use super::prelude::*;
use burn_store::TensorSnapshot;
impl NodeCodegen for onnx_ir::conv1d::Conv1dNode {
fn inputs(&self) -> &[Argument] {
&self.inputs
}
fn outputs(&self) -> &[Argument] {
&self.outputs
}
fn field(&self) -> Option<Field> {
let name = Ident::new(&self.name, Span::call_site());
let weight_shape = self.inputs[1]
.ty
.static_shape_known()
.expect("Conv1d: weight tensor shape must be known at codegen time");
let groups = self.config.groups;
let channels_in = (weight_shape[1] * groups).to_tokens();
let channels_out = weight_shape[0].to_tokens();
let kernel_size = self.config.kernel_size.to_tokens();
let stride = self.config.stride.to_tokens();
let dilation = self.config.dilation.to_tokens();
let groups = groups.to_tokens();
let bias = self.inputs.len() == 3;
let shape = self.inputs[0].ty.static_shape_known();
let input_spatial = shape.as_deref().map(|s| &s[2..]);
let padding = crate::burn::codegen::resolve_auto_pad_1d(
&self.config.auto_pad,
&self.config.padding,
input_spatial,
self.config.kernel_size,
self.config.stride,
self.config.dilation,
)
.to_tokens();
Some(Field::new(
self.name.clone(),
quote! {
Conv1d<B>
},
quote! {
let #name = Conv1dConfig::new(#channels_in, #channels_out, #kernel_size)
.with_stride(#stride)
.with_padding(#padding)
.with_dilation(#dilation)
.with_groups(#groups)
.with_bias(#bias)
.init(device);
},
))
}
fn forward(&self, scope: &mut ScopeAtPosition<'_>) -> TokenStream {
let input = scope.arg(self.inputs.first().unwrap());
let output = arg_to_ident(self.outputs.first().unwrap());
let field = Ident::new(&self.name, Span::call_site());
quote! {
let #output = self.#field.forward(#input);
}
}
fn register_imports(&self, imports: &mut BurnImports) {
imports.register("burn::nn::PaddingConfig1d");
imports.register("burn::nn::conv::Conv1d");
imports.register("burn::nn::conv::Conv1dConfig");
}
fn collect_snapshots(&self, field_name: &str) -> Vec<TensorSnapshot> {
use crate::burn::node_traits::create_lazy_snapshot;
let mut snapshots = vec![];
if let Some(weight_input) = self.inputs.get(1) {
let weight_path = format!("{}.weight", field_name);
if let Some(snapshot) = create_lazy_snapshot(weight_input, &weight_path, "Conv1d") {
snapshots.push(snapshot);
}
}
if let Some(bias_input) = self.inputs.get(2) {
let bias_path = format!("{}.bias", field_name);
if let Some(snapshot) = create_lazy_snapshot(bias_input, &bias_path, "Conv1d") {
snapshots.push(snapshot);
}
}
snapshots
}
}
#[cfg(test)]
mod tests {
use super::super::test_helpers::*;
use burn::tensor::DType;
use insta::assert_snapshot;
use onnx_ir::conv1d::{Conv1dConfig, Conv1dNode, Conv1dNodeBuilder};
use onnx_ir::padding::{AutoPad, PaddingConfig1d};
fn create_conv1d_node(name: &str) -> Conv1dNode {
let config =
Conv1dConfig::new(3, 1, 1, 1, PaddingConfig1d::Explicit(1, 1), AutoPad::NotSet);
Conv1dNodeBuilder::new(name)
.input_tensor("input", 3, DType::F32)
.input_static_tensor_shape("weight", vec![64, 3, 3], DType::F32)
.input_static_tensor_shape("bias", vec![64], DType::F32)
.output_tensor("output", 3, DType::F32)
.config(config)
.build()
}
fn create_conv1d_node_asymmetric(name: &str) -> Conv1dNode {
let config =
Conv1dConfig::new(3, 1, 1, 1, PaddingConfig1d::Explicit(1, 2), AutoPad::NotSet);
Conv1dNodeBuilder::new(name)
.input_tensor("input", 3, DType::F32)
.input_static_tensor_shape("weight", vec![64, 3, 3], DType::F32)
.input_static_tensor_shape("bias", vec![64], DType::F32)
.output_tensor("output", 3, DType::F32)
.config(config)
.build()
}
#[test]
fn test_conv1d_forward() {
let node = create_conv1d_node("conv1");
let code = codegen_forward_default(&node);
assert_snapshot!(code, @r"
pub fn forward(&self, input: Tensor<B, 3>) -> Tensor<B, 3> {
let output = self.conv1.forward(input);
output
}
");
}
#[test]
fn test_conv1d_forward_with_clone() {
let node = create_conv1d_node("conv1");
let code = codegen_forward_with_clone(&node);
assert_snapshot!(code, @r"
pub fn forward(&self, input: Tensor<B, 3>) -> Tensor<B, 3> {
let output = self.conv1.forward(input.clone());
output
}
");
}
#[test]
fn test_conv1d_field_init_auto_pad_same_upper() {
let config = Conv1dConfig::new(3, 1, 1, 1, PaddingConfig1d::Valid, AutoPad::SameUpper);
let node = Conv1dNodeBuilder::new("conv1")
.input_tensor_shape("input", vec![1, 3, 7], DType::F32)
.input_static_tensor_shape("weight", vec![64, 3, 3], DType::F32)
.input_static_tensor_shape("bias", vec![64], DType::F32)
.output_tensor("output", 3, DType::F32)
.config(config)
.build();
let code = codegen_field_init(&node);
assert_snapshot!(code, @r"
let conv1 = Conv1dConfig::new(3, 64, 3)
.with_stride(1)
.with_padding(PaddingConfig1d::Explicit(1, 1))
.with_dilation(1)
.with_groups(1)
.with_bias(true)
.init(device);
");
}
#[test]
fn test_conv1d_field_init_asymmetric_padding() {
let node = create_conv1d_node_asymmetric("conv1");
let code = codegen_field_init(&node);
assert_snapshot!(code, @r"
let conv1 = Conv1dConfig::new(3, 64, 3)
.with_stride(1)
.with_padding(PaddingConfig1d::Explicit(1, 2))
.with_dilation(1)
.with_groups(1)
.with_bias(true)
.init(device);
");
}
}