Safetensors Model
Introduction
Burn supports importing model weights from the Safetensors format, a secure and efficient alternative to pickle-based formats. Whether you've trained your model in PyTorch or you want to use a pre-trained model that provides weights in Safetensors format, you can easily import them into Burn.
This guide demonstrates the complete workflow for exporting models to Safetensors format and importing them into Burn.
Exporting Models to Safetensors Format
To export a PyTorch model to Safetensors format, you'll need the safetensors
Python library. This
library provides a simple API for saving model weights in the Safetensors format.
Example: Exporting a PyTorch Model
import torch
import torch.nn as nn
from safetensors.torch import save_file
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(2, 2, (2,2))
self.conv2 = nn.Conv2d(2, 2, (2,2), bias=False)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
return x
if __name__ == "__main__":
# Set seed for reproducibility
torch.manual_seed(42)
# Initialize model and ensure it's on CPU
model = Net().to(torch.device("cpu"))
# Extract model weights dictionary
model_weights = model.state_dict()
# Save to Safetensors format
save_file(model_weights, "conv2d.safetensors")
Verifying the Export
You can verify your exported model by viewing the .safetensors
file in
Netron, a neural network visualization tool. A correctly
exported file will display a flat structure of tensors, similar to a PyTorch .pt
weights file.
Importing Safetensors Models into Burn
Importing a Safetensors model into Burn involves two main steps:
- Defining the model architecture in Burn
- Loading the weights from the Safetensors file
Step 1: Define the Model in Burn
First, you need to create a Burn model that matches the architecture of the model you exported:
#![allow(unused)] fn main() { use burn::{ nn::conv::{Conv2d, Conv2dConfig}, prelude::*, }; #[derive(Module, Debug)] pub struct Net<B: Backend> { conv1: Conv2d<B>, conv2: Conv2d<B>, } impl<B: Backend> Net<B> { /// Create a new model. pub fn init(device: &B::Device) -> Self { let conv1 = Conv2dConfig::new([2, 2], [2, 2]) .init(device); let conv2 = Conv2dConfig::new([2, 2], [2, 2]) .with_bias(false) .init(device); Self { conv1, conv2 } } /// Forward pass of the model. pub fn forward(&self, x: Tensor<B, 4>) -> Tensor<B, 4> { let x = self.conv1.forward(x); self.conv2.forward(x) } } }
Step 2: Load the Weights
You have two options for loading the weights:
Option A: Load Dynamically at Runtime
This approach loads the Safetensors file directly at runtime, requiring the burn-import
dependency:
use crate::model; use burn::record::{FullPrecisionSettings, Recorder}; use burn_import::safetensors::SafetensorsFileRecorder; type Backend = burn_ndarray::NdArray<f32>; fn main() { let device = Default::default(); // Load weights from Safetensors file let record = SafetensorsFileRecorder::<FullPrecisionSettings>::default() .load("./conv2d.safetensors".into(), &device) .expect("Should decode state successfully"); // Initialize model and load weights let model = model::Net::<Backend>::init(&device).load_record(record); }
Option B: Pre-convert to Burn's Binary Format
This approach converts the Safetensors file to Burn's optimized binary format during build time,
removing the runtime dependency on burn-import
:
#![allow(unused)] fn main() { // This code would go in build.rs or a separate tool use crate::model; use burn::record::{FullPrecisionSettings, NamedMpkFileRecorder, Recorder}; use burn_import::safetensors::SafetensorsFileRecorder; type Backend = burn_ndarray::NdArray<f32>; fn convert_model() { let device = Default::default(); // Load from Safetensors let recorder = SafetensorsFileRecorder::<FullPrecisionSettings>::default(); let record = recorder .load("./conv2d.safetensors".into(), &device) .expect("Should decode state successfully"); // Save to Burn's binary format let recorder = NamedMpkFileRecorder::<FullPrecisionSettings>::default(); recorder .record(record, "model.mpk".into()) .expect("Failed to save model record"); } // In your application code fn load_model() -> Net<Backend> { let device = Default::default(); // Load from Burn's binary format let record = NamedMpkFileRecorder::<FullPrecisionSettings>::default() .load("./model.mpk".into(), &device) .expect("Should decode state successfully"); Net::<Backend>::init(&device).load_record(record) } }
Note: For examples of pre-converting models, see the
examples/import-model-weights
directory in the Burn repository.
Advanced Configuration Options
Framework-Specific Adapters
When importing Safetensors models, you can specify an adapter type to handle framework-specific tensor transformations. This is crucial when importing models from different ML frameworks, as tensor layouts and naming conventions can vary:
#![allow(unused)] fn main() { let device = Default::default(); // Create load arguments with framework-specific adapter let load_args = LoadArgs::new("model.safetensors".into()) .with_adapter_type(AdapterType::PyTorch); // Default adapter // Load with the specified adapter let record = SafetensorsFileRecorder::<FullPrecisionSettings>::default() .load(load_args, &device) .expect("Should decode state successfully"); }
Available Adapter Types
Adapter Type | Description |
---|---|
PyTorch (default) | Automatically applies PyTorch-specific transformations: - Transposes weights for linear layers - Renames normalization parameters (weight→gamma, bias→beta) |
NoAdapter | Loads tensors directly without any transformations - Useful when importing from frameworks that already match Burn's tensor layout |
Troubleshooting and Advanced Features
Key Remapping for Different Model Architectures
If your Burn model structure doesn't match the parameter names in the Safetensors file, you can remap keys using regular expressions:
#![allow(unused)] fn main() { let device = Default::default(); // Create load arguments with key remapping let load_args = LoadArgs::new("model.safetensors".into()) // Remove "conv" prefix, e.g. "conv.conv1" -> "conv1" .with_key_remap("conv\\.(.*)", "$1"); let record = SafetensorsFileRecorder::<FullPrecisionSettings>::default() .load(load_args, &device) .expect("Should decode state successfully"); let model = Net::<Backend>::init(&device).load_record(record); }
Debugging with Key Inspection
To help with troubleshooting import issues, you can enable debugging to print the original and remapped keys:
#![allow(unused)] fn main() { let device = Default::default(); // Enable debug printing of keys let load_args = LoadArgs::new("model.safetensors".into()) .with_key_remap("conv\\.(.*)", "$1") .with_debug_print(); // Print original and remapped keys let record = SafetensorsFileRecorder::<FullPrecisionSettings>::default() .load(load_args, &device) .expect("Should decode state successfully"); }
Automatic Handling of Non-Contiguous Indices
The SafetensorsFileRecorder automatically handles non-contiguous indices in model layer names. For example, if the source model contains indices with gaps:
"model.layers.0.weight"
"model.layers.0.bias"
"model.layers.2.weight" // Note the gap (no index 1)
"model.layers.2.bias"
"model.layers.4.weight"
"model.layers.4.bias"
The recorder will automatically reindex these to be contiguous while preserving their order:
"model.layers.0.weight"
"model.layers.0.bias"
"model.layers.1.weight" // Reindexed from 2
"model.layers.1.bias"
"model.layers.2.weight" // Reindexed from 4
"model.layers.2.bias"
Partial Model Loading
You can selectively load weights into a partial model, which is useful for:
- Loading only the encoder from an encoder-decoder architecture
- Fine-tuning specific layers while initializing others randomly
- Creating hybrid models combining parts from different sources
Support for Enum Modules
The SafetensorsFileRecorder supports models containing enum modules with new-type variants. The enum variant is automatically selected based on the enum variant type, allowing for flexible model architectures.