Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46346
Allow user to provide additional fusion/quant patterns for fx graph mode
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D24317437
fbshipit-source-id: 719927cce50c74dffa4f848bd5c98995c944a26a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46345
Allow user to add more fusion mappings
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D24317439
fbshipit-source-id: 3b144bbc305e41efbdf3e9fb25dbbeaad9e86c6a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46298
Allow user to specify a list of qualified names for non traceable submodule
or type of the non traceable submodule
See quantize_fx.py for api
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D24294210
fbshipit-source-id: eb1e309065e3dfbf31e63507aaed73587f0dae29
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45920
See docs for new way of defining custom modules
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D24145856
fbshipit-source-id: 488673fba503e39e8e303ed5a776fe36899ea4e3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46095
Adds logging on usage of public quantization APIs. This only works in FB codebase
and is a no-op in OSS.
Test Plan: The test plan is fb-only
Reviewed By: raghuramank100
Differential Revision: D24220817
fbshipit-source-id: a2cc957b5a077a70c318242f4a245426e48f75e5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45919
As discussed with JIT team, we'll run symbolic trace in quantization functions
prepare_fx now takes orginal pytorch model (torch.nn.Module) instead of `GraphModule` as input
Test Plan: Imported from OSS
Reviewed By: supriyar
Differential Revision: D24145857
fbshipit-source-id: 2b7a4ca525a7a8c23a26af54ef594c6a951e4024
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45672
This PR merges all quantization mode and will only expose the following top level functions:
```
prepare_fx
prepare_qat_fx
convert_fx
```
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: z-a-f
Differential Revision: D24053439
fbshipit-source-id: 03d545e26a36bc22a73349061b751eeb35171e64
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45292
This PR merges all quantization mode and will only expose the following top level functions:
```
prepare_fx
prepare_qat_fx
convert_fx
```
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23913105
fbshipit-source-id: 4e335286d6de225839daf51d1df54322d52d68e5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44856
Support following format of qconfig_dict
```python
qconfig_dict = {
# optional, global config
"": qconfig?,
# optional, used for module and function types
# could also be split into module_types and function_types if we prefer
"object_type": [
(nn.Conv2d, qconfig?),
(F.add, qconfig?),
...,
],
# optional, used for module names
"module_name": [
("foo.bar", qconfig?)
...,
],
# optional, matched in order, first match takes precedence
"module_name_regex": [
("foo.*bar.*conv[0-9]+", qconfig?)
...,
]
# priority (in increasing order): global, object_type, module_name_regex, module_name
# qconfig == None means fusion and quantization should be skipped for anything
# matching the rule
}
```
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23751304
fbshipit-source-id: 5b98f4f823502b12ae2150c93019c7b229c49c50
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43910
Adds a debug function to get a representation of all nodes in the
graph, such as
```
name op target args kwargs
x plchdr x () {}
linear_weight gt_prm linear.weight () {}
add_1 cl_fun <bi_fun add> (x, linear_weight) {}
linear_1 cl_mod linear (add_1,) {}
relu_1 cl_meth relu (linear_1,) {}
sum_1 cl_fun <bi_meth sum> (relu_1,) {'dim': -1}
topk_1 cl_fun <bi_meth topk> (sum_1, 3) {}
```
using only Python STL. This is useful for printing internal state of
graphs when working on FX code.
Has some on-by-default logic to shorten things so that node reprs for
toy models and unit tests fit into 80 chars.
Flexible on function name and location, I care more that this is
accessible from both inside PT as well as from debug scripts which
are not checked in.
Test Plan:
see
https://gist.github.com/vkuzo/ed0a50e5d6dc7442668b03bb417bd603 for
example usage
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D23435029
fbshipit-source-id: 1a2df797156a19cedd705e9e700ba7098b5a1376
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43901
Add similar APIs like eager and graph mode on torchscript
- fuse_fx
- quantize_fx (for both post training static and qat)
- quantize_dynamic_fx (for post training dynamic)
- prepare_fx (for both post training static and qat)
- prepare_dynamic_fx (for post training dynamic)
- convert_fx (for all modes)
Test Plan:
Imported from OSS
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23432430
fbshipit-source-id: fc99eb75cbecd6ee7a3aa6c8ec71cd499ff7e3c1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43581
Add similar APIs like eager and graph mode on torchscript
- fuse_fx
- quantize_fx (for both post training static and qat)
- quantize_dynamic_fx (for post training dynamic)
- prepare_fx (for both post training static and qat)
- prepare_dynamic_fx (for post training dynamic)
- convert_fx (for all modes)
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23385091
fbshipit-source-id: b789e54e1a0f3af6b026fd568281984e253e0433
