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(
@@ -528,61 +531,68 @@ def llama_model_forward(
     )
 
 
-def llama_decoder_layer_forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: Optional[bool] = False,
-    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]]]:
+class LlamaDecoderLayer(OriginalLlamaDecoderLayer):
     """
-    Args:
-        hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-        attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-            `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-            returned tensors for more detail.
-        use_cache (`bool`, *optional*):
-            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-            (see `past_key_values`).
-        past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-        cu_seqlens (`torch.Tensor`, *optional*) cumulative sequence len when packing
+    patched version of LlamaDecoderLayer to pass through the precalculated cu_seqlens
     """
 
-    residual = hidden_states
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        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]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cu_seqlens (`torch.Tensor`, *optional*) cumulative sequence len when packing
+        """
 
-    hidden_states = self.input_layernorm(hidden_states)
+        residual = hidden_states
 
-    # Self Attention
-    hidden_states, self_attn_weights, present_key_value = self.self_attn(
-        hidden_states=hidden_states,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_value=past_key_value,
-        output_attentions=output_attentions,
-        use_cache=use_cache,
-        cu_seqlens=cu_seqlens,
-        max_seqlen=max_seqlen,
-    )
-    hidden_states = residual + hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
 
-    # Fully Connected
-    residual = hidden_states
-    hidden_states = self.post_attention_layernorm(hidden_states)
-    hidden_states = self.mlp(hidden_states)
-    hidden_states = residual + hidden_states
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+        hidden_states = residual + hidden_states
 
-    outputs = (hidden_states,)
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
 
-    if output_attentions:
-        outputs += (self_attn_weights,)
+        outputs = (hidden_states,)
 
-    if use_cache:
-        outputs += (present_key_value,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
 
-    return outputs
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs