[Doc] update frontend tutorials to new model based runtime (#6063)

python/tvm/relay/build_module.py

@@ -363,10 +363,8 @@ def _make_executor(self, expr=None):
             raise ValueError("Graph Runtime only supports static graphs, got output type",
                              ret_type)
         num_outputs = len(ret_type.fields) if isinstance(ret_type, _ty.TupleType) else 1
-        graph_json, mod, params = build(self.mod, target=self.target)
-        gmodule = _graph_rt.create(graph_json, mod, self.ctx)
-        if params:
-            gmodule.set_input(**params)
+        mod = build(self.mod, target=self.target)
+        gmodule = _graph_rt.GraphModule(mod['default'](self.ctx))
 
         def _graph_wrapper(*args, **kwargs):
             args = self._convert_args(self.mod["main"], args, kwargs)

tutorials/frontend/build_gcn.py

@@ -337,12 +337,11 @@ def prepare_params(g, data):
 mod["main"] = func
 # Build with Relay
 with tvm.transform.PassContext(opt_level=0): # Currently only support opt_level=0
-    graph, lib, params = relay.build(mod, target, params=params)
+    lib = relay.build(mod, target, params=params)
 
 # Generate graph runtime
 ctx = tvm.context(target, 0)
-m = graph_runtime.create(graph, lib, ctx)
-m.set_input(**params)
+m = graph_runtime.GraphModule(lib['default'](ctx))
 
 ######################################################################
 # Run the TVM model, test for accuracy and verify with DGL

tutorials/frontend/deploy_model_on_android.py

@@ -264,8 +264,8 @@ def transform_image(image):
 mod, params = relay.frontend.from_keras(keras_mobilenet_v2, shape_dict)
 
 with tvm.transform.PassContext(opt_level=3):
-    graph, lib, params = relay.build(mod, target=target,
-                                     target_host=target_host, params=params)
+    lib = relay.build(mod, target=target,
+                      target_host=target_host, params=params)
 
 # After `relay.build`, you will get three return values: graph,
 # library and the new parameter, since we do some optimization that will
@@ -309,14 +309,12 @@ def transform_image(image):
 rlib = remote.load_module('net.so')
 
 # create the remote runtime module
-module = runtime.create(graph, rlib, ctx)
+module = runtime.GraphModule(rlib['default'](ctx))
 
 ######################################################################
 # Execute on TVM
 # --------------
 
-# set parameter (upload params to the remote device. This may take a while)
-module.set_input(**params)
 # set input data
 module.set_input(input_name, tvm.nd.array(x.astype(dtype)))
 # run

tutorials/frontend/deploy_model_on_rasp.py

@@ -180,7 +180,7 @@ def transform_image(image):
     # target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -mtriple=armv7l-linux-gnueabihf -mattr=+neon')
 
 with tvm.transform.PassContext(opt_level=3):
-    graph, lib, params = relay.build(func, target, params=params)
+    lib = relay.build(func, target, params=params)
 
 # After `relay.build`, you will get three return values: graph,
 # library and the new parameter, since we do some optimization that will
@@ -212,9 +212,7 @@ def transform_image(image):
 
 # create the remote runtime module
 ctx = remote.cpu(0)
-module = runtime.create(graph, rlib, ctx)
-# set parameter (upload params to the remote device. This may take a while)
-module.set_input(**params)
+module = runtime.GraphModule(rlib['default'](ctx))
 # set input data
 module.set_input('data', tvm.nd.array(x.astype('float32')))
 # run

tutorials/frontend/deploy_prequantized.py

@@ -82,10 +82,9 @@ def get_synset():
 
 def run_tvm_model(mod, params, input_name, inp, target="llvm"):
     with tvm.transform.PassContext(opt_level=3):
-        json, lib, params = relay.build(mod, target=target, params=params)
+        lib = relay.build(mod, target=target, params=params)
 
-    runtime = tvm.contrib.graph_runtime.create(json, lib, tvm.context(target, 0))
-    runtime.set_input(**params)
+    runtime = tvm.contrib.graph_runtime.GraphModule(lib['default'](tvm.context(target, 0)))
 
     runtime.set_input(input_name, inp)
     runtime.run()

tutorials/frontend/deploy_prequantized_tflite.py

@@ -157,10 +157,9 @@ def run_tflite_model(tflite_model_buf, input_data):
 
 ###############################################################################
 # Lets run TVM compiled pre-quantized model inference and get the TVM prediction.
-def run_tvm(graph, lib, params):
+def run_tvm(lib):
     from tvm.contrib import graph_runtime
-    rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
-    rt_mod.set_input(**params)
+    rt_mod = graph_runtime.GraphModule(lib['default'](tvm.cpu(0)))
     rt_mod.set_input('input', data)
     rt_mod.run()
     tvm_res = rt_mod.get_output(0).asnumpy()
@@ -199,12 +198,11 @@ def run_tvm(graph, lib, params):
 # target platform that you are interested in.
 target = 'llvm'
 with tvm.transform.PassContext(opt_level=3):
-    graph, lib, params = relay.build_module.build(mod, target=target,
-                                                  params=params)
+    lib = relay.build_module.build(mod, target=target, params=params)
 
