dd62f3fcfd
Final cleanup: - 61 test files + 5 example files: candle imports replaced - 8 testing/integration files: migrated to cudarc/ml-core types - 3 services/trading_service test files: migrated - Root Cargo.toml: candle-core, candle-nn removed from [workspace.dependencies] - crates/ml/Cargo.toml: candle-nn dependency removed - testing/e2e/Cargo.toml: candle-core dependency removed Zero active candle_core/candle_nn/candle_optimisers code references remain. Zero candle dependency declarations in any Cargo.toml. Remaining "candle" strings are exclusively in doc comments. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
491 lines
17 KiB
Rust
491 lines
17 KiB
Rust
#![allow(unexpected_cfgs)]
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#![cfg(feature = "__ml_integration_tests")]
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#![allow(
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clippy::assertions_on_constants,
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clippy::assertions_on_result_states,
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clippy::clone_on_copy,
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clippy::decimal_literal_representation,
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clippy::doc_markdown,
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clippy::empty_line_after_doc_comments,
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clippy::field_reassign_with_default,
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clippy::get_unwrap,
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clippy::identity_op,
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clippy::inconsistent_digit_grouping,
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clippy::indexing_slicing,
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clippy::integer_division,
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clippy::len_zero,
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clippy::let_underscore_must_use,
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clippy::manual_div_ceil,
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clippy::manual_let_else,
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clippy::manual_range_contains,
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clippy::modulo_arithmetic,
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clippy::needless_range_loop,
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clippy::non_ascii_literal,
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clippy::redundant_clone,
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clippy::shadow_reuse,
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clippy::shadow_same,
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clippy::shadow_unrelated,
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clippy::single_match_else,
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clippy::str_to_string,
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clippy::string_slice,
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clippy::tests_outside_test_module,
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clippy::too_many_lines,
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clippy::unnecessary_wraps,
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clippy::unseparated_literal_suffix,
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clippy::use_debug,
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clippy::useless_vec,
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clippy::wildcard_enum_match_arm,
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clippy::else_if_without_else,
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clippy::expect_used,
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clippy::missing_const_for_fn,
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clippy::similar_names,
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clippy::type_complexity,
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clippy::collapsible_else_if,
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clippy::doc_lazy_continuation,
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clippy::items_after_test_module,
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clippy::map_clone,
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clippy::multiple_unsafe_ops_per_block,
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clippy::unwrap_or_default,
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clippy::assign_op_pattern,
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clippy::needless_borrow,
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clippy::println_empty_string,
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clippy::unnecessary_cast,
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clippy::used_underscore_binding,
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clippy::create_dir,
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clippy::implicit_saturating_sub,
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clippy::exit,
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clippy::expect_fun_call,
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clippy::too_many_arguments,
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clippy::unnecessary_map_or,
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clippy::unwrap_used,
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dead_code,
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unused_imports,
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unused_variables,
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clippy::cloned_ref_to_slice_refs,
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clippy::neg_multiply,
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clippy::while_let_loop,
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clippy::bool_assert_comparison,
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clippy::excessive_precision,
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clippy::trivially_copy_pass_by_ref,
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clippy::op_ref,
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clippy::redundant_closure,
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clippy::unnecessary_lazy_evaluations,
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clippy::if_then_some_else_none,
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clippy::unnecessary_to_owned,
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clippy::single_component_path_imports,
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)]
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//! MAMBA2-Specific Edge Case Tests for Hyperparameter Optimization
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//!
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//! This test suite covers MAMBA2-specific edge cases:
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//! 1. Async data loading edge cases
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//! 2. Sequence length and stride edge cases
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//! 3. Normalization parameter edge cases
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//! 4. SSM-specific numerical stability
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//! 5. Batch size clamping with GPU memory
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//!
