217 lines
7.9 KiB
Python
217 lines
7.9 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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from typing import Any, Optional, Tuple
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import numpy as np
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import torch
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from torch import nn
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class PositionEmbeddingSine(nn.Module):
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"""
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This is a more standard version of the position embedding, very similar to the one
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used by the Attention is all you need paper, generalized to work on images.
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"""
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def __init__(
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self,
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num_pos_feats,
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temperature: int = 10000,
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normalize: bool = True,
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scale: Optional[float] = None,
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):
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super().__init__()
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assert num_pos_feats % 2 == 0, "Expecting even model width"
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self.num_pos_feats = num_pos_feats // 2
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self.temperature = temperature
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self.normalize = normalize
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if scale is not None and normalize is False:
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raise ValueError("normalize should be True if scale is passed")
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if scale is None:
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scale = 2 * math.pi
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self.scale = scale
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self.cache = {}
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def _encode_xy(self, x, y):
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# The positions are expected to be normalized
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assert len(x) == len(y) and x.ndim == y.ndim == 1
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x_embed = x * self.scale
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y_embed = y * self.scale
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dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
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dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
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pos_x = x_embed[:, None] / dim_t
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pos_y = y_embed[:, None] / dim_t
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pos_x = torch.stack(
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(pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2
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).flatten(1)
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pos_y = torch.stack(
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(pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2
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).flatten(1)
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return pos_x, pos_y
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@torch.no_grad()
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def encode_boxes(self, x, y, w, h):
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pos_x, pos_y = self._encode_xy(x, y)
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pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1)
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return pos
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encode = encode_boxes # Backwards compatibility
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@torch.no_grad()
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def encode_points(self, x, y, labels):
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(bx, nx), (by, ny), (bl, nl) = x.shape, y.shape, labels.shape
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assert bx == by and nx == ny and bx == bl and nx == nl
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pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten())
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pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1)
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pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2)
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return pos
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@torch.no_grad()
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def forward(self, x: torch.Tensor):
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cache_key = (x.shape[-2], x.shape[-1])
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if cache_key in self.cache:
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return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1)
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y_embed = (
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torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device)
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.view(1, -1, 1)
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.repeat(x.shape[0], 1, x.shape[-1])
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)
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x_embed = (
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torch.arange(1, x.shape[-1] + 1, dtype=torch.float32, device=x.device)
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.view(1, 1, -1)
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.repeat(x.shape[0], x.shape[-2], 1)
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)
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if self.normalize:
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eps = 1e-6
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y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
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x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
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dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
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dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
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pos_x = x_embed[:, :, :, None] / dim_t
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pos_y = y_embed[:, :, :, None] / dim_t
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pos_x = torch.stack(
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(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos_y = torch.stack(
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(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
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).flatten(3)
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pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
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self.cache[cache_key] = pos[0]
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return pos
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class PositionEmbeddingRandom(nn.Module):
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"""
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Positional encoding using random spatial frequencies.
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"""
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def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
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super().__init__()
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if scale is None or scale <= 0.0:
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scale = 1.0
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self.register_buffer(
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"positional_encoding_gaussian_matrix",
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scale * torch.randn((2, num_pos_feats)),
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)
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def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
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"""Positionally encode points that are normalized to [0,1]."""
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# assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
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coords = 2 * coords - 1
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coords = coords @ self.positional_encoding_gaussian_matrix
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coords = 2 * np.pi * coords
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# outputs d_1 x ... x d_n x C shape
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return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
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def forward(self, size: Tuple[int, int]) -> torch.Tensor:
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"""Generate positional encoding for a grid of the specified size."""
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h, w = size
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device: Any = self.positional_encoding_gaussian_matrix.device
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grid = torch.ones((h, w), device=device, dtype=torch.float32)
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y_embed = grid.cumsum(dim=0) - 0.5
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x_embed = grid.cumsum(dim=1) - 0.5
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y_embed = y_embed / h
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x_embed = x_embed / w
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pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
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return pe.permute(2, 0, 1) # C x H x W
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def forward_with_coords(
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self, coords_input: torch.Tensor, image_size: Tuple[int, int]
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) -> torch.Tensor:
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"""Positionally encode points that are not normalized to [0,1]."""
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coords = coords_input.clone()
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coords[:, :, 0] = coords[:, :, 0] / image_size[1]
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coords[:, :, 1] = coords[:, :, 1] / image_size[0]
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return self._pe_encoding(coords.to(torch.float)) # B x N x C
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# Rotary Positional Encoding, adapted from:
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# 1. https://github.com/meta-llama/codellama/blob/main/llama/model.py
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# 2. https://github.com/naver-ai/rope-vit
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# 3. https://github.com/lucidrains/rotary-embedding-torch
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def init_t_xy(end_x: int, end_y: int):
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t = torch.arange(end_x * end_y, dtype=torch.float32)
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t_x = (t % end_x).float()
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t_y = torch.div(t, end_x, rounding_mode="floor").float()
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return t_x, t_y
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def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0):
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freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))
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freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim))
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t_x, t_y = init_t_xy(end_x, end_y)
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freqs_x = torch.outer(t_x, freqs_x)
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freqs_y = torch.outer(t_y, freqs_y)
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freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x)
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freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y)
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return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1)
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def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
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ndim = x.ndim
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assert 0 <= 1 < ndim
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assert freqs_cis.shape == (x.shape[-2], x.shape[-1])
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shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)]
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return freqs_cis.view(*shape)
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def apply_rotary_enc(
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xq: torch.Tensor,
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xk: torch.Tensor,
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freqs_cis: torch.Tensor,
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repeat_freqs_k: bool = False,
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):
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xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
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xk_ = (
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torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
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if xk.shape[-2] != 0
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else None
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)
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freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
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xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
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if xk_ is None:
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# no keys to rotate, due to dropout
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return xq_out.type_as(xq).to(xq.device), xk
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# repeat freqs along seq_len dim to match k seq_len
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if repeat_freqs_k:
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r = xk_.shape[-2] // xq_.shape[-2]
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freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1)
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xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
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return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device)
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