fix #5048

src/llamafactory/model/model_utils/attention.py

@@ -36,7 +36,7 @@ def configure_attn_implementation(
         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")
-                require_version("flash_attn>=2.6.0", "To fix: pip install flash_attn>=2.6.0")
+                require_version("flash_attn>=2.6.3", "To fix: pip install flash_attn>=2.6.3")
                 logger.warning("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
                 model_args.flash_attn = "fa2"
             else:

src/llamafactory/model/model_utils/longlora.py

@@ -35,6 +35,7 @@ from transformers.utils.versions import require_version
 
 from ...extras.constants import SUPPORTED_CLASS_FOR_S2ATTN
 from ...extras.logging import get_logger
+from ...extras.packages import is_transformers_version_greater_than_4_43
 
 
 if TYPE_CHECKING:
@@ -50,14 +51,15 @@ transformers_logger = logging.get_logger(__name__)
 # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py
 def llama_attention_forward(
     self: "LlamaAttention",
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
+    hidden_states: "torch.Tensor",
+    attention_mask: Optional["torch.Tensor"] = None,
+    position_ids: Optional["torch.LongTensor"] = None,
     past_key_value: Optional["Cache"] = None,
     output_attentions: bool = False,
-    cache_position: Optional[torch.LongTensor] = None,
+    cache_position: Optional["torch.LongTensor"] = None,
+    position_embeddings: Optional[Tuple["torch.Tensor", "torch.Tensor"]] = None,
     **kwargs,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+) -> Tuple["torch.Tensor", Optional["torch.Tensor"], Optional[Tuple["torch.Tensor"]]]:
     bsz, q_len, _ = hidden_states.size()
 
     query_states: "torch.Tensor" = self.q_proj(hidden_states)
@@ -68,7 +70,11 @@ def llama_attention_forward(
     key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
     value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
 
-    cos, sin = self.rotary_emb(value_states, position_ids)
+    if position_embeddings is None:
+        cos, sin = self.rotary_emb(value_states, position_ids)
+    else:
+        cos, sin = position_embeddings
+
     query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
     if past_key_value is not None:
@@ -130,14 +136,15 @@ def llama_attention_forward(
 # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py
 def llama_flash_attention_2_forward(
     self: "LlamaFlashAttention2",
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
+    hidden_states: "torch.Tensor",
+    attention_mask: Optional["torch.Tensor"] = None,
+    position_ids: Optional["torch.LongTensor"] = None,
     past_key_value: Optional["Cache"] = None,
     output_attentions: bool = False,
-    cache_position: Optional[torch.LongTensor] = None,
+    cache_position: Optional["torch.LongTensor"] = None,
+    position_embeddings: Optional[Tuple["torch.Tensor", "torch.Tensor"]] = None,
     **kwargs,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+) -> Tuple["torch.Tensor", Optional["torch.Tensor"], Optional[Tuple["torch.Tensor"]]]:
     # LlamaFlashAttention2 attention does not support output_attentions
     output_attentions = False
 
@@ -151,7 +158,11 @@ def llama_flash_attention_2_forward(
     key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
     value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
 
-    cos, sin = self.rotary_emb(value_states, position_ids)
+    if position_embeddings is None:
+        cos, sin = self.rotary_emb(value_states, position_ids)
+    else:
+        cos, sin = position_embeddings
+
     query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
     if past_key_value is not None:
@@ -198,9 +209,24 @@ def llama_flash_attention_2_forward(
         if attention_mask is not None:
             attention_mask = attention_mask[:, :groupsz].repeat(num_groups, 1)
 
-    attn_output: "torch.Tensor" = self._flash_attention_forward(
-        query_states, key_states, value_states, attention_mask, query_states.size(1), dropout=dropout_rate
-    )
+    if is_transformers_version_greater_than_4_43():
+        from transformers.modeling_flash_attention_utils import _flash_attention_forward
+
+        attn_output: "torch.Tensor" = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            query_states.size(1),
+            dropout=dropout_rate,
+            sliding_window=getattr(self, "sliding_window", None),
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=self.is_causal,
+        )
+    else:
+        attn_output: "torch.Tensor" = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, query_states.size(1), dropout=dropout_rate
+        )
 
     if getattr(self.config, "group_size_ratio", None) and self.training:  # shift back
         attn_output.reshape(bsz, q_len, self.num_heads, self.head_dim)
@@ -225,14 +251,15 @@ def llama_flash_attention_2_forward(
 # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/llama/modeling_llama.py
 def llama_sdpa_attention_forward(
     self: "LlamaSdpaAttention",
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
+    hidden_states: "torch.Tensor",
+    attention_mask: Optional["torch.Tensor"] = None,
+    position_ids: Optional["torch.LongTensor"] = None,
     past_key_value: Optional["Cache"] = None,
     output_attentions: bool = False,
-    cache_position: Optional[torch.LongTensor] = None,
+    cache_position: Optional["torch.LongTensor"] = None,
+    position_embeddings: Optional[Tuple["torch.Tensor", "torch.Tensor"]] = None,
     **kwargs,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+) -> Tuple["torch.Tensor", Optional["torch.Tensor"], Optional[Tuple["torch.Tensor"]]]:
     if output_attentions:
         transformers_logger.warning_once(
             "SDPA does not support `output_attentions=True`. Falling back to the vanilla attention"
@@ -258,7 +285,11 @@ def llama_sdpa_attention_forward(
     key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
     value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
 
-    cos, sin = self.rotary_emb(value_states, position_ids)
+    if position_embeddings is None:
+        cos, sin = self.rotary_emb(value_states, position_ids)
+    else:
+        cos, sin = position_embeddings
+
     query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
     if past_key_value is not None:
@@ -322,7 +353,7 @@ def llama_sdpa_attention_forward(
 
 
 def _apply_llama_patch() -> None:
-    require_version("transformers>=4.41.2,<=4.42.4", "To fix: pip install transformers>=4.41.2,<=4.42.4")
+    require_version("transformers>=4.41.2,<=4.43.4", "To fix: pip install transformers>=4.41.2,<=4.43.4")
     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

@@ -41,11 +41,11 @@ 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
+from ...extras.packages import is_transformers_version_greater_than_4_43
 
 
 if TYPE_CHECKING:
@@ -114,7 +114,15 @@ def get_unpad_data(attention_mask: "torch.Tensor") -> Tuple["torch.Tensor", "tor
 
 
 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")
+    require_version("transformers>=4.41.2,<=4.43.4", "To fix: pip install transformers>=4.41.2,<=4.43.4")
+    if is_transformers_version_greater_than_4_43():
+        import transformers.modeling_flash_attention_utils
+
+        transformers.modeling_flash_attention_utils._get_unpad_data = get_unpad_data
+        return
+
+    import transformers.models
+
     if model_type == "cohere":
         transformers.models.cohere.modeling_cohere._get_unpad_data = get_unpad_data
     elif model_type == "falcon":