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Support CUDA 4.1. Split filtering into new module.

Browse Source 
The new toolkit generates code for filtering which uses too many
registers, so this change splits filtering into its own module so that
it can have separate register usage limits during compiling. As a bonus,
this should improve startup time in general, since the filtering code
is now fixed and does not need to be recompiled.
This commit is contained in:
Steven Robertson 2011-11-08 14:38:45 -05:00
parent cea91d75bf
commit 3147fd40d2
2 changed files with 94 additions and 114 deletions
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145
cuburn/code/filtering.py
View File

@ -1,15 +1,18 @@
import numpy as np
import pycuda.compiler
from pycuda.gpuarray import vec
from cuburn.code.util import *
class ColorClip(HunkOCode):
def __init__(self, info):
self.defs = self.defs_tmpl.substitute(info=info)
_CODE = '''
#include<math_constants.h>
defs_tmpl = Template('''
__global__
void colorclip(float4 *pixbuf, float gamma, float vibrancy, float highpow,
float linrange, float lingam, float3 bkgd, int fbsize) {
int i = gtid();
float linrange, float lingam, float3 bkgd, int fbsize,
int alpha_output_channel) {
int i = threadIdx.x + blockDim.x * (blockIdx.x + gridDim.x * blockIdx.y);
if (i >= fbsize) return;
float4 pix = pixbuf[i];
@ -61,16 +64,16 @@ void colorclip(float4 *pixbuf, float gamma, float vibrancy, float highpow,
pix.y += (1.0f - vibrancy) * powf(opix.y, gamma);
pix.z += (1.0f - vibrancy) * powf(opix.z, gamma);
{{if info.alpha_output_channel}}
float 1_alpha = 1 / alpha;
pix.x *= 1_alpha;
pix.y *= 1_alpha;
pix.z *= 1_alpha;
{{else}}
pix.x += (1.0f - alpha) * bkgd.x;
pix.y += (1.0f - alpha) * bkgd.y;
pix.z += (1.0f - alpha) * bkgd.z;
{{endif}}
if (alpha_output_channel) {
float one_alpha = 1.0f / alpha;
pix.x *= one_alpha;
pix.y *= one_alpha;
pix.z *= one_alpha;
} else {
pix.x += (1.0f - alpha) * bkgd.x;
pix.y += (1.0f - alpha) * bkgd.y;
pix.z += (1.0f - alpha) * bkgd.z;
}
pix.w = alpha;
// Clamp values. I think this is superfluous, but I'm not certain.
@ -80,24 +83,8 @@ void colorclip(float4 *pixbuf, float gamma, float vibrancy, float highpow,
pixbuf[i] = pix;
}
''')
class DensityEst(HunkOCode):
"""
NOTE: for now, this *must* be invoked with a block size of (32,32,1), and
a grid size of (W/32,1). At least 21 pixel gutters are required, and the
stride and height probably need to be multiples of 32.
"""
def __init__(self, info):
self.info = info
headers = "#include<math_constants.h>\n"
@property
def defs(self):
return self.defs_tmpl.substitute(info=self.info)
defs_tmpl = Template('''
#define W 21 // Filter width (regardless of standard deviation chosen)
#define W2 10 // Half of filter width, rounded down
#define FW 52 // Width of local result storage (NW+W2+W2)
@ -116,9 +103,7 @@ __device__ void de_add(int ibase, int ii, int jj, float4 scaled) {
__global__
void logscale(float4 *pixbuf, float4 *outbuf, float k1, float k2) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
float den;
float4 pix = pixbuf[i];
read_pix(pix, den);
float ls = fmaxf(0, k1 * logf(1.0f + pix.w * k2) / pix.w);
pix.x *= ls;
@ -137,21 +122,19 @@ void logscale(float4 *pixbuf, float4 *outbuf, float k1, float k2) {
__global__
void density_est(float4 *pixbuf, float4 *outbuf,
float est_sd, float neg_est_curve, float est_min,
float k1, float k2) {
float k1, float k2, int height, int stride) {
for (int i = threadIdx.x + 32*threadIdx.y; i < FW2; i += 32)
de_r[i] = de_g[i] = de_b[i] = de_a[i] = 0.0f;
__syncthreads();
for (int imrow = threadIdx.y + W2; imrow < ({{info.acc_height}} - W2); imrow += 32)
for (int imrow = threadIdx.y + W2; imrow < (height - W2); imrow += 32)
{
int idx = {{info.acc_stride}} * imrow +
+ blockIdx.x * 32 + threadIdx.x + W2;
int idx = stride * imrow + blockIdx.x * 32 + threadIdx.x + W2;
float4 in = pixbuf[idx];
float den;
read_pix(in, den);
float den = in.w;
if (in.w > 0 && den > 0) {
if (den > 0) {
// Compute a fast and dirty approximation of a "gradient" using
// a [[-1 0 0][0 0 0][0 0 1]]/4 matrix (and its reflection)
@ -168,10 +151,10 @@ void density_est(float4 *pixbuf, float4 *outbuf,
// like MLAA.