 ###############################################################################
 # Finally, lets call inference on the TVM compiled module.
-tvm_pred, rt_mod = run_tvm(graph, lib, params)
+tvm_pred, rt_mod = run_tvm(lib)
 
 ###############################################################################
 # Accuracy comparison

tutorials/frontend/deploy_sparse.py

@@ -216,15 +216,14 @@ def import_graphdef(
 # tensors instead of sparse aware kernels.
 def run_relay_graph(mod, params, shape_dict, target, ctx):
     with relay.build_config(opt_level=3):
-        graph, lib, params = relay.build(mod, target=target, params=params)
+        lib = relay.build(mod, target=target, params=params)
     input_shape = shape_dict["input_1"]
     dummy_data = np.random.uniform(size=input_shape, low=0, high=input_shape[1]).astype(
         "int32"
     )
 
-    m = graph_runtime.create(graph, lib, ctx)
+    m = graph_runtime.GraphModule(lib['default'](ctx))
     m.set_input(0, dummy_data)
-    m.set_input(**params)
     m.run()
     tvm_output = m.get_output(0)
 

tutorials/frontend/deploy_ssd_gluoncv.py

@@ -88,27 +88,26 @@
 def build(target):
     mod, params = relay.frontend.from_mxnet(block, {"data": dshape})
     with tvm.transform.PassContext(opt_level=3):
-        graph, lib, params = relay.build(mod, target, params=params)
-    return graph, lib, params
+        lib = relay.build(mod, target, params=params)
+    return lib
 
 ######################################################################
 # Create TVM runtime and do inference
 
-def run(graph, lib, params, ctx):
+def run(lib, ctx):
     # Build TVM runtime
-    m = graph_runtime.create(graph, lib, ctx)
+    m = graph_runtime.GraphModule(lib['default'](ctx))
     tvm_input = tvm.nd.array(x.asnumpy(), ctx=ctx)
     m.set_input('data', tvm_input)
-    m.set_input(**params)
     # execute
     m.run()
     # get outputs
     class_IDs, scores, bounding_boxs = m.get_output(0), m.get_output(1), m.get_output(2)
     return class_IDs, scores, bounding_boxs
 
 for target, ctx in target_list:
-    graph, lib, params = build(target)
-    class_IDs, scores, bounding_boxs = run(graph, lib, params, ctx)
+    lib = build(target)
+    class_IDs, scores, bounding_boxs = run(lib, ctx)
 
 ######################################################################
 # Display result

tutorials/frontend/from_caffe2.py

@@ -89,7 +89,7 @@ def transform_image(image):
 # target x86 CPU
 target = 'llvm'
 with transform.PassContext(opt_level=3):
-    graph, lib, params = relay.build(mod, target, params=params)
+    lib = relay.build(mod, target, params=params)
 
 ######################################################################
 # Execute on TVM
@@ -101,11 +101,9 @@ def transform_image(image):
 # context x86 CPU, use tvm.gpu(0) if you run on GPU
 ctx = tvm.cpu(0)
 # create a runtime executor module
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
 # set inputs
 m.set_input(input_name, tvm.nd.array(data.astype('float32')))
-# set related params
-m.set_input(**params)
 # execute
 m.run()
 # get outputs

tutorials/frontend/from_coreml.py

@@ -75,9 +75,7 @@
 mod, params = relay.frontend.from_coreml(mlmodel, shape_dict)
 
 with tvm.transform.PassContext(opt_level=3):
-    graph, lib, params = relay.build(mod,
-                                     target,
-                                     params=params)
+    lib = relay.build(mod, target, params=params)
 
 ######################################################################
 # Execute on TVM
@@ -86,10 +84,9 @@
 from tvm.contrib import graph_runtime
 ctx = tvm.cpu(0)
 dtype = 'float32'
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
 # set inputs
 m.set_input('image', tvm.nd.array(x.astype(dtype)))
-m.set_input(**params)
 # execute
 m.run()
 # get outputs

tutorials/frontend/from_darknet.py

@@ -101,10 +101,7 @@
 shape = {'data': data.shape}
 print("Compiling the model...")
 with tvm.transform.PassContext(opt_level=3):
-    graph, lib, params = relay.build(mod,
-                                     target=target,
-                                     target_host=target_host,
-                                     params=params)
+    lib = relay.build(mod, target=target, target_host=target_host, params=params)
 
 [neth, netw] = shape['data'][2:] # Current image shape is 608x608
 ######################################################################
@@ -122,11 +119,10 @@
 # The process is no different from other examples.
 from tvm.contrib import graph_runtime
 
