mirror of https://github.com/tracel-ai/burn.git
Readme updates (#325)
* Update text-generation readme for Mac users * Update root readme to reference import crate * Update import's readme * Update torch backend
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Dilshod Tadjibaev
GitHub
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@ -42,6 +42,7 @@ __Sections__
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* [NdArray](https://github.com/burn-rs/burn/tree/main/burn-ndarray) backend featuring [`no_std`](#no_std-support) compatibility for any platform 👌
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* [Autodiff](https://github.com/burn-rs/burn/tree/main/burn-autodiff) backend enabling differentiability for all backends 🌟
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* [Dataset](https://github.com/burn-rs/burn/tree/main/burn-dataset) crate with a variety of utilities and sources 📚
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* [Import](https://github.com/burn-rs/burn/tree/main/burn-import) crate for seamless integration of pretrained models 📦
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## Get Started
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@ -1,11 +1,12 @@
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# Burn Import
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`burn-import` is a crate designed to facilitate importing models trained in other machine learning
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frameworks into the Burn framework. This tool generates a Rust source file that aligns the source
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model with Burn's model and converts tensor data into a format compatible with Burn.
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`burn-import` is a crate designed to simplify the process of importing models trained in other
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machine learning frameworks into the Burn framework. This tool generates a Rust source file that
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aligns the imported model with Burn's model and converts tensor data into a format compatible with
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Burn.
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Currently under development, `burn-import` supports importing ONNX models with a limited set of
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operators.
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Currently, `burn-import` supports importing ONNX models with a limited set of operators, as it is
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still under development.
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## Supported ONNX Operators
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@ -19,7 +20,9 @@ operators.
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### Importing ONNX models
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In `build.rs`, add the following:
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To import ONNX models, follow these steps:
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1. Add the following code to your `build.rs` file:
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```rust
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use burn_import::onnx::ModelCodeGen;
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@ -27,12 +30,12 @@ use burn_import::onnx::ModelCodeGen;
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fn main() {
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ModelCodeGen::new()
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.input("src/model/mnist.onnx") // Path to the ONNX model
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.out_dir("model/") // Directory to output the generated Rust source file (under target/)
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.out_dir("model/") // Directory for the generated Rust source file (under target/)
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.run_from_script();
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}
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```
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Then, add the following to mod.rs under `src/model`:
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2. Add the following code to the `mod.rs` file under `src/model`:
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```rust
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pub mod mnist {
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@ -40,7 +43,7 @@ pub mod mnist {
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}
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```
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Finally, in your code, you can use the imported model as follows:
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3. Use the imported model in your code as shown below:
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```rust
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use burn::tensor;
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@ -63,48 +66,46 @@ fn main() {
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}
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```
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You can view the working example in the `examples/onnx-inference` directory.
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A working example can be found in the [`examples/onnx-inference`](https://github.com/burn-rs/burn/tree/main/examples/onnx-inference) directory.
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### Adding new operators
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This section explains how to add support for new operators to `burn-import`.
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To add support for new operators to `burn-import`, follow these steps:
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1. Optimize the ONNX model using [onnxoptimizer](https://github.com/onnx/optimizer). It will remove
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uncessary operator/constants and make the model easier to understand.
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2. Use [Netron](https://github.com/lutzroeder/netron) app to visualize the ONNX model.
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3. Generate artifact files to help to see what the ONNX model (`my-model.onnx) looks like and its
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components.
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```bash
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1. Optimize the ONNX model using [onnxoptimizer](https://github.com/onnx/optimizer). This will
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remove unnecessary operators and constants, making the model easier to understand.
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2. Use the [Netron](https://github.com/lutzroeder/netron) app to visualize the ONNX model.
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3. Generate artifact files for the ONNX model (`my-model.onnx`) and its components:
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```
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cargo r -- ./my-model.onnx ./
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```
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4. You will run into an error saying that the operator is not supported. Implement missing
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operators. Hopefully, at least `my-model.graph.txt` is generated before the error occurs. This
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file contains information about the ONNX model.
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5. The newly generated `my-model.graph.txt` file will contain IR information about the model. This
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file is useful for understanding the structure of the model and the operators it uses. The
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`my-model.rs` file will contain an actual Burn model in rust code. `my-model.json` will contain
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the data of the model.
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6. The following is the explaination of onnx modules (under `srs/onnx`):
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- `from_onnx.rs`: This module contains logic for converting ONNX data objects into IR
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(Intermediate Representation) objects. This module must contain anything that deals with ONNX
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directly.
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- `ir.rs`: This module contains the IR objects that are used to represent the ONNX model. These
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objects are used to generate the Burn model.
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- `to_burn.rs` - This module contains logic for converting IR objects into Burn model source code
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and data. Nothing in this module should deal with ONNX directly.
