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FinGPT/FinGPT_Inference_Llama2_13B_falcon_7B_for_Beginners.ipynb
Bruce Yang ByFinTech 6edcd7d013 Created using Colaboratory
2023-11-06 15:06:57 +08:00

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"Requirement already satisfied: transformers==4.32.0 in /usr/local/lib/python3.10/dist-packages (4.32.0)\n",
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]
}
],
"source": [
"!pip install transformers==4.32.0 peft==0.5.0\n",
"!pip install sentencepiece\n",
"!pip install accelerate\n",
"!pip install torch\n",
"!pip install peft\n",
"!pip install datasets\n",
"!pip install bitsandbytes"
]
},
{
"cell_type": "code",
"source": [
"#for the `load_in_8bit=True` error\n",
"!pip install protobuf transformers==4.32.0 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate"
],
"metadata": {
"id": "Mtui5C82IwR-"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Inference with Single Task: Sentiment"
],
"metadata": {
"id": "uTyL2Ct70bmo"
}
},
{
"cell_type": "code",
"source": [
"from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizerFast\n",
"from peft import PeftModel # 0.5.0"
],
"metadata": {
"id": "UFzsvG4ZbXVO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Load Models\n",
"base_model = \"NousResearch/Llama-2-13b-hf\"\n",
"peft_model = \"FinGPT/fingpt-sentiment_llama2-13b_lora\"\n",
"tokenizer = LlamaTokenizerFast.from_pretrained(base_model, trust_remote_code=True)\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"model = LlamaForCausalLM.from_pretrained(base_model, trust_remote_code=True, device_map = \"cuda:0\", load_in_8bit = True,)\n",
"model = PeftModel.from_pretrained(model, peft_model)\n",
"model = model.eval()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542,
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},
"id": "ZMxTm7wFYrla",
"outputId": "baaf81ce-d002-4694-f5f9-0ffc0279d1f8"
},
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
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"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n"
]
},
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}
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},
{
"output_type": "display_data",
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b55185e5b62449faa453918e1b3caa7c"
}
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"version_major": 2,
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}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:362: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
" warnings.warn(\n",
"/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:367: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "36fd83bef2bb48b4aeb16b4e083e8f4c"
}
},
"metadata": {}
},
{
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3b853f40d8ab4dbab3bb7ba46f7eeb19"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Make prompts\n",
"prompt = [\n",
"'''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}\n",
"Input: FINANCING OF ASPOCOMP 'S GROWTH Aspocomp is aggressively pursuing its growth strategy by increasingly focusing on technologically more demanding HDI printed circuit boards PCBs .\n",
"Answer: ''',\n",
"'''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}\n",
"Input: According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .\n",
"Answer: ''',\n",
"'''Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}\n",
"Input: A tinyurl link takes users to a scamming site promising that users can earn thousands of dollars by becoming a Google ( NASDAQ : GOOG ) Cash advertiser .\n",
"Answer: ''',\n",
"]\n",
"\n",
"# Generate results\n",
"tokens = tokenizer(prompt, return_tensors='pt', padding=True, max_length=512)\n",
"res = model.generate(**tokens, max_length=512)\n",
"res_sentences = [tokenizer.decode(i) for i in res]\n",
"out_text = [o.split(\"Answer: \")[1] for o in res_sentences]\n",
"\n",
"# show results\n",
"for sentiment in out_text:\n",
" print(sentiment)\n",
"\n",
"# Output:\n",
"# positive\n",
"# neutral\n",
"# negative\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0tTwD4E9Yroi",
"outputId": "c1fe1451-1ff7-42a8-8f9c-336c7b537dbd"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2436: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
" warnings.warn(\n",
"/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1411: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
