Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

change fvisitor to mutator to match tvm name convention/type signature #3

Closed
wants to merge 88 commits into from
Closed

Conversation

MarisaKirisame
Copy link
Collaborator

No description provided.

jroesch and others added 30 commits September 5, 2018 16:18
This commit adds a simplified version of type.h
and expr.h from the previous Relay version.

We implement the basic data types and the
associated machinery for exporting these
to Python, as well as tests that they
can be constructed, all fields are live,
and can be printed using `str`.
Remove the tests for features we don't currently support, and fix the tests which were left.
Introduce both Environment and type inference Python interfaces for testing.
@MarisaKirisame MarisaKirisame deleted the mutator branch September 17, 2018 23:26
jroesch pushed a commit that referenced this pull request Jul 23, 2020
…generating (apache#5962)

* Code migration Start (#1)

* Init commit: Code migration Start

* Add loop_state.cc/h

* Add ComputeDAG basic test

* Split transform_step out & Update more UTs (#3)

* Split transform_step out

* Update GetProducers & GetConsumers

* Update UTs

* Add UT for CacheReadWrite & Some bug fix

* Add search_task, measure and serialization (#4)

* Add FollowSplit & FollowFusedSplit tests

* Update dag.InferBound & its UT

* Add search_task, measure and serialization

* Update Serialization UT

* Add MetaTileRewritePolicy (#5)

* Add feature

* Add cost_model, meta_tile_rewrite_policy

* Add MetaTileRewritePolicy basic UT

* Basic Python API for State (#6)

* Add Basic Python API for State

* Add UTs for State

* Add Python API: Measure & Task (#7)

* Update the return value of state operation

* Add task

* Copy measure.py & utils.py

* Fix LocalBuilder

* Fix LocalRunner

* Add ansor.auto_schedule() API; First AutoSchedule working version(#8)

* Add basic Python support for ansor.auto_schedule

* Update AutoSchedule API

* Bug fix for get the attach point of a fused iter

* Update UT after infer bug fix

* Bug fix & Add python serialization API (#10)

* Delete C++ UT hack since Python is ready

* Add ndarray.non_empty

* Update Serialization python API

* Improve code style, python wrapper and test cases (#11)

* Update c++ code style and unit test

* Update python State wrapper and test cases

* fix unit tests

* Add RPCRunner & OpenCL/CUDA test (#12)

* Add RPCRunner & OpenCL search test

* Add CUDA search test

* Add RPCRunner test

* rebase to upstream/master

* Add Ansor basic tutorial (#13)

* Add basic tutorial

* migrate feature extraction (#14)

* Add XGBModel & RPCRunnerWarpper (#15)

* Add XGBModel & RPCRunnerWarpper

* Revert "Add Parallel Granularity Mutation"

* Migrate workload_registry.py (apache#16)

* add workload registry

* update

* update

* add task scheduler (apache#17)

* Add conv2d cuda tutorial with workload registry (apache#18)

* add tune_test.py (the old tune_wkl.py) (apache#19)

* add tune_test.py (the old tune_wkl.py)

* update

* fix measure

* fix for gpu

* Code refine for tune_test.py & Add a pre load callback (apache#20)

* Bug fix for tutorials

* Add PreLoadMeasuredStates

* Add search_callback support for task tuner

* Code refine for tune_test.py

* Update

* Update

* Update

* Update

* Bug fix

* Add python custom sketch rule (apache#21)

* Add custom sketch rule

* Bug fix

* Ansor Relay Integration (without layout rewrite) (apache#22)

* relay integration

* Add tune_op_subgraph.py & Some code clean for tune_network.py (apache#23)

* Add single op tune scripts

* Add tune subgraph support

* Merge all op & all subgraph to one file

* Rename file

* add explicit_unroll_max_extent (apache#25)

* Add Index simplification & API update (apache#26)

* Add vectorized cooperative_fetching test

* Update math simplify for vectorized CF

* File rename

* Update tune_network

* API update

* Update PreLoadMeasuredStates & Some bug fix (apache#27)

* Add a threading wrapper to fix the test bug

* Set default TVM_USE_AUTO_SCHEDULER to false

* Update PreLoadMeasuredStates callback

* Add tensorize step for loop_state (apache#31)

* Add tensorize step

* State python api update (apache#33)

