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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, + "referenced_widgets": [ + "d91e6c58289b4a009ec0e7a7afe39749", + "5d1003fac344490b91ca4d6aa4828451", + "41b0f2bc50684e1586efec7ca30b5569", + "a8a2790885fe423990c57e6dee9560b6", + "9b324cf7ce0d477094b86434c54a8615", + "ee29fab7aa354c5fbed36d0fc9a4401d", + "8637b3182bee44089b116eeddea74ed3", + "0034c0aa9a2f431cacfa963a076f90a4", + "4637d86046834689b7f5033406d7c487", + "335e170ac0574854a148ec3e3a9e1717", + "c06d41a499a84a859e076eacccaed40c", + 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"17c53c6988244889810f7454222c6ad2", + "4bd054ce5f8f4f129317f48300586443" + ] + }, + "id": "ZMxTm7wFYrla", + "outputId": "baaf81ce-d002-4694-f5f9-0ffc0279d1f8" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "(…)3b-hf/resolve/main/tokenizer_config.json: 0%| | 0.00/746 [00:00\n", + " neutral\n", + " negative\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