fix the lora yaml for l3

examples/dbrx/16bit-lora.yaml

@@ -22,7 +22,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-qlora_fsdp_alt_loader: true
 adapter: lora
 lora_model_dir:
 lora_r: 8

examples/dbrx/8bit-lora.yaml

@@ -22,7 +22,6 @@ wandb_watch:
 wandb_name:
 wandb_log_model:
 
-qlora_fsdp_alt_loader: true
 adapter: lora
 lora_model_dir:
 lora_r: 8

examples/llama-3/lora-8b.yml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B
+base_model: NousResearch/Llama-2-7b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
 
@@ -64,4 +64,4 @@ weight_decay: 0.0
 fsdp:
 fsdp_config:
 special_tokens:
-   pad_token: <|end_of_text|>
+  pad_token: <|end_of_text|>

requirements.txt

@@ -11,7 +11,7 @@ addict
 fire
 PyYAML>=6.0
 requests
-datasets==2.15.0
+datasets>=2.15.0
 flash-attn==2.5.5
 sentencepiece
 wandb
@@ -28,7 +28,7 @@ scipy
 scikit-learn==1.2.2
 pynvml
 art
-fschat @ git+https://github.com/lm-sys/FastChat.git@5095615810cf613dba7f27dd155f571fcff976d8
+fschat==0.2.36
 gradio==3.50.2
 tensorboard
 
@@ -39,6 +39,6 @@ s3fs
 gcsfs
 # adlfs
 
-trl==0.8.5
+trl @ git+https://github.com/huggingface/trl.git@0ee349dcd43b0f4b3169449f16751c38ac4a609f
 zstandard==0.22.0
 fastcore

src/axolotl/cli/preprocess.py

@@ -54,7 +54,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         LOG.warning(msg)
         parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
 
-    if parsed_cfg.rl:  # and parsed_cfg.rl != "orpo":
+    if parsed_cfg.rl and parsed_cfg.rl != "orpo":
         load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
     else:
         load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)

src/axolotl/cli/train.py

@@ -47,7 +47,7 @@ def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
     else:
         register_chatml_template()
 
-    if cfg.rl:  # and cfg.rl != "orpo":
+    if cfg.rl and cfg.rl != "orpo":
         dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
     else:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

src/axolotl/core/trainer_builder.py

@@ -30,7 +30,7 @@ from transformers import (
 )
 from transformers.trainer_utils import seed_worker
 from transformers.utils import is_sagemaker_mp_enabled
-from trl import DPOTrainer, ORPOConfig, ORPOTrainer
+from trl import DPOTrainer
 from trl.trainer.utils import pad_to_length
 
 from axolotl.loraplus import create_loraplus_optimizer
@@ -54,7 +54,6 @@ from axolotl.utils.collators import (
     MambaDataCollator,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
-from axolotl.utils.models import ensure_dtype
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 from axolotl.utils.schedulers import (
     get_cosine_schedule_with_min_lr,
@@ -811,14 +810,6 @@ class AxolotlDPOTrainer(DPOTrainer):
         return res
 
 
-class AxolotlORPOTrainer(ORPOTrainer):
-    """
-    Extend the base ORPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "orpo"]
-
-
 class TrainerBuilderBase(abc.ABC):
     """
     Base class for trainer builder
@@ -1413,7 +1404,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         )
 
 
-class HFRLTrainerBuilder(TrainerBuilderBase):
+class HFDPOTrainerBuilder(TrainerBuilderBase):
     """
     Trainer factory class for DPO Trainer
     """
@@ -1506,15 +1497,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             # default to saving each epoch if not defined
             training_args_kwargs["save_strategy"] = "epoch"
 
