* [Relay] add shape check for concat

* [Relay] add shape check for stack

* add test case for shape mismatch

* [typo] add the missing assert

* fix lint errors.

* replace int with size_t.

* statically cast param->axis to size_t.

* switch to run_infer_type.

* fix checking for negative index

* add static_cast for param->axis

* merge to latest tvm

* fix lint error

* Fix an error with negative index.

* Update transform.h

* Update transform.cc

* Fix upsample layout in keras frontend.

* Fixed group conv being used instead of conv when channels=1

* Add new conv2d test to catch bugs when channels=1.

* Adding support to check if an attribute is present or not without having to get the value.

* - Renaming the method to more appropriate name.

Use a hash map keyed on the descriptor set to avoid bad asymptotic behaviour.

* improve conv2d_transpose x86 performance by reusing conv2d schedule

* parallelize across batches to make large-batch conv2d and conv2d_transpose faster

* improve doc for autotvm.task.space.FallbackConfigEntity.fallback_with_reference_log

* add fallback schedule for schedule_conv2d_transpose_nchw_cuda

* fix pylint

* fix pylint

* unify conv2d_transpose declaration in topi.nn and topi.x86

* Fix graph tuner benchmarking layout transform

* Add test

* [tvm][codegen] Make buffer auto broadcast independent to the order of the input arg

* fix indent

* [TOPI] operator support: logical_and, logical_or, logical_not

* [TOPI] operator support: logical_and, logical_or, logical_not

* [TOPI] fix test cases for operator support: logical_and, logical_or, logical_not

* [TOPI] fix test cases for operator support: logical_not

* QNNLegalize for conv2d

* [QNN] Legalization for Intel x86 QNN Conv2D

* Enable miopen transpose convolution and fp16 support

* linter

* Add support for SquaredDifference and StopGradient; minor fix in BatchMatMul

* Remove stopgradient change

* Resolve PR comment

* Dummy change to retrigger CI

* dummy change to retrigger CI

* Refine policies for define_split

- Rename policy "all" to "factors"
- Add policy "verbose" and "power2"

* Refine search space

* add doc

2) Add EQ support in the loop partition and add test for the same
3) Change typo truc to trunc

Issue:
RPC path get changed into "vta_rpc" from "pynq_rpc", but related
document still use old informaiton.

Solution:
Update RPC path information.

* Fix int8x4 vectorize

* Fix gpu shared/local memory accumulate

* Add test_shared_memory for int8x4

* Adjust test format

* Fix cpplint

* [QNN] Convolution 2D Implementation.

Rebasing. Empty commit.

Clang-format styling.

* Reformatting code.

* Fixing lint issues.

* Support cuBLAS BatchMatMul

* Add test and check target name

This is an alternative implementation of a subset of the TVM runtime API (and
graph runtime) that focuses entirely on reducing code size, at the expense of
functionality (no tvm.extern(..) calls via PackedFunc, CPU only, etc). It might
be worth incrementally expanding the surface area if there's interest.

The motivation for this work was seeing what the minimal useful subset of the
TVM runtime is. This is relevant for e.g. super code-size constrained
applications in e.g. embedded/mobile. The current runtime is more like O(100KiB)
or so, so this might be compelling for some users.

The smaller surface area for auditing might make this relevant for
https://github.com/dmlc/tvm/issues/3159, or the usecases I was thinking about in
https://github.com/dmlc/tvm/issues/2523#issuecomment-459165815 re: the Rust
runtime.

The symbols in the tvm::minimalruntime space (i.e. excluding std:: and
picojson::) are about 5KiB, so I think there's a bunch of room here (i.e. we
could replace picojson:: with [`jsmn`](https://zserge.com/jsmn.html) or
something, and we could replace more of the `std::unordered_map` usage, etc with
custom primitives as well (similar to the `DynArray`).

* Module refactor

* Add load module

* Add support for idempotent import

* Tweak load paths

* Move path around

* Expose C++ import functions in Python

* Fix import

* Add doc string

* Fix

* Fix lint

* Fix lint

* Fix test failure

* Add type solver

* Fix lint

* support LLVM trunk

* guard with USE_LLVM in if condition for c++14

* GREATER_EQUAL -> GREATER

* [Arm] parallel batch axis

* [Relay][Frontend][Keras] Fix ReLU in Keras Converter missed the case

* [Relay][Frontend][Keras] Add test case for ReLU in Keras Converter missed the case

* [Relay][Frontend][Keras] Add test case for ReLU in Keras Converter missed the case

* save

* save

* numpy compatible type inference

* update

* try to fix

* fix

* try to fix

* fix lint

* Update nn.h

* cast to int32

* try to fix

* fix again

* retrigger ci

* add more ops

* stop vectorization for erf

* x

* cleanup

* fix

* add whitelist for vectorizable intrin

* add tf converter

* fix dense

* fix

* add missing intrin

* fix mxnet frontend

* fix nvptx

save

fix

fix grad

* fix cmake for mac os

* rename

* support LLVM trunk

* guard with USE_LLVM in if condition for c++14

* GREATER_EQUAL -> GREATER