fix lint

include/tvm/relay/op_attr_types.h

@@ -268,9 +268,9 @@ class OpImplement : public ObjectRef {
    * \param target The build target.
    * \return The computation schedule.
    */
-  Schedule Schedule(const Attrs& attrs,
-                    const Array<Tensor>& outs,
-                    const Target& target);
+  tvm::Schedule Schedule(const Attrs& attrs,
+                         const Array<Tensor>& outs,
+                         const Target& target);
 };
 
 /*!

include/tvm/schedule.h

@@ -744,11 +744,17 @@ class SingletonNode : public IterVarRelationNode {
 
 class SpecializedConditionNode;
 
+/*!
+ * \brief Specialized condition to enable op specialization
+ */
 class SpecializedCondition : public ObjectRef {
  public:
   SpecializedCondition() {}
   explicit SpecializedCondition(ObjectPtr<Object> n) : ObjectRef(n) {}
-
+  /*!
+   * \brief Get the current specialized condition.
+   * \return The current specialized condition.
+   */
   TVM_DLL static tvm::SpecializedCondition Current();
 
   const SpecializedConditionNode* operator->() const;
@@ -759,14 +765,20 @@ class SpecializedCondition : public ObjectRef {
   // enable with syntax.
   friend class Internal;
   friend class With<SpecializedCondition>;
-
+  /*! \brief Push a new specialized condition onto the thread local stack. */
   TVM_DLL void EnterWithScope();
-
+  /*! \brief Pop a specialized condition off the thread local context stack. */
   TVM_DLL void ExitWithScope();
 };
 
+/*! \brief Container for specialization conditions. */
 class SpecializedConditionNode : public Object {
  public:
+  /*!
+   * \brief List of conditions in conjunctive joint form (CNF).
+   *   Each condition should be a simple expression, e.g., n > 16, m % 8 == 0, etc.,
+   *   where n, m are tvm::Var that represents a dimension in the tensor shape.
+   */
   Array<Expr> clauses;
 
   void VisitAttrs(AttrVisitor* v) {

python/tvm/autotvm/graph_tuner/base_graph_tuner.py

@@ -25,7 +25,7 @@
 import tvm
 from tvm import autotvm, relay
 from tvm.autotvm.task import get_config
-from tvm.autotvm.task.topi_integration import deserialize_args, serialize_args
+from tvm.autotvm.task.topi_integration import serialize_args
 from tvm.autotvm.record import encode, load_from_file
 from tvm.autotvm.measure import MeasureResult, MeasureInput
 
@@ -50,11 +50,9 @@ def get_infer_layout(task_name):
     else:
         raise ValueError("Cannot find infer layout for task %s" % task_name)
 
-#@autotvm.template
 @autotvm.register_customized_task("layout_transform")
 def layout_transform(*args):
     """Autotvm layout transform template."""
-    args = deserialize_args(args)
     cfg = get_config()
     cfg.add_flop(-1)
     data = args[0]

python/tvm/autotvm/record.py

@@ -26,9 +26,9 @@
 import json
 import time
 import os
-import numpy as np
 import itertools
 from collections import OrderedDict
+import numpy as np
 
 from .. import build, lower, target as _target
 

python/tvm/autotvm/task/dispatcher.py

@@ -335,7 +335,7 @@ def _query_inside(self, target, workload):
         if key in self._best_user_defined:
             return self._best_user_defined[key]
         if key in self.best_by_model:
-            inp, res = self.best_by_model[key]
+            inp, _ = self.best_by_model[key]
             return inp.config
 
         # then try matching by target key
@@ -344,7 +344,7 @@ def _query_inside(self, target, workload):
             if key in self._best_user_defined:
                 return self._best_user_defined[key]
             if key in self.best_by_targetkey:
-                inp, res = self.best_by_targetkey[key]
+                inp, _ = self.best_by_targetkey[key]
                 return inp.config
 
         return None

python/tvm/relay/backend/compile_engine.py

@@ -29,15 +29,19 @@
 from .. import expr as _expr
 from .. import op as _op
 from .. import ty as _ty
-from ..expr_functor import ExprFunctor
+from ..expr_functor import ExprVisitor
 from . import _backend
 
 @register_relay_node
 class CachedFunc(NodeBase):
     """Low-level tensor function to back a relay primitive function.
     """
     def __init__(self, target, func_name, inputs, outputs, schedule=None,
-                 lowered_funcs=[], shape_func_param_states=[]):
+                 lowered_funcs=None, shape_func_param_states=None):
+        if lowered_funcs is None:
+            lowered_funcs = []
+        if shape_func_param_states is None:
+            shape_func_param_states = []
         self.__init_handle_by_constructor__(
             _backend._make_CachedFunc, target, func_name, inputs, outputs,
             schedule, lowered_funcs, shape_func_param_states)
@@ -79,6 +83,7 @@ def _get_cache_key(source_func, target):
 
 
 def get_shape(shape):
+    """Convert the shape to correct dtype and vars."""
     ret = []
     for dim in shape:
         if isinstance(dim, tvm.expr.IntImm):
@@ -92,7 +97,9 @@ def get_shape(shape):
     return ret
 
 
-class ScheduleGetter(ExprFunctor):
+class ScheduleGetter(ExprVisitor):
+    """Get the schedule given a fused Relay function"""
+
     MAX_FUNC_NAME_LENGTH = 80
 
     def __init__(self, target):

python/tvm/relay/op/_reduce.py

@@ -17,8 +17,6 @@
 """Backend compiler related feature registration"""
 from __future__ import absolute_import
 
-import topi
-
 from topi.util import get_const_int, get_const_tuple
 from . import op as _reg
 from ...api import convert

python/tvm/relay/op/contrib/_contrib.py

@@ -18,7 +18,6 @@
 """Backend compiler related feature registration"""
 from __future__ import absolute_import
 
-import topi
 from .. import op as reg
 from .. import strategy
 from ..op import OpPattern

python/tvm/relay/op/op.py

@@ -14,10 +14,8 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-#pylint: disable=unused-argument
+#pylint: disable=unused-argument,invalid-name
 """The base node types for the Relay language."""
-import topi
-
 from ..._ffi.function import _init_api
 
 from ..base import register_relay_node

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

@@ -1,3 +1,24 @@
+# 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
+"""Relay op strategies."""
+from __future__ import absolute_import as _abs
+
 from .generic import *
 from . import x86
 from . import arm_cpu

python/tvm/relay/op/strategy/arm_cpu.py

@@ -15,19 +15,20 @@
 # specific language governing permissions and limitations
 # under the License.
 """Definition of ARM CPU operator strategy."""
-# pylint: disable=invalid-name,unused-argument
+# pylint: disable=invalid-name,unused-argument,wildcard-import,unused-wildcard-import
 from __future__ import absolute_import
 
 import topi
 from .generic import *
-from .. import op as _op
 
 @schedule_injective.register("arm_cpu")
-def schedule_injective(_, outs, target):
+def schedule_injective_arm_cpu(_, outs, target):
+    """schedule injective ops for arm cpu"""
     with target:
         return topi.arm_cpu.schedule_injective(outs)
 
 @schedule_concatenate.register("arm_cpu")
-def schedule_concatenate(_, outs, target):
+def schedule_concatenate_arm_cpu(_, outs, target):
+    """schedule concatenate for arm cpu"""
     with target:
         return topi.arm_cpu.schedule_concatenate(outs)