[Relay][BYOCG] Propagate constant to subgraphs (apache#5094)

* bind constant to subgraphs

* con -> constant

src/relay/backend/contrib/codegen_c/codegen.cc

@@ -19,6 +19,7 @@
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/transform.h>
 #include <tvm/relay/type.h>
+#include <tvm/runtime/ndarray.h>
 #include <tvm/runtime/module.h>
 #include <tvm/runtime/object.h>
 
@@ -40,14 +41,63 @@ 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 VarNode* node) final {
     ext_func_args_.push_back(GetRef<Var>(node));
     out_.clear();
     Output output;
     output.name = node->name_hint();
     out_.push_back(output);
   }
 
+  void VisitExpr_(const ConstantNode* cn) final {
+    Constant constant = GetRef<Constant>(cn);
+    if (visited_.count(constant)) {
+      // Note this is for demostration purpose. ConstantNode doesn't necessarily
+      // belong to calls. We need to revisit this when tuples come into play.
+      out_.push_back(visited_[constant]);
+      return;
+    }
+
+    std::ostringstream decl_stream;
+    std::ostringstream buf_stream;
+
+    out_.clear();
+    Output output;
+    output.name = "const_" + std::to_string(const_idx_++);
+    out_.push_back(output);
+    visited_[constant] = output;
+
+    runtime::NDArray array = cn->data;
+    const auto& shape = array.Shape();
+    const DLTensor& dl_tensor = array.ToDLPack()->dl_tensor;
+
+    // Get the number of elements.
+    int64_t num_elems = 1;
+    for (auto i : shape) num_elems *= i;
+
+    const auto* type_node = cn->checked_type().as<TensorTypeNode>();
+    CHECK(type_node);
+    const auto& dtype = GetDtypeString(type_node);
+    // Define a const buffer: float const_0[64] = {1.0, 2.0, ...};
+    //
+    // Technically, you may need: static float* const_0 = (float*)malloc(4 * 64)
+    // to avoid possible stack overflow.
+    buf_stream << dtype << " " << output.name << "[" << num_elems << "] = {";
+    if (dtype == "float") {
+      float* p_flt = static_cast<float*>(dl_tensor.data);
+      for (int64_t i = 0; i < num_elems - 1; i++) buf_stream << p_flt[i] << ", ";
+      if (num_elems) buf_stream << p_flt[num_elems - 1];
+    } else if (dtype == "int") {
+      int* p_flt = static_cast<int*>(dl_tensor.data);
+      for (int64_t i = 0; i < num_elems - 1; i++) buf_stream << p_flt[i] << ", ";
+      if (num_elems) buf_stream << p_flt[num_elems - 1];
+    } else {
+      LOG(FATAL) << "Only float and int are supported for now.";
+    }
+    buf_stream << "};";
+    ext_func_body.insert(ext_func_body.begin(), buf_stream.str());
+  }
+
   void VisitExpr_(const CallNode* call) final {
     std::ostringstream macro_stream;
     std::ostringstream decl_stream;
@@ -138,6 +188,8 @@ class CodegenC : public ExprVisitor, public CodegenCBase {
   int func_idx = 0;
   /*! \brief The index of allocated buffers. */
   int buf_idx_ = 0;
+  /*! \brief The index of global constants. */
+  int const_idx_ = 0;
   /*! \brief The arguments of a C compiler compatible function. */
   Array<Var> ext_func_args_;
   /*! \brief The statements of a C compiler compatible function. */
@@ -148,6 +200,8 @@ class CodegenC : public ExprVisitor, public CodegenCBase {
   std::vector<std::string> buf_decl_;
   /*! \brief The name and index pairs for output. */
   std::vector<Output> out_;
+  /*! \brief The cached expressions. */
+  std::unordered_map<Expr, Output, ObjectHash, ObjectEqual> visited_;
 };
 
 class CSourceCodegen : public CSourceModuleCodegenBase {

src/relay/backend/contrib/codegen_c/codegen_c.h

@@ -197,7 +197,7 @@ class CodegenCBase {
    * \return true if the call's name is equivalent to the given name. Otherwise,
    * false.
    */
-  bool IsOp(const CallNode* call, std::string op_name) const {
+  bool IsOp(const CallNode* call, const std::string& op_name) const {
     const auto* op_node = call->op.as<OpNode>();
     CHECK(op_node) << "Expects a single op.";
     Op op = GetRef<Op>(op_node);
@@ -218,7 +218,7 @@ class CodegenCBase {
    *
    * \return The emitted code string.
    */
-  std::string JitImpl(std::string ext_func_id, const Array<Var>& args,
+  std::string JitImpl(const std::string& ext_func_id, const Array<Var>& args,
                       const std::vector<std::string>& buf_decl,
                       const std::vector<std::string>& body,
                       const std::vector<Output>& out) {