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
 
 # set inputs
 m.set_input('data', tvm.nd.array(data.astype(dtype)))
-m.set_input(**params)
 # execute
 print("Running the test image...")
 

tutorials/frontend/from_mxnet.py

@@ -91,7 +91,7 @@ def transform_image(image):
 # now compile the graph
 target = 'cuda'
 with tvm.transform.PassContext(opt_level=3):
-    graph, lib, params = relay.build(func, target, params=params)
+    lib = relay.build(func, target, params=params)
 
 ######################################################################
 # Execute the portable graph on TVM
@@ -100,10 +100,9 @@ def transform_image(image):
 from tvm.contrib import graph_runtime
 ctx = tvm.gpu(0)
 dtype = 'float32'
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
 # set inputs
 m.set_input('data', tvm.nd.array(x.astype(dtype)))
-m.set_input(**params)
 # execute
 m.run()
 # get outputs

tutorials/frontend/from_pytorch.py

@@ -102,21 +102,17 @@
 target_host = 'llvm'
 ctx = tvm.cpu(0)
 with tvm.transform.PassContext(opt_level=3):
-    graph, lib, params = relay.build(mod,
-                                     target=target,
-                                     target_host=target_host,
-                                     params=params)
+    lib = relay.build(mod, target=target, target_host=target_host, params=params)
 
 ######################################################################
 # Execute the portable graph on TVM
 # ---------------------------------
 # Now we can try deploying the compiled model on target.
 from tvm.contrib import graph_runtime
 dtype = 'float32'
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
 # Set inputs
 m.set_input(input_name, tvm.nd.array(img.astype(dtype)))
-m.set_input(**params)
 # Execute
 m.run()
 # Get outputs

tutorials/frontend/from_tensorflow.py

@@ -145,10 +145,7 @@
 #   lib: target library which can be deployed on target with TVM runtime.
 
 with tvm.transform.PassContext(opt_level=3):
-    graph, lib, params = relay.build(mod,
-                                     target=target,
-                                     target_host=target_host,
-                                     params=params)
+    lib = relay.build(mod, target=target, target_host=target_host, params=params)
 
 ######################################################################
 # Execute the portable graph on TVM
@@ -157,10 +154,9 @@
 
 from tvm.contrib import graph_runtime
 dtype = 'uint8'
-m = graph_runtime.create(graph, lib, ctx)
+m = graph_runtime.GraphModule(lib['default'](ctx))
 # set inputs
 m.set_input('DecodeJpeg/contents', tvm.nd.array(x.astype(dtype)))
-m.set_input(**params)
 # execute
 m.run()
 # get outputs

tutorials/frontend/from_tflite.py

@@ -136,7 +136,7 @@ def extract(path):
 # Build the module against to x86 CPU
 target = "llvm"
 with transform.PassContext(opt_level=3):
-    graph, lib, params = relay.build(mod, target, params=params)
+    lib = relay.build(mod, target, params=params)
 
 ######################################################################
 # Execute on TVM
@@ -146,14 +146,11 @@ def extract(path):
 from tvm.contrib import graph_runtime as runtime
 
 # Create a runtime executor module
-module = runtime.create(graph, lib, tvm.cpu())
+module = runtime.GraphModule(lib['default'](tvm.cpu()))
 
 # Feed input data
 module.set_input(input_tensor, tvm.nd.array(image_data))
 
-# Feed related params
-module.set_input(**params)
-
 # Run
 module.run()
 

tutorials/frontend/using_external_lib.py

@@ -71,13 +71,11 @@
 logging.basicConfig(level=logging.DEBUG) # to dump TVM IR after fusion
 
 target = "cuda"
-graph, lib, params = relay.build_module.build(
-    net, target, params=params)
+lib = relay.build_module.build(net, target, params=params)
 
 ctx = tvm.context(target, 0)
 data = np.random.uniform(-1, 1, size=data_shape).astype("float32")
-module = runtime.create(graph, lib, ctx)
-module.set_input(**params)
+module = runtime.GraphModule(lib['default'](ctx))
 module.set_input("data", data)
 module.run()
 out_shape = (batch_size, out_channels, 224, 224)
@@ -494,13 +492,11 @@
 # To do that, all we need to do is to append the option " -libs=cudnn" to the target string.
 net, params = testing.create_workload(simple_net)
 target = "cuda -libs=cudnn" # use cudnn for convolution
-graph, lib, params = relay.build_module.build(
-        net, target, params=params)
+lib = relay.build_module.build(net, target, params=params)
 
 ctx = tvm.context(target, 0)
 data = np.random.uniform(-1, 1, size=data_shape).astype("float32")
-module = runtime.create(graph, lib, ctx)
-module.set_input(**params)
+module = runtime.GraphModule(lib['default'](ctx))
 module.set_input("data", data)
 module.run()
 out_shape = (batch_size, out_channels, 224, 224)