[version] support transformers 4.48 & Byebye python 3.8

.github/workflows/tests.yml

@@ -22,10 +22,10 @@ jobs:
       fail-fast: false
       matrix:
         python-version:
-          - "3.8"  # TODO: remove py38 in next transformers release
           - "3.9"
           - "3.10"
           - "3.11"
+          - "3.12"
         os:
           - "ubuntu-latest"
           - "windows-latest"

README.md

@@ -377,11 +377,11 @@ huggingface-cli login
 
 | Mandatory    | Minimum | Recommend |
 | ------------ | ------- | --------- |
-| python       | 3.8     | 3.11      |
+| python       | 3.9     | 3.10      |
 | torch        | 1.13.1  | 2.4.0     |
-| transformers | 4.41.2  | 4.43.4    |
-| datasets     | 2.16.0  | 2.20.0    |
-| accelerate   | 0.30.1  | 0.32.0    |
+| transformers | 4.41.2  | 4.45.2    |
+| datasets     | 2.16.0  | 3.2.0     |
+| accelerate   | 0.34.0  | 1.2.1     |
 | peft         | 0.11.1  | 0.12.0    |
 | trl          | 0.8.6   | 0.9.6     |
 
@@ -390,8 +390,8 @@ huggingface-cli login
 | CUDA         | 11.6    | 12.2      |
 | deepspeed    | 0.10.0  | 0.14.0    |
 | bitsandbytes | 0.39.0  | 0.43.1    |
-| vllm         | 0.4.3   | 0.5.0     |
-| flash-attn   | 2.3.0   | 2.6.3     |
+| vllm         | 0.4.3   | 0.6.6     |
+| flash-attn   | 2.3.0   | 2.7.2     |
 
 ### Hardware Requirement
 

README_zh.md

@@ -379,11 +379,11 @@ huggingface-cli login
 
 | 必需项       | 至少     | 推荐      |
 | ------------ | ------- | --------- |
-| python       | 3.8     | 3.11      |
+| python       | 3.9     | 3.10      |
 | torch        | 1.13.1  | 2.4.0     |
-| transformers | 4.41.2  | 4.43.4    |
-| datasets     | 2.16.0  | 2.20.0    |
-| accelerate   | 0.30.1  | 0.32.0    |
+| transformers | 4.41.2  | 4.45.2    |
+| datasets     | 2.16.0  | 3.2.0     |
+| accelerate   | 0.34.0  | 1.2.1     |
 | peft         | 0.11.1  | 0.12.0    |
 | trl          | 0.8.6   | 0.9.6     |
 
@@ -392,8 +392,8 @@ huggingface-cli login
 | CUDA         | 11.6    | 12.2      |
 | deepspeed    | 0.10.0  | 0.14.0    |
 | bitsandbytes | 0.39.0  | 0.43.1    |
-| vllm         | 0.4.3   | 0.5.0     |
-| flash-attn   | 2.3.0   | 2.6.3     |
+| vllm         | 0.4.3   | 0.6.6     |
+| flash-attn   | 2.3.0   | 2.7.2     |
 
 ### 硬件依赖
 

requirements.txt

@@ -1,9 +1,10 @@
-transformers>=4.41.2,<=4.46.1
-datasets>=2.16.0,<=3.1.0
-accelerate>=0.34.0,<=1.0.1
+transformers>=4.41.2,<=4.45.2;python_version<'3.10'
+transformers>=4.41.2,<=4.48.1,!=4.46.*,!=4.47.*,!=4.48.0;python_version>='3.10'
+datasets>=2.16.0,<=3.2.0
+accelerate>=0.34.0,<=1.2.1
 peft>=0.11.1,<=0.12.0
 trl>=0.8.6,<=0.9.6
-tokenizers>=0.19.0,<0.20.4
+tokenizers>=0.19.0,<=0.21.0
 gradio>=4.38.0,<=5.12.0
 pandas>=2.0.0
 scipy

