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.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() -> 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],
),
]
def get_sch_rules_for_dp4a(intrin):
return [
schedule_rule.MultiLevelTilingWithIntrin(
intrin,
structure="SSSRRSRS",
tile_binds=["blockIdx.x", "vthread.x", "threadIdx.x"],
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="must",
levels=[3],
scope="local",
),
),
schedule_rule.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,
),
schedule_rule.CrossThreadReduction(
thread_extents=[4, 8, 16, 32, 64, 128, 256, 512]),
schedule_rule.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 test_multi_level_tiling_dense_dpa4():
m, n, k = 128, 128, 128
X = te.placeholder((m, k), name="X", dtype="int8")
W = te.placeholder((n, k), name="W", dtype="int8")
ak = te.reduce_axis((0, k), name="k")
matmul = te.compute(
(m, n),
lambda i, j: te.sum(
X[i, ak].astype("int32") * W[j, ak].astype("int32"),
axis=ak,
),
name="compute",
)
func = te.create_prim_func([X, W, matmul])
ctx = _create_context(
func,
target=tvm.target.Target("cuda"),
rule=schedule_rule.MultiLevelTilingWithIntrin(
DP4A_INTRIN,
structure="SSSRRSRS",
tile_binds=["blockIdx.x", "vthread.x", "threadIdx.x"],
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="must",
levels=[3],
scope="local",
),
),
)
spaces = ctx.space_generator.generate_design_space(mod=ctx.mod)
expected = [
"""b0 = sch.get_block(name="compute", func_name="main")
sch.annotate(block_or_loop=b0, ann_key="meta_schedule.tiling_structure", ann_val="SSSRRSRS")
l1, l2, l3 = sch.get_loops(block=b0)
l4, l5 = sch.split(loop=l3, factors=[32, 4])
sch.reorder(l5)
b6 = sch.blockize(loop=l5)
sch.annotate(block_or_loop=b6, ann_key="meta_schedule.auto_tensorize", ann_val="dp4a")
l7, l8, l9 = sch.get_loops(block=b6)
v10, v11, v12, v13, v14 = sch.sample_perfect_tile(loop=l7, n=5, max_innermost_factor=64)
l15, l16, l17, l18, l19 = sch.split(loop=l7, factors=[v10, v11, v12, v13, v14])
v20, v21, v22, v23, v24 = sch.sample_perfect_tile(loop=l8, n=5, max_innermost_factor=64)
l25, l26, l27, l28, l29 = sch.split(loop=l8, factors=[v20, v21, v22, v23, v24])
v30, v31, v32 = sch.sample_perfect_tile(loop=l9, n=3, max_innermost_factor=64)
l33, l34, l35 = sch.split(loop=l9, factors=[v30, v31, v32])
sch.reorder(l15, l25, l16, l26, l17, l27, l33, l34, l18, l28, l35, l19, l29)
l36 = sch.fuse(l15, l25)
sch.bind(loop=l36, thread_axis="blockIdx.x")
l37 = sch.fuse(l16, l26)
sch.bind(loop=l37, thread_axis="vthread.x")
l38 = sch.fuse(l17, l27)
sch.bind(loop=l38, thread_axis="threadIdx.x")
b39 = sch.cache_write(block=b6, write_buffer_index=0, storage_scope="local")
sch.reverse_compute_at(block=b39, loop=l38, preserve_unit_loops=True)
b40 = sch.cache_read(block=b6, read_buffer_index=0, storage_scope="shared")
