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Use the best tuner possible #4397

Merged
merged 3 commits into from
Dec 15, 2019
Merged

Use the best tuner possible #4397

merged 3 commits into from
Dec 15, 2019

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vmiheer
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@vmiheer vmiheer commented Nov 21, 2019

Maybe I am wrong but what's the point of using randomtuner when AutoTVM paper says XGBoost is the best? Please ignore this PR if you think otherwise 😃.
@tqchen, what do you think?

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I think the reason of using the random tuner as an example is because it doesn't require XGBoost to be installed in the system.

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vmiheer commented Nov 22, 2019 via email

@vmiheer
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vmiheer commented Nov 22, 2019 via email

@comaniac
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I agree with you that the instructions have to be updated (and you're welcome to contribute a PR for it), but it doesn't matter if install XGboost is easy or hard. It's about whether XGBoost is a required or an optional dependency to TVM. Promoting XGBoost to be one of the required dependencies needs an RFC for discussion.

cc @tqchen

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tqchen commented Nov 25, 2019

I think in this case, we can just add more comments to the block. By keeping the RandomTuner, and add comment saying that the developer can also freely choose from other tuners, including XGBoostTuner

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Thanks Tianqi for the comment.
@vmiheer would you mind to modify the PR according to the discussion? Thanks.

@tqchen tqchen added the status: need update need update based on feedbacks label Dec 1, 2019
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vmiheer commented Dec 2, 2019

Sorry, I am bit busy with end of semester submissions. But I'll make sure I update the review.

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vmiheer commented Dec 14, 2019

Updated.

comaniac
comaniac requested changes Dec 14, 2019
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Thanks for updating. Please address some minor issues.

tutorials/autotvm/tune_simple_template.py Outdated
@@ -32,6 +32,8 @@
# Install dependencies
# --------------------
# To use autotvm package in TVM, we need to install some extra dependencies.
# This step is needed if you decide to use xgboost for tuning. This example
# as it is doesn't need xgboost.
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This sentense looks a bit weird to me. Are you saying something like "This step can be skipped as it doesn't need XGBoost."?

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Yes thank you, I'll use your wording :).

tutorials/autotvm/tune_simple_template.py Outdated
@@ -294,8 +296,9 @@ def matmul(N, L, M, dtype):
builder='local',
runner=autotvm.LocalRunner(number=5))

# begin tuning, log records to file `matmul.log`
tuner = autotvm.tuner.XGBTuner(task)
# begin tuning with RandomTuner, log records to file `matmul.log`
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Capital B.
Also please be aware that this file will be processed to generate a tutorial web page, so all comments will be tutorial texts. Although it's annoying, you need to follow the specific comment rules. For example, a paragraph should start with a line with full of "#". You can simply copy line 31 here to make it work.

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image
I was hoping in-code comment would be sufficient, which won't break the section? Let me know if you still want to create explicit break in the code section :).

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I checked the current version on the website and I agree with you that in-code comment is better for this case.

comaniac
comaniac approved these changes Dec 15, 2019
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Thanks for the changes. I don't have further comments.
@tqchen please help merge if so do you.

@tqchen tqchen merged commit 97a26bf into apache:master Dec 15, 2019
zhiics pushed a commit to zhiics/tvm that referenced this pull request Dec 31, 2019
@vmiheer @zhiics
Use the best tuner possible (apache#4397)
97f5058 
* Use the best tuner possible

* Add comment denoting availability of better tuners

* Fix typos and wording
zhiics added a commit to neo-ai/tvm that referenced this pull request Jan 9, 2020
@zhiics @SWu
@inadob @jwfromm @vmiheer @liangdzou @ElaineBao @comaniac @masahi @liangfu @lhutton1 @tqchen @alexgl-github @icemelon @mbaret @uenoku @Hzfengsy @FrozenGene @cchung100m @yongwww @dmakarov @spectrometerHBH @kice @yzhliu @wyc-ruiker @zhenhuaw-me @KnowingNothing @anijain2305 @optima2005 @zhuochenKIDD @Laurawly
Merge with Apache/incubator-tvm (#71)
303a471 
* Change upstream url

* Fix bias_add gradient (apache#4516)

* Fix bias_add gradient

A change caused collapse_sum_like to reject implicit dimension
broadcasting for bias_add gradient, so switch to explicit sum reduction
on the non-bias axis dimensions.

* Lint fix

* [Bugfix][Frontend][TFlite] Fix wrong function call in TANH tests (apache#4517)

* Replace sigmoid() with tanh() in tests for TANH

* Fixed extra reshape parameter bug. (apache#4524)

* Use the best tuner possible (apache#4397)

* Use the best tuner possible

* Add comment denoting availability of better tuners

* Fix typos and wording

* [ir] use DataType instead of Type for readability because Type has been deprecated (apache#4513)

* add bfloat16 typeflag support (apache#4525)

* fix empty config caused KeyError (apache#4520)

* fix onnx shape dtype (apache#4528)

* fix crash issue in tsim backend (apache#4527)

* PIL is depreciated and should be replaced with pillow (a fork of PIL) (apache#4533)

Change-Id: If2075df5475505f2da87dae7145af5a7ab83d8a4

* [Relay] External codegen (apache#4482)

* Update legacy places from nnvm to relay. (apache#4535)

* Update legacy places from nnvm to relay.

This PR prepares the current mainline to remove nnvm compiler dep.

* remove legacy stage

* Implement 1d deconvolution (apache#4476)

* [relay][op] add expand op (from ONNX) to relay frontend (apache#4483)

* Add Expand to onnx.py

* add test function for expand

* Fix a onnx frontend test

* Add tests for the value itself instead of shape only on test_expand

* Cleaned up some unnecessary modifications.

* [TOPI] Allow batch matmul to be fused into injective ops (apache#4537)

* [TOPI] Fixed nms max_output_size loop (apache#4541)

One of the loops in hybrid_nms used for
performing the max_output_size reordering
was incorrectly designated as parallel
resulting in incorrect behaviour. This patch
changes that loop to a serial loop.

Change-Id: I97184f5887f5f028d8ab339fa2808eb7630a4017

* [DOCS] Mention Ninja build system in install/from_source.rst (apache#4554)

* [DOCS] Mention Ninja build system in install/from_source.rst

* Address comments

* [PYTHON][FFI] Cythonize NDArray.copyto (apache#4549)

* [PYTHON][FFI] Cythonize NDArray.copyto

* Cythonize the shape property

* vm external codegen (apache#4544)

* [COMMUNITY] @cchung100m -> reviewer (apache#4557)

* [VTA] improved virtual memory mapping (apache#4545)

* [VTA] improved virtual memory mapping

* Update virtual_memory.cc

* [IR] fix style in ir_mutator and ir_visitor (apache#4561)

* [RUNTIME][VULKAN] Fix compiler warning (apache#4559)

* [REFACTOR][DTYPE] Isolate dtype to runtime (apache#4560)

dtype.h -> runtime/data_type.h

Changes:
- Rename all old reference of tvm::Type to DataType
- ExprNode.type -> ExprNode.dtype
- Expr.type() -> Expr.dtype()
- Change Expr related functions to expr_operator.
  - DataType::min() -> min_value(DataType)
  - DataType::max() -> max_value(DataType)
- Move type constructor Int, UInt, Float, Handle, Bool into DataType.
  - Int(bits) -> DataType::Int(bits)
  - UInt(bits) -> DataType::UInt(bits)

* Support standardize runtime module (apache#4532)

* [Relay][Frontend][ONNX] Support auto_pad in Conv and ConvTranspose (apache#4563)

* [TEST] Remove nnvm related code in topi and test script (apache#4562)

* [TEST] Remove nnvm related code in topi and test script

* Remove docs dep

* [Relay] add max_pool3d in relay and TF converter (apache#4551)

