[MetaSchedule][Testing] Add unittests for C1D search space

tests/python/unittest/test_meta_schedule_space_cpu.py

@@ -0,0 +1,179 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Tests for MetaSchedule search space on CPU"""
+from tvm import meta_schedule as ms
+from tvm.meta_schedule.testing.space_generation import check_sketches, print_sketches
+from tvm.meta_schedule.testing.te_workload import create_te_workload
+from tvm.script import tir as T
+from tvm.target import Target
+
+
+def _target():
+    return Target("aws/cpu/c5.9xlarge")
+
+
+def test_cpu_c1d():
+    # fmt: off
+    @T.prim_func
+    def c1d_0(inputs: T.Buffer[(1, 256, 64), "float32"], weight: T.Buffer[(3, 64, 128), "float32"], conv1d_nlc: T.Buffer[(1, 128, 128), "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, 258, 64], dtype="float32")
+            conv1d_nlc_global = T.alloc_buffer([1, 128, 128], dtype="float32")
+            for i0, i1, i2 in T.grid(1, 258, 64):
+                with T.block("PadInput"):
+                    i0_1, i1_1, i2_1 = T.axis.remap("SSS", [i0, i1, i2])
+                    T.reads(inputs[i0_1, i1_1 - 1, i2_1])
+                    T.writes(PadInput[i0_1, i1_1, i2_1])
+                    PadInput[i0_1, i1_1, i2_1] = T.if_then_else(1 <= i1_1 and i1_1 < 257, inputs[i0_1, i1_1 - 1, i2_1], T.float32(0), dtype="float32")
+            for i0_0, i1_0, i2_0, i0_1_1, i1_1_1, i2_1_1 in T.grid(1, 1, 2, 1, 1, 8):
+                for i3_0, i4_0, i0_2, i1_2, i2_2, i3_1, i4_1, i0_3, i1_3, i2_3 in T.grid(1, 64, 1, 64, 8, 3, 1, 1, 2, 1):
+                    with T.block("conv1d_nlc"):
+                        n = T.axis.spatial(1, i0_0 + i0_1_1 + i0_2 + i0_3)
+                        l = T.axis.spatial(128, i1_1_1 * 128 + i1_0 * 128 + i1_2 * 2 + i1_3)
+                        co = T.axis.spatial(128, (i2_0 * 8 + i2_1_1) * 8 + i2_2 + i2_3)
+                        rl = T.axis.reduce(3, i3_0 * 3 + i3_1)
+                        rc = T.axis.reduce(64, i4_0 + i4_1)
+                        T.reads(PadInput[n, l * 2 + rl, co // 128 * 64 + rc], weight[rl, rc, co])
+                        T.writes(conv1d_nlc_global[n, l, co])
+                        T.block_attr({"meta_schedule.tiling_structure":"SSRSRS"})
+                        with T.init():
+                            conv1d_nlc_global[n, l, co] = T.float32(0)
+                        conv1d_nlc_global[n, l, co] = conv1d_nlc_global[n, l, co] + PadInput[n, l * 2 + rl, co // 128 * 64 + rc] * weight[rl, rc, co]
+                for ax0, ax1, ax2 in T.grid(1, 128, 8):
+                    with T.block("conv1d_nlc_global"):
+                        v0, v1 = T.axis.remap("SS", [ax0, ax1])
+                        v2 = T.axis.spatial(128, i2_0 * 64 + i2_1_1 * 8 + ax2)
+                        T.reads(conv1d_nlc_global[v0, v1, v2])
+                        T.writes(conv1d_nlc[v0, v1, v2])
+                        conv1d_nlc[v0, v1, v2] = conv1d_nlc_global[v0, v1, v2]
+    @T.prim_func
+    def c1d_1(inputs: T.Buffer[(1, 256, 64), "float32"], weight: T.Buffer[(3, 64, 128), "float32"], conv1d_nlc: T.Buffer[(1, 128, 128), "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, 258, 64], dtype="float32")
+            conv1d_nlc_global = T.alloc_buffer([1, 128, 128], dtype="float32")
+            for i0_0, i1_0, i2_0 in T.grid(1, 1, 2):
+                for i0_1, i1_1, i2_1 in T.grid(1, 1, 8):
+                    for ax0, ax1, ax2 in T.grid(1, 257, 64):
+                        with T.block("PadInput"):
+                            i0 = T.axis.spatial(1, ax0)
+                            i1 = T.axis.spatial(258, ax1)
+                            i2 = T.axis.spatial(64, ax2)
+                            T.reads(inputs[i0, i1 - 1, i2])
+                            T.writes(PadInput[i0, i1, i2])
+                            PadInput[i0, i1, i2] = T.if_then_else(1 <= i1 and i1 < 257, inputs[i0, i1 - 1, i2], T.float32(0), dtype="float32")
+                    for i3_0, i4_0, i0_2, i1_2, i2_2, i3_1, i4_1, i0_3, i1_3, i2_3 in T.grid(1, 64, 1, 64, 8, 3, 1, 1, 2, 1):
+                        with T.block("conv1d_nlc"):
+                            n = T.axis.spatial(1, i0_0 + i0_1 + i0_2 + i0_3)
+                            l = T.axis.spatial(128, i1_1 * 128 + i1_0 * 128 + i1_2 * 2 + i1_3)
+                            co = T.axis.spatial(128, (i2_0 * 8 + i2_1) * 8 + i2_2 + i2_3)
+                            rl = T.axis.reduce(3, i3_0 * 3 + i3_1)
