# 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 cached_property, partial
import chex
import jax
import jax.numpy as jnp
from flax import nnx as nn
from jax import lax
from easydel.infra.base_module import EasyDeLBaseModule
from easydel.infra.factory import TaskType, register_module
from easydel.infra.loss_utils import LossMetrics
from easydel.infra.modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPooling,
CLIPOutput,
CLIPTextModelOutput,
ImageClassifierOutput,
)
from easydel.infra.utils import ACT2FN, control_mlp_sharding
from easydel.layers.attention import AttentionModule, FlexibleAttentionModule
from easydel.layers.linear import ParallelLinear
from .clip_configuration import (
CLIPConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
[docs]def contrastive_loss(logits: jax.Array) -> jax.Array:
"""
Computes the contrastive loss.
Args:
logits (jax.Array): Logits from the model.
Returns:
jax.Array: Contrastive loss.
"""
labels = jnp.arange(len(logits))
return jnp.mean(
-jnp.sum(jax.nn.log_softmax(logits) * jax.nn.one_hot(labels, len(logits)), axis=-1)
)
[docs]def clip_loss(similarity: jax.Array) -> jax.Array:
"""
Computes the CLIP loss.
Args:
similarity (jax.Array): Similarity matrix.
Returns:
jax.Array: CLIP loss.
"""
caption_loss = contrastive_loss(similarity)
image_loss = contrastive_loss(similarity.T)
return (caption_loss + image_loss) / 2.0
[docs]class CLIPVisionEmbeddings(nn.Module):
"""
Constructs the vision embeddings for CLIP.
Attributes:
config (CLIPVisionConfig): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: CLIPVisionConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
self.config = config
embed_dim = config.hidden_size
image_size = config.image_size
patch_size = config.patch_size
self.class_embedding = nn.Param(
jax.nn.initializers.normal(stddev=0.02)(
rngs.params(),
shape=(embed_dim,),
dtype=param_dtype,
),
)
self.patch_embedding = nn.Conv(
config.num_channels,
embed_dim,
kernel_size=(patch_size, patch_size),
strides=(patch_size, patch_size),
padding="VALID",
use_bias=False,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
kernel_init=jax.nn.initializers.normal(),
rngs=rngs,
)
self.num_patches = (image_size // patch_size) ** 2
num_positions = self.num_patches + 1
self.position_embedding = nn.Embed(
num_positions,
embed_dim,
embedding_init=jax.nn.initializers.normal(),
dtype=dtype,
param_dtype=param_dtype,
rngs=rngs,
)
def __call__(self, pixel_values):
"""
Forward pass for vision embeddings.
Args:
pixel_values (chex.Array): Input pixel values (batch_size, num_channels, height, width).
Returns:
chex.Array: Combined class and patch embeddings.
"""
patch_embeds = self.patch_embedding(pixel_values)
batch_size, height, width, channels = patch_embeds.shape
patch_embeds = jnp.reshape(patch_embeds, (batch_size, height * width, channels))
class_embeds = jnp.expand_dims(self.class_embedding.value, axis=(0, 1))
class_embeds = jnp.tile(class_embeds, (batch_size, 1, 1))
embeddings = jnp.concatenate([class_embeds, patch_embeds], axis=1)
embeddings = embeddings + self.position_embedding(
jnp.expand_dims(
jnp.arange(
0, ((self.config.image_size // self.config.patch_size) ** 2) + 1, dtype="i4"
),
axis=0,
)
)
return embeddings
[docs]class CLIPTextEmbeddings(nn.Module):
"""
Constructs the text embeddings for CLIP.
Attributes:
config (CLIPTextConfig): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: CLIPVisionConfig,
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, position_ids):
"""
Forward pass for text embeddings.
Args:
input_ids (chex.Array): Input token IDs.
position_ids (chex.Array): Position IDs.
Returns:
chex.Array: Combined token and position embeddings.
"""
input_embeds = self.token_embedding(input_ids.astype("i4"))
position_embeds = self.position_embedding(position_ids.astype("i4"))
embeddings = input_embeds + position_embeds
return embeddings
[docs]class CLIPAttention(AttentionModule):
"""
CLIP Attention module, supporting both text (causal) and vision (non-causal) attention.
