gemma3 packing fixes (#2449)

* make gemma3 work with packing * multi-gpu e2e for ci * update gemma3 model namespace to use mirror * add gradient checkpointing to multigpu e2e ci * update gemma3 examples for use_reentrant and fix ddp find unused params * fix tests for gemma3 * fix import for test utils * set correct train loss for gemma3 e2e
2025-03-31 17:15:23 -04:00
parent 4d36ecc724
commit 328d598114
8 changed files with 130 additions and 2 deletions
--- a/examples/gemma3/gemma-3-1b-qlora.yml
+++ b/examples/gemma3/gemma-3-1b-qlora.yml
@@ -5,6 +5,9 @@ tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name

+# gemma3 doesn't seem to play nice with ddp
+ddp_find_unused_parameters: true
+
 load_in_8bit: false
 load_in_4bit: true
 strict: false
@@ -54,6 +57,8 @@ fp16:
 tf32: true

 gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
--- a/examples/gemma3/gemma-3-4b-lora.yml
+++ b/examples/gemma3/gemma-3-4b-lora.yml
@@ -7,6 +7,9 @@ skip_prepare_dataset: true
 remove_unused_columns: false
 sample_packing: false

+# gemma3 doesn't seem to play nice with ddp
+ddp_find_unused_parameters: true
+
 chat_template: gemma3
 datasets:
  - path: HuggingFaceH4/llava-instruct-mix-vsft
@@ -48,6 +51,8 @@ fp16:
 tf32: true

 gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
 local_rank:
 logging_steps: 1
 flash_attention: true
--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -524,9 +524,15 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
            and self.cfg.eval_steps
            and self.cfg.save_steps % self.cfg.eval_steps == 0
        ) or False
+
+        # handle ddp
+        ddp_find_unused_parameters = None
+        if self.cfg.ddp:
+            ddp_find_unused_parameters = bool(self.cfg.ddp_find_unused_parameters)
        training_arguments_kwargs["ddp_find_unused_parameters"] = (
-            False if self.cfg.ddp else None
+            ddp_find_unused_parameters
        )
+
        training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
        training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
        report_to = []
--- a/src/axolotl/monkeypatch/multipack.py
+++ b/src/axolotl/monkeypatch/multipack.py
@@ -22,6 +22,7 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
    "phi3",
    "gemma",
    "gemma2",
+    "gemma3",
    "gemma3_text",
    "cohere",
    "cohere2",
--- a/src/axolotl/utils/collators/batching.py
+++ b/src/axolotl/utils/collators/batching.py
@@ -112,6 +112,7 @@ class DataCollatorForSeq2Seq:
            self.local_world_size = dist.get_world_size(group=sp_group)

    def __call__(self, features, return_tensors=None):
+        has_attn_mask = "attention_mask" in features[0].keys()
        labels = None
        if return_tensors is None:
            return_tensors = self.return_tensors
@@ -164,6 +165,8 @@ class DataCollatorForSeq2Seq:
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors=return_tensors,
        )
+        if not has_attn_mask:
+            del features["attention_mask"]

        # prepare decoder_input_ids
        if (
--- a/src/axolotl/utils/trainer.py
+++ b/src/axolotl/utils/trainer.py
@@ -235,7 +235,7 @@ def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2):


 def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
-    if cfg.model_config_type == "mamba":
+    if cfg.model_config_type in ["mamba", "gemma3"]:
        LOG.info("dropping attention_mask column")
        train_dataset = train_dataset.remove_columns("attention_mask")
        if eval_dataset:
--- a/tests/e2e/multigpu/test_gemma3.py
+++ b/tests/e2e/multigpu/test_gemma3.py
@@ -0,0 +1,100 @@
+"""
+E2E tests for multigpu lora tinyllama
+"""
+
+import logging
+import os
+from pathlib import Path
+
+import pytest
+import yaml
+from accelerate.test_utils import execute_subprocess_async
+from huggingface_hub import snapshot_download
+from transformers.testing_utils import get_torch_dist_unique_port
+
+from axolotl.utils.dict import DictDefault
+
+from tests.e2e.utils import check_tensorboard
+
+LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
+os.environ["WANDB_DISABLED"] = "true"
+
+AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
+
+
+@pytest.fixture(scope="session", autouse=True)
+def download_model():
+    # download the model
+    snapshot_download("axolotl-mirrors/gemma-3-4b-pt", repo_type="model")
+
+
+class TestMultiGPUGemma3:
+    """
+    Test case for Gemma3 models using LoRA
+    """
+
+    def test_lora_ddp_packed(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "axolotl-mirrors/gemma-3-4b-pt",
+                "sequence_len": 2048,
+                "ddp_find_unused_parameters": True,
+                "sample_packing": True,
+                "eval_sample_packing": False,
+                "pad_to_sequence_len": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.0,
+                "chat_template": "gemma3",
+                "datasets": [
+                    {
+                        "path": "mlabonne/FineTome-100k",
+                        "type": "chat_template",
+                        "split": "train[:10%]",
+                        "field_messages": "conversations",
+                        "message_field_role": "from",
+                        "message_field_content": "value",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 2,
+                "micro_batch_size": 4,
+                "gradient_checkpointing": True,
+                "gradient_checkpointing_kwargs": {
+                    "use_reentrant": False,
+                },
+                "gradient_accumulation_steps": 2,
+                "output_dir": temp_dir,
+                "learning_rate": 0.0001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "use_tensorboard": True,
+                "bf16": True,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "axolotl",
+                "train",
+                str(Path(temp_dir) / "config.yaml"),
+                "--num-processes",
+                "2",
+                "--main-process-port",
+                f"{get_torch_dist_unique_port()}",
+            ]
+        )
+
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 1.8, "Train Loss is too high"
+        )
--- a/tests/e2e/multigpu/test_llama.py
+++ b/tests/e2e/multigpu/test_llama.py
@@ -58,6 +58,7 @@ class TestMultiGPULlama:
                "max_steps": 2,
                "micro_batch_size": 4,
                "gradient_accumulation_steps": 4,
+                "gradient_checkpointing": True,
                "output_dir": temp_dir,
                "learning_rate": 0.00001,
                "optimizer": "adamw_8bit",
@@ -121,6 +122,7 @@ class TestMultiGPULlama:
                "max_steps": 2,
                "micro_batch_size": 1,
                "gradient_accumulation_steps": gradient_accumulation_steps,
+                "gradient_checkpointing": True,
                "output_dir": temp_dir,
                "learning_rate": 0.00001,
                "optimizer": "adamw_8bit",
@@ -193,6 +195,7 @@ class TestMultiGPULlama:
                "max_steps": 2,
                "micro_batch_size": 4,
                "gradient_accumulation_steps": 4,
+                "gradient_checkpointing": True,
                "output_dir": temp_dir,
                "warmup_steps": 0,
                "learning_rate": 0.00001,
@@ -270,6 +273,7 @@ class TestMultiGPULlama:
                "max_steps": 2,
                "micro_batch_size": 2,
                "gradient_accumulation_steps": 4,
+                "gradient_checkpointing": True,
                "output_dir": temp_dir,
                "warmup_steps": 0,
                "learning_rate": 0.00001,
@@ -330,6 +334,7 @@ class TestMultiGPULlama:
                "max_steps": 2,
                "micro_batch_size": 2,
                "gradient_accumulation_steps": gradient_accumulation_steps,
+                "gradient_checkpointing": True,
                "output_dir": temp_dir,
                "learning_rate": 0.00001,
                "optimizer": "adamw_torch_fused",
@@ -400,6 +405,7 @@ class TestMultiGPULlama:
                "max_steps": 2,
                "micro_batch_size": 4,
                "gradient_accumulation_steps": 2,
+                "gradient_checkpointing": True,
                "output_dir": temp_dir,
                "learning_rate": 0.00001,
                "optimizer": "adamw_torch_fused",
@@ -479,6 +485,7 @@ class TestMultiGPULlama:
                "max_steps": 2,
                "micro_batch_size": 4,
                "gradient_accumulation_steps": 2,
+                "gradient_checkpointing": True,
                "output_dir": temp_dir,
                "learning_rate": 0.00001,
                "optimizer": "adamw_torch_fused",
@@ -781,6 +788,7 @@ class TestMultiGPULlama:
                "max_steps": 2,
                "micro_batch_size": 1,
                "gradient_accumulation_steps": 1,
+                "gradient_checkpointing": True,
                "output_dir": temp_dir,
                "learning_rate": 0.00001,
                "optimizer": "adamw_torch_fused",