 * 1024 * 1024LL; // 32MB
size_t device_texture_headroom = 128 * 1024 * 1024LL; // 128MB
map<device_memory *, CUDAMem> cuda_mem_map;
bool move_texture_to_host = false;
public:
OptiXDevice(DeviceInfo &info_, Stats &stats_, Profiler &profiler_, bool background_)
: Device(info_, stats_, profiler_, background_),
sbt_data(this, "__sbt", MEM_READ_ONLY),
texture_info(this, "__texture_info", MEM_TEXTURE),
launch_params(this, "__params")
{
// Store number of CUDA streams in device info
info.cpu_threads = DebugFlags().optix.cuda_streams;
// Initialize CUDA driver API
check_result_cuda(cuInit(0));
// Retrieve the primary CUDA context for this device
check_result_cuda(cuDeviceGet(&cuda_device, info.num));
check_result_cuda(cuDevicePrimaryCtxRetain(&cuda_context, cuda_device));
// Make that CUDA context current
const CUDAContextScope scope(cuda_context);
// Limit amount of host mapped memory (see init_host_memory in device_cuda.cpp)
size_t default_limit = 4 * 1024 * 1024 * 1024LL;
size_t system_ram = system_physical_ram();
if (system_ram > 0) {
if (system_ram / 2 > default_limit) {
map_host_limit = system_ram - default_limit;
}
else {
map_host_limit = system_ram / 2;
}
}
else {
VLOG(1) << "Mapped host memory disabled, failed to get system RAM";
}
// Check device support for pinned host memory
check_result_cuda(
cuDeviceGetAttribute(&can_map_host, CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY, cuda_device));
// Create OptiX context for this device
OptixDeviceContextOptions options = {};
# ifdef WITH_CYCLES_LOGGING
options.logCallbackLevel = 4; // Fatal = 1, Error = 2, Warning = 3, Print = 4
options.logCallbackFunction =
[](unsigned int level, const char *, const char *message, void *) {
switch (level) {
case 1:
LOG_IF(FATAL, VLOG_IS_ON(1)) << message;
break;
case 2:
LOG_IF(ERROR, VLOG_IS_ON(1)) << message;
break;
case 3:
LOG_IF(WARNING, VLOG_IS_ON(1)) << message;
break;
case 4:
LOG_IF(INFO, VLOG_IS_ON(1)) << message;
break;
}
};
# endif
check_result_optix(optixDeviceContextCreate(cuda_context, &options, &context));
# ifdef WITH_CYCLES_LOGGING
check_result_optix(optixDeviceContextSetLogCallback(
context, options.logCallbackFunction, options.logCallbackData, options.logCallbackLevel));
# endif
// Create launch streams
cuda_stream.resize(info.cpu_threads);
for (int i = 0; i < info.cpu_threads; ++i)
check_result_cuda(cuStreamCreate(&cuda_stream[i], CU_STREAM_NON_BLOCKING));
// Fix weird compiler bug that assigns wrong size
launch_params.data_elements = sizeof(KernelParams);
// Allocate launch parameter buffer memory on device
launch_params.alloc_to_device(info.cpu_threads);
}
~OptiXDevice()
{
// Stop processing any more tasks
task_pool.stop();
// Clean up all memory before destroying context
blas.clear();
sbt_data.free();
texture_info.free();
launch_params.free();
// Make CUDA context current
const CUDAContextScope scope(cuda_context);
// Unload modules
if (cuda_module != NULL)
cuModuleUnload(cuda_module);
if (cuda_filter_module != NULL)
cuModuleUnload(cuda_filter_module);
if (optix_module != NULL)
optixModuleDestroy(optix_module);
for (unsigned int i = 0; i < NUM_PIPELINES; ++i)
if (pipelines[i] != NULL)
optixPipelineDestroy(pipelines[i]);
// Destroy launch streams
for (int i = 0; i < info.cpu_threads; ++i)
cuStreamDestroy(cuda_stream[i]);
// Destroy OptiX and CUDA context
optixDeviceContextDestroy(context);
cuDevicePrimaryCtxRelease(cuda_device);
}
private:
bool show_samples() const override
{
// Only show samples if not rendering multiple tiles in parallel
return info.cpu_threads == 1;
}
BVHLayoutMask get_bvh_layout_mask() const override
{
// OptiX has its own internal acceleration structure format
return BVH_LAYOUT_OPTIX;
}
bool load_kernels(const DeviceRequestedFeatures &requested_features) override
{
if (have_error())
return false; // Abort early if context creation failed already
// Disable baking for now, since its kernel is not well-suited for inlining and is very slow
if (requested_features.use_baking) {
set_error("OptiX implementation does not support baking yet");
return false;
}
// Disable shader raytracing support for now, since continuation callables are slow
if (requested_features.use_shader_raytrace) {
set_error("OptiX implementation does not support shader raytracing yet");
return false;
}
const CUDAContextScope scope(cuda_context);
// Unload existing OptiX module and pipelines first
if (optix_module != NULL) {
optixModuleDestroy(optix_module);
optix_module = NULL;
}
for (unsigned int i = 0; i < NUM_PIPELINES; ++i) {
if (pipelines[i] != NULL) {
optixPipelineDestroy(pipelines[i]);
pipelines[i] = NULL;
}
}
OptixModuleCompileOptions module_options;
module_options.maxRegisterCount = 0; // Do not set an explicit register limit
# ifdef WITH_CYCLES_DEBUG
module_options.optLevel = OPTIX_COMPILE_OPTIMIZATION_LEVEL_0;
module_options.debugLevel = OPTIX_COMPILE_DEBUG_LEVEL_FULL;
# else
module_options.optLevel = OPTIX_COMPILE_OPTIMIZATION_LEVEL_3;
module_options.debugLevel = OPTIX_COMPILE_DEBUG_LEVEL_LINEINFO;
# endif
OptixPipelineCompileOptions pipeline_options;
// Default to no motion blur and two-level graph, since it is the fastest option
pipeline_options.usesMotionBlur = false;
pipeline_options.traversableGraphFlags =
OPTIX_TRAVERSABLE_GRAPH_FLAG_ALLOW_SINGLE_LEVEL_INSTANCING;
pipeline_options.numPayloadValues = 6;
pipeline_options.numAttributeValues = 2; // u, v
# ifdef WITH_CYCLES_DEBUG
pipeline_options.exceptionFlags = OPTIX_EXCEPTION_FLAG_STACK_OVERFLOW |
OPTIX_EXCEPTION_FLAG_TRACE_DEPTH;
# else
pipeline_options.exceptionFlags = OPTIX_EXCEPTION_FLAG_NONE;
# endif
pipeline_options.pipelineLaunchParamsVariableName = "__params"; // See kernel_globals.h
// Keep track of whether motion blur is enabled, so to enable/disable motion in BVH builds
// This is necessary since objects may be reported to have motion if the Vector pass is
// active, but may still need to be rendered without motion blur if that isn't active as well
motion_blur = requested_features.use_object_motion;
if (motion_blur) {
pipeline_options.usesMotionBlur = true;
// Motion blur can insert motion transforms into the traversal graph
// It is no longer a two-level graph then, so need to set flags to allow any configuration
pipeline_options.traversableGraphFlags = OPTIX_TRAVERSABLE_GRAPH_FLAG_ALLOW_ANY;
}
{ // Load and compile PTX module with OptiX kernels
string ptx_data;
const string ptx_filename = "lib/kernel_optix.ptx";
if (!path_read_text(path_get(ptx_filename), ptx_data)) {
set_error("Failed loading OptiX kernel " + ptx_filename + ".");
return false;
}
check_result_optix_ret(optixModuleCreateFromPTX(context,
&module_options,
&pipeline_options,
ptx_data.data(),
ptx_data.size(),
nullptr,
0,
&optix_module));
}
{ // Load CUDA modules because we need some of the utility kernels
int major, minor;
cuDeviceGetAttribute(&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, info.num);
cuDeviceGetAttribute(&minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, info.num);
if (cuda_module == NULL) { // Avoid reloading module if it was already loaded
string cubin_data;
const string cubin_filename = string_printf("lib/kernel_sm_%d%d.cubin", major, minor);
if (!path_read_text(path_get(cubin_filename), cubin_data)) {
set_error("Failed loading pre-compiled CUDA kernel " + cubin_filename + ".");
return false;
}
check_result_cuda_ret(cuModuleLoadData(&cuda_module, cubin_data.data()));
}
if (requested_features.use_denoising && cuda_filter_module == NULL) {
string filter_data;
const string filter_filename = string_printf("lib/filter_sm_%d%d.cubin", major, minor);
if (!path_read_text(path_get(filter_filename), filter_data)) {
set_error("Failed loading pre-compiled CUDA filter kernel " + filter_filename + ".");
return false;
}
check_result_cuda_ret(cuModuleLoadData(&cuda_filter_module, filter_data.data()));
}
}
// Create program groups
OptixProgramGroup groups[NUM_PROGRAM_GROUPS] = {};
OptixProgramGroupDesc group_descs[NUM_PROGRAM_GROUPS] = {};
