comaniac commented on a change in pull request #5915: URL: https://github.com/apache/incubator-tvm/pull/5915#discussion_r454026302
########## File path: src/relay/backend/contrib/arm_compute_lib/codegen.cc ########## @@ -0,0 +1,188 @@ +/* + * 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. + */ + +/*! + * \file src/relay/backend/contrib/arm_compute_lib/codegen_acl.cc + * \brief Implementation of the Relay -> ACL JSON serializer. + */ +#include <tvm/ir/module.h> +#include <tvm/relay/attrs/nn.h> +#include <tvm/relay/type.h> + +#include "../../utils.h" +#include "codegen_acl.h" + +namespace tvm { +namespace relay { +namespace contrib { +namespace arm_compute_lib { + +using JSONGraphNode = tvm::runtime::json::JSONGraphNode; +using JSONGraphNodeEntry = tvm::runtime::json::JSONGraphNodeEntry; + +std::vector<JSONGraphNodeEntry> ACLJSONSerializer::VisitExpr_(const CallNode* cn) { + Expr expr = GetRef<Expr>(cn); + std::string name; + std::shared_ptr<JSONGraphNode> json_node; + + if (cn->op.as<OpNode>()) { + json_node = CreateOp(cn); + } else if (const auto* fn = cn->op.as<FunctionNode>()) { + auto comp = fn->GetAttr<String>(attr::kComposite); + CHECK(comp.defined()) << "Arm Compute Library JSON runtime only supports composite functions."; + name = comp.value(); + if (name == "arm_compute_lib.conv2d") { + json_node = CreateCompositeConvolution(cn); + } else { + LOG(FATAL) << "Unrecognized Arm Compute Library pattern: " << name; + } + } else { + LOG(FATAL) << "Arm Compute Library JSON runtime does not support calls to " + << cn->op->GetTypeKey(); + } + + return AddNode(json_node, GetRef<Expr>(cn)); +} + +std::vector<JSONGraphNodeEntry> ACLJSONSerializer::VisitExpr_(const ConstantNode* cn) { + this->constants_.push_back(cn->data); + return JSONSerializer::VisitExpr_(cn); +} + +std::shared_ptr<JSONGraphNode> ACLJSONSerializer::CreateOp(const CallNode* cn) { + const auto* op = cn->op.as<OpNode>(); + CHECK(op); + const std::string name = op->name; + // Collect inputs + std::vector<JSONGraphNodeEntry> inputs; + for (const auto& arg : cn->args) { + auto res = VisitExpr(arg); + inputs.insert(inputs.end(), res.begin(), res.end()); + } + // Create JSON op + auto json_node = std::make_shared<JSONGraphNode>(name, "kernel", inputs, 1); + SetCallNodeAttribute(json_node, cn); + return json_node; +} + +std::shared_ptr<JSONGraphNode> ACLJSONSerializer::CreateCompositeConvolution(const CallNode* cn) { Review comment: Ditto. s/CreateCompositeConvolution/CreateCompositeConvJSONNode/ ? ########## File path: python/tvm/relay/op/contrib/arm_compute_lib.py ########## @@ -0,0 +1,119 @@ +# 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=invalid-name, unused-argument +"""ACL library supported operators.""" +import tvm +from tvm.relay import transform +from tvm.relay.build_module import bind_params_by_name + +from ...dataflow_pattern import wildcard, is_op, is_constant +from .register import register_pattern_table + + +def is_arm_compute_runtime_present(): Review comment: `is_arm_compute_runtime_enabled` seems better to me. ########## File path: python/tvm/relay/op/contrib/arm_compute_lib.py ########## @@ -0,0 +1,119 @@ +# 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=invalid-name, unused-argument +"""ACL library supported operators.""" +import tvm +from tvm.relay import transform +from tvm.relay.build_module import bind_params_by_name + +from ...dataflow_pattern import wildcard, is_op, is_constant +from .register import register_pattern_table + + +def is_arm_compute_runtime_present(): + """Check if the ACL graph runtime is present. + + Returns + ------- + ret: bool + True if present, False if not. + """ + return tvm.get_global_func("relay.op.is_arm_compute_runtime_enabled", True) + + +def partition_for_arm_compute_lib(mod, params=None): + """Partition the graph greedily offloading supported + operators to Arm Compute Library. + + Parameters + ---------- + mod : Module + The module to run passes on. + params : dict[str, NDArray] Review comment: The right type for `params` should be `Optional[Dict[str, NDArray]]` (capital