Attributes:
config (Union[CLIPTextConfig, CLIPVisionConfig]): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: tp.Union[CLIPTextConfig, CLIPVisionConfig],
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 = isinstance(config, CLIPTextConfig)
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):
"""
Splits hidden states into multiple heads.
Args:
hidden_states (chex.Array): Input hidden states.
Returns:
chex.Array: Reshaped hidden states.
"""
return hidden_states.reshape(
hidden_states.shape[:2] + (self.num_heads, self.head_dim)
)
def _merge_heads(self, hidden_states):
"""
Merges multiple heads back into a single hidden state tensor.
Args:
hidden_states (chex.Array): Input hidden states.
Returns:
chex.Array: Merged 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,
causal_mask: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
):
"""
Forward pass for the CLIP attention module.
Args:
hidden_states (chex.Array): Input hidden states.
attention_mask (Optional[chex.Array]): Mask to prevent attention to certain positions.
causal_mask (Optional[chex.Array]): Causal mask for text attention.
output_attentions (bool): Whether to output attention weights.
Returns:
Tuple[chex.Array, Optional[chex.Array]]: Attention output and optionally attention weights.
"""
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:
assert causal_mask is not None
query_length, key_length = query.shape[1], key.shape[1]
causal_attention_mask = causal_mask[
:, :, key_length - query_length : key_length, :key_length
]
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 = 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 CLIPMLP(nn.Module):
"""
CLIP MLP (Feed-Forward) layer.
Attributes:
config (Union[CLIPTextConfig, CLIPVisionConfig]): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: tp.Union[CLIPTextConfig, CLIPVisionConfig],
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):
"""
Forward pass for the MLP layer.
Args:
hidden_states (chex.Array): Input hidden states.
Returns:
chex.Array: Output hidden states.
"""
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 CLIPEncoderLayer(nn.Module):
"""
Single CLIP encoder layer, combining self-attention and MLP.
Attributes:
config (Union[CLIPTextConfig, CLIPVisionConfig]): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: tp.Union[CLIPTextConfig, CLIPVisionConfig],
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 = CLIPAttention(
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 = CLIPMLP(
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,
causal_mask: tp.Optional[chex.Array] = None,
output_attentions: bool = False,
):
"""
Forward pass for the encoder layer.
Args:
hidden_states (chex.Array): Input hidden states.
attention_mask (Optional[chex.Array]): Attention mask.
causal_mask (Optional[chex.Array]): Causal mask (for text).
output_attentions (bool): Whether to output attention weights.
Returns:
Tuple[chex.Array, ...]: Output hidden states and optional attention weights.
"""
residual = hidden_states
hidden_states = self.layer_norm1(hidden_states)
attn_outputs = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_mask=causal_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 CLIPEncoder(nn.Module):
"""
Transformer encoder consisting of `CLIPEncoderLayer` layers.
Attributes:
config (Union[CLIPTextConfig, CLIPVisionConfig]): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: tp.Union[CLIPTextConfig, CLIPVisionConfig],
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 = [
CLIPEncoderLayer(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
for _ in range(config.num_hidden_layers)
]
@cached_property
def causal_mask(self):
"""
Returns the causal mask if the encoder is for text, otherwise None.
Returns:
Optional[chex.Array]: Causal mask.
"""
if isinstance(self.config, CLIPTextConfig):
return self.config.get_basic_causal_mask()
return None
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,
):
"""
Forward pass for the CLIP encoder.
Args:
inputs_embeds (chex.Array): Input embeddings.
attention_mask (Optional[chex.Array]): Attention mask.
output_attentions (bool): Whether to output attention weights.
output_hidden_states (bool): Whether to output all hidden states.
return_dict (bool): Whether to return a dictionary output.
Returns:
Union[BaseModelOutput, Tuple]: Encoder output (last hidden state, optional hidden states, optional attentions).
"""
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,
causal_mask=self.causal_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 CLIPTextTransformer(EasyDeLBaseModule):
"""
The transformer encoder for the CLIP text model.
Attributes:
config (CLIPTextConfig): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: CLIPTextConfig,
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.embeddings = CLIPTextEmbeddings(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.encoder = CLIPEncoder(
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.eos_token_id = self.config.eos_token_id
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,
):
"""Forward pass for the text transformer.
