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Device agnostic testing (#25870)

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* adds agnostic decorators and availability fns

* renaming decorators and fixing imports

* updating some representative example tests
bloom, opt, and reformer for now

* wip device agnostic functions

* lru cache to device checking functions

* adds `TRANSFORMERS_TEST_DEVICE_SPEC`
if present, imports the target file and updates device to function
mappings

* comments `TRANSFORMERS_TEST_DEVICE_SPEC` code

* extra checks on device name

* `make style; make quality`

* updates default functions for agnostic calls

* applies suggestions from review

* adds `is_torch_available` guard

* Add spec file to docs, rename function dispatch names to backend_*

* add backend import to docs example for spec file

* change instances of  to

* Move register backend to before device check as per @statelesshz changes

* make style

* make opt test require fp16 to run

---------

Co-authored-by: arsalanu <arsalanu@graphcore.ai>
Co-authored-by: arsalanu <hzji210@gmail.com>
This commit is contained in:
Alex McKinney
2023-10-24 15:49:26 +01:00
committed by GitHub
parent 41496b95da
commit 9da451713d
8 changed files with 188 additions and 25 deletions
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19
docs/source/en/testing.md
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@@ -525,6 +525,25 @@ Certain devices will require an additional import after importing `torch` for th
```bash
TRANSFORMERS_TEST_BACKEND="torch_npu" pytest tests/utils/test_logging.py
```
Alternative backends may also require the replacement of device-specific functions. For example `torch.cuda.manual_seed` may need to be replaced with a device-specific seed setter like `torch.npu.manual_seed` to correctly set a random seed on the device. To specify a new backend with backend-specific device functions when running the test suite, create a Python device specification file in the format:
```
import torch
import torch_npu
# !! Further additional imports can be added here !!
# Specify the device name (eg. 'cuda', 'cpu', 'npu')
DEVICE_NAME = 'npu'
# Specify device-specific backends to dispatch to.
# If not specified, will fallback to 'default' in 'testing_utils.py`
MANUAL_SEED_FN = torch.npu.manual_seed
EMPTY_CACHE_FN = torch.npu.empty_cache
DEVICE_COUNT_FN = torch.npu.device_count
```
This format also allows for specification of any additional imports required. To use this file to replace equivalent methods in the test suite, set the environment variable `TRANSFORMERS_TEST_DEVICE_SPEC` to the path of the spec file.
Currently, only `MANUAL_SEED_FN`, `EMPTY_CACHE_FN` and `DEVICE_COUNT_FN` are supported for device-specific dispatch.
### Distributed training