remove ref_model when peft model is passed into grpo trainer

.github/workflows/multi-gpu-e2e.yml

@@ -4,10 +4,6 @@ on:
   pull_request:
     paths:
       - 'tests/e2e/multigpu/*.py'
-      - 'requirements.txt'
-      - 'setup.py'
-      - 'pyproject.toml'
-      - '.github/workflows/multi-gpu-e2e.yml'
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday

cicd/multigpu.py

@@ -37,11 +37,15 @@ temp_dir = tempfile.mkdtemp()
 with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
     f.write(dockerfile_contents)
 
-cicd_image = Image.from_dockerfile(
+cicd_image = (
+    Image.from_dockerfile(
         pathlib.Path(temp_dir) / "Dockerfile",
         force_build=True,
         gpu="A10G",
-).env(df_args)
+    )
+    .env(df_args)
+    .pip_install("fastapi==0.110.0", "pydantic==2.6.3")
+)
 
 app = App("Axolotl CI/CD", secrets=[])
 

docs/config.qmd

@@ -407,10 +407,7 @@ save_total_limit: # Checkpoints saved at a time
 max_steps:
 
 # bool of whether to include tokens trainer per second in the training metrics. This iterates over the entire dataset once, so it takes some time.
-include_tokens_per_second: # Optional[bool]
+include_tokens_per_second:
-
-# whether to find batch size that fits in memory. Passed to underlying transformers Trainer
-auto_find_batch_size: # Optional[bool]
 
 eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128

requirements.txt

@@ -18,7 +18,7 @@ tokenizers>=0.21.0
 accelerate==1.3.0
 datasets==3.2.0
 deepspeed==0.16.1
-trl==0.15.1
+trl==0.15.0
 
 optimum==1.16.2
 hf_transfer

src/axolotl/cli/cloud/modal_.py

@@ -123,6 +123,8 @@ class ModalCloud(Cloud):
         if env := self.get_env():
             image = image.env(env)
 
+        image = image.pip_install("fastapi==0.110.0", "pydantic==2.6.3")
+
         return image
 
     def get_secrets(self):

src/axolotl/core/trainer_builder.py

@@ -59,7 +59,6 @@ from axolotl.core.training_args import (
     AxolotlTrainingArguments,
 )
 from axolotl.integrations.base import PluginManager
-from axolotl.monkeypatch.attention.sequence_parallel import USPRingAttnType, get_extract_fn
 from axolotl.monkeypatch.multipack import SUPPORTED_MULTIPACK_MODEL_TYPES
 from axolotl.monkeypatch.relora import ReLoRACallback
 from axolotl.utils import is_comet_available, is_mlflow_available
@@ -747,11 +746,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             # A100 is best at 64, while others at 8. Let's use the larger so we don't have to check
             # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
             data_collator_kwargs["pad_to_multiple_of"] = 64
-        if self.cfg.sp_ulysses_degree:
-            data_collator_kwargs["sp_extract_fn"] = get_extract_fn(
-                USPRingAttnType.ZIGZAG,
-                sp_ulysses_degree=self.cfg.sp_ulysses_degree
-            )
 
         if self.cfg.reward_model:
             data_collator_kwargs["max_length"] = self.cfg.sequence_len

src/axolotl/core/trainers/grpo/trainer.py

@@ -39,6 +39,15 @@ class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
             self.model = self._enable_gradient_checkpointing(self.model, kwargs["args"])
         # pylint: enable=access-member-before-definition
 
+        # cleanup the ref_model if we have a peft model passed in
+        # TODO remove this after next major trl release
+        if (
+            self.ref_model  # pylint: disable=access-member-before-definition
+            and is_peft_model(self.model)
+        ):
+            del self.ref_model
+            self.ref_model = None
+
     def _enable_gradient_checkpointing(
         self, model: PreTrainedModel, args: GRPOConfig
     ) -> PreTrainedModel:
@@ -78,6 +87,7 @@ class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
             if is_peft_model(unwrapped_model):
                 unwrapped_model.merge_adapter()
                 state_dict = unwrapped_model.state_dict()
+                unwrapped_model.unmerge_adapter()
                 # Remove base_model and base_layer prefixes
                 state_dict = {
                     k.removeprefix("base_model.model.")
@@ -104,5 +114,3 @@ class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
                 self.llm.llm_engine.model_executor.driver_worker.model_runner.model
             )
             llm_model.load_weights(state_dict.items())
-            if is_peft_model(unwrapped_model):
-                unwrapped_model.unmerge_adapter()

src/axolotl/core/training_args.py

@@ -206,16 +206,6 @@ class AxolotlTrainingMixins:
         },
     )
 
-    sp_ulysses_degree: Optional[int] = field(
-        default=None,
-        metadata={"help": "Ulysses parallelism for hybrid sequence parallel long context attn"},
-    )
-
-    sp_ring_degree: Optional[int] = field(
-        default=None,
-        metadata={"help": "Ring attention parallelism for sequence parallel long context attn"},
-    )
-
 
