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
89 lines
3.1 KiB
Rust
89 lines
3.1 KiB
Rust
use super::*;
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use burn_tensor::{TensorData, Tolerance};
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#[test]
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fn should_behave_the_same_with_multithread() {
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let data_1 = TensorData::from([[1.0, 7.0], [13.0, -3.0]]);
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let data_2 = TensorData::from([[4.0, 7.0], [2.0, 3.0]]);
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let with_move = || {
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let device = Default::default();
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let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1.clone(), &device).require_grad();
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let tensor_2 = TestAutodiffTensor::from_data(data_2.clone(), &device).require_grad();
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let tensor_3 = tensor_1.clone().matmul(tensor_2.clone());
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let tensor_4 = tensor_3.clone().matmul(tensor_2.clone());
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let tensor_5 = tensor_4.matmul(tensor_3);
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// Task 1
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let tensor_1_cloned = tensor_1.clone();
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let tensor_2_cloned = tensor_2.clone();
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let tensor_5_cloned = tensor_5.clone();
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let first_call = move || {
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let tensor_6_1 = tensor_5_cloned.matmul(tensor_2_cloned);
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tensor_6_1.matmul(tensor_1_cloned)
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};
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// Task 2
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let tensor_1_cloned = tensor_1.clone();
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let tensor_2_cloned = tensor_2.clone();
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let tensor_5_cloned = tensor_5;
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let second_call = move || {
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let tensor_6_2 = tensor_5_cloned.matmul(tensor_1_cloned);
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tensor_6_2.matmul(tensor_2_cloned)
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};
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let tensor_7_1_handle = std::thread::spawn(first_call);
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let tensor_7_2_handle = std::thread::spawn(second_call);
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let tensor_7_1 = tensor_7_1_handle.join().unwrap();
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let tensor_7_2 = tensor_7_2_handle.join().unwrap();
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let tensor_8 = tensor_7_1.matmul(tensor_7_2);
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let grads = tensor_8.backward();
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let grad_1 = tensor_1.grad(&grads).unwrap();
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let grad_2 = tensor_2.grad(&grads).unwrap();
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(grad_1, grad_2)
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};
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let without_move = || {
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let device = Default::default();
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let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1.clone(), &device).require_grad();
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let tensor_2 = TestAutodiffTensor::from_data(data_2.clone(), &device).require_grad();
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let tensor_3 = tensor_1.clone().matmul(tensor_2.clone());
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let tensor_4 = tensor_3.clone().matmul(tensor_2.clone());
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let tensor_5 = tensor_4.matmul(tensor_3);
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// Task 1
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let tensor_6_1 = tensor_5.clone().matmul(tensor_2.clone());
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let tensor_7_1 = tensor_6_1.matmul(tensor_1.clone());
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// Task 2
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let tensor_6_2 = tensor_5.matmul(tensor_1.clone());
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let tensor_7_2 = tensor_6_2.matmul(tensor_2.clone());
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let tensor_8 = tensor_7_1.matmul(tensor_7_2);
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let grads = tensor_8.backward();
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let grad_1 = tensor_1.grad(&grads).unwrap();
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let grad_2 = tensor_2.grad(&grads).unwrap();
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(grad_1, grad_2)
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};
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let (grad_1, grad_2) = without_move();
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let (grad_1_moved, grad_2_moved) = with_move();
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grad_1
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.into_data()
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.assert_approx_eq::<FloatElem>(&grad_1_moved.into_data(), Tolerance::default());
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grad_2
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.into_data()
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.assert_approx_eq::<FloatElem>(&grad_2_moved.into_data(), Tolerance::default());
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}
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