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add ipynb to ipex example
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examples/ipex/text-generation/README.md

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# IPEX model for text-generation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"IPEX model will replace the linears and some ops. Please note that IPEXModel uses a graph mode model to inference to accelerate the generation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from transformers import AutoTokenizer\n",
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"from optimum.intel.ipex import IPEXModelForCausalLM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Framework not specified. Using pt to export the model.\n",
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"Passing the argument `library_name` to `get_supported_tasks_for_model_type` is required, but got library_name=None. Defaulting to `transformers`. An error will be raised in a future version of Optimum if `library_name` is not provided.\n",
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"/home/jiqingfe/frameworks.ai.pytorch.ipex-cpu/intel_extension_for_pytorch/frontend.py:462: UserWarning: Conv BatchNorm folding failed during the optimize process.\n",
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" warnings.warn(\n",
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"/home/jiqingfe/frameworks.ai.pytorch.ipex-cpu/intel_extension_for_pytorch/frontend.py:469: UserWarning: Linear BatchNorm folding failed during the optimize process.\n",
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" warnings.warn(\n",
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"/home/jiqingfe/miniconda3/envs/ipex/lib/python3.10/site-packages/transformers/modeling_utils.py:4193: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead\n",
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" warnings.warn(\n",
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"/home/jiqingfe/miniconda3/envs/ipex/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py:801: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
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" if batch_size <= 0:\n",
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"Passing the argument `library_name` to `get_supported_tasks_for_model_type` is required, but got library_name=None. Defaulting to `transformers`. An error will be raised in a future version of Optimum if `library_name` is not provided.\n",
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"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
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"access to the `model_dtype` attribute is deprecated and will be removed after v1.18.0, please use `_dtype` instead.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Answer the following yes/no question by reasoning step-by-step please. Can you write a whole Haiku in a single tweet? Yes, you can.\n",
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"\n",
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"Yes, I can write Haikus in one tweet. I have no idea how to do that, but I'm sure\n"
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]
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}
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],
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"source": [
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"model = IPEXModelForCausalLM.from_pretrained(\"gpt2\", torch_dtype=torch.bfloat16, export=True)\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n",
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"input_sentence = [\"Answer the following yes/no question by reasoning step-by-step please. Can you write a whole Haiku in a single tweet?\"]\n",
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"model_inputs = tokenizer(input_sentence, return_tensors=\"pt\")\n",
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"generation_kwargs = dict(max_new_tokens=32, do_sample=False, num_beams=4, num_beam_groups=1, no_repeat_ngram_size=2, use_cache=True)\n",
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"\n",
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"generated_ids = model.generate(**model_inputs, **generation_kwargs)\n",
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"output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
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"print(output)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "ipex",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

examples/ipex/text-generation/run_generation.py

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