Currently we have 2 ways of doing the same thing for torch dispatch and function modes:
`with push_torch_dispatch_mode(X)` or `with X.push(...)`
is now the equivalent of doing
`with X()`
This removes the first API (which is older and private so we don't need to go through a deprecation cycle)
There is some risk here that this might land race with a PR that uses the old API but in general it seems like most are using the `with X()` API or `enable_torch_dispatch_mode(X())` which isn't getting removed.
EDIT: left the `with X.push(...)` API since there were ~3 land races with that over the past day or so. But made it give a warning and ask users to use the other API
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78215
Approved by: https://github.com/ezyang
Context: For a while slow gradcheck CI was skipping nearly all tests and this hid the fact that it should've been failing and timing out (10+h runtime for TestGradients). The CI configuration has since been fixed to correct this, revealing the test failures. This PR reenables slow gradcheck CI and makes it pass again.
This PR:
- makes slow and failing tests run in fast gradcheck mode only
- reduce the input size for slow gradcheck only for unary/binary ufuncs (alternatively, skip the test entirely)
- skip entire test files on slow gradcheck runner if they don't use gradcheck (test_ops, test_meta, test_decomp, test_ops_jit)
- reduces the input size for some ops
Follow ups:
1. Investigate slow mode failures https://github.com/pytorch/pytorch/issues/80411
2. See if we can re-enable slow gradcheck tests for some of the slow tests by reducing the sizes of their inputs
The following are failing in slow mode, they are now running in fast mode only.
```
test_fn_fwgrad_bwgrad___rmod___cuda_float64
test_fn_fwgrad_bwgrad_linalg_householder_product_cuda_complex128
test_fn_fwgrad_bwgrad__masked_prod_cuda_complex128
test_fn_fwgrad_bwgrad__masked_prod_cuda_float64
test_fn_fwgrad_bwgrad_linalg_matrix_power_cuda_complex128
test_fn_fwgrad_bwgrad_cat_cuda_complex128
test_fn_fwgrad_bwgrad_linalg_lu_factor_ex_cuda_float64
test_fn_fwgrad_bwgrad_copysign_cuda_float64
test_fn_fwgrad_bwgrad_cholesky_inverse_cuda_complex128
test_fn_fwgrad_bwgrad_float_power_cuda_complex128
test_fn_fwgrad_bwgrad_fmod_cuda_float64
test_fn_fwgrad_bwgrad_float_power_cuda_float64
test_fn_fwgrad_bwgrad_linalg_lu_cuda_float64
test_fn_fwgrad_bwgrad_remainder_cuda_float64
test_fn_fwgrad_bwgrad_repeat_cuda_complex128
test_fn_fwgrad_bwgrad_prod_cuda_complex128
test_fn_fwgrad_bwgrad_slice_scatter_cuda_float64
test_fn_fwgrad_bwgrad_tile_cuda_complex128
test_fn_fwgrad_bwgrad_pow_cuda_float64
test_fn_fwgrad_bwgrad_pow_cuda_complex128
test_fn_fwgrad_bwgrad_fft_*
test_fn_fwgrad_bwgrad_zero__cuda_complex128
test_fn_gradgrad_linalg_lu_factor_cuda_float64
test_fn_grad_div_trunc_rounding_cuda_float64
test_fn_grad_div_floor_rounding_cuda_float64
```
Marks the OpInfos for the following ops that run slowly in slow gradcheck as `fast_gradcheck` only (the left column represents runtime in seconds):
```
0 918.722 test_fn_fwgrad_bwgrad_nn_functional_conv_transpose3d_cuda_float64
1 795.042 test_fn_fwgrad_bwgrad_nn_functional_unfold_cuda_complex128
2 583.63 test_fn_fwgrad_bwgrad_nn_functional_max_pool3d_cuda_float64
3 516.946 test_fn_fwgrad_bwgrad_svd_cuda_complex128
4 503.179 test_fn_fwgrad_bwgrad_linalg_svd_cuda_complex128
