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[MetaSchedule][Test] Add unittests for C3D #12046

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Jul 9, 2022
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[MetaSchedule][Test] Add unittests for C3D #12046

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198 changes: 198 additions & 0 deletions tests/python/unittest/test_meta_schedule_space_cpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -351,6 +351,204 @@ def c2d_2(inputs: T.Buffer[(1, 224, 224, 3), "float32"], weight: T.Buffer[(7, 7,
)


def test_cpu_c3d():
# fmt: off
@T.prim_func
def c3d_0(inputs: T.Buffer[(1, 16, 224, 224, 3), "float32"], weight: T.Buffer[(7, 7, 7, 3, 64), "float32"], conv3d_ndhwc: T.Buffer[(1, 8, 112, 112, 64), "float32"]) -> None:
# function attr dict
T.func_attr({"global_symbol": "main", "tir.noalias": True})
# body
with T.block("root"):
T.reads()
T.writes()
T.block_attr({"meta_schedule.parallel":288, "meta_schedule.unroll_explicit":512, "meta_schedule.vectorize":64})
PadInput = T.alloc_buffer([1, 22, 230, 230, 3], dtype="float32")
conv3d_ndhwc_global = T.alloc_buffer([1, 8, 112, 112, 64], dtype="float32")
for i0_0, i1_0, i2_0, i3_0, i4_0 in T.grid(1, 2, 4, 1, 2):
for ax0, ax1, ax2, ax3, ax4 in T.grid(1, 13, 61, 229, 3):
with T.block("PadInput"):
i0 = T.axis.spatial(1, ax0)
i1 = T.axis.spatial(22, i1_0 * 8 + ax1)
i2 = T.axis.spatial(230, i2_0 * 56 + ax2)
i3 = T.axis.spatial(230, ax3)
i4 = T.axis.spatial(3, ax4)
T.reads(inputs[i0, i1 - 3, i2 - 3, i3 - 3, i4])
T.writes(PadInput[i0, i1, i2, i3, i4])
PadInput[i0, i1, i2, i3, i4] = T.if_then_else(3 <= i1 and i1 < 19 and 3 <= i2 and i2 < 227 and 3 <= i3 and i3 < 227, inputs[i0, i1 - 3, i2 - 3, i3 - 3, i4], T.float32(0), dtype="float32")
for i0_1, i1_1, i2_1, i3_1, i4_1 in T.grid(1, 4, 4, 14, 1):
for i5_0, i6_0, i7_0, i8_0, i0_2, i1_2, i2_2, i3_2, i4_2, i5_1, i6_1, i7_1, i8_1, i0_3, i1_3, i2_3, i3_3, i4_3 in T.grid(1, 7, 7, 3, 1, 1, 1, 1, 32, 7, 1, 1, 1, 1, 1, 7, 8, 1):
with T.block("conv3d_ndhwc"):
n = T.axis.spatial(1, i0_3 + i0_2 + i0_1 + i0_0)
d = T.axis.spatial(8, i1_0 * 4 + i1_1 + i1_2 + i1_3)
h = T.axis.spatial(112, (i2_0 * 4 + i2_1 + i2_2) * 7 + i2_3)
w = T.axis.spatial(112, (i3_0 * 14 + i3_1 + i3_2) * 8 + i3_3)
co = T.axis.spatial(64, (i4_0 + i4_1) * 32 + i4_2 + i4_3)
rd = T.axis.reduce(7, i5_0 * 7 + i5_1)
rh = T.axis.reduce(7, i6_0 + i6_1)
rw = T.axis.reduce(7, i7_0 + i7_1)
rc = T.axis.reduce(3, i8_0 + i8_1)