float *dens = reinterpret_cast<float*>(pixbuf);
int didx = idx * 4 + 3;
float x = 0.25f * ( dens[didx+{{info.acc_stride*4}}+4]
- dens[didx-{{info.acc_stride*4}}-4] );
float y = 0.25f * ( dens[didx+{{info.acc_stride*4}}-4]
- dens[didx-{{info.acc_stride*4}}+4] );
float x = 0.25f * ( dens[didx+stride*4+4]
- dens[didx-stride*4-4] );
float y = 0.25f * ( dens[didx+stride*4-4]
- dens[didx-stride*4+4] );
float diag_mag = sqrtf(x*x + y*y);
float ls = k1 * logf(1.0f + in.w * k2) / in.w;
@ -272,7 +255,7 @@ void density_est(float4 *pixbuf, float4 *outbuf,
__syncthreads();
// TODO: could coalesce this, but what a pain
for (int i = threadIdx.x; i < FW; i += 32) {
idx = {{info.acc_stride}} * imrow + blockIdx.x * 32 + i + W2;
idx = stride * imrow + blockIdx.x * 32 + i + W2;
int si = threadIdx.y * FW + i;
float *out = reinterpret_cast<float*>(&outbuf[idx]);
atomicAdd(out, de_r[si]);
@ -301,34 +284,70 @@ void density_est(float4 *pixbuf, float4 *outbuf,
__syncthreads();
}
}
'''
''')
class Filtering(object):
def invoke(self, mod, cp, abufd, obufd, stream=None):
# TODO: add no-est version
# TODO: come up with a general way to average these parameters
mod = None
k1 = np.float32(cp.color.brightness * 268 / 256)
@classmethod
def init_mod(cls):
if cls.mod is None:
cls.mod = pycuda.compiler.SourceModule(_CODE,
options=['-use_fast_math', '-maxrregcount', '32'])
def __init__(self):
self.init_mod()
def de(self, ddst, dsrc, info, start, stop, stream=None):
# TODO: use integration to obtain parameter values
t = (start + stop) / 2
cp = info.genome
k1 = np.float32(cp.color.brightness(t) * 268 / 256)
# Old definition of area is (w*h/(s*s)). Since new scale 'ns' is now
# s/w, new definition is (w*h/(s*s*w*w)) = (h/(s*s*w))
area = self.info.height / (cp.camera.scale ** 2 * self.info.width)
k2 = np.float32(1 / (area * self.info.density ))
area = info.height / (cp.camera.scale(t) ** 2 * info.width)
k2 = np.float32(1 / (area * info.density))
if cp.de.radius == 0:
nbins = self.info.acc_height * self.info.acc_stride
fun = mod.get_function("logscale")
t = fun(abufd, obufd, k1, k2,
nbins = info.acc_height * info.acc_stride
fun = self.mod.get_function("logscale")
t = fun(dsrc, ddst, k1, k2,
block=(512, 1, 1), grid=(nbins/512, 1), stream=stream)
else:
# flam3_gaussian_filter() uses an implicit standard deviation of
# 0.5, but the DE filters scale filter distance by the default
# spatial support factor of 1.5, so the effective base SD is
# (0.5/1.5)=1/3.
est_sd = np.float32(cp.de.radius / 3.)
neg_est_curve = np.float32(-cp.de.curve)
est_min = np.float32(cp.de.minimum / 3.)
fun = mod.get_function("density_est")
fun(abufd, obufd, est_sd, neg_est_curve, est_min, k1, k2,
block=(32, 32, 1), grid=(self.info.acc_width/32, 1),
stream=stream)
est_sd = np.float32(cp.de.radius(t) / 3.)
neg_est_curve = np.float32(-cp.de.curve(t))
est_min = np.float32(cp.de.minimum(t) / 3.)
fun = self.mod.get_function("density_est")
fun(dsrc, ddst, est_sd, neg_est_curve, est_min, k1, k2,
np.int32(info.acc_height), np.int32(info.acc_stride),
block=(32, 32, 1), grid=(info.acc_width/32, 1), stream=stream)
def colorclip(self, dbuf, info, start, stop, stream=None):
