TVM實(shí)現(xiàn)hardware backend
官方的矩陣相加的示例如下：
2個(gè)矩陣相加的實(shí)現(xiàn)
for (int i = 0; i < n; ++i) {
C[i] = A[i] + B[i];
}
怎么優(yōu)化？ 可以并行相加，如下
for (int bx = 0; bx < ceil(n / 64); ++bx) {
for (int tx = 0; tx < 64; ++tx) {
int i = bx * 64 + tx;
if (i < n) {
C[i] = A[i] + B[i];
}
}
}
其實(shí)，就是把循環(huán)繼續(xù)拆，一個(gè)循環(huán)拆成2個(gè)循環(huán)。
https://docs.tvm.ai/faq.html
常見問題
如何安裝
請參見安裝TVM。
如何添加新的硬件后端
如果硬件后端支持LLVM，可以通過在target中設(shè)置正確的target三元組，直接生成代碼。
如果目標(biāo)硬件是GPU，嘗試使用cuda、opencl或vulkan后端。
如果目標(biāo)硬件是特殊加速器，檢查VTA：多功能張量加速器，將Codegen導(dǎo)入TVM。
對于上述所有情況，可能希望使用AutoTVM添加特定于目標(biāo)的優(yōu)化模板，參閱使用模板和AutoTVM進(jìn)行自動(dòng)調(diào)整。
除了使用LLVM的矢量化，可以利用硬件內(nèi)部函數(shù)，嵌入微內(nèi)核，參閱使用Tensorize利用硬件內(nèi)部函數(shù)。
TVM與其它IR/DSL項(xiàng)目的關(guān)系
在深度學(xué)習(xí)系統(tǒng)中，通常有兩個(gè)層次的IR抽象。TensorFlow的XLA和英特爾的NGRAPH，都使用計(jì)算圖表示。這種表示是高級的，有助于執(zhí)行通用優(yōu)化，如內(nèi)存重用、布局轉(zhuǎn)換和自動(dòng)微分。
TVM采用了一種低層次的表示，明確地表達(dá)了內(nèi)存布局、并行模式、局部性和硬件原語等的選擇。這一層次的IR更接近直接的目標(biāo)硬件。低級IR采用了現(xiàn)有圖像處理語言（如Halide, darkroom）和循環(huán)變換工具（如基于循環(huán)和多面體的分析）的思想。特別關(guān)注于表達(dá)深度學(xué)習(xí)工作負(fù)載（如重復(fù)性）、針對不同硬件后端的優(yōu)化及嵌入框架，提供端到端編譯堆棧。
TVM與libDNN、cuDNN的關(guān)系
TVM可以將這些庫合并為外部調(diào)用。TVM的一個(gè)目標(biāo)是能夠生成高性能內(nèi)核。將以一種漸進(jìn)的方式發(fā)展TVM，因?yàn)閷W(xué)習(xí)手動(dòng)內(nèi)核制作技術(shù)，作為DSL中的原語添加。
TVM和其它框架，如NNVM、XLA的區(qū)別？
在tvm看來，nnvm和xla都是計(jì)算圖級別的優(yōu)化，屬于high level優(yōu)化，注意做的是內(nèi)存復(fù)用、布局轉(zhuǎn)換和自動(dòng)微分。
tvm采用的是一種low level的表示，進(jìn)行的是內(nèi)存布局、并行模式、局部性和硬件原語等優(yōu)化。
TVM和libDNN、cuDNN關(guān)系？
tvm會去調(diào)用這些庫，與這些庫共存。
nnvm tensor operator
https://docs.tvm.ai/nnvm_top.html
分5個(gè)級別的op：
? level 1: 基礎(chǔ)op（34個(gè)）
? level 2: 卷積op（6個(gè)）
? level 3: 其它tensor op（19個(gè)）
? level 4: 廣播和約簡op（39個(gè)）
? level 5: 視覺op（5個(gè)）
VTA：多功能張量加速器
多功能張量加速器（VTA）是一個(gè)開放、通用、可定制的深度學(xué)習(xí)加速器，具有完整的基于TVM的編譯器堆棧。設(shè)計(jì)VTA是為了揭示主流深度學(xué)習(xí)加速器最顯著和最常見的特征。TVM和VTA一起構(gòu)成一個(gè)端到端的軟硬件深度學(xué)習(xí)系統(tǒng)堆棧，包括硬件設(shè)計(jì)、驅(qū)動(dòng)程序、JIT運(yùn)行時(shí)和基于TVM的優(yōu)化編譯器堆棧。
VTA具有以下主要功能：
通用、模塊化、開源硬件。
簡化了部署到FPGA的工作流程。
模擬器對原型編譯的支持在常規(guī)工作站上傳遞。
用于模擬和FPGA硬件后端的基于Pynq的驅(qū)動(dòng)程序和JIT運(yùn)行時(shí)。
端到端TVM堆棧集成。
將Codegen導(dǎo)入TVM
隨著深度學(xué)習(xí)工作負(fù)載所針對的硬件設(shè)備數(shù)量不斷增加，用戶在各種設(shè)備上實(shí)現(xiàn)高性能所需的知識也不斷增加。為了讓數(shù)據(jù)科學(xué)家在開發(fā)新模型時(shí)，不必?fù)?dān)心性能問題，硬件后端提供商要么為MKLDNN或cuDNN等庫，提供許多常用的深度學(xué)習(xí)算子，要么提供TensorRT等框架，讓用戶以某種方式描述其模型，實(shí)現(xiàn)高性能。然而，當(dāng)用戶嘗試在新的庫或設(shè)備上工作時(shí)，必須學(xué)習(xí)新的編程接口。因此，對統(tǒng)一編程接口的需求變得越來越重要。
1）讓所有用戶和硬件后端提供商站在同一個(gè)頁面上，
2）提供一個(gè)可行的解決方案，允許專用硬件或庫，僅支持廣泛使用的具有極高性能的運(yùn)營商，但將不受支持的算子回退到CPU/GPU等常規(guī)設(shè)備。
將演示作為硬件后端提供商，如何輕松實(shí)現(xiàn)自己的codegen，將注冊為Relay后端編譯器，支持硬件設(shè)備/庫。介紹了兩種基于所需不同圖形表示的codegen：

