diff --git a/use_case_examples/mlp_glwe_dot_product/deai_wheel/deai_dot_products-0.1.0-cp310-cp310-macosx_11_0_arm64.whl b/use_case_examples/mlp_glwe_dot_product/deai_wheel/deai_dot_products-0.1.0-cp310-cp310-macosx_11_0_arm64.whl deleted file mode 100644 index adf17e80b..000000000 Binary files a/use_case_examples/mlp_glwe_dot_product/deai_wheel/deai_dot_products-0.1.0-cp310-cp310-macosx_11_0_arm64.whl and /dev/null differ diff --git a/use_case_examples/mlp_glwe_dot_product/mlp_lora_module.py b/use_case_examples/mlp_glwe_dot_product/mlp_lora_module.py deleted file mode 100644 index 247222afc..000000000 --- a/use_case_examples/mlp_glwe_dot_product/mlp_lora_module.py +++ /dev/null @@ -1,150 +0,0 @@ -import torch -import torch.nn.functional as F -from torch import nn -from utils_lora import compute_grad_output - - -class ForwardModule(nn.Module): - def __init__(self, weight, bias=None): - super(ForwardModule, self).__init__() - self.weight = weight # Assume weight is passed as a pre-initialized tensor - self.bias = bias - - def forward(self, input): - output = input @ self.weight.t() - if self.bias is not None: - return output + self.bias - - -class BackwardModule(nn.Module): - def __init__(self, weight): - super(BackwardModule, self).__init__() - self.weight = weight # This is the same weight used in ForwardModule - - def forward(self, grad_output): - return grad_output @ self.weight - - -class CustomFunction(torch.autograd.Function): - @staticmethod - def forward(ctx, input, forward_module, backward_module): - ctx.backward_module = backward_module - output = forward_module(input) - return output - - @staticmethod - def backward(ctx, grad_output): - backward_module = ctx.backward_module - grad_input = backward_module.forward(grad_output) - return grad_input, None, None # No gradients for the modules - - -class CustomLinear(nn.Module): - def __init__(self, weight, bias=None): - super(CustomLinear, self).__init__() - self.forward_module = ForwardModule(weight, bias=bias) - self.backward_module = BackwardModule(weight) - - def forward(self, input): - return CustomFunction.apply(input, self.forward_module, self.backward_module) - - -class LoRALayerOnly(nn.Module): - def __init__(self, in_features: int, out_features: int, rank: int, alpha: float = 1.0): - super().__init__() - self.rank = rank - self.alpha = alpha - - self.A = nn.Parameter(torch.randn(out_features, rank) * 0.1) - self.B = nn.Parameter(torch.randn(rank, in_features) * 0.1) - - def forward(self, x, fc_x): - return fc_x + self.alpha * F.linear(F.linear(x, self.B), self.A) - - -class MLPWithLoRATrainingAuto(nn.Module): - def __init__( - self, - input_size: int, - hidden_size: int, - output_size: int, - lora_rank: int, - alpha: float = 1.0, - learning_rate=0.05, - use_lora: bool = False, - criterion=None, - optimizer=None, - ): - super().__init__() - self.fc1 = nn.Linear(input_size, hidden_size) - self.fc1_lora = LoRALayerOnly(input_size, hidden_size, lora_rank, alpha) - self.relu = nn.ReLU() - self.fc2 = nn.Linear(hidden_size, output_size) - self.fc2_lora = LoRALayerOnly(hidden_size, output_size, lora_rank, alpha) - - self.learning_rate = learning_rate - self.optimizer_func = optimizer if optimizer is not None else torch.optim.Adam - self.criterion = criterion if criterion is not None else nn.CrossEntropyLoss() - self.calibrate = False - - self.toggle_lora(use_lora) - - def toggle_calibrate(self, enable: bool = True): - self.calibrate = enable - - def inference(self, x): - self.input = x - self.fc1_output = self.fc1(self.input) # server side - - if self.use_lora: - self.fc1_output = self.fc1_lora(self.input, self.fc1_output) - - self.relu_output = self.relu(self.fc1_output) - - output = self.fc2(self.relu_output) # server side - - if self.use_lora: - output = self.fc2_lora(self.relu_output, output) - - return output - - def forward(self, inputs): - # FIXME: handle multi-inputs in hybrid model - if self.training: - x, y = inputs - self.optimizer.zero_grad() - else: - x = inputs - - # some parts on server side - output = self.inference(x) - - if self.training: - _, loss = compute_grad_output(output, y, criterion=self.criterion) - - if not self.calibrate: - self.optimizer.step() - - return loss - - return output - - def toggle_lora(self, enable: bool = True): - self.use_lora = enable - - # Replace linear layer by custom linear layer the first time we enable lora - if enable and not isinstance(self.fc2, CustomLinear): - self.fc2 = CustomLinear(self.fc2.weight, bias=self.fc2.bias) - - for module in self.modules(): - if isinstance(module, LoRALayerOnly): - module.A.requires_grad = enable - module.B.requires_grad = enable - - elif isinstance(module, nn.Linear): - module.weight.requires_grad = not enable # Freeze original weights - module.bias.requires_grad = not enable # Freeze original weights - - self.optimizer = self.optimizer_func( - filter(lambda p: p.requires_grad, self.parameters()), lr=self.learning_rate - ) diff --git a/use_case_examples/mlp_glwe_dot_product/requirements.txt b/use_case_examples/mlp_glwe_dot_product/requirements.txt deleted file mode 100644 index db2cb2b6f..000000000 --- a/use_case_examples/mlp_glwe_dot_product/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ --e ../../. -matplotlib==3.7.5 -jupyter -# deai wheel diff --git a/use_case_examples/mlp_glwe_dot_product/simple_lora_2d_training_auto.ipynb b/use_case_examples/mlp_glwe_dot_product/simple_lora_2d_training_auto.ipynb deleted file mode 100644 index 56500ca76..000000000 --- a/use_case_examples/mlp_glwe_dot_product/simple_lora_2d_training_auto.ipynb +++ /dev/null @@ -1,591 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Low-Rank Approximation fine-tuning\n", - "\n", - "This notebook demonstrates encrypted fine-tuning of a small MLP model with LORA. A model trained\n", - "on an initial dataset is adapted to a second dataset using LORA fine-tuning. \n", - "\n", - "The fine-tuning dataset and the LORA weights that are trained are protected using encryption. Thus, the training\n", - "can be outsourced to a remote server without leaking any sensitive data.