5 460.985 test_fn_fwgrad_bwgrad_linalg_lu_cuda_complex128
6 401.04 test_fn_fwgrad_bwgrad_linalg_lstsq_grad_oriented_cuda_complex128
7 353.671 test_fn_fwgrad_bwgrad_nn_functional_max_pool2d_cuda_float64
8 321.903 test_fn_fwgrad_bwgrad_nn_functional_gaussian_nll_loss_cuda_float64
9 307.951 test_fn_fwgrad_bwgrad_stft_cuda_complex128
10 266.104 test_fn_fwgrad_bwgrad_svd_lowrank_cuda_float64
11 221.032 test_fn_fwgrad_bwgrad_istft_cuda_complex128
12 183.741 test_fn_fwgrad_bwgrad_lu_unpack_cuda_complex128
13 132.019 test_fn_fwgrad_bwgrad_nn_functional_unfold_cuda_float64
14 125.343 test_fn_fwgrad_bwgrad_nn_functional_pad_constant_cuda_complex128
15 124.2 test_fn_fwgrad_bwgrad_kron_cuda_complex128
16 123.721 test_fn_fwgrad_bwgrad_pca_lowrank_cuda_float64
17 121.074 test_fn_fwgrad_bwgrad_nn_functional_max_unpool3d_cuda_float64
18 119.387 test_fn_fwgrad_bwgrad_rot90_cuda_complex128
19 112.889 test_fn_fwgrad_bwgrad__masked_normalize_cuda_complex128
20 107.541 test_fn_fwgrad_bwgrad_dist_cuda_complex128
21 106.727 test_fn_fwgrad_bwgrad_diff_cuda_complex128
22 104.588 test_fn_fwgrad_bwgrad__masked_cumprod_cuda_complex128
23 100.135 test_fn_fwgrad_bwgrad_nn_functional_feature_alpha_dropout_with_train_cuda_float64
24 88.359 test_fn_fwgrad_bwgrad_mH_cuda_complex128
25 86.214 test_fn_fwgrad_bwgrad_nn_functional_max_unpool2d_cuda_float64
26 83.037 test_fn_fwgrad_bwgrad_nn_functional_bilinear_cuda_float64
27 79.987 test_fn_fwgrad_bwgrad__masked_cumsum_cuda_complex128
28 77.822 test_fn_fwgrad_bwgrad_diag_embed_cuda_complex128
29 76.256 test_fn_fwgrad_bwgrad_mT_cuda_complex128
30 74.039 test_fn_fwgrad_bwgrad_linalg_lu_solve_cuda_complex128
```
```
0 334.142 test_fn_fwgrad_bwgrad_unfold_cuda_complex128
1 312.791 test_fn_fwgrad_bwgrad_linalg_lu_factor_cuda_complex128
2 121.963 test_fn_fwgrad_bwgrad_nn_functional_max_unpool3d_cuda_float64
3 108.085 test_fn_fwgrad_bwgrad_diff_cuda_complex128
4 89.418 test_fn_fwgrad_bwgrad_nn_functional_max_unpool2d_cuda_float64
5 72.231 test_fn_fwgrad_bwgrad___rdiv___cuda_complex128
6 69.433 test_fn_fwgrad_bwgrad___getitem___cuda_complex128
7 68.582 test_fn_fwgrad_bwgrad_ldexp_cuda_complex128
8 68.572 test_fn_fwgrad_bwgrad_linalg_pinv_cuda_complex128
9 67.585 test_fn_fwgrad_bwgrad_nn_functional_glu_cuda_float64
10 66.567 test_fn_fwgrad_bwgrad_lu_cuda_float64
```
```
0 630.13 test_fn_gradgrad_nn_functional_conv2d_cuda_complex128
1 81.086 test_fn_gradgrad_linalg_solve_triangular_cuda_complex128
2 71.332 test_fn_gradgrad_norm_cuda_complex128
3 64.308 test_fn_gradgrad__masked_std_cuda_complex128
4 59.519 test_fn_gradgrad_div_no_rounding_mode_cuda_complex128
5 58.836 test_fn_gradgrad_nn_functional_adaptive_avg_pool3
```
Reduces the sizes of the inputs for:
- diff
- diag_embed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80514
Approved by: https://github.com/albanD
This PR uses pytest to run test_ops, test_ops_gradients, and test_ops_jit in parallel in non linux cuda environments to decrease TTS. I am excluding linux cuda because running in parallel results in errors due to running out of memory
Notes:
* update hypothesis version for compatability with pytest
* use rerun-failures to rerun tests (similar to flaky tests, although these test files generally don't have flaky tests)
* reruns are denoted by a rerun tag in the xml. Failed reruns also have the failure tag. Successes (meaning that the test is flaky) do not have the failure tag.