-        if self.cfg.orpo_alpha:
+        training_args = TrainingArguments(
-            # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
-            training_args_kwargs["beta"] = self.cfg.orpo_alpha
-
-        training_args_cls = TrainingArguments
-        if self.cfg.rl == "orpo":
-            training_args_cls = ORPOConfig
-
-        training_args = training_args_cls(
             per_device_train_batch_size=self.cfg.micro_batch_size,
             max_steps=self.cfg.max_steps or total_num_steps,
             gradient_accumulation_steps=self.cfg.gradient_accumulation_steps,
@@ -1547,32 +1530,20 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             dpo_trainer_kwargs[
                 "precompute_ref_log_probs"
             ] = self.cfg.precompute_ref_log_probs
-        if self.cfg.rl in ["dpo", "ipo", "kto_pair"]:
+        dpo_trainer = AxolotlDPOTrainer(
-            trainer_cls = AxolotlDPOTrainer
+            self.model,
-            dpo_trainer_kwargs["beta"] = self.cfg.dpo_beta or 0.1
+            self.model_ref,
-            trainer_cls_args = [self.model, self.model_ref]
-
-            # these aren't used for the ORPO trainer
-            dpo_trainer_kwargs["max_length"] = self.cfg.sequence_len
-            dpo_trainer_kwargs["max_target_length"] = None
-            dpo_trainer_kwargs["max_prompt_length"] = self.cfg.sequence_len
-            dpo_trainer_kwargs["generate_during_eval"] = True
-        elif self.cfg.rl == "orpo":
-            trainer_cls = AxolotlORPOTrainer
-            trainer_cls_args = [self.model]
-        else:
-            raise ValueError(f"Unsupported RL: {self.cfg.rl}")
-        dpo_trainer = trainer_cls(
-            *trainer_cls_args,
             args=training_args,
+            beta=self.cfg.dpo_beta or 0.1,
             train_dataset=self.train_dataset,
             tokenizer=self.tokenizer,
+            max_length=self.cfg.sequence_len,
+            max_target_length=None,
+            max_prompt_length=self.cfg.sequence_len,
+            generate_during_eval=True,
             callbacks=self.get_callbacks(),
             **dpo_trainer_kwargs,
         )
-        if self.cfg.fsdp:
-            ensure_dtype(dpo_trainer.model, dtype=self.cfg.torch_dtype)
-
         dpo_trainer = self.hook_post_create_trainer(dpo_trainer)
         for callback in self.get_post_trainer_create_callbacks(dpo_trainer):
             dpo_trainer.add_callback(callback)

src/axolotl/monkeypatch/fastchat_conversation_turns.py

@@ -123,14 +123,6 @@ def get_turns(  # pylint: disable=too-many-return-statements
             else:
                 yield role, ""
         return
-    if self.sep_style == SeparatorStyle.GEMMA:
-        if self.system_message:
-            raise ValueError("Gemma chat template does not support system messages")
-        for i, (role, message) in enumerate(self.messages):
-            prefix = "<bos>" if i == 0 else ""
-            message_str = message if message else ""
-            yield prefix + "<start_of_turn>" + role + "\n", message_str + "<end_of_turn>\n"
-        return
     if self.sep_style == SeparatorStyle.CHATGLM:
         # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
         # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926

src/axolotl/prompt_strategies/orpo/__init__.py

@@ -6,4 +6,4 @@ from functools import partial
 
 from ..base import load as load_base
 
-load = partial(load_base, module_base="axolotl.prompt_strategies.orpo")
+load = partial(load_base, module="axolotl.prompt_strategies.orpo")

src/axolotl/prompt_strategies/orpo/chat_template.py

@@ -78,57 +78,6 @@ class ORPODatasetParsingStrategy:
         )
         return MessageList(messages=messages)
 
