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[Runtime] add set_output_zero_copy (apache#8497)
* Update graph_executor.h * Update graph_executor.cc * modify zero copy UT add set input zero copy * modify C style * add runtime test * realy build generatr the json Co-authored-by: hwstaff <hwstaff@hwstaffdeMacBook-Pro.local>
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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. | ||
| */ | ||
|
|
||
| #include <gtest/gtest.h> | ||
| #include <tvm/driver/driver_api.h> | ||
| #include <tvm/ir/module.h> | ||
| #include <tvm/relay/analysis.h> | ||
| #include <tvm/relay/expr.h> | ||
| #include <tvm/relay/op_attr_types.h> | ||
| #include <tvm/relay/op_strategy.h> | ||
| #include <tvm/relay/transform.h> | ||
| #include <tvm/relay/type.h> | ||
| #include <tvm/runtime/executor_info.h> | ||
| #include <tvm/runtime/module.h> | ||
| #include <tvm/runtime/packed_func.h> | ||
| #include <tvm/runtime/registry.h> | ||
| #include <tvm/te/operation.h> | ||
| #include <tvm/topi/broadcast.h> | ||
| #include <tvm/topi/generic/injective.h> | ||
|
|
||
| using namespace tvm; | ||
| using namespace tvm::relay; | ||
|
|
||
| TVM_REGISTER_GLOBAL("runtime_test.strategy") | ||
| .set_body_typed([](const Attrs& attrs, const Array<te::Tensor>& inputs, const Type& out_type, | ||
| const Target& target) { | ||
| FTVMCompute fcompute = [](const Attrs& attrs, const Array<te::Tensor>& inputs, | ||
| const Type& out_type) -> Array<te::Tensor> { | ||
| ICHECK_EQ(inputs.size(), 2U); | ||
| return {topi::add(inputs[0], inputs[1])}; | ||
| }; | ||
| FTVMSchedule fschedule = [](const Attrs& attrs, const Array<te::Tensor>& outs, | ||
| const Target& target) { | ||
| With<Target> target_scope(target); | ||
| return topi::generic::schedule_injective(target, outs); | ||
| }; | ||
|
|
||
| auto n = make_object<OpStrategyNode>(); | ||
| auto strategy = tvm::relay::OpStrategy(std::move(n)); | ||
| strategy.AddImplementation(fcompute, fschedule, "runtime_test.strategy", 10); | ||
| return strategy; | ||
| }); | ||
|
|
||
| TEST(Runtime, ZeroCopy) { | ||
| auto tensor_type = relay::TensorType({2, 3}, DataType::Float(32)); | ||
| auto a = relay::Var("a", tensor_type); | ||
| auto b = relay::Var("b", tensor_type); | ||
| auto add_op = relay::Op::Get("add"); | ||
| auto x = relay::Call(add_op, {a, b}, tvm::Attrs(), {}); | ||
| auto c = relay::Var("c", tensor_type); | ||
| auto y = relay::Call(add_op, {x, c}, tvm::Attrs(), {}); | ||
| auto func = relay::Function(relay::FreeVars(y), y, relay::Type(), {}); | ||
| auto A = tvm::runtime::NDArray::Empty({2, 3}, {kDLFloat, 32, 1}, {kDLCPU, 0}); | ||
| auto B = tvm::runtime::NDArray::Empty({2, 3}, {kDLFloat, 32, 1}, {kDLCPU, 0}); | ||
| auto C = tvm::runtime::NDArray::Empty({2, 3}, {kDLFloat, 32, 1}, {kDLCPU, 0}); | ||
| auto Y = tvm::runtime::NDArray::Empty({2, 3}, {kDLFloat, 32, 1}, {kDLCPU, 0}); | ||
|
|
||
| auto pA = static_cast<float*>(A->data); | ||
| auto pB = static_cast<float*>(B->data); | ||
| auto pC = static_cast<float*>(C->data); | ||
| auto pY = static_cast<float*>(Y->data); | ||
|
|
||
| for (int i = 0; i < 6; ++i) { | ||
