[Relay] Prepare DeadCodeElimination for running post LowerTEPass/ManifestAlloc.

include/tvm/relay/transform.h

@@ -62,21 +62,31 @@ TVM_DLL Pass CreateFunctionPass(
     const runtime::TypedPackedFunc<Function(Function, IRModule, PassContext)>& pass_func,
     int opt_level, String name, tvm::Array<String> required);
 
-/*! \brief Remove expressions which does not effect the program result.
+/*! \brief Remove let-bound expressions which do not effect the program result.
  *
- * It will remove let bindings which are not referenced,
- * and inline let bindings that are only used once.
+ * This pass will remove let bindings which are not referenced. If inline_once is True,
+ * let bindings which are only referenced once will also be inlined.
  *
- * For example, this pass should turn `let a = 1 in 2` into `2`,
+ * For example, this pass should turn `let a = 1; 2` into `2`,
  * as the value of the expression does not depend on a.
  *
- * As another example, `let a = 1 in a` will be optimized into 1.
+ * As another example, `let a = 1; a` will be optimized into 1 if inline_once is True.
  *
- * \param inline_once whether or not to inline binding used one.
+ * If ignore_purity is False, possibly side-effecting expressions (such as memory allocation,
+ * random number generation, reading/writing references, or calls to primitive or external
+ * functions) are never elided or inlined. This is sound, but ignore_purity can be set to True
+ * to suppress this check.
+ *
+ * The analysis is fairly conservative, for example it assumes all local functions
+ * may be called more than once, any functions passed as arguments have side effects,
+ * and so on.
+ *
+ * \param inline_once whether or not to inline bindings used exactly once.
+ * \param ignore_purity whether to ignore whether expressions have side-effects
  *
  * \return the pass.
  */
-TVM_DLL Pass DeadCodeElimination(bool inline_once = false);
+TVM_DLL Pass DeadCodeElimination(bool inline_once = false, bool ignore_purity = false);
 
 /*!
  * \brief Convert all expressions of TensorType into GradCell,

python/tvm/relay/transform/transform.py

@@ -209,20 +209,22 @@ def CanonicalizeOps():
     return _ffi_api.CanonicalizeOps()
 
 
-def DeadCodeElimination(inline_once=False):
+def DeadCodeElimination(inline_once=False, ignore_impurity=False):
     """Remove expressions that do not have any users (dead code).
 
     Parameters
     ----------
     inline_once: Optional[Bool]
-        Whether to inline binding that occurs only once.
+        Whether to inline a binding that is referenced exactly once.
+    ignore_impurity: Optional[Bool]
+        Whether to ignore possible side-effects in let-bound expressions.
 
     Returns
     -------
     ret: tvm.transform.Pass
         The registered pass that eliminates the dead code in a Relay program.
     """
-    return _ffi_api.DeadCodeElimination(inline_once)
+    return _ffi_api.DeadCodeElimination(inline_once, ignore_impurity)
 
 
 def LazyGradientInit():

src/relay/op/memory/memory.cc

@@ -90,10 +90,15 @@ RELAY_REGISTER_OP("memory.alloc_storage")
     .set_attrs_type_key("relay.attrs.AllocStorageAttrs")
     .set_support_level(10)
     .set_attr<TOpPattern>("TOpPattern", kOpaque)
-    .set_attr<TOpIsStateful>("TOpIsStateful", false)
+    .set_attr<TOpIsStateful>("TOpIsStateful", true)
     .set_attr<TNonComputational>("TNonComputational", true)
     .set_attr<FInferCorrectLayout>("FInferCorrectLayout", ElemwiseArbitraryLayout);
 
+const Op& MemoryAllocTensorOp() {
+  static const Op& op = Op::Get("memory.alloc_tensor");
+  return op;
+}
+
 Expr AllocTensor(Expr storage, Expr offset, Expr shape, DataType dtype,
                  Array<IndexExpr> assert_shape) {
   auto attrs = make_object<AllocTensorAttrs>();
@@ -106,8 +111,7 @@ Expr AllocTensor(Expr storage, Expr offset, Expr shape, DataType dtype,
     ICHECK(constant_node);
     attrs->const_shape = GetRef<Constant>(constant_node);
   }
-  static const Op& op = Op::Get("memory.alloc_tensor");
-  return Call(op, {storage, offset, shape}, Attrs(attrs), {});
+  return Call(MemoryAllocTensorOp(), {storage, offset, shape}, Attrs(attrs), {});
 }
 
