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use crate::ir::{
ArgType, Argument, AttributeValue, Data, ElementType, Node, NodeType, TensorData, TensorType,
};
use std::collections::HashMap;
/// Builder for creating test node instances with convenient defaults and simple API.
pub struct NodeBuilder {
node_type: NodeType,
name: String,
inputs: Vec<Argument>,
outputs: Vec<Argument>,
attrs: HashMap<String, AttributeValue>,
}
impl NodeBuilder {
/// Create a new builder with the specified node type and name
pub fn new(node_type: NodeType, name: &str) -> Self {
Self {
node_type,
name: name.to_string(),
inputs: Vec::new(),
outputs: Vec::new(),
attrs: HashMap::new(),
}
}
/// Add a generic input with the given name and type
///
/// Note: Prefer using the specialized methods like `input_tensor_f32`,
/// `input_scalar_f32`, etc. for better readability and type safety.
#[doc(hidden)]
pub fn add_input(mut self, name: &str, ty: ArgType) -> Self {
self.inputs.push(Argument {
name: name.to_string(),
ty,
value: None,
passed: true,
});
self
}
/// Add a float32 tensor input with the given name and rank
pub fn input_tensor_f32(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_input(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Float32,
rank,
static_shape,
}),
)
}
/// Add a float64 tensor input with the given name and rank
pub fn input_tensor_f64(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_input(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Float64,
rank,
static_shape,
}),
)
}
/// Add an int32 tensor input with the given name and rank
pub fn input_tensor_i32(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_input(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Int32,
rank,
static_shape,
}),
)
}
/// Add an int64 tensor input with the given name and rank
pub fn input_tensor_i64(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_input(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Int64,
rank,
static_shape,
}),
)
}
/// Add a bool tensor input with the given name and rank
pub fn input_tensor_bool(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_input(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Bool,
rank,
static_shape,
}),
)
}
/// Add a float16 tensor input with the given name and rank
pub fn input_tensor_f16(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_input(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Float16,
rank,
static_shape,
}),
)
}
/// Add a string tensor input with the given name and rank
pub fn input_tensor_string(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_input(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::String,
rank,
static_shape,
}),
)
}
/// Add a scalar input with the given name and element type
pub fn input_scalar(self, name: &str, elem_type: ElementType) -> Self {
self.add_input(name, ArgType::Scalar(elem_type))
}
/// Add a float32 scalar input with the given name
pub fn input_scalar_f32(self, name: &str) -> Self {
self.input_scalar(name, ElementType::Float32)
}
/// Add an int64 scalar input with the given name
pub fn input_scalar_i64(self, name: &str) -> Self {
self.input_scalar(name, ElementType::Int64)
}
/// Add a shape input with the given name and rank
pub fn input_shape(self, name: &str, rank: usize) -> Self {
self.add_input(name, ArgType::Shape(rank))
}
/// Add a tensor input with data value
pub fn input_tensor_with_data(
mut self,
name: &str,
elem_type: ElementType,
rank: usize,
data: Data,
shape: Vec<usize>,
) -> Self {
let arg = Argument {
name: name.to_string(),
ty: ArgType::Tensor(TensorType {
elem_type,
rank,
static_shape: None,
}),
value: Some(TensorData { data, shape }),
passed: true,
};
self.inputs.push(arg);
self
}
/// Add a float32 tensor input with data values
pub fn input_tensor_f32_data(self, name: &str, data: Vec<f32>, shape: Vec<usize>) -> Self {
self.input_tensor_with_data(
name,
ElementType::Float32,
shape.len(),
Data::Float32s(data),
shape,
)
}
/// Add an int64 tensor input with data values
pub fn input_tensor_i64_data(self, name: &str, data: Vec<i64>, shape: Vec<usize>) -> Self {
self.input_tensor_with_data(
name,
ElementType::Int64,
shape.len(),
Data::Int64s(data),
shape,
)
}
/// Add a float32 scalar tensor input (rank 0)
pub fn input_scalar_tensor_f32(mut self, name: &str, value: Option<f32>) -> Self {
let arg = Argument {
name: name.to_string(),
ty: ArgType::Tensor(TensorType {
elem_type: ElementType::Float32,
rank: 0,
static_shape: None,
}),
value: value.map(|val| TensorData {
data: Data::Float32(val),
shape: vec![],
}),
passed: true,
};
self.inputs.push(arg);
self
}
/// Add an int64 scalar tensor input (rank 0)
pub fn input_scalar_tensor_i64(mut self, name: &str, value: i64) -> Self {
let arg = Argument {
name: name.to_string(),
ty: ArgType::Tensor(TensorType {
elem_type: ElementType::Int64,
rank: 0,
static_shape: None,
}),
value: Some(TensorData {
data: Data::Int64(value),
shape: vec![],
}),
passed: true,
};
self.inputs.push(arg);
self
}
/// Add multiple tensor inputs with the same type but different names
pub fn input_tensors_f32<I>(
mut self,
name_prefix: &str,
count: usize,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
for i in 0..count {
self = self.input_tensor_f32(&format!("{name_prefix}_{i}"), rank, static_shape.clone());
}
self
}
/// Add a generic output with the given name and type
///
/// Note: Prefer using the specialized methods like `output_tensor_f32`,
/// `output_scalar_f32`, etc. for better readability and type safety.
