def test_cpu_matmul_relu():
# pylint: disable=line-too-long
expected = [
[
'b0 = sch.get_block(name="C", func_name="main")',
'sch.annotate(block_or_loop=b0, ann_key="meta_schedule.tiling_structure", ann_val="SSRSRS")',
"l1, l2, l3 = sch.get_loops(block=b0)",
"v4, v5, v6, v7 = sch.sample_perfect_tile(loop=l1, n=4, max_innermost_factor=64)",
"l8, l9, l10, l11 = sch.split(loop=l1, factors=[v4, v5, v6, v7])",
"v12, v13, v14, v15 = sch.sample_perfect_tile(loop=l2, n=4, max_innermost_factor=64)",
"l16, l17, l18, l19 = sch.split(loop=l2, factors=[v12, v13, v14, v15])",
"v20, v21 = sch.sample_perfect_tile(loop=l3, n=2, max_innermost_factor=64)",
"l22, l23 = sch.split(loop=l3, factors=[v20, v21])",
"sch.reorder(l8, l16, l9, l17, l22, l10, l18, l23, l11, l19)",
"b24, = sch.get_consumers(block=b0)",
"sch.reverse_compute_at(block=b24, loop=l17, preserve_unit_loops=1)",
],
[
'b0 = sch.get_block(name="C", func_name="main")',
'sch.annotate(block_or_loop=b0, ann_key="meta_schedule.tiling_structure", ann_val="SSRSRS")',
"l1, l2, l3 = sch.get_loops(block=b0)",
"v4, v5, v6, v7 = sch.sample_perfect_tile(loop=l1, n=4, max_innermost_factor=64)",
"l8, l9, l10, l11 = sch.split(loop=l1, factors=[v4, v5, v6, v7])",
"v12, v13, v14, v15 = sch.sample_perfect_tile(loop=l2, n=4, max_innermost_factor=64)",
"l16, l17, l18, l19 = sch.split(loop=l2, factors=[v12, v13, v14, v15])",
"v20, v21 = sch.sample_perfect_tile(loop=l3, n=2, max_innermost_factor=64)",
"l22, l23 = sch.split(loop=l3, factors=[v20, v21])",
"sch.reorder(l8, l16, l9, l17, l22, l10, l18, l23, l11, l19)",
"b24, = sch.get_consumers(block=b0)",
"sch.reverse_compute_at(block=b24, loop=l16, preserve_unit_loops=1)",
],
[
'b0 = sch.get_block(name="C", func_name="main")',
'sch.annotate(block_or_loop=b0, ann_key="meta_schedule.tiling_structure", ann_val="SSRSRS")',
"l1, l2, l3 = sch.get_loops(block=b0)",
"v4, v5, v6, v7 = sch.sample_perfect_tile(loop=l1, n=4, max_innermost_factor=64)",
"l8, l9, l10, l11 = sch.split(loop=l1, factors=[v4, v5, v6, v7])",
"v12, v13, v14, v15 = sch.sample_perfect_tile(loop=l2, n=4, max_innermost_factor=64)",
"l16, l17, l18, l19 = sch.split(loop=l2, factors=[v12, v13, v14, v15])",
"v20, v21 = sch.sample_perfect_tile(loop=l3, n=2, max_innermost_factor=64)",
"l22, l23 = sch.split(loop=l3, factors=[v20, v21])",
"sch.reorder(l8, l16, l9, l17, l22, l10, l18, l23, l11, l19)",
],
]
# pylint: enable=line-too-long
target = Target("llvm")
ctx = _create_context(
create_prim_func(
te_workload.matmul_relu(
n=512,
m=512,
k=512,
)
),
target=target,
rule=multi_level_tiling(target=target),
)
spaces = ctx.space_generator.generate_design_space(mod=ctx.mod)
assert len(spaces) == 3
check_trace(spaces, expected)
def test_cuda_sum_with_trivial_block_iter():
@T.prim_func
def sum_with_trivial_block_iter(
A: T.Buffer[(1, 64, 768), "float32"], B: T.Buffer[(1, 64, 1), "float32"]
) -> None:
for i0, i1, i2, i3 in T.grid(1, 64, 1, 768):
with T.block("sum"):
ax0, ax1, ax2, k2 = T.axis.remap("SSSR", [i0, i1, i2, i3])
T.reads(A[ax0, ax1, k2])
T.writes(B[ax0, ax1, ax2])
with T.init():
B[ax0, ax1, ax2] = T.float32(0)
B[ax0, ax1, ax2] = B[ax0, ax1, ax2] + A[ax0, ax1, k2]
# Expect nothing to happen - the rule is not supposed to be applied in this case
expected = [[]]
target = Target("cuda", host="llvm")
ctx = _create_context(
sum_with_trivial_block_iter,
target=target,
rule=multi_level_tiling(target=target),
)
spaces = ctx.space_generator.generate_design_space(mod=ctx.mod)
assert len(spaces) == 1
check_trace(spaces, expected)
def test_gpu_batch_norm_bmn():
expected = [
[],
[
'b0 = sch.get_block(name="C", func_name="main")',
"b1, = sch.get_consumers(block=b0)",
"l2, = sch.get_loops(block=b1)",
"v3 = sch.sample_categorical(candidates=[4, 8, 16, 32, 64, 128, 256, 512], probs=[0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125])",
"l4, l5 = sch.split(loop=l2, factors=[None, v3], preserve_unit_iters=True)",
'sch.bind(loop=l5, thread_axis="threadIdx.x")',
"sch.compute_at(block=b0, loop=l4, preserve_unit_loops=True)",
'sch.set_scope(block=b0, buffer_index=0, storage_scope="shared")',
"l6, l7, l8, l9 = sch.get_loops(block=b0)",
"l10 = sch.fuse(l8, l9, preserve_unit_iters=True)",
"l11, l12 = sch.split(loop=l10, factors=[None, v3], preserve_unit_iters=True)",
'sch.bind(loop=l12, thread_axis="threadIdx.x")',
],
]
target = Target("nvidia/geforce-rtx-3090", host="llvm")
ctx = _create_context(
create_prim_func(te_workload.norm_bmn(
B=1,
M=512,
N=512,
)),
target=target,
rule=cross_thread_reduction(target=target),
)
spaces = ctx.space_generator.generate_design_space(mod=ctx.mod)
assert len(spaces) == 2
check_trace(spaces, expected)
def test_cpu_matmul():
expected = [
[],
[
'b0 = sch.get_block(name="C", func_name="main")',
"l1, l2, l3 = sch.get_loops(block=b0)",
"v4, v5 = sch.sample_perfect_tile(loop=l3, n=2, max_innermost_factor=64)",
"l6, l7 = sch.split(loop=l3, factors=[v4, v5], preserve_unit_iters=True)",
"b8 = sch.rfactor(loop=l7, factor_axis=2)",
'sch.annotate(block_or_loop=b0, ann_key="meta_schedule.random_compute_producer", ann_val=1)',
],
[
'b0 = sch.get_block(name="C", func_name="main")',
"l1, l2, l3 = sch.get_loops(block=b0)",
"v4, v5 = sch.sample_perfect_tile(loop=l3, n=2, max_innermost_factor=64)",
"l6, l7 = sch.split(loop=l3, factors=[v4, v5], preserve_unit_iters=True)",
"b8 = sch.rfactor(loop=l6, factor_axis=2)",
'sch.annotate(block_or_loop=b0, ann_key="meta_schedule.random_compute_producer", ann_val=1)',
],
]
target = Target("llvm --num-cores=32")
ctx = _create_context(
create_prim_func(te_workload.matmul(
n=4,
m=4,
k=512,
)),
target=target,
rule=add_rfactor(target=target),
)
spaces = ctx.space_generator.generate_design_space(mod=ctx.mod)
assert len(spaces) == 3
check_trace(spaces, expected)
def test_cuda_matmul():
# pylint: disable=line-too-long
expected = [
[
'b0 = sch.get_block(name="C", 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)",
"v4, v5, v6, v7, v8 = sch.sample_perfect_tile(loop=l1, n=5, max_innermost_factor=64)",
"l9, l10, l11, l12, l13 = sch.split(loop=l1, factors=[v4, v5, v6, v7, v8])",
"v14, v15, v16, v17, v18 = sch.sample_perfect_tile(loop=l2, n=5, max_innermost_factor=64)",
"l19, l20, l21, l22, l23 = sch.split(loop=l2, factors=[v14, v15, v16, v17, v18])",
"v24, v25, v26 = sch.sample_perfect_tile(loop=l3, n=3, max_innermost_factor=64)",
"l27, l28, l29 = sch.split(loop=l3, factors=[v24, v25, v26])",
"sch.reorder(l9, l19, l10, l20, l11, l21, l27, l28, l12, l22, l29, l13, l23)",
"l30 = sch.fuse(l9, l19)",
'sch.bind(loop=l30, thread_axis="blockIdx.x")',
"l31 = sch.fuse(l10, l20)",
'sch.bind(loop=l31, thread_axis="vthread.x")',
"l32 = sch.fuse(l11, l21)",
'sch.bind(loop=l32, thread_axis="threadIdx.x")',