| pA[i] = i; | ||
| pB[i] = i + 1; | ||
| pC[i] = i + 2; | ||
| } | ||
| // get schedule | ||
| auto reg = tvm::runtime::Registry::Get("ir.RegisterOpAttr"); | ||
| if (!reg) { | ||
| LOG(FATAL) << "no _Register"; | ||
| } | ||
| auto fs = tvm::runtime::Registry::Get("runtime_test.strategy"); | ||
| if (!fs) { | ||
| LOG(FATAL) << "No test_strategy registered."; | ||
| } | ||
| auto fgeneric = GenericFunc::Get("runtime_test.strategy_generic").set_default(*fs); | ||
| (*reg)("add", "FTVMStrategy", fgeneric, 10); | ||
| Array<Integer> dep; | ||
| dep.push_back(0); | ||
| (*reg)("add", "TShapeDataDependent", dep, 10); | ||
| // build | ||
| auto pfb = tvm::runtime::Registry::Get("relay.build_module._BuildModule"); | ||
| tvm::runtime::Module build_mod = (*pfb)(); | ||
| auto build_f = build_mod.GetFunction("build", false); | ||
| auto json_f = build_mod.GetFunction("get_graph_json", false); | ||
| auto mod_f = build_mod.GetFunction("get_module", false); | ||
| Map<tvm::Integer, tvm::Target> targets; | ||
| Target llvm_tgt = Target("llvm"); | ||
| targets.Set(0, llvm_tgt); | ||
| auto relay_mod = tvm::IRModule::FromExpr(func); | ||
| ICHECK(relay_mod.defined()) << "Module must be defined"; | ||
| build_f(relay_mod, targets, llvm_tgt, runtime::kTvmExecutorGraph, ""); | ||
| // create graph executor | ||
| std::string json = json_f(); | ||
| tvm::runtime::Module mod = mod_f(); | ||
| auto dev = A->device; | ||
| auto pfr = tvm::runtime::Registry::Get("tvm.graph_executor.create"); | ||
| ICHECK(mod.defined()) << "Module must be defined"; | ||
| tvm::runtime::Module run_mod = | ||
| (*pfr)(json, mod, static_cast<int>(dev.device_type), dev.device_id); | ||
| // get function | ||
| auto set_input_f = run_mod.GetFunction("set_input_zero_copy", false); | ||
| auto set_output_f = run_mod.GetFunction("set_output_zero_copy", false); | ||
| auto run_f = run_mod.GetFunction("run", false); | ||
| // set input zero copy | ||
| set_input_f("a", const_cast<DLTensor*>(A.operator->())); | ||
| set_input_f("b", const_cast<DLTensor*>(B.operator->())); | ||
| set_input_f("c", const_cast<DLTensor*>(C.operator->())); | ||
| // set output zero copy | ||
| set_output_f(0, const_cast<DLTensor*>(Y.operator->())); | ||
| run_f(); | ||
| // check correctness | ||
| for (int i = 0; i < 6; ++i) { | ||
| ICHECK_LT(fabs(pY[i] - (i + (i + 1) + (i + 2))), 1e-4); | ||
| } | ||
| // mutate the input a bit and run it again | ||
| for (int i = 0; i < 6; ++i) { | ||
| pB[i] = i + 3; | ||
| } | ||
| run_f(); | ||
| // check correctness | ||
| for (int i = 0; i < 6; ++i) { | ||
| ICHECK_LT(fabs(pY[i] - (i + (i + 3) + (i + 2))), 1e-4); | ||
| } | ||
| // attach a different input and run it again | ||
| auto C2 = tvm::runtime::NDArray::Empty({2, 3}, {kDLFloat, 32, 1}, {kDLCPU, 0}); | ||
| auto pC2 = static_cast<float*>(C2->data); | ||
| for (int i = 0; i < 6; ++i) { | ||
| pC2[i] = i + 4; | ||
| } | ||
| set_input_f("c", const_cast<DLTensor*>(C2.operator->())); | ||
| run_f(); | ||
| // check correctness | ||
| for (int i = 0; i < 6; ++i) { | ||
| ICHECK_LT(fabs(pY[i] - (i + (i + 3) + (i + 4))), 1e-4); | ||
| } | ||
| } |