# Copyright 2023 The EASYDEL Author @erfanzar (Erfan Zare Chavoshi).
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import typing as tp
from functools import partial
import chex
import jax
import jax.numpy as jnp
from eformer.pytree import auto_pytree
from flax import nnx as nn
from jax import image as jimg
from easydel.infra.base_module import EasyDeLBaseModule
from easydel.infra.factory import TaskType, register_module
from easydel.infra.modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPooling,
ImageClassifierOutput,
ModelOutput,
)
from easydel.infra.utils import ACT2FN, control_mlp_sharding
from easydel.layers.attention import AttentionModule, FlexibleAttentionModule
from easydel.layers.linear import ParallelLinear
from .configuration_siglip import SiglipConfig, SiglipTextConfig, SiglipVisionConfig
[docs]@auto_pytree
class SiglipVisionModelOutput(ModelOutput):
image_embeds: tp.Optional[chex.Array] = None
last_hidden_state: chex.Array = None
hidden_states: tp.Optional[tp.Tuple[chex.Array, ...]] = None
attentions: tp.Optional[tp.Tuple[chex.Array, ...]] = None
[docs]@auto_pytree
class SiglipTextModelOutput(ModelOutput):
text_embeds: tp.Optional[chex.Array] = None
last_hidden_state: chex.Array = None
hidden_states: tp.Optional[tp.Tuple[chex.Array, ...]] = None
attentions: tp.Optional[tp.Tuple[chex.Array, ...]] = None
[docs]@auto_pytree
class SiglipOutput(ModelOutput):
loss: tp.Optional[chex.Array] = None
logits_per_image: chex.Array = None
logits_per_text: chex.Array = None
text_embeds: chex.Array = None
image_embeds: chex.Array = None
text_model_output: BaseModelOutputWithPooling = None
vision_model_output: BaseModelOutputWithPooling = None
[docs] def to_tuple(self) -> tp.Tuple[tp.Any]:
return tuple(
self[k]
if k not in ["text_model_output", "vision_model_output"]
else getattr(self, k).to_tuple()
for k in self.keys()
)
[docs]class SiglipVisionEmbeddings(nn.Module):
def __init__(
self,
config: SiglipVisionConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches
self.position_embedding = nn.Embed(
self.num_positions,
self.embed_dim,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.patch_embedding = nn.Conv(
in_features=config.num_channels,
out_features=self.embed_dim,
kernel_size=(self.patch_size, self.patch_size),
strides=(self.patch_size, self.patch_size),
padding="VALID",
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
precision=precision,
)
[docs] def interpolate(self, embeddings: chex.Array, height: int, width: int):
num_patches = embeddings.shape[1]
num_positions = self.position_embedding.weight.shape[0]
if num_patches == num_positions and height == width:
return self.position_embedding(
jnp.arange(
self.num_positions,
dtype="i4",
).reshape(1, -1)
)
patch_pos_embed = self.position_embedding.embedding.unsqueeze(0)
dim = embeddings.shape[-1]
new_height = height // self.patch_size
new_width = width // self.patch_size
sqrt_num_positions = int(num_positions**0.5)
patch_pos_embed = jnp.reshape(
patch_pos_embed, (1, sqrt_num_positions, sqrt_num_positions, dim)
)
patch_pos_embed = jnp.transpose(patch_pos_embed, (0, 3, 1, 2))
patch_pos_embed = jimg.resize(
patch_pos_embed,
(1, dim, new_height, new_width),
method="cubic",
)
return jnp.reshape(jnp.transpose(patch_pos_embed, (0, 2, 3, 1)), (1, -1, dim))
def __call__(self, pixel_values: chex.Array, interpolate_pos_encoding=False):
_, _, height, width = pixel_values.shape
target_dtype = self.patch_embedding.kernel.dtype
pixel_values = pixel_values.transpose(0, 2, 3, 1).astype(dtype=target_dtype)
patch_embeds = self.patch_embedding(pixel_values).transpose(0, 3, 1, 2)
embeddings = jnp.reshape(patch_embeds, patch_embeds.shape[:2] + (-1,))
embeddings = jnp.transpose(embeddings, (0, 2, 1))
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.position_embedding(
jnp.arange(self.num_positions, dtype="i4").reshape(1, -1)
)
return embeddings
[docs]class SiglipTextEmbeddings(nn.Module):
def __init__(
self,
config: SiglipTextConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
embed_dim = config.hidden_size
self.token_embedding = nn.Embed(
config.vocab_size,
embed_dim,
embedding_init=jax.nn.initializers.normal(),
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.position_embedding = nn.Embed(
config.max_position_embeddings,
embed_dim,
embedding_init=jax.nn.initializers.normal(),
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
inputs_embeds: tp.Optional[chex.Array] = None,
) -> chex.Array:
seq_length = (
input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
)
max_position_embedding = self.position_embedding.embedding.shape[0]
if seq_length > max_position_embedding:
raise ValueError(
f"Sequence length must be less than max_position_embeddings (got `sequence length`: "
f"{seq_length} and max_position_embeddings: {max_position_embedding}"
)
if position_ids is None:
position_ids = jnp.arange(seq_length, dtype="i4").reshape(1, -1)
