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"""Self-contained CROMA model and configuration."""
from __future__ import annotations
import itertools
import math
from dataclasses import dataclass
from typing import Optional
import torch
from einops import rearrange
from torch import einsum, nn
from transformers.configuration_utils import PretrainedConfig as PreTrainedConfig
from transformers.modeling_outputs import BaseModelOutputWithPooling
from transformers.modeling_utils import PreTrainedModel
from transformers.processing_utils import Unpack
from transformers.utils import TransformersKwargs, logging
logger = logging.get_logger(__name__)
class CromaConfig(PreTrainedConfig):
model_type = "croma"
def __init__(
self,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=16,
patch_size=8,
image_size=120,
sar_channels=2,
optical_channels=12,
modality="both",
hidden_dropout_prob=0.0,
layer_norm_eps=1e-5,
initializer_range=0.02,
num_patches=None,
**kwargs,
):
super().__init__(**kwargs)
if image_size % patch_size != 0:
raise ValueError(f"`image_size` ({image_size}) must be divisible by `patch_size` ({patch_size}).")
if modality not in {"both", "sar", "optical"}:
raise ValueError(f"`modality` must be one of 'both', 'sar', or 'optical', got {modality!r}.")
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.patch_size = patch_size
self.image_size = image_size
self.sar_channels = sar_channels
self.optical_channels = optical_channels
self.modality = modality
self.hidden_dropout_prob = hidden_dropout_prob
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.num_patches = num_patches if num_patches is not None else (image_size // patch_size) ** 2
def get_2dalibi(num_heads: int, num_patches: int) -> torch.Tensor:
grid_size = int(math.sqrt(num_patches))
points = list(itertools.product(range(grid_size), range(grid_size)))
def get_slopes(n):
def get_slopes_power_of_2(n):
start = 2 ** (-2 ** -(math.log2(n) - 3))
ratio = start
return [start * ratio**i for i in range(n)]
if math.log2(n).is_integer():
return get_slopes_power_of_2(n)
closest_power_of_2 = 2 ** math.floor(math.log2(n))
return get_slopes_power_of_2(closest_power_of_2) + get_slopes(2 * closest_power_of_2)[0::2][
: n - closest_power_of_2
]
slopes = torch.tensor(get_slopes(num_heads), dtype=torch.float32).unsqueeze(1)
idxs = []
for p1 in points:
for p2 in points:
dist = math.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2)
idxs.append(dist * slopes * -1)
all_bias = torch.cat(idxs, dim=1)
return all_bias.view(1, num_heads, num_patches, num_patches)
@dataclass
class CromaModelOutput(BaseModelOutputWithPooling):
sar_hidden_states: Optional[torch.FloatTensor] = None
sar_pooler_output: Optional[torch.FloatTensor] = None
optical_hidden_states: Optional[torch.FloatTensor] = None
optical_pooler_output: Optional[torch.FloatTensor] = None
joint_hidden_states: Optional[torch.FloatTensor] = None
joint_pooler_output: Optional[torch.FloatTensor] = None
class CromaFeedForward(nn.Module):
def __init__(self, config: CromaConfig, mult: int = 4):
super().__init__()
inner_dim = int(config.hidden_size * mult)
self.net = nn.Sequential(
nn.Linear(config.hidden_size, inner_dim),
nn.GELU(),
nn.Dropout(config.hidden_dropout_prob),
nn.Linear(inner_dim, config.hidden_size),
)
self.input_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.input_norm(hidden_states)
return self.net(hidden_states)
class CromaAttention(nn.Module):
def __init__(self, config: CromaConfig):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = config.hidden_size // config.num_attention_heads
self.scale = self.attention_head_size**-0.5
self.to_qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=False)
self.to_out = nn.Linear(config.hidden_size, config.hidden_size)
self.input_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, relative_position_bias: torch.Tensor) -> torch.Tensor:
hidden_states = self.input_norm(hidden_states)
query, key, value = self.to_qkv(hidden_states).chunk(3, dim=-1)
query, key, value = map(
lambda tensor: rearrange(tensor, "b n (h d) -> b h n d", h=self.num_attention_heads),
(query, key, value),
)
attention_scores = einsum("b h i d, b h j d -> b h i j", query, key) * self.scale
