[relay][vm] move vm opt passes to pass manager

python/tvm/relay/backend/vm.py

@@ -20,24 +20,45 @@
 
 Implements a Python interface to compiling and executing on the Relay VM.
 """
+import numpy as np
+
 import tvm
 from tvm._ffi.function import Object
-import numpy as np
-from .. import ir_pass
+from .. import transform
 from ..backend.interpreter import Executor
-from ..expr import GlobalVar, Function, Expr
+from ..expr import GlobalVar, Expr
 from . import _vm
 
 Object = Object
 
-def optimize(expr, mod=None):
-    # TODO: We need to move this optimization code into the optimizer/pass manager
-    ck_expr = ir_pass.infer_type(expr, mod=mod)
-    simplified_expr = ir_pass.simplify_inference(ck_expr)
-    simplified_expr = ir_pass.infer_type(simplified_expr, mod=mod)
-    fused_expr = ir_pass.fuse_ops(simplified_expr, mod=mod)
-    ck_fused = ir_pass.infer_type(fused_expr, mod=mod)
-    return ck_fused
+def optimize(mod):
+    """Perform several optimizations on a module before executing it in the
+    Relay virtual machine.
+
+    Parameters
+    ----------
+    mod : tvm.relay.Module
+        The module to optimize.
+
+    Returns
+    -------
+    ret : tvm.relay.Module
+        The optimized module.
+    """
+    main_func = mod[mod.entry_func]
+
+    opt_passes = []
+    if not main_func.params and isinstance(main_func.body, GlobalVar):
+        opt_passes.append(transform.EtaExpand())
+
+    opt_passes = opt_passes + [
+        transform.SimplifyInference(),
+        transform.FuseOps(),
+        transform.InferType()
+    ]
+
+    seq = transform.Sequential(opt_passes)
+    return seq(mod)
 
 def _convert(arg, cargs):
     if isinstance(arg, np.ndarray):
@@ -76,15 +97,8 @@ def _eval_vm(mod, ctx, *args):
     args: List[tvm.NDArray, np.ndarray]
         The arguments to evaluate.
     """
-    main_func = mod[mod.entry_func]
-
-    if not main_func.params and isinstance(main_func.body, GlobalVar):
-        main_func = ir_pass.eta_expand(main_func.body, mod)
-
-    assert isinstance(main_func, Function)
-    main_func = optimize(mod[mod.entry_func], mod)
-    mod[mod.entry_func] = main_func
 
+    mod = optimize(mod)
     args = list(args)
     assert isinstance(args, list)
     cargs = convert(args)

src/relay/backend/vm/compiler.cc

@@ -27,7 +27,7 @@
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/interpreter.h>
 #include <tvm/logging.h>
-#include <tvm/relay/pass.h>
+#include <tvm/relay/transform.h>
 #include <tvm/runtime/vm.h>
 #include <iostream>
 #include <unordered_map>
@@ -38,15 +38,22 @@
 
 namespace tvm {
 namespace relay {
+
+namespace transform {
+
+Pass LambdaLift();
+Pass InlinePrimitives();
+
+}  // namespace transform
+
 namespace vm {
 
 using namespace tvm::runtime;
 using namespace tvm::runtime::vm;
+using namespace relay::transform;
 
 // (@jroesch): VM passes, eventually declare as passes.
 bool IsClosure(const Function& func);
-Module LambdaLift(const Module& module);
-Module InlinePrimitives(const Module& module);
 
 template <typename T, typename U>
 using NodeMap = std::unordered_map<T, U, NodeHash, NodeEqual>;
@@ -560,10 +567,13 @@ VMFunction CompileFunc(VMCompilerContext* context, const GlobalVar& var, const F
 }
 
 Module OptimizeModule(const Module& mod) {
-  ToANormalForm(mod->entry_func, mod);
-  InlinePrimitives(mod);
-  LambdaLift(mod);
-  return InlinePrimitives(mod);
+  transform::Sequential seq({transform::ToANormalForm(),
+                             transform::InlinePrimitives(),
+                             transform::LambdaLift(),
+                             transform::InlinePrimitives()});
+  auto pass_ctx = transform::PassContext::Create();
+  tvm::With<relay::transform::PassContext> ctx(pass_ctx);
+  return seq(mod);
 }
 
 void PopulateGlobalMap(GlobalMap* global_map, const Module& mod) {

src/relay/backend/vm/inline_primitives.cc

@@ -26,7 +26,7 @@
 #include <tvm/relay/expr.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/logging.h>
-#include <tvm/relay/pass.h>
+#include <tvm/relay/transform.h>
 #include <tvm/runtime/vm.h>
 #include <iostream>
 #include <vector>
@@ -37,6 +37,21 @@ namespace tvm {
 namespace relay {
 namespace vm {
 
+// TODO(@jroesch): write verifier
+
+/* This pass will eliminate primitives which have been lifted by the ANF
+ * transform inlining them directly into call sites.
+ *
+ * This makes VM related code generation easier as the call target is always
+ * a primitive function.
+ *
+ * let prim = fn(...) { ... };
+ * prim(...)
+ *
+ * will become:
+ *
+ * (fn(...) { ... })(...)
+ */
 struct PrimitiveInliner : ExprMutator {
   Module module_;
   std::unordered_map<Var, Expr, NodeHash, NodeEqual> var_map;
@@ -92,55 +107,46 @@ struct PrimitiveInliner : ExprMutator {
     }
   }
 
-  Function Inline(const Function& func) {
-    DLOG(INFO) << "Before inlining primitives: " << std::endl
-                    << "func= " << AsText(func, false) << std::endl;
-
-    auto inlined = FunctionNode::make(func->params, VisitExpr(func->body), func->ret_type,
-                                      func->type_params, func->attrs);
-
-    inlined = Downcast<Function>(DeadCodeElimination(inlined));
-
-    DLOG(INFO) << "After inlining primitives" << std::endl
-                    << "after_func= " << AsText(inlined, false) << std::endl;
-    return inlined;
+  Module Inline() {
+    auto gvar_funcs = module_->functions;
+    for (auto pair : gvar_funcs) {
+      auto global = pair.first;
+      auto func = pair.second;
+      DLOG(INFO) << "Before inlining primitives: " << global
+                 << std::endl << AsText(func, false);
+
+      func = FunctionNode::make(func->params,
+                                VisitExpr(func->body),
+                                func->ret_type,
+                                func->type_params,
+                                func->attrs);
+      module_->Add(global, func, true);
+
+      DLOG(INFO) << "After inlining primitives: " << global
+                 << std::endl << AsText(func, false);
+    }
+    return module_;
   }
 };
 
-// TODO(@jroesch): write verifier
-
-/* This pass will eliminate primitives which have been lifted by the ANF
- * transform inlining them directly into call sites.
- *
- * This makes VM related code generation easier as the call target is always
- * a primitive function.
- *
- * let prim = fn(...) { ... };
- * prim(...)
- *
- * will become:
- *
- * (fn(...) { ... })(...)
- */
-Module InlinePrimitives(const Module& module) {
-  PrimitiveInliner inliner(module);
+}  // namespace vm
 
-  tvm::Map<GlobalVar, Function> updates;
+namespace transform {
 
-  // There is an ordering bug here.
-  for (auto pair : module->functions) {
-    auto global = pair.first;
-    auto func = pair.second;
-    updates.Set(global, inliner.Inline(func));
-  }
+Pass InlinePrimitives() {
+  runtime::TypedPackedFunc<Module(Module, PassContext)> pass_func =
+    [=](Module m, PassContext pc) {
+      return relay::vm::PrimitiveInliner(m).Inline();
+  };
+  auto inline_pass = CreateModulePass(pass_func, 1, "Inline", {});
+  // Eliminate dead code for each function after inlining.
+  return Sequential({inline_pass, DeadCodeElimination()}, "InlinePrimitives");
+}
 
-  for (auto pair : updates) {
-    module->Add(pair.first, pair.second, true);
-  }
+TVM_REGISTER_API("relay._transform.InlinePrimitives")
+.set_body_typed(InlinePrimitives);
 
-  return module;
-}
+}  // namespace transform
 
-}  // namespace vm
 }  // namespace relay
 }  // namespace tvm

src/relay/backend/vm/lambda_lift.cc

@@ -27,6 +27,7 @@
 #include <tvm/relay/expr_functor.h>
 #include <tvm/logging.h>
 #include <tvm/relay/pass.h>
+#include <tvm/relay/transform.h>
 #include <tvm/runtime/vm.h>
 #include <iostream>
 #include <vector>
@@ -54,9 +55,14 @@ Function MarkClosure(const Function& func) {
   return FunctionSetAttr(func, kIsClosure, tvm::Integer(1));
 }
 
+/* The goal of this class is to lift out any nested functions into top-level
+ * functions.
+ *
+ * We will lift a function out into a global which takes the set of the free
+ * vars and then return the new created function.
+ */
 struct LambdaLifter : ExprMutator {
   Module module_;
-  std::vector<std::pair<GlobalVar, Function>> lifted_;
   explicit LambdaLifter(const Module& module) : module_(module) {}
 
   Expr VisitExpr_(const FunctionNode* func_node) final {
@@ -71,16 +77,15 @@ struct LambdaLifter : ExprMutator {
     auto free_type_vars = FreeTypeVars(func, module_);
     auto body = Downcast<Function>(ExprMutator::VisitExpr_(func_node));
 
-    // When performing this optimization there are two
-    // cases.
+    // When performing this optimization there are two cases.
     //
     // The first case in which we have no free variables
     // we can just lift the function into the global
     // environment without needing to allocate a closure.
     //
     //
     // The second case requires that we generate a special
-    // function with makes a distinction between allocating
+    // function which makes a distinction between allocating
     // a closure, and then the code for the closure.
     //
     // We represent a closure allocation by lifting the
@@ -92,7 +97,7 @@ struct LambdaLifter : ExprMutator {
     // function marked as a closure is used to emit allocation
     // code for the closure's environment.
     //
-    // The "inner" function is should be used to generate the
+    // The "inner" function should be used to generate the
     // code for the closure.
     Function lifted_func;
     if (free_vars.size() == 0) {
@@ -107,16 +112,16 @@ struct LambdaLifter : ExprMutator {
     CHECK(lifted_func.defined());
 
     auto name = GenerateName(lifted_func);
-    auto global = this->module_->GetGlobalVar(name);
+    auto global = module_->GetGlobalVar(name);
 
-    lifted_.push_back({global, lifted_func});
+    // Add the lifted function to the module.
+    module_->Add(global, lifted_func);
 
     if (free_vars.size() == 0) {
       return std::move(global);
     } else {
-      // If we need to allocate a closure
-      // we pass the variables in its environment
-      // here.
+      // If we need to allocate a closure,
+      // we pass the variables in its environment here.
       Array<Expr> fvs;
       for (auto fv : free_vars) {
         fvs.push_back(fv);
@@ -125,42 +130,39 @@ struct LambdaLifter : ExprMutator {
     }
   }
 
-  Function Lift(const Function& func) {
-    DLOG(INFO) << "Lifting: " << AsText(func, false) << std::endl;
-    return FunctionNode::make(func->params, VisitExpr(func->body), func->ret_type,
-                              func->type_params, func->attrs);
+  Module Lift() {
+    // There is an ordering bug here.
+    auto glob_funcs = module_->functions;
+    for (auto pair : glob_funcs) {
+      auto func = pair.second;
+      DLOG(INFO) << "Lifting " << AsText(func, false);
+      func = FunctionNode::make(func->params,
+                                VisitExpr(func->body),
+                                func->ret_type,
+                                func->type_params,
+                                func->attrs);
+      module_->Add(pair.first, func, true);
+    }
+    return module_;
   }
 };
 
-/* The goal of this pass is to lift out any nested functions into top-level
- * functions.
- *
- * We will lift the functions out into globals which take the set of the free vars
- * and then return a function whcih has b
- */
-Module LambdaLift(const Module& module) {
-  LambdaLifter lifter(module);
-
-  tvm::Map<GlobalVar, Function> updates;
+}  // namespace vm
 
-  // There is an ordering bug here.
-  for (auto pair : module->functions) {
-    auto global = pair.first;
-    auto func = pair.second;
-    updates.Set(global, lifter.Lift(func));
-  }
+namespace transform {
 
-  for (auto i = lifter.lifted_.begin(); i != lifter.lifted_.end(); i++) {
-    module->Add(i->first, i->second);
-  }
+Pass LambdaLift() {
+  runtime::TypedPackedFunc<Module(Module, PassContext)> pass_func =
+    [=](Module m, PassContext pc) {
+    return relay::vm::LambdaLifter(m).Lift();
+  };
+  return CreateModulePass(pass_func, 1, "LambdaLift", {});
+}
 
-  for (auto pair : updates) {
-    module->Add(pair.first, pair.second, true);
-  }
+TVM_REGISTER_API("relay._transform.LambdaLift")
+.set_body_typed(LambdaLift);
 
-  return module;
-}
+}  // namespace transform
 
-}  // namespace vm
 }  // namespace relay
 }  // namespace tvm