'sch.annotate(block_or_loop=b0, ann_key="meta_schedule.thread_extent_low_inclusive", ann_val=32)',
'sch.annotate(block_or_loop=b0, ann_key="meta_schedule.thread_extent_high_inclusive", ann_val=1024)',
'b33 = sch.cache_write(block=b0, write_buffer_index=0, storage_scope="local")',
"sch.reverse_compute_at(block=b33, loop=l32, preserve_unit_loops=1)",
'b34 = sch.cache_read(block=b0, read_buffer_index=1, storage_scope="shared")',
"sch.compute_at(block=b34, loop=l27, preserve_unit_loops=1)",
"l35, l36, l37, l38, l39, l40 = sch.get_loops(block=b34)",
"l41 = sch.fuse(l39, l40)",
"v42 = sch.sample_categorical(candidates=[1, 2, 3, 4], probs=[0.25, 0.25, 0.25, 0.25])",
'sch.annotate(block_or_loop=b34, ann_key="meta_schedule.cooperative_fetch", ann_val=v42)',
'b43 = sch.cache_read(block=b0, read_buffer_index=2, storage_scope="shared")',
"sch.compute_at(block=b43, loop=l27, preserve_unit_loops=1)",
"l44, l45, l46, l47, l48, l49 = sch.get_loops(block=b43)",
"l50 = sch.fuse(l48, l49)",
"v51 = sch.sample_categorical(candidates=[1, 2, 3, 4], probs=[0.25, 0.25, 0.25, 0.25])",
'sch.annotate(block_or_loop=b43, ann_key="meta_schedule.cooperative_fetch", ann_val=v51)',
]
]
# pylint: enable=line-too-long
target = Target("cuda --max_threads_per_block=1024 --thread_warp_size=32", host="llvm")
ctx = _create_context(
create_prim_func(
te_workload.matmul(
n=512,
m=512,
k=512,
)
),
target=target,
rule=multi_level_tiling(target=target),
)
spaces = ctx.space_generator.generate_design_space(mod=ctx.mod)
assert len(spaces) == 1
check_trace(spaces, expected)
def test_gpu_softmax_mn_after_inline():
expected = [
[],
[
'b0 = sch.get_block(name="T_softmax_maxelem", func_name="main")',
"v1 = sch.sample_categorical(candidates=[4, 8, 16, 32, 64, 128, 256, 512], probs=[0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125])",
"l2, l3 = sch.get_loops(block=b0)",
"l4, l5 = sch.split(loop=l3, factors=[None, v1], preserve_unit_iters=True)",
'sch.bind(loop=l5, thread_axis="threadIdx.x")',
],
[
'b0 = sch.get_block(name="T_softmax_expsum", func_name="main")',
"b1, = sch.get_consumers(block=b0)",
"l2, l3 = sch.get_loops(block=b1)",
"v4 = sch.sample_categorical(candidates=[4, 8, 16, 32, 64, 128, 256, 512], probs=[0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125])",
"l5, l6 = sch.split(loop=l3, factors=[None, v4], preserve_unit_iters=True)",
'sch.bind(loop=l6, thread_axis="threadIdx.x")',
"sch.compute_at(block=b0, loop=l2, preserve_unit_loops=True)",
'sch.set_scope(block=b0, buffer_index=0, storage_scope="shared")',
"l7, l8, l9 = sch.get_loops(block=b0)",
"l10, l11 = sch.split(loop=l9, factors=[None, v4], preserve_unit_iters=True)",
'sch.bind(loop=l11, thread_axis="threadIdx.x")',
],
[
'b0 = sch.get_block(name="T_softmax_maxelem", func_name="main")',
'b1 = sch.get_block(name="T_softmax_expsum", func_name="main")',
"b2, = sch.get_consumers(block=b1)",
"l3, l4 = sch.get_loops(block=b2)",
"v5 = sch.sample_categorical(candidates=[4, 8, 16, 32, 64, 128, 256, 512], probs=[0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125])",
"l6, l7 = sch.split(loop=l4, factors=[None, v5], preserve_unit_iters=True)",
'sch.bind(loop=l7, thread_axis="threadIdx.x")',
"sch.compute_at(block=b1, loop=l3, preserve_unit_loops=True)",
'sch.set_scope(block=b1, buffer_index=0, storage_scope="shared")',
"l8, l9, l10 = sch.get_loops(block=b1)",
"l11, l12 = sch.split(loop=l10, factors=[None, v5], preserve_unit_iters=True)",
