foxhunt/crates/ml/tests/test_var_gradient_flow.rs

#![allow(
    clippy::assertions_on_constants,
    clippy::assertions_on_result_states,
    clippy::clone_on_copy,
    clippy::decimal_literal_representation,
    clippy::doc_markdown,
    clippy::empty_line_after_doc_comments,
    clippy::field_reassign_with_default,
    clippy::get_unwrap,
    clippy::identity_op,
    clippy::inconsistent_digit_grouping,
    clippy::indexing_slicing,
    clippy::integer_division,
    clippy::len_zero,
    clippy::let_underscore_must_use,
    clippy::manual_div_ceil,
    clippy::manual_let_else,
    clippy::manual_range_contains,
    clippy::modulo_arithmetic,
    clippy::needless_range_loop,
    clippy::non_ascii_literal,
    clippy::redundant_clone,
    clippy::shadow_reuse,
    clippy::shadow_same,
    clippy::shadow_unrelated,
    clippy::single_match_else,
    clippy::str_to_string,
    clippy::string_slice,
    clippy::tests_outside_test_module,
    clippy::too_many_lines,
    clippy::unnecessary_wraps,
    clippy::unseparated_literal_suffix,
    clippy::use_debug,
    clippy::useless_vec,
    clippy::wildcard_enum_match_arm,
    clippy::else_if_without_else,
    clippy::expect_used,
    clippy::missing_const_for_fn,
    clippy::similar_names,
    clippy::type_complexity,
    clippy::collapsible_else_if,
    clippy::doc_lazy_continuation,
    clippy::items_after_test_module,
    clippy::map_clone,
    clippy::multiple_unsafe_ops_per_block,
    clippy::unwrap_or_default,
    clippy::assign_op_pattern,
    clippy::needless_borrow,
    clippy::println_empty_string,
    clippy::unnecessary_cast,
    clippy::used_underscore_binding,
    clippy::create_dir,
    clippy::implicit_saturating_sub,
    clippy::exit,
    clippy::expect_fun_call,
    clippy::too_many_arguments,
    clippy::unnecessary_map_or,
    clippy::unwrap_used,
    dead_code,
    unused_imports,
    unused_variables,
    clippy::cloned_ref_to_slice_refs,
    clippy::neg_multiply,
    clippy::while_let_loop,
    clippy::bool_assert_comparison,
    clippy::excessive_precision,
    clippy::trivially_copy_pass_by_ref,
    clippy::op_ref,
    clippy::redundant_closure,
    clippy::unnecessary_lazy_evaluations,
    clippy::if_then_some_else_none,
    clippy::unnecessary_to_owned,
    clippy::single_component_path_imports,
)]
//! Minimal test to understand Var vs Tensor gradient flow with scatter_add

// candle eliminated — test uses native APIs
use ml::MLError;
use tracing::info;

#[test]
fn test_scatter_add_tensor_vs_var() -> Result<(), MLError> {
    let device = Device::cuda_if_available(0)?;

    info!("Testing Tensor::zeros + scatter_add");
    {
        // Input that should have gradients
        let input = Tensor::ones((2, 3), DType::F32, &device)?;

        // Base for scatter (using Tensor::zeros)
        let base = Tensor::zeros((2, 3), DType::F32, &device)?;
        let indices = Tensor::new(&[[0i64, 1i64, 2i64], [0i64, 1i64, 2i64]], &device)?;

        // Scatter
        let result = base.scatter_add(&indices, &input, 1)?;

        // Compute loss and backward
        let loss = result.sum_all()?;
        let grads = loss.backward()?;

        let has_grads = grads.get(&input).is_some();
        info!(has_grads, "Tensor::zeros scatter_add gradient check");
    }

    info!("Testing Var::zeros + scatter_add");
    {
        // Input that should have gradients
        let input = Tensor::ones((2, 3), DType::F32, &device)?;

        // Base for scatter (using Var::zeros)
        let base_var = Var::zeros((2, 3), DType::F32, &device)?;
        let indices = Tensor::new(&[[0i64, 1i64, 2i64], [0i64, 1i64, 2i64]], &device)?;

        // Scatter - need to convert Var to Tensor for scatter_add
        let base_tensor = base_var.as_tensor();
        let result = base_tensor.scatter_add(&indices, &input, 1)?;

        // Compute loss and backward
        let loss = result.sum_all()?;
        let grads = loss.backward()?;

        let has_grads = grads.get(&input).is_some();
        info!(has_grads, "Var::zeros scatter_add gradient check");
    }

    info!("Testing Var::from_tensor (input) + scatter_add");
    {
        // Input wrapped in Var
        let input_tensor = Tensor::ones((2, 3), DType::F32, &device)?;
        let input_var = Var::from_tensor(&input_tensor)?;

        // Base for scatter (regular Tensor)
        let base = Tensor::zeros((2, 3), DType::F32, &device)?;
        let indices = Tensor::new(&[[0i64, 1i64, 2i64], [0i64, 1i64, 2i64]], &device)?;

        // Scatter - use Var as Tensor
        let result = base.scatter_add(&indices, &input_var.as_tensor(), 1)?;

        // Compute loss and backward
        let loss = result.sum_all()?;
        let grads = loss.backward()?;

        let has_grads_tensor = grads.get(&input_tensor).is_some();
        let has_grads_var = grads.get(input_var.as_tensor()).is_some();
        info!(has_grads_tensor, "Var(input) gradient check on tensor");
        info!(has_grads_var, "Var(input) gradient check on var");
    }

    Ok(())
}