#![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, )] //! Tests for true gradient accumulation in DQN training. //! //! Verifies that `train_step_with_accumulation()` performs exactly 1 optimizer //! step per call (not N steps), proving true accumulation works correctly. #![allow(unused_crate_dependencies)] use anyhow::{Context, Result}; use ml::trainers::dqn::{DQNHyperparameters, DQNTrainer}; use std::path::PathBuf; use tracing::info; use tracing::warn; fn get_6e_fut_data_dir() -> Result { let workspace_root = PathBuf::from(env!("CARGO_MANIFEST_DIR")) .parent() .context("Failed to get workspace root")? .to_path_buf(); let data_dir = workspace_root.join("test_data/real/databento/ml_training_small"); if !data_dir.exists() { anyhow::bail!("6E.FUT data not found: {}", data_dir.display()); } Ok(data_dir.to_string_lossy().to_string()) } /// Verify gradient accumulation with steps=4 produces training with finite losses. #[tokio::test] async fn test_accumulation_single_optimizer_step() -> Result<()> { let data_dir = match get_6e_fut_data_dir() { Ok(dir) => dir, Err(e) => { warn!(reason = %e, "Skipping test: data not available"); return Ok(()); } }; let checkpoint_dir = tempfile::tempdir()?; let ckpt_path = checkpoint_dir.path().to_path_buf(); let mut hyperparams = DQNHyperparameters::conservative(); hyperparams.replay_buffer_vram_fraction = 0.0; // Disable AutoReplaySizer for test determinism hyperparams.epochs = 5; hyperparams.batch_size = 32; hyperparams.learning_rate = 0.0001; hyperparams.gradient_accumulation_steps = 4; hyperparams.early_stopping_enabled = false; hyperparams.checkpoint_frequency = 100; let mut trainer = DQNTrainer::new(hyperparams)?; let metrics = trainer .train(&data_dir, "ES.FUT", move |_epoch, checkpoint_data, _is_best| { let path = ckpt_path.join("accum_test.safetensors"); std::fs::write(&path, &checkpoint_data)?; Ok(path.to_string_lossy().to_string()) }) .await?; assert_eq!( metrics.epochs_trained, 5, "Should complete all 5 epochs" ); let loss_history = trainer.loss_history(); assert!( loss_history.len() >= 2, "Need at least 2 epochs of loss history" ); let initial_loss = loss_history.first().copied().unwrap_or(0.0); let final_loss = loss_history.last().copied().unwrap_or(0.0); assert!( initial_loss.is_finite() && final_loss.is_finite(), "Losses must be finite: initial={}, final={}", initial_loss, final_loss ); info!( epochs = metrics.epochs_trained, accumulation_steps = 4, initial_loss, final_loss, effective_batch = 32 * 4, training_time_seconds = metrics.training_time_seconds, "Gradient accumulation test results" ); Ok(()) }