 @dataclass
 class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):

src/axolotl/monkeypatch/attention/sequence_parallel/__init__.py

@@ -1,45 +0,0 @@
-from enum import Enum
-from functools import partial
-
-from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
-from yunchang import set_seq_parallel_pg, EXTRACT_FUNC_DICT
-
-from axolotl.utils.distributed import get_world_size, get_rank
-
-
-class USPRingAttnType(Enum):
-    BASIC = "basic"
-    ZIGZAG = "zigzag"
-    STRIPE = "stripe"
-
-def apply_usp_attn_patch(ring_impl_type: USPRingAttnType):
-    from axolotl.monkeypatch.attention.sequence_parallel.usp import build_usp_fa_forward
-
-    fa_forward = build_usp_fa_forward(ring_impl_type)
-    ALL_ATTENTION_FUNCTIONS["flash_attention_2"] = fa_forward
-
-def get_extract_fn(ring_impl_type: USPRingAttnType, sp_ulysses_degree: int):
-    fn = EXTRACT_FUNC_DICT["basic"]
-    if ring_impl_type.value in EXTRACT_FUNC_DICT.keys():
-        fn = EXTRACT_FUNC_DICT[ring_impl_type.value]
-
-    # map bad key upstream
-    elif ring_impl_type == USPRingAttnType.STRIPE:
-        fn = EXTRACT_FUNC_DICT["strip"]
-
-    world_size = get_world_size()
-    rd = world_size // sp_ulysses_degree
-
-    return partial(fn, rank=get_rank(), world_size=world_size, rd=rd, ud=sp_ulysses_degree)
-
-def set_usp_parallel_group(sp_ulysses_degree):
-    """
-    setup distributed parallel group for USP attention
-    make sure this gets called before building any USP attention modules
-    :param sp_ulysses_degree:
-    :return:
-    """
-    world_size = get_world_size()
-    rank = get_rank()
-    sp_ring_degree = world_size // sp_ulysses_degree
-    set_seq_parallel_pg(sp_ulysses_degree, sp_ring_degree, rank, world_size)

src/axolotl/monkeypatch/attention/sequence_parallel/usp.py

@@ -1,36 +0,0 @@
-from enum import Enum
-from typing import Optional, Tuple, Callable
-
-import torch
-from yunchang import LongContextAttention
-
-from axolotl.monkeypatch.attention.sequence_parallel import USPRingAttnType
-
-
-def build_usp_fa_forward(ring_impl_type: USPRingAttnType) -> Callable:
-    usp_attn = LongContextAttention(ring_impl_type.value)
-
-    def flash_attention_forward(
-            module: torch.nn.Module,  # pylint: disable=unused-argument
-            query: torch.Tensor,
-            key: torch.Tensor,
-            value: torch.Tensor,
-            attention_mask: Optional[torch.Tensor],  # pylint: disable=unused-argument
-            dropout: float = 0.0,
-            scaling: Optional[float] = None,
-            sliding_window: Optional[int] = None,  # pylint: disable=unused-argument
-            softcap: Optional[float] = None,
-            **kwargs,  # pylint: disable=unused-argument
-    ) -> Tuple[torch.Tensor, None]:
-        attn_output = usp_attn(
-            query,
-            key,
-            value,
-            dropout_p=dropout,
-            softmax_scale=scaling,
-            causal=True,
-            softcap=softcap,
-        )
-        return attn_output, None
-
-    return flash_attention_forward

src/axolotl/utils/collators/batching.py

@@ -3,7 +3,7 @@ DataCollator for axolotl to pad labels and position_ids for packed sequences
 """
 
 from dataclasses import dataclass
-from typing import Any, Optional, Union, Callable
+from typing import Any, Optional, Union
 
 import numpy as np
 from transformers import PreTrainedTokenizerBase
@@ -53,7 +53,6 @@ class DataCollatorForSeq2Seq:
     label_pad_token_id: int = -100
     position_pad_token_id: int = 0
     return_tensors: str = "pt"
-    sp_extract_fn: Optional[Callable] = None
 
     def __call__(self, features, return_tensors=None):
         labels = None
@@ -122,10 +121,6 @@ class DataCollatorForSeq2Seq:
 
         return features
 
-    def seq_parallel_split(self, features):
-        if self.sp_extract_fn:
-            pass
-        return features
 
 @dataclass
 class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):

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

@@ -342,7 +342,6 @@ class LoraConfig(BaseModel):
     peft_use_dora: Optional[bool] = None
     peft_use_rslora: Optional[bool] = None
     peft_layer_replication: Optional[List[Tuple[int, int]]] = None
-    peft_init_lora_weights: Optional[Union[bool, str]] = None
 
     qlora_sharded_model_loading: Optional[bool] = Field(
         default=False,
@@ -832,8 +831,6 @@ class AxolotlInputConfig(
 
     eager_attention: Optional[bool] = None
 
-    sp_ulysses_degree: Optional[int] = None
-
     unsloth_cross_entropy_loss: Optional[bool] = None
     unsloth_lora_mlp: Optional[bool] = None
     unsloth_lora_qkv: Optional[bool] = None

src/axolotl/utils/distributed.py

@@ -86,12 +86,6 @@ def get_world_size():
     return int(os.getenv("WORLD_SIZE", "1"))
 
 
-def get_rank():
-    if not is_distributed():
-        return 0
-    return dist.get_rank()
-
-
 @contextmanager
 def zero_only():
     """

src/axolotl/utils/models.py

@@ -1321,8 +1321,6 @@ def load_lora(model, cfg, inference=False, config_only=False):
     if loftq_bits:
         lora_config_kwargs["loftq_config"] = LoftQConfig(loftq_bits=loftq_bits)
         lora_config_kwargs["init_lora_weights"] = "loftq"
-    if cfg.peft_init_lora_weights:
-        lora_config_kwargs["init_lora_weights"] = cfg.peft_init_lora_weights
     if cfg.peft_use_dora:
         lora_config_kwargs["use_dora"] = cfg.peft_use_dora
         LOG.info("Initializing LoRA weights using dora. This might take longer.")

src/axolotl/utils/trainer.py

@@ -346,7 +346,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
                     load_from_cache_file=not cfg.is_preprocess,
                     desc="Add position_id column (PoSE)",
                 )
-    elif cfg.sample_packing or cfg.sp_ulysses_degree:
+    elif cfg.sample_packing:
         drop_long_kwargs = {}
         if filter_map_kwargs:
             drop_long_kwargs["desc"] = "Add position_id column (Sample Packing)"