This is the first PR of a series in an attempt to get the content of #134592 merged as smaller PRs (Given that the original one was closed due to a lack of reviewers).
This specific PR contains:
- Add and use a common raise_on_run_directly method for when a user runs a test file directly which should not be run this way. Print the file which the user should have run.
- Update ao tests.
There will be follow up PRs to update the other test suites but I don't have permissions to create branches directly on pytorch/pytorch so I can't create a stack and therefore will have to create them one at the time.
Cc @jerryzh168
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154612
Approved by: https://github.com/jcaip
`Sparsity` as a term doesn't reflect the tools that are developed by the AO. The `torch/ao/sparsity` also has utilities for structured pruning, which internally we always referred to as just "pruning". To avoid any confusion, we renamed `Sparsity` to `Prune`. We will not be introducing the backwards compatibility, as so far this toolset was kept under silent development.
This change will reflect the changes in the documentation as well.
**TODO:**
- [ ] Change the tutorials
- [ ] Confirm no bc-breakages
- [ ] Reflect the changes in the trackers and RFC docs
Fixes #ISSUE_NUMBER
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84867
Approved by: https://github.com/supriyar
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [X] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [X] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [X] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [X] [Current PR] `torch.nn.qat` → `torch.ao.nn.qat`
- [X] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [X] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- None
Differential Revision: [D36861197](https://our.internmc.facebook.com/intern/diff/D36861197/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36861197/)!
Differential Revision: [D36861197](https://our.internmc.facebook.com/intern/diff/D36861197)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78716
Approved by: https://github.com/jerryzh168
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [X] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [X] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [X] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [X] [Current PR] `torch.nn.qat` → `torch.ao.nn.qat`
- [X] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [X] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- None
Differential Revision: [D36861197](https://our.internmc.facebook.com/intern/diff/D36861197/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36861197/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78716
Approved by: https://github.com/jerryzh168
Summary: No functional changes here, the previous PRs handle the
necessary changes for this to work.
Test Plan: python test/test_ao_sparsity.py TestFxComposability
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81994
Approved by: https://github.com/jerryzh168
Summary: The primary issue was that fusion and matching had to be
updated to handle parametrized modules
Test Plan: python test/test_ao_sparsity.py TestFxComposability
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81993
Approved by: https://github.com/jerryzh168
Summary: sparse_prepare automatically composes with quantized prepare
even in cases with fusion. However, the convert step needed to be updated to handle parametrized
modules.
Test Plan: python test/test_ao_sparsity.py TestFxComposability
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81992
Approved by: https://github.com/jerryzh168
Summary: updated sparsity api to accept tensor_fqn as primary
specification method, i.e. [{'tensor_fqn':
'linear.weight'}]
Pruning API also updated due to knock on changes.
left old api for accepting module_fqns but changed 'fqn' to 'module_fqn'
for clarity (this will break BC)
updated variables in code to use module rather than layer
updated state dict to use tensor_fqn rather than 'fqn' or 'module_fqn'
Test Plan: python test/test_ao_sparsity.py
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79113
Approved by: https://github.com/z-a-f
Summary: The primary issue for enabling sparsity to work with QAT
convert (unlike normal quantization convert) is that when the
parametrized module undergoes the QAT convert, the parametrizations need
to be maintained. If the parametrizations don't
get transfered during the convert, the sparsifier would lose its
connection to the model. In practice this was handled using the
transfer_parametrizations_and_params function to move the weight and
bias and any associated paramerizations to the new module. This PR also adds
tests for transfer_parametrizations_and_params and type_before_parametrizations
to test_nn.py and also added comments to the test code for
composability.
Test Plan: python test/test_ao_sparsity.py TestComposability
python test/test_nn.py TestNN
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74848
Approved by: https://github.com/vkuzo, https://github.com/Lezcano
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74847
Similar to the other PRs in this stack, the main problem was
that fusion needed to detect the original module type of parametrized
module when sparse prepare was called before fusion. In addition, there
was a potential issue with fusion before sparse_prepare but after the
sparse_config is created. However, in practice fusion moves the
references to the original modules into the fused module without issue.
Thus the original sparse_config that pointed to the original modules
gets automatically updated. If the fusion method changes this may cause
an issue since no explicit handling or updating of these pointers was
needed.
Test Plan:
python test/test_ao_sparsity.py TestComposability
Imported from OSS
Reviewed By: vkuzo, andrewor14, jerryzh168
Differential Revision: D35240273
fbshipit-source-id: 62ed66689b285c3fa68f1e149266ab877f1cdd8e
(cherry picked from commit 2adb002c43f702fa1f18637157264fcbc545002a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74846
This PR primarily allows the PTQ convert function to work with
parametrized modules. Given that the parametrized weight is what is used
by default in convert, as long as sparsifier.step() has already been
called, the converted model will use the sparisified weights. There is
currently no way to handle things if sparsifier.step() has not been
called. Lastly, added the is_leaf_or_only_parametrized function because
parametrized modules no longer look like leaves due to the
parametrizations module attached to them
Test Plan:
python test/test_ao_sparsity.py TestComposability
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D35240275
fbshipit-source-id: 48529f2a83edfe6d8a2d2dff8ca3d08a3fb0d553
(cherry picked from commit 9d6361482e2885db964e02b0222cd23c9f4d469e)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74845
This PR adds support for quantization flow to detect
parametrized modules and match them using their original module types.
This mainly involved using the new type_before_parametrizations function rather than
type to check for module mathcing
Test Plan:
python test/test_ao_sparsity.py TestComposability
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D35240274
fbshipit-source-id: 7294d89c9c2e069e51d8b9bafa45c15f92bed124
(cherry picked from commit ed5cdb7b636c42e040d1b4a67b6b94604d06e1ff)