if inputs_embeds is None:
inputs_embeds = self.token_embedding(input_ids)
position_embeddings = self.position_embedding(position_ids)
embeddings = inputs_embeds + position_embeddings
return embeddings
[docs]class SiglipAttention(AttentionModule):
def __init__(
self,
config,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(config=config)
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.dropout = config.attention_dropout
linear_class = partial(
ParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(0.01),
)
self.k_proj = linear_class(self.embed_dim, self.embed_dim)
self.v_proj = linear_class(self.embed_dim, self.embed_dim)
self.q_proj = linear_class(self.embed_dim, self.embed_dim)
self.out_proj = linear_class(self.embed_dim, self.embed_dim)
self.causal = False
self.attention_performer = FlexibleAttentionModule(
base_config=config,
softmax_scale=self.head_dim**-0.5,
dropout_prob=config.attention_dropout,
)
def _split_heads(self, hidden_states):
return hidden_states.reshape(
hidden_states.shape[:2] + (self.num_heads, self.head_dim)
)
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,))
def __call__(
self,
hidden_states: chex.Array,
attention_mask: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
):
query = self.q_proj(hidden_states)
key = self.k_proj(hidden_states)
value = self.v_proj(hidden_states)
query = self._split_heads(query)
key = self._split_heads(key)
value = self._split_heads(value)
causal_attention_mask = None
if self.causal:
raise NotImplementedError()
if attention_mask is not None and causal_attention_mask is not None:
if attention_mask.ndim == 2:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
attention_mask = nn.combine_masks(
attention_mask,
causal_attention_mask,
dtype="i4",
)
elif causal_attention_mask is not None:
attention_mask = causal_attention_mask
elif attention_mask is not None:
if attention_mask.ndim == 2:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
attention_bias = None
if attention_mask is not None:
attention_bias = jax.lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
)
attention_mask = None
attentions = self.attention_performer.forward(
query_states=query,
key_states=key,
value_states=value,
bias=None,
init_bias=lambda: attention_bias,
attention_mask=attention_mask,
segment_ids=None,
causal=self.causal,
dropout_rng=self.rngs.params(),
)
attn_output = self._merge_heads(attentions.attention_outputs)
attn_output = self.out_proj(attn_output)
return attn_output, attentions.attention_weights
[docs]class SiglipMLP(nn.Module):
def __init__(
self,
config: SiglipTextConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.activation_fn = ACT2FN[config.hidden_act]
linear_class = partial(
ParallelLinear,
use_bias=True,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
kernel_init=jax.nn.initializers.normal(0.01),
)
self.fc1 = linear_class(config.hidden_size, config.intermediate_size)
self.fc2 = linear_class(config.intermediate_size, config.hidden_size)
def __call__(self, hidden_states: chex.Array) -> chex.Array:
hidden_states = control_mlp_sharding(hidden_states, self.config.partition_axis)
hidden_states = self.fc2(self.activation_fn(self.fc1(hidden_states)))
return hidden_states
[docs]class SiglipEncoderLayer(nn.Module):
def __init__(
self,
config: SiglipTextConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.self_attn = SiglipAttention(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.layer_norm1 = nn.LayerNorm(
config.hidden_size,
epsilon=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.mlp = SiglipMLP(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.layer_norm2 = nn.LayerNorm(
config.hidden_size,
epsilon=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
def __call__(
self,
hidden_states: chex.Array,
attention_mask: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
):
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
attn_outputs = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = attn_outputs[0]
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,) + attn_outputs[1:]
return outputs
[docs]class SiglipEncoder(nn.Module):
def __init__(
self,
config: SiglipTextConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
self.dtype = dtype
self.param_dtype = param_dtype
self.precision = precision
self.rngs = rngs
self.layers = [
SiglipEncoderLayer(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for _ in range(config.num_hidden_layers)
]
def __call__(
self,
inputs_embeds: chex.Array,
attention_mask: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
hidden_states = inputs_embeds
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = layer(
hidden_states=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions += (layer_outputs[1],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states,)
if not return_dict:
return tuple(v for v in outputs if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
