[CI] Modify Jenkinfile to always display junit report, fix for #8674

.github/ISSUE_TEMPLATE/ci-problem.md

@@ -19,4 +19,4 @@ Provide a link to the specific run that has failed.
 
 ### Flakiness
 
-Have you seen this multiple times in this branch or in other branches?
+Have you seen this multiple times in this branch or in other branches?

.github/ISSUE_TEMPLATE/documentation.md

@@ -19,4 +19,3 @@ Include the title of the document (e.g. "Getting Started with TVM"), and the typ
 If an RFC/discuss post exists, link it here.
 
 Otherwise, specify what actions should be taken to provide additional clarity/readability/reproducibility to the document. Include code snippets from the previous documentation if applicable.
-

Jenkinsfile

@@ -230,11 +230,15 @@ stage('Build') {
           // sh "${docker_run} ${ci_cpu} ./tests/scripts/task_golang.sh"
           // TODO(@jroesch): need to resolve CI issue will turn back on in follow up patch
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_rust.sh"
-          junit "build/pytest-results/*.xml"
         }
       }
     }
-    },
+    post {
+      always {
+        junit "build/pytest-results/*.xml"
+      }
+    }
+  },
   'BUILD: WASM': {
     node('CPU') {
       ws(per_exec_ws("tvm/build-wasm")) {
@@ -277,10 +281,14 @@ stage('Build') {
         timeout(time: max_time, unit: 'MINUTES') {
           sh "${docker_run} ${ci_qemu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_qemu} ./tests/scripts/task_python_microtvm.sh"
-          junit "build/pytest-results/*.xml"
         }
       }
     }
+    post {
+      always {
+        junit "build/pytest-results/*.xml"
+      }
+    }
   }
 }
 
@@ -295,10 +303,14 @@ stage('Unit Test') {
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_sphinx_precheck.sh"
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_python_unittest_gpuonly.sh"
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_python_integration_gpuonly.sh"
-          junit "build/pytest-results/*.xml"
         }
       }
     }
+    post {
+      always {
+        junit "build/pytest-results/*.xml"
+      }
+    }
   },
   'python3: i386': {
     node('CPU') {
@@ -310,10 +322,14 @@ stage('Unit Test') {
           sh "${docker_run} ${ci_i386} ./tests/scripts/task_python_unittest.sh"
           sh "${docker_run} ${ci_i386} ./tests/scripts/task_python_integration.sh"
           sh "${docker_run} ${ci_i386} ./tests/scripts/task_python_vta_fsim.sh"
-          junit "build/pytest-results/*.xml"
         }
       }
     }
+    post {
+      always {
+       junit "build/pytest-results/*.xml"
+      }
+    }
   },
   'python3: arm': {
     node('ARM') {
@@ -324,11 +340,15 @@ stage('Unit Test') {
           sh "${docker_run} ${ci_arm} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_arm} ./tests/scripts/task_python_unittest.sh"
           sh "${docker_run} ${ci_arm} ./tests/scripts/task_python_arm_compute_library.sh"
-          junit "build/pytest-results/*.xml"
           // sh "${docker_run} ${ci_arm} ./tests/scripts/task_python_integration.sh"
         }
       }
     }
+    post {
+      always {
+        junit "build/pytest-results/*.xml"
+      }
+    }
   },
   'java: GPU': {
     node('GPU') {
@@ -353,10 +373,14 @@ stage('Integration Test') {
         timeout(time: max_time, unit: 'MINUTES') {
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_python_topi.sh"
-          junit "build/pytest-results/*.xml"
         }
       }
     }
+    post {
+      always {
+        junit "build/pytest-results/*.xml"
+      }
+    }
   },
   'frontend: GPU': {
     node('GPU') {
@@ -366,10 +390,14 @@ stage('Integration Test') {
         timeout(time: max_time, unit: 'MINUTES') {
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_gpu} ./tests/scripts/task_python_frontend.sh"
-          junit "build/pytest-results/*.xml"
         }
       }
     }
+    post {
+      always {
+        junit "build/pytest-results/*.xml"
+      }
+    }
   },
   'frontend: CPU': {
     node('CPU') {
@@ -379,10 +407,14 @@ stage('Integration Test') {
         timeout(time: max_time, unit: 'MINUTES') {
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_ci_setup.sh"
           sh "${docker_run} ${ci_cpu} ./tests/scripts/task_python_frontend_cpu.sh"
-          junit "build/pytest-results/*.xml"
         }
       }
     }
+    post {
+      always {
+        junit "build/pytest-results/*.xml"
+      }
+    }
   },
   'docs: GPU': {
     node('TensorCore') {

README.md

@@ -26,8 +26,11 @@
 [![WinMacBuild](https://github.com/apache/tvm/workflows/WinMacBuild/badge.svg)](https://github.com/apache/tvm/actions?query=workflow%3AWinMacBuild)
 
 Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the
-productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends.
-TVM works with deep learning frameworks to provide end to end compilation to different backends.
+ASDADA
+productivity-focused
+deep learning frameworks, and the performance- and efficiency-focused hardware backends.
+TVM works with
+deep learning frameworks to provide end to end compilation to different backends.
 
 License
 -------

apps/microtvm/reference-vm/arduino/base-box/base_box_provision.sh

@@ -16,7 +16,7 @@
 # specific language governing permissions and limitations
 # under the License.
 #
-#   Using this script we can reuse docker/install scripts to configure the reference 
+#   Using this script we can reuse docker/install scripts to configure the reference
 #   virtual machine similar to CI QEMU setup.
 #
 

cmake/modules/Hexagon.cmake

@@ -105,4 +105,3 @@ endif()
 file(GLOB RUNTIME_HEXAGON_SRCS src/runtime/hexagon/*.cc)
 list(APPEND RUNTIME_SRCS ${RUNTIME_HEXAGON_SRCS} ${RUNTIME_HEXAGON_SIM_SRCS}
                          ${RUNTIME_HEXAGON_DEVICE_SRCS})
-

cmake/modules/HexagonSDK.cmake

@@ -120,4 +120,3 @@ function(find_hexagon_sdk_root HEXAGON_SDK_PATH HEXAGON_ARCH)
 
   set(FOUND_HEXAGON_SDK_ROOT TRUE)
 endfunction()
-

cmake/modules/contrib/EthosU.cmake

@@ -18,4 +18,4 @@
 if(USE_ETHOSU)
   file(GLOB ETHOSU_RELAY_CONTRIB_SRC src/relay/backend/contrib/ethosu/*)
   list(APPEND COMPILER_SRCS ${ETHOSU_RELAY_CONTRIB_SRC})
-endif(USE_ETHOSU)
+endif(USE_ETHOSU)

docker/install/ubuntu_install_vitis_ai_packages_ci.sh

@@ -6,9 +6,9 @@
 # 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

docs/langref/relay_pattern.rst

@@ -89,7 +89,7 @@ Or a convolution with a specific kernel size:
         x = relay.var('x')
         y = relay.var('y')
         assert is_conv2d.match(relay.op.nn.conv2d(x, y, kernel_size=[3, 3]))
-      
+
 
 
 Matching an Optional Op

tests/python/relay/aot/corstone300.mk

@@ -115,4 +115,4 @@ run: $(build_dir)/aot_test_runner
 
 .DEFAULT: aot_test_runner
 
-.PHONY: run
+.PHONY: run

tests/scripts/task_config_build_i386.sh

@@ -35,4 +35,3 @@ echo set\(USE_VTA_FSIM ON\) >> config.cmake
 echo set\(USE_VTA_TSIM ON\) >> config.cmake
 echo set\(USE_VERILATOR ON\) >> config.cmake
 echo set\(USE_CCACHE OFF\) >> config.cmake
-

tutorials/auto_scheduler/tune_network_cuda.py

@@ -23,7 +23,7 @@
 best performance. This is a tutorial on how to tune a whole neural
 network for NVIDIA GPU with the auto-scheduler.
 
-To auto-tune a neural network, we partition the network into small subgraphs and 
+To auto-tune a neural network, we partition the network into small subgraphs and
 tune them independently. Each subgraph is treated as one search task.
 A task scheduler slices the time and dynamically allocates time resources to
 these tasks. The task scheduler predicts the impact of each task on the end-to-end

tutorials/auto_scheduler/tune_network_x86.py

@@ -24,7 +24,7 @@
 best performance. This is a tutorial on how to tune a whole neural
 network for x86 CPU with the auto-scheduler.
 
-To auto-tune a neural network, we partition the network into small subgraphs and 
+To auto-tune a neural network, we partition the network into small subgraphs and
 tune them independently. Each subgraph is treated as one search task.
 A task scheduler slices the time and dynamically allocates time resources to
 these tasks. The task scheduler predicts the impact of each task on the end-to-end

tutorials/frontend/deploy_sparse.py

@@ -36,7 +36,7 @@
 
 Pruning is a technique primarily used to reduce the parameter size of a model
 by replacing weight values with 0s. Although many methods exist for choosing which
-weights should be set to 0, the most straight forward is by picking the 
+weights should be set to 0, the most straight forward is by picking the
 weights with the smallest value. Typically, weights are pruned to a desired
 sparsity percentage. For example, a 95% sparse model would have only 5% of
 its weights non-zero. Pruning to very high sparsities often requires
@@ -50,8 +50,8 @@
 value and location. The benefit of bunching up pruned weights is that it allows
 an algorithm such as matrix multiplication to skip entire blocks. It turns out
 that some degree of *block sparsity* is very important to realizing significant
-speedups on most hardware available today. 
-This is because when loading memory in most CPUs or GPUs, 
+speedups on most hardware available today.
+This is because when loading memory in most CPUs or GPUs,
 it doesn't save any work to skip reading a single value at a time, instead an entire
 chunk or tile is read in and executed using something like vectorized instructions.