sch.compute_at(block=b40, loop=l33, preserve_unit_loops=True)
l41, l42, l43, l44, l45, l46 = sch.get_loops(block=b40)
l47 = sch.fuse(l45, l46)
v48 = sch.sample_categorical(candidates=[1, 2, 3, 4], probs=[0.25, 0.25, 0.25, 0.25])
sch.annotate(block_or_loop=b40, ann_key="meta_schedule.cooperative_fetch", ann_val=v48)
b49 = sch.cache_read(block=b6, read_buffer_index=1, storage_scope="shared")
sch.compute_at(block=b49, loop=l33, preserve_unit_loops=True)
l50, l51, l52, l53, l54, l55 = sch.get_loops(block=b49)
l56 = sch.fuse(l54, l55)
v57 = sch.sample_categorical(candidates=[1, 2, 3, 4], probs=[0.25, 0.25, 0.25, 0.25])
sch.annotate(block_or_loop=b49, ann_key="meta_schedule.cooperative_fetch", ann_val=v57)""".split(
"\n"
)
]
check_trace(spaces, expected)
def test_multi_level_tiling_conv2d_nchwc_vnni():
target = "llvm -mcpu=cascadelake -num-cores 4"
ctx = _create_context(
Conv2dNCHWcVNNIModule,
target=tvm.target.Target(target),
rule=schedule_rule.MultiLevelTilingWithIntrin(
VNNI_INTRIN,
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",
),
),
)
spaces = ctx.space_generator.generate_design_space(mod=ctx.mod)
expected = [
"""b0 = sch.get_block(name="conv2d_NCHWc_int8", func_name="main")
sch.annotate(block_or_loop=b0, ann_key="meta_schedule.tiling_structure", ann_val="SSRSRS")
l1, l2, l3, l4, l5, l6, l7, l8, l9, l10 = sch.get_loops(block=b0)
l11, l12 = sch.split(loop=l10, factors=[1, 4])
l13, l14 = sch.split(loop=l5, factors=[1, 16])
l15, l16, l17, l18, l19, l20, l21, l22, l23, l24, l25, l26 = sch.get_loops(block=b0)
sch.reorder(l21, l22, l23, l24, l25, l14, l12)
b27 = sch.blockize(loop=l14)
sch.annotate(block_or_loop=b27, ann_key="meta_schedule.auto_tensorize", ann_val="dot_16x4_vnni")
l28, l29, l30, l31, l32, l33, l34, l35, l36, l37 = sch.get_loops(block=b27)
v38, v39, v40, v41 = sch.sample_perfect_tile(loop=l28, n=4, max_innermost_factor=64)
l42, l43, l44, l45 = sch.split(loop=l28, factors=[v38, v39, v40, v41])
v46, v47, v48, v49 = sch.sample_perfect_tile(loop=l29, n=4, max_innermost_factor=64)
l50, l51, l52, l53 = sch.split(loop=l29, factors=[v46, v47, v48, v49])
v54, v55, v56, v57 = sch.sample_perfect_tile(loop=l30, n=4, max_innermost_factor=64)
l58, l59, l60, l61 = sch.split(loop=l30, factors=[v54, v55, v56, v57])
v62, v63, v64, v65 = sch.sample_perfect_tile(loop=l31, n=4, max_innermost_factor=64)
l66, l67, l68, l69 = sch.split(loop=l31, factors=[v62, v63, v64, v65])
v70, v71, v72, v73 = sch.sample_perfect_tile(loop=l32, n=4, max_innermost_factor=64)
l74, l75, l76, l77 = sch.split(loop=l32, factors=[v70, v71, v72, v73])
v78, v79 = sch.sample_perfect_tile(loop=l33, n=2, max_innermost_factor=64)
l80, l81 = sch.split(loop=l33, factors=[v78, v79])
v82, v83 = sch.sample_perfect_tile(loop=l34, n=2, max_innermost_factor=64)
l84, l85 = sch.split(loop=l34, factors=[v82, v83])
v86, v87 = sch.sample_perfect_tile(loop=l35, n=2, max_innermost_factor=64)