-    def get_prompt(self, prompt) -> MessageList:
-        """Map the data to extract everything up to the last turn"""
-        total_msg_len = len(prompt["chosen"])
-        total_msg_turns, remainder = divmod(total_msg_len, 2)
-        assert remainder == 0, "invalid number of turns"
-
-        messages: List[Message] = []
-        if system := prompt.get("system", None):
-            messages.append(Message(role="system", content=system, label=False))
-        for i in range(total_msg_turns):
-            if "prompt" in prompt:
-                messages.append(
-                    Message(role="user", content=prompt["prompt"], label=False)
-                )
-            else:
-                messages.append(
-                    Message(
-                        role="user",
-                        content=prompt["chosen"][i * 2]["content"],
-                        label=False,
-                    )
-                )
-            if i < total_msg_turns - 1:
-                messages.append(
-                    Message(
-                        role="assistant",
-                        content=prompt["chosen"][i * 2 + 1]["content"],
-                        label=False,
-                    )
-                )
-
-        return MessageList(messages=messages)
-
-    def get_chosen(self, prompt) -> MessageList:
-        res = self.get_prompt(prompt)
-        res.messages.append(
-            Message(
-                role="assistant", content=prompt["chosen"][-1]["content"], label=True
-            )
-        )
-        return res
-
-    def get_rejected(self, prompt) -> MessageList:
-        res = self.get_prompt(prompt)
-        res.messages.append(
-            Message(
-                role="assistant", content=prompt["rejected"][-1]["content"], label=True
-            )
-        )
-        return res
-
 
 class ORPOTokenizingStrategy(PromptTokenizingStrategy):
     """
@@ -237,36 +186,3 @@ class ORPOPrompter(Prompter):
                     chat_template=self.chat_template,
                     tokenize=False,
                 ), True
-
-
-def argilla(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-argument
-    dataset_parser = ORPODatasetParsingStrategy()
-
-    chat_template_str = chat_templates(cfg.chat_template)
-
-    def transform_fn(sample, tokenizer=None):
-        res = {}
-
-        res["prompt"] = tokenizer.apply_chat_template(
-            [msg.model_dump() for msg in dataset_parser.get_prompt(sample).messages],
-            add_generation_prompt=True,
-            chat_template=chat_template_str,
-            tokenize=False,
-        )
-        prompt_str_len = len(res["prompt"])
-        res["chosen"] = tokenizer.apply_chat_template(
-            [msg.model_dump() for msg in dataset_parser.get_chosen(sample).messages],
-            add_generation_prompt=False,
-            chat_template=chat_template_str,
-            tokenize=False,
-        )[prompt_str_len:]
-        res["rejected"] = tokenizer.apply_chat_template(
-            [msg.model_dump() for msg in dataset_parser.get_rejected(sample).messages],
-            add_generation_prompt=False,
-            chat_template=chat_template_str,
-            tokenize=False,
-        )[prompt_str_len:]
-
-        return res
-
-    return transform_fn

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -188,7 +188,6 @@ class LoraConfig(BaseModel):
     peft_use_dora: Optional[bool] = None
     peft_use_rslora: Optional[bool] = None
     peft_layer_replication: Optional[List[Tuple[int, int]]] = None
-    qlora_fsdp_alt_loader: Optional[bool] = None
 
     lora_on_cpu: Optional[bool] = None
     gptq: Optional[bool] = None

src/axolotl/utils/data/__init__.py

@@ -1,11 +1,11 @@
 """
 Data processing modules
 """
+from axolotl.utils.data.dpo import load_prepare_dpo_datasets  # noqa: F401
 from axolotl.utils.data.pretraining import (  # noqa: F401
     encode_pretraining,
     wrap_pretraining_dataset,
 )
-from axolotl.utils.data.rl import load_prepare_dpo_datasets  # noqa: F401
 from axolotl.utils.data.sft import (  # noqa: F401
     get_dataset_wrapper,
     load_prepare_datasets,

src/axolotl/utils/data/dpo.py

@@ -1,20 +1,17 @@
 """data handling specific to DPO"""
-import inspect
+
 import logging
-from functools import partial
 from pathlib import Path
 from typing import Any, List
 
 import yaml
-from datasets import DatasetDict, concatenate_datasets, load_dataset, load_from_disk
+from datasets import concatenate_datasets, load_dataset, load_from_disk
 
