Mistral: Sliding Window Attention with Flash Attention and Sample Packing (#732)

* Implement Mistral FA + SWA + Sample Packing

* Handle unbroadcastable tensor

* chore: lint

* Simplify _prepare_decoder_attention_mask

* Uncomment window size

* Upgrade flash-attn to minimum of 2.3.0 to support SWA

* Add original condition to avoid error during inference

* chore: lint

* use torchscript to prevent oom

* chore: pylint

---------

Co-authored-by: Wing Lian <wing.lian@gmail.com>

setup.py

@@ -46,7 +46,7 @@ setup(
     dependency_links=dependency_links,
     extras_require={
         "flash-attn": [
-            "flash-attn>=2.2.1",
+            "flash-attn>=2.3.0",
         ],
         "deepspeed": [
             "deepspeed",

src/axolotl/monkeypatch/mistral_attn_hijack_flash.py

@@ -14,6 +14,9 @@ from flash_attn.flash_attn_interface import (  # pylint: disable=ungrouped-impor
     flash_attn_varlen_qkvpacked_func,
 )
 from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.models.mistral.modeling_mistral import (
+    MistralAttention as OriginalMistralAttention,
+)
 from transformers.models.mistral.modeling_mistral import (
     MistralDecoderLayer as OriginalMistralDecoderLayer,
 )
@@ -42,6 +45,44 @@ def replace_mistral_attn_with_flash_attn(
         )
 
 
+@torch.jit.script
+def _make_sliding_window_causal_mask(
+    bsz: int,
+    tgt_len: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    past_key_values_length: int = 0,
+    sliding_window: int = 4096,
+):
+    """
+    Make causal mask used for sliding window attention
+    """
+    tensor = torch.full(
+        (tgt_len, tgt_len),
+        fill_value=1,
+        device=device,
+    )
+    mask = torch.tril(tensor, diagonal=0)
+    # make the mask banded to account for sliding window
+    # NOTE: HF implementation is wrong as of 14-10-2023 for torch.triu, needs +1
+    mask = torch.triu(mask, diagonal=-sliding_window + 1)
+    mask = torch.log(mask).to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat(
+            [
+                torch.zeros(
+                    tgt_len, past_key_values_length, dtype=dtype, device=device
+                ),
+                mask,
+            ],
+            dim=-1,
+        )
+    return mask[None, None, :, :].expand(
+        bsz, 1, tgt_len, tgt_len + past_key_values_length
+    )
+
+
 # Disable the transformation of the attention mask in LlamaModel as the flash attention
 # requires the attention mask to be the same as the key_padding_mask
 def _prepare_decoder_attention_mask(
@@ -53,11 +94,29 @@ def _prepare_decoder_attention_mask(
     sliding_window,
 ):  # pylint: disable=unused-argument
     # [bsz, seq_len]
+    if attention_mask is None:
+        return attention_mask
+
+    # NOTE: attention mask and sliding masks are only broadcastable in certain scenarios.
+    # Without attention_mask.shape[0] == 1, error will trigger after eval loss but only when wandb is enabled.
+    if input_shape[-1] > 1 and attention_mask.shape[0] == 1:
+        sliding_window_mask = _make_sliding_window_causal_mask(
+            bsz=input_shape[0],
+            tgt_len=input_shape[1],
+            dtype=inputs_embeds.dtype,
+            device=inputs_embeds.device,
+            past_key_values_length=past_key_values_length,
+            sliding_window=sliding_window,
+        )
+        attention_mask = attention_mask + sliding_window_mask
+    else:
+        LOG.info("skipping sliding window mask, not broadcastable with attention mask")
+
     return attention_mask
 
 
 def flashattn_forward(
-    self,
+    self: OriginalMistralAttention,
     hidden_states: torch.Tensor,
     attention_mask: Optional[torch.Tensor] = None,
     position_ids: Optional[torch.LongTensor] = None,
@@ -91,10 +150,41 @@ def flashattn_forward(
         query_states, key_states, cos, sin, position_ids
     )
 
+    use_sliding_windows = (
+        hasattr(self.config, "sliding_window") is not None
+        and kv_seq_len > self.config.sliding_window
+    )
+
+    if use_sliding_windows:
+        window_size = (self.config.sliding_window, self.config.sliding_window)
+    else:
+        window_size = (-1, -1)
+
     if past_key_value is not None:
-        # reuse k, v, self_attention
-        key_states = torch.cat([past_key_value[0], key_states], dim=2)
-        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+        # Activate slicing cache only if the config has a value `sliding_windows` attribute
+        if (
+            hasattr(self.config, "sliding_window")
+            and kv_seq_len > self.config.sliding_window
+        ):
+            slicing_tokens = kv_seq_len - self.config.sliding_window
+
+            past_key = past_key_value[0]
+            past_value = past_key_value[1]
+
+            past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+            past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+            if past_key.shape[-2] != self.config.sliding_window - 1:
+                raise ValueError(
+                    f"past key much have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+                    f" {past_key.shape}"
+                )
+
+            past_key_value = (past_key, past_value) if use_cache else None
+
+        if past_key_value is not None:
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
 
     past_key_value = (key_states, value_states) if use_cache else None
 
@@ -120,7 +210,13 @@ def flashattn_forward(
         qkv = rearrange(qkv, "b s ... -> (b s) ...")
 
         output = flash_attn_varlen_qkvpacked_func(
-            qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=None, causal=True
+            qkv,
+            cu_seqlens,
+            max_seqlen,
+            0.0,
+            softmax_scale=None,
+            causal=True,
+            window_size=window_size,
         )
         output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
     elif query_states.shape == key_states.shape:
@@ -146,6 +242,7 @@ def flashattn_forward(
             0.0,
             softmax_scale=None,
             causal=is_causal,
+            window_size=window_size,
         )
         output = output_pad_fn(output_unpad)
     else:
@@ -157,6 +254,7 @@ def flashattn_forward(
                 query_states,
                 torch.stack([key_states, value_states], 2),
                 causal=is_causal,
+                window_size=window_size,
             )
         else:
             (  # pylint: disable=unbalanced-tuple-unpacking
@@ -191,6 +289,7 @@ def flashattn_forward(
                 0.0,
                 softmax_scale=None,
                 causal=is_causal,
+                window_size=window_size,
             )
             output = output_pad_fn(output_unpad)