[Quant][Inductor] Enable dequant promotion inside inductor #104590
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/104590
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit ff55f12 with merge base 97a291f ( UNSTABLE - The following job failed but was likely due to flakiness present on trunk and has been marked as unstable:
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ghstack-source-id: ce32fa370be90637f06f3598b103cf8cc255fea5 Pull Request resolved: #104590
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ghstack-source-id: ce32fa370be90637f06f3598b103cf8cc255fea5 Pull Request resolved: pytorch#104590
**Summary** Enable the dequant node promotion pass in inductor. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 [ghstack-poisoned]
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ghstack-source-id: 911e81e54cddb952a967388864e550e805abf739 Pull Request resolved: #104590
**Summary** Enable the dequant node promotion pass in inductor. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 [ghstack-poisoned]
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ghstack-source-id: 85b1c839a0930c1ee428ffe1e98acbf9cbaa882c Pull Request resolved: #104590
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ghstack-source-id: 85b1c839a0930c1ee428ffe1e98acbf9cbaa882c Pull Request resolved: pytorch#104590
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ghstack-source-id: 0a45da5b3e5e8c2517a0903337b9f222406fcfc8 Pull Request resolved: #104590
**Summary** Enable the dequant node promotion pass in inductor. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 [ghstack-poisoned]
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ghstack-source-id: ca1ab17fe3d157cd7c9c131223932eb0b7d15acb Pull Request resolved: #104590
**Summary** Enable the dequant node promotion pass in inductor. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 [ghstack-poisoned]
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ghstack-source-id: ca1ab17fe3d157cd7c9c131223932eb0b7d15acb Pull Request resolved: pytorch#104590
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ghstack-source-id: ef586e03bf5af85ad7f191b712fd6dbc73eb6a6c Pull Request resolved: #104590
**Summary** Enable the dequant node promotion pass in inductor. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 [ghstack-poisoned]
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ghstack-source-id: ef586e03bf5af85ad7f191b712fd6dbc73eb6a6c Pull Request resolved: pytorch#104590
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ghstack-source-id: 876008389d339c717c25096c402f717c5839faee Pull Request resolved: #104590
**Summary** Enable the dequant node promotion pass in inductor. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 [ghstack-poisoned]
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Aug 22, 2023
ghstack-source-id: bf12b7057e73abef38f9b7663099908a2456ce7f Pull Request resolved: pytorch#104590
**Summary**
Enable the `dequant pattern` promotion pass in inductor. Since in the qconv weight prepack pass, we will match the `dequant->conv2d` pattern. If the `dequant pattern` has multi user nodes, it will fail to be matched.
Taking the example of
```
conv1
/ \
conv2 conv3
```
After quantization flow, it will generate pattern as
```
dequant1
|
conv1
|
quant2
|
dequant2
/ \
conv2 conv3
```
We need to duplicate `dequant2` into `dequant2` and `dequant3`, in order to make `dequant2->conv2` and `dequant3->conv3` pattern matched.
**Test Plan**
```
python -m pytest test_mkldnn_pattern_matcher.py -k test_dequant_promotion
```
cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov
[ghstack-poisoned]
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Aug 25, 2023
ghstack-source-id: abf5033d4d4a7820bd4c17380f340320b0af014d Pull Request resolved: pytorch#104590
**Summary**
Enable the `dequant pattern` promotion pass in inductor. Since in the qconv weight prepack pass, we will match the `dequant->conv2d` pattern. If the `dequant pattern` has multi user nodes, it will fail to be matched.
Taking the example of
```
conv1
/ \
conv2 conv3
```
After quantization flow, it will generate pattern as
```
dequant1
|
conv1
|
quant2
|
dequant2
/ \
conv2 conv3
```
We need to duplicate `dequant2` into `dequant2` and `dequant3`, in order to make `dequant2->conv2` and `dequant3->conv3` pattern matched.
**Test Plan**
```
python -m pytest test_mkldnn_pattern_matcher.py -k test_dequant_promotion
```
cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov
[ghstack-poisoned]
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Aug 25, 2023
ghstack-source-id: 021dcdd27c054e541d34a2478e45bf90779c8fe0 Pull Request resolved: pytorch#104590
**Summary**
Enable the `dequant pattern` promotion pass in inductor. Since in the qconv weight prepack pass, we will match the `dequant->conv2d` pattern. If the `dequant pattern` has multi user nodes, it will fail to be matched.
Taking the example of
```
conv1
/ \
conv2 conv3
```
After quantization flow, it will generate pattern as
```
dequant1
|
conv1
|
quant2
|
dequant2
/ \
conv2 conv3
```
We need to duplicate `dequant2` into `dequant2` and `dequant3`, in order to make `dequant2->conv2` and `dequant3->conv3` pattern matched.