D for Dict) as it might be `None`. ########## File path: src/runtime/contrib/arm_compute_lib/acl_utils.cc ########## @@ -0,0 +1,116 @@ +/* + * 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. + */ + +/*! + * \file src/runtime/contrib/arm_compute_lib/acl_utils.cc + * \brief Utils and common functions for the interface. + */ + +#include "acl_utils.h" + +#include <arm_compute/runtime/OffsetLifetimeManager.h> +#include <arm_compute/runtime/PoolManager.h> +#include <tvm/runtime/data_type.h> + +namespace tvm { +namespace runtime { +namespace contrib { +namespace arm_compute_lib { + +using JSONGraphNode = tvm::runtime::json::JSONGraphNode; + +void CheckACLError(const arm_compute::Status& status) { + CHECK(status.error_code() == arm_compute::ErrorCode::OK) << "ACL: " << status.error_description(); +} + +arm_compute::Tensor MakeTensor(const JSONGraphNode& tensor_rep, void* data) { + CHECK(tensor_rep.GetOpType() == "input" || tensor_rep.GetOpType() == "const"); + arm_compute::Tensor tensor; + arm_compute::TensorInfo info = MakeTensorInfo(tensor_rep.GetOpShape()[0]); + tensor.allocator()->init(info); + if (data != nullptr) { + CheckACLError(tensor.allocator()->import_memory(data)); + } + return tensor; +} + +arm_compute::Tensor MakeOutputTensor(const std::vector<int64_t>& shape) { + arm_compute::Tensor tensor; + tensor.allocator()->init(MakeTensorInfo(shape)); + return tensor; +} + +arm_compute::TensorInfo MakeTensorInfo(const std::vector<int64_t>& shape) { + arm_compute::TensorShape acl_shape = MakeTensorShape(shape); + return arm_compute::TensorInfo(acl_shape, 1, arm_compute::DataType::F32, + arm_compute::DataLayout::NHWC); +} + +arm_compute::TensorShape MakeTensorShape(const std::vector<int64_t>& shape) { + arm_compute::TensorShape acl_shape; + for (unsigned int i = shape.size(); i > 0; --i) { + acl_shape.set(shape.size() - i, shape[i - 1]); + } + return acl_shape; +} + +std::shared_ptr<arm_compute::MemoryManagerOnDemand> MakeMemoryManager() { + auto lifetime_mgr = std::make_shared<arm_compute::OffsetLifetimeManager>(); + auto pool_mgr = std::make_shared<arm_compute::PoolManager>(); + return std::make_shared<arm_compute::MemoryManagerOnDemand>(lifetime_mgr, pool_mgr); +} + +arm_compute::PadStrideInfo ToACLPadStride(const std::vector<std::string>& pad, + const std::vector<std::string>& stride) { + int pad_0, pad_1, pad_2, pad_3; + + size_t size = pad.size(); + if (size == 1) { + int pad_v = std::stoi(pad[0]); + pad_0 = pad_v; + pad_1 = pad_v; + pad_2 = pad_v; + pad_3 = pad_v; + } else if (size == 2) { + // TVM: height, width -> ACL: left, right, top, bottom + int pad_h = std::stoi(pad[0]); + int pad_w = std::stoi(pad[1]); + pad_0 = pad_w; + pad_1 = pad_w; + pad_2 = pad_h; + pad_3 = pad_h; + } else if (size == 4) { + // TVM: top, left, bottom, right -> ACL: left, right, top, bottom + pad_0 = std::stoi(pad[1]); + pad_1 = std::stoi(pad[3]); + pad_2 = std::stoi(pad[0]); + pad_3 = std::stoi(pad[2]); + } else { + LOG(FATAL) << "Unsupported padding dimensions"; + return arm_compute::PadStrideInfo(); Review comment: FATAL will throw exception and crash the execution so you don't need this return. ########## File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc ########## @@ -0,0 +1,399 @@ +/* + * 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. + */ + +/*! + * \file src/runtime/contrib/arm_compute_lib/acl_runtime.cc + * \brief A simple JSON runtime for Arm Compute Library. + */ + +#include <tvm/runtime/ndarray.h> +#include <tvm/runtime/registry.h> + +#include "../../file_util.h" +#include "../json/json_node.h" +#include "../json/json_runtime.h" + +#ifdef TVM_GRAPH_RUNTIME_ARM_COMPUTE_LIB +#include <arm_compute/core/Types.h> +#include <arm_compute/runtime/NEON/functions/NEConvolutionLayer.h> +#include <arm_compute/runtime/NEON/functions/NEPoolingLayer.h> +#include <arm_compute/runtime/NEON/functions/NEReshapeLayer.h> + +#include "acl_allocator.h" +#include "acl_utils.h" +#endif + +namespace tvm { +namespace runtime { +namespace contrib { + +using namespace tvm::runtime::json; + +#ifdef