[quant] Move the order of x86 engine to avoid changing the default qengine #86631
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…ngine since the default qengine is the last element of the engine in supported_engines list, adding x86 qengine in the end of the list changes the default quantized engine as well. this PR will be a short term fix to revert the changes. We have an issue here to track the proper fix: #86404 [ghstack-poisoned]
jerryzh168
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…ngine since the default qengine is the last element of the engine in supported_engines list, adding x86 qengine in the end of the list changes the default quantized engine as well. this PR will be a short term fix to revert the changes. We have an issue here to track the proper fix: #86404 ghstack-source-id: 503c36271d0e70c7786160102c3f6d8f731f709e Pull Request resolved: #86631
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/86631
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… default qengine" since the default qengine is the last element of the engine in supported_engines list, adding x86 qengine in the end of the list changes the default quantized engine as well. this PR will be a short term fix to revert the changes. We have an issue here to track the proper fix: #86404 [ghstack-poisoned]
jerryzh168
added a commit
that referenced
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Oct 10, 2022
…ngine since the default qengine is the last element of the engine in supported_engines list, adding x86 qengine in the end of the list changes the default quantized engine as well. this PR will be a short term fix to revert the changes. We have an issue here to track the proper fix: #86404 ghstack-source-id: 2a34e3a9ccf729e7fd2522ea872f4d1e6fbd12e4 Pull Request resolved: #86631
vkuzo
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Oct 10, 2022
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Hey @jerryzh168. |
jerryzh168
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…ngine (pytorch#86631) since the default qengine is the last element of the engine in supported_engines list, adding x86 qengine in the end of the list changes the default quantized engine as well. this PR will be a short term fix to revert the changes. We have an issue here to track the proper fix: pytorch#86404 Motivation: a meta internal team found that the inference failed in onednn prepacking with error: "could not create a primitive descriptor for a reorder primitive." in a COPPER_LAKE machine, we are working with intel to repro and fix the problem. in the mean time, we'll revert the changes of default option back to fbgemm Pull Request resolved: pytorch#86631 Approved by: https://github.com/vkuzo
This was referenced Oct 11, 2022
malfet
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Oct 13, 2022
Comment on lines
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| engines.push_back(at::kFBGEMM); | ||
| // The X86 qengine is available if and only if FBGEMM is available | ||
| engines.push_back(at::kX86); | ||
| engines.push_back(at::kFBGEMM); |
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Just curious, order of supported engines is completely irrelevant here, isn't it?
malfet
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…ngine (#86631) (#86726) since the default qengine is the last element of the engine in supported_engines list, adding x86 qengine in the end of the list changes the default quantized engine as well. this PR will be a short term fix to revert the changes. We have an issue here to track the proper fix: #86404 Motivation: a meta internal team found that the inference failed in onednn prepacking with error: "could not create a primitive descriptor for a reorder primitive." in a COPPER_LAKE machine, we are working with intel to repro and fix the problem. in the mean time, we'll revert the changes of default option back to fbgemm Pull Request resolved: #86631 Approved by: https://github.com/vkuzo
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Stack from ghstack (oldest at bottom):
since the default qengine is the last element of the engine in supported_engines list, adding x86 qengine in the end of the list changes the default quantized engine as well. this PR will be a short term fix to revert the changes. We have an issue here to track the proper fix: #86404
Motivation:
a meta internal team found that the inference failed in onednn prepacking with error: "could not create a primitive descriptor for a reorder primitive." in a COPPER_LAKE machine, we are working with intel to repro and fix the problem. in the mean time, we'll revert the changes of default option back to fbgemm