9c3d741a08
Move 17 library crates into crates/, CLI binary into bin/fxt, consolidate 10 test crates into testing/, split config crate from deployment config files. Root directory reduced from 38+ to ~17 directories. All Cargo.toml paths and build.rs proto refs updated. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
63 lines
2.3 KiB
Rust
63 lines
2.3 KiB
Rust
//! Test gradient flow when source is derived from a Var
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use candle_core::{Device, DType, Tensor, Var};
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use ml::MLError;
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#[test]
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fn test_simple_var_gradient_flow() -> Result<(), MLError> {
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let device = Device::cuda_if_available(0)?;
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println!("\n=== Test 1: Simple Var multiplication ===");
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{
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let x_var = Var::new(&[1.0f32, 2.0, 3.0], &device)?;
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let y = (x_var.as_tensor() * 2.0)?;
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let loss = y.sum_all()?;
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let grads = loss.backward()?;
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let has_grads = grads.get(x_var.as_tensor()).is_some();
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println!("Var multiplication: has gradients: {}", has_grads);
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}
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println!("\n=== Test 2: Var used in scatter_add source ===");
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{
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let source_var = Var::new(&[[1.0f32, 2.0, 3.0], [4.0, 5.0, 6.0]], &device)?;
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let base = Tensor::zeros((2, 3), DType::F32, &device)?;
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let indices = Tensor::new(&[[0i64, 1i64, 2i64], [0i64, 1i64, 2i64]], &device)?;
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let result = base.scatter_add(&indices, source_var.as_tensor(), 1)?;
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let loss = result.sum_all()?;
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let grads = loss.backward()?;
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let has_grads = grads.get(source_var.as_tensor()).is_some();
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println!("scatter_add with Var source: has gradients: {}", has_grads);
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if let Some(grad) = grads.get(source_var.as_tensor()) {
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let grad_sum: f32 = grad.sum_all()?.to_scalar()?;
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println!(" Gradient sum: {}", grad_sum);
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}
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}
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println!("\n=== Test 3: Derived tensor from Var in scatter_add ===");
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{
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let base_var = Var::new(&[[1.0f32, 2.0, 3.0], [4.0, 5.0, 6.0]], &device)?;
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let source = (base_var.as_tensor() * 2.0)?; // Derive from Var
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let base = Tensor::zeros((2, 3), DType::F32, &device)?;
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let indices = Tensor::new(&[[0i64, 1i64, 2i64], [0i64, 1i64, 2i64]], &device)?;
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let result = base.scatter_add(&indices, &source, 1)?;
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let loss = result.sum_all()?;
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let grads = loss.backward()?;
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let has_grads = grads.get(base_var.as_tensor()).is_some();
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println!("scatter_add with derived source: has gradients on base_var: {}", has_grads);
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if let Some(grad) = grads.get(base_var.as_tensor()) {
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let grad_sum: f32 = grad.sum_all()?.to_scalar()?;
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println!(" Gradient sum: {}", grad_sum);
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}
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}
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Ok(())
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}
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