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f440f03 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 | """Tests Maris treniņu UI palīgfunkcijām."""
from __future__ import annotations
from pathlib import Path
import pytest
from maris_core.training.space_ui import (
SpaceTrainingRequest,
build_space_training_command,
build_space_training_env,
has_completed_training_artifacts,
list_space_model_choices,
parse_training_progress,
read_log_since,
resolve_optional_persistent_path,
resolve_output_dir,
)
def test_space_training_request_rejects_invalid_repo_id() -> None:
with pytest.raises(ValueError):
SpaceTrainingRequest(dataset_repo="invalid repo")
def test_space_training_request_rejects_non_maris_repo_ids() -> None:
with pytest.raises(ValueError):
SpaceTrainingRequest(dataset_repo="someone-else/not-maris-memory")
with pytest.raises(ValueError):
SpaceTrainingRequest(model_repo="someone-else/not-maris-model")
def test_resolve_output_dir_keeps_path_inside_persistent_root(tmp_path: Path) -> None:
output_dir = resolve_output_dir(str(tmp_path), "runs/session-1")
assert output_dir == tmp_path / "runs" / "session-1"
def test_resolve_output_dir_rejects_escape_attempt(tmp_path: Path) -> None:
with pytest.raises(ValueError):
resolve_output_dir(str(tmp_path), "../escape")
def test_build_space_training_command_prefers_custom_model_name() -> None:
request = SpaceTrainingRequest(model_preset="coding", model_name="Qwen/Qwen2.5-1.5B-Instruct")
command = build_space_training_command("/tmp/train-hf.sh", request)
assert command == [
"bash",
"/tmp/train-hf.sh",
"--model-name",
"Qwen/Qwen2.5-1.5B-Instruct",
]
def test_space_training_request_accepts_custom_model_without_preset() -> None:
request = SpaceTrainingRequest(model_preset="", model_name="meta-llama/Llama-3.2-3B-Instruct")
assert request.model_preset == ""
assert request.model_name == "meta-llama/Llama-3.2-3B-Instruct"
def test_build_space_training_env_uses_preset_and_persistent_storage(tmp_path: Path) -> None:
request = SpaceTrainingRequest(
model_preset="coding",
hub_model_id="MarisUK/maris-ai-lv",
output_subdir="runs/coder",
continue_model_path="runs/checkpoints",
push_to_hub=False,
)
env = build_space_training_env({}, request, str(tmp_path))
assert env["HF_PERSISTENT_DIR"] == str(tmp_path)
assert env["HF_TRAIN_OUTPUT_DIR"] == str(tmp_path / "runs" / "coder")
assert env["HF_LOCAL_MODEL_DIR"] == str(tmp_path / "runs" / "coder")
assert env["HF_MODEL_REPO"] == "MarisUK/maris-ai-lv"
assert env["HF_TRAIN_MODEL_PRESET"] == "coding"
assert env["HF_TRAINING_CONFIG_PATH"] == "huggingface/training-config.json"
assert env["MARIS_TRAIN_CONFIG_PATH"] == "huggingface/training-config.json"
assert env["HF_TRAIN_PUSH_TO_HUB"] == "false"
assert env["HF_TRAIN_CONTINUE_FROM_LATEST"] == "true"
assert env["HF_TRAIN_CONTINUE_MODEL_PATH"] == str(tmp_path / "runs" / "checkpoints")
assert env["HF_TRAIN_DISTRIBUTED_STRATEGY"] == "none"
assert env["MARIS_TRAIN_DISTRIBUTED_STRATEGY"] == "none"
assert env["PYTHONUNBUFFERED"] == "1"
def test_build_space_training_env_clears_inherited_distributed_overrides(tmp_path: Path) -> None:
request = SpaceTrainingRequest(model_preset="balanced")
env = build_space_training_env(
{
"HF_TRAIN_DISTRIBUTED_STRATEGY": "deepspeed",
"MARIS_TRAIN_DISTRIBUTED_STRATEGY": "fsdp",
"HF_TRAIN_DISTRIBUTED_CONFIG_PATH": "/tmp/deepspeed.json",
"MARIS_TRAIN_DISTRIBUTED_CONFIG_PATH": "/tmp/fsdp.json",
},
request,
str(tmp_path),
)
assert env["HF_TRAIN_DISTRIBUTED_STRATEGY"] == "none"
assert env["MARIS_TRAIN_DISTRIBUTED_STRATEGY"] == "none"
assert "HF_TRAIN_DISTRIBUTED_CONFIG_PATH" not in env
assert "MARIS_TRAIN_DISTRIBUTED_CONFIG_PATH" not in env
def test_build_space_training_env_allows_explicit_space_config_override(tmp_path: Path) -> None:
request = SpaceTrainingRequest(model_preset="balanced")
env = build_space_training_env(
{"MARIS_SPACE_TRAIN_CONFIG_PATH": "huggingface/custom-space-config.json"},
request,
str(tmp_path),
)
assert env["HF_TRAINING_CONFIG_PATH"] == "huggingface/custom-space-config.json"
assert env["MARIS_TRAIN_CONFIG_PATH"] == "huggingface/custom-space-config.json"
