def compile(self):
if self.kernel is None:
params = set(self.sources) | set(self.sinks)
seen_decls = set()
seen_params = set()
decls = []
filtered = set()
for param in params:
if param.level == 'register':
if param.name not in seen_decls:
seen_decls.add(param.name)
decls.append('float {}'.format(param.name))
else:
if param.name not in seen_params:
seen_params.add(param.name)
filtered.add(param)
self.params = list(filtered)
self.params.sort(key=lambda x: x.name)
params = []
sinks = set(sink.name for sink in self.sinks)
for param in self.params:
if param.name in sinks:
str = "global float* {}".format(param.name)
else:
str = "global const float* {}".format(param.name)
params.append(str)
params_str = ", ".join(params)
decls = ";\n\t\t\t".join(decls) + ";\n"
kernel_name = get_unique_kernel_name()
kernel = Template("""
__kernel void $name($params) {
int index = get_global_id(0);
if (index < $num_work_items) {
$decls
$body
}
}
""").substitute(name=kernel_name,
params=params_str,
body=self.body,
decls=decls,
num_work_items=self.launch_parameters[0])
# print([p.name for p in self.params])
print(kernel)
self.kernel_str = kernel
kernel = cl.clCreateProgramWithSource(
context, kernel).build()[kernel_name]
kernel.argtypes = tuple(cl.cl_mem for _ in self.params)
self.kernel = kernel
def compile(self):
if self.kernel is None:
params = set(self.sources) | set(self.sinks)
seen_decls = set()
seen_params = set()
decls = []
filtered = set()
for param in params:
if param.level == 'register':
if param.name not in seen_decls:
seen_decls.add(param.name)
decls.append('float {}'.format(param.name))
else:
if param.name not in seen_params:
seen_params.add(param.name)
filtered.add(param)
self.params = list(filtered)
self.params.sort(key=lambda x: x.name)
params = []
sinks = set(sink.name for sink in self.sinks)
for param in self.params:
if param.name in sinks:
str = "global float* {}".format(param.name)
else:
str = "global const float* {}".format(param.name)
params.append(str)
params_str = ", ".join(params)
decls = ";\n\t\t\t".join(decls) + ";\n"
kernel_name = get_unique_kernel_name()
kernel = Template("""
__kernel void $name($params) {
int index = get_global_id(0);
if (index < $num_work_items) {
$decls
$body
}
}
""").substitute(name=kernel_name, params=params_str, body=self.body, decls=decls,
num_work_items=self.launch_parameters[0])
# print([p.name for p in self.params])
print(kernel)
self.kernel_str = kernel
kernel = cl.clCreateProgramWithSource(
context, kernel).build()[kernel_name]
kernel.argtypes = tuple(cl.cl_mem for _ in self.params)
self.kernel = kernel
def get_launcher(cls, sources, sinks, keywords, symbol_table):
kernel_h, kernel_w = keywords['kernel_size']
pad_h, pad_w = keywords['padding']
stride_h, stride_w = keywords['stride']
num, channels, height, width = symbol_table[sources[0].name].shape
channels_col = channels * kernel_h * kernel_w
# height_col = (height + 2 * pad_h - kernel_h) // stride_h + 1
# width_col = (width + 2 * pad_w - kernel_w) // stride_w + 1
out_channels, height_col, width_col = symbol_table[sinks[0].name].shape[1:]
is_1x1 = kernel_w == 1 and kernel_h == 1 and stride_h == 1 and \
stride_w == 1 and pad_h == 0 and pad_w == 0
if not is_1x1:
col_datas = [hmarray((channels_col, height_col * width_col))
for _ in range(len(queues))]
bias_multiplier = hmarray(
(1, np.prod(symbol_table[sinks[0].name].shape[2:])))
bias_multiplier.fill(1.0)
bias_multiplier.sync_ocl()
im2col_global_size = channels * height_col * width_col
im2col = Template("""
__kernel void im2col(global const float* data_im, global float* data_col,
int bot_offset) {
if (get_global_id(0) < $global_size) {
int index = get_global_id(0);
int h_index = index / $width_col;
int w_out = index - h_index * $width_col;
int channel_in = h_index / $height_col;
int h_out = h_index - channel_in * $height_col;
int channel_out = channel_in * $kernel_h * $kernel_w;
int h_in = h_out * $stride_h - $pad_h;
int w_in = w_out * $stride_w - $pad_w;
global float* data_col_ptr = data_col;
data_col_ptr += (channel_out * $height_col + h_out) * $width_col + w_out;
global const float* data_im_ptr = data_im + bot_offset;
data_im_ptr += (channel_in * $height + h_in) * $width + w_in;
#pragma unroll
for (int i = 0; i < $kernel_h; ++i) {
#pragma unroll
for (int j = 0; j < $kernel_w; ++j) {
int h = h_in + i;
int w = w_in + j;
*data_col_ptr = (h >= 0 && w >= 0 && h < $height && w < $width) ?
data_im_ptr[i * $width + j] : 0;
data_col_ptr += $height_col * $width_col;
}
}
}
}
""").substitute(global_size=im2col_global_size, stride_h=stride_h,
stride_w=stride_w, pad_h=pad_h, pad_w=pad_w,
kernel_h=kernel_h, kernel_w=kernel_w, width=width,
height=height, height_col=height_col,
width_col=width_col)
im2col = cl.clCreateProgramWithSource(
context, im2col
).build()['im2col']
im2col.argtypes = (cl.cl_mem, cl.cl_mem, cl.cl_int)
if im2col_global_size % 64:
padded = (im2col_global_size + 63) & (~63)
else:
padded = im2col_global_size
class ConvLauncher(object):
def __init__(self, sources, sinks):
self.sources = sources
self.sinks = sinks
def compile(self):
pass
def launch(self, symbol_table, wait_for):
bottom = symbol_table[sources[0].name]
bot_offset = np.prod(bottom.shape[1:])
weights = symbol_table[sources[1].name]
bias = symbol_table[sources[2].name]
top = symbol_table[sinks[0].name]
top_offset = np.prod(top.shape[1:])
m = weights.shape[0]
n = np.prod(top.shape[2:])
k = np.prod(weights.shape[1:])
# cl.clFinish(queues[0])
evts = []
if is_1x1:
for i in range(bottom.shape[0]):
evt = sgemm(False, False, 1.0, weights, 0, k,
bottom, i * bot_offset, n, 0.0,
top, i * top_offset, n, m, n,
k, queues[i % len(queues)], wait_for=wait_for)
evt = sgemm(False, False, 1.0, bias, 0, 1,
bias_multiplier, 0, n, 1.0, top, i *
top_offset, n, m, n, 1, queues[i % len(queues)], wait_for=evt)
evts.append(evt)
else:
for i in range(bottom.shape[0]):
evt = im2col(bottom.ocl_buf,
col_datas[i % len(queues)].ocl_buf,
i * bot_offset
).on(queues[i % len(queues)], (padded, ),
wait_for=wait_for)
evt = sgemm(False, False, 1.0, weights, 0, k,
col_datas[i % len(queues)],
0, n, 0.0, top, i * top_offset, n, m, n,
k, queues[i % len(queues)], wait_for=evt)
evt = sgemm(False, False, 1.0, bias, 0, 1,
bias_multiplier, 0, n, 1.0, top, i *
top_offset, n, m, n, 1, queues[i % len(queues)], wait_for=evt)
evts.append(evt)
return evts
# for q in queues:
# cl.clFinish(q)
return ConvLauncher(sources, sinks)
def get_launcher(cls, sources, sinks, keywords, symbol_table):
kernel_h, kernel_w = keywords['kernel_size']
pad_h, pad_w = keywords['padding']
stride_h, stride_w = keywords['stride']
num, channels, height, width = symbol_table[sources[0].name].shape
channels_col = channels * kernel_h * kernel_w
# height_col = (height + 2 * pad_h - kernel_h) // stride_h + 1
# width_col = (width + 2 * pad_w - kernel_w) // stride_w + 1
out_channels, height_col, width_col = symbol_table[
sinks[0].name].shape[1:]
is_1x1 = kernel_w == 1 and kernel_h == 1 and stride_h == 1 and \
stride_w == 1 and pad_h == 0 and pad_w == 0
if not is_1x1:
col_datas = [
hmarray((channels_col, height_col * width_col))
for _ in range(len(queues))
]
bias_multiplier = hmarray(
(1, np.prod(symbol_table[sinks[0].name].shape[2:])))
bias_multiplier.fill(1.0)
bias_multiplier.sync_ocl()
im2col_global_size = channels * height_col * width_col
