This PR extracts some test cases from TestPatternMatcher into a newly created TestPatternMatcherGeneric, and uses instantiate_device_type_tests to make them reusable across multiple devices.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150286
Approved by: https://github.com/jansel
**Summary**
It's part of the task to enable max-autotune with GEMM template for WoQ INT4 GEMM on CPU.
This PR adds a lowering pass for `torch.ops.aten_weight_int4pack_mm_for_cpu`. This op is used for WoQ int4 in Torchao. The lowering pass is a prerequisite for max-autotune, which is planed to be enabled for this op in subsequent PRs.
**Test plan**
```
python test/inductor/test_mkldnn_pattern_matcher.py -k test_woq_int4
python test/inductor/test_cpu_cpp_wrapper.py -k test_woq_int4
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145250
Approved by: https://github.com/leslie-fang-intel, https://github.com/jerryzh168
ghstack dependencies: #145245
**Summary**
Enable the CPP Grouped GEMM Fusion, lowering and Grouped GEMM Template following the RFC: https://github.com/pytorch/pytorch/issues/144012
- Support flexible number of GEMMs
- Share activation across GEMMs
- The Grouped GEMM Template supports independent activations
- However, the pattern matcher requires an anchor node, which is as the shared activation across GEMMs
- Each GEMM can have a unique weight but same sizes
- Each GEMM can have a unique bias or None
- Current PR does not yet support biases; this will be addressed in a follow-up epilogue fusion PR
- Each GEMM have its own epilogues
- Epilogue fusion is not yet supported in this PR and will be enabled in an upcoming follow-up epilogue fusion PR
**Test Plan**
```
python -u -m pytest -s -v test/inductor/test_cpu_select_algorithm.py -k test_grouped_linear
python -u -m pytest -s -v test/inductor/test_cpu_select_algorithm.py -k test_grouped_linear_invalid
python -u -m pytest -s -v test/inductor/test_cpu_cpp_wrapper.py -k test_grouped_linear
```
**Example**
Here is the example and generated code
```
batch_size = 4
in_features = 512
out_features = 1024
dtype = torch.bfloat16
class M(torch.nn.Module):
def __init__(self, bias):
super().__init__()
self.linear0 = torch.nn.Linear(in_features, out_features, bias=False)
self.linear1 = torch.nn.Linear(in_features, out_features, bias=False)
def forward(self, x):
return self.linear0(x), self.linear1(x)
if __name__ == "__main__":
with torch.no_grad():
input = torch.randn(batch_size, in_features, dtype=dtype)
m = M(bias=bias).to(dtype=dtype).eval()
cm = torch.compile(m)
act_res = cm(input)
```
Generated Code: https://gist.github.com/leslie-fang-intel/ed2e8d23aeb3586eb504feeace692e16#file-grouped-gemm-generated-code-py
**Next Step**
- Support Epilogue fusion
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143796
Approved by: https://github.com/jgong5, https://github.com/jansel
This PR adds C shim for `QConvPointWisePT2E` and `QConvPointWiseBinaryPT2E` similar to https://github.com/pytorch/pytorch/pull/138439. Besides that, we aligned the implementation of `qconv_pointwise` with `qlinear_pointwise` in the following aspects:
1. The parameter order of `qconv_pointwise` and `qlinear_pointwise` are quite different, we aligned the schema of `qconv_pointwise` to have similar parameter order as `qlinear_pointwise` to make it more consistent.
2. We always converted `x_scale` and `x_zero_point` to Tensors, just like in the lowering of `qlinear_pointwise`. This avoids the need to create two separate C APIs (one for `double x_scale` and `int64_t x_zero_point`, and another for `Tensor` versions). Instead, we only need one API for `Tensor`-based `x_scale` and `x_zero_point`. If we later add dynamic quantization for qconv (which will use `Tensor` for `x_scale` and `x_zero_point`), we can reuse the code from this PR and don't need to change the C shim layer API.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138540
Approved by: https://github.com/jgong5, https://github.com/desertfire
ghstack dependencies: #138691, #138806
Fixes the error of running WOQ-INT8 LLaMA:
```
E In file included from /home/user/inductor/pytorch/torch/include/torch/csrc/inductor/aoti_runtime/arrayref_tensor.h:3,
E from /tmp/torchinductor_user/sw/csw5gfmlzp5iooqvfwl2gwn574frwdpmtrx2y6nu2m6x76d3xcux.cpp:4:
E /tmp/torchinductor_user/sw/csw5gfmlzp5iooqvfwl2gwn574frwdpmtrx2y6nu2m6x76d3xcux.cpp: In function ‘void inductor_entry_impl(AtenTensorOpaque**, AtenTensorOpaque**)’:
E /tmp/torchinductor_user/sw/csw5gfmlzp5iooqvfwl2gwn574frwdpmtrx2y6nu2m6x76d3xcux.cpp:117:33: error: ‘aoti_torch_cpu__weight_int8pack_mm’ was not declared in this scope
E 117 | AOTI_TORCH_ERROR_CODE_CHECK(aoti_torch_cpu__weight_int8pack_mm(convert_arrayref_tensor_to_tensor(arg8_1), _frozen_param0, _frozen_param1, &buf0_handle));
E | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138691
Approved by: https://github.com/leslie-fang-intel, https://github.com/jgong5, https://github.com/desertfire
The test runs all its combination (512) sequentially, so it takes more than 30 minutes to finish or timeout on ASAN after one hour. Parametrizing it will break it up, so individual tests can finish and aren't need to be marked as slow anymore.
