From c1281b96fff223225b9f776f8ac26a673c4417ec Mon Sep 17 00:00:00 2001 From: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Date: Thu, 16 Feb 2023 17:42:28 +0530 Subject: [PATCH] resolving comments and running `jupyter black` --- ...a_clm_accelerate_big_model_inference.ipynb | 86 +- .../peft_prefix_tuning_clm.ipynb | 123 +- .../peft_prompt_tuning_clm.ipynb | 130 +- .../peft_lora_seq2seq.ipynb | 76 +- ...q2seq_accelerate_big_model_inference.ipynb | 28 +- .../peft_prefix_tuning_seq2seq.ipynb | 76 +- .../image_classification_peft_lora.ipynb | 20 +- .../Finetune_flan_t5_large_bnb_peft.ipynb | 16627 ++++--- .../int8_training/Finetune_opt_bnb_peft.ipynb | 18877 +++---- .../peft_bnb_whisper_large_v2_training.ipynb | 41114 ++++++++-------- .../lora_dreambooth_inference.ipynb | 33 +- .../semantic_segmentation_peft_lora.ipynb | 16 +- examples/sequence_classification/LoRA.ipynb | 40 +- .../sequence_classification/P_Tuning.ipynb | 38 +- .../Prompt_Tuning.ipynb | 39 +- .../prefix_tuning.ipynb | 36 +- .../peft_lora_token_cls.ipynb | 193 +- 17 files changed, 38805 insertions(+), 38747 deletions(-) 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", - 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"\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", - 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EpochTraining LossValidation Loss
1No log0.017228

" - ], - "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", - "9030744dbca9427ba8a036a76b5c8bf7", - "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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"1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6f30253108fb4dce9c3de029457ef6f1", - "placeholder": "​", - "style": "IPY_MODEL_1fa2a7e3ff3c4c99ab95e96a28624846", - "value": " 1/1 [00:00<00:00, 22.88ba/s]" - } - } - } - } + "cells": [ + { + "cell_type": "markdown", + "id": "lw1cWgq-DI5k", + "metadata": { + "id": "lw1cWgq-DI5k" + }, + "source": [ + "# 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 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\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", + "aa2c51ad05c14a02a13e5c047779fc05", + "2ce6779fa5904471945fa5738510af64", + "bdff3b35dcdf49e5ba2c5c2498773cb7", + "6994741f3113493b9d5bba278b8732f5", + "808a41f78a7c4ae0b6aafee59c6234ae", + "c7771479ae4e4efab744fad6da586fd3", + "2d7cb8f5477244ce82e7cec5a9f7ee39", + "df8fbdbe9bc341e3a39a7bda99b70be2", + "179a912bbd1e454eba503782b675efa8", + "232fe29739b74dabb3b558c33835eb32", + "978bbbf33d304588af971d22bb2a3690", + "1b839f5b472e44148c9e02a12550fade", + "b560fc36ee8f424f9590e04a042046fe", + "0845ceae96ca4c95ad4ebebac46b691f", + "e1769695dffd4ebeb79a63ff4812fa9e", + "ec73524ed7f14ea0b67f07d72eada173", + "f2a36b126c1b41848e61b0c581ff8c4b", + "6a40d6535f9e4b5b9c9283a1cd67687a", + "03abf3bb217e4622b31cd869ed11aa00", + "24f9db6df6d54ad6bac4891b65184799", + "f66e179caa8b4393bed19a0488821c47", + "cfc7aa04c11d408c9c12cdbd9cff4bb5", + "65aacfc888014bdea55253428b2569c0", + "82849bb4d5da452e87a18ca749ce5d7b", + "2d2fc7abb9fb411c810b2fbeb54d67cc", + "96e2d208830f48cd821be7e59643c