" warnings.warn(\n",
"/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1529: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
" warnings.warn(\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" positive</s>\n",
" neutral</s>\n",
" negative</s>\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Inference with Multi-Task\n",
"* Please Restart runtime, the memory is not enough to run two tasks"
],
"metadata": {
"id": "07-KagX6YtM8"
}
},
{
"cell_type": "code",
"source": [
"from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizerFast\n",
"from peft import PeftModel # 0.5.0"
],
"metadata": {
"id": "QJN4tCASbZxu"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import os\n",
"import datasets\n",
"\n",
"\n",
"template_dict = {\n",
" 'default': 'Instruction: {instruction}\\nInput: {input}\\nAnswer: '\n",
"}\n",
"\n",
"lora_module_dict = {\n",
" 'chatglm2': ['query_key_value'],\n",
" 'falcon': ['query_key_value'],\n",
" 'bloom': ['query_key_value'],\n",
" 'internlm': ['q_proj', 'k_proj', 'v_proj'],\n",
" 'llama2': ['q_proj', 'k_proj', 'v_proj'],\n",
" 'qwen': [\"c_attn\"],\n",
" 'mpt': ['Wqkv'],\n",
"}\n",
"\n",
"\n",
"def get_prompt(template, instruction, input):\n",
"\n",
" if instruction:\n",
" return template_dict[template].format(instruction=instruction, input=input)\n",
" else:\n",
" return input\n",
"\n",
"\n",
"def test_mapping(args, feature):\n",
"\n",
" prompt = get_prompt(\n",
" args.instruct_template,\n",
" feature['instruction'],\n",
" feature['input']\n",
" )\n",
" return {\n",
" \"prompt\": prompt,\n",
" }\n",
"\n",
"\n",
"def tokenize(args, tokenizer, feature):\n",
"\n",
" prompt = get_prompt(\n",
" args.instruct_template,\n",
" feature['instruction'],\n",
" feature['input']\n",
" )\n",
" prompt_ids = tokenizer(\n",
" prompt, padding=False,\n",
" max_length=args.max_length, truncation=True\n",
" )['input_ids']\n",
" target_ids = tokenizer(\n",
" feature['output'].strip(), padding=False,\n",
" max_length=args.max_length, truncation=True,\n",
" add_special_tokens=False\n",
" )['input_ids']\n",
"\n",
" input_ids = prompt_ids + target_ids\n",
" exceed_max_length = len(input_ids) >= args.max_length\n",
"\n",
" # Add EOS Token\n",
" if input_ids[-1] != tokenizer.eos_token_id and not exceed_max_length:\n",
" input_ids.append(tokenizer.eos_token_id)\n",
"\n",
" label_ids = [tokenizer.pad_token_id] * len(prompt_ids) + input_ids[len(prompt_ids):]\n",
"\n",
" return {\n",
" \"input_ids\": input_ids,\n",
" \"labels\": label_ids,\n",
" \"exceed_max_length\": exceed_max_length\n",
" }\n",
"\n",
"\n",
"def parse_model_name(name, from_remote=False):\n",
"\n",
" if name == 'chatglm2':\n",
" return 'THUDM/chatglm2-6b' if from_remote else 'base_models/chatglm2-6b'\n",
" elif name == 'llama2':\n",
" return 'meta-llama/Llama-2-7b-hf' if from_remote else 'base_models/Llama-2-7b-hf'\n",
" # return 'NousResearch/Llama-2-7b-hf' if from_remote else 'base_models/Llama-2-7b-hf-nous'\n",
" elif name == 'falcon':\n",
" return 'tiiuae/falcon-7b' if from_remote else 'base_models/falcon-7b'\n",
" elif name == 'internlm':\n",
" return 'internlm/internlm-7b' if from_remote else 'base_models/internlm-7b'\n",
" elif name == 'qwen':\n",
" return 'Qwen/Qwen-7B' if from_remote else 'base_models/Qwen-7B'\n",
" elif name == 'mpt':\n",
" return 'cekal/mpt-7b-peft-compatible' if from_remote else 'base_models/mpt-7b-peft-compatible'\n",
" # return 'mosaicml/mpt-7b' if from_remote else 'base_models/mpt-7b'\n",
" elif name == 'bloom':\n",
" return 'bigscience/bloom-7b1' if from_remote else 'base_models/bloom-7b1'\n",
" else:\n",
" raise ValueError(f\"Undefined base model {name}\")\n",
"\n",
"\n",
"def load_dataset(names, from_remote=False):\n",
" dataset_names = [d for d in names.split(',')]\n",
" dataset_list = []\n",
" for name in dataset_names:\n",
" rep = 1\n",
" if not os.path.exists(name):\n",
" rep = int(name.split('*')[1]) if '*' in name else 1\n",
" name = ('FinGPT/fingpt-' if from_remote else 'data/fingpt-') + name.split('*')[0]\n",
" tmp_dataset = datasets.load_from_disk(name)\n",
" if 'test' not in tmp_dataset:\n",
" tmp_dataset = tmp_dataset.train_test_split(0.2, shuffle=True, seed=42)\n",
"\n",
" dataset_list.extend([tmp_dataset] * rep)\n",
" return dataset_list\n",
"\n"
],
"metadata": {
"id": "Wpf57NyVbuVs"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def load_model(base_model, peft_model, from_remote=True):\n",
"\n",
" model_name = parse_model_name(base_model, from_remote)\n",
"\n",
" model = AutoModelForCausalLM.from_pretrained(\n",
" model_name, trust_remote_code=True,\n",
" device_map=\"auto\",\n",
" )\n",
" model.model_parallel = True\n",
"\n",
" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
"\n",
" tokenizer.padding_side = \"left\"\n",
" if base_model == 'qwen':\n",
" tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids('<|endoftext|>')\n",
" tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids('<|extra_0|>')\n",
" if not tokenizer.pad_token or tokenizer.pad_token_id == tokenizer.eos_token_id:\n",
" tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
" model.resize_token_embeddings(len(tokenizer))\n",
"\n",
" model = PeftModel.from_pretrained(model, peft_model)\n",
" model = model.eval()\n",
" return model, tokenizer\n",
"\n",
"\n",
"def test_demo(model, tokenizer):\n",
"\n",
" for task_name, input, instruction in zip(demo_tasks, demo_inputs, demo_instructions):\n",
" prompt = 'Instruction: {instruction}\\nInput: {input}\\nAnswer: '.format(\n",
" input=input,\n",
" instruction=instruction\n",
" )\n",
" inputs = tokenizer(\n",
" prompt, return_tensors='pt',\n",
" padding=True, max_length=512,\n",
" return_token_type_ids=False\n",
" )\n",
" inputs = {key: value.to(model.device) for key, value in inputs.items()}\n",
" res = model.generate(\n",
" **inputs, max_length=512, do_sample=False,\n",
" eos_token_id=tokenizer.eos_token_id\n",
" )\n",
" output = tokenizer.decode(res[0], skip_special_tokens=True)\n",
" print(f\"\\n==== {task_name} ====\\n\")\n",
" print(output)"
],
"metadata": {
"id": "yGj4oCaDZkjA"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"FROM_REMOTE=True\n",
"\n",
"base_model = 'falcon'\n",
"peft_model = 'FinGPT/fingpt-mt_falcon-7b_lora' if FROM_REMOTE else 'finetuned_models/MT-falcon-linear_202309210126'\n",
"\n",
"model, tokenizer = load_model(base_model, peft_model, FROM_REMOTE)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 158,
"referenced_widgets": [
"1efeecbb93cb44a7925566249f32e53b",
"767a01ad674d4328bc003889459340d2",
"1f4e64b66dc442af89384c83cb213d97",
"4b67b4b929064817835a2608edb8c924",
"a5ae601634b74be38713ba0fc76562db",
"d93428f90f9c45bb97f3634de33c9c49",
"d6e455ed742f4b098801cf052edb3e5f",
"17fbf4edd72a4deb80c8849f8b42a9c8",
"ac0d9717279c45ef9d1cbafa95a1e3d2",
"6529c8dcfe4047d7909affadae0b0575",
"d1f917fd527442edbe68b3be9658532c"
]
},
"id": "24-Nh82zaWuC",
"outputId": "1a035faf-a3e9-4bfa-acdd-e6729d334c9b"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:transformers_modules.tiiuae.falcon-7b.898df1396f35e447d5fe44e0a3ccaaaa69f30d36.configuration_falcon:\n",
"WARNING: You are currently loading Falcon using legacy code contained in the model repository. Falcon has now been fully ported into the Hugging Face transformers library. For the most up-to-date and high-performance version of the Falcon model code, please update to the latest version of transformers and then load the model without the trust_remote_code=True argument.\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "1efeecbb93cb44a7925566249f32e53b"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Using pad_token, but it is not set yet.\n",
"You are resizing the embedding layer without providing a `pad_to_multiple_of` parameter. This means that the new embeding dimension will be 65025. This might induce some performance reduction as *Tensor Cores* will not be available. For more details about this, or help on choosing the correct value for resizing, refer to this guide: https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"demo_tasks = [\n",
" 'Financial Sentiment Analysis',\n",
" 'Financial Relation Extraction',\n",
" 'Financial Headline Classification',\n",
" 'Financial Named Entity Recognition',\n",
"]\n",
"demo_inputs = [\n",
" \"Glaxo's ViiV Healthcare Signs China Manufacturing Deal With Desano\",\n",
" \"Apple Inc Chief Executive Steve Jobs sought to soothe investor concerns about his health on Monday, saying his weight loss was caused by a hormone imbalance that is relatively simple to treat.\",\n",
" 'gold trades in red in early trade; eyes near-term range at rs 28,300-28,600',\n",
