[trainer] fix pt loss (#7748)

* fix pt loss

* robust

* fix

* test

src/llamafactory/model/model_utils/longlora.py

@@ -23,7 +23,6 @@ from typing import TYPE_CHECKING, Optional
 import torch
 import torch.nn as nn
 import transformers
-from transformers.models.llama.modeling_llama import Cache, apply_rotary_pos_emb, repeat_kv
 
 from ...extras import logging
 from ...extras.constants import SUPPORTED_CLASS_FOR_S2ATTN
@@ -32,7 +31,15 @@ from ...extras.packages import is_transformers_version_greater_than
 
 
 if not is_transformers_version_greater_than("4.48.0"):
-    from transformers.models.llama.modeling_llama import LlamaAttention, LlamaFlashAttention2, LlamaSdpaAttention
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
+    from transformers.models.llama.modeling_llama import (
+        Cache,
+        LlamaAttention,
+        LlamaFlashAttention2,
+        LlamaSdpaAttention,
+        apply_rotary_pos_emb,
+        repeat_kv,
+    )
 
 
 if TYPE_CHECKING:
@@ -206,9 +213,6 @@ def llama_flash_attention_2_forward(
         if attention_mask is not None:
             attention_mask = attention_mask[:, :groupsz].repeat(num_groups, 1)
 
-    if is_transformers_version_greater_than("4.43.0"):
-        from transformers.modeling_flash_attention_utils import _flash_attention_forward
-
         attn_output: torch.Tensor = _flash_attention_forward(
             query_states,
             key_states,
@@ -220,10 +224,6 @@ def llama_flash_attention_2_forward(
             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)
@@ -350,7 +350,7 @@ def llama_sdpa_attention_forward(
 
 
 def _apply_llama_patch() -> None:
-    check_version("transformers>=4.43.0,<4.48.0", mandatory=True)
+    check_version("transformers>=4.45.0,<4.48.0", mandatory=True)
     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

@@ -43,11 +43,6 @@ import torch
 import torch.nn.functional as F
 
 from ...extras import logging
-from ...extras.packages import is_transformers_version_greater_than
-
-
-if is_transformers_version_greater_than("4.43.0"):
-    import transformers.modeling_flash_attention_utils
 
 
 if TYPE_CHECKING:
@@ -116,5 +111,7 @@ def configure_packing(model_args: "ModelArguments", is_trainable: bool) -> None:
     if not is_trainable or not model_args.block_diag_attn:
         return
 
+    import transformers.modeling_flash_attention_utils
+
     transformers.modeling_flash_attention_utils._get_unpad_data = get_unpad_data
     logger.info_rank0("Using block diagonal attention for sequence packing without cross-attention.")