OptixProgramGroupOptions group_options = {}; // There are no options currently
group_descs[PG_RGEN].kind = OPTIX_PROGRAM_GROUP_KIND_RAYGEN;
group_descs[PG_RGEN].raygen.module = optix_module;
// Ignore branched integrator for now (see "requested_features.use_integrator_branched")
group_descs[PG_RGEN].raygen.entryFunctionName = "__raygen__kernel_optix_path_trace";
group_descs[PG_MISS].kind = OPTIX_PROGRAM_GROUP_KIND_MISS;
group_descs[PG_MISS].miss.module = optix_module;
group_descs[PG_MISS].miss.entryFunctionName = "__miss__kernel_optix_miss";
group_descs[PG_HITD].kind = OPTIX_PROGRAM_GROUP_KIND_HITGROUP;
group_descs[PG_HITD].hitgroup.moduleCH = optix_module;
group_descs[PG_HITD].hitgroup.entryFunctionNameCH = "__closesthit__kernel_optix_hit";
group_descs[PG_HITD].hitgroup.moduleAH = optix_module;
group_descs[PG_HITD].hitgroup.entryFunctionNameAH = "__anyhit__kernel_optix_visibility_test";
group_descs[PG_HITS].kind = OPTIX_PROGRAM_GROUP_KIND_HITGROUP;
group_descs[PG_HITS].hitgroup.moduleAH = optix_module;
group_descs[PG_HITS].hitgroup.entryFunctionNameAH = "__anyhit__kernel_optix_shadow_all_hit";
if (requested_features.use_hair) {
// Add curve intersection programs
group_descs[PG_HITD].hitgroup.moduleIS = optix_module;
group_descs[PG_HITD].hitgroup.entryFunctionNameIS = "__intersection__curve";
group_descs[PG_HITS].hitgroup.moduleIS = optix_module;
group_descs[PG_HITS].hitgroup.entryFunctionNameIS = "__intersection__curve";
}
if (requested_features.use_subsurface || requested_features.use_shader_raytrace) {
// Add hit group for local intersections
group_descs[PG_HITL].kind = OPTIX_PROGRAM_GROUP_KIND_HITGROUP;
group_descs[PG_HITL].hitgroup.moduleAH = optix_module;
group_descs[PG_HITL].hitgroup.entryFunctionNameAH = "__anyhit__kernel_optix_local_hit";
}
# ifdef WITH_CYCLES_DEBUG
group_descs[PG_EXCP].kind = OPTIX_PROGRAM_GROUP_KIND_EXCEPTION;
group_descs[PG_EXCP].exception.module = optix_module;
group_descs[PG_EXCP].exception.entryFunctionName = "__exception__kernel_optix_exception";
# endif
if (requested_features.use_baking) {
group_descs[PG_BAKE].kind = OPTIX_PROGRAM_GROUP_KIND_RAYGEN;
group_descs[PG_BAKE].raygen.module = optix_module;
group_descs[PG_BAKE].raygen.entryFunctionName = "__raygen__kernel_optix_bake";
}
if (requested_features.use_true_displacement) {
group_descs[PG_DISP].kind = OPTIX_PROGRAM_GROUP_KIND_RAYGEN;
group_descs[PG_DISP].raygen.module = optix_module;
group_descs[PG_DISP].raygen.entryFunctionName = "__raygen__kernel_optix_displace";
}
if (requested_features.use_background_light) {
group_descs[PG_BACK].kind = OPTIX_PROGRAM_GROUP_KIND_RAYGEN;
group_descs[PG_BACK].raygen.module = optix_module;
group_descs[PG_BACK].raygen.entryFunctionName = "__raygen__kernel_optix_background";
}
check_result_optix_ret(optixProgramGroupCreate(
context, group_descs, NUM_PROGRAM_GROUPS, &group_options, nullptr, 0, groups));
// Get program stack sizes
OptixStackSizes stack_size[NUM_PROGRAM_GROUPS] = {};
// Set up SBT, which in this case is used only to select between different programs
sbt_data.alloc(NUM_PROGRAM_GROUPS);
memset(sbt_data.host_pointer, 0, sizeof(SbtRecord) * NUM_PROGRAM_GROUPS);
for (unsigned int i = 0; i < NUM_PROGRAM_GROUPS; ++i) {
check_result_optix_ret(optixSbtRecordPackHeader(groups[i], &sbt_data[i]));
check_result_optix_ret(optixProgramGroupGetStackSize(groups[i], &stack_size[i]));
}
sbt_data.copy_to_device(); // Upload SBT to device
// Calculate maximum trace continuation stack size
unsigned int trace_css = stack_size[PG_HITD].cssCH;
// This is based on the maximum of closest-hit and any-hit/intersection programs
trace_css = max(trace_css, stack_size[PG_HITD].cssIS + stack_size[PG_HITD].cssAH);
trace_css = max(trace_css, stack_size[PG_HITL].cssIS + stack_size[PG_HITL].cssAH);
trace_css = max(trace_css, stack_size[PG_HITS].cssIS + stack_size[PG_HITS].cssAH);
OptixPipelineLinkOptions link_options;
link_options.maxTraceDepth = 1;
# ifdef WITH_CYCLES_DEBUG
link_options.debugLevel = OPTIX_COMPILE_DEBUG_LEVEL_FULL;
# else
link_options.debugLevel = OPTIX_COMPILE_DEBUG_LEVEL_LINEINFO;
# endif
link_options.overrideUsesMotionBlur = pipeline_options.usesMotionBlur;
{ // Create path tracing pipeline
OptixProgramGroup pipeline_groups[] = {
groups[PG_RGEN],
groups[PG_MISS],
groups[PG_HITD],
groups[PG_HITS],
groups[PG_HITL],
# ifdef WITH_CYCLES_DEBUG
groups[PG_EXCP],
# endif
};
check_result_optix_ret(
optixPipelineCreate(context,
&pipeline_options,
&link_options,
pipeline_groups,
(sizeof(pipeline_groups) / sizeof(pipeline_groups[0])),
nullptr,
0,
&pipelines[PIP_PATH_TRACE]));
// Combine ray generation and trace continuation stack size
const unsigned int css = stack_size[PG_RGEN].cssRG + link_options.maxTraceDepth * trace_css;
// Set stack size depending on pipeline options
check_result_optix_ret(optixPipelineSetStackSize(
pipelines[PIP_PATH_TRACE], 0, 0, css, (pipeline_options.usesMotionBlur ? 3 : 2)));
}
// Only need to create shader evaluation pipeline if one of these features is used:
const bool use_shader_eval_pipeline = requested_features.use_baking ||
requested_features.use_background_light ||
requested_features.use_true_displacement;
if (use_shader_eval_pipeline) { // Create shader evaluation pipeline
OptixProgramGroup pipeline_groups[] = {
groups[PG_BAKE],
groups[PG_DISP],
groups[PG_BACK],
groups[PG_MISS],
groups[PG_HITD],
groups[PG_HITS],
groups[PG_HITL],
# ifdef WITH_CYCLES_DEBUG
groups[PG_EXCP],
# endif
};
check_result_optix_ret(
optixPipelineCreate(context,
&pipeline_options,
&link_options,
pipeline_groups,
(sizeof(pipeline_groups) / sizeof(pipeline_groups[0])),
nullptr,
0,
&pipelines[PIP_SHADER_EVAL]));
// Calculate continuation stack size based on the maximum of all ray generation stack sizes
const unsigned int css = max(stack_size[PG_BAKE].cssRG,
max(stack_size[PG_DISP].cssRG, stack_size[PG_BACK].cssRG)) +
link_options.maxTraceDepth * trace_css;
check_result_optix_ret(optixPipelineSetStackSize(
pipelines[PIP_SHADER_EVAL], 0, 0, css, (pipeline_options.usesMotionBlur ? 3 : 2)));
}
// Clean up program group objects
for (unsigned int i = 0; i < NUM_PROGRAM_GROUPS; ++i) {
optixProgramGroupDestroy(groups[i]);
}
return true;
}
void thread_run(DeviceTask &task, int thread_index) // Main task entry point
{
if (have_error())
return; // Abort early if there was an error previously
if (task.type == DeviceTask::RENDER) {
RenderTile tile;
while (task.acquire_tile(this, tile)) {
if (tile.task == RenderTile::PATH_TRACE)
launch_render(task, tile, thread_index);
else if (tile.task == RenderTile::DENOISE)
launch_denoise(task, tile, thread_index);
task.release_tile(tile);
if (task.get_cancel() && !task.need_finish_queue)
break; // User requested cancellation
else if (have_error())
break; // Abort rendering when encountering an error
}
}
else if (task.type == DeviceTask::SHADER) {
launch_shader_eval(task, thread_index);
}
else if (task.type == DeviceTask::FILM_CONVERT) {
launch_film_convert(task, thread_index);
}
}
void launch_render(DeviceTask &task, RenderTile &rtile, int thread_index)
{
assert(thread_index < launch_params.data_size);
// Keep track of total render time of this tile
const scoped_timer timer(&rtile.buffers->render_time);
WorkTile wtile;
wtile.x = rtile.x;
wtile.y = rtile.y;
wtile.w = rtile.w;
wtile.h = rtile.h;
wtile.offset = rtile.offset;
wtile.stride = rtile.stride;
wtile.buffer = (float *)rtile.buffer;
const int end_sample = rtile.start_sample + rtile.num_samples;
// Keep this number reasonable to avoid running into TDRs
const int step_samples = (info.display_device ? 8 : 32);
// Offset into launch params buffer so that streams use separate data
device_ptr launch_params_ptr = launch_params.device_pointer +
thread_index * launch_params.data_elements;
const CUDAContextScope scope(cuda_context);
for (int sample = rtile.start_sample; sample < end_sample; sample += step_samples) {
// Copy work tile information to device