* see https://docs.google.com/spreadsheets/d/1aO0Rbg3y3ch7ghipt63PG2KNEUppl9a5b18Hmv2CZ4E/edit#gid=602543594 for info on speedup (or slowdown in the case of slow tests)
* expecting windows tests to decrease by 60 minutes total
* slow test infra is expected to stay the same - verified by running pytest and unittest on the same job and check the number of skipped/run tests
* test reports to s3 changed - add entirely new table to keep track of invoking_file times
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79898
Approved by: https://github.com/malfet, https://github.com/janeyx99
freeze_rng_state() is this thing we use to test random operations in
OpInfos: it ensures that everytime the op is called the rng state is the
same.
Unfortunately this doesn't work with functorch, because
- torch.cuda.set_rng_state() clones a Tensor and then grabs its data_ptr
- functorch's modes cause functorch wrappers to get emitted on the
.clone() call (even if the thing being cloned a regular Tensor).
Tensor subclasses also had this problem. This PR applies the same
solution as torch_dispatch did before: we're just going to disable
functorch dispatch when setting the rng state.
In the long run, torch_dispatch should probably have an option to
interpose on torch.cuda.set_rng_state or generator.set_state... but I
didn't want to think very hard right now.
Test Plan:
- tested with functorch tests (those tests were previously being
skipped, now I can unskip some of them).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81006
Approved by: https://github.com/samdow
Summary:
Add test just to check if TransformerEncoder will crash when enumerating over params [with_no_grad, use_torchscript, training].
Motivation for this was that TransformerEncoder fast path (so with_no_grad=True) and use_torchscript=True would crash with the issue that NestedTensor doesn't have size. This was caused because the TransformerEncoder fast path generates a NestedTensor automatically as a perf optimization and torchscript attempts to find intermediate tensor sizes while it optimizes. But NestedTensor has not implemented a size method, so things fail.
This test goes together with this fix https://github.com/pytorch/pytorch/pull/79480
Test Plan:
```
buck build --show-output mode/opt -c fbcode.enable_gpu_sections=true -c fbcode.nvcc_arch=a100 mode/inplace //caffe2/test:transformers
./fbcode/buck-out/gen/caffe2/test/transformers#binary.par
```
Test runs and passes together with the changes from the PR above (I made another diff on top of this with those changes). Does not pass without the fix.
Reviewed By: mikekgfb
Differential Revision: D37222923
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79796
Approved by: https://github.com/zrphercule
Ref #54789
A `bool` has only two valid values, 1 or 0. Any in-memory value
outside of those leads to undefined behavior. So, instead of
`reinterpret_cast`-ing to `bool*` I introduce `c10::load<scalar_t>`
which will read as `unsigned char` and convert to a valid `bool`.
This gets >90% of operators working, but the remaining operators where
skips and xfails have been added will require individual attention.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77122
Approved by: https://github.com/mruberry
The change
1. Returns the input param in `instantiate_parametrized_tests` so that we can use it as a decorator.
Instead of
```python
instantiate_parametrized_tests(SomeTestClass)
```
we can now do
```python
@instantiate_parametrized_tests
class SomeTestClass:
...
```
2. Strips spaces around parameters in `parametrize`.
Both
```python
@parametrize("x,y", [(1, 'foo'), (2, 'bar'), (3, 'baz')])
def test_bar(self, x, y):
...
```
and
```python
@parametrize("x, y", [(1, 'foo'), (2, 'bar'), (3, 'baz')])
def test_bar(self, x, y):
...
```
(or
```python
@parametrize("x, y", [(1, 'foo'), (2, 'bar'), (3, 'baz')])
````
, for that matter), are now parsed to the correct argument names.
3. Decorates test wrappers with `functools.wraps` to preserve the test method names (previously the names become "wrapper_xxx", which prevents the parameterized tests from getting the correct names.)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78228
Approved by: https://github.com/garymm, https://github.com/seemethere
Give context about failures ~and include it in the test report~! Unfortunately I cannot include it easily in the test report through the addExpectedFailures :c as tracebacks are not something I can instantiate. (see revert comment)
Current way doesn't print the stack traces, which has been fine because we don't hide the signal and it shows up at the end:
<img width="545" alt="image" src="https://user-images.githubusercontent.com/31798555/168878182-914edc39-369f-40bc-8b35-9d5cd47a6b1c.png">
However, when we want to hide the signal, we'd like to print the stack traces for each failed attempt, as it won't be shown at the end.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77664
Approved by: https://github.com/suo
This functionality does not seem to be used
and there are some requests to update dependency.
