Flash attention

monai/networks/blocks/crossattention.py

@@ -17,7 +17,7 @@
 import torch.nn as nn
 
 from monai.networks.layers.utils import get_rel_pos_embedding_layer
-from monai.utils import optional_import
+from monai.utils import optional_import, pytorch_after
 
 Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange")
 
@@ -44,6 +44,7 @@ def __init__(
         rel_pos_embedding: Optional[str] = None,
         input_size: Optional[Tuple] = None,
         attention_dtype: Optional[torch.dtype] = None,
+        use_flash_attention: bool = False,
     ) -> None:
         """
         Args:
@@ -55,13 +56,16 @@ def __init__(
             dim_head (int, optional): dimension of each head. Defaults to hidden_size // num_heads.
             qkv_bias (bool, optional): bias term for the qkv linear layer. Defaults to False.
             save_attn (bool, optional): to make accessible the attention matrix. Defaults to False.
-            causal: whether to use causal attention.
-            sequence_length: if causal is True, it is necessary to specify the sequence length.
-            rel_pos_embedding (str, optional): Add relative positional embeddings to the attention map.
-                For now only "decomposed" is supported (see https://arxiv.org/abs/2112.01526). 2D and 3D are supported.
-            input_size (tuple(spatial_dim), optional): Input resolution for calculating the relative
-                positional parameter size.
+            causal (bool, optional): whether to use causal attention.
+            sequence_length (int, optional): if causal is True, it is necessary to specify the sequence length.
+            rel_pos_embedding (str, optional): Add relative positional embeddings to the attention map. For now only
+            "decomposed" is supported (see https://arxiv.org/abs/2112.01526). 2D and 3D are supported.
+            input_size (tuple(spatial_dim), optional): Input resolution for calculating the relative positional
+            parameter size.
             attention_dtype: cast attention operations to this dtype.
+            use_flash_attention: if True, use Pytorch's inbuilt
+            flash attention for a memory efficient attention mechanism (see
+            https://pytorch.org/docs/2.2/generated/torch.nn.functional.scaled_dot_product_attention.html).
         """
 
         super().__init__()
@@ -81,6 +85,20 @@ def __init__(
         if causal and sequence_length is None:
             raise ValueError("sequence_length is necessary for causal attention.")
 
+        if use_flash_attention and not pytorch_after(minor=13, major=1, patch=0):
+            raise ValueError(
+                "use_flash_attention is only supported for PyTorch versions >= 2.0."
+                "Upgrade your PyTorch or set the flag to False."
+            )
+        if use_flash_attention and save_attn:
+            raise ValueError(
+                "save_attn has been set to True, but use_flash_attention is also set"
+                "to True. save_attn can only be used if use_flash_attention is False"
+            )
+
+        if use_flash_attention and rel_pos_embedding is not None:
+            raise ValueError("rel_pos_embedding must be None if you are using flash_attention.")
+
         self.num_heads = num_heads
         self.hidden_input_size = hidden_input_size if hidden_input_size else hidden_size
         self.context_input_size = context_input_size if context_input_size else hidden_size
@@ -94,13 +112,15 @@ def __init__(
         self.out_rearrange = Rearrange("b h l d -> b l (h d)")
         self.drop_output = nn.Dropout(dropout_rate)
         self.drop_weights = nn.Dropout(dropout_rate)
+        self.dropout_rate = dropout_rate
 
         self.scale = self.head_dim**-0.5
         self.save_attn = save_attn
         self.attention_dtype = attention_dtype
 
         self.causal = causal
         self.sequence_length = sequence_length
+        self.use_flash_attention = use_flash_attention
 
         if causal and sequence_length is not None:
             # causal mask to ensure that attention is only applied to the left in the input sequence
@@ -142,26 +162,39 @@ def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None):
             q = q.to(self.attention_dtype)
             k = k.to(self.attention_dtype)
 
-        q = q.view(b, t, self.num_heads, c // self.num_heads).transpose(1, 2)  # (b, nh, t,  hs)
+        q = q.view(b, t, self.num_heads, c // self.num_heads).transpose(1, 2)  # (b, nh, t,  hs) #
         k = k.view(b, kv_t, self.num_heads, c // self.num_heads).transpose(1, 2)  # (b, nh, kv_t, hs)
         v = v.view(b, kv_t, self.num_heads, c // self.num_heads).transpose(1, 2)  # (b, nh, kv_t, hs)
-        att_mat = torch.einsum("blxd,blyd->blxy", q, k) * self.scale
 
-        # apply relative positional embedding if defined
-        att_mat = self.rel_positional_embedding(x, att_mat, q) if self.rel_positional_embedding is not None else att_mat
+        if self.use_flash_attention:
+            x = torch.nn.functional.scaled_dot_product_attention(
+                query=q.transpose(1, 2),
+                key=k.transpose(1, 2),
+                value=v.transpose(1, 2),
+                scale=self.scale,
+                dropout_p=self.dropout_rate,
+                is_causal=self.causal,
+            ).transpose(
+                1, 2
+            )  # Back to (b, nh, t, hs)
+        else:
+            att_mat = torch.einsum("blxd,blyd->blxy", q, k) * self.scale
+            # apply relative positional embedding if defined
+            if self.rel_positional_embedding is not None:
+                att_mat = self.rel_positional_embedding(x, att_mat, q)
 
-        if self.causal:
-            att_mat = att_mat.masked_fill(self.causal_mask[:, :, :t, :kv_t] == 0, float("-inf"))
+            if self.causal:
+                att_mat = att_mat.masked_fill(self.causal_mask[:, :, :t, :kv_t] == 0, float("-inf"))
 
-        att_mat = att_mat.softmax(dim=-1)
+            att_mat = att_mat.softmax(dim=-1)
 
-        if self.save_attn:
-            # no gradients and new tensor;
-            # https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html
-            self.att_mat = att_mat.detach()
+            if self.save_attn:
+                # no gradients and new tensor;
+                # https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html
+                self.att_mat = att_mat.detach()
 
-        att_mat = self.drop_weights(att_mat)
-        x = torch.einsum("bhxy,bhyd->bhxd", att_mat, v)
+            att_mat = self.drop_weights(att_mat)
+            x = torch.einsum("bhxy,bhyd->bhxd", att_mat, v)
         x = self.out_rearrange(x)
         x = self.out_proj(x)
         x = self.drop_output(x)

monai/networks/blocks/selfattention.py

@@ -15,9 +15,10 @@
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 
 from monai.networks.layers.utils import get_rel_pos_embedding_layer
-from monai.utils import optional_import
+from monai.utils import optional_import, pytorch_after
 
 Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange")
 
@@ -42,6 +43,7 @@ def __init__(
         rel_pos_embedding: Optional[str] = None,
         input_size: Optional[Tuple] = None,
         attention_dtype: Optional[torch.dtype] = None,
+        use_flash_attention: bool = False,
     ) -> None:
         """
         Args:
@@ -59,6 +61,9 @@ def __init__(
             input_size (tuple(spatial_dim), optional): Input resolution for calculating the relative
                 positional parameter size.
             attention_dtype: cast attention operations to this dtype.
+            use_flash_attention: if True, use Pytorch's inbuilt
+            flash attention for a memory efficient attention mechanism (see
+            https://pytorch.org/docs/2.2/generated/torch.nn.functional.scaled_dot_product_attention.html).
 
         """
 
@@ -82,6 +87,20 @@ def __init__(
         if causal and sequence_length is None:
             raise ValueError("sequence_length is necessary for causal attention.")
 