'sch.bind(loop=l12, thread_axis="threadIdx.x")',
"b13, b14 = sch.get_consumers(block=b0)",
"l15, l16, l17, l18 = sch.get_loops(block=b13)",
"sch.compute_at(block=b0, loop=l15, preserve_unit_loops=True)",
'sch.set_scope(block=b0, buffer_index=0, storage_scope="shared")',
"l19, l20, l21 = sch.get_loops(block=b0)",
"l22, l23 = sch.split(loop=l21, factors=[None, v5], preserve_unit_iters=True)",
'sch.bind(loop=l23, thread_axis="threadIdx.x")',
],
]
target = Target("nvidia/geforce-rtx-3090", host="llvm")
ctx = _create_context(
mod=Softmax_mn_after_inline,
target=target,
rule=cross_thread_reduction(target=target),
)
spaces = ctx.space_generator.generate_design_space(mod=ctx.mod)
assert len(spaces) == 4
check_trace(spaces, expected)
def test_random_compute_location():
expected = [[
'b0 = sch.get_block(name="move", func_name="main")',
"l1 = sch.sample_compute_location(block=b0)",
"sch.compute_at(block=b0, loop=l1, preserve_unit_loops=True)",
]]
mod = Add
target = Target("llvm")
ctx = _create_context(
mod=mod,
target=target,
rule=RandomComputeLocation(),
)
spaces = ctx.space_generator.generate_design_space(mod=mod)
assert len(spaces) == 1
check_trace(spaces, expected)
def test_cuda_zero_dim_add():
expected = [[
'b0 = sch.get_block(name="C", func_name="main")',
"l1 = sch.add_unit_loop(block_or_loop=b0)",
"l2 = sch.fuse(l1, preserve_unit_iters=True)",
"l3, l4 = sch.split(loop=l2, factors=[None, 1], preserve_unit_iters=True)",
'sch.bind(loop=l3, thread_axis="blockIdx.x")',
'sch.bind(loop=l4, thread_axis="threadIdx.x")',
]]
target = Target("nvidia/geforce-rtx-3080", host="llvm")
ctx = _create_context(
zero_dim_add,
target=target,
rule=auto_bind(target=target),
)
spaces = ctx.space_generator.generate_design_space(mod=ctx.mod)
assert len(spaces) == 1
check_trace(spaces, expected)
def test_cuda_element_wise():
expected = [[
'b0 = sch.get_block(name="C", func_name="main")',
"l1, l2 = sch.get_loops(block=b0)",
"l3 = sch.fuse(l1, l2, preserve_unit_iters=True)",
"v4 = sch.sample_categorical(candidates=[32, 64, 128, 256, 512, 1024], probs=[0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666])",
"l5, l6 = sch.split(loop=l3, factors=[None, v4], preserve_unit_iters=True)",
'sch.bind(loop=l5, thread_axis="blockIdx.x")',
'sch.bind(loop=l6, thread_axis="threadIdx.x")',
]]
target = Target("nvidia/geforce-rtx-3080", host="llvm")
ctx = _create_context(
element_wise,
target=target,
rule=auto_bind(target=target),
)
spaces = ctx.space_generator.generate_design_space(mod=ctx.mod)
assert len(spaces) == 1
check_trace(spaces, expected)
def test_parallel_vectorize_unroll():
expected = [
[
'b0 = sch.get_block(name="root", func_name="main")',
'sch.annotate(block_or_loop=b0, ann_key="meta_schedule.parallel", ann_val=512)',
'sch.annotate(block_or_loop=b0, ann_key="meta_schedule.vectorize", ann_val=32)',
"v1 = sch.sample_categorical(candidates=[0, 16, 64, 512], probs=[0.25, 0.25, 0.25, 0.25])",
'sch.annotate(block_or_loop=b0, ann_key="meta_schedule.unroll_explicit", ann_val=v1)',
]
]
mod = Matmul
target = Target("llvm --num-cores=32")
ctx = _create_context(
mod=mod,
target=target,
rule=parallel_vectorize_unroll(target=target),
)
spaces = ctx.space_generator.generate_design_space(mod=mod)
assert len(spaces) == 1
check_trace(spaces, expected)
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)
