attn refactoring

Author: NouamaneTazi

src/diffusers/models/attention.py

@@ -1,4 +1,5 @@
 import math
+from typing import Optional
 
 import torch
 import torch.nn.functional as F
@@ -10,16 +11,24 @@ class AttentionBlock(nn.Module):
     An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted
     to the N-d case.
     https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
-    Uses three q, k, v linear layers to compute attention
+    Uses three q, k, v linear layers to compute attention.
+
+    Parameters:
+        channels (:obj:`int`): The number of channels in the input and output.
+        num_head_channels (:obj:`int`, *optional*):
+            The number of channels in each head. If None, then `num_heads` = 1.
+        num_groups (:obj:`int`, *optional*, defaults to 32): The number of groups to use for group norm.
+        rescale_output_factor (:obj:`float`, *optional*, defaults to 1.0): The factor to rescale the output by.
+        eps (:obj:`float`, *optional*, defaults to 1e-5): The epsilon value to use for group norm.
     """
 
     def __init__(
         self,
-        channels,
-        num_head_channels=None,
-        num_groups=32,
-        rescale_output_factor=1.0,
-        eps=1e-5,
+        channels: int,
+        num_head_channels: Optional[int] = None,
+        num_groups: int = 32,
+        rescale_output_factor: float = 1.0,
+        eps: float = 1e-5,
     ):
         super().__init__()
         self.channels = channels
@@ -86,10 +95,26 @@ def forward(self, hidden_states):
 class SpatialTransformer(nn.Module):
     """
     Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply
-    standard transformer action. Finally, reshape to image
+    standard transformer action. Finally, reshape to image.
+
+    Parameters:
+        in_channels (:obj:`int`): The number of channels in the input and output.
+        n_heads (:obj:`int`): The number of heads to use for multi-head attention.
+        d_head (:obj:`int`): The number of channels in each head.
+        depth (:obj:`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
+        dropout (:obj:`float`, *optional*, defaults to 0.1): The dropout probability to use.
+        context_dim (:obj:`int`, *optional*): The number of context dimensions to use.
     """
 
-    def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0.0, context_dim=None):
+    def __init__(
+        self,
+        in_channels: int,
+        n_heads: int,
+        d_head: int,
+        depth: int = 1,
+        dropout: float = 0.0,
+        context_dim: Optional[int] = None,
+    ):
         super().__init__()
         self.n_heads = n_heads
         self.d_head = d_head
@@ -112,22 +137,44 @@ def _set_attention_slice(self, slice_size):
         for block in self.transformer_blocks:
             block._set_attention_slice(slice_size)
 
-    def forward(self, x, context=None):
+    def forward(self, hidden_states, context=None):
         # note: if no context is given, cross-attention defaults to self-attention
-        b, c, h, w = x.shape
-        x_in = x
-        x = self.norm(x)
-        x = self.proj_in(x)
-        x = x.permute(0, 2, 3, 1).reshape(b, h * w, c)
+        batch, channel, height, weight = hidden_states.shape
+        residual = hidden_states
+        hidden_states = self.norm(hidden_states)
+        hidden_states = self.proj_in(hidden_states)
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, channel)
         for block in self.transformer_blocks:
-            x = block(x, context=context)
-        x = x.reshape(b, h, w, c).permute(0, 3, 1, 2)
-        x = self.proj_out(x)
-        return x + x_in
+            hidden_states = block(hidden_states, context=context)
+        hidden_states = hidden_states.reshape(batch, height, weight, channel).permute(0, 3, 1, 2)
+        hidden_states = self.proj_out(hidden_states)
+        return hidden_states + residual
 
 
 class BasicTransformerBlock(nn.Module):
-    def __init__(self, dim, n_heads, d_head, dropout=0.0, context_dim=None, gated_ff=True, checkpoint=True):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+        dim (:obj:`int`): The number of channels in the input and output.
+        n_heads (:obj:`int`): The number of heads to use for multi-head attention.
+        d_head (:obj:`int`): The number of channels in each head.
+        dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        context_dim (:obj:`int`, *optional*): The size of the context vector for cross attention.
+        gated_ff (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use a gated feed-forward network.
+        checkpoint (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use checkpointing.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        n_heads: int,
+        d_head: int,
+        dropout=0.0,
+        context_dim: Optional[int] = None,
+        gated_ff: bool = True,
+        checkpoint: bool = True,
+    ):
         super().__init__()
         self.attn1 = CrossAttention(
             query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout
@@ -145,15 +192,30 @@ def _set_attention_slice(self, slice_size):
         self.attn1._slice_size = slice_size
         self.attn2._slice_size = slice_size
 