1. 生成C代碼。
   如果硬件已經(jīng)有一個(gè)經(jīng)過良好優(yōu)化的C/C++庫，如Intel CBLAS/MKL到CPU和NVIDIA CUBLAS到GPU，那么這就是需要的。幸運(yùn)的是，C源代碼模塊與TVM運(yùn)行時(shí)模塊完全兼容，生成的代碼可以由任何具有適當(dāng)編譯標(biāo)志的C/C++編譯器編譯，因此唯一的任務(wù)是實(shí)現(xiàn)一個(gè)codegen，為子圖生成C代碼，實(shí)現(xiàn)一個(gè)C源模塊，集成到TVM運(yùn)行時(shí)模塊中。在下一節(jié)中，將演示如何為硬件實(shí)現(xiàn)C代碼生成器。

2. 希望生成任何其它圖形表示。

硬件可能需要其它形式的圖形表示，如JSON。在這種情況下，不僅需要實(shí)現(xiàn)codegen，還需要實(shí)現(xiàn)定制的TVM Runtime模塊，以讓TVM Runtime知道應(yīng)該如何實(shí)現(xiàn)圖形表示。如果已經(jīng)有一個(gè)完整的用于硬件圖形實(shí)現(xiàn)引擎，如GPU的TyRoSRT，這是一個(gè)可以考慮的解決方案。
完成codegen和Runtime后，可以讓用戶使用自定義標(biāo)記，對模型進(jìn)行注釋利用。最終用戶注釋和啟動(dòng)特定codegen的教程在這里（TBA）。
實(shí)現(xiàn)一個(gè)C代碼生成器
在這一部分中，將演示如何實(shí)現(xiàn)一個(gè)codegen，該codegen使用預(yù)實(shí)現(xiàn)的算子函數(shù)，生成C代碼。為了簡化，示例codegen不依賴于第三方庫。相反，在C中手動(dòng)實(shí)現(xiàn)兩個(gè)宏：
#define CSOURCE_BINARY_OP_1D(p_ID_, p_OP_, p_DIM1_)
extern “C” void p_ID_(float* a, float* b, float* out) {
for (int64_t i = 0; i < p_DIM1_; ++i) {
out[i] = a[i] p_OP_ b[i];
}
}

#define CSOURCE_BINARY_OP_2D(p_ID_, p_OP_, p_DIM1_, p_DIM2_)
extern “C” void p_ID_(float* a, float* b, float* out) {
for (int64_t i = 0; i < p_DIM1_; ++i) {
for (int64_t j = 0; j < p_DIM2_; ++j) {
int64_t k = i * p_DIM2_ + j;
out[k] = a[k] p_OP_ b[k];
}
}
}
使用這兩個(gè)宏，可以生成一維和二維張量的二元運(yùn)算符。如給定一個(gè)子圖，如下所示。假設(shè)所有輸入都是shape為（10,10）的二維張量。
c_compiler_input0
|
add <-- c_compiler_input1
|
subtract <-- c_compiler_input2
|
multiply <-- c_compiler_input3
|
out
目標(biāo)是生成以下可編譯代碼，實(shí)現(xiàn)子圖：
#include <tvm/runtime/c_runtime_api.h>
#include <tvm/runtime/packed_func.h>
#include <dlpack/dlpack.h>
#include
#include
#include

#define GCC_BINARY_OP_1D(p_ID_, p_OP_, p_DIM1_)
extern “C” void p_ID_(float* a, float* b, float* out) {
for (int64_t i = 0; i < p_DIM1_; ++i) {
out[i] = a[i] p_OP_ b[i];
}
}

#define GCC_BINARY_OP_2D(p_ID_, p_OP_, p_DIM1_, p_DIM2_)
extern “C” void p_ID_(float* a, float* b, float* out) {
for (int64_t i = 0; i < p_DIM1_; ++i) {
for (int64_t j = 0; j < p_DIM2_; ++j) {
int64_t k = i * p_DIM2_ + j;
out[k] = a[k] p_OP_ b[k];
}
}
}

// Note 1
GCC_BINARY_OP_2D(gcc_0_0, *, 10, 10);
GCC_BINARY_OP_2D(gcc_0_1, -, 10, 10);
GCC_BINARY_OP_2D(gcc_0_2, +, 10, 10);

// Note 2
extern “C” void gcc_0_(float* gcc_input0, float* gcc_input1,
float* gcc_input2, float* gcc_input3, float* out) {
float* buf_0 = (float*)malloc(4 * 100);
float* buf_1 = (float*)malloc(4 * 100);
gcc_0_2(gcc_input0, gcc_input1, buf_0);
gcc_0_1(buf_0, gcc_input2, buf_1);
gcc_0_0(buf_1, gcc_input3, out);
free(buf_0);
free(buf_1);
}