[docs]class SiglipTextTransformer(EasyDeLBaseModule):
def __init__(
self,
config: SiglipTextConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
embed_dim = config.hidden_size
self.embeddings = SiglipTextEmbeddings(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.encoder = SiglipEncoder(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.final_layer_norm = nn.LayerNorm(
config.hidden_size,
epsilon=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.head = ParallelLinear(
embed_dim,
config.projection_size,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
input_ids: chex.Array,
attention_mask: chex.Array,
position_ids: chex.Array,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
input_shape = input_ids.shape
input_ids = input_ids.reshape(-1, input_shape[-1])
hidden_states = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.final_layer_norm(last_hidden_state)
pooled_output = last_hidden_state[:, -1, :]
pooled_output = self.head(pooled_output)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
[docs]@register_module(
TaskType.BASE_MODULE,
config=SiglipTextConfig,
model_type="siglip_text_model",
)
class SiglipTextModel(EasyDeLBaseModule):
def __init__(
self,
config: SiglipTextConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.text_model = SiglipTextTransformer(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
return_dict: tp.Optional[bool] = None,
) -> tp.Union[tp.Tuple, BaseModelOutputWithPooling]:
return self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict
if return_dict is not None
else self.config.use_return_dict,
)
[docs]class MultiheadAttention(nn.Module):
def __init__(
self,
embed_dim,
num_heads,
bias=True,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
if embed_dim <= 0 or num_heads <= 0:
raise ValueError(
f"embed_dim and num_heads must be greater than 0,"
f" got embed_dim={embed_dim} and num_heads={num_heads} instead"
)
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, (
"embed_dim must be divisible by num_heads"
)
def normal_init(*shape):
return nn.initializers.xavier_uniform()(rngs.param(), shape, param_dtype)
def ze_init(*shape):
return jnp.zeros(shape, param_dtype)
self.in_proj_weight = nn.Param(normal_init(embed_dim * 3, embed_dim))
self.in_proj_bias = nn.Param(ze_init(3 * embed_dim))
self.out_proj = ParallelLinear(
embed_dim,
embed_dim,
use_bias=bias,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
query: chex.Array,
key: chex.Array,
value: chex.Array,
):
qbs, qss, qds = query.shape
b, s, d = value.shape
qb, kb, vb = jnp.split(self.in_proj_bias, 3, -1)
qw, kw, vw = jnp.split(self.in_proj_weight, 3, -1)
qout = ((query @ qw) + qb).reshape(qbs, qss, self.num_heads, -1)
kout = ((key @ kw) + kb).reshape(b, s, self.num_heads, -1)
vout = ((value @ vw) + vb).reshape(b, s, self.num_heads, -1)
attn = jnp.einsum(
"bhqk,bkhd->bqhd",
jax.nn.softmax(
jnp.einsum(
"bqhd,bkhd->bhqk",
qout * (qout.shape[-1] ** -0.5),
kout,
)
),
vout,
)
return self.out_proj(attn.reshape(qbs, qss, qds))
[docs]class SiglipMultiheadAttentionPoolingHead(nn.Module):
def __init__(
self,
config: SiglipTextConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.probe = nn.Param(
jax.random.normal(
rngs.param(),
(1, 1, config.hidden_size),
param_dtype,
)
)
self.attention = MultiheadAttention(
config.hidden_size,
config.num_attention_heads,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.layernorm = nn.LayerNorm(
config.hidden_size,
epsilon=config.layer_norm_eps,
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
self.mlp = SiglipMLP(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(self, hidden_state):
batch_size = hidden_state.shape[0]
probe = self.probe.value.repeat(batch_size, 0)
hidden_state = self.attention(probe, hidden_state, hidden_state)
residual = hidden_state
hidden_state = self.layernorm(hidden_state)
hidden_state = residual + self.mlp(hidden_state)
return hidden_state[:, 0]
[docs]@register_module(
TaskType.BASE_VISION,
config=SiglipVisionConfig,
model_type="siglip_vision_model",
)
class SiglipVisionModel(nn.Module):
def __init__(
self,
config: SiglipVisionConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.vision_model = SiglipVisionTransformer(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
pixel_values,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
return_dict: tp.Optional[bool] = None,
interpolate_pos_encoding: bool = False,
) -> tp.Union[tp.Tuple, BaseModelOutputWithPooling]:
return self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
[docs]@register_module(
TaskType.BASE_MODULE,
config=SiglipConfig,
model_type="siglip",
)
class SiglipModel(EasyDeLBaseModule):
def __init__(
self,
config: SiglipConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
if not isinstance(config.text_config, SiglipTextConfig):
raise TypeError(
"config.text_config is expected to be of type SiglipTextConfig but is of type"
f" {type(config.text_config)}."
