mirror of
https://github.com/huggingface/candle.git
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Merge branch 'refs/heads/random' into operators-random-exp
# Conflicts: # candle-onnx/tests/ops.rs
This commit is contained in:
B1rtek
@ -971,6 +971,46 @@ pub fn simple_eval(
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};
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values.insert(node.output[0].clone(), output);
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}
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"RandomUniform" => {
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let dt: i64 = get_attr_opt(node, "dtype")?.copied().unwrap_or(1); // 1 is float
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// type by
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// default
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let dtype = match DataType::try_from(dt as i32) {
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Ok(dt) => match dtype(dt) {
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Some(DType::U8 | DType::U32 | DType::I64) => {
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bail!(
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"unsupported 'dtype' value {dt:?}, only floats are allowed, for RandomUnifrom {}",
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node.name
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)
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}
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Some(dt) => dt,
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None => {
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bail!(
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"unsupported 'dtype' value {dt:?} for RandomUnifrom {}",
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node.name
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)
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}
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},
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Err(_) => {
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bail!(
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"unsupported 'dtype' value {dt:?} for RandomUniform {}",
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node.name
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)
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}
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};
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let low: f32 = get_attr_opt(node, "low")?.copied().unwrap_or(0.0);
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let high: f32 = get_attr_opt(node, "high")?.copied().unwrap_or(1.0);
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let seed: Option<f32> = get_attr_opt(node, "seed")?.copied();
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if seed.is_some() {
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bail!("seed for RandomUniform is currently not supported")
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};
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let shape: Vec<usize> = get_attr::<[i64]>(node, "shape")?
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.iter()
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.map(|x| *x as usize)
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.collect();
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let output = Tensor::rand(low, high, shape, &Device::Cpu)?.to_dtype(dtype)?;
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values.insert(node.output[0].clone(), output);
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}
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op_type => bail!("unsupported op_type {op_type} for op {node:?}"),
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}
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}
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@ -1878,6 +1878,152 @@ fn test_sqrt() -> Result<()> {
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Ok(())
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}
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// "RandomUniform"
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#[test]
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fn test_random_uniform() -> Result<()> {
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test(vec![3, 2, 1, 4], None, None)?;
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test(vec![2, 2, 2, 2], Some(-10.0), None)?;
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test(vec![2, 2, 2, 2], None, Some(10.0))?;
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test(vec![1, 2, 3, 4], Some(-10.0), Some(10.0))?;
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fn test(shape: Vec<i64>, low: Option<f32>, high: Option<f32>) -> Result<()> {
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let att_low = AttributeProto {
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name: "low".to_string(),
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ref_attr_name: "low".to_string(),
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i: 0,
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doc_string: "low".to_string(),
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r#type: 1, // FLOAT
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f: low.unwrap_or(0.0),
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s: vec![],
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t: None,
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g: None,
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sparse_tensor: None,
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tp: None,
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floats: vec![],
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ints: vec![],
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strings: vec![],
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tensors: vec![],
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graphs: vec![],
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sparse_tensors: vec![],
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type_protos: vec![],
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};
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let att_high = AttributeProto {
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name: "high".to_string(),
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ref_attr_name: "high".to_string(),
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i: 0,
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doc_string: "high".to_string(),
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r#type: 1, // FLOAT
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f: high.unwrap_or(1.0),
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s: vec![],
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t: None,
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g: None,
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sparse_tensor: None,
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tp: None,
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floats: vec![],
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ints: vec![],
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strings: vec![],
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tensors: vec![],
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graphs: vec![],
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sparse_tensors: vec![],
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type_protos: vec![],
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};
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let att_shape = AttributeProto {
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name: "shape".to_string(),
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ref_attr_name: "shape".to_string(),
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i: 0,
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doc_string: "shape".to_string(),
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r#type: 7, // INTS
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f: 0.0,
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s: vec![],
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t: None,
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g: None,
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sparse_tensor: None,
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tp: None,
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floats: vec![],
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ints: shape,
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strings: vec![],
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tensors: vec![],
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graphs: vec![],
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sparse_tensors: vec![],
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type_protos: vec![],
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};
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let att_dtype = AttributeProto {
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name: "dtype".to_string(),
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ref_attr_name: "dtype".to_string(),
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i: 11, // DOUBLE
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doc_string: "dtype".to_string(),
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r#type: 2, // INT
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f: 0.0,
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s: vec![],
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t: None,
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g: None,
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sparse_tensor: None,
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tp: None,
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floats: vec![],
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ints: vec![],
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strings: vec![],
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tensors: vec![],
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graphs: vec![],
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sparse_tensors: vec![],
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type_protos: vec![],
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};
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let attrs = {
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let mut mut_attrs = vec![att_shape, att_dtype];
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if low.is_some() {
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mut_attrs.push(att_low);
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}
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if high.is_some() {
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mut_attrs.push(att_high);
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}
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mut_attrs
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};
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let manual_graph = create_model_proto_with_graph(Some(GraphProto {
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node: vec![NodeProto {
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op_type: "RandomUniform".to_string(),
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domain: "".to_string(),
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attribute: attrs,
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input: vec![],
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output: vec![OUTPUT_Z.to_string()],
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name: "".to_string(),
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doc_string: "".to_string(),
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}],
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name: "".to_string(),
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initializer: vec![],
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input: vec![],
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output: vec![ValueInfoProto {
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name: OUTPUT_Z.to_string(),
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doc_string: "".to_string(),
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r#type: None,
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}],
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value_info: vec![],
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doc_string: "".to_string(),
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sparse_initializer: vec![],
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quantization_annotation: vec![],
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}));
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let eval = candle_onnx::simple_eval(&manual_graph, HashMap::new())?;
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assert_eq!(eval.len(), 1);
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let z = eval.get(OUTPUT_Z).expect("Output 'z' not found");
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let min = z
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.flatten_all()?
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.to_vec1()?
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.into_iter()
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.reduce(f64::min)
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.unwrap();
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let max = z
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.flatten_all()?
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.to_vec1()?
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.into_iter()
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.reduce(f64::max)
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.unwrap();
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assert!(min >= low.unwrap_or(0.0).into());
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assert!(max <= high.unwrap_or(1.0).into());
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assert_ne!(min, max);
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Ok(())
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}
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Ok(())
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}
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// "Range"
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#[test]
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fn test_range() -> Result<()> {
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