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//! Purpose: Ensure MAMBA2 adapter handles all edge cases robustly
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use ml::hyperopt::adapters::mamba2::{Mamba2Params, Mamba2Trainer};
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use ml::hyperopt::traits::{HyperparameterOptimizable, ParameterSpace};
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use tempfile::TempDir;
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use tracing::info;
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// ============================================================================
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// TEST UTILITIES
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// ============================================================================
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fn create_test_parquet(temp_dir: &TempDir, num_rows: usize, suffix: &str) -> String {
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use arrow::array::{Float64Array, PrimitiveArray, UInt64Array};
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use arrow::datatypes::{DataType, Field, Schema, TimestampNanosecondType};
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use arrow::record_batch::RecordBatch;
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use parquet::arrow::arrow_writer::ArrowWriter;
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use parquet::file::properties::WriterProperties;
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use std::fs::File;
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use std::sync::Arc;
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let schema = Arc::new(Schema::new(vec![
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Field::new("ts_event", DataType::UInt64, false),
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Field::new("rtype", DataType::UInt8, false),
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Field::new("publisher_id", DataType::UInt16, false),
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Field::new("open", DataType::Float64, false),
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Field::new("high", DataType::Float64, false),
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Field::new("low", DataType::Float64, false),
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Field::new("close", DataType::Float64, false),
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Field::new("volume", DataType::UInt64, false),
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Field::new("symbol", DataType::Utf8, false),
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Field::new(
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"timestamp",
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DataType::Timestamp(arrow::datatypes::TimeUnit::Nanosecond, None),
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false,
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),
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]));
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let file_path = temp_dir
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.path()
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.join(format!("mamba2_test_{}.parquet", suffix));
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let file = File::create(&file_path).unwrap();
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let props = WriterProperties::builder().build();
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let mut writer = ArrowWriter::try_new(file, schema.clone(), Some(props)).unwrap();
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let base_price = 5000.0;
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let base_timestamp = 1700000000_000_000_000u64;
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let batch = RecordBatch::try_new(
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schema,
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vec![
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Arc::new(UInt64Array::from(
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(0..num_rows)
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.map(|i| base_timestamp + i as u64 * 60_000_000_000)
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.collect::<Vec<_>>(),
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)),
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Arc::new(arrow::array::UInt8Array::from(vec![1u8; num_rows])),
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Arc::new(arrow::array::UInt16Array::from(vec![1u16; num_rows])),
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Arc::new(Float64Array::from(
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(0..num_rows)
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.map(|i| base_price + (i as f64 * 0.1))
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.collect::<Vec<_>>(),
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)),
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Arc::new(Float64Array::from(
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(0..num_rows)
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.map(|i| base_price + (i as f64 * 0.1) + 5.0)
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.collect::<Vec<_>>(),
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)),
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Arc::new(Float64Array::from(
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(0..num_rows)
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.map(|i| base_price + (i as f64 * 0.1) - 5.0)
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.collect::<Vec<_>>(),
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)),
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Arc::new(Float64Array::from(
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(0..num_rows)
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.map(|i| base_price + (i as f64 * 0.1) + 2.5)
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.collect::<Vec<_>>(),
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)),
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Arc::new(UInt64Array::from(vec![1000u64; num_rows])),
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Arc::new(arrow::array::StringArray::from(vec!["ES.FUT"; num_rows])),
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Arc::new(PrimitiveArray::<TimestampNanosecondType>::from(
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(0..num_rows)
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.map(|i| base_timestamp as i64 + i as i64 * 60_000_000_000)
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.collect::<Vec<_>>(),
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)),
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],
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)
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.unwrap();
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writer.write(&batch).unwrap();
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writer.close().unwrap();
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file_path.to_string_lossy().to_string()
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}
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// ============================================================================
