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44b98c09ca
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8 Commits

Author SHA1 Message Date
Jane Xu
44b98c09ca [BE] migrate all assertRaises tests to OptimizerInfo test_errors (#116315) 
Removes a part of the sparse adam test and the following three tests: `test_fused_optimizer_raises`, `test_duplicate_params_across_param_groups`, `test_duplicate_params_in_one_param_group`

```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (d2d129de)]$ python test/test_optim.py -k test_fused_optimizer_raises -k test_duplicate_params_across_param_groups -k test_duplicate_params_in_one_param_group
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
...
----------------------------------------------------------------------
Ran 3 tests in 0.023s

OK
```

Increases coverage by testing the duplicate param tests on ALL the optims instead of just one each. Also fixes SparseAdam bug which was accidentally calling torch.unbind through list instead of putting params in a list. This bug was caught by migrating the weird warning stuff to just one easy warning context manager, which checks that nothing else gets raised.

The new test_errors does not run slower than before, overhead is still king:
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (d2d129de)]$ python test/test_optim.py -k test_errors
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
..........................
----------------------------------------------------------------------
Ran 26 tests in 10.337s

OK
```

Compared to test_errors BEFORE my commit :p
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (b47aa696)]$ python test/test_optim.py -k test_errors
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
.............sssssssssssss
----------------------------------------------------------------------
Ran 26 tests in 11.980s

OK (skipped=13)
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (b47aa696)]$
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/116315
Approved by: https://github.com/mikaylagawarecki
 2023-12-27 00:08:31 +00:00
Jane Xu
edf1ea622d Move step is noop tests (#115299) 
As stated. I do notice there is perhaps opportunity to abstract, but the tests as written are also super understandable and more abstraction might not be desirable.

This PR _increases coverage_. The original tests each tested 12 default configs (left out Rprop). Now the tests test ~80 configs, and then foreach + fused on top of that! Test time, we basically increase over 10-fold, but this test is tiny so we are not worried:

Old:
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (5ca9672c)]$ python test/test_optim.py -k test_step_is_noop_when_params_have_no_grad
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
.
----------------------------------------------------------------------
Ran 1 test in 0.028s

OK
```

New (includes the old test):
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (5ca9672c)]$ python test/test_optim.py -k test_step_is_noop_when_params_have_no_grad
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
...........................
----------------------------------------------------------------------
Ran 27 tests in 0.456s

OK
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/115299
Approved by: https://github.com/albanD
ghstack dependencies: #114802, #115023, #115025
 2023-12-20 22:49:44 +00:00
Jane Xu
8f3a0594e9 Move tests depending on listed configs to OptimizerInfo (#115025) 
Removing 4 tests:
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (7539011b)]$ python test/test_optim.py -v -k test_fused_optimizers_with_large_tensors -k test_fused_optimizers_with_varying_tensors -k test_multi_tensor_optimizers_with_large_tensors -k test_multi_tensor_optimizers_with_varying_tensors
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
test_fused_optimizers_with_large_tensors (optim.test_optim.TestOptim) ... ok
test_fused_optimizers_with_varying_tensors (optim.test_optim.TestOptim) ... ok
test_multi_tensor_optimizers_with_large_tensors (optim.test_optim.TestOptim) ... ok
test_multi_tensor_optimizers_with_varying_tensors (optim.test_optim.TestOptim) ... ok

----------------------------------------------------------------------
Ran 4 tests in 22.731s

