Fix build failure

Author: wweic

Jenkinsfile

@@ -98,7 +98,6 @@ stage('Build') {
            echo set\\(USE_GRAPH_RUNTIME ON\\) >> config.cmake
            echo set\\(USE_STACKVM_RUNTIME ON\\) >> config.cmake
            echo set\\(USE_GRAPH_RUNTIME_DEBUG ON\\) >> config.cmake
-           echo set\\(USE_ANTLR ON\\) >> config.cmake
            echo set\\(USE_BLAS openblas\\) >> config.cmake
            echo set\\(CMAKE_CXX_COMPILER g++\\) >> config.cmake
            echo set\\(CMAKE_CXX_FLAGS -Werror\\) >> config.cmake
@@ -134,7 +133,6 @@ stage('Build') {
            echo set\\(USE_LLVM llvm-config-4.0\\) >> config.cmake
            echo set\\(USE_NNPACK ON\\) >> config.cmake
            echo set\\(NNPACK_PATH /NNPACK/build/\\) >> config.cmake
-           echo set\\(USE_ANTLR ON\\) >> config.cmake
            echo set\\(CMAKE_CXX_COMPILER g++\\) >> config.cmake
            echo set\\(CMAKE_CXX_FLAGS -Werror\\) >> config.cmake
            """
@@ -143,7 +141,6 @@ stage('Build') {
         timeout(time: max_time, unit: 'MINUTES') {
           sh "${docker_run} tvmai/ci-cpu ./tests/scripts/task_cpp_unittest.sh"
           sh "${docker_run} tvmai/ci-cpu ./tests/scripts/task_python_vta.sh"
-          sh "${docker_run} tvmai/ci-cpu ./tests/scripts/task_rust.sh"
           sh "${docker_run} tvmai/ci-cpu ./tests/scripts/task_golang.sh"
           sh "${docker_run} tvmai/ci-cpu ./tests/scripts/task_python_unittest.sh"
           sh "${docker_run} tvmai/ci-cpu ./tests/scripts/task_python_integration.sh"

cmake/modules/ANTLR.cmake

@@ -3,7 +3,7 @@ if(USE_ANTLR)
         /usr/local/lib/antlr-*-complete.jar
         /usr/local/Cellar/*antlr-*-complete.jar)
 
-  if(DEFINED ANTLR4)
+if(ANTLR4)
     # Get the first element of the list of antlr jars.
     # Sort and reverse the list so the item selected is the highest
     #   version in lib or else in Cellar if no lib installation exists.

nnvm/python/nnvm/top/nn.py

@@ -61,10 +61,9 @@ def schedule_log_softmax(_, outs, target):
 @reg.register_compute("dense")
 def compute_dense(attrs, inputs, _):
     """Compute definition of dense"""
-    with tvm.target.create(attrs.get_string("target")):
-        if attrs.get_bool("use_bias"):
-            return topi.nn.dense(inputs[0], inputs[1], bias=inputs[2])
-        return topi.nn.dense(inputs[0], inputs[1])
+    if attrs.get_bool("use_bias"):
+        return topi.nn.dense(inputs[0], inputs[1], bias=inputs[2])
+    return topi.nn.dense(inputs[0], inputs[1])
 
 @reg.register_schedule("dense")
 def schedule_dense(_, outs, target):

nnvm/src/pass/subgraph/tensorrt_subgraph_property.cc

@@ -173,6 +173,8 @@ class TensorRTSubgraphProperty: public SubgraphProperty {
         this->GetAttr<std::unordered_set<std::string>>("op_names"));
   }
 
