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/*
* Copyright 2011-2021 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.
*/
#include "integrator/shader_eval.h"
#include "device/device.h"
#include "device/device_queue.h"
#include "device/cpu/kernel.h"
#include "device/cpu/kernel_thread_globals.h"
#include "util/util_logging.h"
#include "util/util_progress.h"
#include "util/util_tbb.h"
CCL_NAMESPACE_BEGIN
ShaderEval::ShaderEval(Device *device, Progress &progress) : device_(device), progress_(progress)
{
DCHECK_NE(device_, nullptr);
}
bool ShaderEval::eval(const ShaderEvalType type,
const int max_num_points,
const function<int(device_vector<KernelShaderEvalInput> &)> &fill_input,
const function<void(device_vector<float4> &)> &read_output)
{
bool first_device = true;
bool success = true;
device_->foreach_device([&](Device *device) {
if (!first_device) {
LOG(ERROR) << "Multi-devices are not yet fully implemented, will evaluate shader on a "
"single device.";
return;
}
first_device = false;
device_vector<KernelShaderEvalInput> input(device, "ShaderEval input", MEM_READ_ONLY);
device_vector<float4> output(device, "ShaderEval output", MEM_READ_WRITE);
/* Allocate and copy device buffers. */
DCHECK_EQ(input.device, device);
DCHECK_EQ(output.device, device);
DCHECK_LE(output.size(), input.size());
input.alloc(max_num_points);
int num_points = fill_input(input);
if (num_points == 0) {
return;
}
input.copy_to_device();
output.alloc(num_points);
output.zero_to_device();
/* Evaluate on CPU or GPU. */
success = (device->info.type == DEVICE_CPU) ? eval_cpu(device, type, input, output) :
eval_gpu(device, type, input, output);
/* Copy data back from device if not canceled. */
if (success) {
output.copy_from_device(0, 1, output.size());
read_output(output);
}
input.free();
output.free();
});
return success;
}
bool ShaderEval::eval_cpu(Device *device,
const ShaderEvalType type,
device_vector<KernelShaderEvalInput> &input,
device_vector<float4> &output)
{
vector<CPUKernelThreadGlobals> kernel_thread_globals;
device->get_cpu_kernel_thread_globals(kernel_thread_globals);
/* Find required kernel function. */
const CPUKernels &kernels = *(device->get_cpu_kernels());
/* Simple parallel_for over all work items. */
const int64_t work_size = output.size();
KernelShaderEvalInput *input_data = input.data();
float4 *output_data = output.data();
bool success = true;
tbb::task_arena local_arena(device->info.cpu_threads);
local_arena.execute([&]() {
tbb::parallel_for(int64_t(0), work_size, [&](int64_t work_index) {
/* TODO: is this fast enough? */
if (progress_.get_cancel()) {
success = false;
return;
}
const int thread_index = tbb::this_task_arena::current_thread_index();
KernelGlobals *kg = &kernel_thread_globals[thread_index];
switch (type) {
case SHADER_EVAL_DISPLACE:
kernels.shader_eval_displace(kg, input_data, output_data, work_index);
break;
case SHADER_EVAL_BACKGROUND:
kernels.shader_eval_background(kg, input_data, output_data, work_index);
break;
}
});
});
return success;
}
bool ShaderEval::eval_gpu(Device *device,
const ShaderEvalType type,
device_vector<KernelShaderEvalInput> &input,
device_vector<float4> &output)
{
/* Find required kernel function. */
DeviceKernel kernel;
switch (type) {
case SHADER_EVAL_DISPLACE:
kernel = DEVICE_KERNEL_SHADER_EVAL_DISPLACE;
break;
case SHADER_EVAL_BACKGROUND:
kernel = DEVICE_KERNEL_SHADER_EVAL_BACKGROUND;
break;
};
/* Create device queue. */
unique_ptr<DeviceQueue> queue = device->gpu_queue_create();
queue->init_execution();
/* Execute work on GPU in chunk, so we can cancel.
* TODO : query appropriate size from device.*/
const int chunk_size = 65536;
const int work_size = output.size();
void *d_input = (void *)input.device_pointer;
void *d_output = (void *)output.device_pointer;
for (int d_offset = 0; d_offset < work_size; d_offset += chunk_size) {
int d_work_size = min(chunk_size, work_size - d_offset);
void *args[] = {&d_input, &d_output, &d_offset, &d_work_size};
queue->enqueue(kernel, d_work_size, args);
queue->synchronize();
if (progress_.get_cancel()) {
return false;
}
}
return true;
}
CCL_NAMESPACE_END
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