attention_scores = attention_scores + relative_position_bias
attention_probs = attention_scores.softmax(dim=-1)
attention_probs = self.dropout(attention_probs)
context = einsum("b h i j, b h j d -> b h i d", attention_probs, value)
context = rearrange(context, "b h n d -> b n (h d)")
return self.to_out(context)
class CromaCrossAttention(nn.Module):
def __init__(self, config: CromaConfig):
super().__init__()
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = config.hidden_size // config.num_attention_heads
self.scale = self.attention_head_size**-0.5
self.to_q = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.to_k = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.to_v = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.to_out = nn.Linear(config.hidden_size, config.hidden_size)
self.input_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(
self,
hidden_states: torch.Tensor,
context: torch.Tensor,
relative_position_bias: torch.Tensor,
) -> torch.Tensor:
hidden_states = self.input_norm(hidden_states)
context = self.input_norm(context)
query = self.to_q(hidden_states)
key = self.to_k(context)
value = self.to_v(context)
query, key, value = map(
lambda tensor: rearrange(tensor, "b n (h d) -> b h n d", h=self.num_attention_heads),
(query, key, value),
)
attention_scores = einsum("b h i d, b h j d -> b h i j", query, key) * self.scale
attention_scores = attention_scores + relative_position_bias
attention_probs = attention_scores.softmax(dim=-1)
attention_probs = self.dropout(attention_probs)
context = einsum("b h i j, b h j d -> b h i d", attention_probs, value)
context = rearrange(context, "b h n d -> b n (h d)")
return self.to_out(context)
class CromaEncoder(nn.Module):
def __init__(self, config: CromaConfig, depth: int, final_norm: bool = True):
super().__init__()
self.layers = nn.ModuleList(
[
nn.ModuleList([CromaAttention(config), CromaFeedForward(config)])
for _ in range(depth)
]
)
self.norm_out = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) if final_norm else None
def forward(self, hidden_states: torch.Tensor, relative_position_bias: torch.Tensor) -> torch.Tensor:
for self_attn, ffn in self.layers:
hidden_states = self_attn(hidden_states, relative_position_bias) + hidden_states
hidden_states = ffn(hidden_states) + hidden_states
if self.norm_out is not None:
return self.norm_out(hidden_states)
return hidden_states
class CromaCrossEncoder(nn.Module):
def __init__(self, config: CromaConfig, depth: int):
super().__init__()
self.layers = nn.ModuleList(
[
nn.ModuleList([CromaAttention(config), CromaCrossAttention(config), CromaFeedForward(config)])
for _ in range(depth)
]
)
self.norm_out = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
context: torch.Tensor,
relative_position_bias: torch.Tensor,
) -> torch.Tensor:
for self_attn, cross_attn, ffn in self.layers:
hidden_states = self_attn(hidden_states, relative_position_bias) + hidden_states
hidden_states = cross_attn(hidden_states, context, relative_position_bias) + hidden_states
hidden_states = ffn(hidden_states) + hidden_states
return self.norm_out(hidden_states)
class CromaViTEncoder(nn.Module):
def __init__(self, config: CromaConfig, depth: int, in_channels: int):
super().__init__()
self.patch_size = config.patch_size
pixels_per_patch = config.patch_size * config.patch_size * in_channels
self.linear_input = nn.Linear(pixels_per_patch, config.hidden_size)
self.transformer = CromaEncoder(config, depth=depth)
def forward(self, pixel_values: torch.Tensor, relative_position_bias: torch.Tensor) -> torch.Tensor:
hidden_states = rearrange(
pixel_values,
"b c (h i) (w j) -> b (h w) (c i j)",
i=self.patch_size,
j=self.patch_size,
)
hidden_states = self.linear_input(hidden_states)
return self.transformer(hidden_states, relative_position_bias)
class CromaGapHead(nn.Module):
def __init__(self, config: CromaConfig):
super().__init__()
self.net = nn.Sequential(
nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps),
nn.Linear(config.hidden_size, 4 * config.hidden_size),
nn.GELU(),
nn.Linear(4 * config.hidden_size, config.hidden_size),
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return self.net(hidden_states.mean(dim=1))
class CromaPreTrainedModel(PreTrainedModel):