-    def forward(self, x, context=None):
-        x = self.attn1(self.norm1(x)) + x
-        x = self.attn2(self.norm2(x), context=context) + x
-        x = self.ff(self.norm3(x)) + x
-        return x
+    def forward(self, hidden_states, context=None):
+        hidden_states = hidden_states.contiguous() if hidden_states.device.type == "mps" else hidden_states
+        hidden_states = self.attn1(self.norm1(hidden_states)) + hidden_states
+        hidden_states = self.attn2(self.norm2(hidden_states), context=context) + hidden_states
+        hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
+        return hidden_states
 
 
 class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
+    r"""
+    A cross attention layer.
+
+    Parameters:
+        query_dim (:obj:`int`): The number of channels in the query.
+        context_dim (:obj:`int`, *optional*):
+            The number of channels in the context. If not given, defaults to `query_dim`.
+        heads (:obj:`int`,  *optional*, defaults to 8): The number of heads to use for multi-head attention.
+        dim_head (:obj:`int`,  *optional*, defaults to 64): The number of channels in each head.
+        dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
+    """
+
+    def __init__(
+        self, query_dim: int, context_dim: Optional[int] = None, heads: int = 8, dim_head: int = 64, dropout: int = 0.0
+    ):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = context_dim if context_dim is not None else query_dim
@@ -174,77 +236,104 @@ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.
     def reshape_heads_to_batch_dim(self, tensor):
         batch_size, seq_len, dim = tensor.shape
         head_size = self.heads
-        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
-        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
-        return tensor
+        tensor2 = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
+        tensor3 = tensor2.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
+        return tensor3
 
     def reshape_batch_dim_to_heads(self, tensor):
         batch_size, seq_len, dim = tensor.shape
         head_size = self.heads
-        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
-        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
-        return tensor
+        tensor2 = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
+        tensor3 = tensor2.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
+        return tensor3
 
-    def forward(self, x, context=None, mask=None):
-        batch_size, sequence_length, dim = x.shape
+    def forward(self, hidden_states, context=None, mask=None):
+        batch_size, sequence_length, dim = hidden_states.shape
 
-        q = self.to_q(x)
-        context = context if context is not None else x
-        k = self.to_k(context)
-        v = self.to_v(context)
+        query = self.to_q(hidden_states)
+        context = context if context is not None else hidden_states
+        key = self.to_k(context)
+        value = self.to_v(context)
 
-        q = self.reshape_heads_to_batch_dim(q)
-        k = self.reshape_heads_to_batch_dim(k)
-        v = self.reshape_heads_to_batch_dim(v)
+        query = self.reshape_heads_to_batch_dim(query)
+        key = self.reshape_heads_to_batch_dim(key)
+        value = self.reshape_heads_to_batch_dim(value)
 
         # TODO(PVP) - mask is currently never used. Remember to re-implement when used
 
         # attention, what we cannot get enough of
-        hidden_states = self._attention(q, k, v, sequence_length, dim)
+        hidden_states = self._attention(query, key, value, sequence_length, dim)
 
         return self.to_out(hidden_states)
 
     def _attention(self, query, key, value, sequence_length, dim):
         batch_size_attention = query.shape[0]
-        hidden_states = torch.zeros(
-            (batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
-        )
-        slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0]
-        for i in range(hidden_states.shape[0] // slice_size):
-            start_idx = i * slice_size
-            end_idx = (i + 1) * slice_size
-            attn_slice = (
-                torch.einsum("b i d, b j d -> b i j", query[start_idx:end_idx], key[start_idx:end_idx]) * self.scale
-            )
-            attn_slice = attn_slice.softmax(dim=-1)
-            attn_slice = torch.einsum("b i j, b j d -> b i d", attn_slice, value[start_idx:end_idx])
-
-            hidden_states[start_idx:end_idx] = attn_slice
+        # hidden_states = torch.zeros(
+        #     (batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
+        # )
+        slice_size = self._slice_size if self._slice_size is not None else batch_size_attention
+        # for i in range(hidden_states.shape[0] // slice_size):
+            # start_idx = i * slice_size
+            # end_idx = (i + 1) * slice_size
+        # qslice = query[start_idx:end_idx]
+        qslice = query
+        # kslice = key[start_idx:end_idx].transpose(1, 2)
+        kslice = key.transpose(1, 2)
+        attn_slice = torch.matmul(qslice, kslice) * self.scale
+        attn_slice = attn_slice.softmax(dim=-1)
+        # vslice = value[start_idx:end_idx]
+        vslice = value
+        hidden_states = torch.matmul(attn_slice, vslice)
+
+
+        # hidden_states = torch.cat(attn_slices, dim=0)
+
 
         # reshape hidden_states
         hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
         return hidden_states
 
 
 class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
+    r"""
+    A feed-forward layer.
+
+    Parameters:
+        dim (:obj:`int`): The number of channels in the input.
+        dim_out (:obj:`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
+        mult (:obj:`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
+        glu (:obj:`bool`, *optional*, defaults to :obj:`False`): Whether to use GLU activation.
+        dropout (:obj:`float`, *optional*, defaults to 0.0): The dropout probability to use.
+    """
+
+    def __init__(
+        self, dim: int, dim_out: Optional[int] = None, mult: int = 4, glu: bool = False, dropout: float = 0.0
+    ):
         super().__init__()
         inner_dim = int(dim * mult)
         dim_out = dim_out if dim_out is not None else dim
         project_in = GEGLU(dim, inner_dim)
 
         self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
 
-    def forward(self, x):
-        return self.net(x)
+    def forward(self, hidden_states):
+        return self.net(hidden_states)
 
 
 # feedforward
 class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out):
+    r"""
+    A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202.
+
+    Parameters:
+        dim_in (:obj:`int`): The number of channels in the input.
+        dim_out (:obj:`int`): The number of channels in the output.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int):
         super().__init__()
         self.proj = nn.Linear(dim_in, dim_out * 2)
 
-    def forward(self, x):
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
+    def forward(self, hidden_states):
+        hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)
+        return hidden_states * F.gelu(gate)