add code for reading from multi files in one directory
This commit is contained in:
BUAADreamer
parent
2ba5d69c7f
commit
3dd5f9a874
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@ -1,107 +1,116 @@
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{
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"alpaca_en": {
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"hf_hub_url": "tatsu-lab/alpaca"
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},
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"alpaca_zh": {
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"file_name": "alpaca_data_zh_51k.json",
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"file_sha1": "e655af3db557a4197f7b0cf92e1986b08fae6311"
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},
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"alpaca_gpt4_en": {
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"file_name": "alpaca_gpt4_data_en.json",
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"file_sha1": "647f4ad447bd993e4b6b6223d1be15208bab694a"
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},
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"alpaca_gpt4_zh": {
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"file_name": "alpaca_gpt4_data_zh.json",
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"file_sha1": "3eaa3bda364ccdd59925d7448a698256c31ef845"
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},
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"belle_0.5m": {
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"hf_hub_url": "BelleGroup/train_0.5M_CN"
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},
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"belle_1m": {
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"hf_hub_url": "BelleGroup/train_1M_CN"
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},
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"belle_2m": {
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"hf_hub_url": "BelleGroup/train_2M_CN"
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},
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"belle_dialog": {
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"hf_hub_url": "BelleGroup/generated_chat_0.4M"
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},
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"belle_math": {
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"hf_hub_url": "BelleGroup/school_math_0.25M"
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},
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"belle_multiturn": {
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"hf_hub_url": "BelleGroup/multiturn_chat_0.8M"
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},
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"guanaco": {
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"hf_hub_url": "JosephusCheung/GuanacoDataset"
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},
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"firefly": {
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"hf_hub_url": "YeungNLP/firefly-train-1.1M",
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"columns": {
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"prompt": "input",
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"query": "",
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"response": "target",
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"history": ""
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}
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},
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"codealpaca": {
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"hf_hub_url": "sahil2801/CodeAlpaca-20k"
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},
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"alpaca_cot": {
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"hf_hub_url": "QingyiSi/Alpaca-CoT"
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},
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"webqa": {
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"hf_hub_url": "suolyer/webqa",
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"columns": {
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"prompt": "input",
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"query": "",
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"response": "output",
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"history": ""
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}
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},
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"ultra_chat": {
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"script_url": "ultra_chat",
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"columns": {
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"prompt": "instruction",
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"query": "",
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"response": "output",
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"history": "history"
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}
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},
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"example": {
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"script_url": "example_dataset",
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"columns": {
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"prompt": "instruction",
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"query": "input",
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"response": "output",
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"history": "history"
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}
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},
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"comparison_gpt4_en": {
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"file_name": "comparison_gpt4_data_en.json",
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"file_sha1": "eeb295ce0ab011c37af52596460c8a57d07ad19f"
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},
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"comparison_gpt4_zh": {
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"file_name": "comparison_gpt4_data_zh.json",
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"file_sha1": "b99a41c1c864019d9b0c07dbcd5df0560cf33ce0"
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},
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"hh_rlhf_en": {
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"script_url": "hh_rlhf_en",
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"columns": {
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"prompt": "instruction",
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"query": "",
