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[MetaSchedule][Test] Add unittests for NRM #12250

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Aug 1, 2022
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[MetaSchedule][Test] Add unittests for NRM #12250

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125 changes: 125 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 @@ -1536,6 +1536,130 @@ def t2d_2(inputs: T.Buffer[(1, 4, 4, 512), "float32"], weight: T.Buffer[(4, 4, 5
)


def test_cpu_nrm():
# fmt: off
@T.prim_func
def nrm_0(A: T.Buffer[(1, 256, 256), "float32"], D: T.Buffer[1, "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":0, "meta_schedule.vectorize":64})
C = T.alloc_buffer([1], dtype="float32")
C_rf = T.alloc_buffer([1, 32768], dtype="float32")
for i0, i1_i2_fused_0, i1_i2_fused_1 in T.grid(1, 32768, 2):
with T.block("C_rf"):
vi1_i2_fused_0, b, vi1_i2_fused_1 = T.axis.remap("SSR", [i1_i2_fused_0, i0, i1_i2_fused_1])
T.reads(A[b, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) // 256, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) % 256])
T.writes(C_rf[b, vi1_i2_fused_0])
with T.init():
C_rf[b, vi1_i2_fused_0] = T.float32(0)
C_rf[b, vi1_i2_fused_0] = C_rf[b, vi1_i2_fused_0] + A[b, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) // 256, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) % 256] * A[b, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) // 256, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) % 256]
for i0, i1_i2_fused_0 in T.grid(1, 32768):
with T.block("C"):
vi1_i2_fused_0, b = T.axis.remap("RS", [i1_i2_fused_0, i0])
T.reads(C_rf[b, vi1_i2_fused_0])
T.writes(C[b])
with T.init():
C[b] = T.float32(0)
C[b] = C[b] + C_rf[b, vi1_i2_fused_0]
for i0 in T.serial(1):
with T.block("D"):
b = T.axis.spatial(1, i0)
T.reads(C[b])
T.writes(D[b])
D[b] = T.sqrt(C[b], dtype="float32")
@T.prim_func
def nrm_1(A: T.Buffer[(1, 256, 256), "float32"], D: T.Buffer[1, "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})
C = T.alloc_buffer([1], dtype="float32")
C_rf = T.alloc_buffer([1, 2], dtype="float32")
for i0, i1_i2_fused_0, i1_i2_fused_1 in T.grid(1, 32768, 2):
with T.block("C_rf"):
vi1_i2_fused_1, b, vi1_i2_fused_0 = T.axis.remap("SSR", [i1_i2_fused_1, i0, i1_i2_fused_0])
T.reads(A[b, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) // 256, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) % 256])
T.writes(C_rf[b, vi1_i2_fused_1])
with T.init():
C_rf[b, vi1_i2_fused_1] = T.float32(0)
C_rf[b, vi1_i2_fused_1] = C_rf[b, vi1_i2_fused_1] + A[b, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) // 256, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) % 256] * A[b, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) // 256, (vi1_i2_fused_0 * 2 + vi1_i2_fused_1) % 256]
for i0, i1_i2_fused_1 in T.grid(1, 2):
with T.block("C"):
vi1_i2_fused_1, b = T.axis.remap("RS", [i1_i2_fused_1, i0])
T.reads(C_rf[b, vi1_i2_fused_1])
T.writes(C[b])
with T.init():
C[b] = T.float32(0)
C[b] = C[b] + C_rf[b, vi1_i2_fused_1]
for i0 in T.serial(1):
with T.block("D"):
b = T.axis.spatial(1, i0)
T.reads(C[b])
T.writes(D[b])
D[b] = T.sqrt(C[b], dtype="float32")
@T.prim_func
def nrm_2(A: T.Buffer[(1, 256, 256), "float32"], D: T.Buffer[1, "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":0, "meta_schedule.vectorize":64})
C = T.alloc_buffer([1], dtype="float32")
for i0, i1, i2 in T.grid(1, 256, 256):
with T.block("C"):
b, i, j = T.axis.remap("SRR", [i0, i1, i2])
T.reads(A[b, i, j])
T.writes(C[b])
with T.init():
C[b] = T.float32(0)
C[b] = C[b] + A[b, i, j] * A[b, i, j]
for i0 in T.serial(1):
with T.block("D"):
b = T.axis.spatial(1, i0)
T.reads(C[b])
T.writes(D[b])
D[b] = T.sqrt(C[b], dtype="float32")
# fmt: on
decision_0 = [
("SamplePerfectTile", [32768, 2]),
("SampleCategorical", 0),
("SampleComputeLocation", -1),
("SampleComputeLocation", -1),
]
decision_1 = [
("SamplePerfectTile", [32768, 2]),
("SampleCategorical", 1),
("SampleComputeLocation", -1),
("SampleComputeLocation", -1),
]
decision_2 = [
("SampleCategorical", 0),
("SampleComputeLocation", -1),
]
mod = create_te_workload("NRM", 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=[nrm_0, nrm_1, nrm_2],
expected_decisions=[decision_0, decision_1, decision_2],
)