wtile.num_samples = min(step_samples, end_sample - sample);
wtile.start_sample = sample;
check_result_cuda(cuMemcpyHtoDAsync(launch_params_ptr + offsetof(KernelParams, tile),
&wtile,
sizeof(wtile),
cuda_stream[thread_index]));
OptixShaderBindingTable sbt_params = {};
sbt_params.raygenRecord = sbt_data.device_pointer + PG_RGEN * sizeof(SbtRecord);
# ifdef WITH_CYCLES_DEBUG
sbt_params.exceptionRecord = sbt_data.device_pointer + PG_EXCP * sizeof(SbtRecord);
# endif
sbt_params.missRecordBase = sbt_data.device_pointer + PG_MISS * sizeof(SbtRecord);
sbt_params.missRecordStrideInBytes = sizeof(SbtRecord);
sbt_params.missRecordCount = 1;
sbt_params.hitgroupRecordBase = sbt_data.device_pointer + PG_HITD * sizeof(SbtRecord);
sbt_params.hitgroupRecordStrideInBytes = sizeof(SbtRecord);
sbt_params.hitgroupRecordCount = 3; // PG_HITD, PG_HITL, PG_HITS
// Launch the ray generation program
check_result_optix(optixLaunch(pipelines[PIP_PATH_TRACE],
cuda_stream[thread_index],
launch_params_ptr,
launch_params.data_elements,
&sbt_params,
// Launch with samples close to each other for better locality
wtile.w * wtile.num_samples,
wtile.h,
1));
// Wait for launch to finish
check_result_cuda(cuStreamSynchronize(cuda_stream[thread_index]));
// Update current sample, so it is displayed correctly
rtile.sample = wtile.start_sample + wtile.num_samples;
// Update task progress after the kernel completed rendering
task.update_progress(&rtile, wtile.w * wtile.h * wtile.num_samples);
if (task.get_cancel() && !task.need_finish_queue)
return; // Cancel rendering
}
}
void launch_denoise(DeviceTask &task, RenderTile &rtile, int thread_index)
{
const CUDAContextScope scope(cuda_context);
// Run CUDA denoising kernels
DenoisingTask denoising(this, task);
denoising.functions.construct_transform = function_bind(
&OptiXDevice::denoising_construct_transform, this, &denoising, thread_index);
denoising.functions.accumulate = function_bind(
&OptiXDevice::denoising_accumulate, this, _1, _2, _3, _4, &denoising, thread_index);
denoising.functions.solve = function_bind(
&OptiXDevice::denoising_solve, this, _1, &denoising, thread_index);
denoising.functions.divide_shadow = function_bind(
&OptiXDevice::denoising_divide_shadow, this, _1, _2, _3, _4, _5, &denoising, thread_index);
denoising.functions.non_local_means = function_bind(
&OptiXDevice::denoising_non_local_means, this, _1, _2, _3, _4, &denoising, thread_index);
denoising.functions.combine_halves = function_bind(&OptiXDevice::denoising_combine_halves,
this,
_1,
_2,
_3,
_4,
_5,
_6,
&denoising,
thread_index);
denoising.functions.get_feature = function_bind(
&OptiXDevice::denoising_get_feature, this, _1, _2, _3, _4, _5, &denoising, thread_index);
denoising.functions.write_feature = function_bind(
&OptiXDevice::denoising_write_feature, this, _1, _2, _3, &denoising, thread_index);
denoising.functions.detect_outliers = function_bind(
&OptiXDevice::denoising_detect_outliers, this, _1, _2, _3, _4, &denoising, thread_index);
denoising.filter_area = make_int4(rtile.x, rtile.y, rtile.w, rtile.h);
denoising.render_buffer.samples = rtile.sample = rtile.start_sample + rtile.num_samples;
denoising.buffer.gpu_temporary_mem = true;
denoising.run_denoising(&rtile);
task.update_progress(&rtile, rtile.w * rtile.h);
}
void launch_shader_eval(DeviceTask &task, int thread_index)
{
unsigned int rgen_index = PG_BACK;
if (task.shader_eval_type >= SHADER_EVAL_BAKE)
rgen_index = PG_BAKE;
if (task.shader_eval_type == SHADER_EVAL_DISPLACE)
rgen_index = PG_DISP;
const CUDAContextScope scope(cuda_context);
device_ptr launch_params_ptr = launch_params.device_pointer +
thread_index * launch_params.data_elements;
for (int sample = 0; sample < task.num_samples; ++sample) {
ShaderParams params;
params.input = (uint4 *)task.shader_input;
params.output = (float4 *)task.shader_output;
params.type = task.shader_eval_type;
params.filter = task.shader_filter;
params.sx = task.shader_x;
params.offset = task.offset;
params.sample = sample;
check_result_cuda(cuMemcpyHtoDAsync(launch_params_ptr + offsetof(KernelParams, shader),
&params,
sizeof(params),
cuda_stream[thread_index]));
OptixShaderBindingTable sbt_params = {};
sbt_params.raygenRecord = sbt_data.device_pointer + rgen_index * sizeof(SbtRecord);
# ifdef WITH_CYCLES_DEBUG
sbt_params.exceptionRecord = sbt_data.device_pointer + PG_EXCP * sizeof(SbtRecord);
# endif
sbt_params.missRecordBase = sbt_data.device_pointer + PG_MISS * sizeof(SbtRecord);
sbt_params.missRecordStrideInBytes = sizeof(SbtRecord);
sbt_params.missRecordCount = 1;
sbt_params.hitgroupRecordBase = sbt_data.device_pointer + PG_HITD * sizeof(SbtRecord);
sbt_params.hitgroupRecordStrideInBytes = sizeof(SbtRecord);
sbt_params.hitgroupRecordCount = 3; // PG_HITD, PG_HITL, PG_HITS
check_result_optix(optixLaunch(pipelines[PIP_SHADER_EVAL],
cuda_stream[thread_index],
launch_params_ptr,
launch_params.data_elements,
&sbt_params,
task.shader_w,
1,
1));
check_result_cuda(cuStreamSynchronize(cuda_stream[thread_index]));
task.update_progress(NULL);
}
}
void launch_film_convert(DeviceTask &task, int thread_index)
{
const CUDAContextScope scope(cuda_context);
CUfunction film_convert_func;
check_result_cuda(cuModuleGetFunction(&film_convert_func,
cuda_module,
task.rgba_byte ? "kernel_cuda_convert_to_byte" :
"kernel_cuda_convert_to_half_float"));
float sample_scale = 1.0f / (task.sample + 1);
CUdeviceptr rgba = (task.rgba_byte ? task.rgba_byte : task.rgba_half);
void *args[] = {&rgba,
&task.buffer,
&sample_scale,
&task.x,
&task.y,
&task.w,
&task.h,
&task.offset,
&task.stride};
int threads_per_block;
check_result_cuda(cuFuncGetAttribute(
&threads_per_block, CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK, film_convert_func));
const int num_threads_x = (int)sqrt(threads_per_block);
const int num_blocks_x = (task.w + num_threads_x - 1) / num_threads_x;
const int num_threads_y = (int)sqrt(threads_per_block);
const int num_blocks_y = (task.h + num_threads_y - 1) / num_threads_y;
check_result_cuda(cuLaunchKernel(film_convert_func,
num_blocks_x,
num_blocks_y,
1, /* blocks */
num_threads_x,
num_threads_y,
1, /* threads */
0,
cuda_stream[thread_index],
args,
0));
check_result_cuda(cuStreamSynchronize(cuda_stream[thread_index]));
task.update_progress(NULL);
}
bool build_optix_bvh(const OptixBuildInput &build_input,
uint16_t num_motion_steps,
device_memory &out_data,
OptixTraversableHandle &out_handle)
{
out_handle = 0;
const CUDAContextScope scope(cuda_context);
// Compute memory usage
OptixAccelBufferSizes sizes = {};
OptixAccelBuildOptions options;
options.operation = OPTIX_BUILD_OPERATION_BUILD;
options.buildFlags = OPTIX_BUILD_FLAG_PREFER_FAST_TRACE;
options.motionOptions.numKeys = num_motion_steps;
options.motionOptions.flags = OPTIX_MOTION_FLAG_START_VANISH | OPTIX_MOTION_FLAG_END_VANISH;
options.motionOptions.timeBegin = 0.0f;
options.motionOptions.timeEnd = 1.0f;
check_result_optix_ret(
optixAccelComputeMemoryUsage(context, &options, &build_input, 1, &sizes));
// Allocate required output buffers
device_only_memory<char> temp_mem(this, "temp_build_mem");
temp_mem.alloc_to_device(sizes.tempSizeInBytes);
out_data.type = MEM_DEVICE_ONLY;
out_data.data_type = TYPE_UNKNOWN;
out_data.data_elements = 1;
out_data.data_size = sizes.outputSizeInBytes;
mem_alloc(out_data);
// Finally build the acceleration structure
check_result_optix_ret(optixAccelBuild(context,
NULL,
&options,
&build_input,
1,
temp_mem.device_pointer,
sizes.tempSizeInBytes,
out_data.device_pointer,
sizes.outputSizeInBytes,
&out_handle,
NULL,
0));
// Wait for all operations to finish
check_result_cuda_ret(cuStreamSynchronize(NULL));
return true;
}
bool build_optix_bvh(BVH *bvh, device_memory &out_data) override
{
assert(bvh->params.top_level);
unsigned int num_instances = 0;
unordered_map<Mesh *, vector<OptixTraversableHandle>> meshes;
// Clear all previous AS
blas.clear();
// Build bottom level acceleration structures (BLAS)
// Note: Always keep this logic in sync with bvh_optix.cpp!