TVM_GRAPH_RUNTIME_ARM_COMPUTE_LIB +using namespace arm_compute_lib; + +/*! + * \brief ACL objects we cache in order to avoid needing to construct + * a new layer each time. + */ +struct CachedLayer { + std::shared_ptr<arm_compute::IFunction> function; + std::vector<arm_compute::Tensor> inputs; + std::vector<arm_compute::Tensor> const_inputs; + std::vector<arm_compute::Tensor> outputs; +}; +#endif + +class ACLRuntime : public JSONRuntimeBase { + public: + /*! + * \brief The ACL runtime module. Deserialize the provided functions + * on creation and store in the layer cache. + * + * \param symbol_name The name of the function. + * \param graph_json serialized JSON representation of a sub-graph. + * \param const_names The names of each constant in the sub-graph. + * \params consts An array of constants pre-transposed to the correct layout expected by ACL. + */ + explicit ACLRuntime(const std::string& symbol_name, const std::string& graph_json, + const Array<String>& const_names, const Array<NDArray>& consts) + : JSONRuntimeBase(symbol_name, graph_json, const_names) { + this->constants_ = consts; + } + + /*! + * \brief Get a packed function. + * + * \param name The name/symbol of the function. + * \param sptr_to_self The pointer to the module node. + * \return The packed function. + */ + PackedFunc GetFunction(const std::string& name, const ObjectPtr<Object>& sptr_to_self) override { + if (name == "get_symbol") { + return PackedFunc( + [sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { *rv = this->symbol_name_; }); + } else if (name == "get_const_vars") { + return PackedFunc( + [sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { *rv = this->const_names_; }); + } else if (this->symbol_name_ == name) { + return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { + CHECK(this->initialized_) << "The module has not been initialized"; + + // Bind argument tensors to data entries. + this->SetInputOutputBuffers(args); + // Execute the subgraph. + this->Run(); + }); + } else if ("__init_" + this->symbol_name_ == name) { + // The function to initialize constant tensors. + return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { + this->Init(); + this->initialized_ = true; + *rv = 0; + }); + } else { + return PackedFunc(nullptr); + } + } + + /*! + * \brief Save a compiled network to a binary stream, which can then be + * serialized to disk. + * + * \param stream The stream to save the binary. + */ + void SaveToBinary(dmlc::Stream* stream) override { + // Save the symbol + stream->Write(symbol_name_); + // Save the graph + stream->Write(graph_json_); + // Save the required const names + std::vector<std::string> const_names; + for (const auto& it : const_names_) { + const_names.push_back(it); + } + stream->Write(const_names); + // Save the required constant data + stream->Write(constants_.size()); + for (const auto& it : constants_) { + it.Save(stream); + } + } + + /*! + * \brief Load a compiled network from stream. + * + * \param strm The binary stream to load. + * \return The created ACL module. + */ + static Module LoadFromBinary(void* strm) { + dmlc::Stream* stream = static_cast<dmlc::Stream*>(strm); + std::string symbol; + std::string graph_json; + std::vector<std::string> consts; + // Load the symbol + CHECK(stream->Read(&symbol)) << "Loading symbol name failed"; + CHECK(stream->Read(&graph_json)) << "Loading graph json failed"; + CHECK(stream->Read(&consts)) << "Loading the const name list failed"; + Array<String> const_names; + for (const auto& it : consts) { + const_names.push_back(it); + } + size_t const_data_count; + CHECK(stream->Read(&const_data_count)); + Array<NDArray> const_data; + for (size_t i = 0; i < const_data_count; ++i) { + runtime::NDArray temp; + CHECK(temp.Load(stream)) << "Failed to load constant"; + const_data.push_back(temp); + } + auto n = make_object<ACLRuntime>(symbol, graph_json, const_names, const_data); + return Module(n); + } + + /*! + * \brief The type key of the module. + * + * \return module type key. + */ + const char* type_key() const override { return "arm_compute_lib"; } + + /*! + * \brief