* Start to update api

* Add compute_dag to state

* API update

* kernel layout rewrite (apache#28)

* kernel layout rewrite

* remove some hacks

* add defuse_ops pass and move kernel_layout_rewrite pass after fuse_ops pass

* set TVM_RELAY_DISABLE_BUILD_CACHE for task extraction and prepare_layout_rewrite

* [cache flush] port cache flush to ansor (apache#32)

* Improve relay integration (apache#34)

* tmp checkpoint

* Improve relay integration

* Improve relay integration

* Fix xgb error & Simplify dispatcher (apache#35)

* Rename "MetaTileRewritePolicy" to "SketchPolicy". (apache#36)

* Rename "MetaTileRewritePolicy" to "SketchPolicy".

* Add a new class for auto_unroll_max_step, storage_offset in StageNode

* fix tune_op_subgraph.py

* rebase

* Migrate all node::make to noderef's construct function (apache#37)

* Start to move xxxnode::make to noderef()

* Update

* Update

* Finish transform_step

* Finish comute dag & auto schedule

* Update

* Update

* Update

* Update

* Update

* Code refine

* Code refine

* Code refine

* Update

* Update

* Some lint fix & Recover the double constructor of tvm::PrimExpr (apache#39)

* lint fix

* clang-format-fix

* pylint fix

* Update

* Recover the double constructor of tvm::PrimExpr

* Fix pylint

* pylint fix

* pylint fix

* Add MutateComputeLocation and MutateParallel in evolutionary search (apache#40)

* Add MutateComputeLocation and MutateParallel in evolutionary search

* fix lint

* Improve loop state python API (stage_tensors -> stage_ops) (apache#41)

* improve loop state python API (stage_tensors -> stage_ops)

* fix

* ComputeDAG bug fix & Add Custom TensorCore Matmul Example (apache#42)

* Bug Fix

* Sample example of Custom TensorCore Matmul

* Rever Commits, Start to build minimum Ansor system

* Code clean for minimum Ansor system

* Bug fix & Delete AccessAnalyzer

* Delete attachmap & Code clean

* Doc update

Update statenode::stages from vector to Array

* Headfile update & Python doc update

* clang-format fix

* pylint fix

* Update

* Doc update

* Update

* Bug fix after code merge to the new master

* clang-format fix

* Update

* Update

* Update std::vector to Array; Update verbosity setting; Some commemts
addressed

* std::vector->Array & std::string->String

* Add init_state to ComputeDAG

* Update

* Update some unordered_map to Map

* clang-format fix

* Comments addressed
Delete ReplayAndInferBound
Delete ReplaySteps & InferBoundCommon

* Lint fix

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Rename ansor namespace to auto_schedule

* Update

* Rename ThreadPool to ParallelFor

* Add parallel_for

* Remove ThreadPool

* Update python/tvm/auto_schedule/auto_schedule.py

* trigger CI

Co-authored-by: Lianmin Zheng <lianminzheng@gmail.com>
Co-authored-by: Minmin Sun (孙敏敏) <minmin.smm@alibaba-inc.com>
Co-authored-by: Zhao Wu <zhaowu@apache.org>
jroesch pushed a commit that referenced this pull request Oct 5, 2020
* Change onnx importer to use dynamic upsampling3d (#3)

fix pylint

* Refactor ONNX frontend to be dynamic

Make OneHot dynamic

Support BatchMatMul with dynamically shaped inputs

fix dynamic broadcast

Add null checks to broadcast_to rel functions

fail more isolated broadcast_to test

use StructuralEqual instead of pointer comparisions in dynamic_to_static pass

add an optional weight freeze argument to onnx importer

convert onnx resize to dynamic op

add dynamic expand to onnx importer

add a shape_func for power

fix BERTSquad, lint

handle onnx graph initializer parameters more intelligently

* Dynamic ONNX importer: Upsampling and Pad (#2)

fix lint

fix Call reference

fix a type issue with expand

fix a bad test refactor

respond to review comments, fix batch matmul tests

* black format

* fix batch matmul test

* add dynamic strided slice to the onnx importer

* fix clip importer

* fix qnn tutorial

* fix bad merge, respond to review comments

* add a simple dynamic model test

* Add dynamic-shaped autopadding to convolution and pooling ops

* fix dynamic issues in a few ops

* fix pylint

* disable tests onnxrt doesn't support

* fix pytorch test

* respond to review comments

* add documentation about partially supporting dynamic shapes

Co-authored-by: Lily Orth-Smith <lorthsmith@octoml.ai>
jroesch pushed a commit that referenced this pull request Jun 29, 2021
…-out (apache#8010)