l88, l89 = sch.split(loop=l35, factors=[v86, v87])
v90, v91 = sch.sample_perfect_tile(loop=l36, n=2, max_innermost_factor=64)
l92, l93 = sch.split(loop=l36, factors=[v90, v91])
v94, v95 = sch.sample_perfect_tile(loop=l37, n=2, max_innermost_factor=64)
l96, l97 = sch.split(loop=l37, factors=[v94, v95])
sch.reorder(l42, l50, l58, l66, l74, l43, l51, l59, l67, l75, l80, l84, l88, l92, l96, l44, l52, l60, l68, l76, l81, l85, l89, l93, l97, l45, l53, l61, l69, l77)
b98 = sch.cache_write(block=b27, write_buffer_index=0, storage_scope="global")
sch.reverse_compute_at(block=b98, loop=l75, preserve_unit_loops=True)""".split(
"\n"
),
"""b0 = sch.get_block(name="conv2d_NCHWc_int8", func_name="main")
sch.annotate(block_or_loop=b0, ann_key="meta_schedule.tiling_structure", ann_val="SSRSRS")
l1, l2, l3, l4, l5, l6, l7, l8, l9, l10 = sch.get_loops(block=b0)
l11, l12 = sch.split(loop=l10, factors=[1, 4])
l13, l14 = sch.split(loop=l5, factors=[1, 16])
l15, l16, l17, l18, l19, l20, l21, l22, l23, l24, l25, l26 = sch.get_loops(block=b0)
sch.reorder(l21, l22, l23, l24, l25, l14, l12)
b27 = sch.blockize(loop=l14)
sch.annotate(block_or_loop=b27, ann_key="meta_schedule.auto_tensorize", ann_val="dot_16x4_vnni")
l28, l29, l30, l31, l32, l33, l34, l35, l36, l37 = sch.get_loops(block=b27)
v38, v39, v40, v41 = sch.sample_perfect_tile(loop=l28, n=4, max_innermost_factor=64)
l42, l43, l44, l45 = sch.split(loop=l28, factors=[v38, v39, v40, v41])
v46, v47, v48, v49 = sch.sample_perfect_tile(loop=l29, n=4, max_innermost_factor=64)
l50, l51, l52, l53 = sch.split(loop=l29, factors=[v46, v47, v48, v49])
v54, v55, v56, v57 = sch.sample_perfect_tile(loop=l30, n=4, max_innermost_factor=64)
l58, l59, l60, l61 = sch.split(loop=l30, factors=[v54, v55, v56, v57])
v62, v63, v64, v65 = sch.sample_perfect_tile(loop=l31, n=4, max_innermost_factor=64)
l66, l67, l68, l69 = sch.split(loop=l31, factors=[v62, v63, v64, v65])
v70, v71, v72, v73 = sch.sample_perfect_tile(loop=l32, n=4, max_innermost_factor=64)
l74, l75, l76, l77 = sch.split(loop=l32, factors=[v70, v71, v72, v73])
v78, v79 = sch.sample_perfect_tile(loop=l33, n=2, max_innermost_factor=64)
l80, l81 = sch.split(loop=l33, factors=[v78, v79])
v82, v83 = sch.sample_perfect_tile(loop=l34, n=2, max_innermost_factor=64)
l84, l85 = sch.split(loop=l34, factors=[v82, v83])
v86, v87 = sch.sample_perfect_tile(loop=l35, n=2, max_innermost_factor=64)
l88, l89 = sch.split(loop=l35, factors=[v86, v87])
v90, v91 = sch.sample_perfect_tile(loop=l36, n=2, max_innermost_factor=64)
l92, l93 = sch.split(loop=l36, factors=[v90, v91])
v94, v95 = sch.sample_perfect_tile(loop=l37, n=2, max_innermost_factor=64)
l96, l97 = sch.split(loop=l37, factors=[v94, v95])
sch.reorder(l42, l50, l58, l66, l74, l43, l51, l59, l67, l75, l80, l84, l88, l92, l96, l44, l52, l60, l68, l76, l81, l85, l89, l93, l97, l45, l53, l61, l69, l77)