if __name__ == "__main__":
test_cpu_c1d()
test_cpu_c2d()
Expand All @@ -1546,3 +1670,4 @@ def t2d_2(inputs: T.Buffer[(1, 4, 4, 512), "float32"], weight: T.Buffer[(4, 4, 5
test_cpu_gmm()
test_cpu_grp()
test_cpu_t2d()
test_cpu_nrm()
83 changes: 83 additions & 0 deletions tests/python/unittest/test_meta_schedule_space_cuda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -833,6 +833,88 @@ def t2d_0(inputs: T.Buffer[(1, 4, 4, 512), "float32"], weight: T.Buffer[(4, 4, 5
)


def test_cuda_nrm():
# fmt: off
@T.prim_func
def nrm_0(A: T.Buffer[(1, 256, 256), "float32"], D: T.Buffer[1, "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})
C = T.alloc_buffer([1], dtype="float32")
for i0_fused_0 in T.thread_binding(1, thread="blockIdx.x"):
for i0_fused_1 in T.thread_binding(1, thread="threadIdx.x"):
for i1, i2 in T.grid(256, 256):
with T.block("C"):
b = T.axis.spatial(1, 0)
i, j = T.axis.remap("RR", [i1, i2])
T.reads(A[b, i, j])
T.writes(C[b])
with T.init():
C[b] = T.float32(0)
C[b] = C[b] + A[b, i, j] * A[b, i, j]
for i0_fused_0 in T.thread_binding(1, thread="blockIdx.x"):
for i0_fused_1 in T.thread_binding(1, thread="threadIdx.x"):
with T.block("D"):
b = T.axis.spatial(1, 0)
T.reads(C[b])
T.writes(D[b])
D[b] = T.sqrt(C[b], dtype="float32")
@T.prim_func
def nrm_1(A: T.Buffer[(1, 256, 256), "float32"], D: T.Buffer[1, "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":1024})
C_shared = T.alloc_buffer([1], dtype="float32", scope="shared")
for i0_0_fused in T.thread_binding(1, thread="blockIdx.x"):
for ax0, ax1_ax2_fused_0 in T.grid(1, 512):
for ax1_ax2_fused_1 in T.thread_binding(128, thread="threadIdx.x"):
with T.block("C"):
b = T.axis.spatial(1, ax0)
i = T.axis.reduce(256, (ax1_ax2_fused_0 * 128 + ax1_ax2_fused_1) // 256)
j = T.axis.reduce(256, (ax1_ax2_fused_0 * 128 + ax1_ax2_fused_1) % 256)
T.reads(A[b, i, j])
T.writes(C_shared[b])
with T.init():
C_shared[b] = T.float32(0)
C_shared[b] = C_shared[b] + A[b, i, j] * A[b, i, j]
for i0_1 in T.thread_binding(128, thread="threadIdx.x"):
with T.block("D"):
b = T.axis.spatial(1, i0_1)
T.where(0 * 128 + i0_1 < 1)
T.reads(C_shared[b])
T.writes(D[b])
D[b] = T.sqrt(C_shared[b], dtype="float32")
# fmt: on
decision_0 = [
("SampleCategorical", 3),
]
decision_1 = [
("SampleCategorical", 5),
("SampleCategorical", 4),
]
mod = create_te_workload("NRM", 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=[nrm_0, nrm_1],
expected_decisions=[decision_0, decision_1],
)


if __name__ == "__main__":
test_cuda_c1d()
test_cuda_c2d()
Expand All @@ -843,3 +925,4 @@ def t2d_0(inputs: T.Buffer[(1, 4, 4, 512), "float32"], weight: T.Buffer[(4, 4, 5
test_cuda_gmm()
test_cuda_grp()
test_cuda_t2d()
test_cuda_nrm()