diff --git a/docker/Dockerfile.ci_gpu b/docker/Dockerfile.ci_gpu
index d9ca255502fa..5c13104cda16 100644
--- a/docker/Dockerfile.ci_gpu
+++ b/docker/Dockerfile.ci_gpu
@@ -77,9 +77,6 @@ RUN bash /install/ubuntu_install_onnx.sh
 COPY install/ubuntu_install_tflite.sh /install/ubuntu_install_tflite.sh
 RUN bash /install/ubuntu_install_tflite.sh
 
-COPY install/ubuntu_install_caffe2.sh /install/ubuntu_install_caffe2.sh
-RUN bash /install/ubuntu_install_caffe2.sh
-
 COPY install/ubuntu_install_dgl.sh /install/ubuntu_install_dgl.sh
 RUN bash /install/ubuntu_install_dgl.sh
 
diff --git a/docker/install/ubuntu_install_onnx.sh b/docker/install/ubuntu_install_onnx.sh
index 8fc8157e1b2c..f94df2d64a17 100755
--- a/docker/install/ubuntu_install_onnx.sh
+++ b/docker/install/ubuntu_install_onnx.sh
@@ -36,5 +36,5 @@ pip3 install \
 pip3 install future
 
 pip3 install \
-    torch==1.10.1 \
-    torchvision==0.11.2
+    torch==1.11.0 \
+    torchvision==0.12.0
diff --git a/gallery/how_to/compile_models/from_caffe2.py b/gallery/how_to/compile_models/from_caffe2.py
deleted file mode 100644
index 263f98c9454f..000000000000
--- a/gallery/how_to/compile_models/from_caffe2.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# 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.
-"""
-Compile Caffe2 Models
-=====================
-**Author**: `Hiroyuki Makino <https://makihiro.github.io/>`_
-
-This article is an introductory tutorial to deploy Caffe2 models with Relay.
-
-For us to begin with, Caffe2 should be installed.
-
-A quick solution is to install via conda
-
-.. code-block:: bash
-
-    # for cpu
-    conda install pytorch-nightly-cpu -c pytorch
-    # for gpu with CUDA 8
-    conda install pytorch-nightly cuda80 -c pytorch
-
-or please refer to official site
-https://caffe2.ai/docs/getting-started.html
-"""
-
-######################################################################
-# Load pretrained Caffe2 model
-# ----------------------------
-# We load a pretrained resnet50 classification model provided by Caffe2.
-from caffe2.python.models.download import ModelDownloader
-
-mf = ModelDownloader()
-
-
-class Model:
-    def __init__(self, model_name):
-        self.init_net, self.predict_net, self.value_info = mf.get_c2_model(model_name)
-
-
-resnet50 = Model("resnet50")
-
-######################################################################
-# Load a test image
-# ------------------
-# A single cat dominates the examples!
-from tvm.contrib.download import download_testdata
-from PIL import Image
-from matplotlib import pyplot as plt
-import numpy as np
-
-img_url = "https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true"
-img_path = download_testdata(img_url, "cat.png", module="data")
-img = Image.open(img_path).resize((224, 224))
-plt.imshow(img)
-plt.show()
-# input preprocess
-def transform_image(image):
-    image = np.array(image) - np.array([123.0, 117.0, 104.0])
-    image /= np.array([58.395, 57.12, 57.375])
-    image = image.transpose((2, 0, 1))
-    image = image[np.newaxis, :].astype("float32")
-    return image
-
-
-data = transform_image(img)
-
-######################################################################
-# Compile the model on Relay
-# --------------------------
-
-# Caffe2 input tensor name, shape and type
-input_name = resnet50.predict_net.op[0].input[0]
-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, 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 transform.PassContext(opt_level=3):
-    lib = relay.build(mod, target, params=params)
-
-######################################################################
-# Execute on TVM
-# ---------------
-# The process is no different from other examples.
-import tvm
-from tvm import te
-from tvm.contrib import graph_executor
-
-# context x86 CPU, use tvm.cuda(0) if you run on GPU
-dev = tvm.cpu(0)
-# create a runtime executor module
-m = graph_executor.GraphModule(lib["default"](dev))
-# set inputs
-m.set_input(input_name, tvm.nd.array(data.astype("float32")))
-# execute
-m.run()
-# get outputs
-tvm_out = m.get_output(0)
-top1_tvm = np.argmax(tvm_out.numpy()[0])
-
-#####################################################################
-# Look up synset name
-# -------------------
-# Look up prediction top 1 index in 1000 class synset.
-from caffe2.python import workspace
-
-synset_url = "".join(
-    [
-        "https://gist.githubusercontent.com/zhreshold/",
-        "4d0b62f3d01426887599d4f7ede23ee5/raw/",
-        "596b27d23537e5a1b5751d2b0481ef172f58b539/",
-        "imagenet1000_clsid_to_human.txt",
-    ]
-)
-synset_name = "imagenet1000_clsid_to_human.txt"
-synset_path = download_testdata(synset_url, synset_name, module="data")
-with open(synset_path) as f:
-    synset = eval(f.read())
-print("Relay top-1 id: {}, class name: {}".format(top1_tvm, synset[top1_tvm]))
-# confirm correctness with caffe2 output
-p = workspace.Predictor(resnet50.init_net, resnet50.predict_net)
-caffe2_out = p.run({input_name: data})
-top1_caffe2 = np.argmax(caffe2_out)
-print("Caffe2 top-1 id: {}, class name: {}".format(top1_caffe2, synset[top1_caffe2]))
diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index e0bc9358cc9b..a5411ce4d0b6 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -2039,6 +2039,14 @@ def stack(self, inputs, input_types):
             assert isinstance(ty, tvm.ir.TypeCall) and ty.func == list_ty, msg
             return self.tensor_array_stack(inputs, input_types)
 
