ultralytics-ascend/ultralytics/models/sam2/modules/memory_attention.py

# Ultralytics YOLO 🚀, AGPL-3.0 license

import copy
from typing import Optional

import torch
from torch import Tensor, nn

from .sam2_blocks import RoPEAttention


class MemoryAttentionLayer(nn.Module):
    """Implements a memory attention layer with self-attention and cross-attention mechanisms for neural networks."""

    def __init__(
        self,
        d_model: int = 256,
        dim_feedforward: int = 2048,
        dropout: float = 0.1,
        pos_enc_at_attn: bool = False,
        pos_enc_at_cross_attn_keys: bool = True,
        pos_enc_at_cross_attn_queries: bool = False,
    ):
        """Initializes a MemoryAttentionLayer with self-attention, cross-attention, and feedforward components."""
        super().__init__()
        self.d_model = d_model
        self.dim_feedforward = dim_feedforward
        self.dropout_value = dropout
        self.self_attn = RoPEAttention(embedding_dim=256, num_heads=1, downsample_rate=1)
        self.cross_attn_image = RoPEAttention(
            rope_k_repeat=True,
            embedding_dim=256,
            num_heads=1,
            downsample_rate=1,
            kv_in_dim=64,
        )

        # Implementation of Feedforward model
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)

        self.activation = nn.ReLU()

        # Where to add pos enc
        self.pos_enc_at_attn = pos_enc_at_attn
        self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries
        self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys

    def _forward_sa(self, tgt, query_pos):
        """Performs self-attention on input tensor using positional encoding and RoPE attention mechanism."""
        tgt2 = self.norm1(tgt)
        q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2
        tgt2 = self.self_attn(q, k, v=tgt2)
        tgt = tgt + self.dropout1(tgt2)
        return tgt

    def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0):
        """Performs cross-attention between target and memory tensors using RoPEAttention mechanism."""
        kwds = {}
        if num_k_exclude_rope > 0:
            assert isinstance(self.cross_attn_image, RoPEAttention)
            kwds = {"num_k_exclude_rope": num_k_exclude_rope}

        # Cross-Attention
        tgt2 = self.norm2(tgt)
        tgt2 = self.cross_attn_image(
            q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2,
            k=memory + pos if self.pos_enc_at_cross_attn_keys else memory,
            v=memory,
            **kwds,
        )
        tgt = tgt + self.dropout2(tgt2)
        return tgt

    def forward(
        self,
        tgt,
        memory,
        pos: Optional[Tensor] = None,
        query_pos: Optional[Tensor] = None,
        num_k_exclude_rope: int = 0,
    ) -> torch.Tensor:
        """Performs self-attention, cross-attention, and MLP operations on input tensors for memory-based attention."""
        tgt = self._forward_sa(tgt, query_pos)
        tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope)
        # MLP
        tgt2 = self.norm3(tgt)
        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
        tgt = tgt + self.dropout3(tgt2)
        return tgt


class MemoryAttention(nn.Module):
    """Memory attention module for processing sequential data with self and cross-attention mechanisms."""

    def __init__(
        self,
        d_model: int,
        pos_enc_at_input: bool,
        layer: nn.Module,
        num_layers: int,
        batch_first: bool = True,  # Do layers expect batch first input?
    ):
        """Initializes MemoryAttention module with layers and normalization for attention processing."""
        super().__init__()
        self.d_model = d_model
        self.layers = nn.ModuleList([copy.deepcopy(layer) for _ in range(num_layers)])
        self.num_layers = num_layers
        self.norm = nn.LayerNorm(d_model)
        self.pos_enc_at_input = pos_enc_at_input
        self.batch_first = batch_first

    def forward(
        self,
        curr: torch.Tensor,  # self-attention inputs
        memory: torch.Tensor,  # cross-attention inputs
        curr_pos: Optional[Tensor] = None,  # pos_enc for self-attention inputs
        memory_pos: Optional[Tensor] = None,  # pos_enc for cross-attention inputs
        num_obj_ptr_tokens: int = 0,  # number of object pointer *tokens*
    ):
        """Applies self-attention and cross-attention to input tensors, processing through multiple layers."""
        if isinstance(curr, list):
            assert isinstance(curr_pos, list)
            assert len(curr) == len(curr_pos) == 1
            curr, curr_pos = (
                curr[0],
                curr_pos[0],
            )

        assert curr.shape[1] == memory.shape[1], "Batch size must be the same for curr and memory"

        output = curr
        if self.pos_enc_at_input and curr_pos is not None:
            output = output + 0.1 * curr_pos

        if self.batch_first:
            # Convert to batch first
            output = output.transpose(0, 1)
            curr_pos = curr_pos.transpose(0, 1)
            memory = memory.transpose(0, 1)
            memory_pos = memory_pos.transpose(0, 1)

        for layer in self.layers:
            kwds = {}
            if isinstance(layer.cross_attn_image, RoPEAttention):
                kwds = {"num_k_exclude_rope": num_obj_ptr_tokens}

            output = layer(
                tgt=output,
                memory=memory,
                pos=memory_pos,
                query_pos=curr_pos,
                **kwds,
            )
        normed_output = self.norm(output)

        if self.batch_first:
            # Convert back to seq first
            normed_output = normed_output.transpose(0, 1)
            curr_pos = curr_pos.transpose(0, 1)

        return normed_output