Windows Support for cpp_rpc #4857

jmorrill · 2020-02-11T01:47:09Z

Cherry picked PR from (PR #4548).

This adds Windows support to the C++ RPC server.
To enable building it make sure the CMake option USE_CXX_RPC is set to ON.

Notables:

To workaround the lack for fork() sys call in Windows, the RPC socket is passed (WSADuplicateSocket magic) to a new child process.
To workaround the lack of tar on Windows, wsl must be installed as tar is executed with "wsl tar -C..."
Clang is used for compilation, so make sure LLVM binaries are installed and in the system path.
Updated CMakeLists.txt to use v3.9 on Windows to use INTERPROCEDURAL_OPTIMIZATION for better optimization of the libraries tvm.dll and topi.dll.
CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS is used on the tvm_runtime.dll so exports always work when using the graph runtime from C++ projects.

I have not verified I have not broken the Linux build of this in some way. Certain liberties were also taken as I didn't expect to ever be making this PR, so apologies :)

To debug, I use these command args:

server --host=10.1.30.5 --port=9000 --port-end=9090 --tracker=10.1.30.5:9190 --key=1080ti

@soiferj @FrozenGene

FrozenGene · 2020-02-14T02:53:40Z

Thanks @jmorrill to bring C++ RPC to Windows and change build system to CMake!

I maybe have no time to review it this week, but I have a glance in CMakeLists.txt. In our original C++ RPC Makefile, we will distinguish Android and Linux. Because we can't link pthread on Android, but we must link pthread on Linux. Seems you don't handle this?

jmorrill · 2020-02-16T02:53:07Z

@FrozenGene Thanks for taking a peek. I totally understand limited time.

You are right, I do not include a case for Android to skip linking with pthread. My only excuse was can't really find a solid answer to this in CMake. "if(ANDROID)" would be nice if that's how it is done. I'll be very happy to implement any suggestion though!

I made this "first class" option from the main CMake because a) Its a much better implementation on Windows than the python version, which took a bunch of hacks to make decent performance. b) Was very easy to link to the tvm libs given its a sub CMake project.

FrozenGene · 2020-02-17T08:53:57Z

@FrozenGene Thanks for taking a peek. I totally understand limited time.

You are right, I do not include a case for Android to skip linking with pthread. My only excuse was can't really find a solid answer to this in CMake. "if(ANDROID)" would be nice if that's how it is done. I'll be very happy to implement any suggestion though!

I made this "first class" option from the main CMake because a) Its a much better implementation on Windows than the python version, which took a bunch of hacks to make decent performance. b) Was very easy to link to the tvm libs given its a sub CMake project.

I agree all about the advantages of CMake. For Android, I think the CMAKE_SYSTEM_NAME maybe could help us. But it requires the users to specify the build os, I prefer we do it automatically, i.e. we could use the compiler option -dumpmachine to get we are in the android or linux.

For example, when I run:
~/ndk_tools/bin/clang -dumpmachine, it will output: aarch64-none-linux-android
If I run arm linux compiler, it will output: aarch64-linux-gnueabi.

So I think we could this to distinguish android or linux. If we can not run this command successfully, we do the default things.

Please make sure this Android ndk tools and [linaro linux] (https://www.linaro.org/downloads/) could compile cpp/rpc successfully, because they are important platforms to use cpp_rpc. (Note. these two tools has windows os support too)

soiferj · 2020-02-18T00:03:24Z

apps/cpp_rpc/rpc_env.cc

+#if defined(__linux__) || defined(__ANDROID__)
+  const std::string untar_cmd = "tar -C ";
+#elif defined(_WIN32)
+  const std::string untar_cmd = "wsl tar -C ";


I don't think it's ideal to have to force Windows users to enable WSL. Can we use Python tarfile instead? Are there any other options?

I considered some open-source C and C++ libraries, but shied away with license and time to implement reasons.

I think Python tarfile would be a good pick, especially if it comes packaged with python....as long as having python is not a burden. I would like to test the speed though, mostly around python startup time and determine what is "acceptable" or not.

As a side note, it seems you can just send a precompiled dll (no tar), by changing the build_func:

from tvm.contrib import cc
....
builder=autotvm.LocalBuilder(n_parallel=18, timeout=25, build_func=cc.create_shared)

I just did a few tests in powershell:
Measure-Command {start-process "python tar-test.py" -Wait}

Where tar-test.py is just: "import tarfile"

Measure-Command {start-process "wsl tar --help" -Wait}

It doesnt look like python startup is much worse than "wsl tar". Both around ~30ms.

How would you suggest this be implemented? Generate a python script at runtime in the tmp folder?

Looks like I do not need to create a python script at runtime. It looks like something like this would work on windows:

python -m tarfile -e file.tar outdir/

update

Just committed the changes to use python to untar

tqchen · 2020-02-26T23:47:01Z

@soiferj @FrozenGene please followup, and https://docs.tvm.ai/contribute/code_review.html#approve-and-request-changes-explicitly

tqchen · 2020-04-10T02:02:16Z

@jmorrill @FrozenGene it would be great if we can followup :)

jmorrill · 2020-04-10T06:47:39Z

@jmorrill @FrozenGene it would be great if we can followup :)

Sorry about that. I had changed around how the untar works based on @soiferj's suggestion. But on @FrozenGene's concerns about android, it was beyond my expertise. Basically, I could try to do it, but wouldn't even know where to begin to know if it were right :).

If everyone is too busy, I could withdraw the PR until a later time.

FrozenGene · 2020-04-10T07:16:09Z

@jmorrill @FrozenGene it would be great if we can followup :)

Sorry about that. I had changed around how the untar works based on @soiferj's suggestion. But on @FrozenGene's concerns about android, it was beyond my expertise. Basically, I could try to do it, but wouldn't even know where to begin to know if it were right :).

If everyone is too busy, I could withdraw the PR until a later time.

For Android / Embed Linux, if we can not change to CMake easily, we could keep our current Makefile system.

jmorrill · 2020-04-10T07:48:55Z

For Android / Embed Linux, if we can not change to CMake easily, we could keep our current Makefile system.

I know visual studio has "nmake", which can work with Makefiles. I can experiment with that and see if I can do similar to what is being done currently with the Makefile.

FrozenGene · 2020-04-10T08:11:48Z

For Android / Embed Linux, if we can not change to CMake easily, we could keep our current Makefile system.

I know visual studio has "nmake", which can work with Makefiles. I can experiment with that and see if I can do similar to what is being done currently with the Makefile.

OK. This is one way. Another way is for Windows, we could use your pr's cmake. But for Android / Embed Linux, we could still use current Makefile system.

soiferj

Sorry for the late reply. Everything looks good to me given that other concerns are resolved!

jmorrill · 2020-04-10T17:17:20Z

Ok cool. I'm do another commit in the next day or so as I think I can undo a lot of changes I made to the main CMake file and see what you guys think.

… cpp_rpc_windows

jmorrill · 2020-04-11T23:23:03Z

Ok @FrozenGene I have made some changes to the CMakeLists.txt

Should I place "tvm_option(USE_CPP_RPC "Build CPP RPC" OFF)" with an "if(WIN32)" around it? It will compile for linux, just it won't have the android options available in your Makefile.

Also I have:

if(WIN32)
  cmake_minimum_required(VERSION 3.9)
else()
  cmake_minimum_required(VERSION 3.2)
endif()

This gives a min cmake version of 3.9 on Windows. It is not needed for this, but was hoping to sneak in interprocedural optimizations in Windows builds for extra perf. (ex set_property(TARGET tvm PROPERTY INTERPROCEDURAL_OPTIMIZATION TRUE) ) .

I can remove that if its a problem and find a way to do it via compiler switches in a later PR.

FrozenGene · 2020-04-13T03:29:08Z

Ok @FrozenGene I have made some changes to the CMakeLists.txt

Should I place "tvm_option(USE_CPP_RPC "Build CPP RPC" OFF)" with an "if(WIN32)" around it? It will compile for linux, just it won't have the android options available in your Makefile.

Also I have:
if(WIN32)
  cmake_minimum_required(VERSION 3.9)
else()
  cmake_minimum_required(VERSION 3.2)
endif()
This gives a min cmake version of 3.9 on Windows. It is not needed for this, but was hoping to sneak in interprocedural optimizations in Windows builds for extra perf. (ex set_property(TARGET tvm PROPERTY INTERPROCEDURAL_OPTIMIZATION TRUE) ) .

I can remove that if its a problem and find a way to do it via compiler switches in a later PR.

