Summary:
To align with thrift-python, we are adding the int base class for `non-Flag` enums. In order to not break production code, the annotation `python.NoIntBaseClassDeprecated` is added to opt-out some enums
After the related customer code logic changes, we can now safely remove the annotations that were added earlier.
Our ultimate goal is to unconditionally add the `int` base to `thrift-py3` enums.
Test Plan:
```
buck test 'fbcode//mode/opt' fbcode//caffe2/torch/fb/training_toolkit/applications/bulk_eval/tests:evaluator_test -- --exact 'caffe2/torch/fb/training_toolkit/applications/bulk_eval/tests:evaluator_test - test_setup_evaluation_utils (caffe2.torch.fb.training_toolkit.applications.bulk_eval.tests.evaluator_test.EvaluatorTest)'
```
Reviewed By: ahilger
Differential Revision: D71446522
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149744
Approved by: https://github.com/izaitsevfb, https://github.com/huydhn
Add a mode to fx_codegen_and_compile() to compile in a separate process. This is to prepare for async compile where we'll compile and run eager in parallel (and also be able to move the compile phase to a remote computer).
Added a test based which runs the test_torchinductor tests with subprocess compiling turned on.
Fixed the test which caused the previous version (#146134) to be reverted:
```
$ PYTORCH_TEST_WITH_ROCM=1 PYTORCH_TEST_WITH_SLOW=1 PYTORCH_TEST_SKIP_FAST=1 python test/inductor/test_compile_subprocess.py CpuTests.test_conv_bn_fuse_cpu
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148635
Approved by: https://github.com/jamesjwu
Add a mode to `fx_codegen_and_compile()` to compile in a separate process. This is to prepare for async compile where we'll compile and run eager in parallel (and also be able to move the compile phase to a remote computer).
Added a test based which runs the test_torchinductor tests with subprocess compiling turned on.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146134
Approved by: https://github.com/jamesjwu
Previously, parametrized tests with class arguments, for example
```
@parametrize("this_cls", (Foo, Bar))
```
would create parametrized tests with names `test_foo_this_cls0` and `test_foo_this_cls1`. With this change, we instead should get `test_foo_this_cls_Foo` and `test_foo_this_cls_Bar`
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133546
Approved by: https://github.com/eellison
This PR
* makes changes to the workflow files and scripts so we can run CI workflows on the MI300 runners
* skips and fixes several tests, failed on MI300, observed in https://github.com/pytorch/pytorch/pull/140989
Skipped due to unsupported Float8_e4m3fn data type on MI300 (need to update test code to use datatypes supported by MI300):
- distributed.tensor.parallel.test_micro_pipeline_tp.py::MicroPipelineTPTest::test_fuse_all_gather_scaled_matmul_A_dims_\*_gather_dim_\* (24 tests across inductor/distributed configs)
- distributed.tensor.parallel.test_micro_pipeline_tp.py::test_fuse_scaled_matmul_reduce_scatter_A_dims_\*_scatter_dim_\* (12 tests across inductor/distributed configs))
- inductor.test_loop_ordering::LoopOrderingTest::test_fp8_cast_and_t
- inductor.test_loop_ordering::LoopOrderingTest::test_fp8_pattern_2
Skipped due to AssertionError on MI300:
- inductor.test_mkldnn_pattern_matcher.py::test_qconv2d_int8_mixed_bf16
- distributed._tools.test_sac_ilp::TestSACILP::test_sac_ilp_case1
Skipped:
- test_cuda.py::TestCudaMallocAsync::test_clock_speed
- test_cuda.py::TestCudaMallocAsync::test_power_draw
- test_torch.py::TestTorchDeviceTypeCUDA::test_deterministic_cumsum_cuda
Skipped flaky tests on MI300:
- distributed.test_c10d_gloo.py::ProcessGroupGlooTest::test_gather_stress_cuda
- inductor.test_cpu_repro::CPUReproTests::test_lstm_packed_unbatched_False* (256 tests)
Fixed:
- test_matmul_cuda.py::TestFP8MatmulCudaCUDA::test_float8_basics_cuda
Features:
- inductor/test_fp8.py - declare a new function to convert FP8 datatypes to ROCm supported FP8 datatypes. It keeps test names for CUDA and ROCm and allows to enable Inductor FP8 tests on CPU
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143673
Approved by: https://github.com/jeffdaily, https://github.com/malfet, https://github.com/pruthvistony
Co-authored-by: saienduri <saimanas.enduri@amd.com>
Co-authored-by: Jithun Nair <jithun.nair@amd.com>
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
See #144006
```py
__________________________________________ CudaReproTests.test_repeated_masked_load __________________________________________
RuntimeError: First class dim doesn't work with python 3.12
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/jansel/conda/envs/pytorch/lib/python3.12/unittest/case.py", line 58, in testPartExecutor
yield
File "/home/jansel/conda/envs/pytorch/lib/python3.12/unittest/case.py", line 634, in run
self._callTestMethod(testMethod)
File "/home/jansel/conda/envs/pytorch/lib/python3.12/unittest/case.py", line 589, in _callTestMethod
if method() is not None:
^^^^^^^^
File "/home/jansel/pytorch/torch/testing/_internal/common_utils.py", line 3108, in wrapper
method(*args, **kwargs)
File "/home/jansel/pytorch/test/inductor/test_cuda_repro.py", line 1678, in test_repeated_masked_load
from functorch.einops import rearrange
File "/home/jansel/pytorch/functorch/einops/__init__.py", line 1, in <module>
from .rearrange import rearrange
File "/home/jansel/pytorch/functorch/einops/rearrange.py", line 7, in <module>
from functorch._C import dim as _C
ImportError: initialization failed
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144006
Approved by: https://github.com/Skylion007
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
Changes:
1. Bump `ruff` from 0.7.4 to 0.8.4
2. Change `%`-formatted strings to f-string
3. Change arguments with the `__`-prefix to positional-only arguments with the `/` separator in function signature.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143753
Approved by: https://github.com/Skylion007
Changes by apply order:
