Addressing comments - 11

Change-Id: Iad028144d7b394b2dd2fce41a35ca689d1680200

python/tvm/relay/build_module.py

@@ -116,7 +116,6 @@ def build(self, mod, target=None, target_host=None, params=None, executor="graph
 
         Returns
         -------
-<<<<<<< HEAD
         graph_json : str
             The json string that can be accepted by graph executor.
 
@@ -125,10 +124,6 @@ def build(self, mod, target=None, target_host=None, params=None, executor="graph
 
         params : dict
             The parameters of the final graph.
-=======
-        factory_module : tvm.relay.backend.executor_factory.ExecutorFactoryModule
-            The runtime factory for the TVM executor.
->>>>>>> f65012308... Addressing comments - 3
         """
         target = _update_target(target)
         target, target_host = Target.check_and_update_host_consist(
@@ -288,28 +283,8 @@ def build(ir_mod, target=None, target_host=None, params=None, mod_name="default"
 
     Returns
     -------
-<<<<<<< HEAD
-<<<<<<< HEAD
     factory_module : tvm.relay.backend.executor_factory.ExecutorFactoryModule
             The runtime factory for the TVM graph executor.
-=======
-    internal_repr : str or tir.PrimFunc
-        The internal representation the executor uses to execute the
-        network. Can be a string representing the json graph (if we are
-        building for graph executor) or the PrimFunc representing the
-        AOT runner function
-=======
-    executor_config : str
-        The internal configuration the executor uses to execute the
-        network.
->>>>>>> db667146e... addressing comments - 6
-
-    mod : tvm.Module
-        The module containing necessary libraries.
-
-    params : dict
-        The parameters of the final graph.
->>>>>>> f65012308... Addressing comments - 3
     """
     # pylint: enable=line-too-long
     # fmt: on

src/relay/backend/aot_executor_codegen.cc

@@ -22,7 +22,6 @@
  * \brief Graph runtime codegen
  */
 
-#include <dmlc/any.h>
 #include <tvm/ir/module.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/runtime/device_api.h>
@@ -212,7 +211,7 @@ class AOTExecutorCodegen : public ExprVisitor {
    */
   void CreateFuncCall(Call call, std::string func_name) {
     tvm::Array<PrimExpr> args{tvm::tir::StringImm(func_name)};
-    std::vector<tir::Stmt> CreateFuncCall_stmts;
+    std::vector<tir::Stmt> create_func_call_stmts;
 
     // Pack the inputs
     for (Expr arg : call->args) {
@@ -228,9 +227,9 @@ class AOTExecutorCodegen : public ExprVisitor {
     }
 
     // Use tvm_call_packed to execute the function
-    CreateFuncCall_stmts.push_back(tir::Evaluate(
+    create_func_call_stmts.push_back(tir::Evaluate(
         tvm::tir::Call(DataType::Int(32), tvm::tir::builtin::tvm_call_cpacked(), args)));
-    tir::Stmt body = tir::SeqStmt(CreateFuncCall_stmts);
+    tir::Stmt body = tir::SeqStmt(create_func_call_stmts);
     stmts_.push_back(body);
   }
 

tests/crt/aot_memory_test.cc

@@ -27,21 +27,21 @@ TEST(AOTMemory, Allocate) {
   static uint8_t model_memory[96];
   tvm_workspace_t tvm_runtime_workspace;
 
-  StackMemoryManager_Init(&tvm_runtime_workspace, model_memory, 96);
+  ASSERT_EQ(StackMemoryManager_Init(&tvm_runtime_workspace, model_memory, 96), kTvmErrorNoError);
   void* block_one = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_one);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_one), kTvmErrorNoError);
   ASSERT_EQ(block_one, &model_memory[0]);
 
   void* block_two = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 2, &block_two);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 2, &block_two), kTvmErrorNoError);
   ASSERT_EQ(block_two, &model_memory[16 + STACK_ALLOCATOR_TAG_SIZE_BYTES]);
 
   void* two_blocks = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 24, &two_blocks);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 24, &two_blocks), kTvmErrorNoError);
   ASSERT_EQ(two_blocks, &model_memory[32 + 2 * STACK_ALLOCATOR_TAG_SIZE_BYTES]);
 
   void* block_three = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_three);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_three), kTvmErrorNoError);
   ASSERT_EQ(block_three, &model_memory[64 + 3 * STACK_ALLOCATOR_TAG_SIZE_BYTES]);
 }
 
@@ -51,24 +51,24 @@ TEST(AOTMemory, Allocate) {
 TEST(AOTMemory, Free) {
   static uint8_t model_memory[80];
   tvm_workspace_t tvm_runtime_workspace;
-  StackMemoryManager_Init(&tvm_runtime_workspace, model_memory, 80);
+  ASSERT_EQ(StackMemoryManager_Init(&tvm_runtime_workspace, model_memory, 80), kTvmErrorNoError);
 
   void* block_one = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_one);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_one), kTvmErrorNoError);
   ASSERT_EQ(block_one, &model_memory[0]);
 
   void* block_two = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_two);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_two), kTvmErrorNoError);
   ASSERT_EQ(block_two, &model_memory[16 + STACK_ALLOCATOR_TAG_SIZE_BYTES]);
   ASSERT_EQ(kTvmErrorNoError, StackMemoryManager_Free(&tvm_runtime_workspace, block_two));
 
   void* two_blocks = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 2, &two_blocks);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 2, &two_blocks), kTvmErrorNoError);
   ASSERT_EQ(two_blocks, &model_memory[16 + STACK_ALLOCATOR_TAG_SIZE_BYTES]);
   ASSERT_EQ(kTvmErrorNoError, StackMemoryManager_Free(&tvm_runtime_workspace, two_blocks));
 
   void* block_three = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_three);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_three), kTvmErrorNoError);
   ASSERT_EQ(block_three, &model_memory[16 + STACK_ALLOCATOR_TAG_SIZE_BYTES]);
 }
 
@@ -78,18 +78,19 @@ TEST(AOTMemory, Free) {
 TEST(AOTMemory, OverAllocate) {
   static uint8_t model_memory[72];
   tvm_workspace_t tvm_runtime_workspace;
-  StackMemoryManager_Init(&tvm_runtime_workspace, model_memory, 80);
+  ASSERT_EQ(StackMemoryManager_Init(&tvm_runtime_workspace, model_memory, 80), kTvmErrorNoError);
 
   void* block_one = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_one);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_one), kTvmErrorNoError);
   ASSERT_EQ(block_one, &model_memory[0]);
 
   void* block_two = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_two);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_two), kTvmErrorNoError);
   ASSERT_EQ(block_two, &model_memory[16 + STACK_ALLOCATOR_TAG_SIZE_BYTES]);
 
   void* two_blocks = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 64, &two_blocks);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 64, &two_blocks),
+            kTvmErrorPlatformNoMemory);
   ASSERT_EQ(two_blocks, (void*)NULL);
 }
 
@@ -99,14 +100,14 @@ TEST(AOTMemory, OverAllocate) {
 TEST(AOTMemory, FreeOutOfOrder) {
   static uint8_t model_memory[80];
   tvm_workspace_t tvm_runtime_workspace;
-  StackMemoryManager_Init(&tvm_runtime_workspace, model_memory, 80);
+  ASSERT_EQ(StackMemoryManager_Init(&tvm_runtime_workspace, model_memory, 80), kTvmErrorNoError);
 
   void* block_one = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_one);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_one), kTvmErrorNoError);
   ASSERT_EQ(block_one, &model_memory[0]);
 
   void* block_two = NULL;
-  StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_two);
+  ASSERT_EQ(StackMemoryManager_Allocate(&tvm_runtime_workspace, 1, &block_two), kTvmErrorNoError);
   ASSERT_EQ(block_two, &model_memory[16 + STACK_ALLOCATOR_TAG_SIZE_BYTES]);
 
   ASSERT_EXIT(StackMemoryManager_Free(&tvm_runtime_workspace, block_one),

tests/python/relay/aot/aot_test_utils.py

@@ -109,9 +109,9 @@ def create_main(test_name, input_list, output_list, output_path):
         main_file.write("tvm_runtime_run(&network, inputs, outputs);")
 
         for i in range(0, len(output_list)):
-            is_real_dtype = output_list[i].dtype == "float32"
+            is_float_dtype = output_list[i].dtype == "float32"
             main_file.write("for (int i = 0; i<output_data%i_len; i++){\n" % i)
-            if is_real_dtype:
+            if is_float_dtype:
                 main_file.write(
                     'if (fabs(output_data%s[i]-expected_output_data%s[i]) > 0.001f){printf("ko\\n");return -1;}\n'
                     % (i, i)