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[MetaSchedule][Test] Add unittests for CBR
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@junrushao
junrushao committed Aug 1, 2022
1 parent 12502cc commit 61ab725
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Showing 4 changed files with 268 additions and 2 deletions.
2 changes: 1 addition & 1 deletion python/tvm/meta_schedule/testing/te_workload.py
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Original file line number Diff line number Diff line change
Expand Up @@ -868,7 +868,7 @@ def create_te_workload(name: str, idx: int) -> tir.PrimFunc:
(2048, 2048),
],
),
"C2d-BN-RELU": (
"CBR": (
conv2d_nhwc_bn_relu,
[
(1, 224, 224, 3, 64, 7, 2, 3),
Expand Down
175 changes: 175 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 @@ -2244,6 +2244,180 @@ def sfm_8(A: T.Buffer[(256, 256), "float32"], T_softmax_norm: T.Buffer[(256, 256
)


def test_cpu_cbr():
# fmt: off
@T.prim_func
def cbr_0(data: T.Buffer[(1, 224, 224, 3), "float32"], kernel: T.Buffer[(7, 7, 3, 64), "float32"], bias: T.Buffer[64, "float32"], bn_offset: T.Buffer[64, "float32"], bn_scale: T.Buffer[64, "float32"], compute: T.Buffer[(1, 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})
Conv2dOutput = T.alloc_buffer([1, 112, 112, 64], dtype="float32")
for i0_0, i1_0, i2_0, i3_0, i0_1, i1_1, i2_1, i3_1, i4_0, i5_0, i6_0, i0_2, i1_2, i2_2, i3_2, i4_1, i5_1, i6_1, i0_3, i1_3, i2_3, i3_3 in T.grid(1, 2, 7, 1, 1, 2, 2, 32, 7, 7, 1, 1, 1, 4, 1, 1, 1, 3, 1, 28, 2, 2):
with T.block("Conv2dOutput"):
nn = T.axis.spatial(1, i0_3 + i0_0 + i0_1 + i0_2)
yy = T.axis.spatial(112, i1_0 * 56 + i1_1 * 28 + i1_2 * 28 + i1_3)
xx = T.axis.spatial(112, i2_0 * 16 + i2_1 * 8 + i2_2 * 2 + i2_3)
ff = T.axis.spatial(64, i3_0 * 64 + i3_1 * 2 + i3_2 * 2 + i3_3)
ry = T.axis.reduce(7, i4_1 + i4_0)
rx = T.axis.reduce(7, i5_0 + i5_1)
rc = T.axis.reduce(3, i6_0 * 3 + i6_1)
T.reads(data[nn, yy * 2 + ry - 3, xx * 2 + rx - 3, rc], kernel[ry, rx, rc, ff])
T.writes(Conv2dOutput[nn, yy, xx, ff])
T.block_attr({"meta_schedule.tiling_structure":"SSRSRS"})
with T.init():
Conv2dOutput[nn, yy, xx, ff] = T.float32(0)
Conv2dOutput[nn, yy, xx, ff] = Conv2dOutput[nn, yy, xx, ff] + T.if_then_else(3 <= yy * 2 + ry and yy * 2 + ry < 227 and 3 <= xx * 2 + rx and xx * 2 + rx < 227, data[nn, yy * 2 + ry - 3, xx * 2 + rx - 3, rc], T.float32(0), dtype="float32") * kernel[ry, rx, rc, ff]
for i0, i1, i2, i3 in T.grid(1, 112, 112, 64):
with T.block("compute"):
i0_4, i1_4, i2_4, i3_4 = T.axis.remap("SSSS", [i0, i1, i2, i3])
T.reads(Conv2dOutput[i0_4, i1_4, i2_4, i3_4], bias[i3_4], bn_scale[i3_4], bn_offset[i3_4])
T.writes(compute[i0_4, i1_4, i2_4, i3_4])
compute[i0_4, i1_4, i2_4, i3_4] = T.max((Conv2dOutput[i0_4, i1_4, i2_4, i3_4] + bias[i3_4]) * bn_scale[i3_4] + bn_offset[i3_4], T.float32(0))
@T.prim_func
def cbr_1(data: T.Buffer[(1, 224, 224, 3), "float32"], kernel: T.Buffer[(7, 7, 3, 64), "float32"], bias: T.Buffer[64, "float32"], bn_offset: T.Buffer[64, "float32"], bn_scale: T.Buffer[64, "float32"], compute: T.Buffer[(1, 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})
PaddedInput = T.alloc_buffer([1, 230, 230, 3], dtype="float32")
Conv2dOutput = T.alloc_buffer([1, 112, 112, 64], dtype="float32")
for i0_0, i1_0 in T.grid(1, 2):
for ax0, ax1, ax2, ax3 in T.grid(1, 117, 229, 3):
with T.block("PaddedInput"):
i0 = T.axis.spatial(1, ax0)
i1 = T.axis.spatial(230, i1_0 * 112 + ax1)
i2 = T.axis.spatial(230, ax2)
i3 = T.axis.spatial(3, ax3)
T.reads(data[i0, i1 - 3, i2 - 3, i3])
T.writes(PaddedInput[i0, i1, i2, i3])
PaddedInput[i0, i1, i2, i3] = T.if_then_else(3 <= i1 and i1 < 227 and 3 <= i2 and i2 < 227, data[i0, i1 - 3, i2 - 3, i3], T.float32(0), dtype="float32")
for i2_0, i3_0, i0_1, i1_1, i2_1, i3_1 in T.grid(7, 1, 1, 2, 2, 32):
for i4_0, i5_0, i6_0, i0_2, i1_2, i2_2, i3_2, i4_1, i5_1, i6_1, i0_3, i1_3, i2_3, i3_3 in T.grid(7, 7, 1, 1, 1, 4, 1, 1, 1, 3, 1, 28, 2, 2):
with T.block("Conv2dOutput"):
nn = T.axis.spatial(1, i0_3 + i0_0 + i0_1 + i0_2)
yy = T.axis.spatial(112, i1_0 * 56 + i1_1 * 28 + i1_2 * 28 + i1_3)
xx = T.axis.spatial(112, i2_0 * 16 + i2_1 * 8 + i2_2 * 2 + i2_3)
ff = T.axis.spatial(64, i3_0 * 64 + i3_1 * 2 + i3_2 * 2 + i3_3)
ry = T.axis.reduce(7, i4_1 + i4_0)
rx = T.axis.reduce(7, i5_0 + i5_1)
rc = T.axis.reduce(3, i6_0 * 3 + i6_1)
T.reads(PaddedInput[nn, yy * 2 + ry, xx * 2 + rx, rc], kernel[ry, rx, rc, ff])
T.writes(Conv2dOutput[nn, yy, xx, ff])
T.block_attr({"meta_schedule.tiling_structure":"SSRSRS"})
with T.init():
Conv2dOutput[nn, yy, xx, ff] = T.float32(0)
Conv2dOutput[nn, yy, xx, ff] = Conv2dOutput[nn, yy, xx, ff] + PaddedInput[nn, yy * 2 + ry, xx * 2 + rx, rc] * kernel[ry, rx, rc, ff]
for ax0, ax1, ax2, ax3 in T.grid(1, 28, 8, 2):
with T.block("compute"):
i0 = T.axis.spatial(1, ax0)
i1 = T.axis.spatial(112, i1_0 * 56 + i1_1 * 28 + ax1)
i2 = T.axis.spatial(112, i2_0 * 16 + i2_1 * 8 + ax2)
i3 = T.axis.spatial(64, i3_1 * 2 + ax3)
T.reads(Conv2dOutput[i0, i1, i2, i3], bias[i3], bn_scale[i3], bn_offset[i3])
T.writes(compute[i0, i1, i2, i3])
compute[i0, i1, i2, i3] = T.max((Conv2dOutput[i0, i1, i2, i3] + bias[i3]) * bn_scale[i3] + bn_offset[i3], T.float32(0))
@T.prim_func
def cbr_2(data: T.Buffer[(1, 224, 224, 3), "float32"], kernel: T.Buffer[(7, 7, 3, 64), "float32"], bias: T.Buffer[64, "float32"], bn_offset: T.Buffer[64, "float32"], bn_scale: T.Buffer[64, "float32"], compute: T.Buffer[(1, 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})
PaddedInput = T.alloc_buffer([1, 230, 230, 3], dtype="float32")
Conv2dOutput = T.alloc_buffer([1, 112, 112, 64], dtype="float32")