1. Replace all `".."` and `os.pardir` usage with `os.path.dirname(...)`.
2. Replace nested `os.path.dirname(os.path.dirname(...))` call with `str(Path(...).parent.parent)`.
3. Reorder `.absolute()` ~/ `.resolve()`~ and `.parent`: always resolve the path first.
`.parent{...}.absolute()` -> `.absolute().parent{...}`
4. Replace chained `.parent x N` with `.parents[${N - 1}]`: the code is easier to read (see 5.)
`.parent.parent.parent.parent` -> `.parents[3]`
5. ~Replace `.parents[${N - 1}]` with `.parents[${N} - 1]`: the code is easier to read and does not introduce any runtime overhead.~
~`.parents[3]` -> `.parents[4 - 1]`~
6. ~Replace `.parents[2 - 1]` with `.parent.parent`: because the code is shorter and easier to read.~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129374
Approved by: https://github.com/justinchuby, https://github.com/malfet
There are a number of cases where pattern matching differs based on the presence of ACL, causing the tests to fail. This adds `TEST_ACL` and `skipIfACL` so that these tests can still run with different values or be entirely skipped if necessary.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141921
Approved by: https://github.com/malfet
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
# Motivation
This PR enables the XPU quantized convolution. The operators it registers are `onednn::qconv_prepack`, `onednn::qconv1d_pointwise`, `onednn::qconv2d_pointwise`, `onednn::qconv3d_pointwise`. We share same operator schemas as Intel CPU backend as both would call kernels implemented in oneDNN library.
# Details
The implemented operators would be further integrated into pt2e quant flow. In this PR, we validated the kernel functionality via the UT in `test/inductor/test_mkldnn_pattern_matcher.py` where CPU backend defines a series of UT for quantized convolution. Also, we extend the device support for inductor lowering pass and inductor IR defined in `torch/_inductor/fx_passes/quantization.py` and `torch/_inductor/mkldnn_ir.py`. The overall picture would be that, CPU and GPU backend could share the general optimization pass(op fusion) and quantization inductor IR. After lowering, the final kernel would be dispatched to different implementation in oneDNN library.
In this PR, we share the same int8 quantizer in CPU, namely, `X68InductorQuantizer`. In next PR #139578, we will append a `XPUIndcutorQuantizer` which will customized the pt2e behaviors at XPU backend. The capability of `XPUInductorQuantizer` would gradually grow along with the development of quantized operators in XPU.
# Validation
* UT testing
```bash
python test/inductor/test_mkldnn_pattern_matcher.py -v \
-k test_qconv2d_xpu \
-k test_qconv2d_silu_xpu \
-k test_qconv2d_relu6_xpu \
-k test_qconv2d_hardtanh_xpu \
-k test_qconv2d_hardswish_xpu
```
* Runtime exemplification
```bash
#qconv2d
onednn_verbose,primitive,exec,gpu:0,convolution,jit:ir,forward_training,src_u8::blocked:acdb::f0 wei_s8::blocked:acdb::f0 bia_undef::undef::: dst_f32::blocked:acdb::f0,attr-scratchpad:user attr-scales:src0:0:f32+wei:1:f32 attr-zero-points:src0:0:s32 attr-post-ops:binary_add:f32:2+eltwise_linear:1,alg:convolution_direct,mb1_ic128oc128_ih6oh4kh3sh1dh0ph0_iw6ow4kw3sw1dw0pw0,0.0668945
#qconv2d_silu
onednn_verbose,primitive,exec,gpu:0,convolution,jit:ir,forward_training,src_u8::blocked:acdb::f0 wei_s8::blocked:acdb::f0 bia_undef::undef::: dst_u8::blocked:acdb::f0,attr-scratchpad:user attr-scales:src0:0:f32+wei:1:f32 attr-zero-points:src0:0:s32 attr-post-ops:eltwise_swish:1+binary_add:f32:2+eltwise_linear:0.0124779:22,alg:convolution_direct,mb1_ic3oc128_ih8oh6kh3sh1dh0ph0_iw8ow6kw3sw1dw0pw0,0.0881348
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133080
Approved by: https://github.com/guangyey, https://github.com/EikanWang, https://github.com/atalman