b98 = sch.cache_write(block=b27, write_buffer_index=0, storage_scope="global")
sch.reverse_compute_at(block=b98, loop=l74, preserve_unit_loops=True)""".split(
"\n"
),
"""b0 = sch.get_block(name="conv2d_NCHWc_int8", func_name="main")
sch.annotate(block_or_loop=b0, ann_key="meta_schedule.tiling_structure", ann_val="SSRSRS")
l1, l2, l3, l4, l5, l6, l7, l8, l9, l10 = sch.get_loops(block=b0)
l11, l12 = sch.split(loop=l10, factors=[1, 4])
l13, l14 = sch.split(loop=l5, factors=[1, 16])
l15, l16, l17, l18, l19, l20, l21, l22, l23, l24, l25, l26 = sch.get_loops(block=b0)
sch.reorder(l21, l22, l23, l24, l25, l14, l12)
b27 = sch.blockize(loop=l14)
sch.annotate(block_or_loop=b27, ann_key="meta_schedule.auto_tensorize", ann_val="dot_16x4_vnni")
l28, l29, l30, l31, l32, l33, l34, l35, l36, l37 = sch.get_loops(block=b27)
v38, v39, v40, v41 = sch.sample_perfect_tile(loop=l28, n=4, max_innermost_factor=64)
l42, l43, l44, l45 = sch.split(loop=l28, factors=[v38, v39, v40, v41])
v46, v47, v48, v49 = sch.sample_perfect_tile(loop=l29, n=4, max_innermost_factor=64)
l50, l51, l52, l53 = sch.split(loop=l29, factors=[v46, v47, v48, v49])
v54, v55, v56, v57 = sch.sample_perfect_tile(loop=l30, n=4, max_innermost_factor=64)
l58, l59, l60, l61 = sch.split(loop=l30, factors=[v54, v55, v56, v57])
v62, v63, v64, v65 = sch.sample_perfect_tile(loop=l31, n=4, max_innermost_factor=64)
l66, l67, l68, l69 = sch.split(loop=l31, factors=[v62, v63, v64, v65])
v70, v71, v72, v73 = sch.sample_perfect_tile(loop=l32, n=4, max_innermost_factor=64)
l74, l75, l76, l77 = sch.split(loop=l32, factors=[v70, v71, v72, v73])
v78, v79 = sch.sample_perfect_tile(loop=l33, n=2, max_innermost_factor=64)
l80, l81 = sch.split(loop=l33, factors=[v78, v79])
v82, v83 = sch.sample_perfect_tile(loop=l34, n=2, max_innermost_factor=64)
l84, l85 = sch.split(loop=l34, factors=[v82, v83])
v86, v87 = sch.sample_perfect_tile(loop=l35, n=2, max_innermost_factor=64)
l88, l89 = sch.split(loop=l35, factors=[v86, v87])
v90, v91 = sch.sample_perfect_tile(loop=l36, n=2, max_innermost_factor=64)
l92, l93 = sch.split(loop=l36, factors=[v90, v91])
v94, v95 = sch.sample_perfect_tile(loop=l37, n=2, max_innermost_factor=64)
l96, l97 = sch.split(loop=l37, factors=[v94, v95])
sch.reorder(l42, l50, l58, l66, l74, l43, l51, l59, l67, l75, l80, l84, l88, l92, l96, l44, l52, l60, l68, l76, l81, l85, l89, l93, l97, l45, l53, l61, l69, l77)""".split(
"\n"
),
]
check_trace(spaces, expected)
disallow_if_then_else=True,
require_injective=True,
require_ordered=True,
disallow_op=["tir.exp"],
),
schedule_rule.AddRFactor(max_jobs_per_core=16, max_innermost_factor=64),
schedule_rule.MultiLevelTilingWithIntrin(
VNNI_INTRIN,
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(),
]
def get_sch_rules_for_dp4a(intrin):
return [