OK
```

For the same 4 but more granular:
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (7539011b)]$ python test/test_optim.py  -v -k test_fused_large_tensor -k test_fused_mixed_device_dtype -k test_foreach_large_tensor -k test_foreach_mixed_device_dtype
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
test_foreach_large_tensor_ASGD_cpu_float16 (__main__.TestOptimRenewedCPU) ... skipped 'Only runs on cuda'
....
test_fused_mixed_device_dtype_Adam_cpu_float32 (__main__.TestOptimRenewedCPU) ... skipped 'Only runs on cuda'
test_foreach_large_tensor_ASGD_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_large_tensor_Adadelta_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_large_tensor_Adagrad_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_large_tensor_AdamW_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_large_tensor_Adam_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_large_tensor_NAdam_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_large_tensor_RAdam_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_large_tensor_RMSprop_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_large_tensor_Rprop_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_large_tensor_SGD_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_mixed_device_dtype_ASGD_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_mixed_device_dtype_Adadelta_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_mixed_device_dtype_Adagrad_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_mixed_device_dtype_AdamW_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_mixed_device_dtype_Adam_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_mixed_device_dtype_Adamax_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_mixed_device_dtype_NAdam_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_mixed_device_dtype_RAdam_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_mixed_device_dtype_RMSprop_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_mixed_device_dtype_Rprop_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_mixed_device_dtype_SGD_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_fused_large_tensor_AdamW_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_fused_large_tensor_Adam_cuda_float16 (__main__.TestOptimRenewedCUDA) ... ok
test_fused_mixed_device_dtype_AdamW_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok
test_fused_mixed_device_dtype_Adam_cuda_float32 (__main__.TestOptimRenewedCUDA) ... ok

----------------------------------------------------------------------
Ran 50 tests in 50.785s

OK (skipped=25)
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/115025
Approved by: https://github.com/albanD
ghstack dependencies: #114802, #115023
 2023-12-20 22:49:44 +00:00
Jane Xu
05d60931b3 Migrate test_peak_mem_multi_tensor_optimizers to OptimizerInfo (#115023) 
Replace the following:
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (1bbf1c6f)]$ python test/test_optim.py -k test_peak_mem_multi_tensor_optimizers
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
.
----------------------------------------------------------------------
Ran 1 test in 38.599s

OK
```

with 11 tests (one for each foreach optim :))
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (1bbf1c6f)]$ python test/test_optim.py -k TestOptimRenewedCUDA.test_foreach_memory
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
...........
----------------------------------------------------------------------
Ran 11 tests in 39.293s

OK
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/115023
Approved by: https://github.com/albanD
ghstack dependencies: #114802
 2023-12-20 22:49:44 +00:00
Jane Xu
4fb92b591d [BE] remove redundant _test_derived_optimizers by migrating more to OptimizerInfo (#114802) 
New tests look like:
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (af8fca04)]$ python test/test_optim.py -v -k TestOptimRenewedCUDA.test_fused
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
test_fused_AdamW_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_fused_Adam_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok

----------------------------------------------------------------------
Ran 2 tests in 34.591s

OK
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (af8fca04)]$ python test/test_optim.py
-v -k test_set_default_dtype_works_with_foreach
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
test_set_default_dtype_works_with_foreach_ASGD_cpu_float64 (__main__.TestOptimRenewedCPU) ... skipped 'Only runs on cuda'
...
test_set_default_dtype_works_with_foreach_ASGD_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_set_default_dtype_works_with_foreach_Adadelta_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_set_default_dtype_works_with_foreach_Adagrad_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_set_default_dtype_works_with_foreach_AdamW_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_set_default_dtype_works_with_foreach_Adam_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_set_default_dtype_works_with_foreach_Adamax_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_set_default_dtype_works_with_foreach_NAdam_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_set_default_dtype_works_with_foreach_RAdam_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_set_default_dtype_works_with_foreach_RMSprop_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_set_default_dtype_works_with_foreach_Rprop_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_set_default_dtype_works_with_foreach_SGD_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok

----------------------------------------------------------------------
Ran 22 tests in 32.915s

OK (skipped=11)
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/114802
Approved by: https://github.com/albanD
 2023-12-20 22:49:44 +00:00
Jane Xu
056a882cb9 add markDynamoStrictTest to TestOptimRenewed, removing flakiness (#115947) 
fixes #115406 fixes #115394 fixes #115393 fixes #115392 fixes #115391

Pull Request resolved: https://github.com/pytorch/pytorch/pull/115947
Approved by: https://github.com/albanD, https://github.com/zou3519
 2023-12-16 01:33:32 +00:00
Jane Xu
21cca2494d Move test_multi_tensor_optimizers to use OptimizerInfos (#114797) 
This PR aims for parity+ compared to the old testing for the simplest foreach test case.

Test coverage increase: we now test foreach optimizers with CPU as well as on GPU.