+        if use_flash_attention and not pytorch_after(minor=13, major=1, patch=0):
+            raise ValueError(
+                "use_flash_attention is only supported for PyTorch versions >= 2.0."
+                "Upgrade your PyTorch or set the flag to False."
+            )
+        if use_flash_attention and save_attn:
+            raise ValueError(
+                "save_attn has been set to True, but use_flash_attention is also set"
+                "to True. save_attn can only be used if use_flash_attention is False."
+            )
+
+        if use_flash_attention and rel_pos_embedding is not None:
+            raise ValueError("rel_pos_embedding must be None if you are using flash_attention.")
+
         self.num_heads = num_heads
         self.hidden_input_size = hidden_input_size if hidden_input_size else hidden_size
         self.out_proj = nn.Linear(self.inner_dim, self.hidden_input_size)
@@ -91,12 +110,14 @@ def __init__(
         self.out_rearrange = Rearrange("b h l d -> b l (h d)")
         self.drop_output = nn.Dropout(dropout_rate)
         self.drop_weights = nn.Dropout(dropout_rate)
+        self.dropout_rate = dropout_rate
         self.scale = self.dim_head**-0.5
         self.save_attn = save_attn
         self.att_mat = torch.Tensor()
         self.attention_dtype = attention_dtype
         self.causal = causal
         self.sequence_length = sequence_length
+        self.use_flash_attention = use_flash_attention
 
         if causal and sequence_length is not None:
             # causal mask to ensure that attention is only applied to the left in the input sequence
@@ -130,23 +151,34 @@ def forward(self, x):
             q = q.to(self.attention_dtype)
             k = k.to(self.attention_dtype)
 
-        att_mat = torch.einsum("blxd,blyd->blxy", q, k) * self.scale
+        if self.use_flash_attention:
+            x = F.scaled_dot_product_attention(
+                query=q.transpose(1, 2),
+                key=k.transpose(1, 2),
+                value=v.transpose(1, 2),
+                scale=self.scale,
+                dropout_p=self.dropout_rate,
+                is_causal=self.causal,
+            ).transpose(1, 2)
+        else:
+            att_mat = torch.einsum("blxd,blyd->blxy", q, k) * self.scale
 
-        # apply relative positional embedding if defined
-        att_mat = self.rel_positional_embedding(x, att_mat, q) if self.rel_positional_embedding is not None else att_mat
+            # apply relative positional embedding if defined
+            if self.rel_positional_embedding is not None:
+                att_mat = self.rel_positional_embedding(x, att_mat, q)
 
-        if self.causal:
-            att_mat = att_mat.masked_fill(self.causal_mask[:, :, : x.shape[1], : x.shape[1]] == 0, float("-inf"))
+            if self.causal:
+                att_mat = att_mat.masked_fill(self.causal_mask[:, :, : x.shape[-2], : x.shape[-2]] == 0, float("-inf"))
 
-        att_mat = att_mat.softmax(dim=-1)
+            att_mat = att_mat.softmax(dim=-1)
 
-        if self.save_attn:
-            # no gradients and new tensor;
-            # https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html
-            self.att_mat = att_mat.detach()
+            if self.save_attn:
+                # no gradients and new tensor;
+                # https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html
+                self.att_mat = att_mat.detach()
 
-        att_mat = self.drop_weights(att_mat)
-        x = torch.einsum("bhxy,bhyd->bhxd", att_mat, v)
+            att_mat = self.drop_weights(att_mat)
+            x = torch.einsum("bhxy,bhyd->bhxd", att_mat, v)
         x = self.out_rearrange(x)
         x = self.out_proj(x)
         x = self.drop_output(x)

monai/networks/blocks/spatialattention.py

@@ -33,6 +33,7 @@ class SpatialAttentionBlock(nn.Module):
         num_channels: number of input channels. Must be divisible by num_head_channels.
         num_head_channels: number of channels per head.
         attention_dtype: cast attention operations to this dtype.
+        use_flash_attention: if True, use flash attention for a memory efficient attention mechanism.
 