T.reads(PadInput[n, d * 2 + rd, h * 2 + rh, w * 2 + rw, co // 64 * 3 + rc], weight[rd, rh, rw, rc, co])
T.writes(conv3d_ndhwc_global[n, d, h, w, co])
T.block_attr({"meta_schedule.tiling_structure":"SSRSRS"})
with T.init():
conv3d_ndhwc_global[n, d, h, w, co] = T.float32(0)
conv3d_ndhwc_global[n, d, h, w, co] = conv3d_ndhwc_global[n, d, h, w, co] + PadInput[n, d * 2 + rd, h * 2 + rh, w * 2 + rw, co // 64 * 3 + rc] * weight[rd, rh, rw, rc, co]
for ax0, ax1, ax2, ax3, ax4 in T.grid(1, 1, 7, 8, 32):
with T.block("conv3d_ndhwc_global"):
v0 = T.axis.spatial(1, ax0)
v1 = T.axis.spatial(8, i1_0 * 4 + i1_1 + ax1)
v2 = T.axis.spatial(112, i2_0 * 28 + i2_1 * 7 + ax2)
v3 = T.axis.spatial(112, i3_1 * 8 + ax3)
v4 = T.axis.spatial(64, i4_0 * 32 + ax4)
T.reads(conv3d_ndhwc_global[v0, v1, v2, v3, v4])
T.writes(conv3d_ndhwc[v0, v1, v2, v3, v4])
conv3d_ndhwc[v0, v1, v2, v3, v4] = conv3d_ndhwc_global[v0, v1, v2, v3, v4]
@T.prim_func
def c3d_1(inputs: T.Buffer[(1, 16, 224, 224, 3), "float32"], weight: T.Buffer[(7, 7, 7, 3, 64), "float32"], conv3d_ndhwc: T.Buffer[(1, 8, 112, 112, 64), "float32"]) -> None:
# function attr dict
T.func_attr({"global_symbol": "main", "tir.noalias": True})
# body
with T.block("root"):
T.reads()
T.writes()
T.block_attr({"meta_schedule.parallel":288, "meta_schedule.unroll_explicit":64, "meta_schedule.vectorize":64})
PadInput = T.alloc_buffer([1, 22, 230, 230, 3], dtype="float32")
conv3d_ndhwc_global = T.alloc_buffer([1, 8, 112, 112, 64], dtype="float32")
for i0_0, i1_0, i2_0, i3_0, i4_0 in T.grid(1, 2, 4, 1, 2):
for i0_1, i1_1, i2_1, i3_1 in T.grid(1, 4, 4, 14):
for ax0, ax1, ax2, ax3, ax4 in T.grid(1, 7, 19, 21, 3):
with T.block("PadInput"):
i0 = T.axis.spatial(1, ax0)
i1 = T.axis.spatial(22, i1_0 * 8 + i1_1 * 2 + ax1)
i2 = T.axis.spatial(230, i2_0 * 56 + i2_1 * 14 + ax2)
i3 = T.axis.spatial(230, i3_1 * 16 + ax3)
i4 = T.axis.spatial(3, ax4)
T.reads(inputs[i0, i1 - 3, i2 - 3, i3 - 3, i4])
T.writes(PadInput[i0, i1, i2, i3, i4])
PadInput[i0, i1, i2, i3, i4] = T.if_then_else(3 <= i1 and i1 < 19 and 3 <= i2 and i2 < 227 and 3 <= i3 and i3 < 227, inputs[i0, i1 - 3, i2 - 3, i3 - 3, i4], T.float32(0), dtype="float32")
for i4_1, i5_0, i6_0, i7_0, i8_0, i0_2, i1_2, i2_2, i3_2, i4_2, i5_1, i6_1, i7_1, i8_1, i0_3, i1_3, i2_3, i3_3, i4_3 in T.grid(1, 1, 7, 7, 3, 1, 1, 1, 1, 32, 7, 1, 1, 1, 1, 1, 7, 8, 1):
with T.block("conv3d_ndhwc"):
n = T.axis.spatial(1, i0_3 + i0_2 + i0_1 + i0_0)
d = T.axis.spatial(8, i1_0 * 4 + i1_1 + i1_2 + i1_3)