// Note 3
extern “C” int gcc_0_wrapper(DLTensor* arg0, DLTensor* arg1, DLTensor* arg2,
DLTensor* arg3, DLTensor* out) {
gcc_0_(static_cast<float*>(arg0->data), static_cast<float*>(arg1->data),
static_cast<float*>(arg2->data), static_cast<float*>(arg3->data),
static_cast<float*>(out->data));
return 0;
}
TVM_DLL_EXPORT_TYPED_FUNC(gcc_0, gcc_0_wrapper);
Here we highlight the notes marked in the above code:
? Note 1 is the function implementation for the three nodes in the subgraph.
? Note 2 is a function to execute the subgraph by allocating intermediate buffers and invoking corresponding functions.
? Note 3 is a TVM runtime compatible wrapper function. It accepts a list of input tensors and one output tensor (the last argument), casts them to the right data type, and invokes the subgraph function described in Note 2. In addition, TVM_DLL_EXPORT_TYPED_FUNC is a TVM macro that generates another function gcc_0 with unified the function arguments by packing all tensors to TVMArgs. As a result, the TVM runtime can directly invoke gcc_0 to execute the subgraph without additional efforts. With the above code generated, TVM is able to compile it along with the rest parts of the graph and export a single library for deployment.
In the rest of this section, we will implement a codegen step-by-step to generate the above code. Your own codegen has to be located at src/relay/backend/contrib//. In our example, we name our codegen “codegen_c” and put it under /src/relay/backend/contrib/codegen_c/. Feel free to check this file for a complete implementation.
Specifically, we are going to implement two classes in this file and here is their relationship:
subgraph subgraph
TVM backend -----------------------------> CSourceCodegen -------------> CodegenC
^ | ^ |
| | | |
---------------------------------------- ------------------------
generated C source runtime module generated C code
When TVM backend finds a function (subgraph) in a Relay graph is annotated with the registered compiler tag (ccompiler in this example), TVM backend invokes CSourceCodegen and passes the subgraph. CSourceCodegen’s member function CreateCSourceModule will 1) generate C code for the subgraph, and 2) wrap the generated C code to a C source runtime module for TVM backend to compile and deploy. In particular, the C code generation is transparent to the CodegenC class because it provides many useful utilities to ease the code generation implementation. The following sections will implement these two classes in the bottom-up order.
Implement CodegenC
In src/relay/backend/contrib/codegen_c/codegen.cc, we first create a codegen class skeleton under the namespace of tvm.relay.contrib:
#include <tvm/relay/expr_functor.h>
#include <tvm/relay/transform.h>
#include <tvm/relay/type.h>
#include <tvm/runtime/module.h>
#include <tvm/runtime/object.h>

#include
#include

#include “codegen_c.h”

namespace tvm {
namespace relay {
namespace contrib {

class CodegenC : public ExprVisitor, public CodegenCBase {
public:
explicit CodegenC(const std::string& id) { this->ext_func_id_ = id; }

void VisitExpr_(const VarNode* node) { ; }
void VisitExpr_(const CallNode* call) final { ; }
std::string JIT() { ; }

private:
/*! \brief The function id that represents a C source function. /
std::string ext_func_id_ = “”;
/! \brief The index of a wrapped C function. /
int func_idx = 0;
/! \brief The index of allocated buffers. /
int buf_idx_ = 0;
/! \brief The arguments of a C compiler compatible function. /
std::vectorstd::string ext_func_args_;
/! \brief The statements of a C compiler compatible function. /
std::vectorstd::string ext_func_body;
/! \brief The declaration statements of a C compiler compatible function. /
std::vectorstd::string func_decl_;
/! \brief The declaration statements of buffers. /
std::vectorstd::string buf_decl_;
/! \brief The name and index pairs for output. /
std::vector<std::pair<std::string, int>> out_;
}
The CodegenC class inherits two classes: ExprVisitor provides abilities to traverse subgraphs and collects the required information and generate subgraph functions such as gcc_0_; CodegenCBase provides abilities and utilities to generate wrapper functions such as gcc_0 in the above example. As can be seen, we only need to implement three functions in this codegen class to make it work.
Code Generation for Operators
We first implement VisitExpr_(const CallNode call). This function visits all call nodes when traversing the subgraph. Each call node contains an operator that we want to offload to your hardware. As a result, we need to generate the corresponding C code with correct operators in topological order. We implement this function step-by-step as follows.

1. Generate the function declaration
   Example Result: GCC_BINARY_OP_2D(gcc_0_0, *, 10, 10);
   To generate the function declaration, as shown above, we need 1) a function name (e.g., gcc_0_0), 2) the type of operator (e.g., *), and 3) the input tensor shape (e.g., (10, 10)). Fortunately, this information can be obtained easily from CallNode:
   std::ostringstream macro_stream;
   std::ostringstream decl_stream;
   std::ostringstream buf_stream;

// Generate a unique function name you like.
std::string func_name = ext_func_id_ + “_” + std::to_string(func_idx++);

// Make function declaration string.
macro_stream << “CSOURCE_BINARY_OP_” << call->args.size() << “D(” << func_name << ", ";

// Check the operator type.
if (IsOp(call, “add”)) {
macro_stream << “+”;
} else if (IsOp(call, “subtract”)) {
macro_stream << “-”;
} else if (IsOp(call, “multiply”)) {
macro_stream << “*”;
} else {
LOG(FATAL) << “Unrecognized op”;
}