for (Object *ob : bvh->objects) {
// Skip meshes for which acceleration structure already exists
if (meshes.find(ob->mesh) != meshes.end())
continue;
Mesh *const mesh = ob->mesh;
vector<OptixTraversableHandle> handles;
// Build BLAS for curve primitives
if (bvh->params.primitive_mask & PRIMITIVE_ALL_CURVE && mesh->num_curves() > 0) {
const size_t num_curves = mesh->num_curves();
const size_t num_segments = mesh->num_segments();
size_t num_motion_steps = 1;
Attribute *motion_keys = mesh->curve_attributes.find(ATTR_STD_MOTION_VERTEX_POSITION);
if (motion_blur && mesh->use_motion_blur && motion_keys) {
num_motion_steps = mesh->motion_steps;
}
device_vector<OptixAabb> aabb_data(this, "temp_aabb_data", MEM_READ_ONLY);
aabb_data.alloc(num_segments * num_motion_steps);
// Get AABBs for each motion step
for (size_t step = 0; step < num_motion_steps; ++step) {
// The center step for motion vertices is not stored in the attribute
const float3 *keys = mesh->curve_keys.data();
size_t center_step = (num_motion_steps - 1) / 2;
if (step != center_step) {
size_t attr_offset = (step > center_step) ? step - 1 : step;
// Technically this is a float4 array, but sizeof(float3) is the same as sizeof(float4)
keys = motion_keys->data_float3() + attr_offset * mesh->curve_keys.size();
}
size_t i = step * num_segments;
for (size_t j = 0; j < num_curves; ++j) {
const Mesh::Curve c = mesh->get_curve(j);
for (size_t k = 0; k < c.num_segments(); ++i, ++k) {
BoundBox bounds = BoundBox::empty;
c.bounds_grow(k, keys, mesh->curve_radius.data(), bounds);
aabb_data[i].minX = bounds.min.x;
aabb_data[i].minY = bounds.min.y;
aabb_data[i].minZ = bounds.min.z;
aabb_data[i].maxX = bounds.max.x;
aabb_data[i].maxY = bounds.max.y;
aabb_data[i].maxZ = bounds.max.z;
}
}
}
// Upload AABB data to GPU
aabb_data.copy_to_device();
vector<device_ptr> aabb_ptrs;
aabb_ptrs.reserve(num_motion_steps);
for (size_t step = 0; step < num_motion_steps; ++step) {
aabb_ptrs.push_back(aabb_data.device_pointer + step * num_segments * sizeof(OptixAabb));
}
// Disable visibility test anyhit program, since it is already checked during intersection
// Those trace calls that require anyhit can force it with OPTIX_RAY_FLAG_ENFORCE_ANYHIT
unsigned int build_flags = OPTIX_GEOMETRY_FLAG_DISABLE_ANYHIT;
OptixBuildInput build_input = {};
build_input.type = OPTIX_BUILD_INPUT_TYPE_CUSTOM_PRIMITIVES;
build_input.aabbArray.aabbBuffers = (CUdeviceptr *)aabb_ptrs.data();
build_input.aabbArray.numPrimitives = num_segments;
build_input.aabbArray.strideInBytes = sizeof(OptixAabb);
build_input.aabbArray.flags = &build_flags;
build_input.aabbArray.numSbtRecords = 1;
build_input.aabbArray.primitiveIndexOffset = mesh->prim_offset;
// Allocate memory for new BLAS and build it
blas.emplace_back(this, "blas");
handles.emplace_back();
if (!build_optix_bvh(build_input, num_motion_steps, blas.back(), handles.back()))
return false;
}
// Build BLAS for triangle primitives
if (bvh->params.primitive_mask & PRIMITIVE_ALL_TRIANGLE && mesh->num_triangles() > 0) {
const size_t num_verts = mesh->verts.size();
size_t num_motion_steps = 1;
Attribute *motion_keys = mesh->attributes.find(ATTR_STD_MOTION_VERTEX_POSITION);
if (motion_blur && mesh->use_motion_blur && motion_keys) {
num_motion_steps = mesh->motion_steps;
}
device_vector<int> index_data(this, "temp_index_data", MEM_READ_ONLY);
index_data.alloc(mesh->triangles.size());
memcpy(index_data.data(), mesh->triangles.data(), mesh->triangles.size() * sizeof(int));
device_vector<float3> vertex_data(this, "temp_vertex_data", MEM_READ_ONLY);
vertex_data.alloc(num_verts * num_motion_steps);
for (size_t step = 0; step < num_motion_steps; ++step) {
const float3 *verts = mesh->verts.data();
size_t center_step = (num_motion_steps - 1) / 2;
// The center step for motion vertices is not stored in the attribute
if (step != center_step) {
verts = motion_keys->data_float3() +
(step > center_step ? step - 1 : step) * num_verts;
}
memcpy(vertex_data.data() + num_verts * step, verts, num_verts * sizeof(float3));
}
// Upload triangle data to GPU
index_data.copy_to_device();
vertex_data.copy_to_device();
vector<device_ptr> vertex_ptrs;
vertex_ptrs.reserve(num_motion_steps);
for (size_t step = 0; step < num_motion_steps; ++step) {
vertex_ptrs.push_back(vertex_data.device_pointer + num_verts * step * sizeof(float3));
}
// No special build flags for triangle primitives
unsigned int build_flags = OPTIX_GEOMETRY_FLAG_NONE;
OptixBuildInput build_input = {};
build_input.type = OPTIX_BUILD_INPUT_TYPE_TRIANGLES;
build_input.triangleArray.vertexBuffers = (CUdeviceptr *)vertex_ptrs.data();
build_input.triangleArray.numVertices = num_verts;
build_input.triangleArray.vertexFormat = OPTIX_VERTEX_FORMAT_FLOAT3;
build_input.triangleArray.vertexStrideInBytes = sizeof(float3);
build_input.triangleArray.indexBuffer = index_data.device_pointer;
build_input.triangleArray.numIndexTriplets = mesh->num_triangles();
build_input.triangleArray.indexFormat = OPTIX_INDICES_FORMAT_UNSIGNED_INT3;
build_input.triangleArray.indexStrideInBytes = 3 * sizeof(int);
build_input.triangleArray.flags = &build_flags;
// The SBT does not store per primitive data since Cycles already allocates separate
// buffers for that purpose. OptiX does not allow this to be zero though, so just pass in
// one and rely on that having the same meaning in this case.
build_input.triangleArray.numSbtRecords = 1;
// Triangle primitives are packed right after the curve primitives of this mesh
build_input.triangleArray.primitiveIndexOffset = mesh->prim_offset + mesh->num_segments();
// Allocate memory for new BLAS and build it
blas.emplace_back(this, "blas");
handles.emplace_back();
if (!build_optix_bvh(build_input, num_motion_steps, blas.back(), handles.back()))
return false;
}
meshes.insert({mesh, handles});
}
// Fill instance descriptions
device_vector<OptixAabb> aabbs(this, "tlas_aabbs", MEM_READ_ONLY);
aabbs.alloc(bvh->objects.size() * 2);
device_vector<OptixInstance> instances(this, "tlas_instances", MEM_READ_ONLY);
instances.alloc(bvh->objects.size() * 2);
for (Object *ob : bvh->objects) {
// Skip non-traceable objects
if (!ob->is_traceable())
continue;
// Create separate instance for triangle/curve meshes of an object
for (OptixTraversableHandle handle : meshes[ob->mesh]) {
OptixAabb &aabb = aabbs[num_instances];
aabb.minX = ob->bounds.min.x;
aabb.minY = ob->bounds.min.y;
aabb.minZ = ob->bounds.min.z;
aabb.maxX = ob->bounds.max.x;
aabb.maxY = ob->bounds.max.y;
aabb.maxZ = ob->bounds.max.z;
OptixInstance &instance = instances[num_instances++];
memset(&instance, 0, sizeof(instance));
// Clear transform to identity matrix
instance.transform[0] = 1.0f;
instance.transform[5] = 1.0f;
instance.transform[10] = 1.0f;
// Set user instance ID to object index
instance.instanceId = ob->get_device_index();
// Volumes have a special bit set in the visibility mask so a trace can mask only volumes
// See 'scene_intersect_volume' in bvh.h
instance.visibilityMask = (ob->mesh->has_volume ? 3 : 1);
// Insert motion traversable if object has motion
if (motion_blur && ob->use_motion()) {
blas.emplace_back(this, "motion_transform");
device_only_memory<uint8_t> &motion_transform_gpu = blas.back();
motion_transform_gpu.alloc_to_device(sizeof(OptixSRTMotionTransform) +
(max(ob->motion.size(), 2) - 2) *
sizeof(OptixSRTData));
// Allocate host side memory for motion transform and fill it with transform data
OptixSRTMotionTransform &motion_transform = *reinterpret_cast<OptixSRTMotionTransform *>(
motion_transform_gpu.host_pointer = new uint8_t[motion_transform_gpu.memory_size()]);
motion_transform.child = handle;
motion_transform.motionOptions.numKeys = ob->motion.size();
motion_transform.motionOptions.flags = OPTIX_MOTION_FLAG_NONE;
motion_transform.motionOptions.timeBegin = 0.0f;
motion_transform.motionOptions.timeEnd = 1.0f;
OptixSRTData *const srt_data = motion_transform.srtData;
array<DecomposedTransform> decomp(ob->motion.size());
transform_motion_decompose(decomp.data(), ob->motion.data(), ob->motion.size());
for (size_t i = 0; i < ob->motion.size(); ++i) {
// scaling
srt_data[i].a = decomp[i].z.x; // scale.x.y
srt_data[i].b = decomp[i].z.y; // scale.x.z
srt_data[i].c = decomp[i].w.x; // scale.y.z
srt_data[i].sx = decomp[i].y.w; // scale.x.x
srt_data[i].sy = decomp[i].z.w; // scale.y.y
srt_data[i].sz = decomp[i].w.w; // scale.z.z
srt_data[i].pvx = 0;
srt_data[i].pvy = 0;
srt_data[i].pvz = 0;
// rotation
srt_data[i].qx = decomp[i].x.x;
srt_data[i].qy = decomp[i].x.y;
srt_data[i].qz = decomp[i].x.z;
srt_data[i].qw = decomp[i].x.w;
// transform
srt_data[i].tx = decomp[i].y.x;
srt_data[i].ty = decomp[i].y.y;
srt_data[i].tz = decomp[i].y.z;
}
// Upload motion transform to GPU
mem_copy_to(motion_transform_gpu);
delete[] reinterpret_cast<uint8_t *>(motion_transform_gpu.host_pointer);
motion_transform_gpu.host_pointer = 0;
// Disable instance transform if object uses motion transform already
instance.flags = OPTIX_INSTANCE_FLAG_DISABLE_TRANSFORM;
// Get traversable handle to motion transform
optixConvertPointerToTraversableHandle(context,
motion_transform_gpu.device_pointer,
OPTIX_TRAVERSABLE_TYPE_SRT_MOTION_TRANSFORM,
&instance.traversableHandle);
}
else {
instance.traversableHandle = handle;
if (ob->mesh->is_instanced()) {
// Set transform matrix
memcpy(instance.transform, &ob->tfm, sizeof(instance.transform));
}
else {
// Disable instance transform if mesh already has it applied to vertex data
instance.flags = OPTIX_INSTANCE_FLAG_DISABLE_TRANSFORM;
// Non-instanced objects read ID from prim_object, so
// distinguish them from instanced objects with high bit set
instance.instanceId |= 0x800000;
}
}
}
}
// Upload instance descriptions
aabbs.resize(num_instances);
aabbs.copy_to_device();
instances.resize(num_instances);
instances.copy_to_device();
// Build top-level acceleration structure
OptixBuildInput build_input = {};
build_input.type = OPTIX_BUILD_INPUT_TYPE_INSTANCES;
build_input.instanceArray.instances = instances.device_pointer;
build_input.instanceArray.numInstances = num_instances;
build_input.instanceArray.aabbs = aabbs.device_pointer;
build_input.instanceArray.numAabbs = num_instances;
return build_optix_bvh(build_input, 0 /* TLAS has no motion itself */, out_data, tlas_handle);
}
void update_texture_info()
{
if (need_texture_info) {
texture_info.copy_to_device();
need_texture_info = false;
}
}
void update_launch_params(const char *name, size_t offset, void *data, size_t data_size)
{
const CUDAContextScope scope(cuda_context);
for (int i = 0; i < info.cpu_threads; ++i)
check_result_cuda(
cuMemcpyHtoD(launch_params.device_pointer + i * launch_params.data_elements + offset,
data,
data_size));
// Set constant memory for CUDA module
// TODO(pmours): This is only used for tonemapping (see 'launch_film_convert').
