diff --git "a/hw_midm_7B_food_order_understanding.ipynb" "b/hw_midm_7B_food_order_understanding.ipynb" new file mode 100644--- /dev/null +++ "b/hw_midm_7B_food_order_understanding.ipynb" @@ -0,0 +1,4660 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "28e4c4d1-a73f-437b-a1bd-c2cc3874924a" + }, + "source": [ + "# 강의 11주차: midm-food-order-understanding\n", + "\n", + "1. KT-AI/midm-bitext-S-7B-inst-v1 를 주문 문장 이해에 미세 튜닝\n", + "\n", + "- food-order-understanding-small-3200.json (학습)\n", + "- food-order-understanding-small-800.json (검증)\n", + "\n", + "\n", + "종속적인 필요 내용\n", + "- huggingface 계정 설정 및 llama-2 사용 승인\n", + "- 로깅을 위한 wandb\n", + "\n", + "\n", + "history\n", + "\n", + "v1.2\n", + "- KT-AI/midm-bitext-S-7B-inst-v1 에 safetensors 포맷이 올라왔기에, 해당 리포에서 받도록 설정 변경\n", + "- 전체 과정 재검증" + ], + "id": "28e4c4d1-a73f-437b-a1bd-c2cc3874924a" + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nDZe_wqKU6J3", + "outputId": "f5eafd9b-f24e-4d79-a260-de4f8cf0071a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n", + "Collecting peft\n", + " Downloading peft-0.7.0-py3-none-any.whl (168 kB)\n", + "\u001b[2K 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wandb, datasets, trl, peft, optimum\n", + "Successfully installed GitPython-3.1.40 accelerate-0.25.0 bitsandbytes-0.41.3.post1 coloredlogs-15.0.1 datasets-2.15.0 dill-0.3.7 docker-pycreds-0.4.0 docstring-parser-0.15 einops-0.7.0 gitdb-4.0.11 humanfriendly-10.0 multiprocess-0.70.15 optimum-1.15.0 peft-0.7.0 pyarrow-hotfix-0.6 sentencepiece-0.1.99 sentry-sdk-1.38.0 setproctitle-1.3.3 shtab-1.6.5 smmap-5.0.1 trl-0.7.4 tyro-0.6.0 wandb-0.16.1\n" + ] + } + ], + "source": [ + "pip install transformers peft accelerate optimum bitsandbytes trl wandb einops" + ], + "id": "nDZe_wqKU6J3" + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "51eb00d7-2928-41ad-9ae9-7f0da7d64d6d", + "outputId": "5e0dc56b-95ed-4008-ee80-cc7c131a30a9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import os\n", + "from dataclasses import dataclass, field\n", + "from typing import Optional\n", + "import re\n", + "\n", + "import torch\n", + "import tyro\n", + "from accelerate import Accelerator\n", + "from datasets import load_dataset, Dataset\n", + "from peft import AutoPeftModelForCausalLM, LoraConfig\n", + "from tqdm import tqdm\n", + "from transformers import (\n", + " AutoModelForCausalLM,\n", + " AutoTokenizer,\n", + " BitsAndBytesConfig,\n", + " TrainingArguments,\n", + ")\n", + "\n", + "from trl import SFTTrainer\n", + "\n", + "from trl.trainer import ConstantLengthDataset" + ], + "id": "51eb00d7-2928-41ad-9ae9-7f0da7d64d6d" + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "19ab06a9c77949deb5d895f3960c7936", + "d49698f512a94ddcb3e95d555b32815e", + "c8cc0bb42794455b950d8002dca66831", + "12e4579291b14280a57d507e8d683258", + "ea5f11e553144f0ea85c562dd00114a5", + "cc37abdcfbfc4963b6a432b6a17e27d5", + "b774019fa1344b5dab82b05200f2e842", + "6543b74164434bcf969d80aeb6e20b0e", + "5c6cd6a747344866868ad6734d921bcb", + "af65e983558e489aa3bac902931473d1", + "cee02bd8ea0c4c90b77ac1819f1a6566", + "0d883aa7040f4290abf85c3e33f9ce4f", + "10136afec3ba4a9692dba5b244ff3338", + "931d0fada0a14aab81b0f2b2466df5fd", + "f4ca565564774bf7930e7e65e0d042ae", + "f3d74e756dfb4a2ea51134216879810b", + "f9e56abdf7d84ceda732df869862afc2", + "2c6c6be9c8ce47ac8d360d1010a9239d", + "bbb5aa17e2b14da3b670ec