+    def sub(self, inputs, input_types):
+        if len(inputs) == 3:
+            data0, data1, alpha = self.pytorch_promote_types(inputs, input_types)
+            return get_relay_op("subtract")(data0, alpha * data1)
+        else:
+            data0, data1= self.pytorch_promote_types(inputs, input_types)
+            return get_relay_op("subtract")(data0, data1)
+
     def rsub(self, inputs, input_types):
         data0, data1, alpha = self.pytorch_promote_types(inputs, input_types)
 
@@ -2859,7 +2867,10 @@ def all_any_common(self, op, inputs, input_types):
             inp = inputs[0]
         return op(inp, axis=dim, keepdims=keepdim)
 
-    def searchsorted_common(self, sorted_sequence, values, out_int32, right):
+    def searchsorted_common(
+        self, sorted_sequence, values, out_int32, right, side=None, out=None, sorter=None
+    ):
+        assert side is None and out is None and sorter is None, "unsupported parameters"
         dtype = "int32" if out_int32 else "int64"
         values_shape = _infer_shape(values)
 
@@ -2959,7 +2970,7 @@ def create_convert_map(self):
             "aten::pixel_shuffle": self.pixel_shuffle,
             "aten::device": self.none,
             "prim::device": self.none,
-            "aten::sub": self.make_elemwise("subtract"),
+            "aten::sub": self.sub,
             "aten::max": self.max,
             "aten::min": self.min,
             "aten::mul": self.make_elemwise("multiply"),
diff --git a/tests/python/frontend/pytorch/qnn_test.py b/tests/python/frontend/pytorch/qnn_test.py
index 8c9ce16acc1e..6e87b9ee4f6f 100644
--- a/tests/python/frontend/pytorch/qnn_test.py
+++ b/tests/python/frontend/pytorch/qnn_test.py
@@ -743,9 +743,10 @@ def __init__(self, inputsize=(128, 128)):
             self.backbone = Backbone()
 
         def fuse_model(self):
+            fuse_modules_qat = getattr(torch.ao.quantization, "fuse_modules_qat", fuse_modules)
             for idx, m in enumerate(self.modules()):
                 if type(m) == ConvBnRelu:
-                    torch.quantization.fuse_modules(m, ["conv", "bn", "relu"], inplace=True)
+                    fuse_modules_qat(m, ["conv", "bn", "relu"], inplace=True)
 
         def forward(self, input):
             input = self.quant(input)
diff --git a/tests/scripts/task_python_frontend.sh b/tests/scripts/task_python_frontend.sh
index af1c03bc2def..d7e1b5113f7c 100755
--- a/tests/scripts/task_python_frontend.sh
+++ b/tests/scripts/task_python_frontend.sh
@@ -56,9 +56,6 @@ function shard2 {
         i=$((i+1))
     done
 
-    echo "Running relay caffe2 frontend test..."
-    run_pytest cython python-frontend-caffe2 tests/python/frontend/caffe2
-
     echo "Running relay DarkNet frontend test..."
     run_pytest cython python-frontend-darknet tests/python/frontend/darknet