Initialize runtime. Create ACL layer from JSON + * representation. + */ + void Init() { + CHECK_EQ(this->constants_.size(), const_idx_.size()) + << "The number of input constants must match the number expected."; + this->SetupConstants(this->constants_); +#ifdef TVM_GRAPH_RUNTIME_ARM_COMPUTE_LIB + BuildEngine(); +#endif + } + + // Do not accept constants from MetadataModule as they should be transposed + // by the ACL codegen so they have the correct expected layout. + void Init(const Array<NDArray>& constants) override { LOG(FATAL) << "Not implemented."; } + + /*! + * \brief Unpack inputs and outputs and run inference on a given layer. + * + * \param args Access inputs and outputs. + * \param function The layer to execute inference on. + * \return Status of inference. + */ + void Run() override { +#ifdef TVM_GRAPH_RUNTIME_ARM_COMPUTE_LIB + for (size_t i = 0; i < input_nodes_.size(); ++i) { + auto nid = input_nodes_[i]; + uint32_t eid = EntryID(nid, 0); + if (nodes_[nid].GetOpType() == "input") { + void* data = data_entry_[eid]->data; + CheckACLError(layer_.inputs[i].allocator()->import_memory(data)); + } + } + + for (size_t i = 0; i < outputs_.size(); ++i) { + uint32_t eid = EntryID(outputs_[i]); + void* data = data_entry_[eid]->data; + CheckACLError(layer_.outputs[i].allocator()->import_memory(data)); + } + + this->layer_.function->run(); +#else + LOG(FATAL) << "Cannot call run on Arm Compute Library module without runtime enabled. " + << "Please build with USE_ACL_GRAPH_RUNTIME."; +#endif + } + + /*! + * \brief Get the JSON generated by codegen. + * + * \param format the format to return (only JSON for the time being) + * \return A string of JSON. + */ + std::string GetSource(const std::string& format) override { + if (format == "json") { + return graph_json_; + } + LOG(FATAL) << "Format not supported by Arm Compute Library runtime."; + return ""; + } + + private: +#ifdef TVM_GRAPH_RUNTIME_ARM_COMPUTE_LIB + /*! + * \brief Build ACL layer from JSON representation and cache. + * + * \note For the time being only one layer or operator is supported + * per engine. + */ + void BuildEngine() { + std::shared_ptr<arm_compute::MemoryManagerOnDemand> mm = MakeMemoryManager(); + int num_pools = 0; + + for (size_t i = 0; i < input_nodes_.size(); ++i) { + uint32_t nid = input_nodes_[i]; + const auto& node = nodes_[nid]; + if (node.GetOpType() == "input") { + layer_.inputs.push_back(MakeTensor(node)); + } else if (node.GetOpType() == "const") { + uint32_t eid = EntryID(nid, 0); + void* data = data_entry_[eid]->data; + layer_.const_inputs.push_back(MakeTensor(node, data)); + } + } + + for (size_t nid = 0; nid < nodes_.size(); ++nid) { + const auto& node = nodes_[nid]; + if (node.GetOpType() == "kernel") { + auto op_name = node.GetOpName(); + if ("nn.conv2d" == op_name || "arm_compute_lib.conv2d" == op_name) { + CreateConvolution2DLayer(&layer_, node, mm); + num_pools++; + } else if ("nn.max_pool2d" == op_name) { + CreatePoolingLayer(&layer_, node); + } else if ("reshape" == op_name) { + CreateReshapeLayer(&layer_, node); + } else { + LOG(FATAL) << "Unsupported op: " << op_name; + } + // Only expect one op for the time being + break; Review comment: Didn't catch this comment. You meant the current ACL runtime only supports one kernel node? If so you should error out if there are more than one kernel nodes; otherwise the functionality is incorrect. ########## File path: src/relay/backend/contrib/arm_compute_lib/codegen_acl.h ########## @@ -0,0 +1,143 @@ +/* + * 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. + */ + +/*! + * \file src/relay/backend/contrib/arm_compute_lib/codegen_acl.h + * \brief The Relay -> ACL JSON schema compiler. + */ + +#ifndef TVM_RELAY_BACKEND_CONTRIB_ARM_COMPUTE_LIB_CODEGEN_ACL_H_ +#define TVM_RELAY_BACKEND_CONTRIB_ARM_COMPUTE_LIB_CODEGEN_ACL_H_ + +#include <tvm/relay/expr_functor.h> + +#include <map> +#include <memory> +#include <string> +#include <utility> +#include <vector> + +#include "../../../../runtime/contrib/json/json_node.h" +#include "../codegen_json/codegen_json.h" + +namespace