[REFACTOR][PY][API-CHANGE] establish tvm.ir, migrate corresponding files

apps/benchmark/util.py

@@ -34,7 +34,7 @@ def get_network(name, batch_size, dtype='float32'):
 
     Returns
     -------
-    net: relay.Module
+    net: tvm.IRModule
         The relay function of network definition
     params: dict
         The random parameters for benchmark
@@ -70,7 +70,7 @@ def get_network(name, batch_size, dtype='float32'):
         net, params = relay.frontend.from_mxnet(block, shape={'data': input_shape}, dtype=dtype)
         net = net["main"]
         net = relay.Function(net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs)
-        net = relay.Module.from_expr(net)
+        net = tvm.IRModule.from_expr(net)
     else:
         raise ValueError("Unsupported network: " + name)
 

docs/api/python/tvm.rst

@@ -21,8 +21,6 @@ The user facing API for computation declaration.
 
 .. autosummary::
 
-   tvm.load_json
-   tvm.save_json
    tvm.var
    tvm.size_var
    tvm.const
@@ -47,8 +45,7 @@ The user facing API for computation declaration.
    tvm.max
    tvm.tag_scope
 
-.. autofunction:: tvm.load_json
-.. autofunction:: tvm.save_json
+
 .. autofunction:: tvm.var
 .. autofunction:: tvm.size_var
 .. autofunction:: tvm.const

include/tvm/ir/expr.h

@@ -178,7 +178,7 @@ class RelayExpr : public BaseExpr {
 
 class GlobalVar;
 /*!
- * \brief Global variable that leaves in the top-level module.
+ * \brief Global variable that lives in the top-level module.
  *
  * A GlobalVar only refers to function definitions.
  * This is used to enable recursive calls between function.

include/tvm/ir/type.h

@@ -141,11 +141,12 @@ enum TypeKind : int {
 };
 
 /*!
- * \brief Type parameter in the function.
- *  This can be viewed as template parameter in c++ template function.
+ * \brief Type parameter in functions.
+ *
+ * A type variable can be viewed as template parameter in c++ template function.
  *
  * For example, in the following pesudo code,
- * the TypeVar of f is TypeVar(kind=kShapeVar, var=n).
+ * the TypeVar of f is TypeVar("n", kind=kShapeVar).
  * This function can take in a Tensor with shape=(3, 3) and
  * returns a Tensor with shape=(9,)
  *

include/tvm/ir/type_relation.h

@@ -165,7 +165,7 @@ using TypeRelationFn =
                       const TypeReporter& reporter)>;
 
 /*!
- * \brief User defined type relation, is an input-output relation on types.
+ * \brief User defined type relation, it is an input-output relation on types.
  *
  * TypeRelation is more generalized than type call as it allows inference
  * of both inputs and outputs.

python/tvm/__init__.py

@@ -33,6 +33,12 @@
 from .runtime.ndarray import vpi, rocm, opengl, ext_dev, micro_dev
 from .runtime import ndarray as nd
 
+# tvm.ir
+from .ir import IRModule
+from .ir import transform
+from .ir import container
+from . import ir
+
 # others
 from . import tensor
 from . import arith
@@ -41,10 +47,8 @@
 from . import make
 from . import ir_pass
 from . import codegen
-from . import container
 from . import schedule
 
-from . import attrs
 from . import ir_builder
 from . import target
 from . import generic

python/tvm/_ffi/_ctypes/object.py

@@ -87,6 +87,7 @@ def __init_handle_by_constructor__(self, fconstructor, *args):
         instead of creating a new Node.
         """
         # assign handle first to avoid error raising
+        # pylint: disable=not-callable
         self.handle = None
         handle = __init_by_constructor__(fconstructor, args)
         if not isinstance(handle, ObjectHandle):

python/tvm/api.py

@@ -19,9 +19,11 @@
 from numbers import Integral as _Integral
 
 import tvm._ffi
-import tvm.runtime._ffi_node_api
+import tvm.ir
 
 from tvm.runtime import convert, const, DataType
+from tvm.ir import container as _container
+
 from ._ffi.base import string_types, TVMError
 from ._ffi.registry import register_func, get_global_func, extract_ext_funcs
 
@@ -30,9 +32,7 @@
 from . import expr as _expr
 from . import tensor as _tensor
 from . import schedule as _schedule
-from . import container as _container
 from . import tag as _tag
-from . import json_compact
 
 int8 = "int8"
 int32 = "int32"
@@ -71,66 +71,6 @@ def max_value(dtype):
     """
     return _api_internal._max_value(dtype)
 
-
-def get_env_func(name):
-    """Get an EnvFunc by a global name.
-
-    Parameters
-    ----------
-    name: str
-        The name of the global function.
-
-    Returns
-    -------
-    env_func : EnvFunc
-        The result env function.
-
-    Note
-    ----
-    EnvFunc is a Object wrapper around
-    global function that can be serialized via its name.
-    This can be used to serialize function field in the language.
-    """
-    return _api_internal._EnvFuncGet(name)
-
-
-def load_json(json_str):
-    """Load tvm object from json_str.
-
-    Parameters
-    ----------
-    json_str : str
-        The json string
-
-    Returns
-    -------
-    node : Object
-        The loaded tvm node.
-    """
-
-    try:
-        return tvm.runtime._ffi_node_api.LoadJSON(json_str)
-    except TVMError:
-        json_str = json_compact.upgrade_json(json_str)
-        return tvm.runtime._ffi_node_api.LoadJSON(json_str)
-
-
-def save_json(node):
-    """Save tvm object as json string.
-
-    Parameters
-    ----------
-    node : Object
-        A TVM object to be saved.
-
-    Returns
-    -------
-    json_str : str
-        Saved json string.
-    """
-    return tvm.runtime._ffi_node_api.SaveJSON(node)
-
-
 def var(name="tindex", dtype=int32):
     """Create a new variable with specified name and dtype
 
@@ -688,7 +628,7 @@ def _IterVar(dom, name, iter_type, thread_tag=''):
                 raise TypeError("need to be list of ranges")
             dom = Range(dom[0], dom[1])
 
-        if not isinstance(dom, _container.Range):
+        if not isinstance(dom, tvm.ir.Range):
             raise TypeError("dom need to be Range")
     name = name if name else 'iter'
     v = var(name)

python/tvm/autotvm/graph_tuner/base_graph_tuner.py

@@ -141,7 +141,7 @@ def __init__(self, graph, input_shapes, records, target_ops,
             self._logger.propagate = False
 
         # Generate workload and schedule dictionaries.
-        if isinstance(graph, relay.Module):
+        if isinstance(graph, tvm.IRModule):
             graph = graph["main"]
 
         if isinstance(graph, relay.expr.Function):

python/tvm/autotvm/graph_tuner/utils/traverse_graph.py

@@ -20,6 +20,7 @@
 
 import topi
 
+import tvm
 from tvm import relay, autotvm
 from tvm.relay import transform
 from tvm.relay.expr import Call, Function, TupleGetItem, Var, Constant, Tuple
@@ -83,7 +84,7 @@ def expr2graph(expr, target_ops, node_dict, node_list):
 
 def _infer_type(node):
     """A method to infer the type of a relay expression."""
-    mod = relay.Module.from_expr(node)
+    mod = tvm.IRModule.from_expr(node)
     mod = transform.InferType()(mod)
     entry = mod["main"]
     return entry if isinstance(node, relay.Function) else entry.body
@@ -136,7 +137,7 @@ def _traverse_expr(node):
                     free_var = relay.Var("var_%d" % i, input_type)
                     params.append(free_var)
                 call = relay.Call(node.op, params, node.attrs)
-                mod = relay.Module.from_expr(relay.Function(params, call))
+                mod = tvm.IRModule.from_expr(relay.Function(params, call))
                 relay.backend.compile_engine.get().clear()
                 build_thread = threading.Thread(target=relay.build,
                                                 args=(mod,

python/tvm/autotvm/graph_tuner/utils/utils.py

@@ -16,6 +16,7 @@
 # under the License.
 # pylint: disable=eval-used,invalid-name,too-many-arguments
 """Utility functions"""
+import tvm
 from tvm import relay
 from tvm.relay import transform
 
@@ -136,7 +137,7 @@ def bind_inputs(expr, input_shapes=None, input_dtypes="float32"):
             rebind_dict[var] = updated_input_dict[var.name_hint]
     updated_expr = relay.expr.bind(expr, rebind_dict)
 
-    mod = relay.Module.from_expr(updated_expr)
+    mod = tvm.IRModule.from_expr(updated_expr)
     mod = transform.InferType()(mod)
     entry = mod["main"]
     return entry if isinstance(updated_expr, relay.Function) else entry.body

python/tvm/autotvm/task/relay_integration.py

@@ -63,7 +63,7 @@ def extract_from_program(mod, params, ops, target, target_host=None,
 
     Parameters
     ----------
-    mod: relay.module.Module or relay.expr.Function
+    mod: tvm.IRModule or relay.expr.Function
         The module or function to tune
     params: dict of str to numpy array
         The associated parameters of the program
@@ -95,7 +95,7 @@ def extract_from_multiple_program(mods, params, ops, target, target_host=None,
 
     Parameters
     ----------
-    mods: List[relay.module.Module] or List[relay.expr.Function]
+    mods: List[tvm.IRModule] or List[relay.expr.Function]
         The list of modules or functions to tune
     params: List of dict of str to numpy array
         The associated parameters of the programs
@@ -151,8 +151,8 @@ def extract_from_multiple_program(mods, params, ops, target, target_host=None,
 
         for mod, param in zip(mods, params):
             if isinstance(mod, relay.expr.Function):
-                mod = relay.Module.from_expr(mod)
-            assert isinstance(mod, relay.module.Module), \
+                mod = tvm.IRModule.from_expr(mod)
+            assert isinstance(mod, tvm.IRModule), \
                 "only support relay Module or Function to be tuned"
             relay.backend.compile_engine.get().clear()
             # wrap build call in thread to avoid multiprocessing problems

python/tvm/build_module.py

@@ -24,17 +24,19 @@
 import tvm.runtime
 
 from tvm.runtime import Object, ndarray
+from tvm.ir import container
 from . import api
 from . import _api_internal
 from . import tensor
 from . import schedule
 from . import expr
 from . import ir_pass
 from . import stmt as _stmt
-from . import container
 from . import codegen
 from . import target as _target
 from . import make
+from .stmt import LoweredFunc
+
 
 class DumpIR(object):
     """
@@ -58,16 +60,16 @@ def decorate(self, func):
         def dump(*args, **kwargs):
             """dump function"""
             retv = func(*args, **kwargs)
-            if not isinstance(retv, (_stmt.Stmt, container.LoweredFunc, container.Array)):
+            if not isinstance(retv, (_stmt.Stmt, LoweredFunc, container.Array)):
                 return retv
             fname = func.func_name if hasattr(func, 'func_name') else func.__name__
             pname = str(self._pass_id) + "_" + fname + "_ir.cc"
             with open(pname, "a") as f:
-                out = retv.body if isinstance(retv, container.LoweredFunc) else retv
+                out = retv.body if isinstance(retv, LoweredFunc) else retv
                 f.write(str(out))
                 if isinstance(retv, container.Array):
                     for x in retv:
-                        out = x.body if isinstance(x, container.LoweredFunc) else x
+                        out = x.body if isinstance(x, LoweredFunc) else x
                         f.write("---------%s\n%s\n-----------\n"%(x.name, str(out)))
                 self._pass_id += 1
             return retv
@@ -459,7 +461,7 @@ def _build_for_device(flist, target, target_host):
             raise ValueError(
                 "Direct host side access to device memory is detected in %s. "
                 "Did you forget to bind?" % func.name)
-        if func.func_type == container.LoweredFunc.MixedFunc:
+        if func.func_type == LoweredFunc.MixedFunc:
             if current_build_config().detect_global_barrier:
                 func = ir_pass.ThreadSync(func, "global")
             func = ir_pass.ThreadSync(func, "shared")
@@ -471,9 +473,9 @@ def _build_for_device(flist, target, target_host):
             fhost.append(fsplits[0])
             for x in fsplits[1:]:
                 fdevice.append(x)
-        elif func.func_type == container.LoweredFunc.HostFunc:
+        elif func.func_type == LoweredFunc.HostFunc:
             fhost.append(func)
-        elif func.func_type == container.LoweredFunc.DeviceFunc:
+        elif func.func_type == LoweredFunc.DeviceFunc:
             fdevice.append(func)
         else:
             raise ValueError("unknown function type %d" % func.func_type)
@@ -586,9 +588,9 @@ def build(inputs,
         flist = lower(inputs, args,
                       name=name,
                       binds=binds)
-        if isinstance(flist, container.LoweredFunc):
+        if isinstance(flist, LoweredFunc):
             flist = [flist]
-    elif isinstance(inputs, container.LoweredFunc):
+    elif isinstance(inputs, LoweredFunc):
         if args:
             raise ValueError("args must be done when build from LoweredFunc.")
         flist = [inputs]
@@ -612,7 +614,7 @@ def build(inputs,
                              "_target.Target when inputs is dict.")
         fname_set = set()
         for x in flist:
-            if not isinstance(x, container.LoweredFunc):
+            if not isinstance(x, LoweredFunc):
                 raise ValueError("inputs must be Schedule, LoweredFunc, list "
                                  "of LoweredFunc, or dict of str to list of "
                                  "LoweredFunc.")

python/tvm/contrib/sparse.py

@@ -38,7 +38,7 @@ def __init__(self, arg1, ctx=None, shape=None):
             The corresponding a dense numpy array,
             or a tuple for constructing a sparse matrix directly.
 
-        ctx: tvm.TVMContext
+        ctx: tvmContext
             The corresponding context.
 
         shape : tuple of int

python/tvm/hybrid/calls.py

@@ -16,12 +16,11 @@
 # under the License.
 """Intrinsics of TVM-Python Hybrid Script for Python compilation time
 semantic support."""
-
+from tvm.ir.container import Array
 from .. import api as _api
 from .. import expr as _expr
 from .. import make as _make
 from .. import target as _tgt
-from ..container import Array
 from .. import ir_pass
 from ..stmt import For
 from .util import _internal_assert