[test] fix: shrink CP packed SFT functional model

tests/functional_tests/test_groups/training/test_seqpacking_cp_example.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import gc
 import os
 
 import pytest
@@ -32,6 +33,28 @@
 )
 
 
+def _set_existing_attr(target: object, name: str, value: object) -> None:
+    if not hasattr(target, name):
+        raise ValueError(f"{type(target).__name__} has no field {name!r}")
+    setattr(target, name, value)
+
+
+def _make_functional_test_model_small(model: object) -> None:
+    # Keep this checkpoint-loading functional test far below runner memory limits.
+    # The path under test is CP + sequence packing + pretrained checkpoint loading,
+    # not the full Llama 3.2 1B model shape.
+    for name, value in {
+        "num_layers": 2,
+        "hidden_size": 256,
+        "ffn_hidden_size": 1024,
+        "num_attention_heads": 4,
+        "num_query_groups": 4,
+        "kv_channels": 64,
+        "seq_length": 256,
+    }.items():
+        _set_existing_attr(model, name, value)
+
+
 class TestPeftSftExample:
     """Run the PEFT SFT example as a functional test with packed sequences + CP."""
 
@@ -57,10 +80,10 @@ def test_sft_example_runs_with_cp_and_packing(self, tmp_path):
         torch.distributed.barrier()
 
         pretrain_cfg = llama32_1b_pretrain_config()
+        _make_functional_test_model_small(pretrain_cfg.model)
         pretrain_cfg.model.tensor_model_parallel_size = 1
         pretrain_cfg.model.pipeline_model_parallel_size = 1
         pretrain_cfg.model.context_parallel_size = 2
-        pretrain_cfg.model.seq_length = 256
         pretrain_cfg.dataset.seq_length = 256
         pretrain_cfg.train.train_iters = 1
         pretrain_cfg.train.global_batch_size = 2
@@ -75,6 +98,7 @@ def test_sft_example_runs_with_cp_and_packing(self, tmp_path):
         pretrain_cfg.checkpoint.load = None
 
         cfg = llama32_1b_sft_config()
+        _make_functional_test_model_small(cfg.model)
         cfg.tokenizer.tokenizer_type = "HuggingFaceTokenizer"
         cfg.tokenizer.tokenizer_model = "meta-llama/Llama-3.2-1B"
         cfg.model.calculate_per_token_loss = True
@@ -115,7 +139,6 @@ def test_sft_example_runs_with_cp_and_packing(self, tmp_path):
             rewrite=False,
         )
 
-        cfg.model.seq_length = 256
         cfg.checkpoint.save_interval = cfg.train.train_iters
         cfg.checkpoint.save = sft_checkpoint_dir
         cfg.checkpoint.load = None
@@ -129,6 +152,9 @@ def test_sft_example_runs_with_cp_and_packing(self, tmp_path):
                 ckpt_format=pretrain_cfg.checkpoint.ckpt_format,
                 storage_writers_per_rank=pretrain_cfg.checkpoint.storage_writers_per_rank,
             )
+            gc.collect()
+            torch.cuda.empty_cache()
+            torch.distributed.barrier()
 
             finetune(cfg, forward_step)
             verify_checkpoint_files(