// Extract the input tensor shape.
auto in_shape = GetShape(call->args[0]->checked_type());
for (size_t i = 0; i < in_shape.size(); ++i) {
macro_stream << ", " << in_shape[i];
}
macro_stream << “);”;
func_decl_.push_back(macro_stream.str());
As can be seen, we push the generated code to class member variables func_decl_. It means after we finish traversing the entire subgraph, we have collected all required function declarations and the only thing we need to do is having them compiled by GCC. The rest implementation of VisitExpr_(const CallNode* call) also follow this concept.
2. Generate the function call
Example Result: gcc_0_0(buf_1, gcc_input3, out);
After generating the function declaration, we need to generate a function call with proper inputs and outputs. To know which inputs or buffers we should put when calling this function, we have to visit its arguments:
bool first = true;
decl_stream << func_name << “(”;
for (size_t i = 0; i < call->args.size(); ++i) {
VisitExpr(call->args[i]); // Note 1
for (auto out : out_) {
if (!first) {
decl_stream << ", ";
}
first = false;
decl_stream << out.first;
}
}
// Note 2
Again, we want to highlight the notes in the above code:
Note 1: VisitExpr(call->args[i]) is a recursive call to visit arguments of the current function. An argument could be an output of another node or an input tensor. In our example implementation, we make sure every node updates a class variable out_ before leaving the visitor. Here is an illustration:
arg_node arg_node <- Visit arg (Note 1) arg_node
| | |
curr_node <- Process curr_node curr_node <- Put “buf_0” as an input buffer

(a) out_ = {} (b) out_ = {} ? out_ = {(“buf_0”, 20)}
We can see in the above figure, class variable out_ is empty before visiting the argument node, and it was filled with the output buffer name and size of arg_node. As a result, when we finished visiting the argument node, we know the proper input buffer we should put by looking at out_. You will find out how we update out_ at the end of this section as well as the next section.
Note 2: You may notice that we did not close the function call string in this step. The current function call string looks like: gcc_0_0(buf_1, gcc_input3. This is because we have not put the last argument (i.e., the output) to this call. The output of a function call could be either an allocated temporary buffer or the subgraph output tensor. For simplify, in this example, we allocate an output buffer for every call node (next step) and copy the result in the very last buffer to the output tensor.
3. Generate the output buffer
Example Result: float* buf_0 = (float*)malloc(4 * 100);
As mentioned in the previous step, in addition to the subgraph input and output tensors, we may also need buffers to keep the intermediate results. To generate the buffer, we extract the shape information to determine the buffer type and size:
// This example only supports single output.
auto type_node = call->checked_type().as();
ICHECK(type_node != nullptr && runtime::TypeMatch(type_node->dtype, kDLFloat, 32))
<< “Only support single output tensor with float type”;

// Generate a unique buffer name.
std::string out = “buf_” + std::to_string(buf_idx_++);

// Extract the shape to be the buffer size.
auto out_shape = GetShape(call->checked_type());
int out_size = 1;
for (size_t i = 0; i < out_shape.size(); ++i) {
out_size *= out_shape[i];
}

// Make the buffer allocation and push to the buffer declarations.
buf_stream << "float* " << out << " = (float*)std::malloc(4 * " << out_size << “);”;
buf_decl_.push_back(buf_stream.str());
After we have allocated the output buffer, we can now close the function call string and push the generated function call to a class variable ext_func_body.
decl_stream << ", " << out << “);”;
ext_func_body.push_back(decl_stream.str());
4. Update output buffer
To let the next node, which accepts the output of the current call node as its input, know which buffer it should take, we need to update the class variable out_ before leaving this visit function:
out_.clear();
out_.push_back({out, out_size});
Congratulations! we have finished the most difficult function in this class. In the next two sections, we just need to make up some minor missing parts in this function.
Code Generation for Input Variables
Recall that we collected the input buffer information by visiting the arguments of a call node (2nd step in the previous section), and handled the case when its argument is another call node (4th step). In this section, we demonstrate how to handle other nodes by taking VarNode as an example.
VarNode represents input tensors in a model. The only but important information it has is a name hint (e.g., data, weight, etc). When visiting a VarNode, we simply update class variable out_ to pass the name hint so that the descendant call nodes can generate the correct function call.
void VisitExpr_(const VarNode* node) {
ext_func_args_.push_back(node->name_hint());
out_.clear();
out_.push_back({node->name_hint(), 0});
}
Note that in this example we assume the subgraph we are offloading has only call nodes and variable nodes. If your subgraphs contain other types of nodes, such as TupleNode, then you also need to visit them and bypass the output buffer information.
Code Emitting
The final part in this codegen class is a JIT function that emits a C function for the subgraph and uses the C code we just generated as the function body. Remember, in addition to the subgraph function we generated in the previous sections, we also need a wrapper function with a unified argument for TVM runtime to invoke and pass data. Fortunately, the base class we inherited already provides an implementation, JitImpl, to generate the function. For example, we can invoke JitImpl as follows:
JitImpl(“gcc_0” /* Subgraph symbol (ID) /,
{“gcc_input0”, “gcc_input1”, “gcc_input2”, “gcc_input3”} / Input arguments /,
{“float buf_0 = (float)malloc(4 * 20)”, …} / Buffer allocations /,
{“gcc_0_2(gcc_input0, gcc_input1, buf_0);”} / Function body /,
{“out”} / Output */);
The above call will generate three functions (one from the TVM wrapper macro):

