def _sch_rules() -> List[ScheduleRule]:
from tvm.meta_schedule import schedule_rule as M
return [
M.AutoInline(
into_producer=False,
into_consumer=True,
inline_const_tensor=True,
disallow_if_then_else=True,
require_injective=True,
require_ordered=True,
disallow_op=["tir.exp"],
),
M.AddRFactor(max_jobs_per_core=16, max_innermost_factor=64),
M.MultiLevelTiling(
structure="SSRSRS",
tile_binds=None,
max_innermost_factor=64,
vector_load_lens=None,
reuse_read=None,
reuse_write=M.ReuseType(
req="may",
levels=[1, 2],
scope="global",
),
),
M.ParallelizeVectorizeUnroll(
max_jobs_per_core=16,
max_vectorize_extent=64,
unroll_max_steps=[0, 16, 64, 512],
unroll_explicit=True,
),
M.RandomComputeLocation(),
]
def _sch_rules():
from tvm.meta_schedule import ( # pylint: disable=import-outside-toplevel
schedule_rule as M, )
return [
M.AutoInline(
into_producer=False,
into_consumer=True,
# into_cache_only=False,
inline_const_tensor=True,
disallow_if_then_else=False,
require_injective=False,
require_ordered=False,
disallow_op=None,
),
M.MultiLevelTiling(
structure="SSSRRSRS",
tile_binds=["blockIdx.x", "blockIdx.y", "threadIdx.y"],
# use_tensor_core=True,
max_innermost_factor=64,
vector_load_lens=[1, 2, 3, 4],
reuse_read=schedule_rule.ReuseType(
req="must",
levels=[4],
scope="shared",
),
reuse_write=schedule_rule.ReuseType(
req="no",
levels=[],
scope="",
),
),
M.AutoInline(
into_producer=True,
into_consumer=True,
# into_cache_only=True,
inline_const_tensor=True,
disallow_if_then_else=False,
require_injective=False,
require_ordered=False,
disallow_op=None,
),
M.ParallelizeVectorizeUnroll(
max_jobs_per_core=-1, # disable parallelize
max_vectorize_extent=-1, # disable vectorize
unroll_max_steps=[0, 16, 64, 512, 1024],
unroll_explicit=True,
),
]
def _sch_rules() -> List[ScheduleRule]:
from tvm.meta_schedule import schedule_rule as M
return [
M.MultiLevelTiling(
structure="SSSRRSRS",
tile_binds=["blockIdx.x", "vthread.x", "threadIdx.x"],
max_innermost_factor=64,
vector_load_lens=[1, 2, 3, 4],
reuse_read=M.ReuseType(
req="must",
levels=[4],
scope="shared",
),
reuse_write=M.ReuseType(
req="must",
levels=[3],
scope="local",
),
),
M.AutoInline(
into_producer=True,
into_consumer=True,
inline_const_tensor=True,
disallow_if_then_else=False,
require_injective=False,
require_ordered=False,
disallow_op=None,
),
M.CrossThreadReduction(
thread_extents=[4, 8, 16, 32, 64, 128, 256, 512]),
M.ParallelizeVectorizeUnroll(
max_jobs_per_core=-1, # disable parallelize
max_vectorize_extent=-1, # disable vectorize
unroll_max_steps=[0, 16, 64, 512, 1024],
unroll_explicit=True,
),
M.AutoBind(
max_threadblocks=256,
thread_extents=[32, 64, 128, 256, 512, 1024],
),
]
tile_binds=None,
max_innermost_factor=64,
vector_load_lens=None,
reuse_read=None,
reuse_write=schedule_rule.ReuseType(
req="may",
levels=[1, 2],
scope="global",
),
),
schedule_rule.MultiLevelTiling(
structure="SSRSRS",
tile_binds=None,
max_innermost_factor=64,
vector_load_lens=None,
reuse_read=None,
reuse_write=schedule_rule.ReuseType(
req="may",
levels=[1, 2],
scope="global",
),
),
schedule_rule.ParallelizeVectorizeUnroll(
max_jobs_per_core=16,
max_vectorize_extent=64,
unroll_max_steps=[0, 16, 64, 512],
unroll_explicit=True,
),
schedule_rule.RandomComputeLocation(),
]