From 51c9a64f0da483ed38c1d0515affea31886dafe1 Mon Sep 17 00:00:00 2001 From: jfrery Date: Fri, 20 Dec 2024 09:48:28 +0100 Subject: [PATCH 1/3] chore: update lora notebook + fix order inputs in llama --- src/concrete/ml/torch/lora.py | 1 + .../lora_finetuning/GPT2FineTuneHybrid.ipynb | 578 +++++++++++++----- .../lora_finetuning/LLamaFineTuning.ipynb | 15 +- 3 files changed, 437 insertions(+), 157 deletions(-) diff --git a/src/concrete/ml/torch/lora.py b/src/concrete/ml/torch/lora.py index d80cdab0a..b4888927e 100644 --- a/src/concrete/ml/torch/lora.py +++ b/src/concrete/ml/torch/lora.py @@ -198,6 +198,7 @@ def forward(self, inputs: Tuple[Tensor, ...]) -> Tuple[Tensor, Union[Tensor, Non len(inputs) >= 2 and len(inputs) <= 3 ), "Expected at least two inputs in the tuple: inputs (x) and targets (y)" + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4682 # Unpack depending on how many inputs we have if len(inputs) == 2: input_ids, labels = inputs diff --git a/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb index 51a99b21b..5013dbb4f 100644 --- a/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb +++ b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb @@ -71,8 +71,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "What is FHE?\n", - "FHE is a new type of energy storage that is designed to be used in a variety of applications. It is used to store energy in\n" + "What is FHE? FHE is a new type of energy storage that is designed to be used in a variety of applications. It is used to store energy in\n" ] } ], @@ -134,7 +133,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9775e413ec264b2eb14ee53dbc381474", + "model_id": "c42cfd53d8724112a8e28dfce132ef22", "version_major": 2, "version_minor": 0 }, @@ -249,7 +248,7 @@ "outputs": [], "source": [ "# Define training arguments\n", - "EPOCHS = 20\n", + "EPOCHS = 40\n", "PER_DEVICE_TRAIN_BATCH_SIZE = 4\n", "\n", "training_args = TrainingArguments(\n", @@ -444,7 +443,7 @@ " generate_and_print(prompt, peft_model, tokenizer, SEED)\n", " hybrid_model.set_fhe_mode(fhe)\n", "\n", - " print(\"\\n\" + \"-\" * 50) # Separator for readability\n", + " print(\"\\n\" + \"-\" * 50)\n", " epoch_pbar.update(1)\n", "\n", " # Save model checkpoint\n", @@ -474,7 +473,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 0%| | 0/100 [00:00" ] @@ -876,7 +1201,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "bd666f38", "metadata": {}, "outputs": [], @@ -890,7 +1215,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "id": "3e91ad0b", "metadata": {}, "outputs": [ @@ -898,8 +1223,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "who invented FHE?\n", - "FHE was first proposed by Craig Gentry in 2009, making it easier to use plaintexts on encrypted data. His breakthrough demonstrated ability to\n" + "Who invented Fully Homomorphic Encryption? Fully Homomorphized PoE (FHE) was first proposed by Craig Gentry in 2009. His concept was inspired by traditional encryption\n" ] } ], @@ -909,13 +1233,13 @@ "torch.manual_seed(SEED)\n", "\n", "fine_tuned_model.enable_adapter_layers()\n", - "prompt = \"who invented FHE?\"\n", + "prompt = \"Who invented Fully Homomorphic Encryption?\"\n", "generate_and_print(prompt, fine_tuned_model, tokenizer, SEED)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 24, "id": "21e2a1d1", "metadata": {}, "outputs": [ @@ -923,8 +1247,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "What is FHE?\n", - "FHE is a new type of energy storage that is designed to be used in a variety of applications. It is used to store energy in\n" + "Who invented Fully Homomorphic Encryption? The first time I saw a computer fully fully homomorphic encryption, I was in the middle of a conversation with a friend of mine who was\n" ] } ], @@ -935,13 +1258,13 @@ "\n", "peft_model.disable_adapter_layers()\n", "\n", - "prompt = \"What is FHE?\"\n", + "prompt = \"Who invented Fully Homomorphic Encryption?