[CPU] Support CPU Code generation (#17)

* README.md fixed

* test fix

* cpu backend update

* cpu test case

CMakeLists.txt

@@ -99,6 +99,8 @@ tilelang_file_glob(GLOB TILE_LANG_SRCS
   src/transform/*.cc
   src/op/*.cc
   src/target/utils.cc
+  src/target/codegen_cpp.cc
+  src/target/rt_mod_cpp.cc
 )
 
 # Include CUDA source files if CUDA is enabled

src/ir.cc

@@ -7,13 +7,33 @@
  *
  */
 
+#include <tvm/arith/analyzer.h>
 #include <tvm/script/ir_builder/tir/ir.h>
 
 namespace tvm {
 namespace tl {
 
+constexpr const char *tilelang_is_cpu_kernel_frame =
+    "tilelang.is_cpu_kernel_frame";
+
 using namespace script::ir_builder::tir;
 
+static ForFrame MakeIterVarFrame(std::string name, PrimExpr dom) {
+  using namespace tvm::tir;
+  Var var = Var(name);
+  // Create a frame that represents a loop over the given domain.
+  ObjectPtr<ForFrameNode> n = make_object<ForFrameNode>();
+  n->vars.push_back(var);
+  n->doms.push_back(Range(0, dom));
+  n->f_make_for_loop = [](Array<Var> vars, Array<Range> doms,
+                          Stmt body) -> Stmt {
+    ICHECK_EQ(vars.size(), 1);
+    ICHECK_EQ(doms.size(), 1);
+    return For(vars[0], doms[0]->min, doms[0]->extent, ForKind::kSerial, body);
+  };
+  return ForFrame(n);
+}
+
 ForFrame ParallelFor(Array<PrimExpr> extents,
                      Map<String, ObjectRef> annotations) {
   using namespace tvm::tir;
@@ -121,6 +141,23 @@ KernelLaunchFrame KernelLaunch(Array<PrimExpr> grid_size,
                                Array<PrimExpr> block_size,
                                Map<String, ObjectRef> attrs) {
   ObjectPtr<KernelLaunchFrameNode> n = make_object<KernelLaunchFrameNode>();
+
+  // If the kernel is a CPU kernel, we don't need to launch any threads.
+  bool is_cpu_kernel_frame =
+      attrs.defined() && attrs.count(tilelang_is_cpu_kernel_frame);
+
+  if (is_cpu_kernel_frame) {
+    ICHECK(grid_size.size() >= 0);
+    ICHECK(block_size.size() == 0) << "CPU kernel cannot have block size";
+    ICHECK(attrs.defined());
+    // create grid loop var
+    for (int i = 0; i < grid_size.size(); i++) {
+      n->frames.push_back(
+          MakeIterVarFrame("block_var_" + std::to_string(i), grid_size[i]));
+    }
+    // Launch CPU Kernel
+  } else {
+    // Launch GPU Kernel
     ICHECK(grid_size.size() <= 3);
     if (grid_size.size() > 0)
       n->frames.push_back(LaunchThread("blockIdx.x", grid_size[0]));
@@ -139,6 +176,8 @@ KernelLaunchFrame KernelLaunch(Array<PrimExpr> grid_size,
     } else {
       n->frames.push_back(Block(""));
     }
+  }
+
   if (attrs.defined()) {
     auto empty_block = Block("");
     empty_block->annotations = attrs;

src/op/elem.cc

@@ -138,6 +138,8 @@ For Copy::MakeSIMTLoop(arith::Analyzer *analyzer) const {
 }
 
 Stmt Copy::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
+  Target target = T.target;
+  bool is_cpu_target = target->GetTargetDeviceType() == kDLCPU;
   Stmt ldsm_stmt = LowerLDSMCopy(T, analyzer);
   if (ldsm_stmt.defined())
     return ldsm_stmt;
@@ -148,12 +150,19 @@ Stmt Copy::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const {
   auto simt_loop = MakeSIMTLoop(analyzer);
   auto fused_loop = Downcast<For>(ParallelLoopFuser::Fuse(simt_loop));
 
+  For vectorized_thread_loop;
   auto par_op = std::make_unique<ParallelOp>(fused_loop);
+
+  if (is_cpu_target) {
+    vectorized_thread_loop = VectorizeLoop(fused_loop);
+  } else {
     par_op->InferLayout({T.target, T.block_size, T.layout_map, T.buffer_remap},
                         InferLevel::kFree);
     auto thread_loop = PartitionLoop(par_op->GetRoot(), T.thread_var, analyzer,
                                      par_op->GetLoopLayout());
-  auto vectorized_thread_loop = VectorizeLoop(thread_loop);
+    vectorized_thread_loop = VectorizeLoop(thread_loop);
+  }
+
   if (par_op->GetPredicate(T.thread_var).defined()) {
     return IfThenElse(par_op->GetPredicate(T.thread_var).value(),
                       vectorized_thread_loop);

src/target/codegen_cpp.cc

+/*
+ * 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 codegen_c_host.cc
+ */
+#include "codegen_cpp.h"
+
+#include <tvm/relay/executor.h>
+#include <tvm/relay/runtime.h>
+#include <tvm/runtime/crt/error_codes.h>
+#include <tvm/runtime/module.h>
+#include <tvm/target/codegen.h>
+
+#include <algorithm>
+#include <string>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+
+#include "support/str_escape.h"
+#include "target/build_common.h"
+#include "target/func_registry_generator.h"
+#include "target/source/codegen_params.h"
+
+namespace tvm {
+namespace codegen {
+
+CodeGenTileLangCPP::CodeGenTileLangCPP() {
+  module_name_ = name_supply_->FreshName("__tvm_module_ctx");
+}
+
+void CodeGenTileLangCPP::Init(bool output_ssa, bool emit_asserts,
+                              bool emit_fwd_func_decl, std::string target_str,
+                              const std::unordered_set<std::string> &devices) {
+  emit_asserts_ = emit_asserts;
+  emit_fwd_func_decl_ = emit_fwd_func_decl;
+  declared_globals_.clear();
+  decl_stream << "// tilelang target: " << target_str << "\n";
+  decl_stream << "#include <tl_templates/cpp/common.h>\n";
+  decl_stream << "#include <tl_templates/cpp/gemm.h>\n";
+  decl_stream << "\n";
+  CodeGenC::Init(output_ssa);
+}
+
+void CodeGenTileLangCPP::InitGlobalContext() {
+  decl_stream << "void* " << tvm::runtime::symbol::tvm_module_ctx
+              << " = NULL;\n";
+}
+
+void CodeGenTileLangCPP::DefineModuleName() {
+  decl_stream << "void* " << module_name_ << " = NULL;\n";
+}
+
+void CodeGenTileLangCPP::GenerateForwardFunctionDeclarations(
+    String global_symbol,
+
+    const Array<Type> &arg_types, const Type &ret_type) {
+  if (!emit_fwd_func_decl_) {
+    return;
+  }
+  for (auto &func_already_defined : GetFunctionNames()) {
+    if (global_symbol == func_already_defined) {
+      return;
+    }
+  }
+  this->PrintFuncPrefix(fwd_decl_stream);
+  this->PrintType(ret_type, fwd_decl_stream);
+  fwd_decl_stream << " " << global_symbol << "(";
+  for (size_t i = 0; i < arg_types.size(); ++i) {
+    if (i > 0) {
+      fwd_decl_stream << ", ";
+    }
+    CodeGenSourceBase::PrintType(arg_types[i], fwd_decl_stream);
+  }
+  fwd_decl_stream << ");\n";
+}
+
+void CodeGenTileLangCPP::PrintFuncPrefix(std::ostream &os) { // NOLINT(*)
+  os << "#ifdef __cplusplus\n"
+     << "extern \"C\"\n"
+     << "#endif\n";
+}
+
+void CodeGenTileLangCPP::PrintType(DataType t, std::ostream &os) { // NOLINT(*)
+  int lanes = t.lanes();
+  if (t.is_handle()) {
+    ICHECK_EQ(lanes, 1) << "does not support vector types";
+    os << "void*";
+    return;
+  }
+  if (t.is_void()) {
+    os << "void";
+    return;
+  }
+  if (t == DataType::Bool()) {
+    os << "bool";
+    return;
+  }
+  bool fail = false;
+  if (t.is_float()) {
+    switch (t.bits()) {
+    case 16:
+      os << "half";
+      break;
+    case 32:
+      os << "float";
+      break;
+    case 64:
+      os << "double";
+      break;
+    default:
+      fail = true;
+      break;
+    }
+    if (!fail && lanes == 1)
+      return;
+    if (!fail && (lanes >= 2 && lanes <= 16)) {
+      os << lanes;
+      return;
+    }
+  } else if (t.is_uint() || t.is_int()) {
+    if (t.is_uint()) {
+      os << 'u';
+    }
+    switch (t.bits()) {
+    case 8:
+      os << "int8_t";
+      break;
+    case 16:
+      os << "int16_t";
+      break;
+    case 32:
+      os << "int32_t";
+      break;
+    case 64:
+      os << "int64_t";
+      break;
+    case 1:
+      os << "int32_t";
+      break;
+    default:
+      fail = true;
+      break;
+    }
+    if (!fail && lanes == 1)
+      return;
+    if (!fail && (lanes >= 2 && lanes <= 16)) {
+      os << lanes;
+      return;
+    }
+  }
+  LOG(FATAL) << "Cannot convert type " << t << " to C type";
+}
+
+void CodeGenTileLangCPP::VisitExpr_(const BroadcastNode *op,
+                                    std::ostream &os) { // NOLINT(*)
+  std::string v = PrintExpr(op->value);
+  int lanes = op->dtype.lanes();
+  os << "((";
+  PrintType(op->dtype, os);
+  os << ")(";
+  for (int i = 0; i < lanes; ++i) {
+    if (i != 0)
+      os << ", ";
+    os << v;
+  }
+  os << "))";
+}
+
+void CodeGenTileLangCPP::PrintGetFuncFromBackend(
+    const std::string &func_name, const std::string &packed_func_name) {
+  this->PrintIndent();
+  this->stream << "if (" << packed_func_name << " == NULL) {\n";
+  int packed_func_if_scope = this->BeginScope();
+  this->PrintIndent();
+  this->stream << "if (TVMBackendGetFuncFromEnv(" << module_name_ << ", \""
+               << func_name << "\""
+               << ", &" << packed_func_name << ") != 0) {\n";
+  int get_func_env_scope = this->BeginScope();
+  this->PrintIndent();
+  this->stream << "return -1;\n";
+  this->EndScope(get_func_env_scope);
+  this->PrintIndent();
+  this->stream << "}\n";
+  this->EndScope(packed_func_if_scope);
+  this->PrintIndent();
+  this->stream << "}\n";
+}
+
+void CodeGenTileLangCPP::PrintFuncCall(const std::string &packed_func_name,
+                                       int num_args) {
+  this->PrintIndent();
+  std::string ret_val = name_supply_->FreshName("ret_val");
+  std::string ret_type_code = name_supply_->FreshName("ret_type_code");
+  this->stream << "TVMValue " << ret_val << ";\n";
+  this->PrintIndent();
+  this->stream << "int " << ret_type_code << ";\n";
+  this->PrintIndent();
+  this->stream << "if (TVMFuncCall(" << packed_func_name << ", "
+               << "(TVMValue*) stack_value"
+               << ", "
+               << "(int*) stack_tcode"
+               << ", " << num_args << ", "
+               << "&" << ret_val << ", "
+               << "&" << ret_type_code << ") != 0) {\n";
+  int func_call_scope = this->BeginScope();
+  this->PrintIndent();
+  this->stream << "return -1;\n";
+  this->EndScope(func_call_scope);
+  this->PrintIndent();
+  this->stream << "}\n";
+}
+
+void CodeGenTileLangCPP::PrintFuncCallC(
+    const std::string &packed_func_name, int num_args,
+    const std::string &resource_handle_name) {
+  this->PrintIndent();
+  std::string ret_val = name_supply_->FreshName("ret_val");
+  std::string ret_type_code = name_supply_->FreshName("ret_type_code");
+  this->stream << "TVMValue " << ret_val << ";\n";
+  this->PrintIndent();
+  this->stream << "int " << ret_type_code << ";\n";
+  this->PrintIndent();
+
+  this->stream << "if (" << packed_func_name << "( "
+               << "(TVMValue*) stack_value "
+               << ", "
+               << "(int*) stack_tcode"
+               << ", " << num_args << ", "
+               << "&" << ret_val << ", "
+               << "&" << ret_type_code << ", " << resource_handle_name
+               << ") != 0){\n";
+
+  int func_call_scope = this->BeginScope();
+  this->PrintIndent();
+  this->stream << "return -1;\n";
+  this->EndScope(func_call_scope);
+  this->PrintIndent();
+  this->stream << "}\n";
+}
+
+void CodeGenTileLangCPP::AddFunction(const PrimFunc &f) {
+  // clear previous generated state.
+  this->InitFuncState(f);
+  // reserve keywords
+  ReserveKeywordsAsUnique();
+
+  auto global_symbol = f->GetAttr<String>(tvm::attr::kGlobalSymbol);
+  ICHECK(global_symbol.defined())
+      << "CodeGenC: Expect PrimFunc to have the global_symbol attribute";
+  bool no_alias = f->HasNonzeroAttr(tir::attr::kNoAlias);
+
+  this->PrintFuncPrefix(stream);
+  CodeGenC::PrintType(f->ret_type, stream);
+  this->PrintExtraAttrs(f, stream);
+  this->stream << " " << static_cast<std::string>(global_symbol.value()) << "(";
+
+  for (size_t i = 0; i < f->params.size(); ++i) {
+    tir::Var v = f->params[i];
+    std::string vid = AllocVarID(v.get());
+    if (i != 0)
+      stream << ", ";
+    if (v.dtype().is_handle()) {
+      // work around for grid constant parameters.
+      if (auto *ptr = v->type_annotation.as<PointerTypeNode>()) {
+        if (ptr->storage_scope == "grid_constant") {
+          stream << "__grid_constant__ const ";
+          CodeGenC::PrintType(ptr->element_type, stream);
+          stream << ' ' << vid;
+          continue;
+        }
+      }
+
+      auto it = alloc_storage_scope_.find(v.get());
+      if (it != alloc_storage_scope_.end()) {
+        PrintStorageScope(it->second, stream);
+      }
+
+      CodeGenC::PrintType(GetType(v), stream);
+      if (auto *ptr = v->type_annotation.as<PointerTypeNode>()) {
+        if (auto *prim = ptr->element_type.as<PrimTypeNode>()) {
+          RegisterHandleType(v.get(), prim->dtype);
+        }
+      }
+
+      if (no_alias) {
+        PrintRestrict(v, stream);
+      }
+    } else {
+      CodeGenC::PrintType(GetType(v), stream);
+    }
+    stream << ' ' << vid;
+  }
+  stream << ") {\n";
+  this->PreFunctionBody(f);
+  int func_scope = this->BeginScope();
+  this->PrintStmt(f->body);
+  this->EndScope(func_scope);
+  this->PrintIndent();
+  this->stream << "}\n\n";
+}
+
+std::string CodeGenTileLangCPP::GetPackedName(const CallNode *op) {
+  const StringImmNode *s = op->args[0].as<StringImmNode>();
+  ICHECK(s != nullptr)
+      << "tvm_call_packed_lowered expects first argument as function name";
+  std::string func_name = s->value;
+  std::string packed_func_name = func_name + "_packed";
+  std::string unique_name;
+  auto it = declared_globals_.find(packed_func_name);
+  if (it != declared_globals_.end()) {
+    unique_name = it->second;
+  } else {
+    unique_name = name_supply_->FreshName(packed_func_name);
+    declared_globals_[packed_func_name] = unique_name;
+    decl_stream << "static void* " << unique_name << " = NULL;\n";
+  }
+  return unique_name;
+}
+
+CodeGenTileLangCPP::FunctionInfo
+CodeGenTileLangCPP::GetFunctionInfo(const CallNode *op,
+                                    bool has_resource_handle) {
+  const StringImmNode *s = op->args[0].as<StringImmNode>();
+  ICHECK(s != nullptr)
+      << "tvm_call_[c]packed_lowered expects first argument as function name";
+  int64_t begin = op->args[3].as<IntImmNode>()->value;
+  int64_t end = op->args[4].as<IntImmNode>()->value;
+  int64_t num_args = end - begin;
+  ICHECK_GE(num_args, 0);
+  std::string func_name = s->value;
+
+  if (has_resource_handle) {
+    const StringImmNode *resource_handle_var = op->args[5].as<StringImmNode>();
+    if (resource_handle_var != nullptr) {
+      std::string resource_handle_name = resource_handle_var->value;
+      return {func_name, num_args - 1, resource_handle_name};
+    } else {
+      // The final arg should be "(void*) NULL" to indicate the empty
+      // resource_handle.
+      num_args--;
+
+      const CallNode *reinterpret_call = op->args[5].as<CallNode>();
+      ICHECK_NE(reinterpret_call, (void *)nullptr)
+          << "At CallNode to " << s
+          << "arg 5: Expect either StringImm naming the resource_handle var "
+             "from interface API or "
+          << "reinterpret(0); got: " << op->args[5];
+      ICHECK_EQ(reinterpret_call->op, builtin::reinterpret())
+          << "At CallNode to " << s
+          << "arg 5: Expect either StringImm naming the resource_handle var "
+             "from interface API or "
+          << "reinterpret(0); got: " << op->args[5];
+      ICHECK(is_zero(reinterpret_call->args[0]))
+          << "At CallNode to " << s
+          << " arg 5: Expect either StringImm naming the "
+             "resource_handle var from interface API, or "
+          << "zero; got " << op->args[5];
+    }
+  }
+  return {func_name, num_args, "NULL"};
+}
+
+void CodeGenTileLangCPP::VisitExpr_(const CallNode *op,
+                                    std::ostream &os) { // NOLINT(*)
+  if (op->op.same_as(builtin::tvm_stack_alloca())) {
+    std::string stack_name = name_supply_->FreshName("stack");
+    const std::string &type = op->args[0].as<StringImmNode>()->value;
+    const IntImmNode *num = op->args[1].as<IntImmNode>();
+    ICHECK(num != nullptr);
+    static_assert(alignof(TVMValue) % alignof(DLTensor) == 0, "invariant");
+    size_t unit = sizeof(TVMValue);
+    size_t size = 0;
+    if (type == "shape") {
+      size = (num->value * sizeof(tvm_index_t) + unit - 1) / unit;
+    } else if (type == "arg_value") {
+      size = (num->value * sizeof(TVMValue) + unit - 1) / unit;
+    } else if (type == "arg_tcode") {
+      size = (num->value * sizeof(int) + unit - 1) / unit;
+    } else if (type == "array") {
+      size = (num->value * sizeof(DLTensor) + unit - 1) / unit;
+    } else {
+      LOG(FATAL) << "Unknown stack alloca type " << type;
+    }
+    this->PrintIndent();
+    this->stream << "TVMValue " << stack_name << "[" << size << "];\n";
+    os << stack_name;
+  } else if (op->op.same_as(builtin::tvm_call_packed_lowered())) {
+    auto function_info = GetFunctionInfo(op, false /* has_resource_handle */);
+    std::string func_name_packed = GetPackedName(op);
+    this->PrintGetFuncFromBackend(function_info.func_name, func_name_packed);
+    this->PrintFuncCall(func_name_packed, function_info.num_args);
+  } else if (op->op.same_as(builtin::tvm_call_cpacked_lowered())) {
+    auto function_info = GetFunctionInfo(op, true /* has_resource_handle */);
+    this->PrintFuncCallC(function_info.func_name, function_info.num_args,
+                         function_info.resource_handle_name);
+  } else if (op->op.same_as(builtin::tvm_throw_last_error())) {
+    this->PrintIndent();
+    this->stream << "return -1;\n";
+  } else {
+    CodeGenC::VisitExpr_(op, os);
+  }
+}
+
+void CodeGenTileLangCPP::VisitStmt_(const AssertStmtNode *op) { // NOLINT(*)
+  if (emit_asserts_) {
+    std::string cond = PrintExpr(op->condition);
+    PrintIndent();
+    stream << "if (!(" << cond << ")) {\n";
+    int assert_if_scope = this->BeginScope();
+    PrintIndent();
+    stream << "TVMAPISetLastError(\"" << op->message.as<StringImmNode>()->value
+           << "\");\n";
+    PrintIndent();
+    stream << "return -1;\n";
+    this->EndScope(assert_if_scope);
+    PrintIndent();
+    stream << "}\n";
+  }
+  this->PrintStmt(op->body);
+}
+
+void CodeGenTileLangCPP::VisitStmt_(const AllocateNode *op) {
+  ICHECK(!is_zero(op->condition));
+  std::string vid = AllocVarID(op->buffer_var.get());
+
+  this->PrintIndent();
+  std::string scope = GetPtrStorageScope(op->buffer_var);
+  const VarNode *buffer = op->buffer_var.as<VarNode>();
+
+  PrintType(op->dtype, stream);
+  size_t constant_size = op->ConstantAllocationSize();
+  ICHECK_GT(constant_size, 0)
+      << "Can only handle constant size stack allocation for now";
+
+  stream << ' ' << vid << '[' << constant_size << "];\n";
+
+  RegisterHandleType(op->buffer_var.get(), op->dtype);
+  this->PrintStmt(op->body);
+}
+
+void CodeGenTileLangCPP::VisitExpr_(const MinNode *op,
+                                    std::ostream &os) { // NOLINT(*)
+  PrintTernaryCondExpr(op, "<", os);
+}
+
+void CodeGenTileLangCPP::VisitExpr_(const MaxNode *op,
+                                    std::ostream &os) { // NOLINT(*)
+  PrintTernaryCondExpr(op, ">", os);
+}
+
+template <typename T>
+inline void
+CodeGenTileLangCPP::PrintTernaryCondExpr(const T *op, const char *compare,
+                                         std::ostream &os) { // NOLINT(*)
+  std::ostringstream temp_a;
+  VisitExpr(op->a, temp_a);
+  std::string a_id = SSAGetID(temp_a.str(), op->a.dtype());
+  std::ostringstream temp_b;
+  VisitExpr(op->b, temp_b);
+  std::string b_id = SSAGetID(temp_b.str(), op->b.dtype());
+
+  os << "((" << a_id << ") " << compare << " (" << b_id << ") "
+     << "? (" << a_id << ") : (" << b_id << "))";
+}
+
+} // namespace codegen
+} // namespace tvm

src/target/codegen_cpp.h

+/*
+ * 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 codegen_c_host.h
+ * \brief Generate C host code.
+ */
+#ifndef TVM_TARGET_SOURCE_CODEGEN_C_HOST_H_
+#define TVM_TARGET_SOURCE_CODEGEN_C_HOST_H_
+
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+
+#include "target/source/codegen_c.h"
+#include "tvm/target/codegen.h"
+#include "tvm/tir/expr.h"
+
+namespace tvm {
+namespace codegen {
+
+class CodeGenTileLangCPP : public CodeGenC {
+public:
+  CodeGenTileLangCPP();
+  void Init(bool output_ssa, bool emit_asserts, bool emit_fwd_func_decl,
+            std::string target_str,
+            const std::unordered_set<std::string> &devices);
+
+  void InitGlobalContext();
+  // Override this as a work around for non tvm runtime code generations
+  void AddFunction(const PrimFunc &f);
+
+  /*!
+   * \brief Add functions from the (unordered) range to the current module in a
+   * deterministic order. This helps with debugging.
+   *
+   * \param functions A vector of unordered range of current module.
+   */
+  void AddFunctionsOrdered(
+      std::vector<std::pair<tvm::GlobalVar, tvm::BaseFunc>> functions);
+  void DefineModuleName();
+
+  using CodeGenC::PrintType;
+  void PrintType(DataType t, std::ostream &os) final; // NOLINT(*)
+  void PrintFuncPrefix(std::ostream &os) final;       // NOLINT(*)
+
+  // overload visitor functions
+  void VisitExpr_(const BroadcastNode *op, std::ostream &os) final; // NOLINT(*)
+  void VisitExpr_(const CallNode *op, std::ostream &os) override;   // NOLINT(*)
+  // overload min and max to use the ternary operator, so we don't rely on the
+  // standard library implementations
+  void VisitExpr_(const MinNode *op, std::ostream &os) final; // NOLINT(*)
+  void VisitExpr_(const MaxNode *op, std::ostream &os) final; // NOLINT(*)
+
+  void VisitStmt_(const AssertStmtNode *op) final; // NOLINT(*)
+  void VisitStmt_(const AllocateNode *op) final;   // NOLINT(*)
+
+  void GenerateForwardFunctionDeclarations(String global_symbol,
+                                           const Array<Type> &arg_types,
+                                           const Type &ret_type) override;
+  Array<String> GetFunctionNames() { return function_names_; }
+
+private:
+  /* \brief Internal structure to store information about function calls */
+  struct FunctionInfo {
+    /* \brief function name */
+    std::string func_name;
+    /* number of arguments required by the function */
+    int64_t num_args;
+    /* \brief name of resource_handle to pass */
+    std::string resource_handle_name;
+  };
+  std::string module_name_;
+  /* \brief mapping global packed func to the unique name */
+  std::unordered_map<std::string, std::string> declared_globals_;
+  /* \brief names of the functions declared in this module */
+  Array<String> function_names_;
+  /*! \brief whether to emit asserts in the resulting C code */
+  bool emit_asserts_;
+  /*! \brief whether to emit forwared function declarations in the resulting C
+   * code */
+  bool emit_fwd_func_decl_;
+
+  FunctionInfo GetFunctionInfo(const CallNode *op, bool has_resource_handle);
+  std::string GetPackedName(const CallNode *op);
+  void PrintGetFuncFromBackend(const std::string &func_name,
+                               const std::string &packed_func_name);
+  void PrintFuncCall(const std::string &packed_func_name, int num_args);
+  void PrintFuncCallC(const std::string &packed_func_name, int num_args,
+                      const std::string &resource_handle_name);
+
+  /*!
+   * \brief Print ternary conditional operator implementing binary `op`
+   * Forces the operands to be in SSA form.
+   * \param op binary operator being expressed
+   * \param compare string representation of comparison operator
+   * \param os stream reference to print into
+   */
+  template <typename T>
+  inline void PrintTernaryCondExpr(const T *op, const char *compare,
+                                   std::ostream &os); // NOLINT(*)
+};
+
+} // namespace codegen
+} // namespace tvm
+
+#endif // TVM_TARGET_SOURCE_CODEGEN_C_HOST_H_

src/target/rt_mod_cpp.cc

+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT License.
+
+#include "codegen_cpp.h"
+
+namespace tvm {
+namespace codegen {
+
+runtime::Module BuildCPPHost(IRModule mod, Target target) {
+  using tvm::runtime::Registry;
+  bool output_ssa = false;
+  bool emit_asserts = false;
+  bool emit_fwd_func_decl = true;
+
+  std::unordered_set<std::string> devices;
+  if (mod->GetAttr<Map<GlobalVar, String>>("device_contexts") != nullptr) {
+    Map<GlobalVar, String> device_contexts =
+        mod->GetAttr<Map<GlobalVar, String>>("device_contexts").value();
+    for (auto const &context : device_contexts) {
+      devices.insert(context.second.data());
+    }
+  }
+
+  CodeGenTileLangCPP cg;
+  cg.Init(output_ssa, emit_asserts, emit_fwd_func_decl, target->str(), devices);
+  cg.SetConstantsByteAlignment(
+      target->GetAttr<Integer>("constants-byte-alignment").value_or(16));
+
+  auto is_aot_executor_fn = [](const PrimFunc &func) -> bool {
+    return func->GetAttr<Bool>("runner_function", Bool(false)).value();
+  };
+
+  std::vector<std::pair<GlobalVar, PrimFunc>> funcs;
+  for (auto [gvar, base_func] : mod->functions) {
+    ICHECK(base_func->IsInstance<PrimFuncNode>())
+        << "CodegenCHost: Can only take PrimFunc";
+    auto prim_func = Downcast<PrimFunc>(base_func);
+    funcs.push_back({gvar, prim_func});
+  }
+
+  // Sort functions
+  auto sort_key = [&is_aot_executor_fn](const auto &kv) {
+    return std::tuple{is_aot_executor_fn(kv.second), kv.first->name_hint};
+  };
+  std::sort(funcs.begin(), funcs.end(),
+            [&sort_key](const auto &kv_a, const auto &kv_b) {
+              return sort_key(kv_a) < sort_key(kv_b);
+            });
+
+  // Declare all functions first.  This ensures that all functions,
+  // including the __tvm_main__ used in AOT, have access to forward
+  // declarations of other functions in the IRModule.
+  for (const auto &[gvar, prim_func] : funcs) {
+    cg.DeclareFunction(gvar, prim_func);
+  }
+
+  // Codegen all functions.  Passing emit_fwd_func_decl=true adds a
+  // forward declaration for any `builtin::call_extern`, based on the
+  // arguments provided to it.
+  for (const auto &[gvar, prim_func] : funcs) {
+    cg.AddFunction(prim_func);
+  }
+
+  if (target->GetAttr<Bool>("system-lib").value_or(Bool(false))) {
+    ICHECK_EQ(target->GetAttr<String>("runtime").value_or(""), "c")
+        << "c target only supports generating C runtime SystemLibs";
+  }
+
+  std::string code = cg.Finish();
+  return CSourceModuleCreate(code, "c", cg.GetFunctionNames());
+}
+
+TVM_REGISTER_GLOBAL("target.build.tilelang_cpp").set_body_typed(BuildCPPHost);
+
+} // namespace codegen
+} // namespace tvm