I suggest when we build CPP_RPC on non-Windows platforms, we will raise one fatal and prompt users to use app/Makefile to build CPP RPC.

For CMake version, It is better to use the same version on Linux / Windows. We cold add it in compiler options in later pr.

jmorrill · 2020-04-15T03:57:08Z

For CMake version, It is better to use the same version on Linux / Windows. We cold add it in compiler options in later pr.

Removed all the extra CMakeLists.txt stuff I had in there and do a FATAL if USE_CPP_RPC is set on a non-Windows platform.

Added instructions in the README.

Two other things I want to check with you guys is the small modification I made to src/support/ring_buffer.h. The original would cause a crash on Windows.

Also the same with src/runtime/rpc/rpc_socket_impl.cc change, which shouldn't be an issue (put try/catch in a destructor).

FrozenGene · 2020-04-15T04:03:36Z

For CMake version, It is better to use the same version on Linux / Windows. We cold add it in compiler options in later pr.

Removed all the extra CMakeLists.txt stuff I had in there and do a FATAL if USE_CPP_RPC is set on a non-Windows platform.

Added instructions in the README.

Two other things I want to check with you guys is the small modification I made to src/support/ring_buffer.h. The original would cause a crash on Windows.

Also the same with src/runtime/rpc/rpc_socket_impl.cc change, which shouldn't be an issue (put try/catch in a destructor).

i think it is fine

FrozenGene · 2020-04-15T08:49:46Z

Thanks for the great work! @jmorrill This is merged now. Thanks for the review @soiferj !

* Windows Support for cpp_rpc * Add missing patches that fix crashes under Windows * On Windows, use python to untar vs wsl * remove some CMakeLists.txt stuff * more minor CMakeLists.txt changes * Remove items from CMakeLists.txt * Minor CMakeLists.txt changes * More minor CMakeLists.txt changes * Even more minor CMakeLists.txt changes * Modify readme

* [Relay][Frontend][TFLite] Add parser support for shape and range Signed-off-by: Dhruva Ray <dhruvaray@gmail.com> * [RELAY][PYTORCH]isNan, isinf, isfinite, ceil, clamp, round ops (apache#5316) * [RELAY][PYTORCH]isNan, isinf, isfinite, ceil, clamp, round ops * Review comments * [TIR] Refactor MakePackedAPI to target dependent stage. (apache#5326) Previously MakePackedAPI was in the target independent stage, but never the less requires the device_type information that will be binded at a later target dependent stage. The previous implementation was due to the limitation of LoweredFunc which can not carry buffer_map info(so they have to be lowered right away). This is no longer the case after the unified IR refactor. This PR migrates MakePackedAPI to a target dependent stage and removes the un-necessary BindDevice pass. * [RELAY] Remove re-exports of tvm.transform (apache#5337) * [LLVM] Use llvm::FunctionCallee in IRBuilder::CreateCall with LLVM 11+ (apache#5338) The older variants of CreateCall have been deprecated and were recently removed from LLVM. This caused compilation failures. * [CI] Fix build.sh to propagate --network=host to the docker build command (apache#5336) * when passing --net=host to build.sh it needs to be also sent as --network=host to "docker build", so that both build and run will use the same network configuration * [Runtime][Relay][Cleanup] Clean up for memory pass to enable heterogenous execution support. (apache#5324) * Cleanup type pack and unpack for tuples. * Clean up the memory_pass using common helpers * Clean up memory.cc * Refactor pass * Add doc strings * Fix CPPlint * Fix PyLint * Fix * Apply suggestions from code review Co-Authored-By: Zhi <5145158+zhiics@users.noreply.github.com> * Fix typo Co-authored-by: Zhi <5145158+zhiics@users.noreply.github.com> * Windows Support for cpp_rpc (apache#4857) * Windows Support for cpp_rpc * Add missing patches that fix crashes under Windows * On Windows, use python to untar vs wsl * remove some CMakeLists.txt stuff * more minor CMakeLists.txt changes * Remove items from CMakeLists.txt * Minor CMakeLists.txt changes * More minor CMakeLists.txt changes * Even more minor CMakeLists.txt changes * Modify readme * [PYTORCH]Take, Topk op support (apache#5332) * [PYTORCH]take, topk op support * Ci Failure fix * [TOPI] Using x86 schedules for ARM conv2d. (apache#5334) * [TOPI] Improve get_valid_count and nms performance for CUDA (apache#5339) * get_valid_count updated to have correct results * speedup nms * update nms * revert back nms * recover one test for get_valid_count * [PYTHON] Enhance with_attr API, cleanup MakeAPILegacy in testcases (apache#5335) * [TIR] Remove ProducerConsumer and AllocateNode::new_expr (apache#5333) * [TIR] Remove ProducerConsumer and AllocateNode::new_expr This PR removes two legacy IR parts in TIR that are deprecated. ProducerConsumer node only serves as a hint markup and may no longer be informative after extensive transformations in the pass. If necessary, we can add related info via AttrStmt. The new_expr field in the AllocateNode is deprecated since it can just be replaced by a LetStmt. - Remove dependencies of passes on ProducerConsumer. - Remove ProducerConsumer from the IR. - Remove the deprecated fields (new_expr, free_function) from AllocateNode. * Fix additional testcases * [BYOC] Prevent duplicate outputs in subgraph Tuple (apache#5320) * Fix duplicate output in partitiongraph * Add test case * Fix test_annotated_regions with duplicate compiler_end outputs * Revert "Fix duplicate output in partitiongraph" This reverts commit e1f8ef3. * Prevent duplicate outputs in Tuple in PartitionGraph * Fix lint * Add another test case for when regions are merged, and when TupleGetItem was duplicated * Pull GetFunctionOutput out of branch, improve description of GetFunctionOutput * Use std::move for GetFunctionOutput. Fix typo with testcase name * Use tvm.transform.Sequential * [Tutorial, QNN] Add tutorial for loading quantized PyTorch model (apache#5321) * add pytorch tutorial code and doc stub * add more docs * formatting, more docs * typo fix * try make sphinx happy * add performance section * type and nit fix * format fix * [DOCS] Bring relay docs to the top-level flat view (apache#5343) - Changes most of the relay docs to use autosummary. - Bring relay API docs to the top-level flat view for easier discovery - Removed a few cases of re-exports. * [TOPI][PYTORCH]Logical & Bitwise operator support (apache#5341) * [RELAY][BYOC] Register pattern tables from external codegens (apache#5262) * [RELAY][BYOC] Register pattern tables from external codegens This adds utility functions to support registering and retrieving pattern tables used by MergeComposite for external codegens. Change-Id: I5be165a321440e48b15ff6aff4970e0c67496aaa * Updated DNNL tests to use pattern table mechanism * Removed pattern table standalone test * Change reg to _op * [RUNTIME][CRT] support DLTensor whose ndim == 0 (apache#5344) Signed-off-by: windclarion <windclarion@gmail.com> * [BYOC][FIX] Fix typo in "default" (apache#5348) Default annotations were incorrectly being named 'defualt' which results in them not being removed in PartitionGraph. * enable tsim and fsim for GPU build (apache#5352) * [CRT]Compilation warnings fixed for 32bit and 64bit compilation (apache#5349) * [PYTORCH]Tensor creation ops support (apache#5347) * [Hexagon] Add