// Could be removed by moving those functions to filter CUDA module.
size_t bytes = 0;
CUdeviceptr mem = 0;
check_result_cuda(cuModuleGetGlobal(&mem, &bytes, cuda_module, name));
assert(mem != 0 && bytes == data_size);
check_result_cuda(cuMemcpyHtoD(mem, data, data_size));
}
void mem_alloc(device_memory &mem) override
{
if (mem.type == MEM_PIXELS && !background) {
// Always fall back to no interop for now
// TODO(pmours): Support OpenGL interop when moving CUDA memory management to common code
background = true;
}
else if (mem.type == MEM_TEXTURE) {
assert(!"mem_alloc not supported for textures.");
return;
}
generic_alloc(mem);
}
CUDAMem *generic_alloc(device_memory &mem, size_t pitch_padding = 0)
{
CUDAContextScope scope(cuda_context);
CUdeviceptr device_pointer = 0;
size_t size = mem.memory_size() + pitch_padding;
CUresult mem_alloc_result = CUDA_ERROR_OUT_OF_MEMORY;
const char *status = "";
/* First try allocating in device memory, respecting headroom. We make
* an exception for texture info. It is small and frequently accessed,
* so treat it as working memory.
*
* If there is not enough room for working memory, we will try to move
* textures to host memory, assuming the performance impact would have
* been worse for working memory. */
bool is_texture = (mem.type == MEM_TEXTURE) && (&mem != &texture_info);
bool is_image = is_texture && (mem.data_height > 1);
size_t headroom = (is_texture) ? device_texture_headroom : device_working_headroom;
size_t total = 0, free = 0;
cuMemGetInfo(&free, &total);
/* Move textures to host memory if needed. */
if (!move_texture_to_host && !is_image && (size + headroom) >= free && can_map_host) {
move_textures_to_host(size + headroom - free, is_texture);
cuMemGetInfo(&free, &total);
}
/* Allocate in device memory. */
if (!move_texture_to_host && (size + headroom) < free) {
mem_alloc_result = cuMemAlloc(&device_pointer, size);
if (mem_alloc_result == CUDA_SUCCESS) {
status = " in device memory";
}
}
/* Fall back to mapped host memory if needed and possible. */
void *shared_pointer = 0;
if (mem_alloc_result != CUDA_SUCCESS && can_map_host) {
if (mem.shared_pointer) {
/* Another device already allocated host memory. */
mem_alloc_result = CUDA_SUCCESS;
shared_pointer = mem.shared_pointer;
}
else if (map_host_used + size < map_host_limit) {
/* Allocate host memory ourselves. */
mem_alloc_result = cuMemHostAlloc(
&shared_pointer, size, CU_MEMHOSTALLOC_DEVICEMAP | CU_MEMHOSTALLOC_WRITECOMBINED);
assert((mem_alloc_result == CUDA_SUCCESS && shared_pointer != 0) ||
(mem_alloc_result != CUDA_SUCCESS && shared_pointer == 0));
}
if (mem_alloc_result == CUDA_SUCCESS) {
cuMemHostGetDevicePointer_v2(&device_pointer, shared_pointer, 0);
map_host_used += size;
status = " in host memory";
}
else {
status = " failed, out of host memory";
}
}
else if (mem_alloc_result != CUDA_SUCCESS) {
status = " failed, out of device and host memory";
}
if (mem.name) {
VLOG(1) << "Buffer allocate: " << mem.name << ", "
<< string_human_readable_number(mem.memory_size()) << " bytes. ("
<< string_human_readable_size(mem.memory_size()) << ")" << status;
}
if (mem_alloc_result != CUDA_SUCCESS) {
set_error(string_printf("Buffer allocate %s", status));
return NULL;
}
mem.device_pointer = (device_ptr)device_pointer;
mem.device_size = size;
stats.mem_alloc(size);
if (!mem.device_pointer) {
return NULL;
}
/* Insert into map of allocations. */
CUDAMem *cmem = &cuda_mem_map[&mem];
if (shared_pointer != 0) {
/* Replace host pointer with our host allocation. Only works if
* CUDA memory layout is the same and has no pitch padding. Also
* does not work if we move textures to host during a render,
* since other devices might be using the memory. */
if (!move_texture_to_host && pitch_padding == 0 && mem.host_pointer &&
mem.host_pointer != shared_pointer) {
memcpy(shared_pointer, mem.host_pointer, size);
/* A call to device_memory::host_free() should be preceded by
* a call to device_memory::device_free() for host memory
* allocated by a device to be handled properly. Two exceptions
* are here and a call in CUDADevice::generic_alloc(), where
* the current host memory can be assumed to be allocated by
* device_memory::host_alloc(), not by a device */
mem.host_free();
mem.host_pointer = shared_pointer;
}
mem.shared_pointer = shared_pointer;
mem.shared_counter++;
cmem->use_mapped_host = true;
}
else {
cmem->use_mapped_host = false;
}
return cmem;
}
void tex_alloc(device_memory &mem)
{
CUDAContextScope scope(cuda_context);
/* General variables for both architectures */
string bind_name = mem.name;
size_t dsize = datatype_size(mem.data_type);
size_t size = mem.memory_size();
CUaddress_mode address_mode = CU_TR_ADDRESS_MODE_WRAP;
switch (mem.extension) {
case EXTENSION_REPEAT:
address_mode = CU_TR_ADDRESS_MODE_WRAP;
break;
case EXTENSION_EXTEND:
address_mode = CU_TR_ADDRESS_MODE_CLAMP;
break;
case EXTENSION_CLIP:
address_mode = CU_TR_ADDRESS_MODE_BORDER;
break;
default:
assert(0);
break;
}
CUfilter_mode filter_mode;
if (mem.interpolation == INTERPOLATION_CLOSEST) {
filter_mode = CU_TR_FILTER_MODE_POINT;
}
else {
filter_mode = CU_TR_FILTER_MODE_LINEAR;
}
/* Data Storage */
if (mem.interpolation == INTERPOLATION_NONE) {
generic_alloc(mem);
generic_copy_to(mem);
// Update data storage pointers in launch parameters
# define KERNEL_TEX(data_type, tex_name) \
if (strcmp(mem.name, #tex_name) == 0) \
update_launch_params( \
mem.name, offsetof(KernelParams, tex_name), &mem.device_pointer, sizeof(device_ptr));
# include "kernel/kernel_textures.h"
# undef KERNEL_TEX
return;
}
/* Image Texture Storage */
CUarray_format_enum format;
switch (mem.data_type) {
case TYPE_UCHAR:
format = CU_AD_FORMAT_UNSIGNED_INT8;
break;
case TYPE_UINT16:
format = CU_AD_FORMAT_UNSIGNED_INT16;
break;
case TYPE_UINT:
format = CU_AD_FORMAT_UNSIGNED_INT32;
break;
case TYPE_INT:
format = CU_AD_FORMAT_SIGNED_INT32;
break;
case TYPE_FLOAT:
format = CU_AD_FORMAT_FLOAT;
break;
case TYPE_HALF:
format = CU_AD_FORMAT_HALF;
break;
default:
assert(0);
return;
}
CUDAMem *cmem = NULL;
CUarray array_3d = NULL;
size_t src_pitch = mem.data_width * dsize * mem.data_elements;
size_t dst_pitch = src_pitch;
if (mem.data_depth > 1) {
/* 3D texture using array, there is no API for linear memory. */
CUDA_ARRAY3D_DESCRIPTOR desc;
desc.Width = mem.data_width;
desc.Height = mem.data_height;
desc.Depth = mem.data_depth;
desc.Format = format;
desc.NumChannels = mem.data_elements;
desc.Flags = 0;
VLOG(1) << "Array 3D allocate: " << mem.name << ", "
<< string_human_readable_number(mem.memory_size()) << " bytes. ("
<< string_human_readable_size(mem.memory_size()) << ")";
check_result_cuda(cuArray3DCreate(&array_3d, &desc));
if (!array_3d) {
return;
}
CUDA_MEMCPY3D param;
memset(&param, 0, sizeof(param));
param.dstMemoryType = CU_MEMORYTYPE_ARRAY;
param.dstArray = array_3d;
param.srcMemoryType = CU_MEMORYTYPE_HOST;
param.srcHost = mem.host_pointer;
param.srcPitch = src_pitch;
param.WidthInBytes = param.srcPitch;
param.Height = mem.data_height;
param.Depth = mem.data_depth;
check_result_cuda(cuMemcpy3D(&param));
mem.device_pointer = (device_ptr)array_3d;
mem.device_size = size;
stats.mem_alloc(size);
cmem = &cuda_mem_map[&mem];
cmem->texobject = 0;
cmem->array = array_3d;
}
else if (mem.data_height > 0) {
/* 2D texture, using pitch aligned linear memory. */
int alignment = 0;
check_result_cuda(cuDeviceGetAttribute(
&alignment, CU_DEVICE_ATTRIBUTE_TEXTURE_PITCH_ALIGNMENT, cuda_device));
dst_pitch = align_up(src_pitch, alignment);
size_t dst_size = dst_pitch * mem.data_height;
cmem = generic_alloc(mem, dst_size - mem.memory_size());
if (!cmem) {
return;
}
CUDA_MEMCPY2D param;
memset(&param, 0, sizeof(param));
param.dstMemoryType = CU_MEMORYTYPE_DEVICE;
param.dstDevice = mem.device_pointer;
param.dstPitch = dst_pitch;
param.srcMemoryType = CU_MEMORYTYPE_HOST;
param.srcHost = mem.host_pointer;
param.srcPitch = src_pitch;
param.WidthInBytes = param.srcPitch;
param.Height = mem.data_height;
check_result_cuda(cuMemcpy2DUnaligned(&param));
}
else {
/* 1D texture, using linear memory. */
cmem = generic_alloc(mem);