f32 = np.float32
t = (start + stop) / 2
cp = info.genome
nbins = info.acc_height * info.acc_stride
# TODO: implement integration over cubic splines?
gam = f32(1 / cp.color.gamma(t))
vib = f32(cp.color.vibrancy(t))
hipow = f32(cp.color.highlight_power(t))
lin = f32(cp.color.gamma_threshold(t))
lingam = f32(lin ** (gam-1.0) if lin > 0 else 0)
bkgd = vec.make_float3(
cp.color.background.r(t),
cp.color.background.g(t),
cp.color.background.b(t))
color_fun = self.mod.get_function("colorclip")
blocks = int(np.ceil(np.sqrt(nbins / 256)))
color_fun(dbuf, gam, vib, hipow, lin, lingam, bkgd, np.int32(nbins),
np.int32(0),
block=(256, 1, 1), grid=(blocks, blocks), stream=stream)

63
cuburn/render.py
View File

@ -1,6 +1,5 @@
import os
import sys
import math
import re
import time as timemod
import tempfile
@ -18,7 +17,6 @@ from fr0stlib.pyflam3.constants import *
import pycuda.compiler
import pycuda.driver as cuda
import pycuda.tools
from pycuda.gpuarray import vec
import cuburn.genome
from cuburn import affine
@ -50,13 +48,13 @@ class Renderer(object):
def __init__(self, info):
self.info = info
self._iter = self._de = self.src = self.cubin = self.mod = None
self._iter = self.src = self.cubin = self.mod = None
self.packed_genome = None
# Ensure class options don't get contaminated on an instance
self.cmp_options = list(self.cmp_options)
def compile(self, keep=None, cmp_options=None):
def compile(self, keep=None, cmp_options=None, jit_options=[]):
"""
Compile a kernel capable of rendering every frame in this animation.
The resulting compiled kernel is stored in the ``cubin`` property;
@ -72,41 +70,18 @@ class Renderer(object):
cmp_options = self.cmp_options if cmp_options is None else cmp_options
self._iter = iter.IterCode(self.info)
self._de = filtering.DensityEst(self.info)
cclip = filtering.ColorClip(self.info)
self._iter.packer.finalize()
self.src = util.assemble_code(util.BaseCode, mwc.MWC, self._iter.packer,
self._iter, cclip, self._de)
self._iter)
with open(os.path.join(tempfile.gettempdir(), 'kernel.cu'), 'w') as fp:
fp.write(self.src)
self.cubin = pycuda.compiler.compile(
self.src, keep=keep, options=cmp_options,
cache_dir=False if keep else None)
return self.src
def copy(self):
"""
Return a copy of this animation without any references to the current
CUDA context. This can be used to load an animation in multiple CUDA
contexts without recompiling, so that rendering can proceed across
multiple devices - but managing that is up to you.
"""
import copy
new = copy.copy(self)
new.mod = None
return new
def load(self, jit_options=[]):
"""
Replace the currently loaded CUDA module in the active CUDA context
with the compiled code's module. A reference is kept to the module,
meaning that rendering should henceforth only be called from the
thread and context in which this function was called.
"""
if self.cubin is None:
self.compile()
self.mod = cuda.module_from_buffer(self.cubin, jit_options)
with open('/tmp/iter_kern.cubin', 'wb') as fp:
fp.write(self.cubin)
return self.src
def render(self, times):
"""
@ -126,6 +101,8 @@ class Renderer(object):
if times == []:
return
filt = filtering.Filtering()
reset_rb_fun = self.mod.get_function("reset_rb")
packer_fun = self.mod.get_function("interp_iter_params")
palette_fun = self.mod.get_function("interp_palette_hsv")
@ -193,8 +170,6 @@ class Renderer(object):
last_idx = None
for idx, start, stop in times:
cen_cp = cuburn.genome.HacketyGenome(info.genome, (start+stop)/2)
width = np.float32((stop-start) / info.palette_height)
palette_fun(d_palmem, d_palint_times, d_palint_vals,
np.float32(start), width,
@ -234,25 +209,11 @@ class Renderer(object):
grid=(ntemporal_samples, 1),
texrefs=[tref], stream=stream)
stream.synchronize()
util.BaseCode.fill_dptr(self.mod, d_out, 4 * nbins, stream)
self._de.invoke(self.mod, cen_cp, d_accum, d_out, stream)
f32 = np.float32
# TODO: implement integration over cubic splines?
gam = f32(1 / cen_cp.color.gamma)
vib = f32(cen_cp.color.vibrancy)
hipow = f32(cen_cp.color.highlight_power)
lin = f32(cen_cp.color.gamma_threshold)
lingam = f32(math.pow(lin, gam-1.0) if lin > 0 else 0)
bkgd = vec.make_float3(
cen_cp.color.background.r,
cen_cp.color.background.g,
cen_cp.color.background.b)
color_fun = self.mod.get_function("colorclip")
blocks = int(np.ceil(np.sqrt(nbins / 256)))
color_fun(d_out, gam, vib, hipow, lin, lingam, bkgd, np.int32(nbins),
block=(256, 1, 1), grid=(blocks, blocks), stream=stream)
filt.de(d_out, d_accum, info, start, stop, stream)
filt.colorclip(d_out, info, start, stop, stream)
cuda.memcpy_dtoh_async(h_out_a, d_out, stream)
if event_b: