[Refactor] move vm.py under runtime and adt to runtime.container.py

python/tvm/autotvm/task/relay_integration.py

@@ -39,6 +39,7 @@ def _lower(mod,
     """
     # pylint: disable=import-outside-toplevel
     from tvm import relay
+    from tvm import runtime
     from tvm.relay.backend import graph_runtime_codegen
 
     if hasattr(target, 'device_name') and target.device_name == "vta":
@@ -49,7 +50,7 @@ def _lower(mod,
                 grc = graph_runtime_codegen.GraphRuntimeCodegen(None, target)
                 grc.codegen(mod["main"])
     # default case
-    compiler = relay.vm.VMCompiler()
+    compiler = runtime.vm.VMCompiler()
     if params:
         compiler.set_params(params)
     compiler.lower(mod, target=target)

python/tvm/container.py

@@ -17,7 +17,7 @@
 """Container data structures used in TVM DSL."""
 import tvm._ffi
 
-from tvm.runtime import Object, ObjectTypes
+from tvm.runtime import Object
 from tvm.runtime.container import getitem_helper
 from tvm.runtime import _ffi_node_api
 from . import _api_internal
@@ -104,56 +104,3 @@ class LoweredFunc(Object):
     MixedFunc = 0
     HostFunc = 1
     DeviceFunc = 2
-
-
-@tvm._ffi.register_object("vm.ADT")
-class ADT(Object):
-    """Algebatic data type(ADT) object.
-
-    Parameters
-    ----------
-    tag : int
-        The tag of ADT.
-
-    fields : list[Object] or tuple[Object]
-        The source tuple.
-    """
-    def __init__(self, tag, fields):
-        for f in fields:
-            assert isinstance(f, ObjectTypes), "Expect object or " \
-            "tvm NDArray type, but received : {0}".format(type(f))
-        self.__init_handle_by_constructor__(_ADT, tag, *fields)
-
-    @property
-    def tag(self):
-        return _GetADTTag(self)
-
-    def __getitem__(self, idx):
-        return getitem_helper(
-            self, _GetADTFields, len(self), idx)
-
-    def __len__(self):
-        return _GetADTSize(self)
-
-
-def tuple_object(fields=None):
-    """Create a ADT object from source tuple.
-
-    Parameters
-    ----------
-    fields : list[Object] or tuple[Object]
-        The source tuple.
-
-    Returns
-    -------
-    ret : ADT
-        The created object.
-    """
-    fields = fields if fields else []
-    for f in fields:
-        assert isinstance(f, ObjectTypes), "Expect object or tvm " \
-        "NDArray type, but received : {0}".format(type(f))
-    return _Tuple(*fields)
-
-
-tvm._ffi._init_api("tvm.container")

python/tvm/relay/__init__.py

@@ -36,8 +36,6 @@
 from . import debug
 from . import param_dict
 from . import feature
-from .backend import vm
-from .backend import profiler_vm
 
 # Root operators
 from .op import Op

python/tvm/relay/backend/interpreter.py

@@ -20,7 +20,7 @@
 
 import numpy as np
 
-from tvm import container
+from tvm.runtime import container
 from . import _backend
 from .. import _make, analysis, transform
 from .. import module

python/tvm/relay/build_module.py

@@ -22,14 +22,14 @@
 import numpy as np
 
 from tvm import expr as tvm_expr
+from tvm.runtime import vm
 from .. import nd as _nd, target as _target, autotvm
 from ..contrib import graph_runtime as _graph_rt
 from . import _build_module
 from . import ty as _ty
 from . import expr as _expr
 from .module import Module as _Module
 from .backend import interpreter as _interpreter
-from .backend.vm import VMExecutor
 
 def _update_target(target):
     target = target if target else _target.current_target()
@@ -408,5 +408,5 @@ def create_executor(kind="debug",
     if kind == "graph":
         return GraphExecutor(mod, ctx, target)
     if kind == "vm":
-        return VMExecutor(mod, ctx, target)
+        return vm.VMExecutor(mod, ctx, target)
     raise RuntimeError("unknown execution strategy: {0}".format(kind))

python/tvm/relay/testing/py_converter.py

@@ -32,15 +32,15 @@
 #     import numpy
 #     import tvm
 #     from tvm import relay
-#     from tvm import import container as _container
 #     from tvm import nd
+#     from tvm.runtime import import container as _container
 #     from tvm.relay.backend.interpreter import RefValue, ConstructorValue
 PROLOGUE = [
     ast.Import([alias('numpy', None)]),
     ast.Import([alias('tvm', None)]),
     ast.ImportFrom('tvm', [alias('relay', None)], 0),
     ast.ImportFrom('tvm', [alias('nd', None)], 0),
-    ast.ImportFrom('tvm', [alias('container', '_container')],
+    ast.ImportFrom('tvm.runtime', [alias('container', '_container')],
                    0),
     ast.ImportFrom('tvm.relay.backend.interpreter',
                    [alias('RefValue', None),

python/tvm/runtime/container.py

@@ -15,6 +15,9 @@
 # specific language governing permissions and limitations
 # under the License.
 """Runtime container structures."""
+import tvm._ffi
+
+from tvm.runtime import Object, ObjectTypes
 
 def getitem_helper(obj, elem_getter, length, idx):
     """Helper function to implement a pythonic getitem function.
@@ -54,3 +57,56 @@ def getitem_helper(obj, elem_getter, length, idx):
     if idx < 0:
         idx += length
     return elem_getter(obj, idx)
+
+
+@tvm._ffi.register_object("vm.ADT")
+class ADT(Object):
+    """Algebatic data type(ADT) object.
+
+    Parameters
+    ----------
+    tag : int
+        The tag of ADT.
+
+    fields : list[Object] or tuple[Object]
+        The source tuple.
+    """
+    def __init__(self, tag, fields):
+        for f in fields:
+            assert isinstance(f, ObjectTypes), "Expect object or " \
+            "tvm NDArray type, but received : {0}".format(type(f))
+        self.__init_handle_by_constructor__(_ADT, tag, *fields)
+
+    @property
+    def tag(self):
+        return _GetADTTag(self)
+
+    def __getitem__(self, idx):
+        return getitem_helper(
+            self, _GetADTFields, len(self), idx)
+
+    def __len__(self):
+        return _GetADTSize(self)
+
+
+def tuple_object(fields=None):
+    """Create a ADT object from source tuple.
+
+    Parameters
+    ----------
+    fields : list[Object] or tuple[Object]
+        The source tuple.
+
+    Returns
+    -------
+    ret : ADT
+        The created object.
+    """
+    fields = fields if fields else []
+    for f in fields:
+        assert isinstance(f, ObjectTypes), "Expect object or tvm " \
+        "NDArray type, but received : {0}".format(type(f))
+    return _Tuple(*fields)
+
+
+tvm._ffi._init_api("tvm.runtime.container")

python/tvm/relay/backend/vm.py → python/tvm/runtime/vm.py

@@ -24,13 +24,13 @@
 
 import tvm
 import tvm.runtime.ndarray as _nd
-from tvm.runtime import Object
-from tvm import autotvm, container
+from tvm.runtime import Object, container
+from tvm import autotvm
 from tvm.relay import expr as _expr
 from tvm._ffi.runtime_ctypes import TVMByteArray
 from tvm._ffi import base as _base
+from tvm.relay.backend.interpreter import Executor
 from . import _vm
-from .interpreter import Executor
 
 def _convert(arg, cargs):
     if isinstance(arg, _expr.Constant):
@@ -117,7 +117,7 @@ def save(self):
             # create a Relay VM.
             ctx = tvm.cpu()
             target = "llvm"
-            executable = relay.vm.compile(mod, target)
+            executable = tvm.runtime.vm.compile(mod, target)
             code, lib = executable.save()
             # save and load the code and lib file.
             tmp = tvm.contrib.util.tempdir()
@@ -128,10 +128,10 @@ def save(self):
             loaded_lib = tvm.runtime.load_module(path_lib)
             loaded_code = bytearray(open(tmp.relpath("code.ro"), "rb").read())
             # deserialize.
-            des_exec = relay.vm.Executable.load_exec(loaded_code, loaded_code)
+            des_exec = tvm.runtime.vm.Executable.load_exec(loaded_code, loaded_code)
             # execute the deserialized executable.
             x_data = np.random.rand(10, 10).astype('float32')
-            des_vm = relay.vm.VirtualMachine(des_exec)
+            des_vm = tvm.runtime.vm.VirtualMachine(des_exec)
             des_vm.init(ctx)
             res = des_vm.run(x_data)
             print(res.asnumpy())
@@ -556,7 +556,7 @@ class VMExecutor(Executor):
 
     Useful interface for experimentation and debugging
     the VM can also be used directly from the API.
-    supported by `tvm.relay.vm`.
+    supported by `tvm.runtime.vm`.
 
     Parameters
     ----------

src/runtime/container.cc

@@ -32,22 +32,22 @@ namespace runtime {
 
 using namespace vm;
 
-TVM_REGISTER_GLOBAL("container._GetADTTag")
+TVM_REGISTER_GLOBAL("runtime.container._GetADTTag")
 .set_body([](TVMArgs args, TVMRetValue* rv) {
   ObjectRef obj = args[0];
   const auto& adt = Downcast<ADT>(obj);
   *rv = static_cast<int64_t>(adt.tag());
 });
 
-TVM_REGISTER_GLOBAL("container._GetADTSize")
+TVM_REGISTER_GLOBAL("runtime.container._GetADTSize")
 .set_body([](TVMArgs args, TVMRetValue* rv) {
   ObjectRef obj = args[0];
   const auto& adt = Downcast<ADT>(obj);
   *rv = static_cast<int64_t>(adt.size());
 });
 
 
-TVM_REGISTER_GLOBAL("container._GetADTFields")
+TVM_REGISTER_GLOBAL("runtime.container._GetADTFields")
 .set_body([](TVMArgs args, TVMRetValue* rv) {
   ObjectRef obj = args[0];
   int idx = args[1];
@@ -56,7 +56,7 @@ TVM_REGISTER_GLOBAL("container._GetADTFields")
   *rv = adt[idx];
 });
 
-TVM_REGISTER_GLOBAL("container._Tuple")
+TVM_REGISTER_GLOBAL("runtime.container._Tuple")
 .set_body([](TVMArgs args, TVMRetValue* rv) {
   std::vector<ObjectRef> fields;
   for (auto i = 0; i < args.size(); ++i) {
@@ -65,7 +65,7 @@ TVM_REGISTER_GLOBAL("container._Tuple")
   *rv = ADT::Tuple(fields);
 });
 
-TVM_REGISTER_GLOBAL("container._ADT")
+TVM_REGISTER_GLOBAL("runtime.container._ADT")
 .set_body([](TVMArgs args, TVMRetValue* rv) {
   int itag = args[0];
   size_t tag = static_cast<size_t>(itag);

tests/python/frontend/tensorflow/test_forward.py

@@ -62,7 +62,7 @@ def convert_to_list(x):
 def vmobj_to_list(o):
     if isinstance(o, tvm.nd.NDArray):
         return [o.asnumpy().tolist()]
-    elif isinstance(o, tvm.container.ADT):
+    elif isinstance(o, tvm.runtime.container.ADT):
         result = []
         for f in o:
             result.extend(vmobj_to_list(f))

tests/python/relay/benchmarking/benchmark_vm.py

@@ -19,7 +19,8 @@
 
 import tvm
 from tvm.contrib import graph_runtime
-from tvm import relay, container
+from tvm import relay
+from tvm.runtime import container
 from tvm.relay import testing
 from tvm.relay import vm
 

tests/python/relay/test_adt.py

@@ -117,7 +117,7 @@ def tree_to_dict(t):
 def vmobj_to_list(o, dtype="float32"):
     if isinstance(o, tvm.nd.NDArray):
         return [o.asnumpy().tolist()]
-    elif isinstance(o, tvm.container.ADT):
+    elif isinstance(o, tvm.runtime.container.ADT):
         if len(o) == 0:
             tensor_nil = p.get_var("tensor_nil", dtype=dtype)
             if tensor_nil.tag == o.tag:

tests/python/relay/test_backend_interpreter.py

@@ -18,7 +18,8 @@
 import tvm
 import tvm.testing
 from tvm import nd
-from tvm import relay, container
+from tvm import relay
+from tvm.runtime import container
 from tvm.relay.backend.interpreter import RefValue, ConstructorValue
 from tvm.relay.scope_builder import ScopeBuilder
 from tvm.relay import testing, create_executor

tests/python/relay/test_external_codegen.py

@@ -18,12 +18,12 @@
 import os
 import sys
 import numpy as np
-import pytest
 
 import tvm
 import tvm.relay.testing
 import tvm.relay.transform
 from tvm import relay
+from tvm import runtime
 from tvm.contrib import util
 
 def check_result(mod, map_inputs, out_shape, result, tol=1e-5, target="llvm",
@@ -49,11 +49,11 @@ def update_lib(lib):
 
     def check_vm_result():
         with relay.build_config(opt_level=3, disabled_pass=["AlterOpLayout"]):
-            exe = relay.vm.compile(mod, target=target)
+            exe = runtime.vm.compile(mod, target=target)
         code, lib = exe.save()
         lib = update_lib(lib)
-        exe = relay.vm.Executable.load_exec(code, lib)
-        vm = relay.vm.VirtualMachine(exe)
+        exe = runtime.vm.Executable.load_exec(code, lib)
+        vm = runtime.vm.VirtualMachine(exe)
         vm.init(ctx)
         out = vm.run(**map_inputs)
         tvm.testing.assert_allclose(out.asnumpy(), result, rtol=tol, atol=tol)

tests/python/relay/test_pass_partition_graph.py

@@ -24,6 +24,7 @@
 import tvm.relay.testing
 import tvm.relay.transform as transform
 from tvm import relay
+from tvm import runtime
 from tvm.contrib import util
 from tvm.relay.annotation import compiler_begin, compiler_end
 from tvm.relay.expr_functor import ExprMutator
@@ -182,17 +183,17 @@ def update_lib(lib):
         lib_name = 'lib.so'
         lib_path = tmp_path.relpath(lib_name)
         lib.export_library(lib_path, fcompile=False, **kwargs)
-        lib = tvm.runtime.load_module(lib_path)
+        lib = runtime.load_module(lib_path)
 
         return lib
 
     def check_vm_result():
         with relay.build_config(opt_level=3, disabled_pass=["AlterOpLayout"]):
-            exe = relay.vm.compile(mod, target=target, params=params)
+            exe = runtime.vm.compile(mod, target=target, params=params)
         code, lib = exe.save()
         lib = update_lib(lib)
-        exe = relay.vm.Executable.load_exec(code, lib)
-        vm = relay.vm.VirtualMachine(exe)
+        exe = runtime.vm.Executable.load_exec(code, lib)
+        vm = runtime.vm.VirtualMachine(exe)
         vm.init(ctx)
         out = vm.run(**map_inputs)
         tvm.testing.assert_allclose(out.asnumpy(), result, rtol=tol, atol=tol)