Feat: Add support for gemma3_text and add e2e for gemma2 (#2406)

docs/config.qmd

@@ -513,7 +513,6 @@ lr_div_factor: # Learning rate div factor
 # in the examples/ for your model and fine-tuning use case.
 #
 # Valid values for 'optimizer' include:
-# - adamw_hf
 # - adamw_torch
 # - adamw_torch_fused
 # - adamw_torch_xla

examples/gemma3/qlora.yml

@@ -0,0 +1,74 @@
+base_model: google/gemma-3-1b-it
+# optionally might have model_type or tokenizer_type
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+# huggingface repo
+chat_template: gemma3_text
+datasets:
+  - path: cgato/SlimOrcaDedupCleaned
+    type: chat_template
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+adapter: qlora
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+
+sequence_len: 2048
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+
+gradient_accumulation_steps: 4
+micro_batch_size: 1
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: true
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_ratio: 0.1
+evals_per_epoch:
+eval_table_size:
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:

requirements.txt

@@ -12,7 +12,7 @@ liger-kernel==0.5.3
 packaging==23.2
 
 peft==0.15.0
-transformers==4.49.0
+transformers==4.50.0
 tokenizers>=0.21.1
 accelerate==1.5.2
 datasets==3.4.1

src/axolotl/integrations/liger/__init__.py

@@ -114,3 +114,5 @@ class LigerPlugin(BasePlugin):
                 modeling_mod.CrossEntropyLoss = LigerCrossEntropyLoss
             if cfg.liger_fused_linear_cross_entropy:
                 modeling_mod.DeepseekV2ForCausalLM.forward = deepseekv2_lce_forward
+        elif cfg.model_config_type in ["gemma3_text", "deepseek_v3"]:
+            raise ValueError(f"Unsupported model config type: {cfg.model_config_type}")

src/axolotl/monkeypatch/multipack.py

@@ -22,6 +22,7 @@ SUPPORTED_MULTIPACK_MODEL_TYPES = [
     "phi3",
     "gemma",
     "gemma2",
+    "gemma3_text",
     "gemmoe",
     "starcoder2",
     "deepseek_v2",

src/axolotl/utils/schemas/enums.py

@@ -23,6 +23,7 @@ class ChatTemplate(str, Enum):
     mistral_v2v3 = "mistral_v2v3"  # pylint: disable=invalid-name
     mistral_v3_tekken = "mistral_v3_tekken"  # pylint: disable=invalid-name
     gemma = "gemma"  # pylint: disable=invalid-name
+    gemma3_text = "gemma3_text"  # pylint: disable=invalid-name
     cohere = "cohere"  # pylint: disable=invalid-name
     llama3 = "llama3"  # pylint: disable=invalid-name
     llama3_2_vision = "llama3_2_vision"  # pylint: disable=invalid-name

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

@@ -144,7 +144,7 @@ def test_swiglu_mlp_integration(small_llama_model):
 def test_geglu_model_integration():
     """Test GeGLU activation with Gemma model."""
     model = AutoModelForCausalLM.from_pretrained(
-        "mhenrichsen/gemma-2b", torch_dtype=torch.float16, device_map="cuda"
+        "mhenrichsen/gemma-2b", torch_dtype=torch.float16, device_map="auto"
     )
     peft_config = get_peft_config(
         {
@@ -347,7 +347,7 @@ def test_model_architecture(model_config):
     """Test LoRA kernel patches across different model architectures."""
     # Load model with appropriate dtype
     model = AutoModelForCausalLM.from_pretrained(
-        model_config["name"], torch_dtype=model_config["dtype"], device_map="cuda"
+        model_config["name"], torch_dtype=model_config["dtype"], device_map="auto"
     )
 
     # Apply LoRA configuration

tests/e2e/test_deepseekv3.py

@@ -1,5 +1,5 @@
 """
-E2E tests for lora llama
+E2E tests for deepseekv3
 """
 