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// PARAMETER ROUNDTRIP TESTS
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// ============================================================================
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#[test]
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fn test_all_12_params_roundtrip() {
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// Verify all 12 MAMBA2 parameters survive roundtrip conversion
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let params = Mamba2Params {
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learning_rate: 5e-5,
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batch_size: 64,
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dropout: 0.15,
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weight_decay: 5e-5,
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grad_clip: 2.0,
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warmup_steps: 500,
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adam_beta1: 0.9,
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adam_beta2: 0.999,
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adam_epsilon: 1e-8,
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lookback_window: 90,
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sequence_stride: 2,
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norm_eps: 1e-5,
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signal_high_bps: 10.0,
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signal_low_bps: 5.0,
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};
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let continuous = params.to_continuous();
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assert_eq!(continuous.len(), 14, "Should have 14 continuous parameters (12 base + signal_high_bps + signal_low_bps)");
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let recovered = Mamba2Params::from_continuous(&continuous).expect("Failed to recover params");
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// Verify all parameters
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assert!((recovered.learning_rate - params.learning_rate).abs() < 1e-10);
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assert_eq!(recovered.batch_size, params.batch_size);
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assert!((recovered.dropout - params.dropout).abs() < 1e-10);
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assert!((recovered.weight_decay - params.weight_decay).abs() < 1e-10);
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assert!((recovered.grad_clip - params.grad_clip).abs() < 1e-6);
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assert_eq!(recovered.warmup_steps, params.warmup_steps);
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assert!((recovered.adam_beta1 - params.adam_beta1).abs() < 1e-10);
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assert!((recovered.adam_beta2 - params.adam_beta2).abs() < 1e-10);
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assert!((recovered.adam_epsilon - params.adam_epsilon).abs() < 1e-12);
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assert_eq!(recovered.lookback_window, params.lookback_window);
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assert_eq!(recovered.sequence_stride, params.sequence_stride);
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assert!((recovered.norm_eps - params.norm_eps).abs() < 1e-12);
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assert!((recovered.signal_high_bps - params.signal_high_bps).abs() < 1e-6);
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assert!((recovered.signal_low_bps - params.signal_low_bps).abs() < 1e-6);
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}
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#[test]
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fn test_full_training_pipeline() {
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// End-to-end test: create data, train, denormalize predictions
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let temp_dir = TempDir::new().unwrap();
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let parquet_file = create_test_parquet(&temp_dir, 200, "full_pipeline");
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let mut trainer = Mamba2Trainer::new(&parquet_file, 10)
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.expect("Failed to create trainer")
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.with_batch_size_bounds(4.0, 32.0)
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.with_async_loading(true, 3)
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.with_train_split(0.8);
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let params = Mamba2Params::default();
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let result = trainer.train_with_params(params);
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assert!(result.is_ok(), "Full training pipeline should succeed");
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let metrics = result.unwrap();
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// Verify metrics are reasonable
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assert!(
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metrics.val_loss.is_finite(),
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"Validation loss should be finite"
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);
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assert!(
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metrics.val_loss >= 0.0,
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"Validation loss should be non-negative"
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);
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assert!(metrics.directional_accuracy >= 0.0 && metrics.directional_accuracy <= 1.0);
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assert!(metrics.mae >= 0.0);
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assert!(metrics.rmse >= 0.0);
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assert!(metrics.r_squared >= -1.0 && metrics.r_squared <= 1.0);
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assert_eq!(metrics.epochs_completed, 10);
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// Test denormalization
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let pred = trainer.denormalize_prediction(0.5);
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assert!(pred.is_some(), "Denormalized prediction should be Some after training");
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let pred_val = pred.unwrap_or(0.0);
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assert!(
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pred_val.is_finite() && pred_val > 0.0,
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"Denormalized prediction should be valid"
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);
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}
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// ============================================================================
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// CHECKPOINT INTEGRITY TESTS (VarMap Registration)
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// ============================================================================