93e", + "8d79b7d0c3cb4f8d99fb20941c35856f", + "2b4d68606bdf4758b812f5a8057af595", + "f9620e01cd6749f88b722a42ff68c502", + "06c3d8c44fbe4f0886cf397d9a3c4b81", + "d72e8b3419f240f2bdce253cce9d24e3", + "2fe0a2fa22a0498da983ec38150216e6", + "f3784e85cef34bdba64b611a1f5883e4", + "d7d9e2e2090d4226ad89e5ba9cec33df", + "5cc620a232bf4d418c3fc882f4c1cd0c", + "f742450a607c4ed0bff98ac9b7685d40", + "e87b05e685b040f7a99450bfbab72433", + "e1c8e6f843604161bbb6cbd269488469", + "34333da7eb0f49a6b83df76e35dcdd93", + "c708031a279e4e55ac7833e6697f93bd", + "e293930c8e2c4eadbda53005e21ec450", + "945ac449c2e84fd6b5a7805b017343f2", + "06cea508d7504b228f6cebc66742d200", + "9d0500a0f5f74be39e5edfbbcd7a64fc", + "0363c4d4dc1449148b09061763ea119a", + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EpochTraining LossValidation Loss
1No log0.017228

" + ], + "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", + "9030744dbca9427ba8a036a76b5c8bf7", + "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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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" - } - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 408, - "referenced_widgets": [ - "d4de260ffd8a440eb87eb900fc1bb1d3", - "8602b545a9f8474dbb3cc178ac0b8e60", - "b46919912ee54f6f9f2ce9080be1c61a", - "50374e3ab81c4626a182e61fc03b94ce", - "2144bc2897dc40b29f060e30ace12275", - "949ca70002ca4472bbc21fea4d7ac745", - "49943c9dadca43a584b3f354ba45280c", - "6123e53fb26b41f0af9a3a3348ae1afd", - "285ef943d540400ab827c462945a259c", - "95727290446244ccb9626f4594949675", - "61b54aa6c9e94ee1bc45c15a9e3f7917", - "fc2d5ffe254d425b939252ec46ec27cc", - "f65af2e868244edeb0cc9402534874a8", - "e466054f08004bbcabb24e400cb3c7fc", - "6ea40800dfd849e3b106bae71fc53ae3", - "722a01f42b7d4c38836a4546ecb38108", - "1bd5179cdb474b65aa06eca3520ad37b", - "04d367124a3b419ab1fa1dfd4f9004c3", - "4ea667f48b9f4e1f9da7c5a0d3025b85", - "9b96c63630654773acd38b8b88371f28", - "cb69ae47666a4603a07a8778e2ae7d6e", - "8f5e9f2d11d54fd2a08dcbff9f6da05c", - "c6f712eadc4d49019b2bd355968cc155", - "5fd979c05fd34311af877ce1a988ead8", - "179165ff4c0e4586aaf3a40b8502f428", - "cad1d8326e474a4f9ee13db9005121dd", - "b049ac0b3d2e44ba9b1fc1cb2dd3de62", - "921c268ecd724b6a8869dae8f81d558a", - "4cf85a85c0764f15b5134c81b360e910", - "5936d9eb59e147eea6482006decfe0ee", - "fad61c68edc84c8197afeafded84280d", - "451309d8b3ca4cd9b6f538640477039e", - "c7621e14aa16421d9758321e433b92e4", - "5aa74b9b30614172b07f88873cf89471", - "cf39fb025e3d4635a5695135a56d9f64", - "deaf9732f33446a3be015d2ec16aba76", - "bdfd856dd8ee4ae09205ad9d1b9cc806", - "7a49f7a55b054b6d829f290a1a426a7a", - "5b695486a0ac442b8b6a8ef2ebf4e57a", - "03be7cb91cba4afab795aab7aa242ee1", - "8f7890f54d514f80b1eb17905cf0f964", - "ae660d51267544a89f0ad199cc12b6fa", - "d5bb8d7359274c8e9cc79df563175137", - "98d5a80ec50c46c18f9ee991e2982115", - "e73e5388182040a8937ccf1748171a87", - "f797f7b8b13f4b3cb871522d34498631", - "41f47c864e094cacb1c550d37ddfe80e", - "104d982b444947fe8e4fbb2c2f082616", - "2cf8581583c641fb95d1e16aff7d4cd1", - "11808e9097424dd0b22a5af6c77813f3", - "dc08