**Test Plan**
```
python -m pytest test_mkldnn_pattern_matcher.py -k test_dequant_promotion
```
cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov
[ghstack-poisoned]
**Summary**
Enable the `dequant pattern` promotion pass in inductor. Since in the qconv weight prepack pass, we will match the `dequant->conv2d` pattern. If the `dequant pattern` has multi user nodes, it will fail to be matched.
Taking the example of
```
conv1
/ \
conv2 conv3
```
After quantization flow, it will generate pattern as
```
dequant1
|
conv1
|
quant2
|
dequant2
/ \
conv2 conv3
```
We need to duplicate `dequant2` into `dequant2` and `dequant3`, in order to make `dequant2->conv2` and `dequant3->conv3` pattern matched.
**Test Plan**
```
python -m pytest test_mkldnn_pattern_matcher.py -k test_dequant_promotion
```
cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov
[ghstack-poisoned]
|
@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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… inside inductor (#105455) **Summary** Enable the `dequant-conv2d-unary_postop(relu)-quant` pattern fusion and lowering inside inductor. **Test Plan** ``` clear && python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_unary ``` Pull Request resolved: #105455 Approved by: https://github.com/jgong5, https://github.com/eellison ghstack dependencies: #104580, #104581, #104588, #104590
pytorchmergebot
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Aug 25, 2023
…rn fusion inside inductor (#105456) **Summary** Enable the `dequant-conv2d-binary_postop(add)-unary_postop(relu)-quant` pattern fusion and lowering inside inductor. **Test Plan** ``` clear && python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_binary ``` Pull Request resolved: #105456 Approved by: https://github.com/jgong5, https://github.com/eellison ghstack dependencies: #104580, #104581, #104588, #104590, #105455
pytorchmergebot
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Aug 26, 2023
…ol2d) (#105639) **Summary** In this PR, we mainly enable 2 things. - Enable the skeleton of quantization recipe for single quantizable operators in `X86InductorQuantizer`. - Add quantization recipe of `maxpool2d` and annotate it as input./output share observer. **Test Plan** ``` python -m pytest test_x86inductor_quantizer.py -k test_maxpool2d_recipe ``` Pull Request resolved: #105639 Approved by: https://github.com/jgong5, https://github.com/jerryzh168 ghstack dependencies: #104580, #104581, #104588, #104590, #105455, #105456
pytorchmergebot
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**Summary** Enable the `dq-maxpool2d-q` pattern match and lower into `torch.ops.quantized.max_pool2d`. **Test Plan** ``` python -m pytest test_mkldnn_pattern_matcher.py -k test_qmaxpool2d python -m pytest test_quantized_op.py -k test_max_pool2d_pt2e ``` Pull Request resolved: #105906 Approved by: https://github.com/jgong5, https://github.com/eellison ghstack dependencies: #104580, #104581, #104588, #104590, #105455, #105456, #105639
pytorchmergebot
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**Summary** After oneDNN 3.1 upgrade, we don't need to do the weight scale reciprocal calculation. So, remove the redundant reciprocal calculation to optimize QConv performance and using IDeep version API to implement it in this PR: - This QConv implementation expects to work functionally both with current IDeep version and the following IDeep upgrade in PR: #107565. - With the following IDeep upgrade in PR: #107565, the QConv has better performance since the redundant reciprocal calculation are removed. Pull Request resolved: #105996 Approved by: https://github.com/jgong5, https://github.com/jerryzh168 ghstack dependencies: #104580, #104581, #104588, #104590, #105455, #105456, #105639, #105906
pytorchmergebot
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…ht scale reciprocal calculation (#107565) **Summary** Upgrade IDeep which includes 1 IDeep change as IDeep PR: intel/ideep#226 - For IDeep PR: intel/ideep#226 which has done 2 things: - Remove the redundant QConv weight scale reciprocal calculation. - Pump IDEEP_VERSION_REVISION version from 0 to 1. So only QConv related calculation will be impacted and we already use IDeep version API in #105996 to make the corresponding change in PyTorch. Pull Request resolved: #107565 Approved by: https://github.com/jgong5, https://github.com/jerryzh168 ghstack dependencies: #104580, #104581, #104588, #104590, #105455, #105456, #105639, #105906, #105996
voznesenskym
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**Summary**
Enable the `dequant pattern` promotion pass in inductor. Since in the qconv weight prepack pass, we will match the `dequant->conv2d` pattern. If the `dequant pattern` has multi user nodes, it will fail to be matched.