src/tl_templates/cpu/common.h

+#include <math.h>
+#include <stdbool.h>

src/transform/annotate_device_regions.cc

+/*
+ * 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 annotate_device_regions.cc
+ * \brief Split device function from host.
+ */
+#include "tir/transforms/ir_utils.h"
+#include <tvm/ir/transform.h>
+#include <tvm/runtime/registry.h>
+#include <tvm/target/target.h>
+#include <tvm/tir/builtin.h>
+#include <tvm/tir/expr.h>
+#include <tvm/tir/stmt_functor.h>
+#include <tvm/tir/transform.h>
+
+namespace tvm {
+namespace tl {
+
+using namespace tir;
+
+class DeviceRegionAnnotater : public StmtMutator {
+public:
+  explicit DeviceRegionAnnotater(Target device_target)
+      : device_target_(device_target) {}
+
+  Stmt VisitStmt_(const AttrStmtNode *op) final {
+    if (op->attr_key == tvm::attr::kTarget) {
+      // If a target attribute already exists, use it as-is.
+      return GetRef<Stmt>(op);
+    } else if (op->attr_key == tir::attr::thread_extent ||
+               op->attr_key == tir::attr::pipeline_exec_scope ||
+               op->attr_key == tir::attr::device_scope) {
+      // These attributes are only allowed in device-side code, so
+      // they should be annotated with the function's default target.
+      Stmt body = GetRef<Stmt>(op);
+      return AttrStmt(device_target_, tvm::attr::kTarget, 0, body);
+    } else {
+      // All other annotations are ignored
+      return StmtMutator::VisitStmt_(op);
+    }
+  }
+
+private:
+  Target device_target_;
+};
+
+tvm::transform::Pass AnnotateDeviceRegions() {
+  using namespace tir::transform;
+  auto pass_func = [](PrimFunc func, IRModule mod,
+                      tvm::transform::PassContext ctx) -> PrimFunc {
+    auto opt_target = func->GetAttr<Target>(tvm::attr::kTarget);
+    ICHECK(opt_target) << "AnnotateDeviceRegions: Require the target attribute";
+    Target target = opt_target.value();
+    Target device_target = target.WithoutHost();
+
+    if (target->GetHost()) {
+      if (device_target->kind->name == "c") {
+        // Annotate the function with the device target
+        auto func_body = func->body;
+        func.CopyOnWrite()->body =
+            AttrStmt(device_target, tvm::attr::kTarget, 0, func_body);
+      }
+
+      DeviceRegionAnnotater mutator(target.WithoutHost());
+      func.CopyOnWrite()->body = mutator(func->body);
+    }
+    return func;
+  };
+
+  return CreatePrimFuncPass(pass_func, 0, "tl.AnnotateDeviceRegions", {});
+}
+
+TVM_REGISTER_GLOBAL("tl.transform.AnnotateDeviceRegions")
+    .set_body_typed(AnnotateDeviceRegions);
+
+} // namespace tl
+} // namespace tvm

src/transform/layout_inference.cc

@@ -35,10 +35,86 @@
 #include "common/loop_fusion_utils.h"
 #include "loop_partition.h"
 #include "loop_vectorize.h"
+#include "runtime/thread_storage_scope.h"
+#include "tir/transforms/ir_utils.h"
 
 namespace tvm {
 namespace tl {
 
+using namespace tir;
+
+using runtime::StorageRank;
+using runtime::StorageScope;
+
+static bool IsDynamicSharedMemory(Var buffer_var) {
+  StorageScope storage_scope =
+      runtime::StorageScope::Create(GetPtrStorageScope(buffer_var));
+  return storage_scope.rank == runtime::StorageRank::kShared &&
+         storage_scope.tag == ".dyn";
+}
+
+static bool IsStaticSharedMemory(Var buffer_var) {
+  StorageScope storage_scope =
+      runtime::StorageScope::Create(GetPtrStorageScope(buffer_var));
+  return storage_scope.rank == runtime::StorageRank::kShared &&
+         storage_scope.tag == "";
+}
+
+static bool isLocalFragment(Var buffer_var) {
+  StorageScope storage_scope =
+      runtime::StorageScope::Create(GetPtrStorageScope(buffer_var));
+  return storage_scope.rank == runtime::StorageRank::kLocal &&
+         storage_scope.tag == ".fragment";
+}
+
+/*!
+ * \brief collect the mapping from the buffer var to its allocate
+ */
+class AllocateCollector : public StmtExprVisitor {
+public:
+  void VisitStmt_(const AllocateNode *op) final {
+    if (IsDynamicSharedMemory(op->buffer_var)) {
+      dyn_shmem_allocs_[op->buffer_var.get()] = op;
+    } else if (IsStaticSharedMemory(op->buffer_var)) {
+      static_shmem_allocs_[op->buffer_var.get()] = op;
+    } else if (isLocalFragment(op->buffer_var)) {
+      local_fragment_allocs_[op->buffer_var.get()] = op;
+    }
+    StmtExprVisitor::VisitStmt_(op);
+  }
+  void VisitStmt_(const BlockNode *op) final {
+    for (auto buffer : op->alloc_buffers) {
+      if (IsDynamicSharedMemory(buffer->data)) {
+        dyn_shmem_allocs_[buffer->data.get()] = op;
+      } else if (IsStaticSharedMemory(buffer->data)) {
+        static_shmem_allocs_[buffer->data.get()] = op;
+      } else if (isLocalFragment(buffer->data)) {
+        local_fragment_allocs_[buffer->data.get()] = op;
+      }
+    }
+    StmtExprVisitor::VisitStmt_(op);
+  }
+
+  void VisitStmt_(const AllocateConstNode *op) final {
+    StmtExprVisitor::VisitStmt_(op);
+  }
+
+  void VisitStmt_(const SeqStmtNode *op) final {
+    StmtExprVisitor::VisitStmt_(op);
+  }
+
+  void VisitStmt_(const AttrStmtNode *op) final {
+    StmtExprVisitor::VisitStmt_(op);
+  }
+
+  // The dynamic mapping from the original buffer var to its allocate
+  std::unordered_map<const VarNode *, const Object *> dyn_shmem_allocs_;
+  // The static mapping from the original buffer var to its allocate
+  std::unordered_map<const VarNode *, const Object *> static_shmem_allocs_;
+  // The local fragment mapping from the original buffer var to its allocate
+  std::unordered_map<const VarNode *, const Object *> local_fragment_allocs_;
+};
+
 using namespace tir;
 using arith::IRMutatorWithAnalyzer;
 
@@ -50,37 +126,113 @@ struct LayoutInferenceResult {
 
 class BufferUseDefCollector : public StmtExprVisitor {
 public:
-  BufferUseDefCollector() = default;
+  BufferUseDefCollector(bool skip_thread_partition)
+      : skip_thread_partition_(skip_thread_partition) {}
 
   LayoutInferenceResult Run() {
+    // Basic consistency check: infer_list_ and thread_var_vec_ should have the
+    // same size
+    ICHECK_EQ(infer_list_.size(), thread_var_vec_.size())
+        << "Size mismatch: infer_list_ and thread_var_vec_ must match in "
+           "length.";
+
+    // If needed, you can also check that annotated_layout_map_ is not empty, or
+    // anything else relevant to your setup.
+
+    // Copy the annotated layout map to local variable
     Map<Buffer, Layout> layout_map = annotated_layout_map_;
     int num_infer = infer_list_.size();
 
-    // maintain a bfs queue and infer common layout
+    // Prepare BFS queue for iterative inference
     std::queue<int> q;
     std::vector<bool> in_queue(num_infer, true);
-    for (int i = 0; i < num_infer; i++)
-      q.push(i);
+    for (int i = 0; i < num_infer; i++) {
+      // Check that each infer_list_ entry is valid
+      ICHECK(infer_list_[i] != nullptr)
+          << "infer_list_[" << i
+          << "] is null. The inference object is not allocated properly.";
 
+      // Check that each thread_var_vec_ entry is defined
+      if (!thread_var_vec_[i].defined() && skip_thread_partition_) {
+        // TODO(lei): This is a hack for cpu backend
+        if (!thread_var_.defined()) {
+          // Fake thread var to inference predicate for the buffer
+          thread_var_ = IterVar(Range::FromMinExtent(PrimExpr(0), PrimExpr(1)),
+                                Var(""), IterVarType::kDataPar);
+        }
+        thread_var_vec_[i] = thread_var_;
+      }
+      q.push(i);
+    }
     auto run_infer_step = [&](int cur_infer_id, InferLevel level,
                               bool update_queue) {
+      // Range check for cur_infer_id
+      ICHECK_GE(cur_infer_id, 0)
+          << "cur_infer_id is negative, which is invalid.";
+      ICHECK_LT(cur_infer_id, num_infer)
+          << "cur_infer_id " << cur_infer_id << " is out of range, must be < "
+          << num_infer << ".";
+
+      // Make sure we can safely access infer_list_[cur_infer_id] and
+      // thread_var_vec_[cur_infer_id]
       auto &next = infer_list_[cur_infer_id];
       auto iter_var = thread_var_vec_[cur_infer_id];
+
+      // Double-check that 'next' is valid
+      ICHECK(next != nullptr) << "infer_list_[" << cur_infer_id
+                              << "] is null inside run_infer_step.";
+
+      // Check iter_var->dom and dom->extent
+      ICHECK(iter_var.defined())
+          << "thread_var_vec_[" << cur_infer_id << "] is not defined.";
+      ICHECK(iter_var->dom.defined())
+          << "iter_var->dom is not defined for infer_list_[" << cur_infer_id
+          << "].";
+      ICHECK(iter_var->dom->extent.defined())
+          << "iter_var->dom->extent is not defined for infer_list_["
+          << cur_infer_id << "].";
+
+      const int64_t *extent_ptr = as_const_int(iter_var->dom->extent);
+      ICHECK(extent_ptr != nullptr)
+          << "iter_var->dom->extent is not a constant integer, which is "
+             "required for layout inference.";
+
+      // Run InferLayout
       auto updates = next->InferLayout(
-          LayoutInferArgs{
-              target_,
-              static_cast<size_t>(*as_const_int(iter_var->dom->extent)),
+          LayoutInferArgs{target_, static_cast<size_t>(*extent_ptr),
                           layout_map},
           level);
+
+      // Process the returned updates
       for (const auto &[buffer, layout] : updates) {
+        // Basic validity checks
+        ICHECK(buffer.defined()) << "InferLayout returned an undefined buffer.";
+        ICHECK(layout.defined()) << "InferLayout returned an undefined layout.";
+
         if (layout_map.count(buffer)) {
+          // If already in map, ensure they are structurally equal
           ICHECK(StructuralEqual()(layout, layout_map[buffer]))
-              << "Get different layout for " << buffer;
+              << "Get different layout for " << buffer
+              << " in cur_infer_id = " << cur_infer_id;
         } else {
+          // Otherwise, update map
           layout_map.Set(buffer, layout);
           if (!update_queue)
             continue;
+
+          // Check if buffer exists in use_list_
+          ICHECK(use_list_.count(buffer))
+              << "Buffer " << buffer << " not found in use_list_. "
+              << "Potential mismatch between inference updates and use_list_.";
+
+          // Push back into BFS queue
           for (int idx : use_list_[buffer]) {
+            ICHECK_GE(idx, 0) << "Index in use_list_ for buffer " << buffer
+                              << " is negative.";
+            ICHECK_LT(idx, num_infer)
+                << "Index in use_list_ for buffer " << buffer
+                << " out of range: " << idx << " >= " << num_infer << ".";
+
             if (!in_queue[idx] && idx != cur_infer_id) {
               in_queue[idx] = true;
               q.push(idx);
@@ -89,49 +241,66 @@ public:
         }
       }
     };
+
     auto finish_infer_queue = [&]() {
       while (!q.empty()) {
         int cur_infer_id = q.front();
         q.pop();
+        // Range check again, just to be safe
+        ICHECK_GE(cur_infer_id, 0);
+        ICHECK_LT(cur_infer_id, num_infer);
+
         in_queue[cur_infer_id] = false;
         run_infer_step(cur_infer_id, InferLevel::kCommon, true);
       }
     };
 
-    // step 1, infer strict layout
+    // step 1: infer strict layout
     for (int i = 0; i < num_infer; i++) {
       run_infer_step(i, InferLevel::kStrict, false);
     }
+    // step 2: infer common layout with BFS
 
-    // step2, infer common layout with bfs
     finish_infer_queue();
-
-    // step 3, relax the infer constraint to free and rerun.
+    // step 3: relax constraints to free and re-run
     for (int i = 0; i < num_infer; i++) {
       run_infer_step(i, InferLevel::kFree, true);
       finish_infer_queue();
     }
 
-    // Check that all fragments have been inferred
+    // Check that all local.fragment buffers have inferred layouts
     for (const auto &[buffer, _] : use_list_) {
-      if (buffer.scope() == "local.fragment" && layout_map.count(buffer) == 0)
-        LOG_ERROR << "The layout for fragment " << buffer
+      if (buffer.scope() == "local.fragment") {
+        ICHECK_NE(layout_map.count(buffer), 0)
+            << "The layout for fragment " << buffer
             << " can not be inferred correctly.";
       }
+    }
 
-    // Collect the layout for for nodes
+    // Collect layout info for For nodes
     Map<For, Fragment> for_map;
     Map<For, PrimExpr> predicate_map;
     for (auto &base_infer : infer_list_) {
+      // Check if base_infer is valid
+      ICHECK(base_infer != nullptr) << "Null pointer encountered in "
+                                       "infer_list_ while collecting for_map.";
+
       if (auto for_infer = dynamic_cast<ParallelOp *>(base_infer.get())) {
+        // Check that the loop layout is defined
         ICHECK(for_infer->GetLoopLayout().defined())
-            << "The Layout for Parallel for can not be inferred correctly : \n"
+            << "The Layout for Parallel for cannot be inferred correctly:\n"
             << for_infer->GetRoot();
         for_map.Set(for_infer->GetRoot(), for_infer->GetLoopLayout());
-        if (auto predicate = for_infer->GetPredicate(thread_var_->var))
+
+        // thread_var_ should be defined if we rely on it
+        ICHECK(thread_var_.defined())
+            << "thread_var_ is not defined. Cannot retrieve predicate.";
+
+        if (auto predicate = for_infer->GetPredicate(thread_var_->var)) {
           predicate_map.Set(for_infer->GetRoot(), predicate.value());
         }
       }
+    }
 
     return {layout_map, for_map, predicate_map};
   }
@@ -231,26 +400,28 @@ private:
   std::vector<IterVar> thread_var_vec_;
   Target target_;
   LayoutMap annotated_layout_map_;
+  bool skip_thread_partition_{false};
 };
 
 class LayoutInferencer : public IRMutatorWithAnalyzer {
 public:
-  static PrimFunc Substitute(PrimFunc f) {
+  static PrimFunc Substitute(PrimFunc f, bool skip_thread_partition = false) {
     arith::Analyzer analyzer;
     PrimFuncNode *fptr = f.CopyOnWrite();
     fptr->body = ParallelLoopFuser::Fuse(f->body);
-    BufferUseDefCollector collector;
+    BufferUseDefCollector collector(skip_thread_partition);
     collector.Collect(f);
     auto result = collector.Run();
-    LayoutInferencer substituter(result, &analyzer);
+    LayoutInferencer substituter(result, skip_thread_partition, &analyzer);
     fptr->body = substituter.VisitStmt(f->body);
     return f;
   }
 
 private:
   LayoutInferencer(const LayoutInferenceResult result,
-                   arith::Analyzer *analyzer)
-      : arith::IRMutatorWithAnalyzer(analyzer), result_(result){};
+                   bool skip_thread_partition, arith::Analyzer *analyzer)
+      : arith::IRMutatorWithAnalyzer(analyzer), result_(result),
+        skip_thread_partition_(skip_thread_partition){};
 
   Stmt VisitStmt_(const BlockNode *op) final {
     Block block = Downcast<Block>(IRMutatorWithAnalyzer::VisitStmt_(op));
@@ -270,8 +441,12 @@ private:
     For for_node = Downcast<For>(IRMutatorWithAnalyzer::VisitStmt_(op));
     if (result_.for_map.count(GetRef<For>(op))) {
       auto loop_layout = result_.for_map[GetRef<For>(op)];
+
+      if (!skip_thread_partition_) {
+        // If none thread bindings are provided, partition the loop
         for_node =
             PartitionLoop(for_node, thread_var_->var, analyzer_, loop_layout);
+      }
       for_node = VectorizeLoop(for_node);
       if (result_.predicate_map.count(GetRef<For>(op))) {
         return IfThenElse(result_.predicate_map[GetRef<For>(op)], for_node);
@@ -296,12 +471,22 @@ private:
 private:
   const LayoutInferenceResult result_;
   IterVar thread_var_;
+  bool skip_thread_partition_{false};
 };
 
 tvm::transform::Pass LayoutInference() {
   using namespace tir::transform;
   auto pass_func = [=](PrimFunc f, IRModule m, PassContext ctx) {
-    return LayoutInferencer::Substitute(std::move(f));
+    AllocateCollector collector;
+    collector(f->body);
+    // TODO(Lei): This is a hack to avoid the issue of thread partition
+    // for cpu backend. We should remove this after we have a better
+    // solution for thread partition detect.
+    bool need_thread_partition = (collector.dyn_shmem_allocs_.size() > 1 ||
+                                  collector.static_shmem_allocs_.size() > 1 ||
+                                  collector.local_fragment_allocs_.size() > 1);
+    bool skip_thread_partition = !need_thread_partition;
+    return LayoutInferencer::Substitute(std::move(f), skip_thread_partition);
   };
   return CreatePrimFuncPass(pass_func, 0, "tl.LayoutInference", {});
 }