\n", - "\n", - "The hybrid model approach is applied to fine-tuning: only the linear layers of the original model are outsourced\n", - "to the server. The forward and backward passes on these original weights are performed with encrypted activations\n", - "and gradients. The LORA weights are kept by the client, and the client performs the forward and backward \n", - "passes on the LORA weights. \n", - "\n", - "## Data preparation\n", - "\n", - "Two datasets are generated: one for the original training, and a second one on which\n", - "LORA fine-tuning is performed. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import shutil\n", - "from pathlib import Path\n", - "\n", - "import torch\n", - "from mlp_lora_module import MLPWithLoRATrainingAuto\n", - "from sklearn.datasets import make_circles, make_moons\n", - "from sklearn.model_selection import train_test_split\n", - "from utils_lora import plot_decision_boundary\n", - "\n", - "from concrete.ml.torch.hybrid_model import HybridFHEModel\n", - "\n", - "torch.manual_seed(0)\n", - "torch.use_deterministic_algorithms(True)\n", - "\n", - "\n", - "N_SAMPLES = 1000\n", - "\n", - "\n", - "def prepare_data(X, y, test_size=0.3, random_state=42):\n", - " X_train, X_test, y_train, y_test = train_test_split(\n", - " X, y, test_size=test_size, random_state=random_state\n", - " )\n", - " X_train = torch.tensor(X_train, dtype=torch.float32)\n", - " X_test = torch.tensor(X_test, dtype=torch.float32)\n", - " y_train = torch.tensor(y_train, dtype=torch.long)\n", - " y_test = torch.tensor(y_test, dtype=torch.long)\n", - " return X_train, X_test, y_train, y_test\n", - "\n", - "\n", - "# Generate synthetic 2D data\n", - "X1, y1 = make_moons(n_samples=N_SAMPLES, noise=0.2, random_state=42)\n", - "X2, y2 = make_circles(n_samples=N_SAMPLES, noise=0.2, factor=0.5, random_state=42)\n", - "\n", - "# Prepare data\n", - "X1_train, X1_test, y1_train, y1_test = prepare_data(X1, y1)\n", - "X2_train, X2_test, y2_train, y2_test = prepare_data(X2, y2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the MLP with LORA layers\n", - "\n", - "The LORA rank determines the number of total LORA weights that the model will posess. The number\n", - "of LORA weights will be much lower than the total number of weights." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch [10/100], Loss: 0.2349\n", - "Epoch [20/100], Loss: 0.1244\n", - "Epoch [30/100], Loss: 0.0816\n", - "Epoch [40/100], Loss: 0.0679\n", - "Epoch [50/100], Loss: 0.0607\n", - "Epoch [60/100], Loss: 0.0580\n", - "Epoch [70/100], Loss: 0.0565\n", - "Epoch [80/100], Loss: 0.0556\n", - "Epoch [90/100], Loss: 0.0549\n", - "Epoch [100/100], Loss: 0.0545\n" - ] - } - ], - "source": [ - "# Initialize the model\n", - "input_size = 2\n", - "hidden_size = 128\n", - "output_size = 2\n", - "lora_rank = 1\n", - "num_epochs = 100\n", - "\n", - "model = MLPWithLoRATrainingAuto(input_size, hidden_size, output_size, lora_rank=lora_rank)\n", - "\n", - "# Training loop for the first task with visualization\n", - "model.train()\n", - "for epoch in range(num_epochs):\n", - " model.optimizer.zero_grad()\n", - " outputs = model.inference(X1_train)\n", - " loss = model.criterion(outputs, y1_train)\n", - " loss.backward()\n", - " model.optimizer.step()\n", - " if (epoch + 1) % 10 == 0:\n", - " print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the original model on the first dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy on the first task: 97.67%\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model.eval()\n", - "with torch.no_grad():\n", - " outputs = model(X1_test)\n", - " _, predicted = torch.max(outputs, 1)\n", - " accuracy = (predicted == y1_test).sum().item() / y1_test.size(0)\n", - " print(f\"Accuracy on the first task: {accuracy*100:.2f}%\")\n", - " plot_decision_boundary(\n", - " model, X1_test.numpy(), y1_test.numpy(), \"Task 1 (float) - Test Set\", use_inference=True\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert the original model to an FHE hybrid model with LORA layers" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "_Map_base::at", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 15\u001b[0m\n\u001b[1;32m 12\u001b[0m inputset \u001b[38;5;241m=\u001b[39m (x_train_mixed, y_train_mixed)\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# Compile the model to use FHE\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m \u001b[43mhybrid_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_bits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m8\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(hybrid_model\u001b[38;5;241m.