tvm { +namespace relay { +namespace contrib { +namespace arm_compute_lib { + +/*! + * \brief Generates an ACLModule from a relay expression. This "compilation" + * does not require ACL since the actual conversion using ACL APIs is + * deferred until creation of the runtime. This step simply serializes the + * relay program into a JSON string. + */ +class ACLJSONSerializer : public backend::contrib::JSONSerializer { + using JSONGraphNode = tvm::runtime::json::JSONGraphNode; + using JSONGraphNodeEntry = tvm::runtime::json::JSONGraphNodeEntry; + + public: + ACLJSONSerializer(const std::string& symbol, const Expr& expr) : JSONSerializer(symbol, expr) {} + + std::vector<JSONGraphNodeEntry> VisitExpr_(const CallNode* cn) override; + std::vector<JSONGraphNodeEntry> VisitExpr_(const ConstantNode* cn) override; + + /*! + * \brief Get the constant data transposed when pre-processing the + * input function. + * + * \return An array of constants + */ + Array<runtime::NDArray> GetParamsData(); + + private: + /*! + * \brief Create a JSON representation of an operator. + * + * \param call The call to be represented. + * \return A JSON representation of a specific operator. + */ + std::shared_ptr<JSONGraphNode> CreateOp(const CallNode* cn); + std::shared_ptr<JSONGraphNode> CreateCompositeConvolution(const CallNode* cn); + + /* \brief Transposed constant tensors to serialize. Arm Compute Library expects constant tensors + * in OHWI format. */ + Array<runtime::NDArray> constants_; +}; + +/*! + * \brief Pre-process a module containing functions ready for ACL codegen. + * + * For now we enforce OHWI kernel layout and fold the transforms away. + * + * \param mod The module to be pre-processed. + * \return The processed module. + */ +IRModule PreProcessModule(const IRModule& mod); + +/*! + * \brief Create a runtime module for ACL. + * + * This consists of a series of "serialized functions" which each represent a + * sub-graph to be computed by ACL and will each be executed independently from + * one another. Each function consists of serialized JSON describing the sub-graph + * and serialized constant tensors. + * + * \note The ACL runtime module only currently supports a single operator per + * sub-graph currently. + * + * \param ref The ext_func Relay expression/module to be executed using extern ops. + * \return A runtime module. + */ +runtime::Module ACLCompiler(const ObjectRef& ref); + +/*! + * \brief Get the external symbol of the Relay function name. + * + * \param func The provided function. + * + * \return An external symbol. + */ +std::string GetExtSymbol(const Function& func) { Review comment: This is implemented in `CSourceModuleCodegenBase` already. Maybe we can move this function out of `CSourceModuleCodegenBase` so that it can be used by all backend under contrib. cc @zhiics ########## File path: python/tvm/relay/op/contrib/arm_compute_lib.py ########## @@ -0,0 +1,119 @@ +# 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=invalid-name, unused-argument +"""ACL library supported operators.""" +import tvm +from tvm.relay import transform +from tvm.relay.build_module import bind_params_by_name + +from ...dataflow_pattern import wildcard, is_op, is_constant +from .register import register_pattern_table + + +def is_arm_compute_runtime_present(): + """Check if the ACL graph runtime is present. + + Returns + ------- + ret: bool + True if present, False if not. + """ + return tvm.get_global_func("relay.op.is_arm_compute_runtime_enabled", True) + + +def partition_for_arm_compute_lib(mod, params=None): + """Partition the graph greedily offloading supported + operators to Arm Compute Library. + + Parameters + ---------- + mod : Module + The module to run passes on. + params : dict[str, NDArray] + Constant input parameters. + + Returns + ------- + ret : annotated and partitioned module. + """ + if params: + mod['main'] = bind_params_by_name(mod['main'], params) + + seq = tvm.transform.Sequential([transform.MergeComposite(pattern_table()), + transform.AnnotateTarget('arm_compute_lib'), + transform.PartitionGraph()]) + + return