1. The subgraph function gcc_0_ (with one more underline at the end of the function name) with all C code we generated to execute a subgraph.
2. The wrapper function gcc_0__wrapper_ with a list of DLTensor arguments that casts data to the right type and invokes gcc_0_.
3. The TVM runtime compatible function gcc_0 with TVM unified function arguments that unpacks TVM packed tensors and invokes gcc_0__wrapper_.
   Accordingly, the only thing we need in JIT implementation is passing all subgraph function code we generated to JitImpl:
   std::string JIT() {
   // Write function macros
   for (auto decl : func_decl_) {
   code_stream_ << decl << “\n”;
   }
   return JitImpl(ext_func_id_, ext_func_args_, buf_decl_, ext_func_body, out_);
   }
   All variables (ext_func_id, etc) we passed are class variables and were filled when we traversed the subgraph.
   Implement CSourceCodegen
   Again, let’s create a class skeleton and implement the required functions. Note that it inherits CSourceModuleCodegenBase
   class CSourceCodegen : public CSourceModuleCodegenBase {
   public:
   // Pass a subgraph function, and generate the C code.
   void GenCFunc(const Function& func) { ; }

// Use GenCFunc to generate the C code and wrap it as a C source module.
runtime::Module CreateCSourceModule(const NodeRef& ref) override { ; }

private:
std::ostringstream code_stream_;
};
Implement GenCFunc
GenCFunc simply uses the CodegenC we just implemented to traverse a Relay function (subgraph) and obtains the generated C code. The builtin function GetExtSymbol retrieves a unique symbol name (e.g., gcc_0) in the Relay function and we must use it as the C function name, because this symbol is going to be used for DSO runtime lookup.
void GenCFunc(const Function& func) {
ICHECK(func.defined()) << “Input error: expect a Relay function.”;

// Record the external symbol for runtime lookup.
auto sid = GetExtSymbol(func);

CodeGenC builder(sid);
builder.VisitExpr(func->body);
code_stream_ << builder.JIT();
}
Implement CreateCSourceModule
This function creates a runtime module for the external library. In this example, we create a CSourceModule that can be directly compiled and linked together with a TVM generated DSOModule. After you have implemented CodegenC, implementing this function is relatively straightforward:
runtime::Module CreateCSourceModule(const NodeRef& ref) override {
// Create headers
code_stream_ << “#include \n”;
code_stream_ << “#include \n”;
code_stream_ << “#include \n”;
code_stream_ << “#include <stdio.h>\n”;
code_stream_ << “#include \n”;
code_stream_ << “#include <tvm/runtime/c_runtime_api.h>\n”;
code_stream_ << “#include <dlpack/dlpack.h>\n”;

// Append some common macro for operator definition.
const char* operator_macro = R"op_macro(
#define CSOURCE_BINARY_OP_1D(p_ID_, p_OP_, p_DIM1_)
extern “C” void p_ID_(float* a, float* b, float* out) {
for (int64_t i = 0; i < p_DIM1_; ++i) {
out[i] = a[i] p_OP_ b[i];
}
}

#define CSOURCE_BINARY_OP_2D(p_ID_, p_OP_, p_DIM1_, p_DIM2_)
extern “C” void p_ID_(float* a, float* b, float* out) {
for (int64_t i = 0; i < p_DIM1_; ++i) {
for (int64_t j = 0; j < p_DIM2_; ++j) {
int64_t k = i * p_DIM2_ + j;
out[k] = a[k] p_OP_ b[k];
}
}
}
)op_macro";

code_stream_ << operator_macro << “\n\n”;

// Generate C code for the subgraph.
if (ref->IsInstance()) {
GenCFunc(Downcast(ref));
} else if (ref->IsInstancerelay::ModuleNode()) {
relay::Module mod = Downcastrelay::Module(ref);
for (const auto& it : mod->functions) {
GenCFunc(Downcast(it.second));
}
} else {
LOG(FATAL) << “The input ref is expected to be a Relay function or module”
<< “\n”;
}