h = T.axis.spatial(112, (i2_0 * 4 + i2_1 + i2_2) * 7 + i2_3)
w = T.axis.spatial(112, (i3_0 * 14 + i3_1 + i3_2) * 8 + i3_3)
co = T.axis.spatial(64, (i4_0 + i4_1) * 32 + i4_2 + i4_3)
rd = T.axis.reduce(7, i5_0 * 7 + i5_1)
rh = T.axis.reduce(7, i6_0 + i6_1)
rw = T.axis.reduce(7, i7_0 + i7_1)
rc = T.axis.reduce(3, i8_0 + i8_1)
T.reads(PadInput[n, d * 2 + rd, h * 2 + rh, w * 2 + rw, co // 64 * 3 + rc], weight[rd, rh, rw, rc, co])
T.writes(conv3d_ndhwc_global[n, d, h, w, co])
T.block_attr({"meta_schedule.tiling_structure":"SSRSRS"})
with T.init():
conv3d_ndhwc_global[n, d, h, w, co] = T.float32(0)
conv3d_ndhwc_global[n, d, h, w, co] = conv3d_ndhwc_global[n, d, h, w, co] + PadInput[n, d * 2 + rd, h * 2 + rh, w * 2 + rw, co // 64 * 3 + rc] * weight[rd, rh, rw, rc, co]
for ax0, ax1, ax2, ax3, ax4 in T.grid(1, 4, 28, 112, 32):
with T.block("conv3d_ndhwc_global"):
v0 = T.axis.spatial(1, ax0)
v1 = T.axis.spatial(8, i1_0 * 4 + ax1)
v2 = T.axis.spatial(112, i2_0 * 28 + ax2)
v3 = T.axis.spatial(112, ax3)
v4 = T.axis.spatial(64, i4_0 * 32 + ax4)
T.reads(conv3d_ndhwc_global[v0, v1, v2, v3, v4])
T.writes(conv3d_ndhwc[v0, v1, v2, v3, v4])
conv3d_ndhwc[v0, v1, v2, v3, v4] = conv3d_ndhwc_global[v0, v1, v2, v3, v4]
@T.prim_func
def c3d_2(inputs: T.Buffer[(1, 16, 224, 224, 3), "float32"], weight: T.Buffer[(7, 7, 7, 3, 64), "float32"], conv3d_ndhwc: T.Buffer[(1, 8, 112, 112, 64), "float32"]) -> None:
# function attr dict
T.func_attr({"global_symbol": "main", "tir.noalias": True})
# body
with T.block("root"):
T.reads()
T.writes()
T.block_attr({"meta_schedule.parallel":288, "meta_schedule.unroll_explicit":16, "meta_schedule.vectorize":64})
PadInput = T.alloc_buffer([1, 22, 230, 230, 3], dtype="float32")
for i0_0, i1_0, i2_0, i3_0, i4_0, i0_1, i1_1, i2_1, i3_1 in T.grid(1, 2, 4, 1, 2, 1, 4, 4, 14):
for ax0, ax1, ax2, ax3, ax4 in T.grid(1, 7, 19, 21, 3):
with T.block("PadInput"):
i0 = T.axis.spatial(1, ax0)
i1 = T.axis.spatial(22, i1_0 * 8 + i1_1 * 2 + ax1)
i2 = T.axis.spatial(230, i2_0 * 56 + i2_1 * 14 + ax2)
i3 = T.axis.spatial(230, i3_1 * 16 + ax3)
i4 = T.axis.spatial(3, ax4)
T.reads(inputs[i0, i1 - 3, i2 - 3, i3 - 3, i4])
T.writes(PadInput[i0, i1, i2, i3, i4])
PadInput[i0, i1, i2, i3, i4] = T.if_then_else(3 <= i1 and i1 < 19 and 3 <= i2 and i2 < 227 and 3 <= i3 and i3 < 227, inputs[i0, i1 - 3, i2 - 3, i3 - 3, i4], T.float32(0), dtype="float32")
for i4_1, i5_0, i6_0, i7_0, i8_0, i0_2, i1_2, i2_2, i3_2, i4_2, i5_1, i6_1, i7_1, i8_1, i0_3, i1_3, i2_3, i3_3, i4_3 in T.grid(1, 1, 7, 7, 3, 1, 1, 1, 1, 32, 7, 1, 1, 1, 1, 1, 7, 8, 1):