seq(mod) + + +@register_pattern_table("arm_compute_lib") +def pattern_table(): Review comment: It's better to improve the naming of this function as it is called directly in this file in addition to register to the global pattern table. Something like `arm_compute_lib_pattern_table` for `get_arm_compute_lib_pattern_table` would be better to me. ########## File path: src/runtime/contrib/arm_compute_lib/acl_runtime.cc ########## @@ -0,0 +1,399 @@ +/* + * 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. + */ + +/*! + * \file src/runtime/contrib/arm_compute_lib/acl_runtime.cc + * \brief A simple JSON runtime for Arm Compute Library. + */ + +#include <tvm/runtime/ndarray.h> +#include <tvm/runtime/registry.h> + +#include "../../file_util.h" +#include "../json/json_node.h" +#include "../json/json_runtime.h" + +#ifdef TVM_GRAPH_RUNTIME_ARM_COMPUTE_LIB +#include <arm_compute/core/Types.h> +#include <arm_compute/runtime/NEON/functions/NEConvolutionLayer.h> +#include <arm_compute/runtime/NEON/functions/NEPoolingLayer.h> +#include <arm_compute/runtime/NEON/functions/NEReshapeLayer.h> + +#include "acl_allocator.h" +#include "acl_utils.h" +#endif + +namespace tvm { +namespace runtime { +namespace contrib { + +using namespace tvm::runtime::json; + +#ifdef TVM_GRAPH_RUNTIME_ARM_COMPUTE_LIB +using namespace arm_compute_lib; + +/*! + * \brief ACL objects we cache in order to avoid needing to construct + * a new layer each time. + */ +struct CachedLayer { + std::shared_ptr<arm_compute::IFunction> function; + std::vector<arm_compute::Tensor> inputs; + std::vector<arm_compute::Tensor> const_inputs; + std::vector<arm_compute::Tensor> outputs; +}; +#endif + +class ACLRuntime : public JSONRuntimeBase { + public: + /*! + * \brief The ACL runtime module. Deserialize the provided functions + * on creation and store in the layer cache. + * + * \param symbol_name The name of the function. + * \param graph_json serialized JSON representation of a sub-graph. + * \param const_names The names of each constant in the sub-graph. + * \params consts An array of constants pre-transposed to the correct layout expected by ACL. + */ + explicit ACLRuntime(const std::string& symbol_name, const std::string& graph_json, + const Array<String>& const_names, const Array<NDArray>& consts) + : JSONRuntimeBase(symbol_name, graph_json, const_names) { + this->constants_ = consts; + } + + /*! + * \brief Get a packed function. + * + * \param name The name/symbol of the function. + * \param sptr_to_self The pointer to the module node. + * \return The packed function. + */ + PackedFunc GetFunction(const std::string& name, const ObjectPtr<Object>& sptr_to_self) override { + if (name == "get_symbol") { + return PackedFunc( + [sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { *rv = this->symbol_name_; }); + } else if (name == "get_const_vars") { + return PackedFunc( + [sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { *rv = this->const_names_; }); + } else if (this->symbol_name_ == name) { + return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { + CHECK(this->initialized_) << "The module has not been initialized"; + + // Bind argument tensors to data entries. + this->SetInputOutputBuffers(args); + // Execute the subgraph. + this->Run(); + }); + } else if ("__init_" + this->symbol_name_ == name) { + // The function to initialize constant tensors. + return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) { + this->Init(); + this->initialized_ = true; + *rv = 0; + }); + } else { + return PackedFunc(nullptr); + } + } + + /*! + * \brief Save a compiled network to a binary stream, which can then be + * serialized to disk. + * + * \param stream The stream to save the binary. + */ + void SaveToBinary(dmlc::Stream* stream) override { + // Save the symbol + stream->Write(symbol_name_); + // Save the graph + stream->Write(graph_json_); + // Save the required const names + std::vector<std::string> const_names; + for (const auto& it : const_names_) { + const_names.push_back(it); + } + stream->Write(const_names); + // Save