Added checks for dtype and device of OpInfo's sample_inputs #54949
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IvanYashchuk
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module: tests
💊 CI failures summary and remediationsAs of commit fb24dbe (more details on the Dr. CI page):
🕵️ 1 new failure recognized by patternsThe following CI failures do not appear to be due to upstream breakages:
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Codecov Report
@@ Coverage Diff @@
## master #54949 +/- ##
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+ Coverage 40.88% 76.98% +36.10%
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Files 564 1892 +1328
Lines 69995 186418 +116423
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+ Hits 28620 143521 +114901
- Misses 41375 42897 +1522 |
H-Huang
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mruberry
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Mar 30, 2021
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Neat. Note that, in general, when requesting a sample input for a CUDA device not ALL tensors are expected to be on CUDA. But requiring the "input" tensor be on the device and of the correct dtype seems reasonable, and this would be easy to modify in the future.
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cc @heitorschueroff for a pointer This is going to need a rebase to handle tensor lists inputs, too |
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@mruberry I modified the code for checking of tensor lists input. Only the first tensor in the list is checked. |
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This was referenced Apr 3, 2021
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Summary: #### Reason for relanding Line 1607 of `torch/testing/_internal/common_methods_invocations.py` of #50999 had `dtype` instead of `dtype=torch.bool`, so 4 of the 9 sample inputs for `bool` had incorrect dtype. This bug was caught by #54949. 1. Added support for pow() on CPU for `float16` (`Half`) and `bfloat16` types. Both `pow(Tensor, Scalar)` and `pow(Tensor, Tensor)` are now supported for the aforementioned types. However autograd isn't supported for `Float16` on CPU yet, as `log_vml_cpu` can't be enabled for it. 2. heitorschueroff added `pow_tensor_scalar_optimized_kernel` to refactor & simplify `PowKernel.cpp`. It provides a common path for all the complex types & floating point types (except Float16, due to lack of complete AVX2 vectorization support for it). It replaced code that had previously been duplicated for (float, double) and complex types, so PowKernel.cpp looks a lot cleaner now. 3. Enabled (unskipped) some tests for `erf`, `erfc`,`erfinv`, `tan` and `linalg.vector.norm` which were being skipped earlier due to `pow()` not having been implemented for `float16` & `bfloat16`. 4. Added an OpInfo for `pow()` & enabled some test cases for `pow()`. 5. Extended the coverage of existing tests for `pow` in `test_binary_ufuncs.py` in order to enable comparison with `numpy`, even with discontiguous tensors, and added a test to ensure that a runtime error is raised for `pow`'s inplace variant if resizing the base tensor is required during its invocation. 6. Added `float16` & `bfloat16` to `square`'s dtype lists in its `UnaryUfuncInfo`. 7. Removed redundant `dtypesIfCPU` and `dtypesIfCUDA` from `OpInfo`s where they are equal to `dtypes`. Pull Request resolved: #55280 Reviewed By: jbschlosser Differential Revision: D27591772 Pulled By: heitorschueroff fbshipit-source-id: c7420811b32595bb3353149a61e54a73f2eb352b
krshrimali
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May 19, 2021
…orch#55280) Summary: #### Reason for relanding Line 1607 of `torch/testing/_internal/common_methods_invocations.py` of pytorch#50999 had `dtype` instead of `dtype=torch.bool`, so 4 of the 9 sample inputs for `bool` had incorrect dtype. This bug was caught by pytorch#54949. 1. Added support for pow() on CPU for `float16` (`Half`) and `bfloat16` types. Both `pow(Tensor, Scalar)` and `pow(Tensor, Tensor)` are now supported for the aforementioned types. However autograd isn't supported for `Float16` on CPU yet, as `log_vml_cpu` can't be enabled for it. 2. heitorschueroff added `pow_tensor_scalar_optimized_kernel` to refactor & simplify `PowKernel.cpp`. It provides a common path for all the complex types & floating point types (except Float16, due to lack of complete AVX2 vectorization support for it). It replaced code that had previously been duplicated for (float, double) and complex types, so PowKernel.cpp looks a lot cleaner now. 3. Enabled (unskipped) some tests for `erf`, `erfc`,`erfinv`, `tan` and `linalg.vector.norm` which were being skipped earlier due to `pow()` not having been implemented for `float16` & `bfloat16`. 4. Added an OpInfo for `pow()` & enabled some test cases for `pow()`. 5. Extended the coverage of existing tests for `pow` in `test_binary_ufuncs.py` in order to enable comparison with `numpy`, even with discontiguous tensors, and added a test to ensure that a runtime error is raised for `pow`'s inplace variant if resizing the base tensor is required during its invocation. 6. Added `float16` & `bfloat16` to `square`'s dtype lists in its `UnaryUfuncInfo`. 7. Removed redundant `dtypesIfCPU` and `dtypesIfCUDA` from `OpInfo`s where they are equal to `dtypes`. Pull Request resolved: pytorch#55280 Reviewed By: jbschlosser Differential Revision: D27591772 Pulled By: heitorschueroff fbshipit-source-id: c7420811b32595bb3353149a61e54a73f2eb352b
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Currently, it's not tested whether
op.sample_inputsactually used the provided dtype and device arguments. This PR fixes that introducing asserts intest_supported_dtypes.This will help to detect incorrectly generated inputs in the future.