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feat: update workspace paths and enhance gitignore

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- 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
This commit is contained in:
Ben_Kosytorz
2026-03-05 19:39:14 +01:00
parent 4bb7ca9074
commit 3a67c0979c
1605 changed files with 537032 additions and 2 deletions
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1
crates/stable-diffusion-burn/burn-crates/burn-backend-tests/tests/fused_ops/mod.rs Normal file
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mod reduce_broadcasted;

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crates/stable-diffusion-burn/burn-crates/burn-backend-tests/tests/fused_ops/reduce_broadcasted.rs Normal file
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use super::*;
use burn_tensor::{TensorData, Tolerance, backend::Backend};
#[test]
fn test_reduce_broadcasted_1() {
let device = Default::default();
let tensor = TestTensorInt::<1>::arange(0..32, &device)
.reshape([4, 8])
.float();
let fused_on_read = TestTensorInt::<1>::arange(0..32, &device)
.reshape([4, 8])
.float();
let fused_on_write = TestTensorInt::<1>::arange(0..4, &device)
.reshape([4, 1])
.float();
// Forces previous tensors to be materialized.
TestBackend::sync(&device).unwrap();
let x = tensor + fused_on_read.clone();
let x = x.sum_dim(1);
let x = x + fused_on_write;
// Broadcast
let end = x + fused_on_read;
let actual = end.into_data();
let expected = TensorData::from([
[56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0],
[193.0, 194.0, 195.0, 196.0, 197.0, 198.0, 199.0, 200.0],
[330.0, 331.0, 332.0, 333.0, 334.0, 335.0, 336.0, 337.0],
[467.0, 468.0, 469.0, 470.0, 471.0, 472.0, 473.0, 474.0],
]);
actual.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
}
#[test]
fn test_reduce_broadcasted_2() {
let device = Default::default();
let tensor = TestTensorInt::<1>::arange(0..32, &device)
.reshape([4, 8])
.float();
let fused_on_read = TestTensorInt::<1>::arange(0..32, &device)
.reshape([4, 8])
.float();
let fused_on_write = TestTensorInt::<1>::arange(16..48, &device)
.reshape([4, 8])
.float();
// Second fuse on read
let y = TestTensorInt::<1>::arange(32..64, &device)
.reshape([4, 8])
.float();
// Forces previous tensors to be materialized.
TestBackend::sync(&device).unwrap();
let x = tensor + fused_on_read.clone();
let x = x.sum_dim(1);
let x = x + fused_on_write;
let x = x.mean_dim(1);
let end = x + y;
TestBackend::sync(&device).unwrap();
let actual = end.into_data();
let expected = TensorData::from([
[107.5, 108.5, 109.5, 110.5, 111.5, 112.5, 113.5, 114.5],
[251.5, 252.5, 253.5, 254.5, 255.5, 256.5, 257.5, 258.5],
[395.5, 396.5, 397.5, 398.5, 399.5, 400.5, 401.5, 402.5],
[539.5, 540.5, 541.5, 542.5, 543.5, 544.5, 545.5, 546.5],
]);
actual.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
}
#[test]
fn test_reduce_broadcasted_3() {
let device = Default::default();
let tensor = TestTensorInt::<1>::arange(0..32, &device)
.reshape([4, 8])
.float();
let fused_on_read = TestTensorInt::<1>::arange(0..32, &device)
.reshape([4, 8])
.float();
let fused_on_write = TestTensorInt::<1>::arange(0..4, &device)
.reshape([4, 1])
.float();
// Second fuse on read
let y = TestTensorInt::<1>::arange(32..64, &device)
.reshape([4, 8])
.float();
// Forces previous tensors to be materialized.
TestBackend::sync(&device).unwrap();
let x = tensor + fused_on_read.clone();
let x = x.sum_dim(1);
let x = x + fused_on_write;
// Broadcast
let x = x + fused_on_read;
// Second reduce
let x = x.mean_dim(1);
let end = x + y;
let actual = end.into_data();
let expected = TensorData::from([
[91.5, 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 98.5],
[236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5, 243.5],
[381.5, 382.5, 383.5, 384.5, 385.5, 386.5, 387.5, 388.5],
[526.5, 527.5, 528.5, 529.5, 530.5, 531.5, 532.5, 533.5],
]);
actual.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
}
#[test]
fn test_reduce_broadcasted_4_reused_partial() {
let device = Default::default();
let tensor = TestTensorInt::<1>::arange(0..32, &device)
.reshape([4, 8])
.float();
let fused_on_read = TestTensorInt::<1>::arange(0..32, &device)
.reshape([4, 8])
.float();
let fused_on_write = TestTensorInt::<1>::arange(0..4, &device)
.reshape([4, 1])
.float();
let y = TestTensorInt::<1>::arange(32..64, &device)
.reshape([4, 8])
.float();
// Forces previous tensors to be materialized.
TestBackend::sync(&device).unwrap();
// In fusion we have to create a global buffer to keep the intermediate data for now.
let x_previous = tensor + fused_on_read;
let x = x_previous.clone().sum_dim(1);
let x = x * fused_on_write;
// Broadcast
let x = x + x_previous;
// Second reduce
let x = x.mean_dim(1);
// Second fuse on read
let end = x + y;
let actual = end.into_data();
let expected = TensorData::from([
[39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0],
[247.0, 248.0, 249.0, 250.0, 251.0, 252.0, 253.0, 254.0],
[711.0, 712.0, 713.0, 714.0, 715.0, 716.0, 717.0, 718.0],
[
1431.0, 1432.0, 1433.0, 1434.0, 1435.0, 1436.0, 1437.0, 1438.0,
],
]);
actual.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
}