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fix jit trace error for model forward sequence is not aligned with jit.trace tuple input sequence, update related doc (#19891)

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* fix jit trace error for classification usecase, update related doc

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* add implementation in torch 1.14.0

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* update_doc

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* update_doc

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
This commit is contained in:
Wang, Yi
2022-11-03 22:50:03 +08:00
committed by GitHub
parent 737bff6a36
commit 2564f0c21d
3 changed files with 48 additions and 18 deletions
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10
docs/source/en/perf_train_cpu.mdx
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@@ -19,7 +19,7 @@ IPEX is optimized for CPUs with AVX-512 or above, and functionally works for CPU
Low precision data type BFloat16 has been natively supported on the 3rd Generation Xeon® Scalable Processors (aka Cooper Lake) with AVX512 instruction set and will be supported on the next generation of Intel® Xeon® Scalable Processors with Intel® Advanced Matrix Extensions (Intel® AMX) instruction set with further boosted performance. The Auto Mixed Precision for CPU backend has been enabled since PyTorch-1.10. At the same time, the support of Auto Mixed Precision with BFloat16 for CPU and BFloat16 optimization of operators has been massively enabled in Intel® Extension for PyTorch, and partially upstreamed to PyTorch master branch. Users can get better performance and user experience with IPEX Auto Mixed Precision.
Check more detailed information for [Auto Mixed Precision](https://intel.github.io/intel-extension-for-pytorch/1.11.200/tutorials/features/amp.html).
Check more detailed information for [Auto Mixed Precision](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/amp.html).
### IPEX installation:
@@ -37,7 +37,13 @@ For PyTorch-1.11:
pip install intel_extension_for_pytorch==1.11.200+cpu -f https://software.intel.com/ipex-whl-stable
```
Check more approaches for [IPEX installation](https://intel.github.io/intel-extension-for-pytorch/1.11.200/tutorials/installation.html).
For PyTorch-1.12:
```
pip install intel_extension_for_pytorch==1.12.300+cpu -f https://software.intel.com/ipex-whl-stable
```
Check more approaches for [IPEX installation](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/installation.html).
### Usage in Trainer
To enable auto mixed precision with IPEX in Trainer, users should add `use_ipex`, `bf16` and `no_cuda` in training command arguments.