Add `third_party` to torch_cpu include directories if compiling with
Caffe2 support, as `caffe2/quantization/server/conv_dnnlowp_op.cc` depends on `third_party/fbgemm/src/RefImplementations.h`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75394
Approved by: https://github.com/janeyx99, https://github.com/seemethere
Adds a prototype tracer with no caching support and the `ElementwiseUnaryPythonRefInfo` class. A reference for `floor` is added to test the latter, and the elementwise binary reference inputs are extended to also return noncontiguous inputs. The SampleInput transform operation has been updated to return an actual SampleInput instead of a tuple to facilitate uniform handling of (transformed) SampleInputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76388
Approved by: https://github.com/ngimel
As per title.
### When running `python run_test.py -h`
It used to show:
- The general unittest parser help that we print via a second thread 35545d85dc/torch/testing/_internal/common_utils.py (L467-L470)
- The common_utils's parser help
<details><summary>Full result</summary>
<p>
```bash
$ python run_test.py -h
usage: run_test.py [-h] [-v] [-q] [--locals] [-f] [-c] [-b] [-k TESTNAMEPATTERNS] [tests [tests ...]]
positional arguments:
tests a list of any number of test modules, classes and test methods.
optional arguments:
-h, --help show this help message and exit
-v, --verbose Verbose output
-q, --quiet Quiet output
--locals Show local variables in tracebacks
-f, --failfast Stop on first fail or error
-c, --catch Catch Ctrl-C and display results so far
-b, --buffer Buffer stdout and stderr during tests
-k TESTNAMEPATTERNS Only run tests which match the given substring
Examples:
run_test.py - run default set of tests
run_test.py MyTestSuite - run suite 'MyTestSuite'
run_test.py MyTestCase.testSomething - run MyTestCase.testSomething
run_test.py MyTestCase - run all 'test*' test methods
in MyTestCase
usage: run_test.py [-h] [--subprocess] [--seed SEED] [--accept] [--jit_executor JIT_EXECUTOR] [--repeat REPEAT] [--test_bailouts]
[--save-xml [SAVE_XML]] [--discover-tests] [--log-suffix LOG_SUFFIX] [--run-parallel RUN_PARALLEL]
[--import-slow-tests [IMPORT_SLOW_TESTS]] [--import-disabled-tests [IMPORT_DISABLED_TESTS]]
optional arguments:
-h, --help show this help message and exit
--subprocess whether to run each test in a subprocess
--seed SEED
--accept
--jit_executor JIT_EXECUTOR
--repeat REPEAT
--test_bailouts
--save-xml [SAVE_XML]
--discover-tests
--log-suffix LOG_SUFFIX
--run-parallel RUN_PARALLEL
--import-slow-tests [IMPORT_SLOW_TESTS]
--import-disabled-tests [IMPORT_DISABLED_TESTS]
```
</p>
</details>
It now prints:
- The general unittest parser help the same way. Should we remove this? We can't merge them unfortunately as inittest does not accept parent / does not expose the parser for us to take it as a parent.
- The combined common_utils + run_test parsers help
<details><summary>Full result</summary>
<p>
```bash
$ python run_test.py -h
usage: run_test.py [-h] [-v] [-q] [--locals] [-f] [-c] [-b] [-k TESTNAMEPATTERNS] [tests [tests ...]]
positional arguments:
tests a list of any number of test modules, classes and test methods.
