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[AOT] Add CreateExecutorMetadata analysis pass
AOT requires the ExecutorCodegenMetadata object to be populated containing various pieces of information about the compiled module. This commit adds a separate analysis pass to create the metadata + some tests for the new pass.
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/* | ||
* 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. | ||
*/ | ||
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/*! | ||
* \file src/relay/backend/aot/create_executor_metadata.cc | ||
* \brief Create the ExecutorCodegenMetadata from a compiled IRModule. | ||
*/ | ||
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#include "./create_executor_metadata.h" | ||
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#include "../utils.h" | ||
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namespace tvm { | ||
namespace relay { | ||
namespace backend { | ||
namespace aot { | ||
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ExecutorCodegenMetadata CreateExecutorMetadata(const IRModule& mod, String mod_name, | ||
Executor executor, Integer workspace_byte_alignment, | ||
Integer constant_byte_alignment) { | ||
// Get relevant executor config information | ||
std::string interface_api = executor->GetAttr<String>("interface-api").value_or("packed"); | ||
bool unpacked_api = executor->GetAttr<Bool>("unpacked-api").value_or(Bool(false)); | ||
// Get the input vars | ||
auto tir_main_func = Downcast<tir::PrimFunc>(mod->Lookup(runtime::symbol::tvm_module_main)); | ||
Array<tir::Var> inputs = tir_main_func->GetAttr<Array<tir::Var>>("input_vars").value(); | ||
Array<TensorType> input_tensor_types; | ||
for (const auto& input : inputs) { | ||
auto buffer = tir_main_func->buffer_map.Get(input).value(); | ||
input_tensor_types.push_back(TensorType(buffer->shape, buffer->dtype)); | ||
} | ||
// Extract USMP metadata to pass onto metadata sources | ||
Map<tir::Var, tir::usmp::AllocatedPoolInfo> pool_var_info; | ||
std::vector<tir::Var> pool_vars; | ||
Optional<Array<tir::usmp::AllocatedPoolInfo>> allocated_pool_infos = | ||
tir_main_func->GetAttr<Array<tir::usmp::AllocatedPoolInfo>>(tvm::attr::kPoolArgs); | ||
if (allocated_pool_infos) { | ||
for (const tir::usmp::AllocatedPoolInfo& allocated_pool_info : allocated_pool_infos.value()) { | ||
int pool_var_index = allocated_pool_info->pool_var_idx.value()->value; | ||
pool_vars.push_back(tir_main_func->params[pool_var_index]); | ||
pool_var_info.Set(tir_main_func->params[pool_var_index], allocated_pool_info); | ||
} | ||
} | ||
Map<String, tir::usmp::PoolAllocation> io_pool_allocations = | ||
mod->GetAttr<Map<String, tir::usmp::PoolAllocation>>(tvm::attr::kIOTensorPoolAllocations) | ||
.value_or({}); | ||
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Array<tir::Var> outputs = tir_main_func->GetAttr<Array<tir::Var>>("output_vars").value(); | ||
Array<TensorType> output_tensor_types; | ||
std::vector<String> output_var_names; | ||
for (const auto& output : outputs) { | ||
auto buffer = tir_main_func->buffer_map.Get(output).value(); | ||
output_tensor_types.push_back(TensorType(buffer->shape, buffer->dtype)); | ||
output_var_names.push_back(output->name_hint); | ||
} | ||
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return ExecutorCodegenMetadata(inputs, input_tensor_types, output_var_names, output_tensor_types, | ||
pool_vars, {} /*devices*/, runtime::kTvmExecutorAot, mod_name, | ||
interface_api, unpacked_api, workspace_byte_alignment, | ||
constant_byte_alignment, pool_var_info, io_pool_allocations); | ||
} | ||
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TVM_REGISTER_GLOBAL("relay.backend.aot.CreateExecutorMetadata") | ||
.set_body_typed(CreateExecutorMetadata); | ||
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} // namespace aot | ||
} // namespace backend | ||
} // namespace relay | ||
} // namespace tvm |
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/* | ||
* 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. | ||
*/ | ||
#ifndef TVM_RELAY_BACKEND_AOT_CREATE_EXECUTOR_METADATA_H_ | ||
#define TVM_RELAY_BACKEND_AOT_CREATE_EXECUTOR_METADATA_H_ | ||
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#include <tvm/ir/module.h> | ||
#include <tvm/relay/executor.h> | ||
#include <tvm/runtime/container/string.h> | ||
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#include "../utils.h" | ||
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namespace tvm { | ||
namespace relay { | ||
namespace backend { | ||
namespace aot { | ||
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/*! \brief Create ExecutorCodegenMetadata needed for AOT execution. | ||
* \param mod The module. | ||
* \param mod_name The module name. | ||
* \param executor The executor configuration. | ||
* \param workspace_byte_alignment The alignment of the workspace pool. | ||
* \param constant_byte_alignment The alignment of the constant pool. | ||
* \return The ExecutorCodegenMetadata. | ||
*/ | ||
ExecutorCodegenMetadata CreateExecutorMetadata(const IRModule& mod, String mod_name, | ||
Executor executor, Integer workspace_byte_alignment, | ||
Integer constant_byte_alignment); | ||
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} // namespace aot | ||
} // namespace backend | ||
} // namespace relay | ||
} // namespace tvm | ||
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#endif // TVM_RELAY_BACKEND_AOT_CREATE_EXECUTOR_METADATA_H_ |
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tests/python/relay/aot/test_aot_create_executor_metadata.py
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# 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 | ||
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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 | ||
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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() | ||
) | ||
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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 | ||
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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, | ||
} | ||
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executor_metadata = CreateExecutorMetadata(mod, "test_mod", executor, 16, 1) | ||
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_check_executor_metadata(executor_metadata, expected_metadata) | ||
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if __name__ == "__main__": | ||
tvm.testing.main() |