⚗️ Transmuter

Transmuter runs ONNX models on Nx. Load a model from Hugging Face and run it on any Nx backend: EMLX on Apple silicon, or EXLA on CUDA or on the CPU.

Transmuter does not port model architectures. An ONNX file contains the forward pass that the exporter traced from the original model. Transmuter changes each ONNX operator into Nx operations. A new architecture needs no new Elixir code. It needs only the operators that its export uses.

The gap

Elixir has three ways to run Hugging Face models today. Each way has a limit:

• Bumblebee runs models with great accuracy on all Nx backends. But a person must port each architecture to Axon by hand, and a port takes weeks of work and review. Hugging Face gets new architectures faster than people can port them.
• Ortex runs ONNX models with ONNX Runtime. But it runs outside of Nx: you cannot put it in a defn function, it has no Apple silicon GPU path, and it is in maintenance mode.
• axon_onnx changed ONNX graphs into Axon models. It stopped in 2024. It set all shapes when it loaded a model, and many ONNX graphs need shapes that change.

Transmuter fills this gap. It runs the exported graph directly as Nx operations, so:

• Models that Bumblebee does not have run with no new code. Examples are SAM, Depth Anything, Grounding DINO, OWLv2, Florence-2, RF-DETR, Kokoro and Stable Diffusion.
• The result is plain Nx. It runs on EMLX and EXLA, it works with Nx.Serving, and Nx.Defn.grad can differentiate it.
• Shapes are set when the function compiles, not when the model loads. Thus decoders with a KV cache and graphs with If branches work.

When a model has no ONNX file on Hugging Face, export it one time with Optimum. In the 300 most downloaded transformers models, 62% of downloads already have an ONNX file on Hugging Face.

Example

model = Transmuter.load!({:hf, "Xenova/all-MiniLM-L6-v2", "onnx/model.onnx"}, backend: EMLX.Backend)

inputs = %{
  "input_ids" => Nx.tensor([[101, 7592, 2088, 102]]),
  "attention_mask" => Nx.tensor([[1, 1, 1, 1]]),
  "token_type_ids" => Nx.tensor([[0, 0, 0, 0]])
}

run = Transmuter.compile(model, inputs, compiler: EMLX)
%{"last_hidden_state" => hidden} = run.(inputs)

# Sentence embedding: mean of the token vectors, then L2 normalization.
mask = Nx.new_axis(inputs["attention_mask"], -1)
embedding = Nx.sum(Nx.multiply(hidden, mask), axes: [1]) |> Nx.divide(Nx.sum(mask, axes: [1]))
embedding = Nx.divide(embedding, Nx.LinAlg.norm(embedding, axes: [1], keep_axes: true))

The export sets the names of the inputs and outputs. model.inputs and model.outputs show them. For this export, model.outputs is ["last_hidden_state"].

Transmuter runs the graph that is in the file, and nothing more. The graph of all-MiniLM-L6-v2 stops at the token vectors, because sentence-transformers adds the pooling outside of the model. Thus the example above does the pooling. Bumblebee servings, such as Bumblebee.Text.text_embedding/3, also do the tokenization, pooling and batching for you. Transmuter has no servings yet. For tokenization, use tokenizers.

Transmuter.compile/3 compiles the model for the shapes of the inputs that you give. For other shapes, compile again.

Transmuter.run/4 is the function that Transmuter.compile/3 compiles. It reads the ONNX graph and calls one Nx function for each operator. You can use it in two ways:

• Call it directly. Each operator runs immediately, one after the other. This is eager execution. It is slower, but the model can read values while it runs.
• Give it to Nx.Defn.jit/2 or Nx.Defn.value_and_grad/2. Nx then records the operators as one graph, and the backend compiles that graph.

Installation

def deps do
  [
    {:transmuter, "~> 0.1"},
    {:emlx, "~> 0.5"}
  ]
end

Use EXLA instead of EMLX for CUDA.