* [Relay] add max_pool3d in relay and TF converter

* fix comments

* Remove nnvm (apache#4565)

* [VTA][Chisel] End-to-end Inference with Chisel VTA (apache#4574)

* [VTA][Chisel] End-to-end Inference with Chisel VTA

* Update TensorAlu.scala

* remove unnecessary cast to int32 (apache#4573)

* Fix llvm-enabled build by adding missing intrinsics headers (apache#4575)

* [DEPRECATION] Remove NNVM compiler (apache#4571)

* Remove NNVM compiler

* [Relay/Topi][Op] Added native DepthToSpace and SpaceToDepth Operators (apache#4566)

* Added tvm function stencil for subpixel operations to topi.

* Topi subpixel operators added and tested.

* Added subpixel attrs.

* Added depth_to_space relay attributes.

* depth_to_space fully working.

* Fixed NHWC shape bug.

* SpaceToDepth in and all tests passing.

* lint fixes.

* Added string include

* Fixed topi formatting.

* Added DCR/CDR mode to depthtospace operator.

* [DOC] fix doc in api.py (apache#4580)

* [DEPRECATION] Cleanup legacy verilog support (apache#4576)

This PR cleans up the left over code for legacy verilog support which was experimental.
The new hardware backend path is now support by VTA via TSIM.

* [RUNTIME] Remove Extension VTable in favor of Unified Object system. (apache#4578)

Before the unified object protocol, we support pass
additional extension objects around by declaring a type as an extension type.
The old extension mechanism requires the types to register their
constructor and deleter to a VTable and does not enjoy the benefit of the
self-contained deletion property of the new Object system.

This PR upgrades the extension example to make use of the new object system
and removed the old Extension VTable.

Note that the register_extension funtion in the python side continues to work
when the passed argument does not require explicit container copy/deletion,
which covers the current usecases of the extension mechanism.

* Some Windows and MSVC fixes (apache#4569)

* fix python exception creation in Windows

* better string conversion for msvc

* fix cpp style issue

* [NEWS] add v0.6 release (apache#4558)

* [NEWS] add v0.6 release

* remove link prefix

* fix issue number

* [DOCS]fix typos in autotvm tutorial (apache#4585)

* [Quantization, Calibrate] Fix context creation when current_target is explicity set (apache#4582)

* [Container] Fix NDArray SaveDLTensor declaration and implementation signature different (apache#4586)

* [TOPI][AutoTVM] NHWC conv2d templates for ARM (apache#3859)

* [AutoTVM][TOPI] NHWC conv2d templates (spatial pack) for ARM

As some frontends (tflite for example) are using NHWC as the default
layout, we are enabling NHWC schedule templates in TOPI and AutoTVM.

* some comments fix

* [FIX][TOPI][X86] schedule dense pack (apache#4539)

* [Relay] Convert Layout Pass. (apache#4335)

* [Relay][AlterLayout] Broadcast with scalar shape (apache#4577)

* [TOPI] add 3D upsampling Op. (apache#4584)

* [TOPI] add 3D upsampling Op.

* fix lint issues

* change align_corners to coordinate_transformation_mode

* fix resize3d half_pixel

* make a simple function and clean up trilinear_resize3d_python

* fix doc

* [Runtime] add necessary const qualifier for NDArray container of parameters (apache#4590)

* [autotvm] fix typos in comment (apache#4591)

* fix tf.compat.v1 issue for tf verison <=1.12 (apache#4593)

* [FRONTEND][TF] conv2d_transpose 'SAME' support kernel more than 1x1 (apache#4484)

* [FRONTEND][TF] conv3d_transpose 'SAME' support kernel more than 1x1

* revised per as review comments

* add more fallback wolkaround to make all tests pass

* [GraphRuntime] Support parameter out in the graph runtime debug (apache#4598)

* [GraphRuntime] Support parameter out in the graph runtime debug

* Dummy commit to trigger build

* [Perf] Add CublasLt extern support for better Igemm performance (apache#4550)

* cublaslt added

* fix lint

* address comments

* address more comments

* Trigger CI

* Trigger CI

* fix codegenc (apache#4597)

* [REFACTOR][RUNTIME] Update NDArray use the Unified Object System (apache#4581)

* [REFACTOR][RUNTIME] Move NDArray to Object System.