hexagon_posix.cc to TVM/RT sources in the right place (apache#5346) This file was added before the variable with TVM/RT was initialized. The initialization overwrote the addition. * [TOPI-ARM] Do not alter layout if layout is NHWC (apache#5350) * [TOPI-ARM] Do not alter layout if layout is NHWC * Add test. * [TIR] Make lower_warp_memory support extent(threadIdx.x) < warp_size (apache#5307) * support extent(threadIdx.x) < warp_size in lower_warp_memory * more docs for lower_warp_memory * [RELAY][PYTORCH]GroupNorm op support added (apache#5358) * docker: Drop caffe2 download progess bars (apache#5359) Change-Id: Ia15c3c8f41f75423814e559f6fdb062098f19464 * fix fuse over functions that are handled by external codegen (apache#5365) * [RUNTIME] FastRPC interface for Hexagon runtime (apache#5353) * [RUNTIME] FastRPC interface for Hexagon runtime Co-authored-by: Ravishankar Kolachana <quic_rkolacha@quicinc.com> Co-authored-by: Krzysztof Parzyszek <kparzysz@quicinc.com> * Explain store offset in a comment in launcher Co-authored-by: Abhikrant Sharma <quic_abhikran@quicinc.com> Co-authored-by: Ravishankar Kolachana <quic_rkolacha@quicinc.com> * [TIR][REFACTOR] Migrate low-level passes in tvm.lower to the Unified IR pass manager. (apache#5364) - Migrate BoundCheckers and Simplify - Migrate RewriteUnsafeSelect and RemoveNoOp - Migrate UnrollLoop and StorageRewrite - Migrate InjectDoubleBuffer and InjectVirtualThread - Migrate LoopPartition and Vectorize - Migrate CoProcSync, LiftAttrScope, InjectCopyIntrin We still keep ir_pass registerations for now. Need a separate PR to refactor the parts before the StorageFlatten. * [TIR] Fix lower_warp_memory when there are >1 warp buffers (apache#5368) * fix recursion in lower_warp_memory * post-order mutation * Add cuda target check to dense tensorcore schedule. (apache#5376) * Remove developer facing api from frontend exports. (apache#5375) * [TIR][REFACTOR] Remove te::Tensor dependencies from TIR passes. (apache#5372) * [TIR][REFACTOR] Remove te::Tensor dependencies from TIR passes. te::Tensor is an useful object for tensor expression, but brings un-necessary reverse dependency in TIR nodes such as Provide and Realize. This PR is a first step to remove this dependency. We will use Buffer in all the places where the te::Tensor was used. The rough correspondence are: - Provide -> BufferStore - Realize -> BufferRealize - HalideCall -> BufferLoad. After this change, we can not use IRModule of PrimFuncs cleanly to represent TIR at any point of the optimizations. Buffer will serve as the abstraction for the TIR data models to represent the intermediate storages and their constraints. We still keep Realize/HalideCall and Provide as TIR nodes for now to make the change minimum. Right after ScheduleOps, we call SchedulePostProcToPrimFunc to canonicalize the temporary IR generated by TE(which contains these nodes) to the TIR. The TIR optimizations are now mostly migrated to to the pass manager. Followup PRs are needed to migrate the remaining few passes. * Fix dev tutorial * [PYTORCH]Unary Ops (apache#5378) * [TIR][REFACTOR] RewriteForTensorCore -> te/schedule (apache#5379) * [TIR][REFACTIR] RewriteForTensorCore -> te/schedule RewriteForTensor depends on the schedule information, which makes it differ from a typical pass(which should get all the information from the input TIR). As a result, we refactor it as a SchedulePostProc step for now. We should revisit it later as we introduce more support for tensor core patterns in the TIR. * Fix VTA to fit the new IR Pattern * [Blocksparse] Pipeline for lowering dense model to sparse-dense (apache#5377) * [REFACTOR][TE] Inline -> te/schedule/operation_inline.h (apache#5386) Rationale: inline is a transformation used in te to rewrite its internal expressions. It is not a formal IRModule->IRModule transform pass. Also removed the python test as the test is covered by stage.compute_inline. * [ARITH] Remove the legacy Simplify, migrate to Analyzer. (apache#5385) The legacy Simplify/CanonicalSimplify are now a thin wrapper around the Analyzer. This PR removes these functions and migrated every place that requires simplification to enforce Analyzer creation. The new API would encourage more Analyzer sharing and potentially enable context-aware analyzer-based simplification. * [ARITH] Remove legacy const pattern functions (apache#5387) * Add ability to have multiple copies of same input to onnx_inputs. (apache#5389) * [Topi, ARM] Disbale Winograd for quantized tensors. (apache#5363) * [Topi, ARM] Disbale Winograd for quantized tensors. * Relaxing float * Fix test_ir_type. (apache#5390) * The void return type is not None/nullptr, it's VoidType or TupleType([]). * Tf2 test fixups (apache#5391) * Fix oversight in importing tf.compat.v1 as tf. * Actually disable test for lstm in TF2.1 Since the testing framework actually uses pytest, the version check needs to be moved. * [PTYTHON] Migrate VTA TIR passes to the new pass manager. (apache#5397) * [LLVM] Use ArrayRef<int> in calls to CreateShuffleVector (apache#5399) This switch was made in LLVM 11. Previously this function was expecting mask indices of type uint32_t. This variant is now deprecated. * [KERAS]Minimum & AlphaDropout op support (apache#5380) * Factor out import of common tflite.Operator in tflite frontend. (apache#5355) * Restructure imports in tflite frontend. These python modules are needed for every tflite file parsed. Factorize out imports of the common most ones. Now that the import of operator is common, asserts can be commonized. Loses 473 lines of duplication. * Only restrict to tflite.Operator * [Fix] Remove the duplicate PrintIR pass in Relay (apache#5403) * Update dmlc-core to latest (apache#5401) * [TIR] Enhance Substitute, python bindings for Substitute/PostOrderVisit/IRTransform. (apache#5400) Substitute now takes a std::function to customize more replacing behaviors. Co-authored-by: Siyuan Feng <hzfengsy@sjtu.edu.cn> Co-authored-by: Siyuan Feng <hzfengsy@sjtu.edu.cn> * [Relay] Fix memory leak when accessing NDArray (apache#5413) * Customize SI prefix in logging (apache#5411) * Customize SI prefix in logging * Include unit test * [LLVM] Replace calls to Type::getVectorNumElements (apache#5398) This function has recently been removed from LLVM 11. Use alternative way to obtain vector element count (VectorType::getNumElements) which works for all LLVM versions. * Don't remove() TempDirectory in __del__ after atexit hook runs. (apache#5414) * Use atexit to remove TempDirectory before interpreter shutdown. * Can't rely on complex functions from __del__ anyway. * Fixes warning message on my box: Exception ignored in: <function TempDirectory.__del__ at 0x12be10680> Traceback (most recent call last): File ".../tvm/python/tvm/contrib/util.py", line 55, in __del__ File ".../tvm/python/tvm/contrib/util.py", line 51, in remove File "/usr/local/opt/python/Frameworks/Python.framework/Versions/3.7/lib/python3.7/shutil.py", line 509, in rmtree AttributeError: 'NoneType' object has no attribute 'path' * [TIR][REFACTOR] Remove ir_pass in favor of analysis/transform. (apache#5415) This PR removes ir_pass(old style pass functions) in favor of analysis/transform(new style pass manager). * [RUNTIME][CONTRIB] CoreML Runtime (apache#5283) * [RUNTIME][CONTRIB] CoreML Runtime * fix lint * fix CI * use xcrun to compile coreml model * [DOCS] Migrate HLS documents from md to rst (apache#5419) * fix [RUNTIME][VULKAN] vkBuffer released before memory copy command send to GPU (apache#5388) (apache#5418) * [Frontend] Asymmetric padding of convolution support (apache#4803) * [cuDNN] Add cuDNN grouped convolutions support (apache#5319) Signed-off-by: Wei Pan <weip@nvidia.com> * [CI] Migrate Tensorflow and Tensorflow lite in CI to 2.1.0 (apache#5392) * Migrate Tensorflow and TFLite in the CI up to 1.15.2 The latest stable version of Tensorflow and Tensorflow lite in the 1.x series is 1.15.2. The tflite frontend is receiving support for versions of tflite > 1.14 but there is no consistent testing. There are 2 failures already in the source base with tf 1.15 and I'm concerned this will just get exacerbated over time if we don't have CI picking this up and I view this as a stepping stone towards stepping CI to TF2.x. The test failures that I have commented will get issues raised for them as issues to be fixed. * Comment out run of qnn_mobilenet_v3_net This is another test that fails with TFlite 1.15.2 * Skip the qnn_mobilenet_v3 test in the pytest fashion. * Switch docker versions to support Tensorflow 2.1.0 * Fix up pytest imports and usage. * Skip these tests currently for Tensorflow 2.1.0 * [DOCS] Migrate some markdowns to rst, fix sphinx3 warnings (apache#5416) * [DOCS] Migrate some markdowns to rst, fix sphinx3 warnings * Add note block * [BYOC] Use Non-Recursive Visitor/Mutator (apache#5410) * Non-Recursive AnnotatedTarget and MergeAnnotation * Non-Recursive AnnotatedRegionSet and RegionMerger * [RFC] Pytest environment improvements (apache#5421) * [RFC] Pass pytest options globally. In many places having a global pytest flag is useful . For me with the build and test of tvm , I would like to be able to globally pass in pytest options as part of development flow or CI flows where one would like to measure other things regularly that need measurements including pytest coverage data that I would like to experiment with across the stack. This has been achieved with an additional setup-pytest-env.sh file in tests/scripts rather than putting in something in every single task test script and something I would like to avoid. This now means the -v option to pytest is superfluous. I did consider having a pytest.ini file but that doesn't allow me to pass any old environment variable in and this seems to be the compromise. * Improve other use case documentation * Rationalize pytest environment. * Remove the setting from docker/with_same_user. * Take the opportunity to migrate common PYTHONPATH and TVM_PATH into the common environment setting. * Fixup vta fsim * Be more explicit with common PYTHONPATH * Fix python path for task_python_vta_fsim.sh properly * Fix nit in documentation. * [MXNET]DepthToSpace & SpaceToDepth Operator (apache#5408) * Add option to specify flatbuffers location (apache#5425) * [FRONTEND][MXNET] support elemwise logic ops (apache#5361) * [PY][FFI] Introduce PyNativeObject, enable runtime.String to subclass str (apache#5426) To make runtime.String to work as naturally as possible in the python side, we make it sub-class the python's str object. Note that however, we cannot sub-class Object at the same time due to python's type layout constraint. We introduce a PyNativeObject class to handle this kind of object sub-classing and updated the FFI to handle PyNativeObject classes. * [PYTORCH]where, addcdiv, addcmul op support (apache#5383) * [PYTORCH]Where, addcdiv, addcmul op support * Review comments fixed * [FRONTEND][TFLITE]Gather, StridedSlice op support added (apache#4788) * [FRONTEND][TFLITE]Gather, StridedSlice op added * Review comments fixed * misc fixes for ROCm (pointer lifetime, runtime::String refactor) (apache#5431) * Corrected TVM autotuning on GPU (apache#5432) Added missing "tir" in tvm.tir.analysis.verify_gpu_code(f, kwargs) * [RUNTIME][OBJECT] Introduce static slots for common objects. (apache#5423) The _type_child_slots can be used to enable quick type checking optimization by checking the whether the type index is within the bound. This PR enables these static slots: - Introduce a static assert to avoid the scenario when a developer forget to _type_child_slots when the field is set for the type's parent. - Revamp and assign static type index to common runtime objects - Add a DumpTypeTable call to allow developer monitor the current situation of type table and offers suggestions for the slots(ideally the slots equals the number of children so there is no overflow. * [RELAY][PYTORCH]cosh,sinh,log2,log10,log1p op support (apache#5395) * [RELAY][PYTORCH]cosh,sinh,log2,log10,log1p op support * Review comment fixed * Gradient testcase added * [PYTORCH]Rsub, Embedded, OneHot ops support (apache#5434) * fix miopen pad (apache#5433) * [TOPI,RELAY][TFLITE] Sparse to dense operator Signed-off-by: Dhruva Ray <dhruvaray@gmail.com> * Add TopK to ONNX Frontend (apache#5441) * Add TopK to ONNX Frontend * respond to review comments * [CodeGen] Cleanup generated code (apache#5424) - remove unnecessary white spaces from storage kind - do not start a new scope for vectorization as temporary variables are alll uniquely generated. The above two changes make vectorized code much cleaner. Signed-off-by: Wei Pan <weip@nvidia.com> * [RELAY] Move frontend utils (apache#5345) * [RELAY] Move frontend utils The util file currently under frontend is used from outside of frontend (in qnn/op/legalizations). This suggests that the file should be pushed up to a higher level. The benefit from this change is that importing qnn no longer also imports all the frontends. * Inline get_scalar_from_constant Change-Id: I1cc64e9ecb0eadb6ac0f7b62e6ea174644af4ad4 * Remove util.py from Relay Change-Id: If9cd7cf3fc0bd1861a3a9b5604f338e084d8db96 * Shorten functions Change-Id: Ieb537d82e6ee52421ff05a90cd00a03679ffebf2 * Line length Change-Id: I1d216b7e73a060c4f118f5da50ce58b18eba907f * [KERAS]Embedding layer (apache#5444) * [Docs] VTA install doc migration from md to rst (apache#5442) * Improve IntervalSet's floormod (apache#5367) * use param name in documentation Signed-off-by: Dhruva Ray <dhruvaray@gmail.com> * [ONNX]GatherNd, Round, IsNaN, IsInf (apache#5445) * [relay][topi] Add operation relay.nn.dilate() which calls topi.nn.dilate() (apache#5331) * Add operation relay.nn.dilate() which calls topi.nn.dilate(). * Fix typo * Set op pattern to injective * sphinx doc errors fixed Signed-off-by: Dhruva Ray <dhruvaray@gmail.com> * [Pytorch] fix translation of transpose when axis argument is as a list (apache#5451) * incorporated code review comments Signed-off-by: Dhruva Ray <dhruvaray@gmail.com> * Fixed indentation Signed-off-by: Dhruva Ray <dhruvaray@gmail.com> Co-authored-by: Samuel <siju.samuel@huawei.com> Co-authored-by: Tianqi Chen <tqchen@users.noreply.github.com> Co-authored-by: Krzysztof Parzyszek <kparzysz@quicinc.com> Co-authored-by: Leandro Nunes <leandro.nunes@arm.com> Co-authored-by: Jared Roesch <jroesch@octoml.ai> Co-authored-by: Zhi <5145158+zhiics@users.noreply.github.com> Co-authored-by: jmorrill <jeremiah.morrill@gmail.com> Co-authored-by: Animesh Jain <anijain@umich.edu> Co-authored-by: Leyuan Wang <laurawly@gmail.com> Co-authored-by: Trevor Morris <trevmorr@amazon.com> Co-authored-by: masahi <masahi129@gmail.com> Co-authored-by: mbaret <55580676+mbaret@users.noreply.github.com> Co-authored-by: windclarion <windclarion@gmail.com> Co-authored-by: Tang, Shizhi <rd0x01@gmail.com> Co-authored-by: Marcus Shawcroft <marcus.shawcroft@arm.com> Co-authored-by: Abhikrant Sharma <quic_abhikran@quicinc.com> Co-authored-by: Ravishankar Kolachana <quic_rkolacha@quicinc.com> Co-authored-by: Josh Fromm <jwfromm@uw.edu> Co-authored-by: shoubhik <shoubhikbhatti@gmail.com> Co-authored-by: Bing Xu <antinucleon@gmail.com> Co-authored-by: Andrew Reusch <areusch@octoml.ai> Co-authored-by: Ramana Radhakrishnan <ramana.radhakrishnan@arm.com> Co-authored-by: Haichen Shen <shenhaichen@gmail.com> Co-authored-by: Siyuan Feng <hzfengsy@sjtu.edu.cn> Co-authored-by: MORITA Kazutaka <morita.kazutaka@gmail.com> Co-authored-by: samwyi <samwyi@yahoo.com> Co-authored-by: Zhao Wu <zhaowu@apache.org> Co-authored-by: Wei Pan <60017475+wpan11nv@users.noreply.github.com> Co-authored-by: Cody Yu <comaniac0422@gmail.com> Co-authored-by: Michal Piszczek <imichaljp@gmail.com> Co-authored-by: Thomas Viehmann <tv.code@beamnet.de> Co-authored-by: JishinMaster <francois.turban@gmail.com> Co-authored-by: Matthew Brookhart <matthewbrookhart@gmail.com> Co-authored-by: Thierry Moreau <tmoreau@octoml.ai> Co-authored-by: yongfeng-nv <49211903+yongfeng-nv@users.noreply.github.com> Co-authored-by: notoraptor <notoraptor@users.noreply.github.com> Co-authored-by: Nikolay Nez <34389970+n-nez@users.noreply.github.com>