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"response": "output",
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"history": "history"
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}
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},
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"wiki_demo": {
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"file_name": "wiki_demo.txt",
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"file_sha1": "b2288edb05b233e5b35250fd4b308a5fa21fa66d",
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"columns": {
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"prompt": "text",
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"query": "",
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"response": "",
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"history": ""
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}
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"alpaca_en": {
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"hf_hub_url": "tatsu-lab/alpaca"
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},
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"alpaca_zh": {
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"file_name": "alpaca_data_zh_51k.json",
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"file_sha1": "e655af3db557a4197f7b0cf92e1986b08fae6311"
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},
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"alpaca_gpt4_en": {
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"file_name": "alpaca_gpt4_data_en.json",
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"file_sha1": "647f4ad447bd993e4b6b6223d1be15208bab694a"
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},
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"alpaca_gpt4_zh": {
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"file_name": "alpaca_gpt4_data_zh.json",
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"file_sha1": "3eaa3bda364ccdd59925d7448a698256c31ef845"
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},
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"belle_0.5m": {
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"hf_hub_url": "BelleGroup/train_0.5M_CN"
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},
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"belle_1m": {
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"hf_hub_url": "BelleGroup/train_1M_CN"
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},
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"belle_2m": {
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"hf_hub_url": "BelleGroup/train_2M_CN"
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},
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"belle_dialog": {
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"hf_hub_url": "BelleGroup/generated_chat_0.4M"
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},
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"belle_math": {
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"hf_hub_url": "BelleGroup/school_math_0.25M"
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},
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"belle_multiturn": {
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"hf_hub_url": "BelleGroup/multiturn_chat_0.8M"
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},
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"guanaco": {
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"hf_hub_url": "JosephusCheung/GuanacoDataset"
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},
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"firefly": {
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"hf_hub_url": "YeungNLP/firefly-train-1.1M",
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"columns": {
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"prompt": "input",
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"query": "",
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"response": "target",
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"history": ""
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}
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},
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"codealpaca": {
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"hf_hub_url": "sahil2801/CodeAlpaca-20k"
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},
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"alpaca_cot": {
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"hf_hub_url": "QingyiSi/Alpaca-CoT"
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},
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"webqa": {
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"hf_hub_url": "suolyer/webqa",
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"columns": {
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"prompt": "input",
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"query": "",
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"response": "output",
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"history": ""
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}
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},
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"ultra_chat": {
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"script_url": "ultra_chat",
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"columns": {
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"prompt": "instruction",
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"query": "",
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"response": "output",
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"history": "history"
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}
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},
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"example": {
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"script_url": "example_dataset",
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"columns": {
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"prompt": "instruction",
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"query": "input",
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"response": "output",
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"history": "history"
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}
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},
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"comparison_gpt4_en": {
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"file_name": "comparison_gpt4_data_en.json",
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"file_sha1": "eeb295ce0ab011c37af52596460c8a57d07ad19f"
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},
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"comparison_gpt4_zh": {