config_class = CromaConfig
base_model_prefix = "croma"
main_input_name = "optical_pixel_values"
input_modalities = ("image",)
supports_gradient_checkpointing = False
_no_split_modules = ["CromaEncoder", "CromaCrossEncoder"]
def _init_weights(self, module):
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
class CromaModel(CromaPreTrainedModel):
def __init__(self, config: CromaConfig):
super().__init__(config)
self.modality = config.modality
self.register_buffer(
"attn_bias",
get_2dalibi(config.num_attention_heads, config.num_patches),
persistent=False,
)
cross_depth = config.num_hidden_layers // 2
if config.modality in {"sar", "both"}:
self.sar_encoder = CromaViTEncoder(config, depth=cross_depth, in_channels=config.sar_channels)
self.sar_gap_ffn = CromaGapHead(config)
if config.modality in {"optical", "both"}:
self.optical_encoder = CromaViTEncoder(
config, depth=config.num_hidden_layers, in_channels=config.optical_channels
)
self.optical_gap_ffn = CromaGapHead(config)
if config.modality == "both":
self.cross_encoder = CromaCrossEncoder(config, depth=cross_depth)
self.post_init()
def _resolve_primary_outputs(
self,
sar_hidden_states,
sar_pooler_output,
optical_hidden_states,
optical_pooler_output,
joint_hidden_states,
joint_pooler_output,
):
if joint_hidden_states is not None:
return joint_hidden_states, joint_pooler_output
if self.modality == "sar" or sar_hidden_states is not None:
return sar_hidden_states, sar_pooler_output
return optical_hidden_states, optical_pooler_output
def forward(
self,
sar_pixel_values: Optional[torch.Tensor] = None,
optical_pixel_values: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
**kwargs: Unpack[TransformersKwargs],
) -> CromaModelOutput:
if return_dict is None:
return_dict = self.config.use_return_dict
has_sar = sar_pixel_values is not None
has_optical = optical_pixel_values is not None
if self.modality == "both":
if not has_sar and not has_optical:
raise ValueError("Provide at least one of `sar_pixel_values` or `optical_pixel_values`.")
elif self.modality == "sar" and not has_sar:
raise ValueError("Modality is set to 'sar', but `sar_pixel_values` is None.")
elif self.modality == "optical" and not has_optical:
raise ValueError("Modality is set to 'optical', but `optical_pixel_values` is None.")
attn_bias = self.attn_bias
sar_hidden_states = sar_pooler_output = None
optical_hidden_states = optical_pooler_output = None
joint_hidden_states = joint_pooler_output = None
if self.modality in {"sar", "both"} and has_sar:
sar_pixel_values = sar_pixel_values.to(dtype=self.dtype)
attn_bias = attn_bias.to(device=sar_pixel_values.device, dtype=sar_pixel_values.dtype)
sar_hidden_states = self.sar_encoder(sar_pixel_values, attn_bias)
sar_pooler_output = self.sar_gap_ffn(sar_hidden_states)
if self.modality in {"optical", "both"} and has_optical:
optical_pixel_values = optical_pixel_values.to(dtype=self.dtype)
attn_bias = attn_bias.to(device=optical_pixel_values.device, dtype=optical_pixel_values.dtype)
optical_hidden_states = self.optical_encoder(optical_pixel_values, attn_bias)
optical_pooler_output = self.optical_gap_ffn(optical_hidden_states)
if self.modality == "both" and has_sar and has_optical:
joint_hidden_states = self.cross_encoder(sar_hidden_states, optical_hidden_states, attn_bias)
joint_pooler_output = joint_hidden_states.mean(dim=1)
last_hidden_state, pooler_output = self._resolve_primary_outputs(
sar_hidden_states,
sar_pooler_output,
optical_hidden_states,
optical_pooler_output,
joint_hidden_states,
joint_pooler_output,
)
if not return_dict:
return (
last_hidden_state,
pooler_output,
sar_hidden_states,
sar_pooler_output,
optical_hidden_states,
optical_pooler_output,
joint_hidden_states,
joint_pooler_output,
)
return CromaModelOutput(
last_hidden_state=last_hidden_state,
pooler_output=pooler_output,
sar_hidden_states=sar_hidden_states,
sar_pooler_output=sar_pooler_output,
optical_hidden_states=optical_hidden_states,
optical_pooler_output=optical_pooler_output,
joint_hidden_states=joint_hidden_states,
joint_pooler_output=joint_pooler_output,
)
__all__ = ["CromaConfig", "CromaModel", "CromaModelOutput", "CromaPreTrainedModel"]
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