[3x] add recommendation examples

examples/.config/model_params_pytorch_3x.json

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+{
+  "pytorch": {
+    "dlrm_ipex": {
+      "model_src_dir": "recommendation/dlrm/static_quant/ipex",
+      "dataset_location": "/mnt/local_disk3/dataset/dlrm/dlrm/input",
+      "input_model": "/mnt/local_disk3/dataset/dlrm/dlrm/dlrm_weight/tb00_40M.pt",
+      "main_script": "dlrm_s_pytorch.py",
+      "batch_size": 16384
+    }
+  }
+}

examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/mx/README.md → examples/3.x_api/pytorch/nlp/huggingface_models/language-modeling/quantization/mx_quant/README.md

@@ -1,6 +1,7 @@
 # Run
 
 ## Run WOQ MX FP4 model
+
 ``` python
 python run_clm_no_trainer.py --model [model_name_or_id] --quantize --accuracy --tasks lambada_openai --w_dtype fp4 --woq
-```
+```

examples/3.x_api/pytorch/recommendation/dlrm/static_quant/ipex/CODE_OF_CONDUCT.md

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+# Code of Conduct
+
+Facebook has adopted a Code of Conduct that we expect project participants to adhere to.
+Please read the [full text](https://code.fb.com/codeofconduct/)
+so that you can understand what actions will and will not be tolerated.

examples/3.x_api/pytorch/recommendation/dlrm/static_quant/ipex/CONTRIBUTING.md

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+# Contributing to DLRM
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `master`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Facebook's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## Coding Style
+* 4 spaces for indentation rather than tabs
+* 80 character line length
+* in general, please maintain a consistent style with the rest of the code
+
+## License
+By contributing to DLRM, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

examples/3.x_api/pytorch/recommendation/dlrm/static_quant/ipex/LICENSE

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+MIT License
+
+Copyright (c) Facebook, Inc. and its affiliates.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

examples/3.x_api/pytorch/recommendation/dlrm/static_quant/ipex/README.md

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+Step-by-Step
+============
+
+This document is used to list steps of reproducing PyTorch DLRM tuning zoo result. and original DLRM README is in [DLRM README](https://github.com/facebookresearch/dlrm/blob/master/README.md)
+
+> **Note**
+>
+> Please  ensure your PC have >370G memory to run DLRM
+> IPEX version >= 1.11
+
+# Prerequisite
+
+### 1. Environment
+
+PyTorch 1.11 or higher version is needed with pytorch_fx backend.
+
+  ```shell
+  # Install dependency
+  cd examples/pytorch/recommendation/dlrm/quantization/ptq/ipex
+  pip install -r requirements.txt
+  ```
+> Note: Validated PyTorch [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+### 2. Prepare Dataset
+
+  The code supports interface with the [Criteo Terabyte Dataset](https://labs.criteo.com/2013/12/download-terabyte-click-logs/)
+
+  1. download the raw data files day_0.gz, ...,day_23.gz and unzip them.
+  2. Specify the location of the unzipped text files day_0, ...,day_23, using --raw-data-file=<path/day> (the day number will be appended automatically), please refer "Run" command.
+
+### 3. Prepare pretrained model
+
+  Download the DLRM PyTorch weights (`tb00_40M.pt`, 90GB) from the
+[MLPerf repo](https://github.com/mlcommons/inference/tree/master/recommendation/dlrm/pytorch#more-information-about-the-model-weights)
+
+# Run
+### tune with INC
+  ```shell
+  cd examples/pytorch/recommendation/dlrm/quantization/ptq/ipex
+  bash run_quant.sh --input_model="/path/of/pretrained/model" --dataset_location="/path/of/dataset"
+  ```
+
+### benchmark
+```shell
+bash run_benchmark.sh --input_model="/path/of/pretrained/model" --dataset_location="/path/of/dataset" --mode=accuracy --int8=true
+```
+
+
+Examples of enabling Intel® Neural Compressor
+=========================
+
+This is a tutorial of how to enable DLRM model with Intel® Neural Compressor.
+
+
+### Code update
+
+We need update dlrm_s_pytorch.py like below
+
+```python
+class DLRM_DataLoader(object):
+    def __init__(self, loader=None):
+        self.loader = loader
+        self.batch_size = loader.dataset.batch_size
+    def __iter__(self):
+        for X_test, lS_o_test, lS_i_test, T in self.loader:
+            yield (X_test, lS_o_test, lS_i_test), T
+```
+
+```python
+	def eval_func(model):
+		args.int8 = False if model.ipex_config_path is None else True
+		args.int8_configure = "" \
+			if model.ipex_config_path is None else model.ipex_config_path
+		with torch.no_grad():
+			return inference(
+				args,
+				model,
+				best_acc_test,
+				best_auc_test,
+				test_ld,
+				trace=args.int8
+			)
+
+	eval_dataloader = DLRM_DataLoader(train_ld)
+	from neural_compressor import PostTrainingQuantConfig, quantization
+	conf = PostTrainingQuantConfig(backend="ipex")
+	q_model = quantization.fit(
+						dlrm,
+						conf=conf,
+						eval_func=eval_func,
+						calib_dataloader=eval_dataloader
+						)
+	q_model.save("saved_results")
+```