Also, the test seems to run OOM on a 2xlarge with `std::bad_alloc` memory error. Maybe, this would also fix the issue (pending CI testing)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137447
Approved by: https://github.com/albanD, https://github.com/malfet
The test runs all its combination (512) sequentially, so it takes more than 30 minutes to finish or timeout on ASAN after one hour. Parametrizing it will break it up, so individual tests can finish and aren't need to be marked as slow anymore.
Also, the test seems to run OOM on a 2xlarge with `std::bad_alloc` memory error. Maybe, this would also fix the issue (pending CI testing)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137447
Approved by: https://github.com/albanD, https://github.com/malfet
Summary:
Tests in test_mkldnn_pattern_matcher.py can take too long to finish. Splitting them into smaller tests, using `parametrize`.
I guess this means this test file has some refactoring opportunities as well. Next time would be the parametrize the add functions.
Differential Revision: D63723925
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137153
Approved by: https://github.com/desertfire
Summary: Fixed a bunch of fbcode imports that happened to work but confused autodeps. After this autodeps still suggests "improvements" to TARGETS (which breaks our builds) but at least it can find all the imports.
Test Plan:
```
fbpython fbcode/tools/build/buck/linters/lint_autoformat.py --linter=autodeps --default-exec-timeout=1800 -- fbcode/caffe2/TARGETS fbcode/caffe2/test/TARGETS
```
Before:
```
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/testing.py:229) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fbur$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export.py:87) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fburl$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_serdes.py:9) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fb$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_serdes.py:10) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https://fburl$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_retraceability.py:7) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See https:$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_retraceability.py:6) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See ht$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export_nonstrict.py:7) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See http$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_export_nonstrict.py:6) when processing rule "test_export". Please make sure it's listed in the srcs parameter of another rule. See $
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "test_export" (from caffe2/test/export/test_export_training_ir_to_run_decomp.py:8) when processing rule "test_export". Please make sure it's listed in the srcs parameter of an$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "testing" (from caffe2/test/export/test_export_training_ir_to_run_decomp.py:10) when processing rule "test_export". Please make sure it's listed in the srcs parameter of anoth$
ERROR while processing caffe2/test/TARGETS: Found "//python/typeshed_internal:typeshed_internal_library" owner for "cv2" but it is protected by visibility rules: [] (from caffe2/test/test_bundled_images.py:7) when processing rule "test_bundled_$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "caffe2.test.profiler_test_cpp_thread_lib" (from caffe2/test/profiler/test_cpp_thread.py:29) when processing rule "profiler_test_cpp_thread". Please make sure it's listed in t$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._utils_internal.get_file_path_2" (from caffe2/test/test_custom_ops.py:23) when processing rule "custom_ops". Please make sure it's listed in the srcs parameter of anoth$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._utils_internal.get_file_path_2" (from caffe2/test/test_public_bindings.py:13) when processing rule "public_bindings". Please make sure it's listed in the srcs paramete$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._C._profiler.symbolize_tracebacks" (from caffe2/test/test_cuda.py:3348) when processing rule "test_cuda". Please make sure it's listed in the srcs parameter of another $
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for "torch._C._profiler.gather_traceback" (from caffe2/test/test_cuda.py:3348) when processing rule "test_cuda". Please make sure it's listed in the srcs parameter of another rule$
ERROR while processing caffe2/test/TARGETS: Cannot find an owner for include <torch/csrc/autograd/profiler_kineto.h> (from caffe2/test/profiler/test_cpp_thread.cpp:2) when processing profiler_test_cpp_thread_lib. Some things to try:
```
Differential Revision: D62049222
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135614
Approved by: https://github.com/oulgen, https://github.com/laithsakka
Summary: For S444023
Test Plan:
Revert prevented the NaN errors - f639391901
Training job ran for 7767 iterations. NaN errors show up within the first 1k.
