328 lines
11 KiB
Python
328 lines
11 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import math
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import warnings
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from functools import partial
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from typing import Tuple, Type
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import torch
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import torch.nn.functional as F
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from torch import nn, Tensor
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from ..position_encoding import apply_rotary_enc, compute_axial_cis
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from ..sam2_utils import MLP
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from ...utils.misc import get_sdpa_settings
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warnings.simplefilter(action="ignore", category=FutureWarning)
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OLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = get_sdpa_settings()
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class TwoWayTransformer(nn.Module):
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def __init__(
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self,
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depth: int,
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embedding_dim: int,
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num_heads: int,
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mlp_dim: int,
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activation: Type[nn.Module] = nn.ReLU,
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attention_downsample_rate: int = 2,
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) -> None:
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"""
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A transformer decoder that attends to an input image using
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queries whose positional embedding is supplied.
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Args:
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depth (int): number of layers in the transformer
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embedding_dim (int): the channel dimension for the input embeddings
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num_heads (int): the number of heads for multihead attention. Must
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divide embedding_dim
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mlp_dim (int): the channel dimension internal to the MLP block
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activation (nn.Module): the activation to use in the MLP block
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"""
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super().__init__()
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self.depth = depth
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self.embedding_dim = embedding_dim
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self.num_heads = num_heads
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self.mlp_dim = mlp_dim
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self.layers = nn.ModuleList()
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for i in range(depth):
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self.layers.append(
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TwoWayAttentionBlock(
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embedding_dim=embedding_dim,
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num_heads=num_heads,
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mlp_dim=mlp_dim,
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activation=activation,
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attention_downsample_rate=attention_downsample_rate,
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skip_first_layer_pe=(i == 0),
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)
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)
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self.final_attn_token_to_image = Attention(
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embedding_dim, num_heads, downsample_rate=attention_downsample_rate
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)
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self.norm_final_attn = nn.LayerNorm(embedding_dim)
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def forward(
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self,
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image_embedding: Tensor,
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image_pe: Tensor,
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point_embedding: Tensor,
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) -> Tuple[Tensor, Tensor]:
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"""
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Args:
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image_embedding (torch.Tensor): image to attend to. Should be shape
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B x embedding_dim x h x w for any h and w.
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image_pe (torch.Tensor): the positional encoding to add to the image. Must
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have the same shape as image_embedding.
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point_embedding (torch.Tensor): the embedding to add to the query points.
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Must have shape B x N_points x embedding_dim for any N_points.
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Returns:
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torch.Tensor: the processed point_embedding
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torch.Tensor: the processed image_embedding
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"""
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# BxCxHxW -> BxHWxC == B x N_image_tokens x C
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bs, c, h, w = image_embedding.shape
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image_embedding = image_embedding.flatten(2).permute(0, 2, 1)
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image_pe = image_pe.flatten(2).permute(0, 2, 1)
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# Prepare queries
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queries = point_embedding
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keys = image_embedding
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# Apply transformer blocks and final layernorm
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for layer in self.layers:
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queries, keys = layer(
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queries=queries,
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keys=keys,
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query_pe=point_embedding,
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key_pe=image_pe,
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)
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# Apply the final attention layer from the points to the image
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q = queries + point_embedding
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k = keys + image_pe
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attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys)
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queries = queries + attn_out
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queries = self.norm_final_attn(queries)
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return queries, keys
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class TwoWayAttentionBlock(nn.Module):
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def __init__(
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self,
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embedding_dim: int,
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num_heads: int,
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mlp_dim: int = 2048,
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activation: Type[nn.Module] = nn.ReLU,
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attention_downsample_rate: int = 2,
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skip_first_layer_pe: bool = False,
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) -> None:
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"""
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A transformer block with four layers: (1) self-attention of sparse
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inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp
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block on sparse inputs, and (4) cross attention of dense inputs to sparse
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inputs.
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Arguments:
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embedding_dim (int): the channel dimension of the embeddings
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num_heads (int): the number of heads in the attention layers
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mlp_dim (int): the hidden dimension of the mlp block
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activation (nn.Module): the activation of the mlp block
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skip_first_layer_pe (bool): skip the PE on the first layer
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"""
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super().__init__()
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self.self_attn = Attention(embedding_dim, num_heads)
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self.norm1 = nn.LayerNorm(embedding_dim)
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self.cross_attn_token_to_image = Attention(
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embedding_dim, num_heads, downsample_rate=attention_downsample_rate
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)
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self.norm2 = nn.LayerNorm(embedding_dim)
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self.mlp = MLP(
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embedding_dim, mlp_dim, embedding_dim, num_layers=2, activation=activation
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)
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self.norm3 = nn.LayerNorm(embedding_dim)
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self.norm4 = nn.LayerNorm(embedding_dim)
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self.cross_attn_image_to_token = Attention(
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embedding_dim, num_heads, downsample_rate=attention_downsample_rate
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)
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self.skip_first_layer_pe = skip_first_layer_pe
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def forward(
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self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor
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) -> Tuple[Tensor, Tensor]:
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# Self attention block
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if self.skip_first_layer_pe:
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queries = self.self_attn(q=queries, k=queries, v=queries)
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else:
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q = queries + query_pe
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attn_out = self.self_attn(q=q, k=q, v=queries)
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queries = queries + attn_out
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queries = self.norm1(queries)
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# Cross attention block, tokens attending to image embedding
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q = queries + query_pe
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k = keys + key_pe
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attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys)
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queries = queries + attn_out
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queries = self.norm2(queries)
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# MLP block
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mlp_out = self.mlp(queries)
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queries = queries + mlp_out
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queries = self.norm3(queries)
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# Cross attention block, image embedding attending to tokens
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q = queries + query_pe
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k = keys + key_pe
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attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries)
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keys = keys + attn_out
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keys = self.norm4(keys)
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return queries, keys
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class Attention(nn.Module):
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"""
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An attention layer that allows for downscaling the size of the embedding
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after projection to queries, keys, and values.