// Create a CSourceModule
const auto* pf = runtime::Registry::Get(“module.csource_module_create”);
ICHECK(pf != nullptr) << “Cannot find csource module to create the external runtime module”;
return (pf)(code_stream_.str(), “cc”);
}
Register Your Codegen
The last step is registering your codegen to TVM backend. We first implement a simple function to invoke our codegen and generate a runtime module.
runtime::Module CCompiler(const NodeRef& ref) {
CSourceCodegen csource;
return csource.CreateCSourceModule(ref);
}
Finally, we register this function to TVM backend:
TVM_REGISTER_GLOBAL(“relay.ext.ccompiler”).set_body_typed(CCompiler);
where ccompiler is a customized tag to let TVM know this is the codegen it should use to generate and offload subgraphs when the subgraph is annotated with ccompiler.
Finally, a good practice is to set up a CMake configuration flag to include your compiler only for your customers. We first create a cmake file: cmake/modules/contrib/CODEGENC.cmake:
if(USE_CODEGENC)
file(GLOB CSOURCE_RELAY_CONTRIB_SRC src/relay/backend/contrib/codegen_c/codegen.cc)
list(APPEND COMPILER_SRCS ${CSOURCE_RELAY_CONTRIB_SRC})
endif(USE_CODEGENC)
So that users can configure whether to include your compiler when configuring TVM using config.cmake:
set(USE_CODEGENC ON)
Implement a Codegen for Your Representation
Although we have demonstrated how to implement a C codegen, your hardware may require other forms of graph representation, such as JSON. In this case, you could modify CodegenC class we have implemented to generate your own graph representation and implement a customized runtime module to let TVM runtime know how this graph representation should be executed.
To simplify, we define a graph representation named “ExampleJSON” in this guide. ExampleJSON does not mean the real JSON but just a simple representation for graphs without a control flow. For example, assuming we have the following subgraph named subgraph_0:
input0
|
add <-- input1
|
subtract <-- input2
|
multiply <-- input3
|
out
Then the ExampleJON of this subgraph looks like:
subgraph_0
input 0 10 10
input 1 10 10
input 2 10 10
input 3 10 10
add 4 inputs: 0 1 shape: 10 10
sub 5 inputs: 4 2 shape: 10 10
mul 6 inputs: 5 3 shape: 10 10
The input keyword declares an input tensor with its ID and shape; while the other statements describes computations in inputs: [input ID] shape: [shape] syntax.
In this section, our goal is to implement the following customized TVM runtime module to execute ExampleJSON graphs.
runtime::Module ExampleJsonCompiler(const NodeRef& ref) {
ExampleJsonCodeGen codegen(ref);
std::string code = codegen.gen(); // Note 1
const auto pf = runtime::Registry::Get(“module.examplejson_module_create”); // Note 2
ICHECK(pf != nullptr) << “Cannot find ExampleJson module to create the external runtime module”;
return (*pf)(code);
}
TVM_REGISTER_GLOBAL(“relay.ext.examplejsoncompiler”).set_body_typed(ExampleJsonCompiler);
Note 1: We will implement a customized codegen later to generate a ExampleJSON code string by taking a subgraph.
Note 2: This line obtains a pointer to a function for creating the customized runtime module. You can see that it takes subgraph code in ExampleJSON format we just generated and initializes a runtime module.
In the following sections, we are going to introduce 1) how to implement ExampleJsonCodeGen and 2) how to implement and register examplejson_module_create.
Implement ExampleJsonCodeGen
Similar to the C codegen, we also derive ExampleJsonCodeGen from ExprVisitor to make use of visitor patterns for subgraph traversing. On the other hand, we do not have to inherit CodegenCBase because we do not need TVM C++ wrappers. The codegen class is implemented as follows:
#include <tvm/relay/expr_functor.h>
#include <tvm/relay/transform.h>
#include <tvm/relay/type.h>
#include <tvm/runtime/module.h>
#include <tvm/runtime/object.h>

#include
#include

namespace tvm {
namespace relay {
namespace contrib {

class ExampleJsonCodeGen : public ExprVisitor {
public:
explicit ExampleJsonCodeGen();

// Note 1
void VisitExpr_(const VarNode* node) { /* Skip in this example. */ }
void VisitExpr_(const CallNode* call) final { /* Skip in this example. */ }// Note 2
std::string gen(NodeRef& ref) {this->code = "";if (ref->IsInstance<FunctionNode>()) {this->visit(Downcast<Function>(ref));} else if (ref->IsInstance<relay::ModuleNode>()) {relay::Module mod = Downcast<relay::Module>(ref);for (const auto& it : mod->functions) {this->visit(Downcast<Function>(it.second));}} else {LOG(FATAL) << "The input ref is expected to be a Relay function or module";}return this->code;
}

private:
/*! \brief The function id that represents a C source function. */
std::string code;
}
Note 1: We again implement corresponding visitor functions to generate ExampleJSON code and store it to a class variable code (we skip the visitor function implementation in this example as their concepts are basically the same as C codegen). After finished the graph visiting, we should have an ExampleJSON graph in code.
Note 2: We define an internal API gen to take a subgraph and generate a ExampleJSON code. This API can be in an arbitrary name you prefer.
The next step is to implement a customized runtime to make use of the output of ExampleJsonCodeGen.
Implement a Customized Runtime
In this section, we will implement a customized TVM runtime step-by-step and register it to TVM runtime modules. The customized runtime should be located at src/runtime/contrib//. In our example, we name our runtime “example_ext_runtime”.
Again, we first define a customized runtime class as follows. The class has to be derived from TVM ModuleNode in order to be compatible with other TVM runtime modules.
#include <dmlc/logging.h>
#include <tvm/runtime/c_runtime_api.h>
#include <tvm/runtime/memory.h>
#include <tvm/runtime/module.h>
#include <tvm/runtime/ndarray.h>
#include <tvm/runtime/object.h>
#include <tvm/runtime/packed_func.h>
#include <tvm/runtime/registry.h>

#include
#include
#include

namespace tvm {
namespace runtime {
class ExampleJsonModule : public ModuleNode {
public:
explicit ExampleJsonModule(std::string graph_json);

