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pkgs/xformers/components/attention/global_tokens.py

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+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+
+from xformers.components.attention import (
+    Attention,
+    AttentionConfig,
+    AttentionMask,
+    maybe_sparsify,
+    register_attention,
+    sparsify,
+)
+from xformers.components.attention.attention_patterns import (
+    causal_1d_pattern,
+    global_token_pattern,
+)
+from xformers.components.attention.core import scaled_dot_product_attention
+
+
+@dataclass
+class GlobalAttentionConfig(AttentionConfig):
+    attention_query_mask: torch.Tensor  # Mark the queries which have global attention
+    causal: Optional[bool]
+    force_sparsity: Optional[bool]
+
+
+@register_attention("global", GlobalAttentionConfig)
+class GlobalAttention(Attention):
+    def __init__(
+        self,
+        dropout: float,
+        attention_query_mask: torch.Tensor,
+        causal: bool = False,
+        force_sparsity: bool = False,
+        *_,
+        **__,
+    ):
+        r"""
+        Global attention, as proposed for instance in BigBird_ or Longformer_.
+
+        Global means in that case that the queries positively labelled in the ```attention_query_mask``` can attend
+        to all the other queries. The queries negatively labelled in the ```attention_query_mask``` cannot attend to
+        any other query.
+
+        This implementation is sparse-aware, meaning that the empty attention parts will not be represented in memory.
+
+        Args:
+            dropout (float): probability of an element to be zeroed
+            attention_query_mask (torch.Tensor): if true, this query can attend to all the others
+
+        """
+        super().__init__()
+
+        assert attention_query_mask.dtype == torch.bool, "A boolean mask is expected"
+        assert (
+            attention_query_mask.shape[1] == 1
+            and attention_query_mask.shape[0] > attention_query_mask.shape[1]
+        ), "A N x 1 query mask is expected"
+
+        self.attn_drop = nn.Dropout(dropout, inplace=False)
+        self.attention_mask = global_token_pattern(attention_query_mask[:, 0])
+        self.force_sparsity = force_sparsity
+
+        if causal:
+            self.attention_mask &= causal_1d_pattern(attention_query_mask.shape[1])
+
+        self.attention_mask = (
+            sparsify(self.attention_mask)
+            if self.force_sparsity
+            else maybe_sparsify(self.attention_mask)
+        )
+
+        # Properties specific to this attention mechanism
+        self.requires_same_k_q_dimensions = True
+        self.supports_attention_mask = False
+        self.supports_key_padding_mask = False
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        att_mask: Optional[Union[torch.Tensor, AttentionMask]] = None,
+        *_,
+        **__,
+    ):
+        # Make sure that the mask is on the right device
+        if self.attention_mask.device != q.device:
+            self.attention_mask = self.attention_mask.to(q.device)
+
+        # Mask-aware attention
+        if att_mask is not None:
+            if att_mask.dtype == torch.bool and isinstance(
+                self.attention_mask, AttentionMask
+            ):
+                if not isinstance(att_mask, AttentionMask):
+                    att_mask = AttentionMask.from_bool(att_mask)
+                mask = self.attention_mask + att_mask
+            else:
+                mask = self.attention_mask & att_mask
+        else:
+            mask = self.attention_mask
+
+        # Handle q/k/v which would not fit the mask
+        seq_len = q.shape[-2]
+        q_, k_, v_ = map(lambda x: self._maybe_pad_sequence(x, mask), (q, k, v))
+
+        # Normal attention with the global tokens mask
+        att = scaled_dot_product_attention(
+            q=q_, k=k_, v=v_, att_mask=mask, dropout=self.attn_drop
+        )
+
+        # Take into account an hypothetical padding
+        return att[:, :seq_len, :]