 TVM_REGISTER_GLOBAL("relay.op.memory._make.alloc_tensor").set_body_typed(AllocTensor);
@@ -196,7 +200,7 @@ RELAY_REGISTER_OP("memory.alloc_tensor")
     .set_attrs_type_key("relay.attrs.AllocTensorAttrs")
     .set_support_level(10)
     .set_attr<TOpPattern>("TOpPattern", kOpaque)
-    .set_attr<TOpIsStateful>("TOpIsStateful", false)
+    .set_attr<TOpIsStateful>("TOpIsStateful", true)
     .set_attr<TNonComputational>("TNonComputational", true)
     .set_attr<FInferCorrectLayout>("FInferCorrectLayout", ElemwiseArbitraryLayout);
 

src/relay/op/memory/memory.h

@@ -35,6 +35,8 @@ namespace tvm {
 namespace relay {
 
 Expr AllocStorage(Expr size, Expr alignment, SEScope se_scope, DataType dtype_hint);
+/*! \brief Returns the "memory.alloc_tensor" operator. */
+const Op& MemoryAllocTensorOp();
 Expr AllocTensor(Expr storage, Expr offset, tvm::relay::Expr shape, DataType dtype,
                  Array<IndexExpr> assert_shape);
 Expr ToTupleType(const Type& ty, const std::vector<Expr>& exprs);

src/relay/op/vm/vm.cc

@@ -61,7 +61,7 @@ Expr ShapeOf(Expr expr) {
   auto attrs = make_object<ShapeOfAttrs>();
   attrs->dtype = DataType::Int(64);
   static const Op& op = Op::Get("vm.shape_of");
-  return Call(op, {expr}, Attrs(attrs), {});
+  return Call(op, {std::move(expr)}, Attrs(std::move(attrs)), {});
 }
 
 TVM_REGISTER_GLOBAL("relay.op.vm.shape_of").set_body_typed(ShapeOf);
@@ -156,7 +156,9 @@ bool InvokeTVMOpRel(const Array<Type>& types, int num_inputs, const Attrs& attrs
   if (func_type->ret_type.as<TensorTypeNode>()) {
     ex_output = TupleType({func_type->ret_type});
   } else {
-    ICHECK(func_type->ret_type.as<TupleTypeNode>()) << "should be tuple type";
+    ICHECK(func_type->ret_type.as<TupleTypeNode>())
+        << "expecting function result to be tuple type. Types:" << std::endl
+        << PrettyPrint(types);
     ex_output = func_type->ret_type;
   }
   auto ex_input = TupleType(func_type->arg_types);
@@ -167,10 +169,14 @@ bool InvokeTVMOpRel(const Array<Type>& types, int num_inputs, const Attrs& attrs
 }
 
 Expr InvokeTVMOp(Expr func, Expr inputs, Expr outputs) {
-  return Call(Op::Get("vm.invoke_tvm_op"), {func, inputs, outputs}, Attrs());
+  static const Op& op = Op::Get("vm.invoke_tvm_op");
+  return Call(op, {std::move(func), std::move(inputs), std::move(outputs)}, {});
 }
 
-TVM_REGISTER_GLOBAL("relay.op.vm.invoke_tvm_op").set_body_typed(InvokeTVMOp);
+TVM_REGISTER_GLOBAL("relay.op.vm.invoke_tvm_op")
+    .set_body_typed([](Expr func, Expr inputs, Expr outputs) {
+      return InvokeTVMOp(std::move(func), std::move(inputs), std::move(outputs));
+    });
 
 RELAY_REGISTER_OP("vm.invoke_tvm_op")
     .describe(R"code(Invoke an operation compiled by TVM.)code" TVM_ADD_FILELINE)
@@ -179,9 +185,10 @@ RELAY_REGISTER_OP("vm.invoke_tvm_op")
     .add_argument("ins", "Tuple", "The input tensors.")
     .add_argument("outs", "Tuple", "The output tensors.")
     .add_type_rel("InvokeTVMOp", InvokeTVMOpRel)
+    .set_attrs_type_key("DictAttrs")
     .set_support_level(10)
     .set_attr<TOpPattern>("TOpPattern", kOpaque)
-    .set_attr<TOpIsStateful>("TOpIsStateful", false)
+    .set_attr<TOpIsStateful>("TOpIsStateful", true)
     .set_attr<TNonComputational>("TNonComputational", true)
     .set_attr<FInferCorrectLayout>("FInferCorrectLayout", ElemwiseArbitraryLayout);
 
@@ -217,7 +224,7 @@ Expr ReshapeTensor(Expr data, Expr shape, Array<PrimExpr> newshape) {
   static const Op& op = Op::Get("vm.reshape_tensor");
   auto attrs = make_object<ReshapeTensorAttrs>();
   attrs->newshape = std::move(newshape);
-  return Call(op, {data, shape}, Attrs(attrs), {});
+  return Call(op, {std::move(data), std::move(shape)}, Attrs(std::move(attrs)), {});
 }
 
 TVM_REGISTER_GLOBAL("relay.op.vm.reshape_tensor").set_body_typed(ReshapeTensor);