src/transform/make_packed_api.cc

+/*
+ * 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 make_packed_api.cc Lower PrimFunc to use the packed function API.
+ */
+#include <tvm/runtime/device_api.h>
+#include <tvm/runtime/registry.h>
+#include <tvm/target/target.h>
+#include <tvm/tir/analysis.h>
+#include <tvm/tir/buffer.h>
+#include <tvm/tir/builtin.h>
+#include <tvm/tir/expr.h>
+#include <tvm/tir/stmt_functor.h>
+#include <tvm/tir/transform.h>
+
+#include <unordered_set>
+#include <utility>
+#include <vector>
+
+#include "tir/transforms/arg_binder.h"
+#include "tir/transforms/ir_utils.h"
+
+namespace tvm {
+namespace tl {
+using namespace tir;
+static constexpr const char *kDeviceContextVar = "device_api_context";
+
+namespace {
+class ReturnRewriter : public StmtMutator {
+public:
+  explicit ReturnRewriter(Var ret_var, Var ret_tcode)
+      : ret_var_(ret_var), ret_tcode_(ret_tcode) {}
+
+  Stmt VisitStmt_(const ForNode *node) override {
+    if (node->kind == ForKind::kParallel)
+      in_parallel_ += 1;
+    Stmt ret = StmtMutator::VisitStmt_(node);
+    if (node->kind == ForKind::kParallel)
+      in_parallel_ -= 1;
+    return ret;
+  }
+
+  Stmt VisitStmt_(const EvaluateNode *node) override {
+    Stmt ret = StmtMutator::VisitStmt_(node);
+    const EvaluateNode *eval = ret.as<EvaluateNode>();
+    ICHECK(eval);
+    if (const CallNode *call = eval->value.as<CallNode>()) {
+      if (call->op.same_as(builtin::ret())) {
+        ICHECK_EQ(in_parallel_, 0)
+            << "tir.ret cannot be used in parallel scope.";
+        ICHECK_EQ(call->args.size(), 1) << "tir.ret expect a single argument.";
+        ret = WriteToOut(call->args[0]);
+      }
+    }
+    return ret;
+  }
+
+private:
+  struct ConvertedInfo {
+    int tcode{-1};
+    PrimExpr expr;
+    Buffer dummy_val_buffer;
+    Buffer dummy_tcode_buffer;
+  };
+
+  ConvertedInfo ConvertForFFI(PrimExpr val) {
+    ConvertedInfo info;
+
+    // convert val's data type to FFI data type, return type code
+    DataType dtype = val.dtype();
+    if (dtype.is_int() || dtype.is_uint()) {
+      info.tcode = kTVMArgInt;
+      info.expr = Cast(DataType::Int(64), val);
+    } else if (dtype.is_float()) {
+      info.tcode = kTVMArgFloat;
+      info.expr = Cast(DataType::Float(64), val);
+    } else if (dtype.is_void()) {
+      info.tcode = kTVMNullptr;
+      info.expr = val;
+    } else {
+      LOG(FATAL) << "data type " << dtype << " not supported yet";
+    }
+
+    // If multiple return locations have the same data type, use the
+    // same dummy buffer declaration.
+    auto it = dummy_val_buffer_map_.find(info.tcode);
+    if (it != dummy_val_buffer_map_.end()) {
+      info.dummy_val_buffer = it->second;
+    } else {
+      info.dummy_val_buffer =
+          Buffer(ret_var_, info.expr.dtype(), {1}, {1}, ConstInt32(0),
+                 ret_var_->name_hint, 0, 0, kDefault);
+      dummy_val_buffer_map_[info.tcode] = info.dummy_val_buffer;
+    }
+
+    // The tcode is always a 32-bit int, so we don't need to have a separate
+    // map.
+    if (!dummy_tcode_buffer_.defined()) {
+      dummy_tcode_buffer_ =
+          Buffer(ret_tcode_, DataType::Int(32), {1}, {1}, ConstInt32(0),
+                 ret_tcode_->name_hint, 0, 0, kDefault);
+    }
+    info.dummy_tcode_buffer = dummy_tcode_buffer_;
+
+    return info;
+  }
+
+  Stmt WriteToOut(PrimExpr val) {
+    auto info = ConvertForFFI(val);
+    Stmt store_val = BufferStore(info.dummy_val_buffer, info.expr, {0});
+    Stmt store_tcode = BufferStore(info.dummy_tcode_buffer, info.tcode, {0});
+    Stmt ret_zero = Evaluate(tvm::ret(0));
+    return SeqStmt({store_val, store_tcode, ret_zero});
+  }
+
+  Var ret_var_;
+  Var ret_tcode_;
+  int in_parallel_{0};
+
+  std::unordered_map<int, Buffer> dummy_val_buffer_map_;
+  Buffer dummy_tcode_buffer_;
+};
+
+Stmt RewriteReturn(Stmt body, Var ret_var, Var ret_tcode) {
+  ReturnRewriter rewriter(ret_var, ret_tcode);
+  return rewriter(body);
+}
+
+class SubroutineCallRewriter : public StmtExprMutator {
+public:
+  static Optional<Stmt> Apply(const Map<GlobalVar, String> &packed_func_methods,
+                              Stmt stmt) {
+    SubroutineCallRewriter rewriter(packed_func_methods);
+    stmt = rewriter.VisitStmt(std::move(stmt));
+    if (rewriter.made_change_) {
+      return stmt;
+    } else {
+      return NullOpt;
+    }
+  }
+
+private:
+  explicit SubroutineCallRewriter(
+      const Map<GlobalVar, String> &packed_func_methods)
+      : packed_func_methods(packed_func_methods) {}
+
+  PrimExpr VisitExpr_(const CallNode *op) override {
+    auto node = Downcast<Call>(StmtExprMutator::VisitExpr_(op));
+
+    if (auto *gvar_ptr = node->op.as<GlobalVarNode>()) {
+      auto gvar = GetRef<GlobalVar>(gvar_ptr);
+      if (auto symbol = packed_func_methods.Get(gvar)) {
+        Array<PrimExpr> cpacked_args;
+        cpacked_args.push_back(tir::StringImm(symbol.value()));
+        for (auto arg : node->args) {
+          cpacked_args.push_back(arg);
+        }
+
+        // push an empty handle to be compatible with current cpacked convention
+        cpacked_args.push_back(tir::make_zero(DataType::Handle()));
+        made_change_ = true;
+        return tir::Call(node->dtype, tir::builtin::tvm_call_cpacked(),
+                         cpacked_args);
+      }
+    }
+
+    return node;
+  }
+  const Map<GlobalVar, String> &packed_func_methods;
+  bool made_change_{false};
+};
+
+} // namespace
+
+inline Stmt MakeAssertEQ(PrimExpr lhs, PrimExpr rhs, std::string msg) {
+  return AssertStmt(lhs == rhs, tvm::tir::StringImm(msg), Evaluate(0));
+}
+
+inline Stmt MakeAssertNotNull(PrimExpr ptr, std::string msg) {
+  Call isnull(DataType::Bool(), builtin::isnullptr(), {ptr});
+  return AssertStmt(!isnull, tvm::tir::StringImm(msg), Evaluate(0));
+}
+
+/* \brief Return the global_symbol of the function, if it should be updated
+ *
+ * \param func The function to be inspected
+ *
+ * \returns The global_symbol to be used for the function at call
+ * sites, or NullOpt if the function is to remain unchanged.
+ */
+Optional<String> RequiresPackedAPI(const PrimFunc &func) {
+  // A function with an explicit calling convention has already been
+  // lowered, and should not be modified.
+  if (auto opt = func->GetAttr<Integer>(tvm::attr::kCallingConv)) {
+    if (CallingConv(opt.value()->value) != CallingConv::kDefault) {
+      return NullOpt;
+    }
+  }
+
+  // Internal function calls do not need the PackedFunc API
+  auto global_symbol = func->GetAttr<String>(tvm::attr::kGlobalSymbol);
+  if (!global_symbol.defined()) {
+    return NullOpt;
+  }
+
+  return global_symbol;
+}
+
+PrimFunc MakePackedAPI(PrimFunc func) {
+  auto global_symbol = RequiresPackedAPI(func);
+  if (!global_symbol.defined()) {
+    return func;
+  }
+  std::string name_hint = global_symbol.value();
+
+  Target target = [&]() {
+    auto opt = func->GetAttr<Target>(tvm::attr::kTarget);
+    ICHECK(opt) << "MakePackedAPI required the function to be annotated with "
+                   "tvm::attr::kTarget ("
+                << tvm::attr::kTarget
+                << "), but the function only has attributes " << func->attrs;
+    return opt.value();
+  }();
+  int target_device_type = target->GetTargetDeviceType();
+
+  // A function without a host target has already been lowered.
+  Target target_host;
+  if (auto opt = target->GetHost()) {
+    target_host = opt.value();
+  } else {
+    return func;
+  }
+
+  auto *func_ptr = func.CopyOnWrite();
+  const Stmt nop = Evaluate(0);
+  int num_args = static_cast<int>(func_ptr->params.size());
+
+  // Data field definitions
+  // The packed fields
+  Var v_packed_args("args", DataType::Handle());
+  Buffer buf_packed_arg_type_ids =
+      decl_buffer({IntImm(DataType::Int(32), func_ptr->params.size())},
+                  DataType::Int(32), "arg_type_ids");
+  Var v_num_packed_args("num_args", DataType::Int(32));
+  Var v_out_ret_value("out_ret_value", PointerType(PrimType(DataType::Void())));
+  Var v_out_ret_tcode("out_ret_tcode",
+                      PointerType(PrimType(DataType::Int(32))));
+  Var v_resource_handle("resource_handle", DataType::Handle());
+  // The arguments of the function.
+
+  // The device context
+  Var device_id("dev_id");
+  Integer device_type(target_device_type);
+  // seq_init gives sequence of initialization
+  // seq_check gives sequence of later checks after init
+  std::vector<Stmt> seq_init, seq_check, arg_buffer_declarations;
+  std::unordered_map<const VarNode *, PrimExpr> vmap;
+  ArgBinder binder(&vmap);
+
+  // ---------------------------
+  // local function definitions
+  // load i-th argument as type t
+  auto f_arg_value = [&](DataType t, int i) {
+    Array<PrimExpr> call_args{
+        v_packed_args, IntImm(DataType::Int(32), i),
+        IntImm(DataType::Int(32), builtin::kTVMValueContent)};
+    // load 64 bit version
+    DataType api_type = APIType(t);
+    PrimExpr res = Call(api_type, builtin::tvm_struct_get(), call_args);
+    // cast to the target version.
+    if (api_type != t) {
+      res = Cast(t, res);
+    }
+    return res;
+  };
+
+  // Find the device API context argument based on name
+  for (const auto &param : func_ptr->params) {
+    if (param->name_hint == kDeviceContextVar) {
+      num_args--;
+      v_resource_handle = param;
+      break;
+    }
+  }
+
+  // Assert correct type codes for each argument.  This must be done
+  // *before* any initialization steps produced by
+  // `binder.BindDLTensor()`.  The validity of those initialization
+  // steps depends on the correct types being present, and must not
+  // occur before the type codes are actually checked.
+  seq_init.push_back(
+      MakeAssertEQ(v_num_packed_args, num_args, [&]() -> std::string {
+        std::ostringstream error_message;
+        error_message << name_hint << ": num_args should be " << num_args;
+        return error_message.str();
+      }()));
+
+  seq_init.push_back(MakeAssertNotNull(
+      v_packed_args, name_hint + ": TVMValue* arg pointer was NULL"));
+  seq_init.push_back(MakeAssertNotNull(
+      buf_packed_arg_type_ids->data, name_hint + ": int* type_codes was NULL"));
+
+  seq_init.emplace_back(DeclBuffer(buf_packed_arg_type_ids, nop));
+
+  // Need to delay binding of the buffers, in case some arguments also
+  // appear in the buffer.
+  std::vector<std::pair<PrimExpr, Var>> var_def;
+  std::vector<std::pair<Var, Buffer>> buffer_def;
+
+  for (int i = 0; i < static_cast<int>(func_ptr->params.size()); ++i) {
+    Var param = func_ptr->params[i];
+
+    // Ignore the device context argument, as it will still be passed
+    // as a native argument.
+    if (param->name_hint == kDeviceContextVar) {
+      continue;
+    }
+
+    var_def.emplace_back(f_arg_value(param.dtype(), i), param);
+    if (func_ptr->buffer_map.count(param)) {
+      buffer_def.emplace_back(param, func_ptr->buffer_map[param]);
+    }
+
+    // type code checks
+    Var tcode(param->name_hint + ".code", DataType::Int(32));
+    seq_init.emplace_back(LetStmt(
+        tcode,
+        BufferLoad(buf_packed_arg_type_ids, {IntImm(DataType::Int(32), i)}),
+        nop));
+    DataType t = param.dtype();
+    if (t.is_handle()) {
+      std::ostringstream msg;
+      msg << name_hint << ": Expect arg[" << i << "] to be pointer";
+      seq_init.emplace_back(
+          AssertStmt(tcode == kTVMOpaqueHandle || tcode == kTVMNDArrayHandle ||
+                         tcode == kTVMDLTensorHandle || tcode == kTVMNullptr,
+                     tvm::tir::StringImm(msg.str()), nop));
+    } else if (t.is_int() || t.is_uint()) {
+      std::ostringstream msg;
+      msg << name_hint << ": Expect arg[" << i << "] to be int";
+      seq_init.emplace_back(
+          AssertStmt(tcode == kDLInt, tvm::tir::StringImm(msg.str()), nop));
+    } else {
+      ICHECK(t.is_float());
+      std::ostringstream msg;
+      msg << name_hint << ": Expect arg[" << i << "] to be float";
+      seq_init.emplace_back(
+          AssertStmt(tcode == kDLFloat, tvm::tir::StringImm(msg.str()), nop));
+    }
+  }
+
+  Array<Var> args{v_packed_args,     buf_packed_arg_type_ids->data,
+                  v_num_packed_args, v_out_ret_value,
+                  v_out_ret_tcode,   v_resource_handle};
+
+  // Arg definitions are defined before buffer binding to avoid the use before
+  // def errors.
+  //
+  // For example, for auto broadcasting, checks are required to guarantee that
+  // either 0 or the original stride will be correctly used. Checks here have
+  // to use the args that may have no let binding yet. Therefore, hoisting let
+  // binding for args before buffer declaration is needed.
+  for (const auto &[expr, param] : var_def) {
+    binder.Bind(param, expr, name_hint + "." + param->name_hint, true);
+  }
+
+  for (const auto &kv : buffer_def) {
+    binder.BindDLTensor(kv.second, device_type, device_id, kv.first,
+                        name_hint + "." + kv.first->name_hint);
+    arg_buffer_declarations.push_back(DeclBuffer(kv.second, nop));
+  }
+
+  func =
+      WithAttrs(std::move(func),
+                {{tvm::attr::kCallingConv, Integer(CallingConv::kCPackedFunc)},
+                 {tvm::attr::kTarget, target_host}});
+  Stmt body = RewriteReturn(func_ptr->body, v_out_ret_value, v_out_ret_tcode);
+  body = AttrStmt(make_zero(DataType::Int(32)), tir::attr::compute_scope,
+                  StringImm(name_hint + "_compute_"), body);
+  // Set device context
+  if (vmap.count(device_id.get())) {
+    ObjectRef node = String("default");
+    seq_check.push_back(AttrStmt(node, tir::attr::device_id, device_id, nop));
+    seq_check.push_back(
+        AttrStmt(node, tir::attr::device_type, device_type, nop));
+
+    bool need_set_device =
+        (target_device_type != kDLMicroDev &&
+         (
+             // or is c source target
+             target_device_type != kDLCPU || target->kind->name != "llvm"));
+
+    if (need_set_device) {
+      Stmt set_device =
+          Evaluate(Call(DataType::Int(32), builtin::tvm_call_packed(),
+                        {StringImm(runtime::symbol::tvm_set_device),
+                         device_type, device_id}));
+      body = SeqStmt({set_device, body});
+    }
+  }
+
+  // Return error code of zero on success
+  body = SeqStmt({body, Evaluate(ret(Integer(0)))});
+
+  body = MergeNest({seq_init, binder.init_nest(), seq_check, binder.asserts(),
+                    arg_buffer_declarations},
+                   body);
+  func_ptr->body = body;
+  func_ptr->params = args;
+
+  Array<Var> undefined = UndefinedVars(func_ptr->body, func_ptr->params);
+  ICHECK_EQ(undefined.size(), 0)
+      << "In PrimFunc " << name_hint << " variables " << undefined
+      << " are used, but are not passed in as API arguments";
+
+  func_ptr->buffer_map = Map<Var, Buffer>();
+  func_ptr->checked_type_ = func_ptr->func_type_annotation();
+  func_ptr->ret_type = PrimType(DataType::Int(32)); // return the function.
+  return func;
+}
+
+tvm::transform::Pass MakePackedAPI() {
+  using tvm::transform::Pass;
+  auto pass_func = [](IRModule mod, tvm::transform::PassContext ctx) {
+    Map<GlobalVar, String> packed_func_methods;
+    for (const auto &[gvar, base_func] : mod->functions) {
+      if (auto opt = base_func.as<PrimFunc>()) {
+        auto prim_func = opt.value();
+        if (auto global_symbol = RequiresPackedAPI(prim_func)) {
+          packed_func_methods.Set(gvar, global_symbol.value());
+        }
+      }
+    }
+
+    IRModuleNode *mptr = mod.CopyOnWrite();
+    IRModule updates;
+
+    for (const auto &[gvar, base_func] : mptr->functions) {
+      if (auto opt = base_func.as<PrimFunc>()) {
+        auto func = opt.value();
+        auto orig_func = func;
+
+        if (auto body = SubroutineCallRewriter::Apply(packed_func_methods,
+                                                      func->body)) {
+          func.CopyOnWrite()->body = body.value();
+        }
+
+        func = MakePackedAPI(std::move(func));
+
+        if (!func.same_as(orig_func)) {
+          updates->Add(gvar, func);
+        }
+      }
+    }
+
+    if (updates->functions.size()) {
+      mod.CopyOnWrite()->Update(updates);
+    }
+    return mod;
+  };
+
+  return tvm::transform::CreateModulePass(pass_func, 0, "tl.MakePackedAPI", {});
+}
+
+TVM_REGISTER_GLOBAL("tl.transform.MakePackedAPI").set_body_typed([]() {
+  return MakePackedAPI();
+});
+
+} // namespace tl
+} // namespace tvm

testing/python/cpu/test_tilelang_cpu_gemm.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import tilelang
+import tilelang.testing
+from tilelang import tvm as tvm
+import tilelang.language as T
+
+
+def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"):
+    num_stages = 0
+
+    @T.prim_func
+    def matmul(
+            A: T.Buffer((M, K), dtype),
+            B: T.Buffer((K, N), dtype),
+            C: T.Buffer((M, N), dtype),
+    ):
+        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), is_cpu=True) as (bx, by):
+            A_local = T.alloc_local((block_M, block_K), dtype)
+            B_local = T.alloc_local((block_K, block_N), dtype)
+            C_local = T.alloc_local((block_M, block_N), accum_dtype)
+
+            T.clear(C_local)
+
+            # Apply layout optimizations or define your own layout
+            # (Optional).
+            # T.annotate_layout(
+            #     {
+            #         A_local: make_swizzle_layout(A_local),
+            #         B_local: make_swizzle_layout(B_local),
+            #     }
+            # )
+
+            for ko in T.Pipelined(K // block_K, num_stages=num_stages):
+
+                T.copy(A[by * block_M, ko * block_K], A_local)
+
+                # Or Copy with Parallel
+                for k, j in T.Parallel(block_K, block_N):
+                    B_local[k, j] = B[ko * block_K + k, by * block_N + j]
+
+                for i, j, k in T.grid(block_M, block_N, block_K):
+                    C_local[i, j] += A_local[i, k] * B_local[k, j]
+
+            T.copy(C_local, C[by * block_M, bx * block_N])
+
+    return matmul
+
+
+def assert_matmul_codegen(M=1024, N=1024, K=1024, block_M=128, block_N=128, block_K=32):
+    func = matmul(M, N, K, block_M, block_N, block_K)
+
+    rt_mod, _ = tilelang.lower(func, target="c")
+
+    code = rt_mod.imported_modules[0].get_source()
+
+    assert code is not None, "Code generation failed"
+
+
+def test_matmul_codegen():
+    assert_matmul_codegen(M=1024, N=1024, K=1024, block_M=128, block_N=128, block_K=32)
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()