Reviewed By: nmacchioni
Differential Revision: D62224747
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135186
Approved by: https://github.com/kit1980
Summary:
When a user sets config.profiler_mark_wrapper_call, RECORD_FUNCTION annotations are added to the code. This requires importing the header <ATen/record_function.h>, but the conditional for doing so didn't check
config.profiler_mark_wrapper_call.
Test Plan:
This case is already covered in test_profiler_mark_wrapper_call.
```
(pytorch-3.10) [gabeferns@devvm2252.cco0 ~/pytorch (missing-profile-include)]$ TORCHINDUCTOR_ABI_COMPATIBLE=1 TORCHINDUCTOR_CPP_WRAPPER=1 python test/inductor/test_torchinductor.py -k CpuTests.test_profiler_mark_wrapper_call_cpu
stats [('calls_captured', 1), ('unique_graphs', 1)]
inductor [('fxgraph_cache_miss', 1)]
aot_autograd [('total', 1), ('ok', 1)]
.
----------------------------------------------------------------------
Ran 1 test in 8.080s
OK
```
Fixes https://github.com/pytorch/pytorch/issues/131339
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132419
Approved by: https://github.com/jgong5, https://github.com/desertfire
Fix the compilation error:
```cpp
/tmp/tmpywg34bca/tg/ctg7wbli6pvydsjr2xsxamdbamkquhlincuky3dzopa3ilrxqdwt.cpp:401:24: error: cannot convert ‘at::Tensor’ to ‘const bfloat16*’ {aka ‘const c10::BFloat16*’}
401 | cpp_fused_div_mm_0(arg2_1, constant2, _frozen_param1, buf1);
| ^~~~~~
| |
| at::Tensor
```
The generated code after the fix will be:
```cpp
cpp_fused_div_mm_0((bfloat16*)(arg2_1.data_ptr()), (bfloat16*)(constant2.data_ptr()), (bfloat16*)(_frozen_param1.data_ptr()), (bfloat16*)(buf1.data_ptr()));
```
Multiple changes are required for ABI compatible mode. Separate it into a follow-up PR in this ghstack: https://github.com/pytorch/pytorch/pull/131841
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129557
Approved by: https://github.com/leslie-fang-intel
https://github.com/pytorch/pytorch/pull/126717 will skip the tests in both ABI compatible and non-ABI compatible mode.
It's not expected to skip them in non-ABI compatible mode since they can actually run successfully in such mode but only have issues in ABI compatible mode.
We leverage the existing `xfail_list` for those that will only fail in ABI compatible mode.
- `test_qlinear_add` is already in the `xfail_list`.
- `test_linear_packed` doesn't fail either in my local run (running with `TORCHINDUCTOR_ABI_COMPATIBLE=1`) or in the CI of this PR so I didn't add it into `xfail_list`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128506
Approved by: https://github.com/jgong5, https://github.com/desertfire
Summary: Found during testing with remote caching: Use the same output logger object between graph.py and codecache.py since it's patched in `run_and_get_cpp_code`. That allows us to capture any logging produced from the codecache path when using `run_and_get_cpp_code`. I'm also fixing a few tests that were passing mistakenly because logging was missing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128794
Approved by: https://github.com/oulgen, https://github.com/leslie-fang-intel
https://github.com/pytorch/pytorch/pull/126717 will skip the tests in both ABI compatible and non-ABI compatible mode.
It's not expected to skip them in non-ABI compatible mode since they can actually run successfully in such mode but only have issues in ABI compatible mode.
We leverage the existing `xfail_list` for those that will only fail in ABI compatible mode.
- `test_qlinear_add` is already in the `xfail_list`.
- `test_linear_packed` doesn't fail either in my local run (running with `TORCHINDUCTOR_ABI_COMPATIBLE=1`) or in the CI of this PR so I didn't add it into `xfail_list`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128506
Approved by: https://github.com/jgong5, https://github.com/desertfire
https://github.com/pytorch/pytorch/pull/126717 will skip the tests in both ABI compatible and non-ABI compatible mode.
It's not expected to skip them in non-ABI compatible mode since they can actually run successfully in such mode but only have issues in ABI compatible mode.
We leverage the existing `xfail_list` for those that will only fail in ABI compatible mode.
- `test_qlinear_add` is already in the `xfail_list`.
- `test_linear_packed` doesn't fail either in my local run (running with `TORCHINDUCTOR_ABI_COMPATIBLE=1`) or in the CI of this PR so I didn't add it into `xfail_list`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128506
Approved by: https://github.com/jgong5, https://github.com/desertfire
