This PR is a result of collaboration with @rdspring1 and @mruberry on primTorch.
It adds the following prims:
- `fmax`
- `fmin`
- `fmod`
And adds the following refs:
- `fmax`
- `fmin`
- `fmod`
- `logical_xor`
The work is in progress as there are some tests that fail.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78023
Approved by: https://github.com/mruberry
This PR...
**Issues Found**
- https://github.com/pytorch/pytorch/issues/78058
- https://github.com/pytorch/pytorch/issues/78054
- https://github.com/pytorch/pytorch/issues/78053
- https://github.com/pytorch/pytorch/issues/78050
- https://github.com/pytorch/pytorch/issues/77932
**Testing**
- disables stride consistency checks in test_ops and test_meta pending resolution of https://github.com/pytorch/pytorch/issues/78050
- skips chalf in reference tests (addressing https://github.com/pytorch/pytorch/issues/78054)
- splits test test_python_reference_consistency in one test for the ctx where torch.foo is torch.foo, and another for when torch.foo is refs.foo
- updates test names to be more natural and consistent:
- test_python_reference_errors -> test_python_ref_errors
- test_python_reference_consistency -> test_python_ref and test_python_ref_torch_fallback
- test_python_reference_meta_functions -> test_python_ref_meta
- test_reference_testing -> test_numpy_ref
- updates test_python_ref and test_python_ref_torch_fallback to check that the reference is more accurate than the torch op if the reference and torch op results are not close, a warning is raised when this occurs (addressing https://github.com/pytorch/pytorch/issues/77687)
- adds reference inputs for broadcast_tensors
- Updates the "fill_" OpInfo to "fill", adding a NumPy reference and making it an elementwise unary operator
- Adds 1D no element sample inputs to the cat OpInfo and updates the NumPy reference to handle them and type promotion correctly
- Adds reference inputs for elementwise ternary operations, like clamp
- Adds a NumPy reference for clamp
- Adds reference inputs to where's OpInfo
- Makes softplus an elementwise unary OpInfo
- Removes the great majority of Python reference OpInfo skips and xfails due to the above test changes
- Adds Python reference OpInfos for fill, dropout, clamp, broadcast_tensors, and where
**Prims**
- adds the fill, empty_strided, and uniform prims
- removes the empty, empty_like, full, and full_like prims -- these are now references that use empty_strided and fill
- renames the "concatenate" and "select" prims to "cat" and "where", respectively, to be consistent with PyTorch
- extends the `_elementwise_meta` operation to accepts tensors that don't participate in type promotion, like the `cond` tensor in `where`
- fixes a bug in the stride propagation of broadcast_in_dim
- moves some error checks from prims.cat to prims.where to refs.cat and refs.where, respectively, consistent with our new policy of doing as much error checking in the ref as possible
**Utils**
- adds the canoicalize_device, extract_shape, and extract_shape_from_varargs helpers
- adds the elementwise_unary_scalar_wrapper -- this allows elementwise unary operators to take and return scalar values (ex. refs.sin(1) will return .84...)
**Refs**
- adds the fill, broadcast_tensors, clamp, empty_strided, ones, zeros, and uniform references
- adds the nn.functional.dropout reference
- fixes refs.cat to handle 1D tensors with no inputs consistent with eager mode
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78026
Approved by: https://github.com/ngimel
This PR...
**Issues Found**
- https://github.com/pytorch/pytorch/issues/78058
- https://github.com/pytorch/pytorch/issues/78054
- https://github.com/pytorch/pytorch/issues/78053
- https://github.com/pytorch/pytorch/issues/78050
- https://github.com/pytorch/pytorch/issues/77932
**Testing**
- disables stride consistency checks in test_ops and test_meta pending resolution of https://github.com/pytorch/pytorch/issues/78050
- skips chalf in reference tests (addressing https://github.com/pytorch/pytorch/issues/78054)
- splits test test_python_reference_consistency in one test for the ctx where torch.foo is torch.foo, and another for when torch.foo is refs.foo
- updates test names to be more natural and consistent:
- test_python_reference_errors -> test_python_ref_errors
- test_python_reference_consistency -> test_python_ref and test_python_ref_torch_fallback
- test_python_reference_meta_functions -> test_python_ref_meta
- test_reference_testing -> test_numpy_ref
- updates test_python_ref and test_python_ref_torch_fallback to check that the reference is more accurate than the torch op if the reference and torch op results are not close, a warning is raised when this occurs (addressing https://github.com/pytorch/pytorch/issues/77687)
- adds reference inputs for broadcast_tensors
- Updates the "fill_" OpInfo to "fill", adding a NumPy reference and making it an elementwise unary operator
- Adds 1D no element sample inputs to the cat OpInfo and updates the NumPy reference to handle them and type promotion correctly
- Adds reference inputs for elementwise ternary operations, like clamp
- Adds a NumPy reference for clamp
- Adds reference inputs to where's OpInfo
- Makes softplus an elementwise unary OpInfo
- Removes the great majority of Python reference OpInfo skips and xfails due to the above test changes
- Adds Python reference OpInfos for fill, dropout, clamp, broadcast_tensors, and where
**Prims**
- adds the fill, empty_strided, and uniform prims
- removes the empty, empty_like, full, and full_like prims -- these are now references that use empty_strided and fill
- renames the "concatenate" and "select" prims to "cat" and "where", respectively, to be consistent with PyTorch
- extends the `_elementwise_meta` operation to accepts tensors that don't participate in type promotion, like the `cond` tensor in `where`
- fixes a bug in the stride propagation of broadcast_in_dim
- moves some error checks from prims.cat to prims.where to refs.cat and refs.where, respectively, consistent with our new policy of doing as much error checking in the ref as possible
**Utils**
- adds the canoicalize_device, extract_shape, and extract_shape_from_varargs helpers
- adds the elementwise_unary_scalar_wrapper -- this allows elementwise unary operators to take and return scalar values (ex. refs.sin(1) will return .84...)
**Refs**
- adds the fill, broadcast_tensors, clamp, empty_strided, ones, zeros, and uniform references
- adds the nn.functional.dropout reference
- fixes refs.cat to handle 1D tensors with no inputs consistent with eager mode
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78026
Approved by: https://github.com/ngimel
This PR...
**Filed the Following Issues**
- https://github.com/pytorch/pytorch/issues/77553
- https://github.com/pytorch/pytorch/issues/77526
- https://github.com/pytorch/pytorch/issues/77600
**Testing**
- Updates test_dtypes to longer attempt to test the backward of sample inputs where no inputs require grad
- Adds a new test_python_reference_errors; it ensures the meta operations for references throw errors as expected
- Updates compare_tensor_meta to better handle CUDA devices, and (temporarily) restricts stride checking to the CUDA device type
- Elementwise unary and elementwise binary operators now have arbitrarily strided reference inputs
- Reference inputs for _like functions are added
- An OpInfo for torch.empty is added
- Reference inputs for torch.clone are added
- A NumPy reference for clone is added
- Adds OpInfos for refs.empty and refs.empty_like
**Prims**
- Renames the "max" and "min" prims have been renamed to "maximum" and "minimum," respectively, to better conform to their ATen names
- Adds the empty, empty_like, full, and full_like prims
- Fixes the elementwise meta function's stride propagation
- Fixes clone's meta function's stride propagation
- Fixes convert_element_type's meta's stride propagation
- Adds a (temporary) _to_dtype pprivate prim that casts a tensor while preserving its stride permutation
- Removes the _set prim comment
- Adds utils.compute_elementwise_output_strides, which computes the correct output strides for elementwise operations
- Corrects an issue where utils.make_contiguous_strides_for was creating the incorrect strides for tensors with no elements
**References**
- Adds the empty, empty_like, full, full_like, and ones_like refs
- Extends make_elementwise_unary_reference to accept an additional callable to perform extra input validation
- Adds an extra validation function to handle refs.neg(BoolTensor)
- Updates the isfinite ref to call ones_like when appropriate
- Models Python scalar handling for elementwise binary operations
- Added a 64 dim check for the amin and amax references
- opmath is now a flag that can be set separately for cpu and CUDA
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77542
Approved by: https://github.com/ezyang
This PR does a number of things:
- Move linalg.vector_norm to structured kernels and simplify the logic
- Fixes a number of prexisting issues with the dtype kwarg of these ops
- Heavily simplifies and corrects the logic of `linalg.matrix_norm` and `linalg.norm` to be consistent with the docs
- Before the `_out` versions of these functions were incorrect
- Their implementation is now as efficient as expected, as it avoids reimplementing these operations whenever possible.
- Deprecates `torch.frobenius_norm` and `torch.nuclear_norm`, as they were exposed in the API and they are apparently being used in mobile (??!!) even though they were not documented and their implementation was slow.
- I'd love to get rid of these functions already, but I guess we have to go through their deprecation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76547
Approved by: https://github.com/mruberry
We don't have any coverage for meta tensor correctness for backwards
because torch function mode can only allow us to interpose on
Python torch API calls, but backwards invocations happen from C++.
To make this possible, I add torch_dispatch_meta test which runs the
tests with __torch_dispatch__
While doing this, I needed to generate fresh expected failure / skip
lists for the new test suite, and I discovered that my original
scaffolding for this purpose was woefully insufficient. So I rewrote
how the test framework worked, and at the same time rewrote the
__torch_function__ code to also use the new logic. Here's whats
new:
- Expected failure / skip is now done on a per function call basis,
rather than the entire test. This means that separate OpInfo
samples for a function don't affect each other.
- There are now only two lists: expect failure list (where the test
consistently fails on all runs) and skip list (where the test
sometimes passes and fails.
- We explicitly notate the dtype that failed. I considered detecting
when something failed on all dtypes, but this was complicated and
listing everything out seemed to be nice and simple. To keep the
dtypes short, I introduce a shorthand notation for dtypes.
- Conversion to meta tensors is factored into its own class
MetaConverter
- To regenerate the expected failure / skip lists, just run with
PYTORCH_COLLECT_EXPECT and filter on a specific test type
(test_meta or test_dispatch_meta) for whichever you want to update.
Other misc fixes:
- Fix max_pool1d to work with BFloat16 in all circumstances, by making
it dispatch and then fixing a minor compile error (constexpr doesn't
work with BFloat16)
- Add resolve_name for turning random torch API functions into string
names
- Add push classmethod to the Mode classes, so that you can more easily
push a mode onto the mode stack
- Add some more skips for missing LAPACK
- Added an API to let you query if there's already a registration for
a function, added a test to check that we register_meta for all
decompositions (except detach, that decomp is wrong lol), and then
update all the necessary sites to make the test pass.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77477
Approved by: https://github.com/zou3519
Decompositions can be used to fill in meta support where necessary,
assuming the operations they decompose to support meta key.
This PR adds register_meta kwarg to register_decomposition that
optionally lets you register the meta to the C++ dispatch table
for meta tensors. I use this to then get the meta function for
where and huber_loss for free.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77353
Approved by: https://github.com/mruberry