PackedFunc GetFunction(const std::string& name,
const ObjectPtr& sptr_to_self) final;

const char* type_key() const { return “examplejson”; }

void SaveToBinary(dmlc::Stream* stream) final;

static Module LoadFromBinary(void* strm);

static Module Create(const std::string& path);

std::string GetSource(const std::string& format = “”);

void Run(int id, const std::vector& inputs, int output);

void ParseJson(const std::string& json);

private:
/* \brief The json string that represents a computational graph. /
std::string graph_json_;
/ \brief The subgraph that being processed. /
std::string curr_subgraph_;
/! \brief A simple graph from subgraph id to node entries. /
std::map<std::string, std::vector > graph_;
/ \brief A simple pool to contain the tensor for each node in the graph. /
std::vector data_entry_;
/ \brief A mapping from node id to op name. */
std::vectorstd::string op_id_;
};
In particular, there are some functions derived from ModuleNode that we must implement in ExampleJsonModule:
? Constructor: The constructor of this class should accept a subgraph (in your representation), process and store it in any format you like. The saved subgraph could be used by the following two functions.
? GetFunction: This is the most important function in this class. When TVM runtime wants to execute a subgraph with your compiler tag, TVM runtime invokes this function from your customized runtime module. It provides the function name as well as runtime arguments, and GetFunction should return a packed function implementation for TVM runtime to execute.
? SaveToBinary and LoadFromBinary: SaveToBinary serialize the runtime module to a binary format for later deployment. This function will be called by TVM when users use export_library API. On the other hand, since we are now using our own graph representation, we have to make sure that LoadFromBinary is able to construct the same runtime module by taking the serialized binary generated by SaveToBinary.
? GetSource (optional): If you would like to see the generated ExampleJSON code, you can implement this function to dump it; otherwise you can skip the implementation.
Other functions and class variables will be introduced along with the implementation of above must-have functions.
Implement Constructor
explicit ExampleJsonModule(std::string graph_json) {
this->graph_json_ = graph_json;
ParseJson(this->graph_json_);
}
Then, we implement ParseJson to parse a subgraph in ExampleJSON format and construct a graph in memory for later usage. Since we do not support subgraph with branches in this example, we simply use an array to store every nodes in a subgraph in order.
void ParseJson(const std::string& json) {
std::string line;
std::string curr_subgraph;
std::stringstream ss(json);

while (std::getline(ss, line, ‘\n’)) {
std::stringstream ss2(line);
std::string token;
int id = 0;

ss2 >> token;
if (token.find("subgraph_") != std::string::npos) {curr_subgraph = token;continue;
}ss2 >> id;
if (op_id_.size() <= static_cast<size_t>(id)) {op_id_.resize(id + 1);data_entry_.resize(id + 1);
}int64_t total_elements = 1;
std::vector<int64_t> shape;
if (token == "input") {int64_t size = 0;while (ss2 >> size) {total_elements *= size;shape.push_back(size);}
} else {op_id_[id] = token; // Note 1bool shape_data = false;NodeEntry entry;while (ss2 >> token) {if (token == "shape:") {shape_data = true;} else if (shape_data) {total_elements *= std::stoll(token);shape.push_back(std::stoll(token));} else if (token != "inputs:") {entry.inputs.push_back(std::stoi(token));}}entry.id = id;entry.output = id;graph_[curr_subgraph].push_back(entry); // Note 2
}
DLDevice dev;
dev.device_type = static_cast<DLDeviceType>(1);
dev.device_id = 0;
data_entry_[id] = NDArray::Empty(shape, DLDataType{kDLFloat, 32, 1}, dev); // Note 3

}
}
Note 1: We use a class variable op_id_ to map from subgraph node ID to the operator name (e.g., add) so that we can invoke the corresponding operator function in runtime.
Note 2: We use a class variable graph_ to map from subgraph name to an array of nodes. GetFunction will query graph nodes by a subgraph ID in runtime.
Note 3: We use a class variable data_entry_ to map from a subgraph node ID to a tensor data placeholder. We will put inputs and outputs to the corresponding data entry in runtime.
Implement GetFunction
After the construction, we should have the above class variables ready. We then implement GetFunction to provide executable subgraph functions to TVM runtime:
PackedFunc GetFunction(const std::string& name,
const ObjectPtr& sptr_to_self) final {
if (this->graph_.find(name) != this->graph_.end()) {
this->curr_subgraph_ = name;
return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {

  // Copy input tensors to corresponding data entries.for (auto i = 0; i < args.size(); ++i) {ICHECK(args[i].type_code() == kNDArrayContainer || args[i].type_code() == kArrayHandle)<< "Expect NDArray or DLTensor as inputs\n";if (args[i].type_code() == kArrayHandle) {DLTensor* arg = args[i];this->data_entry_[i].CopyFrom(arg);} else {NDArray arg = args[i];this->data_entry_[i].CopyFrom(arg);}}// Execute the subgraph.for (const auto& it : this->graph_[this->curr_subgraph_]) {this->Run(it.id, it.inputs, it.output);}ICHECK_GT(graph_.count(this->curr_subgraph_), 0U);// Copy the output from a data entry back to TVM runtime argument.auto out_idx = graph_[this->curr_subgraph_].back().output;if (args[args.size() - 1].type_code() == kArrayHandle) {DLTensor* arg = args[args.size() - 1];this->data_entry_[out_idx].CopyTo(arg);} else {NDArray arg = args[args.size() - 1];this->data_entry_[out_idx].CopyTo(arg);}*rv = data_entry_.back();
});

} else {
LOG(FATAL) << "Unknown subgraph: " << name << “\n”;
return PackedFunc();
}
}
As can be seen, GetFunction is composed of three major parts. The first part copies data from TVM runtime arguments to the corresponding data entries we assigned in the constructor. The second part executes the subgraph with Run function (will implement later) and saves the results to another data entry. The third part copies the results from the output data entry back to the corresponding TVM runtime argument for output.
Implement Run
Now let’s implement Run function. This function accepts 1) a subgraph ID, 2) a list of input data entry indexs, and 3) an output data entry index.
void Run(int id, const std::vector& inputs, int output) {
// Make a list data entry indexs.
std::vector args(inputs.begin(), inputs.end());
args.push_back(output);