Taking the example of
```
conv1
/ \
conv2 conv3
```
After quantization flow, it will generate pattern as
```
dequant1
|
conv1
|
quant2
|
dequant2
/ \
conv2 conv3
```
We need to duplicate `dequant2` into `dequant2` and `dequant3`, in order to make `dequant2->conv2` and `dequant3->conv3` pattern matched.
**Test Plan**
```
python -m pytest test_mkldnn_pattern_matcher.py -k test_dequant_promotion
```
Pull Request resolved: #104590
Approved by: https://github.com/jgong5, https://github.com/eellison
ghstack dependencies: #104580, #104581, #104588
voznesenskym
pushed a commit
that referenced
this pull request
Aug 27, 2023
… inside inductor (#105455) **Summary** Enable the `dequant-conv2d-unary_postop(relu)-quant` pattern fusion and lowering inside inductor. **Test Plan** ``` clear && python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_unary ``` Pull Request resolved: #105455 Approved by: https://github.com/jgong5, https://github.com/eellison ghstack dependencies: #104580, #104581, #104588, #104590
voznesenskym
pushed a commit
that referenced
this pull request
Aug 27, 2023
…rn fusion inside inductor (#105456) **Summary** Enable the `dequant-conv2d-binary_postop(add)-unary_postop(relu)-quant` pattern fusion and lowering inside inductor. **Test Plan** ``` clear && python -m pytest test_mkldnn_pattern_matcher.py -k test_qconv2d_binary ``` Pull Request resolved: #105456 Approved by: https://github.com/jgong5, https://github.com/eellison ghstack dependencies: #104580, #104581, #104588, #104590, #105455
voznesenskym
pushed a commit
that referenced
this pull request
Aug 27, 2023
…ol2d) (#105639) **Summary** In this PR, we mainly enable 2 things. - Enable the skeleton of quantization recipe for single quantizable operators in `X86InductorQuantizer`. - Add quantization recipe of `maxpool2d` and annotate it as input./output share observer. **Test Plan** ``` python -m pytest test_x86inductor_quantizer.py -k test_maxpool2d_recipe ``` Pull Request resolved: #105639 Approved by: https://github.com/jgong5, https://github.com/jerryzh168 ghstack dependencies: #104580, #104581, #104588, #104590, #105455, #105456
voznesenskym
pushed a commit
that referenced
this pull request
Aug 27, 2023
**Summary** Enable the `dq-maxpool2d-q` pattern match and lower into `torch.ops.quantized.max_pool2d`. **Test Plan** ``` python -m pytest test_mkldnn_pattern_matcher.py -k test_qmaxpool2d python -m pytest test_quantized_op.py -k test_max_pool2d_pt2e ``` Pull Request resolved: #105906 Approved by: https://github.com/jgong5, https://github.com/eellison ghstack dependencies: #104580, #104581, #104588, #104590, #105455, #105456, #105639
voznesenskym
pushed a commit
that referenced
this pull request
Aug 27, 2023
**Summary** After oneDNN 3.1 upgrade, we don't need to do the weight scale reciprocal calculation. So, remove the redundant reciprocal calculation to optimize QConv performance and using IDeep version API to implement it in this PR: - This QConv implementation expects to work functionally both with current IDeep version and the following IDeep upgrade in PR: #107565. - With the following IDeep upgrade in PR: #107565, the QConv has better performance since the redundant reciprocal calculation are removed. Pull Request resolved: #105996 Approved by: https://github.com/jgong5, https://github.com/jerryzh168 ghstack dependencies: #104580, #104581, #104588, #104590, #105455, #105456, #105639, #105906
voznesenskym
pushed a commit
that referenced
this pull request
Aug 27, 2023
…ht scale reciprocal calculation (#107565) **Summary** Upgrade IDeep which includes 1 IDeep change as IDeep PR: intel/ideep#226 - For IDeep PR: intel/ideep#226 which has done 2 things: - Remove the redundant QConv weight scale reciprocal calculation. - Pump IDEEP_VERSION_REVISION version from 0 to 1. So only QConv related calculation will be impacted and we already use IDeep version API in #105996 to make the corresponding change in PyTorch. Pull Request resolved: #107565 Approved by: https://github.com/jgong5, https://github.com/jerryzh168 ghstack dependencies: #104580, #104581, #104588, #104590, #105455, #105456, #105639, #105906, #105996
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Stack from ghstack (oldest at bottom):
Summary
Enable the
dequant patternpromotion pass in inductor. Since in the qconv weight prepack pass, we will match thedequant->conv2dpattern. If thedequant patternhas multi user nodes, it will fail to be matched.Taking the example of
After quantization flow, it will generate pattern as
We need to duplicate
dequant2intodequant2anddequant3, in order to makedequant2->conv2anddequant3->conv3pattern matched.Test Plan
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @ngimel @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov