black formatting

ignore copied file
fix linting

.mypy.ini

@@ -5,6 +5,9 @@ exclude = venv
 [mypy-alpaca_lora_4bit.*]
 ignore_missing_imports = True
 
+[mypy-axolotl.monkeypatch.*]
+ignore_errors = True
+
 [mypy-flash_attn.*]
 ignore_missing_imports = True
 
@@ -31,3 +34,6 @@ ignore_missing_imports = True
 
 [mypy-addict]
 ignore_missing_imports = True
+
+[mypy-xformers.*]
+ignore_missing_imports = True

src/axolotl/monkeypatch/llama_attn_hijack_xformers.py

@@ -1,18 +1,18 @@
-'''
+"""
 Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-generation-webui/main/modules/llama_attn_hijack.py and made some adjustments
-'''
+"""
 
 import logging
 import math
 from typing import Optional, Tuple
 
 import torch
-import torch.nn as nn
 import transformers.models.llama.modeling_llama
+from torch import nn
 
 try:
     import xformers.ops
-except Exception:
+except ImportError:
     logging.error("xformers not found! Please install it before trying to use it.")
 
 
@@ -22,7 +22,9 @@ def hijack_llama_attention():
 
 
 def hijack_llama_sdp_attention():
-    transformers.models.llama.modeling_llama.LlamaAttention.forward = sdp_attention_forward
+    transformers.models.llama.modeling_llama.LlamaAttention.forward = (
+        sdp_attention_forward
+    )
     logging.info("Replaced attention with sdp_attention")
 
 
@@ -37,15 +39,32 @@ def xformers_forward(
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
     bsz, q_len, _ = hidden_states.size()
 
-    query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    query_states = (
-    key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        self.q_proj(hidden_states)
-    value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    key_states = (
+        self.k_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    value_states = (
+        self.v_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
 
     kv_seq_len = key_states.shape[-2]
     if past_key_value is not None:
         kv_seq_len += past_key_value[0].shape[-2]
     cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+    (
+        query_states,
+        key_states,
+    ) = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
     # [bsz, nh, t, hd]
 
     if past_key_value is not None:
@@ -65,13 +84,22 @@ def xformers_forward(
         # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
         if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
             # input and output should be of form (bsz, q_len, num_heads, head_dim)
-            attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=None)
+            attn_output = xformers.ops.memory_efficient_attention(
+                query_states, key_states, value_states, attn_bias=None
+            )
         else:
             # input and output should be of form (bsz, q_len, num_heads, head_dim)
-            attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=xformers.ops.LowerTriangularMask())
+            attn_output = xformers.ops.memory_efficient_attention(
+                query_states,
+                key_states,
+                value_states,
+                attn_bias=xformers.ops.LowerTriangularMask(),
+            )
         attn_weights = None
     else:
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+        attn_weights = torch.matmul(
+            query_states, key_states.transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
 
         if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
             raise ValueError(
@@ -85,10 +113,14 @@ def xformers_forward(
                     f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                 )
             attn_weights = attn_weights + attention_mask
-            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
+            attn_weights = torch.max(
+                attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
+            )
 
         # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
         attn_output = torch.matmul(attn_weights, value_states)
 
         if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
@@ -115,15 +147,32 @@ def sdp_attention_forward(
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
     bsz, q_len, _ = hidden_states.size()
 
-    query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    query_states = (
-    key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        self.q_proj(hidden_states)
-    value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    key_states = (
+        self.k_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
+    value_states = (
+        self.v_proj(hidden_states)
+        .view(bsz, q_len, self.num_heads, self.head_dim)
+        .transpose(1, 2)
+    )
 
     kv_seq_len = key_states.shape[-2]
     if past_key_value is not None:
         kv_seq_len += past_key_value[0].shape[-2]
     cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+    (
+        query_states,
+        key_states,
+    ) = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(
+        query_states, key_states, cos, sin, position_ids
+    )
     # [bsz, nh, t, hd]
 
     if past_key_value is not None:
@@ -135,10 +184,18 @@ def sdp_attention_forward(
 
     # We only apply sdp attention if we don't need to output the whole attention matrix
     if not output_attentions:
-        attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask, is_causal=False)
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            is_causal=False,
+        )
         attn_weights = None
     else:
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+        attn_weights = torch.matmul(
+            query_states, key_states.transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
 
         if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
             raise ValueError(
@@ -152,10 +209,14 @@ def sdp_attention_forward(
                     f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                 )
             attn_weights = attn_weights + attention_mask
-            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
+            attn_weights = torch.max(
+                attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
+            )
 
         # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
         attn_output = torch.matmul(attn_weights, value_states)
 
         if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):