 import logging

tests/e2e/test_gemma2.py

@@ -0,0 +1,133 @@
+"""
+E2E tests for gemma2
+"""
+
+import logging
+import os
+from pathlib import Path
+
+import pytest
+
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestGemma2:
+    """
+    Test case for Gemma2 models
+    """
+
+    @pytest.mark.parametrize(
+        "sample_packing",
+        [True, False],
+    )
+    def test_lora_gemma2(self, temp_dir, sample_packing):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "axolotl-ai-co/gemma-2-33M",
+                "trust_remote_code": True,
+                "sample_packing": sample_packing,
+                "flash_attention": True,
+                "sequence_len": 2048,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0,
+                "datasets": [
+                    {
+                        "path": "mlabonne/FineTome-100k",
+                        "type": "chat_template",
+                        "field_messages": "conversations",
+                        "message_property_mappings": {
+                            "role": "from",
+                            "content": "value",
+                        },
+                        "drop_system_message": True,
+                        "split": "train[:1%]",
+                    },
+                ],
+                "special_tokens": {
+                    "bos_token": "<bos>",
+                    "eos_token": "<eos>",
+                },
+                "chat_template": "gemma",  # gemma2's template is same as gemma
+                "num_epochs": 1,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 5,
+                "save_safetensors": True,
+                "bf16": True,
+            }
+        )
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
+
+    @pytest.mark.parametrize(
+        "sample_packing",
+        [True, False],
+    )
+    def test_fft_gemma2(self, temp_dir, sample_packing):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "axolotl-ai-co/gemma-2-33M",
+                "trust_remote_code": True,
+                "sample_packing": sample_packing,
+                "flash_attention": True,
+                "sequence_len": 2048,
+                "val_set_size": 0,
+                "datasets": [
+                    {
+                        "path": "mlabonne/FineTome-100k",
+                        "type": "chat_template",
+                        "field_messages": "conversations",
+                        "message_property_mappings": {
+                            "role": "from",
+                            "content": "value",
+                        },
+                        "split": "train[:1%]",
+                        "drop_system_message": True,
+                    },
+                ],
+                "chat_template": "gemma",  # gemma2's template is same as gemma
+                "special_tokens": {
+                    "bos_token": "<bos>",
+                    "eos_token": "<eos>",
+                },
+                "num_epochs": 1,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 5,
+                "save_safetensors": True,
+                "bf16": True,
+            }
+        )
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "model.safetensors").exists()

tests/e2e/test_gemma3_text.py

@@ -0,0 +1,131 @@
+"""
+E2E tests for gemma3_text
+"""
+
+import logging
+import os
+from pathlib import Path
+
+import pytest
+
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestGemma3Text:
+    """
+    Test case for Gemma3Text models
+    """
+
+    @pytest.mark.parametrize(
+        "sample_packing",
+        [True, False],
+    )
+    def test_lora_gemma3_text(self, temp_dir, sample_packing):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "axolotl-ai-co/gemma-3-34M",
+                "trust_remote_code": True,
+                "sample_packing": sample_packing,
+                "flash_attention": True,
+                "sequence_len": 2048,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0,
+                "datasets": [
+                    {
+                        "path": "mlabonne/FineTome-100k",
+                        "type": "chat_template",
+                        "field_messages": "conversations",
+                        "message_property_mappings": {
+                            "role": "from",
+                            "content": "value",
+                        },
+                        "split": "train[:1%]",
+                    },
+                ],
+                "special_tokens": {
+                    "bos_token": "<bos>",
+                    "eos_token": "<eos>",
+                },
+                "chat_template": "gemma3_text",
+                "num_epochs": 1,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 5,
+                "save_safetensors": True,
+                "bf16": True,
+            }
+        )
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "adapter_model.safetensors").exists()
+
+    @pytest.mark.parametrize(
+        "sample_packing",
+        [True, False],
+    )
+    def test_fft_gemma3_text(self, temp_dir, sample_packing):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "axolotl-ai-co/gemma-3-34M",
+                "trust_remote_code": True,
+                "sample_packing": sample_packing,
+                "flash_attention": True,
+                "sequence_len": 2048,
+                "val_set_size": 0,
+                "datasets": [
+                    {
+                        "path": "mlabonne/FineTome-100k",
+                        "type": "chat_template",
+                        "field_messages": "conversations",
+                        "message_property_mappings": {
+                            "role": "from",
+                            "content": "value",
+                        },
+                        "split": "train[:1%]",
+                    },
+                ],
+                "chat_template": "gemma3_text",
+                "special_tokens": {
+                    "bos_token": "<bos>",
+                    "eos_token": "<eos>",
+                },
+                "num_epochs": 1,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 4,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_bnb_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 5,
+                "save_safetensors": True,
+                "bf16": True,
+            }
+        )
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "model.safetensors").exists()

tests/e2e/test_schedulers.py

@@ -54,7 +54,7 @@ class TestCustomSchedulers(unittest.TestCase):
                 "gradient_accumulation_steps": 1,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
-                "optimizer": "adamw_hf",
+                "optimizer": "adamw_torch_fused",
                 "max_steps": 20,
                 "lr_scheduler": "rex",
                 "warmup_steps": 5,