fix gemma2 attention

src/llamafactory/__init__.py

@@ -12,7 +12,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Level: api, webui > chat, eval, train > data, model > hparams > extras
+r"""
+Efficient fine-tuning of large language models.
+
+Level:
+  api, webui > chat, eval, train > data, model > hparams > extras
+
+Dependency graph:
+  main:
+    transformers>=4.41.2
+    datasets>=2.16.0
+    accelerate>=0.30.1
+    peft>=0.11.1
+    trl>=0.8.6
+  attention:
+    transformers>=4.42.4 (gemma+fa2)
+  longlora:
+    transformers>=4.41.2,<=4.42.4
+  packing:
+    transformers>=4.41.2,<=4.42.4
+  patcher:
+    transformers==4.41.2 (chatglm)
+"""
+
 
 from .cli import VERSION
 

src/llamafactory/data/collator.py

@@ -28,11 +28,10 @@ def prepare_4d_attention_mask(attention_mask_with_indices: "torch.Tensor", dtype
     while handles packed sequences and transforms the mask to lower triangular form to prevent future peeking.
 
     e.g.
-    ```
+    ```python
+    # input
     [[1, 1, 2, 2, 2, 0]]
-    ```
-    ->
-    ```
+    # output
     [
         [
             [

src/llamafactory/model/model_utils/attention.py

@@ -15,6 +15,7 @@
 from typing import TYPE_CHECKING
 
 from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available
+from transformers.utils.versions import require_version
 
 from ...extras.logging import get_logger
 
@@ -31,15 +32,17 @@ logger = get_logger(__name__)
 def configure_attn_implementation(
     config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool
 ) -> None:
-    if getattr(config, "model_type", None) == "gemma2" and is_trainable:  # gemma2 adopts soft-cap attention
-        if model_args.flash_attn == "auto":
-            logger.warning("Gemma-2 models should use eager attention in training, change `flash_attn` to disabled.")
-            model_args.flash_attn = "disabled"
-        elif model_args.flash_attn != "disabled":
-            logger.warning(
-                "Gemma-2 models should use eager attention in training, but you set `flash_attn: {}`. "
-                "Will proceed at your own risk.".format(model_args.flash_attn)
-            )
+    if getattr(config, "model_type", None) == "gemma2" and is_trainable:
+        if model_args.flash_attn == "auto" or model_args.flash_attn == "fa2":
+            if is_flash_attn_2_available():
+                require_version("transformers>=4.42.4", "To fix: pip install transformers>=4.42.4")
+                logger.warning("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
+                model_args.flash_attn = "fa2"
+            else:
+                logger.warning("Gemma-2 should use eager attention, change `flash_attn` to disabled.")
+                model_args.flash_attn = "disabled"
+        elif model_args.flash_attn == "sdpa":
+            raise ValueError("Gemma-2 should use soft-capping attention, while the SDPA attention is not compatible.")
 
     if model_args.flash_attn == "auto":
         return

src/llamafactory/model/model_utils/longlora.py

@@ -326,7 +326,7 @@ def llama_sdpa_attention_forward(
 
 
 def _apply_llama_patch() -> None:
-    require_version("transformers>=4.41.2,<=4.42.3", "To fix: pip install transformers>=4.41.2,<=4.42.3")
+    require_version("transformers>=4.41.2,<=4.42.4", "To fix: pip install transformers>=4.41.2,<=4.42.4")
     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

@@ -42,6 +42,7 @@ from typing import TYPE_CHECKING, Tuple
 import torch
 import torch.nn.functional as F
 import transformers.models
+from transformers.utils.versions import require_version
 
 from ...extras.constants import SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN
 from ...extras.logging import get_logger
@@ -61,14 +62,13 @@ def get_seqlens_in_batch(attention_mask: "torch.Tensor") -> "torch.Tensor":
     Gets the sequnce lengths in the current batch.
 
     e.g.
-    ```
+    ```python
+    # input
     [
         [1, 1, 2, 2, 2, 0],
         [1, 2, 2, 3, 3, 3],
     ]
-    ```
-    ->
-    ```
+    # output
     [2, 3, 1, 2, 3]
     ```
     """
@@ -94,14 +94,13 @@ def get_unpad_data(attention_mask: "torch.Tensor") -> Tuple["torch.Tensor", "tor
         max_seqlen_in_batch: the largest seqlen in the current batch.
 
     e.g.
-    ```
+    ```python
+    # input
     [
         [1, 1, 2, 2, 2, 0],
         [1, 2, 2, 3, 3, 3],
     ]
-    ```
-    ->
-    ```
+    # output
     [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11]
     [0, 2, 5, 6, 8, 11]
     3
@@ -114,7 +113,8 @@ def get_unpad_data(attention_mask: "torch.Tensor") -> Tuple["torch.Tensor", "tor
     return indices, cu_seqlens, max_seqlen_in_batch
 
 
-def patch_for_block_diag_attn(model_type: str) -> None:
+def _patch_for_block_diag_attn(model_type: str) -> None:
+    require_version("transformers>=4.41.2,<=4.42.4", "To fix: pip install transformers>=4.41.2,<=4.42.4")
     if model_type == "cohere":
         transformers.models.cohere.modeling_cohere._get_unpad_data = get_unpad_data
     elif model_type == "falcon":
@@ -143,7 +143,7 @@ def configure_packing(config: "PretrainedConfig", model_args: "ModelArguments",
 
     model_type = getattr(config, "model_type", None)
     if model_type in SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN:
-        patch_for_block_diag_attn(model_type)
+        _patch_for_block_diag_attn(model_type)
         logger.info("Using block diagonal attention for sequence packing without cross-attention.")
     else:
         raise ValueError("Current model does not support block diagonal attention.")

src/llamafactory/model/model_utils/quantization.py

@@ -126,7 +126,6 @@ def configure_quantization(
             require_version("autoawq", "To fix: pip install autoawq")
 
         if quant_method == QuantizationMethod.AQLM:
-            require_version("transformers>=4.39.0", "To fix: pip install transformers>=4.39.0")
             require_version("aqlm>=1.1.0", "To fix: pip install aqlm[gpu]>=1.1.0")
             quantization_config["bits"] = 2
 

src/llamafactory/model/patcher.py

@@ -21,6 +21,7 @@ from peft import PeftModel
 from transformers import PreTrainedModel, PreTrainedTokenizerBase, is_torch_npu_available
 from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.modeling_utils import is_fsdp_enabled
+from transformers.utils.versions import require_version
 
 from ..extras.logging import get_logger
 from ..extras.misc import infer_optim_dtype
@@ -88,6 +89,9 @@ def patch_config(
     if getattr(config, "model_type", None) == "qwen2" and is_trainable and model_args.flash_attn == "fa2":
         setattr(config, "use_cache", False)  # qwen2 does not support use_cache when using flash attn
 
+    if getattr(config, "model_type", None) == "chatglm":
+        require_version("transformers==4.41.2", "To fix: pip install transformers==4.41.2")
+
     # deepspeed zero3 is not compatible with low_cpu_mem_usage
     init_kwargs["low_cpu_mem_usage"] = model_args.low_cpu_mem_usage and (not is_deepspeed_zero3_enabled())