* 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

* [DOCS] Fix Sphinx Warnings: the target found for cross-reference warnings

* Fix the warning: undefined label

* Initial TEDD for publishing.

* 1. Fix lint issues. 2. Print intrin.body instead of intrin.name in Schedule Tree.  3. Add examples to top level APIs' comments.  4. Top level APIs don't print Dot string by default, unless outputdotstring is True.

* Fix more lint issues.

* Update top level API argument names and use raw strings to avoid Python lint warnings in the tests.

* Disable TEDD verification, but keep TE construction.

* Stop importing tedd to avoid failure.

* Separate data extraction and visualization. 1. Add API tedd.dump_json(schedule) to dump a json string for the schedule data for visualization.  2. Update tests.  3. Add a tutorial.  4. Add range information to IterVars.

* Update TEDD about InferBound failure.  1. TEDD doesn't call inferbound for DFG. 2. Update tutorial about the InferBound failure.

* 1. Import IPython only if SVG is requested.  This is required to fix a tutorial publishing faliure.  2. Fix test about IPython availability check.

* save

* save

* remove

* remove cerr

* remove unnecessary spliting in the cached chunk

* remove unnecessary spliting in the cached chunk

- llvm::StringRef to std::string conversion is explicit now.

Signed-off-by: Wei Pan <wpan11nv@nvidia.com>

* Support int args and no extra buffers

* Fixes

* remove testing code

* fix style

* more style

* use const args

* style

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

* Add a PyTorch to Relay parser

* Add alexnet, googlenet, mnasnet, shufflenet wip

* Fix lint

* Remove fix for shufflenet

* Lower check

* Pull changes from neo-ai/tvm changes

* Remove commented out section

* Use infer_shape everywhere

* Change back to using trace instead of path in from_pytorch

* Parse state_dict to add param names

* Umbrella single_op under test_forwards

* Remove print and cleanup call

* Check if update to test broke CI

* Retrigger CI

* Add back in updated tests

* Try splitting up tests

* First pass at flexible typing, implemented for ones

* Add int32 for all ops

* Remove print statements

* Fix lint

* Broad except

* Add other tensor types

* Temporarily use old tests

* Retrigger CI

* Lower type names

* Use numpy to convert in dense op

* Fix lint

* Remove print

* Need to cleanup but verify int32 works for add

* Rough tests for different types, a lot of types are not supported on CPU

* Probably doesn't build, need to save work as I have to switch branches (constantly)

* Parse param type

* Remove print stmt in parser

* Clean up some code

* Working on flaot32 for bn

* Add resnet18 double type

* Fix lint

* Temporarily move PT tests first

* Temporarily add back refactored tests to fix mem issue

* Add more type test and temp remove some tests

* Comment out tests, hopefully CI prints a trace

* Get stack trace

* Remove operator dict key, rename op_name to node_id, remove dead code

* Make relay map a list

* Remove some hacky string stuff

* Move to PyTorch 1.4

* Remove input_type as param

* Remove _get_fill_value, fix full ops

* Remove unused code and combine ops for identity and none

* Remove fn_param

* Clean up main loop

* Remove useless if/else for outputs

* Remove ir_names, only used once

* Remove some string hacking

* Remove string parsing to get output name

* Fix bug with output sizes of nodes

* Use attributeNames in parse ops

* Remove continue and add_op in parse_op

* Do this everywhere, use assert instead of explciitly type casting

* Remove unnecessary swap

* Slight refactor for elemwise input parse

* Use a copy of graph everywhere

* Rename nid_to_node_name

* Refactor parse import prereqs

* Clean up input node kind check

* Clean up conditionals

* Clean up add_op

* Cleanup type for ones and zeros op

* Fix lint

* Add torch install to CI

* Actually use torch

* Try moving import torch to only where it's needed

* Import torch for CI

* Use take op for select

* Temporarily add ignore for jit inline pass for CI

* Use CompleteTensorType, might be a PT 1.2 only thing

* Use different types in elemwise op

* Use float16 ones

* Fix float16 test

* Remove the temp docker changes

* Remove temp test

* Temporarily comment out original tests

* Remove file

* Empty cache after each test

* Add some prints and lower input sizes

* Try using no grad

* Trying to globally set grad off

* Use no grad for torchvision

* Remove xfail tests

* Remove VGG and AlexNet due to some issues

* Combine pooling tests

* Remove extra test file

* Remove single op, remove larger pooling tests

* Remove maxpool3

* Remove debug prints

* Remove inference call and add no_grad in measure latency

* Use standard string start char

* Remove redundant infer_shape in slice

* Convert most to checks to just expr

* Remove extra paren

* More refactor of isinstance

* Add helper for creating typed constants

* Assert instead of return when no matching type

* Remove network variants

* Add no_grad when forward, remove deatch, fix lint

* Change isinstance to expr in transpose

* Use opnotimplemented, refactor

* Fix full ops, remove duplicate tests

* Never use shape field unless we know the type

* Remove comma, retrigger CI

* Add paren, retrigger CI

* Use inline if-else for flags

* Throw exception instead of assert

* Remove version check for CI

* Check version when doing inline pass

* Fix lint

* Lower more input sizes

* Add new line, conv2d only accepts weight as expr

* Use tvm.runtime.ndarray

* Remove change to torch version install

* Try no grad for mobilenet

* Fix lint

* Fix lint again

* Revert to last passing

* Delete test files

* Ignore lint

* Revert back

* Comment out mobilenet

* Clean up compare compiled and baseline outputs

* Use IRModule

* Add todos

* Refactor use_bias

* Add todo for fix conv op channels

* Change input to data type

* Remove todo

* Handle channel multiplier > 1

* Use opencv reisze method for preprocessing of image in darknet

* Use opencv reisze method for preprocessing of image in darknet

* Fix pylint issues

GaussianDropout & GaussianNoise are active only during training time. This can be skipped during inference.