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"file_name": "comparison_gpt4_data_zh.json",
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"file_sha1": "b99a41c1c864019d9b0c07dbcd5df0560cf33ce0"
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},
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"hh_rlhf_en": {
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"script_url": "hh_rlhf_en",
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"columns": {
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"prompt": "instruction",
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"query": "",
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"response": "output",
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"history": "history"
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}
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},
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"wiki_demo": {
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"file_name": "wiki_demo.txt",
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"file_sha1": "b2288edb05b233e5b35250fd4b308a5fa21fa66d",
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"columns": {
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"prompt": "text",
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"query": "",
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"response": "",
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"history": ""
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}
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},
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"pretrain_data": {
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"file_name": "pretrain_data",
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"columns": {
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"prompt": "content",
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"query": "",
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"response": "",
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"history": ""
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}
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}
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}
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@ -0,0 +1,7 @@
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[
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{
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"id": 0,
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"title": "拥有自己的航空器",
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"content": "想自己驾驶飞机或从事通用航空事业的人，大都想拥有自己的航空器。\"自己的\"意思包括自己购买、自己制造、可供自己使用（租用或借用）等等。\n花自己的钱买一架飞机来开一开，国内有些人或企业已实现了这个愿望。现在一架国产超轻型的“蜜蜂”飞机售价在l0万元以下，进口的一些单发的双座飞机售价在100万元之内。据估计，全国大约有几十万人具有这种购买能力。\n自己造一架飞机来开也是一个好创意。美国的通航飞机中有l／5是自制的。有的自制飞机甚至还创造了世界飞行纪录。今天自己造飞机比当年莱特兄弟容易多了。飞机的基本构造已无秘密可言，各种飞机部件和材料都不难买到。尤其主要的是，技术进步大大改进了配件的性能，与此同时，配件的重量也下降了很多。莱特兄弟当年使用的12马力汽油发动机比现在30马力的同类产品还重。如果有人有志于此而且具备造飞机的种种条件，应该说这个目标也是可以实现的。有两点值得注意，一是在莱特兄弟造飞机时没有前人经验，全靠自己摸索。现在不同了，航空制造已有了上百年的知识和经验可供后人学习和利用。现在如果谁想自己造飞机就不用闭门造车了。制造者本人首先应该去学习和掌握一些必要知识和经验才行。其次，在莱特兄弟时代，没有国家民航当局，他们的航空活动不受法规约束。今天就不一样了，所有要升空的航空器必须先接受民航当局的鉴定，以保证飞行安全。绝不允许以生命为赌注的任何冒险行为。\n租用飞机也是实现自驾飞机的方式之一。国内也还有另一种形式，即参加飞行驾驶学校接受培训，当然所交的学费价格是不菲的。预计未来在我国必将出现出各类飞行俱乐部。到那时，飞行爱好者可以租用飞机去上天过一把瘾了。"
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}
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]
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@ -0,0 +1,7 @@
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[
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{
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"id": 0,
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"title": "大卫·亨利",
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"content": "大卫·亨利\n\n大卫·克莱顿·亨利（David Clayton Henrie，），美国演员。近来在迪士尼频道原创电视影集《少年魔法师》（Wizards of Waverly Place）当中演出贾斯汀·鲁索（Justin Russo）一角。\n\n大卫·亨利出生在加州Mission Viejo，在凤凰城长大。他的胞弟劳伦斯·亨利（Lorenzo Henrie）也是演员。大卫·亨利就读夏安传统学校。家中是信奉罗马天主教。 \n\n大卫在2007年拍摄少年魔法师期间认识女演员露西·海尔（Lucy Hale），之后与其交往，于2009年分手。\n\n10岁时，大卫·亨利和SAG在凤凰城签订了合约，并开始走出去试镜。 9岁的时候，在沙加缅度进行商业拍摄，SAG董事建议大卫·亨利搬到洛杉矶。在10岁那年夏天，他和他的家人搬到了好莱坞。他预定他的前2支商业试镜，扮演主要角色为汉堡王和桂格燕麦。他初演电视节目为Providence。 \n\n到了13岁，大卫有了他的第一次重大突破，在福克斯公司的喜剧The Pitts饰演 Petey Pitt一角。大卫下出作品为的Hallmark movie为Monster Maker，和琳达布莱儿、乔治甘迺迪共同演出，并要求回来Hallmark movie公司。 \n\n在18岁时，大卫得到了迪士尼频道原创系列演出机会，该节目2007年10月12日首播。大卫2008年参加了迪士尼频道的游戏节目。他是绿色团队的队长，隔年，为旋风队队长。他在迪士尼原创电影《少年魔法师》之后在《酷爸的疯狂假期》中有饰演一角。\n"
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}
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]
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@ -56,7 +56,6 @@ require_version("accelerate>=0.19.0", "To fix: pip install accelerate>=0.19.0")
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require_version("peft>=0.3.0", "To fix: pip install peft>=0.3.0")
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require_version("trl>=0.4.1", "To fix: pip install trl>=0.4.1")
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logger = get_logger(__name__)
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@ -92,10 +91,12 @@ def _init_adapter(
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if model_args.checkpoint_dir is not None:
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if finetuning_args.finetuning_type != "lora":
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assert is_mergeable and len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
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load_trainable_params(model, model_args.checkpoint_dir[0]) # load model checkpoints for non-peft methods
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assert is_mergeable and len(
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model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
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load_trainable_params(model, model_args.checkpoint_dir[0]) # load model checkpoints for non-peft methods
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else:
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assert is_mergeable or len(model_args.checkpoint_dir) == 1, "Quantized model only accepts a single checkpoint."
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assert is_mergeable or len(
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model_args.checkpoint_dir) == 1, "Quantized model only accepts a single checkpoint."
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if finetuning_args.finetuning_type == "lora":
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logger.info("Fine-tuning method: LoRA")
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@ -105,7 +106,8 @@ def _init_adapter(
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assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], CONFIG_NAME)), \
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"The given checkpoint is not a LoRA checkpoint, please specify `--finetuning_type full/freeze` instead."
