Crates.io | edge-transformers |
lib.rs | edge-transformers |
version | 0.1.2 |
source | src |
created_at | 2023-03-15 16:37:03.1478 |
updated_at | 2023-04-01 15:23:55.658194 |
description | A Rust wrapper over ONNXRuntime that implements Huggingface's Optimum pipelines for inference and generates bindings for C# and C. |
homepage | |
repository | https://github.com/npc-engine/edge-transformers |
max_upload_size | |
id | 810840 |
size | 411,797 |
Edge transformers is a Rust implementation of Huggingface's pipelines based on ONNX Runtime backend.
Model export feature/task | Class name |
---|---|
causal-lm | ConditionalGenerationPipeline |
causal-lm-with-past | ConditionalGenerationPipelineWithPKVs |
default | EmbeddingPipeline |
seq2seq-lm | Seq2SeqGenerationPipeline or OptimumSeq2SeqPipeline |
seq2seq-lm-with-past | OptimumSeq2SeqPipelineWithPKVs |
sequence-classification | SequenceClassificationPipeline |
token-classification | TokenClassificationPipeline |
Your linker must be able to find onnxruntime.dll and edge-transformers.dll (or *.so on Linux).
You can find C and C# wrappers in c
and csharp
folders respectively.
Documentation is WIP, refer to Rust documentation for now.
using EdgeTransformers;
...
var env = EnvContainer.New();
var conditionalGen = ConditionalGenerationPipelineFFI.FromPretrained(
env.Context, "optimum/gpt2", DeviceFFI.CPU, GraphOptimizationLevelFFI.Level3
);
var outp = conditionalGen.GenerateTopkSampling("Hello", 2, 50, 0.9f);
Assert.IsNotNull(outp);
...
Batch processing is supported, but is a bit unintuitive and requires StringBatch class.
using EdgeTransformers;
...
var env = EnvContainer.New();
var condPipelinePkv = ConditionalGenerationPipelineFFI.FromPretrained(
env.Context, "optimum/gpt2", DeviceFFI.DML, GraphOptimizationLevelFFI.All);
var string_batch = StringBatch.New();
string_batch.Add("Hello world");
string_batch.Add("Hello world");
var o_batch_pkv = condPipelinePkv.GenerateRandomSamplingBatch(string_batch.Context, 10, 0.5f);
Debug.LogFormat("Cond generation output 0: {0} 1: {1}", o_batch_pkv[0].ascii_string, o_batch_pkv[1].ascii_string);
...
TODO
use std::fs;
use ort::environment::Environment;
use ort::{GraphOptimizationLevel, LoggingLevel};
use edge_transformers::{ConditionalGenerationPipelineWithPKVs, TopKSampler, Device};
let environment = Environment::builder()
.with_name("test")
.with_log_level(LoggingLevel::Verbose)
.build()
.unwrap();
let sampler = TopKSampler::new(50, 0.9);
let pipeline = ConditionalGenerationPipelineWithPKVs::from_pretrained(
environment.into_arc(),
"optimum/gpt2".to_string(),
Device::CPU,
GraphOptimizationLevel::Level3,
).unwrap();
let input = "This is a test";
println!("{}", pipeline.generate(input, 10, &sampler).unwrap());
Please refer to ONNX Runtime bindings docs on detailed how to.
Tests require to be running on a single thread at least for the first time. The reason is that they use *::from_pretrained function that downloads data from Huggingface Hub and some tests rely on the same files being downloaded. Second time they can run in parallel because they use cached files.
e.g. First time command:
cargo test -- --test-threads=1
e.g. Second time:
cargo test