#[doc(hidden)]
pub fn add_output(mut self, name: &str, ty: ArgType) -> Self {
self.outputs.push(Argument {
name: name.to_string(),
ty,
value: None,
passed: true,
});
self
}
/// Add a float32 tensor output with the given name and rank
pub fn output_tensor_f32(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_output(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Float32,
rank,
static_shape,
}),
)
}
/// Add a float64 tensor output with the given name and rank
pub fn output_tensor_f64(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_output(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Float64,
rank,
static_shape,
}),
)
}
/// Add an int32 tensor output with the given name and rank
pub fn output_tensor_i32(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_output(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Int32,
rank,
static_shape,
}),
)
}
/// Add an int64 tensor output with the given name and rank
pub fn output_tensor_i64(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_output(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Int64,
rank,
static_shape,
}),
)
}
/// Add a bool tensor output with the given name and rank
pub fn output_tensor_bool(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_output(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Bool,
rank,
static_shape,
}),
)
}
/// Add a float16 tensor output with the given name and rank
pub fn output_tensor_f16(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_output(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::Float16,
rank,
static_shape,
}),
)
}
/// Add a string tensor output with the given name and rank
pub fn output_tensor_string(
self,
name: &str,
rank: usize,
static_shape: Option<Vec<usize>>,
) -> Self {
self.add_output(
name,
ArgType::Tensor(TensorType {
elem_type: ElementType::String,
rank,
static_shape,
}),
)
}
/// Add a scalar output with the given name and element type
pub fn output_scalar(self, name: &str, elem_type: ElementType) -> Self {
self.add_output(name, ArgType::Scalar(elem_type))
}
/// Add a float32 scalar output with the given name
pub fn output_scalar_f32(self, name: &str) -> Self {
self.output_scalar(name, ElementType::Float32)
}
/// Add an int64 scalar output with the given name
pub fn output_scalar_i64(self, name: &str) -> Self {
self.output_scalar(name, ElementType::Int64)
}
/// Add a shape output with the given name and rank
pub fn output_shape(self, name: &str, rank: usize) -> Self {
self.add_output(name, ArgType::Shape(rank))
}
/// Add an integer attribute
pub fn attr_int(mut self, name: &str, value: i64) -> Self {
self.attrs
.insert(name.to_string(), AttributeValue::Int64(value));
self
}
/// Add a float attribute
pub fn attr_float(mut self, name: &str, value: f32) -> Self {
self.attrs
.insert(name.to_string(), AttributeValue::Float32(value));
self
}
/// Add a string attribute
pub fn attr_string(mut self, name: &str, value: &str) -> Self {
self.attrs
.insert(name.to_string(), AttributeValue::String(value.to_string()));
self
}
/// Add an integer array attribute
pub fn attr_ints(mut self, name: &str, values: Vec<i64>) -> Self {
self.attrs
.insert(name.to_string(), AttributeValue::Int64s(values));
self
}
/// Add a float array attribute
pub fn attr_floats(mut self, name: &str, values: Vec<f32>) -> Self {
self.attrs
.insert(name.to_string(), AttributeValue::Float32s(values));
self
}
/// Add a string array attribute
pub fn attr_strings(mut self, name: &str, values: Vec<String>) -> Self {
self.attrs
.insert(name.to_string(), AttributeValue::Strings(values));
self
}
/// Add a tensor attribute
pub fn attr_tensor(mut self, name: &str, tensor: TensorData) -> Self {
self.attrs
.insert(name.to_string(), AttributeValue::Tensor(tensor));
self
}
/// Add a default output with the given name
pub fn output_default(mut self, name: &str) -> Self {
self.outputs.push(Argument {
name: name.to_string(),
ty: ArgType::default(),
value: None,
passed: true,
});
self
}
/// Build the node
pub fn build(self) -> Node {
Node {
node_type: self.node_type,
name: self.name,
inputs: self.inputs,
outputs: self.outputs,
attrs: self.attrs,
}
}
}