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[Frontend] Asymmetric padding of convolution support #4803

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merged 1 commit into from Apr 23, 2020
Merged

[Frontend] Asymmetric padding of convolution support #4803

merged 1 commit into from Apr 23, 2020

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FrozenGene
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@FrozenGene FrozenGene commented Feb 3, 2020 •
edited

As we have supported asymmetric padding, we could avoid extra pad operator in the frontend now. Our tf frontend has done this. This pr complete this support in the tflite / coreml / keras frontend. Existing test files covers new change.

Note: This is not conflict with pr #4787, which could handle it and legalize it in the topi. Like our MXNet frontend could still keep 2D padding if it doesn't have asymmetric padding. However, like tflite / tf / keras / coreml has asymmetric padding, this pr could make us no extra pad operator now and could have better performance described in the rfc #2682

@inadob @optima2005 @anijain2305 @Huyuwei Could you help to review this? Thanks.

@FrozenGene
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Currently our QNN seems doesn't like passing asymmetric padding directly to it. Change to WIP to indicate I am working on it. I will notice to review when it is complete. Thanks.

@FrozenGene FrozenGene changed the title [Frontend] Asymmetric padding of convolution support [WIP][Frontend] Asymmetric padding of convolution support Feb 3, 2020
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Initial Investigation:

For QNN, before asymmetric padding support, we will handle it in tflite frontend inserting nn.pad so that the attr['padding'] is always 0 even it is same padding. However, the things become different when we have asymmetric padding support. If we pass 4D padding (top, left, bottom, right) directly to QNN, if we are in the target of CPU, we will call

def helper_no_fast_int8_hw_legalization

if we don't have int8 acceleration. there isn't pad handling here.

The logic of QNN is always do like Conv2DPadInput. This is not perfect way because we will always have pad operator before QNN. We should have pad_const and let the tvm backend to handle pad value.

I prefer this PR make FP32 be asymmetric padding and leave QNN padding support as previous way (i.e. inserting nn.pad in tflite frontend). However, I wish we could add one attribute pad_const to Conv2DAttr and related api to support asymmetric padding computation of QNN. How about you? @anijain2305

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@FrozenGene I will take a look in few hours (traveling right now). Happy to help.

@inadob
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inadob commented Feb 3, 2020

I ran into this issue when I used a quantized model with convolution #4807. Is this helpful?

@FrozenGene
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I ran into this issue when I used a quantized model with convolution #4807. Is this helpful?

Right! This is one issue too. My raised question is when we pass 4D padding directly to QNN, it has problem too (no nn.pad in tflite frontend). I think your pr should be merged too. cc @anijain2305

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So, if I understand correctly there are 2 issues here

  • If you try to pass the asymmetric padding via QNN conv2d, QNN conv2d is not able to handle 4 dimensions. I agree that needs to change.
  • Currently, even if I make QNN conv2d handle 4 dimensions, QNN calls Pad explicity, which might lead to sub-optimal performance. So, suggestion here is to add a pad_const value in Conv2D attrs and let Conv2D schedule handle padding. Correct?

I can handle them in 2 separate PRs. For Conv2D attrs, I will have to put RFC to gather everybody thoughts on changing the Conv2D API. Not sure, if everybody will be happy with changing extensively used Conv2D API.

@FrozenGene do you have any rough numbers for what is the padding overhead if we have it a separate operator? For C5 Cascade/Skylake servers, I don't see pad taking any significant time. So, just curious. (maybe bad schedule for other platforms)

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FrozenGene commented Feb 4, 2020 •
edited

Thanks for the quick response @anijain2305 !

I can handle them in 2 separate PRs. For Conv2D attrs, I will have to put RFC to gather everybody thoughts on changing the Conv2D API. Not sure, if everybody will be happy with changing extensively used Conv2D API.

Yes. I agree we should file a RFC, because this affects many aspects, even Conv1D, Conv3D, because we should unify these attrs too.

@FrozenGene do you have any rough numbers for what is the padding overhead if we have it a separate operator? For C5 Cascade/Skylake servers, I don't see pad taking any significant time. So, just curious. (maybe bad schedule for other platforms)

I am very sad that I lost the number. I don't know whether @Rasterer has record number. Previous testing on one arm device i.MX6 , ARM cortex A9 cpu, which has performance impact. Your skylake CPU is too strong so that we maybe can not observe significant overhead.

Meanwhile, you could put an eye on this PR #4807, it shouldn't fail on qnn mobilenet v1 test. Its logic is correct.