+                            rc = T.axis.reduce(64, i4_0 + i4_1)
+                            T.reads(PadInput[n, l * 2 + rl, co // 128 * 64 + rc], weight[rl, rc, co])
+                            T.writes(conv1d_nlc_global[n, l, co])
+                            T.block_attr({"meta_schedule.tiling_structure":"SSRSRS"})
+                            with T.init():
+                                conv1d_nlc_global[n, l, co] = T.float32(0)
+                            conv1d_nlc_global[n, l, co] = conv1d_nlc_global[n, l, co] + PadInput[n, l * 2 + rl, co // 128 * 64 + rc] * weight[rl, rc, co]
+                for ax0, ax1, ax2 in T.grid(1, 128, 64):
+                    with T.block("conv1d_nlc_global"):
+                        v0, v1 = T.axis.remap("SS", [ax0, ax1])
+                        v2 = T.axis.spatial(128, i2_0 * 64 + ax2)
+                        T.reads(conv1d_nlc_global[v0, v1, v2])
+                        T.writes(conv1d_nlc[v0, v1, v2])
+                        conv1d_nlc[v0, v1, v2] = conv1d_nlc_global[v0, v1, v2]
+
+    @T.prim_func
+    def c1d_2(inputs: T.Buffer[(1, 256, 64), "float32"], weight: T.Buffer[(3, 64, 128), "float32"], conv1d_nlc: T.Buffer[(1, 128, 128), "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})
+            for i0_0, i1_0, i2_0, i0_1, i1_1, i2_1, i3_0, i4_0, i0_2, i1_2, i2_2, i3_1, i4_1, i0_3, i1_3, i2_3 in T.grid(1, 1, 2, 1, 1, 8, 1, 64, 1, 64, 8, 3, 1, 1, 2, 1):
+                with T.block("conv1d_nlc"):
+                    n = T.axis.spatial(1, i0_0 + i0_1 + i0_2 + i0_3)
+                    l = T.axis.spatial(128, i1_1 * 128 + i1_0 * 128 + i1_2 * 2 + i1_3)
+                    co = T.axis.spatial(128, (i2_0 * 8 + i2_1) * 8 + i2_2 + i2_3)
+                    rl = T.axis.reduce(3, i3_0 * 3 + i3_1)
+                    rc = T.axis.reduce(64, i4_0 + i4_1)
+                    T.reads(inputs[n, l * 2 + rl - 1, co // 128 * 64 + rc], weight[rl, rc, co])
+                    T.writes(conv1d_nlc[n, l, co])
+                    T.block_attr({"meta_schedule.tiling_structure":"SSRSRS"})
+                    with T.init():
+                        conv1d_nlc[n, l, co] = T.float32(0)
+                    conv1d_nlc[n, l, co] = conv1d_nlc[n, l, co] + T.if_then_else(1 <= l * 2 + rl and l * 2 + rl < 257, inputs[n, l * 2 + rl - 1, co // 128 * 64 + rc], T.float32(0), dtype="float32") * weight[rl, rc, co]
+    # fmt: on
+
+    decision_0 = [
+        ("SamplePerfectTile", [1, 1, 1, 1]),
+        ("SamplePerfectTile", [1, 1, 64, 2]),
+        ("SamplePerfectTile", [2, 8, 8, 1]),
+        ("SamplePerfectTile", [1, 3]),
+        ("SamplePerfectTile", [64, 1]),
+        ("SampleCategorical", 3),
+        ("SampleComputeLocation", -1),
+    ]
+    decision_1 = [
+        ("SamplePerfectTile", [1, 1, 1, 1]),
+        ("SamplePerfectTile", [1, 1, 64, 2]),
+        ("SamplePerfectTile", [2, 8, 8, 1]),
+        ("SamplePerfectTile", [1, 3]),
+        ("SamplePerfectTile", [64, 1]),
+        ("SampleCategorical", 3),
+        ("SampleComputeLocation", 5),
+    ]
+    decision_2 = [
+        ("SamplePerfectTile", [1, 1, 1, 1]),
+        ("SamplePerfectTile", [1, 1, 64, 2]),
+        ("SamplePerfectTile", [2, 8, 8, 1]),
+        ("SamplePerfectTile", [1, 3]),
+        ("SamplePerfectTile", [64, 1]),
+        ("SampleCategorical", 1),
+        ("SampleComputeLocation", -2),
+    ]
+
+    mod = create_te_workload("C1D", 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=[c1d_0, c1d_1, c1d_2],
+        expected_decisions=[decision_0, decision_1, decision_2],
+    )
+
+
+if __name__ == "__main__":
+    test_cpu_c1d()

tests/python/unittest/test_meta_schedule_space_cuda.py

@@ -29,7 +29,7 @@ def _target():
 def test_cuda_c1d():
     # fmt: off
     @T.prim_func
-    def mod_0(inputs: T.Buffer[(1, 256, 64), "float32"], weight: T.Buffer[(3, 64, 128), "float32"], conv1d_nlc: T.Buffer[(1, 128, 128), "float32"]) -> None:
+    def c1d_0(inputs: T.Buffer[(1, 256, 64), "float32"], weight: T.Buffer[(3, 64, 128), "float32"], conv1d_nlc: T.Buffer[(1, 128, 128), "float32"]) -> None:
         # function attr dict
         T.func_attr({"global_symbol": "main", "tir.noalias": True})
         # body
@@ -106,7 +106,7 @@ def mod_0(inputs: T.Buffer[(1, 256, 64), "float32"], weight: T.Buffer[(3, 64, 12
     check_sketches(
         mod,
         sketches=actual,
-        expected_mods=[mod_0],
+        expected_mods=[c1d_0],
         expected_decisions=[decision_0],
     )