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- `coalesce.rs`: This module contains the logic to coalesce multiple ONNX operators into a single
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Burn operator. This is useful for operators that are not supported by Burn, but can be
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represented by a combination of supported operators.
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- `op_configuration.rs` - This module contains helper functions for configuring burn operators
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from operator nodes.
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- `shape_inference.rs` - This module contains helper functions for inferring shapes of tensors
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for inputs and outputs of operators.
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7. Add unit tests for the new operator in `burn-import/tests/onnx_tests.rs` file. Add the ONNX file
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and expected output to `tests/data` directory. Please be sure the ONNX file is small. If the ONNX
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file is too large, the repository size will grow too large and will be difficult to maintain and
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clone. See the existing unit tests for examples.
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4. Implement the missing operators when you encounter an error stating that the operator is not
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supported. Ideally, the `my-model.graph.txt` file is generated before the error occurs, providing
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information about the ONNX model.
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5. The newly generated `my-model.graph.txt` file contains IR information about the model, while the
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`my-model.rs` file contains an actual Burn model in Rust code. The `my-model.json` file contains
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the model data.
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6. The `srs/onnx` directory contains the following ONNX modules (continued):
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- `coalesce.rs`: Coalesces multiple ONNX operators into a single Burn operator. This is useful
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for operators that are not supported by Burn but can be represented by a combination of
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supported operators.
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- `op_configuration.rs`: Contains helper functions for configuring Burn operators from operator
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nodes.
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- `shape_inference.rs`: Contains helper functions for inferring shapes of tensors for inputs and
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outputs of operators.
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7. Add unit tests for the new operator in the `burn-import/tests/onnx_tests.rs` file. Add the ONNX
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file and expected output to the `tests/data` directory. Ensure the ONNX file is small, as large
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files can increase repository size and make it difficult to maintain and clone. Refer to existing
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unit tests for examples.
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## Resources
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1. [PyTorch ONNX](https://pytorch.org/docs/stable/onnx.html)
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2. [ONNX Intro](https://onnx.ai/onnx/intro/)
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1. [PyTorch to ONNX](https://pytorch.org/docs/stable/onnx.html)
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2. [ONNX to Pytorch](https://github.com/ENOT-AutoDL/onnx2torch)
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3. [ONNX Intro](https://onnx.ai/onnx/intro/)
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4. [ONNX Operators](https://onnx.ai/onnx/operators/index.html)
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5. [ONNX Protos](https://onnx.ai/onnx/api/classes.html)
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6. [ONNX Optimizer](https://github.com/onnx/optimizer)
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7. [Netron](https://github.com/lutzroeder/netron)
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@ -1,6 +1,45 @@
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# Burn Tch
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# Burn Torch Backend
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> [Burn](https://github.com/burn-rs/burn) tch backend
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[Burn](https://github.com/burn-rs/burn) Torch backend
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[](https://crates.io/crates/burn-tch)
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[](https://github.com/burn-rs/burn-tch/blob/master/README.md)
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This crate provides a Torch backend for [Burn](https://github.com/burn-rs/burn) utilizing the
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[tch-rs](https://github.com/LaurentMazare/tch-rs) crate, which offers a Rust interface to the
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[PyTorch](https://pytorch.org/) C++ API.
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The backend supports CPU (multithreaded), [CUDA](https://pytorch.org/docs/stable/notes/cuda.html)
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(multiple GPUs), and [MPS](https://pytorch.org/docs/stable/notes/mps.html) devices (MacOS).
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## Usage Example
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```rust
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#[cfg(feature = "tch-gpu")]
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mod tch_gpu {
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use burn_autodiff::ADBackendDecorator;
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use burn_tch::{TchBackend, TchDevice};
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use mnist::training;
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pub fn run() {
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#[cfg(not(target_os = "macos"))]
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let device = TchDevice::Cuda(0);
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#[cfg(target_os = "macos")]
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let device = TchDevice::Mps;
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training::run::<ADBackendDecorator<TchBackend<f32>>>(device);
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}
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}
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#[cfg(feature = "tch-cpu")]
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mod tch_cpu {
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use burn_autodiff::ADBackendDecorator;
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use burn_tch::{TchBackend, TchDevice};
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use mnist::training;
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pub fn run() {
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let device = TchDevice::Cpu;
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training::run::<ADBackendDecorator<TchBackend<f32>>>(device);
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}
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}
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```
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@ -2,6 +2,8 @@
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The example can be run like so:
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## CUDA users
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```bash
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git clone https://github.com/burn-rs/burn.git
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cd burn
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export TORCH_CUDA_VERSION=cu113
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cargo run --example text-generation --release
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```
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## Mac users
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```bash
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git clone https://github.com/burn-rs/burn.git
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cd burn
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# Use the --release flag to really speed up training.
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cargo run --example text-generation --release
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```
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