" 'This LOAN AND SECURITY AGREEMENT dated January 27 , 1999 , between SILICON VALLEY BANK (\" Bank \"), a California - chartered bank with its principal place of business at 3003 Tasman Drive , Santa Clara , California 95054 with a loan production office located at 40 William St ., Ste .',\n",
"]\n",
"demo_instructions = [\n",
" 'What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.',\n",
" 'Given phrases that describe the relationship between two words/phrases as options, extract the word/phrase pair and the corresponding lexical relationship between them from the input text. The output format should be \"relation1: word1, word2; relation2: word3, word4\". Options: product/material produced, manufacturer, distributed by, industry, position held, original broadcaster, owned by, founded by, distribution format, headquarters location, stock exchange, currency, parent organization, chief executive officer, director/manager, owner of, operator, member of, employer, chairperson, platform, subsidiary, legal form, publisher, developer, brand, business division, location of formation, creator.',\n",
" 'Does the news headline talk about price going up? Please choose an answer from {Yes/No}.',\n",
" 'Please extract entities and their types from the input sentence, entity types should be chosen from {person/organization/location}.',\n",
"]"
],
"metadata": {
"id": "kTsoodGCHwDi"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"test_demo(model, tokenizer)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ckn6fnNFL7Si",
"outputId": "3b16c5c8-51a9-40ff-bb54-73badac97b8b"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:2436: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
" warnings.warn(\n",
"Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n",
"Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"==== Financial Sentiment Analysis ====\n",
"\n",
"Instruction: What is the sentiment of this news? Please choose an answer from {negative/neutral/positive}.\n",
"Input: Glaxo's ViiV Healthcare Signs China Manufacturing Deal With Desano\n",
"Answer: positive\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"==== Financial Relation Extraction ====\n",
"\n",
"Instruction: Given phrases that describe the relationship between two words/phrases as options, extract the word/phrase pair and the corresponding lexical relationship between them from the input text. The output format should be \"relation1: word1, word2; relation2: word3, word4\". Options: product/material produced, manufacturer, distributed by, industry, position held, original broadcaster, owned by, founded by, distribution format, headquarters location, stock exchange, currency, parent organization, chief executive officer, director/manager, owner of, operator, member of, employer, chairperson, platform, subsidiary, legal form, publisher, developer, brand, business division, location of formation, creator.\n",
"Input: Apple Inc Chief Executive Steve Jobs sought to soothe investor concerns about his health on Monday, saying his weight loss was caused by a hormone imbalance that is relatively simple to treat.\n",
"Answer: employer: Steve Jobs, Apple Inc\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"==== Financial Headline Classification ====\n",
"\n",
"Instruction: Does the news headline talk about price going up? Please choose an answer from {Yes/No}.\n",
"Input: gold trades in red in early trade; eyes near-term range at rs 28,300-28,600\n",
"Answer: No\n",
"\n",
"==== Financial Named Entity Recognition ====\n",
"\n",
"Instruction: Please extract entities and their types from the input sentence, entity types should be chosen from {person/organization/location}.\n",
"Input: This LOAN AND SECURITY AGREEMENT dated January 27, 1999, between SILICON VALLEY BANK (\" Bank \"), a California - chartered bank with its principal place of business at 3003 Tasman Drive, Santa Clara, California 95054 with a loan production office located at 40 William St., Ste.\n",
"Answer: SILICON VALLEY BANK is an organization, Bank is an organization, California is a location, bank is an organization, 3003 Tasman Drive is a location, Santa Clara is a location, California is a location, 40 William St is a location.\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "hsO0Wg9y0h_E"
},
"execution_count": null,
"outputs": []
}
]
}
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