if (!cmem) {
return;
}
check_result_cuda(cuMemcpyHtoD(mem.device_pointer, mem.host_pointer, size));
}
/* Kepler+, bindless textures. */
int flat_slot = 0;
if (string_startswith(mem.name, "__tex_image")) {
int pos = string(mem.name).rfind("_");
flat_slot = atoi(mem.name + pos + 1);
}
else {
assert(0);
}
CUDA_RESOURCE_DESC resDesc;
memset(&resDesc, 0, sizeof(resDesc));
if (array_3d) {
resDesc.resType = CU_RESOURCE_TYPE_ARRAY;
resDesc.res.array.hArray = array_3d;
resDesc.flags = 0;
}
else if (mem.data_height > 0) {
resDesc.resType = CU_RESOURCE_TYPE_PITCH2D;
resDesc.res.pitch2D.devPtr = mem.device_pointer;
resDesc.res.pitch2D.format = format;
resDesc.res.pitch2D.numChannels = mem.data_elements;
resDesc.res.pitch2D.height = mem.data_height;
resDesc.res.pitch2D.width = mem.data_width;
resDesc.res.pitch2D.pitchInBytes = dst_pitch;
}
else {
resDesc.resType = CU_RESOURCE_TYPE_LINEAR;
resDesc.res.linear.devPtr = mem.device_pointer;
resDesc.res.linear.format = format;
resDesc.res.linear.numChannels = mem.data_elements;
resDesc.res.linear.sizeInBytes = mem.device_size;
}
CUDA_TEXTURE_DESC texDesc;
memset(&texDesc, 0, sizeof(texDesc));
texDesc.addressMode[0] = address_mode;
texDesc.addressMode[1] = address_mode;
texDesc.addressMode[2] = address_mode;
texDesc.filterMode = filter_mode;
texDesc.flags = CU_TRSF_NORMALIZED_COORDINATES;
check_result_cuda(cuTexObjectCreate(&cmem->texobject, &resDesc, &texDesc, NULL));
/* Resize once */
if (flat_slot >= texture_info.size()) {
/* Allocate some slots in advance, to reduce amount
* of re-allocations. */
texture_info.resize(flat_slot + 128);
}
/* Set Mapping and tag that we need to (re-)upload to device */
TextureInfo &info = texture_info[flat_slot];
info.data = (uint64_t)cmem->texobject;
info.cl_buffer = 0;
info.interpolation = mem.interpolation;
info.extension = mem.extension;
info.width = mem.data_width;
info.height = mem.data_height;
info.depth = mem.data_depth;
need_texture_info = true;
}
void mem_copy_to(device_memory &mem) override
{
if (mem.type == MEM_PIXELS) {
assert(!"mem_copy_to not supported for pixels.");
}
else if (mem.type == MEM_TEXTURE) {
tex_free(mem);
tex_alloc(mem);
}
else {
if (!mem.device_pointer) {
generic_alloc(mem);
}
generic_copy_to(mem);
}
}
void generic_copy_to(device_memory &mem)
{
if (mem.host_pointer && mem.device_pointer) {
CUDAContextScope scope(cuda_context);
/* If use_mapped_host of mem is false, the current device only
* uses device memory allocated by cuMemAlloc regardless of
* mem.host_pointer and mem.shared_pointer, and should copy
* data from mem.host_pointer. */
if (cuda_mem_map[&mem].use_mapped_host == false || mem.host_pointer != mem.shared_pointer) {
check_result_cuda(
cuMemcpyHtoD((CUdeviceptr)mem.device_pointer, mem.host_pointer, mem.memory_size()));
}
}
}
void mem_copy_from(device_memory &mem, int y, int w, int h, int elem) override
{
if (mem.type == MEM_PIXELS && !background) {
assert(!"mem_copy_from not supported for pixels.");
}
else if (mem.type == MEM_TEXTURE) {
assert(!"mem_copy_from not supported for textures.");
}
else {
// Calculate linear memory offset and size
const size_t size = elem * w * h;
const size_t offset = elem * y * w;
if (mem.host_pointer && mem.device_pointer) {
const CUDAContextScope scope(cuda_context);
check_result_cuda(cuMemcpyDtoH(
(char *)mem.host_pointer + offset, (CUdeviceptr)mem.device_pointer + offset, size));
}
else if (mem.host_pointer) {
memset((char *)mem.host_pointer + offset, 0, size);
}
}
}
void mem_zero(device_memory &mem) override
{
if (mem.host_pointer)
memset(mem.host_pointer, 0, mem.memory_size());
if (!mem.device_pointer)
mem_alloc(mem); // Need to allocate memory first if it does not exist yet
/* If use_mapped_host of mem is false, mem.device_pointer currently
* refers to device memory regardless of mem.host_pointer and
* mem.shared_pointer. */
if (mem.device_pointer &&
(cuda_mem_map[&mem].use_mapped_host == false || mem.host_pointer != mem.shared_pointer)) {
const CUDAContextScope scope(cuda_context);
check_result_cuda(cuMemsetD8((CUdeviceptr)mem.device_pointer, 0, mem.memory_size()));
}
}
void mem_free(device_memory &mem) override
{
if (mem.type == MEM_PIXELS && !background) {
assert(!"mem_free not supported for pixels.");
}
else if (mem.type == MEM_TEXTURE) {
tex_free(mem);
}
else {
generic_free(mem);
}
}
void generic_free(device_memory &mem)
{
if (mem.device_pointer) {
CUDAContextScope scope(cuda_context);
const CUDAMem &cmem = cuda_mem_map[&mem];
/* If cmem.use_mapped_host is true, reference counting is used
* to safely free a mapped host memory. */
if (cmem.use_mapped_host) {
assert(mem.shared_pointer);
if (mem.shared_pointer) {
assert(mem.shared_counter > 0);
if (--mem.shared_counter == 0) {
if (mem.host_pointer == mem.shared_pointer) {
mem.host_pointer = 0;
}
cuMemFreeHost(mem.shared_pointer);
mem.shared_pointer = 0;
}
}
map_host_used -= mem.device_size;
}
else {
/* Free device memory. */
cuMemFree(mem.device_pointer);
}
stats.mem_free(mem.device_size);
mem.device_pointer = 0;
mem.device_size = 0;
cuda_mem_map.erase(cuda_mem_map.find(&mem));
}
}
void tex_free(device_memory &mem)
{
if (mem.device_pointer) {
CUDAContextScope scope(cuda_context);
const CUDAMem &cmem = cuda_mem_map[&mem];
if (cmem.texobject) {
/* Free bindless texture. */
cuTexObjectDestroy(cmem.texobject);
}
if (cmem.array) {
/* Free array. */
cuArrayDestroy(cmem.array);
stats.mem_free(mem.device_size);
mem.device_pointer = 0;
mem.device_size = 0;
cuda_mem_map.erase(cuda_mem_map.find(&mem));
}
else {
generic_free(mem);
}
}
}
void move_textures_to_host(size_t size, bool for_texture)
{
/* Signal to reallocate textures in host memory only. */
move_texture_to_host = true;
while (size > 0) {
/* Find suitable memory allocation to move. */
device_memory *max_mem = NULL;
size_t max_size = 0;
bool max_is_image = false;
foreach (auto &pair, cuda_mem_map) {
device_memory &mem = *pair.first;
CUDAMem *cmem = &pair.second;
bool is_texture = (mem.type == MEM_TEXTURE) && (&mem != &texture_info);
bool is_image = is_texture && (mem.data_height > 1);
/* Can't move this type of memory. */
if (!is_texture || cmem->array) {
continue;
}
/* Already in host memory. */
if (cmem->use_mapped_host) {
continue;
}
/* For other textures, only move image textures. */
if (for_texture && !is_image) {
continue;
}
/* Try to move largest allocation, prefer moving images. */
if (is_image > max_is_image || (is_image == max_is_image && mem.device_size > max_size)) {
max_is_image = is_image;
max_size = mem.device_size;
max_mem = &mem;
}
}
/* Move to host memory. This part is mutex protected since
* multiple CUDA devices could be moving the memory. The
* first one will do it, and the rest will adopt the pointer. */
if (max_mem) {
VLOG(1) << "Move memory from device to host: " << max_mem->name;
static thread_mutex move_mutex;
thread_scoped_lock lock(move_mutex);
/* Preserve the original device pointer, in case of multi device
* we can't change it because the pointer mapping would break. */
device_ptr prev_pointer = max_mem->device_pointer;
size_t prev_size = max_mem->device_size;
tex_free(*max_mem);
tex_alloc(*max_mem);
size = (max_size >= size) ? 0 : size - max_size;
max_mem->device_pointer = prev_pointer;
max_mem->device_size = prev_size;
}
else {
break;
}
}
/* Update texture info array with new pointers. */
update_texture_info();
move_texture_to_host = false;
}
void const_copy_to(const char *name, void *host, size_t size) override
{
if (strcmp(name, "__data") == 0) {
assert(size <= sizeof(KernelData));
// Fix traversable handle on multi devices
KernelData *const data = (KernelData *)host;
*(OptixTraversableHandle *)&data->bvh.scene = tlas_handle;
update_launch_params(name, offsetof(KernelParams, data), host, size);
}
}
device_ptr mem_alloc_sub_ptr(device_memory &mem, int offset, int /*size*/) override
{
return (device_ptr)(((char *)mem.device_pointer) + mem.memory_elements_size(offset));
}
void task_add(DeviceTask &task) override
{
// Upload texture information to device if it has changed since last launch
update_texture_info();
// Split task into smaller ones
list<DeviceTask> tasks;
task.split(tasks, info.cpu_threads);
// Queue tasks in internal task pool
struct OptiXDeviceTask : public DeviceTask {
OptiXDeviceTask(OptiXDevice *device, DeviceTask &task, int task_index) : DeviceTask(task)
{
// Using task index parameter instead of thread index, since number of CUDA streams may
// differ from number of threads
run = function_bind(&OptiXDevice::thread_run, device, *this, task_index);
}
};
int task_index = 0;
for (DeviceTask &task : tasks)
task_pool.push(new OptiXDeviceTask(this, task, task_index++));
}
void task_wait() override
{
// Wait for all queued tasks to finish
task_pool.wait_work();
}
void task_cancel() override
{
// Cancel any remaining tasks in the internal pool
task_pool.cancel();
}
# define CUDA_GET_BLOCKSIZE(func, w, h) \
int threads; \
check_result_cuda_ret( \
cuFuncGetAttribute(&threads, CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK, func)); \
threads = (int)sqrt((float)threads); \
int xblocks = ((w) + threads - 1) / threads; \
int yblocks = ((h) + threads - 1) / threads;
# define CUDA_LAUNCH_KERNEL(func, args) \
check_result_cuda_ret(cuLaunchKernel( \
func, xblocks, yblocks, 1, threads, threads, 1, 0, cuda_stream[thread_index], args, 0));
/* Similar as above, but for 1-dimensional blocks. */
# define CUDA_GET_BLOCKSIZE_1D(func, w, h) \
int threads; \
check_result_cuda_ret( \
cuFuncGetAttribute(&threads, CU_FUNC_ATTRIBUTE_MAX_THREADS_PER_BLOCK, func)); \
int xblocks = ((w) + threads - 1) / threads; \
int yblocks = h;
# define CUDA_LAUNCH_KERNEL_1D(func, args) \
check_result_cuda_ret(cuLaunchKernel( \
func, xblocks, yblocks, 1, threads, 1, 1, 0, cuda_stream[thread_index], args, 0));
bool denoising_non_local_means(device_ptr image_ptr,
device_ptr guide_ptr,
device_ptr variance_ptr,
device_ptr out_ptr,
DenoisingTask *task,
int thread_index)
{
if (have_error())
return false;
int stride = task->buffer.stride;
int w = task->buffer.width;
int h = task->buffer.h;
int r = task->nlm_state.r;
int f = task->nlm_state.f;
float a = task->nlm_state.a;
float k_2 = task->nlm_state.k_2;
int pass_stride = task->buffer.pass_stride;
int num_shifts = (2 * r + 1) * (2 * r + 1);
int channel_offset = task->nlm_state.is_color ? task->buffer.pass_stride : 0;
int frame_offset = 0;
CUdeviceptr difference = (CUdeviceptr)task->buffer.temporary_mem.device_pointer;
CUdeviceptr blurDifference = difference + sizeof(float) * pass_stride * num_shifts;
CUdeviceptr weightAccum = difference + 2 * sizeof(float) * pass_stride * num_shifts;
CUdeviceptr scale_ptr = 0;
check_result_cuda_ret(
cuMemsetD8Async(weightAccum, 0, sizeof(float) * pass_stride, cuda_stream[thread_index]));
check_result_cuda_ret(
cuMemsetD8Async(out_ptr, 0, sizeof(float) * pass_stride, cuda_stream[thread_index]));
{
CUfunction cuNLMCalcDifference, cuNLMBlur, cuNLMCalcWeight, cuNLMUpdateOutput;
check_result_cuda_ret(cuModuleGetFunction(
&cuNLMCalcDifference, cuda_filter_module, "kernel_cuda_filter_nlm_calc_difference"));
check_result_cuda_ret(
cuModuleGetFunction(&cuNLMBlur, cuda_filter_module, "kernel_cuda_filter_nlm_blur"));
check_result_cuda_ret(cuModuleGetFunction(
&cuNLMCalcWeight, cuda_filter_module, "kernel_cuda_filter_nlm_calc_weight"));
check_result_cuda_ret(cuModuleGetFunction(
&cuNLMUpdateOutput, cuda_filter_module, "kernel_cuda_filter_nlm_update_output"));
check_result_cuda_ret(cuFuncSetCacheConfig(cuNLMCalcDifference, CU_FUNC_CACHE_PREFER_L1));
check_result_cuda_ret(cuFuncSetCacheConfig(cuNLMBlur, CU_FUNC_CACHE_PREFER_L1));
check_result_cuda_ret(cuFuncSetCacheConfig(cuNLMCalcWeight, CU_FUNC_CACHE_PREFER_L1));
check_result_cuda_ret(cuFuncSetCacheConfig(cuNLMUpdateOutput, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE_1D(cuNLMCalcDifference, w * h, num_shifts);
void *calc_difference_args[] = {&guide_ptr,
&variance_ptr,
&scale_ptr,
&difference,
&w,
&h,
&stride,
&pass_stride,
&r,
&channel_offset,
&frame_offset,
&a,
&k_2};
void *blur_args[] = {&difference, &blurDifference, &w, &h, &stride, &pass_stride, &r, &f};
void *calc_weight_args[] = {
&blurDifference, &difference, &w, &h, &stride, &pass_stride, &r, &f};
void *update_output_args[] = {&blurDifference,
&image_ptr,
&out_ptr,
&weightAccum,
&w,
&h,
&stride,
&pass_stride,
&channel_offset,
&r,
&f};
CUDA_LAUNCH_KERNEL_1D(cuNLMCalcDifference, calc_difference_args);
CUDA_LAUNCH_KERNEL_1D(cuNLMBlur, blur_args);
CUDA_LAUNCH_KERNEL_1D(cuNLMCalcWeight, calc_weight_args);
CUDA_LAUNCH_KERNEL_1D(cuNLMBlur, blur_args);
CUDA_LAUNCH_KERNEL_1D(cuNLMUpdateOutput, update_output_args);
}
{
CUfunction cuNLMNormalize;
check_result_cuda_ret(cuModuleGetFunction(
&cuNLMNormalize, cuda_filter_module, "kernel_cuda_filter_nlm_normalize"));
check_result_cuda_ret(cuFuncSetCacheConfig(cuNLMNormalize, CU_FUNC_CACHE_PREFER_L1));
void *normalize_args[] = {&out_ptr, &weightAccum, &w, &h, &stride};
CUDA_GET_BLOCKSIZE(cuNLMNormalize, w, h);
CUDA_LAUNCH_KERNEL(cuNLMNormalize, normalize_args);
check_result_cuda_ret(cuStreamSynchronize(cuda_stream[thread_index]));
}
return !have_error();
}
bool denoising_construct_transform(DenoisingTask *task, int thread_index)
{
if (have_error())
return false;
CUfunction cuFilterConstructTransform;
check_result_cuda_ret(cuModuleGetFunction(&cuFilterConstructTransform,
cuda_filter_module,
"kernel_cuda_filter_construct_transform"));
check_result_cuda_ret(
cuFuncSetCacheConfig(cuFilterConstructTransform, CU_FUNC_CACHE_PREFER_SHARED));
CUDA_GET_BLOCKSIZE(cuFilterConstructTransform, task->storage.w, task->storage.h);
void *args[] = {&task->buffer.mem.device_pointer,
&task->tile_info_mem.device_pointer,
&task->storage.transform.device_pointer,
&task->storage.rank.device_pointer,
&task->filter_area,
&task->rect,
&task->radius,
&task->pca_threshold,
&task->buffer.pass_stride,
&task->buffer.frame_stride,
&task->buffer.use_time};
CUDA_LAUNCH_KERNEL(cuFilterConstructTransform, args);
check_result_cuda_ret(cuCtxSynchronize());
return !have_error();
}
bool denoising_accumulate(device_ptr color_ptr,
device_ptr color_variance_ptr,
device_ptr scale_ptr,
int frame,
DenoisingTask *task,
int thread_index)
{
if (have_error())
return false;
int r = task->radius;
int f = 4;
float a = 1.0f;
float k_2 = task->nlm_k_2;
int w = task->reconstruction_state.source_w;
int h = task->reconstruction_state.source_h;
int stride = task->buffer.stride;
int frame_offset = frame * task->buffer.frame_stride;
int t = task->tile_info->frames[frame];
int pass_stride = task->buffer.pass_stride;
int num_shifts = (2 * r + 1) * (2 * r + 1);
CUdeviceptr difference = (CUdeviceptr)task->buffer.temporary_mem.device_pointer;
CUdeviceptr blurDifference = difference + sizeof(float) * pass_stride * num_shifts;
CUfunction cuNLMCalcDifference, cuNLMBlur, cuNLMCalcWeight, cuNLMConstructGramian;
check_result_cuda_ret(cuModuleGetFunction(
&cuNLMCalcDifference, cuda_filter_module, "kernel_cuda_filter_nlm_calc_difference"));
check_result_cuda_ret(
cuModuleGetFunction(&cuNLMBlur, cuda_filter_module, "kernel_cuda_filter_nlm_blur"));
check_result_cuda_ret(cuModuleGetFunction(
&cuNLMCalcWeight, cuda_filter_module, "kernel_cuda_filter_nlm_calc_weight"));
check_result_cuda_ret(cuModuleGetFunction(
&cuNLMConstructGramian, cuda_filter_module, "kernel_cuda_filter_nlm_construct_gramian"));
check_result_cuda_ret(cuFuncSetCacheConfig(cuNLMCalcDifference, CU_FUNC_CACHE_PREFER_L1));
check_result_cuda_ret(cuFuncSetCacheConfig(cuNLMBlur, CU_FUNC_CACHE_PREFER_L1));
check_result_cuda_ret(cuFuncSetCacheConfig(cuNLMCalcWeight, CU_FUNC_CACHE_PREFER_L1));
check_result_cuda_ret(
cuFuncSetCacheConfig(cuNLMConstructGramian, CU_FUNC_CACHE_PREFER_SHARED));
CUDA_GET_BLOCKSIZE_1D(cuNLMCalcDifference,
task->reconstruction_state.source_w *
task->reconstruction_state.source_h,
num_shifts);
void *calc_difference_args[] = {&color_ptr,
&color_variance_ptr,
&scale_ptr,
&difference,
&w,
&h,
&stride,
&pass_stride,
&r,
&pass_stride,
&frame_offset,
&a,
&k_2};
void *blur_args[] = {&difference, &blurDifference, &w, &h, &stride, &pass_stride, &r, &f};
void *calc_weight_args[] = {
&blurDifference, &difference, &w, &h, &stride, &pass_stride, &r, &f};