for i0_0, i1_0 in T.grid(1, 2):
for ax0, ax1, ax2, ax3 in T.grid(1, 117, 229, 3):
with T.block("PaddedInput"):
i0 = T.axis.spatial(1, ax0)
i1 = T.axis.spatial(230, i1_0 * 112 + ax1)
i2 = T.axis.spatial(230, ax2)
i3 = T.axis.spatial(3, ax3)
T.reads(data[i0, i1 - 3, i2 - 3, i3])
T.writes(PaddedInput[i0, i1, i2, i3])
PaddedInput[i0, i1, i2, i3] = T.if_then_else(3 <= i1 and i1 < 227 and 3 <= i2 and i2 < 227, data[i0, i1 - 3, i2 - 3, i3], T.float32(0), dtype="float32")
for i2_0, i3_0 in T.grid(7, 1):
for i0_1, i1_1, i2_1, i3_1, i4_0, i5_0, i6_0, i0_2, i1_2, i2_2, i3_2, i4_1, i5_1, i6_1, i0_3, i1_3, i2_3, i3_3 in T.grid(1, 2, 2, 32, 7, 7, 1, 1, 1, 4, 1, 1, 1, 3, 1, 28, 2, 2):
with T.block("Conv2dOutput"):
nn = T.axis.spatial(1, i0_3 + i0_0 + i0_1 + i0_2)
yy = T.axis.spatial(112, i1_0 * 56 + i1_1 * 28 + i1_2 * 28 + i1_3)
xx = T.axis.spatial(112, i2_0 * 16 + i2_1 * 8 + i2_2 * 2 + i2_3)
ff = T.axis.spatial(64, i3_0 * 64 + i3_1 * 2 + i3_2 * 2 + i3_3)
ry = T.axis.reduce(7, i4_1 + i4_0)
rx = T.axis.reduce(7, i5_0 + i5_1)
rc = T.axis.reduce(3, i6_0 * 3 + i6_1)
T.reads(PaddedInput[nn, yy * 2 + ry, xx * 2 + rx, rc], kernel[ry, rx, rc, ff])
T.writes(Conv2dOutput[nn, yy, xx, ff])
T.block_attr({"meta_schedule.tiling_structure":"SSRSRS"})
with T.init():
Conv2dOutput[nn, yy, xx, ff] = T.float32(0)
Conv2dOutput[nn, yy, xx, ff] = Conv2dOutput[nn, yy, xx, ff] + PaddedInput[nn, yy * 2 + ry, xx * 2 + rx, rc] * kernel[ry, rx, rc, ff]
for ax0, ax1, ax2, ax3 in T.grid(1, 56, 16, 64):
with T.block("compute"):
i0 = T.axis.spatial(1, ax0)
i1 = T.axis.spatial(112, i1_0 * 56 + ax1)
i2 = T.axis.spatial(112, i2_0 * 16 + ax2)
i3 = T.axis.spatial(64, ax3)
T.reads(Conv2dOutput[i0, i1, i2, i3], bias[i3], bn_scale[i3], bn_offset[i3])
T.writes(compute[i0, i1, i2, i3])
compute[i0, i1, i2, i3] = T.max((Conv2dOutput[i0, i1, i2, i3] + bias[i3]) * bn_scale[i3] + bn_offset[i3], T.float32(0))
# fmt: on
decision_0 = [
("SamplePerfectTile", [1, 1, 1, 1]),
("SamplePerfectTile", [2, 2, 1, 28]),
("SamplePerfectTile", [7, 2, 4, 2]),
("SamplePerfectTile", [1, 32, 1, 2]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [1, 3]),
("SampleCategorical", 2),
("SampleComputeLocation", -2),
]
decision_1 = [
("SamplePerfectTile", [1, 1, 1, 1]),
("SamplePerfectTile", [2, 2, 1, 28]),
("SamplePerfectTile", [7, 2, 4, 2]),
("SamplePerfectTile", [1, 32, 1, 2]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [1, 3]),
("SampleCategorical", 3),
("SampleComputeLocation", 1),
]
decision_2 = [
("SamplePerfectTile", [1, 1, 1, 1]),
("SamplePerfectTile", [2, 2, 1, 28]),
("SamplePerfectTile", [7, 2, 4, 2]),
("SamplePerfectTile", [1, 32, 1, 2]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [7, 1]),
("SamplePerfectTile", [1, 3]),
("SampleCategorical", 2),
("SampleComputeLocation", 1),