tilelang/engine/lower.py

@@ -5,16 +5,26 @@
 import tilelang as tl
 import os
 import os.path as osp
-from typing import Literal, Union
+from typing import Union, Optional
 from tilelang import tvm as tvm
 from tvm import tir, relay
+from tvm.ir import CallingConv
 from tvm.target import Target
 from tilelang.contrib import hipcc, nvcc
-from tilelang.utils import determine_target
+from tilelang.utils.target import determine_target
 
 
 def is_device_call(func: tir.PrimFunc):
-    return bool(func.attrs and "calling_conv" in func.attrs and func.attrs["calling_conv"] == 2)
+    attrs = func.attrs
+
+    # consider c source as a device call
+    if "target" in attrs:
+        target = attrs["target"]
+        if target.kind.name == "c":
+            return True
+
+    return bool(func.attrs and "calling_conv" in func.attrs and
+                func.attrs["calling_conv"] == CallingConv.DEVICE_KERNEL_LAUNCH)
 
 
 def is_host_call(func: tir.PrimFunc):
@@ -95,13 +105,26 @@ def extrac_params(func: tir.PrimFunc):
     return tensor_types
 
 
+def canon_target_host(target: Union[str, Target], target_host: Optional[Union[str, Target]]):
+
+    def target_is_c(target):
+        if isinstance(target, str):
+            return target == "c"
+        return target.kind.name == "c"
+
+    if not target_host:
+        target_host = "llvm" if tvm.runtime.enabled("llvm") else "stackvm"
+
+    return target_host
+
+
 def lower(
     func_or_mod: Union[tir.PrimFunc, tvm.IRModule],
-    target: Union[Literal["auto", "cuda", "hip"], Target] = "auto",
-    target_host="llvm",
+    target: Union[str, Target] = "auto",
+    target_host: Optional[Union[str, Target]] = None,
     runtime_only=False,
 ):
-    # TODO(lei): Append C Source code host generation to the runtime
+
     mod = func_or_mod
     if isinstance(func_or_mod, tir.PrimFunc):
         func = func_or_mod
@@ -111,6 +134,8 @@ def lower(
     if isinstance(target, str):
         target = determine_target(target)
 
+    target_host = canon_target_host(target, target_host)
+
     target_host = tvm.target.Target.canon_target(target_host)
     target = tvm.target.Target(target, target_host)
 
@@ -167,13 +192,13 @@ def lower(
     mod = tl.transform.LowerHopperIntrin()(mod)
     mod = tir.transform.InjectPTXAsyncCopy()(mod)
 
-    mod = tir.transform.AnnotateDeviceRegions()(mod)
+    mod = tl.transform.AnnotateDeviceRegions()(mod)
     mod = tir.transform.SplitHostDevice()(mod)
     mod = tir.transform.MergeSharedMemoryAllocations()(mod)
     mod = tir.transform.ThreadSync("shared")(mod)
     mod = tir.transform.ThreadSync("shared.dyn")(mod)
 
-    mod = tir.transform.MakePackedAPI()(mod)
+    mod = tl.transform.MakePackedAPI()(mod)
     mod = tir.transform.LowerDeviceKernelLaunch()(mod)
     host_mod = tir.transform.Filter(is_host_call)(mod)
     host_mod = tir.transform.BindTarget(target_host)(host_mod)
@@ -187,6 +212,8 @@ def lower(
 
     if target_host.kind.name == "llvm":
         host_mod = tvm._ffi.get_global_func("target.build.llvm")(host_mod, target_host)
+    elif target_host.kind.name == "c":
+        host_mod = tvm._ffi.get_global_func("target.build.tilelang_cpp")(host_mod, target_host)
     else:
         raise ValueError("Target host is not supported")
 
@@ -201,6 +228,10 @@ def lower(
         device_mod = tvm._ffi.get_global_func("target.build.tilelang_cuda")(device_mod, target)
     elif target.kind.name == "hip":
         device_mod = tvm._ffi.get_global_func("target.build.tilelang_hip")(device_mod, target)
+    elif target.kind.name == "c":
+        device_mod = tvm._ffi.get_global_func("target.build.tilelang_cpp")(device_mod, target)
+    elif target.kind.name == "llvm":
+        device_mod = tvm._ffi.get_global_func("target.build.llvm")(device_mod, target)
     else:
         raise ValueError("Target is not supported")
 

tilelang/language/kernel.py

@@ -6,7 +6,7 @@ from typing import Union, List, Tuple, Optional
 from collections import deque
 from tvm import tir
 from tvm.tir import Var
-from tvm.script.ir_builder.tir.frame import TIRFrame
+from tvm.script.ir_builder.tir.frame import TIRFrame, BlockFrame
 from tvm._ffi import register_object
 from tilelang import _ffi_api
 
@@ -73,12 +73,21 @@ class KernelLaunchFrame(TIRFrame):
         """
         super().__enter__()
         _kernel_launch_frame_stack.push(self)
-
         # If we have exactly 5 frames, return the single iter_var.var.
         if len(self.frames) == 5:
             return self.frames[0].iter_var.var
 
+        last_block_frame = self.frames[-1]
+        assert isinstance(last_block_frame, BlockFrame), "Last frame must be a block frame"
+
+        maybe_cpu = last_block_frame.annotations.get("tilelang.is_cpu_kernel_frame", False)
+
+        if maybe_cpu:
+            # CPU kernel frame, return a list of for frame items.
+            return [frame.vars[0] for frame in self.frames[0:-1]]
+        else:
             # Otherwise, return a list of iter_var.var objects (excluding the last 4 frames).
+            # As 4 frames for threadIdx.x, threadIdx.y, threadIdx.z and block frame with attributes
             return [frame.iter_var.var for frame in self.frames[0:-4]]
 
     def __exit__(self, ptype, value, trace):
@@ -148,7 +157,8 @@ class KernelLaunchFrame(TIRFrame):
 
 def Kernel(
     *blocks: List[tir.PrimExpr],
-    threads: Union[int, List[int], Tuple] = 128,
+    threads: Optional[Union[int, List[int], Tuple]] = None,
+    is_cpu: bool = False,
     prelude: Optional[str] = None,
 ):
     """Tools to quickly construct a GPU kernel launch frame.
@@ -161,11 +171,13 @@ def Kernel(
         A integer representing blockDim.x
         Or a list of integers representing blockDim.(x|y|z)
         if the value is -1, we skip the threadIdx.x binding.
+    is_cpu : bool
+        Whether the kernel is running on CPU.
+        Thus we will not bind threadIdx.x, threadIdx.y, threadIdx.z.
+        and blockIdx.x, blockIdx.y, blockIdx.z.
     prelude : str
         The import c code of the kernel,
         will be injected before the generated kernel code.
-    layout_annotation: Optional[Map[tir.Buffer, tir.IndexMap]]
-        The layout annotation map, used to annotate the layout of the buffers.
 
     Returns
     -------
@@ -174,6 +186,9 @@ def Kernel(
     """
     attrs: dict = {}
 
+    if not is_cpu and threads is None:
+        threads = 128  # default thread number
+
     if isinstance(threads, int):
         threads = [threads, 1, 1]
     elif isinstance(threads, list):
@@ -181,7 +196,10 @@ def Kernel(
     elif isinstance(threads, tuple):
         threads = list(threads) + [1] * (3 - len(threads))
     else:
-        raise ValueError("threads must be an integer or a list of integers")
+        assert is_cpu, "threads must be an integer or a list of integers"
+
+    if is_cpu:
+        attrs["tilelang.is_cpu_kernel_frame"] = True
 
     if prelude is not None:
         attrs["pragma_import_c"] = prelude

tilelang/transform/__init__.py

@@ -164,3 +164,25 @@ def LegalizeSafeMemoryAccess():
         The result pass
     """
     return _ffi_api.LegalizeSafeMemoryAccess()  # type: ignore
+
+
+def MakePackedAPI():
+    """MakePackedAPI
+
+    Returns
+    -------
+    fpass : tvm.transform.Pass
+        The result pass
+    """
+    return _ffi_api.MakePackedAPI()  # type: ignore
+
+
+def AnnotateDeviceRegions():
+    """AnnotateDeviceRegions
+
+    Returns
+    -------
+    fpass : tvm.transform.Pass
+        The result pass
+    """
+    return _ffi_api.AnnotateDeviceRegions()  # type: ignore

tilelang/utils/target.py

@@ -7,6 +7,14 @@ from tvm.target import Target
 from tvm.contrib import rocm
 from tilelang.contrib import nvcc
 
+AVALIABLE_TARGETS = {
+    "auto",
+    "cuda",
+    "hip",
+    "c",  # represent c source backend
+    "llvm",
+}
+
 
 def check_cuda_availability() -> bool:
     """
@@ -64,8 +72,6 @@ def determine_target(target: Union[str, Target, Literal["auto"]]) -> Union[str,
             raise ValueError("No CUDA or HIP available on this system.")
     else:
         # Validate the target if it's not "auto"
-        assert isinstance(target, Target) or target in [
-            "cuda",
-            "hip",
-        ], f"Target {target} is not supported"
+        assert isinstance(
+            target, Target) or target in AVALIABLE_TARGETS, f"Target {target} is not supported"
         return target