* 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

* Support 3d Convolution with the ONNX frontend

* add unit tests for conv3d in onnx frontend

respond to PR formatting requests

add x86 schedules to conv3d ncdhw test

fix a doc string format issue

refactor for changed upsream API

* first attempt at conv3d autotuning

add default schedule for conv3d_ncdhw

fill in autotvm integration

add a fallback for invalid schedules

fix fallback

fix reduction order to get simd working correctly

* [intrin] exp2

* [intrin] exp10

* [intrin] log2/10

* [intrins] exp10

* [test] math intrin

This reverts commit 585f9ce6.

This reverts commit fe74b37a.

* [QNN] Support 4D padding.

* Empty commit.

Co-authored-by: Ubuntu <ubuntu@ip-172-31-38-96.us-west-2.compute.internal>

* [TFLITE]elu, leaky_relu, lrn, log_softmax activation functions

* removed ops present in pr 4805

* review_comments updated

- This patch allows CUDA backend to emit correct code for
  selects with vector conditions, which may be produced
  by floordiv op lowering etc..

- This already works for llvm BE, as llvm select instruction
  supports vector conditions.

Signed-off-by: Wei Pan <weip@nvidia.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.

Finally implement tan as sin/cos in llvm.

* Add support for prim::If and prim::Loop with test cases

* rebase and fix tests

* add some comments

* simplifying, fix float cast

* parse -> convert

* recursivly retrive ops in get_all_op_names

* use multiple return values from block correctly, simplify loop convert

* choose dtype properly for zeros and ones

* simplifying, replace convert_inputs with _get_relay_input_vars

* fix for while loop with non input dependent init cond

* add assert on loop var update

* move the condition around

* better testing for seg models

* rebase fix, disable inception v3 in quant test as it is too slow to
load with torch-1.4 + torchvision 0.5

* simplify and add more comparison op converter

This reverts commit d992468d.

* A composite function should not be primitive since we still may need to perform passes on it.

Change-Id: If62d06d265234861a6ec0df7749dc1c339c1055c

* [1] New test case added for fuse

* [2] New test case added for fuse

* [3] New test case added for fuse

* [4] New test case added for fuse

* [5] Early check added

This reverts commit f346c602.

This reverts commit fc7f0783.

* [FRONTEND][TENSORFLOW] support multiply outputs

* [TENSORFLOW][TEST] add tf_testing.AddShapesToGraphDef test

* update frontend test

* retrigger CI

* outline and inline lifted functions for external codegen

* add batch_norm test

* test batch_norm inline

* 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

* 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.

* op based external compiler annotation

* Use TVM register directly

* Small fix

* test graph

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

* 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

* [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

* 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

* 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.

* [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

* 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

* 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>