d237860e4788a8ceeff4518c2612", - "7bbf2c40a4ae46fe9e5885db08975263", - "f00cbc4a89f5492787ec489da65ff70b", - "8a7cd2194113493a841b00d034b5f1ba", - "e9673363e85448d494ac9ac5d7ba0efb", - "a994beafbf3f4c20880a7bbe3898db36", - "7769c261781f4f5483c7e9d58c1a5573", - "4a713a8fe16f4e81aa841c69711fa136", - "08d036904ecf47ed88e129ba6e2b285c", - "9f1182fdddab43b59ee98bc701965a17", - "c2302f535b114ad780bfa440445c2e28", - "8fe032f285ba4858b8efd68119c217b0", - "4ca695fca3d140c6ab4e1e1d34df807b", - "b77ceaf55dc04810963cdd01126478f6", - "0ccdefcb25e14d229b2634ffae4a6d3d", - "11d6b952503c4824b36b66e228f87599", - "1e9391f6c89c4d08859ef3413edb19be", - "039bbda2402f469eb21ba7ec7ea589a6", - "5da6eef8fb0048219159f38a68727b64", - "c006e62ee6d04831b2b89273ef04a8ac", - "af0634c2539a43989902daef47776901", - "62fea00ef0364af287e6097b964d00c4", - "9e5ef73f8b244845a6b9002fa5c35d15", - "5cb09c84e1e144e0a92580fb5e1ce2fe", - "0f561c1660744251a8f710b69d434c87", - "9e5f870ef80242f5af09fad70f84ea62", - "6f173ca73dd545deb22b8cd0470d925f", - "4e6d5943bc374b388b93ed115e44b6a5", - "cbe2a6ea41834e95a27d6f02c3c0eeec", - "7beb5f4efefc4593abac253df74d1405", - "370cefbaeebf41f582b7507ad493055f", - "e3df9dda16e244aa9b61d54f9e21ef40", - "23d2ad64a17041b7a006dad1e041e0a1", - "c1fd6a1234274a44b838a09f3f5380c6", - "81bb51d088374394becd9a45ec3b17d4", - "9e762779e5434bb7afcc295b61c2f4e4", - "f539b7a4665449de9eac209a20629969", - "32d528db79ad4f6f836ab2e0df5ac426", - "1ca7684b79c5438fa06b047bd2b3283f", - "a07688185bff4c4b8cbed3af3b4cf802", - "0272f1d9f93f4dd788363a8409cdfd69", - "27b41d23d2c64127ba3ae8464958f855", - "2b54032c0d8e4a2897aed1ac1c79af14", - "ed8fa1048e814f2fa3666899fc42e55a", - "97daf559100c44ac983562fea93c5fac", - "72e511b775604d899ff5b3fa2ebe9fc4", - "da946f86590447d2ab98b9da468fa66b", - "54fe79d5c7254117a2209927a7248dd4", - "1d122e4eaad54e06961288484f31e18b", - "d46b5725c35142a89617e46c0e8d3679", - "c5493c23fd5542738ffd1ff5f09a6a67", - "a1a80d3460984c2496ada5a634875934", - "598c5584ffba4f26815c4e87bb1595c4", - "fe93f25323604447be0bb1d24a0c2c59", - "00c2e2d3ee8b45818ba84da12c6b11e2", - "6ccea64c2e614a9fbdcc2f716cecaea0", - "63fc9a9eebca4f2db2ed8a385fc5e204", - "9a4860dfeac944db85e6e532599bc1cb", - "3b946e1bbab24629b98307275fbe7cbb", - "d9d36f8ff5f747bf90fbc8a7d35a6664" - ] - }, - "id": "cg3fiQOvmI3Q", - "outputId": "135a7675-6a4d-4786-b5dc-34cb867f40c7" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "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", - "
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StepTraining Loss
12.364400
22.200400
32.302300
42.184700
51.878700
62.307200
72.193800
82.446200
92.458900
102.020000
111.941200
121.931000
132.055900
141.975100
152.015100
162.095600
171.768300
182.155700
192.402300
202.124600
212.314900
221.908500
232.078800
241.941900
251.879800
261.927500
271.371400
281.977600
292.055000
301.915800
311.958100
322.195900
332.001000
342.025000
351.576900
361.879800
371.821600
381.727800
391.995700
401.698600
412.129300
422.025800
431.696500
441.984700
452.051100
462.054400
471.765600
482.063100
491.746900
501.873000
512.391300