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"""
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def __init__(
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self,
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embedding_dim: int,
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num_heads: int,
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downsample_rate: int = 1,
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dropout: float = 0.0,
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kv_in_dim: int = None,
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) -> None:
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super().__init__()
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self.embedding_dim = embedding_dim
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self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim
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self.internal_dim = embedding_dim // downsample_rate
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self.num_heads = num_heads
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assert (
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self.internal_dim % num_heads == 0
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), "num_heads must divide embedding_dim."
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self.q_proj = nn.Linear(embedding_dim, self.internal_dim)
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self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
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self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim)
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self.out_proj = nn.Linear(self.internal_dim, embedding_dim)
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self.dropout_p = dropout
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def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
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b, n, c = x.shape
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x = x.reshape(b, n, num_heads, c // num_heads)
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return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head
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def _recombine_heads(self, x: Tensor) -> Tensor:
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b, n_heads, n_tokens, c_per_head = x.shape
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x = x.transpose(1, 2)
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return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C
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def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
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# Input projections
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q = self.q_proj(q)
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k = self.k_proj(k)
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v = self.v_proj(v)
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# Separate into heads
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q = self._separate_heads(q, self.num_heads)
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k = self._separate_heads(k, self.num_heads)
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v = self._separate_heads(v, self.num_heads)
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dropout_p = self.dropout_p if self.training else 0.0
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# Attention
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with torch.backends.cuda.sdp_kernel(
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enable_flash=USE_FLASH_ATTN,
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# if Flash attention kernel is off, then math kernel needs to be enabled
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enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON,
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enable_mem_efficient=OLD_GPU,
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):
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out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
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out = self._recombine_heads(out)
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out = self.out_proj(out)
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return out
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class RoPEAttention(Attention):
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"""Attention with rotary position encoding."""
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def __init__(
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self,
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*args,
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rope_theta=10000.0,
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# whether to repeat q rope to match k length
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# this is needed for cross-attention to memories
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rope_k_repeat=False,
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feat_sizes=(32, 32), # [w, h] for stride 16 feats at 512 resolution
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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self.compute_cis = partial(
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compute_axial_cis, dim=self.internal_dim // self.num_heads, theta=rope_theta
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)
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freqs_cis = self.compute_cis(end_x=feat_sizes[0], end_y=feat_sizes[1])
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self.freqs_cis = freqs_cis
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self.rope_k_repeat = rope_k_repeat
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def forward(
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self, q: Tensor, k: Tensor, v: Tensor, num_k_exclude_rope: int = 0
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) -> Tensor:
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# Input projections
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q = self.q_proj(q)
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k = self.k_proj(k)
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v = self.v_proj(v)
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# Separate into heads
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q = self._separate_heads(q, self.num_heads)
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k = self._separate_heads(k, self.num_heads)
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v = self._separate_heads(v, self.num_heads)
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# Apply rotary position encoding
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w = h = math.sqrt(q.shape[-2])
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self.freqs_cis = self.freqs_cis.to(q.device)
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if self.freqs_cis.shape[0] != q.shape[-2]:
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self.freqs_cis = self.compute_cis(end_x=w, end_y=h).to(q.device)
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if q.shape[-2] != k.shape[-2]:
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assert self.rope_k_repeat
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num_k_rope = k.size(-2) - num_k_exclude_rope
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q, k[:, :, :num_k_rope] = apply_rotary_enc(
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q,
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k[:, :, :num_k_rope],
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freqs_cis=self.freqs_cis,
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repeat_freqs_k=self.rope_k_repeat,
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)
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dropout_p = self.dropout_p if self.training else 0.0
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# Attention
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with torch.backends.cuda.sdp_kernel(
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enable_flash=USE_FLASH_ATTN,
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# if Flash attention kernel is off, then math kernel needs to be enabled
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enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON,
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enable_mem_efficient=OLD_GPU,
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):
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out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p)
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out = self._recombine_heads(out)
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out = self.out_proj(out)
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return out
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