optional arguments:
-h, --help show this help message and exit
-v, --verbose Verbose output
-q, --quiet Quiet output
--locals Show local variables in tracebacks
-f, --failfast Stop on first fail or error
-c, --catch Catch Ctrl-C and display results so far
-b, --buffer Buffer stdout and stderr during tests
-k TESTNAMEPATTERNS Only run tests which match the given substring
Examples:
run_test.py - run default set of tests
run_test.py MyTestSuite - run suite 'MyTestSuite'
run_test.py MyTestCase.testSomething - run MyTestCase.testSomething
run_test.py MyTestCase - run all 'test*' test methods
in MyTestCase
Ignoring disabled issues: []
usage: run_test.py [-h] [--subprocess] [--seed SEED] [--accept] [--jit_executor JIT_EXECUTOR] [--repeat REPEAT] [--test_bailouts]
[--save-xml [SAVE_XML]] [--discover-tests] [--log-suffix LOG_SUFFIX] [--run-parallel RUN_PARALLEL]
[--import-slow-tests [IMPORT_SLOW_TESTS]] [--import-disabled-tests [IMPORT_DISABLED_TESTS]] [-v] [--jit]
[--distributed-tests] [-core] [-pt] [-c] [-i TESTS [TESTS ...]] [-x TESTS [TESTS ...]] [-f TESTS] [-l TESTS]
[--bring-to-front TESTS [TESTS ...]] [--ignore-win-blocklist] [--continue-through-error]
[--export-past-test-times [EXPORT_PAST_TEST_TIMES]] [--shard SHARD SHARD] [--exclude-jit-executor]
[--exclude-distributed-tests] [--run-specified-test-cases [RUN_SPECIFIED_TEST_CASES]]
[--use-specified-test-cases-by {include,bring-to-front}] [--dry-run]
[additional_unittest_args [additional_unittest_args ...]]
Run the PyTorch unit test suite
positional arguments:
additional_unittest_args
additional arguments passed through to unittest, e.g., python run_test.py -i sparse -- TestSparse.test_factory_size_check
optional arguments:
-h, --help show this help message and exit
--subprocess whether to run each test in a subprocess
--seed SEED
--accept
--jit_executor JIT_EXECUTOR
--repeat REPEAT
--test_bailouts
--save-xml [SAVE_XML]
--discover-tests
--log-suffix LOG_SUFFIX
--run-parallel RUN_PARALLEL
--import-slow-tests [IMPORT_SLOW_TESTS]
--import-disabled-tests [IMPORT_DISABLED_TESTS]
-v, --verbose print verbose information and test-by-test results
--jit, --jit run all jit tests
--distributed-tests, --distributed-tests
run all distributed tests
-core, --core Only run core tests, or tests that validate PyTorch's ops, modules,and autograd. They are defined by CORE_TEST_LIST.
-pt, --pytest If true, use `pytest` to execute the tests. E.g., this runs TestTorch with pytest in verbose and coverage mode: python run_test.py -vci torch -pt
-c, --coverage enable coverage
-i TESTS [TESTS ...], --include TESTS [TESTS ...]
select a set of tests to include (defaults to ALL tests). tests must be a part of the TESTS list defined in run_test.py
-x TESTS [TESTS ...], --exclude TESTS [TESTS ...]
select a set of tests to exclude
-f TESTS, --first TESTS
select the test to start from (excludes previous tests)
-l TESTS, --last TESTS
select the last test to run (excludes following tests)
--bring-to-front TESTS [TESTS ...]
select a set of tests to run first. This can be used in situations where you want to run all tests, but care more about some set, e.g. after making a change to a specific component
--ignore-win-blocklist
always run blocklisted windows tests
--continue-through-error
Runs the full test suite despite one of the tests failing
--export-past-test-times [EXPORT_PAST_TEST_TIMES]
dumps test times from previous S3 stats into a file, format JSON
--shard SHARD SHARD runs a shard of the tests (taking into account other selections), e.g., --shard 2 3 will break up the selected tests into 3 shards and run the tests in the 2nd shard (the first number should not exceed the second)
--exclude-jit-executor
exclude tests that are run for a specific jit config
--exclude-distributed-tests
exclude distributed tests
--run-specified-test-cases [RUN_SPECIFIED_TEST_CASES]
load specified test cases file dumped from previous OSS CI stats, format CSV. If all test cases should run for a <test_module> please add a single row:
test_filename,test_case_name
...
<test_module>,__all__
...
how we use the stats will be based on option "--use-specified-test-cases-by".
--use-specified-test-cases-by {include,bring-to-front}
used together with option "--run-specified-test-cases". When specified test case file is set, this option allows the user to control whether to only run the specified test modules or to simply bring the specified modules to front and also run the remaining modules. Note: regardless of this option, we will only run the specified test cases within a specified test module. For unspecified test modules with the bring-to-front option, all test cases will be run, as one may expect.
