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>
76 lines
2.7 KiB
Rust
76 lines
2.7 KiB
Rust
//! Minimal test to understand Var vs Tensor gradient flow with scatter_add
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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_scatter_add_tensor_vs_var() -> Result<(), MLError> {
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let device = Device::cuda_if_available(0)?;
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println!("\n=== Testing Tensor::zeros + scatter_add ===");
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{
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// Input that should have gradients
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let input = Tensor::ones((2, 3), DType::F32, &device)?;
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// Base for scatter (using Tensor::zeros)
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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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// Scatter
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let result = base.scatter_add(&indices, &input, 1)?;
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// Compute loss and backward
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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(&input).is_some();
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println!("Tensor::zeros -> has gradients: {}", has_grads);
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}
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println!("\n=== Testing Var::zeros + scatter_add ===");
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{
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// Input that should have gradients
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let input = Tensor::ones((2, 3), DType::F32, &device)?;
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// Base for scatter (using Var::zeros)
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let base_var = Var::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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// Scatter - need to convert Var to Tensor for scatter_add
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let base_tensor = base_var.as_tensor();
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let result = base_tensor.scatter_add(&indices, &input, 1)?;
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// Compute loss and backward
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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(&input).is_some();
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println!("Var::zeros -> has gradients: {}", has_grads);
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}
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println!("\n=== Testing Var::from_tensor (input) + scatter_add ===");
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{
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// Input wrapped in Var
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let input_tensor = Tensor::ones((2, 3), DType::F32, &device)?;
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let input_var = Var::from_tensor(&input_tensor)?;
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// Base for scatter (regular Tensor)
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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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// Scatter - use Var as Tensor
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let result = base.scatter_add(&indices, &input_var.as_tensor(), 1)?;
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// Compute loss and backward
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let loss = result.sum_all()?;
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let grads = loss.backward()?;
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let has_grads_tensor = grads.get(&input_tensor).is_some();
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let has_grads_var = grads.get(input_var.as_tensor()).is_some();
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println!("Var(input) -> has gradients on tensor: {}", has_grads_tensor);
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println!("Var(input) -> has gradients on var: {}", has_grads_var);
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}
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Ok(())
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}
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