* [Frontend][Torch] Check graph inputs match expected

* error/warn when missing/unused graph inputs

* Change to use get_graph_input_names

* fix unordered dictionary problem for python version 3.5

* modify style

* default value of stride in torch.nn.functional.avg_pool is None

* delete prev modifications

* add testcase for nn.functional.avg_pool2d

Co-authored-by: Jon Soifer <jonso@microsoft.com>

* Add relay operation relay.op.tan.

* Update tan implementation in TVM.

* Update tests.

* Add shape function for tan.

* Add missing main test to python/frontend/tensorflow/test_forward.

* Revert, back to sin/cos.

* Revert "Revert, back to sin/cos."

This reverts commit 4da5b503b921585ba9d80944b29136142b575c40.

* Fix implementation of tan in cuda. Do not support tan for float16.

Simplify topi/tests/python/test_topi_math. Add testing for tan with float32 and float64.

Try again to implement tan as sin/cos in llvm.

* Conditions updated to cover better user scenarios

* [1] New test case added

* [2] New test case added

* [3] Proper variable name used

* [4] Review Comments handled

* [5] Review comments handled

* [6] Review comments handled

* qnn support initial import

* fix upsampling num input

* imagenet tests added

* add qunatized module tests

* quantized module tests working

* imagenet test working

* fix lint

* remove top level torch import to fix ci error

* disable lint warning on outside toplevel import

* revert parse -> convert change

* add comments to qnn translation

* address comments, add sample outputs

* add more comments

* refactor bias add and requantize step

* Set split node's range to minimum of ext and split factor or split nparts, but only when PassDownDomain is called with allow_missing == false, i.e. by InferBound.  Add a helper PassUpThreadBinding() to get a map telling whether an IterVar has at least one leaf IterVar deriving from it binding to a thread. Add two unit tests.

* Enhance LoopVectorizer for vectorizing by 0.  Found at least one case from testtopi/tests/python/test_topi_transform.py::test_tile.

* Revert changes vectorize_loop.cc; when parent's ext is zero, set split's range to the factor or nparts.

* Update with comments.

* Refactor the ext tightening predicate.

* Fix reference types.

* Integrate tvm.te changes.

* Trivial comment change to trigger CI.

* Trivial comment correction to trigger testing.

* fix unordered dictionary problem for python version 3.5

* modify style

* op based external compiler annotation

* Use TVM register directly

* Small fix

* test graph

Co-authored-by: Cody Yu <comaniac0422@gmail.com>

* Sets xgboost dependency to be 0.90, preventing
   segfaults during TVM python unit tests execution

 * This is discussed in issue #4953

* tf frontend read variable op

* pylint fix

* tf frontend freezed graph pruned ops

* add inline pass

* IsInline -> IsMarkedInlined

* fix comment

* TFLite Floor_div & floor_mod parsing code

* Review comment updated

* [Relay][FastMath] Relay pass to use fast exp/tanh

* Adding required_pass to the tests.

* FastMath test changes.

* add custom conversion map

* add roi align test using custom convert map

* refactor test

* add support for upsampling op and test on segmentation models

* remove redundant no_grad

* add upsampling test case

* make the default custom map None, instead of empty dict

* updated tests, remove packaging and drop PT 1.2 support

* add better support for aten::to and tests

* add a note on dilation in x86

* Added CopyFromBytes and CopyToBytes convenience methods.  Fixed typos.

* Removed unneed argument check

* Use TVMArrayCopyFrom/ToBytes methods

* Moved CopyFrom/ToBytes to ndarray.cc

* CopyToBytes impl was using CopyFromBytes.  Fixed

* changed inline to TVM_DLL

* Used impl from TVMArrayCopyTo/FromBytes into NDArray CopyTo/FromBytes

* Move implementation of all CopyFrom/ToBytes into a common impls

* make arg const

* simplify method impl

* [Frontend][TFLite] Add parser support for l2_normalization

* TF doesn't provide uint8 support
* TFL does the normalization only if it's over the last axis
* TFL uses only the default value for expilon

* Change error message

* [DOCS] Fix sphinx precheck

* ignore keras warnings

* Remove more warnings

* The initial import of refactored implementation, all tests passed

* enable mobilenet v2 test

* minor cleanup

* reorg

* fix lint

* use input names that come with torch IR

* fix typo

* introduce parse_operators

* fix lint

* add _ prefix

Introduce the check stage to the unittest stage for now
so we don't have to rebuild CI images.
As we make additional CPU images to make use of the sphinx,
consider move it to an earlier stage.

* [DOCS] Sphinx -- Introduce alias detection.

Background: some of our namespaces import function from another
namespace. For example tvm.te imports most of the operators from tvm.tir.

Previously we manually exclude these aliases from the doc.
However that means we can not link them by the alias name.

This PR adds a sphinx callback plugin to detect such aliases, and create a rubric block
on the button of its current docstring `Alias of the original class`.
It is done in a way so that we can refer to the generated docs.

We also fixed a few docs errors.

* Fix most of the issues

* [Runtime] Fixed TVM_DLL_EXPORT_TYPED_FUNC to work on Windows

* fix style

Co-authored-by: Jon Soifer <jonso@microsoft.com>

* move contrib

* lint

* address comment

* address comment

* make_loss test case

* mxnet frontend make_loss support

* added comment for make_loss

* pylint fix

* Update mxnet.py

* [REFACTOR][PY][API-CHANGE] Remove legacy python files.

Remove legacy python files.
Use the te namespace for most of the tensor expression primitives.

- tvm.create_schedule -> tvm.te.create_schedule
- tvm.placeholder -> tvm.te.placeholder
- tvm.compute -> tvm.te.compute

* Remove top-level exposures.

* [TUTORIAL] Fix tedd tutorial after strategy change

* Remove scale, remove link to external gdoc

* [VTA] YoloV3 Support

Issue:
YoloV3 use some operator and logic that not get good support by
existing vta logic, like nn.pad, upsample, and 255 output channel.

Solution:
add related logic to let darknet YoloV3 can running on VTA

* Fix small(0, or 1 heigh/width) detect frame issue.

* add yolov3-tiny turtorial

* add os import

* address review comments.

* rename tutorial file with a short name.

* rename deploy_vision_on_vta.py into deploy_classification.py.

* address review comment, fix plint eror in deploy_detection.py

* [Frontend][TFLite] Add parser support for square operator

* Add parser implementation
* Add relevant tests
* Note: 'square' is an unary elemwise operator but it's added separately
  in the parser since there is no Relay 'square' op
  and instead we have to use 'multiply'

* Change relay operation from 'multiply' to 'power'

* Remove a redundant line as requested

* call graph for relay

* CallGraphEntryNode->CallGraphEntry, __getitem__->print_var

* fix typos

* Add a tutorial for PyTorch

* Fix sphinx formatting, add version support

* Remove space

* Remove version check

* Some refactoring

* Use no grad

* Rename input

* Update cat img source

* bump up dev version

* update