]
mod = create_te_workload("CBR", 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=[cbr_0, cbr_1, cbr_2],
expected_decisions=[decision_0, decision_1, decision_2],
)


if __name__ == "__main__":
test_cpu_c1d()
test_cpu_c2d()
Expand All @@ -2256,3 +2430,4 @@ def sfm_8(A: T.Buffer[(256, 256), "float32"], T_softmax_norm: T.Buffer[(256, 256
test_cpu_t2d()
test_cpu_nrm()
test_cpu_sfm()
test_cpu_cbr()
91 changes: 91 additions & 0 deletions tests/python/unittest/test_meta_schedule_space_cuda.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1127,6 +1127,96 @@ def sfm_3(A: T.Buffer[(256, 256), "float32"], T_softmax_norm: T.Buffer[(256, 256
)


def test_cuda_cbr():
# fmt: off
@T.prim_func
def cbr_0(data: T.Buffer[(1, 224, 224, 3), "float32"], kernel: T.Buffer[(7, 7, 3, 64), "float32"], bias: T.Buffer[64, "float32"], bn_offset: T.Buffer[64, "float32"], bn_scale: T.Buffer[64, "float32"], compute: T.Buffer[(1, 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.unroll_explicit":512})
Conv2dOutput_local = T.alloc_buffer([1, 112, 112, 64], dtype="float32", scope="local")
PaddedInput_shared = T.alloc_buffer([1, 230, 230, 3], dtype="float32", scope="shared")
kernel_shared = T.alloc_buffer([7, 7, 3, 64], dtype="float32", scope="shared")
for i0_0_i1_0_i2_0_i3_0_fused in T.thread_binding(14, thread="blockIdx.x"):
for i0_1_i1_1_i2_1_i3_1_fused in T.thread_binding(4, thread="vthread.x"):
for i0_2_i1_2_i2_2_i3_2_fused in T.thread_binding(128, thread="threadIdx.x"):
for i4_0, i5_0, i6_0 in T.grid(7, 1, 3):
for ax0_ax1_ax2_ax3_fused in T.serial(8251):
with T.block("PaddedInput_shared"):
v0 = T.axis.spatial(1, 0)
v1 = T.axis.spatial(230, ax0_ax1_ax2_ax3_fused // 37 + i4_0)
v2 = T.axis.spatial(230, i0_0_i1_0_i2_0_i3_0_fused // 2 * 32 + ax0_ax1_ax2_ax3_fused % 37)
v3 = T.axis.spatial(3, i6_0)
T.reads(data[v0, v1 - 3, v2 - 3, v3])
T.writes(PaddedInput_shared[v0, v1, v2, v3])
T.block_attr({"meta_schedule.cooperative_fetch":1})
PaddedInput_shared[v0, v1, v2, v3] = T.if_then_else(3 <= v1 and v1 < 227 and 3 <= v2 and v2 < 227, data[v0, v1 - 3, v2 - 3, v3], T.float32(0), dtype="float32")
for ax0_ax1_ax2_ax3_fused in T.serial(224):
with T.block("kernel_shared"):
v0 = T.axis.spatial(7, i4_0)
v1 = T.axis.spatial(7, ax0_ax1_ax2_ax3_fused // 32)
v2 = T.axis.spatial(3, i6_0)
v3 = T.axis.spatial(64, i0_0_i1_0_i2_0_i3_0_fused % 2 * 32 + ax0_ax1_ax2_ax3_fused % 32)
T.reads(kernel[v0, v1, v2, v3])
T.writes(kernel_shared[v0, v1, v2, v3])
T.block_attr({"meta_schedule.cooperative_fetch":1})
kernel_shared[v0, v1, v2, v3] = kernel[v0, v1, v2, v3]
for i4_1, i5_1, i6_1, i0_3, i1_3, i2_3, i3_3, i4_2, i5_2, i6_2, i0_4, i1_4, i2_4, i3_4 in T.grid(1, 1, 1, 1, 1, 1, 2, 1, 7, 1, 1, 7, 1, 8):
with T.block("Conv2dOutput"):
nn = T.axis.spatial(1, i0_3 + i0_4)
