def sort_within_ub(instance: tik.Tik, src, cols):
"""
sort_within_ub
"""
with instance.new_stmt_scope():
dst = instance.Tensor(src.dtype,
src.shape,
scope=tik.scope_ubuf,
name="ub_sort_within")
_vrpsort16(instance, dst, src, cnt=cols)
if cols > 16:
result_ub = _merge_region(instance,
out_ub=src,
dst=src,
src=dst,
rows=1,
cols=cols)
else:
result_ub = dst
if result_ub.name != src.name:
burst = math.ceil(cols * src.shape[1] *
common_util.get_data_size(src.dtype) / 32)
instance.data_move(src, result_ub, 0, 1, burst, 0, 0)
return src
def __init__(self, input_values, inst: tik.Tik):
self.tik_instance = inst
dtype = "int64"
# data in tiling_gm likes:
# 0---- 1---- 2---- 3----
# axis, out_dim, max_inner_dim, min_inner_dim,
# 4---- 5----
# output_inner_length, input_count
# 6---- 7----
# reserve, reserve
# 8---- 9----
# first_inner_dims, first_output_idx,
# second_inner_dims, second_output_idx
# ...
self.dtype = dtype
self.input_values = input_values
self.axis = inst.Scalar(dtype, name="axis")
self.out_dim = inst.Scalar(dtype, name="out_dim")
self.max_inner_dim = inst.Scalar(dtype, name="max_inner_dim")
self.min_inner_dim = inst.Scalar(dtype, name="min_inner_dim")
self.output_inner_length = inst.Scalar(dtype,
name="output_inner_length")
tiling_ub_size = max(len(input_values) * 2, 8)
tiling_gm_size = 8 + tiling_ub_size
tiling_gm_size = ceil_32bytes_align_count(tiling_gm_size, dtype)
tiling_ub_size = ceil_32bytes_align_count(tiling_ub_size, dtype)
self.tiling_ub_size = tiling_ub_size
self.tiling_gm = inst.Tensor(dtype, (tiling_gm_size, ),
name="tiling_gm",
scope=tik.scope_gm)
self._need_ub_size = (self.tiling_ub_size *
common_util.get_data_size(dtype))
self._tiling_ub = None
self._out_dim = None
self._inner_dim = None
def __init__(self, input_x_shape, inst: tik.Tik):
"""
tiling param
:param input_x_shape: input shape
:param inst: tik instance
"""
self.tik_instance = inst
dtype = "int64"
self.dtype = dtype
# input_shape, output_shape, begin, end, stride
tiling_gm_size = len(input_x_shape) * 5
self.tiling_gm = inst.Tensor(dtype, (tiling_gm_size,), name="tiling_gm", scope=tik.scope_gm)
def gen_shape(name, index):
name += str(index)
return inst.Scalar(dtype, name=name)
self.input_shape = tuple(map(lambda x: gen_shape("input_dim", x[0]), enumerate(input_x_shape)))
self.begin = tuple(map(lambda x: gen_shape("begin_", x[0]), enumerate(input_x_shape)))
self.end = tuple(map(lambda x: gen_shape("end_", x[0]), enumerate(input_x_shape)))
self.stride = tuple(map(lambda x: gen_shape("stride_", x[0]), enumerate(input_x_shape)))
self.output_shape = tuple(map(lambda x: gen_shape("out_dim_", x[0]), enumerate(input_x_shape)))
self.out_dim = inst.Scalar(dtype, name="out_dim")
def _merge_recur(instance: tik.Tik,
out_ub,
dst_ub,
src_ub,
last_dim,
total_region_list,
level,
region_offset=0):
"""
_merge_recur
merge multi sorted region proposal list to one sorted region proposal list
"""
# vmrgsort4 can merger at most 4 sorted region list
def is_next_to_last_merge():
return 1 < math.ceil(total_region_list / 4) <= 4
