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From tqc...@apache.org
Subject [incubator-tvm-site] branch asf-site updated: Docs build at Wed Jun 17 14:20:48 PDT 2020
Date Wed, 17 Jun 2020 21:21:04 GMT
This is an automated email from the ASF dual-hosted git repository.

tqchen pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-tvm-site.git


The following commit(s) were added to refs/heads/asf-site by this push:
     new 1f8a6ea  Docs build at Wed Jun 17 14:20:48 PDT 2020
1f8a6ea is described below

commit 1f8a6ea146bf5c5c3878709a110b62e8b3eb43b2
Author: tqchen <tqchen@octoml.ai>
AuthorDate: Wed Jun 17 14:20:48 2020 -0700

    Docs build at Wed Jun 17 14:20:48 PDT 2020
---
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 1722 files changed, 87485 insertions(+), 52356 deletions(-)

diff --git a/docs/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py b/docs/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py
index e01a4ec..35a308c 100644
--- a/docs/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py
+++ b/docs/_downloads/02fc8627299fa0b05eb017773b471bfa/from_tflite.py
@@ -128,14 +128,14 @@ input_shape = (1, 224, 224, 3)
 input_dtype = "float32"
 
 # Parse TFLite model and convert it to a Relay module
-from tvm import relay
+from tvm import relay, transform
 mod, params = relay.frontend.from_tflite(tflite_model,
                                          shape_dict={input_tensor: input_shape},
                                          dtype_dict={input_tensor: input_dtype})
 
 # Build the module against to x86 CPU
 target = "llvm"
-with relay.build_config(opt_level=3):
+with transform.PassContext(opt_level=3):
     graph, lib, params = relay.build(mod, target, params=params)
 
 ######################################################################
diff --git a/docs/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb b/docs/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb
index f9ff631..ff25080 100644
--- a/docs/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb
+++ b/docs/_downloads/0c30ce88b67b0e8d46494348ab36c9fb/from_tflite.ipynb
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "# TFLite input tensor name, shape and type\ninput_tensor = \"input\"\ninput_shape = (1, 224, 224, 3)\ninput_dtype = \"float32\"\n\n# Parse TFLite model and convert it to a Relay module\nfrom tvm import relay\nmod, params = relay.frontend.from_tflite(tflite_model,\n                                         shape_dict={input_tensor: input_shape},\n                                         dtype_dict={input_tensor: input_dtype})\n\n# Build the module against to x86 CPU\ntarget = \"ll [...]
+        "# TFLite input tensor name, shape and type\ninput_tensor = \"input\"\ninput_shape = (1, 224, 224, 3)\ninput_dtype = \"float32\"\n\n# Parse TFLite model and convert it to a Relay module\nfrom tvm import relay, transform\nmod, params = relay.frontend.from_tflite(tflite_model,\n                                         shape_dict={input_tensor: input_shape},\n                                         dtype_dict={input_tensor: input_dtype})\n\n# Build the module against to x86 CPU\nta [...]
       ]
     },
     {
diff --git a/docs/_downloads/10e16681be542cc483fa89e9b4678a27/opt_matmul_auto_tensorcore.py b/docs/_downloads/10e16681be542cc483fa89e9b4678a27/opt_matmul_auto_tensorcore.py
index 50cc1eb..7dbd475 100644
--- a/docs/_downloads/10e16681be542cc483fa89e9b4678a27/opt_matmul_auto_tensorcore.py
+++ b/docs/_downloads/10e16681be542cc483fa89e9b4678a27/opt_matmul_auto_tensorcore.py
@@ -287,10 +287,9 @@ def tune_and_evaluate(M, N, L, dtype, layout):
   print(best_config)
   with autotvm.apply_history_best('matmul.log'):
     with tvm.target.create("cuda"):
-        with tvm.target.build_config():
-            s, arg_bufs = test_gemm(N, L, M, dtype, layout)
-            print(tvm.lower(s, arg_bufs, simple_mode=True))
-            func = tvm.build(s, arg_bufs)
+          s, arg_bufs = test_gemm(N, L, M, dtype, layout)
+          print(tvm.lower(s, arg_bufs, simple_mode=True))
+          func = tvm.build(s, arg_bufs)
   dev_module = func.imported_modules[0]
   print(dev_module.get_source())
 
diff --git a/docs/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb b/docs/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb
index 31f4c53..3c67a09 100644
--- a/docs/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb
+++ b/docs/_downloads/1604460dde2b82fb9db809bb388890f8/deploy_prequantized_tflite.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nDeploy a Framework-prequantized Model with TVM - Part 3 (TFLite)\n================================================================\n**Author**: `Siju Samuel <https://github.com/siju-samuel>`_\nWelcome to part 3 of the Deploy Framework-Prequantized Model with TVM tutorial.\nIn this part, we will start with a Quantized TFLite graph and then compile and execute it via TVM.\n\n\nFor more details on quantizing the model using TFLite, readers are encouraged to\ngo through `Convertin [...]
+        "\nDeploy a Framework-prequantized Model with TVM - Part 3 (TFLite)\n================================================================\n**Author**: `Siju Samuel <https://github.com/siju-samuel>`_\n\nWelcome to part 3 of the Deploy Framework-Prequantized Model with TVM tutorial.\nIn this part, we will start with a Quantized TFLite graph and then compile and execute it via TVM.\n\n\nFor more details on quantizing the model using TFLite, readers are encouraged to\ngo through `Convert [...]
       ]
     },
     {
@@ -223,7 +223,7 @@
       },
       "outputs": [],
       "source": [
-        "target = 'llvm'\nwith relay.build_config(opt_level=3):\n    graph, lib, params = relay.build_module.build(mod, target=target,\n                                                  params=params)"
+        "target = 'llvm'\nwith tvm.transform.PassContext(opt_level=3):\n    graph, lib, params = relay.build_module.build(mod, target=target,\n                                                  params=params)"
       ]
     },
     {
diff --git a/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py b/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
index 4195075..a6fe45b 100644
--- a/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
+++ b/docs/_downloads/272a5a893d007658546dc0eaf0a7aeed/tune_relay_cuda.py
@@ -222,7 +222,7 @@ def tune_and_evaluate(tuning_opt):
     # compile kernels with history best records
     with autotvm.apply_history_best(log_file):
         print("Compile...")
-        with relay.build_config(opt_level=3):
+        with tvm.transform.PassContext(opt_level=3):
             graph, lib, params = relay.build_module.build(
                 mod, target=target, params=params)
 
diff --git a/docs/_downloads/289640e7ded85c57931fc0537d443c5f/relay_pass_infra.ipynb b/docs/_downloads/289640e7ded85c57931fc0537d443c5f/relay_pass_infra.ipynb
index 12bb90f..bc8691a 100644
--- a/docs/_downloads/289640e7ded85c57931fc0537d443c5f/relay_pass_infra.ipynb
+++ b/docs/_downloads/289640e7ded85c57931fc0537d443c5f/relay_pass_infra.ipynb
@@ -152,7 +152,7 @@
       },
       "outputs": [],
       "source": [
-        "with relay.build_config(opt_level=3):\n    mod2 = seq(mod)\nprint(mod2)"
+        "with tvm.transform.PassContext(opt_level=3):\n    mod2 = seq(mod)\nprint(mod2)"
       ]
     },
     {
@@ -170,7 +170,7 @@
       },
       "outputs": [],
       "source": [
-        "with relay.build_config(opt_level=3, disabled_pass=[\"EliminateCommonSubexpr\"]):\n    mod3 = seq(mod)\nprint(mod3)"
+        "with tvm.transform.PassContext(opt_level=3, disabled_pass=[\"EliminateCommonSubexpr\"]):\n    mod3 = seq(mod)\nprint(mod3)"
       ]
     },
     {
@@ -188,7 +188,7 @@
       },
       "outputs": [],
       "source": [
-        "with relay.build_config(opt_level=3):\n    mod4 = seq(mod)\nprint(mod4)\n\nseq1 = tvm.transform.Sequential([relay.transform.AlterOpLayout()])\nwith relay.build_config(opt_level=3):\n    with tvm.target.create(\"llvm\"):\n        mod5 = seq1(mod)\nprint(mod5)"
+        "with tvm.transform.PassContext(opt_level=3):\n    mod4 = seq(mod)\nprint(mod4)\n\nseq1 = tvm.transform.Sequential([relay.transform.AlterOpLayout()])\nwith tvm.transform.PassContext(opt_level=3):\n    with tvm.target.create(\"llvm\"):\n        mod5 = seq1(mod)\nprint(mod5)"
       ]
     },
     {
@@ -224,7 +224,7 @@
       },
       "outputs": [],
       "source": [
-        "f = example()\nmod = tvm.IRModule.from_expr(f)\nseq = tvm.transform.Sequential([relay.transform.FoldConstant(),\n                                tvm.transform.PrintIR(),\n                                relay.transform.EliminateCommonSubexpr(),\n                                relay.transform.FuseOps(),\n                                tvm.transform.PrintIR()])\nwith relay.build_config(opt_level=3):\n    mod = seq(mod)\n\nprint(\"done\")"
+        "f = example()\nmod = tvm.IRModule.from_expr(f)\nseq = tvm.transform.Sequential([relay.transform.FoldConstant(),\n                                tvm.transform.PrintIR(),\n                                relay.transform.EliminateCommonSubexpr(),\n                                relay.transform.FuseOps(),\n                                tvm.transform.PrintIR()])\nwith tvm.transform.PassContext(opt_level=3):\n    mod = seq(mod)\n\nprint(\"done\")"
       ]
     }
   ],
diff --git a/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb b/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
index ddd7c4f..eacfead 100644
--- a/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
+++ b/docs/_downloads/2c0ed53a9ebd68caf76cd8235fae2711/tune_relay_mobile_gpu.ipynb
@@ -126,7 +126,7 @@
       },
       "outputs": [],
       "source": [
-        "def tune_and_evaluate(tuning_opt):\n    # extract workloads from relay program\n    print(\"Extract tasks...\")\n    mod, params, input_shape, _ = get_network(network, batch_size=1)\n    tasks = autotvm.task.extract_from_program(mod[\"main\"],\n                                              target=target,\n                                              target_host=target_host,\n                                              params=params,\n                                           [...]
+        "def tune_and_evaluate(tuning_opt):\n    # extract workloads from relay program\n    print(\"Extract tasks...\")\n    mod, params, input_shape, _ = get_network(network, batch_size=1)\n    tasks = autotvm.task.extract_from_program(mod[\"main\"],\n                                              target=target,\n                                              target_host=target_host,\n                                              params=params,\n                                           [...]
       ]
     },
     {
diff --git a/docs/_downloads/2e974b05b6d59fcf944f96d27106b994/from_keras.ipynb b/docs/_downloads/2e974b05b6d59fcf944f96d27106b994/from_keras.ipynb
index 54e5370..b78d755 100644
--- a/docs/_downloads/2e974b05b6d59fcf944f96d27106b994/from_keras.ipynb
+++ b/docs/_downloads/2e974b05b6d59fcf944f96d27106b994/from_keras.ipynb
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "shape_dict = {'input_1': data.shape}\nmod, params = relay.frontend.from_keras(keras_resnet50, shape_dict)\n# compile the model\ntarget = 'cuda'\nctx = tvm.gpu(0)\nwith relay.build_config(opt_level=3):\n    executor = relay.build_module.create_executor('graph', mod, ctx, target)"
+        "shape_dict = {'input_1': data.shape}\nmod, params = relay.frontend.from_keras(keras_resnet50, shape_dict)\n# compile the model\ntarget = 'cuda'\nctx = tvm.gpu(0)\nwith tvm.transform.PassContext(opt_level=3):\n    executor = relay.build_module.create_executor('graph', mod, ctx, target)"
       ]
     },
     {
diff --git a/docs/_downloads/2f6dcf56b15f857f94b6d320c1ace6e5/from_coreml.ipynb b/docs/_downloads/2f6dcf56b15f857f94b6d320c1ace6e5/from_coreml.ipynb
index 6f98860..abb835c 100644
--- a/docs/_downloads/2f6dcf56b15f857f94b6d320c1ace6e5/from_coreml.ipynb
+++ b/docs/_downloads/2f6dcf56b15f857f94b6d320c1ace6e5/from_coreml.ipynb
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "target = 'llvm'\nshape_dict = {'image': x.shape}\n\n# Parse CoreML model and convert into Relay computation graph\nmod, params = relay.frontend.from_coreml(mlmodel, shape_dict)\n\nwith relay.build_config(opt_level=3):\n    graph, lib, params = relay.build(mod,\n                                     target,\n                                     params=params)"
+        "target = 'llvm'\nshape_dict = {'image': x.shape}\n\n# Parse CoreML model and convert into Relay computation graph\nmod, params = relay.frontend.from_coreml(mlmodel, shape_dict)\n\nwith tvm.transform.PassContext(opt_level=3):\n    graph, lib, params = relay.build(mod,\n                                     target,\n                                     params=params)"
       ]
     },
     {
diff --git a/docs/_downloads/30015213c2882505d466865fafaed52d/from_caffe2.ipynb b/docs/_downloads/30015213c2882505d466865fafaed52d/from_caffe2.ipynb
index 70a7e7f..03cd570 100644
--- a/docs/_downloads/30015213c2882505d466865fafaed52d/from_caffe2.ipynb
+++ b/docs/_downloads/30015213c2882505d466865fafaed52d/from_caffe2.ipynb
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "# Caffe2 input tensor name, shape and type\ninput_name = resnet50.predict_net.op[0].input[0]\nshape_dict = {input_name: data.shape}\ndtype_dict = {input_name: data.dtype}\n\n# parse Caffe2 model and convert into Relay computation graph\nfrom tvm import relay\nmod, params = relay.frontend.from_caffe2(resnet50.init_net, resnet50.predict_net, shape_dict, dtype_dict)\n\n# compile the model\n# target x86 CPU\ntarget = 'llvm'\nwith relay.build_config(opt_level=3):\n    graph, lib, par [...]
+        "# Caffe2 input tensor name, shape and type\ninput_name = resnet50.predict_net.op[0].input[0]\nshape_dict = {input_name: data.shape}\ndtype_dict = {input_name: data.dtype}\n\n# parse Caffe2 model and convert into Relay computation graph\nfrom tvm import relay, transform\nmod, params = relay.frontend.from_caffe2(resnet50.init_net, resnet50.predict_net, shape_dict, dtype_dict)\n\n# compile the model\n# target x86 CPU\ntarget = 'llvm'\nwith transform.PassContext(opt_level=3):\n    g [...]
       ]
     },
     {
diff --git a/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb b/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
index 6a993e9..5dfb414 100644
--- a/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
+++ b/docs/_downloads/38606228ff7130fbd6473b7c0625ddcd/deploy_model_on_android.ipynb
@@ -119,7 +119,7 @@
       },
       "outputs": [],
       "source": [
-        "local_demo = True\n\n# by default on CPU target will execute.\n# select 'cpu', 'opencl' and 'vulkan'\ntest_target = 'cpu'\n\n# Change target configuration.\n# Run `adb shell cat /proc/cpuinfo` to find the arch.\narch = 'arm64'\ntarget = 'llvm -target=%s-linux-android' % arch\ntarget_host = None\n\nif local_demo:\n    target_host = None\n    target = 'llvm'\nelif test_target == 'opencl':\n    target_host = target\n    target = 'opencl'\nelif test_target == 'vulkan':\n    target_h [...]
+        "local_demo = True\n\n# by default on CPU target will execute.\n# select 'cpu', 'opencl' and 'vulkan'\ntest_target = 'cpu'\n\n# Change target configuration.\n# Run `adb shell cat /proc/cpuinfo` to find the arch.\narch = 'arm64'\ntarget = 'llvm -target=%s-linux-android' % arch\ntarget_host = None\n\nif local_demo:\n    target_host = None\n    target = 'llvm'\nelif test_target == 'opencl':\n    target_host = target\n    target = 'opencl'\nelif test_target == 'vulkan':\n    target_h [...]
       ]
     },
     {
diff --git a/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py b/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
index 17ec9cb..bc5b523 100644
--- a/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
+++ b/docs/_downloads/3961fdfa7abff1b6dc996faa43b4c40f/deploy_model_on_android.py
@@ -263,7 +263,7 @@ input_name = 'input_1'
 shape_dict = {input_name: x.shape}
 mod, params = relay.frontend.from_keras(keras_mobilenet_v2, shape_dict)
 
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     graph, lib, params = relay.build(mod, target=target,
                                      target_host=target_host, params=params)
 
diff --git a/docs/_downloads/39e437b36375e97c049f64073eade7a6/relay_quick_start.py b/docs/_downloads/39e437b36375e97c049f64073eade7a6/relay_quick_start.py
index b2174a0..e52a99a 100644
--- a/docs/_downloads/39e437b36375e97c049f64073eade7a6/relay_quick_start.py
+++ b/docs/_downloads/39e437b36375e97c049f64073eade7a6/relay_quick_start.py
@@ -96,7 +96,7 @@ print(mod.astext(show_meta_data=False))
 
 opt_level = 3
 target = tvm.target.cuda()
-with relay.build_config(opt_level=opt_level):
+with tvm.transform.PassContext(opt_level=opt_level):
     graph, lib, params = relay.build(mod, target, params=params)
 
 #####################################################################
diff --git a/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb b/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
index 17b3109..2e10e0a 100644
--- a/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
+++ b/docs/_downloads/48bd751ebaae08fce134e559f86a25cc/tune_relay_vta.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "def compile_network(env, target, model, start_pack, stop_pack):\n\n    # Populate the shape and data type dictionary\n    dtype_dict = {\"data\": 'float32'}\n    shape_dict = {\"data\": (env.BATCH, 3, 224, 224)}\n\n    # Get off the shelf gluon model, and convert to relay\n    gluon_model = vision.get_model(model, pretrained=True)\n    mod, params = relay.frontend.from_mxnet(gluon_model, shape_dict)\n\n    # Update shape and type dictionary\n    shape_dict.update({k: v.shape for [...]
+        "def compile_network(env, target, model, start_pack, stop_pack):\n\n    # Populate the shape and data type dictionary\n    dtype_dict = {\"data\": 'float32'}\n    shape_dict = {\"data\": (env.BATCH, 3, 224, 224)}\n\n    # Get off the shelf gluon model, and convert to relay\n    gluon_model = vision.get_model(model, pretrained=True)\n    mod, params = relay.frontend.from_mxnet(gluon_model, shape_dict)\n\n    # Update shape and type dictionary\n    shape_dict.update({k: v.shape for [...]
       ]
     },
     {
@@ -144,7 +144,7 @@
       },
       "outputs": [],
       "source": [
-        "def tune_and_evaluate(tuning_opt):\n\n    if env.TARGET != \"sim\":\n        # Get remote from fleet node\n        remote = autotvm.measure.request_remote(env.TARGET,\n                                                tracker_host,\n                                                tracker_port,\n                                                timeout=10000)\n        # Reconfigure the JIT runtime and FPGA.\n        vta.reconfig_runtime(remote)\n        vta.program_fpga(remote, bitst [...]
+        "def tune_and_evaluate(tuning_opt):\n\n    if env.TARGET != \"sim\":\n        # Get remote from fleet node\n        remote = autotvm.measure.request_remote(env.TARGET,\n                                                tracker_host,\n                                                tracker_port,\n                                                timeout=10000)\n        # Reconfigure the JIT runtime and FPGA.\n        vta.reconfig_runtime(remote)\n        vta.program_fpga(remote, bitst [...]
       ]
     },
     {
diff --git a/docs/_downloads/50b174352ccf0a0defcbd8e6b40145e2/from_tensorflow.py b/docs/_downloads/50b174352ccf0a0defcbd8e6b40145e2/from_tensorflow.py
index 0ebd733..b7b3d69 100644
--- a/docs/_downloads/50b174352ccf0a0defcbd8e6b40145e2/from_tensorflow.py
+++ b/docs/_downloads/50b174352ccf0a0defcbd8e6b40145e2/from_tensorflow.py
@@ -144,7 +144,7 @@ print("Tensorflow protobuf imported to relay frontend.")
 #   params: final params after compilation.
 #   lib: target library which can be deployed on target with TVM runtime.
 
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     graph, lib, params = relay.build(mod,
                                      target=target,
                                      target_host=target_host,
diff --git a/docs/_downloads/52b04835c256bb81bbf4187f18950a0a/relay_pass_infra.py b/docs/_downloads/52b04835c256bb81bbf4187f18950a0a/relay_pass_infra.py
index 980d96c..df40733 100644
--- a/docs/_downloads/52b04835c256bb81bbf4187f18950a0a/relay_pass_infra.py
+++ b/docs/_downloads/52b04835c256bb81bbf4187f18950a0a/relay_pass_infra.py
@@ -160,7 +160,7 @@ print(mod1)
 # however, provides a configuration interface
 # for users to customize the optimization level that they want to execute.
 
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     mod2 = seq(mod)
 print(mod2)
 
@@ -173,7 +173,7 @@ print(mod2)
 # EliminateCommonSubexpr as following. The printed module will again show two
 # identical addition operations.
 
-with relay.build_config(opt_level=3, disabled_pass=["EliminateCommonSubexpr"]):
+with tvm.transform.PassContext(opt_level=3, disabled_pass=["EliminateCommonSubexpr"]):
     mod3 = seq(mod)
 print(mod3)
 
@@ -182,12 +182,12 @@ print(mod3)
 # provides a means to make pass target-aware. For example, the layout
 # alteration pass falls in such category.
 
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     mod4 = seq(mod)
 print(mod4)
 
 seq1 = tvm.transform.Sequential([relay.transform.AlterOpLayout()])
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     with tvm.target.create("llvm"):
         mod5 = seq1(mod)
 print(mod5)
@@ -242,7 +242,7 @@ seq = tvm.transform.Sequential([relay.transform.FoldConstant(),
                                 relay.transform.EliminateCommonSubexpr(),
                                 relay.transform.FuseOps(),
                                 tvm.transform.PrintIR()])
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     mod = seq(mod)
 
 print("done")
diff --git a/docs/_downloads/5c443f88ea44ce77c5ccade429af6e74/deploy_prequantized_tflite.py b/docs/_downloads/5c443f88ea44ce77c5ccade429af6e74/deploy_prequantized_tflite.py
index 3cdd423..ecd283a 100644
--- a/docs/_downloads/5c443f88ea44ce77c5ccade429af6e74/deploy_prequantized_tflite.py
+++ b/docs/_downloads/5c443f88ea44ce77c5ccade429af6e74/deploy_prequantized_tflite.py
@@ -18,6 +18,7 @@
 Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)
 ================================================================
 **Author**: `Siju Samuel <https://github.com/siju-samuel>`_
+
 Welcome to part 3 of the Deploy Framework-Prequantized Model with TVM tutorial.
 In this part, we will start with a Quantized TFLite graph and then compile and execute it via TVM.
 
@@ -197,7 +198,7 @@ mod, params = relay.frontend.from_tflite(tflite_model,
 # Lets now the compile the Relay module. We use the "llvm" target here. Please replace it with the
 # target platform that you are interested in.
 target = 'llvm'
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     graph, lib, params = relay.build_module.build(mod, target=target,
                                                   params=params)
 
diff --git a/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py b/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
index 571dde6..a92b1ee 100644
--- a/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
+++ b/docs/_downloads/612f9e42b0247df5c8ab277534e2af65/tune_relay_vta.py
@@ -92,7 +92,7 @@ def compile_network(env, target, model, start_pack, stop_pack):
 
     # Perform quantization in Relay
     # Note: We set opt_level to 3 in order to fold batch norm
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         with relay.quantize.qconfig(global_scale=8.0, skip_conv_layers=[0]):
             mod = relay.quantize.quantize(mod, params=params)
 
@@ -392,19 +392,19 @@ def tune_and_evaluate(tuning_opt):
     with autotvm.tophub.context(target, extra_files=[log_file]):
         # Compile network
         print("Compile...")
-        with relay.build_config(opt_level=3, disabled_pass={"AlterOpLayout"}):
-            if target.device_name != "vta":
+        if target.device_name != "vta":
+            with tvm.transform.PassContext(opt_level=3, disabled_pass={"AlterOpLayout"}):
                 graph, lib, params = relay.build(relay_prog,
-                                                 target=target,
-                                                 params=params,
-                                                 target_host=env.target_host)
-            else:
-                with vta.build_config():
-                    graph, lib, params = relay.build(
-                        relay_prog,
-                        target=target,
-                        params=params,
-                        target_host=env.target_host)
+                                                target=target,
+                                                params=params,
+                                                target_host=env.target_host)
+        else:
+            with vta.build_config(opt_level=3, disabled_pass={"AlterOpLayout"}):
+                graph, lib, params = relay.build(
+                    relay_prog,
+                    target=target,
+                    params=params,
+                    target_host=env.target_host)
 
         # Export library
         print("Upload...")
diff --git a/docs/_downloads/65bd9927a152de6eed3444185b24287f/tensorize.ipynb b/docs/_downloads/65bd9927a152de6eed3444185b24287f/tensorize.ipynb
index 49493fe..6ce0db4 100644
--- a/docs/_downloads/65bd9927a152de6eed3444185b24287f/tensorize.ipynb
+++ b/docs/_downloads/65bd9927a152de6eed3444185b24287f/tensorize.ipynb
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "def intrin_gemv(m, l):\n    a = te.placeholder((l,), name='a')\n    b = te.placeholder((m, l), name='b')\n    k = te.reduce_axis((0, l), name='k')\n    c = te.compute((m,), lambda i: te.sum(a[k] * b[i, k], axis=k), name='c')\n    Ab = tvm.tir.decl_buffer(a.shape, a.dtype,\n                         name=\"A\",\n                         offset_factor=1,\n                         strides=[1])\n    Bb = tvm.tir.decl_buffer(b.shape, b.dtype,\n                         name=\"B\",\n    [...]
+        "def intrin_gemv(m, l):\n    a = te.placeholder((l,), name='a')\n    b = te.placeholder((m, l), name='b')\n    k = te.reduce_axis((0, l), name='k')\n    c = te.compute((m,), lambda i: te.sum(a[k] * b[i, k], axis=k), name='c')\n    Ab = tvm.tir.decl_buffer(a.shape, a.dtype,\n                         name=\"A\",\n                         offset_factor=1,\n                         strides=[1])\n    Bb = tvm.tir.decl_buffer(b.shape, b.dtype,\n                         name=\"B\",\n    [...]
       ]
     },
     {
@@ -188,7 +188,7 @@
       },
       "outputs": [],
       "source": [
-        "def gemv_impl():\n    cc_code = \"\"\"\n      extern \"C\" int gemv_update(float *cc, float *aa, float *bb, int m, int l, int stride) {\n        for (int i = 0; i < m; ++i) {\n            for (int j = 0; j < l; ++j) {\n                cc[i] += aa[j] * bb[i * stride + j];\n            }\n        }\n        return 0;\n      }\n      extern \"C\" int gemv_reset(float *cc, int m) {\n        for (int i = 0; i < m; ++i) {\n            cc[i] = 0.0;\n        }\n        return 0;\n       [...]
+        "def gemv_impl():\n    cc_code = \"\"\"\n      extern \"C\" int gemv_update(float *cc, float *aa, float *bb, int m, int l, int stride) {\n        for (int i = 0; i < m; ++i) {\n            for (int j = 0; j < l; ++j) {\n                cc[i] += aa[j] * bb[i * stride + j];\n            }\n        }\n        return 0;\n      }\n      extern \"C\" int gemv_reset(float *cc, int m) {\n        for (int i = 0; i < m; ++i) {\n            cc[i] = 0.0;\n        }\n        return 0;\n       [...]
       ]
     },
     {
diff --git a/docs/_downloads/66258823cf03d1875c9ea84a82131101/deploy_detection.ipynb b/docs/_downloads/66258823cf03d1875c9ea84a82131101/deploy_detection.ipynb
index 372bb0a..8ce07ae 100644
--- a/docs/_downloads/66258823cf03d1875c9ea84a82131101/deploy_detection.ipynb
+++ b/docs/_downloads/66258823cf03d1875c9ea84a82131101/deploy_detection.ipynb
@@ -123,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "# Load pre-configured AutoTVM schedules\nwith autotvm.tophub.context(target):\n    net = __darknetffi__.dlopen(darknet_lib_path).load_network(cfg_path.encode('utf-8'),\n                                                               weights_path.encode('utf-8'),\n                                                               0)\n    dshape = (env.BATCH, net.c, net.h, net.w)\n    dtype = 'float32'\n\n    # Measure build start time\n    build_start = time.time()\n\n    # Start fron [...]
+        "# Load pre-configured AutoTVM schedules\nwith autotvm.tophub.context(target):\n    net = __darknetffi__.dlopen(darknet_lib_path).load_network(cfg_path.encode('utf-8'),\n                                                               weights_path.encode('utf-8'),\n                                                               0)\n    dshape = (env.BATCH, net.c, net.h, net.w)\n    dtype = 'float32'\n\n    # Measure build start time\n    build_start = time.time()\n\n    # Start fron [...]
       ]
     },
     {
diff --git a/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py b/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py
index 766451c..9973a08 100644
--- a/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py
+++ b/docs/_downloads/696dd37904ef92773435ca321ff41bfb/from_onnx.py
@@ -74,7 +74,7 @@ input_name = '1'
 shape_dict = {input_name: x.shape}
 mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
 
-with relay.build_config(opt_level=1):
+with tvm.transform.PassContext(opt_level=1):
     intrp = relay.build_module.create_executor('graph', mod, tvm.cpu(0), target)
 
 ######################################################################
diff --git a/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb b/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb
index 6c66225..30ff482 100644
--- a/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb
+++ b/docs/_downloads/6c8a9d3bc4c689f8680a968349965ee5/from_pytorch.ipynb
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "target = 'llvm'\ntarget_host = 'llvm'\nctx = tvm.cpu(0)\nwith relay.build_config(opt_level=3):\n    graph, lib, params = relay.build(mod,\n                                     target=target,\n                                     target_host=target_host,\n                                     params=params)"
+        "target = 'llvm'\ntarget_host = 'llvm'\nctx = tvm.cpu(0)\nwith tvm.transform.PassContext(opt_level=3):\n    graph, lib, params = relay.build(mod,\n                                     target=target,\n                                     target_host=target_host,\n                                     params=params)"
       ]
     },
     {
diff --git a/docs/_downloads/70d345c5409f99cb5de9dc44f147ff6f/build_gcn.py b/docs/_downloads/70d345c5409f99cb5de9dc44f147ff6f/build_gcn.py
index 6ac518e..19719a5 100644
--- a/docs/_downloads/70d345c5409f99cb5de9dc44f147ff6f/build_gcn.py
+++ b/docs/_downloads/70d345c5409f99cb5de9dc44f147ff6f/build_gcn.py
@@ -336,7 +336,7 @@ func = relay.build_module.bind_params_by_name(func, params)
 mod = tvm.IRModule()
 mod["main"] = func
 # Build with Relay
-with relay.build_config(opt_level=0): # Currently only support opt_level=0
+with tvm.transform.PassContext(opt_level=0): # Currently only support opt_level=0
     graph, lib, params = relay.build(mod, target, params=params)
 
 # Generate graph runtime
diff --git a/docs/_downloads/72871483681951fd0400ddc905113f11/from_caffe2.py b/docs/_downloads/72871483681951fd0400ddc905113f11/from_caffe2.py
index 8fad80d..5988525 100644
--- a/docs/_downloads/72871483681951fd0400ddc905113f11/from_caffe2.py
+++ b/docs/_downloads/72871483681951fd0400ddc905113f11/from_caffe2.py
@@ -82,13 +82,13 @@ shape_dict = {input_name: data.shape}
 dtype_dict = {input_name: data.dtype}
 
 # parse Caffe2 model and convert into Relay computation graph
-from tvm import relay
+from tvm import relay, transform
 mod, params = relay.frontend.from_caffe2(resnet50.init_net, resnet50.predict_net, shape_dict, dtype_dict)
 
 # compile the model
 # target x86 CPU
 target = 'llvm'
-with relay.build_config(opt_level=3):
+with transform.PassContext(opt_level=3):
     graph, lib, params = relay.build(mod, target, params=params)
 
 ######################################################################
diff --git a/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb b/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
index b7e4b22..f9d16a8 100644
--- a/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
+++ b/docs/_downloads/739deb9ab034a5315ce6ba6bf7e5ff44/tune_relay_cuda.ipynb
@@ -112,7 +112,7 @@
       },
       "outputs": [],
       "source": [
-        "def tune_and_evaluate(tuning_opt):\n    # extract workloads from relay program\n    print(\"Extract tasks...\")\n    mod, params, input_shape, out_shape = get_network(network, batch_size=1)\n    tasks = autotvm.task.extract_from_program(mod[\"main\"], target=target,\n                                              params=params,\n                                              ops=(relay.op.get(\"nn.conv2d\"),))\n\n    # run tuning tasks\n    print(\"Tuning...\")\n    tune_tasks(tas [...]
+        "def tune_and_evaluate(tuning_opt):\n    # extract workloads from relay program\n    print(\"Extract tasks...\")\n    mod, params, input_shape, out_shape = get_network(network, batch_size=1)\n    tasks = autotvm.task.extract_from_program(mod[\"main\"], target=target,\n                                              params=params,\n                                              ops=(relay.op.get(\"nn.conv2d\"),))\n\n    # run tuning tasks\n    print(\"Tuning...\")\n    tune_tasks(tas [...]
       ]
     },
     {
diff --git a/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb b/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb
index 2927b78..4608363 100644
--- a/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb
+++ b/docs/_downloads/78da213eae381b8ff94cc356ee7c5423/deploy_prequantized.ipynb
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "def get_transform():\n    import torchvision.transforms as transforms\n    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                                     std=[0.229, 0.224, 0.225])\n    return transforms.Compose([\n            transforms.Resize(256),\n            transforms.CenterCrop(224),\n            transforms.ToTensor(),\n            normalize,\n        ])\n\n\ndef get_real_image(im_height, im_width):\n    img_url = 'https://github.com/dmlc/mxnet.js/blob [...]
+        "def get_transform():\n    import torchvision.transforms as transforms\n    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                                     std=[0.229, 0.224, 0.225])\n    return transforms.Compose([\n            transforms.Resize(256),\n            transforms.CenterCrop(224),\n            transforms.ToTensor(),\n            normalize,\n        ])\n\n\ndef get_real_image(im_height, im_width):\n    img_url = 'https://github.com/dmlc/mxnet.js/blob [...]
       ]
     },
     {
diff --git a/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb b/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb
index b4e2fe2..11356f1 100644
--- a/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb
+++ b/docs/_downloads/8246644805c8dfcb0b33ca356cc1fafc/deploy_ssd_gluoncv.ipynb
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "block = model_zoo.get_model(model_name, pretrained=True)\n\ndef build(target):\n    mod, params = relay.frontend.from_mxnet(block, {\"data\": dshape})\n    with relay.build_config(opt_level=3):\n        graph, lib, params = relay.build(mod, target, params=params)\n    return graph, lib, params"
+        "block = model_zoo.get_model(model_name, pretrained=True)\n\ndef build(target):\n    mod, params = relay.frontend.from_mxnet(block, {\"data\": dshape})\n    with tvm.transform.PassContext(opt_level=3):\n        graph, lib, params = relay.build(mod, target, params=params)\n    return graph, lib, params"
       ]
     },
     {
diff --git a/docs/_downloads/835a4def1e256b7a1f711621fc031418/from_darknet.ipynb b/docs/_downloads/835a4def1e256b7a1f711621fc031418/from_darknet.ipynb
index c2c176b..cc1892e 100644
--- a/docs/_downloads/835a4def1e256b7a1f711621fc031418/from_darknet.ipynb
+++ b/docs/_downloads/835a4def1e256b7a1f711621fc031418/from_darknet.ipynb
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "target = 'llvm'\ntarget_host = 'llvm'\nctx = tvm.cpu(0)\ndata = np.empty([batch_size, net.c, net.h, net.w], dtype)\nshape = {'data': data.shape}\nprint(\"Compiling the model...\")\nwith relay.build_config(opt_level=3):\n    graph, lib, params = relay.build(mod,\n                                     target=target,\n                                     target_host=target_host,\n                                     params=params)\n\n[neth, netw] = shape['data'][2:] # Current image  [...]
+        "target = 'llvm'\ntarget_host = 'llvm'\nctx = tvm.cpu(0)\ndata = np.empty([batch_size, net.c, net.h, net.w], dtype)\nshape = {'data': data.shape}\nprint(\"Compiling the model...\")\nwith tvm.transform.PassContext(opt_level=3):\n    graph, lib, params = relay.build(mod,\n                                     target=target,\n                                     target_host=target_host,\n                                     params=params)\n\n[neth, netw] = shape['data'][2:] # Current [...]
       ]
     },
     {
diff --git a/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py b/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py
index ef707fe..25df341 100644
--- a/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py
+++ b/docs/_downloads/83dedc6352b4016772e17480ef01345d/deploy_model_on_rasp.py
@@ -179,7 +179,7 @@ else:
     # The above line is a simple form of
     # target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -target=armv7l-linux-gnueabihf -mattr=+neon')
 
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     graph, lib, params = relay.build(func, target, params=params)
 
 # After `relay.build`, you will get three return values: graph,
diff --git a/docs/_downloads/85ba00b8ada85b8c5367f37b526a8caa/tune_relay_x86.py b/docs/_downloads/85ba00b8ada85b8c5367f37b526a8caa/tune_relay_x86.py
index 15ce2de..dcc5b25 100644
--- a/docs/_downloads/85ba00b8ada85b8c5367f37b526a8caa/tune_relay_x86.py
+++ b/docs/_downloads/85ba00b8ada85b8c5367f37b526a8caa/tune_relay_x86.py
@@ -189,7 +189,7 @@ def tune_and_evaluate(tuning_opt):
     # compile kernels with graph-level best records
     with autotvm.apply_graph_best(graph_opt_sch_file):
         print("Compile...")
-        with relay.build_config(opt_level=3):
+        with tvm.transform.PassContext(opt_level=3):
             graph, lib, params = relay.build_module.build(
                 mod, target=target, params=params)
 
diff --git a/docs/_downloads/870680567a5bf1e4697356b416e302b4/opt_matmul_auto_tensorcore.ipynb b/docs/_downloads/870680567a5bf1e4697356b416e302b4/opt_matmul_auto_tensorcore.ipynb
index 6287d6e..6eb4e22 100644
--- a/docs/_downloads/870680567a5bf1e4697356b416e302b4/opt_matmul_auto_tensorcore.ipynb
+++ b/docs/_downloads/870680567a5bf1e4697356b416e302b4/opt_matmul_auto_tensorcore.ipynb
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "# check whether the gpu has tensorcore\nif not tvm.gpu(0).exist or not tvm.runtime.enabled(\"cuda\"):\n  print(\"skip because cuda is not enabled..\")\n  sys.exit(0)\n\nctx = tvm.gpu()\nif not nvcc.have_tensorcore(ctx.compute_version):\n  print('the gpu has no tensorcore, skipping...')\n  sys.exit(0)\n\nM, N, L = 512, 32, 512\ndtype = 'float16'\nlayout = 'NN'\nif len(sys.argv) >= 4:\n  M, N, L = int(sys.argv[1]), int(sys.argv[2]), int(sys.argv[3])\nif len(sys.argv) >= 5:\n  dtyp [...]
+        "# check whether the gpu has tensorcore\nif not tvm.gpu(0).exist or not tvm.runtime.enabled(\"cuda\"):\n  print(\"skip because cuda is not enabled..\")\n  sys.exit(0)\n\nctx = tvm.gpu()\nif not nvcc.have_tensorcore(ctx.compute_version):\n  print('the gpu has no tensorcore, skipping...')\n  sys.exit(0)\n\nM, N, L = 512, 32, 512\ndtype = 'float16'\nlayout = 'NN'\nif len(sys.argv) >= 4:\n  M, N, L = int(sys.argv[1]), int(sys.argv[2]), int(sys.argv[3])\nif len(sys.argv) >= 5:\n  dtyp [...]
       ]
     },
     {
diff --git a/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py b/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py
index 4027977..d6183d6 100644
--- a/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py
+++ b/docs/_downloads/8a7f17665207908e373e8146da09443a/deploy_prequantized.py
@@ -81,7 +81,7 @@ def get_synset():
 
 
 def run_tvm_model(mod, params, input_name, inp, target="llvm"):
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         json, lib, params = relay.build(mod, target=target, params=params)
 
     runtime = tvm.contrib.graph_runtime.create(json, lib, tvm.context(target, 0))
diff --git a/docs/_downloads/9a7956336431664ba6d628347b08f5cb/opt_conv_tensorcore.ipynb b/docs/_downloads/9a7956336431664ba6d628347b08f5cb/opt_conv_tensorcore.ipynb
index 3f3e083..e8ff100 100644
--- a/docs/_downloads/9a7956336431664ba6d628347b08f5cb/opt_conv_tensorcore.ipynb
+++ b/docs/_downloads/9a7956336431664ba6d628347b08f5cb/opt_conv_tensorcore.ipynb
@@ -130,7 +130,7 @@
       },
       "outputs": [],
       "source": [
-        "ctx = tvm.gpu(0)\nif nvcc.have_tensorcore(ctx.compute_version):\n    with tvm.target.build_config(auto_unroll_max_step=16):\n        func = tvm.build(s, [A, W, Conv], 'cuda')\n    a_np = np.random.uniform(size=data_shape).astype(A.dtype)\n    w_np = np.random.uniform(size=kernel_shape).astype(W.dtype)\n    a = tvm.nd.array(a_np, ctx)\n    w = tvm.nd.array(w_np, ctx)\n    c = tvm.nd.array(np.zeros(output_shape, dtype=Conv.dtype), ctx)\n    evaluator = func.time_evaluator(func.ent [...]
+        "ctx = tvm.gpu(0)\nif nvcc.have_tensorcore(ctx.compute_version):\n    with tvm.transform.PassContext(config={\"tir.UnrollLoop\": {\n        \"auto_max_step\": 16\n    }}):\n        func = tvm.build(s, [A, W, Conv], 'cuda')\n    a_np = np.random.uniform(size=data_shape).astype(A.dtype)\n    w_np = np.random.uniform(size=kernel_shape).astype(W.dtype)\n    a = tvm.nd.array(a_np, ctx)\n    w = tvm.nd.array(w_np, ctx)\n    c = tvm.nd.array(np.zeros(output_shape, dtype=Conv.dtype), ctx [...]
       ]
     },
     {
diff --git a/docs/_downloads/9cf0213876be0a9cc4aaa52a1ebd9586/low_level_custom_pass.py b/docs/_downloads/9cf0213876be0a9cc4aaa52a1ebd9586/low_level_custom_pass.py
index 03eae1c..17f864f 100644
--- a/docs/_downloads/9cf0213876be0a9cc4aaa52a1ebd9586/low_level_custom_pass.py
+++ b/docs/_downloads/9cf0213876be0a9cc4aaa52a1ebd9586/low_level_custom_pass.py
@@ -84,7 +84,7 @@ print(ir)
 
 loops = []
 def find_width8(op):
-    """ Find all the 'For' nodes whose extent can be divided by 8. """
+    """ Find all the 'tir.For' nodes whose extent can be divided by 8. """
     if isinstance(op, tvm.tir.For):
         if isinstance(op.extent, tvm.tir.IntImm):
             if op.extent.value % 8 == 0:
@@ -129,7 +129,7 @@ def vectorize(f, mod, ctx):
     # The last list arugment indicates what kinds of nodes will be transformed.
     # Thus, in this case only `For` nodes will call `vectorize8`
     return f.with_body(
-        tvm.tir.stmt_functor.ir_transform(f.body, None, vectorize8, ['For']))
+        tvm.tir.stmt_functor.ir_transform(f.body, None, vectorize8, ['tir.For']))
 
 
 #####################################################################
@@ -138,9 +138,8 @@ def vectorize(f, mod, ctx):
 # So far, we are done with writing this IR transformation pass. What we need to do next is to glue
 # this pass to TVM's lower pass.
 #
-# In TVM, there is a property called ``BuildConfig``. You can use this property to customize your
-# own lowering options. In this case, we inject the pass written above into the TVM standard lowering
-# pass by feeding **a list of tuple** as argument to ``add_lower_pass``. "Tuple" indicates different
+# In this case, we inject the pass written above into the TVM standard lowering
+# pass by feeding **a list of tuple** as argument to ``tir.add_lower_pass``. "Tuple" indicates different
 # phases of lowering. In TVM, there are four phases of lowering and user-customized ones will be
 # called after each phase is done.
 #
@@ -154,7 +153,7 @@ def vectorize(f, mod, ctx):
 # Thus, a good place to put this transformation pass is just after Phase 1.
 #
 
-with tvm.target.build_config(add_lower_pass=[(1, vectorize)]) as cfg:
+with tvm.transform.PassContext(config={"tir.add_lower_pass": [(1, vectorize)]}):
     print(tvm.lower(sch, [a, b, c]))
 
 #####################################################################
@@ -164,5 +163,5 @@ with tvm.target.build_config(add_lower_pass=[(1, vectorize)]) as cfg:
 # - Use ``tvm.tir.stmt_functor.post_order_visit`` to gather information on each IR nodes.
 # - Use ``tvm.tir.stmt_functor.ir_transform`` to transform IR nodes.
 # - Wrap up two above to write an IR-transformation function.
-# - Use ``tvm.target.build_config`` to put this function to TVM lowering pass
+# - Use ``tvm.transform.PassContext`` to put this function to TVM lowering pass
 #
diff --git a/docs/_downloads/a2903d9ade85eae7ce486f80ddaf9cfe/relay_quick_start.ipynb b/docs/_downloads/a2903d9ade85eae7ce486f80ddaf9cfe/relay_quick_start.ipynb
index faebab0..d9ac3bd 100644
--- a/docs/_downloads/a2903d9ade85eae7ce486f80ddaf9cfe/relay_quick_start.ipynb
+++ b/docs/_downloads/a2903d9ade85eae7ce486f80ddaf9cfe/relay_quick_start.ipynb
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "opt_level = 3\ntarget = tvm.target.cuda()\nwith relay.build_config(opt_level=opt_level):\n    graph, lib, params = relay.build(mod, target, params=params)"
+        "opt_level = 3\ntarget = tvm.target.cuda()\nwith tvm.transform.PassContext(opt_level=opt_level):\n    graph, lib, params = relay.build(mod, target, params=params)"
       ]
     },
     {
diff --git a/docs/_downloads/a7ce44923ffcc359fd2e532ac1f62c9e/from_darknet.py b/docs/_downloads/a7ce44923ffcc359fd2e532ac1f62c9e/from_darknet.py
index e2c1ea5..6d84463 100644
--- a/docs/_downloads/a7ce44923ffcc359fd2e532ac1f62c9e/from_darknet.py
+++ b/docs/_downloads/a7ce44923ffcc359fd2e532ac1f62c9e/from_darknet.py
@@ -100,7 +100,7 @@ ctx = tvm.cpu(0)
 data = np.empty([batch_size, net.c, net.h, net.w], dtype)
 shape = {'data': data.shape}
 print("Compiling the model...")
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     graph, lib, params = relay.build(mod,
                                      target=target,
                                      target_host=target_host,
diff --git a/docs/_downloads/b9891d1a23f84eec3271025d99d005f7/tune_relay_x86.ipynb b/docs/_downloads/b9891d1a23f84eec3271025d99d005f7/tune_relay_x86.ipynb
index 15d4431..e705461 100644
--- a/docs/_downloads/b9891d1a23f84eec3271025d99d005f7/tune_relay_x86.ipynb
+++ b/docs/_downloads/b9891d1a23f84eec3271025d99d005f7/tune_relay_x86.ipynb
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "def tune_and_evaluate(tuning_opt):\n    # extract workloads from relay program\n    print(\"Extract tasks...\")\n    mod, params, data_shape, out_shape = get_network(model_name, batch_size)\n    tasks = autotvm.task.extract_from_program(mod[\"main\"], target=target,\n                                              params=params,\n                                              ops=(relay.op.get(\"nn.conv2d\"),))\n\n    # run tuning tasks\n    tune_kernels(tasks, **tuning_opt)\n    t [...]
+        "def tune_and_evaluate(tuning_opt):\n    # extract workloads from relay program\n    print(\"Extract tasks...\")\n    mod, params, data_shape, out_shape = get_network(model_name, batch_size)\n    tasks = autotvm.task.extract_from_program(mod[\"main\"], target=target,\n                                              params=params,\n                                              ops=(relay.op.get(\"nn.conv2d\"),))\n\n    # run tuning tasks\n    tune_kernels(tasks, **tuning_opt)\n    t [...]
       ]
     },
     {
diff --git a/docs/_downloads/baa4de13ce6d932de43e0eb5c4cb8f16/tensorize.py b/docs/_downloads/baa4de13ce6d932de43e0eb5c4cb8f16/tensorize.py
index 6224c10..8a77c77 100644
--- a/docs/_downloads/baa4de13ce6d932de43e0eb5c4cb8f16/tensorize.py
+++ b/docs/_downloads/baa4de13ce6d932de43e0eb5c4cb8f16/tensorize.py
@@ -115,8 +115,7 @@ def intrin_gemv(m, l):
                                 bb.access_ptr("r"),
                                 m, l, bb.strides[0]))
         return ib.get()
-    with tvm.target.build_config(offset_factor=1):
-        return te.decl_tensor_intrin(c.op, intrin_func, binds={a: Ab, b: Bb, c: Cb})
+    return te.decl_tensor_intrin(c.op, intrin_func, binds={a: Ab, b: Bb, c: Cb})
 
 ######################################################################
 # Here :code:`te.decl_tensor_intrin` declares how to execute the computation :code:`c.op`.
@@ -269,8 +268,7 @@ def intrin_gemv(m, l):
         def _reduce_update():
             return _body()
         return _body(), _reduce_reset(), _reduce_update()
-    with tvm.target.build_config(offset_factor=1):
-        return te.decl_tensor_intrin(c.op, intrin_func, binds={a: Ab, b: Bb, c: Cb})
+    return te.decl_tensor_intrin(c.op, intrin_func, binds={a: Ab, b: Bb, c: Cb})
 
 ######################################################################
 # Note that :code:`intrin_func` now returns a triplet:
diff --git a/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py b/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
index ffd3e8b..3b07097 100644
--- a/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
+++ b/docs/_downloads/baf1373314e0e040008107ff2571b4cd/tune_relay_arm.py
@@ -311,7 +311,7 @@ def tune_and_evaluate(tuning_opt):
     # compile kernels with history best records
     with autotvm.apply_history_best(log_file):
         print("Compile...")
-        with relay.build_config(opt_level=3):
+        with tvm.transform.PassContext(opt_level=3):
             graph, lib, params = relay.build_module.build(
                 mod, target=target, params=params)
 
diff --git a/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb b/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb
index bc36e78..f22dcd8 100644
--- a/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb
+++ b/docs/_downloads/beb2188d497d67b66bcfbc2c254dccb7/deploy_model_on_rasp.ipynb
@@ -155,7 +155,7 @@
       },
       "outputs": [],
       "source": [
-        "local_demo = True\n\nif local_demo:\n    target = tvm.target.create('llvm')\nelse:\n    target = tvm.target.arm_cpu('rasp3b')\n    # The above line is a simple form of\n    # target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -target=armv7l-linux-gnueabihf -mattr=+neon')\n\nwith relay.build_config(opt_level=3):\n    graph, lib, params = relay.build(func, target, params=params)\n\n# After `relay.build`, you will get three return values: graph,\n# library and the new  [...]
+        "local_demo = True\n\nif local_demo:\n    target = tvm.target.create('llvm')\nelse:\n    target = tvm.target.arm_cpu('rasp3b')\n    # The above line is a simple form of\n    # target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -target=armv7l-linux-gnueabihf -mattr=+neon')\n\nwith tvm.transform.PassContext(opt_level=3):\n    graph, lib, params = relay.build(func, target, params=params)\n\n# After `relay.build`, you will get three return values: graph,\n# library and t [...]
       ]
     },
     {
diff --git a/docs/_downloads/c316f4b828b813e437473ee752bacdf9/build_gcn.ipynb b/docs/_downloads/c316f4b828b813e437473ee752bacdf9/build_gcn.ipynb
index 910f1eb..ed1884c 100644
--- a/docs/_downloads/c316f4b828b813e437473ee752bacdf9/build_gcn.ipynb
+++ b/docs/_downloads/c316f4b828b813e437473ee752bacdf9/build_gcn.ipynb
@@ -177,7 +177,7 @@
       },
       "outputs": [],
       "source": [
-        "model_params = {}\nfor param_tensor in torch_model.state_dict():\n    model_params[param_tensor] = torch_model.state_dict()[param_tensor].numpy()\n\nfor i in range(num_layers+1):\n    params[\"layers.%d.weight\"%(i)] = model_params[\"layers.%d.weight\"%(i)]\n    params[\"layers.%d.bias\"%(i)] = model_params[\"layers.%d.bias\"%(i)]\n\n# Set the TVM build target\ntarget = 'llvm' # Currently only support `llvm` as target\n\nfunc = relay.Function(relay.analysis.free_vars(output), ou [...]
+        "model_params = {}\nfor param_tensor in torch_model.state_dict():\n    model_params[param_tensor] = torch_model.state_dict()[param_tensor].numpy()\n\nfor i in range(num_layers+1):\n    params[\"layers.%d.weight\"%(i)] = model_params[\"layers.%d.weight\"%(i)]\n    params[\"layers.%d.bias\"%(i)] = model_params[\"layers.%d.bias\"%(i)]\n\n# Set the TVM build target\ntarget = 'llvm' # Currently only support `llvm` as target\n\nfunc = relay.Function(relay.analysis.free_vars(output), ou [...]
       ]
     },
     {
diff --git a/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb b/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
index cf6b413..93455af 100644
--- a/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
+++ b/docs/_downloads/c4d683ae80a4b8a011286f239422638a/deploy_classification.ipynb
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "# Load pre-configured AutoTVM schedules\nwith autotvm.tophub.context(target):\n\n    # Populate the shape and data type dictionary for ImageNet classifier input\n    dtype_dict = {\"data\": 'float32'}\n    shape_dict = {\"data\": (env.BATCH, 3, 224, 224)}\n\n    # Get off the shelf gluon model, and convert to relay\n    gluon_model = vision.get_model(model, pretrained=True)\n\n    # Measure build start time\n    build_start = time.time()\n\n    # Start front end compilation\n    [...]
+        "# Load pre-configured AutoTVM schedules\nwith autotvm.tophub.context(target):\n\n    # Populate the shape and data type dictionary for ImageNet classifier input\n    dtype_dict = {\"data\": 'float32'}\n    shape_dict = {\"data\": (env.BATCH, 3, 224, 224)}\n\n    # Get off the shelf gluon model, and convert to relay\n    gluon_model = vision.get_model(model, pretrained=True)\n\n    # Measure build start time\n    build_start = time.time()\n\n    # Start front end compilation\n    [...]
       ]
     },
     {
diff --git a/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py b/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py
index 6126df0..e2fc3c5 100644
--- a/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py
+++ b/docs/_downloads/ca08de6c440df207921d807474d26f06/deploy_ssd_gluoncv.py
@@ -87,7 +87,7 @@ block = model_zoo.get_model(model_name, pretrained=True)
 
 def build(target):
     mod, params = relay.frontend.from_mxnet(block, {"data": dshape})
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         graph, lib, params = relay.build(mod, target, params=params)
     return graph, lib, params
 
diff --git a/docs/_downloads/e09aef52edc37570c0178591a87d328c/from_tensorflow.ipynb b/docs/_downloads/e09aef52edc37570c0178591a87d328c/from_tensorflow.ipynb
index 2646e66..23ee2ad 100644
--- a/docs/_downloads/e09aef52edc37570c0178591a87d328c/from_tensorflow.ipynb
+++ b/docs/_downloads/e09aef52edc37570c0178591a87d328c/from_tensorflow.ipynb
@@ -134,7 +134,7 @@
       },
       "outputs": [],
       "source": [
-        "with relay.build_config(opt_level=3):\n    graph, lib, params = relay.build(mod,\n                                     target=target,\n                                     target_host=target_host,\n                                     params=params)"
+        "with tvm.transform.PassContext(opt_level=3):\n    graph, lib, params = relay.build(mod,\n                                     target=target,\n                                     target_host=target_host,\n                                     params=params)"
       ]
     },
     {
diff --git a/docs/_downloads/e3748c6e5b8a427385ff1afdf1562c3c/opt_conv_tensorcore.py b/docs/_downloads/e3748c6e5b8a427385ff1afdf1562c3c/opt_conv_tensorcore.py
index 44b9de3..cd40a91 100644
--- a/docs/_downloads/e3748c6e5b8a427385ff1afdf1562c3c/opt_conv_tensorcore.py
+++ b/docs/_downloads/e3748c6e5b8a427385ff1afdf1562c3c/opt_conv_tensorcore.py
@@ -331,7 +331,9 @@ print(tvm.lower(s, [A, W, Conv], simple_mode=True))
 
 ctx = tvm.gpu(0)
 if nvcc.have_tensorcore(ctx.compute_version):
-    with tvm.target.build_config(auto_unroll_max_step=16):
+    with tvm.transform.PassContext(config={"tir.UnrollLoop": {
+        "auto_max_step": 16
+    }}):
         func = tvm.build(s, [A, W, Conv], 'cuda')
     a_np = np.random.uniform(size=data_shape).astype(A.dtype)
     w_np = np.random.uniform(size=kernel_shape).astype(W.dtype)
diff --git a/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py b/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
index ad74608..4748f41 100644
--- a/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
+++ b/docs/_downloads/e41367a7f459e4f4dca82180009c1539/tune_relay_mobile_gpu.py
@@ -308,7 +308,7 @@ def tune_and_evaluate(tuning_opt):
     # compile kernels with history best records
     with autotvm.apply_history_best(log_file):
         print("Compile...")
-        with relay.build_config(opt_level=3):
+        with tvm.transform.PassContext(opt_level=3):
             graph, lib, params = relay.build_module.build(
                 mod, target=target, params=params, target_host=target_host)
         # export library
diff --git a/docs/_downloads/e732d71c83de9fd8c6c7a72184d3ee0a/from_coreml.py b/docs/_downloads/e732d71c83de9fd8c6c7a72184d3ee0a/from_coreml.py
index 2a0c8db..beac483 100644
--- a/docs/_downloads/e732d71c83de9fd8c6c7a72184d3ee0a/from_coreml.py
+++ b/docs/_downloads/e732d71c83de9fd8c6c7a72184d3ee0a/from_coreml.py
@@ -74,7 +74,7 @@ shape_dict = {'image': x.shape}
 # Parse CoreML model and convert into Relay computation graph
 mod, params = relay.frontend.from_coreml(mlmodel, shape_dict)
 
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     graph, lib, params = relay.build(mod,
                                      target,
                                      params=params)
diff --git a/docs/_downloads/e87c21d127b0b825efcf978b9f8e2cd7/low_level_custom_pass.ipynb b/docs/_downloads/e87c21d127b0b825efcf978b9f8e2cd7/low_level_custom_pass.ipynb
index 991bc88..f8b0ee5 100644
--- a/docs/_downloads/e87c21d127b0b825efcf978b9f8e2cd7/low_level_custom_pass.ipynb
+++ b/docs/_downloads/e87c21d127b0b825efcf978b9f8e2cd7/low_level_custom_pass.ipynb
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "loops = []\ndef find_width8(op):\n    \"\"\" Find all the 'For' nodes whose extent can be divided by 8. \"\"\"\n    if isinstance(op, tvm.tir.For):\n        if isinstance(op.extent, tvm.tir.IntImm):\n            if op.extent.value % 8 == 0:\n                loops.append(op)"
+        "loops = []\ndef find_width8(op):\n    \"\"\" Find all the 'tir.For' nodes whose extent can be divided by 8. \"\"\"\n    if isinstance(op, tvm.tir.For):\n        if isinstance(op.extent, tvm.tir.IntImm):\n            if op.extent.value % 8 == 0:\n                loops.append(op)"
       ]
     },
     {
@@ -87,14 +87,14 @@
       },
       "outputs": [],
       "source": [
-        "def vectorize8(op):\n    \"\"\" Split can vectorize the loops found in `find_width8`. \"\"\"\n    if op in loops:\n        extent = op.extent.value\n        name = op.loop_var.name\n        lo, li = te.var(name + '.outer'), te.var(name + '.inner')\n        body = tvm.tir.stmt_functor.substitute(op.body, {op.loop_var: lo * 8 + li})\n        body = tvm.tir.For(li, 0, 8, tvm.tir.For.Vectorized, 0, body)\n        body = tvm.tir.For(lo, 0, extent // 8, tvm.tir.For.Serial, 0, body)\n  [...]
+        "def vectorize8(op):\n    \"\"\" Split can vectorize the loops found in `find_width8`. \"\"\"\n    if op in loops:\n        extent = op.extent.value\n        name = op.loop_var.name\n        lo, li = te.var(name + '.outer'), te.var(name + '.inner')\n        body = tvm.tir.stmt_functor.substitute(op.body, {op.loop_var: lo * 8 + li})\n        body = tvm.tir.For(li, 0, 8, tvm.tir.For.Vectorized, 0, body)\n        body = tvm.tir.For(lo, 0, extent // 8, tvm.tir.For.Serial, 0, body)\n  [...]
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Glue to Lowering\n----------------\nSo far, we are done with writing this IR transformation pass. What we need to do next is to glue\nthis pass to TVM's lower pass.\n\nIn TVM, there is a property called ``BuildConfig``. You can use this property to customize your\nown lowering options. In this case, we inject the pass written above into the TVM standard lowering\npass by feeding **a list of tuple** as argument to ``add_lower_pass``. \"Tuple\" indicates different\nphases of lower [...]
+        "Glue to Lowering\n----------------\nSo far, we are done with writing this IR transformation pass. What we need to do next is to glue\nthis pass to TVM's lower pass.\n\nIn this case, we inject the pass written above into the TVM standard lowering\npass by feeding **a list of tuple** as argument to ``tir.add_lower_pass``. \"Tuple\" indicates different\nphases of lowering. In TVM, there are four phases of lowering and user-customized ones will be\ncalled after each phase is done.\n [...]
       ]
     },
     {
@@ -105,14 +105,14 @@
       },
       "outputs": [],
       "source": [
-        "with tvm.target.build_config(add_lower_pass=[(1, vectorize)]) as cfg:\n    print(tvm.lower(sch, [a, b, c]))"
+        "with tvm.transform.PassContext(config={\"tir.add_lower_pass\": [(1, vectorize)]}):\n    print(tvm.lower(sch, [a, b, c]))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Quick View\n----------\nThis tutorial gives a quick view of writing a customized IR transformation pass:\n- Use ``tvm.tir.stmt_functor.post_order_visit`` to gather information on each IR nodes.\n- Use ``tvm.tir.stmt_functor.ir_transform`` to transform IR nodes.\n- Wrap up two above to write an IR-transformation function.\n- Use ``tvm.target.build_config`` to put this function to TVM lowering pass\n\n\n"
+        "Quick View\n----------\nThis tutorial gives a quick view of writing a customized IR transformation pass:\n- Use ``tvm.tir.stmt_functor.post_order_visit`` to gather information on each IR nodes.\n- Use ``tvm.tir.stmt_functor.ir_transform`` to transform IR nodes.\n- Wrap up two above to write an IR-transformation function.\n- Use ``tvm.transform.PassContext`` to put this function to TVM lowering pass\n\n\n"
       ]
     }
   ],
diff --git a/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb b/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb
index 289a695..af31db1 100644
--- a/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb
+++ b/docs/_downloads/e92c7219a1cd7838e61f9683f4228a7f/from_onnx.ipynb
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "target = 'llvm'\n\ninput_name = '1'\nshape_dict = {input_name: x.shape}\nmod, params = relay.frontend.from_onnx(onnx_model, shape_dict)\n\nwith relay.build_config(opt_level=1):\n    intrp = relay.build_module.create_executor('graph', mod, tvm.cpu(0), target)"
+        "target = 'llvm'\n\ninput_name = '1'\nshape_dict = {input_name: x.shape}\nmod, params = relay.frontend.from_onnx(onnx_model, shape_dict)\n\nwith tvm.transform.PassContext(opt_level=1):\n    intrp = relay.build_module.create_executor('graph', mod, tvm.cpu(0), target)"
       ]
     },
     {
diff --git a/docs/_downloads/ea0c81cab71096d16b825a33fd276c58/from_mxnet.py b/docs/_downloads/ea0c81cab71096d16b825a33fd276c58/from_mxnet.py
index d0e4c4a..6e6b2d7 100644
--- a/docs/_downloads/ea0c81cab71096d16b825a33fd276c58/from_mxnet.py
+++ b/docs/_downloads/ea0c81cab71096d16b825a33fd276c58/from_mxnet.py
@@ -90,7 +90,7 @@ func = relay.Function(func.params, relay.nn.softmax(func.body), None, func.type_
 ######################################################################
 # now compile the graph
 target = 'cuda'
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     graph, lib, params = relay.build(func, target, params=params)
 
 ######################################################################
diff --git a/docs/_downloads/ed4fcfc19b39f91a14d817676480a454/deploy_detection.py b/docs/_downloads/ed4fcfc19b39f91a14d817676480a454/deploy_detection.py
index efcd2c4..5039488 100644
--- a/docs/_downloads/ed4fcfc19b39f91a14d817676480a454/deploy_detection.py
+++ b/docs/_downloads/ed4fcfc19b39f91a14d817676480a454/deploy_detection.py
@@ -207,7 +207,7 @@ with autotvm.tophub.context(target):
     if target.device_name == "vta":
     # Perform quantization in Relay
     # Note: We set opt_level to 3 in order to fold batch norm
-        with relay.build_config(opt_level=3):
+        with tvm.transform.PassContext(opt_level=3):
             with relay.quantize.qconfig(global_scale=33.0,
                                         skip_conv_layers=[0],
                                         store_lowbit_output=True,
diff --git a/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py b/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
index 62fb321..3a36785 100644
--- a/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
+++ b/docs/_downloads/ef2454f5c8b007c53ff214943df808ad/deploy_classification.py
@@ -171,7 +171,7 @@ with autotvm.tophub.context(target):
     if target.device_name == "vta":
         # Perform quantization in Relay
         # Note: We set opt_level to 3 in order to fold batch norm
-        with relay.build_config(opt_level=3):
+        with tvm.transform.PassContext(opt_level=3):
             with relay.quantize.qconfig(global_scale=8.0,
                                         skip_conv_layers=[0]):
                 mod = relay.quantize.quantize(mod, params=params)
@@ -188,16 +188,16 @@ with autotvm.tophub.context(target):
         relay_prog = mod["main"]
 
     # Compile Relay program with AlterOpLayout disabled
-    with relay.build_config(opt_level=3, disabled_pass={"AlterOpLayout"}):
-        if target.device_name != "vta":
+    if target.device_name != "vta":
+        with tvm.transform.PassContext(opt_level=3, disabled_pass={"AlterOpLayout"}):
+            graph, lib, params = relay.build(
+                relay_prog, target=target,
+                params=params, target_host=env.target_host)
+    else:
+        with vta.build_config(opt_level=3, disabled_pass={"AlterOpLayout"}):
             graph, lib, params = relay.build(
                 relay_prog, target=target,
                 params=params, target_host=env.target_host)
-        else:
-            with vta.build_config():
-                graph, lib, params = relay.build(
-                    relay_prog, target=target,
-                    params=params, target_host=env.target_host)
 
     # Measure Relay build time
     build_time = time.time() - build_start
diff --git a/docs/_downloads/f2f3dea1548903b315df5847ee60f87d/from_mxnet.ipynb b/docs/_downloads/f2f3dea1548903b315df5847ee60f87d/from_mxnet.ipynb
index 387afa2..7e5d275 100644
--- a/docs/_downloads/f2f3dea1548903b315df5847ee60f87d/from_mxnet.ipynb
+++ b/docs/_downloads/f2f3dea1548903b315df5847ee60f87d/from_mxnet.ipynb
@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "target = 'cuda'\nwith relay.build_config(opt_level=3):\n    graph, lib, params = relay.build(func, target, params=params)"
+        "target = 'cuda'\nwith tvm.transform.PassContext(opt_level=3):\n    graph, lib, params = relay.build(func, target, params=params)"
       ]
     },
     {
diff --git a/docs/_downloads/f59fd8b968f7dcde34ed872c8527c192/from_keras.py b/docs/_downloads/f59fd8b968f7dcde34ed872c8527c192/from_keras.py
index 928a8ac..7ece790 100644
--- a/docs/_downloads/f59fd8b968f7dcde34ed872c8527c192/from_keras.py
+++ b/docs/_downloads/f59fd8b968f7dcde34ed872c8527c192/from_keras.py
@@ -79,7 +79,7 @@ mod, params = relay.frontend.from_keras(keras_resnet50, shape_dict)
 # compile the model
 target = 'cuda'
 ctx = tvm.gpu(0)
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     executor = relay.build_module.create_executor('graph', mod, ctx, target)
 
 ######################################################################
diff --git a/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py b/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py
index 8354b0e..53d29a9 100644
--- a/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py
+++ b/docs/_downloads/f83f0c3da8a2ab10657c61e034b7218d/from_pytorch.py
@@ -101,7 +101,7 @@ mod, params = relay.frontend.from_pytorch(scripted_model,
 target = 'llvm'
 target_host = 'llvm'
 ctx = tvm.cpu(0)
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
     graph, lib, params = relay.build(mod,
                                      target=target,
                                      target_host=target_host,
diff --git a/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb b/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
index c82d762..2fa3551 100644
--- a/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
+++ b/docs/_downloads/f8f7a2adf30f5033603d79cdbacd9235/tune_relay_arm.ipynb
@@ -126,7 +126,7 @@
       },
       "outputs": [],
       "source": [
-        "def tune_and_evaluate(tuning_opt):\n    # extract workloads from relay program\n    print(\"Extract tasks...\")\n    mod, params, input_shape, _ = get_network(network, batch_size=1)\n    tasks = autotvm.task.extract_from_program(mod[\"main\"], target=target,\n                                              params=params,\n                                              ops=(relay.op.get(\"nn.conv2d\"),))\n\n    # run tuning tasks\n    print(\"Tuning...\")\n    tune_tasks(tasks, **tu [...]
+        "def tune_and_evaluate(tuning_opt):\n    # extract workloads from relay program\n    print(\"Extract tasks...\")\n    mod, params, input_shape, _ = get_network(network, batch_size=1)\n    tasks = autotvm.task.extract_from_program(mod[\"main\"], target=target,\n                                              params=params,\n                                              ops=(relay.op.get(\"nn.conv2d\"),))\n\n    # run tuning tasks\n    print(\"Tuning...\")\n    tune_tasks(tasks, **tu [...]
       ]
     },
     {
diff --git a/docs/_sources/api/python/index.rst.txt b/docs/_sources/api/python/index.rst.txt
index c279dc2..bee6e56 100644
--- a/docs/_sources/api/python/index.rst.txt
+++ b/docs/_sources/api/python/index.rst.txt
@@ -37,9 +37,11 @@ Python API
    relay/transform
    relay/analysis
    relay/backend
+   relay/dataflow_pattern
    relay/testing
    autotvm
    rpc
+   micro
    contrib
    graph_runtime
    vta/index
diff --git a/docs/_sources/api/python/micro.rst.txt b/docs/_sources/api/python/micro.rst.txt
new file mode 100644
index 0000000..1a93f74
--- /dev/null
+++ b/docs/_sources/api/python/micro.rst.txt
@@ -0,0 +1,23 @@
+..  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.
+
+tvm.micro
+---------
+.. automodule:: tvm.micro
+   :members:
+   :imported-members:
+   :autosummary:
diff --git a/docs/_sources/api/python/relay/dataflow_pattern.rst.txt b/docs/_sources/api/python/relay/dataflow_pattern.rst.txt
new file mode 100644
index 0000000..fe1d4e9
--- /dev/null
+++ b/docs/_sources/api/python/relay/dataflow_pattern.rst.txt
@@ -0,0 +1,25 @@
+..  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.
+
+tvm.relay.dataflow_pattern
+--------------------------
+
+.. automodule:: tvm.relay.dataflow_pattern
+    :members:
+    :imported-members:
+    :exclude-members: Object, Node
+    :autosummary:
diff --git a/docs/_sources/api/python/topi.rst.txt b/docs/_sources/api/python/topi.rst.txt
index cef2999..53f2f3c 100644
--- a/docs/_sources/api/python/topi.rst.txt
+++ b/docs/_sources/api/python/topi.rst.txt
@@ -46,14 +46,17 @@ List of operators
    topi.reinterpret
    topi.transpose
    topi.flip
+   topi.reverse_sequence
    topi.strided_slice
    topi.expand_dims
    topi.reshape
    topi.unravel_index
+   topi.sparse_to_dense
    topi.squeeze
    topi.concatenate
    topi.split
    topi.take
+   topi.gather
    topi.gather_nd
    topi.full
    topi.full_like
@@ -150,14 +153,17 @@ topi
 .. autofunction:: topi.reinterpret
 .. autofunction:: topi.transpose
 .. autofunction:: topi.flip
+.. autofunction:: topi.reverse_sequence
 .. autofunction:: topi.strided_slice
 .. autofunction:: topi.expand_dims
 .. autofunction:: topi.reshape
 .. autofunction:: topi.unravel_index
+.. autofunction:: topi.sparse_to_dense
 .. autofunction:: topi.squeeze
 .. autofunction:: topi.concatenate
 .. autofunction:: topi.split
 .. autofunction:: topi.take
+.. autofunction:: topi.gather
 .. autofunction:: topi.gather_nd
 .. autofunction:: topi.full
 .. autofunction:: topi.full_like
@@ -212,6 +218,8 @@ topi.nn
 .. autofunction:: topi.nn.conv2d_hwcn
 .. autofunction:: topi.nn.depthwise_conv2d_nchw
 .. autofunction:: topi.nn.depthwise_conv2d_nhwc
+.. autofunction:: topi.nn.conv3d_ncdhw
+.. autofunction:: topi.nn.conv3d_transpose_ncdhw
 .. autofunction:: topi.nn.fifo_buffer
 
 topi.image
@@ -231,6 +239,8 @@ topi.generic
 
 .. autofunction:: topi.generic.schedule_conv2d_nchw
 .. autofunction:: topi.generic.schedule_depthwise_conv2d_nchw
+.. autofunction:: topi.generic.schedule_conv3d_ncdhw
+.. autofunction:: topi.generic.schedule_conv3d_transpose_ncdhw
 .. autofunction:: topi.generic.schedule_reduce
 .. autofunction:: topi.generic.schedule_broadcast
 .. autofunction:: topi.generic.schedule_injective
diff --git a/docs/_sources/dev/codebase_walkthrough.rst.txt b/docs/_sources/dev/codebase_walkthrough.rst.txt
index a66328f..8674c8e 100644
--- a/docs/_sources/dev/codebase_walkthrough.rst.txt
+++ b/docs/_sources/dev/codebase_walkthrough.rst.txt
@@ -84,7 +84,7 @@ This function is mapped to the C++ function in ``include/tvm/schedule.h``.
 ::
 
    inline Schedule create_schedule(Array<Operation> ops) {
-     return ScheduleNode::make(ops);
+     return Schedule(ops);
    }
 
 ``Schedule`` consists of collections of ``Stage`` and output ``Operation``.
diff --git a/docs/_sources/dev/convert_layout.rst.txt b/docs/_sources/dev/convert_layout.rst.txt
index ee5350c..07ebc20 100644
--- a/docs/_sources/dev/convert_layout.rst.txt
+++ b/docs/_sources/dev/convert_layout.rst.txt
@@ -246,7 +246,7 @@ In order to specify the layouts to convert to, we create a mapping of heavily-la
     # RemoveUnunsedFunctions is used to clean up the graph.
     seq = tvm.transform.Sequential([relay.transform.RemoveUnusedFunctions(),
                                     relay.transform.ConvertLayout(desired_layouts)])
-    with relay.transform.PassContext(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         mod = seq(mod)
 
     # Call relay compilation
diff --git a/docs/_sources/dev/relay_add_op.rst.txt b/docs/_sources/dev/relay_add_op.rst.txt
index f494cc6..7dca251 100644
--- a/docs/_sources/dev/relay_add_op.rst.txt
+++ b/docs/_sources/dev/relay_add_op.rst.txt
@@ -99,7 +99,7 @@ the arguments to the call node, as below.
     TVM_REGISTER_GLOBAL("relay.op._make.add")
         .set_body_typed<Expr(Expr, Expr)>([](Expr lhs, Expr rhs) {
             static const Op& op = Op::Get("add");
-          return CallNode::make(op, {lhs, rhs}, Attrs(), {});
+          return Call(op, {lhs, rhs}, Attrs(), {});
         });
 
 Including a Python API Hook
diff --git a/docs/_sources/dev/relay_add_pass.rst.txt b/docs/_sources/dev/relay_add_pass.rst.txt
index 3eb9586..a82ae4f 100644
--- a/docs/_sources/dev/relay_add_pass.rst.txt
+++ b/docs/_sources/dev/relay_add_pass.rst.txt
@@ -138,7 +138,7 @@ is shown below.
       if (g->tuple == t) {
         return GetRef<Expr>(g);
       } else {
-        return TupleGetItemNode::make(t, g->index);
+        return TupleGetItem(t, g->index);
       }
     }
 
@@ -261,7 +261,7 @@ the pass.
             body.same_as(op->body)) {
           return GetRef<Expr>(op);
         } else {
-          return LetNode::make(var, value, body);
+          return Let(var, value, body);
         }
       }
     }
diff --git a/docs/_sources/dev/relay_pass_infra.rst.txt b/docs/_sources/dev/relay_pass_infra.rst.txt
index b40b06e..446a91b 100644
--- a/docs/_sources/dev/relay_pass_infra.rst.txt
+++ b/docs/_sources/dev/relay_pass_infra.rst.txt
@@ -344,13 +344,13 @@ registration.
 .. code:: c++
 
     // Create a simple Relay program.
-    auto tensor_type = relay::TensorTypeNode::make({}, tvm::Bool());
-    auto x = relay::VarNode::make("x", relay::Type());
-    auto f = relay::FunctionNode::make(tvm::Array<relay::Var>{ x }, x, relay::Type(), {});
+    auto tensor_type = relay::TensorType({}, tvm::Bool());
+    auto x = relay::Var("x", relay::Type());
+    auto f = relay::Function(tvm::Array<relay::Var>{ x }, x, relay::Type(), {});
 
-    auto y = relay::VarNode::make("y", tensor_type);
-    auto call = relay::CallNode::make(f, tvm::Array<relay::Expr>{ y });
-    auto fx = relay::FunctionNode::make(tvm::Array<relay::Var>{ y }, call, relay::Type(), {});
+    auto y = relay::Var("y", tensor_type);
+    auto call = relay::Call(f, tvm::Array<relay::Expr>{ y });
+    auto fx = relay::Function(tvm::Array<relay::Var>{ y }, call, relay::Type(), {});
 
     // Create a module for optimization.
     auto mod = IRModule::FromExpr(fx);
diff --git a/docs/_sources/langref/relay_op.rst.txt b/docs/_sources/langref/relay_op.rst.txt
index 798d440..86e0c0d 100644
--- a/docs/_sources/langref/relay_op.rst.txt
+++ b/docs/_sources/langref/relay_op.rst.txt
@@ -69,6 +69,8 @@ This level enables typical convnet models.
 
    tvm.relay.nn.conv2d
    tvm.relay.nn.conv2d_transpose
+   tvm.relay.nn.conv3d
+   tvm.relay.nn.conv3d_transpose
    tvm.relay.nn.dense
    tvm.relay.nn.max_pool2d
    tvm.relay.nn.max_pool3d
@@ -118,6 +120,7 @@ This level enables additional math and transform operators.
    tvm.relay.zeros_like
    tvm.relay.ones
    tvm.relay.ones_like
+   tvm.relay.gather
    tvm.relay.gather_nd
    tvm.relay.full
    tvm.relay.full_like
@@ -129,7 +132,9 @@ This level enables additional math and transform operators.
    tvm.relay.repeat
    tvm.relay.tile
    tvm.relay.reverse
+   tvm.relay.reverse_sequence
    tvm.relay.unravel_index
+   tvm.relay.sparse_to_dense
 
 
 **Level 4: Broadcast and Reductions**
@@ -224,4 +229,4 @@ This level supports dialect operators.
    :nosignatures:
 
    tvm.relay.qnn.op.requantize
-   tvm.relay.qnn.op.conv2d
\ No newline at end of file
+   tvm.relay.qnn.op.conv2d
diff --git a/docs/_sources/langref/relay_pattern.rst.txt b/docs/_sources/langref/relay_pattern.rst.txt
index f56d496..962dcc6 100644
--- a/docs/_sources/langref/relay_pattern.rst.txt
+++ b/docs/_sources/langref/relay_pattern.rst.txt
@@ -28,17 +28,40 @@ Such a language is not just useful for building a rewriter but also providing ex
 
 In the backend world, we could use the same machinery to build a higher level API using bring your own code generation. This API takes set of patterns describing your hardware capabilities and an external compiler, providing a relatively smooth heterogeneous experience out of the box.
 
-Examples
-========
+Pattern Examples
+================
 
-There are quite a few properties that are worth matching of operators below we examine how to match tree properties, and expand on some use cases that are not fully explored in the prototype. The first example is a simple case where we want to match one operator with a single input OR another operator with a single input, see the below diagram for a graphical representation and corresponding code::
+There are quite a few properties of operators that are worth matching. Below we examine how to match tree properties, and expand on some use cases that are not fully explored in the prototype. This section
+demonstrates how to write patterns. It is recommended to check `tests/python/relay/test_dataflow_pattern.py`_
+for more use cases.
+
+.. _tests/python/relay/test_dataflow_pattern.py: https://github.com/apache/incubator-tvm/blob/master/tests/python/relay/test_dataflow_pattern.py
+
+.. note::
+
+    If you cannot find the corresponding pattern node to match the Relay node you want,
+    you are welcome to raise an issue or submit a PR to add it.
+
+Matching One of Two Ops
+***********************
+
+The first example is a simple case where we want to match one operator with a single input OR
+another operator with a single input:
+
+.. code-block:: python
 
     def test_match_op_or():
         is_add_or_sub = is_op('add') | is_op('subtract')
         assert is_add_or_sub.match(relay.op.op.get("add"))
         assert is_add_or_sub.match(relay.op.op.get("subtract"))
 
-The next example is a dense operation with any operator that is marked element-wise::
+
+Matching an Op with Attributes
+******************************
+
+The next example is a dense operation with any operator that is marked element-wise:
+
+.. code-block:: python
 
     def test_no_match_attr():
         op = is_op('nn.dense').has_attr({"TOpPattern": K_ELEMWISE})
@@ -47,11 +70,130 @@ The next example is a dense operation with any operator that is marked element-w
         y = relay.var('y')
         assert not op_pat.match(relay.op.nn.dense(x, y))
 
+Here is another example to match an op with a specific attribute:
+
+.. code-block:: python
+
+    def test_match_data_layout():
+        is_conv2d = is_op('nn.conv2d')(wildcard(), wildcard()).has_attr({"data_layout": "NHWC"})
+        x = relay.var('x')
+        y = relay.var('y')
+        assert not is_conv2d.match(relay.op.nn.conv2d(x, y))
+
+Matching an Optional Op
+***********************
+
+The next example is matching a pattern with one optional operator. In this pattern,
+we can match the graph of conv2d+bias_add+relu or the graph of conv2d+bias_add.
+
+.. code-block:: python
+
+    def test_match_optional():
+        conv_node = is_op('nn.conv2d')(wildcard(), wildcard())
+        bias_node = is_op('nn.bias_add')(conv_node, wildcard())
+        pat = bias_node.optional(lambda x: is_op('nn.relu')(x))
+
+        x = relay.var('x')
+        y = relay.var('y')
+        z = relay.var('z')
+        conv2d = relay.op.nn.conv2d(x, y)
+        bias = relay.op.nn.bias_add(conv2d, z)
+        assert pat.match(bias)
+        relu = relay.op.nn.relu(bias)
+        assert pat.match(relu)
+
+Matching Non-Call Nodes
+***********************
+
+Sometimes we may also want to match a pattern that includes Tuple or TupleGetItem nodes.
+Since there are not call nodes, we need to use specific pattern nodes to match them:
+
+.. code-block:: python
+
+    def test_match_tuple():
+        x = relay.var('x')
+        y = relay.var('y')
+        z = relay.var('z')
+        tuple_pattern = is_tuple((wildcard(), wildcard(), wildcard()))
+        assert tuple_pattern.match(relay.expr.Tuple((x,y,z)))
+
+The next example is matching a pattern of batch_norm -> get(0) -> relu:
+
+.. code-block:: python
+
+    def test_match_tuple_get_item():
+        bn_node = is_op('nn.batch_norm')(wildcard(), wildcard(), wildcard(), wildcard(), wildcard())
+        tuple_get_item_node = is_tuple_get_item(bn_node, 0)
+        pat = is_op('nn.relu')(tuple_get_item_node)
+
+        x = relay.var('x', shape=(1, 8))
+        gamma = relay.var("gamma", shape=(8,))
+        beta = relay.var("beta", shape=(8,))
+        moving_mean = relay.var("moving_mean", shape=(8,))
+        moving_var = relay.var("moving_var", shape=(8,))
+        bn_node = relay.nn.batch_norm(x, gamma, beta, moving_mean, moving_var)
+        tuple_get_item_node = bn_node[0]
+        out = relay.nn.relu(tuple_get_item_node)
+        pat.match(out)
+
+The next example is matching a constant node regarding its values. This is useful to check
+if a specific parameter in a subgraph has been bound or not.
+
+.. code-block:: python
+
+    def test_match_constant():
+        conv2d = is_op('nn.conv2d')(wildcard(), is_constant())
+        pattern = is_op('nn.bias_add')(conv2d, wildcard())
+
+        x = relay.var('x', shape=(1, 3, 224, 224))
+        w = relay.var('w', shape=(3, 3, 3, 3))
+        b = relay.var('b', shape=(3, ))
+        conv2d = relay.op.nn.conv2d(x, w)
+        out = relay.op.nn.bias_add(conv2d, b)
+        func = relay.Function([x, w, b], out)
+        mod = tvm.IRModule.from_expr(func)
+
+        # Two inputs of the conv2d in the graph are VarNode by default, so no match.
+        assert not pattern.match(mod['main'].body)
+
+        # The second input (weight) has been bind with constant values so it is now a constant node.
+        mod["main"] = bind_params_by_name(mod["main"],
+                                        {'w': tvm.nd.array(np.ones(shape=(3, 3, 3, 3)))})
+        assert pattern.match(mod['main'].body)
+
+On the other hand, if you need to match the constant with a specific value, you can directly
+use ``is_expr``. This could be useful for algebraic simplify.
+
+.. code-block:: python
+
+    def test_match_plus_zero():
+        zero = (is_expr(relay.const(0)) | is_expr(relay.const(0.0)))
+        pattern = wildcard() + zero
+        
+        x = relay.Var('x')
+        y = x + relay.const(0)
+        assert pattern.match(y)
+
+The next example is matching function nodes with a specific attribute:
+
+.. code-block:: python
+
+    def test_match_function():
+        pattern = wildcard().has_attr({"Composite": "add"})
+
+        x = relay.var('x')
+        y = relay.var('y')
+        f = relay.Function([x, y], x + y).with_attr("Composite", "add")
+        assert pattern.match(f)
+
+Matching Diamonds and Post-Dominator Graphs
+*******************************************
+
 The next example is matching a diamond with two inputs at the top of the diamond::
 
     def test_match_diamond():
         # Pattern
-        is_conv2d = is_op('nn.conv2d')(is_input(), is_input())
+        is_conv2d = is_op('nn.conv2d')(is_var(), is_var())
         path1 = is_op('nn.relu')(is_conv2d)
         path2 = is_op('nn.leaky_relu')(is_conv2d)
         diamond = is_op('add')(path1, path2)
@@ -67,11 +209,11 @@ The next example is matching a diamond with two inputs at the top of the diamond
         # Check
         assert diamond.match(out)
 
-The final example  is matching diamonds with a post-dominator relationship. We embed dominator analysis as type of matching in the pattern language in order to allow for pattern matching with unknown topology. This is important because we want to be able to use the language to describe fuse patterns, like elementwise operations followed by a conv2d::
+The final example is matching diamonds with a post-dominator relationship. We embed dominator analysis as type of matching in the pattern language in order to allow for pattern matching with unknown topology. This is important because we want to be able to use the language to describe fuse patterns, like elementwise operations followed by a conv2d::
 
     def test_match_dom_diamond():
         # Pattern
-        is_conv2d = is_op('nn.conv2d')(is_input(), is_input())
+        is_conv2d = is_op('nn.conv2d')(is_var(), is_var())
         reduction = is_op('add')(wildcard(), wildcard())
         diamond = dominates(is_conv2d, is_elemwise, reduction)
 
@@ -86,8 +228,8 @@ The final example  is matching diamonds with a post-dominator relationship. We e
         # Check
         assert diamond.match(out)
 
-Design
-======
+Pattern Language Design
+=======================
 
 The pattern language proposed is designed to be a mirror of Relay's IR with additional support for common scenarios. The goal of the pattern language is to provide a regular-expression like capability for matching data-flow graphs and doing rewriting.
 
@@ -98,7 +240,12 @@ The high level design is to introduce a language of patterns for now we propose
             | pattern(pattern1, ... patternN)
             | has_type(pattern, type)
             | has_attr(pattern, attrs)
-            | is_input(name)
+            | is_var(name)
+            | is_constant()
+            | is_expr(expr)
+            | is_op(op_name)
+            | is_tuple()
+            | is_tuple_get_item()
             | pattern1 `|` pattern2
             | dominates(parent_pattern, path_pattern, child_pattern)
 
@@ -124,10 +271,10 @@ Attribute Pattern
 
 Check that the operator matched by the pattern has an attribute with a particular value.
 
-Input
-*****
+Variable Pattern
+****************
 
-Check that the expression is an input, i.e has no parents and is a variable.
+Check that the expression is a relay Variable, and optional provide a name to match to the Variable name.
 
 
 Alternate
@@ -139,3 +286,124 @@ Domination
 **********
 
 Match child pattern, find a match for the parent pattern, insuring that the child ultimately dominates the parrent (i.e., no nodes outside the pattern use outputs of the parent), and that ever node betwen the child and the pattern matches the path pattern.
+
+Applications
+============
+
+The pattern language provides not only the pattern matching but also pattern processing.
+Here we introduce two pattern processing approaches and provide some examples.
+
+Pattern Rewriting
+*****************
+
+If you would like to replace the matched pattern with another subgraph, you can leverage
+the ``rewrite`` transformation. Here is an example of rewriting a series of arithmetic operators
+with a single batch_norm op:
+
+.. code-block:: python
+
+    class BatchnormCallback(DFPatternCallback):
+        # A callback class to rewrite the matched pattern to a batch_norm op.
+        def __init__(self):
+            self.x = wildcard()
+            self.var = wildcard()
+            self.mean = wildcard()
+            self.beta = wildcard()
+            self.gamma = wildcard()
+            self.eps = wildcard()
+            
+            self.pattern = self.gamma * (self.x - self.mean)/is_op("sqrt")(self.var + self.eps) + self.beta
+
+        def callback(self, pre, post, node_map):
+            x = node_map[self.x][0]
+            var = node_map[self.var][0]
+            mean = node_map[self.mean][0]
+            beta = node_map[self.beta][0]
+            gamma = node_map[self.gamma][0]
+            eps = node_map[self.eps][0]
+            return relay.op.nn.batch_norm(x, gamma, beta, mean, var, epsilon = eps.data.asnumpy().item())[0]
+
+        # A graph of arithmetic operators that are functional equivalent to batch_norm.
+        x = relay.var('x')
+        var = relay.var('var')
+        mean = relay.var('mean')
+        beta = relay.var('beta')
+        gamma = relay.var('gamma')
+        BN = gamma * (x - mean)/relay.op.sqrt(var + relay.const(1e-5)) + beta
+
+        from tvm.relay.dataflow_pattern import rewrite
+        out = rewrite(BatchnormCallback(), BN)
+        assert tvm.ir.structural_equal(out, relay.op.nn.batch_norm(x, gamma, beta, mean, var, epsilon = 1e-5)[0])
+
+The function ``def callback(self, pre, post, node_map)`` will be invoked when the rewriter matches
+``self.pattern``. ``node_map`` is a dictionary mapping from pattern nodes to matched nodes in the graph.
+
+Pattern Partitioning
+********************
+
+If you would like to perform a more complex processing for matched subgraphs and you are not
+satisfied with ``rewrite``, you may consider partitioning the matched subgraphs to a separate
+Relay function and perform other processes to the function. Here we use ``pattern.partition``
+to create a new Relay function for each matched subgraph. The functionality is similar to
+the op fusion pass in TVM:
+
+.. code-block:: python
+
+    # A pattern matching conv2d+relu.
+    pattern = is_op("nn.relu")(is_op("nn.conv2d")(wildcard(), wildcard()))
+
+    # A graph.
+    x = relay.var('input')
+    w = relay.var('weight')
+    conv2d = relay.op.nn.conv2d(x, w)
+    relu = relay.op.nn.relu(conv2d)
+    print('relu')
+    # free_var %x: Tensor[(1, 3, 224, 224), float32]
+    # free_var %w: Tensor[(3, 3, 3, 3), float32]
+    # %0 = nn.conv2d(%x, %w, padding=[0, 0, 0, 0]) /* ty=Tensor[(1, 3, 222, 222), float32] */;
+    # free_var %b: Tensor[(3), float32]
+    # nn.bias_add(%0, %b) /* ty=Tensor[(1, 3, 222, 222), float32] */
+
+    # After partition.
+    print(pattern.partition(relu))
+    # free_var %x: Tensor[(1, 3, 224, 224), float32]
+    # free_var %w: Tensor[(3, 3, 3, 3), float32]
+    # free_var %b: Tensor[(3), float32]
+    # %1 = fn (%FunctionVar_0_0, %FunctionVar_0_1,
+    #          %FunctionVar_0_2, PartitionedFromPattern="nn.conv2d_nn.bias_add_") {
+    #   %0 = nn.conv2d(%FunctionVar_0_0, %FunctionVar_0_1, padding=[0, 0, 0, 0]);
+    #   nn.bias_add(%0, %FunctionVar_0_2)
+    # };
+    # %1(%x, %w, %b)
+
+Note that you can also specify the attributes for the created functions:
+
+.. code-block:: python
+
+    print(pattern.partition(relu, {'Composite': 'one_layer'}))
+    # free_var %x: Tensor[(1, 3, 224, 224), float32]
+    # free_var %w: Tensor[(3, 3, 3, 3), float32]
+    # free_var %b: Tensor[(3), float32]
+    # %1 = fn (%FunctionVar_0_0, %FunctionVar_0_1,
+    #          %FunctionVar_0_2, Composite="one_layer",
+    #                            PartitionedFromPattern="nn.conv2d_nn.bias_add_") {
+    #   %0 = nn.conv2d(%FunctionVar_0_0, %FunctionVar_0_1, padding=[0, 0, 0, 0]);
+    #   nn.bias_add(%0, %FunctionVar_0_2)
+    # };
+    # %1(%x, %w, %b)
+
+If you need a customized checking function that cannot be specified using pattern language,
+you can specify ``check`` function when partitioning. The following example demonstrates a
+case that checks input data layout of a subgraph:
+
+.. code-block:: python
+
+    def check(pre):
+        conv = pre.args[0]
+        return (conv.attrs.data_layout == "NCHW") and bool(conv.checked_type.shape[0] == 1)
+
+    pattern.partition(relu, check=check)
+
+In this example, we check if the first argument of the matched subgraph (i.e., ``pre.args[0]``)
+has data layout "NCHW" and if its batch size is 1. This feature is useful if the conditions
+of matching a pattern cannot be verified by analyzing the pattern itself.
diff --git a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
index cd650b9..bba1d45 100644
--- a/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,11 +5,11 @@
 
 Computation times
 =================
-**00:52.399** total execution time for **tutorials_autotvm** files:
-
-- **00:29.032**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
-- **00:22.806**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
-- **00:00.153**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
-- **00:00.137**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
-- **00:00.135**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
-- **00:00.135**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+**00:53.012** total execution time for **tutorials_autotvm** files:
+
+- **00:28.544**: :ref:`sphx_glr_tutorials_autotvm_tune_simple_template.py` (``tune_simple_template.py``)
+- **00:23.866**: :ref:`sphx_glr_tutorials_autotvm_tune_conv2d_cuda.py` (``tune_conv2d_cuda.py``)
+- **00:00.169**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_x86.py` (``tune_relay_x86.py``)
+- **00:00.147**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_arm.py` (``tune_relay_arm.py``)
+- **00:00.144**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_cuda.py` (``tune_relay_cuda.py``)
+- **00:00.143**: :ref:`sphx_glr_tutorials_autotvm_tune_relay_mobile_gpu.py` (``tune_relay_mobile_gpu.py``)
diff --git a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
index 055a4ca..635e539 100644
--- a/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_conv2d_cuda.rst.txt
@@ -234,26 +234,26 @@ for this template
        7 unroll_explicit: OtherOption([0, 1]) len=2
     )
     Get devices for measurement successfully!
-    No: 1   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 2   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 3   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 4   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 5   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 6   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 7   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 8   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 9   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 10  GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 11  GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 12  GFLOPS: 70.83/70.83     result: MeasureResult(costs=(0.003268532825,), error_no=0, all_cost=1.603607416152954, timestamp=1590214109.7394354)    [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,8533140
-    No: 13  GFLOPS: 0.00/70.83      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 14  GFLOPS: 0.00/70.83      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 15  GFLOPS: 0.00/70.83      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 16  GFLOPS: 0.00/70.83      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 17  GFLOPS: 0.00/70.83      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 18  GFLOPS: 0.00/70.83      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (3) /workspace/build/libtvm.so(+0x4c2c04) [0x7ffa99076c04]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7ffa990766a6]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
-    No: 19  GFLOPS: 789.84/789.84   result: MeasureResult(costs=(0.0002930994545454545,), error_no=0, all_cost=1.9248723983764648, timestamp=1590214114.025119)     [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
-    No: 20  GFLOPS: 6.97/789.84     result: MeasureResult(costs=(0.03322324,), error_no=0, all_cost=1.646322250366211, timestamp=1590214114.9072316)        [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7502165
+    No: 1   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 2   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 3   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 4   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 5   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 6   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 7   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 8   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 9   GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 10  GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 11  GFLOPS: 0.00/0.00       result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 12  GFLOPS: 63.43/63.43     result: MeasureResult(costs=(0.003649818875,), error_no=0, all_cost=1.636338472366333, timestamp=1592418257.054786)     [('tile_f', [-1, 1, 1, 1]), ('tile_y', [-1, 1, 1, 7]), ('tile_x', [-1, 1, 1, 1]), ('tile_rc', [-1, 16, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 3]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,8533140
+    No: 13  GFLOPS: 0.00/63.43      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 14  GFLOPS: 0.00/63.43      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 15  GFLOPS: 0.00/63.43      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 16  GFLOPS: 0.00/63.43      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 17  GFLOPS: 0.00/63.43      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 18  GFLOPS: 0.00/63.43      result: MeasureResult(costs=(InstantiationError('Traceback (most recent call last):\n  [bt] (4) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (3) /workspace/build/libtvm.so(+0x4cc4d4) [0x7f25939fe4d4]\n  [bt] (2) /workspace/build/libtvm.so(tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const+0x3e6) [0x7f25939fdf76]\n  [bt] (1) /workspace/build/libtvm.so(tvm::tir::transform::PrimFunc [...]
+    No: 19  GFLOPS: 777.37/777.37   result: MeasureResult(costs=(0.00029780138189845474,), error_no=0, all_cost=2.0230767726898193, timestamp=1592418261.583229)    [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
+    No: 20  GFLOPS: 6.95/777.37     result: MeasureResult(costs=(0.03328762325,), error_no=0, all_cost=1.7611923217773438, timestamp=1592418262.514915)     [('tile_f', [-1, 2, 1, 16]), ('tile_y', [-1, 1, 1, 1]), ('tile_x', [-1, 7, 1, 1]), ('tile_rc', [-1, 2, 32]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 1, 1]), ('auto_unroll_max_step', 512), ('unroll_explicit', 1)],None,7502165
 
 
 
@@ -307,7 +307,7 @@ and measure running time.
 
     Best config:
     [('tile_f', [-1, 2, 1, 1]), ('tile_y', [-1, 1, 7, 1]), ('tile_x', [-1, 1, 7, 1]), ('tile_rc', [-1, 32, 1]), ('tile_ry', [-1, 1, 3]), ('tile_rx', [-1, 3, 1]), ('auto_unroll_max_step', 1500), ('unroll_explicit', 1)],None,9699801
-    Time cost of this operator: 0.000354
+    Time cost of this operator: 0.000389
 
 
 
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
index 33e5a27..1f7addb 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_arm.rst.txt
@@ -337,7 +337,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
         # compile kernels with history best records
         with autotvm.apply_history_best(log_file):
             print("Compile...")
-            with relay.build_config(opt_level=3):
+            with tvm.transform.PassContext(opt_level=3):
                 graph, lib, params = relay.build_module.build(
                     mod, target=target, params=params)
 
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
index 0a14896..8f83263 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_cuda.rst.txt
@@ -250,7 +250,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
         # compile kernels with history best records
         with autotvm.apply_history_best(log_file):
             print("Compile...")
-            with relay.build_config(opt_level=3):
+            with tvm.transform.PassContext(opt_level=3):
                 graph, lib, params = relay.build_module.build(
                     mod, target=target, params=params)
 
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt
index 641e488..f343f9f 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_mobile_gpu.rst.txt
@@ -334,7 +334,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
         # compile kernels with history best records
         with autotvm.apply_history_best(log_file):
             print("Compile...")
-            with relay.build_config(opt_level=3):
+            with tvm.transform.PassContext(opt_level=3):
                 graph, lib, params = relay.build_module.build(
                     mod, target=target, params=params, target_host=target_host)
             # export library
diff --git a/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt b/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt
index a18eff3..5764df8 100644
--- a/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_relay_x86.rst.txt
@@ -211,7 +211,7 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
         # compile kernels with graph-level best records
         with autotvm.apply_graph_best(graph_opt_sch_file):
             print("Compile...")
-            with relay.build_config(opt_level=3):
+            with tvm.transform.PassContext(opt_level=3):
                 graph, lib, params = relay.build_module.build(
                     mod, target=target, params=params)
 
diff --git a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
index d1e5f04..eecf258 100644
--- a/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
+++ b/docs/_sources/tutorials/autotvm/tune_simple_template.rst.txt
@@ -361,16 +361,16 @@ used to get the best config later.
  .. code-block:: none
 
     Get devices for measurement successfully!
-    No: 1   GFLOPS: 7.05/7.05       result: MeasureResult(costs=(0.0380749568,), error_no=0, all_cost=0.8678317070007324, timestamp=1590214075.6415935)     [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
-    No: 2   GFLOPS: 2.28/7.05       result: MeasureResult(costs=(0.11760239880000001,), error_no=0, all_cost=2.1851308345794678, timestamp=1590214077.9269166)      [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
-    No: 3   GFLOPS: 7.43/7.43       result: MeasureResult(costs=(0.036118973799999995,), error_no=0, all_cost=1.1442148685455322, timestamp=1590214078.9067369)     [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
-    No: 4   GFLOPS: 4.50/7.43       result: MeasureResult(costs=(0.059688098599999996,), error_no=0, all_cost=1.3526537418365479, timestamp=1590214080.259163)      [('tile_y', [-1, 16]), ('tile_x', [-1, 32])],None,54
-    No: 5   GFLOPS: 6.40/7.43       result: MeasureResult(costs=(0.041930579,), error_no=0, all_cost=0.9435508251190186, timestamp=1590214081.3267553)      [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
-    No: 6   GFLOPS: 6.49/7.43       result: MeasureResult(costs=(0.0413677544,), error_no=0, all_cost=0.943490743637085, timestamp=1590214082.3985152)      [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
-    No: 7   GFLOPS: 27.63/27.63     result: MeasureResult(costs=(0.0097154568,), error_no=0, all_cost=0.5158059597015381, timestamp=1590214082.9561296)     [('tile_y', [-1, 1]), ('tile_x', [-1, 128])],None,70
-    No: 8   GFLOPS: 21.87/27.63     result: MeasureResult(costs=(0.012276801399999999,), error_no=0, all_cost=0.5109748840332031, timestamp=1590214083.5522263)     [('tile_y', [-1, 4]), ('tile_x', [-1, 512])],None,92
-    No: 9   GFLOPS: 0.64/27.63      result: MeasureResult(costs=(0.4200816336,), error_no=0, all_cost=7.005804777145386, timestamp=1590214090.669096)       [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
-    No: 10  GFLOPS: 0.00/27.63      result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n  [bt] (5) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]\n  [bt] (4) /workspace/build/libtvm.so(+0xe35dd2) [0x7ffa999e9dd2]\n  [bt] (3) /workspace/build/libtvm.so(tvm::runtime::RPCWrappedFunc::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const+0x235) [0x7ffa999ec7c5]\n  [bt] (2) /workspace/build/libtvm.so(tvm::runtime::RPCClientSession::CallF [...]
+    No: 1   GFLOPS: 6.66/6.66       result: MeasureResult(costs=(0.040317947199999996,), error_no=0, all_cost=0.9689126014709473, timestamp=1592418223.5055163)     [('tile_y', [-1, 8]), ('tile_x', [-1, 8])],None,33
+    No: 2   GFLOPS: 2.20/6.66       result: MeasureResult(costs=(0.1219625674,), error_no=0, all_cost=2.2740604877471924, timestamp=1592418225.8873503)     [('tile_y', [-1, 8]), ('tile_x', [-1, 2])],None,13
+    No: 3   GFLOPS: 7.04/7.04       result: MeasureResult(costs=(0.038118197799999996,), error_no=0, all_cost=1.473351001739502, timestamp=1592418226.9196477)      [('tile_y', [-1, 16]), ('tile_x', [-1, 128])],None,74
+    No: 4   GFLOPS: 4.35/7.04       result: MeasureResult(costs=(0.061688380200000004,), error_no=0, all_cost=1.7359662055969238, timestamp=1592418228.323466)      [('tile_y', [-1, 16]), ('tile_x', [-1, 32])],None,54
+    No: 5   GFLOPS: 5.80/7.04       result: MeasureResult(costs=(0.046256228999999996,), error_no=0, all_cost=1.5193562507629395, timestamp=1592418229.4731617)     [('tile_y', [-1, 128]), ('tile_x', [-1, 8])],None,37
+    No: 6   GFLOPS: 6.15/7.04       result: MeasureResult(costs=(0.0436479732,), error_no=0, all_cost=1.2018964290618896, timestamp=1592418230.6016724)     [('tile_y', [-1, 64]), ('tile_x', [-1, 8])],None,36
+    No: 7   GFLOPS: 25.13/25.13     result: MeasureResult(costs=(0.0106810936,), error_no=0, all_cost=0.6128425598144531, timestamp=1592418231.2234533)     [('tile_y', [-1, 1]), ('tile_x', [-1, 128])],None,70
+    No: 8   GFLOPS: 21.21/25.13     result: MeasureResult(costs=(0.012655053199999999,), error_no=0, all_cost=0.5169622898101807, timestamp=1592418231.8622746)     [('tile_y', [-1, 4]), ('tile_x', [-1, 512])],None,92
+    No: 9   GFLOPS: 0.84/25.13      result: MeasureResult(costs=(0.3187542196,), error_no=0, all_cost=5.419588088989258, timestamp=1592418237.4022605)      [('tile_y', [-1, 512]), ('tile_x', [-1, 2])],None,19
+    No: 10  GFLOPS: 0.00/25.13      result: MeasureResult(costs=(RuntimeError('Traceback (most recent call last):\n  [bt] (5) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]\n  [bt] (4) /workspace/build/libtvm.so(+0xe791f2) [0x7f25943ab1f2]\n  [bt] (3) /workspace/build/libtvm.so(tvm::runtime::RPCWrappedFunc::operator()(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*) const+0x26b) [0x7f25943ae56b]\n  [bt] (2) /workspace/build/libtvm.so(tvm::runtime::RPCClientSession::CallF [...]
 
 
 
diff --git a/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt b/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt
index e3f17e7..0c50c9a 100644
--- a/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt
+++ b/docs/_sources/tutorials/cross_compilation_and_rpc.rst.txt
@@ -235,7 +235,7 @@ device and returns the measured cost. Network overhead is excluded.
 
  .. code-block:: none
 
-    1.683e-07 secs/op
+    1.723e-07 secs/op
 
 
 
diff --git a/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt b/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt
index 9beb91a..6925319 100644
--- a/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt
+++ b/docs/_sources/tutorials/dev/low_level_custom_pass.rst.txt
@@ -75,9 +75,9 @@ our customized lowering pass to manipulate the IR directly instead of using sche
     primfn(a_1: handle, b_1: handle, c_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
       buffers = {a: Buffer(a_2: handle, float32, [128], []),
-                 c: Buffer(c_2: handle, float32, [128], []),
-                 b: Buffer(b_2: handle, float32, [128], [])}
-      buffer_map = {b_1: b, c_1: c, a_1: a} {
+                 b: Buffer(b_2: handle, float32, [128], []),
+                 c: Buffer(c_2: handle, float32, [128], [])}
+      buffer_map = {c_1: c, b_1: b, a_1: a} {
       for (i: int32, 0, 128) {
         c_2[i] = ((float32*)a_2[i]) + (float32*)b_2[i]))
       }
@@ -115,7 +115,7 @@ return value of ``func`` will be ignored.
 
     loops = []
     def find_width8(op):
-        """ Find all the 'For' nodes whose extent can be divided by 8. """
+        """ Find all the 'tir.For' nodes whose extent can be divided by 8. """
         if isinstance(op, tvm.tir.For):
             if isinstance(op.extent, tvm.tir.IntImm):
                 if op.extent.value % 8 == 0:
@@ -169,7 +169,7 @@ this value.
         # The last list arugment indicates what kinds of nodes will be transformed.
         # Thus, in this case only `For` nodes will call `vectorize8`
         return f.with_body(
-            tvm.tir.stmt_functor.ir_transform(f.body, None, vectorize8, ['For']))
+            tvm.tir.stmt_functor.ir_transform(f.body, None, vectorize8, ['tir.For']))
 
 
 
@@ -183,9 +183,8 @@ Glue to Lowering
 So far, we are done with writing this IR transformation pass. What we need to do next is to glue
 this pass to TVM's lower pass.
 
-In TVM, there is a property called ``BuildConfig``. You can use this property to customize your
-own lowering options. In this case, we inject the pass written above into the TVM standard lowering
-pass by feeding **a list of tuple** as argument to ``add_lower_pass``. "Tuple" indicates different
+In this case, we inject the pass written above into the TVM standard lowering
+pass by feeding **a list of tuple** as argument to ``tir.add_lower_pass``. "Tuple" indicates different
 phases of lowering. In TVM, there are four phases of lowering and user-customized ones will be
 called after each phase is done.
 
@@ -203,7 +202,7 @@ Thus, a good place to put this transformation pass is just after Phase 1.
 .. code-block:: default
 
 
-    with tvm.target.build_config(add_lower_pass=[(1, vectorize)]) as cfg:
+    with tvm.transform.PassContext(config={"tir.add_lower_pass": [(1, vectorize)]}):
         print(tvm.lower(sch, [a, b, c]))
 
 
@@ -237,7 +236,7 @@ This tutorial gives a quick view of writing a customized IR transformation pass:
 - Use ``tvm.tir.stmt_functor.post_order_visit`` to gather information on each IR nodes.
 - Use ``tvm.tir.stmt_functor.ir_transform`` to transform IR nodes.
 - Wrap up two above to write an IR-transformation function.
-- Use ``tvm.target.build_config`` to put this function to TVM lowering pass
+- Use ``tvm.transform.PassContext`` to put this function to TVM lowering pass
 
 
 
diff --git a/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt b/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt
index 2cde6d2..b758cc8 100644
--- a/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt
+++ b/docs/_sources/tutorials/dev/relay_pass_infra.rst.txt
@@ -294,7 +294,7 @@ for users to customize the optimization level that they want to execute.
 .. code-block:: default
 
 
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         mod2 = seq(mod)
     print(mod2)
 
@@ -334,7 +334,7 @@ identical addition operations.
 .. code-block:: default
 
 
-    with relay.build_config(opt_level=3, disabled_pass=["EliminateCommonSubexpr"]):
+    with tvm.transform.PassContext(opt_level=3, disabled_pass=["EliminateCommonSubexpr"]):
         mod3 = seq(mod)
     print(mod3)
 
@@ -371,12 +371,12 @@ alteration pass falls in such category.
 .. code-block:: default
 
 
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         mod4 = seq(mod)
     print(mod4)
 
     seq1 = tvm.transform.Sequential([relay.transform.AlterOpLayout()])
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         with tvm.target.create("llvm"):
             mod5 = seq1(mod)
     print(mod5)
@@ -502,7 +502,7 @@ them.
                                     relay.transform.EliminateCommonSubexpr(),
                                     relay.transform.FuseOps(),
                                     tvm.transform.PrintIR()])
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         mod = seq(mod)
 
     print("done")
diff --git a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
index dac8111..afb89de 100644
--- a/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/dev/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**00:00.508** total execution time for **tutorials_dev** files:
+**00:00.472** total execution time for **tutorials_dev** files:
 
-- **00:00.371**: :ref:`sphx_glr_tutorials_dev_relay_pass_infra.py` (``relay_pass_infra.py``)
-- **00:00.137**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
+- **00:00.321**: :ref:`sphx_glr_tutorials_dev_relay_pass_infra.py` (``relay_pass_infra.py``)
+- **00:00.151**: :ref:`sphx_glr_tutorials_dev_low_level_custom_pass.py` (``low_level_custom_pass.py``)
diff --git a/docs/_sources/tutorials/frontend/build_gcn.rst.txt b/docs/_sources/tutorials/frontend/build_gcn.rst.txt
index bbf29b0..d364c9c 100644
--- a/docs/_sources/tutorials/frontend/build_gcn.rst.txt
+++ b/docs/_sources/tutorials/frontend/build_gcn.rst.txt
@@ -430,7 +430,7 @@ Export the weigths from PyTorch model to Python Dict
     mod = tvm.IRModule()
     mod["main"] = func
     # Build with Relay
-    with relay.build_config(opt_level=0): # Currently only support opt_level=0
+    with tvm.transform.PassContext(opt_level=0): # Currently only support opt_level=0
         graph, lib, params = relay.build(mod, target, params=params)
 
     # Generate graph runtime
diff --git a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
index 9a5ea82..689fd88 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_android.rst.txt
@@ -314,7 +314,7 @@ to run this tutorial with a real device.
     shape_dict = {input_name: x.shape}
     mod, params = relay.frontend.from_keras(keras_mobilenet_v2, shape_dict)
 
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         graph, lib, params = relay.build(mod, target=target,
                                          target_host=target_host, params=params)
 
@@ -415,7 +415,7 @@ Execute on TVM
 
     TVM prediction top-1: tiger cat
     Evaluate inference time cost...
-    Mean inference time (std dev): 3.64 ms (0.04 ms)
+    Mean inference time (std dev): 5.55 ms (0.04 ms)
 
 
 
diff --git a/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt b/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt
index 692f729..e5c3dea 100644
--- a/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_model_on_rasp.rst.txt
@@ -240,7 +240,7 @@ to run this tutorial with a real device.
         # The above line is a simple form of
         # target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837 -target=armv7l-linux-gnueabihf -mattr=+neon')
 
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         graph, lib, params = relay.build(func, target, params=params)
 
     # After `relay.build`, you will get three return values: graph,
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
index be17abc..e600e83 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized.rst.txt
@@ -84,7 +84,7 @@ Helper functions to run the demo
 
 
     def run_tvm_model(mod, params, input_name, inp, target="llvm"):
-        with relay.build_config(opt_level=3):
+        with tvm.transform.PassContext(opt_level=3):
             json, lib, params = relay.build(mod, target=target, params=params)
 
         runtime = tvm.contrib.graph_runtime.create(json, lib, tvm.context(target, 0))
@@ -352,7 +352,7 @@ Here we give an example of how to measure performance of TVM compiled models.
 
  .. code-block:: none
 
-    Elapsed average ms: 10.2562221
+    Elapsed average ms: 14.098849819999998
 
 
 
diff --git a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
index 339400b..830791a 100644
--- a/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_prequantized_tflite.rst.txt
@@ -10,6 +10,7 @@
 Deploy a Framework-prequantized Model with TVM - Part 3 (TFLite)
 ================================================================
 **Author**: `Siju Samuel <https://github.com/siju-samuel>`_
+
 Welcome to part 3 of the Deploy Framework-Prequantized Model with TVM tutorial.
 In this part, we will start with a Quantized TFLite graph and then compile and execute it via TVM.
 
@@ -284,7 +285,7 @@ target platform that you are interested in.
 .. code-block:: default
 
     target = 'llvm'
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         graph, lib, params = relay.build_module.build(mod, target=target,
                                                       params=params)
 
@@ -360,7 +361,7 @@ Here we give an example of how to measure performance of TVM compiled models.
 
  .. code-block:: none
 
-    Elapsed average ms: 24.98644659
+    Elapsed average ms: 35.247464699999995
 
 
 
@@ -393,7 +394,7 @@ Here we give an example of how to measure performance of TVM compiled models.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  58.848 seconds)
+   **Total running time of the script:** ( 2 minutes  10.254 seconds)
 
 
 .. _sphx_glr_download_tutorials_frontend_deploy_prequantized_tflite.py:
diff --git a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
index c8b8d36..9f616a0 100644
--- a/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
+++ b/docs/_sources/tutorials/frontend/deploy_ssd_gluoncv.rst.txt
@@ -117,7 +117,7 @@ Convert and compile model for CPU.
 
     def build(target):
         mod, params = relay.frontend.from_mxnet(block, {"data": dshape})
-        with relay.build_config(opt_level=3):
+        with tvm.transform.PassContext(opt_level=3):
             graph, lib, params = relay.build(mod, target, params=params)
         return graph, lib, params
 
diff --git a/docs/_sources/tutorials/frontend/from_caffe2.rst.txt b/docs/_sources/tutorials/frontend/from_caffe2.rst.txt
index d74941f..ddb9a53 100644
--- a/docs/_sources/tutorials/frontend/from_caffe2.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_caffe2.rst.txt
@@ -105,13 +105,13 @@ Compile the model on Relay
     dtype_dict = {input_name: data.dtype}
 
     # parse Caffe2 model and convert into Relay computation graph
-    from tvm import relay
+    from tvm import relay, transform
     mod, params = relay.frontend.from_caffe2(resnet50.init_net, resnet50.predict_net, shape_dict, dtype_dict)
 
     # compile the model
     # target x86 CPU
     target = 'llvm'
-    with relay.build_config(opt_level=3):
+    with transform.PassContext(opt_level=3):
         graph, lib, params = relay.build(mod, target, params=params)
 
 
diff --git a/docs/_sources/tutorials/frontend/from_coreml.rst.txt b/docs/_sources/tutorials/frontend/from_coreml.rst.txt
index 5e005e0..eb48cb6 100644
--- a/docs/_sources/tutorials/frontend/from_coreml.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_coreml.rst.txt
@@ -110,7 +110,7 @@ We should be familiar with the process right now.
     # Parse CoreML model and convert into Relay computation graph
     mod, params = relay.frontend.from_coreml(mlmodel, shape_dict)
 
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         graph, lib, params = relay.build(mod,
                                          target,
                                          params=params)
diff --git a/docs/_sources/tutorials/frontend/from_darknet.rst.txt b/docs/_sources/tutorials/frontend/from_darknet.rst.txt
index bf439e3..af3f0c7 100644
--- a/docs/_sources/tutorials/frontend/from_darknet.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_darknet.rst.txt
@@ -133,7 +133,7 @@ compile the model
     data = np.empty([batch_size, net.c, net.h, net.w], dtype)
     shape = {'data': data.shape}
     print("Compiling the model...")
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         graph, lib, params = relay.build(mod,
                                          target=target,
                                          target_host=target_host,
diff --git a/docs/_sources/tutorials/frontend/from_keras.rst.txt b/docs/_sources/tutorials/frontend/from_keras.rst.txt
index b8597c0..410c54d 100644
--- a/docs/_sources/tutorials/frontend/from_keras.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_keras.rst.txt
@@ -121,7 +121,7 @@ convert the keras model(NHWC layout) to Relay format(NCHW layout).
     # compile the model
     target = 'cuda'
     ctx = tvm.gpu(0)
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         executor = relay.build_module.create_executor('graph', mod, ctx, target)
 
 
diff --git a/docs/_sources/tutorials/frontend/from_mxnet.rst.txt b/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
index 1069214..b24a761 100644
--- a/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_mxnet.rst.txt
@@ -124,7 +124,7 @@ now compile the graph
 .. code-block:: default
 
     target = 'cuda'
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         graph, lib, params = relay.build(func, target, params=params)
 
 
diff --git a/docs/_sources/tutorials/frontend/from_onnx.rst.txt b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
index fbe90cf..e201470 100644
--- a/docs/_sources/tutorials/frontend/from_onnx.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_onnx.rst.txt
@@ -112,7 +112,7 @@ Compile the model with relay
     shape_dict = {input_name: x.shape}
     mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
 
-    with relay.build_config(opt_level=1):
+    with tvm.transform.PassContext(opt_level=1):
         intrp = relay.build_module.create_executor('graph', mod, tvm.cpu(0), target)
 
 
@@ -125,7 +125,7 @@ Compile the model with relay
 
  .. code-block:: none
 
-    /workspace/docs/../python/tvm/relay/frontend/onnx.py:1999: UserWarning: Mismatched attribute type in ' : kernel_shape'
+    /workspace/docs/../python/tvm/relay/frontend/onnx.py:2286: UserWarning: Mismatched attribute type in ' : kernel_shape'
 
     ==> Context: Bad node spec: input: "1" input: "2" output: "11" op_type: "Conv" attribute { name: "kernel_shape" ints: 5 ints: 5 } attribute { name: "strides" ints: 1 ints: 1 } attribute { name: "pads" ints: 2 ints: 2 ints: 2 ints: 2 } attribute { name: "dilations" ints: 1 ints: 1 } attribute { name: "group" i: 1 }
       warnings.warn(str(e))
@@ -151,7 +151,7 @@ Execute on TVM
 
  .. code-block:: none
 
-
    ...47%, 0.01 MB, 46 KB/s, 0 seconds passed
    ...94%, 0.02 MB, 92 KB/s, 0 seconds passed
    ...100%, 0.02 MB, 139 KB/s, 0 seconds passed
+
    ...47%, 0.01 MB, 755 KB/s, 0 seconds passed
    ...94%, 0.02 MB, 1410 KB/s, 0 seconds passed
    ...100%, 0.02 MB, 2077 KB/s, 0 seconds passed
     Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 32, 224, 224), 'float32'), ('TENSOR', (9, 32, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
     Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 64, 224, 224), 'float32'), ('TENSOR', (32, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
     Cannot find config for target=llvm, workload=('conv2d_NCHWc.x86', ('TENSOR', (1, 1, 224, 224), 'float32'), ('TENSOR', (64, 1, 5, 5), 'float32'), (1, 1), (2, 2, 2, 2), (1, 1), 'NCHW', 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
diff --git a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
index a76b2d8..dbd520b 100644
--- a/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_pytorch.rst.txt
@@ -149,7 +149,7 @@ Compile the graph to llvm target with given input specification.
     target = 'llvm'
     target_host = 'llvm'
     ctx = tvm.cpu(0)
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         graph, lib, params = relay.build(mod,
                                          target=target,
                                          target_host=target_host,
diff --git a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
index 606fe21..8ff019d 100644
--- a/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_tensorflow.rst.txt
@@ -197,9 +197,9 @@ Results:
 
     ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
     ANTLR runtime and generated code versions disagree: 4.8!=4.7.2
-    /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:2822: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
+    /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:2843: UserWarning: Ignore the passed shape. Shape in graphdef will be used for operator DecodeJpeg/contents.
       "will be used for operator %s." % node.name)
-    /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:620: UserWarning: DecodeJpeg: It's a pass through, please handle preprocessing before input
+    /workspace/docs/../python/tvm/relay/frontend/tensorflow.py:684: UserWarning: DecodeJpeg: It's a pass through, please handle preprocessing before input
       warnings.warn("DecodeJpeg: It's a pass through, please handle preprocessing before input")
     Tensorflow protobuf imported to relay frontend.
 
@@ -218,7 +218,7 @@ Results:
 .. code-block:: default
 
 
-    with relay.build_config(opt_level=3):
+    with tvm.transform.PassContext(opt_level=3):
         graph, lib, params = relay.build(mod,
                                          target=target,
                                          target_host=target_host,
diff --git a/docs/_sources/tutorials/frontend/from_tflite.rst.txt b/docs/_sources/tutorials/frontend/from_tflite.rst.txt
index 7f8d67b..a23bc38 100644
--- a/docs/_sources/tutorials/frontend/from_tflite.rst.txt
+++ b/docs/_sources/tutorials/frontend/from_tflite.rst.txt
@@ -170,14 +170,14 @@ Compile the model with relay
     input_dtype = "float32"
 
     # Parse TFLite model and convert it to a Relay module
-    from tvm import relay
+    from tvm import relay, transform
     mod, params = relay.frontend.from_tflite(tflite_model,
                                              shape_dict={input_tensor: input_shape},
                                              dtype_dict={input_tensor: input_dtype})
 
     # Build the module against to x86 CPU
     target = "llvm"
-    with relay.build_config(opt_level=3):
+    with transform.PassContext(opt_level=3):
         graph, lib, params = relay.build(mod, target, params=params)
 
 
diff --git a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
index 9fe9826..68534b2 100644
--- a/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,22 +5,22 @@
 
 Computation times
 =================
-**05:10.844** total execution time for **tutorials_frontend** files:
+**05:38.916** total execution time for **tutorials_frontend** files:
 
-- **01:58.848**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
-- **00:30.833**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
-- **00:24.721**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
-- **00:19.896**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
-- **00:19.457**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
-- **00:17.535**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
-- **00:16.073**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
-- **00:11.211**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
-- **00:09.834**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
-- **00:09.023**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
-- **00:08.170**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
-- **00:06.642**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
-- **00:05.908**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
-- **00:04.626**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
-- **00:04.493**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
-- **00:02.112**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
-- **00:01.462**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
+- **02:10.254**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized_tflite.py` (``deploy_prequantized_tflite.py``)
+- **00:30.124**: :ref:`sphx_glr_tutorials_frontend_deploy_ssd_gluoncv.py` (``deploy_ssd_gluoncv.py``)
+- **00:29.983**: :ref:`sphx_glr_tutorials_frontend_deploy_prequantized.py` (``deploy_prequantized.py``)
+- **00:28.080**: :ref:`sphx_glr_tutorials_frontend_from_tensorflow.py` (``from_tensorflow.py``)
+- **00:21.902**: :ref:`sphx_glr_tutorials_frontend_from_tflite.py` (``from_tflite.py``)
+- **00:18.913**: :ref:`sphx_glr_tutorials_frontend_deploy_quantized.py` (``deploy_quantized.py``)
+- **00:17.142**: :ref:`sphx_glr_tutorials_frontend_from_darknet.py` (``from_darknet.py``)
+- **00:10.471**: :ref:`sphx_glr_tutorials_frontend_from_caffe2.py` (``from_caffe2.py``)
+- **00:10.211**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_android.py` (``deploy_model_on_android.py``)
+- **00:08.790**: :ref:`sphx_glr_tutorials_frontend_from_keras.py` (``from_keras.py``)
+- **00:07.050**: :ref:`sphx_glr_tutorials_frontend_deploy_model_on_rasp.py` (``deploy_model_on_rasp.py``)
+- **00:06.472**: :ref:`sphx_glr_tutorials_frontend_from_pytorch.py` (``from_pytorch.py``)
+- **00:06.164**: :ref:`sphx_glr_tutorials_frontend_from_coreml.py` (``from_coreml.py``)
+- **00:04.943**: :ref:`sphx_glr_tutorials_frontend_from_mxnet.py` (``from_mxnet.py``)
+- **00:04.864**: :ref:`sphx_glr_tutorials_frontend_build_gcn.py` (``build_gcn.py``)
+- **00:02.175**: :ref:`sphx_glr_tutorials_frontend_using_external_lib.py` (``using_external_lib.py``)
+- **00:01.378**: :ref:`sphx_glr_tutorials_frontend_from_onnx.py` (``from_onnx.py``)
diff --git a/docs/_sources/tutorials/index.rst.txt b/docs/_sources/tutorials/index.rst.txt
index 701c615..bdbda07 100644
--- a/docs/_sources/tutorials/index.rst.txt
+++ b/docs/_sources/tutorials/index.rst.txt
@@ -326,7 +326,7 @@ Compile Deep Learning Models
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip=" For more details on quantizing the model using TFLite, readers are encouraged to go through `C...">
+    <div class="sphx-glr-thumbcontainer" tooltip="Welcome to part 3 of the Deploy Framework-Prequantized Model with TVM tutorial. In this part, w...">
 
 .. only:: html
 
diff --git a/docs/_sources/tutorials/language/reduction.rst.txt b/docs/_sources/tutorials/language/reduction.rst.txt
index 6a60c06..8165bf1 100644
--- a/docs/_sources/tutorials/language/reduction.rst.txt
+++ b/docs/_sources/tutorials/language/reduction.rst.txt
@@ -140,13 +140,13 @@ axis by different factors. The result is a nested reduction.
       buffer_map = {B_1: B, A_1: A} {
       for (i.outer: int32, 0, floordiv((n + 31), 32)) {
         for (i.inner: int32, 0, 32) {
-          if @likely((((i.outer*32) + i.inner) < n), dtype=bool, type="pure_intrin", index=0) {
+          if @likely((((i.outer*32) + i.inner) < n), dtype=bool, type="pure_intrin") {
             B_2[(((i.outer*32) + i.inner)*stride_2)] = 0f32
           }
           for (k.outer: int32, 0, floordiv((m + 15), 16)) {
             for (k.inner: int32, 0, 16) {
-              if @likely((((i.outer*32) + i.inner) < n), dtype=bool, type="pure_intrin", index=0) {
-                if @likely((((k.outer*16) + k.inner) < m), dtype=bool, type="pure_intrin", index=0) {
+              if @likely((((i.outer*32) + i.inner) < n), dtype=bool, type="pure_intrin") {
+                if @likely((((k.outer*16) + k.inner) < m), dtype=bool, type="pure_intrin") {
                   B_2[(((i.outer*32) + i.inner)*stride_2)] = ((float32*)B_2[(((i.outer*32) + i.inner)*stride_2)]) + (float32*)A_2[((((i.outer*32) + i.inner)*stride) + (((k.outer*16) + k.inner)*stride_1))]))
                 }
               }
@@ -186,13 +186,13 @@ If we are building a GPU kernel, we can bind the rows of B to GPU threads.
       buffer_map = {B_1: B, A_1: A} {
       attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = floordiv((n + 31), 32);
       attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32 {
-        if @likely((((blockIdx.x*32) + threadIdx.x) < n), dtype=bool, type="pure_intrin", index=0) {
+        if @likely((((blockIdx.x*32) + threadIdx.x) < n), dtype=bool, type="pure_intrin") {
           B_2[(((blockIdx.x*32) + threadIdx.x)*stride_2)] = 0f32
         }
         for (k.outer: int32, 0, floordiv((m + 15), 16)) {
           for (k.inner: int32, 0, 16) {
-            if @likely((((blockIdx.x*32) + threadIdx.x) < n), dtype=bool, type="pure_intrin", index=0) {
-              if @likely((((k.outer*16) + k.inner) < m), dtype=bool, type="pure_intrin", index=0) {
+            if @likely((((blockIdx.x*32) + threadIdx.x) < n), dtype=bool, type="pure_intrin") {
+              if @likely((((k.outer*16) + k.inner) < m), dtype=bool, type="pure_intrin") {
                 B_2[(((blockIdx.x*32) + threadIdx.x)*stride_2)] = ((float32*)B_2[(((blockIdx.x*32) + threadIdx.x)*stride_2)]) + (float32*)A_2[((((blockIdx.x*32) + threadIdx.x)*stride) + (((k.outer*16) + k.inner)*stride_1))]))
               }
             }
@@ -246,7 +246,7 @@ result B.rf. The factored dimension becomes the first dimension of B.rf.
           for (i: int32, 0, n) {
             B.rf[((k.inner*n) + i)] = 0f32
             for (k.outer: int32, 0, floordiv((m + 15), 16)) {
-              if @likely((((k.outer*16) + k.inner) < m), dtype=bool, type="pure_intrin", index=0) {
+              if @likely((((k.outer*16) + k.inner) < m), dtype=bool, type="pure_intrin") {
                 B.rf[((k.inner*n) + i)] = ((float32*)B.rf[((k.inner*n) + i)]) + (float32*)A_2[((i*stride) + (((k.outer*16) + k.inner)*stride_1))]))
               }
             }
@@ -284,7 +284,7 @@ the first axis of reduced result of B.f
 
  .. code-block:: none
 
-    [reduce(combiner=comm_reducer(result=[(x + y)], lhs=[x], rhs=[y], identity_element=[0f]), source=[B.rf(k.inner.v, ax0)], axis=[iter_var(k.inner.v, range(min=0, ext=16))], where=(bool)1, value_index=0)]
+    [reduce(combiner=comm_reducer(result=[(x + y)], lhs=[x], rhs=[y], identity_element=[0f]), source=[B.rf[k.inner.v, ax0]], axis=[iter_var(k.inner.v, range(min=0, ext=16))], where=(bool)1, value_index=0)]
 
 
 
diff --git a/docs/_sources/tutorials/language/scan.rst.txt b/docs/_sources/tutorials/language/scan.rst.txt
index 795b4dd..a3dd3c2 100644
--- a/docs/_sources/tutorials/language/scan.rst.txt
+++ b/docs/_sources/tutorials/language/scan.rst.txt
@@ -105,13 +105,13 @@ To split on the time iteration, user can schedule on scan_op.scan_axis instead.
       buffer_map = {scan_1: scan, X_1: X} {
       attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = floordiv((n + 255), 256);
       attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 256;
-      if @likely((((blockIdx.x*256) + threadIdx.x) < n), dtype=bool, type="pure_intrin", index=0) {
+      if @likely((((blockIdx.x*256) + threadIdx.x) < n), dtype=bool, type="pure_intrin") {
         scan_2[(((blockIdx.x*256) + threadIdx.x)*stride_3)] = (float32*)X_2[(((blockIdx.x*256) + threadIdx.x)*stride_1)])
       }
       for (scan.idx: int32, 0, (m - 1)) {
         attr [IterVar(blockIdx.x, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = floordiv((n + 255), 256);
         attr [IterVar(threadIdx.x, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 256;
-        if @likely((((blockIdx.x*256) + threadIdx.x) < n), dtype=bool, type="pure_intrin", index=0) {
+        if @likely((((blockIdx.x*256) + threadIdx.x) < n), dtype=bool, type="pure_intrin") {
           scan_2[(((scan.idx + 1)*stride_2) + (((blockIdx.x*256) + threadIdx.x)*stride_3))] = ((float32*)scan_2[((scan.idx*stride_2) + (((blockIdx.x*256) + threadIdx.x)*stride_3))]) + (float32*)X_2[(((scan.idx + 1)*stride) + (((blockIdx.x*256) + threadIdx.x)*stride_1))]))
         }
       }
@@ -210,12 +210,12 @@ the body of scan to be compute_at locations outside the scan loop.
         for (scan.idx: int32, 0, (m - 1)) {
           for (i.outer: int32, 0, floordiv((n + 31), 32)) {
             for (i_1: int32, 0, 32) {
-              if @likely((((i.outer*32) + i_1) < n), dtype=bool, type="pure_intrin", index=0) {
+              if @likely((((i.outer*32) + i_1) < n), dtype=bool, type="pure_intrin") {
                 s1[i_1] = ((float32*)scan_2[((scan.idx*stride_2) + (((i.outer*32) + i_1)*stride_3))])*2f32)
               }
             }
             for (i.inner: int32, 0, 32) {
-              if @likely((((i.outer*32) + i.inner) < n), dtype=bool, type="pure_intrin", index=0) {
+              if @likely((((i.outer*32) + i.inner) < n), dtype=bool, type="pure_intrin") {
                 scan_2[(((scan.idx + 1)*stride_2) + (((i.outer*32) + i.inner)*stride_3))] = ((float32*)s1[i.inner]) + (float32*)X_2[(((scan.idx + 1)*stride) + (((i.outer*32) + i.inner)*stride_1))]))
               }
             }
@@ -267,21 +267,21 @@ The following example demonstrates how we can build recurrence with two states.
     primfn(X_1: handle, scan.v0_1: handle, scan.v1_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
       buffers = {X: Buffer(X_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
-                 scan.v1: Buffer(scan.v1_2: handle, float32, [m, l: int32], [stride_2: int32, stride_3: int32], type="auto"),
-                 scan.v0: Buffer(scan.v0_2: handle, float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
-      buffer_map = {scan.v0_1: scan.v0, scan.v1_1: scan.v1, X_1: X} {
+                 scan.v0: Buffer(scan.v0_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+                 scan.v1: Buffer(scan.v1_2: handle, float32, [m, l: int32], [stride_4: int32, stride_5: int32], type="auto")}
+      buffer_map = {scan.v1_1: scan.v1, scan.v0_1: scan.v0, X_1: X} {
       for (i: int32, 0, n) {
-        scan.v0_2[(i*stride_5)] = (float32*)X_2[(i*stride_1)])
+        scan.v0_2[(i*stride_3)] = (float32*)X_2[(i*stride_1)])
       }
       for (i_1: int32, 0, l) {
-        scan.v1_2[(i_1*stride_3)] = 0f32
+        scan.v1_2[(i_1*stride_5)] = 0f32
       }
       for (scan.idx: int32, 0, (m - 1)) {
         for (i_2: int32, 0, n) {
-          scan.v0_2[(((scan.idx + 1)*stride_4) + (i_2*stride_5))] = ((float32*)scan.v0_2[((scan.idx*stride_4) + (i_2*stride_5))]) + (float32*)X_2[(((scan.idx + 1)*stride) + (i_2*stride_1))]))
+          scan.v0_2[(((scan.idx + 1)*stride_2) + (i_2*stride_3))] = ((float32*)scan.v0_2[((scan.idx*stride_2) + (i_2*stride_3))]) + (float32*)X_2[(((scan.idx + 1)*stride) + (i_2*stride_1))]))
         }
         for (i_3: int32, 0, l) {
-          scan.v1_2[(((scan.idx + 1)*stride_2) + (i_3*stride_3))] = ((float32*)scan.v1_2[((scan.idx*stride_2) + (i_3*stride_3))]) + (float32*)scan.v0_2[(scan.idx*stride_4)]))
+          scan.v1_2[(((scan.idx + 1)*stride_4) + (i_3*stride_5))] = ((float32*)scan.v1_2[((scan.idx*stride_4) + (i_3*stride_5))]) + (float32*)scan.v0_2[(scan.idx*stride_2)]))
         }
       }
     }
diff --git a/docs/_sources/tutorials/language/schedule_primitives.rst.txt b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
index 364fa36..42072a3 100644
--- a/docs/_sources/tutorials/language/schedule_primitives.rst.txt
+++ b/docs/_sources/tutorials/language/schedule_primitives.rst.txt
@@ -136,7 +136,7 @@ split
       buffer_map = {B_1: B, A_1: A} {
       for (i.outer: int32, 0, floordiv((m + 31), 32)) {
         for (i.inner: int32, 0, 32) {
-          if @likely((((i.outer*32) + i.inner) < m), dtype=bool, type="pure_intrin", index=0) {
+          if @likely((((i.outer*32) + i.inner) < m), dtype=bool, type="pure_intrin") {
             B_2[(((i.outer*32) + i.inner)*stride_1)] = ((float32*)A_2[(((i.outer*32) + i.inner)*stride)])*2f32)
           }
         }
@@ -177,7 +177,7 @@ contrary with :code:`factor`.
       buffer_map = {B_1: B, A_1: A} {
       for (i.outer: int32, 0, 32) {
         for (i.inner: int32, 0, floordiv((m + 31), 32)) {
-          if @likely(((i.inner + (i.outer*floordiv((m + 31), 32))) < m), dtype=bool, type="pure_intrin", index=0) {
+          if @likely(((i.inner + (i.outer*floordiv((m + 31), 32))) < m), dtype=bool, type="pure_intrin") {
             B_2[((i.inner + (i.outer*floordiv((m + 31), 32)))*stride_1)] = (float32*)A_2[((i.inner + (i.outer*floordiv((m + 31), 32)))*stride)])
           }
         }
@@ -222,8 +222,8 @@ axises.
         for (j.outer: int32, 0, floordiv((n + 4), 5)) {
           for (i.inner: int32, 0, 10) {
             for (j.inner: int32, 0, 5) {
-              if @likely((((i.outer*10) + i.inner) < m), dtype=bool, type="pure_intrin", index=0) {
-                if @likely((((j.outer*5) + j.inner) < n), dtype=bool, type="pure_intrin", index=0) {
+              if @likely((((i.outer*10) + i.inner) < m), dtype=bool, type="pure_intrin") {
+                if @likely((((j.outer*5) + j.inner) < n), dtype=bool, type="pure_intrin") {
                   B_2[((((i.outer*10) + i.inner)*stride_2) + (((j.outer*5) + j.inner)*stride_3))] = (float32*)A_2[((((i.outer*10) + i.inner)*stride) + (((j.outer*5) + j.inner)*stride_1))])
                 }
               }
@@ -272,8 +272,8 @@ fuse
       for (i.outer: int32, 0, floordiv((m + 9), 10)) {
         for (j.outer: int32, 0, floordiv((n + 4), 5)) {
           for (i.inner.j.inner.fused: int32, 0, 50) {
-            if @likely((((i.outer*10) + floordiv(i.inner.j.inner.fused, 5)) < m), dtype=bool, type="pure_intrin", index=0) {
-              if @likely((((j.outer*5) + floormod(i.inner.j.inner.fused, 5)) < n), dtype=bool, type="pure_intrin", index=0) {
+            if @likely((((i.outer*10) + floordiv(i.inner.j.inner.fused, 5)) < m), dtype=bool, type="pure_intrin") {
+              if @likely((((j.outer*5) + floormod(i.inner.j.inner.fused, 5)) < n), dtype=bool, type="pure_intrin") {
                 B_2[((((i.outer*10) + floordiv(i.inner.j.inner.fused, 5))*stride_2) + (((j.outer*5) + floormod(i.inner.j.inner.fused, 5))*stride_3))] = (float32*)A_2[((((i.outer*10) + floordiv(i.inner.j.inner.fused, 5))*stride) + (((j.outer*5) + floormod(i.inner.j.inner.fused, 5))*stride_1))])
               }
             }
@@ -322,8 +322,8 @@ reorder
         for (j.outer: int32, 0, floordiv((n + 4), 5)) {
           for (i.outer: int32, 0, floordiv((m + 9), 10)) {
             for (j.inner: int32, 0, 5) {
-              if @likely((((i.outer*10) + i.inner) < m), dtype=bool, type="pure_intrin", index=0) {
-                if @likely((((j.outer*5) + j.inner) < n), dtype=bool, type="pure_intrin", index=0) {
+              if @likely((((i.outer*10) + i.inner) < m), dtype=bool, type="pure_intrin") {
+                if @likely((((j.outer*5) + j.inner) < n), dtype=bool, type="pure_intrin") {
                   B_2[((((i.outer*10) + i.inner)*stride_2) + (((j.outer*5) + j.inner)*stride_3))] = (float32*)A_2[((((i.outer*10) + i.inner)*stride) + (((j.outer*5) + j.inner)*stride_1))])
                 }
               }
@@ -371,7 +371,7 @@ in gpu programming.
       buffer_map = {B_1: B, A_1: A} {
       attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = floordiv((n + 63), 64);
       attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
-      if @likely((((blockIdx.x*64) + threadIdx.x) < n), dtype=bool, type="pure_intrin", index=0) {
+      if @likely((((blockIdx.x*64) + threadIdx.x) < n), dtype=bool, type="pure_intrin") {
         B_2[(((blockIdx.x*64) + threadIdx.x)*stride_1)] = ((float32*)A_2[(((blockIdx.x*64) + threadIdx.x)*stride)])*2f32)
       }
     }
@@ -408,14 +408,14 @@ tensors at the root separately by default.
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
       buffers = {A: Buffer(A_2: handle, float32, [m: int32], [stride: int32], type="auto"),
-                 B: Buffer(B_2: handle, float32, [m], [stride_1: int32], type="auto"),
-                 C: Buffer(C_2: handle, float32, [m], [stride_2: int32], type="auto")}
-      buffer_map = {C_1: C, B_1: B, A_1: A} {
+                 C: Buffer(C_2: handle, float32, [m], [stride_1: int32], type="auto"),
+                 B: Buffer(B_2: handle, float32, [m], [stride_2: int32], type="auto")}
+      buffer_map = {B_1: B, C_1: C, A_1: A} {
       for (i: int32, 0, m) {
-        B_2[(i*stride_1)] = ((float32*)A_2[(i*stride)]) + 1f32)
+        B_2[(i*stride_2)] = ((float32*)A_2[(i*stride)]) + 1f32)
       }
       for (i_1: int32, 0, m) {
-        C_2[(i_1*stride_2)] = ((float32*)B_2[(i_1*stride_1)])*2f32)
+        C_2[(i_1*stride_1)] = ((float32*)B_2[(i_1*stride_2)])*2f32)
       }
     }
 
@@ -492,12 +492,12 @@ tensor is required.
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {A: Buffer(A_2: handle, float32, [m: int32], [stride: int32], type="auto"),
-                 B: Buffer(B_2: handle, float32, [m], [stride_1: int32], type="auto"),
+      buffers = {B: Buffer(B_2: handle, float32, [m: int32], [stride: int32], type="auto"),
+                 A: Buffer(A_2: handle, float32, [m], [stride_1: int32], type="auto"),
                  C: Buffer(C_2: handle, float32, [m], [stride_2: int32], type="auto")}
       buffer_map = {C_1: C, B_1: B, A_1: A} {
       for (i: int32, 0, m) {
-        C_2[(i*stride_2)] = (((float32*)A_2[(i*stride)]) + 1f32)*2f32)
+        C_2[(i*stride_2)] = (((float32*)A_2[(i*stride_1)]) + 1f32)*2f32)
       }
     }
 
diff --git a/docs/_sources/tutorials/language/sg_execution_times.rst.txt b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
index edbc7a3..728d3e8 100644
--- a/docs/_sources/tutorials/language/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/language/sg_execution_times.rst.txt
@@ -5,13 +5,13 @@
 
 Computation times
 =================
-**00:03.728** total execution time for **tutorials_language** files:
+**00:03.887** total execution time for **tutorials_language** files:
 
-- **00:01.349**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
-- **00:00.682**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
-- **00:00.494**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
-- **00:00.460**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
-- **00:00.280**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
-- **00:00.166**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
-- **00:00.157**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
-- **00:00.141**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
+- **00:01.442**: :ref:`sphx_glr_tutorials_language_intrin_math.py` (``intrin_math.py``)
+- **00:00.697**: :ref:`sphx_glr_tutorials_language_tensorize.py` (``tensorize.py``)
+- **00:00.505**: :ref:`sphx_glr_tutorials_language_reduction.py` (``reduction.py``)
+- **00:00.501**: :ref:`sphx_glr_tutorials_language_scan.py` (``scan.py``)
+- **00:00.234**: :ref:`sphx_glr_tutorials_language_extern_op.py` (``extern_op.py``)
+- **00:00.190**: :ref:`sphx_glr_tutorials_language_tedd.py` (``tedd.py``)
+- **00:00.169**: :ref:`sphx_glr_tutorials_language_schedule_primitives.py` (``schedule_primitives.py``)
+- **00:00.149**: :ref:`sphx_glr_tutorials_language_tuple_inputs.py` (``tuple_inputs.py``)
diff --git a/docs/_sources/tutorials/language/tensorize.rst.txt b/docs/_sources/tutorials/language/tensorize.rst.txt
index 01bd05b..0abc7ac 100644
--- a/docs/_sources/tutorials/language/tensorize.rst.txt
+++ b/docs/_sources/tutorials/language/tensorize.rst.txt
@@ -71,8 +71,8 @@ The following lines describe the computation :code:`A * B^T` in TVM.
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {A: Buffer(A_2: handle, float32, [1024, 64], []),
-                 B: Buffer(B_2: handle, float32, [512, 64], []),
+      buffers = {B: Buffer(B_2: handle, float32, [512, 64], []),
+                 A: Buffer(A_2: handle, float32, [1024, 64], []),
                  C: Buffer(C_2: handle, float32, [1024, 512], [])}
       buffer_map = {C_1: C, B_1: B, A_1: A} {
       for (i: int32, 0, 1024) {
@@ -120,8 +120,8 @@ Thus we break down the matmul loops to make the innermost loops a (16x64) GEMV.
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {B: Buffer(B_2: handle, float32, [512, 64], []),
-                 A: Buffer(A_2: handle, float32, [1024, 64], []),
+      buffers = {A: Buffer(A_2: handle, float32, [1024, 64], []),
+                 B: Buffer(B_2: handle, float32, [512, 64], []),
                  C: Buffer(C_2: handle, float32, [1024, 512], [])}
       buffer_map = {C_1: C, B_1: B, A_1: A} {
       for (i: int32, 0, 1024) {
@@ -187,8 +187,7 @@ which is done in :code:`intrin_func` below.
                                     bb.access_ptr("r"),
                                     m, l, bb.strides[0]))
             return ib.get()
-        with tvm.target.build_config(offset_factor=1):
-            return te.decl_tensor_intrin(c.op, intrin_func, binds={a: Ab, b: Bb, c: Cb})
+        return te.decl_tensor_intrin(c.op, intrin_func, binds={a: Ab, b: Bb, c: Cb})
 
 
 
@@ -243,7 +242,7 @@ such placeholder can be put to let TVM automatically bind the inferred value for
       buffer_map = {C_1: C, B_1: B, A_1: A} {
       for (i: int32, 0, 1024) {
         for (j.outer: int32, 0, 32) {
-          @gemv_update(@tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin", index=0), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle, type="intrin", index=0), @tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin", index=0), A_2, (i*64), 64, 1, dtype=handle, type="intrin", index=0), @tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin", index=0), B_2, (j.outer*1024), 1024, 1, dtype=handle, type="intrin", index=0), 16, 64, 64, dtype=int32, [...]
+          @gemv_update(@tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin"), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle, type="intrin"), @tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin"), A_2, (i*64), 64, 1, dtype=handle, type="intrin"), @tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin"), B_2, (j.outer*1024), 1024, 1, dtype=handle, type="intrin"), 16, 64, 64, dtype=int32, type="extern")
         }
       }
     }
@@ -307,12 +306,12 @@ The importing needs to happen before the tensorized GEMV being executed.
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {B: Buffer(B_2: handle, float32, [512, 64], []),
-                 A: Buffer(A_2: handle, float32, [1024, 64], []),
+      buffers = {A: Buffer(A_2: handle, float32, [1024, 64], []),
+                 B: Buffer(B_2: handle, float32, [512, 64], []),
                  C: Buffer(C_2: handle, float32, [1024, 512], [])}
       buffer_map = {C_1: C, B_1: B, A_1: A} {
-      attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpxbed81_y/input0.cc'
-    source_filename = "/tmp/tmpxbed81_y/input0.cc"
+      attr [IterVar(i: int32, (nullptr), "DataPar", "")] "pragma_import_llvm" = "; ModuleID = '/tmp/tmpcw7zd4dq/input0.cc'
+    source_filename = "/tmp/tmpcw7zd4dq/input0.cc"
     target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
     target triple = "x86_64-pc-linux-gnu"
 
@@ -405,7 +404,7 @@ The importing needs to happen before the tensorized GEMV being executed.
     ";
       for (i, 0, 1024) {
         for (j.outer: int32, 0, 32) {
-          @gemv_update(@tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin", index=0), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle, type="intrin", index=0), @tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin", index=0), A_2, (i*64), 64, 1, dtype=handle, type="intrin", index=0), @tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin", index=0), B_2, (j.outer*1024), 1024, 1, dtype=handle, type="intrin", index=0), 16, 64, 64, dtype=int32, [...]
+          @gemv_update(@tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin"), C_2, ((i*512) + (j.outer*16)), 16, 2, dtype=handle, type="intrin"), @tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin"), A_2, (i*64), 64, 1, dtype=handle, type="intrin"), @tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin"), B_2, (j.outer*1024), 1024, 1, dtype=handle, type="intrin"), 16, 64, 64, dtype=int32, type="extern")
         }
       }
     }
@@ -529,8 +528,7 @@ which defines the "update" computing strategy.
             def _reduce_update():
                 return _body()
             return _body(), _reduce_reset(), _reduce_update()
-        with tvm.target.build_config(offset_factor=1):
-            return te.decl_tensor_intrin(c.op, intrin_func, binds={a: Ab, b: Bb, c: Cb})
+        return te.decl_tensor_intrin(c.op, intrin_func, binds={a: Ab, b: Bb, c: Cb})
 
 
 
diff --git a/docs/_sources/tutorials/language/tuple_inputs.rst.txt b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
index 72dd0d1..1911840 100644
--- a/docs/_sources/tutorials/language/tuple_inputs.rst.txt
+++ b/docs/_sources/tutorials/language/tuple_inputs.rst.txt
@@ -64,15 +64,15 @@ together in the next schedule procedure.
 
     primfn(A0_1: handle, A1_1: handle, B.v0_1: handle, B.v1_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {A0: Buffer(A0_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
-                 A1: Buffer(A1_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
-                 B.v0: Buffer(B.v0_2: handle, float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
+      buffers = {A1: Buffer(A1_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+                 B.v0: Buffer(B.v0_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+                 A0: Buffer(A0_2: handle, float32, [m, n], [stride_4: int32, stride_5: int32], type="auto"),
                  B.v1: Buffer(B.v1_2: handle, float32, [m, n], [stride_6: int32, stride_7: int32], type="auto")}
       buffer_map = {B.v1_1: B.v1, B.v0_1: B.v0, A1_1: A1, A0_1: A0} {
       for (i: int32, 0, m) {
         for (j: int32, 0, n) {
-          B.v0_2[((i*stride_4) + (j*stride_5))] = ((float32*)A0_2[((i*stride) + (j*stride_1))]) + 2f32)
-          B.v1_2[((i*stride_6) + (j*stride_7))] = ((float32*)A1_2[((i*stride_2) + (j*stride_3))])*3f32)
+          B.v0_2[((i*stride_2) + (j*stride_3))] = ((float32*)A0_2[((i*stride_4) + (j*stride_5))]) + 2f32)
+          B.v1_2[((i*stride_6) + (j*stride_7))] = ((float32*)A1_2[((i*stride) + (j*stride_1))])*3f32)
         }
       }
     }
@@ -133,17 +133,17 @@ with :py:func:`te.comm_reducer` as below:
 
     primfn(idx_1: handle, val_1: handle, T.v0_1: handle, T.v1_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {idx: Buffer(idx_2: handle, int32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
-                 val: Buffer(val_2: handle, int32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
-                 T.v0: Buffer(T.v0_2: handle, int32, [m], [stride_4: int32], type="auto"),
+      buffers = {val: Buffer(val_2: handle, int32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+                 T.v0: Buffer(T.v0_2: handle, int32, [m], [stride_2: int32], type="auto"),
+                 idx: Buffer(idx_2: handle, int32, [m, n], [stride_3: int32, stride_4: int32], type="auto"),
                  T.v1: Buffer(T.v1_2: handle, int32, [m], [stride_5: int32], type="auto")}
       buffer_map = {T.v1_1: T.v1, T.v0_1: T.v0, val_1: val, idx_1: idx} {
       for (i: int32, 0, m) {
-        T.v0_2[(i*stride_4)] = -1
+        T.v0_2[(i*stride_2)] = -1
         T.v1_2[(i*stride_5)] = -2147483648
         for (k: int32, 0, n) {
-          T.v0_2[(i*stride_4)] = @tvm_if_then_else(((int32*)val_2[((i*stride_2) + (k*stride_3))]) <= (int32*)T.v1_2[(i*stride_5)])), (int32*)T.v0_2[(i*stride_4)]), (int32*)idx_2[((i*stride) + (k*stride_1))]), dtype=int32, type="intrin", index=0)
-          T.v1_2[(i*stride_5)] = @tvm_if_then_else(((int32*)val_2[((i*stride_2) + (k*stride_3))]) <= (int32*)T.v1_2[(i*stride_5)])), (int32*)T.v1_2[(i*stride_5)]), (int32*)val_2[((i*stride_2) + (k*stride_3))]), dtype=int32, type="intrin", index=0)
+          T.v0_2[(i*stride_2)] = @tvm_if_then_else(((int32*)val_2[((i*stride) + (k*stride_1))]) <= (int32*)T.v1_2[(i*stride_5)])), (int32*)T.v0_2[(i*stride_2)]), (int32*)idx_2[((i*stride_3) + (k*stride_4))]), dtype=int32, type="intrin")
+          T.v1_2[(i*stride_5)] = @tvm_if_then_else(((int32*)val_2[((i*stride) + (k*stride_1))]) <= (int32*)T.v1_2[(i*stride_5)])), (int32*)T.v1_2[(i*stride_5)]), (int32*)val_2[((i*stride) + (k*stride_1))]), dtype=int32, type="intrin")
         }
       }
     }
@@ -191,8 +191,8 @@ in terms of operation.
 
     primfn(A0_1: handle, A1_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {A1: Buffer(A1_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
-                 A0: Buffer(A0_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
+      buffers = {A0: Buffer(A0_2: handle, float32, [m: int32, n: int32], [stride: int32, stride_1: int32], type="auto"),
+                 A1: Buffer(A1_2: handle, float32, [m, n], [stride_2: int32, stride_3: int32], type="auto"),
                  C: Buffer(C_2: handle, float32, [m, n], [stride_4: int32, stride_5: int32], type="auto")}
       buffer_map = {C_1: C, A1_1: A1, A0_1: A0} {
       attr [B.v0: handle] "storage_scope" = "global";
@@ -201,11 +201,11 @@ in terms of operation.
       allocate(B.v1, float32, [n]);
       for (i: int32, 0, m) {
         for (j: int32, 0, n) {
-          B.v0[j] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))]) + 2f32)
-          B.v1[j] = ((float32*)A0_2[((i*stride_2) + (j*stride_3))])*3f32)
+          B.v0[j] = ((float32*)A0_2[((i*stride) + (j*stride_1))]) + 2f32)
+          B.v1[j] = ((float32*)A0_2[((i*stride) + (j*stride_1))])*3f32)
         }
         for (j_1: int32, 0, n) {
-          C_2[((i*stride_4) + (j_1*stride_5))] = ((float32*)A1_2[((i*stride) + (j_1*stride_1))]) + (float32*)B.v0[j_1]))
+          C_2[((i*stride_4) + (j_1*stride_5))] = ((float32*)A1_2[((i*stride_2) + (j_1*stride_3))]) + (float32*)B.v0[j_1]))
         }
       }
     }
diff --git a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
index 605ab7a..3210c23 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_cuda.rst.txt
@@ -293,7 +293,7 @@ latency of convolution.
 
  .. code-block:: none
 
-    Convolution: 19.556336 ms
+    Convolution: 19.651037 ms
 
 
 
diff --git a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
index d65796b..a768242 100644
--- a/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_conv_tensorcore.rst.txt
@@ -376,7 +376,7 @@ one time.
               for (ax3: int32, 0, 2) {
                 for (ax4.ax5.fused.outer: int32, 0, 8) {
                   attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
-                  Apad.shared[((((((threadIdx.y*3072) + (threadIdx.z*1536)) + (ax2*512)) + (ax3*256)) + (ax4.ax5.fused.outer*32)) + threadIdx.x)] = @tvm_if_then_else(((((1 <= (floordiv(blockIdx.z, 14) + kh)) && ((floordiv(blockIdx.z, 14) + kh) < 15)) && (1 <= (ax2 + floormod(blockIdx.z, 14)))) && ((ax2 + floormod(blockIdx.z, 14)) < 15)), (float16*)A_2[(((((((((((blockIdx.x*6422528) + (threadIdx.y*1605632)) + (threadIdx.z*802816)) + (kh*57344)) + (blockIdx.z*4096)) + (ax2*4096)) + (ic.out [...]
+                  Apad.shared[((((((threadIdx.y*3072) + (threadIdx.z*1536)) + (ax2*512)) + (ax3*256)) + (ax4.ax5.fused.outer*32)) + threadIdx.x)] = @tvm_if_then_else(((((1 <= (floordiv(blockIdx.z, 14) + kh)) && ((floordiv(blockIdx.z, 14) + kh) < 15)) && (1 <= (ax2 + floormod(blockIdx.z, 14)))) && ((ax2 + floormod(blockIdx.z, 14)) < 15)), (float16*)A_2[(((((((((((blockIdx.x*6422528) + (threadIdx.y*1605632)) + (threadIdx.z*802816)) + (kh*57344)) + (blockIdx.z*4096)) + (ax2*4096)) + (ic.out [...]
                 }
               }
             }
@@ -461,9 +461,9 @@ by mapping the 2D convolution to tensor intrinsics
     primfn(A_1: handle, W_1: handle, Conv_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
       buffers = {A: Buffer(A_2: handle, float16, [16, 14, 14, 16, 16, 16], []),
-                 W: Buffer(W_2: handle, float16, [3, 3, 16, 32, 16, 16], []),
-                 Conv: Buffer(Conv_2: handle, float32, [16, 14, 14, 32, 16, 16], [])}
-      buffer_map = {Conv_1: Conv, W_1: W, A_1: A} {
+                 Conv: Buffer(Conv_2: handle, float32, [16, 14, 14, 32, 16, 16], []),
+                 W: Buffer(W_2: handle, float16, [3, 3, 16, 32, 16, 16], [])}
+      buffer_map = {W_1: W, Conv_1: Conv, A_1: A} {
       attr [IterVar(blockIdx.z: int32, (nullptr), "ThreadIndex", "blockIdx.z")] "thread_extent" = 196;
       attr [Conv.wmma.accumulator: handle] "storage_scope" = "wmma.accumulator";
       allocate(Conv.wmma.accumulator, float32, [2048]);
@@ -481,7 +481,7 @@ by mapping the 2D convolution to tensor intrinsics
       attr [IterVar(threadIdx.z: int32, (nullptr), "ThreadIndex", "threadIdx.z")] "thread_extent" = 2 {
         for (n.c.init: int32, 0, 2) {
           for (o.c.init: int32, 0, 4) {
-            @tvm_fill_fragment(Conv.wmma.accumulator, 16, 16, 16, ((n.c.init*4) + o.c.init), 0f32, dtype=handle, type="intrin", index=0)
+            @tvm_fill_fragment(Conv.wmma.accumulator, 16, 16, 16, ((n.c.init*4) + o.c.init), 0f32, dtype=handle, type="intrin")
           }
         }
         for (ic.outer: int32, 0, 8) {
@@ -490,7 +490,7 @@ by mapping the 2D convolution to tensor intrinsics
               for (ax3: int32, 0, 2) {
                 for (ax4.ax5.fused.outer: int32, 0, 8) {
                   attr [IterVar(threadIdx.x: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 32;
-                  Apad.shared[((((((threadIdx.y*3072) + (threadIdx.z*1536)) + (ax2*512)) + (ax3*256)) + (ax4.ax5.fused.outer*32)) + threadIdx.x)] = @tvm_if_then_else(((((1 <= (floordiv(blockIdx.z, 14) + kh)) && ((floordiv(blockIdx.z, 14) + kh) < 15)) && (1 <= (ax2 + floormod(blockIdx.z, 14)))) && ((ax2 + floormod(blockIdx.z, 14)) < 15)), (float16*)A_2[(((((((((((blockIdx.x*6422528) + (threadIdx.y*1605632)) + (threadIdx.z*802816)) + (kh*57344)) + (blockIdx.z*4096)) + (ax2*4096)) + (ic.out [...]
+                  Apad.shared[((((((threadIdx.y*3072) + (threadIdx.z*1536)) + (ax2*512)) + (ax3*256)) + (ax4.ax5.fused.outer*32)) + threadIdx.x)] = @tvm_if_then_else(((((1 <= (floordiv(blockIdx.z, 14) + kh)) && ((floordiv(blockIdx.z, 14) + kh) < 15)) && (1 <= (ax2 + floormod(blockIdx.z, 14)))) && ((ax2 + floormod(blockIdx.z, 14)) < 15)), (float16*)A_2[(((((((((((blockIdx.x*6422528) + (threadIdx.y*1605632)) + (threadIdx.z*802816)) + (kh*57344)) + (blockIdx.z*4096)) + (ax2*4096)) + (ic.out [...]
                 }
               }
             }
@@ -503,14 +503,14 @@ by mapping the 2D convolution to tensor intrinsics
             for (ic.inner: int32, 0, 2) {
               for (kw: int32, 0, 3) {
                 for (ax0: int32, 0, 2) {
-                  @tvm_load_matrix_sync(Apad.shared.wmma.matrix_a, 16, 16, 16, ax0, @tvm_access_ptr(@type_annotation(, dtype=float16, type="pure_intrin", index=0), Apad.shared, ((((threadIdx.y*3072) + (ax0*1536)) + (kw*512)) + (ic.inner*256)), 256, 1, dtype=handle, type="intrin", index=0), 16, "row_major", dtype=handle, type="intrin", index=0)
+                  @tvm_load_matrix_sync(Apad.shared.wmma.matrix_a, 16, 16, 16, ax0, @tvm_access_ptr(@type_annotation(, dtype=float16, type="pure_intrin"), Apad.shared, ((((threadIdx.y*3072) + (ax0*1536)) + (kw*512)) + (ic.inner*256)), 256, 1, dtype=handle, type="intrin"), 16, "row_major", dtype=handle, type="intrin")
                 }
                 for (ax3_1: int32, 0, 4) {
-                  @tvm_load_matrix_sync(W.shared.wmma.matrix_b, 16, 16, 16, ax3_1, @tvm_access_ptr(@type_annotation(, dtype=float16, type="pure_intrin", index=0), W.shared, ((((kw*4096) + (ic.inner*2048)) + (threadIdx.z*1024)) + (ax3_1*256)), 256, 1, dtype=handle, type="intrin", index=0), 16, "row_major", dtype=handle, type="intrin", index=0)
+                  @tvm_load_matrix_sync(W.shared.wmma.matrix_b, 16, 16, 16, ax3_1, @tvm_access_ptr(@type_annotation(, dtype=float16, type="pure_intrin"), W.shared, ((((kw*4096) + (ic.inner*2048)) + (threadIdx.z*1024)) + (ax3_1*256)), 256, 1, dtype=handle, type="intrin"), 16, "row_major", dtype=handle, type="intrin")
                 }
                 for (n.c: int32, 0, 2) {
                   for (o.c: int32, 0, 4) {
-                    @tvm_mma_sync(Conv.wmma.accumulator, ((n.c*4) + o.c), Apad.shared.wmma.matrix_a, n.c, W.shared.wmma.matrix_b, o.c, Conv.wmma.accumulator, ((n.c*4) + o.c), dtype=handle, type="intrin", index=0)
+                    @tvm_mma_sync(Conv.wmma.accumulator, ((n.c*4) + o.c), Apad.shared.wmma.matrix_a, n.c, W.shared.wmma.matrix_b, o.c, Conv.wmma.accumulator, ((n.c*4) + o.c), dtype=handle, type="intrin")
                   }
                 }
               }
@@ -519,7 +519,7 @@ by mapping the 2D convolution to tensor intrinsics
         }
         for (n.inner: int32, 0, 2) {
           for (o.inner: int32, 0, 4) {
-            @tvm_store_matrix_sync(Conv.wmma.accumulator, 16, 16, 16, ((n.inner*4) + o.inner), @tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin", index=0), Conv_2, (((((((blockIdx.x*12845056) + (threadIdx.y*3211264)) + (n.inner*1605632)) + (blockIdx.z*8192)) + (blockIdx.y*2048)) + (threadIdx.z*1024)) + (o.inner*256)), 256, 2, dtype=handle, type="intrin", index=0), 16, "row_major", dtype=handle, type="intrin", index=0)
+            @tvm_store_matrix_sync(Conv.wmma.accumulator, 16, 16, 16, ((n.inner*4) + o.inner), @tvm_access_ptr(@type_annotation(, dtype=float32, type="pure_intrin"), Conv_2, (((((((blockIdx.x*12845056) + (threadIdx.y*3211264)) + (n.inner*1605632)) + (blockIdx.z*8192)) + (blockIdx.y*2048)) + (threadIdx.z*1024)) + (o.inner*256)), 256, 2, dtype=handle, type="intrin"), 16, "row_major", dtype=handle, type="intrin")
           }
         }
       }
@@ -541,7 +541,9 @@ be able to run on our build server
 
     ctx = tvm.gpu(0)
     if nvcc.have_tensorcore(ctx.compute_version):
-        with tvm.target.build_config(auto_unroll_max_step=16):
+        with tvm.transform.PassContext(config={"tir.UnrollLoop": {
+            "auto_max_step": 16
+        }}):
             func = tvm.build(s, [A, W, Conv], 'cuda')
         a_np = np.random.uniform(size=data_shape).astype(A.dtype)
         w_np = np.random.uniform(size=kernel_shape).astype(W.dtype)
@@ -561,7 +563,7 @@ be able to run on our build server
 
  .. code-block:: none
 
-    conv2d with tensor core: 6.304898 ms
+    conv2d with tensor core: 6.328851 ms
 
 
 
diff --git a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
index 1a07ca6..04bac12 100644
--- a/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_gemm.rst.txt
@@ -118,8 +118,8 @@ Then we write a baseline implementation, the simplest way to write a matrix mult
 
  .. code-block:: none
 
-    Numpy running time: 0.007487
-    Baseline: 5.854661
+    Numpy running time: 0.007867
+    Baseline: 6.026107
 
 
 
@@ -206,7 +206,7 @@ fill 32 * 32 * sizeof(float) which is 4KB in the cache whose total size is 32KB
 
  .. code-block:: none
 
-    Opt1: 0.112672
+    Opt1: 0.124150
 
 
 
@@ -231,9 +231,9 @@ Here is the generated IR after blocking.
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
       buffers = {A: Buffer(A_2: handle, float32, [1024, 1024], []),
-                 C: Buffer(C_2: handle, float32, [1024, 1024], []),
-                 B: Buffer(B_2: handle, float32, [1024, 1024], [])}
-      buffer_map = {B_1: B, C_1: C, A_1: A} {
+                 B: Buffer(B_2: handle, float32, [1024, 1024], []),
+                 C: Buffer(C_2: handle, float32, [1024, 1024], [])}
+      buffer_map = {C_1: C, B_1: B, A_1: A} {
       for (x.outer: int32, 0, 32) {
         for (y.outer: int32, 0, 32) {
           for (x.inner.init: int32, 0, 32) {
@@ -300,7 +300,7 @@ In this tutorial, we chose to vectorize the inner loop row data since it is cach
 
  .. code-block:: none
 
-    Opt2: 0.125738
+    Opt2: 0.124186
 
 
 
@@ -389,7 +389,7 @@ the access pattern for A matrix is more cache friendly.
 
  .. code-block:: none
 
-    Opt3: 0.059627
+    Opt3: 0.069148
 
 
 
@@ -497,7 +497,7 @@ the corresponding value from the packed array.
 
  .. code-block:: none
 
-    Opt4: 0.060532
+    Opt4: 0.064161
 
 
 
@@ -607,7 +607,7 @@ write to C when all the block results are ready.
 
  .. code-block:: none
 
-    Opt5: 0.061617
+    Opt5: 0.071793
 
 
 
@@ -723,7 +723,7 @@ Futhermore, we can also utilize multi-core processors to do the thread-level par
 
  .. code-block:: none
 
-    Opt6: 0.014839
+    Opt6: 0.022614
 
 
 
@@ -747,10 +747,10 @@ Here is the generated IR after parallelization.
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {B: Buffer(B_2: handle, float32, [1024, 1024], []),
-                 A: Buffer(A_2: handle, float32, [1024, 1024], []),
-                 C: Buffer(C_2: handle, float32, [1024, 1024], [])}
-      buffer_map = {C_1: C, B_1: B, A_1: A} {
+      buffers = {A: Buffer(A_2: handle, float32, [1024, 1024], []),
+                 C: Buffer(C_2: handle, float32, [1024, 1024], []),
+                 B: Buffer(B_2: handle, float32, [1024, 1024], [])}
+      buffer_map = {B_1: B, C_1: C, A_1: A} {
       attr [packedB: handle] "storage_scope" = "global";
       allocate(packedB, float32x32, [32768]) {
         for (x: int32, 0, 32) "parallel" {
diff --git a/docs/_sources/tutorials/optimize/opt_matmul_auto_tensorcore.rst.txt b/docs/_sources/tutorials/optimize/opt_matmul_auto_tensorcore.rst.txt
index 223d72b..767c9ad 100644
--- a/docs/_sources/tutorials/optimize/opt_matmul_auto_tensorcore.rst.txt
+++ b/docs/_sources/tutorials/optimize/opt_matmul_auto_tensorcore.rst.txt
@@ -296,10 +296,9 @@ and run the kernel to compare with numpy to check whether the results are correc
       print(best_config)
       with autotvm.apply_history_best('matmul.log'):
         with tvm.target.create("cuda"):
-            with tvm.target.build_config():
-                s, arg_bufs = test_gemm(N, L, M, dtype, layout)
-                print(tvm.lower(s, arg_bufs, simple_mode=True))
-                func = tvm.build(s, arg_bufs)
+              s, arg_bufs = test_gemm(N, L, M, dtype, layout)
+              print(tvm.lower(s, arg_bufs, simple_mode=True))
+              func = tvm.build(s, arg_bufs)
       dev_module = func.imported_modules[0]
       print(dev_module.get_source())
 
diff --git a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
index 23fe737..b044deb 100644
--- a/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,9 +5,9 @@
 
 Computation times
 =================
-**00:26.701** total execution time for **tutorials_optimize** files:
+**00:28.349** total execution time for **tutorials_optimize** files:
 
-- **00:24.971**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
-- **00:00.905**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
-- **00:00.677**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
-- **00:00.147**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
+- **00:26.434**: :ref:`sphx_glr_tutorials_optimize_opt_gemm.py` (``opt_gemm.py``)
+- **00:01.002**: :ref:`sphx_glr_tutorials_optimize_opt_conv_tensorcore.py` (``opt_conv_tensorcore.py``)
+- **00:00.752**: :ref:`sphx_glr_tutorials_optimize_opt_conv_cuda.py` (``opt_conv_cuda.py``)
+- **00:00.161**: :ref:`sphx_glr_tutorials_optimize_opt_matmul_auto_tensorcore.py` (``opt_matmul_auto_tensorcore.py``)
diff --git a/docs/_sources/tutorials/relay_quick_start.rst.txt b/docs/_sources/tutorials/relay_quick_start.rst.txt
index f4992d8..a20c6cf 100644
--- a/docs/_sources/tutorials/relay_quick_start.rst.txt
+++ b/docs/_sources/tutorials/relay_quick_start.rst.txt
@@ -210,7 +210,7 @@ in this example. Then the machine code will be generated as the module library.
 
     opt_level = 3
     target = tvm.target.cuda()
-    with relay.build_config(opt_level=opt_level):
+    with tvm.transform.PassContext(opt_level=opt_level):
         graph, lib, params = relay.build(mod, target, params=params)
 
 
@@ -223,7 +223,7 @@ in this example. Then the machine code will be generated as the module library.
 
  .. code-block:: none
 
-
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+
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     Cannot find config for target=cuda -model=unknown, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 64, 56, 56), 'float32'), ('TENSOR', (64, 64, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
     Cannot find config for target=cuda -model=unknown, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 128, 28, 28), 'float32'), ('TENSOR', (128, 128, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
     Cannot find config for target=cuda -model=unknown, workload=('conv2d_nchw.cuda', ('TENSOR', (1, 256, 14, 14), 'float32'), ('TENSOR', (256, 256, 3, 3), 'float32'), (1, 1), (1, 1, 1, 1), (1, 1), 'float32'). A fallback configuration is used, which may bring great performance regression.
diff --git a/docs/_sources/tutorials/sg_execution_times.rst.txt b/docs/_sources/tutorials/sg_execution_times.rst.txt
index eaf32a2..5a43a2a 100644
--- a/docs/_sources/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/sg_execution_times.rst.txt
@@ -5,8 +5,8 @@
 
 Computation times
 =================
-**00:09.733** total execution time for **tutorials** files:
+**00:09.016** total execution time for **tutorials** files:
 
-- **00:09.280**: :ref:`sphx_glr_tutorials_relay_quick_start.py` (``relay_quick_start.py``)
-- **00:00.318**: :ref:`sphx_glr_tutorials_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
-- **00:00.134**: :ref:`sphx_glr_tutorials_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
+- **00:08.536**: :ref:`sphx_glr_tutorials_relay_quick_start.py` (``relay_quick_start.py``)
+- **00:00.342**: :ref:`sphx_glr_tutorials_tensor_expr_get_started.py` (``tensor_expr_get_started.py``)
+- **00:00.139**: :ref:`sphx_glr_tutorials_cross_compilation_and_rpc.py` (``cross_compilation_and_rpc.py``)
diff --git a/docs/_sources/tutorials/topi/intro_topi.rst.txt b/docs/_sources/tutorials/topi/intro_topi.rst.txt
index d008862..42e786c 100644
--- a/docs/_sources/tutorials/topi/intro_topi.rst.txt
+++ b/docs/_sources/tutorials/topi/intro_topi.rst.txt
@@ -196,12 +196,12 @@ we can schedule the following series of operations ending with :code:`topi.sum`
       allocate(reduce_temp0, float32, [1]) {
         T_divide_red.rf[0] = 0f32
         for (k0.k1.fused.k2.fused.outer: int32, 0, 10) {
-          if @likely((((((k0.k1.fused.k2.fused.outer*1024) + threadIdx.x) < 10000) && (((k0.k1.fused.k2.fused.outer*1024) + threadIdx.x) < 10000)) && (((k0.k1.fused.k2.fused.outer*1024) + threadIdx.x) < 10000)), dtype=bool, type="pure_intrin", index=0) {
+          if @likely((((((k0.k1.fused.k2.fused.outer*1024) + threadIdx.x) < 10000) && (((k0.k1.fused.k2.fused.outer*1024) + threadIdx.x) < 10000)) && (((k0.k1.fused.k2.fused.outer*1024) + threadIdx.x) < 10000)), dtype=bool, type="pure_intrin") {
             T_divide_red.rf[0] = ((float32*)T_divide_red.rf[0]) + ((((float32*)a_2[((k0.k1.fused.k2.fused.outer*1024) + threadIdx.x)]) + (float32*)b_2[floormod(((k0.k1.fused.k2.fused.outer*1024) + threadIdx.x), 100)])) + ((float32*)a_2[((k0.k1.fused.k2.fused.outer*1024) + threadIdx.x)])*(float32*)b_2[floormod(((k0.k1.fused.k2.fused.outer*1024) + threadIdx.x), 100)])))*0.5f32))
           }
         }
-        attr [meta[CommReducer][0]] "reduce_scope" = @reinterpret(0u64, dtype=handle, type="pure_intrin", index=0);
-        @tvm_thread_allreduce(1u32, (float32*)T_divide_red.rf[0]), True, reduce_temp0, threadIdx.x, dtype=handle, type="intrin", index=0)
+        attr [meta[tir.CommReducer][0]] "reduce_scope" = @reinterpret(0u64, dtype=handle, type="pure_intrin");
+        @tvm_thread_allreduce(1u32, (float32*)T_divide_red.rf[0]), True, reduce_temp0, threadIdx.x, dtype=handle, type="intrin")
         if (threadIdx.x == 0) {
           T_divide_red[0] = (float32*)reduce_temp0[0])
         }
@@ -230,7 +230,7 @@ As you can see, scheduled stages of computation have been accumulated and we can
 
  .. code-block:: none
 
-    [stage(a, 0x144145e20), stage(b, 0x143f95d70), stage(T_add, 0xbf254da0), stage(T_multiply, 0x155b05590), stage(T_elemwise_sum, 0x12e4a3720), stage(T_divide, 0x144195490), stage(T_divide_red.rf, 0x1441445f0), stage(T_divide_red, 0x131381f50)]
+    [stage(a, 0x12858c960), stage(b, 0x13bb5b610), stage(T_add, 0xc0ddf7a0), stage(T_multiply, 0x14f9e45e0), stage(T_elemwise_sum, 0x13d37cad0), stage(T_divide, 0x11cc3d6e0), stage(T_divide_red.rf, 0x11cc374f0), stage(T_divide_red, 0x11c91e300)]
 
 
 
@@ -283,41 +283,41 @@ TOPI also provides common neural nets operations such as _softmax_ with optimize
       attr = {"tir.noalias": True, "global_symbol": "main"}
       buffers = {tarray: Buffer(tarray_2: handle, float32, [512, 512], [])}
       buffer_map = {tarray_1: tarray} {
-      attr [T_softmax_maxelem: handle] "storage_scope" = "global";
-      allocate(T_softmax_maxelem, float32, [512]);
-      attr [T_softmax_exp: handle] "storage_scope" = "global";
-      allocate(T_softmax_exp, float32, [262144]) {
-        attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 512 {
-          T_softmax_maxelem[blockIdx.x] = -3.40282e+38f32
-          for (k: int32, 0, 512) {
-            T_softmax_maxelem[blockIdx.x] = max((float32*)T_softmax_maxelem[blockIdx.x]), (float32*)tarray_2[((blockIdx.x*512) + k)]))
+      attr [T_softmax_norm: handle] "storage_scope" = "global";
+      allocate(T_softmax_norm, float32x4, [65536]);
+      attr [IterVar(blockIdx.x: int32, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 512;
+      attr [normal_reduce_temp0: handle] "storage_scope" = "local";
+      allocate(normal_reduce_temp0, float32, [1]);
+      attr [reduce_temp0: handle] "storage_scope" = "local";
+      allocate(reduce_temp0, float32, [1]);
+      attr [T_softmax_exp: handle] "storage_scope" = "warp";
+      allocate(T_softmax_exp, float32, [512]);
+      attr [normal_reduce_temp0_1: handle] "storage_scope" = "local";
+      allocate(normal_reduce_temp0_1, float32, [1]);
+      attr [reduce_temp0_1: handle] "storage_scope" = "local";
+      allocate(reduce_temp0_1, float32, [1]) {
+        attr [IterVar(threadIdx.x: int32, [0:32], "ThreadIndex", "threadIdx.x")] "thread_extent" = 32 {
+          normal_reduce_temp0[0] = -3.40282e+38f32
+          for (k.inner: int32, 0, 16) {
+            normal_reduce_temp0[0] = max((float32*)normal_reduce_temp0[0]), (float32*)tarray_2[(((blockIdx.x*512) + (threadIdx.x*16)) + k.inner)]))
+          }
+          attr [meta[tir.CommReducer][0]] "reduce_scope" = @reinterpret(0u64, dtype=handle, type="pure_intrin");
+          @tvm_thread_allreduce(1u32, (float32*)normal_reduce_temp0[0]), True, reduce_temp0, threadIdx.x, dtype=handle, type="intrin")
+          for (i1.inner.outer: int32, 0, 4) {
+            T_softmax_exp[ramp(((threadIdx.x*16) + (i1.inner.outer*4)), 1, 4)] = @exp(((float32x4*)tarray_2[ramp((((blockIdx.x*512) + (threadIdx.x*16)) + (i1.inner.outer*4)), 1, 4)]) - broadcast((float32*)reduce_temp0[0]), 4)), dtype=float32x4, type="pure_intrin")
           }
         }
-        attr [IterVar(blockIdx.x, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 512;
-        for (i1: int32, 0, 512) {
-          T_softmax_exp[((blockIdx.x*512) + i1)] = @exp(((float32*)tarray_2[((blockIdx.x*512) + i1)]) - (float32*)T_softmax_maxelem[blockIdx.x])), dtype=float32, type="pure_intrin", index=0)
-        }
-        attr [IterVar(blockIdx.x, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 512;
-        attr [T_softmax_expsum.rf: handle] "storage_scope" = "local";
-        allocate(T_softmax_expsum.rf, float32, [1]);
-        attr [reduce_temp0: handle] "storage_scope" = "local";
-        allocate(reduce_temp0, float32, [1]);
-        attr [IterVar(threadIdx.x: int32, [0:64], "ThreadIndex", "threadIdx.x")] "thread_extent" = 64 {
-          T_softmax_expsum.rf[0] = 0f32
-          for (k.outer: int32, 0, 8) {
-            T_softmax_expsum.rf[0] = ((float32*)T_softmax_expsum.rf[0]) + (float32*)T_softmax_exp[(((blockIdx.x*512) + (k.outer*64)) + threadIdx.x)]))
+        attr [IterVar(threadIdx.x, [0:32], "ThreadIndex", "threadIdx.x")] "thread_extent" = 32 {
+          normal_reduce_temp0_1[0] = 0f32
+          for (k.inner_1: int32, 0, 16) {
+            normal_reduce_temp0_1[0] = ((float32*)normal_reduce_temp0_1[0]) + (float32*)T_softmax_exp[((threadIdx.x*16) + k.inner_1)]))
           }
-          attr [meta[CommReducer][0]] "reduce_scope" = @reinterpret(0u64, dtype=handle, type="pure_intrin", index=0);
-          @tvm_thread_allreduce(1u32, (float32*)T_softmax_expsum.rf[0]), True, reduce_temp0, threadIdx.x, dtype=handle, type="intrin", index=0)
-          if (threadIdx.x == 0) {
-            T_softmax_maxelem[blockIdx.x] = (float32*)reduce_temp0[0])
+          attr [meta[tir.CommReducer][1]] "reduce_scope" = @reinterpret(0u64, dtype=handle, type="pure_intrin");
+          @tvm_thread_allreduce(1u32, (float32*)normal_reduce_temp0_1[0]), True, reduce_temp0_1, threadIdx.x, dtype=handle, type="intrin")
+          for (i1.inner.outer_1: int32, 0, 4) {
+            T_softmax_norm[ramp((((blockIdx.x*512) + (threadIdx.x*16)) + (i1.inner.outer_1*4)), 1, 4)] = ((float32x4*)T_softmax_exp[ramp(((threadIdx.x*16) + (i1.inner.outer_1*4)), 1, 4)]) / broadcast((float32*)reduce_temp0_1[0]), 4))
           }
         }
-        attr [IterVar(blockIdx.x, (nullptr), "ThreadIndex", "blockIdx.x")] "thread_extent" = 512;
-        attr [IterVar(threadIdx.x, [0:64], "ThreadIndex", "threadIdx.x")] "thread_extent" = 64;
-        for (i1.inner: int32, 0, 8) {
-          T_softmax_exp[(((blockIdx.x*512) + (threadIdx.x*8)) + i1.inner)] = ((float32*)T_softmax_exp[(((blockIdx.x*512) + (threadIdx.x*8)) + i1.inner)]) / (float32*)T_softmax_maxelem[blockIdx.x]))
-        }
       }
     }
 
@@ -398,31 +398,19 @@ We can fuse :code:`topi.nn.conv2d` and :code:`topi.nn.relu` together.
             attr [IterVar(threadIdx.z_1: int32, (nullptr), "ThreadIndex", "threadIdx.z")] "thread_extent" = 1;
             attr [IterVar(threadIdx.y_1: int32, (nullptr), "ThreadIndex", "threadIdx.y")] "thread_extent" = 1;
             attr [IterVar(threadIdx.x_1: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 16 {
-              pad_temp.shared[(threadIdx.x_1*7)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (2 <= ((blockIdx.x*112) + (threadIdx.x_1*7)))), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 450)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 1)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (1 <= ((blockIdx.x*112) + (threadIdx.x_1*7)))), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 449)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 2)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 448)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 3)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 4)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 5)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 6)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)]), 0f32, dtype=float32, type="pure_intrin", index=0)
+              pad_temp.shared[(threadIdx.x_1*7)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (2 <= ((blockIdx.x*112) + (threadIdx.x_1*7)))), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 450)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 1)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (1 <= ((blockIdx.x*112) + (threadIdx.x_1*7)))), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 449)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 2)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 448)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 3)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 4)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 5)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 6)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)]), 0f32, dtype=float32, type="pure_intrin")
             }
             attr [IterVar(threadIdx.z_2: int32, (nullptr), "ThreadIndex", "threadIdx.z")] "thread_extent" = 1;
             attr [IterVar(threadIdx.y_2: int32, (nullptr), "ThreadIndex", "threadIdx.y")] "thread_extent" = 1;
             attr [IterVar(threadIdx.x_2: int32, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 16;
-            if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-              if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                  if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                    if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                      if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                        if @likely((((blockIdx.z*2) + threadIdx.x_2) < 10), dtype=bool, type="pure_intrin", index=0) {
-                          placeholder.shared[threadIdx.x_2] = (float32*)placeholder_5[((((blockIdx.z*150) + (threadIdx.x_2*75)) + (rc.outer*25)) + (ry.outer*5))])
-                        }
-                      }
-                    }
-                  }
-                }
-              }
+            if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin") {
+              placeholder.shared[threadIdx.x_2] = (float32*)placeholder_5[((((blockIdx.z*150) + (threadIdx.x_2*75)) + (rc.outer*25)) + (ry.outer*5))])
             }
             compute_1[0] = ((float32*)compute_1[0]) + ((float32*)pad_temp.shared[threadIdx.x])*(float32*)placeholder.shared[0])))
             compute_1[2] = ((float32*)compute_1[2]) + ((float32*)pad_temp.shared[(threadIdx.x + 16)])*(float32*)placeholder.shared[0])))
@@ -441,31 +429,19 @@ We can fuse :code:`topi.nn.conv2d` and :code:`topi.nn.relu` together.
             attr [IterVar(threadIdx.z_1, (nullptr), "ThreadIndex", "threadIdx.z")] "thread_extent" = 1;
             attr [IterVar(threadIdx.y_1, (nullptr), "ThreadIndex", "threadIdx.y")] "thread_extent" = 1;
             attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 16 {
-              pad_temp.shared[(threadIdx.x_1*7)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (1 <= ((blockIdx.x*112) + (threadIdx.x_1*7)))), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 449)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 1)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 448)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 2)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 3)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 4)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 5)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 6)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)]), 0f32, dtype=float32, type="pure_intrin", index=0)
+              pad_temp.shared[(threadIdx.x_1*7)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (1 <= ((blockIdx.x*112) + (threadIdx.x_1*7)))), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 449)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 1)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 448)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 2)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 3)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 4)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 5)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 6)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)]), 0f32, dtype=float32, type="pure_intrin")
             }
             attr [IterVar(threadIdx.z_2, (nullptr), "ThreadIndex", "threadIdx.z")] "thread_extent" = 1;
             attr [IterVar(threadIdx.y_2, (nullptr), "ThreadIndex", "threadIdx.y")] "thread_extent" = 1;
             attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 16;
-            if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-              if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                  if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                    if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                      if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                        if @likely((((blockIdx.z*2) + threadIdx.x_2) < 10), dtype=bool, type="pure_intrin", index=0) {
-                          placeholder.shared[threadIdx.x_2] = (float32*)placeholder_5[(((((blockIdx.z*150) + (threadIdx.x_2*75)) + (rc.outer*25)) + (ry.outer*5)) + 1)])
-                        }
-                      }
-                    }
-                  }
-                }
-              }
+            if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin") {
+              placeholder.shared[threadIdx.x_2] = (float32*)placeholder_5[(((((blockIdx.z*150) + (threadIdx.x_2*75)) + (rc.outer*25)) + (ry.outer*5)) + 1)])
             }
             compute_1[0] = ((float32*)compute_1[0]) + ((float32*)pad_temp.shared[threadIdx.x])*(float32*)placeholder.shared[0])))
             compute_1[2] = ((float32*)compute_1[2]) + ((float32*)pad_temp.shared[(threadIdx.x + 16)])*(float32*)placeholder.shared[0])))
@@ -484,31 +460,19 @@ We can fuse :code:`topi.nn.conv2d` and :code:`topi.nn.relu` together.
             attr [IterVar(threadIdx.z_1, (nullptr), "ThreadIndex", "threadIdx.z")] "thread_extent" = 1;
             attr [IterVar(threadIdx.y_1, (nullptr), "ThreadIndex", "threadIdx.y")] "thread_extent" = 1;
             attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 16 {
-              pad_temp.shared[(threadIdx.x_1*7)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 448)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 1)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 2)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 3)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 4)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 5)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 6)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 442)]), 0f32, dtype=float32, type="pure_intrin", index=0)
+              pad_temp.shared[(threadIdx.x_1*7)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 448)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 1)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 2)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 3)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 4)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 5)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 6)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 442)]), 0f32, dtype=float32, type="pure_intrin")
             }
             attr [IterVar(threadIdx.z_2, (nullptr), "ThreadIndex", "threadIdx.z")] "thread_extent" = 1;
             attr [IterVar(threadIdx.y_2, (nullptr), "ThreadIndex", "threadIdx.y")] "thread_extent" = 1;
             attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 16;
-            if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-              if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                  if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                    if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                      if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                        if @likely((((blockIdx.z*2) + threadIdx.x_2) < 10), dtype=bool, type="pure_intrin", index=0) {
-                          placeholder.shared[threadIdx.x_2] = (float32*)placeholder_5[(((((blockIdx.z*150) + (threadIdx.x_2*75)) + (rc.outer*25)) + (ry.outer*5)) + 2)])
-                        }
-                      }
-                    }
-                  }
-                }
-              }
+            if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin") {
+              placeholder.shared[threadIdx.x_2] = (float32*)placeholder_5[(((((blockIdx.z*150) + (threadIdx.x_2*75)) + (rc.outer*25)) + (ry.outer*5)) + 2)])
             }
             compute_1[0] = ((float32*)compute_1[0]) + ((float32*)pad_temp.shared[threadIdx.x])*(float32*)placeholder.shared[0])))
             compute_1[2] = ((float32*)compute_1[2]) + ((float32*)pad_temp.shared[(threadIdx.x + 16)])*(float32*)placeholder.shared[0])))
@@ -527,31 +491,19 @@ We can fuse :code:`topi.nn.conv2d` and :code:`topi.nn.relu` together.
             attr [IterVar(threadIdx.z_1, (nullptr), "ThreadIndex", "threadIdx.z")] "thread_extent" = 1;
             attr [IterVar(threadIdx.y_1, (nullptr), "ThreadIndex", "threadIdx.y")] "thread_extent" = 1;
             attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 16 {
-              pad_temp.shared[(threadIdx.x_1*7)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 1)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 2)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 3)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 4)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 5)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 442)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 6)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (((blockIdx.x*112) + (threadIdx.x_1*7)) < 217)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 441)]), 0f32, dtype=float32, type="pure_intrin", index=0)
+              pad_temp.shared[(threadIdx.x_1*7)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 447)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 1)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 2)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 3)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 4)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 5)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 442)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 6)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (((blockIdx.x*112) + (threadIdx.x_1*7)) < 217)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 441)]), 0f32, dtype=float32, type="pure_intrin")
             }
             attr [IterVar(threadIdx.z_2, (nullptr), "ThreadIndex", "threadIdx.z")] "thread_extent" = 1;
             attr [IterVar(threadIdx.y_2, (nullptr), "ThreadIndex", "threadIdx.y")] "thread_extent" = 1;
             attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 16;
-            if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-              if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                  if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                    if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                      if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                        if @likely((((blockIdx.z*2) + threadIdx.x_2) < 10), dtype=bool, type="pure_intrin", index=0) {
-                          placeholder.shared[threadIdx.x_2] = (float32*)placeholder_5[(((((blockIdx.z*150) + (threadIdx.x_2*75)) + (rc.outer*25)) + (ry.outer*5)) + 3)])
-                        }
-                      }
-                    }
-                  }
-                }
-              }
+            if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin") {
+              placeholder.shared[threadIdx.x_2] = (float32*)placeholder_5[(((((blockIdx.z*150) + (threadIdx.x_2*75)) + (rc.outer*25)) + (ry.outer*5)) + 3)])
             }
             compute_1[0] = ((float32*)compute_1[0]) + ((float32*)pad_temp.shared[threadIdx.x])*(float32*)placeholder.shared[0])))
             compute_1[2] = ((float32*)compute_1[2]) + ((float32*)pad_temp.shared[(threadIdx.x + 16)])*(float32*)placeholder.shared[0])))
@@ -570,31 +522,19 @@ We can fuse :code:`topi.nn.conv2d` and :code:`topi.nn.relu` together.
             attr [IterVar(threadIdx.z_1, (nullptr), "ThreadIndex", "threadIdx.z")] "thread_extent" = 1;
             attr [IterVar(threadIdx.y_1, (nullptr), "ThreadIndex", "threadIdx.y")] "thread_extent" = 1;
             attr [IterVar(threadIdx.x_1, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 16 {
-              pad_temp.shared[(threadIdx.x_1*7)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 1)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 2)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 3)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 4)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 442)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 5)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (((blockIdx.x*112) + (threadIdx.x_1*7)) < 217)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 441)]), 0f32, dtype=float32, type="pure_intrin", index=0)
-              pad_temp.shared[((threadIdx.x_1*7) + 6)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (((blockIdx.x*112) + (threadIdx.x_1*7)) < 216)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 440)]), 0f32, dtype=float32, type="pure_intrin", index=0)
+              pad_temp.shared[(threadIdx.x_1*7)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 446)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 1)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 445)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 2)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 444)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 3)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 443)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 4)] = @tvm_if_then_else(((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 442)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 5)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (((blockIdx.x*112) + (threadIdx.x_1*7)) < 217)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 441)]), 0f32, dtype=float32, type="pure_intrin")
+              pad_temp.shared[((threadIdx.x_1*7) + 6)] = @tvm_if_then_else((((2 <= (blockIdx.y + ry.outer)) && ((blockIdx.y + ry.outer) < 226)) && (((blockIdx.x*112) + (threadIdx.x_1*7)) < 216)), (float32*)placeholder_4[((((((rc.outer*50176) + (blockIdx.y*224)) + (ry.outer*224)) + (blockIdx.x*112)) + (threadIdx.x_1*7)) - 440)]), 0f32, dtype=float32, type="pure_intrin")
             }
             attr [IterVar(threadIdx.z_2, (nullptr), "ThreadIndex", "threadIdx.z")] "thread_extent" = 1;
             attr [IterVar(threadIdx.y_2, (nullptr), "ThreadIndex", "threadIdx.y")] "thread_extent" = 1;
             attr [IterVar(threadIdx.x_2, (nullptr), "ThreadIndex", "threadIdx.x")] "thread_extent" = 16;
-            if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-              if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                  if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                    if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                      if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin", index=0) {
-                        if @likely((((blockIdx.z*2) + threadIdx.x_2) < 10), dtype=bool, type="pure_intrin", index=0) {
-                          placeholder.shared[threadIdx.x_2] = (float32*)placeholder_5[(((((blockIdx.z*150) + (threadIdx.x_2*75)) + (rc.outer*25)) + (ry.outer*5)) + 4)])
-                        }
-                      }
-                    }
-                  }
-                }
-              }
+            if @likely((threadIdx.x_2 < 2), dtype=bool, type="pure_intrin") {
+              placeholder.shared[threadIdx.x_2] = (float32*)placeholder_5[(((((blockIdx.z*150) + (threadIdx.x_2*75)) + (rc.outer*25)) + (ry.outer*5)) + 4)])
             }
             compute_1[0] = ((float32*)compute_1[0]) + ((float32*)pad_temp.shared[threadIdx.x])*(float32*)placeholder.shared[0])))
             compute_1[2] = ((float32*)compute_1[2]) + ((float32*)pad_temp.shared[(threadIdx.x + 16)])*(float32*)placeholder.shared[0])))
diff --git a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
index 7c7c583..fc5424b 100644
--- a/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
+++ b/docs/_sources/tutorials/topi/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
 
 Computation times
 =================
-**00:00.528** total execution time for **tutorials_topi** files:
+**00:00.536** total execution time for **tutorials_topi** files:
 
-- **00:00.528**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
+- **00:00.536**: :ref:`sphx_glr_tutorials_topi_intro_topi.py` (``intro_topi.py``)
diff --git a/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
index 2a5f494..3bae1f9 100644
--- a/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/sg_execution_times.rst.txt
@@ -5,6 +5,6 @@
 
 Computation times
 =================
-**00:03.566** total execution time for **vta_tutorials_autotvm** files:
+**00:03.860** total execution time for **vta_tutorials_autotvm** files:
 
-- **00:03.566**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
+- **00:03.860**: :ref:`sphx_glr_vta_tutorials_autotvm_tune_relay_vta.py` (``tune_relay_vta.py``)
diff --git a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
index af248ca..8d33862 100644
--- a/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
+++ b/docs/_sources/vta/tutorials/autotvm/tune_relay_vta.rst.txt
@@ -95,7 +95,7 @@ Perform vta-specific compilation with Relay from a Gluon model
 
         # Perform quantization in Relay
         # Note: We set opt_level to 3 in order to fold batch norm
-        with relay.build_config(opt_level=3):
+        with tvm.transform.PassContext(opt_level=3):
             with relay.quantize.qconfig(global_scale=8.0, skip_conv_layers=[0]):
                 mod = relay.quantize.quantize(mod, params=params)
 
@@ -427,19 +427,19 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
         with autotvm.tophub.context(target, extra_files=[log_file]):
             # Compile network
             print("Compile...")
-            with relay.build_config(opt_level=3, disabled_pass={"AlterOpLayout"}):
-                if target.device_name != "vta":
+            if target.device_name != "vta":
+                with tvm.transform.PassContext(opt_level=3, disabled_pass={"AlterOpLayout"}):
                     graph, lib, params = relay.build(relay_prog,
-                                                     target=target,
-                                                     params=params,
-                                                     target_host=env.target_host)
-                else:
-                    with vta.build_config():
-                        graph, lib, params = relay.build(
-                            relay_prog,
-                            target=target,
-                            params=params,
-                            target_host=env.target_host)
+                                                    target=target,
+                                                    params=params,
+                                                    target_host=env.target_host)
+            else:
+                with vta.build_config(opt_level=3, disabled_pass={"AlterOpLayout"}):
+                    graph, lib, params = relay.build(
+                        relay_prog,
+                        target=target,
+                        params=params,
+                        target_host=env.target_host)
 
             # Export library
             print("Upload...")
@@ -481,14 +481,14 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
  .. code-block:: none
 
     Extract tasks...
-
    ...1%, 0.01 MB, 31 KB/s, 0 seconds passed
    ...2%, 0.02 MB, 62 KB/s, 0 seconds passed
    ...3%, 0.02 MB, 94 KB/s, 0 seconds passed
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    ...8%, 0.06 MB, 248 KB/s, 0 seconds passed
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    ...11%, 0.09 MB, 341 KB/s, 0 seconds passed
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    ...14%, 0.10 MB, 402 KB/s, 0 seconds passed
    ...15%, 0.11 MB, 432 KB/s, 0 seconds passed
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    ...21%, 0.16 MB, 613 KB/s, 0 seconds passed
    ...22%, 0.16 MB, 643 KB/s, 0 seconds passed
     ...23%, 0.17 MB, 673 KB/s, 0 seconds passed
    ...25%, 0.18 MB, 702 KB/s, 0 seconds passed
    ...26%, 0.19 MB, 732 KB/s, 0 seconds passed
    ...27%, 0.20 MB, 762 KB/s, 0 seconds passed
    ...28%, 0.20 MB, 792 KB/s, 0 seconds passed
    ...29%, 0.21 MB, 821 KB/s, 0 seconds passed
    ...30%, 0.22 MB, 851 KB/s, 0 seconds passed
    ...31%, 0.23 MB, 881 KB/s, 0 seconds passed
    ...32%, 0.23 MB, 911 KB/s, 0 seconds passed
    ...33%, 0.24 MB, 940 KB/s, 0 seconds passed
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    ...35%, 0.26 MB, 999 KB/s, 0 seconds passed
    ...36%, 0.27 MB, 1028 KB/s, 0 seconds passed
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    ...40%, 0.29 MB, 1115 KB/s, 0 seconds passed
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    ...45%, 0.33 MB, 1260 KB/
 s, 0 seconds passed
    ...46%, 0.34 MB, 1289 KB/s, 0 seconds passed
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    ...48%, 0.35 MB, 1347 KB/s, 0 seconds passed
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+
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 onds passed
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    .
 ..90%, 0.65 MB, 3095 KB/s, 0 seconds passed
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     Exception in thread Thread-9:
     Traceback (most recent call last):
       File "/usr/lib/python3.6/threading.py", line 916, in _bootstrap_inner
         self.run()
       File "/usr/lib/python3.6/threading.py", line 864, in run
         self._target(*self._args, **self._kwargs)
-      File "/workspace/docs/../python/tvm/autotvm/task/relay_integration.py", line 49, in _lower
+      File "/workspace/docs/../python/tvm/autotvm/task/relay_integration.py", line 48, in _lower
         grc.codegen(mod["main"])
       File "/workspace/docs/../python/tvm/relay/backend/graph_runtime_codegen.py", line 88, in codegen
         arr = self._get_param_by_name(key)
@@ -497,11 +497,11 @@ Finally, we launch tuning jobs and evaluate the end-to-end performance.
       File "tvm/_ffi/_cython/./packed_func.pxi", line 245, in tvm._ffi._cy3.core.FuncCall3
       File "tvm/_ffi/_cython/./base.pxi", line 160, in tvm._ffi._cy3.core.CALL
     tvm._ffi.base.TVMError: Traceback (most recent call last):
-      [bt] (3) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7ffa999ba6a1]
-      [bt] (2) /workspace/build/libtvm.so(std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::relay::backend::GraphRuntimeCodegenModule::GetFunction(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, tvm::runtime::ObjectPtr<tvm::runtime::Object> const&)::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#5}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)+0x6f) [0x7ffa9 [...]
-      [bt] (1) /workspace/build/libtvm.so(tvm::runtime::TVMArgValue::operator std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >[abi:cxx11]() const+0x141) [0x7ffa98ff3121]
-      [bt] (0) /workspace/build/libtvm.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x67) [0x7ffa98fe0f87]
-      File "/workspace/include/tvm/runtime/packed_func.h", line 484
+      [bt] (3) /workspace/build/libtvm.so(TVMFuncCall+0x61) [0x7f259437cd11]
+      [bt] (2) /workspace/build/libtvm.so(std::_Function_handler<void (tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*), tvm::relay::backend::GraphRuntimeCodegenModule::GetFunction(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, tvm::runtime::ObjectPtr<tvm::runtime::Object> const&)::{lambda(tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)#5}>::_M_invoke(std::_Any_data const&, tvm::runtime::TVMArgs&&, tvm::runtime::TVMRetValue*&&)+0x6f) [0x7f259 [...]
+      [bt] (1) /workspace/build/libtvm.so(tvm::runtime::TVMArgValue::operator std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >[abi:cxx11]() const+0x141) [0x7f259397c201]
+      [bt] (0) /workspace/build/libtvm.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x67) [0x7f259396a047]
+      File "/workspace/include/tvm/runtime/packed_func.h", line 491
     TVMError: Check failed: type_code_ == kTVMStr (8 vs. 11) : expected str but get Object
 
     Extracted 10 conv2d tasks:
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
index aa69074..f8ac6af 100644
--- a/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/deploy_classification.rst.txt
@@ -192,7 +192,7 @@ The compilation steps are:
         if target.device_name == "vta":
             # Perform quantization in Relay
             # Note: We set opt_level to 3 in order to fold batch norm
-            with relay.build_config(opt_level=3):
+            with tvm.transform.PassContext(opt_level=3):
                 with relay.quantize.qconfig(global_scale=8.0,
                                             skip_conv_layers=[0]):
                     mod = relay.quantize.quantize(mod, params=params)
@@ -209,16 +209,16 @@ The compilation steps are:
             relay_prog = mod["main"]
 
         # Compile Relay program with AlterOpLayout disabled
-        with relay.build_config(opt_level=3, disabled_pass={"AlterOpLayout"}):
-            if target.device_name != "vta":
+        if target.device_name != "vta":
+            with tvm.transform.PassContext(opt_level=3, disabled_pass={"AlterOpLayout"}):
+                graph, lib, params = relay.build(
+                    relay_prog, target=target,
+                    params=params, target_host=env.target_host)
+        else:
+            with vta.build_config(opt_level=3, disabled_pass={"AlterOpLayout"}):
                 graph, lib, params = relay.build(
                     relay_prog, target=target,
                     params=params, target_host=env.target_host)
-            else:
-                with vta.build_config():
-                    graph, lib, params = relay.build(
-                        relay_prog, target=target,
-                        params=params, target_host=env.target_host)
 
         # Measure Relay build time
         build_time = time.time() - build_start
@@ -243,8 +243,8 @@ The compilation steps are:
 
  .. code-block:: none
 
-
    ...12%, 0.01 MB, 43 KB/s, 0 seconds passed
    ...25%, 0.02 MB, 86 KB/s, 0 seconds passed
    ...38%, 0.02 MB, 129 KB/s, 0 seconds passed
    ...51%, 0.03 MB, 172 KB/s, 0 seconds passed
    ...64%, 0.04 MB, 215 KB/s, 0 seconds passed
    ...77%, 0.05 MB, 257 KB/s, 0 seconds passed
    ...90%, 0.05 MB, 299 KB/s, 0 seconds passed
    ...100%, 0.06 MB, 341 KB/s, 0 seconds passed
-    resnet18_v1 inference graph built in 3.88s!
+
    ...12%, 0.01 MB, 36 KB/s, 0 seconds passed
    ...25%, 0.02 MB, 72 KB/s, 0 seconds passed
    ...38%, 0.02 MB, 109 KB/s, 0 seconds passed
    ...51%, 0.03 MB, 145 KB/s, 0 seconds passed
    ...64%, 0.04 MB, 181 KB/s, 0 seconds passed
    ...77%, 0.05 MB, 217 KB/s, 0 seconds passed
    ...90%, 0.05 MB, 253 KB/s, 0 seconds passed
    ...100%, 0.06 MB, 288 KB/s, 0 seconds passed
+    resnet18_v1 inference graph built in 4.19s!
 
 
 
diff --git a/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt b/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt
index a39379d..a84ad23 100644
--- a/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/deploy_detection.rst.txt
@@ -265,7 +265,7 @@ The compilation steps are:
         if target.device_name == "vta":
         # Perform quantization in Relay
         # Note: We set opt_level to 3 in order to fold batch norm
-            with relay.build_config(opt_level=3):
+            with tvm.transform.PassContext(opt_level=3):
                 with relay.quantize.qconfig(global_scale=33.0,
                                             skip_conv_layers=[0],
                                             store_lowbit_output=True,
@@ -315,7 +315,7 @@ The compilation steps are:
 
  .. code-block:: none
 
-    yolov3-tiny inference graph built in 4.97s!
+    yolov3-tiny inference graph built in 5.53s!
 
 
 
diff --git a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
index c82815d..e00fd79 100644
--- a/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/frontend/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**00:56.299** total execution time for **vta_tutorials_frontend** files:
+**00:54.502** total execution time for **vta_tutorials_frontend** files:
 
-- **00:33.305**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
-- **00:22.994**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
+- **00:32.900**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_detection.py` (``deploy_detection.py``)
+- **00:21.602**: :ref:`sphx_glr_vta_tutorials_frontend_deploy_classification.py` (``deploy_classification.py``)
diff --git a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
index 053abb5..fa8d003 100644
--- a/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
+++ b/docs/_sources/vta/tutorials/matrix_multiply.rst.txt
@@ -304,8 +304,8 @@ After we construct the schedule, by default the schedule computes
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {A: Buffer(A_2: handle, int8, [1, 16, 1, 16], []),
-                 B: Buffer(B_2: handle, int8, [16, 16, 16, 16], []),
+      buffers = {B: Buffer(B_2: handle, int8, [16, 16, 16, 16], []),
+                 A: Buffer(A_2: handle, int8, [1, 16, 1, 16], []),
                  C: Buffer(C_2: handle, int8, [1, 16, 1, 16], [])}
       buffer_map = {C_1: C, B_1: B, A_1: A} {
       attr [A_buf: handle] "storage_scope" = "global";
@@ -539,8 +539,8 @@ by the VTA runtime JIT compiler.
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {A: Buffer(A_2: handle, int8, [1, 16, 1, 16], []),
-                 B: Buffer(B_2: handle, int8, [16, 16, 16, 16], []),
+      buffers = {B: Buffer(B_2: handle, int8, [16, 16, 16, 16], []),
+                 A: Buffer(A_2: handle, int8, [1, 16, 1, 16], []),
                  C: Buffer(C_2: handle, int8, [1, 16, 1, 16], [])}
       buffer_map = {C_1: C, B_1: B, A_1: A} {
       attr [C_buf: handle] "storage_scope" = "local.acc_buffer";
@@ -548,36 +548,36 @@ by the VTA runtime JIT compiler.
       attr [B_buf: handle] "storage_scope" = "local.wgt_buffer" {
         attr [IterVar(vta: int32, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
           attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushGEMMOp" {
-            @VTAUopLoopBegin(16, 1, 0, 0, dtype=int32, type="extern", index=0)
-            @VTAUopPush(0, 1, 0, 0, 0, 0, 0, 0, dtype=int32, type="extern", index=0)
-            @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+            @VTAUopLoopBegin(16, 1, 0, 0, dtype=int32, type="extern")
+            @VTAUopPush(0, 1, 0, 0, 0, 0, 0, 0, dtype=int32, type="extern")
+            @VTAUopLoopEnd(, dtype=int32, type="extern")
           }
-          @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin", index=0)
+          @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin")
         }
         for (ko: int32, 0, 16) {
           attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 1 {
-            @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin", index=0)
-            @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), A_2, ko, 1, 1, 1, 0, 0, 0, 0, 0, 2, dtype=int32, type="extern", index=0)
-            @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), B_2, ko, 1, 16, 16, 0, 0, 0, 0, 0, 1, dtype=int32, type="extern", index=0)
-            @vta.coproc_dep_push(1, 2, dtype=int32, type="intrin", index=0)
+            @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin")
+            @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), A_2, ko, 1, 1, 1, 0, 0, 0, 0, 0, 2, dtype=int32, type="extern")
+            @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), B_2, ko, 1, 16, 16, 0, 0, 0, 0, 0, 1, dtype=int32, type="extern")
+            @vta.coproc_dep_push(1, 2, dtype=int32, type="intrin")
           }
           attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
-            @vta.coproc_dep_pop(1, 2, dtype=int32, type="intrin", index=0)
+            @vta.coproc_dep_pop(1, 2, dtype=int32, type="intrin")
             attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushGEMMOp" {
-              @VTAUopLoopBegin(16, 1, 0, 1, dtype=int32, type="extern", index=0)
-              @VTAUopPush(0, 0, 0, 0, 0, 0, 0, 0, dtype=int32, type="extern", index=0)
-              @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+              @VTAUopLoopBegin(16, 1, 0, 1, dtype=int32, type="extern")
+              @VTAUopPush(0, 0, 0, 0, 0, 0, 0, 0, dtype=int32, type="extern")
+              @VTAUopLoopEnd(, dtype=int32, type="extern")
             }
-            @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin", index=0)
+            @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin")
           }
         }
-        @vta.coproc_dep_push(2, 3, dtype=int32, type="intrin", index=0)
-        @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin", index=0)
+        @vta.coproc_dep_push(2, 3, dtype=int32, type="intrin")
+        @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin")
         attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 3 {
-          @vta.coproc_dep_pop(2, 3, dtype=int32, type="intrin", index=0)
-          @VTAStoreBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), 0, 4, C_2, 0, 16, 1, 16, dtype=int32, type="extern", index=0)
+          @vta.coproc_dep_pop(2, 3, dtype=int32, type="intrin")
+          @VTAStoreBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), 0, 4, C_2, 0, 16, 1, 16, dtype=int32, type="extern")
         }
-        @vta.coproc_sync(, dtype=int32, type="intrin", index=0)
+        @vta.coproc_sync(, dtype=int32, type="intrin")
       }
     }
 
diff --git a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
index c010585..0b6419d 100644
--- a/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/convolution_opt.rst.txt
@@ -271,7 +271,7 @@ Those include:
           for (i2: int32, 0, 16) {
             for (i3: int32, 0, 16) {
               for (i5: int32, 0, 16) {
-                data_buf[((((i1*4096) + (i2*256)) + (i3*16)) + i5)] = @tvm_if_then_else(((((1 <= i2) && (i2 < 15)) && (1 <= i3)) && (i3 < 15)), (int8*)data_2[(((((i1*3136) + (i2*224)) + (i3*16)) + i5) - 240)]), 0i8, dtype=int8, type="pure_intrin", index=0)
+                data_buf[((((i1*4096) + (i2*256)) + (i3*16)) + i5)] = @tvm_if_then_else(((((1 <= i2) && (i2 < 15)) && (1 <= i3)) && (i3 < 15)), (int8*)data_2[(((((i1*3136) + (i2*224)) + (i3*16)) + i5) - 240)]), 0i8, dtype=int8, type="pure_intrin")
               }
             }
           }
@@ -311,7 +311,7 @@ Those include:
           for (i2_2: int32, 0, 14) {
             for (i3_2: int32, 0, 14) {
               for (i5_2: int32, 0, 16) {
-                res_conv[((((i1_2*3136) + (i2_2*224)) + (i3_2*16)) + i5_2)] = @shift_right((int32*)res_conv[((((i1_2*3136) + (i2_2*224)) + (i3_2*16)) + i5_2)]), 8, dtype=int32, type="pure_intrin", index=0)
+                res_conv[((((i1_2*3136) + (i2_2*224)) + (i3_2*16)) + i5_2)] = @shift_right((int32*)res_conv[((((i1_2*3136) + (i2_2*224)) + (i3_2*16)) + i5_2)]), 8, dtype=int32, type="pure_intrin")
               }
             }
           }
@@ -467,7 +467,7 @@ below.
           for (i2: int32, 0, 16) {
             for (i3: int32, 0, 16) {
               for (i5: int32, 0, 16) {
-                data_buf[((((i1*4096) + (i2*256)) + (i3*16)) + i5)] = @tvm_if_then_else(((((1 <= i2) && (i2 < 15)) && (1 <= i3)) && (i3 < 15)), (int8*)data_2[(((((i1*3136) + (i2*224)) + (i3*16)) + i5) - 240)]), 0i8, dtype=int8, type="pure_intrin", index=0)
+                data_buf[((((i1*4096) + (i2*256)) + (i3*16)) + i5)] = @tvm_if_then_else(((((1 <= i2) && (i2 < 15)) && (1 <= i3)) && (i3 < 15)), (int8*)data_2[(((((i1*3136) + (i2*224)) + (i3*16)) + i5) - 240)]), 0i8, dtype=int8, type="pure_intrin")
               }
             }
           }
@@ -518,8 +518,8 @@ below.
             for (i2_2: int32, 0, 7) {
               for (i3_2: int32, 0, 14) {
                 for (i5_2: int32, 0, 16) {
-                  res_conv[((((i1_2*1568) + (i2_2*224)) + (i3_2*16)) + i5_2)] = @shift_right((int32*)res_conv[((((i1_2*1568) + (i2_2*224)) + (i3_2*16)) + i5_2)]), 8, dtype=int32, type="pure_intrin", index=0)
-                  res_conv[(((((i1_2*1568) + (i2_2*224)) + (i3_2*16)) + i5_2) + 12544)] = @shift_right((int32*)res_conv[(((((i1_2*1568) + (i2_2*224)) + (i3_2*16)) + i5_2) + 12544)]), 8, dtype=int32, type="pure_intrin", index=0)
+                  res_conv[((((i1_2*1568) + (i2_2*224)) + (i3_2*16)) + i5_2)] = @shift_right((int32*)res_conv[((((i1_2*1568) + (i2_2*224)) + (i3_2*16)) + i5_2)]), 8, dtype=int32, type="pure_intrin")
+                  res_conv[(((((i1_2*1568) + (i2_2*224)) + (i3_2*16)) + i5_2) + 12544)] = @shift_right((int32*)res_conv[(((((i1_2*1568) + (i2_2*224)) + (i3_2*16)) + i5_2) + 12544)]), 8, dtype=int32, type="pure_intrin")
                 }
               }
             }
@@ -636,103 +636,103 @@ and mapping the shift, and clipping computation to the vector ALU.
 
     primfn(data_1: handle, kernel_1: handle, res_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {data: Buffer(data_2: handle, int8, [1, 16, 14, 14, 1, 16], []),
-                 kernel: Buffer(kernel_2: handle, int8, [16, 16, 3, 3, 16, 16], []),
-                 res: Buffer(res_2: handle, int8, [1, 16, 14, 14, 1, 16], [])}
-      buffer_map = {res_1: res, kernel_1: kernel, data_1: data} {
+      buffers = {res: Buffer(res_2: handle, int8, [1, 16, 14, 14, 1, 16], []),
+                 data: Buffer(data_2: handle, int8, [1, 16, 14, 14, 1, 16], []),
+                 kernel: Buffer(kernel_2: handle, int8, [16, 16, 3, 3, 16, 16], [])}
+      buffer_map = {kernel_1: kernel, res_1: res, data_1: data} {
       attr [res_conv: handle] "storage_scope" = "local.acc_buffer";
       attr [data_buf: handle] "storage_scope" = "local.inp_buffer";
       attr [kernel_buf: handle] "storage_scope" = "local.wgt_buffer" {
-        @vta.coproc_dep_push(3, 2, dtype=int32, type="intrin", index=0)
-        @vta.coproc_dep_push(3, 2, dtype=int32, type="intrin", index=0)
+        @vta.coproc_dep_push(3, 2, dtype=int32, type="intrin")
+        @vta.coproc_dep_push(3, 2, dtype=int32, type="intrin")
         for (i2.outer: int32, 0, 2) {
           for (cthread.s: int32, 0, 2) {
             attr [IterVar(vta: int32, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
-              @vta.coproc_dep_pop(3, 2, dtype=int32, type="intrin", index=0)
+              @vta.coproc_dep_pop(3, 2, dtype=int32, type="intrin")
               attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushGEMMOp" {
-                @VTAUopLoopBegin(8, 98, 0, 0, dtype=int32, type="extern", index=0)
-                @VTAUopLoopBegin(7, 14, 0, 0, dtype=int32, type="extern", index=0)
+                @VTAUopLoopBegin(8, 98, 0, 0, dtype=int32, type="extern")
+                @VTAUopLoopBegin(7, 14, 0, 0, dtype=int32, type="extern")
                 for (j.init: int32, 0, 14) {
-                  @VTAUopPush(0, 1, ((cthread.s*784) + j.init), 0, 0, 0, 0, 0, dtype=int32, type="extern", index=0)
+                  @VTAUopPush(0, 1, ((cthread.s*784) + j.init), 0, 0, 0, 0, 0, dtype=int32, type="extern")
                 }
-                @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
-                @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+                @VTAUopLoopEnd(, dtype=int32, type="extern")
+                @VTAUopLoopEnd(, dtype=int32, type="extern")
               }
-              @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin", index=0)
+              @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin")
             }
           }
           for (ic.outer: int32, 0, 16) {
             attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 1 {
-              @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin", index=0)
-              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), data_2, ((((ic.outer*196) + (i2.outer*98)) + (max((1 - (i2.outer*7)), 0)*14)) - 14), 14, ((9 - max((1 - (i2.outer*7)), 0)) - max(((i2.outer*7) - 6), 0)), 14, 1, max((1 - (i2.outer*7)), 0), 1, max(((i2.outer*7) - 6), 0), 0, 2, dtype=int32, type="extern", index=0)
-              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), kernel_2, (ic.outer*9), 9, 8, 144, 0, 0, 0, 0, 0, 1, dtype=int32, type="extern", index=0)
-              @vta.coproc_dep_push(1, 2, dtype=int32, type="intrin", index=0)
+              @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin")
+              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), data_2, ((((ic.outer*196) + (i2.outer*98)) + (max((1 - (i2.outer*7)), 0)*14)) - 14), 14, ((9 - max((1 - (i2.outer*7)), 0)) - max(((i2.outer*7) - 6), 0)), 14, 1, max((1 - (i2.outer*7)), 0), 1, max(((i2.outer*7) - 6), 0), 0, 2, dtype=int32, type="extern")
+              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), kernel_2, (ic.outer*9), 9, 8, 144, 0, 0, 0, 0, 0, 1, dtype=int32, type="extern")
+              @vta.coproc_dep_push(1, 2, dtype=int32, type="intrin")
             }
             attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 1 {
-              @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin", index=0)
-              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), data_2, ((((ic.outer*196) + (i2.outer*98)) + (max((1 - (i2.outer*7)), 0)*14)) - 14), 14, ((9 - max((1 - (i2.outer*7)), 0)) - max(((i2.outer*7) - 6), 0)), 14, 1, max((1 - (i2.outer*7)), 0), 1, max(((i2.outer*7) - 6), 0), 144, 2, dtype=int32, type="extern", index=0)
-              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), kernel_2, ((ic.outer*9) + 1152), 9, 8, 144, 0, 0, 0, 0, 72, 1, dtype=int32, type="extern", index=0)
-              @vta.coproc_dep_push(1, 2, dtype=int32, type="intrin", index=0)
+              @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin")
+              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), data_2, ((((ic.outer*196) + (i2.outer*98)) + (max((1 - (i2.outer*7)), 0)*14)) - 14), 14, ((9 - max((1 - (i2.outer*7)), 0)) - max(((i2.outer*7) - 6), 0)), 14, 1, max((1 - (i2.outer*7)), 0), 1, max(((i2.outer*7) - 6), 0), 144, 2, dtype=int32, type="extern")
+              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), kernel_2, ((ic.outer*9) + 1152), 9, 8, 144, 0, 0, 0, 0, 72, 1, dtype=int32, type="extern")
+              @vta.coproc_dep_push(1, 2, dtype=int32, type="intrin")
             }
             for (cthread.s_1: int32, 0, 2) {
               attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
-                @vta.coproc_dep_pop(1, 2, dtype=int32, type="intrin", index=0)
+                @vta.coproc_dep_pop(1, 2, dtype=int32, type="intrin")
                 attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushGEMMOp" {
-                  @VTAUopLoopBegin(8, 98, 0, 9, dtype=int32, type="extern", index=0)
-                  @VTAUopLoopBegin(7, 14, 16, 0, dtype=int32, type="extern", index=0)
+                  @VTAUopLoopBegin(8, 98, 0, 9, dtype=int32, type="extern")
+                  @VTAUopLoopBegin(7, 14, 16, 0, dtype=int32, type="extern")
                   for (dy: int32, 0, 3) {
                     for (dx: int32, 0, 3) {
                       for (j: int32, 0, 14) {
-                        @VTAUopPush(0, 0, ((cthread.s_1*784) + j), ((((cthread.s_1*144) + (dy*16)) + j) + dx), (((cthread.s_1*72) + (dy*3)) + dx), 0, 0, 0, dtype=int32, type="extern", index=0)
+                        @VTAUopPush(0, 0, ((cthread.s_1*784) + j), ((((cthread.s_1*144) + (dy*16)) + j) + dx), (((cthread.s_1*72) + (dy*3)) + dx), 0, 0, 0, dtype=int32, type="extern")
                       }
                     }
                   }
-                  @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
-                  @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+                  @VTAUopLoopEnd(, dtype=int32, type="extern")
+                  @VTAUopLoopEnd(, dtype=int32, type="extern")
                 }
-                @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin", index=0)
+                @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin")
               }
             }
           }
-          @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin", index=0)
-          @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin", index=0)
+          @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin")
+          @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin")
           for (cthread.s_2: int32, 0, 2) {
             attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
               attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushALUOp" {
-                @VTAUopLoopBegin(784, 1, 1, 0, dtype=int32, type="extern", index=0)
-                @VTAUopPush(1, 0, (cthread.s_2*784), (cthread.s_2*784), 0, 3, 1, 8, dtype=int32, type="extern", index=0)
-                @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+                @VTAUopLoopBegin(784, 1, 1, 0, dtype=int32, type="extern")
+                @VTAUopPush(1, 0, (cthread.s_2*784), (cthread.s_2*784), 0, 3, 1, 8, dtype=int32, type="extern")
+                @VTAUopLoopEnd(, dtype=int32, type="extern")
               }
               attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushALUOp" {
-                @VTAUopLoopBegin(784, 1, 1, 0, dtype=int32, type="extern", index=0)
-                @VTAUopPush(1, 0, (cthread.s_2*784), (cthread.s_2*784), 0, 1, 1, 0, dtype=int32, type="extern", index=0)
-                @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+                @VTAUopLoopBegin(784, 1, 1, 0, dtype=int32, type="extern")
+                @VTAUopPush(1, 0, (cthread.s_2*784), (cthread.s_2*784), 0, 1, 1, 0, dtype=int32, type="extern")
+                @VTAUopLoopEnd(, dtype=int32, type="extern")
               }
               attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushALUOp" {
-                @VTAUopLoopBegin(784, 1, 1, 0, dtype=int32, type="extern", index=0)
-                @VTAUopPush(1, 0, (cthread.s_2*784), (cthread.s_2*784), 0, 0, 1, 127, dtype=int32, type="extern", index=0)
-                @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+                @VTAUopLoopBegin(784, 1, 1, 0, dtype=int32, type="extern")
+                @VTAUopPush(1, 0, (cthread.s_2*784), (cthread.s_2*784), 0, 0, 1, 127, dtype=int32, type="extern")
+                @VTAUopLoopEnd(, dtype=int32, type="extern")
               }
-              @vta.coproc_dep_push(2, 3, dtype=int32, type="intrin", index=0)
+              @vta.coproc_dep_push(2, 3, dtype=int32, type="intrin")
             }
           }
           for (cthread.s_3: int32, 0, 2) {
             attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 3 {
-              @vta.coproc_dep_pop(2, 3, dtype=int32, type="intrin", index=0)
+              @vta.coproc_dep_pop(2, 3, dtype=int32, type="intrin")
               for (i1.inner: int32, 0, 8) {
                 for (i2.inner: int32, 0, 7) {
                   for (i3.inner: int32, 0, 14) {
-                    @VTAStoreBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), ((((cthread.s_3*784) + (i1.inner*98)) + (i2.inner*14)) + i3.inner), 4, res_2, (((((cthread.s_3*1568) + (i1.inner*196)) + (i2.outer*98)) + (i2.inner*14)) + i3.inner), 1, 1, 1, dtype=int32, type="extern", index=0)
+                    @VTAStoreBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), ((((cthread.s_3*784) + (i1.inner*98)) + (i2.inner*14)) + i3.inner), 4, res_2, (((((cthread.s_3*1568) + (i1.inner*196)) + (i2.outer*98)) + (i2.inner*14)) + i3.inner), 1, 1, 1, dtype=int32, type="extern")
                   }
                 }
               }
-              @vta.coproc_dep_push(3, 2, dtype=int32, type="intrin", index=0)
+              @vta.coproc_dep_push(3, 2, dtype=int32, type="intrin")
             }
           }
         }
-        @vta.coproc_dep_pop(3, 2, dtype=int32, type="intrin", index=0)
-        @vta.coproc_dep_pop(3, 2, dtype=int32, type="intrin", index=0)
-        @vta.coproc_sync(, dtype=int32, type="intrin", index=0)
+        @vta.coproc_dep_pop(3, 2, dtype=int32, type="intrin")
+        @vta.coproc_dep_pop(3, 2, dtype=int32, type="intrin")
+        @vta.coproc_sync(, dtype=int32, type="intrin")
       }
     }
 
diff --git a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
index baf6936..815b29e 100644
--- a/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/matrix_multiply_opt.rst.txt
@@ -203,9 +203,9 @@ Those include:
     primfn(data_1: handle, weight_1: handle, res_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
       buffers = {data: Buffer(data_2: handle, int8, [1, 64, 1, 16], []),
-                 res: Buffer(res_2: handle, int8, [1, 64, 1, 16], []),
-                 weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], [])}
-      buffer_map = {weight_1: weight, res_1: res, data_1: data} {
+                 weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
+                 res: Buffer(res_2: handle, int8, [1, 64, 1, 16], [])}
+      buffer_map = {res_1: res, weight_1: weight, data_1: data} {
       attr [data_buf: handle] "storage_scope" = "global";
       allocate(data_buf, int8, [1024]);
       attr [weight_buf: handle] "storage_scope" = "global";
@@ -238,7 +238,7 @@ Those include:
         }
         for (i1_2: int32, 0, 64) {
           for (i3_2: int32, 0, 16) {
-            res_gem[((i1_2*16) + i3_2)] = @shift_right((int32*)res_gem[((i1_2*16) + i3_2)]), 8, dtype=int32, type="pure_intrin", index=0)
+            res_gem[((i1_2*16) + i3_2)] = @shift_right((int32*)res_gem[((i1_2*16) + i3_2)]), 8, dtype=int32, type="pure_intrin")
           }
         }
         for (i1_3: int32, 0, 64) {
@@ -364,8 +364,8 @@ below:
 
     primfn(data_1: handle, weight_1: handle, res_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
-                 data: Buffer(data_2: handle, int8, [1, 64, 1, 16], []),
+      buffers = {data: Buffer(data_2: handle, int8, [1, 64, 1, 16], []),
+                 weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
                  res: Buffer(res_2: handle, int8, [1, 64, 1, 16], [])}
       buffer_map = {res_1: res, weight_1: weight, data_1: data} {
       attr [data_buf: handle] "storage_scope" = "global";
@@ -407,7 +407,7 @@ below:
           }
           for (i1_2: int32, 0, 16) {
             for (i3_2: int32, 0, 16) {
-              res_gem[((i1_2*16) + i3_2)] = @shift_right((int32*)res_gem[((i1_2*16) + i3_2)]), 8, dtype=int32, type="pure_intrin", index=0)
+              res_gem[((i1_2*16) + i3_2)] = @shift_right((int32*)res_gem[((i1_2*16) + i3_2)]), 8, dtype=int32, type="pure_intrin")
             }
           }
           for (i1_3: int32, 0, 16) {
@@ -507,72 +507,72 @@ and mapping the shift, and clipping computation to the vector ALU.
 
     primfn(data_1: handle, weight_1: handle, res_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
-                 data: Buffer(data_2: handle, int8, [1, 64, 1, 16], []),
+      buffers = {data: Buffer(data_2: handle, int8, [1, 64, 1, 16], []),
+                 weight: Buffer(weight_2: handle, int8, [64, 64, 16, 16], []),
                  res: Buffer(res_2: handle, int8, [1, 64, 1, 16], [])}
       buffer_map = {res_1: res, weight_1: weight, data_1: data} {
       attr [res_gem: handle] "storage_scope" = "local.acc_buffer";
       attr [data_buf: handle] "storage_scope" = "local.inp_buffer";
       attr [weight_buf: handle] "storage_scope" = "local.wgt_buffer" {
-        @vta.coproc_dep_push(3, 2, dtype=int32, type="intrin", index=0)
+        @vta.coproc_dep_push(3, 2, dtype=int32, type="intrin")
         for (i1.outer: int32, 0, 4) {
           attr [IterVar(vta: int32, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
-            @vta.coproc_dep_pop(3, 2, dtype=int32, type="intrin", index=0)
+            @vta.coproc_dep_pop(3, 2, dtype=int32, type="intrin")
             attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushGEMMOp" {
-              @VTAUopLoopBegin(16, 1, 0, 0, dtype=int32, type="extern", index=0)
-              @VTAUopPush(0, 1, 0, 0, 0, 0, 0, 0, dtype=int32, type="extern", index=0)
-              @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+              @VTAUopLoopBegin(16, 1, 0, 0, dtype=int32, type="extern")
+              @VTAUopPush(0, 1, 0, 0, 0, 0, 0, 0, dtype=int32, type="extern")
+              @VTAUopLoopEnd(, dtype=int32, type="extern")
             }
-            @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin", index=0)
+            @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin")
           }
           for (ic.outer: int32, 0, 4) {
             attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 1 {
-              @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin", index=0)
-              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), data_2, (ic.outer*16), 16, 1, 16, 0, 0, 0, 0, 0, 2, dtype=int32, type="extern", index=0)
-              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), weight_2, ((i1.outer*1024) + (ic.outer*16)), 16, 16, 64, 0, 0, 0, 0, 0, 1, dtype=int32, type="extern", index=0)
-              @vta.coproc_dep_push(1, 2, dtype=int32, type="intrin", index=0)
+              @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin")
+              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), data_2, (ic.outer*16), 16, 1, 16, 0, 0, 0, 0, 0, 2, dtype=int32, type="extern")
+              @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), weight_2, ((i1.outer*1024) + (ic.outer*16)), 16, 16, 64, 0, 0, 0, 0, 0, 1, dtype=int32, type="extern")
+              @vta.coproc_dep_push(1, 2, dtype=int32, type="intrin")
             }
             attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
-              @vta.coproc_dep_pop(1, 2, dtype=int32, type="intrin", index=0)
+              @vta.coproc_dep_pop(1, 2, dtype=int32, type="intrin")
               attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushGEMMOp" {
-                @VTAUopLoopBegin(16, 1, 0, 16, dtype=int32, type="extern", index=0)
-                @VTAUopLoopBegin(16, 0, 1, 1, dtype=int32, type="extern", index=0)
-                @VTAUopPush(0, 0, 0, 0, 0, 0, 0, 0, dtype=int32, type="extern", index=0)
-                @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
-                @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+                @VTAUopLoopBegin(16, 1, 0, 16, dtype=int32, type="extern")
+                @VTAUopLoopBegin(16, 0, 1, 1, dtype=int32, type="extern")
+                @VTAUopPush(0, 0, 0, 0, 0, 0, 0, 0, dtype=int32, type="extern")
+                @VTAUopLoopEnd(, dtype=int32, type="extern")
+                @VTAUopLoopEnd(, dtype=int32, type="extern")
               }
-              @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin", index=0)
+              @vta.coproc_dep_push(2, 1, dtype=int32, type="intrin")
             }
           }
-          @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin", index=0)
+          @vta.coproc_dep_pop(2, 1, dtype=int32, type="intrin")
           attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
             attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushALUOp" {
-              @VTAUopLoopBegin(16, 1, 1, 0, dtype=int32, type="extern", index=0)
-              @VTAUopPush(1, 0, 0, 0, 0, 3, 1, 8, dtype=int32, type="extern", index=0)
-              @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+              @VTAUopLoopBegin(16, 1, 1, 0, dtype=int32, type="extern")
+              @VTAUopPush(1, 0, 0, 0, 0, 3, 1, 8, dtype=int32, type="extern")
+              @VTAUopLoopEnd(, dtype=int32, type="extern")
             }
             attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushALUOp" {
-              @VTAUopLoopBegin(16, 1, 1, 0, dtype=int32, type="extern", index=0)
-              @VTAUopPush(1, 0, 0, 0, 0, 1, 1, 0, dtype=int32, type="extern", index=0)
-              @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+              @VTAUopLoopBegin(16, 1, 1, 0, dtype=int32, type="extern")
+              @VTAUopPush(1, 0, 0, 0, 0, 1, 1, 0, dtype=int32, type="extern")
+              @VTAUopLoopEnd(, dtype=int32, type="extern")
             }
             attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushALUOp" {
-              @VTAUopLoopBegin(16, 1, 1, 0, dtype=int32, type="extern", index=0)
-              @VTAUopPush(1, 0, 0, 0, 0, 0, 1, 127, dtype=int32, type="extern", index=0)
-              @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+              @VTAUopLoopBegin(16, 1, 1, 0, dtype=int32, type="extern")
+              @VTAUopPush(1, 0, 0, 0, 0, 0, 1, 127, dtype=int32, type="extern")
+              @VTAUopLoopEnd(, dtype=int32, type="extern")
             }
-            @vta.coproc_dep_push(2, 3, dtype=int32, type="intrin", index=0)
+            @vta.coproc_dep_push(2, 3, dtype=int32, type="intrin")
           }
           attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 3 {
-            @vta.coproc_dep_pop(2, 3, dtype=int32, type="intrin", index=0)
+            @vta.coproc_dep_pop(2, 3, dtype=int32, type="intrin")
             for (i1.inner: int32, 0, 16) {
-              @VTAStoreBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), i1.inner, 4, res_2, ((i1.outer*16) + i1.inner), 1, 1, 1, dtype=int32, type="extern", index=0)
+              @VTAStoreBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), i1.inner, 4, res_2, ((i1.outer*16) + i1.inner), 1, 1, 1, dtype=int32, type="extern")
             }
-            @vta.coproc_dep_push(3, 2, dtype=int32, type="intrin", index=0)
+            @vta.coproc_dep_push(3, 2, dtype=int32, type="intrin")
           }
         }
-        @vta.coproc_sync(, dtype=int32, type="intrin", index=0)
-        @vta.coproc_dep_pop(3, 2, dtype=int32, type="intrin", index=0)
+        @vta.coproc_sync(, dtype=int32, type="intrin")
+        @vta.coproc_dep_pop(3, 2, dtype=int32, type="intrin")
       }
     }
 
diff --git a/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
index 3581136..ec433ab 100644
--- a/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/optimize/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**00:02.883** total execution time for **vta_tutorials_optimize** files:
+**00:03.090** total execution time for **vta_tutorials_optimize** files:
 
-- **00:02.494**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
-- **00:00.389**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
+- **00:02.674**: :ref:`sphx_glr_vta_tutorials_optimize_convolution_opt.py` (``convolution_opt.py``)
+- **00:00.416**: :ref:`sphx_glr_vta_tutorials_optimize_matrix_multiply_opt.py` (``matrix_multiply_opt.py``)
diff --git a/docs/_sources/vta/tutorials/sg_execution_times.rst.txt b/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
index 0326164..0a03c0e 100644
--- a/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
+++ b/docs/_sources/vta/tutorials/sg_execution_times.rst.txt
@@ -5,7 +5,7 @@
 
 Computation times
 =================
-**00:00.707** total execution time for **vta_tutorials** files:
+**00:00.766** total execution time for **vta_tutorials** files:
 
-- **00:00.356**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
-- **00:00.352**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
+- **00:00.389**: :ref:`sphx_glr_vta_tutorials_matrix_multiply.py` (``matrix_multiply.py``)
+- **00:00.377**: :ref:`sphx_glr_vta_tutorials_vta_get_started.py` (``vta_get_started.py``)
diff --git a/docs/_sources/vta/tutorials/vta_get_started.rst.txt b/docs/_sources/vta/tutorials/vta_get_started.rst.txt
index 875d7f6..a34b485 100644
--- a/docs/_sources/vta/tutorials/vta_get_started.rst.txt
+++ b/docs/_sources/vta/tutorials/vta_get_started.rst.txt
@@ -301,9 +301,9 @@ After we construct the schedule, by default the schedule computes
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
       buffers = {A: Buffer(A_2: handle, int32, [1, 64, 1, 16], []),
-                 C: Buffer(C_2: handle, int8, [1, 64, 1, 16], []),
-                 B: Buffer(B_2: handle, int32, [1, 64, 1, 16], [])}
-      buffer_map = {B_1: B, C_1: C, A_1: A} {
+                 B: Buffer(B_2: handle, int32, [1, 64, 1, 16], []),
+                 C: Buffer(C_2: handle, int8, [1, 64, 1, 16], [])}
+      buffer_map = {C_1: C, B_1: B, A_1: A} {
       attr [A_buf: handle] "storage_scope" = "global";
       allocate(A_buf, int32, [1024]);
       attr [B_buf: handle] "storage_scope" = "global";
@@ -423,26 +423,26 @@ with an :code:`env.alu` pragma.
 
     primfn(A_1: handle, B_1: handle, C_1: handle) -> ()
       attr = {"tir.noalias": True, "global_symbol": "main"}
-      buffers = {B: Buffer(B_2: handle, int32, [1, 64, 1, 16], []),
-                 A: Buffer(A_2: handle, int32, [1, 64, 1, 16], []),
+      buffers = {A: Buffer(A_2: handle, int32, [1, 64, 1, 16], []),
+                 B: Buffer(B_2: handle, int32, [1, 64, 1, 16], []),
                  C: Buffer(C_2: handle, int8, [1, 64, 1, 16], [])}
       buffer_map = {C_1: C, B_1: B, A_1: A} {
       attr [A_buf: handle] "storage_scope" = "local.acc_buffer" {
         attr [IterVar(vta: int32, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 2 {
-          @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), A_2, 0, 64, 1, 64, 0, 0, 0, 0, 0, 3, dtype=int32, type="extern", index=0)
-          @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), B_2, 0, 64, 1, 64, 0, 0, 0, 0, 64, 3, dtype=int32, type="extern", index=0)
+          @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), A_2, 0, 64, 1, 64, 0, 0, 0, 0, 0, 3, dtype=int32, type="extern")
+          @VTALoadBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), B_2, 0, 64, 1, 64, 0, 0, 0, 0, 64, 3, dtype=int32, type="extern")
           attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_uop_scope" = "VTAPushALUOp" {
-            @VTAUopLoopBegin(64, 1, 1, 0, dtype=int32, type="extern", index=0)
-            @VTAUopPush(1, 0, 0, 64, 0, 2, 0, 0, dtype=int32, type="extern", index=0)
-            @VTAUopLoopEnd(, dtype=int32, type="extern", index=0)
+            @VTAUopLoopBegin(64, 1, 1, 0, dtype=int32, type="extern")
+            @VTAUopPush(1, 0, 0, 64, 0, 2, 0, 0, dtype=int32, type="extern")
+            @VTAUopLoopEnd(, dtype=int32, type="extern")
           }
-          @vta.coproc_dep_push(2, 3, dtype=int32, type="intrin", index=0)
+          @vta.coproc_dep_push(2, 3, dtype=int32, type="intrin")
         }
         attr [IterVar(vta, (nullptr), "ThreadIndex", "vta")] "coproc_scope" = 3 {
-          @vta.coproc_dep_pop(2, 3, dtype=int32, type="intrin", index=0)
-          @VTAStoreBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern", index=0), dtype=handle, type="intrin", index=0), 0, 4, C_2, 0, 64, 1, 64, dtype=int32, type="extern", index=0)
+          @vta.coproc_dep_pop(2, 3, dtype=int32, type="intrin")
+          @VTAStoreBuffer2D(@tvm_thread_context(@VTATLSCommandHandle(, dtype=handle, type="extern"), dtype=handle, type="intrin"), 0, 4, C_2, 0, 64, 1, 64, dtype=int32, type="extern")
         }
-        @vta.coproc_sync(, dtype=int32, type="intrin", index=0)
+        @vta.coproc_sync(, dtype=int32, type="intrin")
       }
     }
 