Previously NDArray has its own object reference counting mechanism.
This PR migrates NDArray to the unified object protocol.

The calling convention of NDArray remained intact.
That means NDArray still has its own type_code and
its handle is still DLTensor compatible.

In order to do so, this PR added a few minimum runtime type
detection in TVMArgValue and RetValue only when the corresponding
type is a base type(ObjectRef) that could also refer to NDArray.

This means that even if we return a base reference object ObjectRef
which refers to the NDArray. The type_code will still be translated
correctly as kNDArrayContainer.
If we assign a non-base type(say Expr) that we know is not compatible
with NDArray during compile time, no runtime type detection will be performed.

This PR also adopts the object protocol for NDArray sub-classing and
removed the legacy NDArray subclass protocol.
Examples in apps/extension are now updated to reflect that.

Making NDArray as an Object brings all the benefits of the object system.
For example, we can now use the Array container to store NDArrays.

* Address review comments

* [Relay][Convert Layout] Handling batch norm layout change. (apache#4600)

* [relay][refactor] Cache Op::Get in passes to reduce lookup overhead (apache#4594)

* Refactor to use IsOp utility

* retrigger CI

* Update dmlc_tvm_commit_id.txt

* disable one test_batch_norm unit test for now to check CI

* enable test_batch_norm

Co-authored-by: SWu <[email protected]>
Co-authored-by: Ina Dobreva <[email protected]>
Co-authored-by: Josh Fromm <[email protected]>
Co-authored-by: miheer vaidya <[email protected]>
Co-authored-by: Liang ZOU <[email protected]>
Co-authored-by: YixinBao <[email protected]>
Co-authored-by: Cody Yu <[email protected]>
Co-authored-by: masahi <[email protected]>
Co-authored-by: Liangfu Chen <[email protected]>
Co-authored-by: lhutton1 <[email protected]>
Co-authored-by: Tianqi Chen <[email protected]>
Co-authored-by: Alex Gladkov <[email protected]>
Co-authored-by: Takato Yamada <[email protected]>
Co-authored-by: Haichen Shen <[email protected]>
Co-authored-by: mbarrett97 <[email protected]>
Co-authored-by: Hideto Ueno <[email protected]>
Co-authored-by: Siyuan Feng <[email protected]>
Co-authored-by: Zhao Wu <[email protected]>
Co-authored-by: Neo Chien <[email protected]>
Co-authored-by: Yong Wu <[email protected]>
Co-authored-by: Dmitri Makarov <[email protected]>
Co-authored-by: Bohan Hou <[email protected]>
Co-authored-by: kice <[email protected]>
Co-authored-by: Yizhi Liu <[email protected]>
Co-authored-by: Wang Yucheng <[email protected]>
Co-authored-by: 王振华(Zhenhua WANG) <[email protected]>
Co-authored-by: deepIgnorance <[email protected]>
Co-authored-by: Animesh Jain <[email protected]>
Co-authored-by: optima2005 <[email protected]>
Co-authored-by: zhuochen <[email protected]>
Co-authored-by: Leyuan Wang <[email protected]>
zhiics pushed a commit to neo-ai/tvm that referenced this pull request Jan 11, 2020
@vmiheer @zhiics
Use the best tuner possible (apache#4397)
e4da5e8 
* Use the best tuner possible

* Add comment denoting availability of better tuners

* Fix typos and wording
@zhiics zhiics mentioned this pull request Sep 15, 2020
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@vmiheer @comaniac @tqchen