yy = T.axis.spatial(112, i0_1_i1_1_i2_1_i3_1_fused // 2 * 56 + i0_2_i1_2_i2_2_i3_2_fused // 16 * 7 + i1_3 * 7 + i1_4)
xx = T.axis.spatial(112, i2_4 + i0_0_i1_0_i2_0_i3_0_fused // 2 * 16 + i0_2_i1_2_i2_2_i3_2_fused % 16 + i2_3)
ff = T.axis.spatial(64, i0_0_i1_0_i2_0_i3_0_fused % 2 * 32 + i0_1_i1_1_i2_1_i3_1_fused % 2 * 16 + i3_3 * 8 + i3_4)
ry = T.axis.reduce(7, i4_0 + i4_1 + i4_2)
rx = T.axis.reduce(7, i5_0 * 7 + i5_1 * 7 + i5_2)
rc = T.axis.reduce(3, i6_1 + i6_2 + i6_0)
T.reads(PaddedInput_shared[nn, yy * 2 + ry, xx * 2 + rx, rc], kernel_shared[ry, rx, rc, ff])
T.writes(Conv2dOutput_local[nn, yy, xx, ff])
T.block_attr({"meta_schedule.thread_extent_high_inclusive":1024, "meta_schedule.thread_extent_low_inclusive":32, "meta_schedule.tiling_structure":"SSSRRSRS"})
with T.init():
Conv2dOutput_local[nn, yy, xx, ff] = T.float32(0)
Conv2dOutput_local[nn, yy, xx, ff] = Conv2dOutput_local[nn, yy, xx, ff] + PaddedInput_shared[nn, yy * 2 + ry, xx * 2 + rx, rc] * kernel_shared[ry, rx, rc, ff]
for ax0, ax1, ax2, ax3 in T.grid(1, 7, 1, 16):
with T.block("Conv2dOutput_local"):
v0 = T.axis.spatial(1, ax0)
v1 = T.axis.spatial(112, i0_1_i1_1_i2_1_i3_1_fused // 2 * 56 + i0_2_i1_2_i2_2_i3_2_fused // 16 * 7 + ax1)
v2 = T.axis.spatial(112, i0_0_i1_0_i2_0_i3_0_fused // 2 * 16 + i0_2_i1_2_i2_2_i3_2_fused % 16 + ax2)
v3 = T.axis.spatial(64, i0_0_i1_0_i2_0_i3_0_fused % 2 * 32 + i0_1_i1_1_i2_1_i3_1_fused % 2 * 16 + ax3)
T.reads(Conv2dOutput_local[v0, v1, v2, v3], bias[v3], bn_scale[v3], bn_offset[v3])
T.writes(compute[v0, v1, v2, v3])
compute[v0, v1, v2, v3] = T.max((Conv2dOutput_local[v0, v1, v2, v3] + bias[v3]) * bn_scale[v3] + bn_offset[v3], T.float32(0))
# fmt: on
decision_0 = [
("SamplePerfectTile", [1, 1, 1, 1, 1]),
("SamplePerfectTile", [1, 2, 8, 1, 7]),
("SamplePerfectTile", [7, 1, 16, 1, 1]),
("SamplePerfectTile", [2, 2, 1, 2, 8]),
("SamplePerfectTile", [7, 1, 1]),
("SamplePerfectTile", [1, 1, 7]),
("SamplePerfectTile", [3, 1, 1]),
("SampleCategorical", 0),
("SampleCategorical", 0),
("SampleCategorical", 3),
]
mod = create_te_workload("CBR", 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=[cbr_0],
expected_decisions=[decision_0],
)


if __name__ == "__main__":
test_cuda_c1d()
test_cuda_c2d()
Expand All @@ -1139,3 +1229,4 @@ def sfm_3(A: T.Buffer[(256, 256), "float32"], T_softmax_norm: T.Buffer[(256, 256
test_cuda_t2d()
test_cuda_nrm()
test_cuda_sfm()
test_cuda_cbr()
2 changes: 1 addition & 1 deletion tests/python/unittest/test_tir_analysis_estimate_tir_flops.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@
("GMM", 4194304),
("GRP", 28901376),
("T2D", 268435456),
("C2d-BN-RELU", 239239168),
("CBR", 239239168),
("TBG", 25165824),
("NRM", 131072),
("SFM", 262144),
Expand Down

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