Before:
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (19136605)]$ python test/test_optim.py -v -k test_multi_tensor_optimizers
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
test_multi_tensor_optimizers (optim.test_optim.TestOptim) ... ok

----------------------------------------------------------------------
Ran 1 test in 7.253s

OK
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (19136605)]$
```

Now, we get granular test cases at the cost of overhead!
```
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (19136605)]$ python test/test_optim.py -v -k test_foreach
/home/janeyx/.conda/envs/pytorch-3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.0
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
test_foreach_ASGD_cpu_float64 (__main__.TestOptimRenewedCPU) ... ok
test_foreach_Adadelta_cpu_float64 (__main__.TestOptimRenewedCPU) ... ok
test_foreach_Adagrad_cpu_float64 (__main__.TestOptimRenewedCPU) ... ok
test_foreach_AdamW_cpu_float64 (__main__.TestOptimRenewedCPU) ... ok
test_foreach_Adam_cpu_float64 (__main__.TestOptimRenewedCPU) ... ok
test_foreach_Adamax_cpu_float64 (__main__.TestOptimRenewedCPU) ... ok
test_foreach_NAdam_cpu_float64 (__main__.TestOptimRenewedCPU) ... ok
test_foreach_RAdam_cpu_float64 (__main__.TestOptimRenewedCPU) ... ok
test_foreach_RMSprop_cpu_float64 (__main__.TestOptimRenewedCPU) ... ok
test_foreach_Rprop_cpu_float64 (__main__.TestOptimRenewedCPU) ... ok
test_foreach_SGD_cpu_float64 (__main__.TestOptimRenewedCPU) ... ok
test_foreach_ASGD_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_Adadelta_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_Adagrad_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_AdamW_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_Adam_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_Adamax_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_NAdam_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_RAdam_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_RMSprop_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_Rprop_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok
test_foreach_SGD_cuda_float64 (__main__.TestOptimRenewedCUDA) ... ok

----------------------------------------------------------------------
Ran 22 tests in 30.954s

OK
(pytorch-3.10) [janeyx@devgpu023.odn1 ~/local/pytorch (19136605)]$
```

Why the increase in time?
Two reasons:
1. overhead. Any _CUDA_ *Info test (OpInfo, ModuleInfo, OptimizerInfo) will wrap itself with the `CudaNonDefaultStream` policy, and `CudaNonDefaultStream.__enter__` when called for the first time will go through all visible CUDA devices and synchronize each of them, thus forcing the CUDAContext to be init'd. Doing this for all 8 devices takes ~10-15s. Also, test parametrization costs a little overhead too, but not to the level init'ing CUDA context does.
2. We test more! Now, we have 72 configs (in the foreach optimizer world) whereas we only had 59 before.

Next steps for the future:
- consider adding more Tensor LR configs (like a Tensor LR without capturable in the single tensor case)
- this is likely the next PR or 2: migrate all uses of _test_derived_optimizers in test_optim to TestOptimRenewed

Pull Request resolved: https://github.com/pytorch/pytorch/pull/114797
Approved by: https://github.com/albanD
 2023-12-07 19:37:56 +00:00
Jane Xu
d78fe039eb Introduce OptimizerInfos + add a test_errors (#114178) 
Introduce OptimizerInfos + use them to refactor out the error testing.

Why OptimizerInfos?
- cleaner, easier way to test all configs of optimizers
- would plug in well with devicetype to auto-enable tests for devices like MPS, meta
- would allow for more granular testing. currently, lots of functionality is tested in `_test_basic_cases` and some of that should be broken down more.

What did I do for error testing?
- I moved out some error cases from `_test_basic_cases` into a new test_errors parametrized test.
- The new test has to live in TestOptimRenewed (bikeshedding welcome) because the parametrized tests need to take in device and dtype and hook correctly, and not all tests in TestOptim do that.
- TestOptimRenewed also is migrating to the toplevel test/test_optim.py now because importing TestOptimRenewed does not work (because of test instantiation, TestOptimRenewed gets replaced with TestOptimRenewedDevice for CPU, CUDA, and whatever other device).

Is there any change in test coverage?
- INCREASE: The error case where a single Parameter (vs a container of them) are passed in has now expanded to all optims instead of only LBFGS
- DECREASE: Not much. The only thing is we no longer test two error cases for foreach=True AND foreach=False, which I think is redundant. (Highlighted in comments)

Possible but not urgent next step: test ALL possible error cases by going through all the constructors.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/114178
Approved by: https://github.com/albanD
 2023-12-05 22:58:36 +00:00