\u001b[39m_all_layers_are_pure_linear)\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/src/concrete/ml/torch/hybrid_model.py:643\u001b[0m, in \u001b[0;36mHybridFHEModel.compile_model\u001b[0;34m(self, x, n_bits, rounding_threshold_bits, p_error, device, configuration)\u001b[0m\n\u001b[1;32m 636\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_q_modules[name] \u001b[38;5;241m=\u001b[39m build_quantized_module(\n\u001b[1;32m 637\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_modules[name],\n\u001b[1;32m 638\u001b[0m calibration_data_tensor,\n\u001b[1;32m 639\u001b[0m n_bits\u001b[38;5;241m=\u001b[39mn_bits,\n\u001b[1;32m 640\u001b[0m rounding_threshold_bits\u001b[38;5;241m=\u001b[39mrounding_threshold_bits,\n\u001b[1;32m 641\u001b[0m )\n\u001b[1;32m 642\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 643\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_q_modules[name] \u001b[38;5;241m=\u001b[39m \u001b[43mcompile_torch_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 644\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprivate_modules\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 645\u001b[0m \u001b[43m \u001b[49m\u001b[43mcalibration_data_tensor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 646\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_bits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 647\u001b[0m \u001b[43m \u001b[49m\u001b[43mrounding_threshold_bits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrounding_threshold_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 648\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfiguration\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfiguration\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 649\u001b[0m \u001b[43m \u001b[49m\u001b[43mp_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mp_error\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 650\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 652\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mremote_modules[name]\u001b[38;5;241m.\u001b[39mprivate_q_module \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_q_modules[name]\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/src/concrete/ml/torch/compile.py:342\u001b[0m, in \u001b[0;36mcompile_torch_model\u001b[0;34m(torch_model, torch_inputset, import_qat, configuration, artifacts, show_mlir, n_bits, rounding_threshold_bits, p_error, global_p_error, verbose, inputs_encryption_status, reduce_sum_copy, device)\u001b[0m\n\u001b[1;32m 330\u001b[0m assert_true(\n\u001b[1;32m 331\u001b[0m \u001b[38;5;28misinstance\u001b[39m(torch_model, torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mModule),\n\u001b[1;32m 332\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe compile_torch_model function must be called on a torch.nn.Module\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 333\u001b[0m )\n\u001b[1;32m 335\u001b[0m assert_false(\n\u001b[1;32m 336\u001b[0m has_any_qnn_layers(torch_model),\n\u001b[1;32m 337\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe compile_torch_model was called on a torch.nn.Module that contains \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 338\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBrevitas quantized layers. These models must be imported \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 339\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124musing compile_brevitas_qat_model instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 340\u001b[0m )\n\u001b[0;32m--> 342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_compile_torch_or_onnx_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 343\u001b[0m \u001b[43m \u001b[49m\u001b[43mtorch_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 344\u001b[0m \u001b[43m \u001b[49m\u001b[43mtorch_inputset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 345\u001b[0m \u001b[43m \u001b[49m\u001b[43mimport_qat\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 346\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfiguration\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfiguration\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 347\u001b[0m \u001b[43m \u001b[49m\u001b[43martifacts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43martifacts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 348\u001b[0m \u001b[43m \u001b[49m\u001b[43mshow_mlir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_mlir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 349\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_bits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 350\u001b[0m \u001b[43m \u001b[49m\u001b[43mrounding_threshold_bits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrounding_threshold_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[43m \u001b[49m\u001b[43mp_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mp_error\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 352\u001b[0m \u001b[43m \u001b[49m\u001b[43mglobal_p_error\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mglobal_p_error\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 353\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 354\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_encryption_status\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_encryption_status\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 355\u001b[0m \u001b[43m \u001b[49m\u001b[43mreduce_sum_copy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreduce_sum_copy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 356\u001b[0m \u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 357\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/src/concrete/ml/torch/compile.py:224\u001b[0m, in \u001b[0;36m_compile_torch_or_onnx_model\u001b[0;34m(model, torch_inputset, import_qat, configuration, artifacts, show_mlir, n_bits, rounding_threshold_bits, p_error, global_p_error, verbose, inputs_encryption_status, reduce_sum_copy, composition_mapping, device)\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 219\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComposition must be enabled in \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mconfiguration\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m in order to trigger a re-quantization \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstep on the circuit\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms outputs.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 221\u001b[0m )\n\u001b[1;32m 223\u001b[0m \u001b[38;5;66;03m# Build the quantized module\u001b[39;00m\n\u001b[0;32m--> 224\u001b[0m quantized_module \u001b[38;5;241m=\u001b[39m \u001b[43mbuild_quantized_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 225\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 226\u001b[0m \u001b[43m \u001b[49m\u001b[43mtorch_inputset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputset_as_numpy_tuple\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 227\u001b[0m \u001b[43m \u001b[49m\u001b[43mimport_qat\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimport_qat\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 228\u001b[0m \u001b[43m \u001b[49m\u001b[43mn_bits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 229\u001b[0m \u001b[43m \u001b[49m\u001b[43mrounding_threshold_bits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrounding_threshold_bits\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 230\u001b[0m \u001b[43m \u001b[49m\u001b[43mreduce_sum_copy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreduce_sum_copy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 231\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 233\u001b[0m \u001b[38;5;66;03m# Check that p_error or global_p_error is not set in both the configuration and in the direct\u001b[39;00m\n\u001b[1;32m 234\u001b[0m \u001b[38;5;66;03m# parameters\u001b[39;00m\n\u001b[1;32m 235\u001b[0m check_there_is_no_p_error_options_in_configuration(configuration)\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/src/concrete/ml/torch/compile.py:124\u001b[0m, in \u001b[0;36mbuild_quantized_module\u001b[0;34m(model, torch_inputset, import_qat, n_bits, rounding_threshold_bits, reduce_sum_copy)\u001b[0m\n\u001b[1;32m 114\u001b[0m dummy_input_for_tracing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\n\u001b[1;32m 115\u001b[0m (\n\u001b[1;32m 116\u001b[0m torch\u001b[38;5;241m.\u001b[39mfrom_numpy(val[[\u001b[38;5;241m0\u001b[39m], ::])\u001b[38;5;241m.\u001b[39mfloat()\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m inputset_as_numpy_tuple\n\u001b[1;32m 121\u001b[0m )\n\u001b[1;32m 123\u001b[0m \u001b[38;5;66;03m# Create corresponding numpy model\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m numpy_model \u001b[38;5;241m=\u001b[39m \u001b[43mNumpyModule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdummy_input_for_tracing\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 126\u001b[0m \u001b[38;5;66;03m# Quantize with post-training static method, to have a model with integer weights\u001b[39;00m\n\u001b[1;32m 127\u001b[0m post_training \u001b[38;5;241m=\u001b[39m PostTrainingQATImporter \u001b[38;5;28;01mif\u001b[39;00m import_qat \u001b[38;5;28;01melse\u001b[39;00m PostTrainingAffineQuantization\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/src/concrete/ml/torch/numpy_module.py:51\u001b[0m, in \u001b[0;36mNumpyModule.__init__\u001b[0;34m(self, model, dummy_input, debug_onnx_output_file_path)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, nn\u001b[38;5;241m.\u001b[39mModule):\n\u001b[1;32m 40\u001b[0m \n\u001b[1;32m 41\u001b[0m \u001b[38;5;66;03m# mypy\u001b[39;00m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m 43\u001b[0m dummy_input \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 44\u001b[0m ), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdummy_input must be provided if model is a torch.nn.Module\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 46\u001b[0m (\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_numpy_preprocessing,\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_onnx_preprocessing,\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnumpy_forward,\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_onnx_model,\n\u001b[0;32m---> 51\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[43mget_equivalent_numpy_forward_from_torch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 52\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdummy_input\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug_onnx_output_file_path\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, onnx\u001b[38;5;241m.\u001b[39mModelProto):\n\u001b[1;32m 57\u001b[0m onnx_model_opset_version \u001b[38;5;241m=\u001b[39m get_onnx_opset_version(model)\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/src/concrete/ml/onnx/convert.py:153\u001b[0m, in \u001b[0;36mget_equivalent_numpy_forward_from_torch\u001b[0;34m(torch_module, dummy_input, output_onnx_file)\u001b[0m\n\u001b[1;32m 150\u001b[0m arguments \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(inspect\u001b[38;5;241m.\u001b[39msignature(torch_module\u001b[38;5;241m.\u001b[39mforward)\u001b[38;5;241m.\u001b[39mparameters)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;66;03m# Export to ONNX\u001b[39;00m\n\u001b[0;32m--> 153\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43monnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 154\u001b[0m \u001b[43m \u001b[49m\u001b[43mtorch_module\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 155\u001b[0m \u001b[43m \u001b[49m\u001b[43mdummy_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 156\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput_onnx_file_path\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 157\u001b[0m \u001b[43m \u001b[49m\u001b[43mopset_version\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mOPSET_VERSION_FOR_ONNX_EXPORT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 158\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_names\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43marguments\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 159\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 160\u001b[0m equivalent_onnx_model \u001b[38;5;241m=\u001b[39m onnx\u001b[38;5;241m.