loops = total_region_list // 4
remain = total_region_list % 4
if is_next_to_last_merge() and dst_ub.name == out_ub.name:
dst_ub = instance.Tensor(out_ub.dtype,
out_ub.shape,
scope=tik.scope_ubuf,
name="ub_merge_recur")
merge_n0 = 16 * (4**(level - 1))
merge_n1 = merge_n0
merge_n2 = merge_n0
merge_n3 = merge_n0
merge_repeat = loops
need_tail_process = False
if loops > 0 and remain == 0:
if merge_n0 * 4 * loops > last_dim:
merge_repeat = loops - 1
n012 = merge_n0 + merge_n1 + merge_n2
merge_left = last_dim - ((merge_n0 * 4 * (loops - 1)) + n012)
need_tail_process = True
if merge_repeat > 0:
ub_offset = region_offset
src_list = (src_ub[ub_offset], src_ub[ub_offset + merge_n0 * 8],
src_ub[ub_offset + merge_n0 * 8 + merge_n1 * 8],
src_ub[ub_offset + merge_n0 * 8 + merge_n1 * 8 +
merge_n2 * 8])
element_count_list = (merge_n0, merge_n1, merge_n2, merge_n3)
valid_bit = 15
instance.vmrgsort4(dst_ub[ub_offset], src_list, element_count_list,
False, valid_bit, merge_repeat)
if need_tail_process:
tail_offset = 4 * merge_n0 * merge_repeat * 8
ub_offset = region_offset + tail_offset
src_list = (src_ub[ub_offset], src_ub[ub_offset + merge_n0 * 8],
src_ub[ub_offset + merge_n0 * 8 + merge_n1 * 8],
src_ub[ub_offset + merge_n0 * 8 + merge_n1 * 8 +
merge_n2 * 8])
element_count_list = (merge_n0, merge_n1, merge_n2, merge_left)
valid_bit = 15
instance.vmrgsort4(dst_ub[ub_offset],
src_list,
element_count_list,
False,
valid_bit,
repeat_times=1)
if loops > 0:
offset = 4 * loops * 16 * (4**(level - 1))
else:
offset = 0
if remain == 3:
merge_n0 = 16 * (4**(level - 1))
merge_n1 = merge_n0
merge_n2 = last_dim - (offset + merge_n0 + merge_n1)
ub_offset = region_offset + offset * 8
src_list = (src_ub[ub_offset], src_ub[ub_offset + merge_n0 * 8],
src_ub[ub_offset + merge_n0 * 8 + merge_n1 * 8],
src_ub[ub_offset + merge_n0 * 8 + merge_n1 * 8 +
merge_n2 * 8])
element_count_list = (merge_n0, merge_n1, merge_n2, 0)
valid_bit = 2**remain - 1
instance.vmrgsort4(dst_ub[ub_offset],
src_list,
element_count_list,
False,
valid_bit,
repeat_times=1)
elif remain == 2:
merge_n0 = 16 * (4**(level - 1))
merge_n1 = last_dim - (offset + merge_n0)
ub_offset = region_offset + offset * 8
src_list = (src_ub[ub_offset], src_ub[ub_offset + merge_n0 * 8],
src_ub[ub_offset + merge_n0 * 8 + merge_n1 * 8],
src_ub[ub_offset + merge_n0 * 8 + merge_n1 * 8 +
merge_n2 * 8])
element_count_list = (merge_n0, merge_n1, 0, 0)
valid_bit = 2**remain - 1
instance.vmrgsort4(dst_ub[ub_offset],
src_list,
element_count_list,
False,
valid_bit,
repeat_times=1)
elif remain == 1:
merge_n0 = last_dim - offset
num_blocks_write = (
merge_n0 * 8 * common_util.get_data_size(src_ub.dtype) + 31) // 32
ub_offset = region_offset + offset * 8
instance.data_move(dst_ub[ub_offset], src_ub[ub_offset], 0, 1,
num_blocks_write, 0, 0)
next_total_region_list = math.ceil(total_region_list / 4)
if next_total_region_list <= 1:
return dst_ub
if is_next_to_last_merge():
src_ub = out_ub
return _merge_recur(instance, out_ub, src_ub, dst_ub, last_dim,
next_total_region_list, level + 1, region_offset)