89deeec57c", + "0ddc6bfb294843e5941ae7075d21c98b", + "846c79b8fe604cdbb600a452b0b28890", + "437522ee9a05480f963c02533b70c155", + "96c40a82d6904a56b276a8bff5191bce", + "802d6b5803c140b2a97ed4ab95320858", + "dd5081c5bb0041418ff07c40bf0a4226", + "6e290d8144924ba8826f18254975ebd7", + "55333d6acaf94557a41857a286ee2ae3", + "7ef70cedb6c64f508ba3ed1391875899", + "38f6d70290a14dfdaec394fae7f0e026", + "8e46fc712d574626af5f92fad3a1f719", + "4a3b4746a8234656b08a19abb6e79879", + "fd4cf399c58c4c61be021ff7b9859eb8" + ] + }, + "id": "tX7gYxZaVhYL", + "outputId": "89626642-cd90-40c0-8a2c-c54505dc3faa" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
,\n", + "ignore_data_skip=False,\n", + "include_inputs_for_metrics=False,\n", + "include_tokens_per_second=False,\n", + "jit_mode_eval=False,\n", + "label_names=None,\n", + "label_smoothing_factor=0.0,\n", + "learning_rate=0.0001,\n", + "length_column_name=length,\n", + "load_best_model_at_end=False,\n", + "local_rank=0,\n", + "log_level=passive,\n", + "log_level_replica=warning,\n", + "log_on_each_node=True,\n", + "logging_dir=./results/runs/Dec11_12-42-36_1df8970d58b9,\n", + "logging_first_step=False,\n", + "logging_nan_inf_filter=True,\n", + "logging_steps=50,\n", + "logging_strategy=steps,\n", + "lr_scheduler_type=cosine,\n", + "max_grad_norm=0.3,\n", + "max_steps=300,\n", + "metric_for_best_model=None,\n", + "mp_parameters=,\n", + "neftune_noise_alpha=None,\n", + "no_cuda=False,\n", + "num_train_epochs=1,\n", + "optim=paged_adamw_32bit,\n", + "optim_args=None,\n", + "output_dir=/gdrive/MyDrive/nlp/lora-midm-7b-food-order-understanding,\n", + "overwrite_output_dir=False,\n", + "past_index=-1,\n", + "per_device_eval_batch_size=1,\n", + "per_device_train_batch_size=1,\n", + "prediction_loss_only=False,\n", + "push_to_hub=False,\n", + "push_to_hub_model_id=None,\n", + "push_to_hub_organization=None,\n", + "push_to_hub_token=,\n", + "ray_scope=last,\n", + "remove_unused_columns=False,\n", + "report_to=['wandb'],\n", + "resume_from_checkpoint=None,\n", + "run_name=midm-7b-food-order-understanding,\n", + "save_on_each_node=False,\n", + "save_safetensors=True,\n", + "save_steps=500,\n", + "save_strategy=epoch,\n", + "save_total_limit=20,\n", + "seed=42,\n", + "skip_memory_metrics=True,\n", + "split_batches=False,\n", + "tf32=None,\n", + "torch_compile=False,\n", + "torch_compile_backend=None,\n", + "torch_compile_mode=None,\n", + "torchdynamo=None,\n", + "tpu_metrics_debug=False,\n", + "tpu_num_cores=None,\n", + "use_cpu=False,\n", + "use_ipex=False,\n", + "use_legacy_prediction_loop=False,\n", + "use_mps_device=False,\n", + "warmup_ratio=0.03,\n", + "warmup_steps=0,\n", + "weight_decay=0.05,\n", + "), packing=True, peft_config=LoraConfig(peft_type=, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type='CAUSAL_LM', inference_mode=False, r=8, target_modules={'c_attn', 'c_proj', 'c_fc'}, lora_alpha=16, lora_dropout=0.05, fan_in_fan_out=False, bias='none', modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={}), merge_with_final_checkpoint=False)\n" + ] + } + ], + "source": [ + "print(script_args)" + ], + "id": "bac62c01-21ef-491e-a686-cf4988186c58" + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "1ff1422e-184d-4438-b033-40ae8bdaa5fd" + }, + "outputs": [], + "source": [ + "bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=torch.bfloat16,\n", + ")" + ], + "id": "1ff1422e-184d-4438-b033-40ae8bdaa5fd" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "elg7gcB-5zb7" + }, + "source": [ + "원본인 'KT-AI/midm-bitext-S-7B-inst-v1' 는 *.bin 형태로 모델을 제공한다.\n", + "- 코랩에서 CPU 메모리 부족 발생\n", + "\n", + "해결책\n", + "- safetensors로 변환한 모델을 업로드 하고 이를 사용하기로 한다." + ], + "id": "elg7gcB-5zb7" + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "9d5a048b3ca044b383a2a46ccd6fc735", + "a7504c93953341799ca0ea374a28b8b3", + "edeb5e9dc0b24e40969178ef7b51f8f2", + "4bad7037bf7941b68e29e5e459cc37e3", + "144680fb16934cb997443a64ed98e452", + "6f27e95e7db14eb1b879b3487ccccca2", + "70f769deb8e64ce08079512320244677", + "2e4a57c9eff34a00b257d46edd5260a1", + "9c8f26d1e5934050b4faa175910bda2f", + "7548ccbe23784574b40583df92efd7bd", + "a3c3191c185e49ab99d33f7b21f96160" + ] + }, + "id": "15c8425e-bb0b-40c5-bfe8-385bac699b9d", + "outputId": "9b31a0a8-75cd-4959-b646-c75c57a467ec" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00, auto_mapping=None, base_model_name_or_path=None, revision=None, task_type='CAUSAL_LM', inference_mode=False, r=8, target_modules={'c_attn', 'c_proj', 'c_fc'}, lora_alpha=16, lora_dropout=0.05, fan_in_fan_out=False, bias='none', modules_to_save=None, init_lora_weights=True, layers_to_transform=None, layers_pattern=None, rank_pattern={}, alpha_pattern={}, megatron_config=None, megatron_core='megatron.core', loftq_config={})" + ] + }, + "metadata": {}, + "execution_count": 27 + } + ], + "source": [ + "peft_config" + ], + "id": "4420fcc4-2bac-413d-b7aa-89455c512419" + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "f47f9584-3988-46b8-a062-29dcde75a0e2" + }, + "outputs": [], + "source": [ + "tokenizer = AutoTokenizer.from_pretrained(\n", + " script_args.model_name,\n", + " trust_remote_code=True,\n", + " cache_dir=script_args.cache_dir,\n", + ")\n", + "\n", + "if getattr(tokenizer, \"pad_token\", None) is None:\n", + " tokenizer.pad_token = tokenizer.eos_token\n", + "tokenizer.padding_side = \"right\" # Fix weird overflow issue with fp16 training\n", + "\n", + "tokenizer.add_special_tokens(dict(bos_token=''))\n", + "\n", + "base_model.config.pad_token_id = tokenizer.pad_token_id\n", + "base_model.config.bos_token_id = tokenizer.bos_token_id" + ], + "id": "f47f9584-3988-46b8-a062-29dcde75a0e2" + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "abd17c83-ab8d-44cb-b69b-fc0936c2cec5" + }, + "outputs": [], + "source": [ + "training_args = script_args.training_args" + ], + "id": "abd17c83-ab8d-44cb-b69b-fc0936c2cec5" + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204, + "referenced_widgets": [ + "aa28d83f38294c0297c7676bff941b45", + "b983c81a99df4ea5aeb2a1ef57c97d95", + "dd92c120efc444888cd26e30c52b7f98", + "71097d7543df4eaf94cad0b6b044fbb8", + "a9b65ab23d5140289ea83bea49e0fe96", + "3247f8f75ae54daba8bff3c95250b423", + "290148245f054460bd6cabcc5ed4bd16", + "8a82bea59a7247adb7cb62a7cae0745e", + "a906a5ff4e304cc3989678deceed32c8", + "d87e517c7b4b4a98a7e0bb147ccb69e4", + "16a594026f6c41a19cddf9fc161d95f9", + "1b183fe98d914301a721b370d76aef59", + "bb910288ab4640c48e6f1cfb853dbb35", + "bb90d242064843b7b5587cbe3d3dacd2", + "9dde2095b07548c5b0a88e28bbfefc7d", + "af45e927806e4a74a9c88a8189ffd29f", + "9a23b7f565c949fb87d1fb349cac8885", + "d84906e22a194eaeb155fd71c2531393", + "e388f07004d844b79f110ce0257767b7", + "b1c5e75a0e634c289fecdb2880a3eca2", + "cc4b1e6b9b3f4826a1c5ed8a4983c06e", + "3775d1703e6e4219b87fbf47d3b99a66", + "495bfee5f80048ffa378a16e0262634d", + "3dda2783edf4420896f6be4e77376c98", + "8daac184a6c8493f84ff1ad5fcbf1275", + "7ffdfe57191e40039039b0edddad3d0e", + "8eaa28d1794b473ab27421ed758851a2", + "3bcc52c89214449f91574a2788ab430a", + "bd8b8209e3d84c7aa931f78bf49c1c58", + "3aba06b0f4f04a18a9435cfa6dcf3976", + "4552a5742de34e36829a0188bcdd1009", + "2293502f9a45447281de04ecaf08bc36", + "be23c79641ad4f4bad169f98b1946771" + ] + }, + "id": "62e8139f-5179-4c75-84a7-0c818ab0a35a", + "outputId": "cbef930d-4d99-4694-acf0-355177dc588c" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading data files: 0%| | 0/1 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m trainer = SFTTrainer(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbase_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m 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\u001b[0muse_gradient_checkpointing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 1.10 GiB. GPU 0 has a total capacty of 14.75 GiB of which 840.81 MiB is free. Process 4895 has 13.92 GiB memory in use. Of the allocated memory 12.95 GiB is allocated by PyTorch, and 164.28 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF" + ] + } + ], + "source": [ + "trainer = SFTTrainer(\n", + " model=base_model,\n", + " train_dataset=train_dataset,\n", + " eval_dataset=None,\n", + " peft_config=peft_config,\n", + " packing=script_args.packing,\n", + " max_seq_length=script_args.seq_length,\n", + " tokenizer=tokenizer,\n", + " args=training_args,\n", + ")" + ], + "id": "4ba80a64-0ec7-4b29-ac95-7b3d34549f17" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6qPxfovivMuH", + "outputId": "ab322c87-b1d6-4f9e-a65d-4f520dfd761f" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "MidmLMHeadModel(\n", + " (transformer): MidmModel(\n", + " (wte): Embedding(72192, 4096)\n", + " (rotary_pos_emb): RotaryEmbedding()\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " (h): ModuleList(\n", + " (0-31): 32 x MidmBlock(\n", + " (ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (attn): MidmAttention(\n", + " (c_attn): Linear4bit(\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=12288, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (base_layer): Linear4bit(in_features=4096, out_features=12288, bias=False)\n", + " )\n", + " (c_proj): Linear4bit(\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=4096, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", + " )\n", + " (attn_dropout): Dropout(p=0.0, inplace=False)\n", + " (resid_dropout): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (ln_2): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (mlp): MidmMLP(\n", + " (c_fc): Linear4bit(\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=21760, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (base_layer): Linear4bit(in_features=4096, out_features=21760, bias=False)\n", + " )\n", + " (c_proj): Linear4bit(\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=10880, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=4096, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (base_layer): Linear4bit(in_features=10880, out_features=4096, bias=False)\n", + " )\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (ln_f): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (lm_head): Linear(in_features=4096, out_features=72192, bias=False)\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 29 + } + ], + "source": [ + "base_model" + ], + "id": "6qPxfovivMuH" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gw9xbeUgbZEo", + "outputId": "935a69c7-0175-4aef-f40c-465778d9c03c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "PeftModelForCausalLM(\n", + " (base_model): LoraModel(\n", + " (model): MidmLMHeadModel(\n", + " (transformer): MidmModel(\n", + " (wte): Embedding(72192, 4096)\n", + " (rotary_pos_emb): RotaryEmbedding()\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " (h): ModuleList(\n", + " (0-31): 32 x MidmBlock(\n", + " (ln_1): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (attn): MidmAttention(\n", + " (c_attn): Linear4bit(\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=12288, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (base_layer): Linear4bit(in_features=4096, out_features=12288, bias=False)\n", + " )\n", + " (c_proj): Linear4bit(\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=4096, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", + " )\n", + " (attn_dropout): Dropout(p=0.0, inplace=False)\n", + " (resid_dropout): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (ln_2): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " (mlp): MidmMLP(\n", + " (c_fc): Linear4bit(\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=4096, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=21760, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (base_layer): Linear4bit(in_features=4096, out_features=21760, bias=False)\n", + " )\n", + " (c_proj): Linear4bit(\n", + " (lora_dropout): ModuleDict(\n", + " (default): Dropout(p=0.05, inplace=False)\n", + " )\n", + " (lora_A): ModuleDict(\n", + " (default): Linear(in_features=10880, out_features=8, bias=False)\n", + " )\n", + " (lora_B): ModuleDict(\n", + " (default): Linear(in_features=8, out_features=4096, bias=False)\n", + " )\n", + " (lora_embedding_A): ParameterDict()\n", + " (lora_embedding_B): ParameterDict()\n", + " (base_layer): Linear4bit(in_features=10880, out_features=4096, bias=False)\n", + " )\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (ln_f): LayerNorm((4096,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (lm_head): Linear(in_features=4096, out_features=72192, bias=False)\n", + " )\n", + " )\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 30 + } + ], + "source": [ + "trainer.model" + ], + "id": "gw9xbeUgbZEo" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "edb204be-ec15-4800-af49-6cfbad2f7f9a", + "outputId": "56085fde-5f7f-43a3-87cf-657d7c79b2f6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "trainable params: 16744448 || all params: 3821510656 || trainable%: 0.4381630592527648\n" + ] + } + ], + "source": [ + "print_trainable_parameters(base_model)" + ], + "id": "edb204be-ec15-4800-af49-6cfbad2f7f9a" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sVA-VzeTigHs", + "outputId": "816c71a2-6cda-48d6-96eb-8113086e651b" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "7795015808" + ] + }, + "metadata": {}, + "execution_count": 24 + } + ], + "source": [ + "base_model.get_memory_footprint()" + ], + "id": "sVA-VzeTigHs" + }, + { + "cell_type": "code", + "source": [ + "trainer.model.print_trainable_parameters()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CTZmx_faQ-Xj", + "outputId": "58b6b60b-52b4-49e3-a422-13f1d3039085" + }, + "id": "CTZmx_faQ-Xj", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "trainable params: 16,744,448 || all params: 7,034,347,520 || trainable%: 0.23803839591934178\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "76sRe172fGlm" + }, + "source": [ + "midm 모델을 주문 문장 이해에 적용시 특징\n", + "- 모델 로딩 과정에서 CPU도 5.1기가, 디스크 42.4기가, GPU 메모리: 7,4 기가\n", + "\n", + "구글 코랩 T-4 GPU: 300스텝 (13:47초 예상)\n", + "\n", + "시퀀스 길이 384의 경우\n", + "- 14.7 G / 15.0 G 사용\n", + "- 메모리 오버플로우 발생시 이보다 줄일 것" + ], + "id": "76sRe172fGlm" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 475 + }, + "id": "14019fa9-0c6f-4729-ac99-0d407af375b8", + "outputId": "7893d25b-44c1-4847-d193-c3a62054d86f" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " window._wandbApiKey = new Promise((resolve, reject) => {\n", + " function loadScript(url) {\n", + " return new Promise(function(resolve, reject) {\n", + " let newScript = document.createElement(\"script\");\n", + " newScript.onerror = reject;\n", + " newScript.onload = resolve;\n", + " document.body.appendChild(newScript);\n", + " newScript.src = url;\n", + " });\n", + " }\n", + " loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n", + " const iframe = document.createElement('iframe')\n", + " iframe.style.cssText = \"width:0;height:0;border:none\"\n", + " document.body.appendChild(iframe)\n", + " const handshake = new Postmate({\n", + " container: iframe,\n", + " url: 'https://wandb.ai/authorize'\n", + " });\n", + " const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n", + " handshake.then(function(child) {\n", + " child.on('authorize', data => {\n", + " clearTimeout(timeout)\n", + " resolve(data)\n", + " });\n", + " });\n", + " })\n", + " });\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n", + "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ··········\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "Tracking run with wandb version 0.16.0" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "Run data is saved locally in /content/wandb/run-20231125_070528-q7nwfg3b" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "Syncing run midm-7b-food-order-understanding to Weights & Biases (docs)
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" + ] + }, + "metadata": {} + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "TrainOutput(global_step=300, training_loss=0.6013818422953288, metrics={'train_runtime': 937.6794, 'train_samples_per_second': 0.64, 'train_steps_per_second': 0.32, 'total_flos': 9315508499251200.0, 'train_loss': 0.6013818422953288, 'epoch': 0.19})" + ] + }, + "metadata": {}, + "execution_count": 31 + } + ], + "source": [ + "trainer.train()" + ], + "id": "14019fa9-0c6f-4729-ac99-0d407af375b8" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "id": "3Y4FQSyRghQt", + "outputId": "a97204d2-e42f-46ef-c954-09f6f0cda6ca" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'/gdrive/MyDrive/Lectures/2023/nlp/lora-midm-7b-food-order-understanding'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 32 + } + ], + "source": [ + "script_args.training_args.output_dir" + ], + "id": "3Y4FQSyRghQt" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "49f05450-da2a-4edd-9db2-63836a0ec73a" + }, + "outputs": [], + "source": [ + "trainer.save_model(script_args.training_args.output_dir)" + ], + "id": "49f05450-da2a-4edd-9db2-63836a0ec73a" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "652f307e-e1d7-43ae-b083-dba2d94c2296" + }, + "source": [ + "# 추론 테스트" + ], + "id": "652f307e-e1d7-43ae-b083-dba2d94c2296" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ea8a1fea-7499-4386-9dea-0509110f61af" + }, + "outputs": [], + "source": [ + "from transformers import pipeline, TextStreamer" + ], + "id": "ea8a1fea-7499-4386-9dea-0509110f61af" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "52626888-1f6e-46b6-a8dd-836622149ff5" + }, + "outputs": [], + "source": [ + "instruction_prompt_template = \"\"\"###System;다음은 매장에서 고객이 음식을 주문하는 주문 문장이다. 이를 분석하여 음식명, 옵션명, 수량을 추출하여 고객의 의도를 이해하고자 한다.\n", + "분석 결과를 완성해주기 바란다.\n", + "\n", + "### 주문 문장: {0} ### 분석 결과:\n", + "\"\"\"\n", + "\n", + "prompt_template = \"\"\"###System;{System}\n", + "###User;{User}\n", + "###Midm;\"\"\"\n", + "\n", + "default_system_msg = (\n", + " \"너는 먼저 사용자가 입력한 주문 문장을 분석하는 에이전트이다. 이로부터 주문을 구성하는 음식명, 옵션명, 수량을 차례대로 추출해야 한다.\"\n", + ")" + ], + "id": "52626888-1f6e-46b6-a8dd-836622149ff5" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "46e844fa-8f63-4359-a4fb-df66e8171796" + }, + "outputs": [], + "source": [ + "evaluation_queries = [\n", + " \"오늘은 비가오니깐 이거 먹자. 삼선짬뽕 곱배기 하나하구요, 사천 탕수육 중짜 한그릇 주세요.\",\n", + " \"아이스아메리카노 톨사이즈 한잔 하고요. 딸기스무디 한잔 주세요. 또, 콜드브루라떼 하나요.\",\n", + " \"참이슬 한병, 코카콜라 1.5리터 한병, 테슬라 한병이요.\",\n", + " \"꼬막무침 1인분하고요, 닭도리탕 중자 주세요. 그리고 소주도 한병 주세요.\",\n", + " \"김치찌개 3인분하고요, 계란말이 주세요.\",\n", + " \"불고기버거세트 1개하고요 감자튀김 추가해주세요.\",\n", + " \"불닭볶음면 1개랑 사리곰탕면 2개 주세요.\",\n", + " \"카페라떼 아이스 샷추가 한잔하구요. 스콘 하나 주세요\",\n", + " \"여기요 춘천닭갈비 4인분하고요. 라면사리 추가하겠습니다. 콜라 300ml 두캔주세요.\",\n", + " \"있잖아요 조랭이떡국 3인분하고요. 떡만두 한세트 주세요.\",\n", + " \"깐풍탕수 2인분 하고요 콜라 1.5리터 한병이요.\",\n", + "]" + ], + "id": "46e844fa-8f63-4359-a4fb-df66e8171796" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1919cf1f-482e-4185-9d06-e3cea1918416" + }, + "outputs": [], + "source": [ + "def wrapper_generate(model, input_prompt, do_stream=False):\n", + " data = tokenizer(input_prompt, return_tensors=\"pt\")\n", + " streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n", + " input_ids = data.input_ids[..., :-1]\n", + " with torch.no_grad():\n", + " pred = model.generate(\n", + " input_ids=input_ids.cuda(),\n", + " streamer=streamer if do_stream else None,\n", + " use_cache=True,\n", + " max_new_tokens=float('inf'),\n", + " do_sample=False\n", + " )\n", + " decoded_text = tokenizer.batch_decode(pred, skip_special_tokens=True)\n", + " decoded_text = decoded_text[0].replace(\"<[!newline]>\", \"\\n\")\n", + " return (decoded_text[len(input_prompt):])" + ], + "id": "1919cf1f-482e-4185-9d06-e3cea1918416" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eaac1f6f-c823-4488-8edb-2f931ddf0daa", + "outputId": "930fa82a-0858-44d3-ef83-f688ccf80c1b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1473: 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 and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "eval_dic = {i:wrapper_generate(model=base_model, input_prompt=prompt_template.format(System=default_system_msg, User=evaluation_queries[i]))for i, query in enumerate(evaluation_queries)}" + ], + "id": "eaac1f6f-c823-4488-8edb-2f931ddf0daa" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fefd04ba-2ed8-4f84-bdd0-86d52b3f39f6", + "outputId": "7411d306-e523-4a41-865b-d02a53608245" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "- 분석 결과 0: 음식명:삼선짬뽕, 옵션:곱배기, 수량:하나\n", + "- 분석 결과 1: 음식명:사천 탕수육, 옵션:중짜, 수량:한그릇\n" + ] + } + ], + "source": [ + "print(eval_dic[0])" + ], + "id": "fefd04ba-2ed8-4f84-bdd0-86d52b3f39f6" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3f471e3a-723b-4df5-aa72-46f571f6bab6" + }, + "source": [ + "# 미세튜닝된 모델 로딩 후 테스트" + ], + "id": "3f471e3a-723b-4df5-aa72-46f571f6bab6" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "a43bdd07-7555-42b2-9888-a614afec892f" + }, + "outputs": [], + "source": [ + "bnb_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_quant_type=\"nf4\",\n", + " bnb_4bit_compute_dtype=torch.bfloat16,\n", + ")" + ], + "id": "a43bdd07-7555-42b2-9888-a614afec892f" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "7b86e0db29d64b44bb2e7feb52958679", + "dbe8587bc57d49f7bd31e1e764a3c1d9", + "b65474856f654107986b92c2781676d2", + "f85e191cdff3450496c6c6aacd08ed7d", + "04ba6c7451784c9bb49e8ec16cc16440", + "5ef9c8dc73614ffa9d14118ed64c85c7", + "da1e45c3cc1c49758b7c24c35c31751b", + "2afbbc0d3f0949c8bbb56e666d2cfa82", + "4a88a1ed8ed8405b978477b2d11ba3b4", + "ee5b8d93923b4875bf2f4863ca2f5628", + "3f215b1ef9d24b45a6b161e019403325" + ] + }, + "id": "39db2ee4-23c8-471f-89b2-bca34964bf81", + "outputId": "ad6582fd-8378-4170-f099-2dcf4f9fa441" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00'))\n", + "\n", + "trained_model.config.pad_token_id = tokenizer.pad_token_id\n", + "trained_model.config.bos_token_id = tokenizer.bos_token_id" + ], + "id": "b0b75ca4-730d-4bde-88bb-a86462a76d52" + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X1tRCa4EiYXp" + }, + "source": [ + "추론 과정에서는 GPU 메모리를 약 5.5 GB 활용" + ], + "id": "X1tRCa4EiYXp" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true, + "base_uri": "https://localhost:8080/" + }, + "id": "e374555b-9f8a-4617-8ea7-c1e6ee1b2999", + "outputId": "526d2827-6422-4399-d7ed-107b822b2bb2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1473: 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 and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "- 분석 결과 0: 음식명:삼선짬뽕, 옵션:곱배기, 수량:하나<[!newline]>- 분석 결과 1: 음식명:사천 탕수육, 옵션:중짜, 수량:한그릇\n", + "- 분석 결과 0: 음식명:아이스아메리카노,옵션:톨사이즈,수량:한잔<[!newline]>- 분석 결과 1: 음식명:딸기스무디,수량:한잔<[!newline]>- 분석 결과 2: 음식명:콜드브루라떼,수량:하나\n", + "- 분석 결과 0: 음식명:참이슬,수량:한병<[!newline]>- 분석 결과 1: 음식명:코카콜라,옵션:1.5리터,수량:한병<[!newline]>- 분석 결과 2: 음식명:테슬라,수량:한병\n", + "- 분석 결과 0: 음식명:꼬막무침, 수량:1인분<[!newline]>- 분석 결과 1: 음식명:닭도리탕, 옵션:중자<[!newline]>- 분석 결과 2: 음식명:소주, 수량:한병\n", + "- 분석 결과 0: 음식명:김치찌개, 수량:3인분<[!newline]>- 분석 결과 1: 음식명:계란말이\n", + "- 분석 결과 0: 음식명:불고기버거세트, 수량:1개<[!newline]>- 분석 결과 1: 음식명:감자튀김, 수량:추가\n", + "- 분석 결과 0: " + ] + } + ], + "source": [ + "eval_dic = {i:wrapper_generate(model=trained_model, do_stream=True, input_prompt=prompt_template.format(System=default_system_msg, User=evaluation_queries[i]))for i, query in enumerate(evaluation_queries)}" + ], + "id": "e374555b-9f8a-4617-8ea7-c1e6ee1b2999" + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5d055bb0-5e5f-4221-a634-45d903c0f3b5" + }, + "outputs": [], + "source": [ + "print(eval_dic[0])" + ], + "id": "5d055bb0-5e5f-4221-a634-45d903c0f3b5" + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "7b86e0db29d64b44bb2e7feb52958679": { + "model_module": 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