diff --git a/docs/api/doxygen/algorithm_8h.html b/docs/api/doxygen/algorithm_8h.html
index e41eadd..9f82ef7 100644
--- a/docs/api/doxygen/algorithm_8h.html
+++ b/docs/api/doxygen/algorithm_8h.html
@@ -94,11 +94,12 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
 <div class="contents">
 <div class="textblock"><code>#include &lt;<a class="el" href="ir_2attrs_8h_source.html">tvm/ir/attrs.h</a>&gt;</code><br />
 <code>#include &lt;<a class="el" href="base_8h_source.html">tvm/relay/base.h</a>&gt;</code><br />
+<code>#include &lt;<a class="el" href="relay_2expr_8h_source.html">tvm/relay/expr.h</a>&gt;</code><br />
 <code>#include &lt;string&gt;</code><br />
 </div><div class="textblock"><div class="dynheader">
 Include dependency graph for algorithm.h:</div>
 <div class="dyncontent">
-<div class="center"><iframe scrolling="no" frameborder="0" src="algorithm_8h__incl.svg" width="4136" height="1440"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
+<div class="center"><iframe scrolling="no" frameborder="0" src="algorithm_8h__incl.svg" width="5171" height="1530"><p><b>This browser is not able to show SVG: try Firefox, Chrome, Safari, or Opera instead.</b></p></iframe>
 </div>
 </div>
 </div>
diff --git a/docs/api/doxygen/algorithm_8h__incl.svg b/docs/api/doxygen/algorithm_8h__incl.svg
index 2bb8819..11aae6c 100644
--- a/docs/api/doxygen/algorithm_8h__incl.svg
+++ b/docs/api/doxygen/algorithm_8h__incl.svg
@@ -4,1182 +4,1468 @@
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diff --git a/docs/api/doxygen/algorithm_8h_source.html b/docs/api/doxygen/algorithm_8h_source.html
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 <div class="ttc" id="namespacetvm_html_a28c693333c2b15702b1a9a57dec0fbf5"><div class="ttname"><a href="namespacetvm.html#a28c693333c2b15702b1a9a57dec0fbf5">tvm::NullValue&lt; DataType &gt;</a></div><div class="ttdeci">DataType NullValue&lt; DataType &gt;()</div><div class="ttdef"><b>Definition:</b> attrs.h:90</div></div>
-<div class="ttc" id="structtvm_1_1relay_1_1TopKAttrs_html_a2d9b93c511148c070e7d5974d2620c6e"><div class="ttname"><a href="structtvm_1_1relay_1_1TopKAttrs.html#a2d9b93c511148c070e7d5974d2620c6e">tvm::relay::TopKAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(TopKAttrs,&quot;relay.attrs.TopkAttrs&quot;)</div><div class="ttdef"><b>Definition:</b> algorithm.h:61</div></div>
+<div class="ttc" id="structtvm_1_1relay_1_1TopKAttrs_html_a2d9b93c511148c070e7d5974d2620c6e"><div class="ttname"><a href="structtvm_1_1relay_1_1TopKAttrs.html#a2d9b93c511148c070e7d5974d2620c6e">tvm::relay::TopKAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(TopKAttrs,&quot;relay.attrs.TopkAttrs&quot;)</div><div class="ttdef"><b>Definition:</b> algorithm.h:62</div></div>
 <div class="ttc" id="base_8h_html"><div class="ttname"><a href="base_8h.html">base.h</a></div><div class="ttdoc">Base classes for the Relay IR. </div></div>
-<div class="ttc" id="structtvm_1_1relay_1_1ArgsortAttrs_html_a867e4a526a4b5e2512e658839d6c1a9e"><div class="ttname"><a href="structtvm_1_1relay_1_1ArgsortAttrs.html#a867e4a526a4b5e2512e658839d6c1a9e">tvm::relay::ArgsortAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(ArgsortAttrs,&quot;relay.attrs.ArgsortAttrs&quot;)</div><div class="ttdef"><b>Definition:</b> algorithm.h:41</div></div>
-<div class="ttc" id="classtvm_1_1AttrsNode_html"><div class="ttname"><a href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a></div><div class="ttdoc">The base class of the all the Use &quot;curiously recurring template pattern&quot;. </div><div class="ttdef"><b>Definition:</b> attrs.h:621</div></div>
-<div class="ttc" id="structtvm_1_1relay_1_1TopKAttrs_html_a5717dfe7edcd2817fc35b2e84dc2305d"><div class="ttname"><a href="structtvm_1_1relay_1_1TopKAttrs.html#a5717dfe7edcd2817fc35b2e84dc2305d">tvm::relay::TopKAttrs::ret_type</a></div><div class="ttdeci">std::string ret_type</div><div class="ttdef"><b>Definition:</b> algorithm.h:58</div></div>
-<div class="ttc" id="structtvm_1_1relay_1_1TopKAttrs_html"><div class="ttname"><a href="structtvm_1_1relay_1_1TopKAttrs.html">tvm::relay::TopKAttrs</a></div><div class="ttdef"><b>Definition:</b> algorithm.h:54</div></div>
-<div class="ttc" id="structtvm_1_1relay_1_1ArgsortAttrs_html_a533842c9351d0ec7f8e2673e94987957"><div class="ttname"><a href="structtvm_1_1relay_1_1ArgsortAttrs.html#a533842c9351d0ec7f8e2673e94987957">tvm::relay::ArgsortAttrs::dtype</a></div><div class="ttdeci">DataType dtype</div><div class="ttdef"><b>Definition:</b> algorithm.h:39</div></div>
-<div class="ttc" id="structtvm_1_1relay_1_1TopKAttrs_html_a8c10ab4cb2a3bf5b38b624dc1eff7b46"><div class="ttname"><a href="structtvm_1_1relay_1_1TopKAttrs.html#a8c10ab4cb2a3bf5b38b624dc1eff7b46">tvm::relay::TopKAttrs::k</a></div><div class="ttdeci">int k</div><div class="ttdef"><b>Definition:</b> algorithm.h:55</div></div>
+<div class="ttc" id="structtvm_1_1relay_1_1ArgsortAttrs_html_a867e4a526a4b5e2512e658839d6c1a9e"><div class="ttname"><a href="structtvm_1_1relay_1_1ArgsortAttrs.html#a867e4a526a4b5e2512e658839d6c1a9e">tvm::relay::ArgsortAttrs::TVM_DECLARE_ATTRS</a></div><div class="ttdeci">TVM_DECLARE_ATTRS(ArgsortAttrs,&quot;relay.attrs.ArgsortAttrs&quot;)</div><div class="ttdef"><b>Definition:</b> algorithm.h:42</div></div>
+<div class="ttc" id="classtvm_1_1AttrsNode_html"><div class="ttname"><a href="classtvm_1_1AttrsNode.html">tvm::AttrsNode</a></div><div class="ttdoc">The base class of the all the Use &quot;curiously recurring template pattern&quot;. </div><div class="ttdef"><b>Definition:</b> attrs.h:634</div></div>
+<div class="ttc" id="structtvm_1_1relay_1_1TopKAttrs_html_a5717dfe7edcd2817fc35b2e84dc2305d"><div class="ttname"><a href="structtvm_1_1relay_1_1TopKAttrs.html#a5717dfe7edcd2817fc35b2e84dc2305d">tvm::relay::TopKAttrs::ret_type</a></div><div class="ttdeci">std::string ret_type</div><div class="ttdef"><b>Definition:</b> algorithm.h:59</div></div>
+<div class="ttc" id="structtvm_1_1relay_1_1TopKAttrs_html_a5f602cd29db662fad78e59159c371c3e"><div class="ttname"><a href="structtvm_1_1relay_1_1TopKAttrs.html#a5f602cd29db662fad78e59159c371c3e">tvm::relay::TopKAttrs::k</a></div><div class="ttdeci">Optional&lt; Integer &gt; k</div><div class="ttdef"><b>Definition:</b> algorithm.h:56</div></div>
+<div class="ttc" id="structtvm_1_1relay_1_1TopKAttrs_html"><div class="ttname"><a href="structtvm_1_1relay_1_1TopKAttrs.html">tvm::relay::TopKAttrs</a></div><div class="ttdef"><b>Definition:</b> algorithm.h:55</div></div>
+<div class="ttc" id="structtvm_1_1relay_1_1ArgsortAttrs_html_a533842c9351d0ec7f8e2673e94987957"><div class="ttname"><a href="structtvm_1_1relay_1_1ArgsortAttrs.html#a533842c9351d0ec7f8e2673e94987957">tvm::relay::ArgsortAttrs::dtype</a></div><div class="ttdeci">DataType dtype</div><div class="ttdef"><b>Definition:</b> algorithm.h:40</div></div>
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diff --git a/docs/api/doxygen/analyzer_8h_source.html b/docs/api/doxygen/analyzer_8h_source.html
index 5e8ffe0..17bd1b6 100644
--- a/docs/api/doxygen/analyzer_8h_source.html
+++ b/docs/api/doxygen/analyzer_8h_source.html
@@ -89,22 +89,23 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
 <div class="title">analyzer.h</div>  </div>
 </div><!--header-->
 <div class="contents">
-<a href="analyzer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Licensed to the Apache Software Foundation (ASF) under one</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> * or more co [...]
+<a href="analyzer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Licensed to the Apache Software Foundation (ASF) under one</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> * or more co [...]
 <div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundNode_html_a035f9270597638a44a55dd2552f4ad20"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundNode.html#a035f9270597638a44a55dd2552f4ad20">tvm::arith::ConstIntBoundNode::kPosInf</a></div><div class="ttdeci">static const constexpr int64_t kPosInf</div><div class="ttdoc">Number to represent +inf. </div><div class="ttdef"><b>Definition:</b> analyzer.h:76</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundNode_html_a7187224ffaf688dd1aa0f6069b393f92"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundNode.html#a7187224ffaf688dd1aa0f6069b393f92">tvm::arith::ConstIntBoundNode::max_value</a></div><div class="ttdeci">int64_t max_value</div><div class="ttdef"><b>Definition:</b> analyzer.h:64</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1ModularSetNode_html_a5b5bf53e26881ec83777f2a3c41a4031"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#a5b5bf53e26881ec83777f2a3c41a4031">tvm::arith::ModularSetNode::VisitAttrs</a></div><div class="ttdeci">void VisitAttrs(tvm::AttrVisitor *v)</div><div class="ttdef"><b>Definition:</b> analyzer.h:180</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundNode_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundNode.html">tvm::arith::ConstIntBoundNode</a></div><div class="ttdoc">Constant integer up and lower bound(inclusive). Useful for value bound analysis. </div><div class="ttdef"><b>Definition:</b> analyzer.h:61</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1ModularSetNode_html_a0531d08d8529454c726a9a20a2002ee6"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html#a0531d08d8529454c726a9a20a2002ee6">tvm::arith::ModularSetNode::coeff</a></div><div class="ttdeci">int64_t coeff</div><div class="ttdoc">linear co-efficient </div><div class="ttdef"><b>Definition:</b> analyzer.h:176</div></div>
+<div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundAnalyzer_html_a14736850005ba70395b09c2fb9113770"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html#a14736850005ba70395b09c2fb9113770">tvm::arith::ConstIntBoundAnalyzer::BoundMapType</a></div><div class="ttdeci">std::unordered_map&lt; PrimExpr, ConstIntBound, ObjectPtrHash, ObjectPtrEqual &gt; BoundMapType</div><div class="ttdef"><b>Definition:</b> analyzer.h:110</div></div>
 <div class="ttc" id="classtvm_1_1SEqualReducer_html"><div class="ttname"><a href="classtvm_1_1SEqualReducer.html">tvm::SEqualReducer</a></div><div class="ttdoc">A Reducer class to reduce the structural equality result of two objects. </div><div class="ttdef"><b>Definition:</b> structural_equal.h:102</div></div>
 <div class="ttc" id="namespacetvm_html_a7f0738778e1a4f1725bea3d6c801aab4"><div class="ttname"><a href="namespacetvm.html#a7f0738778e1a4f1725bea3d6c801aab4">tvm::Bind</a></div><div class="ttdeci">Type Bind(const Type &amp;type, const Map&lt; TypeVar, Type &gt; &amp;args_map)</div><div class="ttdoc">Bind free type variables in the type. </div></div>
 <div class="ttc" id="ir_2expr_8h_html"><div class="ttname"><a href="ir_2expr_8h.html">expr.h</a></div><div class="ttdoc">Base expr nodes in TVM. </div></div>
 <div class="ttc" id="namespacetvm_html"><div class="ttname"><a href="namespacetvm.html">tvm</a></div><div class="ttdef"><b>Definition:</b> analyzer.h:36</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1ModularSetNode_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSetNode.html">tvm::arith::ModularSetNode</a></div><div class="ttdoc">Range of a linear integer function. Use to do specify the possible index values. </div><div class="ttdef"><b>Definition:</b> analyzer.h:173</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1CanonicalSimplifier_html"><div class="ttname"><a href="classtvm_1_1arith_1_1CanonicalSimplifier.html">tvm::arith::CanonicalSimplifier</a></div><div class="ttdoc">Canonical-form based simplifier. </div><div class="ttdef"><b>Definition:</b> analyzer.h:279</div></div>
-<div class="ttc" id="classtvm_1_1tir_1_1Var_html"><div class="ttname"><a href="classtvm_1_1tir_1_1Var.html">tvm::tir::Var</a></div><div class="ttdoc">a named variable in TVM </div><div class="ttdef"><b>Definition:</b> var.h:87</div></div>
+<div class="ttc" id="classtvm_1_1tir_1_1Var_html"><div class="ttname"><a href="classtvm_1_1tir_1_1Var.html">tvm::tir::Var</a></div><div class="ttdoc">a named variable in TIR </div><div class="ttdef"><b>Definition:</b> var.h:87</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundNode_html_a0d39b4e097e1e0e4fffd85d3d2b22459"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundNode.html#a0d39b4e097e1e0e4fffd85d3d2b22459">tvm::arith::ConstIntBoundNode::SEqualReduce</a></div><div class="ttdeci">bool SEqualReduce(const ConstIntBoundNode *other, SEqualReducer equal) const </div><div class="ttdef"><b>Definition:</b> analyzer.h:71</div></div>
 <div class="ttc" id="classtvm_1_1AttrVisitor_html"><div class="ttname"><a href="classtvm_1_1AttrVisitor.html">tvm::AttrVisitor</a></div><div class="ttdoc">Visitor class for to get the attributesof a AST/IR node. The content is going to be called for each f...</div><div class="ttdef"><b>Definition:</b> reflection.h:52</div></div>
-<div class="ttc" id="classtvm_1_1Range_html"><div class="ttname"><a href="classtvm_1_1Range.html">tvm::Range</a></div><div class="ttdoc">Range constainer. </div><div class="ttdef"><b>Definition:</b> expr.h:438</div></div>
+<div class="ttc" id="classtvm_1_1Range_html"><div class="ttname"><a href="classtvm_1_1Range.html">tvm::Range</a></div><div class="ttdoc">Range constainer. </div><div class="ttdef"><b>Definition:</b> expr.h:440</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1ModularSet_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ModularSet.html">tvm::arith::ModularSet</a></div><div class="ttdoc">reference of ModularSetNode </div><div class="ttdef"><b>Definition:</b> analyzer.h:197</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1Analyzer_html_a0d054ea2ea5b7e99f0883c00672ec831"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#a0d054ea2ea5b7e99f0883c00672ec831">tvm::arith::Analyzer::int_set</a></div><div class="ttdeci">IntSetAnalyzer int_set</div><div class="ttdoc">sub-analyzer: int set </div><div class="ttdef"><b>Definition:</b> analyzer.h:396</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundAnalyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html">tvm::arith::ConstIntBoundAnalyzer</a></div><div class="ttdoc">Analyzer to get constant integer bound over expression. </div><div class="ttdef"><b>Definition:</b> analyzer.h:108</div></div>
@@ -122,17 +123,16 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
 <div class="ttc" id="classtvm_1_1arith_1_1ConstraintContext_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstraintContext.html">tvm::arith::ConstraintContext</a></div><div class="ttdoc">Constraint context. </div><div class="ttdef"><b>Definition:</b> analyzer.h:323</div></div>
 <div class="ttc" id="namespacetvm_1_1te_html_ae0c71f84710b436cbe0b32289d0838f4"><div class="ttname"><a href="namespacetvm_1_1te.html#ae0c71f84710b436cbe0b32289d0838f4">tvm::te::var</a></div><div class="ttdeci">Var var(std::string name_hint, DataType t=DataType::Int(32))</div><div class="ttdoc">Construct a new Var expression. </div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1IntSetAnalyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1IntSetAnalyzer.html">tvm::arith::IntSetAnalyzer</a></div><div class="ttdoc">Integer set analyzer. </div><div class="ttdef"><b>Definition:</b> analyzer.h:349</div></div>
-<div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundAnalyzer_html_a453ffd907b8dcac5c9ceb0f23291fa4e"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundAnalyzer.html#a453ffd907b8dcac5c9ceb0f23291fa4e">tvm::arith::ConstIntBoundAnalyzer::BoundMapType</a></div><div class="ttdeci">std::unordered_map&lt; PrimExpr, ConstIntBound, ObjectHash, ObjectEqual &gt; BoundMapType</div><div class="ttdef"><b>Definition:</b> analyzer.h:110</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundNode_html_a0761897bf16ab73b848bf360e9b195a3"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundNode.html#a0761897bf16ab73b848bf360e9b195a3">tvm::arith::ConstIntBoundNode::min_value</a></div><div class="ttdeci">int64_t min_value</div><div class="ttdef"><b>Definition:</b> analyzer.h:63</div></div>
 <div class="ttc" id="namespacetvm_1_1tir_1_1transform_html_a817801e8c9488f712804d2d0b821acf0"><div class="ttname"><a href="namespacetvm_1_1tir_1_1transform.html#a817801e8c9488f712804d2d0b821acf0">tvm::tir::transform::Simplify</a></div><div class="ttdeci">Pass Simplify()</div><div class="ttdoc">Run arithmetic simplifications on the statements and expressions. </div></div>
 <div class="ttc" id="namespacetopi_html_ae10e1fb8c765a9ae15c12f45199375b3"><div class="ttname"><a href="namespacetopi.html#ae10e1fb8c765a9ae15c12f45199375b3">topi::equal</a></div><div class="ttdeci">tvm::PrimExpr equal(const tvm::PrimExpr &amp;a, const tvm::PrimExpr &amp;b)</div><div class="ttdef"><b>Definition:</b> broadcast.h:390</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1Analyzer_html_a435eba3ac3a839d3c53b74acfdc10146"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#a435eba3ac3a839d3c53b74acfdc10146">tvm::arith::Analyzer::const_int_bound</a></div><div class="ttdeci">ConstIntBoundAnalyzer const_int_bound</div><div class="ttdoc">sub-analyzer: const integer bound </div><div class="ttdef"><b>Definition:</b> analyzer.h:388</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1ConstIntBoundNode_html_a652c9c965a3942f1ca45f7929ddd554c"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBoundNode.html#a652c9c965a3942f1ca45f7929ddd554c">tvm::arith::ConstIntBoundNode::_type_key</a></div><div class="ttdeci">static constexpr const char * _type_key</div><div class="ttdef"><b>Definition:</b> analyzer.h:83</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1RewriteSimplifier_html"><div class="ttname"><a href="classtvm_1_1arith_1_1RewriteSimplifier.html">tvm::arith::RewriteSimplifier</a></div><div class="ttdoc">Rewrite-rule based simplifier. </div><div class="ttdef"><b>Definition:</b> analyzer.h:245</div></div>
-<div class="ttc" id="classtvm_1_1Map_html"><div class="ttname"><a href="classtvm_1_1Map.html">tvm::Map</a></div><div class="ttdoc">Map container of NodeRef-&gt;NodeRef in DSL graph. Map implements copy on write semantics, which means map is mutable but copy will happen when array is referenced in more than two places. </div><div class="ttdef"><b>Definition:</b> container.h:91</div></div>
+<div class="ttc" id="classtvm_1_1Map_html"><div class="ttname"><a href="classtvm_1_1Map.html">tvm::Map</a></div><div class="ttdoc">Map container of NodeRef-&gt;NodeRef in DSL graph. Map implements copy on write semantics, which means map is mutable but copy will happen when array is referenced in more than two places. </div><div class="ttdef"><b>Definition:</b> container.h:103</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1Analyzer_html_acac92a9522deabe289fea99efbd9eaec"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html#acac92a9522deabe289fea99efbd9eaec">tvm::arith::Analyzer::modular_set</a></div><div class="ttdeci">ModularSetAnalyzer modular_set</div><div class="ttdoc">sub-analyzer: modular set </div><div class="ttdef"><b>Definition:</b> analyzer.h:390</div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1ConstIntBound_html"><div class="ttname"><a href="classtvm_1_1arith_1_1ConstIntBound.html">tvm::arith::ConstIntBound</a></div><div class="ttdoc">reference class to ConstIntBoundNode </div><div class="ttdef"><b>Definition:</b> analyzer.h:91</div></div>
-<div class="ttc" id="classtvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode. </div><div class="ttdef"><b>Definition:</b> expr.h:101</div></div>
+<div class="ttc" id="classtvm_1_1PrimExpr_html"><div class="ttname"><a href="classtvm_1_1PrimExpr.html">tvm::PrimExpr</a></div><div class="ttdoc">Reference to PrimExprNode. </div><div class="ttdef"><b>Definition:</b> expr.h:103</div></div>
 <div class="ttc" id="int__set_8h_html"><div class="ttname"><a href="int__set_8h.html">int_set.h</a></div><div class="ttdoc">Integer set. </div></div>
 <div class="ttc" id="classtvm_1_1arith_1_1Analyzer_html"><div class="ttname"><a href="classtvm_1_1arith_1_1Analyzer.html">tvm::arith::Analyzer</a></div><div class="ttdoc">Analyzer that contains bunch of sub-analyzers. </div><div class="ttdef"><b>Definition:</b> analyzer.h:380</div></div>
 <div class="ttc" id="with_8h_html"><div class="ttname"><a href="with_8h.html">with.h</a></div><div class="ttdoc">RAII wrapper function to enter and exit a context object similar to python&amp;#39;s with syntax...</div></div>
diff --git a/docs/api/doxygen/annotated.html b/docs/api/doxygen/annotated.html
index 8a641c9..29a096e 100644
--- a/docs/api/doxygen/annotated.html
+++ b/docs/api/doxygen/annotated.html
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 <tr id="row_2_2_30_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConcatenateAttrs.html" target="_self">ConcatenateAttrs</a></td><td class="desc">Attributes used in concatenate operators </td></tr>
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 <tr id="row_2_2_32_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantNode.html" target="_self">ConstantNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> tensor type </td></tr>
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-<tr id="row_2_2_34_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html" target="_self">ConstructorValueObj</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_35_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html" target="_self">Conv1DAttrs</a></td><td class="desc">Attributes used in 1D convolution operators </td></tr>
-<tr id="row_2_2_36_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html" target="_self">Conv1DTransposeAttrs</a></td><td class="desc">Attributes used in 1D transposed convolution operator </td></tr>
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-<tr id="row_2_2_38_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html" target="_self">Conv2DTransposeAttrs</a></td><td class="desc">Attributes used in transposed convolution operator </td></tr>
-<tr id="row_2_2_39_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradAttrs.html" target="_self">Conv2DWinogradAttrs</a></td><td class="desc">Attributes used in convolution operators with winograd algorithm </td></tr>
-<tr id="row_2_2_40_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradNNPACKWeightTransformAttrs.html" target="_self">Conv2DWinogradNNPACKWeightTransformAttrs</a></td><td class="desc">Attributes used in winograd weight transformation operators </td></tr>
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-<tr id="row_2_2_42_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DWinogradAttrs.html" target="_self">Conv3DWinogradAttrs</a></td><td class="desc">Attributes used in 3d winograd convolution operators </td></tr>
-<tr id="row_2_2_43_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConvWinogradWeightTransformAttrs.html" target="_self">ConvWinogradWeightTransformAttrs</a></td><td class="desc">Attributes used in winograd weight transformation operators </td></tr>
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-<tr id="row_2_2_45_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CropAndResizeAttrs.html" target="_self">CropAndResizeAttrs</a></td><td class="desc">Attributes used in image crop_and_resize operator </td></tr>
-<tr id="row_2_2_46_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DebugAttrs.html" target="_self">DebugAttrs</a></td><td class="desc">Options for the debug operators </td></tr>
-<tr id="row_2_2_47_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DeformableConv2DAttrs.html" target="_self">DeformableConv2DAttrs</a></td><td class="desc">Attributes for DeformableConv2D operator </td></tr>
-<tr id="row_2_2_48_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DenseAttrs.html" target="_self">DenseAttrs</a></td><td class="desc">Attributes for dense operator </td></tr>
-<tr id="row_2_2_49_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DeviceCopyAttrs.html" target="_self">DeviceCopyAttrs</a></td><td class="desc">Options for the device copy operators </td></tr>
-<tr id="row_2_2_50_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPattern.html" target="_self">DFPattern</a></td><td class="desc">Managed reference to dataflow patterns </td></tr>
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-<tr id="row_2_2_53_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" target="_self">DFPatternFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first <a class="el" href="classtvm_1_1relay_1_1DFPattern.html" title="Managed reference to dataflow patterns. ">DFPattern</a> argument </td></tr>
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-<tr id="row_2_2_55_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternNode.html" target="_self">DFPatternNode</a></td><td class="desc">Base type of all dataflow patterns </td></tr>
-<tr id="row_2_2_56_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html" target="_self">DFPatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" title="A dynamical functor that dispatches on in the first DFPattern argument. ">DFPatternFunctor</a [...]
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-<tr id="row_2_2_62_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html" target="_self">ExpandDimsAttrs</a></td><td class="desc">Attributes used in expand_dims operators </td></tr>
-<tr id="row_2_2_63_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" target="_self">ExprFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerful Visitor, since it allows you to define function signatures of Visit <a class="el" href="classtvm_1_1rel [...]
-<tr id="row_2_2_64_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html" target="_self">ExprFunctor&lt; R(const Expr &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_65_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html" target="_self">ExprMutator</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu...">ExprFunctor</a> w [...]
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-<tr id="row_2_2_68_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprRewriter.html" target="_self">ExprRewriter</a></td><td class="desc">A non-iterating Expression Rewriter </td></tr>
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-<tr id="row_2_2_70_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html" target="_self">FeatureSet</a></td><td class="desc">A finite set of Feature </td></tr>
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-<tr id="row_2_2_72_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Function.html" target="_self">Function</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
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-<tr id="row_2_2_78_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Id.html" target="_self">Id</a></td><td class="desc"></td></tr>
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-<tr id="row_2_2_81_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IfNode.html" target="_self">IfNode</a></td><td class="desc">Container of <a class="el" href="classtvm_1_1relay_1_1If.html">If</a> </td></tr>
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-<tr id="row_2_2_86_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1L2NormalizeAttrs.html" target="_self">L2NormalizeAttrs</a></td><td class="desc">Attributes for L2Normalize operator </td></tr>
-<tr id="row_2_2_87_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LayerNormAttrs.html" target="_self">LayerNormAttrs</a></td><td class="desc">Attributes used in layer_norm operator </td></tr>
-<tr id="row_2_2_88_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LayoutTransformAttrs.html" target="_self">LayoutTransformAttrs</a></td><td class="desc">Attributes for LayoutTransform operator </td></tr>
-<tr id="row_2_2_89_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LeakyReluAttrs.html" target="_self">LeakyReluAttrs</a></td><td class="desc">Attributes for leaky relu operator </td></tr>
-<tr id="row_2_2_90_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Let.html" target="_self">Let</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_91_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1LetNode.html" target="_self">LetNode</a></td><td class="desc">A binding of a sub-network </td></tr>
-<tr id="row_2_2_92_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LRNAttrs.html" target="_self">LRNAttrs</a></td><td class="desc">Attributes for LRN operator </td></tr>
-<tr id="row_2_2_93_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Match.html" target="_self">Match</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_94_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MatchNode.html" target="_self">MatchNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Match.html">Match</a> container node </td></tr>
-<tr id="row_2_2_95_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool1DAttrs.html" target="_self">MaxPool1DAttrs</a></td><td class="desc">Attributes for 1D max pool operator </td></tr>
-<tr id="row_2_2_96_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool2DAttrs.html" target="_self">MaxPool2DAttrs</a></td><td class="desc">Attributes for max pool operator </td></tr>
-<tr id="row_2_2_97_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool3DAttrs.html" target="_self">MaxPool3DAttrs</a></td><td class="desc">Attributes for 3D max pool operator </td></tr>
-<tr id="row_2_2_98_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MirrorPadAttrs.html" target="_self">MirrorPadAttrs</a></td><td class="desc">Attributes used for the MirrorPadding operator </td></tr>
-<tr id="row_2_2_99_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MixedModeMutator.html" target="_self">MixedModeMutator</a></td><td class="desc">Non-recursive DFS Graph Traversal for Custom Rewriting Passes </td></tr>
-<tr id="row_2_2_100_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MixedModeVisitor.html" target="_self">MixedModeVisitor</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html" title="A simple visitor wrapper around ExprFunctor. Recursively visit the content. ">ExprVisitor</a> which traverses th [...]
-<tr id="row_2_2_101_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html" target="_self">MultiBoxPriorAttrs</a></td><td class="desc">Attributes used in multibox_prior operators </td></tr>
-<tr id="row_2_2_102_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html" target="_self">MultiBoxTransformLocAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_103_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1NdarraySizeAttrs.html" target="_self">NdarraySizeAttrs</a></td><td class="desc">Attributes for ndarray_size operator </td></tr>
-<tr id="row_2_2_104_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1NonMaximumSuppressionAttrs.html" target="_self">NonMaximumSuppressionAttrs</a></td><td class="desc">Attributes used in non_maximum_suppression operator </td></tr>
-<tr id="row_2_2_105_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html" target="_self">OnDeviceAttrs</a></td><td class="desc">Options for the device annotation operators </td></tr>
-<tr id="row_2_2_106_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html" target="_self">OneHotAttrs</a></td><td class="desc">Attributes used in one-hot operator </td></tr>
-<tr id="row_2_2_107_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html" target="_self">OpImplementation</a></td><td class="desc">Operator implementation class </td></tr>
-<tr id="row_2_2_108_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementationNode.html" target="_self">OpImplementationNode</a></td><td class="desc">Operator implementation that includes compute and schedule function </td></tr>
-<tr id="row_2_2_109_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html" target="_self">OpSpecialization</a></td><td class="desc">Operator specialization class </td></tr>
-<tr id="row_2_2_110_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecializationNode.html" target="_self">OpSpecializationNode</a></td><td class="desc">Specialized implementations for operators under certain conditions </td></tr>
-<tr id="row_2_2_111_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html" target="_self">OpStrategy</a></td><td class="desc">Operator strategy class </td></tr>
-<tr id="row_2_2_112_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategyNode.html" target="_self">OpStrategyNode</a></td><td class="desc">Operator strategy to choose implementation </td></tr>
-<tr id="row_2_2_113_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1PadAttrs.html" target="_self">PadAttrs</a></td><td class="desc">Attributes used for the padding operator </td></tr>
-<tr id="row_2_2_114_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Pattern.html" target="_self">Pattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> is the base type for an ADT match pattern in Relay </td></tr>
-<tr id="row_2_2_115_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html" target="_self">PatternConstructor</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_116_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructorNode.html" target="_self">PatternConstructorNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
-<tr id="row_2_2_117_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" target="_self">PatternFunctor</a></td><td class="desc">A dynamical functor on ADT patterns that dispatches on its first argument. You can use this as a more powerful visitor, since it allows you to define the types of further arguments to VisitPattern </ [...]
-<tr id="row_2_2_118_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_07const_01Pattern_01_6n_00_01Args_8_8_8_08_4.html" target="_self">PatternFunctor&lt; R(const Pattern &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_119_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternMutator.html" target="_self">PatternMutator</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu...">ExprFuncto [...]
-<tr id="row_2_2_120_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternNode.html" target="_self">PatternNode</a></td><td class="desc">Base type for declaring relay pattern </td></tr>
-<tr id="row_2_2_121_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html" target="_self">PatternTuple</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_122_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTupleNode.html" target="_self">PatternTupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
-<tr id="row_2_2_123_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVar.html" target="_self">PatternVar</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_124_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html" target="_self">PatternVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
-<tr id="row_2_2_125_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html" target="_self">PatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" title="A dynamical functor on ADT patterns that dispatches on its first argument. You can use this as a m [...]
-<tr id="row_2_2_126_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html" target="_self">PatternWildcard</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_127_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html" target="_self">PatternWildcardNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">PatternWildcard</a> container node </td></tr>
-<tr id="row_2_2_128_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1PReluAttrs.html" target="_self">PReluAttrs</a></td><td class="desc">Attributes for prelu operator </td></tr>
-<tr id="row_2_2_129_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ProposalAttrs.html" target="_self">ProposalAttrs</a></td><td class="desc">Attributes used in proposal operators </td></tr>
-<tr id="row_2_2_130_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosure.html" target="_self">RecClosure</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_131_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosureObj.html" target="_self">RecClosureObj</a></td><td class="desc">The container type of <a class="el" href="classtvm_1_1relay_1_1RecClosure.html">RecClosure</a> </td></tr>
-<tr id="row_2_2_132_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReduceAttrs.html" target="_self">ReduceAttrs</a></td><td class="desc">Attributes for Reduce operators </td></tr>
-<tr id="row_2_2_133_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreate.html" target="_self">RefCreate</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_134_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreateNode.html" target="_self">RefCreateNode</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_135_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefRead.html" target="_self">RefRead</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_136_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefReadNode.html" target="_self">RefReadNode</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_137_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefValue.html" target="_self">RefValue</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_138_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RefValueObj.html" target="_self">RefValueObj</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_139_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWrite.html" target="_self">RefWrite</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_140_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWriteNode.html" target="_self">RefWriteNode</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_141_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RelayNode.html" target="_self">RelayNode</a></td><td class="desc">This is the base node container of all relay structures </td></tr>
-<tr id="row_2_2_142_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RepeatAttrs.html" target="_self">RepeatAttrs</a></td><td class="desc">Attributes used in repeat operators </td></tr>
-<tr id="row_2_2_143_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeAttrs.html" target="_self">ReshapeAttrs</a></td><td class="desc">Attributes used in reshape operators </td></tr>
-<tr id="row_2_2_144_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Resize3dAttrs.html" target="_self">Resize3dAttrs</a></td><td class="desc">Attributes used in image resize3d operator </td></tr>
-<tr id="row_2_2_145_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ResizeAttrs.html" target="_self">ResizeAttrs</a></td><td class="desc">Attributes used in image resize operator </td></tr>
-<tr id="row_2_2_146_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReverseAttrs.html" target="_self">ReverseAttrs</a></td><td class="desc">Attributes used in reverse operators </td></tr>
-<tr id="row_2_2_147_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIAlignAttrs.html" target="_self">ROIAlignAttrs</a></td><td class="desc">Attributes used in roi_align operators </td></tr>
-<tr id="row_2_2_148_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIPoolAttrs.html" target="_self">ROIPoolAttrs</a></td><td class="desc">Attributes used in roi_pool operators </td></tr>
-<tr id="row_2_2_149_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SequenceMaskAttrs.html" target="_self">SequenceMaskAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_150_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeFuncAttrs.html" target="_self">ShapeFuncAttrs</a></td><td class="desc">Options for the shape function operator </td></tr>
-<tr id="row_2_2_151_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeOfAttrs.html" target="_self">ShapeOfAttrs</a></td><td class="desc">Attributes for ShapeOf operator </td></tr>
-<tr id="row_2_2_152_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SliceLikeAttrs.html" target="_self">SliceLikeAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_153_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SoftmaxAttrs.html" target="_self">SoftmaxAttrs</a></td><td class="desc">Attributes used in softmax operators </td></tr>
-<tr id="row_2_2_154_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseDenseAttrs.html" target="_self">SparseDenseAttrs</a></td><td class="desc">Attributes for sparse_dense operator </td></tr>
-<tr id="row_2_2_155_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseTransposeAttrs.html" target="_self">SparseTransposeAttrs</a></td><td class="desc">Attributes for sparse_transpose operator </td></tr>
-<tr id="row_2_2_156_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SplitAttrs.html" target="_self">SplitAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_157_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SqueezeAttrs.html" target="_self">SqueezeAttrs</a></td><td class="desc">Attributes used in squeeze operators </td></tr>
-<tr id="row_2_2_158_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StackAttrs.html" target="_self">StackAttrs</a></td><td class="desc">Attributes used in stack operators </td></tr>
-<tr id="row_2_2_159_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html" target="_self">StridedSliceAttrs</a></td><td class="desc">Attributes for StridedSlice operator </td></tr>
-<tr id="row_2_2_160_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SubPixelAttrs.html" target="_self">SubPixelAttrs</a></td><td class="desc">Attributes used in subpixel operators </td></tr>
-<tr id="row_2_2_161_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TakeAttrs.html" target="_self">TakeAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_162_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExpr.html" target="_self">TempExpr</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_163_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExprNode.html" target="_self">TempExprNode</a></td><td class="desc">Base class of the temporary expression </td></tr>
-<tr id="row_2_2_164_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TileAttrs.html" target="_self">TileAttrs</a></td><td class="desc">Attributes used in tile operators </td></tr>
-<tr id="row_2_2_165_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TopKAttrs.html" target="_self">TopKAttrs</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_166_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TransposeAttrs.html" target="_self">TransposeAttrs</a></td><td class="desc">Attributes used in transpose operators </td></tr>
-<tr id="row_2_2_167_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Tuple.html" target="_self">Tuple</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_168_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItem.html" target="_self">TupleGetItem</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_169_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemNode.html" target="_self">TupleGetItemNode</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_170_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPattern.html" target="_self">TupleGetItemPattern</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_171_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPatternNode.html" target="_self">TupleGetItemPatternNode</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_172_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleNode.html" target="_self">TupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
-<tr id="row_2_2_173_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePattern.html" target="_self">TuplePattern</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_174_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePatternNode.html" target="_self">TuplePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
-<tr id="row_2_2_175_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePattern.html" target="_self">TypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
-<tr id="row_2_2_176_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePatternNode.html" target="_self">TypePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Types </td></tr>
-<tr id="row_2_2_177_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSampling3DAttrs.html" target="_self">UpSampling3DAttrs</a></td><td class="desc">Attributes for upsampling3d operator </td></tr>
-<tr id="row_2_2_178_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSamplingAttrs.html" target="_self">UpSamplingAttrs</a></td><td class="desc">Attributes for upsampling operator </td></tr>
-<tr id="row_2_2_179_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Var.html" target="_self">Var</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_180_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarNode.html" target="_self">VarNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
-<tr id="row_2_2_181_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPattern.html" target="_self">VarPattern</a></td><td class="desc"></td></tr>
-<tr id="row_2_2_182_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPatternNode.html" target="_self">VarPatternNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
-<tr id="row_2_2_183_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPattern.html" target="_self">WildcardPattern</a></td><td class="desc">A pattern which matches anything </td></tr>
-<tr id="row_2_2_184_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPatternNode.html" target="_self">WildcardPatternNode</a></td><td class="desc">Wildcard <a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> </td></tr>
-<tr id="row_2_2_185_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1YoloReorgAttrs.html" target="_self">YoloReorgAttrs</a></td><td class="desc">Attributes used in yolo reorg operators </td></tr>
+<tr id="row_2_2_33_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPattern.html" target="_self">ConstantPattern</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_34_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstantPatternNode.html" target="_self">ConstantPatternNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Constant.html">Constant</a> </td></tr>
+<tr id="row_2_2_35_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ConstructorValue.html" target="_self">ConstructorValue</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_36_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConstructorValueObj.html" target="_self">ConstructorValueObj</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_37_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv1DAttrs.html" target="_self">Conv1DAttrs</a></td><td class="desc">Attributes used in 1D convolution operators </td></tr>
+<tr id="row_2_2_38_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv1DTransposeAttrs.html" target="_self">Conv1DTransposeAttrs</a></td><td class="desc">Attributes used in 1D transposed convolution operator </td></tr>
+<tr id="row_2_2_39_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DAttrs.html" target="_self">Conv2DAttrs</a></td><td class="desc">Attributes used in convolution operators </td></tr>
+<tr id="row_2_2_40_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DTransposeAttrs.html" target="_self">Conv2DTransposeAttrs</a></td><td class="desc">Attributes used in transposed convolution operator </td></tr>
+<tr id="row_2_2_41_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradAttrs.html" target="_self">Conv2DWinogradAttrs</a></td><td class="desc">Attributes used in convolution operators with winograd algorithm </td></tr>
+<tr id="row_2_2_42_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv2DWinogradNNPACKWeightTransformAttrs.html" target="_self">Conv2DWinogradNNPACKWeightTransformAttrs</a></td><td class="desc">Attributes used in winograd weight transformation operators </td></tr>
+<tr id="row_2_2_43_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DAttrs.html" target="_self">Conv3DAttrs</a></td><td class="desc">Attributes used in convolution operators </td></tr>
+<tr id="row_2_2_44_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DTransposeAttrs.html" target="_self">Conv3DTransposeAttrs</a></td><td class="desc">Attributes used in transposed convolution operator </td></tr>
+<tr id="row_2_2_45_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Conv3DWinogradAttrs.html" target="_self">Conv3DWinogradAttrs</a></td><td class="desc">Attributes used in 3d winograd convolution operators </td></tr>
+<tr id="row_2_2_46_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ConvWinogradWeightTransformAttrs.html" target="_self">ConvWinogradWeightTransformAttrs</a></td><td class="desc">Attributes used in winograd weight transformation operators </td></tr>
+<tr id="row_2_2_47_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CorrelationAttrs.html" target="_self">CorrelationAttrs</a></td><td class="desc">Attributes used in correlation operators </td></tr>
+<tr id="row_2_2_48_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1CropAndResizeAttrs.html" target="_self">CropAndResizeAttrs</a></td><td class="desc">Attributes used in image crop_and_resize operator </td></tr>
+<tr id="row_2_2_49_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePattern.html" target="_self">DataTypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_2_2_50_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DataTypePatternNode.html" target="_self">DataTypePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Types </td></tr>
+<tr id="row_2_2_51_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DebugAttrs.html" target="_self">DebugAttrs</a></td><td class="desc">Options for the debug operators </td></tr>
+<tr id="row_2_2_52_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DeformableConv2DAttrs.html" target="_self">DeformableConv2DAttrs</a></td><td class="desc">Attributes for DeformableConv2D operator </td></tr>
+<tr id="row_2_2_53_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DenseAttrs.html" target="_self">DenseAttrs</a></td><td class="desc">Attributes for dense operator </td></tr>
+<tr id="row_2_2_54_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DeviceCopyAttrs.html" target="_self">DeviceCopyAttrs</a></td><td class="desc">Options for the device copy operators </td></tr>
+<tr id="row_2_2_55_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPattern.html" target="_self">DFPattern</a></td><td class="desc">Managed reference to dataflow patterns </td></tr>
+<tr id="row_2_2_56_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallback.html" target="_self">DFPatternCallback</a></td><td class="desc">Managed reference to dataflow pattern callbacks </td></tr>
+<tr id="row_2_2_57_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternCallbackNode.html" target="_self">DFPatternCallbackNode</a></td><td class="desc">Base type of all dataflow pattern callbacks </td></tr>
+<tr id="row_2_2_58_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" target="_self">DFPatternFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first <a class="el" href="classtvm_1_1relay_1_1DFPattern.html" title="Managed reference to dataflow patterns. ">DFPattern</a> argument </td></tr>
+<tr id="row_2_2_59_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor_3_01R_07const_01DFPattern_01_6n_00_01Args_8_8_8_08_4.html" target="_self">DFPatternFunctor&lt; R(const DFPattern &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_60_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternNode.html" target="_self">DFPatternNode</a></td><td class="desc">Base type of all dataflow patterns </td></tr>
+<tr id="row_2_2_61_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DFPatternVisitor.html" target="_self">DFPatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1DFPatternFunctor.html" title="A dynamical functor that dispatches on in the first DFPattern argument. ">DFPatternFunctor</a [...]
+<tr id="row_2_2_62_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DilateAttrs.html" target="_self">DilateAttrs</a></td><td class="desc">Attributes used in dilate operator </td></tr>
+<tr id="row_2_2_63_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Dilation2DAttrs.html" target="_self">Dilation2DAttrs</a></td><td class="desc">Attributes used in dilation operators </td></tr>
+<tr id="row_2_2_64_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPattern.html" target="_self">DominatorPattern</a></td><td class="desc">A pattern which matches a variable length dominator path </td></tr>
+<tr id="row_2_2_65_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1DominatorPatternNode.html" target="_self">DominatorPatternNode</a></td><td class="desc">Dominated Graph <a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> <a class="el" href="classtvm_1_1rela [...]
+<tr id="row_2_2_66_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1DropoutAttrs.html" target="_self">DropoutAttrs</a></td><td class="desc">Attributes used in dropout operator </td></tr>
+<tr id="row_2_2_67_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ExpandDimsAttrs.html" target="_self">ExpandDimsAttrs</a></td><td class="desc">Attributes used in expand_dims operators </td></tr>
+<tr id="row_2_2_68_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" target="_self">ExprFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerful Visitor, since it allows you to define function signatures of Visit <a class="el" href="classtvm_1_1rel [...]
+<tr id="row_2_2_69_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprFunctor_3_01R_07const_01Expr_01_6n_00_01Args_8_8_8_08_4.html" target="_self">ExprFunctor&lt; R(const Expr &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_70_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprMutator.html" target="_self">ExprMutator</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu...">ExprFunctor</a> w [...]
+<tr id="row_2_2_71_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPattern.html" target="_self">ExprPattern</a></td><td class="desc">A pattern which matches a literal expression </td></tr>
+<tr id="row_2_2_72_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprPatternNode.html" target="_self">ExprPatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Relay Expression </td></tr>
+<tr id="row_2_2_73_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprRewriter.html" target="_self">ExprRewriter</a></td><td class="desc">A non-iterating Expression Rewriter </td></tr>
+<tr id="row_2_2_74_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html" target="_self">ExprVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu...">Ex [...]
+<tr id="row_2_2_75_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FeatureSet.html" target="_self">FeatureSet</a></td><td class="desc">A finite set of Feature </td></tr>
+<tr id="row_2_2_76_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1FIFOBufferAttrs.html" target="_self">FIFOBufferAttrs</a></td><td class="desc">Attributes for FIFO buffer operator </td></tr>
+<tr id="row_2_2_77_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Function.html" target="_self">Function</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" title="Relay Function container. ">FunctionNode</a> </td></tr>
+<tr id="row_2_2_78_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1FunctionNode.html" target="_self">FunctionNode</a></td><td class="desc">Relay <a class="el" href="classtvm_1_1relay_1_1Function.html" title="Managed reference to FunctionNode. ">Function</a> container </td></tr>
+<tr id="row_2_2_79_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GatherAttrs.html" target="_self">GatherAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_80_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GetValidCountsAttrs.html" target="_self">GetValidCountsAttrs</a></td><td class="desc">Attributes used in get_valid_counts operator </td></tr>
+<tr id="row_2_2_81_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GlobalPool2DAttrs.html" target="_self">GlobalPool2DAttrs</a></td><td class="desc">Attributes for global pool operator </td></tr>
+<tr id="row_2_2_82_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GridSampleAttrs.html" target="_self">GridSampleAttrs</a></td><td class="desc">Attributes used in image grid_sample operator </td></tr>
+<tr id="row_2_2_83_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1GroupNormAttrs.html" target="_self">GroupNormAttrs</a></td><td class="desc">Attributes used in group_norm operator </td></tr>
+<tr id="row_2_2_84_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Id.html" target="_self">Id</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_85_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IdNode.html" target="_self">IdNode</a></td><td class="desc">The unique identifier of variables </td></tr>
+<tr id="row_2_2_86_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1If.html" target="_self">If</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_87_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1IfNode.html" target="_self">IfNode</a></td><td class="desc">Container of <a class="el" href="classtvm_1_1relay_1_1If.html">If</a> </td></tr>
+<tr id="row_2_2_88_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1InitOpAttrs.html" target="_self">InitOpAttrs</a></td><td class="desc">Attributes that specify a tensor </td></tr>
+<tr id="row_2_2_89_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1InstanceNormAttrs.html" target="_self">InstanceNormAttrs</a></td><td class="desc">Attributes used in instance_norm operator </td></tr>
+<tr id="row_2_2_90_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosure.html" target="_self">InterpreterClosure</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_91_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1InterpreterClosureObj.html" target="_self">InterpreterClosureObj</a></td><td class="desc">The container type of Closures used by the interpreter </td></tr>
+<tr id="row_2_2_92_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1L2NormalizeAttrs.html" target="_self">L2NormalizeAttrs</a></td><td class="desc">Attributes for L2Normalize operator </td></tr>
+<tr id="row_2_2_93_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LayerNormAttrs.html" target="_self">LayerNormAttrs</a></td><td class="desc">Attributes used in layer_norm operator </td></tr>
+<tr id="row_2_2_94_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LayoutTransformAttrs.html" target="_self">LayoutTransformAttrs</a></td><td class="desc">Attributes for LayoutTransform operator </td></tr>
+<tr id="row_2_2_95_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LeakyReluAttrs.html" target="_self">LeakyReluAttrs</a></td><td class="desc">Attributes for leaky relu operator </td></tr>
+<tr id="row_2_2_96_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Let.html" target="_self">Let</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_97_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1LetNode.html" target="_self">LetNode</a></td><td class="desc">A binding of a sub-network </td></tr>
+<tr id="row_2_2_98_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1LRNAttrs.html" target="_self">LRNAttrs</a></td><td class="desc">Attributes for LRN operator </td></tr>
+<tr id="row_2_2_99_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Match.html" target="_self">Match</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_100_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MatchNode.html" target="_self">MatchNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Match.html">Match</a> container node </td></tr>
+<tr id="row_2_2_101_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool1DAttrs.html" target="_self">MaxPool1DAttrs</a></td><td class="desc">Attributes for 1D max pool operator </td></tr>
+<tr id="row_2_2_102_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool2DAttrs.html" target="_self">MaxPool2DAttrs</a></td><td class="desc">Attributes for max pool operator </td></tr>
+<tr id="row_2_2_103_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MaxPool3DAttrs.html" target="_self">MaxPool3DAttrs</a></td><td class="desc">Attributes for 3D max pool operator </td></tr>
+<tr id="row_2_2_104_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MirrorPadAttrs.html" target="_self">MirrorPadAttrs</a></td><td class="desc">Attributes used for the MirrorPadding operator </td></tr>
+<tr id="row_2_2_105_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MixedModeMutator.html" target="_self">MixedModeMutator</a></td><td class="desc">Non-recursive DFS Graph Traversal for Custom Rewriting Passes </td></tr>
+<tr id="row_2_2_106_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1MixedModeVisitor.html" target="_self">MixedModeVisitor</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprVisitor.html" title="A simple visitor wrapper around ExprFunctor. Recursively visit the content. ">ExprVisitor</a> which traverses th [...]
+<tr id="row_2_2_107_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MultiBoxPriorAttrs.html" target="_self">MultiBoxPriorAttrs</a></td><td class="desc">Attributes used in multibox_prior operators </td></tr>
+<tr id="row_2_2_108_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1MultiBoxTransformLocAttrs.html" target="_self">MultiBoxTransformLocAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_109_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1NdarraySizeAttrs.html" target="_self">NdarraySizeAttrs</a></td><td class="desc">Attributes for ndarray_size operator </td></tr>
+<tr id="row_2_2_110_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1NonMaximumSuppressionAttrs.html" target="_self">NonMaximumSuppressionAttrs</a></td><td class="desc">Attributes used in non_maximum_suppression operator </td></tr>
+<tr id="row_2_2_111_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1OnDeviceAttrs.html" target="_self">OnDeviceAttrs</a></td><td class="desc">Options for the device annotation operators </td></tr>
+<tr id="row_2_2_112_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1OneHotAttrs.html" target="_self">OneHotAttrs</a></td><td class="desc">Attributes used in one-hot operator </td></tr>
+<tr id="row_2_2_113_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementation.html" target="_self">OpImplementation</a></td><td class="desc">Operator implementation class </td></tr>
+<tr id="row_2_2_114_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpImplementationNode.html" target="_self">OpImplementationNode</a></td><td class="desc">Operator implementation that includes compute and schedule function </td></tr>
+<tr id="row_2_2_115_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecialization.html" target="_self">OpSpecialization</a></td><td class="desc">Operator specialization class </td></tr>
+<tr id="row_2_2_116_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpSpecializationNode.html" target="_self">OpSpecializationNode</a></td><td class="desc">Specialized implementations for operators under certain conditions </td></tr>
+<tr id="row_2_2_117_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategy.html" target="_self">OpStrategy</a></td><td class="desc">Operator strategy class </td></tr>
+<tr id="row_2_2_118_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1OpStrategyNode.html" target="_self">OpStrategyNode</a></td><td class="desc">Operator strategy to choose implementation </td></tr>
+<tr id="row_2_2_119_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1PadAttrs.html" target="_self">PadAttrs</a></td><td class="desc">Attributes used for the padding operator </td></tr>
+<tr id="row_2_2_120_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Pattern.html" target="_self">Pattern</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> is the base type for an ADT match pattern in Relay </td></tr>
+<tr id="row_2_2_121_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructor.html" target="_self">PatternConstructor</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_122_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternConstructorNode.html" target="_self">PatternConstructorNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
+<tr id="row_2_2_123_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" target="_self">PatternFunctor</a></td><td class="desc">A dynamical functor on ADT patterns that dispatches on its first argument. You can use this as a more powerful visitor, since it allows you to define the types of further arguments to VisitPattern </ [...]
+<tr id="row_2_2_124_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternFunctor_3_01R_07const_01Pattern_01_6n_00_01Args_8_8_8_08_4.html" target="_self">PatternFunctor&lt; R(const Pattern &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_125_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternMutator.html" target="_self">PatternMutator</a></td><td class="desc">A wrapper around <a class="el" href="classtvm_1_1relay_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu...">ExprFuncto [...]
+<tr id="row_2_2_126_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternNode.html" target="_self">PatternNode</a></td><td class="desc">Base type for declaring relay pattern </td></tr>
+<tr id="row_2_2_127_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTuple.html" target="_self">PatternTuple</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_128_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternTupleNode.html" target="_self">PatternTupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
+<tr id="row_2_2_129_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVar.html" target="_self">PatternVar</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_130_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVarNode.html" target="_self">PatternVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternVar.html">PatternVar</a> container node </td></tr>
+<tr id="row_2_2_131_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternVisitor.html" target="_self">PatternVisitor</a></td><td class="desc">A simple visitor wrapper around <a class="el" href="classtvm_1_1relay_1_1PatternFunctor.html" title="A dynamical functor on ADT patterns that dispatches on its first argument. You can use this as a m [...]
+<tr id="row_2_2_132_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html" target="_self">PatternWildcard</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_133_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1PatternWildcardNode.html" target="_self">PatternWildcardNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1PatternWildcard.html">PatternWildcard</a> container node </td></tr>
+<tr id="row_2_2_134_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1PReluAttrs.html" target="_self">PReluAttrs</a></td><td class="desc">Attributes for prelu operator </td></tr>
+<tr id="row_2_2_135_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ProposalAttrs.html" target="_self">ProposalAttrs</a></td><td class="desc">Attributes used in proposal operators </td></tr>
+<tr id="row_2_2_136_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosure.html" target="_self">RecClosure</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_137_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RecClosureObj.html" target="_self">RecClosureObj</a></td><td class="desc">The container type of <a class="el" href="classtvm_1_1relay_1_1RecClosure.html">RecClosure</a> </td></tr>
+<tr id="row_2_2_138_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReduceAttrs.html" target="_self">ReduceAttrs</a></td><td class="desc">Attributes for Reduce operators </td></tr>
+<tr id="row_2_2_139_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreate.html" target="_self">RefCreate</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_140_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefCreateNode.html" target="_self">RefCreateNode</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_141_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefRead.html" target="_self">RefRead</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_142_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefReadNode.html" target="_self">RefReadNode</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_143_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefValue.html" target="_self">RefValue</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_144_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RefValueObj.html" target="_self">RefValueObj</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_145_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWrite.html" target="_self">RefWrite</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_146_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RefWriteNode.html" target="_self">RefWriteNode</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_147_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1RelayNode.html" target="_self">RelayNode</a></td><td class="desc">This is the base node container of all relay structures </td></tr>
+<tr id="row_2_2_148_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1RepeatAttrs.html" target="_self">RepeatAttrs</a></td><td class="desc">Attributes used in repeat operators </td></tr>
+<tr id="row_2_2_149_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReshapeAttrs.html" target="_self">ReshapeAttrs</a></td><td class="desc">Attributes used in reshape operators </td></tr>
+<tr id="row_2_2_150_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1Resize3dAttrs.html" target="_self">Resize3dAttrs</a></td><td class="desc">Attributes used in image resize3d operator </td></tr>
+<tr id="row_2_2_151_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ResizeAttrs.html" target="_self">ResizeAttrs</a></td><td class="desc">Attributes used in image resize operator </td></tr>
+<tr id="row_2_2_152_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReverseAttrs.html" target="_self">ReverseAttrs</a></td><td class="desc">Attributes used in reverse operators </td></tr>
+<tr id="row_2_2_153_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ReverseSequenceAttrs.html" target="_self">ReverseSequenceAttrs</a></td><td class="desc">Attributes used in reverse_sequence operators </td></tr>
+<tr id="row_2_2_154_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIAlignAttrs.html" target="_self">ROIAlignAttrs</a></td><td class="desc">Attributes used in roi_align operators </td></tr>
+<tr id="row_2_2_155_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ROIPoolAttrs.html" target="_self">ROIPoolAttrs</a></td><td class="desc">Attributes used in roi_pool operators </td></tr>
+<tr id="row_2_2_156_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ScatterAttrs.html" target="_self">ScatterAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_157_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SequenceMaskAttrs.html" target="_self">SequenceMaskAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_158_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeFuncAttrs.html" target="_self">ShapeFuncAttrs</a></td><td class="desc">Options for the shape function operator </td></tr>
+<tr id="row_2_2_159_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1ShapeOfAttrs.html" target="_self">ShapeOfAttrs</a></td><td class="desc">Attributes for ShapeOf operator </td></tr>
+<tr id="row_2_2_160_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePattern.html" target="_self">ShapePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_2_2_161_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1ShapePatternNode.html" target="_self">ShapePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Shapes </td></tr>
+<tr id="row_2_2_162_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SliceLikeAttrs.html" target="_self">SliceLikeAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_163_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SoftmaxAttrs.html" target="_self">SoftmaxAttrs</a></td><td class="desc">Attributes used in softmax operators </td></tr>
+<tr id="row_2_2_164_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseDenseAttrs.html" target="_self">SparseDenseAttrs</a></td><td class="desc">Attributes for sparse_dense operator </td></tr>
+<tr id="row_2_2_165_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseToDenseAttrs.html" target="_self">SparseToDenseAttrs</a></td><td class="desc">Attributes used in sparse_to_dense operator </td></tr>
+<tr id="row_2_2_166_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SparseTransposeAttrs.html" target="_self">SparseTransposeAttrs</a></td><td class="desc">Attributes for sparse_transpose operator </td></tr>
+<tr id="row_2_2_167_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SplitAttrs.html" target="_self">SplitAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_168_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SqueezeAttrs.html" target="_self">SqueezeAttrs</a></td><td class="desc">Attributes used in squeeze operators </td></tr>
+<tr id="row_2_2_169_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StackAttrs.html" target="_self">StackAttrs</a></td><td class="desc">Attributes used in stack operators </td></tr>
+<tr id="row_2_2_170_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1StridedSliceAttrs.html" target="_self">StridedSliceAttrs</a></td><td class="desc">Attributes for StridedSlice operator </td></tr>
+<tr id="row_2_2_171_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1SubPixelAttrs.html" target="_self">SubPixelAttrs</a></td><td class="desc">Attributes used in subpixel operators </td></tr>
+<tr id="row_2_2_172_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TakeAttrs.html" target="_self">TakeAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_173_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExpr.html" target="_self">TempExpr</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_174_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TempExprNode.html" target="_self">TempExprNode</a></td><td class="desc">Base class of the temporary expression </td></tr>
+<tr id="row_2_2_175_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TileAttrs.html" target="_self">TileAttrs</a></td><td class="desc">Attributes used in tile operators </td></tr>
+<tr id="row_2_2_176_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TopKAttrs.html" target="_self">TopKAttrs</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_177_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1TransposeAttrs.html" target="_self">TransposeAttrs</a></td><td class="desc">Attributes used in transpose operators </td></tr>
+<tr id="row_2_2_178_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Tuple.html" target="_self">Tuple</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_179_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItem.html" target="_self">TupleGetItem</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_180_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemNode.html" target="_self">TupleGetItemNode</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_181_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPattern.html" target="_self">TupleGetItemPattern</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_182_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleGetItemPatternNode.html" target="_self">TupleGetItemPatternNode</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_183_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TupleNode.html" target="_self">TupleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
+<tr id="row_2_2_184_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePattern.html" target="_self">TuplePattern</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_185_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TuplePatternNode.html" target="_self">TuplePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Tuple.html">Tuple</a> container </td></tr>
+<tr id="row_2_2_186_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePattern.html" target="_self">TypePattern</a></td><td class="desc">A pattern which matches a type in another pattern </td></tr>
+<tr id="row_2_2_187_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1TypePatternNode.html" target="_self">TypePatternNode</a></td><td class="desc"><a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> for Types </td></tr>
+<tr id="row_2_2_188_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSampling3DAttrs.html" target="_self">UpSampling3DAttrs</a></td><td class="desc">Attributes for upsampling3d operator </td></tr>
+<tr id="row_2_2_189_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1UpSamplingAttrs.html" target="_self">UpSamplingAttrs</a></td><td class="desc">Attributes for upsampling operator </td></tr>
+<tr id="row_2_2_190_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1Var.html" target="_self">Var</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_191_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarNode.html" target="_self">VarNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
+<tr id="row_2_2_192_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPattern.html" target="_self">VarPattern</a></td><td class="desc"></td></tr>
+<tr id="row_2_2_193_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1VarPatternNode.html" target="_self">VarPatternNode</a></td><td class="desc">Container for <a class="el" href="classtvm_1_1relay_1_1Var.html">Var</a> </td></tr>
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+<tr id="row_2_2_195_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1relay_1_1WildcardPatternNode.html" target="_self">WildcardPatternNode</a></td><td class="desc">Wildcard <a class="el" href="classtvm_1_1relay_1_1Pattern.html" title="Pattern is the base type for an ADT match pattern in Relay. ">Pattern</a> </td></tr>
+<tr id="row_2_2_196_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1relay_1_1YoloReorgAttrs.html" target="_self">YoloReorgAttrs</a></td><td class="desc">Attributes used in yolo reorg operators </td></tr>
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 <tr id="row_2_3_0_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1threading_1_1ThreadGroup.html" target="_self">ThreadGroup</a></td><td class="desc">A platform-agnostic abstraction for managing a collection of thread pool threads </td></tr>
@@ -350,9 +361,9 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
 <tr id="row_2_3_1_8_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1vm_1_1VMFunction.html" target="_self">VMFunction</a></td><td class="desc">A representation of a Relay function in the VM </td></tr>
 <tr id="row_2_3_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ADT.html" target="_self">ADT</a></td><td class="desc">Reference to algebraic data type objects </td></tr>
 <tr id="row_2_3_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ADTObj.html" target="_self">ADTObj</a></td><td class="desc">An object representing a structure or enumeration </td></tr>
-<tr id="row_2_3_4_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_2_3_4_" class="arrow" onclick="toggleFolder('2_3_4_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Array.html" target="_self">Array</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array container of ObjectRef in DSL graph. Array implements copy-on-writ [...]
+<tr id="row_2_3_4_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_2_3_4_" class="arrow" onclick="toggleFolder('2_3_4_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Array.html" target="_self">Array</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array, container representing a contigious sequence of ObjectRefs. ">Arr [...]
 <tr id="row_2_3_4_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1Array_1_1ValueConverter.html" target="_self">ValueConverter</a></td><td class="desc"></td></tr>
-<tr id="row_2_3_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html" target="_self">ArrayNode</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array container of ObjectRef in DSL graph. Array implements copy-on-write semantics, which means array is mutable but copy will happen when array i [...]
+<tr id="row_2_3_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ArrayNode.html" target="_self">ArrayNode</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1Array.html" title="Array, container representing a contigious sequence of ObjectRefs. ">Array</a> node content in array </td></tr>
 <tr id="row_2_3_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DataType.html" target="_self">DataType</a></td><td class="desc">Runtime primitive data type </td></tr>
 <tr id="row_2_3_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1DeviceAPI.html" target="_self">DeviceAPI</a></td><td class="desc">TVM Runtime Device API, abstracts the device specific interface for memory management </td></tr>
 <tr id="row_2_3_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1InplaceArrayBase.html" target="_self">InplaceArrayBase</a></td><td class="desc">Base template for classes with array like memory layout </td></tr>
@@ -365,9 +376,9 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
 <tr id="row_2_3_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1NullOptType.html" target="_self">NullOptType</a></td><td class="desc">Helper to represent nullptr for optional </td></tr>
 <tr id="row_2_3_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ObjAllocatorBase.html" target="_self">ObjAllocatorBase</a></td><td class="desc">Base class of object allocators that implements make. Use curiously recurring template pattern </td></tr>
 <tr id="row_2_3_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1Object.html" target="_self">Object</a></td><td class="desc">Base class of all object containers </td></tr>
-<tr id="row_2_3_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectEqual.html" target="_self">ObjectEqual</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference. ">ObjectRef</a> equal functor </td></tr>
-<tr id="row_2_3_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectHash.html" target="_self">ObjectHash</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference. ">ObjectRef</a> hash functor </td></tr>
-<tr id="row_2_3_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html" target="_self">ObjectPtr</a></td><td class="desc">A custom smart pointer for <a class="el" href="classtvm_1_1runtime_1_1Object.html" title="base class of all object containers. ">Object</a> </td></tr>
+<tr id="row_2_3_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ObjectPtr.html" target="_self">ObjectPtr</a></td><td class="desc">A custom smart pointer for <a class="el" href="classtvm_1_1runtime_1_1Object.html" title="base class of all object containers. ">Object</a> </td></tr>
+<tr id="row_2_3_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrEqual.html" target="_self">ObjectPtrEqual</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference. ">ObjectRef</a> equal functor </td></tr>
+<tr id="row_2_3_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectPtrHash.html" target="_self">ObjectPtrHash</a></td><td class="desc"><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" title="Base class of all object reference. ">ObjectRef</a> hash functor </td></tr>
 <tr id="row_2_3_19_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1ObjectRef.html" target="_self">ObjectRef</a></td><td class="desc">Base class of all object reference </td></tr>
 <tr id="row_2_3_20_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker.html" target="_self">ObjectTypeChecker</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> traits for runtime type check during FFI conversion </td></tr>
 <tr id="row_2_3_21_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1ObjectTypeChecker_3_01Array_3_01T_01_4_01_4.html" target="_self">ObjectTypeChecker&lt; Array&lt; T &gt; &gt;</a></td><td class="desc"></td></tr>
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 <tr id="row_2_3_42_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1runtime_1_1TypedPackedFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">TypedPackedFunc&lt; R(Args...)&gt;</a></td><td class="desc">A <a class="el" href="classtvm_1_1runtime_1_1PackedFunc.html" title="Packed function is a type-erased function. The arguments are passed by packed format [...]
 <tr id="row_2_3_43_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1runtime_1_1TypeIndex.html" target="_self">TypeIndex</a></td><td class="desc">Namespace for the list of type index </td></tr>
 <tr id="row_2_4_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_2_4_" class="arrow" onclick="toggleFolder('2_4_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1te.html" target="_self">te</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Tensor</a [...]
-<tr id="row_2_4_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1BaseComputeOpNode.html" target="_self">BaseComputeOpNode</a></td><td class="desc">A Compute op that compute a tensor on certain domain. This is the base class for ComputeOp (operating on a scalar at a time) and TensorComputeOp (operating on a TensorSlice at a time) </td></tr>
-<tr id="row_2_4_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html" target="_self">ComputeOpNode</a></td><td class="desc">A Compute op that compute a tensor on certain domain </td></tr>
-<tr id="row_2_4_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ExternOpNode.html" target="_self">ExternOpNode</a></td><td class="desc">External computation that cannot be splitted </td></tr>
-<tr id="row_2_4_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1FuseNode.html" target="_self">FuseNode</a></td><td class="desc">Fuse two domains into one domain </td></tr>
-<tr id="row_2_4_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1HybridOpNode.html" target="_self">HybridOpNode</a></td><td class="desc">A computation operator that generated by hybrid script </td></tr>
-<tr id="row_2_4_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarAttr.html" target="_self">IterVarAttr</a></td><td class="desc">Additional scheduable attributes about IterVar </td></tr>
-<tr id="row_2_4_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarAttrNode.html" target="_self">IterVarAttrNode</a></td><td class="desc">Node container for IterVar attr </td></tr>
-<tr id="row_2_4_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarRelation.html" target="_self">IterVarRelation</a></td><td class="desc">The schedule relation between IterVars can be Split, Fuse </td></tr>
-<tr id="row_2_4_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarRelationNode.html" target="_self">IterVarRelationNode</a></td><td class="desc">Base node of iteration var </td></tr>
-<tr id="row_2_4_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Operation.html" target="_self">Operation</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Operation.html" title="Operation that produces tensors. ">Operation</a> that produces tensors </td></tr>
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-<tr id="row_2_4_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ScanOpNode.html" target="_self">ScanOpNode</a></td><td class="desc">Symbolic scan </td></tr>
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-<tr id="row_2_4_23_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorComputeOpNode.html" target="_self">TensorComputeOpNode</a></td><td class="desc">A TenorCompute op that compute a tensor with an tensor intrinsic </td></tr>
-<tr id="row_2_4_24_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1te_1_1TensorDom.html" target="_self">TensorDom</a></td><td class="desc">Temporary data structure to store union of bounds of each axis of <a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Te [...]
-<tr id="row_2_4_25_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrin.html" target="_self">TensorIntrin</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Tensor</a> intrinsic node </td></tr>
-<tr id="row_2_4_26_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrinCall.html" target="_self">TensorIntrinCall</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Tensor</a> intrinsic calling node </td></tr>
-<tr id="row_2_4_27_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrinCallNode.html" target="_self">TensorIntrinCallNode</a></td><td class="desc"></td></tr>
-<tr id="row_2_4_28_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrinNode.html" target="_self">TensorIntrinNode</a></td><td class="desc">Node to represent a <a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Tensor</a> intrinsic operator </td></tr>
-<tr id="row_2_4_29_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorNode.html" target="_self">TensorNode</a></td><td class="desc">Node to represent a tensor </td></tr>
+<tr id="row_2_4_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1BaseComputeOpNode.html" target="_self">BaseComputeOpNode</a></td><td class="desc">A Compute op that compute a tensor on certain domain. This is the base class for <a class="el" href="classtvm_1_1te_1_1ComputeOp.html" title="Managed reference to ComputeOpNode. ">ComputeOp</a> (ope [...]
+<tr id="row_2_4_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ComputeOp.html" target="_self">ComputeOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html" title="A Compute op that compute a tensor on certain domain. ">ComputeOpNode</a> </td></tr>
+<tr id="row_2_4_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ComputeOpNode.html" target="_self">ComputeOpNode</a></td><td class="desc">A Compute op that compute a tensor on certain domain </td></tr>
+<tr id="row_2_4_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ExternOp.html" target="_self">ExternOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ExternOpNode.html" title="External computation that cannot be splitted. ">ExternOpNode</a> </td></tr>
+<tr id="row_2_4_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ExternOpNode.html" target="_self">ExternOpNode</a></td><td class="desc">External computation that cannot be splitted </td></tr>
+<tr id="row_2_4_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Fuse.html" target="_self">Fuse</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1FuseNode.html" title="Fuse two domains into one domain. ">FuseNode</a> </td></tr>
+<tr id="row_2_4_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1FuseNode.html" target="_self">FuseNode</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Fuse.html" title="Managed reference to FuseNode. ">Fuse</a> two domains into one domain </td></tr>
