fix the patch to work properly and work with FSDP

src/axolotl/monkeypatch/llama_attn_hijack_flash.py

@@ -11,6 +11,9 @@ import transformers
 from einops import rearrange
 from flash_attn.bert_padding import pad_input, unpad_input
 from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.models.llama.modeling_llama import (
+    LlamaDecoderLayer as OriginalLlamaDecoderLayer,
+)
 from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
 
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
@@ -36,12 +39,10 @@ def replace_llama_attn_with_flash_attn(packed: Optional[bool] = False):
     )
     transformers.models.llama.modeling_llama.LlamaAttention.forward = flashattn_forward
     if packed:
+        transformers.models.llama.modeling_llama.LlamaDecoderLayer = LlamaDecoderLayer
         transformers.models.llama.modeling_llama.LlamaModel.forward = (
             llama_model_forward
         )
-        transformers.models.llama.modeling_llama.LlamaDecoderLayer = (
-            llama_decoder_layer_forward
-        )
 
 
 # Disable the transformation of the attention mask in LlamaModel as the flash attention
@@ -159,7 +160,7 @@ def flashattn_forward(
         # only on first autoregressive step q,k,v have same seqlen
         is_causal = past_key_value is not None
 
-    if cu_seqlens and max_seqlen:
+    if cu_seqlens is not None and max_seqlen is not None:
         # special handling using sample packing
         qkv = torch.stack(
             [query_states, key_states, value_states], dim=2
@@ -472,9 +473,9 @@ def llama_model_forward(
         if self.gradient_checkpointing and self.training:
 
             def create_custom_forward(module):
-                def custom_forward(*inputs, **kwargs):
+                def custom_forward(*inputs):
                     # None for past_key_value
-                    return module(*inputs, output_attentions, None, **kwargs)
+                    return module(*inputs)
 
                 return custom_forward
 
@@ -484,8 +485,10 @@ def llama_model_forward(
                 attention_mask,
                 position_ids,
                 None,
-                cu_seqlens=cu_seqlens,
-                max_seqlen=max_seqlen,
+                output_attentions,
+                None,
+                cu_seqlens,
+                max_seqlen,
             )
         else:
             layer_outputs = decoder_layer(
@@ -528,7 +531,12 @@ def llama_model_forward(
     )
 
 
-def llama_decoder_layer_forward(
+class LlamaDecoderLayer(OriginalLlamaDecoderLayer):
+    """
+    patched version of LlamaDecoderLayer to pass through the precalculated cu_seqlens
+    """
+
+    def forward(
         self,
         hidden_states: torch.Tensor,
         attention_mask: Optional[torch.Tensor] = None,
@@ -538,7 +546,9 @@ def llama_decoder_layer_forward(
         use_cache: Optional[bool] = False,
         cu_seqlens: Optional[torch.Tensor] = None,
         max_seqlen: Optional[torch.Tensor] = None,
-) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
         """
         Args:
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`