im2col = Template("""
__kernel void im2col(global const float* data_im, global float* data_col,
int bot_offset) {
if (get_global_id(0) < $global_size) {
int index = get_global_id(0);
int h_index = index / $width_col;
int w_out = index - h_index * $width_col;
int channel_in = h_index / $height_col;
int h_out = h_index - channel_in * $height_col;
int channel_out = channel_in * $kernel_h * $kernel_w;
int h_in = h_out * $stride_h - $pad_h;
int w_in = w_out * $stride_w - $pad_w;
global float* data_col_ptr = data_col;
data_col_ptr += (channel_out * $height_col + h_out) * $width_col + w_out;
global const float* data_im_ptr = data_im + bot_offset;
data_im_ptr += (channel_in * $height + h_in) * $width + w_in;
#pragma unroll
for (int i = 0; i < $kernel_h; ++i) {
#pragma unroll
for (int j = 0; j < $kernel_w; ++j) {
int h = h_in + i;
int w = w_in + j;
*data_col_ptr = (h >= 0 && w >= 0 && h < $height && w < $width) ?
data_im_ptr[i * $width + j] : 0;
data_col_ptr += $height_col * $width_col;
}
}
}
}
""").substitute(global_size=im2col_global_size,
stride_h=stride_h,
stride_w=stride_w,
pad_h=pad_h,
pad_w=pad_w,
kernel_h=kernel_h,
kernel_w=kernel_w,
width=width,
height=height,
height_col=height_col,
width_col=width_col)
im2col = cl.clCreateProgramWithSource(context,
im2col).build()['im2col']
im2col.argtypes = (cl.cl_mem, cl.cl_mem, cl.cl_int)
if im2col_global_size % 64:
padded = (im2col_global_size + 63) & (~63)
else:
padded = im2col_global_size
class ConvLauncher(object):
def __init__(self, sources, sinks):
self.sources = sources
self.sinks = sinks
def compile(self):
pass
def launch(self, symbol_table, wait_for):
bottom = symbol_table[sources[0].name]
bot_offset = np.prod(bottom.shape[1:])
weights = symbol_table[sources[1].name]
bias = symbol_table[sources[2].name]
top = symbol_table[sinks[0].name]
top_offset = np.prod(top.shape[1:])
m = weights.shape[0]
n = np.prod(top.shape[2:])
k = np.prod(weights.shape[1:])
# cl.clFinish(queues[0])
evts = []
if is_1x1:
for i in range(bottom.shape[0]):
evt = sgemm(False,
False,
1.0,
weights,
0,
k,
bottom,
i * bot_offset,
n,
0.0,
top,
i * top_offset,
n,
m,
n,
k,
queues[i % len(queues)],
wait_for=wait_for)
evt = sgemm(False,
False,
1.0,
bias,
0,
1,
bias_multiplier,
0,
n,
1.0,
top,
i * top_offset,
n,
m,
n,
1,
queues[i % len(queues)],
wait_for=evt)
evts.append(evt)
else:
for i in range(bottom.shape[0]):
evt = im2col(bottom.ocl_buf,
col_datas[i % len(queues)].ocl_buf,
i * bot_offset).on(
queues[i % len(queues)],
(padded, ),
wait_for=wait_for)
evt = sgemm(False,
False,
1.0,
weights,
0,
k,
col_datas[i % len(queues)],
0,
n,
0.0,
top,
i * top_offset,
n,
m,
n,
k,
queues[i % len(queues)],
wait_for=evt)
evt = sgemm(False,
False,
1.0,
bias,
0,
1,
bias_multiplier,
0,
n,
1.0,
top,
i * top_offset,
n,
m,
n,
1,
queues[i % len(queues)],
wait_for=evt)
evts.append(evt)
return evts
# for q in queues:
# cl.clFinish(q)
return ConvLauncher(sources, sinks)