void *construct_gramian_args[] = {&t,
&blurDifference,
&task->buffer.mem.device_pointer,
&task->storage.transform.device_pointer,
&task->storage.rank.device_pointer,
&task->storage.XtWX.device_pointer,
&task->storage.XtWY.device_pointer,
&task->reconstruction_state.filter_window,
&w,
&h,
&stride,
&pass_stride,
&r,
&f,
&frame_offset,
&task->buffer.use_time};
CUDA_LAUNCH_KERNEL_1D(cuNLMCalcDifference, calc_difference_args);
CUDA_LAUNCH_KERNEL_1D(cuNLMBlur, blur_args);
CUDA_LAUNCH_KERNEL_1D(cuNLMCalcWeight, calc_weight_args);
CUDA_LAUNCH_KERNEL_1D(cuNLMBlur, blur_args);
CUDA_LAUNCH_KERNEL_1D(cuNLMConstructGramian, construct_gramian_args);
check_result_cuda_ret(cuCtxSynchronize());
return !have_error();
}
bool denoising_solve(device_ptr output_ptr, DenoisingTask *task, int thread_index)
{
if (have_error())
return false;
CUfunction cuFinalize;
check_result_cuda_ret(
cuModuleGetFunction(&cuFinalize, cuda_filter_module, "kernel_cuda_filter_finalize"));
check_result_cuda_ret(cuFuncSetCacheConfig(cuFinalize, CU_FUNC_CACHE_PREFER_L1));
void *finalize_args[] = {&output_ptr,
&task->storage.rank.device_pointer,
&task->storage.XtWX.device_pointer,
&task->storage.XtWY.device_pointer,
&task->filter_area,
&task->reconstruction_state.buffer_params.x,
&task->render_buffer.samples};
CUDA_GET_BLOCKSIZE(
cuFinalize, task->reconstruction_state.source_w, task->reconstruction_state.source_h);
CUDA_LAUNCH_KERNEL(cuFinalize, finalize_args);
check_result_cuda_ret(cuStreamSynchronize(cuda_stream[thread_index]));
return !have_error();
}
bool denoising_combine_halves(device_ptr a_ptr,
device_ptr b_ptr,
device_ptr mean_ptr,
device_ptr variance_ptr,
int r,
int4 rect,
DenoisingTask *task,
int thread_index)
{
if (have_error())
return false;
CUfunction cuFilterCombineHalves;
check_result_cuda_ret(cuModuleGetFunction(
&cuFilterCombineHalves, cuda_filter_module, "kernel_cuda_filter_combine_halves"));
check_result_cuda_ret(cuFuncSetCacheConfig(cuFilterCombineHalves, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(
cuFilterCombineHalves, task->rect.z - task->rect.x, task->rect.w - task->rect.y);
void *args[] = {&mean_ptr, &variance_ptr, &a_ptr, &b_ptr, &rect, &r};
CUDA_LAUNCH_KERNEL(cuFilterCombineHalves, args);
check_result_cuda_ret(cuStreamSynchronize(cuda_stream[thread_index]));
return !have_error();
}
bool denoising_divide_shadow(device_ptr a_ptr,
device_ptr b_ptr,
device_ptr sample_variance_ptr,
device_ptr sv_variance_ptr,
device_ptr buffer_variance_ptr,
DenoisingTask *task,
int thread_index)
{
if (have_error())
return false;
CUfunction cuFilterDivideShadow;
check_result_cuda_ret(cuModuleGetFunction(
&cuFilterDivideShadow, cuda_filter_module, "kernel_cuda_filter_divide_shadow"));
check_result_cuda_ret(cuFuncSetCacheConfig(cuFilterDivideShadow, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(
cuFilterDivideShadow, task->rect.z - task->rect.x, task->rect.w - task->rect.y);
void *args[] = {&task->render_buffer.samples,
&task->tile_info_mem.device_pointer,
&a_ptr,
&b_ptr,
&sample_variance_ptr,
&sv_variance_ptr,
&buffer_variance_ptr,
&task->rect,
&task->render_buffer.pass_stride,
&task->render_buffer.offset};
CUDA_LAUNCH_KERNEL(cuFilterDivideShadow, args);
check_result_cuda_ret(cuStreamSynchronize(cuda_stream[thread_index]));
return !have_error();
}
bool denoising_get_feature(int mean_offset,
int variance_offset,
device_ptr mean_ptr,
device_ptr variance_ptr,
float scale,
DenoisingTask *task,
int thread_index)
{
if (have_error())
return false;
CUfunction cuFilterGetFeature;
check_result_cuda_ret(cuModuleGetFunction(
&cuFilterGetFeature, cuda_filter_module, "kernel_cuda_filter_get_feature"));
check_result_cuda_ret(cuFuncSetCacheConfig(cuFilterGetFeature, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(
cuFilterGetFeature, task->rect.z - task->rect.x, task->rect.w - task->rect.y);
void *args[] = {&task->render_buffer.samples,
&task->tile_info_mem.device_pointer,
&mean_offset,
&variance_offset,
&mean_ptr,
&variance_ptr,
&scale,
&task->rect,
&task->render_buffer.pass_stride,
&task->render_buffer.offset};
CUDA_LAUNCH_KERNEL(cuFilterGetFeature, args);
check_result_cuda_ret(cuStreamSynchronize(cuda_stream[thread_index]));
return !have_error();
}
bool denoising_write_feature(int out_offset,
device_ptr from_ptr,
device_ptr buffer_ptr,
DenoisingTask *task,
int thread_index)
{
if (have_error())
return false;
CUfunction cuFilterWriteFeature;
check_result_cuda_ret(cuModuleGetFunction(
&cuFilterWriteFeature, cuda_filter_module, "kernel_cuda_filter_write_feature"));
check_result_cuda_ret(cuFuncSetCacheConfig(cuFilterWriteFeature, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(cuFilterWriteFeature, task->filter_area.z, task->filter_area.w);
void *args[] = {&task->render_buffer.samples,
&task->reconstruction_state.buffer_params,
&task->filter_area,
&from_ptr,
&buffer_ptr,
&out_offset,
&task->rect};
CUDA_LAUNCH_KERNEL(cuFilterWriteFeature, args);
check_result_cuda_ret(cuStreamSynchronize(cuda_stream[thread_index]));
return !have_error();
}
bool denoising_detect_outliers(device_ptr image_ptr,
device_ptr variance_ptr,
device_ptr depth_ptr,
device_ptr output_ptr,
DenoisingTask *task,
int thread_index)
{
if (have_error())
return false;
CUfunction cuFilterDetectOutliers;
check_result_cuda_ret(cuModuleGetFunction(
&cuFilterDetectOutliers, cuda_filter_module, "kernel_cuda_filter_detect_outliers"));
check_result_cuda_ret(cuFuncSetCacheConfig(cuFilterDetectOutliers, CU_FUNC_CACHE_PREFER_L1));
CUDA_GET_BLOCKSIZE(
cuFilterDetectOutliers, task->rect.z - task->rect.x, task->rect.w - task->rect.y);
void *args[] = {&image_ptr,
&variance_ptr,
&depth_ptr,
&output_ptr,
&task->rect,
&task->buffer.pass_stride};
CUDA_LAUNCH_KERNEL(cuFilterDetectOutliers, args);
check_result_cuda_ret(cuStreamSynchronize(cuda_stream[thread_index]));
return !have_error();
}
};
bool device_optix_init()
{
if (g_optixFunctionTable.optixDeviceContextCreate != NULL)
return true; // Already initialized function table
// Need to initialize CUDA as well
if (!device_cuda_init())
return false;
# ifdef WITH_CUDA_DYNLOAD
// Load NVRTC function pointers for adaptive kernel compilation
if (DebugFlags().cuda.adaptive_compile && cuewInit(CUEW_INIT_NVRTC) != CUEW_SUCCESS) {
VLOG(1)
<< "CUEW initialization failed for NVRTC. Adaptive kernel compilation won't be available.";
}
# endif
const OptixResult result = optixInit();
if (result == OPTIX_ERROR_UNSUPPORTED_ABI_VERSION) {
VLOG(1)
<< "OptiX initialization failed because the installed driver does not support ABI version "
<< OPTIX_ABI_VERSION;
return false;
}
else if (result != OPTIX_SUCCESS) {
VLOG(1) << "OptiX initialization failed with error code " << (unsigned int)result;
return false;
}
// Loaded OptiX successfully!
return true;
}
void device_optix_info(vector<DeviceInfo> &devices)
{
// Simply add all supported CUDA devices as OptiX devices again
vector<DeviceInfo> cuda_devices;
device_cuda_info(cuda_devices);
for (auto it = cuda_devices.begin(); it != cuda_devices.end();) {
DeviceInfo &info = *it;
assert(info.type == DEVICE_CUDA);
info.type = DEVICE_OPTIX;
info.id += "_OptiX";
// Figure out RTX support
CUdevice cuda_device = 0;
CUcontext cuda_context = NULL;
unsigned int rtcore_version = 0;
if (cuDeviceGet(&cuda_device, info.num) == CUDA_SUCCESS &&
cuDevicePrimaryCtxRetain(&cuda_context, cuda_device) == CUDA_SUCCESS) {
OptixDeviceContext optix_context = NULL;
if (optixDeviceContextCreate(cuda_context, nullptr, &optix_context) == OPTIX_SUCCESS) {
optixDeviceContextGetProperty(optix_context,
OPTIX_DEVICE_PROPERTY_RTCORE_VERSION,
&rtcore_version,
sizeof(rtcore_version));
optixDeviceContextDestroy(optix_context);
}
cuDevicePrimaryCtxRelease(cuda_device);
}
// Only add devices with RTX support
if (rtcore_version == 0)
it = cuda_devices.erase(it);
else
++it;
}
devices.insert(devices.end(), cuda_devices.begin(), cuda_devices.end());
}
Device *device_optix_create(DeviceInfo &info, Stats &stats, Profiler &profiler, bool background)
{
return new OptiXDevice(info, stats, profiler, background);
}
CCL_NAMESPACE_END
#endif
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