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.prompt_strategies.dpo import load as load_dpo
-from axolotl.prompt_strategies.orpo import load as load_orpo
 from axolotl.utils.data.utils import md5
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process, zero_first
-from axolotl.utils.models import load_tokenizer
 
 LOG = logging.getLogger("axolotl")
 
@@ -75,29 +72,16 @@ def load_prepare_dpo_datasets(cfg):
                 )
                 split_datasets.insert(i, ds)
 
-        tokenizer = None
         for i, data_set in enumerate(split_datasets):
             _type = dataset_cfgs[i]["type"]
             if _type:
                 if isinstance(_type, DictDefault):
                     _type = "user_defined.default"
-                if _cfg.rl == "orpo":
+                ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
-                    ds_transform_fn = load_orpo(_type, _cfg, dataset_idx=i)
+                split_datasets[i] = data_set.map(
-                else:
-                    ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
-                sig = inspect.signature(ds_transform_fn)
-                if "tokenizer" in sig.parameters:
-                    if not tokenizer:
-                        tokenizer = load_tokenizer(_cfg)
-                    ds_transform_fn = partial(ds_transform_fn, tokenizer=tokenizer)
-
-                data_set = data_set.map(
                     ds_transform_fn,
                     desc="Mapping RL Dataset",
                 )
-                if isinstance(data_set, DatasetDict):
-                    data_set = data_set["train"]
-                split_datasets[i] = data_set
             else:
                 # If no `type` is provided, assume the dataset is already in the expected format with
                 # "prompt", "chosen" and "rejected" already preprocessed

src/axolotl/utils/data/sft.py

@@ -421,7 +421,7 @@ def load_tokenized_prepared_datasets(
 
         if cfg.local_rank == 0:
             LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
-            dataset.save_to_disk(str(prepared_ds_path))
+            dataset.save_to_disk(prepared_ds_path)
             if cfg.push_dataset_to_hub:
                 LOG.info(
                     f"Saving merged prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"

src/axolotl/utils/model_shard_quant.py

@@ -70,7 +70,6 @@ def load_and_quantize(
     to_meta: bool = False,
     verbose: bool = False,
     quant_method: str = "bnb",
-    is_dora: bool = False,
 ):
     """
     Loads `value` tensor into submodule of `module`, optionally skipping `skip_names` and converting to `dtype`.
@@ -109,12 +108,6 @@ def load_and_quantize(
                 # FSDP only syncs parameters and buffers, so the quant_state isn't copied. This
                 # workaround quantizes Params4bit to initialize quant_state on all ranks, then
                 # replaces Params4bit's data with a meta tensor to free memory on non-rank 0.
-                if is_dora:
-                    setattr(
-                        submodule,
-                        "dora_scale",
-                        value.norm(p=2, dim=1).to(dtype=dtype).to("cpu"),
-                    )
                 value = type(param)(
                     value.to(device=device, dtype=dtype).data, **param.__dict__
                 ).cuda(device)
@@ -184,7 +177,6 @@ def load_sharded_model_quant(
     with init_empty_weights():
         model = AutoModelForCausalLM.from_config(
             model_config,
-            attn_implementation=model_config._attn_implementation,  # pylint: disable=protected-access
             trust_remote_code=cfg.trust_remote_code,
         )
         if hasattr(model, "transformer"):
@@ -257,7 +249,6 @@ def load_sharded_model_quant(
             to_meta=(low_memory and cfg.local_rank != 0),
             verbose=verbose,
             quant_method=quant_method,
-            is_dora=cfg.peft_use_dora,
         )
 
     if cfg.local_rank == 0 and verbose:

src/axolotl/utils/models.py

@@ -34,7 +34,6 @@ from transformers import (  # noqa: F401
     PreTrainedTokenizerBase,
 )
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
-from transformers.quantizers import AutoHfQuantizer
 