Windows Support for cpp_rpc

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jmorrill mentioned this pull request Feb 11, 2020

Windows support for autotvm - Do not merge #4548

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Add missing patches that fix crashes under Windows

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tqchen added the status: need review label Feb 11, 2020

soiferj suggested changes Feb 18, 2020

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tqchen assigned FrozenGene Feb 26, 2020

On Windows, use python to untar vs wsl

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soiferj approved these changes Apr 10, 2020

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jmorrill added 3 commits April 11, 2020 14:01

Merge branch 'master' of https://github.com/apache/incubator-tvm into…

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… cpp_rpc_windows

remove some CMakeLists.txt stuff

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more minor CMakeLists.txt changes

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jmorrill added 5 commits April 14, 2020 20:26

Remove items from CMakeLists.txt

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Minor CMakeLists.txt changes

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More minor CMakeLists.txt changes

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Even more minor CMakeLists.txt changes

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Modify readme

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FrozenGene approved these changes Apr 15, 2020

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FrozenGene merged commit afcf939 into apache:master Apr 15, 2020

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ZihengJiang mentioned this pull request Sep 25, 2020

TVM v0.7 Release Note Candidate #6486

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Windows Support for cpp_rpc #4857

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Windows Support for cpp_rpc #4857

Windows Support for cpp_rpc #4857

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