def test_has_completed_training_artifacts_detects_finished_space_run(tmp_path: Path) -> None:
output_dir = tmp_path / "runs" / "demo"
output_dir.mkdir(parents=True)
assert has_completed_training_artifacts(output_dir) is False
(output_dir / "training-metrics.json").write_text("{}", encoding="utf-8")
assert has_completed_training_artifacts(output_dir) is True
def test_list_space_model_choices_exposes_presets() -> None:
choices = list_space_model_choices()
assert {"balanced", "reasoning", "coding", "lightweight"}.issubset(choices)
def test_list_space_model_choices_can_include_large_external_models(monkeypatch) -> None:
monkeypatch.setenv(
"MARIS_TRAIN_EXTRA_MODELS",
(
'{"qwen-880b":{"model_name":"Qwen/Qwen3-880B-Instruct",'
'"label":"Qwen ultra preset",'
'"description":"Large external preset for giant-model experiments."}}'
),
)
choices = list_space_model_choices()
assert choices["qwen-880b"]["model_name"] == "Qwen/Qwen3-880B-Instruct"
assert choices["qwen-880b"]["label"] == "Qwen ultra preset"
def test_space_training_request_defaults_to_balanced_model_selection() -> None:
request = SpaceTrainingRequest(model_preset="", model_name="")
assert request.model_preset == "balanced"
assert request.model_name == ""
def test_space_training_request_accepts_separate_hub_model_id() -> None:
request = SpaceTrainingRequest(
model_repo="",
hub_model_id="MarisUK/maris-ai-lv",
model_preset="",
model_name="meta-llama/Llama-3.2-3B-Instruct",
)
assert request.hub_model_id == "MarisUK/maris-ai-lv"
assert request.model_repo == "MarisUK/maris-ai-lv"
def test_resolve_optional_persistent_path_returns_none_for_empty_value(tmp_path: Path) -> None:
assert resolve_optional_persistent_path(str(tmp_path), "") is None
def test_read_log_since_reads_only_delta(tmp_path: Path) -> None:
log_path = tmp_path / "train.log"
log_path.write_text("line-1\nline-2\n", encoding="utf-8")
first_chunk, first_offset = read_log_since(log_path, 0)
second_chunk, second_offset = read_log_since(log_path, first_offset)
assert first_chunk == "line-1\nline-2\n"
assert second_chunk == ""
assert second_offset == first_offset
def test_parse_training_progress_detects_epoch_and_loss() -> None:
progress = parse_training_progress(
"Epoch 2/4\n{'loss': 0.125, 'epoch': 2.0}\n",
request={"num_epochs": 4},
running=True,
exit_code=None,
)
assert progress["stage"] == "training"
assert progress["percent"] >= 60
assert progress["current_epoch"] == 2.0
assert progress["total_epochs"] == 4
assert progress["loss"] == 0.125
def test_parse_training_progress_reports_structured_preparing_stage() -> None:
progress = parse_training_progress(
'{"maris_training_event": true, "event": "prepare_model", "stage": "preparing", "label": "Ielādē tokenizeri un modeli"}\n',
request={"num_epochs": 3},
running=True,
exit_code=None,
)
assert progress["stage"] == "preparing"
assert progress["label"] == "Ielādē tokenizeri un modeli"
assert progress["percent"] == 20
assert progress["events_detected"] == 1
def test_parse_training_progress_reports_completion() -> None:
progress = parse_training_progress(
"Training complete\n",
request={"num_epochs": 3},
running=False,
exit_code=0,
)
assert progress["stage"] == "completed"
assert progress["percent"] == 100
def test_parse_training_progress_prefers_structured_events() -> None:
progress = parse_training_progress(
"\n".join(
[
'{"maris_training_event": true, "event": "log", "stage": "training", "label": "Trenē modeli · solis 12/40", "epoch": 1.5, "total_epochs": 4, "step": 12, "total_steps": 40, "loss": 0.2451, "eval_loss": 0.1987, "learning_rate": 0.0002, "eta_seconds": 180}',
"Epoch 1/4",
]
),
request={"num_epochs": 4},
running=True,
exit_code=None,
)
assert progress["stage"] == "training"
assert progress["label"] == "Trenē modeli · solis 12/40"
assert progress["current_epoch"] == 1.5
assert progress["total_epochs"] == 4
assert progress["current_step"] == 12
assert progress["total_steps"] == 40
assert progress["loss"] == 0.2451
assert progress["eval_loss"] == 0.1987
assert progress["learning_rate"] == 0.0002
assert progress["eta_seconds"] == 180
assert progress["events_detected"] == 1
|