setup.py

@@ -46,7 +46,7 @@ def get_console_scripts() -> List[str]:
     "torch": ["torch>=1.13.1"],
     "torch-npu": ["torch==2.1.0", "torch-npu==2.1.0.post3", "decorator"],
     "metrics": ["nltk", "jieba", "rouge-chinese"],
-    "deepspeed": ["deepspeed>=0.10.0,<=0.14.4"],
+    "deepspeed": ["deepspeed>=0.10.0,<=0.16.2"],
     "liger-kernel": ["liger-kernel"],
     "bitsandbytes": ["bitsandbytes>=0.39.0"],
     "hqq": ["hqq"],
@@ -92,7 +92,7 @@ def main():
         url="https://github.com/hiyouga/LLaMA-Factory",
         package_dir={"": "src"},
         packages=find_packages("src"),
-        python_requires=">=3.8.0",
+        python_requires=">=3.9.0",
         install_requires=get_requires(),
         extras_require=extra_require,
         entry_points={"console_scripts": get_console_scripts()},
@@ -104,10 +104,10 @@ def main():
             "License :: OSI Approved :: Apache Software License",
             "Operating System :: OS Independent",
             "Programming Language :: Python :: 3",
-            "Programming Language :: Python :: 3.8",
             "Programming Language :: Python :: 3.9",
             "Programming Language :: Python :: 3.10",
             "Programming Language :: Python :: 3.11",
+            "Programming Language :: Python :: 3.12",
             "Topic :: Scientific/Engineering :: Artificial Intelligence",
         ],
     )

src/llamafactory/__init__.py

@@ -20,17 +20,17 @@
 
 Dependency graph:
   main:
-    transformers>=4.41.2,<=4.46.1
-    datasets>=2.16.0,<=3.1.0
-    accelerate>=0.34.0,<=1.0.1
+    transformers>=4.41.2,<=4.48.1,!=4.46.*,!=4.47.*,!=4.48.0
+    datasets>=2.16.0,<=3.2.0
+    accelerate>=0.34.0,<=1.2.1
     peft>=0.11.1,<=0.12.0
     trl>=0.8.6,<=0.9.6
   attention:
     transformers>=4.42.4 (gemma+fa2)
   longlora:
-    transformers>=4.41.2,<=4.46.1
+    transformers>=4.41.2,<4.48.0
   packing:
-    transformers>=4.43.0,<=4.46.1
+    transformers>=4.43.0,<=4.48.1
 
 Disable version checking: DISABLE_VERSION_CHECK=1
 Enable VRAM recording: RECORD_VRAM=1

src/llamafactory/extras/misc.py

@@ -34,6 +34,7 @@
 from transformers.utils.versions import require_version
 
 from . import logging
+from .packages import is_transformers_version_greater_than
 
 
 _is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
@@ -93,11 +94,13 @@ def check_dependencies() -> None:
     r"""
     Checks the version of the required packages.
     """
-    check_version("transformers>=4.41.2,<=4.46.1")
-    check_version("datasets>=2.16.0,<=3.1.0")
-    check_version("accelerate>=0.34.0,<=1.0.1")
+    check_version("transformers>=4.41.2,<=4.48.1,!=4.46.0,!=4.46.1,!=4.46.2,!=4.46.3,!=4.47.0,!=4.47.1,!=4.48.0")
+    check_version("datasets>=2.16.0,<=3.2.0")
+    check_version("accelerate>=0.34.0,<=1.2.1")
     check_version("peft>=0.11.1,<=0.12.0")
     check_version("trl>=0.8.6,<=0.9.6")
+    if is_transformers_version_greater_than("4.46.0") and not is_transformers_version_greater_than("4.48.1"):
+        logger.warning_rank0_once("There are known bugs in transformers v4.46.0-v4.48.0, please use other versions.")
 
 
 def calculate_tps(dataset: Sequence[Dict[str, Any]], metrics: Dict[str, float], stage: Literal["sft", "rm"]) -> float:

src/llamafactory/extras/packages.py

@@ -87,11 +87,6 @@ def is_transformers_version_greater_than(content: str):
     return _get_package_version("transformers") >= version.parse(content)
 
 
-@lru_cache
-def is_transformers_version_equal_to_4_46():
-    return version.parse("4.46.0") <= _get_package_version("transformers") <= version.parse("4.46.1")
-
-
 def is_uvicorn_available():
     return _is_package_available("uvicorn")
 

src/llamafactory/model/model_utils/longlora.py

@@ -350,7 +350,7 @@ def shift(state: "torch.Tensor") -> "torch.Tensor":
 
 
 def _apply_llama_patch() -> None:
-    check_version("transformers>=4.41.2,<=4.46.1")
+    check_version("transformers>=4.41.2,<4.48.0")
     LlamaAttention.forward = llama_attention_forward
     LlamaFlashAttention2.forward = llama_flash_attention_2_forward
     LlamaSdpaAttention.forward = llama_sdpa_attention_forward

src/llamafactory/model/model_utils/packing.py

@@ -118,6 +118,6 @@ def configure_packing(model_args: "ModelArguments", is_trainable: bool) -> None:
     if not is_trainable or not model_args.block_diag_attn:
         return
 
-    check_version("transformers>=4.43.0,<=4.46.1")
+    check_version("transformers>=4.43.0,<=4.48.1")
     transformers.modeling_flash_attention_utils._get_unpad_data = get_unpad_data
     logger.info_rank0("Using block diagonal attention for sequence packing without cross-attention.")

src/llamafactory/train/dpo/trainer.py

@@ -29,7 +29,7 @@
 from typing_extensions import override
 
 from ...extras.constants import IGNORE_INDEX
-from ...extras.packages import is_transformers_version_equal_to_4_46, is_transformers_version_greater_than
+from ...extras.packages import is_transformers_version_greater_than
 from ..callbacks import SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps, nested_detach
 
@@ -282,19 +282,12 @@ def compute_loss(
         self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
     ) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
         r"""
-        Fixes the loss value. See https://github.com/huggingface/transformers/pull/35438 for details.
+        Subclass and override to accept extra kwargs.
         """
-        loss = super().compute_loss(model, inputs, return_outputs)
-        if is_transformers_version_equal_to_4_46() and kwargs.get("num_items_in_batch"):
-            if return_outputs:
-                loss = (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
-            else:
-                loss = loss / self.args.gradient_accumulation_steps
-
-        return loss
+        return super().compute_loss(model, inputs, return_outputs)
 
     @override
-    def log(self, logs: Dict[str, float]) -> None:
+    def log(self, logs: Dict[str, float], *args, **kwargs) -> None:
         r"""
         Log `logs` on the various objects watching training, including stored metrics.
         """
@@ -318,4 +311,4 @@ def log(self, logs: Dict[str, float]) -> None:
             if not key.startswith("dummy_"):
                 logs[key] = metric
 
-        return Trainer.log(self, logs)
+        return Trainer.log(self, logs, *args, **kwargs)

src/llamafactory/train/kto/trainer.py

@@ -28,7 +28,7 @@
 from typing_extensions import override
 
 from ...extras.constants import IGNORE_INDEX
-from ...extras.packages import is_transformers_version_equal_to_4_46, is_transformers_version_greater_than
+from ...extras.packages import is_transformers_version_greater_than
 from ..callbacks import SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler, get_batch_logps, nested_detach
 
@@ -256,19 +256,12 @@ def compute_loss(
         self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
     ) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
         r"""
-        Fixes the loss value. See https://github.com/huggingface/transformers/pull/35438 for details.
+        Subclass and override to accept extra kwargs.
         """
-        loss = super().compute_loss(model, inputs, return_outputs)
-        if is_transformers_version_equal_to_4_46() and kwargs.get("num_items_in_batch"):
-            if return_outputs:
-                loss = (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
-            else:
-                loss = loss / self.args.gradient_accumulation_steps
-
-        return loss
+        return super().compute_loss(model, inputs, return_outputs)
 
     @override
-    def log(self, logs: Dict[str, float]) -> None:
+    def log(self, logs: Dict[str, float], *args, **kwargs) -> None:
         r"""
         Log `logs` on the various objects watching training, including stored metrics.
         """
@@ -304,4 +297,4 @@ def log(self, logs: Dict[str, float]) -> None:
             if not key.startswith("dummy_"):
                 logs[key] = metric
 
