This PR includes the GBID weblink whenever a user encounters a graph break. I also had to include the JSON file in setup.py, so it can be part of the files that are packaged in during CI. It also fixes the issue of the hardcoded error messages stripping away one of the '/' in 'https'.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156033
Approved by: https://github.com/williamwen42
This PR includes the GBID weblink whenever a user encounters a graph break. I also had to include the JSON file in setup.py, so it can be part of the files that are packaged in during CI. It also fixes the issue of the hardcoded error messages stripping away one of the '/' in 'https'.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156033
Approved by: https://github.com/williamwen42
This PR:
- cleans up some existing comments that don't make sense anymore
- hooks up the "custom_op_default_layout_constraint" back (that seems to
have broken)
- cleans up the "lazy registration path" which seems to never get hit
anymore
- adds dislike_padding to nodes that require exact strides
Test Plan:
- tests + CI
disable padding
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148104
Approved by: https://github.com/shunting314, https://github.com/eellison
Added a context manager, `torch._library.fake_profile.register_fake_profile(op_profiles)`, where given an operator profile, it will generate and register a fake impl for the operator based on the operator profile.
The input to `register_fake_profile` is a dictionary mapping operator name to a set of profiles which describe the input and outputs of the operator. Here's an example of a profile for `mylib.foo.default`:
```
"mylib.foo.default": {
OpProfile(
args_profile=(
TensorMetadata(rank=2, dtype=torch.float32, device=torch.device("cpu"), layout=torch.strided,),
TensorMetadata(rank=2, dtype=torch.float32, device=torch.device("cpu"), layout=torch.strided,),
),
out_profile=TensorMetadata(rank=2, dtype=torch.float32, device=torch.device("cpu"), layout=torch.strided,),
)
}
```
`foo`'s profile contains only one profile, which says that for 2 input tensors of rank 2, dtype float32, device cpu, we will return one tensor of rank 2, dtype float32, and device cpu.
This will then generate a fake kernel where given 2 input tensors of rank 2 (and the other tensor metadata), we will output one tensor of rank 2 (and the other tensor metadata). If the operator also supports other input ranks, then we can add to the profile for the fake impl to support more input types.
This profile can either be manually written or created by draft-export, and then checked into the codebase.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150807
Approved by: https://github.com/zou3519
ghstack dependencies: #150806
Added a flag, `allow_override`, to allow overriding existing kernel implementations in `torch.library.register_fake` `library.impl`. The default is false, where if a user tries to register a kernel to a dispatch key that already contains a kernel, it will error. This flag doesn't apply to CustomOpDefs, where overriding a fake kernel is already allowed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150806
Approved by: https://github.com/zou3519
This PR improves opcheck to:
1. directly use torch.testing.assert_close (without a msg override).
This allows it to print the absolute and relative differences and the
number of mismatched elements.
2. take in an atol/rtol tolerance (for if someone just wants to use
opcheck in their testing).
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146488
Approved by: https://github.com/williamwen42
We didn't support multiple levels of vmap. The main problem is, during
the batching rule, we need to exclude the vmap dispatch key
(FuncTorchBatched) like how our C++ batching rules do it.
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137306
Approved by: https://github.com/Chillee
Fixes https://github.com/pytorch/pytorch/issues/136177
The motivation is that torch::deploy doesn't handle this well. The
workaround for users is to use C++ custom ops.
All torch.library APIs ultimately go through the torch.library.Library
object, so we add checks to noop for torch::deploy there.
Test Plan:
- new test
- going to test this internally and hope nothing breaks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136645
Approved by: https://github.com/ezyang
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
By default, Inductor is allowed to manipulate the layout
(strides+storage offset) of input tensors to custom operators.
We want to change it so that the default is that Inductor should respect
the stride order of input tensors to custom operators.
This PR adds a config to toggle the behavior, in the next PR up we'll
change the default. We also make the following changes:
- We add a new operator Tag (flexible_layout), which means that
inductor is allowed to manipulate the layout. When we flip the default,
users can specify they want the old behavior by using this tag.
This is a reland of https://github.com/pytorch/pytorch/pull/126986,
which was previously reverted due to silent incorrectness. We've since
fixed the silent incorrectness
(https://github.com/pytorch/pytorch/pull/133639)
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/135238
Approved by: https://github.com/albanD
Made the following changes:
- mutates_args is now keyword-only and mandatory. This is to align with
torch.library.custom_op (which makes it mandatory because it's easy to
miss)
- op_name is now keyword-only. This helps the readability of the API
- updated all usages of infer_schema
This change is not BC-breaking because we introduced
torch.library.infer_schema a couple of days ago.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130705
Approved by: https://github.com/yushangdi
ghstack dependencies: #131777
Fixes#130284Fixes#130653
- Add `torch.library.register_vmap` to custom ops
- Add `register_vmap` for operators in ops in custom_op_db.
- Make `torch.autograd.Function` support kwarg-only kwargs for vmap
- test operators in op_db with `tests/test_vmap`.
- change `test_vmap` to allow custom `out_dim` and allow "None" in `out_dim` when testing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130589
Approved by: https://github.com/zou3519
Fixes#130284Fixes#130653
- Add `torch.library.register_vmap` to custom ops
- Add `register_vmap` for operators in ops in custom_op_db.
- Make `torch.autograd.Function` support kwarg-only kwargs for vmap
- test operators in op_db with `tests/test_vmap`.
- change `test_vmap` to allow custom `out_dim` and allow "None" in `out_dim` when testing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130589
Approved by: https://github.com/zou3519
Made the following changes:
- mutates_args is now keyword-only and mandatory. This is to align with
torch.library.custom_op (which makes it mandatory because it's easy to
miss)
- op_name is now keyword-only. This helps the readability of the API
- updated all usages of infer_schema
This change is not BC-breaking because we introduced
torch.library.infer_schema a couple of days ago.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130705
Approved by: https://github.com/yushangdi
We add torch.library.Library._register_torch_dispatch_rule. Here, a user
can provide us a specific rule to run for a specific
(torch_dispatch_class, operator) pair. The motivation is that a user
might want to extend a subclass/mode but may not have access to the
source code of the subclass/mode.
I'll make this public in a follow-up PR if we think the approach and API
is good.
Keep in mind that many subclasses will likely deliver their own open
registration solution (DTensor has register_sharding_prop_rule and NJT
has register_jagged_op); _register_torch_dispatch_rule is meant as a
catch-all open registration mechanism for when the subclass hasn't
provided anything more specific.
Test Plan:
- new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130064
Approved by: https://github.com/albanD
