Update ITREX version in ONNXRT WOQ example and fix bugs in hf models (#1333)

Signed-off-by: yuwenzho <yuwen.zhou@intel.com>

Author: yuwenzho

examples/onnxrt/nlp/huggingface_model/question_answering/quantization/ptq_static/main.py

@@ -483,6 +483,8 @@ def eval_func(model, *args):
         if model_args.model_name_or_path == 'mrm8488/spanbert-finetuned-squadv1':
             fp32_op_names = ['/bert/embeddings/word_embeddings/Gather', 
                              '/bert/encoder/layer.[5-7|9]/output/dense/MatMul']
+        elif model_args.model_name_or_path == 'salti/bert-base-multilingual-cased-finetuned-squad':
+            fp32_op_names = ['/bert/encoder/layer.[4-5]/output/dense/MatMul']
         elif model_args.model_name_or_path == 'distilbert-base-uncased-distilled-squad':
             fp32_op_names = ['/distilbert/transformer/layer.[1-5]/ffn/lin[1-2]/MatMul']
         elif model_args.model_name_or_path == 'deepset/roberta-large-squad2':

examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/main.py

@@ -25,7 +25,7 @@
 import onnxruntime as ort
 from torch.nn.functional import pad
 from torch.utils.data import DataLoader
-from intel_extension_for_transformers.evaluation.lm_eval import evaluate
+from intel_extension_for_transformers.llm.evaluation.lm_eval import evaluate
 from optimum.onnxruntime import ORTModelForCausalLM
 from transformers import LlamaConfig, LlamaTokenizer
 

examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/ptq_static/requirements.txt

@@ -1,5 +1,5 @@
-git+https://github.com/intel/intel-extension-for-transformers.git@b8302f99a93e5f09a80431cee2fb384755062664
 git+https://github.com/EleutherAI/lm-evaluation-harness.git@83dbfbf6070324f3e5872f63e49d49ff7ef4c9b3
+intel-extension-for-transformers
 torch
 transformers
 accelerate

examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/weight_only/README.md

@@ -12,6 +12,8 @@ pip install -r requirements.txt
 ```
 > Note: Validated ONNX Runtime [Version](/docs/source/installation_guide.md#validated-software-environment).
 
+> Note: Weight-only quantization in Intel® Neural Compressor is still under development. We encourage you to use the `master` branch to access the latest features.
+
 ## 2. Prepare Model
 
 ```bash

examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/weight_only/main.py

@@ -26,7 +26,7 @@
 import onnxruntime as ort
 from torch.nn.functional import pad
 from torch.utils.data import DataLoader
-from intel_extension_for_transformers.evaluation.lm_eval import evaluate
+from intel_extension_for_transformers.llm.evaluation.lm_eval import evaluate
 from optimum.onnxruntime import ORTModelForCausalLM
 from transformers import LlamaConfig, LlamaTokenizer
 

examples/onnxrt/nlp/huggingface_model/text_generation/llama/quantization/weight_only/requirements.txt

@@ -1,5 +1,5 @@
-git+https://github.com/intel/intel-extension-for-transformers.git@b8302f99a93e5f09a80431cee2fb384755062664
 git+https://github.com/EleutherAI/lm-evaluation-harness.git@83dbfbf6070324f3e5872f63e49d49ff7ef4c9b3
+intel-extension-for-transformers
 torch
 transformers
 accelerate
@@ -8,4 +8,4 @@ onnxruntime
 onnxruntime-extensions; python_version < '3.11'
 datasets
 optimum
-evaluate
+evaluate