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if (is_trainable and model_args.resume_lora_training) or (not is_mergeable): # continually train on the lora weights
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if (is_trainable and model_args.resume_lora_training) or (
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not is_mergeable): # continually train on the lora weights
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checkpoints_to_merge, lastest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
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else:
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checkpoints_to_merge = model_args.checkpoint_dir
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@ -117,10 +119,10 @@ def _init_adapter(
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if len(checkpoints_to_merge) > 0:
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logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))
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if lastest_checkpoint is not None: # resume lora training or quantized inference
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if lastest_checkpoint is not None: # resume lora training or quantized inference
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model = PeftModel.from_pretrained(model, lastest_checkpoint, is_trainable=is_trainable)
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if is_trainable and lastest_checkpoint is None: # create new lora weights while training
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if is_trainable and lastest_checkpoint is None: # create new lora weights while training
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lora_config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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inference_mode=False,
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@ -168,7 +170,7 @@ def load_pretrained(
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padding_side="left",
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**config_kwargs
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)
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tokenizer.pad_token_id = 0 if tokenizer.pad_token_id is None else tokenizer.pad_token_id # set as the <unk> token
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tokenizer.pad_token_id = 0 if tokenizer.pad_token_id is None else tokenizer.pad_token_id # set as the <unk> token
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config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
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is_mergeable = True
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@ -184,9 +186,11 @@ def load_pretrained(
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)
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elif model_args.quantization_bit == 4:
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require_version("bitsandbytes>=0.39.0", "To fix: pip install bitsandbytes>=0.39.0")
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require_version("transformers>=4.30.0.dev0", "To fix: pip install git+https://github.com/huggingface/transformers.git")
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require_version("transformers>=4.30.0.dev0",
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"To fix: pip install git+https://github.com/huggingface/transformers.git")
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require_version("peft>=0.4.0.dev0", "To fix: pip install git+https://github.com/huggingface/peft.git")
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require_version("accelerate>=0.20.0.dev0", "To fix: pip install git+https://github.com/huggingface/accelerate.git")
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require_version("accelerate>=0.20.0.dev0",
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"To fix: pip install git+https://github.com/huggingface/accelerate.git")
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config_kwargs["load_in_4bit"] = True
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config_kwargs["quantization_config"] = BitsAndBytesConfig(
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load_in_4bit=True,
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@ -214,10 +218,10 @@ def load_pretrained(
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model = prepare_model_for_training(model) if is_trainable else model
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model = _init_adapter(model, model_args, finetuning_args, is_trainable, is_mergeable)
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if stage == "rm" or stage == "ppo": # add value head
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if stage == "rm" or stage == "ppo": # add value head
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model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
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if stage == "ppo": # load reward model
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if stage == "ppo": # load reward model
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assert is_trainable, "PPO stage cannot be performed at evaluation."
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assert model_args.reward_model is not None, "Reward model is necessary for PPO training."
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logger.info("Load reward model from {}".format(model_args.reward_model))
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@ -230,8 +234,8 @@ def load_pretrained(
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model._is_int8_training_enabled = True
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if not is_trainable:
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model.requires_grad_(False) # fix all model params
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model = model.half() if model_args.quantization_bit is None else model # cast from fp32 to fp16
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model.requires_grad_(False) # fix all model params
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model = model.half() if model_args.quantization_bit is None else model # cast from fp32 to fp16
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print_trainable_params(model)
|
||||
|
||||
|
|
@ -241,11 +245,11 @@ def load_pretrained(
|
|||
def prepare_args(
|
||||
stage: Literal["pt", "sft", "rm", "ppo"]
|
||||
) -> Tuple[ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments]:
|
||||
|
||||
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments, FinetuningArguments))
|
||||
|
||||
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
|
||||
model_args, data_args, training_args, finetuning_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
||||
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
|
||||
model_args, data_args, training_args, finetuning_args = parser.parse_json_file(
|
||||
json_file=os.path.abspath(sys.argv[1]))
|
||||
else:
|
||||
model_args, data_args, training_args, finetuning_args = parser.parse_args_into_dataclasses()
|
||||
|
||||
|
|
@ -286,7 +290,7 @@ def prepare_args(
|
|||
logger.warning("`ddp_find_unused_parameters` needs to be set as False in DDP training.")
|
||||
training_args.ddp_find_unused_parameters = False
|
||||
|
||||
training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning
|
||||
training_args.optim = "adamw_torch" if training_args.optim == "adamw_hf" else training_args.optim # suppress warning
|
||||
|
||||
if model_args.quantization_bit is not None:
|
||||
if training_args.fp16:
|
||||
|
|
@ -310,10 +314,9 @@ def prepare_args(
|
|||
|
||||
|
||||
def prepare_infer_args() -> Tuple[ModelArguments, DataTrainingArguments, FinetuningArguments]:
|
||||
|
||||
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, FinetuningArguments))
|
||||
|
||||
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
|
||||
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # Provide arguments with a json file.