with T.block("conv3d_ndhwc"):
n = T.axis.spatial(1, i0_3 + i0_2 + i0_1 + i0_0)
d = T.axis.spatial(8, i1_0 * 4 + i1_1 + i1_2 + i1_3)
h = T.axis.spatial(112, (i2_0 * 4 + i2_1 + i2_2) * 7 + i2_3)
w = T.axis.spatial(112, (i3_0 * 14 + i3_1 + i3_2) * 8 + i3_3)
co = T.axis.spatial(64, (i4_0 + i4_1) * 32 + i4_2 + i4_3)
rd = T.axis.reduce(7, i5_0 * 7 + i5_1)
rh = T.axis.reduce(7, i6_0 + i6_1)
rw = T.axis.reduce(7, i7_0 + i7_1)
rc = T.axis.reduce(3, i8_0 + i8_1)
T.reads(PadInput[n, d * 2 + rd, h * 2 + rh, w * 2 + rw, co // 64 * 3 + rc], weight[rd, rh, rw, rc, co])
T.writes(conv3d_ndhwc[n, d, h, w, co])
T.block_attr({"meta_schedule.tiling_structure":"SSRSRS"})
with T.init():
conv3d_ndhwc[n, d, h, w, co] = T.float32(0)
conv3d_ndhwc[n, d, h, w, co] = conv3d_ndhwc[n, d, h, w, co] + PadInput[n, d * 2 + rd, h * 2 + rh, w * 2 + rw, co // 64 * 3 + rc] * weight[rd, rh, rw, rc, co]
# fmt: on

decision_0 = [
("SamplePerfectTile", [1, 1, 1, 1]),
("SamplePerfectTile", [2, 4, 1, 1]),
("SamplePerfectTile", [4, 4, 1, 7]),
("SamplePerfectTile", [1, 14, 1, 8]),
("SamplePerfectTile", [2, 1, 32, 1]),
("SamplePerfectTile", [1, 7]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [3, 1]),
("SampleCategorical", 3),
("SampleComputeLocation", 4),
]
decision_1 = [
("SamplePerfectTile", [1, 1, 1, 1]),
("SamplePerfectTile", [2, 4, 1, 1]),
("SamplePerfectTile", [4, 4, 1, 7]),
("SamplePerfectTile", [1, 14, 1, 8]),
("SamplePerfectTile", [2, 1, 32, 1]),
("SamplePerfectTile", [1, 7]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [3, 1]),
("SampleCategorical", 2),
("SampleComputeLocation", 8),
]
decision_2 = [
("SamplePerfectTile", [1, 1, 1, 1]),
("SamplePerfectTile", [2, 4, 1, 1]),
("SamplePerfectTile", [4, 4, 1, 7]),
("SamplePerfectTile", [1, 14, 1, 8]),
("SamplePerfectTile", [2, 1, 32, 1]),
("SamplePerfectTile", [1, 7]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [3, 1]),
("SampleCategorical", 1),
("SampleComputeLocation", 8),
]

mod = create_te_workload("C3D", 0)
actual = ms.TuneContext(
mod=mod,
target=_target(),
space_generator=ms.space_generator.PostOrderApply(),
sch_rules="default",
).generate_design_space()
check_sketches(
mod,
sketches=actual,
expected_mods=[c3d_0, c3d_1, c3d_2],
expected_decisions=[decision_0, decision_1, decision_2],
)


if __name__ == "__main__":
test_cpu_c1d()
test_cpu_c2d()
test_cpu_c3d()
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