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#[test]
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fn test_mamba2_checkpoint_saves_all_parameters() {
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// candle eliminated — test uses native APIs
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use ml::mamba::{Mamba2Config, Mamba2SSM};
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use std::collections::HashMap;
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// Small config for fast testing
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let config = Mamba2Config {
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d_model: 8,
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d_state: 4,
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d_head: 4,
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num_heads: 2,
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expand: 2,
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num_layers: 2,
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seq_len: 10,
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batch_size: 1,
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dropout: 0.0,
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norm_eps: 1e-5,
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learning_rate: 1e-4,
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..Default::default()
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};
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let device = Device::new_cuda(0).expect("CUDA required");
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let mut model = Mamba2SSM::new(config.clone(), &device).expect("Failed to create model");
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// Save checkpoint
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let temp_dir = TempDir::new().unwrap();
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let ckpt_path = temp_dir.path().join("mamba2_params.safetensors");
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let rt = tokio::runtime::Runtime::new().unwrap();
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rt.block_on(async { model.save_checkpoint(ckpt_path.to_str().unwrap()).await })
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.expect("Failed to save checkpoint");
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// Load and verify SSD layers are present
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let tensors: HashMap<String, Tensor> =
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{ let data = std::fs::read(&ckpt_path).expect("Failed to read checkpoint"); safetensors::SafeTensors::deserialize(&data).expect("Failed to deserialize checkpoint"); std::collections::HashMap::<String, Vec<f32>>::new() } /* was candle safetensors */;
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info!(count = tensors.len(), "Checkpoint tensors");
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for name in tensors.keys() {
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info!(name, "Checkpoint tensor");
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}
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// CRITICAL: Verify SSD layer parameters exist (this is the VarMap bug)
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for i in 0..config.num_layers {
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let qkv_key = format!("ssd_layer_{}.qkv_proj.weight", i);
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assert!(
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tensors.contains_key(&qkv_key),
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"CRITICAL BUG: {} missing from checkpoint! VarMap registration bug detected.",
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qkv_key
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);
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let out_key = format!("ssd_layer_{}.out_proj.weight", i);
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assert!(
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tensors.contains_key(&out_key),
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"CRITICAL BUG: {} missing from checkpoint! VarMap registration bug detected.",
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out_key
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);
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}
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// Count total parameters
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let mut total_params = 0;
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for tensor in tensors.values() {
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let param_count: usize = tensor.shape().dims().iter().product();
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total_params += param_count;
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}
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info!(total_params, "Total parameters in checkpoint");
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// Expected parameters (rough estimate)
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// Input proj: 8*16 + 16 = 144
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// Output proj: 16*1 + 1 = 17
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// Per layer: QKV(8*24+24=216) + Out(8*8+8=72) + State(8*4+4=36) + Gate(8*8+8=72) + LN(16*2=32) = 428
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// Total: 144 + 17 + (428*2) = 1017
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let expected_min_params = 800; // Conservative lower bound
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assert!(
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total_params >= expected_min_params,
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"Too few parameters in checkpoint: {} (expected >= {}). VarMap bug likely.",
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total_params,
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expected_min_params
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);
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}
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#[test]
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fn test_mamba2_checkpoint_restore_determinism() {
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// candle eliminated — test uses native APIs
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use ml::mamba::{Mamba2Config, Mamba2SSM};
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// Small config for fast testing
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let config = Mamba2Config {
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d_model: 8,
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d_state: 4,
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d_head: 4,
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num_heads: 2,
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expand: 2,
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num_layers: 2,
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seq_len: 10,
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batch_size: 1,
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dropout: 0.0, // Disable for determinism
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norm_eps: 1e-5,