the required constant data + stream->Write(constants_.size()); + for (const auto& it : constants_) { + it.Save(stream); + } + } + + /*! + * \brief Load a compiled network from stream. + * + * \param strm The binary stream to load. + * \return The created ACL module. + */ + static Module LoadFromBinary(void* strm) { + dmlc::Stream* stream = static_cast<dmlc::Stream*>(strm); + std::string symbol; + std::string graph_json; + std::vector<std::string> consts; + // Load the symbol + CHECK(stream->Read(&symbol)) << "Loading symbol name failed"; + CHECK(stream->Read(&graph_json)) << "Loading graph json failed"; + CHECK(stream->Read(&consts)) << "Loading the const name list failed"; + Array<String> const_names; + for (const auto& it : consts) { + const_names.push_back(it); + } + size_t const_data_count; + CHECK(stream->Read(&const_data_count)); + Array<NDArray> const_data; + for (size_t i = 0; i < const_data_count; ++i) { + runtime::NDArray temp; + CHECK(temp.Load(stream)) << "Failed to load constant"; + const_data.push_back(temp); + } + auto n = make_object<ACLRuntime>(symbol, graph_json, const_names, const_data); + return Module(n); + } + + /*! + * \brief The type key of the module. + * + * \return module type key. + */ + const char* type_key() const override { return "arm_compute_lib"; } + + /*! + * \brief Initialize runtime. Create ACL layer from JSON + * representation. + */ + void Init() { + CHECK_EQ(this->constants_.size(), const_idx_.size()) + << "The number of input constants must match the number expected."; + this->SetupConstants(this->constants_); +#ifdef TVM_GRAPH_RUNTIME_ARM_COMPUTE_LIB + BuildEngine(); +#endif + } + + // Do not accept constants from MetadataModule as they should be transposed + // by the ACL codegen so they have the correct expected layout. + void Init(const Array<NDArray>& constants) override { LOG(FATAL) << "Not implemented."; } Review comment: it seems to me that even the constants are transposed by the preprocess sequence, you can still leave them to MetadataModule? If that's the case, you can reuse `Init(const Array<NDArray>& constants)`, and you don't have to save/load constants from binary. ########## File path: src/relay/backend/contrib/arm_compute_lib/codegen.cc ########## @@ -0,0 +1,188 @@ +/* + * 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. + */ + +/*! + * \file src/relay/backend/contrib/arm_compute_lib/codegen_acl.cc + * \brief Implementation of the Relay -> ACL JSON serializer. + */ +#include <tvm/ir/module.h> +#include <tvm/relay/attrs/nn.h> +#include <tvm/relay/type.h> + +#include "../../utils.h" +#include "codegen_acl.h" + +namespace tvm { +namespace relay { +namespace contrib { +namespace arm_compute_lib { + +using JSONGraphNode = tvm::runtime::json::JSONGraphNode; +using JSONGraphNodeEntry = tvm::runtime::json::JSONGraphNodeEntry; + +std::vector<JSONGraphNodeEntry> ACLJSONSerializer::VisitExpr_(const CallNode* cn) { + Expr expr = GetRef<Expr>(cn); + std::string name; + std::shared_ptr<JSONGraphNode> json_node; + + if (cn->op.as<OpNode>()) { + json_node = CreateOp(cn); + } else if (const auto* fn = cn->op.as<FunctionNode>()) { + auto comp = fn->GetAttr<String>(attr::kComposite); + CHECK(comp.defined()) << "Arm Compute Library JSON runtime only supports composite functions."; + name = comp.value(); + if (name == "arm_compute_lib.conv2d") { + json_node = CreateCompositeConvolution(cn); + } else { + LOG(FATAL) << "Unrecognized Arm Compute Library pattern: " << name; + } + } else { + LOG(FATAL) << "Arm Compute Library JSON runtime does not support calls to " + << cn->op->GetTypeKey(); + } + + return AddNode(json_node, GetRef<Expr>(cn)); +} + +std::vector<JSONGraphNodeEntry> ACLJSONSerializer::VisitExpr_(const ConstantNode* cn) { + this->constants_.push_back(cn->data); + return JSONSerializer::VisitExpr_(cn); +} + +std::shared_ptr<JSONGraphNode> ACLJSONSerializer::CreateOp(const CallNode* cn) { Review comment: Would s/CreateOp/CreateOpJSONNode/ be better? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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