|
||||
model_args, data_args, finetuning_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
||||
else:
|
||||
model_args, data_args, finetuning_args = parser.parse_args_into_dataclasses()
|
||||
|
|
@ -331,7 +334,6 @@ def prepare_data(
|
|||
model_args: ModelArguments,
|
||||
data_args: DataTrainingArguments
|
||||
) -> Dataset:
|
||||
|
||||
def checksum(file_path, hash):
|
||||
with open(file_path, "rb") as datafile:
|
||||
binary_data = datafile.read()
|
||||
|
|
@ -340,7 +342,7 @@ def prepare_data(
|
|||
logger.warning("Checksum failed for {}. It may vary depending on the platform.".format(file_path))
|
||||
|
||||
max_samples = data_args.max_samples
|
||||
all_datasets: List[Dataset] = [] # support multiple datasets
|
||||
all_datasets: List[Dataset] = [] # support multiple datasets
|
||||
|
||||
for dataset_attr in data_args.dataset_list:
|
||||
|
||||
|
|
@ -361,7 +363,7 @@ def prepare_data(
|
|||
checksum(data_file, dataset_attr.file_sha1)
|
||||
else:
|
||||
logger.warning("Checksum failed: missing SHA-1 hash value in dataset_info.json.")
|
||||
|
||||
print(extension)
|
||||
raw_datasets = load_dataset(
|
||||
extension if extension in ["csv", "json"] else "text",
|
||||
data_files=data_file,
|
||||
|
|
@ -383,11 +385,11 @@ def prepare_data(
|
|||
("query_column", "query"),
|
||||
("response_column", "response"),
|
||||
("history_column", "history")
|
||||
]: # every dataset will have 4 columns same as each other
|
||||
]: # every dataset will have 4 columns same as each other
|
||||
if getattr(dataset_attr, column_name) != target_name:
|
||||
if getattr(dataset_attr, column_name):
|
||||
dataset = dataset.rename_column(getattr(dataset_attr, column_name), target_name)
|
||||
else: # None or empty string
|
||||
else: # None or empty string
|
||||
dataset = dataset.add_column(target_name, dummy_data)
|
||||
all_datasets.append(dataset)
|
||||
|
||||
|
|
@ -406,7 +408,6 @@ def preprocess_data(
|
|||
training_args: Seq2SeqTrainingArguments,
|
||||
stage: Literal["pt", "sft", "rm", "ppo"]
|
||||
) -> Dataset:
|
||||
|
||||
column_names = list(dataset.column_names)
|
||||
prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
|
||||
prompt_template = Template(data_args.prompt_template)
|
||||
|
|
@ -429,7 +430,8 @@ def preprocess_data(
|
|||
# we drop the small remainder, and if the total_length < block_size, we exclude this batch
|
||||
total_length = (total_length // data_args.max_source_length) * data_args.max_source_length
|
||||
# split by chunks of max_source_length
|
||||
result = [concatenated_ids[i: i+data_args.max_source_length] for i in range(0, total_length, data_args.max_source_length)]
|
||||
result = [concatenated_ids[i: i + data_args.max_source_length] for i in
|
||||
range(0, total_length, data_args.max_source_length)]
|
||||
return {
|
||||
"input_ids": result,
|
||||
"labels": result.copy()
|
||||
|
|
@ -442,9 +444,9 @@ def preprocess_data(
|
|||
source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
|
||||
target_ids = tokenizer.encode(text=answer, add_special_tokens=False)
|
||||
|
||||
if len(source_ids) > data_args.max_source_length - 1: # bos token
|
||||
if len(source_ids) > data_args.max_source_length - 1: # bos token
|
||||
source_ids = source_ids[:data_args.max_source_length - 1]
|
||||
if len(target_ids) > data_args.max_target_length - 1: # eos token
|
||||
if len(target_ids) > data_args.max_target_length - 1: # eos token
|
||||
target_ids = target_ids[:data_args.max_target_length - 1]
|
||||
|
||||
input_ids = source_ids + [tokenizer.bos_token_id] + target_ids + [tokenizer.eos_token_id]
|
||||
|
|
@ -461,9 +463,9 @@ def preprocess_data(
|
|||
source_ids = tokenizer.encode(text=prompt, add_special_tokens=False)
