add more common cfg to DefaultOptions #3
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areusch
reviewed
Aug 4, 2020
tests/python/unittest/test_crt.py
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| micro_binary = tvm.micro.build_static_runtime(workspace, compiler, mod, lib_opts, bin_opts) | ||
| lib_opts['cflags'].pop() | ||
| micro_binary = tvm.micro.build_static_runtime( | ||
| workspace, compiler, mod, lib_opts=opts['lib_opts'], bin_opts=opts['bin_opts']) |
| lib_opts = copy.deepcopy(bin_opts) | ||
| lib_opts['profile']['common'].append('-Werror') | ||
| lib_opts['cflags'] = ['-Wno-error=incompatible-pointer-types'] | ||
| opts = tvm.micro.DefaultOptions(f'{project_dir}/crt') |
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can you also update whatever is in tests/micro?
areusch
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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>
areusch
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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>
areusch
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Sep 20, 2022
* Implementation of call_dps. * Implementation of PackedFuncExpr. * Test CallDPS for TIR function. * Rename. * Add header and comments. * Update. * Address comments.
areusch
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Feb 24, 2023
* Implementation of call_dps. * Implementation of PackedFuncExpr. * Test CallDPS for TIR function. * Rename. * Add header and comments. * Update. * Address comments.
areusch
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Feb 24, 2023
* [IR] Introduce StructInfo * StructInfoFunctor and Analysis Support * [TVMScript] Parse type/shape annotation with StructInfo * remove runtime type assign * Remove type/shape during parsing (#2) * Normalizer prep: simple checks and legacy function renaming. * Struct info deduction in BlockBuilder. * Two TODOs * StructInfo Normalizer Fixes (#3) * StructInfo AST Fix * Fix Extern Func Deduction and shape mutator. * Update VoidStructInfo & globalvar (#4) * Fix passes and proper sinfo propagation. * Refactor EraseToWellDefined to Enable Remapping * [WIP] First stab at symbolic param tracking * Update EraseToWellDefined to support symbolic shape return (#5) * fix R.shape with ndim (apache#6) * Remove update shape/type * Address review comment, AnnotateTypeShape=>AnnotateStructInfo * Update include/tvm/script/ir_builder/relax/frame.h Co-authored-by: Ruihang Lai <ruihangl@cs.cmu.edu> * Address comments * Update printer to use structinfo (apache#7) * Update Error mechanism to prep for obj loc based reporting * Symbolic shape aware function call return value derivation. The main flow works as follows: - Match and populate shape_var_map and var_map by visit each pair of param and call arguments. - Call EraseToWellDefined to map the ret parameter to new result. * [ANALYSIS] Refactor well-form to only look at struct info. * Update comments according to reviews. * Update include/tvm/relax/struct_info.h Co-authored-by: Ruihang Lai <ruihangl@cs.cmu.edu> Co-authored-by: Siyuan Feng <Hzfengsy@sjtu.edu.cn> Co-authored-by: Tianqi Chen <tqchen> Co-authored-by: Ruihang Lai <ruihangl@cs.cmu.edu>
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