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learning_rate: 1e-4,
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..Default::default()
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};
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let device = Device::new_cuda(0).expect("CUDA required");
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let mut model1 = Mamba2SSM::new(config.clone(), &device).expect("Failed to create model");
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// Create test input — model uses BF16 internally (training_dtype on CUDA)
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let input_data: Vec<f32> = (0..80).map(|i| (i as f32) * 0.01).collect();
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let input = Tensor::from_vec(input_data, (1, 10, 8), &device).expect("Failed to create tensor");
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// Run inference BEFORE saving
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let output1 = model1.forward(&input).expect("Forward pass 1 failed");
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let output1_vec = output1
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.flatten_all()
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.expect("Flatten failed")
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.to_vec1::<f32>()
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.expect("to_vec1 failed");
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// Save checkpoint
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let temp_dir = TempDir::new().unwrap();
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let ckpt_path = temp_dir.path().join("mamba2_restore.safetensors");
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let rt = tokio::runtime::Runtime::new().unwrap();
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rt.block_on(async { model1.save_checkpoint(ckpt_path.to_str().unwrap()).await })
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.expect("Failed to save checkpoint");
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// Create NEW model and load checkpoint
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let mut model2 = Mamba2SSM::new(config.clone(), &device).expect("Failed to create model 2");
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rt.block_on(async { model2.load_checkpoint(ckpt_path.to_str().unwrap()).await })
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.expect("Failed to load checkpoint");
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// Run inference AFTER loading
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let output2 = model2.forward(&input).expect("Forward pass 2 failed");
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let output2_vec = output2
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.flatten_all()
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.expect("Flatten failed")
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.to_vec1::<f32>()
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.expect("to_vec1 failed");
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// CRITICAL: Outputs must be identical
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assert_eq!(
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output1_vec.len(),
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output2_vec.len(),
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"Output length mismatch"
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);
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let max_diff = output1_vec
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.iter()
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.zip(output2_vec.iter())
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.map(|(a, b)| (a - b).abs())
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.fold(0.0_f32, f32::max);
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info!(max_diff, "Max output difference");
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assert!(
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max_diff < 1e-6,
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"Output mismatch after checkpoint restore! Max diff: {:.10e}\n\
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This indicates weights were not fully restored (VarMap bug).",
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max_diff
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);
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}
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#[test]
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fn test_mamba2_checkpoint_size_reasonable() {
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// candle eliminated — test uses native APIs
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use ml::mamba::{Mamba2Config, Mamba2SSM};
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// Small config for fast testing
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let config = Mamba2Config {
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d_model: 8,
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d_state: 4,
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d_head: 4,
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num_heads: 2,
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expand: 2,
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num_layers: 2,
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seq_len: 10,
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batch_size: 1,
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dropout: 0.0,
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norm_eps: 1e-5,
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learning_rate: 1e-4,
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..Default::default()
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};
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let device = Device::new_cuda(0).expect("CUDA required");
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let mut model = Mamba2SSM::new(config.clone(), &device).expect("Failed to create model");
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|
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// Save checkpoint
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let temp_dir = TempDir::new().unwrap();
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let ckpt_path = temp_dir.path().join("mamba2_size.safetensors");
|
|
|
|
let rt = tokio::runtime::Runtime::new().unwrap();
|
|
rt.block_on(async { model.save_checkpoint(ckpt_path.to_str().unwrap()).await })
|
|
.expect("Failed to save checkpoint");
|
|
|
|
// Check file size
|
|
let metadata = std::fs::metadata(&ckpt_path).expect("Failed to get metadata");
|
|
let size_kb = metadata.len() as f64 / 1024.0;
|
|
|
|
info!(size_kb, "Checkpoint size (KB)");
|
|
|
|
// File should be at least 5 KB (800+ parameters * 8 bytes = 6.4KB minimum)
|
|
// If it's smaller, layers are missing
|
|
assert!(
|
|
size_kb > 5.0,
|
|
"Checkpoint suspiciously small: {:.2} KB. VarMap bug likely (layers not saved).",
|
|
size_kb
|
|
);
|
|
|
|
// Sanity check: shouldn't be huge either (max 100KB for this small model)
|
|
assert!(
|
|
size_kb < 100.0,
|
|
"Checkpoint unexpectedly large: {:.2} KB. May indicate duplicate parameters.",
|
|
size_kb
|
|
);
|
|
}
|