+<tr id="row_2_4_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1HybridOp.html" target="_self">HybridOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1HybridOpNode.html" title="A computation operator that generated by hybrid script. ">HybridOpNode</a> </td></tr>
+<tr id="row_2_4_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1HybridOpNode.html" target="_self">HybridOpNode</a></td><td class="desc">A computation operator that generated by hybrid script </td></tr>
+<tr id="row_2_4_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarAttr.html" target="_self">IterVarAttr</a></td><td class="desc">Additional scheduable attributes about IterVar </td></tr>
+<tr id="row_2_4_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarAttrNode.html" target="_self">IterVarAttrNode</a></td><td class="desc">Node container for IterVar attr </td></tr>
+<tr id="row_2_4_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarRelation.html" target="_self">IterVarRelation</a></td><td class="desc">The schedule relation between IterVars can be <a class="el" href="classtvm_1_1te_1_1Split.html" title="Managed reference to SplitNode. ">Split</a>, <a class="el" href="classtvm_1_1te_1_1Fuse.html" titl [...]
+<tr id="row_2_4_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1IterVarRelationNode.html" target="_self">IterVarRelationNode</a></td><td class="desc">Base node of iteration var </td></tr>
+<tr id="row_2_4_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Operation.html" target="_self">Operation</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Operation.html" title="Operation that produces tensors. ">Operation</a> that produces tensors </td></tr>
+<tr id="row_2_4_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1OperationNode.html" target="_self">OperationNode</a></td><td class="desc">Base class of all operation nodes </td></tr>
+<tr id="row_2_4_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1PlaceholderOp.html" target="_self">PlaceholderOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html" title="A placeholder op represents an input placeholder. ">PlaceholderOpNode</a> </td></tr>
+<tr id="row_2_4_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1PlaceholderOpNode.html" target="_self">PlaceholderOpNode</a></td><td class="desc">A placeholder op represents an input placeholder </td></tr>
+<tr id="row_2_4_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Rebase.html" target="_self">Rebase</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1RebaseNode.html" title="Rebase the iteration to make min to be 0. This is useful to normalize the Schedule to make every leaf...">RebaseNode</a> </td></tr>
+<tr id="row_2_4_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1RebaseNode.html" target="_self">RebaseNode</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Rebase.html" title="Managed reference to RebaseNode. ">Rebase</a> the iteration to make min to be 0. This is useful to normalize the <a class="el" href="classtvm_1_1te_1_1Sc [...]
+<tr id="row_2_4_19_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ScanOp.html" target="_self">ScanOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1ScanOpNode.html" title="Symbolic scan. ">ScanOpNode</a> </td></tr>
+<tr id="row_2_4_20_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ScanOpNode.html" target="_self">ScanOpNode</a></td><td class="desc">Symbolic scan </td></tr>
+<tr id="row_2_4_21_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Schedule.html" target="_self">Schedule</a></td><td class="desc">Global schedule container For operations and all the operations they depend on. The schedule per <a class="el" href="classtvm_1_1te_1_1Operation.html" title="Operation that produces tensors. ">Operation</a> is named [...]
+<tr id="row_2_4_22_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1ScheduleNode.html" target="_self">ScheduleNode</a></td><td class="desc">Node container for schedule </td></tr>
+<tr id="row_2_4_23_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Singleton.html" target="_self">Singleton</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1SingletonNode.html" title="Singleton iterator [0, 1) ">SingletonNode</a> </td></tr>
+<tr id="row_2_4_24_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1SingletonNode.html" target="_self">SingletonNode</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Singleton.html" title="Managed reference to SingletonNode. ">Singleton</a> iterator [0, 1) </td></tr>
+<tr id="row_2_4_25_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1SpecializedCondition.html" target="_self">SpecializedCondition</a></td><td class="desc">Specialized condition to enable op specialization </td></tr>
+<tr id="row_2_4_26_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1SpecializedConditionNode.html" target="_self">SpecializedConditionNode</a></td><td class="desc">Container for specialization conditions </td></tr>
+<tr id="row_2_4_27_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Split.html" target="_self">Split</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1SplitNode.html" title="Split the parent domain into product of outer and iter. ">SplitNode</a> </td></tr>
+<tr id="row_2_4_28_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1SplitNode.html" target="_self">SplitNode</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Split.html" title="Managed reference to SplitNode. ">Split</a> the parent domain into product of outer and iter </td></tr>
+<tr id="row_2_4_29_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Stage.html" target="_self">Stage</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Stage.html" title="Stage, contains scheduling for a stage of computation. ">Stage</a>, contains scheduling for a stage of computation </td></tr>
+<tr id="row_2_4_30_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1StageNode.html" target="_self">StageNode</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Stage.html" title="Stage, contains scheduling for a stage of computation. ">Stage</a> </td></tr>
+<tr id="row_2_4_31_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_2_4_31_" class="arrow" onclick="toggleFolder('2_4_31_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Tensor.html" target="_self">Tensor</a></td><td class="desc"><a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation  [...]
+<tr id="row_2_4_31_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1Tensor_1_1Slice.html" target="_self">Slice</a></td><td class="desc">Data structure to represent a slice that fixes first k coordinates. This is used to enable syntax sugar of <a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible inp [...]
+<tr id="row_2_4_32_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorComputeOp.html" target="_self">TensorComputeOp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorComputeOpNode.html" title="A TenorCompute op that compute a tensor with an tensor intrinsic. ">TensorComputeOpNode</a> </td></tr>
+<tr id="row_2_4_33_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorComputeOpNode.html" target="_self">TensorComputeOpNode</a></td><td class="desc">A TenorCompute op that compute a tensor with an tensor intrinsic </td></tr>
+<tr id="row_2_4_34_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1te_1_1TensorDom.html" target="_self">TensorDom</a></td><td class="desc">Temporary data structure to store union of bounds of each axis of <a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Te [...]
+<tr id="row_2_4_35_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrin.html" target="_self">TensorIntrin</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorIntrinNode.html" title="Node to represent a Tensor intrinsic operator. ">TensorIntrinNode</a> </td></tr>
+<tr id="row_2_4_36_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrinCall.html" target="_self">TensorIntrinCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1te_1_1TensorIntrinCallNode.html">TensorIntrinCallNode</a> </td></tr>
+<tr id="row_2_4_37_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrinCallNode.html" target="_self">TensorIntrinCallNode</a></td><td class="desc"></td></tr>
+<tr id="row_2_4_38_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorIntrinNode.html" target="_self">TensorIntrinNode</a></td><td class="desc">Node to represent a <a class="el" href="classtvm_1_1te_1_1Tensor.html" title="Tensor structure representing a possible input, or intermediate computation result. ">Tensor</a> intrinsic operator </td></tr>
+<tr id="row_2_4_39_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1te_1_1TensorNode.html" target="_self">TensorNode</a></td><td class="desc">Node to represent a tensor </td></tr>
 <tr id="row_2_5_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_2_5_" class="arrow" onclick="toggleFolder('2_5_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1tir.html" target="_self">tir</a></td><td class="desc"></td></tr>
-<tr id="row_2_5_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AddNode.html" target="_self">AddNode</a></td><td class="desc"><ul>
+<tr id="row_2_5_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Add.html" target="_self">Add</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AddNode.html" title="a + b ">AddNode</a> </td></tr>
+<tr id="row_2_5_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AddNode.html" target="_self">AddNode</a></td><td class="desc"><ul>
 <li>b </li>
 </ul>
 </td></tr>
-<tr id="row_2_5_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AllocateNode.html" target="_self">AllocateNode</a></td><td class="desc">Allocate a buffer that can be used in body </td></tr>
-<tr id="row_2_5_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AndNode.html" target="_self">AndNode</a></td><td class="desc">&amp;&amp; b </td></tr>
-<tr id="row_2_5_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AnyNode.html" target="_self">AnyNode</a></td><td class="desc">Any shape </td></tr>
-<tr id="row_2_5_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AssertStmtNode.html" target="_self">AssertStmtNode</a></td><td class="desc">Assert condition, if an error occurs, return the error message </td></tr>
-<tr id="row_2_5_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html" target="_self">AttrStmtNode</a></td><td class="desc">Define certain auxiliary attribute for the body to be a symbolic value. This provide auxiliary information for IR passes that transforms body </td></tr>
-<tr id="row_2_5_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html" target="_self">BijectiveLayout</a></td><td class="desc">Bijective function mapping for data layout transformation. Given two <a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Layout is to describe how data is organized within an N-dimention tensor. [...]
-<tr id="row_2_5_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayoutNode.html" target="_self">BijectiveLayoutNode</a></td><td class="desc"></td></tr>
-<tr id="row_2_5_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BinaryOpNode.html" target="_self">BinaryOpNode</a></td><td class="desc">Base template to implement binary ops </td></tr>
-<tr id="row_2_5_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html" target="_self">BroadcastNode</a></td><td class="desc">Create a vector where all the elements are value </td></tr>
-<tr id="row_2_5_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Buffer.html" target="_self">Buffer</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Buffer.html" title="Buffer is a symbolic n-darray structure. It is a composition of primitive symbolic types...">Buffer</a> is a symbolic n-darray structure. It is a composition o [...]
-<tr id="row_2_5_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html" target="_self">BufferLoad</a></td><td class="desc"></td></tr>
-<tr id="row_2_5_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html" target="_self">BufferLoadNode</a></td><td class="desc">Load value from the high dimension buffer </td></tr>
-<tr id="row_2_5_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferNode.html" target="_self">BufferNode</a></td><td class="desc">Node to represent a buffer </td></tr>
-<tr id="row_2_5_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html" target="_self">BufferRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html" title="Annotate the region where the buffer need to be read and write in the body. We only need to allocate ...">BufferR [...]
-<tr id="row_2_5_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html" target="_self">BufferRealizeNode</a></td><td class="desc">Annotate the region where the buffer need to be read and write in the body. We only need to allocate the space for the corresponding region </td></tr>
-<tr id="row_2_5_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferStore.html" target="_self">BufferStore</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html" title="Store value to the high dimension buffer. ">BufferStoreNode</a> </td></tr>
-<tr id="row_2_5_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html" target="_self">BufferStoreNode</a></td><td class="desc">Store value to the high dimension buffer </td></tr>
-<tr id="row_2_5_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CallNode.html" target="_self">CallNode</a></td><td class="desc">Call node </td></tr>
-<tr id="row_2_5_19_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CastNode.html" target="_self">CastNode</a></td><td class="desc">Cast value from one data type to another </td></tr>
-<tr id="row_2_5_20_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CmpOpNode.html" target="_self">CmpOpNode</a></td><td class="desc">Base template to implement comparison ops </td></tr>
-<tr id="row_2_5_21_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducer.html" target="_self">CommReducer</a></td><td class="desc"></td></tr>
-<tr id="row_2_5_22_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html" target="_self">CommReducerNode</a></td><td class="desc">A commutative reducer node to represent a commutative binary operator with identity element </td></tr>
-<tr id="row_2_5_23_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DivNode.html" target="_self">DivNode</a></td><td class="desc">/ b in the C semnatics </td></tr>
-<tr id="row_2_5_24_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EQNode.html" target="_self">EQNode</a></td><td class="desc">== b </td></tr>
-<tr id="row_2_5_25_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html" target="_self">EvaluateNode</a></td><td class="desc">Evaluates an expression. This is mostly used for putting a Call node into <a class="el" href="classtvm_1_1tir_1_1Stmt.html" title="Container of all statements. ">Stmt</a> </td></tr>
-<tr id="row_2_5_26_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1tir_1_1ExprDeepEqual.html" target="_self">ExprDeepEqual</a></td><td class="desc">Compare two expressions recursively and check if they are equal to each other without var remapping </td></tr>
-<tr id="row_2_5_27_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html" target="_self">ExprFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerful Visitor, since it allows you to define function signatures of Visit Function </td></tr>
-<tr id="row_2_5_28_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprFunctor_3_01R_07const_01PrimExpr_01_6n_00_01Args_8_8_8_08_4.html" target="_self">ExprFunctor&lt; R(const PrimExpr &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
-<tr id="row_2_5_29_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html" target="_self">ExprMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html" title="ExprMutator that mutates expressions. ">ExprMutator</a> that mutates expressions </td></tr>
-<tr id="row_2_5_30_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html" target="_self">ExprVisitor</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html" title="ExprVisitor. ">ExprVisitor</a> </td></tr>
-<tr id="row_2_5_31_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html" target="_self">FloorDivNode</a></td><td class="desc">Floor division, floor(a/b) </td></tr>
-<tr id="row_2_5_32_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorModNode.html" target="_self">FloorModNode</a></td><td class="desc">The remainder of the floordiv </td></tr>
-<tr id="row_2_5_33_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ForNode.html" target="_self">ForNode</a></td><td class="desc">A for loop, with poissible type annotations </td></tr>
-<tr id="row_2_5_34_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FreeNode.html" target="_self">FreeNode</a></td><td class="desc">Free the resources in the buffer before the scope ends </td></tr>
-<tr id="row_2_5_35_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FunctionBaseNode.html" target="_self">FunctionBaseNode</a></td><td class="desc">Base node of internal functions </td></tr>
-<tr id="row_2_5_36_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FunctionRef.html" target="_self">FunctionRef</a></td><td class="desc">Reference to a function </td></tr>
-<tr id="row_2_5_37_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GENode.html" target="_self">GENode</a></td><td class="desc">&gt;= b </td></tr>
-<tr id="row_2_5_38_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GTNode.html" target="_self">GTNode</a></td><td class="desc">&gt; b </td></tr>
-<tr id="row_2_5_39_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html" target="_self">IfThenElseNode</a></td><td class="desc">IfThenElse statment </td></tr>
-<tr id="row_2_5_40_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IterVar.html" target="_self">IterVar</a></td><td class="desc">Iteration Variable, represents an iteration over an integer interval </td></tr>
-<tr id="row_2_5_41_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IterVarNode.html" target="_self">IterVarNode</a></td><td class="desc">An iteration variable representing an iteration over a one dimensional interval </td></tr>
-<tr id="row_2_5_42_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Layout.html" target="_self">Layout</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Layout is to describe how data is organized within an N-dimention tensor. It is composed of upper cas...">Layout</a> is to describe how data is organized withi [...]
-<tr id="row_2_5_43_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html" target="_self">LayoutAxis</a></td><td class="desc"></td></tr>
-<tr id="row_2_5_44_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LayoutNode.html" target="_self">LayoutNode</a></td><td class="desc"></td></tr>
-<tr id="row_2_5_45_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1tir_1_1LENode.html" target="_self">LENode</a></td><td class="desc">&lt;= b </td></tr>
-<tr id="row_2_5_46_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LetNode.html" target="_self">LetNode</a></td><td class="desc">Let binding. Bind var to value then evaluate body </td></tr>
-<tr id="row_2_5_47_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html" target="_self">LetStmtNode</a></td><td class="desc">Let binding, bind var to value, then run body </td></tr>
-<tr id="row_2_5_48_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LoadNode.html" target="_self">LoadNode</a></td><td class="desc">Load the value from buffer_var </td></tr>
-<tr id="row_2_5_49_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LTNode.html" target="_self">LTNode</a></td><td class="desc">&lt; b </td></tr>
-<tr id="row_2_5_50_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MaxNode.html" target="_self">MaxNode</a></td><td class="desc">Max(a, b) </td></tr>
-<tr id="row_2_5_51_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MinNode.html" target="_self">MinNode</a></td><td class="desc">Min(a, b) </td></tr>
-<tr id="row_2_5_52_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ModNode.html" target="_self">ModNode</a></td><td class="desc">% b in the C semnatics </td></tr>
-<tr id="row_2_5_53_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MulNode.html" target="_self">MulNode</a></td><td class="desc"><ul>
+<tr id="row_2_5_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Allocate.html" target="_self">Allocate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AllocateNode.html" title="Allocate a buffer that can be used in body. ">AllocateNode</a> </td></tr>
+<tr id="row_2_5_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AllocateNode.html" target="_self">AllocateNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Allocate.html" title="Managed reference to AllocateNode. ">Allocate</a> a buffer that can be used in body </td></tr>
+<tr id="row_2_5_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1And.html" target="_self">And</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AndNode.html" title="a &amp;&amp; b ">AndNode</a> </td></tr>
+<tr id="row_2_5_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AndNode.html" target="_self">AndNode</a></td><td class="desc">&amp;&amp; b </td></tr>
+<tr id="row_2_5_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Any.html" target="_self">Any</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AnyNode.html" title="Any shape. ">AnyNode</a> </td></tr>
+<tr id="row_2_5_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AnyNode.html" target="_self">AnyNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Any.html" title="Managed reference to AnyNode. ">Any</a> shape </td></tr>
+<tr id="row_2_5_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AssertStmt.html" target="_self">AssertStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AssertStmtNode.html" title="Assert condition, if an error occurs, return the error message. ">AssertStmtNode</a> </td></tr>
+<tr id="row_2_5_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AssertStmtNode.html" target="_self">AssertStmtNode</a></td><td class="desc">Assert condition, if an error occurs, return the error message </td></tr>
+<tr id="row_2_5_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AttrStmt.html" target="_self">AttrStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html" title="Define certain auxiliary attribute for the body to be a symbolic value. This provide auxiliary inform...">AttrStmtNode</a> </td></tr>
+<tr id="row_2_5_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1AttrStmtNode.html" target="_self">AttrStmtNode</a></td><td class="desc">Define certain auxiliary attribute for the body to be a symbolic value. This provide auxiliary information for IR passes that transforms body </td></tr>
+<tr id="row_2_5_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayout.html" target="_self">BijectiveLayout</a></td><td class="desc">Bijective function mapping for data layout transformation. Given two <a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Managed reference to LayoutNode. ">Layout</a>, <a class="el" href="class [...]
+<tr id="row_2_5_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BijectiveLayoutNode.html" target="_self">BijectiveLayoutNode</a></td><td class="desc"></td></tr>
+<tr id="row_2_5_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BinaryOpNode.html" target="_self">BinaryOpNode</a></td><td class="desc">Base template to implement binary ops </td></tr>
+<tr id="row_2_5_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Broadcast.html" target="_self">Broadcast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html" title="Create a vector where all the elements are value. ">BroadcastNode</a> </td></tr>
+<tr id="row_2_5_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BroadcastNode.html" target="_self">BroadcastNode</a></td><td class="desc">Create a vector where all the elements are value </td></tr>
+<tr id="row_2_5_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Buffer.html" target="_self">Buffer</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Buffer.html" title="Buffer is a symbolic n-darray structure. It is a composition of primitive symbolic types...">Buffer</a> is a symbolic n-darray structure. It is a composition o [...]
+<tr id="row_2_5_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoad.html" target="_self">BufferLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html" title="Load value from the high dimension buffer. ">BufferLoadNode</a> </td></tr>
+<tr id="row_2_5_19_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferLoadNode.html" target="_self">BufferLoadNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Load.html" title="Managed reference to LoadNode. ">Load</a> value from the high dimension buffer </td></tr>
+<tr id="row_2_5_20_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferNode.html" target="_self">BufferNode</a></td><td class="desc">Node to represent a buffer </td></tr>
+<tr id="row_2_5_21_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRealize.html" target="_self">BufferRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html" title="Annotate the region where the buffer need to be read and write in the body. We only need to allocate ...">BufferR [...]
+<tr id="row_2_5_22_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferRealizeNode.html" target="_self">BufferRealizeNode</a></td><td class="desc">Annotate the region where the buffer need to be read and write in the body. We only need to allocate the space for the corresponding region </td></tr>
+<tr id="row_2_5_23_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferStore.html" target="_self">BufferStore</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html" title="Store value to the high dimension buffer. ">BufferStoreNode</a> </td></tr>
+<tr id="row_2_5_24_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1BufferStoreNode.html" target="_self">BufferStoreNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Store.html" title="Managed reference to StoreNode. ">Store</a> value to the high dimension buffer </td></tr>
+<tr id="row_2_5_25_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Call.html" target="_self">Call</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CallNode.html" title="Call node. ">CallNode</a> </td></tr>
+<tr id="row_2_5_26_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CallNode.html" target="_self">CallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Call.html" title="Managed reference to CallNode. ">Call</a> node </td></tr>
+<tr id="row_2_5_27_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Cast.html" target="_self">Cast</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CastNode.html" title="Cast value from one data type to another. ">CastNode</a> </td></tr>
+<tr id="row_2_5_28_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CastNode.html" target="_self">CastNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Cast.html" title="Managed reference to CastNode. ">Cast</a> value from one data type to another </td></tr>
+<tr id="row_2_5_29_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CmpOpNode.html" target="_self">CmpOpNode</a></td><td class="desc">Base template to implement comparison ops </td></tr>
+<tr id="row_2_5_30_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducer.html" target="_self">CommReducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html" title="A commutative reducer node to represent a commutative binary operator with identity element...">CommReducerNode</a> </td></tr>
+<tr id="row_2_5_31_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1CommReducerNode.html" target="_self">CommReducerNode</a></td><td class="desc">A commutative reducer node to represent a commutative binary operator with identity element </td></tr>
+<tr id="row_2_5_32_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataProducer.html" target="_self">DataProducer</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html" title="Base node for data producers. ">DataProducerNode</a> </td></tr>
+<tr id="row_2_5_33_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DataProducerNode.html" target="_self">DataProducerNode</a></td><td class="desc">Base node for data producers </td></tr>
+<tr id="row_2_5_34_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Div.html" target="_self">Div</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1DivNode.html" title="a / b in the C semnatics. ">DivNode</a> </td></tr>
+<tr id="row_2_5_35_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1DivNode.html" target="_self">DivNode</a></td><td class="desc">/ b in the C semnatics </td></tr>
+<tr id="row_2_5_36_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EQ.html" target="_self">EQ</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EQNode.html" title="a == b ">EQNode</a> </td></tr>
+<tr id="row_2_5_37_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EQNode.html" target="_self">EQNode</a></td><td class="desc">== b </td></tr>
+<tr id="row_2_5_38_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Evaluate.html" target="_self">Evaluate</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html" title="Evaluates an expression. This is mostly used for putting a Call node into Stmt. ">EvaluateNode</a> </td></tr>
+<tr id="row_2_5_39_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1EvaluateNode.html" target="_self">EvaluateNode</a></td><td class="desc">Evaluates an expression. This is mostly used for putting a <a class="el" href="classtvm_1_1tir_1_1Call.html" title="Managed reference to CallNode. ">Call</a> node into <a class="el" href="classtvm_1_1tir_1_ [...]
+<tr id="row_2_5_40_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1tir_1_1ExprDeepEqual.html" target="_self">ExprDeepEqual</a></td><td class="desc">Compare two expressions recursively and check if they are equal to each other without var remapping </td></tr>
+<tr id="row_2_5_41_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html" target="_self">ExprFunctor</a></td><td class="desc">A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerful Visitor, since it allows you to define function signatures of Visit Function </td></tr>
+<tr id="row_2_5_42_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprFunctor_3_01R_07const_01PrimExpr_01_6n_00_01Args_8_8_8_08_4.html" target="_self">ExprFunctor&lt; R(const PrimExpr &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
+<tr id="row_2_5_43_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html" target="_self">ExprMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1ExprMutator.html" title="ExprMutator that mutates expressions. ">ExprMutator</a> that mutates expressions </td></tr>
+<tr id="row_2_5_44_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html" target="_self">ExprVisitor</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1ExprVisitor.html" title="ExprVisitor. ">ExprVisitor</a> </td></tr>
+<tr id="row_2_5_45_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorDiv.html" target="_self">FloorDiv</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html" title="Floor division, floor(a/b) ">FloorDivNode</a> </td></tr>
+<tr id="row_2_5_46_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorDivNode.html" target="_self">FloorDivNode</a></td><td class="desc">Floor division, floor(a/b) </td></tr>
+<tr id="row_2_5_47_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorMod.html" target="_self">FloorMod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FloorModNode.html" title="The remainder of the floordiv. ">FloorModNode</a> </td></tr>
+<tr id="row_2_5_48_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FloorModNode.html" target="_self">FloorModNode</a></td><td class="desc">The remainder of the floordiv </td></tr>
+<tr id="row_2_5_49_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1For.html" target="_self">For</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ForNode.html" title="A for loop, with poissible type annotations. ">ForNode</a> </td></tr>
+<tr id="row_2_5_50_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ForNode.html" target="_self">ForNode</a></td><td class="desc">A for loop, with poissible type annotations </td></tr>
+<tr id="row_2_5_51_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Free.html" target="_self">Free</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1FreeNode.html" title="Free the resources in the buffer before the scope ends. ">FreeNode</a> </td></tr>
+<tr id="row_2_5_52_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1FreeNode.html" target="_self">FreeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Free.html" title="Managed reference to FreeNode. ">Free</a> the resources in the buffer before the scope ends </td></tr>
+<tr id="row_2_5_53_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GE.html" target="_self">GE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GENode.html" title="a &gt;= b ">GENode</a> </td></tr>
+<tr id="row_2_5_54_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GENode.html" target="_self">GENode</a></td><td class="desc">&gt;= b </td></tr>
+<tr id="row_2_5_55_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GT.html" target="_self">GT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1GTNode.html" title="a &gt; b ">GTNode</a> </td></tr>
+<tr id="row_2_5_56_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1GTNode.html" target="_self">GTNode</a></td><td class="desc">&gt; b </td></tr>
+<tr id="row_2_5_57_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html" target="_self">IfThenElse</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html" title="IfThenElse statment. ">IfThenElseNode</a> </td></tr>
+<tr id="row_2_5_58_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IfThenElseNode.html" target="_self">IfThenElseNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1IfThenElse.html" title="Managed reference to IfThenElseNode. ">IfThenElse</a> statment </td></tr>
+<tr id="row_2_5_59_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IterVar.html" target="_self">IterVar</a></td><td class="desc">Iteration Variable, represents an iteration over an integer interval </td></tr>
+<tr id="row_2_5_60_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1IterVarNode.html" target="_self">IterVarNode</a></td><td class="desc">An iteration variable representing an iteration over a one dimensional interval </td></tr>
+<tr id="row_2_5_61_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Layout.html" target="_self">Layout</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LayoutNode.html" title="Layout is to describe how data is organized within an N-dimention tensor. It is composed of upper cas...">LayoutNode</a> </td></tr>
+<tr id="row_2_5_62_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LayoutAxis.html" target="_self">LayoutAxis</a></td><td class="desc"></td></tr>
+<tr id="row_2_5_63_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LayoutNode.html" target="_self">LayoutNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Layout.html" title="Managed reference to LayoutNode. ">Layout</a> is to describe how data is organized within an N-dimention tensor. It is composed of upper cases, lower c [...]
+<tr id="row_2_5_64_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LE.html" target="_self">LE</a></td><td class="desc">Managed reference to <a class="el" href="structtvm_1_1tir_1_1LENode.html" title="a &lt;= b ">LENode</a> </td></tr>
+<tr id="row_2_5_65_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1tir_1_1LENode.html" target="_self">LENode</a></td><td class="desc">&lt;= b </td></tr>
+<tr id="row_2_5_66_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Let.html" target="_self">Let</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetNode.html" title="Let binding. Bind var to value then evaluate body. ">LetNode</a> </td></tr>
+<tr id="row_2_5_67_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LetNode.html" target="_self">LetNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Let.html" title="Managed reference to LetNode. ">Let</a> binding. Bind var to value then evaluate body </td></tr>
+<tr id="row_2_5_68_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LetStmt.html" target="_self">LetStmt</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html" title="Let binding, bind var to value, then run body. ">LetStmtNode</a> </td></tr>
+<tr id="row_2_5_69_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LetStmtNode.html" target="_self">LetStmtNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Let.html" title="Managed reference to LetNode. ">Let</a> binding, bind var to value, then run body </td></tr>
+<tr id="row_2_5_70_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Load.html" target="_self">Load</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LoadNode.html" title="Load the value from buffer_var. ">LoadNode</a> </td></tr>
+<tr id="row_2_5_71_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LoadNode.html" target="_self">LoadNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Load.html" title="Managed reference to LoadNode. ">Load</a> the value from buffer_var </td></tr>
+<tr id="row_2_5_72_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LT.html" target="_self">LT</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1LTNode.html" title="a &lt; b ">LTNode</a> </td></tr>
+<tr id="row_2_5_73_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1LTNode.html" target="_self">LTNode</a></td><td class="desc">&lt; b </td></tr>
+<tr id="row_2_5_74_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Max.html" target="_self">Max</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MaxNode.html" title="max(a, b) ">MaxNode</a> </td></tr>
+<tr id="row_2_5_75_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MaxNode.html" target="_self">MaxNode</a></td><td class="desc">Max(a, b) </td></tr>
+<tr id="row_2_5_76_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Min.html" target="_self">Min</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MinNode.html" title="min(a, b) ">MinNode</a> </td></tr>
+<tr id="row_2_5_77_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MinNode.html" target="_self">MinNode</a></td><td class="desc">Min(a, b) </td></tr>
+<tr id="row_2_5_78_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mod.html" target="_self">Mod</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ModNode.html" title="a % b in the C semnatics. ">ModNode</a> </td></tr>
+<tr id="row_2_5_79_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ModNode.html" target="_self">ModNode</a></td><td class="desc">% b in the C semnatics </td></tr>
+<tr id="row_2_5_80_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Mul.html" target="_self">Mul</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1MulNode.html" title="a * b ">MulNode</a> </td></tr>
+<tr id="row_2_5_81_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1MulNode.html" target="_self">MulNode</a></td><td class="desc"><ul>
 <li>b </li>
 </ul>
 </td></tr>
-<tr id="row_2_5_54_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NENode.html" target="_self">NENode</a></td><td class="desc">!= b </td></tr>
-<tr id="row_2_5_55_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NotNode.html" target="_self">NotNode</a></td><td class="desc">!a </td></tr>
-<tr id="row_2_5_56_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1OrNode.html" target="_self">OrNode</a></td><td class="desc">|| b </td></tr>
-<tr id="row_2_5_57_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Prefetch.html" target="_self">Prefetch</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrefetchNode.html" title="A prefetch hint for abuffer. ">PrefetchNode</a> </td></tr>
-<tr id="row_2_5_58_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrefetchNode.html" target="_self">PrefetchNode</a></td><td class="desc">A prefetch hint for abuffer </td></tr>
-<tr id="row_2_5_59_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFunc.html" target="_self">PrimFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" title="Primitive functions that contains TIR statements. ">PrimFuncNode</a> </td></tr>
-<tr id="row_2_5_60_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" target="_self">PrimFuncNode</a></td><td class="desc">Primitive functions that contains TIR statements </td></tr>
-<tr id="row_2_5_61_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProvideNode.html" target="_self">ProvideNode</a></td><td class="desc">Store value into mult-dimensional array defined by func </td></tr>
-<tr id="row_2_5_62_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1RampNode.html" target="_self">RampNode</a></td><td class="desc">Construct a vector with lanes elements where its i-th element equals base + i * stride. This is useful to construct a index for a continuous vector load </td></tr>
-<tr id="row_2_5_63_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1RealizeNode.html" target="_self">RealizeNode</a></td><td class="desc">Annotate the bounds where func need to be written and read in body. We will need to allocate space for the corresponding regions </td></tr>
-<tr id="row_2_5_64_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ReduceNode.html" target="_self">ReduceNode</a></td><td class="desc">Reduction operator operator </td></tr>
-<tr id="row_2_5_65_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SelectNode.html" target="_self">SelectNode</a></td><td class="desc">Return true_value if condition is true, otherwise return false_value </td></tr>
-<tr id="row_2_5_66_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_2_5_66_" class="arrow" onclick="toggleFolder('2_5_66_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SeqStmt.html" target="_self">SeqStmt</a></td><td class="desc">Sequence statement </td></tr>
-<tr id="row_2_5_66_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SeqStmt_1_1Flattener.html" target="_self">Flattener</a></td><td class="desc">Helper class to flatten sequence of arguments into Array </td></tr>
-<tr id="row_2_5_67_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SeqStmtNode.html" target="_self">SeqStmtNode</a></td><td class="desc">The container of seq statement. Represent a sequence of statements </td></tr>
-<tr id="row_2_5_68_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html" target="_self">ShuffleNode</a></td><td class="desc">Shuffle instruction. vec = concat(vectors) result = (vec[indices[0]], vec[indices[1]] ...) </td></tr>
-<tr id="row_2_5_69_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SizeVar.html" target="_self">SizeVar</a></td><td class="desc">Named variable represents a tensor index size </td></tr>
-<tr id="row_2_5_70_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SizeVarNode.html" target="_self">SizeVarNode</a></td><td class="desc">A variable node represent a tensor index size, whose value must be non-negative </td></tr>
-<tr id="row_2_5_71_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Stmt.html" target="_self">Stmt</a></td><td class="desc">Container of all statements </td></tr>
-<tr id="row_2_5_72_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtExprMutator.html" target="_self">StmtExprMutator</a></td><td class="desc">Mutator that recursively mutates stmts and exprs on them </td></tr>
-<tr id="row_2_5_73_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtExprVisitor.html" target="_self">StmtExprVisitor</a></td><td class="desc">Visitor that recursively visit stmts and exprs on them </td></tr>
-<tr id="row_2_5_74_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtFunctor.html" target="_self">StmtFunctor</a></td><td class="desc">Same as <a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu...">ExprFunctor</a> except it is a [...]
-<tr id="row_2_5_75_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8args_08_4.html" target="_self">StmtFunctor&lt; R(const Stmt &amp;n, Args...args)&gt;</a></td><td class="desc"></td></tr>
-<tr id="row_2_5_76_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtMutator.html" target="_self">StmtMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1StmtMutator.html" title="StmtMutator that mutates the statements. ">StmtMutator</a> that mutates the statements </td></tr>
-<tr id="row_2_5_77_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtNode.html" target="_self">StmtNode</a></td><td class="desc">Base node of all statements </td></tr>
-<tr id="row_2_5_78_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtVisitor.html" target="_self">StmtVisitor</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1StmtVisitor.html" title="StmtVisitor. ">StmtVisitor</a> </td></tr>
-<tr id="row_2_5_79_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StoreNode.html" target="_self">StoreNode</a></td><td class="desc">Store value to the buffer </td></tr>
-<tr id="row_2_5_80_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImm.html" target="_self">StringImm</a></td><td class="desc"></td></tr>
-<tr id="row_2_5_81_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImmNode.html" target="_self">StringImmNode</a></td><td class="desc">String constants, only used in asserts </td></tr>
-<tr id="row_2_5_82_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SubNode.html" target="_self">SubNode</a></td><td class="desc"><ul>
+<tr id="row_2_5_82_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NE.html" target="_self">NE</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NENode.html" title="a != b ">NENode</a> </td></tr>
+<tr id="row_2_5_83_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NENode.html" target="_self">NENode</a></td><td class="desc">!= b </td></tr>
+<tr id="row_2_5_84_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Not.html" target="_self">Not</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1NotNode.html" title="!a ">NotNode</a> </td></tr>
+<tr id="row_2_5_85_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1NotNode.html" target="_self">NotNode</a></td><td class="desc">!a </td></tr>
+<tr id="row_2_5_86_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Or.html" target="_self">Or</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1OrNode.html" title="a || b ">OrNode</a> </td></tr>
+<tr id="row_2_5_87_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1OrNode.html" target="_self">OrNode</a></td><td class="desc">|| b </td></tr>
+<tr id="row_2_5_88_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Prefetch.html" target="_self">Prefetch</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrefetchNode.html" title="A prefetch hint for abuffer. ">PrefetchNode</a> </td></tr>
+<tr id="row_2_5_89_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrefetchNode.html" target="_self">PrefetchNode</a></td><td class="desc">A prefetch hint for abuffer </td></tr>
+<tr id="row_2_5_90_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFunc.html" target="_self">PrimFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" title="Primitive functions that contains TIR statements. ">PrimFuncNode</a> </td></tr>
+<tr id="row_2_5_91_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1PrimFuncNode.html" target="_self">PrimFuncNode</a></td><td class="desc">Primitive functions that contains TIR statements </td></tr>
+<tr id="row_2_5_92_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerLoad.html" target="_self">ProducerLoad</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerLoadNode.html" title="Load value from the result produced by the producer. ">ProducerLoadNode</a> </td></tr>
+<tr id="row_2_5_93_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerLoadNode.html" target="_self">ProducerLoadNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Load.html" title="Managed reference to LoadNode. ">Load</a> value from the result produced by the producer </td></tr>
+<tr id="row_2_5_94_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerRealize.html" target="_self">ProducerRealize</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerRealizeNode.html" title="Annotate the bounds where the data produced by the producer need to be written and read in body...">Produce [...]
+<tr id="row_2_5_95_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerRealizeNode.html" target="_self">ProducerRealizeNode</a></td><td class="desc">Annotate the bounds where the data produced by the producer need to be written and read in body. We will need to allocate space for the corresponding regions </td></tr>
+<tr id="row_2_5_96_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerStore.html" target="_self">ProducerStore</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ProducerStoreNode.html" title="Store value into mult-dimensional array that will be read by the consumer of the producer. ">ProducerStoreNode</a [...]
+<tr id="row_2_5_97_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ProducerStoreNode.html" target="_self">ProducerStoreNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Store.html" title="Managed reference to StoreNode. ">Store</a> value into mult-dimensional array that will be read by the consumer of the producer </td></tr>
+<tr id="row_2_5_98_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Ramp.html" target="_self">Ramp</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1RampNode.html" title="Construct a vector with lanes elements where its i-th element equals base + i * stride. This is useful to construct a index for a continuous [...]
+<tr id="row_2_5_99_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1RampNode.html" target="_self">RampNode</a></td><td class="desc">Construct a vector with lanes elements where its i-th element equals base + i * stride. This is useful to construct a index for a continuous vector load </td></tr>
+<tr id="row_2_5_100_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Reduce.html" target="_self">Reduce</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ReduceNode.html" title="Reduction operator operator. ">ReduceNode</a> </td></tr>
+<tr id="row_2_5_101_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ReduceNode.html" target="_self">ReduceNode</a></td><td class="desc">Reduction operator operator </td></tr>
+<tr id="row_2_5_102_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Select.html" target="_self">Select</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SelectNode.html" title="return true_value if condition is true, otherwise return false_value. ">SelectNode</a> </td></tr>
+<tr id="row_2_5_103_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SelectNode.html" target="_self">SelectNode</a></td><td class="desc">Return true_value if condition is true, otherwise return false_value </td></tr>
+<tr id="row_2_5_104_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_2_5_104_" class="arrow" onclick="toggleFolder('2_5_104_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SeqStmt.html" target="_self">SeqStmt</a></td><td class="desc">Sequence statement </td></tr>
+<tr id="row_2_5_104_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SeqStmt_1_1Flattener.html" target="_self">Flattener</a></td><td class="desc">Helper class to flatten sequence of arguments into Array </td></tr>
+<tr id="row_2_5_105_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SeqStmtNode.html" target="_self">SeqStmtNode</a></td><td class="desc">The container of seq statement. Represent a sequence of statements </td></tr>
+<tr id="row_2_5_106_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Shuffle.html" target="_self">Shuffle</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html" title="Shuffle instruction. vec = concat(vectors) result = (vec[indices[0]], vec[indices[1]] ...">ShuffleNode</a> </td></tr>
+<tr id="row_2_5_107_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1ShuffleNode.html" target="_self">ShuffleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Shuffle.html" title="Managed reference to ShuffleNode. ">Shuffle</a> instruction. vec = concat(vectors) result = (vec[indices[0]], vec[indices[1]] ...) </td></tr>
+<tr id="row_2_5_108_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SizeVar.html" target="_self">SizeVar</a></td><td class="desc">Named variable represents a tensor index size </td></tr>
+<tr id="row_2_5_109_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SizeVarNode.html" target="_self">SizeVarNode</a></td><td class="desc">A variable node represent a tensor index size, whose value must be non-negative </td></tr>
+<tr id="row_2_5_110_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Stmt.html" target="_self">Stmt</a></td><td class="desc">Container of all statements </td></tr>
+<tr id="row_2_5_111_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtExprMutator.html" target="_self">StmtExprMutator</a></td><td class="desc">Mutator that recursively mutates stmts and exprs on them </td></tr>
+<tr id="row_2_5_112_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtExprVisitor.html" target="_self">StmtExprVisitor</a></td><td class="desc">Visitor that recursively visit stmts and exprs on them </td></tr>
+<tr id="row_2_5_113_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtFunctor.html" target="_self">StmtFunctor</a></td><td class="desc">Same as <a class="el" href="classtvm_1_1tir_1_1ExprFunctor.html" title="A dynamical functor that dispatches on in the first Expr argument. You can use this as a more powerfu...">ExprFunctor</a> except it is  [...]
+<tr id="row_2_5_114_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtFunctor_3_01R_07const_01Stmt_01_6n_00_01Args_8_8_8args_08_4.html" target="_self">StmtFunctor&lt; R(const Stmt &amp;n, Args...args)&gt;</a></td><td class="desc"></td></tr>
+<tr id="row_2_5_115_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtMutator.html" target="_self">StmtMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1StmtMutator.html" title="StmtMutator that mutates the statements. ">StmtMutator</a> that mutates the statements </td></tr>
+<tr id="row_2_5_116_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtNode.html" target="_self">StmtNode</a></td><td class="desc">Base node of all statements </td></tr>
+<tr id="row_2_5_117_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StmtVisitor.html" target="_self">StmtVisitor</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1StmtVisitor.html" title="StmtVisitor. ">StmtVisitor</a> </td></tr>
+<tr id="row_2_5_118_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Store.html" target="_self">Store</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StoreNode.html" title="Store value to the buffer. ">StoreNode</a> </td></tr>
+<tr id="row_2_5_119_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StoreNode.html" target="_self">StoreNode</a></td><td class="desc"><a class="el" href="classtvm_1_1tir_1_1Store.html" title="Managed reference to StoreNode. ">Store</a> value to the buffer </td></tr>
+<tr id="row_2_5_120_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImm.html" target="_self">StringImm</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1StringImmNode.html" title="String constants, only used in asserts. ">StringImmNode</a> </td></tr>
+<tr id="row_2_5_121_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1StringImmNode.html" target="_self">StringImmNode</a></td><td class="desc">String constants, only used in asserts </td></tr>
+<tr id="row_2_5_122_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Sub.html" target="_self">Sub</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1tir_1_1SubNode.html" title="a - b ">SubNode</a> </td></tr>
+<tr id="row_2_5_123_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1SubNode.html" target="_self">SubNode</a></td><td class="desc"><ul>
 <li>b </li>
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 </td></tr>
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-<tr id="row_2_5_84_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Var.html" target="_self">Var</a></td><td class="desc">Named variable in TVM </td></tr>
-<tr id="row_2_5_85_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1VarNode.html" target="_self">VarNode</a></td><td class="desc">A variable node in the IR </td></tr>
+<tr id="row_2_5_124_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1Var.html" target="_self">Var</a></td><td class="desc">Named variable in TIR </td></tr>
+<tr id="row_2_5_125_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1tir_1_1VarNode.html" target="_self">VarNode</a></td><td class="desc">A variable node in the IR </td></tr>
 <tr id="row_2_6_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_2_6_" class="arrow" onclick="toggleFolder('2_6_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacetvm_1_1transform.html" target="_self">transform</a></td><td class="desc"></td></tr>
 <tr id="row_2_6_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1Pass.html" target="_self">Pass</a></td><td class="desc"></td></tr>
 <tr id="row_2_6_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1transform_1_1PassContext.html" target="_self">PassContext</a></td><td class="desc"><a class="el" href="classtvm_1_1transform_1_1PassContext.html" title="PassContext that is used to configure the pass behavior. ">PassContext</a> that is used to configure the pass behavior </td></tr>
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 <tr id="row_2_22_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseValueEqual.html" target="_self">BaseValueEqual</a></td><td class="desc">Equality definition of base value class </td></tr>
 <tr id="row_2_23_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BaseValueHash.html" target="_self">BaseValueHash</a></td><td class="desc">Hash definition of base value classes </td></tr>
 <tr id="row_2_24_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Bool.html" target="_self">Bool</a></td><td class="desc">Boolean constant </td></tr>
-<tr id="row_2_25_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BuildConfig.html" target="_self">BuildConfig</a></td><td class="desc">Build configuration for compilations </td></tr>
-<tr id="row_2_26_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1BuildConfigNode.html" target="_self">BuildConfigNode</a></td><td class="desc">Container for build configuration options </td></tr>
-<tr id="row_2_27_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Constructor.html" target="_self">Constructor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1ConstructorNode.html" title="ADT constructor. Constructors compare by pointer equality. ">ConstructorNode</a> </td></tr>
-<tr id="row_2_28_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ConstructorNode.html" target="_self">ConstructorNode</a></td><td class="desc">ADT constructor. Constructors compare by pointer equality </td></tr>
-<tr id="row_2_29_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DictAttrs.html" target="_self">DictAttrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1DictAttrsNode.html" title="Specialized attribute type that is backed by a map. The DictAttrsNode implements the Attrs behavior...">DictAttrsNode</a> </td></tr>
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-<tr id="row_2_31_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFunc.html" target="_self">EnvFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1EnvFuncNode.html" title="A serializable function backed by TVM&#39;s global environment. ">EnvFuncNode</a> </td></tr>
-<tr id="row_2_32_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFuncNode.html" target="_self">EnvFuncNode</a></td><td class="desc">A serializable function backed by TVM's global environment </td></tr>
-<tr id="row_2_33_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Error.html" target="_self">Error</a></td><td class="desc">Custom <a class="el" href="classtvm_1_1Error.html" title="Custom Error class to be thrown during compilation. ">Error</a> class to be thrown during compilation </td></tr>
-<tr id="row_2_34_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1ErrorBuilder.html" target="_self">ErrorBuilder</a></td><td class="desc">A wrapper around std::stringstream to build error </td></tr>
-<tr id="row_2_35_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ErrorReporter.html" target="_self">ErrorReporter</a></td><td class="desc">An abstraction around how errors are stored and reported. Designed to be opaque to users, so we can support a robust and simpler error reporting mode, as well as a more complex mode </td></tr>
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-<tr id="row_2_37_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FloatImmNode.html" target="_self">FloatImmNode</a></td><td class="desc">Constant floating point literals in the program </td></tr>
-<tr id="row_2_38_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncType.html" target="_self">FuncType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1FuncTypeNode.html" title="Function type. ">FuncTypeNode</a> </td></tr>
-<tr id="row_2_39_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncTypeNode.html" target="_self">FuncTypeNode</a></td><td class="desc">Function type </td></tr>
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-<tr id="row_2_41_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFuncNode.html" target="_self">GenericFuncNode</a></td><td class="desc">Represents a generic function that can be specialized on a per-target basis </td></tr>
-<tr id="row_2_42_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVar.html" target="_self">GlobalTypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalTypeVarNode.html" title="A global type variable that is used for defining new types or type aliases. ">GlobalTypeVarNode</a> </td></tr>
-<tr id="row_2_43_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVarNode.html" target="_self">GlobalTypeVarNode</a></td><td class="desc">A global type variable that is used for defining new types or type aliases </td></tr>
-<tr id="row_2_44_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVar.html" target="_self">GlobalVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalVarNode.html" title="Global variable that lives in the top-level module. ">GlobalVarNode</a> </td></tr>
-<tr id="row_2_45_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVarNode.html" target="_self">GlobalVarNode</a></td><td class="desc">Global variable that lives in the top-level module </td></tr>
-<tr id="row_2_46_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteType.html" target="_self">IncompleteType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1IncompleteTypeNode.html" title="Intermediate values that is used to indicate incomplete type during type inference. ">IncompleteTypeNode</a> </td></tr>
-<tr id="row_2_47_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteTypeNode.html" target="_self">IncompleteTypeNode</a></td><td class="desc">Intermediate values that is used to indicate incomplete type during type inference </td></tr>
-<tr id="row_2_48_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Integer.html" target="_self">Integer</a></td><td class="desc">Container of constant int that adds more constructors </td></tr>
-<tr id="row_2_49_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImm.html" target="_self">IntImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IntImmNode.html" title="Constant integer literals in the program. ">IntImmNode</a> </td></tr>
-<tr id="row_2_50_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImmNode.html" target="_self">IntImmNode</a></td><td class="desc">Constant integer literals in the program </td></tr>
-<tr id="row_2_51_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModule.html" target="_self">IRModule</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IRModuleNode.html" title="IRModule that holds functions and type definitions. ">IRModuleNode</a> </td></tr>
-<tr id="row_2_52_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModuleNode.html" target="_self">IRModuleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1IRModule.html" title="Managed reference class to IRModuleNode. ">IRModule</a> that holds functions and type definitions </td></tr>
-<tr id="row_2_53_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_2_53_" class="arrow" onclick="toggleFolder('2_53_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Map.html" target="_self">Map</a></td><td class="desc"><a class="el" href="classtvm_1_1Map.html" title="Map container of NodeRef-&gt;NodeRef in DSL graph. Map implements copy on write semantics, which means  [...]
-<tr id="row_2_53_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1Map_1_1ValueConverter.html" target="_self">ValueConverter</a></td><td class="desc"></td></tr>
-<tr id="row_2_54_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MapNode.html" target="_self">MapNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Map.html" title="Map container of NodeRef-&gt;NodeRef in DSL graph. Map implements copy on write semantics, which means map is mutable but copy will happen when array is referenced in more than [...]
-<tr id="row_2_55_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfo.html" target="_self">MemoryInfo</a></td><td class="desc">Defines memory info </td></tr>
-<tr id="row_2_56_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfoNode.html" target="_self">MemoryInfoNode</a></td><td class="desc">Memory information of special memory region. Use <a class="el" href="classtvm_1_1MemoryInfo.html" title="Defines memory info. ">MemoryInfo</a> as its container type </td></tr>
-<tr id="row_2_57_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor.html" target="_self">NodeFunctor</a></td><td class="desc">A dynamically dispatched functor on the type of the first argument </td></tr>
-<tr id="row_2_58_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html" target="_self">NodeFunctor&lt; R(const ObjectRef &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
+<tr id="row_2_25_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Constructor.html" target="_self">Constructor</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1ConstructorNode.html" title="ADT constructor. Constructors compare by pointer equality. ">ConstructorNode</a> </td></tr>
+<tr id="row_2_26_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ConstructorNode.html" target="_self">ConstructorNode</a></td><td class="desc">ADT constructor. Constructors compare by pointer equality </td></tr>
+<tr id="row_2_27_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DictAttrs.html" target="_self">DictAttrs</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1DictAttrsNode.html" title="Specialized attribute type that is backed by a map. The DictAttrsNode implements the Attrs behavior...">DictAttrsNode</a> </td></tr>
+<tr id="row_2_28_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1DictAttrsNode.html" target="_self">DictAttrsNode</a></td><td class="desc">Specialized attribute type that is backed by a map. The <a class="el" href="classtvm_1_1DictAttrsNode.html" title="Specialized attribute type that is backed by a map. The DictAttrsNode implements the Attrs behavio [...]
+<tr id="row_2_29_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFunc.html" target="_self">EnvFunc</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1EnvFuncNode.html" title="A serializable function backed by TVM&#39;s global environment. ">EnvFuncNode</a> </td></tr>
+<tr id="row_2_30_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1EnvFuncNode.html" target="_self">EnvFuncNode</a></td><td class="desc">A serializable function backed by TVM's global environment </td></tr>
+<tr id="row_2_31_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Error.html" target="_self">Error</a></td><td class="desc">Custom <a class="el" href="classtvm_1_1Error.html" title="Custom Error class to be thrown during compilation. ">Error</a> class to be thrown during compilation </td></tr>
+<tr id="row_2_32_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1ErrorBuilder.html" target="_self">ErrorBuilder</a></td><td class="desc">A wrapper around std::stringstream to build error </td></tr>
+<tr id="row_2_33_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1ErrorReporter.html" target="_self">ErrorReporter</a></td><td class="desc">An abstraction around how errors are stored and reported. Designed to be opaque to users, so we can support a robust and simpler error reporting mode, as well as a more complex mode </td></tr>
+<tr id="row_2_34_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FloatImm.html" target="_self">FloatImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1FloatImmNode.html" title="Constant floating point literals in the program. ">FloatImmNode</a> </td></tr>
+<tr id="row_2_35_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FloatImmNode.html" target="_self">FloatImmNode</a></td><td class="desc">Constant floating point literals in the program </td></tr>
+<tr id="row_2_36_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncType.html" target="_self">FuncType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1FuncTypeNode.html" title="Function type. ">FuncTypeNode</a> </td></tr>
+<tr id="row_2_37_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1FuncTypeNode.html" target="_self">FuncTypeNode</a></td><td class="desc">Function type </td></tr>
+<tr id="row_2_38_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFunc.html" target="_self">GenericFunc</a></td><td class="desc">Generic function that can be specialized on a per-target basis </td></tr>
+<tr id="row_2_39_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GenericFuncNode.html" target="_self">GenericFuncNode</a></td><td class="desc">Represents a generic function that can be specialized on a per-target basis </td></tr>
+<tr id="row_2_40_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVar.html" target="_self">GlobalTypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalTypeVarNode.html" title="A global type variable that is used for defining new types or type aliases. ">GlobalTypeVarNode</a> </td></tr>
+<tr id="row_2_41_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalTypeVarNode.html" target="_self">GlobalTypeVarNode</a></td><td class="desc">A global type variable that is used for defining new types or type aliases </td></tr>
+<tr id="row_2_42_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVar.html" target="_self">GlobalVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1GlobalVarNode.html" title="Global variable that lives in the top-level module. ">GlobalVarNode</a> </td></tr>
+<tr id="row_2_43_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1GlobalVarNode.html" target="_self">GlobalVarNode</a></td><td class="desc">Global variable that lives in the top-level module </td></tr>
+<tr id="row_2_44_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteType.html" target="_self">IncompleteType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1IncompleteTypeNode.html" title="Intermediate values that is used to indicate incomplete type during type inference. ">IncompleteTypeNode</a> </td></tr>
+<tr id="row_2_45_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IncompleteTypeNode.html" target="_self">IncompleteTypeNode</a></td><td class="desc">Intermediate values that is used to indicate incomplete type during type inference </td></tr>
+<tr id="row_2_46_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Integer.html" target="_self">Integer</a></td><td class="desc">Container of constant int that adds more constructors </td></tr>
+<tr id="row_2_47_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImm.html" target="_self">IntImm</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IntImmNode.html" title="Constant integer literals in the program. ">IntImmNode</a> </td></tr>
+<tr id="row_2_48_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IntImmNode.html" target="_self">IntImmNode</a></td><td class="desc">Constant integer literals in the program </td></tr>
+<tr id="row_2_49_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModule.html" target="_self">IRModule</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1IRModuleNode.html" title="IRModule that holds functions and type definitions. ">IRModuleNode</a> </td></tr>
+<tr id="row_2_50_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1IRModuleNode.html" target="_self">IRModuleNode</a></td><td class="desc"><a class="el" href="classtvm_1_1IRModule.html" title="Managed reference class to IRModuleNode. ">IRModule</a> that holds functions and type definitions </td></tr>
+<tr id="row_2_51_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_2_51_" class="arrow" onclick="toggleFolder('2_51_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Map.html" target="_self">Map</a></td><td class="desc"><a class="el" href="classtvm_1_1Map.html" title="Map container of NodeRef-&gt;NodeRef in DSL graph. Map implements copy on write semantics, which means  [...]
+<tr id="row_2_51_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1Map_1_1ValueConverter.html" target="_self">ValueConverter</a></td><td class="desc"></td></tr>
+<tr id="row_2_52_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MapNode.html" target="_self">MapNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Map.html" title="Map container of NodeRef-&gt;NodeRef in DSL graph. Map implements copy on write semantics, which means map is mutable but copy will happen when array is referenced in more than [...]
+<tr id="row_2_53_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfo.html" target="_self">MemoryInfo</a></td><td class="desc">Defines memory info </td></tr>
+<tr id="row_2_54_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1MemoryInfoNode.html" target="_self">MemoryInfoNode</a></td><td class="desc">Memory information of special memory region. Use <a class="el" href="classtvm_1_1MemoryInfo.html" title="Defines memory info. ">MemoryInfo</a> as its container type </td></tr>
+<tr id="row_2_55_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor.html" target="_self">NodeFunctor</a></td><td class="desc">A dynamically dispatched functor on the type of the first argument </td></tr>
+<tr id="row_2_56_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1NodeFunctor_3_01R_07const_01ObjectRef_01_6n_00_01Args_8_8_8_08_4.html" target="_self">NodeFunctor&lt; R(const ObjectRef &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
+<tr id="row_2_57_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1ObjectEqual.html" target="_self">ObjectEqual</a></td><td class="desc">String-aware ObjectRef equal functor </td></tr>
+<tr id="row_2_58_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structtvm_1_1ObjectHash.html" target="_self">ObjectHash</a></td><td class="desc">String-aware ObjectRef hash functor </td></tr>
 <tr id="row_2_59_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Op.html" target="_self">Op</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1OpNode.html" title="Primitive Op(builtin intrinsics) ">OpNode</a> </td></tr>
 <tr id="row_2_60_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1OpAttrMap.html" target="_self">OpAttrMap</a></td><td class="desc">Map&lt;Op,ValueType&gt; used to store meta-information about <a class="el" href="classtvm_1_1Op.html" title="Managed reference class to OpNode. ">Op</a> </td></tr>
 <tr id="row_2_61_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1OpNode.html" target="_self">OpNode</a></td><td class="desc">Primitive Op(builtin intrinsics) </td></tr>
@@ -613,36 +674,35 @@ var searchBox = new SearchBox("searchBox", "search",false,'Search');
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 <tr id="row_2_81_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Span.html" target="_self">Span</a></td><td class="desc"></td></tr>
 <tr id="row_2_82_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1SpanNode.html" target="_self">SpanNode</a></td><td class="desc">Stores locations in frontend source that generated a node </td></tr>
-<tr id="row_2_83_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StrMapNode.html" target="_self">StrMapNode</a></td><td class="desc">Specialized map node with string as key </td></tr>
-<tr id="row_2_84_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralEqual.html" target="_self">StructuralEqual</a></td><td class="desc">Content-aware structural equality comparator for objects </td></tr>
-<tr id="row_2_85_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralHash.html" target="_self">StructuralHash</a></td><td class="desc">Content-aware structural hasing </td></tr>
-<tr id="row_2_86_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Target.html" target="_self">Target</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetNode.html" title="Compilation target. ">TargetNode</a> </td></tr>
-<tr id="row_2_87_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetNode.html" target="_self">TargetNode</a></td><td class="desc">Compilation target </td></tr>
-<tr id="row_2_88_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorType.html" target="_self">TensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TensorTypeNode.html" title="This is the most commonly used type in relay. TensorType have a fixed dimension, data type...">TensorTypeNode</a> </td></tr>
-<tr id="row_2_89_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorTypeNode.html" target="_self">TensorTypeNode</a></td><td class="desc">This is the most commonly used type in relay. <a class="el" href="classtvm_1_1TensorType.html" title="Managed reference to TensorTypeNode. ">TensorType</a> have a fixed dimension, data type </td></tr>
-<tr id="row_2_90_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleType.html" target="_self">TupleType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TupleTypeNode.html" title="The type of tuple values. ">TupleTypeNode</a> </td></tr>
-<tr id="row_2_91_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleTypeNode.html" target="_self">TupleTypeNode</a></td><td class="desc">The type of tuple values </td></tr>
-<tr id="row_2_92_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Type.html" target="_self">Type</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeNode.html" title="Type is the base type of all types. ">TypeNode</a> </td></tr>
-<tr id="row_2_93_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCall.html" target="_self">TypeCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeCallNode.html" title="Type function application. ">TypeCallNode</a> </td></tr>
-<tr id="row_2_94_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCallNode.html" target="_self">TypeCallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> function application </td></tr>
-<tr id="row_2_95_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraint.html" target="_self">TypeConstraint</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeConstraintNode.html" title="Potential Constraints in a function. ">TypeConstraintNode</a> </td></tr>
-<tr id="row_2_96_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraintNode.html" target="_self">TypeConstraintNode</a></td><td class="desc">Potential Constraints in a function </td></tr>
-<tr id="row_2_97_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeData.html" target="_self">TypeData</a></td><td class="desc">Stores all data for an Algebraic Data <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> (ADT) </td></tr>
-<tr id="row_2_98_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeDataNode.html" target="_self">TypeDataNode</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeData.html" title="Stores all data for an Algebraic Data Type (ADT). ">TypeData</a> container node </td></tr>
-<tr id="row_2_99_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">TypedEnvFunc</a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#TypedEnvFuncAnchor">TypedEnvFunc&lt;R(Args..)&gt;</a> </td></tr>
-<tr id="row_2_100_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">TypedEnvFunc&lt; R(Args...)&gt;</a></td><td class="desc">A typed version of <a class="el" href="classtvm_1_1EnvFunc.html" title="Managed reference to EnvFuncNode. ">EnvFunc</a>. It is backed by a GlobalFuncNode internally </td></tr>
-<tr id="row_2_101_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">TypeFunctor</a></td><td class="desc"></td></tr>
-<tr id="row_2_102_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html" target="_self">TypeFunctor&lt; R(const Type &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
-<tr id="row_2_103_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeMutator.html" target="_self">TypeMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeMutator.html" title="TypeMutator that mutates expressions. ">TypeMutator</a> that mutates expressions </td></tr>
-<tr id="row_2_104_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeNode.html" target="_self">TypeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> is the base type of all types </td></tr>
-<tr id="row_2_105_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelation.html" target="_self">TypeRelation</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeRelationNode.html" title="User defined type relation, it is an input-output relation on types. ">TypeRelationNode</a> </td></tr>
-<tr id="row_2_106_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelationNode.html" target="_self">TypeRelationNode</a></td><td class="desc">User defined type relation, it is an input-output relation on types </td></tr>
-<tr id="row_2_107_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporter.html" target="_self">TypeReporter</a></td><td class="desc">Container class of <a class="el" href="classtvm_1_1TypeReporter.html" title="Container class of TypeReporter. ">TypeReporter</a> </td></tr>
-<tr id="row_2_108_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporterNode.html" target="_self">TypeReporterNode</a></td><td class="desc">Reporter that reports back to the type resolution information </td></tr>
-<tr id="row_2_109_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVar.html" target="_self">TypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeVarNode.html" title="Type parameter in functions. ">TypeVarNode</a> </td></tr>
-<tr id="row_2_110_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVarNode.html" target="_self">TypeVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> parameter in functions </td></tr>
-<tr id="row_2_111_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVisitor.html" target="_self">TypeVisitor</a></td><td class="desc">A type visitor that recursively visit types </td></tr>
-<tr id="row_2_112_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1With.html" target="_self">With</a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
+<tr id="row_2_83_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralEqual.html" target="_self">StructuralEqual</a></td><td class="desc">Content-aware structural equality comparator for objects </td></tr>
+<tr id="row_2_84_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1StructuralHash.html" target="_self">StructuralHash</a></td><td class="desc">Content-aware structural hasing </td></tr>
+<tr id="row_2_85_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Target.html" target="_self">Target</a></td><td class="desc">Managed reference class to <a class="el" href="classtvm_1_1TargetNode.html" title="Compilation target. ">TargetNode</a> </td></tr>
+<tr id="row_2_86_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TargetNode.html" target="_self">TargetNode</a></td><td class="desc">Compilation target </td></tr>
+<tr id="row_2_87_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorType.html" target="_self">TensorType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TensorTypeNode.html" title="This is the most commonly used type in relay. TensorType have a fixed dimension, data type...">TensorTypeNode</a> </td></tr>
+<tr id="row_2_88_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TensorTypeNode.html" target="_self">TensorTypeNode</a></td><td class="desc">This is the most commonly used type in relay. <a class="el" href="classtvm_1_1TensorType.html" title="Managed reference to TensorTypeNode. ">TensorType</a> have a fixed dimension, data type </td></tr>
+<tr id="row_2_89_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleType.html" target="_self">TupleType</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TupleTypeNode.html" title="The type of tuple values. ">TupleTypeNode</a> </td></tr>
+<tr id="row_2_90_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TupleTypeNode.html" target="_self">TupleTypeNode</a></td><td class="desc">The type of tuple values </td></tr>
+<tr id="row_2_91_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1Type.html" target="_self">Type</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeNode.html" title="Type is the base type of all types. ">TypeNode</a> </td></tr>
+<tr id="row_2_92_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCall.html" target="_self">TypeCall</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeCallNode.html" title="Type function application. ">TypeCallNode</a> </td></tr>
+<tr id="row_2_93_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeCallNode.html" target="_self">TypeCallNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> function application </td></tr>
+<tr id="row_2_94_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraint.html" target="_self">TypeConstraint</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeConstraintNode.html" title="Potential Constraints in a function. ">TypeConstraintNode</a> </td></tr>
+<tr id="row_2_95_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeConstraintNode.html" target="_self">TypeConstraintNode</a></td><td class="desc">Potential Constraints in a function </td></tr>
+<tr id="row_2_96_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeData.html" target="_self">TypeData</a></td><td class="desc">Stores all data for an Algebraic Data <a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> (ADT) </td></tr>
+<tr id="row_2_97_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeDataNode.html" target="_self">TypeDataNode</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeData.html" title="Stores all data for an Algebraic Data Type (ADT). ">TypeData</a> container node </td></tr>
+<tr id="row_2_98_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc.html" target="_self">TypedEnvFunc</a></td><td class="desc">Please refer to <a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html#TypedEnvFuncAnchor">TypedEnvFunc&lt;R(Args..)&gt;</a> </td></tr>
+<tr id="row_2_99_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypedEnvFunc_3_01R_07Args_8_8_8_08_4.html" target="_self">TypedEnvFunc&lt; R(Args...)&gt;</a></td><td class="desc">A typed version of <a class="el" href="classtvm_1_1EnvFunc.html" title="Managed reference to EnvFuncNode. ">EnvFunc</a>. It is backed by a GlobalFuncNode internally </td></tr>
+<tr id="row_2_100_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor.html" target="_self">TypeFunctor</a></td><td class="desc"></td></tr>
+<tr id="row_2_101_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeFunctor_3_01R_07const_01Type_01_6n_00_01Args_8_8_8_08_4.html" target="_self">TypeFunctor&lt; R(const Type &amp;n, Args...)&gt;</a></td><td class="desc"></td></tr>
+<tr id="row_2_102_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeMutator.html" target="_self">TypeMutator</a></td><td class="desc"><a class="el" href="classtvm_1_1TypeMutator.html" title="TypeMutator that mutates expressions. ">TypeMutator</a> that mutates expressions </td></tr>
+<tr id="row_2_103_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeNode.html" target="_self">TypeNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> is the base type of all types </td></tr>
+<tr id="row_2_104_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelation.html" target="_self">TypeRelation</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeRelationNode.html" title="User defined type relation, it is an input-output relation on types. ">TypeRelationNode</a> </td></tr>
+<tr id="row_2_105_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeRelationNode.html" target="_self">TypeRelationNode</a></td><td class="desc">User defined type relation, it is an input-output relation on types </td></tr>
+<tr id="row_2_106_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporter.html" target="_self">TypeReporter</a></td><td class="desc">Container class of <a class="el" href="classtvm_1_1TypeReporter.html" title="Container class of TypeReporter. ">TypeReporter</a> </td></tr>
+<tr id="row_2_107_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeReporterNode.html" target="_self">TypeReporterNode</a></td><td class="desc">Reporter that reports back to the type resolution information </td></tr>
+<tr id="row_2_108_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVar.html" target="_self">TypeVar</a></td><td class="desc">Managed reference to <a class="el" href="classtvm_1_1TypeVarNode.html" title="Type parameter in functions. ">TypeVarNode</a> </td></tr>
+<tr id="row_2_109_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVarNode.html" target="_self">TypeVarNode</a></td><td class="desc"><a class="el" href="classtvm_1_1Type.html" title="Managed reference to TypeNode. ">Type</a> parameter in functions </td></tr>
+<tr id="row_2_110_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1TypeVisitor.html" target="_self">TypeVisitor</a></td><td class="desc">A type visitor that recursively visit types </td></tr>
+<tr id="row_2_111_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classtvm_1_1With.html" target="_self">With</a></td><td class="desc">RAII wrapper function to enter and exit a context object similar to python's with syntax </td></tr>
 <tr id="row_3_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMByteArray.html" target="_self">TVMByteArray</a></td><td class="desc">Byte array type used to pass in byte array When kTVMBytes is used as data type </td></tr>
 <tr id="row_4_" class="even"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structTVMParallelGroupEnv.html" target="_self">TVMParallelGroupEnv</a></td><td class="desc">Environment for TVM parallel task </td></tr>
 <tr id="row_5_"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="unionTVMValue.html" target="_self">TVMValue</a></td><td class="desc">Union type of values being passed through API and function calls </td></tr>
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