// Initialize data holders.
std::vector values(args.size());
std::vector type_codes(args.size());

// Initialize a TVM arg setter with TVMValue and its type code.
TVMArgsSetter setter(values.data(), type_codes.data());

// Set each argument to its corresponding data entry.
if (op_id_[id] == “add” || op_id_[id] == “sub” || op_id_[id] == “mul”) {
for (size_t i = 0; i < args.size(); i++) {
setter(i, data_entry_[args[i]]);
}
}

// Invoke the corresponding operator function.
if (op_id_[id] == “add”) {
Add(values.data(), type_codes.data(), args.size());
} else if (op_id_[id] == “sub”) {
Sub(values.data(), type_codes.data(), args.size());
} else if (op_id_[id] == “mul”) {
Mul(values.data(), type_codes.data(), args.size());
} else {
LOG(FATAL) << "Unknown op: " << op_id_[id] << “\n”;
}
}
Run function mainly has two parts. The first part allocates a list of TVMValue, and maps corresponding data entry blocks. This will become the arguments of our operator functions. The second part than invokes our operator functions. Although we use the same C functions as the previous example, you can replace Add, Sub, and Mul with your own engine. You only need to make sure your engine stores the results to the last argument so that they can be transferred back to TVM runtime.
With above functions implemented, our customized codegen and runtime can now execute subgraphs. The last step is registering an API (examplejson_module_create) to create this module:
TVM_REGISTER_GLOBAL(“module.examplejson_module_create”)
.set_body_typed([](std::string code){
auto n = make_object(code);
return runtime::Module(n);
});
Implement SaveToBinary and LoadFromBinary
So far we have implemented the main features of a customized runtime so that it can be used as other TVM runtimes. However, when users want to save the built runtime to a disk for deployment, TVM has no idea about how to save it. This is the reason we want to implement SaveToBinary and LoadFromBinary, which tell TVM how should this customized runtime be persist and restored.
We first implement SaveToBinary function to allow users to save this module in disk.
void SaveToBinary(dmlc::Stream* stream) final {
stream->Write(this->graph_json_);
}
We can find that this function is pretty simple. Recall that the only argument we took in constructor is a subgraph representation, meaning that we only need a subgraph representation to construct/recover this customized runtime module. As a result, SaveToBinary simply writes the subgraph to an output DMLC stream. That is, when users use export_library API to export the module, the customized module will be an ExampleJSON stream of a subgraph.
Similarity, LoadFromBinary reads the subgraph stream and re-constructs the customized runtime module:
static Module LoadFromBinary(void* strm) {
dmlc::Stream* stream = static_castdmlc::Stream*(strm);
std::string graph_json;
stream->Read(&graph_json);
auto n = tvm::runtime::make_object(graph_json);
return Module(n);
}
We also need to register this function to enable the corresponding Python API:
TVM_REGISTER_GLOBAL(“module.loadbinary_examplejson”)
.set_body_typed(ExampleJsonModule::LoadFromBinary);
The above registration means when users call tvm.runtime.load_module(lib_path) API and the exported library has an ExampleJSON stream, our LoadFromBinary will be invoked to create the same customized runtime module.
In addition, if you want to support module creation directly from an ExampleJSON file, you can also implement a simple function and register a Python API as follows:
static Module Create(const std::string& path) {
std::ifstream filep;
filep.open(path, std::ios::in);
std::string graph_json;
std::string line;
while (std::getline(filep, line)) {
graph_json += line;
graph_json += “\n”;
}
filep.close();
auto n = tvm::runtime::make_object(graph_json);
return Module(n);
}

TVM_REGISTER_GLOBAL(“module.loadfile_examplejson”)
.set_body([](TVMArgs args, TVMRetValue* rv) {
rv = ExampleJsonModule::Create(args[0]);
});
It means users can manually write/modify an ExampleJSON file, and use Python API tvm.runtime.load_module(“mysubgraph.examplejson”, “examplejson”) to construct a customized module.
Summary
In summary, here is a checklist for you to refer:
? A codegen class derived from ExprVisitor and CodegenCBase (only for C codegen) with following functions.
o VisitExpr_(const CallNode call) to collect call node information.
o Other visitor functions you needed to collect subgraph information.
o JIT to generate subgraph code.
o Register codegen.
? A function to create CSourceModule (for C codegen).
? A runtime module class derived from ModuleNode with following functions (for your graph representation).
o Constructor.
o GetFunction to generate a TVM runtime compatible PackedFunc.
o Run to execute a subgraph.
o Register a runtime creation API.
o SaveToBinary and LoadFromBinary to serialize/deserialize customized runtime module.
o Register LoadFromBinary API to support tvm.runtime.load_module(your_module_lib_path).
o (optional) Create to support customized runtime module construction from subgraph file in your representation.
? An annotator to annotate a user Relay program to make use of your compiler and runtime (TBA).

參考鏈接：
https://tvm.apache.org/docs/faq.html
https://tvm.apache.org/docs/dev/how_to/relay_bring_your_own_codegen.html#relay-bring-your-own-codegen
https://tvm.apache.org/docs/how_to/tune_with_autotvm/index.html#tutorials-autotvm-sec
https://tvm.apache.org/docs/how_to/work_with_schedules/tensorize.html#tutorials-tensorize
https://www.jianshu.com/p/7a8d93522b07

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