Correct runtime.load_module (apache#6161)

docs/deploy/hls.rst

@@ -64,11 +64,11 @@ We use two python scripts for this tutorial.
 
       tgt="sdaccel"
 
-      fadd = tvm.runtime.load("myadd.so")
+      fadd = tvm.runtime.load_module("myadd.so")
       if os.environ.get("XCL_EMULATION_MODE"):
-          fadd_dev = tvm.runtime.load("myadd.xclbin")
+          fadd_dev = tvm.runtime.load_module("myadd.xclbin")
       else:
-          fadd_dev = tvm.runtime.load("myadd.awsxclbin")
+          fadd_dev = tvm.runtime.load_module("myadd.awsxclbin")
       fadd.import_module(fadd_dev)
 
       ctx = tvm.context(tgt, 0)

docs/dev/introduction_to_module_serialization.rst

@@ -53,7 +53,7 @@ Let us build one ResNet-18 workload for GPU as an example first.
    resnet18_lib.export_library(path_lib)
 
    # load it back
-   loaded_lib = tvm.runtime.load(path_lib)
+   loaded_lib = tvm.runtime.load_module(path_lib)
    assert loaded_lib.type_key == "library"
    assert loaded_lib.imported_modules[0].type_key == "cuda"
 

docs/dev/relay_bring_your_own_codegen.rst

@@ -905,7 +905,7 @@ We also need to register this function to enable the corresponding Python API:
   TVM_REGISTER_GLOBAL("module.loadbinary_examplejson")
   .set_body_typed(ExampleJsonModule::LoadFromBinary);
 
-The above registration means when users call ``tvm.runtime.load(lib_path)`` API and the exported library has an ExampleJSON stream, our ``LoadFromBinary`` will be invoked to create the same customized runtime module.
+The above registration means when users call ``tvm.runtime.load_module(lib_path)`` API and the exported library has an ExampleJSON stream, our ``LoadFromBinary`` will be invoked to create the same customized runtime module.
 
 In addition, if you want to support module creation directly from an ExampleJSON file, you can also implement a simple function and register a Python API as follows:
 
@@ -930,7 +930,7 @@ In addition, if you want to support module creation directly from an ExampleJSON
       *rv = ExampleJsonModule::Create(args[0]);
   });
 
-It means users can manually write/modify an ExampleJSON file, and use Python API ``tvm.runtime.load("mysubgraph.examplejson", "examplejson")`` to construct a customized module.
+It means users can manually write/modify an ExampleJSON file, and use Python API ``tvm.runtime.load_module("mysubgraph.examplejson", "examplejson")`` to construct a customized module.
 
 *******
 Summary
@@ -954,7 +954,7 @@ In summary, here is a checklist for you to refer:
   * ``Run`` to execute a subgraph.
   * Register a runtime creation API.
   * ``SaveToBinary`` and ``LoadFromBinary`` to serialize/deserialize customized runtime module.
-  * Register ``LoadFromBinary`` API to support ``tvm.runtime.load(your_module_lib_path)``.
+  * Register ``LoadFromBinary`` API to support ``tvm.runtime.load_module(your_module_lib_path)``.
   * (optional) ``Create`` to support customized runtime module construction from subgraph file in your representation.
 
 * An annotator to annotate a user Relay program to make use of your compiler and runtime (TBA).

rust/tvm/examples/resnet/src/build_resnet.py

@@ -112,7 +112,7 @@ def download_img_labels():
 def test_build(build_dir):
     """ Sanity check with random input"""
     graph = open(osp.join(build_dir, "deploy_graph.json")).read()
-    lib = tvm.runtime.load(osp.join(build_dir, "deploy_lib.so"))
+    lib = tvm.runtime.load_module(osp.join(build_dir, "deploy_lib.so"))
     params = bytearray(open(osp.join(build_dir,"deploy_param.params"), "rb").read())
     input_data = tvm.nd.array(np.random.uniform(size=data_shape).astype("float32"))
     ctx = tvm.cpu()