     """
 
@@ -44,6 +45,7 @@ def __init__(
         norm_num_groups: int = 32,
         norm_eps: float = 1e-6,
         attention_dtype: Optional[torch.dtype] = None,
+        use_flash_attention: bool = False,
     ) -> None:
         super().__init__()
 
@@ -54,7 +56,11 @@ def __init__(
             raise ValueError("num_channels must be divisible by num_head_channels")
         num_heads = num_channels // num_head_channels if num_head_channels is not None else 1
         self.attn = SABlock(
-            hidden_size=num_channels, num_heads=num_heads, qkv_bias=True, attention_dtype=attention_dtype
+            hidden_size=num_channels,
+            num_heads=num_heads,
+            qkv_bias=True,
+            attention_dtype=attention_dtype,
+            use_flash_attention=use_flash_attention,
         )
 
     def forward(self, x: torch.Tensor):

monai/networks/blocks/transformerblock.py

@@ -36,15 +36,18 @@ def __init__(
         causal: bool = False,
         sequence_length: int | None = None,
         with_cross_attention: bool = False,
+        use_flash_attention: bool = False,
     ) -> None:
         """
         Args:
             hidden_size (int): dimension of hidden layer.
             mlp_dim (int): dimension of feedforward layer.
             num_heads (int): number of attention heads.
             dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0.
-            qkv_bias (bool, optional): apply bias term for the qkv linear layer. Defaults to False.
+            qkv_bias(bool, optional): apply bias term for the qkv linear layer. Defaults to False.
             save_attn (bool, optional): to make accessible the attention matrix. Defaults to False.
+            use_flash_attention: if True, use Pytorch's inbuilt flash attention for a memory efficient attention mechanism
+             (see https://pytorch.org/docs/2.2/generated/torch.nn.functional.scaled_dot_product_attention.html).
 
         """
 
@@ -66,13 +69,19 @@ def __init__(
             save_attn=save_attn,
             causal=causal,
             sequence_length=sequence_length,
+            use_flash_attention=use_flash_attention,
         )
         self.norm2 = nn.LayerNorm(hidden_size)
         self.with_cross_attention = with_cross_attention
 
         self.norm_cross_attn = nn.LayerNorm(hidden_size)
         self.cross_attn = CrossAttentionBlock(
-            hidden_size=hidden_size, num_heads=num_heads, dropout_rate=dropout_rate, qkv_bias=qkv_bias, causal=False
+            hidden_size=hidden_size,
+            num_heads=num_heads,
+            dropout_rate=dropout_rate,
+            qkv_bias=qkv_bias,
+            causal=False,
+            use_flash_attention=use_flash_attention,
         )
 
     def forward(self, x: torch.Tensor, context: Optional[torch.Tensor] = None) -> torch.Tensor:

monai/networks/nets/diffusion_model_unet.py

@@ -66,6 +66,8 @@ class DiffusionUNetTransformerBlock(nn.Module):
         dropout: dropout probability to use.
         cross_attention_dim: size of the context vector for cross attention.
         upcast_attention: if True, upcast attention operations to full precision.
+        use_flash_attention: if True, use Pytorch's inbuilt flash attention for a memory efficient attention mechanism
+        (see https://pytorch.org/docs/2.2/generated/torch.nn.functional.scaled_dot_product_attention.html).
 
     """
 
@@ -77,6 +79,7 @@ def __init__(
         dropout: float = 0.0,
         cross_attention_dim: int | None = None,
         upcast_attention: bool = False,
+        use_flash_attention: bool = False,
     ) -> None:
         super().__init__()
         self.attn1 = SABlock(
@@ -86,6 +89,7 @@ def __init__(
             dim_head=num_head_channels,
             dropout_rate=dropout,
             attention_dtype=torch.float if upcast_attention else None,
+            use_flash_attention=use_flash_attention,
         )
         self.ff = MLPBlock(hidden_size=num_channels, mlp_dim=num_channels * 4, act="GEGLU", dropout_rate=dropout)
         self.attn2 = CrossAttentionBlock(
@@ -96,6 +100,7 @@ def __init__(
             dim_head=num_head_channels,
             dropout_rate=dropout,
             attention_dtype=torch.float if upcast_attention else None,
+            use_flash_attention=use_flash_attention,
         )
         self.norm1 = nn.LayerNorm(num_channels)
         self.norm2 = nn.LayerNorm(num_channels)