src/relay/transforms/partition_graph.cc

@@ -42,6 +42,8 @@
 #include <utility>
 #include <vector>
 
+#include "../backend/utils.h"
+
 namespace tvm {
 namespace relay {
 namespace partitioning {
@@ -200,14 +202,20 @@ class Partitioner : public ExprMutator {
       auto input = VisitExpr(call->args[0]);
       Array<Var> params;
       Array<Expr> args;
+      std::unordered_map<std::string, runtime::NDArray> params_bind;
 
       // The subgraph may be merged so we need to update it again.
       subgraph = GetSubgraph(GetRef<Call>(call));
       CHECK(subgraph);
 
+      // Record the constants for propagation.
       for (auto pair : subgraph->args) {
         params.push_back(pair.first);
-        args.push_back(pair.second);
+        if (const auto* cn = pair.second.as<ConstantNode>()) {
+          params_bind[pair.first->name_hint()] = cn->data;
+        } else {
+          args.push_back(pair.second);
+        }
       }
 
       auto subgraph_func =
@@ -223,6 +231,11 @@ class Partitioner : public ExprMutator {
                    tvm::tir::StringImmNode::make(compiler_attrs->compiler));
       subgraph_func =
           WithAttr(std::move(subgraph_func), attr::kInline, tvm::Integer(1));
+
+      // Constant propagation
+      if (!params_bind.empty()) {
+        subgraph_func = backend::BindParamsByName(subgraph_func, params_bind);
+      }
       CHECK(!module_->ContainGlobalVar(name))
           << "Global function " << name << " already exists";
       // Create a global function and add it to the IRModule for the subgraph.

tests/python/relay/test_pass_partition_graph.py

@@ -634,6 +634,50 @@ def expected():
     assert relay.analysis.alpha_equal(partitioned, ref_mod)
 
 
+def test_constant_propagation():
+    ones = np.ones(shape=(8, 8), dtype="float32")
+
+    def expected():
+        mod = tvm.IRModule()
+        x = relay.const(ones)
+        y = relay.var("y", shape=(8, 8))
+        x0 = relay.const(ones)
+        y0 = relay.var("y0", shape=(8, 8))
+        add = x0 + y0
+        # Function that uses C compiler
+        func = relay.Function([y0], add)
+        func = func.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
+        func = func.with_attr("Inline", tvm.tir.IntImm("int32", 1))
+        func = func.with_attr("Compiler", tvm.tir.StringImm("ccompiler"))
+        func = func.with_attr("ExternalSymbol",
+                              tvm.tir.StringImm("ccompiler_0"))
+        glb_0 = relay.GlobalVar("ccompiler_0")
+        mod[glb_0] = func
+        add_call = relay.Call(glb_0, [y])
+        log = relay.log(add_call)
+        main = relay.Function([y], log)
+        mod["main"] = main
+        return mod
+
+    x = relay.var("x", shape=(8, 8))
+    y = relay.var("y", shape=(8, 8))
+    add = x + y
+    log = relay.log(add)
+    f = relay.Function([x, y], log)
+    f = relay.build_module.bind_params_by_name(f, {"x": tvm.nd.array(ones)})
+    mod = tvm.IRModule()
+    mod["main"] = f
+    mod = WhiteListAnnotator(["add"], "ccompiler")(mod)
+    mod = transform.PartitionGraph()(mod)
+
+    expected_mod = expected()
+    assert relay.alpha_equal(mod, expected_mod)
+
+    y_data = np.random.rand(8, 8).astype('float32')
+    np_add = ones + y_data
+    check_result(mod, {"y": y_data}, (8, 8), np.log(np_add))
+
+
 if __name__ == "__main__":
     test_multi_node_compiler()
     test_extern_ccompiler_single_op()
@@ -643,3 +687,4 @@ def expected():
     test_extern_dnnl_mobilenet()
     test_function_lifting()
     test_function_lifting_inline()
+    test_constant_propagation()