@tqchen tqchen added the status: need update need update based on feedbacks label Feb 26, 2020
@FrozenGene
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So, if I understand correctly there are 2 issues here

  • If you try to pass the asymmetric padding via QNN conv2d, QNN conv2d is not able to handle 4 dimensions. I agree that needs to change.
  • Currently, even if I make QNN conv2d handle 4 dimensions, QNN calls Pad explicity, which might lead to sub-optimal performance. So, suggestion here is to add a pad_const value in Conv2D attrs and let Conv2D schedule handle padding. Correct?

I can handle them in 2 separate PRs. For Conv2D attrs, I will have to put RFC to gather everybody thoughts on changing the Conv2D API. Not sure, if everybody will be happy with changing extensively used Conv2D API.

@FrozenGene do you have any rough numbers for what is the padding overhead if we have it a separate operator? For C5 Cascade/Skylake servers, I don't see pad taking any significant time. So, just curious. (maybe bad schedule for other platforms)

ping @anijain2305 have you plan to handle it recently?

@anijain2305
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Thanks for reminding. Yes, I will make QNN Conv2D accept 4D padding for now, to make parser cleaner for now. The change of Conv2D attrs to have a pad_const can wait.

@anijain2305
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Sorry for the delay. I could not get to it yesterday. I will get it done in 2 days.

@FrozenGene
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Sorry for the delay. I could not get to it yesterday. I will get it done in 2 days.

No worries. Just do it as your plan.

@anijain2305 anijain2305 mentioned this pull request Mar 11, 2020
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tqchen commented Mar 16, 2020

ping @FrozenGene @anijain2305

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@FrozenGene I have made changes in the QNN side. Maybe you can revisit this PR now.

@FrozenGene
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@FrozenGene I have made changes in the QNN side. Maybe you can revisit this PR now.

Sorry, I missed this notification @anijain2305 . I just saw this message.

About this pr, if we are in QNN mode, our pad value will be zero_point. If we will use params['padding'] = [pad_top, pad_left, pad_bottom, pad_right], here, we don't control the pad_value. Have you handled it in your previous pr?

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Yes, QNN Conv2d accepts 4D padding now. Internally while converting to simpler Relay ops, it will call Relay pad with those 4D pad_values and pad_const = input_zero_point, followed by Relay conv2d. For this PR, we don't need Relay conv2d to accept a pad_const value.

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Yes, QNN Conv2d accepts 4D padding now. Internally while converting to simpler Relay ops, it will call Relay pad with those 4D pad_values and pad_const = input_zero_point, followed by Relay conv2d. For this PR, we don't need Relay conv2d to accept a pad_const value.

Good. This is expected behavior now.

@FrozenGene FrozenGene changed the title [WIP][Frontend] Asymmetric padding of convolution support [Frontend] Asymmetric padding of convolution support Apr 22, 2020
@FrozenGene FrozenGene removed the request for review from Huyuwei April 22, 2020 08:25
@FrozenGene
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@anijain2305 Please help to review it.

@tqchen tqchen added status: need review and removed status: need update need update based on feedbacks labels Apr 23, 2020
@anijain2305 anijain2305 merged commit a3b1397 into apache:master Apr 23, 2020
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Thanks @FrozenGene @tqchen @inadob
This is merged!

dpankratz pushed a commit to dpankratz/incubator-tvm that referenced this pull request Apr 24, 2020
@FrozenGene @dpankratz
[Frontend] Asymmetric padding of convolution support (apache#4803)
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dhruvaray pushed a commit to dhruvaray/incubator-tvm that referenced this pull request Apr 28, 2020
@FrozenGene @dhruvaray
[Frontend] Asymmetric padding of convolution support (apache#4803)
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dhruvaray added a commit to dhruvaray/incubator-tvm that referenced this pull request Apr 28, 2020
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S2d (#2)
3da94a0 
* [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>
trevor-m pushed a commit to trevor-m/tvm that referenced this pull request Jun 9, 2020
@FrozenGene
[Frontend] Asymmetric padding of convolution support (apache#4803)
5b10096
trevor-m pushed a commit to trevor-m/tvm that referenced this pull request Jun 18, 2020
@FrozenGene
[Frontend] Asymmetric padding of convolution support (apache#4803)
aa98259
trevor-m pushed a commit to neo-ai/tvm that referenced this pull request Jun 18, 2020
@FrozenGene @trevor-m
[Frontend] Asymmetric padding of convolution support (apache#4803)
58dfc7d
@ZihengJiang ZihengJiang mentioned this pull request Sep 25, 2020
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