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

* relay op strategy

fix lint

bitpack strategy

bitserial_dense (#6)

* update strategy

* address comments

fix a few topi test

Dense strategy (#5)

* dense

* add biforst; remove comments

* address comment

Refactor x86 conv2d_NCHWc (#4)

* Refactor x86 conv2d

* Add x86 depthwise_conv2d_NCHWc

* Add back topi x86 conv2d_nchw

* Merge x86 conv2d_nchw and conv2d_NCHWc

* Minor fix for x86 conv2d

fix more strategy

Add x86 conv2d_NCHWc_int8 strategy (#8)

* Add x86 conv2d_NCHWc_int8 strategy

* Remove contrib_conv2d_nchwc_int8

* Fix generic conv2d_NCHWc for int8

* Fix topi arm_cpu conv2d_NCHWc_int8

update x86 conv2d

enable specify relay ops to be tuned for autotvm

add cuda conv2d strategy

add conv2d strategy for rocm

add conv2d strategy for hls

add conv2d strategy for arm cpu

add conv2d strategy for mali

add conv2d strategy for bifrost

add conv2d strategy for intel graphics

clean up and fix lint

remove template keys from autotvm

remove 2 in the func name

address comments

fix

* fix bugs

* lint

* address comments

* add name to op implement

* Modify topi tests (#9)

* Add pooling, reorg, softmax and vision

* Add lrn

* fix topi test

* fix more topi test

* lint

* address comments

* x

* fix more tests & bugs

* Modify more tests (#10)

* Modify tests for bitserial_conv2d, bitserial_dense, bitserial_conv2d_rasp and bnn

* Minor fix

* More minor fix

* fix more test

* try to update vta using strategy

* fix cpptest

* x

* fix rebase err

* Fix two tests (#11)

* change autotvm log format

* lint

* minor fix

* try fix vta test

* fix rebase err

* tweak

* tmp hack for vta pass

* fix tutorial

* fix

* fix more tutorials

* fix vta tutorial

* minor

* address comments

* fix

* address comments

* fix cpptest

* fix docs

* change data structure name and api

* address comments

* lint

* fix rebase err

* updates

* fix winograd test

* fix doc

* rebase

* upgrade tophub version number

* fix bug

* re-enable vta tsim test after tophub is upgraded

* fix vta test to use the correct args so the config can be found in tophub

Co-authored-by: Yao Wang <kevinthesunwy@gmail.com>

* get_valid_count accuracy issue fixed for individual tests but not for all tests running together

* minor fix

* initialize valid_count and PrefixSum buffers

* test updated

* udpate relay test as well

* update document

* fix lint

* address comment

* fix lint

* correct atomicAdd identifier name

* [TEST][FLAKY] topi/tests/python/test_topi_sort.py::test_argsort

* upadate test function of argsort like topk

* Shuffle index and get data from shuffled index

* Replace the random.uniform with np.arange

* add TFLite version check for 'ceil' and 'cos'
* fix name check of test_op for positive inputs
* add error message for operator not found in the installed fbs schema

* support cuda tensorcore subbyte int data type in auto tensorcore

* add lisence

* pass cpplint

* fix code review comments

* merge the int4/int1 codegen tutorial into the existing auto tensorcore tutorial

* using master's new API

* disable tuning when cuda is not enabled

* address cr comment

* do not run the tuning

* fix test failure

* fix cpplint error

* fix bool type reduction bug

* 1. fix a index bug 2. fix returned bytes value of int1/int4/uint4

* fix typo

* fix indents

* Fix image scale and cross-ref

- Allows uniform conditions for select expressions (the same as halide)
  exposed by the loop vectorizer.

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

* Fix Python docstrings

* More fixes

* Fix lint

* [REFACTOR] Polish ffi convention.

- Remove the src/api, keep registration local to the c++ function.
- Remove the api_internal as it is no longer needed.

* Update the codebase walk through

* [Relay] Expose FunctionGetAttr to Python

* add test

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

* Basic test working

* Almost all tests working.

* all tests passing.

* Fixed lint.

* Improved Style.

Fixed bugs that occured when using bitwise operators on floating point type expressions. Further crash when using ops <<, >>, %. Finally added regression tests for both types of bug. (#4892)

- Move the related files to tvm.te
- Move build_module.py to tvm.driver

* Fix bug in re-processing call node

* Add test

* Add to main

* temp changes to work from another machine

* fix rest of tests

* fix test_reuse_call_merge

* fix merge

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

various minor editorial updates - style, grammar, typos.

* add additional switch to handle nested call node

* Fix VM compiler for while loop with free var

- Do not emit __shared__ etc. as part of type for casting

- Fix fp16 reduction kernels with compiler errors:

  "no operator "+" matches these operands, volatile half + volatile half

  This patch inserts casts to remove volatile type qualifier following
  volatile loads (fp16 only). CUDA fp16 library headers should add
  volatile member functions.

- Update have_fp16 to include compute 6.1 GPUs, which do support fp16,
  although their fp16 throughput is low. Updated tests.

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

* Support range in index based tuners

* Address comments

* Remove __*state__

* trigger CI

* add test case for per channel dense

* add unit arg in tflite frontend

* update qnn legalize test

* fix output dim index

* [QNN] Doc fix on quantize and convolution

* update test

- This allows to better utilize the memory bandwidth

- Note that not all cases are vectorized for fp16 datatype. For
  instance, when the size is not a multiple of 1024, the inner loop
  may be an expression that cannot be vectorized. In this case, a
  small inner loop is still benefical for latency hidding.

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

- Move related files into the corresponding location as in C++
- Keep the top-level TVM API backward compatible to make minimum changes in topi

Co-Authored-By: Wei Chen <ipondering.weic@gmail.com>

Co-Authored-By: Wei Chen <ipondering.weic@gmail.com>

Add tuneable conv3d_ndhwc schedule

* [FRONTEND][TFLITE] Add support for TFLite_Detection_PostProcess

This adds support for the custom operator
TFLite_Detection_PostProcess which is commonly used in
object detection networks such as SSD Mobilenet. It
only adds support for when use_regular_nms = False.

Change-Id: I819b253c0eb6f0fa55da65d2634e09359b888828

* Added a test for the tflite custom op

Change-Id: Ie5baa092deae9a8bcffd2ebd9f6d346b90e58afd

* Removed trailing comma

Change-Id: Ib08f02b5f1a59a883048bfb36e4321152cd2e7f2

* Added spaces between divide

Change-Id: If1171fc03d211a809cedeb800804394972af4060

* Formatted comment

Change-Id: I3ce7e69b8d2c73aec57369c1c64ea1eec07f087b

* Reduced line length in test

Change-Id: I49eaafc3369070f8f3e85fbb965ad20972096c68

* Set random seed for test

Change-Id: I542a787d11422ea83c52147b2cb1144fcef0dd77

* Fixes to style

Change-Id: I2971b8ecebe08c882b2481a99f67cfbe515e0b1f

* Assert for incorrect number of inputs

Change-Id: I393f3b3b62be73e427498d98456fb1d5a214e0af

* Change comparison to pass linting

The linter was updated, so I needed to fix
a small style issue as a result.

Change-Id: Ia3c954565a00de92e7fb1912eae9ed9875d60c7c