522.494100
532.072300
541.808000
551.911900
562.168100
572.166100
581.921500
591.856000
601.652800
611.605000
622.032500
631.822100
641.623600
651.923200
662.053200
672.114300
681.807700
691.857800
701.854600
712.023000
721.864900
731.769300
741.837700
751.742200
761.895900
771.922800
782.325300
792.231200
802.309500
811.945700
822.072100
831.917400
842.004600
851.951700
861.450600
871.785600
881.668000
891.903100
901.709800
912.312900
922.092100
932.319600
941.603100
951.740000
961.670500
971.611600
981.728900
992.285200
1001.957800
1011.676700
1021.656300
1031.612400
1041.848900
1051.870000
1061.954000
1072.192200
1081.637600
1091.208700
1102.254200
1111.832100
1122.119600
1132.126400
1141.915700
1151.587500
1161.564800
1171.742700
1181.712600
1191.727900
1202.361500
1212.070300
1221.878500
1231.846600
1242.061700
1252.149700
1261.940600
1272.098300
1281.734100
1292.111700
1301.887600
1311.716300
1322.070000
1331.782200
1341.955200
1351.762900
1361.954700
1371.687100
1381.979100
1391.634600
1401.801200
1411.954100
1421.833900
1432.051400
1441.921200
1451.787500
1461.825400
1471.363400
1481.977400
1491.768300
1502.226700
1511.945500

" - ] - }, - "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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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" + } + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 408, + "referenced_widgets": [ + "d4de260ffd8a440eb87eb900fc1bb1d3", + "8602b545a9f8474dbb3cc178ac0b8e60", + "b46919912ee54f6f9f2ce9080be1c61a", + "50374e3ab81c4626a182e61fc03b94ce", + "2144bc2897dc40b29f060e30ace12275", + "949ca70002ca4472bbc21fea4d7ac745", + "49943c9dadca43a584b3f354ba45280c", + "6123e53fb26b41f0af9a3a3348ae1afd", + "285ef943d540400ab827c462945a259c", + "95727290446244ccb9626f4594949675", + "61b54aa6c9e94ee1bc45c15a9e3f7917", + "fc2d5ffe254d425b939252ec46ec27cc", + "f65af2e868244edeb0cc9402534874a8", + "e466054f08004bbcabb24e400cb3c7fc", + "6ea40800dfd849e3b106bae71fc53ae3", + "722a01f42b7d4c38836a4546ecb38108", + "1bd5179cdb474b65aa06eca3520ad37b", + "04d367124a3b419ab1fa1dfd4f9004c3", + "4ea667f48b9f4e1f9da7c5a0d3025b85", + "9b96c63630654773acd38b8b88371f28", + "cb69ae47666a4603a07a8778e2ae7d6e", + "8f5e9f2d11d54fd2a08dcbff9f6da05c", + "c6f712eadc4d49019b2bd355968cc155", + "5fd979c05fd34311af877ce1a988ead8", + "179165ff4c0e4586aaf3a40b8502f428", + "cad1d8326e474a4f9ee13db9005121dd", + "b049ac0b3d2e44ba9b1fc1cb2dd3de62", + "921c268ecd724b6a8869dae8f81d558a", + "4cf85a85c0764f15b5134c81b360e910", + "5936d9eb59e147eea6482006decfe0ee", + "fad61c68edc84c8197afeafded84280d", + "451309d8b3ca4cd9b6f538640477039e", + "c7621e14aa16421d9758321e433b92e4", + "5aa74b9b30614172b07f88873cf89471", + "cf39fb025e3d4635a5695135a56d9f64", + "deaf9732f33446a3be015d2ec16aba76", + "bdfd856dd8ee4ae09205ad9d1b9cc806", + "7a49f7a55b054b6d829f290a1a426a7a", + "5b695486a0ac442b8b6a8ef2ebf4e57a", + "03be7cb91cba4afab795aab7aa242ee1", + "8f7890f54d514f80b1eb17905cf0f964", + "ae660d51267544a89f0ad199cc12b6fa", + "d5bb8d7359274c8e9cc79df563175137", + "98d5a80ec50c46c18f9ee991e2982115", + "e73e5388182040a8937ccf1748171a87", + "f797f7b8b13f4b3cb871522d34498631", + "41f47c864e094cacb1c550d37ddfe80e", + "104d982b444947fe8e4fbb2c2f082616", + "2cf8581583c641fb95d1e16aff7d4cd1", + "11808e9097424dd0b22a5af6c77813f3", + "dc08d237860e4788a8ceeff4518c2612", + "7bbf2c40a4ae46fe9e5885db08975263", + "f00cbc4a89f5492787ec489da65ff70b", + "8a7cd2194113493a841b00d034b5f1ba", + "e9673363e85448d494ac9ac5d7ba0efb", + "a994beafbf3f4c20880a7bbe3898db36", + "7769c261781f4f5483c7e9d58c1a5573", + "4a713a8fe16f4e81aa841c69711fa136", + "08d036904ecf47ed88e129ba6e2b285c", + "9f1182fdddab43b59ee98bc701965a17", + "c2302f535b114ad780bfa440445c2e28", + "8fe032f285ba4858b8efd68119c217b0", + "4ca695fca3d140c6ab4e1e1d34df807b", + "b77ceaf55dc04810963cdd01126478f6", + "0ccdefcb25e14d229b2634ffae4a6d3d", + "11d6b952503c4824b36b66e228f87599", + "1e9391f6c89c4d08859ef3413edb19be", + "039bbda2402f469eb21ba7ec7ea589a6", + "5da6eef8fb0048219159f38a68727b64", + "c006e62ee6d04831b2b89273ef04a8ac", + "af0634c2539a43989902daef47776901", + "62fea00ef0364af287e6097b964d00c4", + "9e5ef73f8b244845a6b9002fa5c35d15", + "5cb09c84e1e144e0a92580fb5e1ce2fe", + "0f561c1660744251a8f710b69d434c87", + "9e5f870ef80242f5af09fad70f84ea62", + "6f173ca73dd545deb22b8cd0470d925f", + "4e6d5943bc374b388b93ed115e44b6a5", + "cbe2a6ea41834e95a27d6f02c3c0eeec", + "7beb5f4efefc4593abac253df74d1405", + "370cefbaeebf41f582b7507ad493055f", + "e3df9dda16e244aa9b61d54f9e21ef40", + "23d2ad64a17041b7a006dad1e041e0a1", + "c1fd6a1234274a44b838a09f3f5380c6", + "81bb51d088374394becd9a45ec3b17d4", + "9e762779e5434bb7afcc295b61c2f4e4", + "f539b7a4665449de9eac209a20629969", + "32d528db79ad4f6f836ab2e0df5ac426", + "1ca7684b79c5438fa06b047bd2b3283f", + "a07688185bff4c4b8cbed3af3b4cf802", + "0272f1d9f93f4dd788363a8409cdfd69", + "27b41d23d2c64127ba3ae8464958f855", + "2b54032c0d8e4a2897aed1ac1c79af14", + "ed8fa1048e814f2fa3666899fc42e55a", + "97daf559100c44ac983562fea93c5fac", + "72e511b775604d899ff5b3fa2ebe9fc4", + "da946f86590447d2ab98b9da468fa66b", + "54fe79d5c7254117a2209927a7248dd4", + "1d122e4eaad54e06961288484f31e18b", + "d46b5725c35142a89617e46c0e8d3679", + "c5493c23fd5542738ffd1ff5f09a6a67", + "a1a80d3460984c2496ada5a634875934", + "598c5584ffba4f26815c4e87bb1595c4", + "fe93f25323604447be0bb1d24a0c2c59", + "00c2e2d3ee8b45818ba84da12c6b11e2", + "6ccea64c2e614a9fbdcc2f716cecaea0", + "63fc9a9eebca4f2db2ed8a385fc5e204", + "9a4860dfeac944db85e6e532599bc1cb", + "3b946e1bbab24629b98307275fbe7cbb", + "d9d36f8ff5f747bf90fbc8a7d35a6664" + ] + }, + "id": "cg3fiQOvmI3Q", + "outputId": "135a7675-6a4d-4786-b5dc-34cb867f40c7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "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", + "

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StepTraining Loss
12.364400
22.200400
32.302300
42.184700
51.878700
62.307200
72.193800
82.446200
92.458900
102.020000
111.941200
121.931000
132.055900
141.975100
152.015100
162.095600
171.768300
182.155700
192.402300
202.124600
212.314900
221.908500
232.078800
241.941900
251.879800
261.927500
271.371400
281.977600
292.055000
301.915800
311.958100
322.195900
332.001000
342.025000
351.576900
361.879800
371.821600
381.727800
391.995700
401.698600
412.129300
422.025800
431.696500
441.984700
452.051100
462.054400
471.765600
482.063100
491.746900
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" + ] + }, + "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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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": { - "base_uri": "https://localhost:8080/" - }, - "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", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 303, - "referenced_widgets": [ - "c60690c2aee74763bf23115553f4e640", - "7d3d6c198e794219ab5db59f0228c8ab", - "5d5ea0207c6148769ad9f15b7b3dd92d", - "642e28d258ca4c30a5df94c5cf7e0471", - "170ee581427d4f30925dc393d124c1be", - "d5d5aa24182a4e04b3fdae1ca7fad52a", - "6dba643113a547ac9b6e121d008791d6", - "cd9bda1053a14890ad9091c63c0a0acf", - "4522666ebbcf4647b06ce81a6316fbbb", - "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", - "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n" - ] - }, - { - "cell_type": "code", - 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"f20fc82bcbf245619ad4dec04d0f999d", - "8016eabfc7aa48c2bc0bf3a13919a675", - "83cb83e404a24fba8cf43610cb1696fc", - "7b70db203ee644fe81006f5d48aa426d", - "efd9d5724dbd435991052b4445c6970f", - "41f65340441f419d91e1d3841921f48a", - "58aba8edefa44c60818891a2651137be", - "e5e8f119a91944f296ff821dd7ecfb1b", - "568a85caf800461b8571e3d206854e6b", - "e2a8a379bd0d4cbdb22fbc3bbb4fdc7a", - "2c7d952f958247b681301d1c6bff4fa7", - "27649a0d303643d8985caf506ef66fc3", - "1855ae0388074d08a4763b7ac76c6948", - "7e5252f608d6468f80deb468cb2556fd", - "bce6b62300d942a1ab89a6f0ceb16d30", - "0239f7263d1a4e8b9475ae48379c068d", - "5b527e45d6a946b28047115fa6b5aa3a", - "3c5eb07f0bff43948ff1f283c3c56b0f", - "3545fcc1e9d7453099c4931f658e0bc8", - "2f314258091a430095794c3fe0aea7e3", - "0ebfa123462342e99ad81b7e5ee00eac", - "8af6bd6e8cae4964a2e372be30220bd9", - "4184f160ab6d4f58bc921fb7ba89cf60", - "575c4d8d503244d8a9f2a5709021b5b2", - "ffce35af1ad84a2c838cca55a26dd3c4", - "d21cd4878c6e49d38dd3abb2e3b3f566", - "969e3cf7f3634c3f90b5fea38c5797ca", - "78501c2a5ac84f9ca0d20bbef340fc9f", - "5b4fbd1102a84670a1eed6f3d25c0bcd", - "621880c98245427881dd5b004b480c6a", - "65c2716dd7f14afa93d3bfaebe85c44f", - "2ff541d18f5844408690be00c7259d59", - "679fa9d2a703494dbb10fbfd19879f1a", - "6717b182e5674afb90006b50dea98cae", - "0887c7aabbbe4d4e8fdc64d2e2657cc7" - ] - }, - "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", - "f9d6d41ffeba43cf94ec9d7a96af6617", - "bed65245e7234977874d35bf78694e35", - "c28c2fc81e0e467cbc0ded0205d1ee85", - "ea941a078b984f51b66b5f1e8f3d1d82", - "e3f1244dbe2c48bc8102f958c3df6467", - "786d3d34cfd24f81996797c55b10b443", - "3a4bb5b9cf864265af8f1a0b989dff2c", - "ac2e1977ddaf4b948c9cc24d84c08b83", - "dd339bf6baf6433e92665e30dc30b062", - "6a544d6dd5954beab32ce3089d8d2aac", - "f933fbdcc26d41b0bb294dabd0337834", - "950e74921e6042868ab6b7b9070d6f69", - "82dc91c5b065459d827863699a9710e1", - "5b7b6f08765c4c1989f945414f2c3cf4", - "735c3606df924b9297ddc05fae3e92d5", - "d5d632dd16f147e090c62aad38c45d3c", - "24191f38e3654b86a5da3576615e2229", - "4b1f4abe697948d5a1cca9b45b2a6f87", - "8527f474f1a549abafe9519e2b4bd338", - "1031dc4b5d2d45f3b14ab12dbd9bca56", - "ab40616ed9b74f438e74d403f848bed4", - "e6c2dc814c324a0c8cb744ca16707479", - "a615446a48624d0f9a009c1f6d8b1a54", - "383d6e891c5249b4b2fabb60d4900488", - "3c3214f235a54864848902f7e53662db", - "73f6e6860b64491284c9442f0deae8b6", - "6d9826a5adb847e8b4cc4486a31b52ce", - "e8b3093587e44164b0ac043414cea0fa", - "96266c5722f54eeeb682b2707b8025dd", - "f0be69583cd1410da6dbc18302d4439b", - "c24ddbaafa5f4a63b391d981a4f10354", - "629725dfcc684b1db1a57850c3bc7bc3", - "add0f175631742d49dd4695bc004e81a", - "5871a46d60f14da99e4bb8ee74405319", - "64b308019fff4095ab7f1812aab4676a", - "03f62b3f5aa64b6390b157cb3f9d2b9f", - "59657f26f4af490980b1d9bea1526e5e", - "1571fa5d8f494704949c5809f3409fa7", - "5ef218872f2646249888def0abed7149", - "03fcbc61c50c4ddc9c3684e4c085bffb", - "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(\"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/", - "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", - "@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": [ - "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", - "08b26adf061b48f59078bf0c0b59e643", - "ef056ad59e314089a012acaa73a54e4f", - "7935e298049f4deeaeb278ba3de92291", - "3afca90970fc4925a05a9a1aa5c8d2f2", - "40aba44ef0a74c0d9a385c803c1365a6", - "ae54388d78dc4b7ebd1b21860421ffe4", - "bb4a47c63d254d4aa3220e85f86d37c4", - "cd585c98560b42c8b4a08df5b853b23c", - "a140cd385e5a4c0a88c5562370982d2e", - "fce64f5690024c698701330f0e5d039a", - "abd0e7c414974e51b280a29bf978f776", - "bf7961a79c2f403a89a4fb6d4b1a02e5", - "1d8636d1d1c3442fbbc2fc83cd03fa44", - "313090dc1f034ab19d5ffc573ea1aa5c", - "966c0400dafe4e3ea2c9baebc2e104fa", - "9087be5d992c4198b3ec2c61f4021164", - "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(\"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": { - "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", - "

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"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", + "7d3d6c198e794219ab5db59f0228c8ab", + "5d5ea0207c6148769ad9f15b7b3dd92d", + "642e28d258ca4c30a5df94c5cf7e0471", + "170ee581427d4f30925dc393d124c1be", + "d5d5aa24182a4e04b3fdae1ca7fad52a", + "6dba643113a547ac9b6e121d008791d6", + "cd9bda1053a14890ad9091c63c0a0acf", + "4522666ebbcf4647b06ce81a6316fbbb", + "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, + "referenced_widgets": [ + 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"41f65340441f419d91e1d3841921f48a", + "58aba8edefa44c60818891a2651137be", + "e5e8f119a91944f296ff821dd7ecfb1b", + "568a85caf800461b8571e3d206854e6b", + "e2a8a379bd0d4cbdb22fbc3bbb4fdc7a", + "2c7d952f958247b681301d1c6bff4fa7", + "27649a0d303643d8985caf506ef66fc3", + "1855ae0388074d08a4763b7ac76c6948", + "7e5252f608d6468f80deb468cb2556fd", + "bce6b62300d942a1ab89a6f0ceb16d30", + "0239f7263d1a4e8b9475ae48379c068d", + "5b527e45d6a946b28047115fa6b5aa3a", + "3c5eb07f0bff43948ff1f283c3c56b0f", + "3545fcc1e9d7453099c4931f658e0bc8", + "2f314258091a430095794c3fe0aea7e3", + "0ebfa123462342e99ad81b7e5ee00eac", + "8af6bd6e8cae4964a2e372be30220bd9", + "4184f160ab6d4f58bc921fb7ba89cf60", + "575c4d8d503244d8a9f2a5709021b5b2", + "ffce35af1ad84a2c838cca55a26dd3c4", + "d21cd4878c6e49d38dd3abb2e3b3f566", + "969e3cf7f3634c3f90b5fea38c5797ca", + "78501c2a5ac84f9ca0d20bbef340fc9f", + "5b4fbd1102a84670a1eed6f3d25c0bcd", + "621880c98245427881dd5b004b480c6a", + "65c2716dd7f14afa93d3bfaebe85c44f", + "2ff541d18f5844408690be00c7259d59", + "679fa9d2a703494dbb10fbfd19879f1a", + "6717b182e5674afb90006b50dea98cae", + "0887c7aabbbe4d4e8fdc64d2e2657cc7" + ] + }, + "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", + "f9d6d41ffeba43cf94ec9d7a96af6617", + "bed65245e7234977874d35bf78694e35", + "c28c2fc81e0e467cbc0ded0205d1ee85", + "ea941a078b984f51b66b5f1e8f3d1d82", + "e3f1244dbe2c48bc8102f958c3df6467", + "786d3d34cfd24f81996797c55b10b443", + "3a4bb5b9cf864265af8f1a0b989dff2c", + "ac2e1977ddaf4b948c9cc24d84c08b83", + "dd339bf6baf6433e92665e30dc30b062", + "6a544d6dd5954beab32ce3089d8d2aac", + "f933fbdcc26d41b0bb294dabd0337834", + "950e74921e6042868ab6b7b9070d6f69", + "82dc91c5b065459d827863699a9710e1", + "5b7b6f08765c4c1989f945414f2c3cf4", + "735c3606df924b9297ddc05fae3e92d5", + "d5d632dd16f147e090c62aad38c45d3c", + "24191f38e3654b86a5da3576615e2229", + "4b1f4abe697948d5a1cca9b45b2a6f87", + "8527f474f1a549abafe9519e2b4bd338", + "1031dc4b5d2d45f3b14ab12dbd9bca56", + "ab40616ed9b74f438e74d403f848bed4", + "e6c2dc814c324a0c8cb744ca16707479", + "a615446a48624d0f9a009c1f6d8b1a54", + "383d6e891c5249b4b2fabb60d4900488", + "3c3214f235a54864848902f7e53662db", + "73f6e6860b64491284c9442f0deae8b6", + "6d9826a5adb847e8b4cc4486a31b52ce", + "e8b3093587e44164b0ac043414cea0fa", + "96266c5722f54eeeb682b2707b8025dd", + "f0be69583cd1410da6dbc18302d4439b", + "c24ddbaafa5f4a63b391d981a4f10354", + "629725dfcc684b1db1a57850c3bc7bc3", + "add0f175631742d49dd4695bc004e81a", + "5871a46d60f14da99e4bb8ee74405319", + "64b308019fff4095ab7f1812aab4676a", + "03f62b3f5aa64b6390b157cb3f9d2b9f", + "59657f26f4af490980b1d9bea1526e5e", + "1571fa5d8f494704949c5809f3409fa7", + "5ef218872f2646249888def0abed7149", + "03fcbc61c50c4ddc9c3684e4c085bffb", + "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", + "08b26adf061b48f59078bf0c0b59e643", + "ef056ad59e314089a012acaa73a54e4f", + "7935e298049f4deeaeb278ba3de92291", + "3afca90970fc4925a05a9a1aa5c8d2f2", + "40aba44ef0a74c0d9a385c803c1365a6", + "ae54388d78dc4b7ebd1b21860421ffe4", + "bb4a47c63d254d4aa3220e85f86d37c4", + "cd585c98560b42c8b4a08df5b853b23c", + "a140cd385e5a4c0a88c5562370982d2e", + "fce64f5690024c698701330f0e5d039a", + "abd0e7c414974e51b280a29bf978f776", + "bf7961a79c2f403a89a4fb6d4b1a02e5", + "1d8636d1d1c3442fbbc2fc83cd03fa44", + "313090dc1f034ab19d5ffc573ea1aa5c", + "966c0400dafe4e3ea2c9baebc2e104fa", + "9087be5d992c4198b3ec2c61f4021164", + "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", + "

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"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\")" ] }, {