-        return Trainer.log(self, logs)
+        return Trainer.log(self, logs, *args, **kwargs)

src/llamafactory/train/pt/trainer.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from types import MethodType
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Optional
 
 import torch
 from transformers import Trainer
@@ -25,7 +25,7 @@
 
 
 if TYPE_CHECKING:
-    from transformers import PreTrainedModel, ProcessorMixin
+    from transformers import ProcessorMixin
 
     from ...hparams import FinetuningArguments
 
@@ -72,21 +72,3 @@ def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
             return torch.utils.data.SequentialSampler(self.train_dataset)
 
         return super()._get_train_sampler()
-
-    @override
-    def compute_loss(
-        self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
-    ) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
-        r"""
-        Fixes the loss value. See https://github.com/huggingface/transformers/pull/35438 for details.
-
-        It should be removed after https://github.com/huggingface/transformers/pull/35651 is merged.
-        """
-        loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
-        if kwargs.get("num_items_in_batch") and not getattr(self, "model_accepts_loss_kwargs", False):
-            if return_outputs:
-                loss = (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
-            else:
-                loss = loss / self.args.gradient_accumulation_steps
-
-        return loss

src/llamafactory/train/rm/trainer.py

@@ -25,7 +25,7 @@
 from typing_extensions import override
 
 from ...extras import logging
-from ...extras.packages import is_transformers_version_equal_to_4_46, is_transformers_version_greater_than
+from ...extras.packages import is_transformers_version_greater_than
 from ..callbacks import FixValueHeadModelCallback, SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
 
@@ -107,10 +107,6 @@ def compute_loss(
         chosen_scores, rejected_scores = chosen_scores.squeeze(), rejected_scores.squeeze()
 
         loss = -torch.nn.functional.logsigmoid(chosen_scores.float() - rejected_scores.float()).mean()
-
-        if is_transformers_version_equal_to_4_46() and kwargs.get("num_items_in_batch"):
-            loss /= self.args.gradient_accumulation_steps  # fixes the loss value for transformers 4.46.0-4.46.1
-
         if return_outputs:
             return loss, (loss, chosen_scores, rejected_scores)
         else:

src/llamafactory/train/sft/trainer.py

@@ -34,7 +34,7 @@
 
 if TYPE_CHECKING:
     from torch.utils.data import Dataset
-    from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
+    from transformers import PreTrainedTokenizer, ProcessorMixin
     from transformers.trainer import PredictionOutput
 
     from ...hparams import FinetuningArguments
@@ -88,24 +88,6 @@ def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]:
 
         return super()._get_train_sampler()
 
-    @override
-    def compute_loss(
-        self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
-    ) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
-        r"""
-        Fixes the loss value. See https://github.com/huggingface/transformers/pull/35438 for details.
-
-        It should be removed after https://github.com/huggingface/transformers/pull/35651 is merged.
-        """
-        loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
-        if kwargs.get("num_items_in_batch") and not getattr(self, "model_accepts_loss_kwargs", False):
-            if return_outputs:
-                loss = (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
-            else:
-                loss = loss / self.args.gradient_accumulation_steps
-
-        return loss
-
     @override
     def prediction_step(
         self,

src/llamafactory/webui/runner.py

@@ -23,7 +23,7 @@
 
 from ..extras.constants import LLAMABOARD_CONFIG, PEFT_METHODS, TRAINING_STAGES
 from ..extras.misc import is_gpu_or_npu_available, torch_gc, use_ray
-from ..extras.packages import is_gradio_available, is_transformers_version_equal_to_4_46
+from ..extras.packages import is_gradio_available
 from .common import (
     DEFAULT_CACHE_DIR,
     DEFAULT_CONFIG_DIR,
@@ -180,7 +180,7 @@ def _parse_train_args(self, data: Dict["Component", Any]) -> Dict[str, Any]:
             plot_loss=True,
             trust_remote_code=True,
             ddp_timeout=180000000,
-            include_num_input_tokens_seen=False if is_transformers_version_equal_to_4_46() else True,  # FIXME
+            include_num_input_tokens_seen=True,
         )
         args.update(json.loads(get("train.extra_args")))
 

tests/model/model_utils/test_attention.py

@@ -14,8 +14,10 @@
 
 import os
 
+import pytest
 from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available
 
+from llamafactory.extras.packages import is_transformers_version_greater_than
 from llamafactory.train.test_utils import load_infer_model
 
 
@@ -27,6 +29,7 @@
 }
 
 
+@pytest.mark.xfail(is_transformers_version_greater_than("4.48"), reason="Attention refactor.")
 def test_attention():
     attention_available = ["disabled"]
     if is_torch_sdpa_available():