refactor : replace iteration with consumed_samples

- Replace context.iteration with consumed_samples (global sample count)
- Add optimizer_step property derived from consumed_samples
- Checkpoint meta.json stores consumed_samples, drops iteration
- CLI --start_batch renamed to --start_samples (per-rank samples)
- Checkpoint dir naming: epoch_X_step_Y instead of epoch_X_iter_Y
- Metric log entries use step and consumed_samples fields
- Backward compat removed (old iteration checkpoints unsupported)

tests/trainer/conftest.py

@@ -52,7 +52,7 @@ def create_train_config(
         batch_per_device: Batch size per device (default: 2)
         grad_accum_steps: Gradient accumulation steps (default: 1)
         max_grad_norm: Maximum gradient norm for clipping (default: 1.0)
-        ckpt_interval: Checkpoint save interval in iterations (default: 5)
+        ckpt_interval: Checkpoint save interval in optimizer steps (default: 5)
         random_seed: Random seed for reproducibility (default: 42)
         **kwargs: Additional arguments passed to TrainConfig
 

tests/trainer/test_early_stopping.py

@@ -44,14 +44,14 @@ def test_early_stopping_simulation(base_test_env, early_stopping_dataset):
         pass
 
     # Resume from latest checkpoint
-    load_dir = os.path.join(base_test_env["test_dir"], "epoch_0_iter_2")
+    load_dir = os.path.join(base_test_env["test_dir"], "epoch_0_step_1")
     trainer = Trainer(train_config)
     trainer.train(resume_dir=load_dir)
 
-    # Verify checkpoint was saved at expected iteration
-    load_dir = os.path.join(base_test_env["test_dir"], "epoch_1_iter_10")
+    # Verify checkpoint was saved at expected step
+    load_dir = os.path.join(base_test_env["test_dir"], "epoch_1_step_5")
     import json
 
     with open(os.path.join(load_dir, "meta.json")) as f:
         meta = json.load(f)
-    assert meta["iteration"] == 10
+    assert meta["consumed_samples"] == 20