[GitHub] [tvm] comaniac commented on a change in pull request #10856: [Metaschedule] Add test case for multi-anchor subgraph

[GitBox <gi...@apache.org>]

comaniac commented on a change in pull request #10856:
URL: https://github.com/apache/tvm/pull/10856#discussion_r840227166



##########
File path: tests/python/unittest/test_meta_schedule_multi_anchor.py
##########
@@ -0,0 +1,131 @@
+# 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.
+import os
+import tempfile
+
+import numpy as np
+
+import tvm
+import tvm.testing
+from tvm import relay
+from tvm.meta_schedule.tune import Parse, extract_task_from_relay
+from tvm.meta_schedule.database import TuningRecord, JSONDatabase
+from tvm.meta_schedule.integration import ApplyHistoryBest
+
+
+def get_dense_dense(data_shape, weight_shape):
+    def multi_dense():
+        p_data = relay.var("p_data", shape=data_shape, dtype="float32")
+        p_weight1 = relay.var("p_weight1", shape=weight_shape, dtype="float32")
+        p_weight2 = relay.var("p_weight2", shape=weight_shape, dtype="float32")
+
+        dense1 = relay.nn.dense(p_data, p_weight1)
+        dense2 = relay.nn.dense(dense1, p_weight2)
+
+        f = relay.Function([p_data, p_weight1, p_weight2], dense2)
+        f = f.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
+        return f
+
+    data = relay.var("data", shape=data_shape, dtype="float32")
+    weight1 = relay.var("weight1", shape=weight_shape, dtype="float32")
+    weight2 = relay.var("weight2", shape=weight_shape, dtype="float32")
+
+    out = relay.Call(multi_dense(), [data, weight1, weight2])
+    return relay.Function([data, weight1, weight2], out)
+
+
+def get_ref(data_np, weight1_np, weight2_np):
+    dense1 = np.dot(data_np, np.transpose(weight1_np))
+    return np.dot(dense1, np.transpose(weight2_np))
+
+
+def schedule_dense_dense(sch):
+    dense1 = sch.get_block("T_matmul_NT")
+    dense2 = sch.get_block("T_matmul_NT_1")
+
+    y1, x1, k1 = sch.get_loops(dense1)
+    y2, x2, k2 = sch.get_loops(dense2)
+
+    # ...
+
+
+def test_dense_dense():
+    M, N, K = 128, 128, 128
+    data_shape = (M, K)
+    weight_shape = (N, K)
+
+    relay_mod = tvm.IRModule.from_expr(get_dense_dense(data_shape, weight_shape))
+
+    # print(relay.transform.InferType()(relay_mod))
+
+    target = "llvm"
+
+    data_np = np.random.randn(*data_shape).astype("float32")
+    weight1_np = np.random.randn(*weight_shape).astype("float32")
+    weight2_np = np.random.randn(*weight_shape).astype("float32")
+
+    params = {"weight1": weight1_np, "weight2": weight2_np}
+
+    extracted_tasks = extract_task_from_relay(relay_mod, target, params)
+
+    assert len(extracted_tasks) == 1
+
+    task = extracted_tasks[0]
+
+    mod = Parse._mod(task.dispatched[0])
+
+    with tempfile.TemporaryDirectory() as work_dir:
+        database = JSONDatabase(
+            path_workload=os.path.join(work_dir, "database_workload.json"),
+            path_tuning_record=os.path.join(work_dir, "database_tuning_record.json"),
+        )
+
+        workload = database.commit_workload(mod)
+
+        sch = tvm.tir.Schedule(mod)
+
+        schedule_dense_dense(sch)
+
+        print(sch.mod.script())

Review comment:
       Remove?




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