 from axolotl.models.mamba import fix_mamba_attn_for_loss
 from axolotl.monkeypatch.multipack import (
@@ -569,7 +568,7 @@ def load_model(
         elif (
             qlora_fsdp
             and cfg.fsdp_config.fsdp_cpu_ram_efficient_loading
-            and cfg.qlora_fsdp_alt_loader
+            and cfg.model_config_type == "dbrx"
         ):
             quant_storage = cfg.torch_dtype
             model = load_sharded_model_quant(
@@ -578,11 +577,6 @@ def load_model(
                 cfg,
                 quant_storage=quant_storage,
             )
-            if model_kwargs["quantization_config"]:
-                hf_quantizer = AutoHfQuantizer.from_config(
-                    model_kwargs["quantization_config"]
-                )
-                model.hf_quantizer = hf_quantizer
             skip_move_to_device = True
         elif (
             model_config.model_type == "llama"
@@ -999,20 +993,3 @@ def load_lora(model, cfg, inference=False, config_only=False):
         setup_quantized_peft_meta_for_training(model)
 
     return model, lora_config
-
-
-def ensure_dtype(model, dtype=torch.bfloat16):
-    for name, module in model.named_modules():
-        try:
-            if module.weight.dtype != dtype:
-                print(f"Converting module {name}: {module.weight.dtype} -> {dtype}")
-                module.to(dtype)
-        except AttributeError:
-            pass
-    for name, param in model.named_parameters():
-        try:
-            if param.data.dtype != dtype:
-                print(f"Converting module {name}: {param.data.dtype} -> {dtype}")
-                param.data = param.data.to(dtype)
-        except AttributeError:
-            pass

src/axolotl/utils/trainer.py

@@ -13,7 +13,7 @@ from datasets import set_caching_enabled
 from torch.utils.data import DataLoader, RandomSampler
 from transformers.utils import is_torch_bf16_gpu_available
 
-from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
+from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFDPOTrainerBuilder
 from axolotl.utils.distributed import is_main_process, reduce_and_broadcast, zero_first
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
@@ -340,8 +340,8 @@ def prepare_optim_env(cfg):
 
 
 def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
-    if cfg.rl in ["dpo", "ipo", "kto_pair", "orpo"]:
+    if cfg.rl in ["dpo", "ipo", "kto_pair"]:
-        trainer_builder = HFRLTrainerBuilder(cfg, model[0], tokenizer)
+        trainer_builder = HFDPOTrainerBuilder(cfg, model[0], tokenizer)
         trainer_builder.model_ref = model[1]
         trainer_builder.peft_config = model[2]
     else:

tests/core/test_trainer_builder.py

@@ -4,7 +4,7 @@ unit tests for axolotl.core.trainer_builder
 
 import pytest
 
-from axolotl.core.trainer_builder import HFRLTrainerBuilder
+from axolotl.core.trainer_builder import HFDPOTrainerBuilder
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model, load_tokenizer
@@ -51,13 +51,13 @@ def fixture_model(cfg, tokenizer):
     return load_model(cfg, tokenizer)
 
 
-class TestHFRLTrainerBuilder:
+class TestHFDPOTrainerBuilder:
     """
     TestCase class for DPO trainer builder
     """
 
     def test_build_training_arguments(self, cfg, model, tokenizer):
-        builder = HFRLTrainerBuilder(cfg, model, tokenizer)
+        builder = HFDPOTrainerBuilder(cfg, model, tokenizer)
         training_arguments = builder.build_training_arguments(100)
         assert training_arguments.adam_beta1 == 0.998
         assert training_arguments.adam_beta2 == 0.9

tests/test_datasets.py

@@ -110,7 +110,7 @@ class TestDatasetPreparation(unittest.TestCase):
         """Usual use case.  Verify datasets saved via `save_to_disk` can be loaded."""
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_ds_name = Path(tmp_dir) / "tmp_dataset"
-            self.dataset.save_to_disk(str(tmp_ds_name))
+            self.dataset.save_to_disk(tmp_ds_name)
 
             prepared_path = Path(tmp_dir) / "prepared"
             cfg = DictDefault(