)
if not isinstance(config.vision_config, SiglipVisionConfig):
raise TypeError(
"config.vision_config is expected to be of type SiglipVisionConfig but is of type"
f" {type(config.vision_config)}."
)
text_config = config.text_config
vision_config = config.vision_config
text_model = SiglipTextModel(
text_config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
vision_model = SiglipVisionModel(
vision_config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.text_model = text_model.text_model
self.vision_model = vision_model.vision_model
self.logit_scale = nn.Param(jax.random.normal(rngs.param(), (1,), param_dtype))
self.logit_bias = nn.Param(jax.random.normal(rngs.param(), (1,), param_dtype))
[docs] def get_text_features(
self,
input_ids: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
return_dict: tp.Optional[bool] = None,
) -> chex.Array:
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = text_outputs[1]
return pooled_output
[docs] def get_image_features(
self,
pixel_values: tp.Optional[chex.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
return_dict: tp.Optional[bool] = None,
interpolate_pos_encoding: bool = False,
) -> chex.Array:
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
pooled_output = vision_outputs[1]
return pooled_output
def __call__(
self,
input_ids: tp.Optional[chex.Array] = None,
pixel_values: tp.Optional[chex.Array] = None,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
return_loss: tp.Optional[bool] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
return_dict: tp.Optional[bool] = None,
interpolate_pos_encoding: bool = False,
) -> tp.Union[tp.Tuple, SiglipOutput]:
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
image_embeds = vision_outputs[1]
text_embeds = text_outputs[1]
# normalized features
image_embeds = image_embeds / jnp.linalg.norm(
image_embeds,
ord=2,
axis=-1,
keepdims=True,
)
text_embeds = text_embeds / jnp.linalg.norm(
text_embeds,
ord=2,
axis=-1,
keepdims=True,
)
# cosine similarity as logits
logits_per_text = jnp.matmul(text_embeds, image_embeds.T)
logit_scale, logit_bias = (self.logit_scale, self.logit_bias)
logits_per_text = logits_per_text * jnp.exp(logit_scale) + logit_bias
logits_per_image = logits_per_text.T
loss = None
if return_loss:
m1_diag1 = -jnp.ones_like(logits_per_text) + 2 * jnp.eye(logits_per_text.shape[0])
loglik = jax.nn.log_sigmoid(m1_diag1 * logits_per_text)
nll = -jnp.sum(loglik, axis=-1)
loss = nll.mean()
if not return_dict:
output = (
logits_per_image,
logits_per_text,
text_embeds,
image_embeds,
text_outputs,
vision_outputs,
)
return ((loss,) + output) if loss is not None else output
return SiglipOutput(
loss=loss,
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
text_embeds=text_embeds,
image_embeds=image_embeds,
text_model_output=text_outputs,
vision_model_output=vision_outputs,
)
[docs]@register_module(
TaskType.IMAGE_CLASSIFICATION,
config=SiglipConfig,
model_type="siglip",
)
class SiglipForImageClassification(EasyDeLBaseModule):
def __init__(
self,
config: SiglipConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.num_labels = config.num_labels
vision_model = SiglipVisionModel(
config.vision_config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.vision_model = vision_model.vision_model
self.use_classif = config.num_labels > 0
# Classifier head
if self.use_classif:
self.classifier = ParallelLinear(
config.vision_config.hidden_size,
config.num_labels,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
pixel_values: tp.Optional[chex.Array] = None,
labels: tp.Optional[chex.Array] = None,
output_attentions: tp.Optional[bool] = None,
output_hidden_states: tp.Optional[bool] = None,
return_dict: tp.Optional[bool] = None,
interpolate_pos_encoding: bool = False,
) -> tp.Union[tuple, ImageClassifierOutput]:
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
outputs = self.vision_model(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
sequence_output = outputs[0]
logits = jnp.mean(sequence_output, axis=1)
if self.use_classif:
logits = self.classifier(logits)
if not return_dict:
output = (logits,) + outputs[2:]
return output
return ImageClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)