* [UnitTests] Explicitly list tests that were enabled by TVM_TEST_TARGETS but were skipped

Previously, these were removed by a filter in
tvm.testing._get_targets(), and weren't listed at all.  With this
change, they are instead removed by pytest.skipif, and show up as
explicitly skipped tests in pytest's summary when using
tvm.testing.parametrize_targets.

* [UnitTests] Automatic parametrize_targets for tests that use (target,dev)

Should make it easier to convert tests from using
tvm.testing.enabled_targets to use pytest's parametrized tests
instead.

* [UnitTests] Added ability to explicitly exclude a target from a particular test

Uses tvm_exclude_targets variable, which can be set (1) in the
conftest.py to apply to a test directory, (2) in a test script to
apply to that module, or (3) on an individual test function to apply
to it.  The @tvm.testing.exclude_targets decorator is provided for
readability in case #3.

* [UnitTests] Refactored test_topi_relu.py to use pytest.mark.parametrize

* [UnitTests] Added tvm_known_failing_targets option for the unittests.

Intended to mark tests that fail for a particular target, and are
intended to be fixed in the future.  Typically, these would result
either from implementing a new test, or from an in-progress
implementation of a new target.

* [UnitTests] Known failing targets now marked with xfail instead of skipif

* [UnitTests] Removed tvm_excluded_targets and tvm_known_failing_targets

These were implemented to exclude or mark as failing an entire file or
directory of tests.  In
https://discuss.tvm.apache.org/t/rfc-parametrized-unit-tests/9946/4,
it was pointed out that the global variables would be vulnerable to
typos in the names, resulting in the option being silently ignored.
The decorators `@tvm.testing.exclude_targets` and
`@tvm.testing.known_failing_targets` do not have this failure mode,
and are the preferred version.

* [UnitTests] Added helper functions to tvm.testing.

- tvm.testing.parameter() defines a parameter that can be passed to
  tests.  Tests that accept more than one parameter are run for all
  combinations of parameter values.

- tvm.testing.parameters() defines multiple sets of parameter values.
  Tests that accept more than one parameter are run once for each set
  of parameter values.

- tvm.testing.fixture() is a decorator that defines setup code.  The
  `cache=True` argument can be passed to avoid repeating expensive
  setup across multiple tests.

* [UnitTests] Bugfix for auto parametrizing of "target"

Previously, if the @parametrize_targets were present, but had other
@pytest.mark.parametrize after it, "target" would get parametrized a
second time.  Now, it checks more than just the closest "parametrize"
marker.

* [UnitTests] Renamed "cache" argument of tvm.testing.fixture to "cache_return_value"

* [UnitTests] Minor updates to parametrized test implementation.

As recommended by @tkonolige:

- Avoid infinite loop if LLVM target isn't enabled

- Update documentation for preferred use cases of
  tvm.testing.parametrize_targets, and recommended alternatives.

* [UnitTests] Minor updates to parametrized test implementation

- Documentation, removed previous example usage of tvm.testing.parametrize_targets

* [UnitTests] Changed accidental use of pytest fixtures to a NameError.

- Previously, a fixture function defined in a module was accessible
  through the global scope, and the function definition is accessible
  if a test function uses that name but fails to declare the fixture
  as a parameter.  Now, it will result in a NameError instead.

* [UnitTests] More careful removal of fixture functions from module global scope.

- Initial implementation only checked hasattr(obj, "_pytestfixturefunction")
  before removing obj, which gave false positives for objects that implement
  __getattr__, such as caffe.layers.  Now, also check that the value
  contained is a FixtureFunctionMarker.

* [UnitTests] Copy cached values when using tvm.testing.fixture(cache_return_value=True)

To avoid unit tests being able to influence each other through a
shared cache, all cached fixtures are passed through copy.deepcopy
prior to use.

* [UnitTests] Added meta-tests for tvm.testing functionality

Co-authored-by: Eric Lunderberg <elunderberg@octoml.ai>
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

3 participants