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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/11/04 14:14:00 UTC

[GitHub] [tvm] mbaret commented on a diff in pull request #13250: [AOT] Add CreateExecutorMetadata analysis pass

mbaret commented on code in PR #13250:
URL: https://github.com/apache/tvm/pull/13250#discussion_r1014071091


##########
tests/python/relay/aot/test_aot_create_executor_metadata.py:
##########
@@ -0,0 +1,128 @@
+# 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.
+# pylint: disable=line-too-long,missing-class-docstring,missing-module-docstring,missing-function-docstring,no-self-argument,unused-argument,invalid-name
+import numpy as np
+
+import tvm
+import tvm.testing
+from tvm.script import tir as T
+from tvm.runtime.ndarray import array
+from tvm.relay.backend import Executor
+from tvm.relay.backend.aot import CreateExecutorMetadata
+from tvm.relay import TensorType
+from tvm.tir.usmp.utils import PoolAllocation
+from tvm.ir.memory_pools import AllocatedPoolInfo, ConstantPoolInfo, WorkspacePoolInfo, ConstantInfo
+
+
+def _check_executor_metadata(executor_metadata, expected_metadata):
+    assert list(executor_metadata.inputs) == expected_metadata["inputs"]
+    assert list(executor_metadata.input_tensor_types) == expected_metadata["input_tensor_types"]
+    assert list(executor_metadata.outputs) == expected_metadata["outputs"]
+    assert list(executor_metadata.output_tensor_types) == expected_metadata["output_tensor_types"]
+    assert list(executor_metadata.pools) == expected_metadata["pools"]
+    assert list(executor_metadata.devices) == expected_metadata["devices"]
+    assert executor_metadata.executor == expected_metadata["executor"]
+    assert executor_metadata.mod_name == expected_metadata["mod_name"]
+    assert executor_metadata.interface_api == expected_metadata["interface_api"]
+    assert executor_metadata.unpacked_api == expected_metadata["unpacked_api"]
+    assert executor_metadata.workspace_alignment == expected_metadata["workspace_alignment"]
+    assert executor_metadata.constant_alignment == expected_metadata["constant_alignment"]
+    assert set(executor_metadata.pool_inputs.keys()) == set(expected_metadata["pool_inputs"].keys())
+    assert set(executor_metadata.io_pool_allocations.keys()) == set(
+        expected_metadata["io_pool_allocations"].keys()
+    )
+
+
+def test_create_executor_metadata_single_func():
+    # fmt: off
+    @tvm.script.ir_module
+    class Module:
+        @T.prim_func
+        def __tvm_main__(
+            a: T.handle, output: T.handle, workspace: T.Ptr[T.uint8], constants: T.Ptr[T.uint8]
+        ) -> None:
+            # function attr dict
+            T.func_attr({"global_symbol": "test_mod___tvm_main__", "runner_function": True, "target": T.target({"kind": "llvm", "tag": "", "keys": ["cpu"]}), "input_vars": [a], "output_vars": [output]})
+            a_buffer = T.match_buffer(a, [5, 7], dtype="float32", align=16)
+            output_buffer = T.match_buffer(output, [5, 7], dtype="float32", align=16)
+            # body
+            sid_3 = T.allocate([140], "int8", "global.workspace")
+            sid_2 = T.allocate([140], "int8", "global.workspace")
+            sid_1 = T.allocate([140], "int8", "global.workspace")
+            constant_0 = T.allocate_const([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "float32", [5, 7])
+            T.evaluate(T.tvm_call_cpacked("test_fused_add_0", a_buffer.data, sid_1.data, T.reinterpret(T.uint64(0), dtype="handle"), dtype="int32"))
+            T.evaluate(T.tvm_call_cpacked("test_fused_add_0", sid_1.data, constant_0.data, T.reinterpret(T.uint64(0), dtype="handle"), dtype="int32"))
+            T.evaluate(T.tvm_call_cpacked("test_fused_add_0", sid_2.data, sid_3.data, T.reinterpret(T.uint64(0), dtype="handle"), dtype="int32"))
+            T.evaluate(T.tvm_call_cpacked("test_fused_add_1", sid_2.data, sid_3.data, output_buffer.data, T.reinterpret(T.uint64(0), dtype="handle"), dtype="int32"))
+    # fmt: on
+
+    target = Module["__tvm_main__"].attrs["target"]
+    executor = Executor("aot", {"interface-api": "c"})
+    workspace_pool_info = AllocatedPoolInfo(
+        WorkspacePoolInfo("sram", [target]),
+        256,
+        3,
+    )
+    constant_pool_info = AllocatedPoolInfo(
+        ConstantPoolInfo(
+            "flash",
+            [target],
+            [ConstantInfo("a", 0, array(np.array([0])))],
+        ),
+        512,
+        2,
+    )
+    io_pool_allocations = {
+        "a": PoolAllocation(WorkspacePoolInfo("sram", [target]), 0),
+        "output": PoolAllocation(WorkspacePoolInfo("sram", [target]), 0),
+    }
+    mod = Module.with_attr("io_tensor_pool_allocations", io_pool_allocations)
+    mod["__tvm_main__"] = mod["__tvm_main__"].with_attr(
+        "pool_args",
+        [
+            constant_pool_info,
+            workspace_pool_info,
+        ],
+    )
+    f = mod["__tvm_main__"]
+    expected_metadata = {
+        "inputs": [f.params[0]],
+        "input_tensor_types": [TensorType((5, 7), "float32")],
+        "outputs": ["output"],
+        "output_tensor_types": [TensorType((5, 7), "float32")],
+        "pools": f.params[2:],
+        "devices": [],
+        "executor": "aot",
+        "mod_name": "test_mod",
+        "interface_api": "c",
+        "unpacked_api": False,
+        "workspace_alignment": 16,
+        "constant_alignment": 1,
+        "pool_inputs": {
+            f.params[2]: workspace_pool_info,
+            f.params[3]: constant_pool_info,
+        },
+        "io_pool_allocations": io_pool_allocations,
+    }
+
+    executor_metadata = CreateExecutorMetadata(mod, "test_mod", executor, 16, 1)
+
+    _check_executor_metadata(executor_metadata, expected_metadata)
+

Review Comment:
   Added a test case :)



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