remove ipex_optimize_model usage (#38632)

* remove ipex_optimize_model usage

Signed-off-by: YAO Matrix <matrix.yao@intel.com>

* update Dockerfile

Signed-off-by: root <root@a4bf01945cfe.jf.intel.com>

---------

Signed-off-by: YAO Matrix <matrix.yao@intel.com>
Signed-off-by: root <root@a4bf01945cfe.jf.intel.com>
Co-authored-by: root <root@a4bf01945cfe.jf.intel.com>

docs/source/en/perf_infer_cpu.md

@@ -78,26 +78,3 @@ python examples/pytorch/question-answering/run_qa.py \
 --no_cuda \
 --jit_mode_eval
 ```
-
-## IPEX
-
-[Intel Extension for PyTorch](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/getting_started.html) (IPEX) offers additional optimizations for PyTorch on Intel CPUs. IPEX further optimizes TorchScript with [graph optimization](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/graph_optimization.html) which fuses operations like Multi-head attention, Concat Linear, Linear + Add, Linear + Gelu, Add + LayerNorm, and more, into single kernels for faster execution.
-
-Make sure IPEX is installed, and set the `--use_opex` and `--jit_mode_eval` flags in [`Trainer`] to enable IPEX graph optimization and TorchScript.
-
-```bash
-!pip install intel_extension_for_pytorch
-```
-
-```bash
-python examples/pytorch/question-answering/run_qa.py \
---model_name_or_path csarron/bert-base-uncased-squad-v1 \
---dataset_name squad \
---do_eval \
---max_seq_length 384 \
---doc_stride 128 \
---output_dir /tmp/ \
---no_cuda \
---use_ipex \
---jit_mode_eval
-```

docs/source/en/perf_train_cpu.md

@@ -17,30 +17,9 @@ rendered properly in your Markdown viewer.
 
 A modern CPU is capable of efficiently training large models by leveraging the underlying optimizations built into the hardware and training on fp16 or bf16 data types.
 
-This guide focuses on how to train large models on an Intel CPU using mixed precision and the [Intel Extension for PyTorch (IPEX)](https://intel.github.io/intel-extension-for-pytorch/index.html) library.
+This guide focuses on how to train large models on an Intel CPU using mixed precision. AMP is enabled for CPU backends training with PyTorch.
 
-You can Find your PyTorch version by running the command below.
-
-```bash
-pip list | grep torch
-```
-
-Install IPEX with the PyTorch version from above.
-
-```bash
-pip install intel_extension_for_pytorch==<version_name> -f https://developer.intel.com/ipex-whl-stable-cpu
-```
-
-> [!TIP]
-> Refer to the IPEX [installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation) guide for more details.
-
-IPEX provides additional performance optimizations for Intel CPUs. These include additional CPU instruction level architecture (ISA) support such as [Intel AVX512-VNNI](https://en.wikichip.org/wiki/x86/avx512_vnni) and [Intel AMX](https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/what-is-intel-amx.html). Both of these features are designed to accelerate matrix multiplication. Older AMD and Intel CPUs with only Intel AVX2, however, aren't guaranteed better performance with IPEX.
-
-IPEX also supports [Auto Mixed Precision (AMP)](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/amp.html) training with the fp16 and bf16 data types. Reducing precision speeds up training and reduces memory usage because it requires less computation. The loss in accuracy from using full-precision is minimal. 3rd, 4th, and 5th generation Intel Xeon Scalable processors natively support bf16, and the 6th generation processor also natively supports fp16 in addition to bf16.
-
-AMP is enabled for CPU backends training with PyTorch.
-
-[`Trainer`] supports AMP training with a CPU by adding the `--use_cpu`, `--use_ipex`, and `--bf16` parameters. The example below demonstrates the [run_qa.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) script.
+[`Trainer`] supports AMP training with CPU by adding the `--use_cpu`, and `--bf16` parameters. The example below demonstrates the [run_qa.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) script.
 
 ```bash
 python run_qa.py \
@@ -54,7 +33,6 @@ python run_qa.py \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir /tmp/debug_squad/ \
- --use_ipex \
  --bf16 \
  --use_cpu
 ```
@@ -65,7 +43,6 @@ These parameters can also be added to [`TrainingArguments`] as shown below.
 training_args = TrainingArguments(
     output_dir="./outputs",
     bf16=True,
-    use_ipex=True,
     use_cpu=True,
 )
 ```

docs/source/en/perf_train_cpu_many.md

@@ -75,8 +75,7 @@ python3 run_qa.py \
  --doc_stride 128  \
  --output_dir /tmp/debug_squad/ \
  --no_cuda \
- --ddp_backend ccl \
- --use_ipex
+ --ddp_backend ccl
 ```
 
 </hfoption>
@@ -115,7 +114,6 @@ python3 run_qa.py \
  --output_dir /tmp/debug_squad/ \
  --no_cuda \
  --ddp_backend ccl \
- --use_ipex \
  --bf16
 ```
 
@@ -201,8 +199,7 @@ spec:
                     --output_dir /tmp/pvc-mount/output_$(date +%Y%m%d_%H%M%S) \
                     --no_cuda \
                     --ddp_backend ccl \
-                    --bf16 \
-                    --use_ipex;
+                    --bf16;
               env:
               - name: LD_PRELOAD
                 value: "/usr/lib/x86_64-linux-gnu/libtcmalloc.so.4.5.9:/usr/local/lib/libiomp5.so"