Implement explicit IR representation of memory alloction (#3560)

CMakeLists.txt

@@ -272,6 +272,7 @@ add_library(tvm_runtime SHARED ${RUNTIME_SRCS})
 if(USE_RELAY_DEBUG)
   message(STATUS "Building Relay in debug mode...")
   set_target_properties(tvm PROPERTIES COMPILE_DEFINITIONS "USE_RELAY_DEBUG")
+  set_target_properties(tvm PROPERTIES COMPILE_DEFINITIONS "DMLC_LOG_DEBUG")
 else()
   set_target_properties(tvm PROPERTIES COMPILE_DEFINITIONS "NDEBUG")
 endif(USE_RELAY_DEBUG)

include/tvm/relay/attrs/memory.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 tvm/relay/attrs/memory.h
+ * \brief Attributes for memory operators.
+ */
+#ifndef TVM_RELAY_ATTRS_MEMORY_H_
+#define TVM_RELAY_ATTRS_MEMORY_H_
+
+#include <tvm/attrs.h>
+#include <tvm/relay/expr.h>
+#include <string>
+
+namespace tvm {
+namespace relay {
+
+/*!
+ * \brief Options for allocating tensors.
+ */
+struct AllocTensorAttrs : public tvm::AttrsNode<AllocTensorAttrs> {
+  Constant const_shape;
+  Array<IndexExpr> assert_shape;
+  DataType dtype;
+
+  TVM_DECLARE_ATTRS(AllocTensorAttrs, "relay.attrs.AllocTensorAttrs") {
+    TVM_ATTR_FIELD(dtype)
+      .describe(
+         "The dtype of the tensor to allocate.")
+      .set_default(Float(32, 1));
+    TVM_ATTR_FIELD(const_shape)
+      .describe(
+         "The shape of constant used to aid in type inference.");
+    TVM_ATTR_FIELD(assert_shape)
+      .describe(
+         "The shape to cast the return type of the allocation to, "\
+         "used to specify the shape obtained via further analysis.");
+  }
+};
+
+/*!
+ * \brief Options for the shape function operator.
+ */
+struct ShapeFuncAttrs : public tvm::AttrsNode<ShapeFuncAttrs> {
+  Array<Integer> is_input;
+
+  TVM_DECLARE_ATTRS(ShapeFuncAttrs, "relay.attrs.ShapeFuncAttrs") {
+    TVM_ATTR_FIELD(is_input)
+      .describe(
+         "A bool indicating whether the shape function should"\
+         "expect shape or input in each position.");
+  }
+};
+
+}  // namespace relay
+}  // namespace tvm
+#endif  // TVM_RELAY_ATTRS_MEMORY_H_

include/tvm/relay/base.h

@@ -47,6 +47,12 @@ namespace relay {
   (*fdebug)("RELAY_DEBUG", __FILE__, __LINE__, __VA_ARGS__); \
 }
 
+#define RELAY_DEBUG_INTERP(...) \
+{ auto fdebug = runtime::Registry::Get("relay.debug_interp"); \
+  CHECK(fdebug) << "Could not find Relay Python debugger function."; \
+  (*fdebug)("RELAY_DEBUG", __FILE__, __LINE__, __VA_ARGS__); \
+}
+
 /*!
  * \brief We always used NodeRef for referencing nodes.
  *

include/tvm/relay/module.h

@@ -76,7 +76,8 @@ class ModuleNode : public RelayNode {
   }
 
   TVM_DLL static Module make(tvm::Map<GlobalVar, Function> global_funcs,
-                             tvm::Map<GlobalTypeVar, TypeData> global_type_defs);
+                             tvm::Map<GlobalTypeVar, TypeData> global_type_defs,
+                             std::unordered_set<std::string> imports = {});
 
   /*!
    * \brief Add a function to the global environment.
@@ -235,6 +236,11 @@ class ModuleNode : public RelayNode {
    */
   TVM_DLL void ImportFromStd(const std::string& path);
 
+  /*!
+   * \brief The set of imported files.
+   */
+  TVM_DLL std::unordered_set<std::string> Imports() const;
+
   /*! \brief Construct a module from a standalone expression.
    *
    * Allows one to optionally pass a global function map and

include/tvm/runtime/object.h

@@ -283,6 +283,8 @@ class Object {
    * \note The deleter will be called when ref_counter_ becomes zero.
    */
   inline void DecRef();
+
+ private:
   /*!
    * \return The usage count of the cell.
    * \note We use stl style naming to be consistent with known API in shared_ptr.
@@ -675,6 +677,16 @@ struct ObjectEqual {
   operator bool() const { return data_ != nullptr; }                    \
   using ContainerType = ObjectName;
 
+#define TVM_DEFINE_OBJECT_REF_METHODS_MUT(TypeName, ParentType, ObjectName) \
+  TypeName() {}                                                             \
+  explicit TypeName(                                                        \
+      ::tvm::runtime::ObjectPtr<::tvm::runtime::Object> n)                  \
+      : ParentType(n) {}                                                    \
+  ObjectName* operator->() {                                    \
+    return static_cast<ObjectName*>(data_.get());                     \
+  }                                                                         \
+  operator bool() const { return data_ != nullptr; }                        \
+  using ContainerType = ObjectName;
 
 // Implementations details below
 // Object reference counting.

include/tvm/runtime/vm.h

@@ -138,6 +138,7 @@ enum class Opcode {
   GetTag = 13U,
   LoadConsti = 14U,
   Fatal = 15U,
+  AllocStorage = 16U,
 };
 
 /*! \brief A single virtual machine instruction.
@@ -158,6 +159,8 @@ struct Instruction {
 
   union {
     struct /* AllocTensor Operands */ {
+      /*! \brief The storage to allocate from. */
+      RegName storage;
       /*! \brief The number of dimensions. */
       uint32_t ndim;
       /*! \brief The shape of tensor. */
@@ -166,6 +169,8 @@ struct Instruction {
       DLDataType dtype;
     } alloc_tensor;
     struct /* AllocTensorReg Operands */ {
+      /*! \brief The storage to allocate from. */
+      RegName storage;
       /*! \brief The register to read the shape out of. */
       RegName shape_register;
       /*! \brief The datatype of tensor to be allocated. */
@@ -253,6 +258,14 @@ struct Instruction {
       /*! \brief The free variables as an array. */
       RegName* free_vars;
     };
+    struct /* AllocStorage Operands */ {
+      /*! \brief The size of the allocation. */
+      RegName allocation_size;
+      /*! \brief The alignment of the allocation. */
+      RegName alignment;
+      /*! \brief The hint of the dtype. */
+      DLDataType dtype_hint;
+    } alloc_storage;
   };
 
   /*! \brief Construct a return instruction.
@@ -274,19 +287,23 @@ struct Instruction {
   static Instruction InvokePacked(Index packed_index, Index arity, Index output_size,
                                   const std::vector<RegName>& args);
   /*! \brief Construct an allocate tensor instruction with constant shape.
+   *  \param storage The storage to allocate out of.
    *  \param shape The shape of the tensor.
    *  \param dtype The dtype of the tensor.
    *  \param dst The destination register.
    *  \return The allocate tensor instruction.
    */
-  static Instruction AllocTensor(std::vector<int64_t> shape, DLDataType dtype, RegName dst);
+  static Instruction AllocTensor(RegName storage,
+                                 const std::vector<int64_t>& shape, DLDataType dtype, RegName dst);
   /*! \brief Construct an allocate tensor instruction with register.
+   *  \param storage The storage to allocate out of.
    *  \param shape_register The register containing the shape.
    *  \param dtype The dtype of the tensor.
    *  \param dst The destination register.
    *  \return The allocate tensor instruction.
    */
-  static Instruction AllocTensorReg(RegName shape_register, DLDataType dtype, RegName dst);
+  static Instruction AllocTensorReg(RegName storage,
+                                    RegName shape_register, DLDataType dtype, RegName dst);
   /*! \brief Construct an allocate datatype instruction.
    *  \param tag The datatype tag.
    *  \param num_fields The number of fields for the datatype.
@@ -295,7 +312,7 @@ struct Instruction {
    *  \return The allocate instruction tensor.
    */
   static Instruction AllocADT(Index tag, Index num_fields, const std::vector<RegName>& fields,
-                                   RegName dst);
+                              RegName dst);
   /*! \brief Construct an allocate closure instruction.
    *  \param func_index The index of the function table.
    *  \param num_freevar The number of free variables.
@@ -364,6 +381,16 @@ struct Instruction {
    */
   static Instruction Move(RegName src, RegName dst);
 
+   /*! \brief Allocate a storage block.
+   *  \param size The size of the allocation.
+   *  \param alignment The allocation's alignment.
+   *  \param dtype_hint The data type hint for the allocator.
+   *  \param dst The destination to place the storage.
+   *  \return The alloc storage instruction.
+   */
+  static Instruction AllocStorage(RegName size, RegName alignment,
+                                  DLDataType dtype_hint, RegName dst);
+
   Instruction();
   Instruction(const Instruction& instr);
   Instruction& operator=(const Instruction& instr);

python/tvm/relay/__init__.py

@@ -59,6 +59,8 @@ from . import quantize
 from . import qnn
 
 from .scope_builder import ScopeBuilder
+# Load Memory pass
+from . import memory_alloc
 
 # Required to traverse large programs
 setrecursionlimit(10000)

python/tvm/relay/backend/compile_engine.py

@@ -99,6 +99,10 @@ class CompileEngine(NodeBase):
             msg += "--------------------------\n"
             raise RuntimeError(msg)
 
+    def lower_shape_func(self, source_func, target=None):
+        key = _get_cache_key(source_func, target)
+        return _backend._CompileEngineLowerShapeFunc(self, key)
+
     def jit(self, source_func, target=None):
         """JIT a source_func to a tvm.Function.
 

python/tvm/relay/debug.py

@@ -25,9 +25,14 @@ def _debugger_init(expr, stack):
     import pdb
     pdb.set_trace()
 
-# pylint: disable=unused-argument
 @register_func("relay.debug")
 def _debug(*args):
+    import pdb
+    pdb.set_trace()
+
+# pylint: disable=unused-argument
+@register_func("relay.debug_interp")
+def _debug_interp(*args):
     _, _, _, ist = args
     print("Relay Debugger")
     print("  You can manipulate the expression under evaluation with the name `expr`.")

python/tvm/relay/expr.py

@@ -317,6 +317,9 @@ class Function(Expr):
 
         return _expr.FunctionSetParams(self, params)
 
+    def set_attribute(self, name, ref):
+        return _expr.FunctionSetAttr(self, name, ref)
+
 
 @register_relay_node
 class Call(Expr):

python/tvm/relay/op/__init__.py

@@ -28,6 +28,7 @@ from .transform import *
 from .algorithm import *
 from . import nn
 from . import annotation
+from . import memory
 from . import image
 from . import vision
 from . import contrib

python/tvm/relay/op/memory/__init__.py

+# 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.
+# pylint: disable=wildcard-import
+"""Operators for manipulating low level memory."""
+from __future__ import absolute_import as _abs
+from .memory import *

python/tvm/relay/op/memory/_make.py

+# 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.
+"""Constructor APIs"""
+from ...._ffi.function import _init_api
+
+_init_api("relay.op.memory._make", __name__)

python/tvm/relay/op/memory/memory.py

+# 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.
+"""Operators for manipulating low-level memory."""
+from __future__ import absolute_import as _abs
+from . import _make
+
+def invoke_tvm_op(func, inputs, outputs):
+    """Call a primitive function with the TVM operator calling convention.
+
+    Parameters
+    ----------
+    inputs : tvm.relay.Expr
+        A tuple of the inputs to pass to the TVM function.
+
+    outputs : tvm.relay.Expr
+        A tuple of the outputs to pass to the TVM function.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The invoke_tvm_op call node.
+    """
+    return _make.invoke_tvm_op(func, inputs, outputs)
+
+def alloc_tensor(storage, shape, dtype='float32', assert_shape=None):
+    """Allocate a tensor with the provided shape, and dtype.
+
+    Parameters
+    ----------
+    storage : tvm.relay.Expr
+        The storage to allocate from.
+
+    shape : tvm.relay.Expr
+        The shape of the tensor to allocate.
+
+    dtype: str
+        The dtype of the tensor.
+
+    assert_shape: Control the static shape when computed by dynamic shape expression.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The alloc_tensor expression.
+    """
+    return _make.alloc_tensor(storage, shape, dtype, assert_shape)
+
+def alloc_storage(size, alignment, dtype_hint='float32'):
+    """Allocate a piece of tensor storage.
+
+    Parameters
+    ----------
+    size : tvm.relay.Expr
+        The size of the allocation.
+    alignment : tvm.relay.Expr
+        The alignment of the allocation.
+    dtype : str
+        The dtype_hint of the allocation.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The alloc_storage expression.
+    """
+    return _make.alloc_storage(size, alignment, dtype_hint)
+
+def shape_func(func, inputs, outputs, dependent=False):
+    """Invoke the shape function of the passed function.
+
+    Parameters
+    ----------
+    func : tvm.relay.Expr
+        The primitive function from which to compute the shape function.
+    inputs : tvm.relay.Tuple
+        The tupled inputs.
+    outputs : tvm.relay.Tuple
+        The tupled outputs.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The shape function expression.
+    """
+    return _make.shape_func(func, inputs, outputs, dependent)

python/tvm/relay/std/core.rly

+/*
+ * 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.
+ */
+v0.0.4
+
+extern type Storage

python/tvm/relay/ty.py

@@ -52,6 +52,9 @@ class Type(RelayNode):
         """
         return TypeCall(self, args)
 
+    def is_dynamic(self):
+        return _make.IsDynamic(self)
+
 @register_relay_node
 class TensorType(Type):
     """A concrete TensorType in Relay.
@@ -317,7 +320,6 @@ class RefType(Type):
     def __init__(self, value):
         self.__init_handle_by_constructor__(_make.RefType, value)
 
-
 def scalar_type(dtype):
     """Creates a scalar type.
 

src/relay/backend/compile_engine.cc

@@ -72,6 +72,10 @@ bool IsDynamic(const Type& ty) {
   return v.is_dyn;
 }
 
+// TODO(@jroesch): MOVE ME
+TVM_REGISTER_API("relay._make.IsDynamic")
+.set_body_typed(IsDynamic);
+
 Array<IndexExpr> GetShape(const Array<IndexExpr>& shape) {
   // for now, we always use int32 shape when possible
   // even if the result of shape inference becomes int64.
@@ -775,6 +779,12 @@ TVM_REGISTER_GLOBAL("relay.backend._CompileEngineLower")
       return self->Lower(key);
     });
 
+TVM_REGISTER_GLOBAL("relay.backend._CompileEngineLowerShapeFunc")
+.set_body_typed<CachedFunc(CompileEngine, CCacheKey)>(
+    [](CompileEngine self, CCacheKey key) {
+      return self->LowerShapeFunc(key);
+    });
+
 TVM_REGISTER_GLOBAL("relay.backend._CompileEngineJIT")
 .set_body_typed<PackedFunc(CompileEngine, CCacheKey)>(
     [](CompileEngine self, CCacheKey key) {

src/relay/backend/interpreter.cc

@@ -458,7 +458,7 @@ class Interpreter :
       if (dattrs->debug_func.defined()) {
         dattrs->debug_func(interp_state);
       } else {
-        RELAY_DEBUG(interp_state);
+        RELAY_DEBUG_INTERP(interp_state);
       }
 
       return args[0];
@@ -479,7 +479,8 @@ class Interpreter :
     if (const auto* tuple_type = func->body->checked_type().as<TupleTypeNode>()) {
       arg_len += tuple_type->fields.size();
     } else {
-      CHECK(func->body->checked_type().as<TensorTypeNode>());
+      CHECK(func->body->checked_type().as<TensorTypeNode>())
+        << func->body->checked_type();
       arg_len += 1;
     }
     std::vector<TVMValue> values(arg_len);

src/relay/backend/utils.h

@@ -48,6 +48,19 @@ namespace backend {
 inline const PackedFunc* GetPackedFunc(const std::string& func_name) {
   return tvm::runtime::Registry::Get(func_name);
 }
+
+/*!
+ * \brief Get a typed packed function.
+ *
+ * \param func_name
+ * \return const PackedFunc*
+ */
+template <typename R, typename... Args>
+inline const runtime::TypedPackedFunc<R(Args...)> GetTypedPackedFunc(const std::string& func_name) {
+  auto *pf = GetPackedFunc(func_name);
+  CHECK(pf != nullptr) << "can not find packed function";
+  return runtime::TypedPackedFunc<R(Args...)>(*pf);
+}
 /*!
  * \brief Convert type to string
  *

src/relay/ir/expr.cc

@@ -355,5 +355,11 @@ TVM_REGISTER_API("relay._expr.TempExprRealize")
   return temp->Realize();
 });
 
+TVM_REGISTER_API("relay._expr.FunctionSetAttr")
+.set_body_typed<Function(Function, std::string, NodeRef)>(
+  [](Function func, std::string name, NodeRef ref) {
+    return FunctionSetAttr(func, name, ref);
+});
+
 }  // namespace relay
 }  // namespace tvm

src/relay/ir/module.cc

@@ -35,13 +35,16 @@ using tvm::IRPrinter;
 using namespace runtime;
 
 Module ModuleNode::make(tvm::Map<GlobalVar, Function> global_funcs,
-                        tvm::Map<GlobalTypeVar, TypeData> global_type_defs) {
+                        tvm::Map<GlobalTypeVar, TypeData> global_type_defs,
+                        std::unordered_set<std::string> imports
+                        ) {
   auto n = make_node<ModuleNode>();
   n->functions = std::move(global_funcs);
   n->type_definitions = std::move(global_type_defs);
   n->global_type_var_map_ = {};
   n->global_var_map_ = {};
   n->constructor_tag_map_ = {};
+  n->import_set_ = imports;
 
   for (const auto& kv : n->functions) {
     // set global var map
@@ -283,9 +286,9 @@ Module ModuleNode::FromExpr(
 }
 
 void ModuleNode::Import(const std::string& path) {
-  LOG(INFO) << "Importing: " << path;
   if (this->import_set_.count(path) == 0) {
     this->import_set_.insert(path);
+    DLOG(INFO) << "Importing: " << path;
     std::fstream src_file(path, std::fstream::in);
     std::string file_contents {
       std::istreambuf_iterator<char>(src_file),
@@ -302,6 +305,10 @@ void ModuleNode::ImportFromStd(const std::string& path) {
   return this->Import(std_path + "/" + path);
 }
 
+std::unordered_set<std::string> ModuleNode::Imports() const {
+  return this->import_set_;
+}
+
 Module FromText(const std::string& source, const std::string& source_name) {
   auto* f = tvm::runtime::Registry::Get("relay.fromtext");
   CHECK(f != nullptr) << "The Relay std_path is not set, please register tvm.relay.std_path.";
@@ -312,7 +319,10 @@ Module FromText(const std::string& source, const std::string& source_name) {
 TVM_REGISTER_NODE_TYPE(ModuleNode);
 
 TVM_REGISTER_API("relay._make.Module")
-.set_body_typed(ModuleNode::make);
+.set_body_typed<Module(tvm::Map<GlobalVar, Function>, tvm::Map<GlobalTypeVar, TypeData>)>(
+[](tvm::Map<GlobalVar, Function> funcs, tvm::Map<GlobalTypeVar, TypeData> types) {
+  return ModuleNode::make(funcs, types, {});
+});
 
 TVM_REGISTER_API("relay._module.Module_Add")
 .set_body([](TVMArgs args, TVMRetValue* ret) {

src/relay/op/annotation/annotation.cc

@@ -24,14 +24,15 @@
  * \brief Registration of annotation operators.
  */
 
+#include <tvm/expr.h>
 #include <tvm/relay/attrs/annotation.h>
 #include <tvm/relay/expr.h>
 #include <tvm/relay/op.h>
 #include <tvm/relay/op_attr_types.h>
 #include <topi/elemwise.h>
 
-#include "../type_relations.h"
 #include "../../pass/alter_op_layout.h"
+#include "../type_relations.h"
 
 namespace tvm {
 namespace relay {

src/relay/op/device_copy.cc

@@ -27,6 +27,7 @@
  * used as "barrier" to avoid fusing operators belonging to differen devices.
  */
 
+#include <tvm/expr.h>
 #include <tvm/relay/attrs/device_copy.h>
 #include <tvm/relay/expr.h>
 #include <tvm/relay/op.h>

src/relay/op/op_common.h

@@ -6,9 +6,9 @@
  * 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
@@ -30,6 +30,8 @@
 #include <tvm/relay/op.h>
 #include <tvm/relay/op_attr_types.h>
 #include <vector>
+#include <string>
+#include <unordered_map>
 #include "type_relations.h"
 #include "../pass/alter_op_layout.h"
 
@@ -105,6 +107,50 @@ namespace relay {
     .set_attr<FInferCorrectLayout>("FInferCorrectLayout",         \
                                    BinaryBroadcastLayout)
 
+
+/*! \brief A helper class for matching and rewriting operators. */
+template<typename R>
+class OpMatch {
+ public:
+  using MatchFunc =
+      std::function<R(const Array<Expr>& args, const Attrs& attrs, const Array<Type>& type_args)>;
+
+  /*! \brief Match an operator with the given name.
+   *  \param op_name The name of the operator to match.
+   *  \param func The function to execute when it matches.
+   *  \return A self-reference for builder style API.
+   */
+  inline OpMatch& Match(const std::string& op_name, MatchFunc func) {
+    auto op = Op::Get(op_name);
+    match_map_.insert({op, func});
+    return *this;
+  }
+
+  /*! \brief Rewrite a call operation based on the operator and the registered
+   *  match functions.
+   * \param call The call to rewrite.
+   * \return The result of rewriting.
+   */
+  inline R operator()(const Call& call) {
+    auto it = match_map_.find(Downcast<Op>(call->op));
+    if (it != match_map_.end()) {
+      return it->second(call->args, call->attrs, call->type_args);
+    } else {
+      if (default_ != nullptr) {
+        return default_(call->args, call->attrs, call->type_args);
+      } else {
+        LOG(FATAL) << "unexpected operation " << call->op;
+      }
+    }
+  }
+
+ private:
+  /*! \brief The match function map. */
+  std::unordered_map<Op, MatchFunc, NodeHash, NodeEqual> match_map_;
+  /*! \brief An optional default case. */
+  MatchFunc default_;
+};
+
 }  // namespace relay
 }  // namespace tvm
 

src/relay/op/tensor/unary.cc

@@ -286,8 +286,8 @@ bool ShapeOfRel(const Array<Type>& types,
   CHECK(tt != nullptr);
   const auto* param = attrs.as<ShapeOfAttrs>();
   CHECK(param != nullptr);
-  auto vector_out = tvm::Integer(tt->shape.size());
-  reporter->Assign(types[1], TensorTypeNode::make({ vector_out }, param->dtype));
+  auto rank_shape = RankShape(tt->shape);
+  reporter->Assign(types[1], TensorTypeNode::make(rank_shape, param->dtype));
   return true;
 }
 

src/relay/op/type_relations.cc

@@ -144,5 +144,13 @@ bool BroadcastCompRel(const Array<Type>& types,
   return false;
 }
 
+Array<IndexExpr> RankShape(const Array<IndexExpr>& shape) {
+  if (shape.size() == 0) {
+    return {};
+  } else {
+    return { tvm::Integer(shape.size()) };
+  }
+}
+
 }  // namespace relay
 }  // namespace tvm

src/relay/op/type_relations.h

@@ -6,9 +6,9 @@
  * 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
@@ -80,6 +80,8 @@ bool BroadcastCompRel(const Array<Type>& types,
                       const Attrs& attrs,
                       const TypeReporter& reporter);
 
+Array<IndexExpr> RankShape(const Array<IndexExpr>& shape);
+
 }  // namespace relay
 }  // namespace tvm
 

src/relay/pass/device_annotation.cc

@@ -28,6 +28,7 @@
  *  3. Collect the device allocation of each expression.
  */
 
+#include <tvm/expr.h>
 #include <tvm/relay/attrs/device_copy.h>
 #include <tvm/relay/attrs/annotation.h>
 #include <tvm/relay/expr.h>

src/relay/pass/fold_constant.cc

@@ -27,6 +27,7 @@
 #include <tvm/relay/interpreter.h>
 #include <tvm/relay/attrs/transform.h>
 #include <tvm/relay/transform.h>
+#include "./pattern_util.h"
 
 namespace tvm {
 namespace relay {
@@ -73,13 +74,12 @@ bool ConstantCheck(const Expr& e) {
 TVM_REGISTER_API("relay._analysis.check_constant")
 .set_body_typed(ConstantCheck);
 
-
 // TODO(tvm-team) consider combine dead-code with constant folder.
 // or make a more powerful partial evaluator.
 class ConstantFolder : public ExprMutator {
  public:
-  explicit ConstantFolder(FInterpreter executor)
-      : executor_(executor) {
+  explicit ConstantFolder(FInterpreter executor, Module module)
+      : executor_(executor), module_(module) {
   }
 
   Expr VisitExpr_(const LetNode* op) final {
@@ -123,6 +123,15 @@ class ConstantFolder : public ExprMutator {
     if (call->op.same_as(Op::Get("shape_of"))) {
       return EvaluateShapeOf(res, origin_args, call->attrs);
     }
+
+    // We should think about potentially constant evaluation over these ops too.
+    if (call->op.same_as(Op::Get("memory.invoke_tvm_op")) ||
+        call->op.same_as(Op::Get("memory.shape_func")) ||
+        call->op.same_as(Op::Get("memory.alloc_tensor")) ||
+        call->op.same_as(Op::Get("memory.alloc_storage"))) {
+      return GetRef<Call>(call);
+    }
+
     bool all_const_args = true;
     for (Expr arg : call->args) {
       if (!checker_.Check(arg)) {
@@ -151,10 +160,16 @@ class ConstantFolder : public ExprMutator {
   FInterpreter executor_;
   // Internal constant checker
   ConstantChecker checker_;
+  // Module
+  Module module_;
 
   // Convert value to expression.
   Expr ValueToExpr(Value value) {
     if (const auto* val = value.as<TensorValueNode>()) {
+      for (auto dim : val->data.Shape()) {
+        CHECK_GT(dim, 0)
+          << "invalid dimension after constant eval";
+      }
       return ConstantNode::make(val->data);
     } else if (const auto* val = value.as<TupleValueNode>()) {
       Array<Expr> fields;
@@ -171,18 +186,33 @@ class ConstantFolder : public ExprMutator {
   Expr ConstEvaluate(Expr expr) {
     std::vector<transform::Pass> passes = {transform::FuseOps(0),
                                            transform::InferType()};
-    auto mod = ModuleNode::FromExpr(expr);
+    Function func;
+    if (expr.as<FunctionNode>()) {
+      func = Downcast<Function>(expr);
+    } else {
+      // TODO(@jroesch): fix this
+      func = FunctionNode::make(FreeVars(expr), expr, Type(), FreeTypeVars(expr, module_), {});
+    }
+    auto mod = ModuleNode::make(
+      {},
+      module_->type_definitions,
+      module_->Imports());
+    auto global = GlobalVarNode::make("main");
+    mod->Add(global, func);
     auto seq = transform::Sequential(passes);
     mod = seq(mod);
     auto entry_func = mod->Lookup("main");
     expr = expr.as<FunctionNode>() == nullptr ? entry_func->body : entry_func;
     return ValueToExpr(executor_(expr));
   }
-  // Evaluate shape_of op
+
+  // Evaluate a call to the shape_of operator for tensors with constant
+  // shapes.
   Expr EvaluateShapeOf(Expr expr, Array<Expr> args, Attrs attrs) {
     Expr input = args[0];
     const auto* param = attrs.as<ShapeOfAttrs>();
     CHECK(param != nullptr);
+
     tvm::Array<IndexExpr> ishape;
     if (const ConstantNode* op = input.as<ConstantNode>()) {
       ishape = op->tensor_type()->shape;
@@ -191,33 +221,48 @@ class ConstantFolder : public ExprMutator {
     } else {
       return expr;
     }
+
     // Get the constant shape
     DLContext ctx;
     ctx.device_type = kDLCPU;
     ctx.device_id = 0;
-    auto val = runtime::NDArray::Empty(
-        {(int64_t)ishape.size()}, Type2TVMType(Int(32)), ctx);
-    int32_t* dims = static_cast<int32_t*>(val->data);
-    using ::tvm::ir::IntImm;
-    for (size_t i = 0; i < ishape.size(); ++i) {
-      if (const IntImm* dim = ishape[i].as<IntImm>()) {
-        dims[i] = dim->value;
-      } else {
-        return expr;
+    runtime::NDArray value;
+    auto cdtype = Type2TVMType(Int(32));
+    if (ishape.size() == 0) {
+      value = runtime::NDArray::Empty({}, cdtype, ctx);
+    } else {
+      CHECK_NE(ishape.size(), 0);
+      std::vector<int64_t> cshape = { static_cast<int64_t>(ishape.size()) };
+      value = runtime::NDArray::Empty(cshape, cdtype, ctx);
+      int32_t* dims = static_cast<int32_t*>(value->data);
+      using ::tvm::ir::IntImm;
+      for (size_t i = 0; i < ishape.size(); ++i) {
+        if (const IntImm* dim = ishape[i].as<IntImm>()) {
+          dims[i] = dim->value;
+        } else {
+          return expr;
+        }
       }
     }
-    Expr shape = ValueToExpr(TensorValueNode::make(val));
+
+    Constant shape = Downcast<Constant>(ValueToExpr(TensorValueNode::make(value)));
+
+    if (shape->data.Shape().size() == 0 && GetScalarFromConstant<int32_t>(shape) == 0) {
+      auto ndarray = runtime::NDArray::Empty({}, cdtype, ctx);
+      shape = ConstantNode::make(ndarray);
+    }
+
     // Cast the constant into correct dtype
     auto cast_attrs = make_node<CastAttrs>();
     cast_attrs->dtype = param->dtype;
     static const Op& cast_op = Op::Get("cast");
-    Expr ret = CallNode::make(cast_op, {shape}, Attrs(cast_attrs), {});
+    Expr ret = CallNode::make(cast_op, { shape }, Attrs(cast_attrs), {});
     return ConstEvaluate(ret);
   }
 };
 
 
-Expr FoldConstant(const Expr& expr) {
+Expr FoldConstant(const Expr& expr, const Module& mod) {
   DLContext ctx;
   ctx.device_type = kDLCPU;
   ctx.device_id = 0;
@@ -227,7 +272,7 @@ Expr FoldConstant(const Expr& expr) {
   With<BuildConfig> fresh_build_ctx(BuildConfig::Create());
 
   return ConstantFolder(CreateInterpreter(
-      Module(nullptr), ctx, target)).Mutate(expr);
+      mod, ctx, target), mod).Mutate(expr);
 }
 
 namespace transform {
@@ -235,7 +280,7 @@ namespace transform {
 Pass FoldConstant() {
   runtime::TypedPackedFunc<Function(Function, Module, PassContext)> pass_func =
     [=](Function f, Module m, PassContext pc) {
-      return Downcast<Function>(FoldConstant(f));
+      return Downcast<Function>(FoldConstant(f, m));
   };
   return CreateFunctionPass(pass_func, 2, "FoldConstant", {});
 }

src/relay/pass/fuse_ops.cc

@@ -862,6 +862,13 @@ class FuseMutator : private ExprMutator {
   Expr VisitExpr_(const CallNode* call) {
     static const Op& stop_fusion = Op::Get("annotation.stop_fusion");
     if (call->op.as<OpNode>()) {
+      static auto fnoncomputational =
+        Op::GetAttr<TNonComputational>("TNonComputational");
+
+      if (fnoncomputational.get(Downcast<Op>(call->op), false)) {
+        return ExprMutator::VisitExpr_(call);
+      }
+
       // If it is a primitive op call
       // then we must have a group assignment for it already.
       CHECK(gmap_.count(call));

src/relay/pass/pass_manager.cc

@@ -314,7 +314,7 @@ Module FunctionPassNode::operator()(const Module& mod,
              << pass_info->opt_level;
 
   // Execute the pass function and return a new module.
-  Module updated_mod = ModuleNode::make(mod->functions, mod->type_definitions);
+  Module updated_mod = ModuleNode::make(mod->functions, mod->type_definitions, mod->Imports());
   std::vector<std::pair<GlobalVar, Function> > updates;
   for (const auto& it : updated_mod->functions) {
     auto updated_func = SkipFunction(it.second)

src/relay/pass/type_infer.cc

@@ -311,8 +311,15 @@ class TypeInferencer : private ExprFunctor<Type(const Expr&)>,
       Match match = GetRef<Match>(op);
       Array<Pattern> unmatched_cases = UnmatchedCases(match, this->mod_);
       if (unmatched_cases.size() != 0) {
-        LOG(FATAL) << "Match clause " << match <<  " does not handle the following cases: "
-                   << unmatched_cases;
+        RelayErrorStream ss;
+        ss << "match expression does not handle the following cases: ";
+        int i = 0;
+        for (auto cs : unmatched_cases) {
+          ss << "case " << i << ": \n" << PrettyPrint(cs);
+        }
+        this->ReportFatalError(
+          match,
+          ss);
       }
     }
 

src/relay/pass/type_solver.cc

@@ -530,8 +530,10 @@ class TypeSolver::Merger : public TypeFunctor<void(const Type&)> {
 };
 
 // constructor
-TypeSolver::TypeSolver(const GlobalVar& current_func, const Module& module,
-                       ErrorReporter* err_reporter)
+TypeSolver::TypeSolver(
+  const GlobalVar& current_func,
+  const Module& module,
+  ErrorReporter* err_reporter)
     : reporter_(make_node<Reporter>(this)),
       current_func(current_func),
       err_reporter_(err_reporter),

src/runtime/vm/executable.cc

@@ -287,9 +287,13 @@ VMInstructionSerializer SerializeInstruction(const Instruction& instr) {
     }
     case Opcode::AllocTensor: {
       // Number of fields = 5 + instr.alloc_tensor.ndim
+      fields.push_back(instr.alloc_tensor.storage);
+
       // Save `DLDataType` and the dst register.
       const auto& dtype = instr.alloc_tensor.dtype;
-      fields.assign({dtype.code, dtype.bits, dtype.lanes});
+      fields.push_back(dtype.code);
+      fields.push_back(dtype.bits);
+      fields.push_back(dtype.lanes);
 
       // The number of dimensions is not needed for constructing an
       // `AllocTensor` instruction as it equals to the length of the `shape`
@@ -305,10 +309,22 @@ VMInstructionSerializer SerializeInstruction(const Instruction& instr) {
       break;
     }
     case Opcode::AllocTensorReg: {
-      // Number of fields = 5
+      // Number of fields = 6
+      fields.push_back(instr.alloc_tensor_reg.storage);
       fields.push_back(instr.alloc_tensor_reg.shape_register);
       // Save `DLDataType` and the dst register.
-      const auto& dtype = instr.alloc_tensor.dtype;
+      const auto& dtype = instr.alloc_tensor_reg.dtype;
+      fields.push_back(dtype.code);
+      fields.push_back(dtype.bits);
+      fields.push_back(dtype.lanes);
+      fields.push_back(instr.dst);
+      break;
+    }
+    case Opcode::AllocStorage: {
+      fields.push_back(instr.alloc_storage.allocation_size);
+      fields.push_back(instr.alloc_storage.alignment);
+      // Save `DLDataType` and the dst register.
+      const auto& dtype = instr.alloc_storage.dtype_hint;
       fields.push_back(dtype.code);
       fields.push_back(dtype.bits);
       fields.push_back(dtype.lanes);
@@ -521,35 +537,39 @@ Instruction DeserializeInstruction(const VMInstructionSerializer& instr) {
       return Instruction::InvokePacked(packed_index, arity, output_size, args);
     }
     case Opcode::AllocTensor: {
-      // Number of fields = 5 + instr.alloc_tensor.ndim
-      DCHECK_GE(instr.fields.size(), 5U);
-      DCHECK_EQ(instr.fields.size(), 5U + static_cast<size_t>(instr.fields[3]));
+      // Number of fields = 6 + instr.alloc_tensor.ndim
+      DCHECK_GE(instr.fields.size(), 6U);
+      DCHECK_EQ(instr.fields.size(), 6U + static_cast<size_t>(instr.fields[4]));
+
+      RegName storage_reg = instr.fields[0];
 
       DLDataType dtype;
-      dtype.code = instr.fields[0];
-      dtype.bits = instr.fields[1];
-      dtype.lanes = instr.fields[2];
+      dtype.code = instr.fields[1];
+      dtype.bits = instr.fields[2];
+      dtype.lanes = instr.fields[3];
 
-      Index ndim = instr.fields[3];
-      RegName dst = instr.fields[4];
+      Index ndim = instr.fields[4];
+      RegName dst = instr.fields[5];
 
-      std::vector<Index> shape = ExtractFields(instr.fields, 5, ndim);
+      std::vector<Index> shape = ExtractFields(instr.fields, 6, ndim);
 
-      return Instruction::AllocTensor(shape, dtype, dst);
+      return Instruction::AllocTensor(storage_reg, shape, dtype, dst);
     }
     case Opcode::AllocTensorReg: {
       // Number of fields = 5
-      DCHECK_EQ(instr.fields.size(), 5U);
-      Index shape_register = instr.fields[0];
+      DCHECK_EQ(instr.fields.size(), 6U);
+
+      RegName storage_reg = instr.fields[0];
+      Index shape_register = instr.fields[1];
 
       DLDataType dtype;
-      dtype.code = instr.fields[1];
-      dtype.bits = instr.fields[2];
-      dtype.lanes = instr.fields[3];
+      dtype.code = instr.fields[2];
+      dtype.bits = instr.fields[3];
+      dtype.lanes = instr.fields[4];
 
-      RegName dst = instr.fields[4];
+      RegName dst = instr.fields[5];
 
-      return Instruction::AllocTensorReg(shape_register, dtype, dst);
+      return Instruction::AllocTensorReg(storage_reg, shape_register, dtype, dst);
     }
     case Opcode::AllocADT: {
       // Number of fields = 3 + instr.num_fields
@@ -575,6 +595,24 @@ Instruction DeserializeInstruction(const VMInstructionSerializer& instr) {
 
       return Instruction::AllocClosure(clo_index, num_freevar, free_vars, dst);
     }
+    case Opcode::AllocStorage: {
+      DCHECK_GE(instr.fields.size(), 6U);
+      Index allocation_size = instr.fields[0];
+      Index alignment = instr.fields[1];
+
+      DLDataType dtype;
+      dtype.code = instr.fields[2];
+      dtype.bits = instr.fields[3];
+      dtype.lanes = instr.fields[4];
+
+      RegName dst = instr.fields[5];
+
+      return Instruction::AllocStorage(
+        allocation_size,
+        alignment,
+        dtype,
+        dst);
+    }
     case Opcode::If: {
       // Number of fields = 4
       DCHECK_EQ(instr.fields.size(), 4U);

src/runtime/vm/memory_manager.cc

@@ -32,6 +32,30 @@ namespace tvm {
 namespace runtime {
 namespace vm {
 
+static void BufferDeleter(NDArray::Container* ptr) {
+  CHECK(ptr->manager_ctx != nullptr);
+  Buffer* buffer = reinterpret_cast<Buffer*>(ptr->manager_ctx);
+  MemoryManager::Global()->GetAllocator(buffer->ctx)->
+      Free(*(buffer));
+  delete buffer;
+  delete ptr;
+}
+
+void StorageObj::Deleter(NDArray::Container* ptr) {
+  // When invoking AllocNDArray we don't own the underlying allocation
+  // and should not delete the buffer, but instead let it be reclaimed
+  // by the storage object's destructor.
+  //
+  // We did bump the reference count by 1 to keep alive the StorageObj
+  // allocation in case this NDArray is the sole owner.
+  //
+  // We decrement the object allowing for the buffer to release our
+  // reference count from allocation.
+  StorageObj* storage = reinterpret_cast<StorageObj*>(ptr->manager_ctx);
+  storage->DecRef();
+  delete ptr;
+}
+
 inline void VerifyDataType(DLDataType dtype) {
   CHECK_GE(dtype.lanes, 1);
   if (dtype.code == kDLFloat) {
@@ -50,6 +74,22 @@ inline size_t GetDataAlignment(const DLTensor& arr) {
   return align;
 }
 
+NDArray StorageObj::AllocNDArray(size_t offset, std::vector<int64_t> shape, DLDataType dtype) {
+  // TODO(@jroesch): generalize later to non-overlapping allocations.
+  CHECK_EQ(offset, 0u);
+  VerifyDataType(dtype);
+  NDArray::Container* container = new NDArray::Container(nullptr, shape, dtype, this->buffer.ctx);
+  container->deleter = StorageObj::Deleter;
+  size_t needed_size = GetDataSize(container->dl_tensor);
+  // TODO(@jroesch): generalize later to non-overlapping allocations.
+  CHECK(needed_size == this->buffer.size)
+    << "size mistmatch required " << needed_size << " found " << this->buffer.size;
+  this->IncRef();
+  container->manager_ctx = reinterpret_cast<void*>(this);
+  container->dl_tensor.data = this->buffer.data;
+  return NDArray(container);
+}
+
 MemoryManager* MemoryManager::Global() {
   static MemoryManager memory_manager;
   return &memory_manager;
@@ -66,15 +106,6 @@ Allocator* MemoryManager::GetAllocator(TVMContext ctx) {
   return allocators_.at(ctx).get();
 }
 
-static void BufferDeleter(NDArray::Container* ptr) {
-  CHECK(ptr->manager_ctx != nullptr);
-  Buffer* buffer = reinterpret_cast<Buffer*>(ptr->manager_ctx);
-  MemoryManager::Global()->GetAllocator(buffer->ctx)->
-      Free(*(buffer));
-  delete buffer;
-  delete ptr;
-}
-
 NDArray Allocator::Empty(std::vector<int64_t> shape, DLDataType dtype, DLContext ctx) {
   VerifyDataType(dtype);
   NDArray::Container* container = new NDArray::Container(nullptr, shape, dtype, ctx);

src/runtime/vm/memory_manager.h

@@ -27,6 +27,7 @@
 
 #include <tvm/runtime/c_runtime_api.h>
 #include <tvm/runtime/ndarray.h>
+#include <tvm/runtime/object.h>
 #include <functional>
 #include <memory>
 #include <mutex>
@@ -108,6 +109,38 @@ class MemoryManager {
   std::unordered_map<TVMContext, std::unique_ptr<Allocator> > allocators_;
 };
 
+/*! \brief An object representing a storage allocation. */
+class StorageObj : public Object {
+ public:
+  /*! \brief The index into the VM function table. */
+  Buffer buffer;
+
+  /*! \brief Allocate an NDArray from a given piece of storage. */
+  NDArray AllocNDArray(size_t offset,
+                       std::vector<int64_t> shape,
+                       DLDataType dtype);
+
+  /*! \brief The deleter for an NDArray when allocated from underlying storage. */
+  static void Deleter(NDArray::Container* ptr);
+
+  ~StorageObj() {
+    auto alloc = MemoryManager::Global()->GetAllocator(buffer.ctx);
+    alloc->Free(buffer);
+  }
+
+  static constexpr const uint32_t _type_index = TypeIndex::kDynamic;
+  static constexpr const char* _type_key = "vm.Storage";
+  TVM_DECLARE_FINAL_OBJECT_INFO(StorageObj, Object);
+};
+
+/*! \brief reference to storage. */
+class Storage : public ObjectRef {
+ public:
+  explicit Storage(Buffer buffer);
+
+  TVM_DEFINE_OBJECT_REF_METHODS_MUT(Storage, ObjectRef, StorageObj);
+};
+
 }  // namespace vm
 }  // namespace runtime
 }  // namespace tvm

tests/python/relay/test_memory_alloc.py

+# 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
+import tvm
+import numpy as np
+from tvm import relay
+from tvm.relay import memory_alloc
+
+def check_vm_alloc(func, check_fn):
+    mod = relay.Module()
+    mod['main'] = func
+    ex = relay.create_executor('vm', mod)
+    args = []
+    for param in func.params:
+        param = param.type_annotation
+        sh = [int(sh) for sh in param.shape]
+        data = np.random.rand(*sh).astype(param.dtype)
+        args.append(tvm.nd.array(data))
+    result = ex.evaluate(mod['main'])(*args)
+    py_res = check_fn(*[arg.asnumpy() for arg in args])
+    np.testing.assert_allclose(result.asnumpy(), py_res)
+
+def storage_type(mod):
+    return relay.TypeCall(mod.get_global_type_var("Storage"), [])
+
+def test_tyck_alloc_storage():
+    mod = relay.Module()
+    mod.import_from_std("core.rly")
+
+def test_tyck_alloc_tensor():
+    mod = relay.Module()
+    mod.import_from_std("core.rly")
+    sto = relay.Var("x", storage_type(mod))
+    sh = relay.const(np.array([1, 2]), dtype="int64")
+    at = relay.op.memory.alloc_tensor(sto, sh)
+    mod['main'] = relay.Function([sto], at)
+    relay.transform.InferType()(mod)
+
+
+def check_add(x):
+    return x + x
+
+def test_add():
+    x = relay.var('x', shape=(2,))
+    z = x + x
+    func = relay.Function([x,], z)
+    check_vm_alloc(func, check_add)
+
+
+def check_add_sub(x, y):
+    z = x + x
+    return z - y
+
+def test_add_sub():
+    x = relay.var('x', shape=(10,))
+    y = relay.var('y', shape=(10,))
+    z = x + x
+    z = z - y
+    func = relay.Function([x, y], z)
+    check_vm_alloc(func, check_add_sub)
+
+if __name__ == "__main__":
+    test_tyck_alloc_tensor()
+    test_add()
+    test_add_sub()

tests/python/relay/test_vm_serialization.py

@@ -107,9 +107,9 @@ def test_serializer():
     assert any(item.startswith('fused_multiply') for item in prim_ops)
 
     code = exe.bytecode
-    assert "main 5 2 5" in code
-    assert "f1 2 1 3" in code
-    assert "f2 2 1 3" in code
+    assert "main 8 2 8" in code
+    assert "f1 5 1 6" in code
+    assert "f2 5 1 6" in code
 
     code, lib = exe.save()
     assert isinstance(code, bytearray)