Applies the remaining flake8-comprehension fixes and checks. This changes replace all remaining unnecessary generator expressions with list/dict/set comprehensions which are more succinct, performant, and better supported by our torch.jit compiler. It also removes useless generators such as 'set(a for a in b)`, resolving it into just the set call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94676
Approved by: https://github.com/ezyang
Fixes#92043.
I'm following numpy's implementation as suggested by @min-jean-cho.
I found out that this implementation still produces overflow if we're working with numbers greater than `finfo.max / 2`, but this is still much better than the previous implementation where it gets overflow with numbers greater than `finfo.max ** 0.5`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92539
Approved by: https://github.com/lezcano
The `ref` property was moved down from `{Unary,Binary}UfuncInfo` into
`OpInfo` quite some time ago, but `OpInfo` uses `None` to signal no
reference is available while the others use `_NOTHING`. This makes
everything consistently use `None`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82348
Approved by: https://github.com/ngimel
The `ref` property was moved down from `{Unary,Binary}UfuncInfo` into
`OpInfo` quite some time ago, but `OpInfo` uses `None` to signal no
reference is available while the others use `_NOTHING`. This makes
everything consistently use `None`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82348
Approved by: https://github.com/ngimel
That check was potentially synchronizing (people were running into synchronization in real workloads) and mostly unneeded.
Type promotion takes care of comparing to values that cannot be safely converted to the type of other argument.
This now works for `comp(int_tensor, float('inf'))` as expected. For `comp(uint8_tensor, large_int)` it silently wraps integer to uint8 whereas it would error out before, but this also happens with other arithmetic ops.
Also adds reference np implementation to comparison op OpInfos, and additional reference inputs to test newly enabled behavior.
Fixes#76805
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78881
Approved by: https://github.com/mruberry
This PR does the following...
Tests:
- fixes test_type_promotion in test_binary_ufuncs to correctly generate scalar cpu tensors
- fixes test_python_reference_consistency to use the Python Reference's reference inputs
- extends Python reference testing to test_conj_view, test_neg_view, and test_neg_conj_view
- adds a NaN propagation sample input for elementwise unary and binary operations
- fixes the UnaryUfuncInfo class to properly register its reference inputs
- Updates the Python Reference OpInfos to skip error inputs when their behavior on scalar inputs is inconsistent with their reference operators
Code organization:
- moves elementwise type promotion functionality to prims.utils
Prims & Refs:
- fixes scalar cpu tensor handling by having them pass through broadcasting and device and shape checks
- adds two decorators, `elementwise_type_promotion_wrapper` and `out_wrapper`, the former allows for elementwise type promotion to be automated and the latter automatically adds the out kwarg and handles it properly
cc @ezyang who also had some thoughts on cpu scalar tensor handling
cc @chillee -- might want to use this new decorator as we converge decompositions and references
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76945
Approved by: https://github.com/ngimel
https://github.com/pytorch/pytorch/pull/76158 added `chalf` support for `mul` on CUDA incorrectly.
Following sample
```python
torch.mul(torch.zeros(3, device='cuda'), 2.5) # CUDA Tensor and CPU Scalar
```
fails with
```
RuntimeError: iter.device(arg).is_cuda() INTERNAL ASSERT FAILED at "../aten/src/ATen/native/cuda/JitLoops.cuh":83, please report a bug to PyTorch. argument 2: expected a CUDA device but found cpu
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76364
Approved by: https://github.com/mruberry
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
This PR makes the following improvements:
- moves the custom skip list for test_normalize_operator_exhaustive in test_fx_experimental to use the typical OpInfo skip architecture. The skips were updated to xfails, and that identified some operators which were no longer failing the test
- redundant tests with OpInfo-based testing in test_jit.py were removed
- test_dtypes was improved so its error messages are clear and it makes test_nondifferentiable redundant; the latter test has been removed
- OpInfo.supports_complex_autograd() is removed in favor of a more accurate and general test for whether the particular dtype is in the backward dtypes of the operator
- gradchecks have been improved to verify that an operator doesn't support grad if it claims not to
- gradchecks have been improved to test the gradient of all input tensors that require gradient
- the concept of "default test dtypes" has been removed
- excessive and mostly redundant out testing for elementwise unary operators has been removed
- metadata for whether an op supports nuanced "safe casting" to out behavior has been removed from OpInfos
- numerous skips have been converted to xfails
- numerous OpInfos have had their metadata fixed based on the new checks
- jit-specific utilities in common_methods_invocations.py have been moved to jit_programming_utils.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75951
Approved by: https://github.com/ngimel
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
Summary:
This adds support for bfloat16 and fp16 types for jiterator by adding at::Half and at::BFloat16 classes to the jiterator code template. The only methods defined in those classes are construction from float and implicit conversion to float. Mathematical operations on them never need to be defined, because jiterator is written in a way to implicitly upcast the inputs to the functor, so all math has to be performed on float only (e.g. compute part of the kernel would always be written as
```
out[j] = i0<float>(arg0[j]);
```
It also adds support for casting to complex outputs, by adding a similar templated class c10::complex<T>. Originally I planned to only support float -> complex complex for it, but to compile fetch_and_cast function we also need complex -> float conversion. We can avoid it by compiling fetch_and_cast for a different subset of types, but I'm not doing it in this PR. Thus, technically, we can compile a kernel that would accept complex inputs and produce wrong results, but we are guarding against it by static asserting that none of the functor datatype are complex, and runtime-checking that none of the inputs are complex.
Adding bfloat16, half and complex support allows us to remove special handling for type promotion tests for gcd.
i0 (that supports half and bfloat16 inputs) is moved to use jiterator.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70157
Reviewed By: mruberry
Differential Revision: D33221645
Pulled By: ngimel
fbshipit-source-id: 9cfe8aba3498a0604c4ea62c217292ea06c826b1
Summary:
ROCm and CUDA type promotion are slightly divergent and need to be updated.
cc jeffdaily sunway513 jithunnair-amd ROCmSupport KyleCZH
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69456
Reviewed By: anjali411, janeyx99
Differential Revision: D32883895
Pulled By: mruberry
fbshipit-source-id: 3b0ba8a9d092c2d7ff20d78da42d4a147b1db12d
Summary:
This PR:
- creates the "jiterator" pattern, allowing elementwise unary and binary kernels that don't accept scalars to be jit compiled when called
- ports the gcd and i1 CUDA kernels to use the jiterator
- extends elementwise binary systemic testing to be comparable to elementwise unary systemic testing
- separates one test case from test_out in test_ops.py
- updates more OpInfos to use expected failures instead of skips
The jiterator currently does not support half, bfloat16 or complex dtypes. It also (as mentioned above) doesn't support scalar inputs. In the future we expect to add support for those datatypes and scalars.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69439
Reviewed By: ngimel
Differential Revision: D32874968
Pulled By: mruberry
fbshipit-source-id: d44bb9cde4f602703e75400ec5a0b209f085e9b3
Summary:
Partially fixes https://github.com/pytorch/pytorch/issues/66066
This PR:
- cleans up op-specific testing from test_autograd. test_autograd should be reserved for testing generic autograd functionality
- tests related to an operator are better colocated
- see the tracker for details
What to think about when moving tests to their correct test suite:
- naming, make sure its not too generic
- how the test is parametrized, sometimes we need to add/remove a device/dtype parameter
- can this be merged with existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67413
Reviewed By: jbschlosser, albanD
Differential Revision: D32031480
Pulled By: soulitzer
fbshipit-source-id: 8e13da1e58a38d5cecbfdfd4fe2b4fe6f816897f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66245Fixes#66053
This PR splits `declare_static_dtype_and_device` into two new methods for
`TensorIteratorBase`: `declare_static_dtype` and `declare_static_device`.
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D31503849
Pulled By: ngimel
fbshipit-source-id: 4b131b691d29ceb5f3709f5d6503997ea0875c54