\u001b[39mload_model(\u001b[38;5;28mstr\u001b[39m(output_onnx_file_path))\n\u001b[1;32m 162\u001b[0m \u001b[38;5;66;03m# Check if the inputs are present in the model's graph\u001b[39;00m\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/onnx/utils.py:551\u001b[0m, in \u001b[0;36mexport\u001b[0;34m(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, custom_opsets, export_modules_as_functions, autograd_inlining, dynamo)\u001b[0m\n\u001b[1;32m 546\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m f \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 547\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 548\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExport destination must be specified for torchscript-onnx export.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 549\u001b[0m )\n\u001b[0;32m--> 551\u001b[0m \u001b[43m_export\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 552\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 553\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 554\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 555\u001b[0m \u001b[43m \u001b[49m\u001b[43mexport_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 556\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 557\u001b[0m \u001b[43m \u001b[49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 558\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 559\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 560\u001b[0m \u001b[43m \u001b[49m\u001b[43moperator_export_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moperator_export_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 561\u001b[0m \u001b[43m \u001b[49m\u001b[43mopset_version\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopset_version\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 562\u001b[0m \u001b[43m \u001b[49m\u001b[43mdo_constant_folding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdo_constant_folding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 563\u001b[0m \u001b[43m \u001b[49m\u001b[43mdynamic_axes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdynamic_axes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 564\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeep_initializers_as_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_initializers_as_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 565\u001b[0m \u001b[43m \u001b[49m\u001b[43mcustom_opsets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_opsets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 566\u001b[0m \u001b[43m \u001b[49m\u001b[43mexport_modules_as_functions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexport_modules_as_functions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 567\u001b[0m \u001b[43m \u001b[49m\u001b[43mautograd_inlining\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautograd_inlining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 568\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 570\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/onnx/utils.py:1648\u001b[0m, in \u001b[0;36m_export\u001b[0;34m(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, fixed_batch_size, custom_opsets, add_node_names, onnx_shape_inference, export_modules_as_functions, autograd_inlining)\u001b[0m\n\u001b[1;32m 1645\u001b[0m dynamic_axes \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 1646\u001b[0m _validate_dynamic_axes(dynamic_axes, model, input_names, output_names)\n\u001b[0;32m-> 1648\u001b[0m graph, params_dict, torch_out \u001b[38;5;241m=\u001b[39m \u001b[43m_model_to_graph\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1649\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1650\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1651\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1652\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1653\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1654\u001b[0m \u001b[43m \u001b[49m\u001b[43moperator_export_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1655\u001b[0m \u001b[43m \u001b[49m\u001b[43mval_do_constant_folding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1656\u001b[0m \u001b[43m \u001b[49m\u001b[43mfixed_batch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfixed_batch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1657\u001b[0m \u001b[43m \u001b[49m\u001b[43mtraining\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1658\u001b[0m \u001b[43m \u001b[49m\u001b[43mdynamic_axes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdynamic_axes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1659\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1661\u001b[0m \u001b[38;5;66;03m# TODO: Don't allocate a in-memory string for the protobuf\u001b[39;00m\n\u001b[1;32m 1662\u001b[0m defer_weight_export \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1663\u001b[0m export_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _exporter_states\u001b[38;5;241m.\u001b[39mExportTypes\u001b[38;5;241m.\u001b[39mPROTOBUF_FILE\n\u001b[1;32m 1664\u001b[0m )\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/onnx/utils.py:1170\u001b[0m, in \u001b[0;36m_model_to_graph\u001b[0;34m(model, args, verbose, input_names, output_names, operator_export_type, do_constant_folding, _disable_torch_constant_prop, fixed_batch_size, training, dynamic_axes)\u001b[0m\n\u001b[1;32m 1167\u001b[0m args \u001b[38;5;241m=\u001b[39m (args,)\n\u001b[1;32m 1169\u001b[0m model \u001b[38;5;241m=\u001b[39m _pre_trace_quant_model(model, args)\n\u001b[0;32m-> 1170\u001b[0m graph, params, torch_out, module \u001b[38;5;241m=\u001b[39m \u001b[43m_create_jit_graph\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1171\u001b[0m params_dict \u001b[38;5;241m=\u001b[39m _get_named_param_dict(graph, params)\n\u001b[1;32m 1173\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/onnx/utils.py:1046\u001b[0m, in \u001b[0;36m_create_jit_graph\u001b[0;34m(model, args)\u001b[0m\n\u001b[1;32m 1041\u001b[0m graph \u001b[38;5;241m=\u001b[39m _C\u001b[38;5;241m.\u001b[39m_propagate_and_assign_input_shapes(\n\u001b[1;32m 1042\u001b[0m graph, flattened_args, param_count_list, \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 1043\u001b[0m )\n\u001b[1;32m 1044\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m graph, params, torch_out, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1046\u001b[0m graph, torch_out \u001b[38;5;241m=\u001b[39m \u001b[43m_trace_and_get_graph_from_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1047\u001b[0m _C\u001b[38;5;241m.\u001b[39m_jit_pass_onnx_lint(graph)\n\u001b[1;32m 1048\u001b[0m state_dict \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39m_unique_state_dict(model)\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/onnx/utils.py:950\u001b[0m, in \u001b[0;36m_trace_and_get_graph_from_model\u001b[0;34m(model, args)\u001b[0m\n\u001b[1;32m 948\u001b[0m prev_autocast_cache_enabled \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mis_autocast_cache_enabled()\n\u001b[1;32m 949\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_autocast_cache_enabled(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 950\u001b[0m trace_graph, torch_out, inputs_states \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_trace_graph\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 951\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 952\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 953\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 954\u001b[0m \u001b[43m \u001b[49m\u001b[43m_force_outplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 955\u001b[0m \u001b[43m \u001b[49m\u001b[43m_return_inputs_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 956\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 957\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_autocast_cache_enabled(prev_autocast_cache_enabled)\n\u001b[1;32m 959\u001b[0m warn_on_static_input_change(inputs_states)\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/jit/_trace.py:1497\u001b[0m, in \u001b[0;36m_get_trace_graph\u001b[0;34m(f, args, kwargs, strict, _force_outplace, return_inputs, _return_inputs_states)\u001b[0m\n\u001b[1;32m 1495\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(args, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 1496\u001b[0m args \u001b[38;5;241m=\u001b[39m (args,)\n\u001b[0;32m-> 1497\u001b[0m outs \u001b[38;5;241m=\u001b[39m \u001b[43mONNXTracedModule\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1498\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_force_outplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_return_inputs_states\u001b[49m\n\u001b[1;32m 1499\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1500\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outs\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/jit/_trace.py:141\u001b[0m, in \u001b[0;36mONNXTracedModule.forward\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(out_vars)\n\u001b[0;32m--> 141\u001b[0m graph, out \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_graph_by_tracing\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 142\u001b[0m \u001b[43m \u001b[49m\u001b[43mwrapper\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 143\u001b[0m \u001b[43m \u001b[49m\u001b[43min_vars\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmodule_state\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[43m \u001b[49m\u001b[43m_create_interpreter_name_lookup_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 146\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_force_outplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_return_inputs:\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m graph, outs[\u001b[38;5;241m0\u001b[39m], ret_inputs[\u001b[38;5;241m0\u001b[39m]\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/jit/_trace.py:132\u001b[0m, in \u001b[0;36mONNXTracedModule.forward..wrapper\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_return_inputs_states:\n\u001b[1;32m 131\u001b[0m inputs_states\u001b[38;5;241m.\u001b[39mappend(_unflatten(in_args, in_desc))\n\u001b[0;32m--> 132\u001b[0m outs\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtrace_inputs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_return_inputs_states:\n\u001b[1;32m 134\u001b[0m inputs_states[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m (inputs_states[\u001b[38;5;241m0\u001b[39m], trace_inputs)\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1543\u001b[0m, in \u001b[0;36mModule._slow_forward\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1541\u001b[0m recording_scopes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 1542\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1543\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1544\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 1545\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m recording_scopes:\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/use_case_examples/mlp_glwe_dot_product/mlp_lora_module.py:49\u001b[0m, in \u001b[0;36mCustomLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mCustomFunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward_module\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Private/Work/concrete-ml/.venv/lib/python3.11/site-packages/torch/autograd/function.py:574\u001b[0m, in \u001b[0;36mFunction.apply\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 571\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_are_functorch_transforms_active():\n\u001b[1;32m 572\u001b[0m \u001b[38;5;66;03m# See NOTE: [functorch vjp and autograd interaction]\u001b[39;00m\n\u001b[1;32m 573\u001b[0m args \u001b[38;5;241m=\u001b[39m _functorch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39munwrap_dead_wrappers(args)\n\u001b[0;32m--> 574\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 576\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_setup_ctx_defined:\n\u001b[1;32m 577\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 578\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn order to use an autograd.Function with functorch transforms \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 579\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(vmap, grad, jvp, jacrev, ...), it must override the setup_context \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 580\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstaticmethod. For more details, please see \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 581\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://pytorch.org/docs/main/notes/extending.func.html\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 582\u001b[0m )\n", - "\u001b[0;31mRuntimeError\u001b[0m: _Map_base::at" - ] - } - ], - "source": [ - "# Enable LORA layers\n", - "model.toggle_lora(enable=True)\n", - "model.train()\n", - "\n", - "# Create the FHE compatible model, outsourcing all linear layers\n", - "hybrid_model = HybridFHEModel(model, [\"fc1\", \"fc2.forward_module\", \"fc2.backward_module\"])\n", - "\n", - "# Sample some data to determine the weight and activation value bounds for training\n", - "inputset_sample = 100\n", - "x_train_mixed = torch.cat((X1_train[:inputset_sample], X2_train[:inputset_sample]), dim=0)\n", - "y_train_mixed = torch.cat((y1_train[:inputset_sample], y2_train[:inputset_sample]), dim=0)\n", - "inputset = (x_train_mixed, y_train_mixed)\n", - "\n", - "# Compile the model to use FHE\n", - "hybrid_model.compile_model(inputset, n_bits=8)\n", - "\n", - "print(hybrid_model._all_layers_are_pure_linear)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the FHE model on the original dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FHE Accuracy on the first task: 97.67%\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hybrid_model.model.toggle_lora(enable=False)\n", - "hybrid_model.model.eval()\n", - "\n", - "with torch.no_grad():\n", - " outputs = hybrid_model(X1_test, fhe=\"execute\")\n", - " _, predicted = torch.max(outputs, 1)\n", - " accuracy = (predicted == y1_test).sum().item() / y1_test.size(0)\n", - " print(f\"FHE Accuracy on the first task: {accuracy*100:.2f}%\")\n", - " plot_decision_boundary(\n", - " hybrid_model,\n", - " X1_test.numpy(),\n", - " y1_test.numpy(),\n", - " \"Task 1 (quant) - Test Set\",\n", - " use_inference=False,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train the model on the second dataset\n", - "\n", - "For now, the LORA weights are not trained and are thus simply randomly intitialized. It is \n", - "now time to enable the LORA weights and fine-tune them on the second dataset. " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch [1/40], Loss: 0.8596\n", - "Epoch [2/40], Loss: 0.7397\n", - "Epoch [3/40], Loss: 0.6114\n", - "Epoch [4/40], Loss: 0.5207\n", - "Epoch [5/40], Loss: 0.4857\n", - "Epoch [6/40], Loss: 0.4767\n", - "Epoch [7/40], Loss: 0.4616\n", - "Epoch [8/40], Loss: 0.4382\n", - "Epoch [9/40], Loss: 0.3965\n", - "Epoch [10/40], Loss: 0.3626\n", - "Epoch [11/40], Loss: 0.3264\n", - "Epoch [12/40], Loss: 0.3020\n", - "Epoch [13/40], Loss: 0.2896\n", - "Epoch [14/40], Loss: 0.2854\n", - "Epoch [15/40], Loss: 0.2835\n", - "Epoch [16/40], Loss: 0.2809\n", - "Epoch [17/40], Loss: 0.2783\n", - "Epoch [18/40], Loss: 0.2704\n", - "Epoch [19/40], Loss: 0.2654\n", - "Epoch [20/40], Loss: 0.2616\n", - "Epoch [21/40], Loss: 0.2605\n", - "Epoch [22/40], Loss: 0.2558\n", - "Epoch [23/40], Loss: 0.2494\n", - "Epoch [24/40], Loss: 0.2429\n", - "Epoch [25/40], Loss: 0.2380\n", - "Epoch [26/40], Loss: 0.2325\n", - "Epoch [27/40], Loss: 0.2296\n", - "Epoch [28/40], Loss: 0.2277\n", - "Epoch [29/40], Loss: 0.2266\n", - "Epoch [30/40], Loss: 0.2244\n", - "Epoch [31/40], Loss: 0.2219\n", - "Epoch [32/40], Loss: 0.2177\n", - "Epoch [33/40], Loss: 0.2160\n", - "Epoch [34/40], Loss: 0.2144\n", - "Epoch [35/40], Loss: 0.2150\n", - "Epoch [36/40], Loss: 0.2172\n", - "Epoch [37/40], Loss: 0.2195\n", - "Epoch [38/40], Loss: 0.2241\n", - "Epoch [39/40], Loss: 0.2286\n", - "Epoch [40/40], Loss: 0.2298\n" - ] - } - ], - "source": [ - "hybrid_model.model.toggle_lora(enable=True)\n", - "hybrid_model.model.train()\n", - "\n", - "LORA_SAMPLES = 50\n", - "\n", - "X2_train_lora = X2_train[:LORA_SAMPLES]\n", - "y2_train_lora = y2_train[:LORA_SAMPLES]\n", - "\n", - "num_epochs = 40\n", - "for epoch in range(num_epochs):\n", - " loss = hybrid_model((X2_train_lora, y2_train_lora), fhe=\"execute\")\n", - " if epoch % 5 == 0:\n", - " print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate the fine-tuned model on the second dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy on the second task: 81.33%\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hybrid_model.model.toggle_lora(True)\n", - "hybrid_model.model.eval()\n", - "\n", - "with torch.no_grad():\n", - " outputs = hybrid_model(X2_test, fhe=\"execute\")\n", - " _, predicted = torch.max(outputs, 1)\n", - " accuracy = (predicted == y2_test).sum().item() / y2_test.size(0)\n", - " print(f\"Accuracy on the second task: {accuracy*100:.2f}%\")\n", - " plot_decision_boundary(\n", - " hybrid_model,\n", - " X2_test.numpy(),\n", - " y2_test.numpy(),\n", - " \"Task 2 (quant) - Test Set\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check the model without LORA weights on the original dataset\n", - "\n", - "When running without the LORA weights you should get the same result as \n", - "as the original model (in FHE)." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy on the second task: 97.67%\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "hybrid_model.model.toggle_lora(False)\n", - "hybrid_model.model.eval()\n", - "\n", - "with torch.no_grad():\n", - " outputs = hybrid_model(X1_test, fhe=\"execute\")\n", - " _, predicted = torch.max(outputs, 1)\n", - " accuracy = (predicted == y1_test).sum().item() / y1_test.size(0)\n", - " print(f\"Accuracy on the second task: {accuracy*100:.2f}%\")\n", - " plot_decision_boundary(\n", - " hybrid_model,\n", - " X1_test.numpy(),\n", - " y1_test.numpy(),\n", - " \"Task 1 (quant) - Test Set\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute the percentage of LORA weights\n", - "\n", - "First, check the total number of weights in the model." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fc1.private_module.weight 256\n", - "fc1.private_module.bias 128\n", - "fc1_lora.A 128\n", - "fc1_lora.B 2\n", - "fc2.forward_module.private_module.weight 256\n", - "fc2.forward_module.private_module.bias 2\n", - "fc2_lora.A 2\n", - "fc2_lora.B 128\n", - "Total number of weights: 902\n" - ] - } - ], - "source": [ - "total_weights = 0\n", - "for name, param in hybrid_model.model.named_parameters():\n", - " total_weights += param.numel()\n", - " print(name, param.numel())\n", - "\n", - "print(f\"Total number of weights: {total_weights}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remove the weights that are outsourced to the server-side. These weights\n", - "are not needed on the client, providing computation time and memory savings. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "path = Path(\"lora_mlp\")\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": "markdown", - "metadata": {}, - "source": [ - "Compute the number of LORA weights" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fc1_lora.A 128\n", - "fc1_lora.B 2\n", - "fc2_lora.A 2\n", - "fc2_lora.B 128\n", - "Total number of weights: 260\n" - ] - } - ], - "source": [ - "total_lora_weights = 0\n", - "for name, param in hybrid_model.model.named_parameters():\n", - " total_lora_weights += param.numel()\n", - " print(name, param.numel())\n", - "\n", - "print(f\"Total number of weights: {total_lora_weights}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Percentage of LORA weights out of the total: 28.82%\n" - ] - } - ], - "source": [ - "print(f\"Percentage of LORA weights out of the total: {total_lora_weights/total_weights*100:.2f}%\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "LORA parameter-efficient fine-tuning helps fine-tune private models on private data. The server, which \n", - "computes activations and gradient using the original weights, has no access to the private training data:\n", - "the activations and gradients are encrypted by the client and stay secret.\n", - "\n", - "The example here shows LORA for an MLP model on low-dimensional data. The percentage of LORA weights is comparatively\n", - "high with respect to a bigger model such as a transformer or LLM. In practice for an LLM the number of LORA \n", - "weights stays under one percent of total weights. Thus the client device has low memory and computation requirements." - ] - } - ], - "metadata": { - "execution": { - "timeout": 10800 - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/use_case_examples/mlp_glwe_dot_product/utils_lora.py b/use_case_examples/mlp_glwe_dot_product/utils_lora.py deleted file mode 100644 index caec0def5..000000000 --- a/use_case_examples/mlp_glwe_dot_product/utils_lora.py +++ /dev/null @@ -1,60 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import torch -import torch.nn.functional as F - -from concrete.ml.quantization import QuantizedModule - - -def plot_decision_boundary( - model, X, y, title, display_points=True, fhe="disable", use_inference=False -): - x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 - y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 - h = 0.01 - - xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) - grid = np.c_[xx.ravel(), yy.ravel()] - - if isinstance(model, QuantizedModule): - Z = model.forward(grid, fhe=fhe) - Z = np.argmax(Z, axis=1) - Z = Z.reshape(xx.shape) - - else: - # model.eval() - - with torch.no_grad(): - grid_tensor = torch.tensor(grid, dtype=torch.float32) - if use_inference: - Z = model.inference(grid_tensor) - else: - Z = model.forward(grid_tensor) - _, Z = torch.max(Z, 1) - Z = Z.numpy().reshape(xx.shape) - - plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8) - - if display_points: - plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral, edgecolors="k") - - plt.title(title) - plt.show() - - -def custom_cross_entropy_loss(output, target, criterion=None): - if criterion is not None: - loss = criterion(output, target) - else: - log_softmax_output = F.log_softmax(output, dim=1) - loss = -log_softmax_output[range(target.shape[0]), target].mean() - return loss - - -def compute_grad_output(output, target, criterion=None): - output.retain_grad() - loss = custom_cross_entropy_loss(output, target, criterion=criterion) - loss.backward() - grad_output = output.grad - - return grad_output, loss