\"\n", "generate_and_print(prompt, peft_model, tokenizer, SEED)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 25, "id": "c97425ee", "metadata": {}, "outputs": [ @@ -961,63 +1284,6 @@ "total_weights_size = print_weights_and_size(hybrid_model.model)" ] }, - { - "cell_type": "code", - "execution_count": 19, - "id": "31367ff5", - "metadata": {}, - "outputs": [], - "source": [ - "# Save the model\n", - "path = Path(\"deployment/gpt2_lora_finetuned\")\n", - "path.mkdir(parents=True, exist_ok=True)\n", - "\n", - "if path.is_dir() and any(path.iterdir()):\n", - " shutil.rmtree(path)\n", - "\n", - "hybrid_model.save_and_clear_private_info(path)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "a1dda636", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of weights: 39717120\n", - "Total number of LoRA weights: 294912\n" - ] - } - ], - "source": [ - "# Print weights and size after saving\n", - "total_weights_size_private = print_weights_and_size(hybrid_model.model)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "506ad2f5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total weights removed: 68.16 %\n" - ] - } - ], - "source": [ - "# Calculate and print the percentage of weights removed\n", - "percentage_removed = (total_weights_size - total_weights_size_private) / total_weights_size * 100\n", - "print(f\"Total weights removed: {percentage_removed:.2f} %\")" - ] - }, { "cell_type": "code", "execution_count": 22, diff --git a/use_case_examples/lora_finetuning/LLamaFineTuning.ipynb b/use_case_examples/lora_finetuning/LLamaFineTuning.ipynb index 7ee8a6810..2bf12280a 100644 --- a/use_case_examples/lora_finetuning/LLamaFineTuning.ipynb +++ b/use_case_examples/lora_finetuning/LLamaFineTuning.ipynb @@ -140,7 +140,20 @@ "\n", "\n", "tokenized_dataset = dataset.map(tokenize_function, batched=True)\n", - "data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)" + "\n", + "\n", + "class LoraTrainerDataCollator(DataCollatorForLanguageModeling):\n", + " def __call__(self, features):\n", + " batch = super().__call__(features)\n", + " # Reorder keys for LoraTrainer\n", + " return {\n", + " \"input_ids\": batch[\"input_ids\"],\n", + " \"labels\": batch[\"labels\"],\n", + " \"attention_mask\": batch[\"attention_mask\"],\n", + " }\n", + "\n", + "\n", + "data_collator = LoraTrainerDataCollator(tokenizer, mlm=False)" ] }, { From c0094ae54b7426c6007690636c1538b0b096a0e3 Mon Sep 17 00:00:00 2001 From: jfrery Date: Fri, 20 Dec 2024 14:45:29 +0100 Subject: [PATCH 2/3] chore: downgrade accelerate --- .../lora_finetuning/GPT2FineTuneHybrid.ipynb | 6 +++--- .../lora_finetuning/LLamaFineTuning.ipynb | 10 +++++++++- use_case_examples/lora_finetuning/README.md | 2 ++ use_case_examples/lora_finetuning/requirements.txt | 2 +- 4 files changed, 15 insertions(+), 5 deletions(-) diff --git a/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb index 5013dbb4f..b0729905c 100644 --- a/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb +++ b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb @@ -19,8 +19,6 @@ "source": [ "# Import necessary libraries\n", "import math\n", - "import shutil\n", - "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import torch\n", @@ -261,7 +259,6 @@ " learning_rate=2e-3,\n", " lr_scheduler_type=\"linear\",\n", " seed=SEED,\n", - " data_seed=SEED,\n", " warmup_steps=10,\n", " weight_decay=0.01,\n", " prediction_loss_only=True,\n", @@ -1299,6 +1296,9 @@ "metadata": { "execution": { "timeout": 10800 + }, + "language_info": { + "name": "python" } }, "nbformat": 4, diff --git a/use_case_examples/lora_finetuning/LLamaFineTuning.ipynb b/use_case_examples/lora_finetuning/LLamaFineTuning.ipynb index 2bf12280a..e86cd43a2 100644 --- a/use_case_examples/lora_finetuning/LLamaFineTuning.ipynb +++ b/use_case_examples/lora_finetuning/LLamaFineTuning.ipynb @@ -175,7 +175,6 @@ " learning_rate=2e-4,\n", " lr_scheduler_type=\"linear\",\n", " seed=SEED,\n", - " data_seed=SEED,\n", " warmup_steps=10,\n", " weight_decay=0.01,\n", " prediction_loss_only=True,\n", @@ -351,6 +350,15 @@ "metadata": { "execution": { "timeout": 10800 + }, + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.11" } }, "nbformat": 4, diff --git a/use_case_examples/lora_finetuning/README.md b/use_case_examples/lora_finetuning/README.md index a1513298f..a4b0872ef 100644 --- a/use_case_examples/lora_finetuning/README.md +++ b/use_case_examples/lora_finetuning/README.md @@ -19,6 +19,8 @@ Fine-tuning large language models typically requires access to sensitive data, w ### Installation +This project requires Python 3.9 or higher. + Install the required packages: diff --git a/use_case_examples/lora_finetuning/requirements.txt b/use_case_examples/lora_finetuning/requirements.txt index da6495fef..86d35cb02 100644 --- a/use_case_examples/lora_finetuning/requirements.txt +++ b/use_case_examples/lora_finetuning/requirements.txt @@ -4,6 +4,6 @@ peft==0.12.0 Jinja2==3.1.4 matplotlib==3.7.5 datasets==3.1.0 -accelerate==1.2.0 +accelerate==1.0.1 jupyter==1.1.1 tqdm==4.67.1 \ No newline at end of file From c607e3493658b6786486e134d95429e5d94297eb Mon Sep 17 00:00:00 2001 From: jfrery Date: Fri, 20 Dec 2024 14:21:41 +0000 Subject: [PATCH 3/3] chore: refresh notebook(s) for use case lora_finetuning --- .../lora_finetuning/GPT2FineTuneHybrid.ipynb | 3790 ++++++++++++++++- 1 file changed, 3607 insertions(+), 183 deletions(-) diff --git a/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb index b0729905c..c5c44d65b 100644 --- a/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb +++ b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb @@ -14,7 +14,14 @@ "cell_type": "code", "execution_count": 1, "id": "eca73e44", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-20T13:59:14.310990Z", + "iopub.status.busy": "2024-12-20T13:59:14.310782Z", + "iopub.status.idle": "2024-12-20T13:59:18.219429Z", + "shell.execute_reply": "2024-12-20T13:59:18.218929Z" + } + }, "outputs": [], "source": [ "# Import necessary libraries\n", @@ -41,8 +48,114 @@ "cell_type": "code", "execution_count": 2, "id": "8b965a1a", - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-20T13:59:18.221836Z", + "iopub.status.busy": "2024-12-20T13:59:18.221449Z", + "iopub.status.idle": "2024-12-20T13:59:22.897185Z", + "shell.execute_reply": "2024-12-20T13:59:22.896409Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8db1a659865848c2b46921271fe692b5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/26.0 [00:00" ] @@ -1200,7 +1692,14 @@ "cell_type": "code", "execution_count": 15, "id": "bd666f38", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-20T14:20:40.555186Z", + "iopub.status.busy": "2024-12-20T14:20:40.555015Z", + "iopub.status.idle": "2024-12-20T14:20:40.558305Z", + "shell.execute_reply": "2024-12-20T14:20:40.557887Z" + } + }, "outputs": [], "source": [ "# Get the fine-tuned model\n", @@ -1212,15 +1711,22 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 16, "id": "3e91ad0b", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-20T14:20:40.560754Z", + "iopub.status.busy": "2024-12-20T14:20:40.560484Z", + "iopub.status.idle": "2024-12-20T14:20:58.755422Z", + "shell.execute_reply": "2024-12-20T14:20:58.754531Z" + } + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Who invented Fully Homomorphic Encryption? Fully Homomorphized PoE (FHE) was first proposed by Craig Gentry in 2009. His concept was inspired by traditional encryption\n" + "Who invented Fully Homomorphic Encryption? Fully Homomorphized Encrypted Data (FHE) was first proposed by Craig Gentry in 2009. His breakthrough demonstrated how to perform\n" ] } ], @@ -1236,9 +1742,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "id": "21e2a1d1", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-20T14:20:58.757983Z", + "iopub.status.busy": "2024-12-20T14:20:58.757705Z", + "iopub.status.idle": "2024-12-20T14:21:02.964246Z", + "shell.execute_reply": "2024-12-20T14:21:02.963505Z" + } + }, "outputs": [ { "name": "stdout", @@ -1261,9 +1774,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "id": "c97425ee", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2024-12-20T14:21:02.966353Z", + "iopub.status.busy": "2024-12-20T14:21:02.966016Z", + "iopub.status.idle": "2024-12-20T14:21:02.970827Z", + "shell.execute_reply": "2024-12-20T14:21:02.970462Z" + } + }, "outputs": [ { "name": "stdout", @@ -1283,9 +1803,16 @@ }, { "cell_type": "code", - "execution_count": 22, + 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