Args:
input_ids (chex.Array): Input token IDs.
attention_mask (chex.Array): Attention mask.
position_ids (chex.Array): Position IDs.
output_attentions (bool): Whether to output attention weights.
output_hidden_states (bool): Whether to output all hidden states.
return_dict (bool): Whether to return a dictionary output.
Returns:
Union[BaseModelOutputWithPooling, Tuple]: Transformer output (last hidden state, pooled output, optional hidden states, optional attentions).
"""
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
)
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)
if self.eos_token_id == 2:
pooled_output = last_hidden_state[
jnp.arange(last_hidden_state.shape[0]),
input_ids.argmax(axis=-1),
]
else:
pooled_output = last_hidden_state[
jnp.arange(last_hidden_state.shape[0]),
(input_ids == self.eos_token_id).argmax(axis=-1),
]
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]class CLIPTextModel(EasyDeLBaseModule):
"""
Bare CLIP text model (transformer) outputting raw hidden-states without any specific head on top.
Attributes:
config (CLIPTextConfig): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: CLIPTextConfig,
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 = CLIPTextTransformer(
config=config,
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,
):
"""Forward pass for the bare CLIP text model.
Args:
input_ids (chex.Array): Input token IDs.
attention_mask (chex.Array): Attention mask.
position_ids (chex.Array): Position IDs.
output_attentions (bool): Whether to output attention weights.
output_hidden_states (bool): Whether to output all hidden states.
return_dict (bool): Whether to return a dictionary output.
Returns:
Union[BaseModelOutputWithPooling, Tuple]: Model output.
"""
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,
)
[docs]class CLIPTextModelWithProjection(EasyDeLBaseModule):
"""
CLIP text model with a projection layer on top.
Attributes:
config (CLIPTextConfig): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: CLIPTextConfig,
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 = CLIPTextTransformer(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.text_projection = ParallelLinear(
config.hidden_size,
config.projection_dim,
use_bias=False,
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,
) -> tp.Union[CLIPTextModelOutput, tp.Tuple]:
"""Forward pass for the CLIP text model with projection.
Args:
input_ids (chex.Array): Input token IDs.
attention_mask (chex.Array): Attention mask.
position_ids (chex.Array): Position IDs.
output_attentions (bool): Whether to output attention weights.
output_hidden_states (bool): Whether to output all hidden states.
return_dict (bool): Whether to return a dictionary output.
Returns:
Union[CLIPTextModelOutput, Tuple]: Model output including projected text embeddings.
"""
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]
text_embeds = self.text_projection(pooled_output)
if not return_dict:
return (text_embeds, text_outputs[0]) + text_outputs[2:]
return CLIPTextModelOutput(
text_embeds=text_embeds,
last_hidden_state=text_outputs.last_hidden_state,
hidden_states=text_outputs.hidden_states,
attentions=text_outputs.attentions,
)
[docs]@register_module(
config=CLIPVisionConfig,
model_type="clip_vision_model",
task_type=TaskType.BASE_VISION,
)
@register_module(
config=CLIPVisionConfig,
model_type="clip_vision_model",
task_type=TaskType.BASE_MODULE,
)
class CLIPVisionModel(EasyDeLBaseModule):
"""
Bare CLIP vision model (transformer) outputting raw hidden-states without any specific head on top.
Attributes:
config (CLIPVisionConfig): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: CLIPVisionConfig,
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.vision_model = CLIPVisionTransformer(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
def __call__(
self,
pixel_values: chex.Array,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
"""Forward pass for the bare CLIP vision model.
Args:
pixel_values (chex.Array): Input pixel values.
output_attentions (bool): Whether to output attention weights.
output_hidden_states (bool): Whether to output all hidden states.
return_dict (bool): Whether to return a dictionary output.
Returns:
Union[BaseModelOutputWithPooling, Tuple]: Model output.
"""
return self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
[docs]@register_module(
config=CLIPVisionConfig,
model_type="clip",
task_type=TaskType.IMAGE_CLASSIFICATION,
)
class CLIPForImageClassification(EasyDeLBaseModule):
"""
CLIP vision model with an image classification head on top (a linear layer on the pooled final hidden state).
Attributes:
config (CLIPVisionConfig): Configuration object.
dtype (jnp.dtype): Data type for computation.
param_dtype (jnp.dtype): Data type for parameters.
precision (jax.lax.PrecisionLike): JAX precision level.
rngs (nn.Rngs): Random number generators.
"""
def __init__(
self,
config: CLIPVisionConfig,
dtype: jnp.dtype = jnp.float32,
param_dtype: jnp.dtype = jnp.float32,
precision: jax.lax.PrecisionLike = None,
*,
rngs: nn.Rngs,
):
"""Initializes the CLIPForImageClassification model."""
super().__init__(
config=config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.vision_model = CLIPVisionTransformer(
config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.classifier = ParallelLinear(
config.vision_config.hidden_size,
config.num_labels,
rngs=rngs,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
)
def __call__(
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,
) -> 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,
)
sequence_output = outputs[0]
sequence_output = jnp.mean(sequence_output[:, 1:, :], axis=1)
if self.config.num_labels > 0:
logits = self.classifier(sequence_output)
else:
logits = sequence_output
if not return_dict:
output = (logits,) + outputs[2:]
return output
return ImageClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[docs]@register_module(
config=CLIPConfig,
model_type="clip",
task_type=TaskType.ZERO_SHOT_IMAGE_CLASSIFICATION,
)
class CLIPModel(EasyDeLBaseModule):
def __init__(
self,
config: CLIPConfig,
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,
)
text_config = self.config.text_config
vision_config = self.config.vision_config
self.projection_dim = self.config.projection_dim
self.text_embed_dim = text_config.hidden_size
self.vision_embed_dim = vision_config.hidden_size
self.text_model = CLIPTextTransformer(
text_config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
self.vision_model = CLIPVisionTransformer(
vision_config,
dtype=dtype,
param_dtype=param_dtype,
precision=precision,
rngs=rngs,
)
linear_class = partial(
ParallelLinear,
dtype=dtype,
param_dtype=param_dtype,
kernel_init=jax.nn.initializers.normal(0.02),
use_bias=False,
rngs=rngs,
)
self.visual_projection = linear_class(
config.vision_config.hidden_size, self.projection_dim
)
self.text_projection = linear_class(
config.text_config.hidden_size, self.projection_dim
)
self.logit_scale = nn.Param(jnp.ones([]) * self.config.logit_scale_init_value)
def __call__(
self,
input_ids: chex.Array,
pixel_values: chex.Array,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
) -> tp.Union[CLIPOutput, tp.Tuple]:
if attention_mask is None and input_ids is not None:
attention_mask = jnp.ones_like(input_ids)
if position_ids is None and attention_mask is not None:
position_ids = attention_mask.cumsum(-1) - 1
return_dict = return_dict if return_dict is not None else self.config.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,
)
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]
image_embeds = self.visual_projection(image_embeds)
text_embeds = text_outputs[1]
text_embeds = self.text_projection(text_embeds)
image_embeds = image_embeds / jnp.linalg.norm(image_embeds, axis=-1, keepdims=True)
text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True)
logit_scale = jnp.exp(self.logit_scale)
logits_per_text = jnp.matmul(text_embeds, image_embeds.T) * logit_scale
logits_per_image = logits_per_text.T
if not return_dict:
return (
logits_per_image,
logits_per_text,
text_embeds,
image_embeds,
text_outputs,
vision_outputs,
)
return CLIPOutput(
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] def get_text_features(
self,
input_ids: chex.Array,
attention_mask: tp.Optional[chex.Array] = None,
position_ids: tp.Optional[chex.Array] = None,
):
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
)
pooled_output = text_outputs[1]
text_features = self.text_projection(pooled_output)
return text_features
[docs] def get_image_features(self, pixel_values: chex.Array):
vision_outputs = self.vision_model(pixel_values=pixel_values)
pooled_output = vision_outputs[1] # pooled_output
image_features = self.visual_projection(pooled_output)
return image_features
[docs] def compute_loss(
self,
*,
labels=None, # just to extract
loss_config=None, # just to extract
loss_kwargs=None, # just to extract
**batch,
) -> tp.Tuple[tp.Any, CLIPOutput]:
batch.pop("return_dict", None)
outputs = self(**batch, return_dict=True)
loss = LossMetrics(loss=clip_loss(outputs.logits_per_text))
outputs = outputs.replace(loss=loss.loss)
return outputs, loss