--dry-run Only list the test that will run.
where TESTS is any of: benchmark_utils/test_benchmark_utils, distributed/_shard/sharded_optim/test_sharded_optim, distributed/_shard/sharded_tensor/ops/test_binary_cmp, distributed/_shard/sharded_tensor/ops/test_elementwise_ops, distributed/_shard/sharded_tensor/ops/test_embedding, distributed/_shard/sharded_tensor/ops/test_embedding_bag, distributed/_shard/sharded_tensor/ops/test_init, distributed/_shard/sharded_tensor/ops/test_linear, distributed/_shard/sharded_tensor/ops/test_math_ops, distributed/_shard/sharded_tensor/test_megatron_prototype, distributed/_shard/sharded_tensor/test_partial_tensor, distributed/_shard/sharded_tensor/test_sharded_tensor, distributed/_shard/sharded_tensor/test_sharded_tensor_reshard, distributed/_shard/sharding_spec/test_sharding_spec, distributed/_shard/test_replicated_tensor, distributed/algorithms/test_join, distributed/elastic/events/lib_test, distributed/elastic/metrics/api_test, distributed/elastic/multiprocessing/api_test, distributed/elastic/timer/api_test, distributed/elastic/timer/local_timer_example, distributed/elastic/timer/local_timer_test, distributed/elastic/utils/distributed_test, distributed/elastic/utils/logging_test, distributed/elastic/utils/util_test, distributed/fsdp/test_flatten_params_wrapper, distributed/fsdp/test_fsdp_apply, distributed/fsdp/test_fsdp_checkpoint, distributed/fsdp/test_fsdp_clip_grad_norm, distributed/fsdp/test_fsdp_comm, distributed/fsdp/test_fsdp_core, distributed/fsdp/test_fsdp_freezing_weights, distributed/fsdp/test_fsdp_grad_acc, distributed/fsdp/test_fsdp_ignored_modules, distributed/fsdp/test_fsdp_input, distributed/fsdp/test_fsdp_memory, distributed/fsdp/test_fsdp_mixed_precision, distributed/fsdp/test_fsdp_multiple_forward, distributed/fsdp/test_fsdp_multiple_wrapping, distributed/fsdp/test_fsdp_optim_state, distributed/fsdp/test_fsdp_overlap, distributed/fsdp/test_fsdp_pure_fp16, distributed/fsdp/test_fsdp_state_dict, distributed/fsdp/test_fsdp_summon_full_params, distributed/fsdp/test_fsdp_traversal, distributed/fsdp/test_fsdp_uneven, distributed/fsdp/test_shard_utils, distributed/fsdp/test_utils, distributed/fsdp/test_wrap, distributed/nn/jit/test_instantiator, distributed/optim/test_zero_redundancy_optimizer, distributed/pipeline/sync/skip/test_api, distributed/pipeline/sync/skip/test_gpipe, distributed/pipeline/sync/skip/test_inspect_skip_layout, distributed/pipeline/sync/skip/test_leak, distributed/pipeline/sync/skip/test_portal, distributed/pipeline/sync/skip/test_stash_pop, distributed/pipeline/sync/skip/test_tracker, distributed/pipeline/sync/skip/test_verify_skippables, distributed/pipeline/sync/test_balance, distributed/pipeline/sync/test_bugs, distributed/pipeline/sync/test_checkpoint, distributed/pipeline/sync/test_copy, distributed/pipeline/sync/test_deferred_batch_norm, distributed/pipeline/sync/test_dependency, distributed/pipeline/sync/test_inplace, distributed/pipeline/sync/test_microbatch, distributed/pipeline/sync/test_phony, distributed/pipeline/sync/test_pipe, distributed/pipeline/sync/test_pipeline, distributed/pipeline/sync/test_stream, distributed/pipeline/sync/test_transparency, distributed/pipeline/sync/test_worker, distributed/rpc/cuda/test_tensorpipe_agent, distributed/rpc/test_faulty_agent, distributed/rpc/test_tensorpipe_agent, distributed/test_c10d_common, distributed/test_c10d_gloo, distributed/test_c10d_nccl, distributed/test_c10d_spawn_gloo, distributed/test_c10d_spawn_nccl, distributed/test_data_parallel, distributed/test_distributed_spawn, distributed/test_launcher, distributed/test_nccl, distributed/test_pg_wrapper, distributed/test_store, distributions/test_constraints, distributions/test_distributions, lazy/test_bindings, lazy/test_extract_compiled_graph, lazy/test_ts_opinfo, test_ao_sparsity, test_autocast, test_autograd, test_binary_ufuncs, test_bundled_inputs, test_complex, test_cpp_api_parity, test_cpp_extensions_aot_ninja, test_cpp_extensions_aot_no_ninja, test_cpp_extensions_jit, test_cuda, test_cuda_primary_ctx, test_dataloader, test_datapipe, test_deploy, test_deploy, test_dispatch, test_expanded_weights, test_foreach, test_function_schema, test_functional_autograd_benchmark, test_functional_optim, test_functionalization, test_futures, test_fx, test_fx_experimental, test_hub, test_import_stats, test_indexing, test_jit, test_jit_autocast, test_jit_cuda_fuser, test_jit_disabled, test_jit_fuser_legacy, test_jit_fuser_te, test_jit_legacy, test_jit_profiling, test_license, test_linalg, test_logging, test_masked, test_mkldnn, test_mobile_optimizer, test_model_dump, test_module_init, test_modules, test_monitor, test_multiprocessing, test_multiprocessing_spawn, test_namedtensor, test_namedtuple_return_api, test_native_functions, test_nestedtensor, test_nn, test_numba_integration, test_numpy_interop, test_openmp, test_ops, test_ops_gradients, test_ops_jit, test_optim, test_overrides, test_package, test_per_overload_api, test_profiler, test_pruning_op, test_public_bindings, test_python_dispatch, test_pytree, test_quantization, test_reductions, test_scatter_gather_ops, test_serialization, test_set_default_mobile_cpu_allocator, test_shape_ops, test_show_pickle, test_sort_and_select, test_sparse, test_sparse_csr, test_spectral_ops, test_stateless, test_tensor_creation_ops, test_tensorboard, test_tensorexpr, test_tensorexpr_pybind, test_testing, test_torch, test_type_hints, test_type_info, test_type_promotion, test_unary_ufuncs, test_utils, test_view_ops, test_vmap, test_vulkan, test_xnnpack_integration
```
</p>
</details>
### When running anything else (for example `python test_autograd.py -h`)
It did not change and still does:
- The general unittest parser help that we print via a second thread
- The common_utils's parser help
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76152
Approved by: https://github.com/malfet, https://github.com/seemethere
Currently `torch.onnx.export(.., operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK)` only issues ATen ops through explicit requests (e.g. `g.at()`) calls inside each op symbolic function. This is done based on specific conditions such as `operator_export_type==OperatorExportTypes.ONNX_ATEN_FALLBACK)` or `is_caffe2_aten_fallback()`
This PR extends the ATen fallback mechanism for scenarios when the symbolic function raises `RuntimeError` during export. The idea is that partial implementation of existing ONNX ops can fallback to ATen as a last resort. That is valuable because each operator can have many input combinations and not all are always implemented.
A minor fix was done to make sure the `overload_name` attribute is added to explicit ATen op fallback requests when a symbolic is not registered to a particular op.
ps: The behavior for builds with BUILD_CAFFE2=1 is not changed to ensure BC.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74759
Approved by: https://github.com/garymm, https://github.com/msaroufim
crossref is a new strategy for performing tests when you want
to run a normal PyTorch API call, separately run some variation of
the API call (e.g., same thing but all the arguments are meta tensors)
and then cross-reference the results to see that they are consistent.
Any logic you add to CrossRefMode will get run on *every* PyTorch API
call that is called in the course of PyTorch's test suite. This can
be a good choice for correctness testing if OpInfo testing is not
exhaustive enough.
For now, the crossref test doesn't do anything except verify that
we can validly push a mode onto the torch function mode stack for all
functions.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75988
Approved by: https://github.com/seemethere
This PR introduces 3 BC changes:
First, this PR propagates `BUILD_CAFFE2` flag to `libtorch` and `libtorch_python`, which is necessary for non-caffe2 ONNX runtimes when using `ONNX_ATEN_FALLBACK` operator export type.
Second, as a complement of https://github.com/pytorch/pytorch/pull/68490, this PR refactors Caffe2's Aten ops symbolics to consider not only the `operator_export_type` (aka `ONNX_ATEN_FALLBACK`) to emit Caffe2 Aten ops, but also whether `BUILD_CAFFE2` (which is called `torch.onnx._CAFFE2_ATEN_FALLBACK` in python binding) is set.
Lastly, it renames `onnx::ATen` to `aten::ATen` for ONNX spec consistency in a BC fashion.
ONNX doesn't have `ATen` op on its spec, but PyTorch ONNX converter emits them. Non-Caffe2 backend engines would be mislead by such operator's name/domain. A non-ideal workaround would be to have Aten ops handled based on its name and ignore the (non-complaint) domain. Moreover, users could incorrectly file bugs to either ONNX or ONNX Runtime when they inspect the model and notice the presence of an unspecified ONNX operator.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73954
Approved by: https://github.com/BowenBao, https://github.com/malfet, https://github.com/garymm, https://github.com/jiafatom
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73875
Previously we had a few settings:
- getExecutor - which toggled between Profiling Executor and Legacy
- getGraphOptimize - if true, overrides PE/Legacy to run with simple executor (no optimizations)
and then...
- getProfilingMode - which would set PE to 0 specializtions.
The last mode is redundant with getGraphOptimize, we should just remove it and use getGraphOptimize in these cases. It would lead to potentially invalid combinations of logic - what does mean if getProfilingMode is true but getExecutor is set to false ? This would lead to a bug in specialize_autograd_zero in this case, see: https://github.com/pytorch/pytorch/blob/master/torch%2Fcsrc%2Fjit%2Fpasses%2Fspecialize_autogradzero.cpp#L93.
The tests here are failing but get fixed with the PR above it, so i'll squash for landing.
Test Plan: Imported from OSS
Reviewed By: cpuhrsch
Differential Revision: D34938130
Pulled By: eellison
fbshipit-source-id: 1a9c0ae7f6d1cfddc2ed3499a5af611053ae5e1b
(cherry picked from commit cf69ce3d155ba7d334022c42fb2cee54bb088c23)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74641
This makes dns hostname lookup failures retryable since in some environments such as Kubernetes they're not guaranteed to be resolvable until the job starts. Retrying this eliminates the race condition.
This also fixes `sandcastle_skip_if` when used on the class instead of the method. Previously they wouldn't inherit from TestCase so just wouldn't run under buck at all.
Fixes https://github.com/pytorch/pytorch/issues/73682
Test Plan:
Added a unit test
```
buck test //caffe2/test/distributed:test_store
```
Reviewed By: aivanou
Differential Revision: D35092284
fbshipit-source-id: d40bf187e52c41f551e4fe41c536b2b0015588ee
(cherry picked from commit f8908309d8ee64c25ee466a6b4922f34f2b7618e)
Summary:
Issue: https://github.com/pytorch/pytorch/issues/69014 (skip reason is not printed for tests running in CI)
Cause: locally tests are fired with testrunner that is built into unittest. When IN_CI env variable is true - tests are fired with xml test runner from unittest-xml-reporting. They have different summary printing properties
Solution: patching printing logic in unittest-xml-reporting when a test is skipped
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74451
Test Plan: examine CI run
Reviewed By: janeyx99
Differential Revision: D35050675
Pulled By: tenpercent
fbshipit-source-id: e3421687d68932079ba156153abeefee368b2fb3
(cherry picked from commit e2356d8ecc618dad9c7c556d40b47c1a6c53f68a)
This PR extends our OpInfo test architecture with "reference inputs," an optional expansion of typical sample inputs that allows for more thorough testing. Currently only the elementwise binary operations implement an extended set of reference inputs. This PR also cleans up some smaller OpInfo-related issues, including several bugs, and it identified https://github.com/pytorch/pytorch/issues/74279.
A reference inputs function can be specified for an OpInfo by filling in its "reference_inputs_func" metadata. If this is done it's recommended that the reference inputs function first call the sample inputs function, then produce additional sample inputs. See `reference_inputs_elementwise_binary` for an example of this pattern.
In addition to implementing reference inputs for the elementwise binary operations, this PR improves their consistency and simplifies how their metadata is represented. The great majority now use a generic sample input function, and those that want extensions start by calling the generic sample input function and then adding additional samples. This removes many older sample input functions. The BinaryUfuncInfo subclass also now allows specifying scalar support more precisely, and reference inputs and error inputs are generated based on this metadata to ensure it's correct.
cc @kshitij12345 @pmeier @zou3519 @Chillee
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74280
Approved by: https://github.com/ngimel