+  virtual ~TensorRTSubgraphProperty() {}
+
  private:
   nnvm::Symbol RemoveFlattenOpNodes(nnvm::Symbol sym) const {
     std::stack<nnvm::Node*> node_stack;

nnvm/src/top/nn/nn.cc

@@ -194,11 +194,12 @@ inline bool BatchNormInferShape(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
-inline bool BatchNormCorrectLayout(const NodeAttrs& attrs,
+inline bool BatchNormCorrectLayout(const NodeAttrs& attrs_const,
                                    std::vector<TShape>* ishapes,
                                    std::vector<Layout> *in_layouts,
                                    const std::vector<Layout> *last_in_layouts,
                                    std::vector<Layout> *out_layouts) {
+  NodeAttrs& attrs = const_cast<NodeAttrs&>(attrs_const);
   const BatchNormParam& param = nnvm::get<BatchNormParam>(attrs.parsed);
   CHECK_EQ(in_layouts->size(), 5U);
   CHECK_EQ(last_in_layouts->size(), 5U);
@@ -593,11 +594,12 @@ inline bool PadInferShape(const nnvm::NodeAttrs& attrs,
   return true;
 }
 
-inline bool PadCorrectLayout(const NodeAttrs& attrs,
+inline bool PadCorrectLayout(const NodeAttrs& attrs_const,
                              std::vector<TShape>* ishapes,
                              std::vector<Layout>* ilayouts,
                              const std::vector<Layout>* last_ilayouts,
                              std::vector<Layout>* olayouts) {
+  NodeAttrs& attrs = const_cast<NodeAttrs&>(attrs_const);
   const PadParam& param = nnvm::get<PadParam>(attrs.parsed);
   const auto& last_layout = last_ilayouts->at(0);
   Layout layout = ilayouts->at(0);

topi/python/topi/cuda/pooling.py

@@ -131,59 +131,3 @@ def traverse(OP):
 
     traverse(outs[0].op)
     return s
-
-
-@avg_pool2d_alter_layout.register(["cuda"])
-def _alter_avg_pool2d_layout(attrs, inputs, tinfo):
-    import nnvm.symbol as sym
-    copy_inputs = [s for s in inputs]
-    new_attrs = {k : attrs[k] for k in attrs.keys()}
-    # NHWC -> NCHW
-    if attrs["layout"] != "NHWC":
-        return None
-    new_attrs["layout"] = "NCHW"
-    if "target" in new_attrs:
-        del new_attrs["target"]
-    return sym.avg_pool2d(*copy_inputs, **new_attrs)
-
-
-@max_pool2d_alter_layout.register(["cuda"])
-def _alter_max_pool2d_layout(attrs, inputs, tinfo):
-    import nnvm.symbol as sym
-    copy_inputs = [s for s in inputs]
-    new_attrs = {k : attrs[k] for k in attrs.keys()}
-    # NHWC -> NCHW
-    if attrs["layout"] != "NHWC":
-        return None
-    new_attrs["layout"] = "NCHW"
-    if "target" in new_attrs:
-        del new_attrs["target"]
-    return sym.max_pool2d(*copy_inputs, **new_attrs)
-
-
-@global_max_pool2d_alter_layout.register(["cuda"])
-def _alter_global_max_pool2d_layout(attrs, inputs, tinfo):
-    import nnvm.symbol as sym
-    copy_inputs = [s for s in inputs]
-    new_attrs = {k : attrs[k] for k in attrs.keys()}
-    # NHWC -> NCHW
-    if attrs["layout"] != "NHWC":
-        return None
-    new_attrs["layout"] = "NCHW"
-    if "target" in new_attrs:
-        del new_attrs["target"]
-    return sym.global_max_pool2d(*copy_inputs, **new_attrs)
-
-
-@global_avg_pool2d_alter_layout.register(["cuda"])
-def _alter_global_avg_pool2d_layout(attrs, inputs, tinfo):
-    import nnvm.symbol as sym
-    copy_inputs = [s for s in inputs]
-    new_attrs = {k : attrs[k] for k in attrs.keys()}
-    # NHWC -> NCHW
-    if attrs["layout"] != "NHWC":
-        return None
-    new_attrs["layout"] = "NCHW"
-    if "target" in new_attrs:
-        del new_attrs["target"]
-    return sym.global_avg_pool2d(*copy_inputs, **new_attrs)