Static inputs and eager execution

Some graphs use the value of an input to set a shape or to select a branch. Give these inputs as Nx.BinaryBackend tensors in the :static_inputs option of Transmuter.compile/3:

• use_cache_branch in merged decoder exports selects an If branch.
• num_logits_to_keep in some decoder exports sets the length of a slice.
• input_ids in Grounding DINO go into NonZero.

Each new value of a static input compiles the model again.

Some graphs calculate a shape inside the graph. For these graphs, call Transmuter.run/4 without compilation. Then Transmuter reads the values while the model runs. Kokoro text-to-speech needs this, because the length of its audio comes from the predicted duration of each phoneme. On an Apple silicon GPU, Kokoro makes 4.45 seconds of audio in 1.4 seconds.

Precision and quantization

Half-precision exports (model_fp16.onnx) run in f16 or bf16. Sums, normalizations, softmax and attention scores use f32 inside the operation. ONNX Runtime on the CPU calculates half-precision graphs in f32. To get the same result, use Transmuter.load!(source, compute_type: :f32).

Quantized exports run with their weights dequantized in the graph:

• model_q4.onnx and model_q4f16.onnx use 4-bit weights with MatMulNBits and GatherBlockQuantized.
• model_quantized.onnx and model_int8.onnx use dynamic int8 with DynamicQuantizeLinear, MatMulInteger and ConvInteger.

With backend: EMLX.Backend, Transmuter.load!/2 keeps MatMulNBits weights packed as MLX quantized tensors. Then mx::quantized_matmul reads the 4-bit or 8-bit weights directly. On other backends, each forward pass dequantizes the weights to floats. These results are from an Apple silicon GPU with one forward pass of 8 tokens:

Dynamic int8 quantizes each activation again. A change of one f32 rounding step can move a value to the next int8 step, and the next layers make that change larger. For example, when all weights of SmolLM2 int8 change by a factor of 1 + 1e-7, its logits change by 22. Thus, compare an int8 model with its own noise.

Fine-tuning

Transmuter.run/4 is plain Nx. Thus Nx.Defn.value_and_grad/2 calculates gradients through the graph, and an optimizer such as Polaris updates the params. Load the model with trainable: true. Then small weights, such as a bias with 1 element, are params and not constants.

bench/finetune.exs fine-tunes a MiniLM cross-encoder. Its gradients and its loss curve for 30 AdamW steps are the same as in PyTorch.

Fine-tune an fp32 export. Inference exports have no dropout, and quantized weights have no gradients.

Coverage

bench/coverage runs the most downloaded ONNX exports for each Hugging Face task. It compares each output with ONNX Runtime. The reference is ONNX Runtime with graph optimizations off, because its default optimizer gives wrong results for some exports. For MatMulNBits, the reference uses accuracy_level 0. At level 4, ONNX Runtime quantizes the activations to int8, and that result is an approximation.

These models do not match by position, or do not run with compilation:

• Grounding DINO runs. But with random inputs, its TopK selection changes when the input has noise of 1e-6. ONNX Runtime has the same difference when it compares with itself.
• Kokoro runs only in eager mode, because its audio length is calculated in the graph.

To run the matrix:

1. Run bench/coverage/select_corpus.py to select the models.
2. Run bench/coverage/prepare.py to download them and record the ONNX Runtime outputs.
3. Run bench/coverage/run_all.py to run each case through Transmuter.
4. Run bench/coverage/report.py to show the matrix.

If a case does not match, run bench/coverage/debug_ort.py and bench/coverage/debug_case.exs. They show the first node where Transmuter and ONNX Runtime give different values.

Tests

mix test downloads five models and compares them with ONNX Runtime. test/support/reference.py shows the reference values. On a Mac, TRANSMUTER_TEST_DEVICE=cpu mix test runs the tests on the CPU, as the Linux CI does.

License

Apache-2.0