Ben_Kosytorz
3a67c0979c
- Updated stablediffusion crate path from "../stable-diffusion-burn" to "./crates/stable-diffusion-burn" for proper workspace resolution - Enhanced .gitignore to include generated model files (.mpk, .pt, .bin, .safetensors, .ckpt) and user_data directory - Added Cargo.lock to gitignore with appropriate comment - Reorganized IDE files section in gitignore for better clarity - Added newline at end of file for proper formatting
79 lines
3.5 KiB
Rust
79 lines
3.5 KiB
Rust
use super::*;
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use burn_tensor::Tolerance;
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use burn_tensor::{Distribution, Tensor, module};
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#[test]
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fn conv2d_should_match_reference_backend() {
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let test_device = Default::default();
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let input =
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Tensor::<TestBackend, 4>::random([6, 16, 32, 32], Distribution::Default, &test_device);
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let weight =
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Tensor::<TestBackend, 4>::random([12, 8, 3, 3], Distribution::Default, &test_device);
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let bias = Tensor::<TestBackend, 1>::random([12], Distribution::Default, &test_device);
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let ref_device = Default::default();
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let input_ref = Tensor::<ReferenceBackend, 4>::from_data(input.to_data(), &ref_device);
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let weight_ref = Tensor::<ReferenceBackend, 4>::from_data(weight.to_data(), &ref_device);
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let bias_ref = Tensor::<ReferenceBackend, 1>::from_data(bias.to_data(), &ref_device);
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let options = burn_tensor::ops::ConvOptions::new([2, 3], [2, 3], [2, 3], 2);
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let output = module::conv2d(input, weight, Some(bias), options.clone());
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let output_ref = module::conv2d(input_ref, weight_ref, Some(bias_ref), options);
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output
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.into_data()
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.assert_approx_eq::<FloatElem>(&output_ref.into_data(), Tolerance::default());
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}
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#[test]
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fn conv2d_should_match_reference_backend_implicit() {
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let test_device = Default::default();
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let input =
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Tensor::<TestBackend, 4>::random([4, 16, 6, 6], Distribution::Default, &test_device);
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let weight =
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Tensor::<TestBackend, 4>::random([16, 16, 3, 3], Distribution::Default, &test_device);
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let bias = Tensor::<TestBackend, 1>::random([16], Distribution::Default, &test_device);
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let ref_device = Default::default();
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let input_ref = Tensor::<ReferenceBackend, 4>::from_data(input.to_data(), &ref_device);
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let weight_ref = Tensor::<ReferenceBackend, 4>::from_data(weight.to_data(), &ref_device);
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let bias_ref = Tensor::<ReferenceBackend, 1>::from_data(bias.to_data(), &ref_device);
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let options = burn_tensor::ops::ConvOptions::new([1, 1], [2, 2], [1, 1], 1);
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let output = module::conv2d(input, weight, Some(bias), options.clone());
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let output_ref = module::conv2d(input_ref, weight_ref, Some(bias_ref), options);
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let tolerance = Tolerance::default();
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output
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.into_data()
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.assert_approx_eq::<FloatElem>(&output_ref.into_data(), tolerance);
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}
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/// Regression test for bias loader in new implicit GEMM
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#[test]
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fn conv2d_should_match_reference_backend_bias_regression() {
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let test_device = Default::default();
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let input = Tensor::<TestBackend, 4>::random([1, 1, 1, 1], Distribution::Default, &test_device);
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let weight =
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Tensor::<TestBackend, 4>::random([32, 1, 3, 3], Distribution::Default, &test_device);
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let bias = Tensor::<TestBackend, 1>::random([32], Distribution::Default, &test_device);
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let ref_device = Default::default();
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let input_ref = Tensor::<ReferenceBackend, 4>::from_data(input.to_data(), &ref_device);
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let weight_ref = Tensor::<ReferenceBackend, 4>::from_data(weight.to_data(), &ref_device);
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let bias_ref = Tensor::<ReferenceBackend, 1>::from_data(bias.to_data(), &ref_device);
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let options = burn_tensor::ops::ConvOptions::new([1, 1], [1, 1], [1, 1], 1);
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let output = module::conv2d(input, weight, Some(bias), options.clone()).permute([0, 2, 3, 1]);
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let output_ref =
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module::conv2d(input_ref, weight_ref, Some(bias_ref), options).permute([0, 2, 3, 1]);
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let tolerance = Tolerance::default();
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output
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.into_data()
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.assert_approx_eq::<FloatElem>(&output_ref.into_data(), tolerance);
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}
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