diff --git a/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb b/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb
index 1d0ca35..385f5dc 100644
--- a/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb
+++ b/examples/causal_language_modeling/peft_lora_clm_accelerate_big_model_inference.ipynb
@@ -30,7 +30,7 @@
"import os\n",
"from transformers import AutoTokenizer\n",
"from torch.utils.data import DataLoader\n",
- "from transformers import default_data_collator,get_linear_schedule_with_warmup\n",
+ "from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm\n",
"from datasets import load_dataset\n",
"\n",
@@ -40,10 +40,10 @@
"dataset_name = \"twitter_complaints\"\n",
"text_column = \"Tweet text\"\n",
"label_column = \"text_label\"\n",
- "max_length=64\n",
+ "max_length = 64\n",
"lr = 1e-3\n",
"num_epochs = 50\n",
- "batch_size=8\n"
+ "batch_size = 8"
]
},
{
@@ -63,7 +63,6 @@
" lambda x: {\"text_label\": [classes[label] for label in x[\"Label\"]]},\n",
" batched=True,\n",
" num_proc=1,\n",
- " \n",
")\n",
"print(dataset)\n",
"dataset[\"train\"][0]"
@@ -118,6 +117,8 @@
" tokenizer.pad_token_id = tokenizer.eos_token_id\n",
"target_max_length = max([len(tokenizer(class_label)[\"input_ids\"]) for class_label in classes])\n",
"print(target_max_length)\n",
+ "\n",
+ "\n",
"def preprocess_function(examples):\n",
" batch_size = len(examples[text_column])\n",
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
@@ -127,44 +128,43 @@
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" label_input_ids = labels[\"input_ids\"][i] + [tokenizer.pad_token_id]\n",
- " #print(i, sample_input_ids, label_input_ids)\n",
- " model_inputs[\"input_ids\"][i] = sample_input_ids + label_input_ids \n",
+ " # print(i, sample_input_ids, label_input_ids)\n",
+ " model_inputs[\"input_ids\"][i] = sample_input_ids + label_input_ids\n",
" labels[\"input_ids\"][i] = [-100] * len(sample_input_ids) + label_input_ids\n",
" model_inputs[\"attention_mask\"][i] = [1] * len(model_inputs[\"input_ids\"][i])\n",
- " #print(model_inputs)\n",
+ " # print(model_inputs)\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" label_input_ids = labels[\"input_ids\"][i]\n",
- " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id]*(max_length-len(sample_input_ids)) + sample_input_ids\n",
- " model_inputs[\"attention_mask\"][i] = [0]*(max_length-len(sample_input_ids)) + model_inputs[\"attention_mask\"][i]\n",
- " labels[\"input_ids\"][i] = [-100]*(max_length-len(sample_input_ids)) + label_input_ids \n",
+ " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
+ " max_length - len(sample_input_ids)\n",
+ " ) + sample_input_ids\n",
+ " model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
+ " \"attention_mask\"\n",
+ " ][i]\n",
+ " labels[\"input_ids\"][i] = [-100] * (max_length - len(sample_input_ids)) + label_input_ids\n",
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
- " labels[\"input_ids\"][i] = torch.tensor(labels[\"input_ids\"][i][:max_length]) \n",
+ " labels[\"input_ids\"][i] = torch.tensor(labels[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
" return model_inputs\n",
"\n",
"\n",
- "\n",
"processed_datasets = dataset.map(\n",
- " preprocess_function,\n",
- " batched=True,\n",
- " num_proc=1,\n",
- " remove_columns=dataset[\"train\"].column_names,\n",
- " load_from_cache_file=False,\n",
- " desc=\"Running tokenizer on dataset\",\n",
- " )\n",
+ " preprocess_function,\n",
+ " batched=True,\n",
+ " num_proc=1,\n",
+ " remove_columns=dataset[\"train\"].column_names,\n",
+ " load_from_cache_file=False,\n",
+ " desc=\"Running tokenizer on dataset\",\n",
+ ")\n",
"\n",
"train_dataset = processed_datasets[\"train\"]\n",
"\n",
"\n",
"train_dataloader = DataLoader(\n",
- " train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
- " )\n",
- "\n",
- "\n",
- "\n",
- " "
+ " train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
+ ")"
]
},
{
@@ -178,23 +178,28 @@
" batch_size = len(examples[text_column])\n",
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
" model_inputs = tokenizer(inputs)\n",
- " #print(model_inputs)\n",
+ " # print(model_inputs)\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
- " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id]*(max_length-len(sample_input_ids)) + sample_input_ids\n",
- " model_inputs[\"attention_mask\"][i] = [0]*(max_length-len(sample_input_ids)) + model_inputs[\"attention_mask\"][i]\n",
+ " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
+ " max_length - len(sample_input_ids)\n",
+ " ) + sample_input_ids\n",
+ " model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
+ " \"attention_mask\"\n",
+ " ][i]\n",
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
" return model_inputs\n",
"\n",
+ "\n",
"processed_datasets = dataset.map(\n",
- " test_preprocess_function,\n",
- " batched=True,\n",
- " num_proc=1,\n",
- " remove_columns=dataset[\"train\"].column_names,\n",
- " load_from_cache_file=False,\n",
- " desc=\"Running tokenizer on dataset\",\n",
- " )\n",
+ " test_preprocess_function,\n",
+ " batched=True,\n",
+ " num_proc=1,\n",
+ " remove_columns=dataset[\"train\"].column_names,\n",
+ " load_from_cache_file=False,\n",
+ " desc=\"Running tokenizer on dataset\",\n",
+ ")\n",
"\n",
"eval_dataset = processed_datasets[\"train\"]\n",
"test_dataset = processed_datasets[\"test\"]\n",
@@ -236,7 +241,8 @@
],
"source": [
"from peft import PeftModel, PeftConfig\n",
- "max_memory={0: \"1GIB\", 1: \"1GIB\", 2: \"2GIB\", 3: \"10GIB\", \"cpu\":\"30GB\"}\n",
+ "\n",
+ "max_memory = {0: \"1GIB\", 1: \"1GIB\", 2: \"2GIB\", 3: \"10GIB\", \"cpu\": \"30GB\"}\n",
"peft_model_id = \"smangrul/twitter_complaints_bigscience_bloomz-7b1_LORA_CAUSAL_LM\"\n",
"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
@@ -251,7 +257,7 @@
"metadata": {},
"outputs": [],
"source": [
- "#model"
+ "# model"
]
},
{
@@ -343,7 +349,7 @@
"with torch.no_grad():\n",
" outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
" print(outputs)\n",
- " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))\n"
+ " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
@@ -397,7 +403,7 @@
"accuracy = correct / total * 100\n",
"print(f\"{accuracy=}\")\n",
"print(f\"{eval_preds[:10]=}\")\n",
- "print(f\"{dataset['train'][label_column][:10]=}\")\n"
+ "print(f\"{dataset['train'][label_column][:10]=}\")"
]
},
{
@@ -416,7 +422,7 @@
" outputs = model.generate(**batch, max_new_tokens=10)\n",
" preds = outputs[:, max_length:].detach().cpu().numpy()\n",
" test_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))\n",
- " if len(test_preds)>100:\n",
+ " if len(test_preds) > 100:\n",
" break\n",
"test_preds"
]
diff --git a/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb b/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb
index 8a1201b..eb25c0c 100644
--- a/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb
+++ b/examples/causal_language_modeling/peft_prefix_tuning_clm.ipynb
@@ -8,31 +8,31 @@
"outputs": [],
"source": [
"from transformers import AutoModelForCausalLM\n",
- "from peft import get_peft_config,get_peft_model, PrefixTuningConfig, TaskType, PeftType\n",
+ "from peft import get_peft_config, get_peft_model, PrefixTuningConfig, TaskType, PeftType\n",
"import torch\n",
"from datasets import load_dataset\n",
"import os\n",
"from transformers import AutoTokenizer\n",
"from torch.utils.data import DataLoader\n",
- "from transformers import default_data_collator,get_linear_schedule_with_warmup\n",
+ "from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm\n",
"from datasets import load_dataset\n",
"\n",
"device = \"cuda\"\n",
"model_name_or_path = \"bigscience/bloomz-560m\"\n",
"tokenizer_name_or_path = \"bigscience/bloomz-560m\"\n",
- "peft_config = PrefixTuningConfig(task_type=TaskType.CAUSAL_LM, \n",
- " num_virtual_tokens=30)\n",
+ "peft_config = PrefixTuningConfig(task_type=TaskType.CAUSAL_LM, num_virtual_tokens=30)\n",
"\n",
"dataset_name = \"twitter_complaints\"\n",
- "checkpoint_name = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}_v1.pt\".replace(\"/\", \"_\")\n",
+ "checkpoint_name = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}_v1.pt\".replace(\n",
+ " \"/\", \"_\"\n",
+ ")\n",
"text_column = \"Tweet text\"\n",
"label_column = \"text_label\"\n",
- "max_length=64\n",
+ "max_length = 64\n",
"lr = 3e-2\n",
"num_epochs = 50\n",
- "batch_size=8\n",
- "\n"
+ "batch_size = 8"
]
},
{
@@ -112,7 +112,6 @@
" lambda x: {\"text_label\": [classes[label] for label in x[\"Label\"]]},\n",
" batched=True,\n",
" num_proc=1,\n",
- " \n",
")\n",
"print(dataset)\n",
"dataset[\"train\"][0]"
@@ -167,6 +166,8 @@
" tokenizer.pad_token_id = tokenizer.eos_token_id\n",
"target_max_length = max([len(tokenizer(class_label)[\"input_ids\"]) for class_label in classes])\n",
"print(target_max_length)\n",
+ "\n",
+ "\n",
"def preprocess_function(examples):\n",
" batch_size = len(examples[text_column])\n",
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
@@ -176,47 +177,45 @@
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" label_input_ids = labels[\"input_ids\"][i] + [tokenizer.pad_token_id]\n",
- " #print(i, sample_input_ids, label_input_ids)\n",
- " model_inputs[\"input_ids\"][i] = sample_input_ids + label_input_ids \n",
+ " # print(i, sample_input_ids, label_input_ids)\n",
+ " model_inputs[\"input_ids\"][i] = sample_input_ids + label_input_ids\n",
" labels[\"input_ids\"][i] = [-100] * len(sample_input_ids) + label_input_ids\n",
" model_inputs[\"attention_mask\"][i] = [1] * len(model_inputs[\"input_ids\"][i])\n",
- " #print(model_inputs)\n",
+ " # print(model_inputs)\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" label_input_ids = labels[\"input_ids\"][i]\n",
- " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id]*(max_length-len(sample_input_ids)) + sample_input_ids\n",
- " model_inputs[\"attention_mask\"][i] = [0]*(max_length-len(sample_input_ids)) + model_inputs[\"attention_mask\"][i]\n",
- " labels[\"input_ids\"][i] = [-100]*(max_length-len(sample_input_ids)) + label_input_ids \n",
+ " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
+ " max_length - len(sample_input_ids)\n",
+ " ) + sample_input_ids\n",
+ " model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
+ " \"attention_mask\"\n",
+ " ][i]\n",
+ " labels[\"input_ids\"][i] = [-100] * (max_length - len(sample_input_ids)) + label_input_ids\n",
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
- " labels[\"input_ids\"][i] = torch.tensor(labels[\"input_ids\"][i][:max_length]) \n",
+ " labels[\"input_ids\"][i] = torch.tensor(labels[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
" return model_inputs\n",
"\n",
"\n",
- "\n",
"processed_datasets = dataset.map(\n",
- " preprocess_function,\n",
- " batched=True,\n",
- " num_proc=1,\n",
- " remove_columns=dataset[\"train\"].column_names,\n",
- " load_from_cache_file=False,\n",
- " desc=\"Running tokenizer on dataset\",\n",
- " )\n",
+ " preprocess_function,\n",
+ " batched=True,\n",
+ " num_proc=1,\n",
+ " remove_columns=dataset[\"train\"].column_names,\n",
+ " load_from_cache_file=False,\n",
+ " desc=\"Running tokenizer on dataset\",\n",
+ ")\n",
"\n",
"train_dataset = processed_datasets[\"train\"]\n",
"eval_dataset = processed_datasets[\"train\"]\n",
"\n",
"\n",
"train_dataloader = DataLoader(\n",
- " train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
- " )\n",
- "eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n",
- "\n",
- "\n",
- "\n",
- "\n",
- " "
+ " train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
+ ")\n",
+ "eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)"
]
},
{
@@ -230,23 +229,28 @@
" batch_size = len(examples[text_column])\n",
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
" model_inputs = tokenizer(inputs)\n",
- " #print(model_inputs)\n",
+ " # print(model_inputs)\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
- " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id]*(max_length-len(sample_input_ids)) + sample_input_ids\n",
- " model_inputs[\"attention_mask\"][i] = [0]*(max_length-len(sample_input_ids)) + model_inputs[\"attention_mask\"][i]\n",
+ " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
+ " max_length - len(sample_input_ids)\n",
+ " ) + sample_input_ids\n",
+ " model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
+ " \"attention_mask\"\n",
+ " ][i]\n",
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
" return model_inputs\n",
"\n",
+ "\n",
"test_dataset = dataset[\"test\"].map(\n",
- " test_preprocess_function,\n",
- " batched=True,\n",
- " num_proc=1,\n",
- " remove_columns=dataset[\"train\"].column_names,\n",
- " load_from_cache_file=False,\n",
- " desc=\"Running tokenizer on dataset\",\n",
- " )\n",
+ " test_preprocess_function,\n",
+ " batched=True,\n",
+ " num_proc=1,\n",
+ " remove_columns=dataset[\"train\"].column_names,\n",
+ " load_from_cache_file=False,\n",
+ " desc=\"Running tokenizer on dataset\",\n",
+ ")\n",
"\n",
"test_dataloader = DataLoader(test_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n",
"next(iter(test_dataloader))"
@@ -308,12 +312,10 @@
}
],
"source": [
- "\n",
"# creating model\n",
"model = AutoModelForCausalLM.from_pretrained(model_name_or_path)\n",
"model = get_peft_model(model, peft_config)\n",
- "model.print_trainable_parameters()\n",
- "\n"
+ "model.print_trainable_parameters()"
]
},
{
@@ -1155,8 +1157,8 @@
" total_loss = 0\n",
" for step, batch in enumerate(tqdm(train_dataloader)):\n",
" batch = {k: v.to(device) for k, v in batch.items()}\n",
- "# print(batch)\n",
- "# print(batch[\"input_ids\"].shape)\n",
+ " # print(batch)\n",
+ " # print(batch[\"input_ids\"].shape)\n",
" outputs = model(**batch)\n",
" loss = outputs.loss\n",
" total_loss += loss.detach().float()\n",
@@ -1174,13 +1176,15 @@
" outputs = model(**batch)\n",
" loss = outputs.loss\n",
" eval_loss += loss.detach().float()\n",
- " eval_preds.extend(tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True))\n",
+ " eval_preds.extend(\n",
+ " tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n",
+ " )\n",
"\n",
- " eval_epoch_loss = eval_loss/len(train_dataloader)\n",
+ " eval_epoch_loss = eval_loss / len(train_dataloader)\n",
" eval_ppl = torch.exp(eval_epoch_loss)\n",
- " train_epoch_loss = total_loss/len(eval_dataloader)\n",
+ " train_epoch_loss = total_loss / len(eval_dataloader)\n",
" train_ppl = torch.exp(train_epoch_loss)\n",
- " print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")\n"
+ " print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")"
]
},
{
@@ -1217,10 +1221,11 @@
"\n",
"with torch.no_grad():\n",
" inputs = {k: v.to(device) for k, v in inputs.items()}\n",
- " outputs = model.generate(input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3)\n",
+ " outputs = model.generate(\n",
+ " input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n",
+ " )\n",
" print(outputs)\n",
- " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))\n",
- " "
+ " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
@@ -1254,11 +1259,12 @@
"outputs": [],
"source": [
"from peft import PeftModel, PeftConfig\n",
+ "\n",
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)\n",
- "model = PeftModel.from_pretrained(model, peft_model_id)\n"
+ "model = PeftModel.from_pretrained(model, peft_model_id)"
]
},
{
@@ -1291,10 +1297,11 @@
"\n",
"with torch.no_grad():\n",
" inputs = {k: v.to(device) for k, v in inputs.items()}\n",
- " outputs = model.generate(input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3)\n",
+ " outputs = model.generate(\n",
+ " input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n",
+ " )\n",
" print(outputs)\n",
- " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))\n",
- " "
+ " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
diff --git a/examples/causal_language_modeling/peft_prompt_tuning_clm.ipynb b/examples/causal_language_modeling/peft_prompt_tuning_clm.ipynb
index ffaa05d..e5ba39b 100644
--- a/examples/causal_language_modeling/peft_prompt_tuning_clm.ipynb
+++ b/examples/causal_language_modeling/peft_prompt_tuning_clm.ipynb
@@ -14,7 +14,7 @@
"import os\n",
"from transformers import AutoTokenizer\n",
"from torch.utils.data import DataLoader\n",
- "from transformers import default_data_collator,get_linear_schedule_with_warmup\n",
+ "from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm\n",
"from datasets import load_dataset\n",
"\n",
@@ -22,22 +22,23 @@
"model_name_or_path = \"bigscience/bloomz-560m\"\n",
"tokenizer_name_or_path = \"bigscience/bloomz-560m\"\n",
"peft_config = PromptTuningConfig(\n",
- " task_type=TaskType.CAUSAL_LM,\n",
- " prompt_tuning_init=PromptTuningInit.TEXT,\n",
- " num_virtual_tokens=8,\n",
- " prompt_tuning_init_text=\"Classify if the tweet is a complaint or not:\",\n",
- " tokenizer_name_or_path=model_name_or_path,\n",
- " )\n",
+ " task_type=TaskType.CAUSAL_LM,\n",
+ " prompt_tuning_init=PromptTuningInit.TEXT,\n",
+ " num_virtual_tokens=8,\n",
+ " prompt_tuning_init_text=\"Classify if the tweet is a complaint or not:\",\n",
+ " tokenizer_name_or_path=model_name_or_path,\n",
+ ")\n",
"\n",
"dataset_name = \"twitter_complaints\"\n",
- "checkpoint_name = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}_v1.pt\".replace(\"/\", \"_\")\n",
+ "checkpoint_name = f\"{dataset_name}_{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}_v1.pt\".replace(\n",
+ " \"/\", \"_\"\n",
+ ")\n",
"text_column = \"Tweet text\"\n",
"label_column = \"text_label\"\n",
- "max_length=64\n",
+ "max_length = 64\n",
"lr = 3e-2\n",
"num_epochs = 50\n",
- "batch_size=8\n",
- "\n"
+ "batch_size = 8"
]
},
{
@@ -57,7 +58,6 @@
" lambda x: {\"text_label\": [classes[label] for label in x[\"Label\"]]},\n",
" batched=True,\n",
" num_proc=1,\n",
- " \n",
")\n",
"print(dataset)\n",
"dataset[\"train\"][0]"
@@ -76,6 +76,8 @@
" tokenizer.pad_token_id = tokenizer.eos_token_id\n",
"target_max_length = max([len(tokenizer(class_label)[\"input_ids\"]) for class_label in classes])\n",
"print(target_max_length)\n",
+ "\n",
+ "\n",
"def preprocess_function(examples):\n",
" batch_size = len(examples[text_column])\n",
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
@@ -85,47 +87,45 @@
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" label_input_ids = labels[\"input_ids\"][i] + [tokenizer.pad_token_id]\n",
- " #print(i, sample_input_ids, label_input_ids)\n",
- " model_inputs[\"input_ids\"][i] = sample_input_ids + label_input_ids \n",
+ " # print(i, sample_input_ids, label_input_ids)\n",
+ " model_inputs[\"input_ids\"][i] = sample_input_ids + label_input_ids\n",
" labels[\"input_ids\"][i] = [-100] * len(sample_input_ids) + label_input_ids\n",
" model_inputs[\"attention_mask\"][i] = [1] * len(model_inputs[\"input_ids\"][i])\n",
- " #print(model_inputs)\n",
+ " # print(model_inputs)\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
" label_input_ids = labels[\"input_ids\"][i]\n",
- " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id]*(max_length-len(sample_input_ids)) + sample_input_ids\n",
- " model_inputs[\"attention_mask\"][i] = [0]*(max_length-len(sample_input_ids)) + model_inputs[\"attention_mask\"][i]\n",
- " labels[\"input_ids\"][i] = [-100]*(max_length-len(sample_input_ids)) + label_input_ids \n",
+ " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
+ " max_length - len(sample_input_ids)\n",
+ " ) + sample_input_ids\n",
+ " model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
+ " \"attention_mask\"\n",
+ " ][i]\n",
+ " labels[\"input_ids\"][i] = [-100] * (max_length - len(sample_input_ids)) + label_input_ids\n",
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
- " labels[\"input_ids\"][i] = torch.tensor(labels[\"input_ids\"][i][:max_length]) \n",
+ " labels[\"input_ids\"][i] = torch.tensor(labels[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
" return model_inputs\n",
"\n",
"\n",
- "\n",
"processed_datasets = dataset.map(\n",
- " preprocess_function,\n",
- " batched=True,\n",
- " num_proc=1,\n",
- " remove_columns=dataset[\"train\"].column_names,\n",
- " load_from_cache_file=False,\n",
- " desc=\"Running tokenizer on dataset\",\n",
- " )\n",
+ " preprocess_function,\n",
+ " batched=True,\n",
+ " num_proc=1,\n",
+ " remove_columns=dataset[\"train\"].column_names,\n",
+ " load_from_cache_file=False,\n",
+ " desc=\"Running tokenizer on dataset\",\n",
+ ")\n",
"\n",
"train_dataset = processed_datasets[\"train\"]\n",
"eval_dataset = processed_datasets[\"train\"]\n",
"\n",
"\n",
"train_dataloader = DataLoader(\n",
- " train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
- " )\n",
- "eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n",
- "\n",
- "\n",
- "\n",
- "\n",
- " "
+ " train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
+ ")\n",
+ "eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)"
]
},
{
@@ -139,23 +139,28 @@
" batch_size = len(examples[text_column])\n",
" inputs = [f\"{text_column} : {x} Label : \" for x in examples[text_column]]\n",
" model_inputs = tokenizer(inputs)\n",
- " #print(model_inputs)\n",
+ " # print(model_inputs)\n",
" for i in range(batch_size):\n",
" sample_input_ids = model_inputs[\"input_ids\"][i]\n",
- " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id]*(max_length-len(sample_input_ids)) + sample_input_ids\n",
- " model_inputs[\"attention_mask\"][i] = [0]*(max_length-len(sample_input_ids)) + model_inputs[\"attention_mask\"][i]\n",
+ " model_inputs[\"input_ids\"][i] = [tokenizer.pad_token_id] * (\n",
+ " max_length - len(sample_input_ids)\n",
+ " ) + sample_input_ids\n",
+ " model_inputs[\"attention_mask\"][i] = [0] * (max_length - len(sample_input_ids)) + model_inputs[\n",
+ " \"attention_mask\"\n",
+ " ][i]\n",
" model_inputs[\"input_ids\"][i] = torch.tensor(model_inputs[\"input_ids\"][i][:max_length])\n",
" model_inputs[\"attention_mask\"][i] = torch.tensor(model_inputs[\"attention_mask\"][i][:max_length])\n",
" return model_inputs\n",
"\n",
+ "\n",
"test_dataset = dataset[\"test\"].map(\n",
- " test_preprocess_function,\n",
- " batched=True,\n",
- " num_proc=1,\n",
- " remove_columns=dataset[\"train\"].column_names,\n",
- " load_from_cache_file=False,\n",
- " desc=\"Running tokenizer on dataset\",\n",
- " )\n",
+ " test_preprocess_function,\n",
+ " batched=True,\n",
+ " num_proc=1,\n",
+ " remove_columns=dataset[\"train\"].column_names,\n",
+ " load_from_cache_file=False,\n",
+ " desc=\"Running tokenizer on dataset\",\n",
+ ")\n",
"\n",
"test_dataloader = DataLoader(test_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n",
"next(iter(test_dataloader))"
@@ -198,12 +203,10 @@
"metadata": {},
"outputs": [],
"source": [
- "\n",
"# creating model\n",
"model = AutoModelForCausalLM.from_pretrained(model_name_or_path)\n",
"model = get_peft_model(model, peft_config)\n",
- "model.print_trainable_parameters()\n",
- "\n"
+ "model.print_trainable_parameters()"
]
},
{
@@ -996,8 +999,8 @@
" total_loss = 0\n",
" for step, batch in enumerate(tqdm(train_dataloader)):\n",
" batch = {k: v.to(device) for k, v in batch.items()}\n",
- "# print(batch)\n",
- "# print(batch[\"input_ids\"].shape)\n",
+ " # print(batch)\n",
+ " # print(batch[\"input_ids\"].shape)\n",
" outputs = model(**batch)\n",
" loss = outputs.loss\n",
" total_loss += loss.detach().float()\n",
@@ -1015,13 +1018,15 @@
" outputs = model(**batch)\n",
" loss = outputs.loss\n",
" eval_loss += loss.detach().float()\n",
- " eval_preds.extend(tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True))\n",
+ " eval_preds.extend(\n",
+ " tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n",
+ " )\n",
"\n",
- " eval_epoch_loss = eval_loss/len(train_dataloader)\n",
+ " eval_epoch_loss = eval_loss / len(train_dataloader)\n",
" eval_ppl = torch.exp(eval_epoch_loss)\n",
- " train_epoch_loss = total_loss/len(eval_dataloader)\n",
+ " train_epoch_loss = total_loss / len(eval_dataloader)\n",
" train_ppl = torch.exp(train_epoch_loss)\n",
- " print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")\n"
+ " print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")"
]
},
{
@@ -1060,10 +1065,11 @@
"\n",
"with torch.no_grad():\n",
" inputs = {k: v.to(device) for k, v in inputs.items()}\n",
- " outputs = model.generate(input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3)\n",
+ " outputs = model.generate(\n",
+ " input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n",
+ " )\n",
" print(outputs)\n",
- " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))\n",
- " "
+ " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
@@ -1109,11 +1115,12 @@
"outputs": [],
"source": [
"from peft import PeftModel, PeftConfig\n",
+ "\n",
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)\n",
- "model = PeftModel.from_pretrained(model, peft_model_id)\n"
+ "model = PeftModel.from_pretrained(model, peft_model_id)"
]
},
{
@@ -1146,10 +1153,11 @@
"\n",
"with torch.no_grad():\n",
" inputs = {k: v.to(device) for k, v in inputs.items()}\n",
- " outputs = model.generate(input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3)\n",
+ " outputs = model.generate(\n",
+ " input_ids=inputs[\"input_ids\"], attention_mask=inputs[\"attention_mask\"], max_new_tokens=10, eos_token_id=3\n",
+ " )\n",
" print(outputs)\n",
- " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))\n",
- " "
+ " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
diff --git a/examples/conditional_generation/peft_lora_seq2seq.ipynb b/examples/conditional_generation/peft_lora_seq2seq.ipynb
index c308738..f22d3c6 100644
--- a/examples/conditional_generation/peft_lora_seq2seq.ipynb
+++ b/examples/conditional_generation/peft_lora_seq2seq.ipynb
@@ -24,14 +24,15 @@
],
"source": [
"from transformers import AutoModelForSeq2SeqLM\n",
- "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, LoraConfig, TaskType\n",
+ "from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, LoraConfig, TaskType\n",
"import torch\n",
"from datasets import load_dataset\n",
"import os\n",
+ "\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
"from transformers import AutoTokenizer\n",
"from torch.utils.data import DataLoader\n",
- "from transformers import default_data_collator,get_linear_schedule_with_warmup\n",
+ "from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm\n",
"from datasets import load_dataset\n",
"\n",
@@ -42,10 +43,10 @@
"checkpoint_name = \"financial_sentiment_analysis_lora_v1.pt\"\n",
"text_column = \"sentence\"\n",
"label_column = \"text_label\"\n",
- "max_length=128\n",
+ "max_length = 128\n",
"lr = 1e-3\n",
"num_epochs = 3\n",
- "batch_size=8\n"
+ "batch_size = 8"
]
},
{
@@ -56,9 +57,7 @@
"outputs": [],
"source": [
"# creating model\n",
- "peft_config = LoraConfig(\n",
- " task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1\n",
- ")\n",
+ "peft_config = LoraConfig(task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1)\n",
"\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n",
"model = get_peft_model(model, peft_config)\n",
@@ -136,17 +135,16 @@
],
"source": [
"# loading dataset\n",
- "dataset = load_dataset(\"financial_phrasebank\", 'sentences_allagree')\n",
+ "dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\")\n",
"dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
"dataset[\"validation\"] = dataset[\"test\"]\n",
- "del(dataset[\"test\"])\n",
+ "del dataset[\"test\"]\n",
"\n",
"classes = dataset[\"train\"].features[\"label\"].names\n",
"dataset = dataset.map(\n",
" lambda x: {\"text_label\": [classes[label] for label in x[\"label\"]]},\n",
" batched=True,\n",
" num_proc=1,\n",
- " \n",
")\n",
"\n",
"dataset[\"train\"][0]"
@@ -190,36 +188,35 @@
"source": [
"# data preprocessing\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
+ "\n",
+ "\n",
"def preprocess_function(examples):\n",
" inputs = examples[text_column]\n",
" targets = examples[label_column]\n",
" model_inputs = tokenizer(inputs, max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
" labels = tokenizer(targets, max_length=3, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
" labels = labels[\"input_ids\"]\n",
- " labels[labels==tokenizer.pad_token_id] = -100\n",
+ " labels[labels == tokenizer.pad_token_id] = -100\n",
" model_inputs[\"labels\"] = labels\n",
" return model_inputs\n",
"\n",
+ "\n",
"processed_datasets = dataset.map(\n",
- " preprocess_function,\n",
- " batched=True,\n",
- " num_proc=1,\n",
- " remove_columns=dataset[\"train\"].column_names,\n",
- " load_from_cache_file=False,\n",
- " desc=\"Running tokenizer on dataset\",\n",
- " )\n",
+ " preprocess_function,\n",
+ " batched=True,\n",
+ " num_proc=1,\n",
+ " remove_columns=dataset[\"train\"].column_names,\n",
+ " load_from_cache_file=False,\n",
+ " desc=\"Running tokenizer on dataset\",\n",
+ ")\n",
"\n",
"train_dataset = processed_datasets[\"train\"]\n",
"eval_dataset = processed_datasets[\"validation\"]\n",
"\n",
"train_dataloader = DataLoader(\n",
- " train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
- " )\n",
- "eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n",
- "\n",
- "\n",
- "\n",
- " "
+ " train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
+ ")\n",
+ "eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)"
]
},
{
@@ -235,7 +232,7 @@
" optimizer=optimizer,\n",
" num_warmup_steps=0,\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
- ")\n"
+ ")"
]
},
{
@@ -323,13 +320,15 @@
" outputs = model(**batch)\n",
" loss = outputs.loss\n",
" eval_loss += loss.detach().float()\n",
- " eval_preds.extend(tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True))\n",
+ " eval_preds.extend(\n",
+ " tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n",
+ " )\n",
"\n",
- " eval_epoch_loss = eval_loss/len(train_dataloader)\n",
+ " eval_epoch_loss = eval_loss / len(train_dataloader)\n",
" eval_ppl = torch.exp(eval_epoch_loss)\n",
- " train_epoch_loss = total_loss/len(eval_dataloader)\n",
+ " train_epoch_loss = total_loss / len(eval_dataloader)\n",
" train_ppl = torch.exp(train_epoch_loss)\n",
- " print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")\n"
+ " print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")"
]
},
{
@@ -350,13 +349,13 @@
],
"source": [
"# print accuracy\n",
- "correct =0\n",
+ "correct = 0\n",
"total = 0\n",
- "for pred,true in zip(eval_preds, dataset[\"validation\"][\"text_label\"]):\n",
- " if pred.strip()==true.strip():\n",
- " correct+=1\n",
- " total+=1 \n",
- "accuracy = correct/total*100\n",
+ "for pred, true in zip(eval_preds, dataset[\"validation\"][\"text_label\"]):\n",
+ " if pred.strip() == true.strip():\n",
+ " correct += 1\n",
+ " total += 1\n",
+ "accuracy = correct / total * 100\n",
"print(f\"{accuracy=} % on the evaluation dataset\")\n",
"print(f\"{eval_preds[:10]=}\")\n",
"print(f\"{dataset['validation']['text_label'][:10]=}\")"
@@ -401,11 +400,12 @@
"outputs": [],
"source": [
"from peft import PeftModel, PeftConfig\n",
+ "\n",
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n",
- "model = PeftModel.from_pretrained(model, peft_model_id)\n"
+ "model = PeftModel.from_pretrained(model, peft_model_id)"
]
},
{
@@ -437,7 +437,7 @@
"with torch.no_grad():\n",
" outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
" print(outputs)\n",
- " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))\n"
+ " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
diff --git a/examples/conditional_generation/peft_lora_seq2seq_accelerate_big_model_inference.ipynb b/examples/conditional_generation/peft_lora_seq2seq_accelerate_big_model_inference.ipynb
index 7e2b25c..132cb01 100644
--- a/examples/conditional_generation/peft_lora_seq2seq_accelerate_big_model_inference.ipynb
+++ b/examples/conditional_generation/peft_lora_seq2seq_accelerate_big_model_inference.ipynb
@@ -14,17 +14,17 @@
"import os\n",
"from transformers import AutoTokenizer\n",
"from torch.utils.data import DataLoader\n",
- "from transformers import default_data_collator,get_linear_schedule_with_warmup\n",
+ "from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm\n",
"from datasets import load_dataset\n",
"\n",
"dataset_name = \"twitter_complaints\"\n",
"text_column = \"Tweet text\"\n",
"label_column = \"text_label\"\n",
- "batch_size=8\n",
+ "batch_size = 8\n",
"\n",
"peft_model_id = \"smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM\"\n",
- "config = PeftConfig.from_pretrained(peft_model_id)\n"
+ "config = PeftConfig.from_pretrained(peft_model_id)"
]
},
{
@@ -35,7 +35,7 @@
"outputs": [],
"source": [
"peft_model_id = \"smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM\"\n",
- "max_memory={0: \"6GIB\", 1: \"0GIB\", 2: \"0GIB\", 3: \"0GIB\", 4: \"0GIB\", \"cpu\":\"30GB\"}\n",
+ "max_memory = {0: \"6GIB\", 1: \"0GIB\", 2: \"0GIB\", 3: \"0GIB\", 4: \"0GIB\", \"cpu\": \"30GB\"}\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, device_map=\"auto\", max_memory=max_memory)\n",
"model = PeftModel.from_pretrained(model, peft_model_id, device_map=\"auto\", max_memory=max_memory)"
@@ -58,7 +58,6 @@
" lambda x: {\"text_label\": [classes[label] for label in x[\"Label\"]]},\n",
" batched=True,\n",
" num_proc=1,\n",
- " \n",
")\n",
"print(dataset)\n",
"dataset[\"train\"][0]"
@@ -73,6 +72,8 @@
"source": [
"tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
"target_max_length = max([len(tokenizer(class_label)[\"input_ids\"]) for class_label in classes])\n",
+ "\n",
+ "\n",
"def preprocess_function(examples):\n",
" inputs = examples[text_column]\n",
" targets = examples[label_column]\n",
@@ -85,6 +86,7 @@
" model_inputs[\"labels\"] = labels\n",
" return model_inputs\n",
"\n",
+ "\n",
"processed_datasets = dataset.map(\n",
" preprocess_function,\n",
" batched=True,\n",
@@ -100,18 +102,14 @@
"\n",
"\n",
"def collate_fn(examples):\n",
- " return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
+ " return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
+ "\n",
"\n",
"train_dataloader = DataLoader(\n",
" train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True\n",
")\n",
"eval_dataloader = DataLoader(eval_dataset, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True)\n",
- "test_dataloader = DataLoader(test_dataset, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True)\n",
- "\n",
- "\n",
- "\n",
- "\n",
- " "
+ "test_dataloader = DataLoader(test_dataset, collate_fn=collate_fn, batch_size=batch_size, pin_memory=True)"
]
},
{
@@ -146,7 +144,7 @@
"with torch.no_grad():\n",
" outputs = model.generate(input_ids=inputs[\"input_ids\"].to(\"cuda\"), max_new_tokens=10)\n",
" print(outputs)\n",
- " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))\n"
+ " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
@@ -201,7 +199,7 @@
"accuracy = correct / total * 100\n",
"print(f\"{accuracy=}\")\n",
"print(f\"{eval_preds[:10]=}\")\n",
- "print(f\"{dataset['train'][label_column][:10]=}\")\n"
+ "print(f\"{dataset['train'][label_column][:10]=}\")"
]
},
{
@@ -220,7 +218,7 @@
" outputs = model.generate(**batch, max_new_tokens=10)\n",
" preds = outputs.detach().cpu().numpy()\n",
" test_preds.extend(tokenizer.batch_decode(preds, skip_special_tokens=True))\n",
- " if len(test_preds)>100:\n",
+ " if len(test_preds) > 100:\n",
" break\n",
"test_preds"
]
diff --git a/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb b/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb
index b514d57..dde8fff 100644
--- a/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb
+++ b/examples/conditional_generation/peft_prefix_tuning_seq2seq.ipynb
@@ -24,15 +24,16 @@
],
"source": [
"from transformers import AutoModelForSeq2SeqLM\n",
- "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, PrefixTuningConfig, TaskType\n",
+ "from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, PrefixTuningConfig, TaskType\n",
"import torch\n",
"from datasets import load_dataset\n",
"import os\n",
+ "\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\"\n",
"from transformers import AutoTokenizer\n",
"from torch.utils.data import DataLoader\n",
- "from transformers import default_data_collator,get_linear_schedule_with_warmup\n",
+ "from transformers import default_data_collator, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm\n",
"from datasets import load_dataset\n",
"\n",
@@ -43,10 +44,10 @@
"checkpoint_name = \"financial_sentiment_analysis_prefix_tuning_v1.pt\"\n",
"text_column = \"sentence\"\n",
"label_column = \"text_label\"\n",
- "max_length=128\n",
+ "max_length = 128\n",
"lr = 1e-2\n",
"num_epochs = 5\n",
- "batch_size=8\n"
+ "batch_size = 8"
]
},
{
@@ -57,9 +58,7 @@
"outputs": [],
"source": [
"# creating model\n",
- "peft_config = PrefixTuningConfig(\n",
- " task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, num_virtual_tokens=20\n",
- ")\n",
+ "peft_config = PrefixTuningConfig(task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, num_virtual_tokens=20)\n",
"\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)\n",
"model = get_peft_model(model, peft_config)\n",
@@ -137,17 +136,16 @@
],
"source": [
"# loading dataset\n",
- "dataset = load_dataset(\"financial_phrasebank\", 'sentences_allagree')\n",
+ "dataset = load_dataset(\"financial_phrasebank\", \"sentences_allagree\")\n",
"dataset = dataset[\"train\"].train_test_split(test_size=0.1)\n",
"dataset[\"validation\"] = dataset[\"test\"]\n",
- "del(dataset[\"test\"])\n",
+ "del dataset[\"test\"]\n",
"\n",
"classes = dataset[\"train\"].features[\"label\"].names\n",
"dataset = dataset.map(\n",
" lambda x: {\"text_label\": [classes[label] for label in x[\"label\"]]},\n",
" batched=True,\n",
" num_proc=1,\n",
- " \n",
")\n",
"\n",
"dataset[\"train\"][0]"
@@ -203,36 +201,35 @@
"source": [
"# data preprocessing\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)\n",
+ "\n",
+ "\n",
"def preprocess_function(examples):\n",
" inputs = examples[text_column]\n",
" targets = examples[label_column]\n",
" model_inputs = tokenizer(inputs, max_length=max_length, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
" labels = tokenizer(targets, max_length=2, padding=\"max_length\", truncation=True, return_tensors=\"pt\")\n",
" labels = labels[\"input_ids\"]\n",
- " labels[labels==tokenizer.pad_token_id] = -100\n",
+ " labels[labels == tokenizer.pad_token_id] = -100\n",
" model_inputs[\"labels\"] = labels\n",
" return model_inputs\n",
"\n",
+ "\n",
"processed_datasets = dataset.map(\n",
- " preprocess_function,\n",
- " batched=True,\n",
- " num_proc=1,\n",
- " remove_columns=dataset[\"train\"].column_names,\n",
- " load_from_cache_file=False,\n",
- " desc=\"Running tokenizer on dataset\",\n",
- " )\n",
+ " preprocess_function,\n",
+ " batched=True,\n",
+ " num_proc=1,\n",
+ " remove_columns=dataset[\"train\"].column_names,\n",
+ " load_from_cache_file=False,\n",
+ " desc=\"Running tokenizer on dataset\",\n",
+ ")\n",
"\n",
"train_dataset = processed_datasets[\"train\"]\n",
"eval_dataset = processed_datasets[\"validation\"]\n",
"\n",
"train_dataloader = DataLoader(\n",
- " train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
- " )\n",
- "eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)\n",
- "\n",
- "\n",
- "\n",
- " "
+ " train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True\n",
+ ")\n",
+ "eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=batch_size, pin_memory=True)"
]
},
{
@@ -248,7 +245,7 @@
" optimizer=optimizer,\n",
" num_warmup_steps=0,\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
- ")\n"
+ ")"
]
},
{
@@ -359,13 +356,15 @@
" outputs = model(**batch)\n",
" loss = outputs.loss\n",
" eval_loss += loss.detach().float()\n",
- " eval_preds.extend(tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True))\n",
+ " eval_preds.extend(\n",
+ " tokenizer.batch_decode(torch.argmax(outputs.logits, -1).detach().cpu().numpy(), skip_special_tokens=True)\n",
+ " )\n",
"\n",
- " eval_epoch_loss = eval_loss/len(train_dataloader)\n",
+ " eval_epoch_loss = eval_loss / len(train_dataloader)\n",
" eval_ppl = torch.exp(eval_epoch_loss)\n",
- " train_epoch_loss = total_loss/len(eval_dataloader)\n",
+ " train_epoch_loss = total_loss / len(eval_dataloader)\n",
" train_ppl = torch.exp(train_epoch_loss)\n",
- " print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")\n"
+ " print(f\"{epoch=}: {train_ppl=} {train_epoch_loss=} {eval_ppl=} {eval_epoch_loss=}\")"
]
},
{
@@ -386,13 +385,13 @@
],
"source": [
"# print accuracy\n",
- "correct =0\n",
+ "correct = 0\n",
"total = 0\n",
- "for pred,true in zip(eval_preds, dataset[\"validation\"][\"text_label\"]):\n",
- " if pred.strip()==true.strip():\n",
- " correct+=1\n",
- " total+=1 \n",
- "accuracy = correct/total*100\n",
+ "for pred, true in zip(eval_preds, dataset[\"validation\"][\"text_label\"]):\n",
+ " if pred.strip() == true.strip():\n",
+ " correct += 1\n",
+ " total += 1\n",
+ "accuracy = correct / total * 100\n",
"print(f\"{accuracy=} % on the evaluation dataset\")\n",
"print(f\"{eval_preds[:10]=}\")\n",
"print(f\"{dataset['validation']['text_label'][:10]=}\")"
@@ -437,11 +436,12 @@
"outputs": [],
"source": [
"from peft import PeftModel, PeftConfig\n",
+ "\n",
"peft_model_id = f\"{model_name_or_path}_{peft_config.peft_type}_{peft_config.task_type}\"\n",
"\n",
"config = PeftConfig.from_pretrained(peft_model_id)\n",
"model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)\n",
- "model = PeftModel.from_pretrained(model, peft_model_id)\n"
+ "model = PeftModel.from_pretrained(model, peft_model_id)"
]
},
{
@@ -475,7 +475,7 @@
"with torch.no_grad():\n",
" outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
" print(outputs)\n",
- " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))\n"
+ " print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
]
},
{
diff --git a/examples/image_classification/image_classification_peft_lora.ipynb b/examples/image_classification/image_classification_peft_lora.ipynb
index f6c2e3a..dc7ac4b 100644
--- a/examples/image_classification/image_classification_peft_lora.ipynb
+++ b/examples/image_classification/image_classification_peft_lora.ipynb
@@ -155,7 +155,7 @@
}
],
"source": [
- "import transformers \n",
+ "import transformers\n",
"import accelerate\n",
"import peft"
]
@@ -204,9 +204,7 @@
},
"outputs": [],
"source": [
- "model_checkpoint = (\n",
- " \"google/vit-base-patch16-224-in21k\" # pre-trained model from which to fine-tune\n",
- ")"
+ "model_checkpoint = \"google/vit-base-patch16-224-in21k\" # pre-trained model from which to fine-tune"
]
},
{
@@ -736,17 +734,13 @@
"\n",
"def preprocess_train(example_batch):\n",
" \"\"\"Apply train_transforms across a batch.\"\"\"\n",
- " example_batch[\"pixel_values\"] = [\n",
- " train_transforms(image.convert(\"RGB\")) for image in example_batch[\"image\"]\n",
- " ]\n",
+ " example_batch[\"pixel_values\"] = [train_transforms(image.convert(\"RGB\")) for image in example_batch[\"image\"]]\n",
" return example_batch\n",
"\n",
"\n",
"def preprocess_val(example_batch):\n",
" \"\"\"Apply val_transforms across a batch.\"\"\"\n",
- " example_batch[\"pixel_values\"] = [\n",
- " val_transforms(image.convert(\"RGB\")) for image in example_batch[\"image\"]\n",
- " ]\n",
+ " example_batch[\"pixel_values\"] = [val_transforms(image.convert(\"RGB\")) for image in example_batch[\"image\"]]\n",
" return example_batch"
]
},
@@ -1099,6 +1093,7 @@
"\n",
"metric = evaluate.load(\"accuracy\")\n",
"\n",
+ "\n",
"# the compute_metrics function takes a Named Tuple as input:\n",
"# predictions, which are the logits of the model as Numpy arrays,\n",
"# and label_ids, which are the ground-truth labels as Numpy arrays.\n",
@@ -1129,6 +1124,7 @@
"source": [
"import torch\n",
"\n",
+ "\n",
"def collate_fn(examples):\n",
" pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n",
" labels = torch.tensor([example[\"label\"] for example in examples])\n",
@@ -2230,10 +2226,10 @@
"\n",
"config = PeftConfig.from_pretrained(repo_name)\n",
"model = model = AutoModelForImageClassification.from_pretrained(\n",
- " config.base_model_name_or_path, \n",
+ " config.base_model_name_or_path,\n",
" label2id=label2id,\n",
" id2label=id2label,\n",
- " ignore_mismatched_sizes=True, # provide this in case you're planning to fine-tune an already fine-tuned checkpoint\n",
+ " ignore_mismatched_sizes=True, # provide this in case you're planning to fine-tune an already fine-tuned checkpoint\n",
")\n",
"# Load the Lora model\n",
"inference_model = PeftModel.from_pretrained(model, repo_name)"
diff --git a/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb b/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb
index 75c41d6..ec42414 100644
--- a/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb
+++ b/examples/int8_training/Finetune_flan_t5_large_bnb_peft.ipynb
@@ -1,8322 +1,8311 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "lw1cWgq-DI5k",
- "metadata": {
- "id": "lw1cWgq-DI5k"
- },
- "source": [
- "# Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` 🤗 "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "kBFPA3-aDT7H",
- "metadata": {
- "id": "kBFPA3-aDT7H"
- },
- "source": [
- "In this notebook we will see how to properly use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large` in a google colab!\n",
- "\n",
- "We will finetune the model on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset, that consists of pairs of text-labels to classify financial-related sentences, if they are either `positive`, `neutral` or `negative`."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ShAuuHCDDkvk",
- "metadata": {
- "id": "ShAuuHCDDkvk"
- },
- "source": [
- "## Install requirements"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "DRQ4ZrJTDkSy",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "DRQ4ZrJTDkSy",
- "outputId": "31b108ee-a34c-4a1f-a970-6fa1809b64c5"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m45.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m26.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
- " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
- " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
- " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
- " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
- " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m65.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
- " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
- ]
- }
- ],
- "source": [
- "!pip install -q bitsandbytes datasets accelerate loralib\n",
- "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "QBdCIrizDxFw",
- "metadata": {
- "id": "QBdCIrizDxFw"
- },
- "source": [
- "## Import model and tokenizer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "dd3c5acc",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 350,
- "referenced_widgets": [
- "59782bac41834abbbd6cbd6614c2e57b",
- "8252a05cb70b46ec8b0480062ea1cb71",
- "b77afc7c1f184de0970feb2df8ac5285",
- "23e494a586cf47d1afa5e619abfcdbba",
- "bdfb4a04e48246a4b0890f52d6dd424b",
- "8669a890db6c456cbc3ada28976be30b",
- "9e5afa2048c74754816b34a34171fcb0",
- "5bfebc75ec424c6cb41b33d210d28d2b",
- "ef2fa44d0105457c9aed3812633dd329",
- "5a6b38b8fc1345e8bd4e8f3aaea546c9",
- "57d4ca686af34205aca630b1c61d4aea",
- "dbdb787728184aa1a6906f96c5e6f929",
- "e3be963920c84c7fbe7e0bc61b8e778d",
- "1275c5a5c88b435a897f88a19c54a0a5",
- "81d2f0953e104fc1ad57295819b6b689",
- "61ab054f49884b1fadf529a39ccc37dc",
- "924e6a8308fc47af929aca1987a12f09",
- "c1757a5b684f4496a4b0e3db544bf44b",
- "a3bb3f44c1754082a4f5169431c5b760",
- "a0186b2194df4a0a9cd1ac49054d68da",
- "bcda86e43607436583f1fbfee08a9786",
- "55cf3bcee7c745948b39eea5f65fc62b",
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- "data": {
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- "model_id": "59782bac41834abbbd6cbd6614c2e57b",
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- "Downloading (…)lve/main/config.json: 0%| | 0.00/662 [00:00\n",
- " \n",
- " \n",
- " [255/255 06:13, Epoch 1/1]\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " | Epoch | \n",
- " Training Loss | \n",
- " Validation Loss | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | 1 | \n",
- " No log | \n",
- " 0.017228 | \n",
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\n",
- " \n",
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"
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- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Saving model checkpoint to temp/checkpoint-100\n",
- "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n",
- "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
- " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
- "Saving model checkpoint to temp/checkpoint-200\n",
- "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n",
- "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
- " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
- "***** Running Evaluation *****\n",
- " Num examples = 227\n",
- " Batch size = 8\n",
- "\n",
- "\n",
- "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
- "\n",
- "\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "TrainOutput(global_step=255, training_loss=0.2569344015682445, metrics={'train_runtime': 377.3565, 'train_samples_per_second': 5.398, 'train_steps_per_second': 0.676, 'total_flos': 1181084919791616.0, 'train_loss': 0.2569344015682445, 'epoch': 1.0})"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "trainer.train()"
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- },
- {
- "cell_type": "markdown",
- "id": "r98VtofiGXtO",
- "metadata": {
- "id": "r98VtofiGXtO"
- },
- "source": [
- "## Qualitatively test our model"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "NIm7z3UNzGPP",
- "metadata": {
- "id": "NIm7z3UNzGPP"
- },
- "source": [
- "Let's have a quick qualitative evaluation of the model, by taking a sample from the dataset that corresponds to a positive label. Run your generation similarly as you were running your model from `transformers`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c95d6173",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "c95d6173",
- "outputId": "ed03a1dc-597a-4053-99d6-eca2cc6da253"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Generate config GenerationConfig {\n",
- " \"_from_model_config\": true,\n",
- " \"decoder_start_token_id\": 0,\n",
- " \"eos_token_id\": 1,\n",
- " \"pad_token_id\": 0,\n",
- " \"transformers_version\": \"4.27.0.dev0\",\n",
- " \"use_cache\": false\n",
- "}\n",
- "\n",
- "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
- " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
- "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1374: 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"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "input sentence: In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\n",
- " output prediction: ['positive']\n"
- ]
- }
- ],
- "source": [
- "model.eval()\n",
- "input_text = \"In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\"\n",
- "inputs = tokenizer(input_text, return_tensors=\"pt\")\n",
- "\n",
- "outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
- "\n",
- "print(\"input sentence: \", input_text)\n",
- "print(\" output prediction: \", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9QqBlwzoGZ3f",
- "metadata": {
- "id": "9QqBlwzoGZ3f"
- },
- "source": [
- "## Share your adapters on 🤗 Hub"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "NT-C8SjcKqUx",
- "metadata": {
- "id": "NT-C8SjcKqUx"
- },
- "source": [
- "Once you have trained your adapter, you can easily share it on the Hub using the method `push_to_hub` . Note that only the adapter weights and config will be pushed"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bcbfa1f9",
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- "text": [
- "Token is valid.\n",
- "Your token has been saved in your configured git credential helpers (store).\n",
- "Your token has been saved to /root/.cache/huggingface/token\n",
- "Login successful\n"
- ]
- }
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- "source": [
- "from huggingface_hub import notebook_login\n",
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- "text": [
- "Uploading the following files to ybelkada/flan-t5-large-lora: adapter_model.bin,adapter_config.json\n"
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- },
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- "Upload 1 LFS files: 0%| | 0/1 [00:00Pro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. "
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+ "# Fine-tune FLAN-T5 using `bitsandbytes`, `peft` & `transformers` 🤗 "
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "cell_type": "markdown",
+ "id": "kBFPA3-aDT7H",
+ "metadata": {
+ "id": "kBFPA3-aDT7H"
+ },
+ "source": [
+ "In this notebook we will see how to properly use `peft` , `transformers` & `bitsandbytes` to fine-tune `flan-t5-large` in a google colab!\n",
+ "\n",
+ "We will finetune the model on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset, that consists of pairs of text-labels to classify financial-related sentences, if they are either `positive`, `neutral` or `negative`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ShAuuHCDDkvk",
+ "metadata": {
+ "id": "ShAuuHCDDkvk"
+ },
+ "source": [
+ "## Install requirements"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "DRQ4ZrJTDkSy",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "DRQ4ZrJTDkSy",
+ "outputId": "31b108ee-a34c-4a1f-a970-6fa1809b64c5"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m45.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m26.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m65.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -q bitsandbytes datasets accelerate loralib\n",
+ "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "QBdCIrizDxFw",
+ "metadata": {
+ "id": "QBdCIrizDxFw"
+ },
+ "source": [
+ "## Import model and tokenizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dd3c5acc",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 350,
+ "referenced_widgets": [
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+ "bdff3b35dcdf49e5ba2c5c2498773cb7",
+ "6994741f3113493b9d5bba278b8732f5",
+ "808a41f78a7c4ae0b6aafee59c6234ae",
+ "c7771479ae4e4efab744fad6da586fd3",
+ "2d7cb8f5477244ce82e7cec5a9f7ee39",
+ "df8fbdbe9bc341e3a39a7bda99b70be2",
+ "179a912bbd1e454eba503782b675efa8",
+ "232fe29739b74dabb3b558c33835eb32",
+ "978bbbf33d304588af971d22bb2a3690",
+ "1b839f5b472e44148c9e02a12550fade",
+ "b560fc36ee8f424f9590e04a042046fe",
+ "0845ceae96ca4c95ad4ebebac46b691f",
+ "e1769695dffd4ebeb79a63ff4812fa9e",
+ "ec73524ed7f14ea0b67f07d72eada173",
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+ "65aacfc888014bdea55253428b2569c0",
+ "82849bb4d5da452e87a18ca749ce5d7b",
+ "2d2fc7abb9fb411c810b2fbeb54d67cc",
+ "96e2d208830f48cd821be7e59643c93e",
+ "8d79b7d0c3cb4f8d99fb20941c35856f",
+ "2b4d68606bdf4758b812f5a8057af595",
+ "f9620e01cd6749f88b722a42ff68c502",
+ "06c3d8c44fbe4f0886cf397d9a3c4b81",
+ "d72e8b3419f240f2bdce253cce9d24e3",
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+ "f3784e85cef34bdba64b611a1f5883e4",
+ "d7d9e2e2090d4226ad89e5ba9cec33df",
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+ "f742450a607c4ed0bff98ac9b7685d40",
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+ "e1c8e6f843604161bbb6cbd269488469",
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+ "f4ff06e2c48d4e58abe64cb7f41dd886",
+ "7fb7e3e2c75d4d03a98e581d4ead0f00",
+ "b8087054f46c44cab9bd62fa23fbf9de",
+ "85ae7ed1ec244a89aeb9f4552c2c9462",
+ "4f57cfa7cb3b4199babf82dc9d93b074",
+ "053de11f995247f6b851909a6a8dfc16",
+ "43d7a9b421be430286b5eb8441d6d465",
+ "ebc26228160046c48279d71770c928d8",
+ "5192bc282c4847cb9df8365fc22a6cc2",
+ "608e9f7a14054573b9bd07f0f74b6345"
+ ]
+ },
+ "id": "dd3c5acc",
+ "outputId": "fab398b4-dace-4f44-89e5-687ec74b18eb"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "59782bac41834abbbd6cbd6614c2e57b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/662 [00:00\n",
+ " \n",
+ " \n",
+ " [255/255 06:13, Epoch 1/1]\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | Epoch | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1 | \n",
+ " No log | \n",
+ " 0.017228 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Saving model checkpoint to temp/checkpoint-100\n",
+ "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n",
+ "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
+ " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
+ "Saving model checkpoint to temp/checkpoint-200\n",
+ "Trainer.model is not a `PreTrainedModel`, only saving its state dict.\n",
+ "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
+ " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
+ "***** Running Evaluation *****\n",
+ " Num examples = 227\n",
+ " Batch size = 8\n",
+ "\n",
+ "\n",
+ "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=255, training_loss=0.2569344015682445, metrics={'train_runtime': 377.3565, 'train_samples_per_second': 5.398, 'train_steps_per_second': 0.676, 'total_flos': 1181084919791616.0, 'train_loss': 0.2569344015682445, 'epoch': 1.0})"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "r98VtofiGXtO",
+ "metadata": {
+ "id": "r98VtofiGXtO"
+ },
+ "source": [
+ "## Qualitatively test our model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "NIm7z3UNzGPP",
+ "metadata": {
+ "id": "NIm7z3UNzGPP"
+ },
+ "source": [
+ "Let's have a quick qualitative evaluation of the model, by taking a sample from the dataset that corresponds to a positive label. Run your generation similarly as you were running your model from `transformers`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c95d6173",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "c95d6173",
+ "outputId": "ed03a1dc-597a-4053-99d6-eca2cc6da253"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Generate config GenerationConfig {\n",
+ " \"_from_model_config\": true,\n",
+ " \"decoder_start_token_id\": 0,\n",
+ " \"eos_token_id\": 1,\n",
+ " \"pad_token_id\": 0,\n",
+ " \"transformers_version\": \"4.27.0.dev0\",\n",
+ " \"use_cache\": false\n",
+ "}\n",
+ "\n",
+ "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
+ " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n",
+ "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1374: 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"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "input sentence: In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\n",
+ " output prediction: ['positive']\n"
+ ]
+ }
+ ],
+ "source": [
+ "model.eval()\n",
+ "input_text = \"In January-September 2009 , the Group 's net interest income increased to EUR 112.4 mn from EUR 74.3 mn in January-September 2008 .\"\n",
+ "inputs = tokenizer(input_text, return_tensors=\"pt\")\n",
+ "\n",
+ "outputs = model.generate(input_ids=inputs[\"input_ids\"], max_new_tokens=10)\n",
+ "\n",
+ "print(\"input sentence: \", input_text)\n",
+ "print(\" output prediction: \", tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9QqBlwzoGZ3f",
+ "metadata": {
+ "id": "9QqBlwzoGZ3f"
+ },
+ "source": [
+ "## Share your adapters on 🤗 Hub"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "NT-C8SjcKqUx",
+ "metadata": {
+ "id": "NT-C8SjcKqUx"
+ },
+ "source": [
+ "Once you have trained your adapter, you can easily share it on the Hub using the method `push_to_hub` . Note that only the adapter weights and config will be pushed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bcbfa1f9",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 359,
+ "referenced_widgets": [
+ "5bb29f3102954b06bec825f6b3a7aaa7",
+ "90516032070a40979181d1d27db10c4f",
+ "4b7dc0fb222b4e2a9bb2ef2501e9fd30",
+ "06069855ef82484f9985e4619095dbe8",
+ "1ece69c53e37413caad8db70d9160ad5",
+ "7ce90db727ea47cc9344176858a2225b",
+ "64f2b70b63cd4e7eb9e22ac2de5589c9",
+ "57dea1b3e04142bb91868a474774d86a",
+ "8ac43334e0ad4a78acda3b876fead058",
+ "04da98e400514cf2847d172916cd0081",
+ "4dbe49547fe94010ad5a30818cfc35bc",
+ "99091ca45c1b4809ba0a1b01af85f528",
+ "06ec124c3dac4fe6b152fb812d20d86d",
+ "7561c47a97444666816422a0418e1675",
+ "45ab5d7049e34dfd8a067643ae887a31",
+ "434e308cac5847f0bee431c7dbb4c04a",
+ "dd993a4a7cdf40448098544c95468a10"
+ ]
+ },
+ "id": "bcbfa1f9",
+ "outputId": "91ef770e-9fc4-4eb2-b02b-24e635101f97"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Token is valid.\n",
+ "Your token has been saved in your configured git credential helpers (store).\n",
+ "Your token has been saved to /root/.cache/huggingface/token\n",
+ "Login successful\n"
+ ]
+ }
+ ],
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "notebook_login()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "rFKJ4vHNGkJw",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 152,
+ "referenced_widgets": [
+ "00f7d043cb184d69b828c204dac2c0ab",
+ "1e409cd3d3a04b558d989d63f0b3b5f7",
+ "d48cbb38078b456fab1634bec5b0a1ba",
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+ "53dd4444c0e14e16a912532898b32d92",
+ "5287ac638c22412ab91c55f3316c9b63",
+ "a43ddb478f044f17adbcfae841ec2114",
+ "45141234ce584f208a9d301faadf75d2",
+ "f62ffbdc24734b999f36058d9edca81f",
+ "972ddebd536d4685bfc3c7c13e5bd8be",
+ "64156e2c54b44fb9aec661d9b57da962",
+ "050de732f51f4af8bb41ab3cad0090a4",
+ "7960ed3beb2a429ba2aca1c6ed032f64",
+ "726a2eedc7434210bc5aa4d0a772b313",
+ "07bf5d621cf944258aaf13954669df56",
+ "93a0896ca66b4111bc4cabe6e1278440",
+ "cfc78731f7d543ce8529cc254d92ddf5",
+ "eacf8e9ed6e847faae2b8ecab283ddc4",
+ "bef1971d92e6479696e3f9a27a757b8a",
+ "821f2f296acb40ae9bb40fc3faf4103d",
+ "11efd993475a4f2aabe7df605bab04dd",
+ "406e4d8561f64d2a94d93a606d02d7d3"
+ ]
+ },
+ "id": "rFKJ4vHNGkJw",
+ "outputId": "07425379-64ad-47e8-ba8f-8d9dc26252b6"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Uploading the following files to ybelkada/flan-t5-large-lora: adapter_model.bin,adapter_config.json\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "00f7d043cb184d69b828c204dac2c0ab",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Upload 1 LFS files: 0%| | 0/1 [00:00Pro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. "
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diff --git a/examples/int8_training/Finetune_opt_bnb_peft.ipynb b/examples/int8_training/Finetune_opt_bnb_peft.ipynb
index 0e28f19..51b2951 100644
--- a/examples/int8_training/Finetune_opt_bnb_peft.ipynb
+++ b/examples/int8_training/Finetune_opt_bnb_peft.ipynb
@@ -1,9440 +1,9443 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "source": [
- "## Fine-tune large models using 🤗 `peft` adapters, `transformers` & `bitsandbytes`\n",
- "\n",
- "In this tutorial we will cover how we can fine-tune large language models using the very recent `peft` library and `bitsandbytes` for loading large models in 8-bit.\n",
- "The fine-tuning method will rely on a recent method called \"Low Rank Adapters\" (LoRA), instead of fine-tuning the entire model you just have to fine-tune these adapters and load them properly inside the model. \n",
- "After fine-tuning the model you can also share your adapters on the 🤗 Hub and load them very easily. Let's get started!"
- ],
- "metadata": {
- "id": "WE5GJ6s7y0Xo"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "### Install requirements\n",
- "\n",
- "First, run the cells below to install the requirements:"
- ],
- "metadata": {
- "id": "TfBzP8gWzkpv"
- }
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "otj46qRbtpnd",
- "outputId": "2aa109f6-3f4e-4887-a16e-336f51e7cc9a"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
- " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
- " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
- " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
- " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
- " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m72.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
- " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
- ]
- }
- ],
- "source": [
- "!pip install -q bitsandbytes datasets accelerate loralib\n",
- "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "### Model loading\n",
- "\n",
- "Here let's load the `opt-6.7b` model, its weights in half-precision (float16) are about 13GB on the Hub! If we load them in 8-bit we would require around 7GB of memory instead."
- ],
- "metadata": {
- "id": "FOtwYRI3zzXI"
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- "\n",
- "===================================BUG REPORT===================================\n",
- "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
- "================================================================================\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Downloading (…)lve/main/config.json: 0%| | 0.00/651 [00:00"
- ],
- "text/html": [
- "\n",
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- " [153/200 26:04 < 08:06, 0.10 it/s, Epoch 0.97/2]\n",
- "
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- " \n",
- " | Step | \n",
- " Training Loss | \n",
- "
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- " \n",
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- " \n",
- " | 1 | \n",
- " 2.364400 | \n",
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- " 2.200400 | \n",
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- " 2.302300 | \n",
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- " | 4 | \n",
- " 2.184700 | \n",
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- " | 5 | \n",
- " 1.878700 | \n",
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- " | 6 | \n",
- " 2.307200 | \n",
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- " \n",
- " | 7 | \n",
- " 2.193800 | \n",
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- " \n",
- " | 8 | \n",
- " 2.446200 | \n",
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- " 2.458900 | \n",
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- " \n",
- " | 10 | \n",
- " 2.020000 | \n",
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- " | 11 | \n",
- " 1.941200 | \n",
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- " | 12 | \n",
- " 1.931000 | \n",
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- " | 13 | \n",
- " 2.055900 | \n",
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- " 1.975100 | \n",
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- " 2.015100 | \n",
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- " | 16 | \n",
- " 2.095600 | \n",
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- " \n",
- " | 17 | \n",
- " 1.768300 | \n",
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- " \n",
- " | 18 | \n",
- " 2.155700 | \n",
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- " 2.402300 | \n",
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- " 2.124600 | \n",
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- " 2.314900 | \n",
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- " 1.908500 | \n",
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- " | 23 | \n",
- " 2.078800 | \n",
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- " | 24 | \n",
- " 1.941900 | \n",
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- " \n",
- " | 25 | \n",
- " 1.879800 | \n",
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- " \n",
- " | 26 | \n",
- " 1.927500 | \n",
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- " 1.958100 | \n",
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- " 2.195900 | \n",
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- " 2.001000 | \n",
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- " 1.879800 | \n",
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- " 1.821600 | \n",
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- " 1.727800 | \n",
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- " | 39 | \n",
- " 1.995700 | \n",
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- " 1.698600 | \n",
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- " 2.129300 | \n",
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- " 2.025800 | \n",
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- " 1.696500 | \n",
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- " 1.984700 | \n",
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- " \n",
- " | 45 | \n",
- " 2.051100 | \n",
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- " \n",
- " | 46 | \n",
- " 2.054400 | \n",
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- " 1.765600 | \n",
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- " 2.063100 | \n",
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- " 1.746900 | \n",
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- " 1.873000 | \n",
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- " \n",
- " | 51 | \n",
- " 2.391300 | \n",
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- " | 52 | \n",
- " 2.494100 | \n",
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- " | 53 | \n",
- " 2.072300 | \n",
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- " | 54 | \n",
- " 1.808000 | \n",
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- " | 55 | \n",
- " 1.911900 | \n",
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- " \n",
- " | 56 | \n",
- " 2.168100 | \n",
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- " 2.166100 | \n",
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- " 1.921500 | \n",
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- " 1.856000 | \n",
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- " 1.652800 | \n",
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- " 1.623600 | \n",
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- " \n",
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- " 1.923200 | \n",
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- " \n",
- " | 66 | \n",
- " 2.053200 | \n",
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- " \n",
- " | 67 | \n",
- " 2.114300 | \n",
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- " 1.807700 | \n",
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- " 1.857800 | \n",
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- " 2.023000 | \n",
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- " 1.864900 | \n",
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- " 1.769300 | \n",
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- " 1.837700 | \n",
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- " 1.742200 | \n",
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- "
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- " \n",
- "
"
- ]
- },
- "metadata": {}
- }
- ],
- "source": [
- "import transformers\n",
- "from datasets import load_dataset\n",
- "data = load_dataset(\"Abirate/english_quotes\")\n",
- "data = data.map(lambda samples: tokenizer(samples['quote']), batched=True)\n",
- "\n",
- "trainer = transformers.Trainer(\n",
- " model=model, \n",
- " train_dataset=data['train'],\n",
- " args=transformers.TrainingArguments(\n",
- " per_device_train_batch_size=4, \n",
- " gradient_accumulation_steps=4,\n",
- " warmup_steps=100, \n",
- " max_steps=200, \n",
- " learning_rate=2e-4, \n",
- " fp16=True,\n",
- " logging_steps=1, \n",
- " output_dir='outputs'\n",
- " ),\n",
- " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
- ")\n",
- "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
- "trainer.train()"
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Share adapters on the 🤗 Hub"
- ],
- "metadata": {
- "id": "Duak7T_B3VpJ"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "from huggingface_hub import notebook_login\n",
- "\n",
- "notebook_login()"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 331,
- "referenced_widgets": [
- "262f01ffc5824b5faa8a61afac12ff67",
- "c1af10b599da43a3a848f3ba816d7acc",
- "be673691e713472980fa1132465714b4",
- "01f8b90f0f184dfdb92c0c3bffb28b0f",
- "9ad2bd0e92174d339a3a91a38253a180",
- "bff17ef6aabd4aa681bfd5ad64b808d9",
- "55633800b60a4336abea6a4adfcfdec1",
- "73d5c6b4034d49b392b103d889bfb3b4",
- "a55954b8d7bf4057b0d7aa6a1cb9e91a",
- "4da42eb3846f423d88e2a6462a0cfce8",
- "dab39ef354a84be3b37b6f151f9d9b9d",
- "b88b03326f464c96a5656eef774e36d5",
- "4eccb670e98043b3b2702821a3060ece",
- "a333501a50df4b9fa9546d8d965e0dc3",
- "1f173cb95c5c44f4b32f6cfe10ee3b03",
- "52c8a7e673f24276a07042388a13b58f",
- "a0f323ccfbc14fc4b7a5e7046b221ce3"
- ]
- },
- "id": "DpYr24pR8T_0",
- "outputId": "20186456-1bd4-4655-b2f2-8f24f9f37fcc"
- },
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Token is valid.\n",
- "Your token has been saved to /root/.cache/huggingface/token\n",
- "Login successful\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "model.push_to_hub(\"ybelkada/opt-6.7b-lora\", use_auth_token=True)"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 133,
- "referenced_widgets": [
- "3dbe077ed0c34e4eb1628418138ccbc6",
- "a579cb7804774ca3aa9efd800d1af57e",
- "9f44e3175835470aba43e239661037b2",
- "549bd12e3af64256a7534903688835a8",
- "a18d9beb34b848ff8cc541d2cb290c4c",
- "876cb42184f54b749c442163290c2c45",
- "43b14e0e1263499dbd592b280cff21b0",
- "7164feaf360d4300a5083f95064b144a",
- "bd7547126a874a45b608afed7ab2958b",
- "26e4b6b94e4540728c59df8e481ce43d",
- "9d1b77500f1c45308d4791a9c443e307",
- "b2693135b6954d35afb3120f3caf4000",
- "aeca2429ee48450a814515cb06acbc3e",
- "434c09950be04d728cd7ca8d6c134dc6",
- "34f58c132d2e4249b1e62a0b57c85999",
- "d148369be26a43949257790cb202728d",
- "0ecf58c5cbcd40908595fccddff1c6d4",
- "fc2314656a2745eb921f636cc3451381",
- "7817f8669b7f449fadf02d7145fa89e2",
- "06e012eea9714ed589344a362b7421bb",
- "504e9e5ced0348cc87aafae0c1c372eb",
- "5b538e8389fb4574a5dfdc554624e3c8"
- ]
- },
- "id": "VxB6UV5XAvvP",
- "outputId": "c3b0133b-f5b1-4283-8367-f06524bea46c"
- },
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "Uploading the following files to ybelkada/opt-6.7b-lora: adapter_config.json,adapter_model.bin\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Upload 1 LFS files: 0%| | 0/1 [00:00
Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. "
- }
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+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Fine-tune large models using 🤗 `peft` adapters, `transformers` & `bitsandbytes`\n",
+ "\n",
+ "In this tutorial we will cover how we can fine-tune large language models using the very recent `peft` library and `bitsandbytes` for loading large models in 8-bit.\n",
+ "The fine-tuning method will rely on a recent method called \"Low Rank Adapters\" (LoRA), instead of fine-tuning the entire model you just have to fine-tune these adapters and load them properly inside the model. \n",
+ "After fine-tuning the model you can also share your adapters on the 🤗 Hub and load them very easily. Let's get started!"
+ ],
+ "metadata": {
+ "id": "WE5GJ6s7y0Xo"
+ }
},
- "nbformat": 4,
- "nbformat_minor": 0
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Install requirements\n",
+ "\n",
+ "First, run the cells below to install the requirements:"
+ ],
+ "metadata": {
+ "id": "TfBzP8gWzkpv"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "otj46qRbtpnd",
+ "outputId": "2aa109f6-3f4e-4887-a16e-336f51e7cc9a"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m72.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+ " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -q bitsandbytes datasets accelerate loralib\n",
+ "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Model loading\n",
+ "\n",
+ "Here let's load the `opt-6.7b` model, its weights in half-precision (float16) are about 13GB on the Hub! If we load them in 8-bit we would require around 7GB of memory instead."
+ ],
+ "metadata": {
+ "id": "FOtwYRI3zzXI"
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+ "text": [
+ "\n",
+ "===================================BUG REPORT===================================\n",
+ "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
+ "================================================================================\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/651 [00:00"
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
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+ " [153/200 26:04 < 08:06, 0.10 it/s, Epoch 0.97/2]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | Step | \n",
+ " Training Loss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1 | \n",
+ " 2.364400 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 2.200400 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 2.302300 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 2.184700 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 1.878700 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 2.307200 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 2.193800 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 2.446200 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 2.458900 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 2.020000 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 1.941200 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " 1.931000 | \n",
+ "
\n",
+ " \n",
+ " | 13 | \n",
+ " 2.055900 | \n",
+ "
\n",
+ " \n",
+ " | 14 | \n",
+ " 1.975100 | \n",
+ "
\n",
+ " \n",
+ " | 15 | \n",
+ " 2.015100 | \n",
+ "
\n",
+ " \n",
+ " | 16 | \n",
+ " 2.095600 | \n",
+ "
\n",
+ " \n",
+ " | 17 | \n",
+ " 1.768300 | \n",
+ "
\n",
+ " \n",
+ " | 18 | \n",
+ " 2.155700 | \n",
+ "
\n",
+ " \n",
+ " | 19 | \n",
+ " 2.402300 | \n",
+ "
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+ " \n",
+ " | 20 | \n",
+ " 2.124600 | \n",
+ "
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+ " \n",
+ " | 21 | \n",
+ " 2.314900 | \n",
+ "
\n",
+ " \n",
+ " | 22 | \n",
+ " 1.908500 | \n",
+ "
\n",
+ " \n",
+ " | 23 | \n",
+ " 2.078800 | \n",
+ "
\n",
+ " \n",
+ " | 24 | \n",
+ " 1.941900 | \n",
+ "
\n",
+ " \n",
+ " | 25 | \n",
+ " 1.879800 | \n",
+ "
\n",
+ " \n",
+ " | 26 | \n",
+ " 1.927500 | \n",
+ "
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+ " \n",
+ " | 27 | \n",
+ " 1.371400 | \n",
+ "
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+ " \n",
+ " | 28 | \n",
+ " 1.977600 | \n",
+ "
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+ " \n",
+ " | 29 | \n",
+ " 2.055000 | \n",
+ "
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+ " \n",
+ " | 30 | \n",
+ " 1.915800 | \n",
+ "
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+ " \n",
+ " | 31 | \n",
+ " 1.958100 | \n",
+ "
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+ " \n",
+ " | 32 | \n",
+ " 2.195900 | \n",
+ "
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+ " \n",
+ " | 33 | \n",
+ " 2.001000 | \n",
+ "
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+ " \n",
+ " | 34 | \n",
+ " 2.025000 | \n",
+ "
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+ " \n",
+ " | 35 | \n",
+ " 1.576900 | \n",
+ "
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+ " \n",
+ " | 36 | \n",
+ " 1.879800 | \n",
+ "
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+ " \n",
+ " | 37 | \n",
+ " 1.821600 | \n",
+ "
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+ " \n",
+ " | 38 | \n",
+ " 1.727800 | \n",
+ "
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+ " \n",
+ " | 39 | \n",
+ " 1.995700 | \n",
+ "
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+ " \n",
+ " | 40 | \n",
+ " 1.698600 | \n",
+ "
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+ " \n",
+ " | 41 | \n",
+ " 2.129300 | \n",
+ "
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+ " \n",
+ " | 42 | \n",
+ " 2.025800 | \n",
+ "
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+ " \n",
+ " | 43 | \n",
+ " 1.696500 | \n",
+ "
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+ " \n",
+ " | 44 | \n",
+ " 1.984700 | \n",
+ "
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+ " \n",
+ " | 45 | \n",
+ " 2.051100 | \n",
+ "
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+ " \n",
+ " | 46 | \n",
+ " 2.054400 | \n",
+ "
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+ " \n",
+ " | 47 | \n",
+ " 1.765600 | \n",
+ "
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+ " \n",
+ " | 48 | \n",
+ " 2.063100 | \n",
+ "
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+ " \n",
+ " | 49 | \n",
+ " 1.746900 | \n",
+ "
\n",
+ " \n",
+ " | 50 | \n",
+ " 1.873000 | \n",
+ "
\n",
+ " \n",
+ " | 51 | \n",
+ " 2.391300 | \n",
+ "
\n",
+ " \n",
+ " | 52 | \n",
+ " 2.494100 | \n",
+ "
\n",
+ " \n",
+ " | 53 | \n",
+ " 2.072300 | \n",
+ "
\n",
+ " \n",
+ " | 54 | \n",
+ " 1.808000 | \n",
+ "
\n",
+ " \n",
+ " | 55 | \n",
+ " 1.911900 | \n",
+ "
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+ " \n",
+ " | 56 | \n",
+ " 2.168100 | \n",
+ "
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+ " \n",
+ " | 57 | \n",
+ " 2.166100 | \n",
+ "
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+ " \n",
+ " | 58 | \n",
+ " 1.921500 | \n",
+ "
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+ " \n",
+ " | 59 | \n",
+ " 1.856000 | \n",
+ "
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+ " \n",
+ " | 60 | \n",
+ " 1.652800 | \n",
+ "
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+ " \n",
+ " | 61 | \n",
+ " 1.605000 | \n",
+ "
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+ " \n",
+ " | 62 | \n",
+ " 2.032500 | \n",
+ "
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+ " \n",
+ " | 63 | \n",
+ " 1.822100 | \n",
+ "
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+ " \n",
+ " | 64 | \n",
+ " 1.623600 | \n",
+ "
\n",
+ " \n",
+ " | 65 | \n",
+ " 1.923200 | \n",
+ "
\n",
+ " \n",
+ " | 66 | \n",
+ " 2.053200 | \n",
+ "
\n",
+ " \n",
+ " | 67 | \n",
+ " 2.114300 | \n",
+ "
\n",
+ " \n",
+ " | 68 | \n",
+ " 1.807700 | \n",
+ "
\n",
+ " \n",
+ " | 69 | \n",
+ " 1.857800 | \n",
+ "
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+ " \n",
+ " | 70 | \n",
+ " 1.854600 | \n",
+ "
\n",
+ " \n",
+ " | 71 | \n",
+ " 2.023000 | \n",
+ "
\n",
+ " \n",
+ " | 72 | \n",
+ " 1.864900 | \n",
+ "
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+ " \n",
+ " | 73 | \n",
+ " 1.769300 | \n",
+ "
\n",
+ " \n",
+ " | 74 | \n",
+ " 1.837700 | \n",
+ "
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+ " \n",
+ " | 75 | \n",
+ " 1.742200 | \n",
+ "
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+ " \n",
+ " | 76 | \n",
+ " 1.895900 | \n",
+ "
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+ " \n",
+ " | 77 | \n",
+ " 1.922800 | \n",
+ "
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+ " \n",
+ " | 78 | \n",
+ " 2.325300 | \n",
+ "
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+ " \n",
+ " | 79 | \n",
+ " 2.231200 | \n",
+ "
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+ " \n",
+ " | 80 | \n",
+ " 2.309500 | \n",
+ "
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+ " \n",
+ " | 81 | \n",
+ " 1.945700 | \n",
+ "
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+ " \n",
+ " | 82 | \n",
+ " 2.072100 | \n",
+ "
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+ " \n",
+ " | 83 | \n",
+ " 1.917400 | \n",
+ "
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+ " \n",
+ " | 84 | \n",
+ " 2.004600 | \n",
+ "
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+ " \n",
+ " | 85 | \n",
+ " 1.951700 | \n",
+ "
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+ " \n",
+ " | 86 | \n",
+ " 1.450600 | \n",
+ "
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+ " \n",
+ " | 87 | \n",
+ " 1.785600 | \n",
+ "
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+ " \n",
+ " | 88 | \n",
+ " 1.668000 | \n",
+ "
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+ " \n",
+ " | 89 | \n",
+ " 1.903100 | \n",
+ "
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+ " \n",
+ " | 90 | \n",
+ " 1.709800 | \n",
+ "
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+ " \n",
+ " | 91 | \n",
+ " 2.312900 | \n",
+ "
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+ " \n",
+ " | 92 | \n",
+ " 2.092100 | \n",
+ "
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+ " \n",
+ " | 93 | \n",
+ " 2.319600 | \n",
+ "
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+ " \n",
+ " | 94 | \n",
+ " 1.603100 | \n",
+ "
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+ " \n",
+ " | 95 | \n",
+ " 1.740000 | \n",
+ "
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+ " \n",
+ " | 96 | \n",
+ " 1.670500 | \n",
+ "
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+ " \n",
+ " | 97 | \n",
+ " 1.611600 | \n",
+ "
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+ " \n",
+ " | 98 | \n",
+ " 1.728900 | \n",
+ "
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+ " \n",
+ " | 99 | \n",
+ " 2.285200 | \n",
+ "
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+ " \n",
+ " | 100 | \n",
+ " 1.957800 | \n",
+ "
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+ " \n",
+ " | 101 | \n",
+ " 1.676700 | \n",
+ "
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+ " \n",
+ " | 102 | \n",
+ " 1.656300 | \n",
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+ " \n",
+ " | 103 | \n",
+ " 1.612400 | \n",
+ "
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+ " \n",
+ " | 104 | \n",
+ " 1.848900 | \n",
+ "
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+ " \n",
+ " | 105 | \n",
+ " 1.870000 | \n",
+ "
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+ " \n",
+ " | 106 | \n",
+ " 1.954000 | \n",
+ "
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+ " \n",
+ " | 107 | \n",
+ " 2.192200 | \n",
+ "
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+ " \n",
+ " | 108 | \n",
+ " 1.637600 | \n",
+ "
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+ " \n",
+ " | 109 | \n",
+ " 1.208700 | \n",
+ "
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+ " \n",
+ " | 110 | \n",
+ " 2.254200 | \n",
+ "
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+ " \n",
+ " | 111 | \n",
+ " 1.832100 | \n",
+ "
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+ " \n",
+ " | 112 | \n",
+ " 2.119600 | \n",
+ "
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+ " \n",
+ " | 113 | \n",
+ " 2.126400 | \n",
+ "
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+ " \n",
+ " | 114 | \n",
+ " 1.915700 | \n",
+ "
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+ " \n",
+ " | 115 | \n",
+ " 1.587500 | \n",
+ "
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+ " \n",
+ " | 116 | \n",
+ " 1.564800 | \n",
+ "
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+ " \n",
+ " | 117 | \n",
+ " 1.742700 | \n",
+ "
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+ " \n",
+ " | 118 | \n",
+ " 1.712600 | \n",
+ "
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+ " | 119 | \n",
+ " 1.727900 | \n",
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+ " | 120 | \n",
+ " 2.361500 | \n",
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+ " | 121 | \n",
+ " 2.070300 | \n",
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+ " \n",
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+ " 1.846600 | \n",
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+ " 2.061700 | \n",
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+ " 2.149700 | \n",
+ "
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+ " 1.940600 | \n",
+ "
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+ " \n",
+ " | 127 | \n",
+ " 2.098300 | \n",
+ "
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+ " \n",
+ " | 128 | \n",
+ " 1.734100 | \n",
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+ " 1.687100 | \n",
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+ " 1.954100 | \n",
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+ " 2.051400 | \n",
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+ " 1.921200 | \n",
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+ " 1.787500 | \n",
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+ " 1.825400 | \n",
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+ " 1.363400 | \n",
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+ " 1.977400 | \n",
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+ " | 149 | \n",
+ " 1.768300 | \n",
+ "
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+ " \n",
+ " | 150 | \n",
+ " 2.226700 | \n",
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+ " \n",
+ " | 151 | \n",
+ " 1.945500 | \n",
+ "
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+ " \n",
+ "
"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "import transformers\n",
+ "from datasets import load_dataset\n",
+ "\n",
+ "data = load_dataset(\"Abirate/english_quotes\")\n",
+ "data = data.map(lambda samples: tokenizer(samples[\"quote\"]), batched=True)\n",
+ "\n",
+ "trainer = transformers.Trainer(\n",
+ " model=model,\n",
+ " train_dataset=data[\"train\"],\n",
+ " args=transformers.TrainingArguments(\n",
+ " per_device_train_batch_size=4,\n",
+ " gradient_accumulation_steps=4,\n",
+ " warmup_steps=100,\n",
+ " max_steps=200,\n",
+ " learning_rate=2e-4,\n",
+ " fp16=True,\n",
+ " logging_steps=1,\n",
+ " output_dir=\"outputs\",\n",
+ " ),\n",
+ " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
+ ")\n",
+ "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Share adapters on the 🤗 Hub"
+ ],
+ "metadata": {
+ "id": "Duak7T_B3VpJ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "notebook_login()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 331,
+ "referenced_widgets": [
+ "262f01ffc5824b5faa8a61afac12ff67",
+ "c1af10b599da43a3a848f3ba816d7acc",
+ "be673691e713472980fa1132465714b4",
+ "01f8b90f0f184dfdb92c0c3bffb28b0f",
+ "9ad2bd0e92174d339a3a91a38253a180",
+ "bff17ef6aabd4aa681bfd5ad64b808d9",
+ "55633800b60a4336abea6a4adfcfdec1",
+ "73d5c6b4034d49b392b103d889bfb3b4",
+ "a55954b8d7bf4057b0d7aa6a1cb9e91a",
+ "4da42eb3846f423d88e2a6462a0cfce8",
+ "dab39ef354a84be3b37b6f151f9d9b9d",
+ "b88b03326f464c96a5656eef774e36d5",
+ "4eccb670e98043b3b2702821a3060ece",
+ "a333501a50df4b9fa9546d8d965e0dc3",
+ "1f173cb95c5c44f4b32f6cfe10ee3b03",
+ "52c8a7e673f24276a07042388a13b58f",
+ "a0f323ccfbc14fc4b7a5e7046b221ce3"
+ ]
+ },
+ "id": "DpYr24pR8T_0",
+ "outputId": "20186456-1bd4-4655-b2f2-8f24f9f37fcc"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Token is valid.\n",
+ "Your token has been saved to /root/.cache/huggingface/token\n",
+ "Login successful\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model.push_to_hub(\"ybelkada/opt-6.7b-lora\", use_auth_token=True)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 133,
+ "referenced_widgets": [
+ "3dbe077ed0c34e4eb1628418138ccbc6",
+ "a579cb7804774ca3aa9efd800d1af57e",
+ "9f44e3175835470aba43e239661037b2",
+ "549bd12e3af64256a7534903688835a8",
+ "a18d9beb34b848ff8cc541d2cb290c4c",
+ "876cb42184f54b749c442163290c2c45",
+ "43b14e0e1263499dbd592b280cff21b0",
+ "7164feaf360d4300a5083f95064b144a",
+ "bd7547126a874a45b608afed7ab2958b",
+ "26e4b6b94e4540728c59df8e481ce43d",
+ "9d1b77500f1c45308d4791a9c443e307",
+ "b2693135b6954d35afb3120f3caf4000",
+ "aeca2429ee48450a814515cb06acbc3e",
+ "434c09950be04d728cd7ca8d6c134dc6",
+ "34f58c132d2e4249b1e62a0b57c85999",
+ "d148369be26a43949257790cb202728d",
+ "0ecf58c5cbcd40908595fccddff1c6d4",
+ "fc2314656a2745eb921f636cc3451381",
+ "7817f8669b7f449fadf02d7145fa89e2",
+ "06e012eea9714ed589344a362b7421bb",
+ "504e9e5ced0348cc87aafae0c1c372eb",
+ "5b538e8389fb4574a5dfdc554624e3c8"
+ ]
+ },
+ "id": "VxB6UV5XAvvP",
+ "outputId": "c3b0133b-f5b1-4283-8367-f06524bea46c"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Uploading the following files to ybelkada/opt-6.7b-lora: adapter_config.json,adapter_model.bin\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Upload 1 LFS files: 0%| | 0/1 [00:00
Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. "
+ }
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diff --git a/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb b/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb
index 0861444..41a60e6 100644
--- a/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb
+++ b/examples/int8_training/peft_bnb_whisper_large_v2_training.ipynb
@@ -1,20533 +1,20587 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "eJrPyQM5Xhv5",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "eJrPyQM5Xhv5",
- "outputId": "cfd6d8c9-964c-492b-b641-8e80e337f783"
- },
- "outputs": [],
- "source": [
- "!add-apt-repository -y ppa:jonathonf/ffmpeg-4\n",
- "!apt update\n",
- "!apt install -y ffmpeg"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "r_Ivl7qlX0dz",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "r_Ivl7qlX0dz",
- "outputId": "2caa9eed-f01a-4603-a527-fe3b0b58b6e2"
- },
- "outputs": [],
- "source": [
- "!pip install datasets>=2.6.1\n",
- "!pip install git+https://github.com/huggingface/transformers\n",
- "!pip install librosa\n",
- "!pip install evaluate>=0.30\n",
- "!pip install jiwer\n",
- "!pip install gradio"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "70JyJoWSX78t",
- "metadata": {
- "colab": {
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- },
- "id": "70JyJoWSX78t",
- "outputId": "38eae8e9-a08b-49f9-aac0-389f19e096fd"
- },
- "outputs": [],
- "source": [
- "!pip install -q bitsandbytes datasets accelerate loralib\n",
- "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ed0OpduhX2JF",
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- ]
- },
- "id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
- "outputId": "b1729004-591e-41c2-c206-9d0e572eb8e5"
- },
- "outputs": [],
- "source": [
- "from datasets import load_dataset, DatasetDict\n",
- "\n",
- "common_voice = DatasetDict()\n",
- "\n",
- "common_voice[\"train\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"mr\", split=\"train+validation\", use_auth_token=True)\n",
- "common_voice[\"test\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"mr\", split=\"test\", use_auth_token=True)\n",
- "\n",
- "print(common_voice)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce",
- "outputId": "dd81bced-f544-4d55-9669-5babb901f842"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DatasetDict({\n",
- " train: Dataset({\n",
- " features: ['audio', 'sentence'],\n",
- " num_rows: 3927\n",
- " })\n",
- " test: Dataset({\n",
- " features: ['audio', 'sentence'],\n",
- " num_rows: 1816\n",
- " })\n",
- "})\n"
- ]
- }
- ],
- "source": [
- "common_voice = common_voice.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"])\n",
- "\n",
- "print(common_voice)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605",
- "metadata": {
- "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
- },
- "source": [
- "## Prepare Feature Extractor, Tokenizer and Data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 49,
- "referenced_widgets": [
- "4d25d9919acf44a19f1b6f8fd625f808",
- "b041efb27b6149fcaf206590f1c1b961",
- "4668602d021a400a8b0ec88599abc85c",
- "92d9732acb964601b27695041f9fbb72",
- "c3ea17f7dd94462986d30b751664d77b",
- "1b1dc31d9a2b4357a71119300c6d899a",
- "7a24ae9d13fb4e82b1993b05f8d71d11",
- "e1141861d9f44d4c95313fd432795b70",
- "6ef55b5db76d4c78854fa0c27165e480",
- "91b103ac79e641b190416acdeb55902c",
- "e9e10f1e53b74509bfc9c0bf11502c5e"
- ]
- },
- "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
- "outputId": "7abb2062-e755-4f1a-e88b-9b7bf2986dbf"
- },
- "outputs": [],
- "source": [
- "from transformers import WhisperFeatureExtractor\n",
- "\n",
- "feature_extractor = WhisperFeatureExtractor.from_pretrained(\"openai/whisper-large-v2\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 209,
- "referenced_widgets": [
- "f436e6a7d3014e2ca44c94455bfeace8",
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- "7cfbb542eba34459ba64b881c1040eee",
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- "b8d3539c8a454217a3b6ffab51259054",
- "05e96819517e417aaf05f5f38c0c8b76",
- "b600ead93bbf44a3a3fe229589c63a61",
- "3d4c614fb775434fb0c5b02d46246ee0",
- "344d1cc53e28411cae839a4ebba1bf58",
- "a1578c0c777f4780a3fdd1635a0909d9",
- "41faf55c8878475f8a986b02ad73e8ce",
- "b5c8221a09df4dfdb74017d4af544b95",
- "65e4b8dae1e4435cb7737a8d5dd13d91",
- "6d932eadfd6a448a9a71d741bb64f428",
- "dce0d285c7d947dfba9ee5bc1a6ebece",
- "bde010029c374a0eb2bb942f380f0e8b",
- "a91f37ce798d411ea2a33cfdb1f01251"
- ]
- },
- "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
- "outputId": "4c094075-3f95-42db-9f4b-4db0c2fbeb91"
- },
- "outputs": [],
- "source": [
- "from transformers import WhisperTokenizer\n",
- "\n",
- "tokenizer = WhisperTokenizer.from_pretrained(\"openai/whisper-large-v2\", language=\"Marathi\", task=\"transcribe\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6",
- "metadata": {
- "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
- },
- "outputs": [],
- "source": [
- "from transformers import WhisperProcessor\n",
- "\n",
- "processor = WhisperProcessor.from_pretrained(\"openai/whisper-large-v2\", language=\"Marathi\", task=\"transcribe\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
- "metadata": {
- "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
- },
- "source": [
- "### Prepare Data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 72
- },
- "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255",
- "outputId": "1f1fe2d1-3ad2-42d4-e6f0-f0929785ae8e"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/common_voice_mr_30585613.mp3', 'array': array([-1.3727526e-15, -1.2400461e-13, -1.5159097e-13, ...,\n",
- " 4.7928120e-06, 3.5631349e-06, 1.6352631e-06], dtype=float32), 'sampling_rate': 48000}, 'sentence': 'आईचे आजारपण वाढत चालले, तसतशी मथीही नीट खातपीतनाशी झाली.'}\n"
- ]
- }
- ],
- "source": [
- "print(common_voice[\"train\"][0])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd",
- "metadata": {
- "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"
- },
- "source": [
- "Since \n",
- "our input audio is sampled at 48kHz, we need to _downsample_ it to \n",
- "16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model. \n",
- "\n",
- "We'll set the audio inputs to the correct sampling rate using dataset's \n",
- "[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n",
- "method. This operation does not change the audio in-place, \n",
- "but rather signals to `datasets` to resample audio samples _on the fly_ the \n",
- "first time that they are loaded:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f12e2e57-156f-417b-8cfb-69221cc198e8",
- "metadata": {
- "id": "f12e2e57-156f-417b-8cfb-69221cc198e8"
- },
- "outputs": [],
- "source": [
- "from datasets import Audio\n",
- "\n",
- "common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707",
- "metadata": {
- "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707"
- },
- "source": [
- "Re-loading the first audio sample in the Common Voice dataset will resample \n",
- "it to the desired sampling rate:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "87122d71-289a-466a-afcf-fa354b18946b",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "87122d71-289a-466a-afcf-fa354b18946b",
- "outputId": "727a709a-2b21-4c54-807f-efd40ea1719c"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/common_voice_mr_30585613.mp3', 'array': array([-4.4097186e-14, -9.4153831e-14, 3.4645775e-13, ...,\n",
- " -7.6018655e-06, -1.8617659e-06, 4.4520480e-06], dtype=float32), 'sampling_rate': 16000}, 'sentence': 'आईचे आजारपण वाढत चालले, तसतशी मथीही नीट खातपीतनाशी झाली.'}\n"
- ]
- }
- ],
- "source": [
- "print(common_voice[\"train\"][0])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "91edc72d-08f8-4f01-899d-74e65ce441fc",
- "metadata": {
- "id": "91edc72d-08f8-4f01-899d-74e65ce441fc"
- },
- "source": [
- "Now we can write a function to prepare our data ready for the model:\n",
- "1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n",
- "2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n",
- "3. We encode the transcriptions to label ids through the use of the tokenizer."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6525c478-8962-4394-a1c4-103c54cce170",
- "metadata": {
- "id": "6525c478-8962-4394-a1c4-103c54cce170"
- },
- "outputs": [],
- "source": [
- "def prepare_dataset(batch):\n",
- " # load and resample audio data from 48 to 16kHz\n",
- " audio = batch[\"audio\"]\n",
- "\n",
- " # compute log-Mel input features from input audio array \n",
- " batch[\"input_features\"] = feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
- "\n",
- " # encode target text to label ids \n",
- " batch[\"labels\"] = tokenizer(batch[\"sentence\"]).input_ids\n",
- " return batch"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13",
- "metadata": {
- "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
- },
- "source": [
- "We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
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- },
- "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b",
- "outputId": "eecac4c8-c5f8-427b-a2ba-0d51fae825ac"
- },
- "outputs": [],
- "source": [
- "common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=2)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c4be572c",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "c4be572c",
- "outputId": "0383124a-d4b1-4abe-a8a1-868ce6b3884e"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Dataset({\n",
- " features: ['input_features', 'labels'],\n",
- " num_rows: 3927\n",
- "})"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "common_voice[\"train\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "263a5a58-0239-4a25-b0df-c625fc9c5810",
- "metadata": {
- "id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
- },
- "source": [
- "## Training and Evaluation"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8d230e6d-624c-400a-bbf5-fa660881df25",
- "metadata": {
- "id": "8d230e6d-624c-400a-bbf5-fa660881df25"
- },
- "source": [
- "### Define a Data Collator"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5",
- "metadata": {
- "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5"
- },
- "outputs": [],
- "source": [
- "import torch\n",
- "\n",
- "from dataclasses import dataclass\n",
- "from typing import Any, Dict, List, Union\n",
- "\n",
- "@dataclass\n",
- "class DataCollatorSpeechSeq2SeqWithPadding:\n",
- " processor: Any\n",
- "\n",
- " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
- " # split inputs and labels since they have to be of different lengths and need different padding methods\n",
- " # first treat the audio inputs by simply returning torch tensors\n",
- " input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
- " batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
- "\n",
- " # get the tokenized label sequences\n",
- " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
- " # pad the labels to max length\n",
- " labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
- "\n",
- " # replace padding with -100 to ignore loss correctly\n",
- " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
- "\n",
- " # if bos token is appended in previous tokenization step,\n",
- " # cut bos token here as it's append later anyways\n",
- " if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
- " labels = labels[:, 1:]\n",
- "\n",
- " batch[\"labels\"] = labels\n",
- " \n",
- " # for INT8 training inputs need to be in fp16\n",
- " #batch[\"input_features\"] = batch[\"input_features\"].to(torch.float16)\n",
- "\n",
- " return batch"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86",
- "metadata": {
- "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86"
- },
- "source": [
- "Let's initialise the data collator we've just defined:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fc834702-c0d3-4a96-b101-7b87be32bf42",
- "metadata": {
- "id": "fc834702-c0d3-4a96-b101-7b87be32bf42"
- },
- "outputs": [],
- "source": [
- "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698",
- "metadata": {
- "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698"
- },
- "source": [
- "### Evaluation Metrics"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "66fee1a7-a44c-461e-b047-c3917221572e",
- "metadata": {
- "id": "66fee1a7-a44c-461e-b047-c3917221572e"
- },
- "source": [
- "We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing \n",
- "ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from 🤗 Evaluate:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b22b4011-f31f-4b57-b684-c52332f92890",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 49,
- "referenced_widgets": [
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- },
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- "outputId": "b0a08086-69b9-4ab4-97ac-dbed295f2e15"
- },
- "outputs": [],
- "source": [
- "import evaluate\n",
- "\n",
- "metric = evaluate.load(\"wer\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508",
- "metadata": {
- "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508"
- },
- "source": [
- "We then simply have to define a function that takes our model \n",
- "predictions and returns the WER metric. This function, called\n",
- "`compute_metrics`, first replaces `-100` with the `pad_token_id`\n",
- "in the `label_ids` (undoing the step we applied in the \n",
- "data collator to ignore padded tokens correctly in the loss).\n",
- "It then decodes the predicted and label ids to strings. Finally,\n",
- "it computes the WER between the predictions and reference labels:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52",
- "metadata": {
- "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52"
- },
- "outputs": [],
- "source": [
- "def compute_metrics(pred):\n",
- " pred_ids = pred.predictions\n",
- " label_ids = pred.label_ids\n",
- "\n",
- " # replace -100 with the pad_token_id\n",
- " label_ids[label_ids == -100] = tokenizer.pad_token_id\n",
- "\n",
- " # we do not want to group tokens when computing the metrics\n",
- " pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
- " label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n",
- "\n",
- " wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
- "\n",
- " return {\"wer\": wer}"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "daf2a825-6d9f-4a23-b145-c37c0039075b",
- "metadata": {
- "id": "daf2a825-6d9f-4a23-b145-c37c0039075b"
- },
- "source": [
- "### Load a Pre-Trained Checkpoint"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "437a97fa-4864-476b-8abc-f28b8166cfa5",
- "metadata": {
- "id": "437a97fa-4864-476b-8abc-f28b8166cfa5"
- },
- "source": [
- "Now let's load the pre-trained Whisper `small` checkpoint. Again, this \n",
- "is trivial through use of 🤗 Transformers!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 220,
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- },
- "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
- "outputId": "163d4b39-7e5d-4126-8d78-846c5d94dca6"
- },
- "outputs": [],
- "source": [
- "from transformers import WhisperForConditionalGeneration\n",
- "\n",
- "model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-large-v2\", load_in_8bit=True, \n",
- " device_map='auto')\n",
- "\n",
- "#model.hf_device_map - this should be {\" \": 0}"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a15ead5f-2277-4a39-937b-585c2497b2df",
- "metadata": {
- "id": "a15ead5f-2277-4a39-937b-585c2497b2df"
- },
- "source": [
- "Override generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "62038ba3-88ed-4fce-84db-338f50dcd04f",
- "metadata": {
- "id": "62038ba3-88ed-4fce-84db-338f50dcd04f"
- },
- "outputs": [],
- "source": [
- "model.config.forced_decoder_ids = None\n",
- "model.config.suppress_tokens = []"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bR-_yaEOPsfQ",
- "metadata": {
- "id": "bR-_yaEOPsfQ"
- },
- "source": [
- "### Post-processing on the model\n",
- "\n",
- "Finally, we need to apply some post-processing on the 8-bit model to enable training, let's freeze all our layers, and cast the layer-norm in `float32` for stability. We also cast the output of the last layer in `float32` for the same reasons."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "Cl_ZQualPt9R",
- "metadata": {
- "id": "Cl_ZQualPt9R"
- },
- "outputs": [],
- "source": [
- "from peft import prepare_model_for_training\n",
- "model = prepare_model_for_training(model, output_embedding_layer_name=\"proj_out\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "Vjl4j4RJPmPR",
- "metadata": {
- "id": "Vjl4j4RJPmPR"
- },
- "source": [
- "### Apply LoRA\n",
- "\n",
- "Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "DQtpDPRHPyOL",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "DQtpDPRHPyOL",
- "outputId": "1effcbde-7acc-4f62-f24b-e6236a43f833"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "trainable params: 15728640 || all params: 1559033600 || trainable%: 1.0088711365810203\n"
- ]
- }
- ],
- "source": [
- "\n",
- "from peft import LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_model \n",
- "\n",
- "config = LoraConfig(\n",
- " r=32,\n",
- " lora_alpha=64,\n",
- " target_modules=[\"q_proj\", \"v_proj\"],\n",
- " lora_dropout=0.05,\n",
- " bias=\"none\"\n",
- ")\n",
- "\n",
- "model = get_peft_model(model, config)\n",
- "model.print_trainable_parameters()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06",
- "metadata": {
- "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06"
- },
- "source": [
- "### Define the Training Configuration"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c21af1e9-0188-4134-ac82-defc7bdcc436",
- "metadata": {
- "id": "c21af1e9-0188-4134-ac82-defc7bdcc436"
- },
- "source": [
- "In the final step, we define all the parameters related to training. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
- "metadata": {
- "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a"
- },
- "outputs": [],
- "source": [
- "from transformers import Seq2SeqTrainingArguments\n",
- "\n",
- "training_args = Seq2SeqTrainingArguments(\n",
- " output_dir=\"temp\", # change to a repo name of your choice\n",
- " per_device_train_batch_size=8,\n",
- " gradient_accumulation_steps=1, # increase by 2x for every 2x decrease in batch size\n",
- " learning_rate=1e-3,\n",
- " warmup_steps=50,\n",
- " num_train_epochs=3,\n",
- " evaluation_strategy = \"epoch\",\n",
- " fp16=True,\n",
- " per_device_eval_batch_size=8,\n",
- " generation_max_length=128,\n",
- " logging_steps=25,\n",
- " remove_unused_columns=False, # required as the PeftModel forward doesn't have the signature of the wrapped model's forward\n",
- " label_names=[\"labels\"], # same reason as above\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b3a944d8-3112-4552-82a0-be25988b3857",
- "metadata": {
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- "**Few Important Notes:**\n",
- "1. `remove_unused_columns=False` and `label_names=[\"labels\"]` are required as the PeftModel's forward doesn't have the signature of the base model's forward.\n",
- "\n",
- "2. INT8 training required autocasting. `predict_with_generate` can't be passed to Trainer because it internally calls transformer's `generate` without autocasting leading to errors. \n",
- "\n",
- "3. Because of point 2, `compute_metrics` shouldn't be passed to `Seq2SeqTrainer` as seen below. (commented out)"
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- "outputId": "e2fabe64-2c50-42ff-a7ca-7773813e9408"
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- "The model is loaded in 8-bit precision. To train this model you need to add additional modules inside the model such as adapters using `peft` library and freeze the model weights. Please check the examples in https://github.com/huggingface/peft for more details.\n",
- "Using cuda_amp half precision backend\n"
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- "source": [
- "from transformers import Seq2SeqTrainer\n",
- "\n",
- "trainer = Seq2SeqTrainer(\n",
- " args=training_args,\n",
- " model=model,\n",
- " train_dataset=common_voice[\"train\"],\n",
- " eval_dataset=common_voice[\"test\"],\n",
- " data_collator=data_collator,\n",
- " #compute_metrics=compute_metrics, \n",
- " tokenizer=processor.feature_extractor,\n",
- ")\n",
- "model.config.use_cache = False # silence the warnings. Please re-enable for inference!"
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- "outputs": [
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- "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:346: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
- " warnings.warn(\n",
- "***** Running training *****\n",
- " Num examples = 3927\n",
- " Num Epochs = 3\n",
- " Instantaneous batch size per device = 8\n",
- " Total train batch size (w. parallel, distributed & accumulation) = 8\n",
- " Gradient Accumulation steps = 1\n",
- " Total optimization steps = 1473\n",
- " Number of trainable parameters = 15728640\n",
- "/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
- " warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
- "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
- " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
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- "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
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Copy a token from your Hugging Face\ntokens page and paste it below.
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+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "5cefac89",
+ "metadata": {},
+ "source": [
+ "# Finetuning Whisper-large-V2 on Colab using PEFT-Lora + BNB INT8 training"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "090fa3ed",
+ "metadata": {},
+ "source": [
+ "In this Colab, we present a step-by-step guide on how to fine-tune Whisper for any multilingual ASR dataset using Hugging Face 🤗 Transformers and 🤗 PEFT. Using 🤗 PEFT and `bitsandbytes`, you can train the `whisper-large-v2` seamlessly on a colab with T4 GPU (16 GB VRAM). In this notebook, with most parts from [fine_tune_whisper.ipynb](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/fine_tune_whisper.ipynb#scrollTo=BRdrdFIeU78w) is adapted to train using PEFT LoRA+BNB INT8.\n",
+ "\n",
+ "For more details on model, datasets and metrics, refer blog [Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper)\n",
+ "\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "625e47a0",
+ "metadata": {},
+ "source": [
+ "## Inital Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eJrPyQM5Xhv5",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "eJrPyQM5Xhv5",
+ "outputId": "cfd6d8c9-964c-492b-b641-8e80e337f783"
+ },
+ "outputs": [],
+ "source": [
+ "!add-apt-repository -y ppa:jonathonf/ffmpeg-4\n",
+ "!apt update\n",
+ "!apt install -y ffmpeg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "r_Ivl7qlX0dz",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "r_Ivl7qlX0dz",
+ "outputId": "2caa9eed-f01a-4603-a527-fe3b0b58b6e2"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install datasets>=2.6.1\n",
+ "!pip install git+https://github.com/huggingface/transformers\n",
+ "!pip install librosa\n",
+ "!pip install evaluate>=0.30\n",
+ "!pip install jiwer\n",
+ "!pip install gradio\n",
+ "!pip install -q bitsandbytes datasets accelerate loralib\n",
+ "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git@main"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "8a528c1a",
+ "metadata": {},
+ "source": [
+ "Linking the notebook to the Hub is straightforward - it simply requires entering your Hub authentication token when prompted. Find your Hub authentication token [here](https://huggingface.co/settings/tokens):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ed0OpduhX2JF",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 303,
+ "referenced_widgets": [
+ "c60690c2aee74763bf23115553f4e640",
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+ "5d5ea0207c6148769ad9f15b7b3dd92d",
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+ "989b3df296504f34a62d31ca0d6d88bb",
+ "e8a7a34c6fb146f0b38a40f389a617fa",
+ "960553d142c446cd8852523887a5cc04",
+ "441820fb176048109e0f8f7e9519d735",
+ "0bb38c654e18429a8396466ebab84504",
+ "1acfc4a2809e41dd995817c3526650dd",
+ "6d5801774beb4b529b227ef2f098614e",
+ "dfdf23cde48c421caebb573060641d6a"
+ ]
+ },
+ "id": "ed0OpduhX2JF",
+ "outputId": "ecc2048a-b46a-4b20-b94a-5912924feb3d"
+ },
+ "outputs": [],
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "notebook_login()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1da5fff",
+ "metadata": {
+ "id": "e1da5fff"
+ },
+ "outputs": [],
+ "source": [
+ "# Select CUDA device index\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
+ "model_name_or_path = \"openai/whisper-large-v2\"\n",
+ "language = \"Marathi\"\n",
+ "language_abbr = \"mr\"\n",
+ "task = \"transcribe\"\n",
+ "dataset_name = \"mozilla-foundation/common_voice_11_0\""
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "805b1c56",
+ "metadata": {},
+ "source": [
+ "## Load Dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
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+ ]
+ },
+ "id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
+ "outputId": "b1729004-591e-41c2-c206-9d0e572eb8e5"
+ },
+ "outputs": [],
+ "source": [
+ "from datasets import load_dataset, DatasetDict\n",
+ "\n",
+ "common_voice = DatasetDict()\n",
+ "\n",
+ "common_voice[\"train\"] = load_dataset(dataset_name, language_abbr, split=\"train+validation\", use_auth_token=True)\n",
+ "common_voice[\"test\"] = load_dataset(dataset_name, language_abbr, split=\"test\", use_auth_token=True)\n",
+ "\n",
+ "print(common_voice)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "20ba635d-518c-47ac-97ee-3cad25f1e0ce",
+ "outputId": "dd81bced-f544-4d55-9669-5babb901f842"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DatasetDict({\n",
+ " train: Dataset({\n",
+ " features: ['audio', 'sentence'],\n",
+ " num_rows: 3927\n",
+ " })\n",
+ " test: Dataset({\n",
+ " features: ['audio', 'sentence'],\n",
+ " num_rows: 1816\n",
+ " })\n",
+ "})\n"
+ ]
+ }
+ ],
+ "source": [
+ "common_voice = common_voice.remove_columns(\n",
+ " [\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"]\n",
+ ")\n",
+ "\n",
+ "print(common_voice)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605",
+ "metadata": {
+ "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
+ },
+ "source": [
+ "## Prepare Feature Extractor, Tokenizer and Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "4d25d9919acf44a19f1b6f8fd625f808",
+ "b041efb27b6149fcaf206590f1c1b961",
+ "4668602d021a400a8b0ec88599abc85c",
+ "92d9732acb964601b27695041f9fbb72",
+ "c3ea17f7dd94462986d30b751664d77b",
+ "1b1dc31d9a2b4357a71119300c6d899a",
+ "7a24ae9d13fb4e82b1993b05f8d71d11",
+ "e1141861d9f44d4c95313fd432795b70",
+ "6ef55b5db76d4c78854fa0c27165e480",
+ "91b103ac79e641b190416acdeb55902c",
+ "e9e10f1e53b74509bfc9c0bf11502c5e"
+ ]
+ },
+ "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
+ "outputId": "7abb2062-e755-4f1a-e88b-9b7bf2986dbf"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import WhisperFeatureExtractor\n",
+ "\n",
+ "feature_extractor = WhisperFeatureExtractor.from_pretrained(model_name_or_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 209,
+ "referenced_widgets": [
+ "f436e6a7d3014e2ca44c94455bfeace8",
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+ "e6c2dc814c324a0c8cb744ca16707479",
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+ "6d9826a5adb847e8b4cc4486a31b52ce",
+ "e8b3093587e44164b0ac043414cea0fa",
+ "96266c5722f54eeeb682b2707b8025dd",
+ "f0be69583cd1410da6dbc18302d4439b",
+ "c24ddbaafa5f4a63b391d981a4f10354",
+ "629725dfcc684b1db1a57850c3bc7bc3",
+ "add0f175631742d49dd4695bc004e81a",
+ "5871a46d60f14da99e4bb8ee74405319",
+ "64b308019fff4095ab7f1812aab4676a",
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+ "e4234c7c29744fc4be99b8b2ebedc9d1",
+ "70aa1fc14b09473e911dad3f9030b15b",
+ "008bead021ea416eb31dd9143d5ae5b9",
+ "ebd79e22ad4e4256a6d88883af2c1eef",
+ "c47ba0b11e074f708338863a35a78f7b",
+ "72b715be2c774235a21602b18d71e75a",
+ "ff9a0ed54bab49aca6f27bf1be66958e",
+ "15b5e415b62146ba96215458cf116431",
+ "8b0dd001d5b04647b1c480aab83d03a2",
+ "0af935ad4f694fb48094e6a119cbcf82",
+ "7cfbb542eba34459ba64b881c1040eee",
+ "fe48e65b2371445bbb01a8d3e9af1f67",
+ "b8d3539c8a454217a3b6ffab51259054",
+ "05e96819517e417aaf05f5f38c0c8b76",
+ "b600ead93bbf44a3a3fe229589c63a61",
+ "3d4c614fb775434fb0c5b02d46246ee0",
+ "344d1cc53e28411cae839a4ebba1bf58",
+ "a1578c0c777f4780a3fdd1635a0909d9",
+ "41faf55c8878475f8a986b02ad73e8ce",
+ "b5c8221a09df4dfdb74017d4af544b95",
+ "65e4b8dae1e4435cb7737a8d5dd13d91",
+ "6d932eadfd6a448a9a71d741bb64f428",
+ "dce0d285c7d947dfba9ee5bc1a6ebece",
+ "bde010029c374a0eb2bb942f380f0e8b",
+ "a91f37ce798d411ea2a33cfdb1f01251"
+ ]
+ },
+ "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
+ "outputId": "4c094075-3f95-42db-9f4b-4db0c2fbeb91"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import WhisperTokenizer\n",
+ "\n",
+ "tokenizer = WhisperTokenizer.from_pretrained(model_name_or_path, language=language, task=task)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6",
+ "metadata": {
+ "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import WhisperProcessor\n",
+ "\n",
+ "processor = WhisperProcessor.from_pretrained(model_name_or_path, language=language, task=task)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
+ "metadata": {
+ "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
+ },
+ "source": [
+ "### Prepare Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 72
+ },
+ "id": "6e6b0ec5-0c94-4e2c-ae24-c791be1b2255",
+ "outputId": "1f1fe2d1-3ad2-42d4-e6f0-f0929785ae8e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/common_voice_mr_30585613.mp3', 'array': array([-1.3727526e-15, -1.2400461e-13, -1.5159097e-13, ...,\n",
+ " 4.7928120e-06, 3.5631349e-06, 1.6352631e-06], dtype=float32), 'sampling_rate': 48000}, 'sentence': 'आईचे आजारपण वाढत चालले, तसतशी मथीही नीट खातपीतनाशी झाली.'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(common_voice[\"train\"][0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd",
+ "metadata": {
+ "id": "5a679f05-063d-41b3-9b58-4fc9c6ccf4fd"
+ },
+ "source": [
+ "Since \n",
+ "our input audio is sampled at 48kHz, we need to _downsample_ it to \n",
+ "16kHz prior to passing it to the Whisper feature extractor, 16kHz being the sampling rate expected by the Whisper model. \n",
+ "\n",
+ "We'll set the audio inputs to the correct sampling rate using dataset's \n",
+ "[`cast_column`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=cast_column#datasets.DatasetDict.cast_column)\n",
+ "method. This operation does not change the audio in-place, \n",
+ "but rather signals to `datasets` to resample audio samples _on the fly_ the \n",
+ "first time that they are loaded:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f12e2e57-156f-417b-8cfb-69221cc198e8",
+ "metadata": {
+ "id": "f12e2e57-156f-417b-8cfb-69221cc198e8"
+ },
+ "outputs": [],
+ "source": [
+ "from datasets import Audio\n",
+ "\n",
+ "common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707",
+ "metadata": {
+ "id": "00382a3e-abec-4cdd-a54c-d1aaa3ea4707"
+ },
+ "source": [
+ "Re-loading the first audio sample in the Common Voice dataset will resample \n",
+ "it to the desired sampling rate:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "87122d71-289a-466a-afcf-fa354b18946b",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "87122d71-289a-466a-afcf-fa354b18946b",
+ "outputId": "727a709a-2b21-4c54-807f-efd40ea1719c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f7e1ef6a2d14f20194999aad5040c5d4bb3ead1377de3e1bbc6e9dba34d18a8a/common_voice_mr_30585613.mp3', 'array': array([-4.4097186e-14, -9.4153831e-14, 3.4645775e-13, ...,\n",
+ " -7.6018655e-06, -1.8617659e-06, 4.4520480e-06], dtype=float32), 'sampling_rate': 16000}, 'sentence': 'आईचे आजारपण वाढत चालले, तसतशी मथीही नीट खातपीतनाशी झाली.'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(common_voice[\"train\"][0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "91edc72d-08f8-4f01-899d-74e65ce441fc",
+ "metadata": {
+ "id": "91edc72d-08f8-4f01-899d-74e65ce441fc"
+ },
+ "source": [
+ "Now we can write a function to prepare our data ready for the model:\n",
+ "1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n",
+ "2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n",
+ "3. We encode the transcriptions to label ids through the use of the tokenizer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6525c478-8962-4394-a1c4-103c54cce170",
+ "metadata": {
+ "id": "6525c478-8962-4394-a1c4-103c54cce170"
+ },
+ "outputs": [],
+ "source": [
+ "def prepare_dataset(batch):\n",
+ " # load and resample audio data from 48 to 16kHz\n",
+ " audio = batch[\"audio\"]\n",
+ "\n",
+ " # compute log-Mel input features from input audio array\n",
+ " batch[\"input_features\"] = feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
+ "\n",
+ " # encode target text to label ids\n",
+ " batch[\"labels\"] = tokenizer(batch[\"sentence\"]).input_ids\n",
+ " return batch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13",
+ "metadata": {
+ "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
+ },
+ "source": [
+ "We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 197,
+ "referenced_widgets": [
+ "466eeda389c442e487742faff05eeb81",
+ "664ff40a4e7346869652ba3be663acf2",
+ "3d8f10b5726b46de934dd0f7ed7244e4",
+ "b803893882ac4358a4676964dfb3fb31",
+ "4ff7e3d07f6a48f8935f0f42b86fc20c",
+ "3e5db1000a6c4871936614e712e769db",
+ "21dcf39a421847a78ea685eec0983fc6",
+ "c4119f7ec7464aab90f17d022e674999",
+ "965ac01b68064dc5a9fc3bb3f244c804",
+ "f5d7433d15de45e997d12568cac536fc",
+ "14e284f308844311a9ad40415091d93f",
+ "58c3766293e64116a131357f6cc66fe5",
+ "abc8f69eae7b46b1b430cf3b7a231b05",
+ "979edd227f0840a4be0233082e452b5a",
+ "12dedd9f089c42f3a8bba9adb01cf9d3",
+ "3042d874fcf544fabc4701938bca7cf3",
+ "baa8f739bb7e402bae7ee479e282e813",
+ "54509d703b7d4416bdee59157f918396",
+ "ec6017ce2fb3431ab823bde05f977a61",
+ "1a540a7cba794122abdb1900061479e2",
+ "d046a46c70ea46ffbb04a3c9f55637d5",
+ "99b628071c814d88a3cd5d72e4c95f01",
+ "e213d1c919314315ada180d49e27dfb6",
+ "0ea1f163e4174684bd6efc2e2433c1d3",
+ "09511a81a89d4754897a3507a84405be",
+ "8338339ab8a242c1a485bce8558f9c39",
+ "8e84abf61e3e45d58efc7ccd0bfe8d37",
+ "47a5e0cfb3564c16acaedb88613b9020",
+ "09e357a7187044b0bd2b841893a2601f",
+ "65e8c45d05be4d41b136439d9785a09c",
+ "1442a54fad7d44ddbafdacaf7f95279a",
+ "4bea6b0455ae4019b33d45491dbf4584",
+ "9c928a14371a4d9aa521953e287fff54",
+ "b8acdb71c3564972a6c8b27e964ec061",
+ "688f552e85714fe6a5d3eda82be0106a",
+ "a8aac44a077040bd9f4638c0c8f7a877",
+ "89f8ede64593475da4e056e9907f370f",
+ "851902cb2b0e494998684409d82dafd1",
+ "328f4d11886d48f49d4578e8e352097f",
+ "a25ef41450eb42ff9b8b618b40080ac0",
+ "04144ed3ff5f423f99179702cee5343f",
+ "8c41e1ee1a2c49b8b3d3d320fbf26262",
+ "4edf0948ed1346ceba1c0148e9c6fe1c",
+ "40f2b2ff75ca4562808fc2c7a870901e"
+ ]
+ },
+ "id": "7b73ab39-ffaf-4b9e-86e5-782963c6134b",
+ "outputId": "eecac4c8-c5f8-427b-a2ba-0d51fae825ac"
+ },
+ "outputs": [],
+ "source": [
+ "common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c4be572c",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "c4be572c",
+ "outputId": "0383124a-d4b1-4abe-a8a1-868ce6b3884e"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Dataset({\n",
+ " features: ['input_features', 'labels'],\n",
+ " num_rows: 3927\n",
+ "})"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "common_voice[\"train\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "263a5a58-0239-4a25-b0df-c625fc9c5810",
+ "metadata": {
+ "id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
+ },
+ "source": [
+ "## Training and Evaluation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8d230e6d-624c-400a-bbf5-fa660881df25",
+ "metadata": {
+ "id": "8d230e6d-624c-400a-bbf5-fa660881df25"
+ },
+ "source": [
+ "### Define a Data Collator"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5",
+ "metadata": {
+ "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "\n",
+ "from dataclasses import dataclass\n",
+ "from typing import Any, Dict, List, Union\n",
+ "\n",
+ "\n",
+ "@dataclass\n",
+ "class DataCollatorSpeechSeq2SeqWithPadding:\n",
+ " processor: Any\n",
+ "\n",
+ " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
+ " # split inputs and labels since they have to be of different lengths and need different padding methods\n",
+ " # first treat the audio inputs by simply returning torch tensors\n",
+ " input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
+ " batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
+ "\n",
+ " # get the tokenized label sequences\n",
+ " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
+ " # pad the labels to max length\n",
+ " labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
+ "\n",
+ " # replace padding with -100 to ignore loss correctly\n",
+ " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
+ "\n",
+ " # if bos token is appended in previous tokenization step,\n",
+ " # cut bos token here as it's append later anyways\n",
+ " if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
+ " labels = labels[:, 1:]\n",
+ "\n",
+ " batch[\"labels\"] = labels\n",
+ "\n",
+ " return batch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86",
+ "metadata": {
+ "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86"
+ },
+ "source": [
+ "Let's initialise the data collator we've just defined:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc834702-c0d3-4a96-b101-7b87be32bf42",
+ "metadata": {
+ "id": "fc834702-c0d3-4a96-b101-7b87be32bf42"
+ },
+ "outputs": [],
+ "source": [
+ "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698",
+ "metadata": {
+ "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698"
+ },
+ "source": [
+ "### Evaluation Metrics"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "66fee1a7-a44c-461e-b047-c3917221572e",
+ "metadata": {
+ "id": "66fee1a7-a44c-461e-b047-c3917221572e"
+ },
+ "source": [
+ "We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing \n",
+ "ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from 🤗 Evaluate:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b22b4011-f31f-4b57-b684-c52332f92890",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "215c3486e13343e091a26d658e1030d2",
+ "999382fb9e764a98893bd5269261d70b",
+ "4e9729771294424f959bbfb6bf8b60f3",
+ "3d2249516ecd43a08b5fba53fddb32e8",
+ "681711ebd0c64a63bee6b5337ef401db",
+ "d37791ea2b6c4152991295ca0edb0fb7",
+ "730f4d93bbd94452a59de43bf3d0a266",
+ "048e5c87cea34014a8f8a4538f4126bd",
+ "fbd27061ff114846aedc99bc2d17f7a7",
+ "5a306409e9e045b2b936267c520f935c",
+ "9f0638198f544a3bbb31a3a78b7bd2c2"
+ ]
+ },
+ "id": "b22b4011-f31f-4b57-b684-c52332f92890",
+ "outputId": "b0a08086-69b9-4ab4-97ac-dbed295f2e15"
+ },
+ "outputs": [],
+ "source": [
+ "import evaluate\n",
+ "\n",
+ "metric = evaluate.load(\"wer\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508",
+ "metadata": {
+ "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508"
+ },
+ "source": [
+ "We then simply have to define a function that takes our model \n",
+ "predictions and returns the WER metric. This function, called\n",
+ "`compute_metrics`, first replaces `-100` with the `pad_token_id`\n",
+ "in the `label_ids` (undoing the step we applied in the \n",
+ "data collator to ignore padded tokens correctly in the loss).\n",
+ "It then decodes the predicted and label ids to strings. Finally,\n",
+ "it computes the WER between the predictions and reference labels:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52",
+ "metadata": {
+ "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52"
+ },
+ "outputs": [],
+ "source": [
+ "def compute_metrics(pred):\n",
+ " pred_ids = pred.predictions\n",
+ " label_ids = pred.label_ids\n",
+ "\n",
+ " # replace -100 with the pad_token_id\n",
+ " label_ids[label_ids == -100] = tokenizer.pad_token_id\n",
+ "\n",
+ " # we do not want to group tokens when computing the metrics\n",
+ " pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
+ " label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n",
+ "\n",
+ " wer = 100 * metric.compute(predictions=pred_str, references=label_str)\n",
+ "\n",
+ " return {\"wer\": wer}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "daf2a825-6d9f-4a23-b145-c37c0039075b",
+ "metadata": {
+ "id": "daf2a825-6d9f-4a23-b145-c37c0039075b"
+ },
+ "source": [
+ "### Load a Pre-Trained Checkpoint"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "437a97fa-4864-476b-8abc-f28b8166cfa5",
+ "metadata": {
+ "id": "437a97fa-4864-476b-8abc-f28b8166cfa5"
+ },
+ "source": [
+ "Now let's load the pre-trained Whisper `small` checkpoint. Again, this \n",
+ "is trivial through use of 🤗 Transformers!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 220,
+ "referenced_widgets": [
+ "d8c1a66480204f1095ff5f6a7dd2e477",
+ "9dc736113ef6477d91aaf71c9969ca74",
+ "79521628c64b4d6f9f22e73749298693",
+ "c7268972f75e4824893ebe7d893a18e1",
+ "a87598d464174703b5f5a5eca23543f3",
+ "e0529b81739144db8912c2d6789e729a",
+ "13d0a97497274652b081cbcefb3fd17d",
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+ "6bc07db3471342a6bbc1b86ca734b3e6",
+ "4f167e9657274d56b8568d48262d1ee6",
+ "b7fbaa9d4bcd40b5bcf8d1475658c5b1",
+ "ba21f5ddf2434cc792b70a70c8c1079e",
+ "21bc84c704874455a57c47239984d28d",
+ "5b226e580df94448bed54b400c7a9a25",
+ "b0237154051343adaa076a9cc6dd711d",
+ "64756452791144859b9c803886c6dc77",
+ "f027b4358c2c41a8a93c617641135bcd",
+ "42cb6114956c4a86980c8dafd5a734ae"
+ ]
+ },
+ "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
+ "outputId": "163d4b39-7e5d-4126-8d78-846c5d94dca6"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import WhisperForConditionalGeneration\n",
+ "\n",
+ "model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path, load_in_8bit=True, device_map=\"auto\")\n",
+ "\n",
+ "# model.hf_device_map - this should be {\" \": 0}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a15ead5f-2277-4a39-937b-585c2497b2df",
+ "metadata": {
+ "id": "a15ead5f-2277-4a39-937b-585c2497b2df"
+ },
+ "source": [
+ "Override generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "62038ba3-88ed-4fce-84db-338f50dcd04f",
+ "metadata": {
+ "id": "62038ba3-88ed-4fce-84db-338f50dcd04f"
+ },
+ "outputs": [],
+ "source": [
+ "model.config.forced_decoder_ids = None\n",
+ "model.config.suppress_tokens = []"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bR-_yaEOPsfQ",
+ "metadata": {
+ "id": "bR-_yaEOPsfQ"
+ },
+ "source": [
+ "### Post-processing on the model\n",
+ "\n",
+ "Finally, we need to apply some post-processing on the 8-bit model to enable training, let's freeze all our layers, and cast the layer-norm in `float32` for stability. We also cast the output of the last layer in `float32` for the same reasons."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "Cl_ZQualPt9R",
+ "metadata": {
+ "id": "Cl_ZQualPt9R"
+ },
+ "outputs": [],
+ "source": [
+ "from peft import prepare_model_for_training\n",
+ "\n",
+ "model = prepare_model_for_training(model, output_embedding_layer_name=\"proj_out\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "Vjl4j4RJPmPR",
+ "metadata": {
+ "id": "Vjl4j4RJPmPR"
+ },
+ "source": [
+ "### Apply LoRA\n",
+ "\n",
+ "Here comes the magic with `peft`! Let's load a `PeftModel` and specify that we are going to use low-rank adapters (LoRA) using `get_peft_model` utility function from `peft`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "DQtpDPRHPyOL",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "DQtpDPRHPyOL",
+ "outputId": "1effcbde-7acc-4f62-f24b-e6236a43f833"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "trainable params: 15728640 || all params: 1559033600 || trainable%: 1.0088711365810203\n"
+ ]
+ }
+ ],
+ "source": [
+ "from peft import LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_model\n",
+ "\n",
+ "config = LoraConfig(r=32, lora_alpha=64, target_modules=[\"q_proj\", \"v_proj\"], lora_dropout=0.05, bias=\"none\")\n",
+ "\n",
+ "model = get_peft_model(model, config)\n",
+ "model.print_trainable_parameters()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "3906d436",
+ "metadata": {},
+ "source": [
+ "We are ONLY using **1%** of the total trainable parameters, thereby performing **Parameter-Efficient Fine-Tuning**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06",
+ "metadata": {
+ "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06"
+ },
+ "source": [
+ "### Define the Training Configuration"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c21af1e9-0188-4134-ac82-defc7bdcc436",
+ "metadata": {
+ "id": "c21af1e9-0188-4134-ac82-defc7bdcc436"
+ },
+ "source": [
+ "In the final step, we define all the parameters related to training. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
+ "metadata": {
+ "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import Seq2SeqTrainingArguments\n",
+ "\n",
+ "training_args = Seq2SeqTrainingArguments(\n",
+ " output_dir=\"temp\", # change to a repo name of your choice\n",
+ " per_device_train_batch_size=8,\n",
+ " gradient_accumulation_steps=1, # increase by 2x for every 2x decrease in batch size\n",
+ " learning_rate=1e-3,\n",
+ " warmup_steps=50,\n",
+ " num_train_epochs=3,\n",
+ " evaluation_strategy=\"epoch\",\n",
+ " fp16=True,\n",
+ " per_device_eval_batch_size=8,\n",
+ " generation_max_length=128,\n",
+ " logging_steps=25,\n",
+ " remove_unused_columns=False, # required as the PeftModel forward doesn't have the signature of the wrapped model's forward\n",
+ " label_names=[\"labels\"], # same reason as above\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b3a944d8-3112-4552-82a0-be25988b3857",
+ "metadata": {
+ "id": "b3a944d8-3112-4552-82a0-be25988b3857"
+ },
+ "source": [
+ "**Few Important Notes:**\n",
+ "1. `remove_unused_columns=False` and `label_names=[\"labels\"]` are required as the PeftModel's forward doesn't have the signature of the base model's forward.\n",
+ "\n",
+ "2. INT8 training required autocasting. `predict_with_generate` can't be passed to Trainer because it internally calls transformer's `generate` without autocasting leading to errors. \n",
+ "\n",
+ "3. Because of point 2, `compute_metrics` shouldn't be passed to `Seq2SeqTrainer` as seen below. (commented out)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d546d7fe-0543-479a-b708-2ebabec19493",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "d546d7fe-0543-479a-b708-2ebabec19493",
+ "outputId": "e2fabe64-2c50-42ff-a7ca-7773813e9408"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The model is loaded in 8-bit precision. To train this model you need to add additional modules inside the model such as adapters using `peft` library and freeze the model weights. Please check the examples in https://github.com/huggingface/peft for more details.\n",
+ "Using cuda_amp half precision backend\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import Seq2SeqTrainer\n",
+ "\n",
+ "trainer = Seq2SeqTrainer(\n",
+ " args=training_args,\n",
+ " model=model,\n",
+ " train_dataset=common_voice[\"train\"],\n",
+ " eval_dataset=common_voice[\"test\"],\n",
+ " data_collator=data_collator,\n",
+ " # compute_metrics=compute_metrics,\n",
+ " tokenizer=processor.feature_extractor,\n",
+ ")\n",
+ "model.config.use_cache = False # silence the warnings. Please re-enable for inference!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
+ "outputId": "cdea5268-f33a-4d48-ea4a-a9c71576f81d"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:346: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+ " warnings.warn(\n",
+ "***** Running training *****\n",
+ " Num examples = 3927\n",
+ " Num Epochs = 3\n",
+ " Instantaneous batch size per device = 8\n",
+ " Total train batch size (w. parallel, distributed & accumulation) = 8\n",
+ " Gradient Accumulation steps = 1\n",
+ " Total optimization steps = 1473\n",
+ " Number of trainable parameters = 15728640\n",
+ "/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
+ " warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n",
+ "/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
+ " warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
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+ " [1473/1473 3:20:30, Epoch 3/3]\n",
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+ " \n",
+ " \n",
+ " | Epoch | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
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+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=1473, training_loss=0.20080567288382556, metrics={'train_runtime': 13136.6638, 'train_samples_per_second': 0.897, 'train_steps_per_second': 0.112, 'total_flos': 2.52799085113344e+19, 'train_loss': 0.20080567288382556, 'epoch': 3.0})"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0576aa2a",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 116,
+ "referenced_widgets": [
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+ "outputId": "6d3fe4aa-f42a-4428-e070-2272f3b87f5c"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Uploading the following files to smangrul/openai-whisper-large-v2-LORA-colab: adapter_model.bin,adapter_config.json\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f309d7a096df4f119e6e6871b56913f1",
+ "version_major": 2,
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diff --git a/examples/lora_dreambooth/lora_dreambooth_inference.ipynb b/examples/lora_dreambooth/lora_dreambooth_inference.ipynb
index 5a8a7f0..a98bbe6 100644
--- a/examples/lora_dreambooth/lora_dreambooth_inference.ipynb
+++ b/examples/lora_dreambooth/lora_dreambooth_inference.ipynb
@@ -51,7 +51,7 @@
"logger = get_logger(__name__)\n",
"\n",
"\n",
- "MODEL_NAME=\"CompVis/stable-diffusion-v1-4\"#\"stabilityai/stable-diffusion-2-1-base\"\n",
+ "MODEL_NAME = \"CompVis/stable-diffusion-v1-4\" # \"stabilityai/stable-diffusion-2-1-base\"\n",
"INSTANCE_PROMPT = \"a photo of sks dog\"\n",
"ckpt_dir = \"/home/sourab/temp/sd_dog_dreambooth/\""
]
@@ -89,31 +89,32 @@
" with open(f\"{ckpt_dir}{instance_prompt}_lora_config.json\", \"r\") as f:\n",
" lora_config = json.load(f)\n",
" print(lora_config)\n",
- " \n",
+ "\n",
" checkpoint = f\"{ckpt_dir}{instance_prompt}_lora.pt\"\n",
" lora_checkpoint_sd = torch.load(checkpoint)\n",
- " unet_lora_ds = {k:v for k,v in lora_checkpoint_sd.items() if \"text_encoder_\" not in k}\n",
- " text_encoder_lora_ds = {k.replace(\"text_encoder_\", \"\"):v for k,v in lora_checkpoint_sd.items() if \"text_encoder_\" in k}\n",
- " \n",
+ " unet_lora_ds = {k: v for k, v in lora_checkpoint_sd.items() if \"text_encoder_\" not in k}\n",
+ " text_encoder_lora_ds = {\n",
+ " k.replace(\"text_encoder_\", \"\"): v for k, v in lora_checkpoint_sd.items() if \"text_encoder_\" in k\n",
+ " }\n",
+ "\n",
" unet_config = LoraConfig(**lora_config[\"peft_config\"])\n",
" pipe.unet = LoraModel(unet_config, pipe.unet)\n",
- " set_peft_model_state_dict(pipe.unet, unet_lora_ds) \n",
- " \n",
+ " set_peft_model_state_dict(pipe.unet, unet_lora_ds)\n",
+ "\n",
" if \"text_encoder_peft_config\" in lora_config:\n",
" text_encoder_config = LoraConfig(**lora_config[\"text_encoder_peft_config\"])\n",
" pipe.text_encoder = LoraModel(text_encoder_config, pipe.text_encoder)\n",
" set_peft_model_state_dict(pipe.text_encoder, text_encoder_lora_ds)\n",
- " \n",
+ "\n",
" if dtype in (torch.float16, torch.bfloat16):\n",
" pipe.unet.half()\n",
" pipe.text_encoder.half()\n",
- " \n",
- " pipe.to(device) \n",
- " return pipe\n",
- " \n",
- "pipe = load_and_set_lora_ckpt(pipe, ckpt_dir, INSTANCE_PROMPT, \"cuda\", torch.float16)\n",
"\n",
- " "
+ " pipe.to(device)\n",
+ " return pipe\n",
+ "\n",
+ "\n",
+ "pipe = load_and_set_lora_ckpt(pipe, ckpt_dir, INSTANCE_PROMPT, \"cuda\", torch.float16)"
]
},
{
@@ -175,9 +176,7 @@
"outputs": [],
"source": [
"prompt = \"sks dog with Eiffel Tower in the background\"\n",
- "image = pipe(prompt, num_inference_steps=50, \n",
- " guidance_scale=7.5, \n",
- " negative_prompt=negative_prompt).images[0]\n",
+ "image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5, negative_prompt=negative_prompt).images[0]\n",
"image"
]
},
diff --git a/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb b/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb
index 77d9494..09ccbb8 100644
--- a/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb
+++ b/examples/semantic_segmentation/semantic_segmentation_peft_lora.ipynb
@@ -170,9 +170,7 @@
"\n",
"repo_id = \"huggingface/label-files\"\n",
"filename = \"ade20k-id2label.json\"\n",
- "id2label = json.load(\n",
- " open(cached_download(hf_hub_url(repo_id, filename, repo_type=\"dataset\")), \"r\")\n",
- ")\n",
+ "id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type=\"dataset\")), \"r\"))\n",
"id2label = {int(k): v for k, v in id2label.items()}\n",
"label2id = {v: k for k, v in id2label.items()}\n",
"num_labels = len(id2label)"
@@ -318,12 +316,8 @@
" per_category_accuracy = metrics.pop(\"per_category_accuracy\").tolist()\n",
" per_category_iou = metrics.pop(\"per_category_iou\").tolist()\n",
"\n",
- " metrics.update(\n",
- " {f\"accuracy_{id2label[i]}\": v for i, v in enumerate(per_category_accuracy)}\n",
- " )\n",
- " metrics.update(\n",
- " {f\"iou_{id2label[i]}\": v for i, v in enumerate(per_category_iou)}\n",
- " )\n",
+ " metrics.update({f\"accuracy_{id2label[i]}\": v for i, v in enumerate(per_category_accuracy)})\n",
+ " metrics.update({f\"iou_{id2label[i]}\": v for i, v in enumerate(per_category_iou)})\n",
"\n",
" return metrics"
]
@@ -1022,9 +1016,7 @@
" color_seg[pred_seg == label, :] = color\n",
"color_seg = color_seg[..., ::-1] # convert to BGR\n",
"\n",
- "img = (\n",
- " np.array(image) * 0.5 + color_seg * 0.5\n",
- ") # plot the image with the segmentation map\n",
+ "img = np.array(image) * 0.5 + color_seg * 0.5 # plot the image with the segmentation map\n",
"img = img.astype(np.uint8)\n",
"\n",
"plt.figure(figsize=(15, 10))\n",
diff --git a/examples/sequence_classification/LoRA.ipynb b/examples/sequence_classification/LoRA.ipynb
index e9dd6da..44c61e4 100644
--- a/examples/sequence_classification/LoRA.ipynb
+++ b/examples/sequence_classification/LoRA.ipynb
@@ -29,13 +29,21 @@
"import torch\n",
"from torch.optim import AdamW\n",
"from torch.utils.data import DataLoader\n",
- "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict, LoraConfig, PeftType, \\\n",
- "PrefixTuningConfig, PromptEncoderConfig\n",
+ "from peft import (\n",
+ " get_peft_config,\n",
+ " get_peft_model,\n",
+ " get_peft_model_state_dict,\n",
+ " set_peft_model_state_dict,\n",
+ " LoraConfig,\n",
+ " PeftType,\n",
+ " PrefixTuningConfig,\n",
+ " PromptEncoderConfig,\n",
+ ")\n",
"\n",
"import evaluate\n",
"from datasets import load_dataset\n",
"from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
- "from tqdm import tqdm\n"
+ "from tqdm import tqdm"
]
},
{
@@ -60,13 +68,7 @@
"metadata": {},
"outputs": [],
"source": [
- "peft_config = LoraConfig(\n",
- " task_type=\"SEQ_CLS\",\n",
- " inference_mode=False,\n",
- " r=8,\n",
- " lora_alpha=16,\n",
- " lora_dropout=0.1\n",
- ")\n",
+ "peft_config = LoraConfig(task_type=\"SEQ_CLS\", inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.1)\n",
"lr = 3e-4"
]
},
@@ -159,19 +161,21 @@
" padding_side = \"left\"\n",
"else:\n",
" padding_side = \"right\"\n",
- " \n",
+ "\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
"if getattr(tokenizer, \"pad_token_id\") is None:\n",
" tokenizer.pad_token_id = tokenizer.eos_token_id\n",
- " \n",
+ "\n",
"datasets = load_dataset(\"glue\", task)\n",
"metric = evaluate.load(\"glue\", task)\n",
"\n",
+ "\n",
"def tokenize_function(examples):\n",
" # max_length=None => use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
" return outputs\n",
"\n",
+ "\n",
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
@@ -182,16 +186,16 @@
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
"\n",
+ "\n",
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
+ "\n",
"# Instantiate dataloaders.\n",
- "train_dataloader = DataLoader(\n",
- " tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size\n",
- ")\n",
+ "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
- ")\n"
+ ")"
]
},
{
@@ -219,7 +223,7 @@
"# Instantiate scheduler\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
- " num_warmup_steps=0.06*(len(train_dataloader) * num_epochs),\n",
+ " num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
@@ -668,7 +672,7 @@
" )\n",
"\n",
"eval_metric = metric.compute()\n",
- "print(eval_metric)\n"
+ "print(eval_metric)"
]
},
{
diff --git a/examples/sequence_classification/P_Tuning.ipynb b/examples/sequence_classification/P_Tuning.ipynb
index 5b9f91d..b811fcd 100644
--- a/examples/sequence_classification/P_Tuning.ipynb
+++ b/examples/sequence_classification/P_Tuning.ipynb
@@ -29,13 +29,20 @@
"import torch\n",
"from torch.optim import AdamW\n",
"from torch.utils.data import DataLoader\n",
- "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict, PeftType, \\\n",
- "PrefixTuningConfig, PromptEncoderConfig\n",
+ "from peft import (\n",
+ " get_peft_config,\n",
+ " get_peft_model,\n",
+ " get_peft_model_state_dict,\n",
+ " set_peft_model_state_dict,\n",
+ " PeftType,\n",
+ " PrefixTuningConfig,\n",
+ " PromptEncoderConfig,\n",
+ ")\n",
"\n",
"import evaluate\n",
"from datasets import load_dataset\n",
"from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
- "from tqdm import tqdm\n"
+ "from tqdm import tqdm"
]
},
{
@@ -60,12 +67,7 @@
"metadata": {},
"outputs": [],
"source": [
- "\n",
- "peft_config = PromptEncoderConfig(\n",
- " task_type=\"SEQ_CLS\",\n",
- " num_virtual_tokens=20,\n",
- " encoder_hidden_size=128\n",
- ")\n",
+ "peft_config = PromptEncoderConfig(task_type=\"SEQ_CLS\", num_virtual_tokens=20, encoder_hidden_size=128)\n",
"lr = 1e-3"
]
},
@@ -111,19 +113,21 @@
" padding_side = \"left\"\n",
"else:\n",
" padding_side = \"right\"\n",
- " \n",
+ "\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
"if getattr(tokenizer, \"pad_token_id\") is None:\n",
" tokenizer.pad_token_id = tokenizer.eos_token_id\n",
- " \n",
+ "\n",
"datasets = load_dataset(\"glue\", task)\n",
"metric = evaluate.load(\"glue\", task)\n",
"\n",
+ "\n",
"def tokenize_function(examples):\n",
" # max_length=None => use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
" return outputs\n",
"\n",
+ "\n",
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
@@ -134,16 +138,16 @@
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
"\n",
+ "\n",
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
+ "\n",
"# Instantiate dataloaders.\n",
- "train_dataloader = DataLoader(\n",
- " tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size\n",
- ")\n",
+ "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
- ")\n"
+ ")"
]
},
{
@@ -171,7 +175,7 @@
"# Instantiate scheduler\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
- " num_warmup_steps=0,#0.06*(len(train_dataloader) * num_epochs),\n",
+ " num_warmup_steps=0, # 0.06*(len(train_dataloader) * num_epochs),\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
@@ -640,7 +644,7 @@
" )\n",
"\n",
"eval_metric = metric.compute()\n",
- "print(eval_metric)\n"
+ "print(eval_metric)"
]
},
{
diff --git a/examples/sequence_classification/Prompt_Tuning.ipynb b/examples/sequence_classification/Prompt_Tuning.ipynb
index ce5c068..10bd633 100644
--- a/examples/sequence_classification/Prompt_Tuning.ipynb
+++ b/examples/sequence_classification/Prompt_Tuning.ipynb
@@ -29,13 +29,21 @@
"import torch\n",
"from torch.optim import AdamW\n",
"from torch.utils.data import DataLoader\n",
- "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict, PeftType, \\\n",
- "PrefixTuningConfig, PromptEncoderConfig, PromptTuningConfig\n",
+ "from peft import (\n",
+ " get_peft_config,\n",
+ " get_peft_model,\n",
+ " get_peft_model_state_dict,\n",
+ " set_peft_model_state_dict,\n",
+ " PeftType,\n",
+ " PrefixTuningConfig,\n",
+ " PromptEncoderConfig,\n",
+ " PromptTuningConfig,\n",
+ ")\n",
"\n",
"import evaluate\n",
"from datasets import load_dataset\n",
"from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
- "from tqdm import tqdm\n"
+ "from tqdm import tqdm"
]
},
{
@@ -60,11 +68,8 @@
"metadata": {},
"outputs": [],
"source": [
- "peft_config = PromptTuningConfig(\n",
- " task_type=\"SEQ_CLS\",\n",
- " num_virtual_tokens=10\n",
- ")\n",
- "lr = 1e-3\n"
+ "peft_config = PromptTuningConfig(task_type=\"SEQ_CLS\", num_virtual_tokens=10)\n",
+ "lr = 1e-3"
]
},
{
@@ -109,19 +114,21 @@
" padding_side = \"left\"\n",
"else:\n",
" padding_side = \"right\"\n",
- " \n",
+ "\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
"if getattr(tokenizer, \"pad_token_id\") is None:\n",
" tokenizer.pad_token_id = tokenizer.eos_token_id\n",
- " \n",
+ "\n",
"datasets = load_dataset(\"glue\", task)\n",
"metric = evaluate.load(\"glue\", task)\n",
"\n",
+ "\n",
"def tokenize_function(examples):\n",
" # max_length=None => use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
" return outputs\n",
"\n",
+ "\n",
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
@@ -132,16 +139,16 @@
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
"\n",
+ "\n",
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
+ "\n",
"# Instantiate dataloaders.\n",
- "train_dataloader = DataLoader(\n",
- " tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size\n",
- ")\n",
+ "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
- ")\n"
+ ")"
]
},
{
@@ -169,7 +176,7 @@
"# Instantiate scheduler\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
- " num_warmup_steps=0.06*(len(train_dataloader) * num_epochs),\n",
+ " num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
@@ -652,7 +659,7 @@
" )\n",
"\n",
"eval_metric = metric.compute()\n",
- "print(eval_metric)\n"
+ "print(eval_metric)"
]
}
],
diff --git a/examples/sequence_classification/prefix_tuning.ipynb b/examples/sequence_classification/prefix_tuning.ipynb
index a7ca16e..ee96b33 100644
--- a/examples/sequence_classification/prefix_tuning.ipynb
+++ b/examples/sequence_classification/prefix_tuning.ipynb
@@ -29,13 +29,20 @@
"import torch\n",
"from torch.optim import AdamW\n",
"from torch.utils.data import DataLoader\n",
- "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict, PeftType, \\\n",
- "PrefixTuningConfig, PromptEncoderConfig\n",
+ "from peft import (\n",
+ " get_peft_config,\n",
+ " get_peft_model,\n",
+ " get_peft_model_state_dict,\n",
+ " set_peft_model_state_dict,\n",
+ " PeftType,\n",
+ " PrefixTuningConfig,\n",
+ " PromptEncoderConfig,\n",
+ ")\n",
"\n",
"import evaluate\n",
"from datasets import load_dataset\n",
"from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
- "from tqdm import tqdm\n"
+ "from tqdm import tqdm"
]
},
{
@@ -60,10 +67,7 @@
"metadata": {},
"outputs": [],
"source": [
- "peft_config = PrefixTuningConfig(\n",
- " task_type=\"SEQ_CLS\",\n",
- " num_virtual_tokens=20\n",
- ")\n",
+ "peft_config = PrefixTuningConfig(task_type=\"SEQ_CLS\", num_virtual_tokens=20)\n",
"lr = 1e-2"
]
},
@@ -128,19 +132,21 @@
" padding_side = \"left\"\n",
"else:\n",
" padding_side = \"right\"\n",
- " \n",
+ "\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
"if getattr(tokenizer, \"pad_token_id\") is None:\n",
" tokenizer.pad_token_id = tokenizer.eos_token_id\n",
- " \n",
+ "\n",
"datasets = load_dataset(\"glue\", task)\n",
"metric = evaluate.load(\"glue\", task)\n",
"\n",
+ "\n",
"def tokenize_function(examples):\n",
" # max_length=None => use the model max length (it's actually the default)\n",
" outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
" return outputs\n",
"\n",
+ "\n",
"tokenized_datasets = datasets.map(\n",
" tokenize_function,\n",
" batched=True,\n",
@@ -151,16 +157,16 @@
"# transformers library\n",
"tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
"\n",
+ "\n",
"def collate_fn(examples):\n",
" return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
"\n",
+ "\n",
"# Instantiate dataloaders.\n",
- "train_dataloader = DataLoader(\n",
- " tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size\n",
- ")\n",
+ "train_dataloader = DataLoader(tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size)\n",
"eval_dataloader = DataLoader(\n",
" tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
- ")\n"
+ ")"
]
},
{
@@ -188,7 +194,7 @@
"# Instantiate scheduler\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
- " num_warmup_steps=0.06*(len(train_dataloader) * num_epochs),\n",
+ " num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),\n",
" num_training_steps=(len(train_dataloader) * num_epochs),\n",
")"
]
@@ -671,7 +677,7 @@
" )\n",
"\n",
"eval_metric = metric.compute()\n",
- "print(eval_metric)\n"
+ "print(eval_metric)"
]
}
],
diff --git a/examples/token_classification/peft_lora_token_cls.ipynb b/examples/token_classification/peft_lora_token_cls.ipynb
index 8997649..f019796 100644
--- a/examples/token_classification/peft_lora_token_cls.ipynb
+++ b/examples/token_classification/peft_lora_token_cls.ipynb
@@ -104,6 +104,7 @@
"source": [
"from PIL import Image, ImageDraw, ImageFont\n",
"import os\n",
+ "\n",
"base_path = \"/home/sourab/temp/data/dataset\"\n",
"\n",
"image = Image.open(os.path.join(base_path, \"training_data/images/0000971160.png\"))\n",
@@ -135,11 +136,11 @@
"source": [
"import json\n",
"\n",
- "with open(os.path.join(base_path, 'training_data/annotations/0000971160.json')) as f:\n",
- " data = json.load(f)\n",
+ "with open(os.path.join(base_path, \"training_data/annotations/0000971160.json\")) as f:\n",
+ " data = json.load(f)\n",
"\n",
- "for annotation in data['form']:\n",
- " print(annotation)"
+ "for annotation in data[\"form\"]:\n",
+ " print(annotation)"
]
},
{
@@ -168,17 +169,17 @@
"\n",
"font = ImageFont.load_default()\n",
"\n",
- "label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}\n",
+ "label2color = {\"question\": \"blue\", \"answer\": \"green\", \"header\": \"orange\", \"other\": \"violet\"}\n",
"\n",
- "for annotation in data['form']:\n",
- " label = annotation['label']\n",
- " general_box = annotation['box']\n",
- " draw.rectangle(general_box, outline=label2color[label], width=2)\n",
- " draw.text((general_box[0] + 10, general_box[1] - 10), label, fill=label2color[label], font=font)\n",
- " words = annotation['words']\n",
- " for word in words:\n",
- " box = word['box']\n",
- " draw.rectangle(box, outline=label2color[label], width=1)\n",
+ "for annotation in data[\"form\"]:\n",
+ " label = annotation[\"label\"]\n",
+ " general_box = annotation[\"box\"]\n",
+ " draw.rectangle(general_box, outline=label2color[label], width=2)\n",
+ " draw.text((general_box[0] + 10, general_box[1] - 10), label, fill=label2color[label], font=font)\n",
+ " words = annotation[\"words\"]\n",
+ " for word in words:\n",
+ " box = word[\"box\"]\n",
+ " draw.rectangle(box, outline=label2color[label], width=1)\n",
"\n",
"image"
]
@@ -260,6 +261,7 @@
"source": [
"from torch.nn import CrossEntropyLoss\n",
"\n",
+ "\n",
"def get_labels(path):\n",
" with open(path, \"r\") as f:\n",
" labels = f.read().splitlines()\n",
@@ -267,6 +269,7 @@
" labels = [\"O\"] + labels\n",
" return labels\n",
"\n",
+ "\n",
"labels = get_labels(\"data/labels.txt\")\n",
"num_labels = len(labels)\n",
"label_map = {i: label for i, label in enumerate(labels)}\n",
@@ -368,27 +371,19 @@
" pad_token_segment_id=4 if args.model_type in [\"xlnet\"] else 0,\n",
" pad_token_label_id=pad_token_label_id,\n",
" )\n",
- " #if args.local_rank in [-1, 0]:\n",
- " #logger.info(\"Saving features into cached file %s\", cached_features_file)\n",
- " #torch.save(features, cached_features_file)\n",
+ " # if args.local_rank in [-1, 0]:\n",
+ " # logger.info(\"Saving features into cached file %s\", cached_features_file)\n",
+ " # torch.save(features, cached_features_file)\n",
"\n",
" if args.local_rank == 0 and mode == \"train\":\n",
" torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache\n",
"\n",
" self.features = features\n",
" # Convert to Tensors and build dataset\n",
- " self.all_input_ids = torch.tensor(\n",
- " [f.input_ids for f in features], dtype=torch.long\n",
- " )\n",
- " self.all_input_mask = torch.tensor(\n",
- " [f.input_mask for f in features], dtype=torch.long\n",
- " )\n",
- " self.all_segment_ids = torch.tensor(\n",
- " [f.segment_ids for f in features], dtype=torch.long\n",
- " )\n",
- " self.all_label_ids = torch.tensor(\n",
- " [f.label_ids for f in features], dtype=torch.long\n",
- " )\n",
+ " self.all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n",
+ " self.all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)\n",
+ " self.all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)\n",
+ " self.all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long)\n",
" self.all_bboxes = torch.tensor([f.boxes for f in features], dtype=torch.long)\n",
"\n",
" def __len__(self):\n",
@@ -441,9 +436,7 @@
" ):\n",
" assert (\n",
" 0 <= all(boxes) <= 1000\n",
- " ), \"Error with input bbox ({}): the coordinate value is not between 0 and 1000\".format(\n",
- " boxes\n",
- " )\n",
+ " ), \"Error with input bbox ({}): the coordinate value is not between 0 and 1000\".format(boxes)\n",
" self.input_ids = input_ids\n",
" self.input_mask = input_mask\n",
" self.segment_ids = segment_ids\n",
@@ -460,9 +453,9 @@
" image_file_path = os.path.join(data_dir, \"{}_image.txt\".format(mode))\n",
" guid_index = 1\n",
" examples = []\n",
- " with open(file_path, encoding=\"utf-8\") as f, open(\n",
- " box_file_path, encoding=\"utf-8\"\n",
- " ) as fb, open(image_file_path, encoding=\"utf-8\") as fi:\n",
+ " with open(file_path, encoding=\"utf-8\") as f, open(box_file_path, encoding=\"utf-8\") as fb, open(\n",
+ " image_file_path, encoding=\"utf-8\"\n",
+ " ) as fi:\n",
" words = []\n",
" boxes = []\n",
" actual_bboxes = []\n",
@@ -546,17 +539,17 @@
" sequence_a_segment_id=0,\n",
" mask_padding_with_zero=True,\n",
"):\n",
- " \"\"\" Loads a data file into a list of `InputBatch`s\n",
- " `cls_token_at_end` define the location of the CLS token:\n",
- " - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]\n",
- " - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]\n",
- " `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)\n",
+ " \"\"\"Loads a data file into a list of `InputBatch`s\n",
+ " `cls_token_at_end` define the location of the CLS token:\n",
+ " - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]\n",
+ " - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]\n",
+ " `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)\n",
" \"\"\"\n",
"\n",
" label_map = {label: i for i, label in enumerate(label_list)}\n",
"\n",
" features = []\n",
- " for (ex_index, example) in enumerate(examples):\n",
+ " for ex_index, example in enumerate(examples):\n",
" file_name = example.file_name\n",
" page_size = example.page_size\n",
" width, height = page_size\n",
@@ -567,17 +560,13 @@
" token_boxes = []\n",
" actual_bboxes = []\n",
" label_ids = []\n",
- " for word, label, box, actual_bbox in zip(\n",
- " example.words, example.labels, example.boxes, example.actual_bboxes\n",
- " ):\n",
+ " for word, label, box, actual_bbox in zip(example.words, example.labels, example.boxes, example.actual_bboxes):\n",
" word_tokens = tokenizer.tokenize(word)\n",
" tokens.extend(word_tokens)\n",
" token_boxes.extend([box] * len(word_tokens))\n",
" actual_bboxes.extend([actual_bbox] * len(word_tokens))\n",
" # Use the real label id for the first token of the word, and padding ids for the remaining tokens\n",
- " label_ids.extend(\n",
- " [label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1)\n",
- " )\n",
+ " label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))\n",
"\n",
" # Account for [CLS] and [SEP] with \"- 2\" and with \"- 3\" for RoBERTa.\n",
" special_tokens_count = 3 if sep_token_extra else 2\n",
@@ -640,9 +629,7 @@
" padding_length = max_seq_length - len(input_ids)\n",
" if pad_on_left:\n",
" input_ids = ([pad_token] * padding_length) + input_ids\n",
- " input_mask = (\n",
- " [0 if mask_padding_with_zero else 1] * padding_length\n",
- " ) + input_mask\n",
+ " input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask\n",
" segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids\n",
" label_ids = ([pad_token_label_id] * padding_length) + label_ids\n",
" token_boxes = ([pad_token_box] * padding_length) + token_boxes\n",
@@ -682,7 +669,7 @@
" page_size=page_size,\n",
" )\n",
" )\n",
- " return features\n"
+ " return features"
]
},
{
@@ -694,16 +681,20 @@
"outputs": [],
"source": [
"from transformers import LayoutLMTokenizer\n",
- "#from .unilm.layoutlm.data.funsd import FunsdDataset, InputFeatures\n",
+ "\n",
+ "# from .unilm.layoutlm.data.funsd import FunsdDataset, InputFeatures\n",
"from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
+ "\n",
"batch_size = 16\n",
- "args = {'local_rank': -1,\n",
- " 'overwrite_cache': True,\n",
- " 'data_dir': '/home/sourab/temp/data/',\n",
- " 'model_name_or_path':'microsoft/layoutlm-base-uncased',\n",
- " 'max_seq_length': 512,\n",
- " 'model_type': 'layoutlm',\n",
- " }\n",
+ "args = {\n",
+ " \"local_rank\": -1,\n",
+ " \"overwrite_cache\": True,\n",
+ " \"data_dir\": \"/home/sourab/temp/data/\",\n",
+ " \"model_name_or_path\": \"microsoft/layoutlm-base-uncased\",\n",
+ " \"max_seq_length\": 512,\n",
+ " \"model_type\": \"layoutlm\",\n",
+ "}\n",
+ "\n",
"\n",
"# class to turn the keys of a dict into attributes (thanks Stackoverflow)\n",
"class AttrDict(dict):\n",
@@ -711,6 +702,7 @@
" super(AttrDict, self).__init__(*args, **kwargs)\n",
" self.__dict__ = self\n",
"\n",
+ "\n",
"args = AttrDict(args)\n",
"\n",
"tokenizer = LayoutLMTokenizer.from_pretrained(\"microsoft/layoutlm-base-uncased\")\n",
@@ -718,15 +710,11 @@
"# the LayoutLM authors already defined a specific FunsdDataset, so we are going to use this here\n",
"train_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode=\"train\")\n",
"train_sampler = RandomSampler(train_dataset)\n",
- "train_dataloader = DataLoader(train_dataset,\n",
- " sampler=train_sampler,\n",
- " batch_size=batch_size)\n",
+ "train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size)\n",
"\n",
"eval_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode=\"test\")\n",
"eval_sampler = SequentialSampler(eval_dataset)\n",
- "eval_dataloader = DataLoader(eval_dataset,\n",
- " sampler=eval_sampler,\n",
- " batch_size=batch_size)"
+ "eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=batch_size)"
]
},
{
@@ -828,14 +816,10 @@
],
"source": [
"from peft import get_peft_config, PeftModel, get_peft_model, LoraConfig, TaskType\n",
+ "\n",
"peft_config = LoraConfig(\n",
- " task_type=TaskType.TOKEN_CLS,\n",
- " inference_mode=False,\n",
- " r=16,\n",
- " lora_alpha=16,\n",
- " lora_dropout=0.1,\n",
- " bias=\"all\"\n",
- " )\n",
+ " task_type=TaskType.TOKEN_CLS, inference_mode=False, r=16, lora_alpha=16, lora_dropout=0.1, bias=\"all\"\n",
+ ")\n",
"peft_config"
]
},
@@ -883,7 +867,7 @@
"source": [
"print(model.model.layoutlm.encoder.layer[0].attention.self.query.weight)\n",
"print(model.model.layoutlm.encoder.layer[0].attention.self.query.lora_A.weight)\n",
- "print(model.model.classifier.weight)\n"
+ "print(model.model.classifier.weight)"
]
},
{
@@ -909,50 +893,52 @@
"source": [
"from transformers import AdamW, get_linear_schedule_with_warmup\n",
"from tqdm import tqdm\n",
+ "\n",
"num_train_epochs = 100\n",
"\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=3e-3)\n",
"lr_scheduler = get_linear_schedule_with_warmup(\n",
" optimizer=optimizer,\n",
- " num_warmup_steps=0.06*(len(train_dataloader) * num_train_epochs),\n",
+ " num_warmup_steps=0.06 * (len(train_dataloader) * num_train_epochs),\n",
" num_training_steps=(len(train_dataloader) * num_train_epochs),\n",
")\n",
"\n",
"\n",
"global_step = 0\n",
"\n",
- "t_total = len(train_dataloader) * num_train_epochs # total number of training steps \n",
+ "t_total = len(train_dataloader) * num_train_epochs # total number of training steps\n",
"\n",
- "#put the model in training mode\n",
+ "# put the model in training mode\n",
"model.train()\n",
"for epoch in range(num_train_epochs):\n",
- " for batch in tqdm(train_dataloader, desc=\"Training\"):\n",
- " input_ids = batch[0].to(device)\n",
- " bbox = batch[4].to(device)\n",
- " attention_mask = batch[1].to(device)\n",
- " token_type_ids = batch[2].to(device)\n",
- " labels = batch[3].to(device)\n",
+ " for batch in tqdm(train_dataloader, desc=\"Training\"):\n",
+ " input_ids = batch[0].to(device)\n",
+ " bbox = batch[4].to(device)\n",
+ " attention_mask = batch[1].to(device)\n",
+ " token_type_ids = batch[2].to(device)\n",
+ " labels = batch[3].to(device)\n",
"\n",
- " # forward pass\n",
- " outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,\n",
- " labels=labels)\n",
- " loss = outputs.loss\n",
+ " # forward pass\n",
+ " outputs = model(\n",
+ " input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels\n",
+ " )\n",
+ " loss = outputs.loss\n",
"\n",
- " # print loss every 100 steps\n",
- " if global_step % 10 == 0:\n",
- " print(f\"Loss after {global_step} steps: {loss.item()}\")\n",
+ " # print loss every 100 steps\n",
+ " if global_step % 10 == 0:\n",
+ " print(f\"Loss after {global_step} steps: {loss.item()}\")\n",
"\n",
- " # backward pass to get the gradients \n",
- " loss.backward()\n",
+ " # backward pass to get the gradients\n",
+ " loss.backward()\n",
"\n",
- " #print(\"Gradients on classification head:\")\n",
- " #print(model.classifier.weight.grad[6,:].sum())\n",
+ " # print(\"Gradients on classification head:\")\n",
+ " # print(model.classifier.weight.grad[6,:].sum())\n",
"\n",
- " # update\n",
- " optimizer.step()\n",
- " lr_scheduler.step()\n",
- " optimizer.zero_grad()\n",
- " global_step += 1"
+ " # update\n",
+ " optimizer.step()\n",
+ " lr_scheduler.step()\n",
+ " optimizer.zero_grad()\n",
+ " global_step += 1"
]
},
{
@@ -1006,8 +992,9 @@
" labels = batch[3].to(device)\n",
"\n",
" # forward pass\n",
- " outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,\n",
- " labels=labels)\n",
+ " outputs = model(\n",
+ " input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels\n",
+ " )\n",
" # get the loss and logits\n",
" tmp_eval_loss = outputs.loss\n",
" logits = outputs.logits\n",
@@ -1021,9 +1008,7 @@
" out_label_ids = labels.detach().cpu().numpy()\n",
" else:\n",
" preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)\n",
- " out_label_ids = np.append(\n",
- " out_label_ids, labels.detach().cpu().numpy(), axis=0\n",
- " )\n",
+ " out_label_ids = np.append(out_label_ids, labels.detach().cpu().numpy(), axis=0)\n",
"\n",
"# compute average evaluation loss\n",
"eval_loss = eval_loss / nb_eval_steps\n",
@@ -1070,7 +1055,7 @@
"metadata": {},
"outputs": [],
"source": [
- "model.save_pretrained(\"peft_layoutlm\")\n"
+ "model.save_pretrained(\"peft_layoutlm\")"
]
},
{