|
||||
target_ids = tokenizer.encode(text=answer, add_special_tokens=False)
|
||||
|
||||
if len(source_ids) > data_args.max_source_length - 1: # bos token
|
||||
if len(source_ids) > data_args.max_source_length - 1: # bos token
|
||||
source_ids = source_ids[:data_args.max_source_length - 1]
|
||||
if len(target_ids) > data_args.max_target_length - 1: # bos token
|
||||
if len(target_ids) > data_args.max_target_length - 1: # bos token
|
||||
target_ids = target_ids[:data_args.max_target_length - 1]
|
||||
|
||||
input_ids = source_ids + [tokenizer.bos_token_id]
|
||||
|
|
@ -481,11 +483,11 @@ def preprocess_data(
|
|||
accept_ids = tokenizer.encode(text=answer[0], add_special_tokens=False)
|
||||
reject_ids = tokenizer.encode(text=answer[1], add_special_tokens=False)
|
||||
|
||||
if len(source_ids) > data_args.max_source_length - 1: # bos token
|
||||
if len(source_ids) > data_args.max_source_length - 1: # bos token
|
||||
source_ids = source_ids[:data_args.max_source_length - 1]
|
||||
if len(accept_ids) > data_args.max_target_length - 1: # eos token
|
||||
if len(accept_ids) > data_args.max_target_length - 1: # eos token
|
||||
accept_ids = accept_ids[:data_args.max_target_length - 1]
|
||||
if len(reject_ids) > data_args.max_target_length - 1: # eos token
|
||||
if len(reject_ids) > data_args.max_target_length - 1: # eos token
|
||||
reject_ids = reject_ids[:data_args.max_target_length - 1]
|
||||
|
||||
accept_ids = source_ids + [tokenizer.bos_token_id] + accept_ids + [tokenizer.eos_token_id]
|
||||
|
|
|
|||
|
|
@ -7,7 +7,6 @@ from dataclasses import asdict, dataclass, field
|
|||
|
||||
@dataclass
|
||||
class DatasetAttr:
|
||||
|
||||
load_from: str
|
||||
dataset_name: Optional[str] = None
|
||||
file_name: Optional[str] = None
|
||||
|
|
@ -68,7 +67,8 @@ class ModelArguments:
|
|||
)
|
||||
checkpoint_dir: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={"help": "Path to the directory(s) containing the delta model checkpoints as well as the configurations."}
|
||||
metadata={
|
||||
"help": "Path to the directory(s) containing the delta model checkpoints as well as the configurations."}
|
||||
)
|
||||
reward_model: Optional[str] = field(
|
||||
default=None,
|
||||
|
|
@ -76,7 +76,8 @@ class ModelArguments:
|
|||
)
|
||||
resume_lora_training: Optional[bool] = field(
|
||||
default=True,
|
||||
metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
|
||||
metadata={
|
||||
"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
|
||||
)
|
||||
plot_loss: Optional[bool] = field(
|
||||
default=False,
|
||||
|
|
@ -84,7 +85,7 @@ class ModelArguments:
|
|||
)
|
||||
|
||||
def __post_init__(self):
|
||||
if self.checkpoint_dir is not None: # support merging multiple lora weights
|
||||
if self.checkpoint_dir is not None: # support merging multiple lora weights
|
||||
self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")]
|
||||
|
||||
|
||||
|
|
@ -146,7 +147,7 @@ class DataTrainingArguments:
|
|||
metadata={"help": "Which template to use for constructing prompts in training and inference."}
|
||||
)
|
||||
|
||||
def __post_init__(self): # support mixing multiple datasets
|
||||
def __post_init__(self): # support mixing multiple datasets
|
||||
dataset_names = [ds.strip() for ds in self.dataset.split(",")]
|
||||
with open(os.path.join(self.dataset_dir, "dataset_info.json"), "r") as f:
|
||||
dataset_info = json.load(f)
|
||||
|
|
@ -155,25 +156,42 @@ class DataTrainingArguments:
|
|||
for name in dataset_names:
|
||||
if name not in dataset_info:
|
||||
raise ValueError("Undefined dataset {} in dataset_info.json.".format(name))
|
||||
|
||||
dataset_attrs = []
|
||||
dataset_attr = None
|
||||
if "hf_hub_url" in dataset_info[name]:
|
||||
dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
|
||||
elif "script_url" in dataset_info[name]:
|
||||
dataset_attr = DatasetAttr("script", dataset_name=dataset_info[name]["script_url"])
|
||||
else:
|
||||
elif os.path.isfile(os.path.join(self.dataset_dir, dataset_info[name]["file_name"])):
|
||||
dataset_attr = DatasetAttr(
|
||||
"file",
|
||||
file_name=dataset_info[name]["file_name"],
|
||||
file_sha1=dataset_info[name]["file_sha1"] if "file_sha1" in dataset_info[name] else None
|
||||
)
|
||||
|
||||
if "columns" in dataset_info[name]:
|
||||
dataset_attr.prompt_column = dataset_info[name]["columns"].get("prompt", None)
|
||||
dataset_attr.query_column = dataset_info[name]["columns"].get("query", None)
|
||||
dataset_attr.response_column = dataset_info[name]["columns"].get("response", None)
|
||||
dataset_attr.history_column = dataset_info[name]["columns"].get("history", None)
|
||||
|
||||
self.dataset_list.append(dataset_attr)
|
||||
else:
|
||||
# Support Directory
|
||||
for file_name in os.listdir(os.path.join(self.dataset_dir, dataset_info[name]["file_name"])):
|
||||
path = os.path.join(dataset_info[name]["file_name"], file_name)
|
||||
dataset_attrs.append(DatasetAttr(
|
||||
"file",
|
||||
file_name=path,
|
||||
file_sha1=dataset_info[name]["file_sha1"] if "file_sha1" in dataset_info[name] else None
|
||||
))
|
||||
if dataset_attr is not None:
|
||||
if "columns" in dataset_info[name]:
|
||||
dataset_attr.prompt_column = dataset_info[name]["columns"].get("prompt", None)
|
||||
dataset_attr.query_column = dataset_info[name]["columns"].get("query", None)
|
||||
dataset_attr.response_column = dataset_info[name]["columns"].get("response", None)
|
||||
dataset_attr.history_column = dataset_info[name]["columns"].get("history", None)
|
||||
self.dataset_list.append(dataset_attr)
|
||||
else:
|
||||
for i, dataset_attr in enumerate(dataset_attrs):
|
||||
if "columns" in dataset_info[name]:
|
||||
dataset_attr.prompt_column = dataset_info[name]["columns"].get("prompt", None)
|
||||
dataset_attr.query_column = dataset_info[name]["columns"].get("query", None)
|
||||
dataset_attr.response_column = dataset_info[name]["columns"].get("response", None)
|
||||
dataset_attr.history_column = dataset_info[name]["columns"].get("history", None)
|
||||
self.dataset_list.append(dataset_attr)
|
||||
|
||||
|
||||
@dataclass
|
||||
|
|
@ -216,14 +234,16 @@ class FinetuningArguments:
|
|||
|
||||
def __post_init__(self):
|
||||
if isinstance(self.lora_target, str):
|
||||
self.lora_target = [target.strip() for target in self.lora_target.split(",")] # support custom target modules of LoRA
|
||||
self.lora_target = [target.strip() for target in
|
||||
self.lora_target.split(",")] # support custom target modules of LoRA
|
||||
|
||||
if self.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
|
||||
trainable_layer_ids = [27-k for k in range(self.num_layer_trainable)]
|
||||
else: # fine-tuning the first n layers if num_layer_trainable < 0
|
||||
if self.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
|
||||
trainable_layer_ids = [27 - k for k in range(self.num_layer_trainable)]
|
||||
else: # fine-tuning the first n layers if num_layer_trainable < 0
|
||||
trainable_layer_ids = [k for k in range(-self.num_layer_trainable)]
|
||||
|
||||
self.trainable_layers = ["layers.{:d}.{}".format(idx, self.name_module_trainable) for idx in trainable_layer_ids]
|
||||
self.trainable_layers = ["layers.{:d}.{}".format(idx, self.name_module_trainable) for idx in
|
||||
trainable_layer_ids]
|
||||
|
||||
assert self.finetuning_type in ["none", "freeze", "lora", "full"], "Invalid fine-tuning method."
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue