defmodule Bumblebee.Vision.ClipVision do
alias Bumblebee.Shared
options =
[
image_size: [
default: 224,
doc: "the size of the input spatial dimensions"
],
num_channels: [
default: 3,
doc: "the number of channels in the input"
],
patch_size: [
default: 32,
doc: "the size of the patch spatial dimensions"
],
hidden_size: [
default: 768,
doc: "the dimensionality of hidden layers"
],
num_blocks: [
default: 12,
doc: "the number of Transformer blocks in the encoder"
],
num_attention_heads: [
default: 12,
doc: "the number of attention heads for each attention layer in the encoder"
],
intermediate_size: [
default: 3072,
docs:
"the dimensionality of the intermediate layer in the transformer feed-forward network (FFN) in the encoder"
],
activation: [
default: :quick_gelu,
doc: "the activation function"
],
attention_dropout_rate: [
default: 0.0,
doc: "the dropout rate for attention weights"
],
layer_norm_epsilon: [
default: 1.0e-5,
doc: "the epsilon used by the layer normalization layers"
]
] ++
Shared.common_options([
:output_hidden_states,
:output_attentions
])
@moduledoc """
The CLIP model for image encoding.
## Architectures
* `:base` - the base image model
## Inputs
* `"pixel_values"` - `{batch_size, image_size, image_size, num_channels}`
Featurized image pixel values.
## Configuration
#{Shared.options_doc(options)}
"""
defstruct [architecture: :base] ++ Shared.option_defaults(options)
@behaviour Bumblebee.ModelSpec
@behaviour Bumblebee.Configurable
import Bumblebee.Utils.Model, only: [join: 2]
alias Bumblebee.Layers
@impl true
def architectures(), do: [:base]
@impl true
def config(spec, opts \\ []) do
Shared.put_config_attrs(spec, opts)
end
@impl true
def input_template(spec) do
%{
"pixel_values" =>
Nx.template({1, spec.image_size, spec.image_size, spec.num_channels}, :f32)
}
end
@impl true
def model(%__MODULE__{architecture: :base} = spec) do
inputs = inputs(spec)
inputs
|> core(spec)
|> Layers.output()
end
defp inputs(spec) do
shape = {nil, spec.image_size, spec.image_size, spec.num_channels}
Bumblebee.Utils.Model.inputs_to_map([
Axon.input("pixel_values", shape: shape)
])
end
defp core(inputs, spec) do
embeddings = embedder(inputs["pixel_values"], spec, name: "embedder")
encoder_outputs =
embeddings
|> Axon.layer_norm(epsilon: spec.layer_norm_epsilon, name: "pre_norm")
|> encoder(spec, name: "encoder")
pooled_state =
encoder_outputs.hidden_state
|> Axon.layer_norm(epsilon: spec.layer_norm_epsilon, name: "post_norm")
|> Layers.take_token(index: 0, axis: 1)
%{
hidden_state: encoder_outputs.hidden_state,
pooled_state: pooled_state,
hidden_states: encoder_outputs.hidden_states,
attentions: encoder_outputs.attentions
}
end
defp embedder(pixel_values, spec, opts) do
name = opts[:name]
patch_embeddings = patch_embedding(pixel_values, spec, name: join(name, "patch_embedding"))
class_embedding =
Layers.learned_embeddings(1, spec.hidden_size,
name: join(name, "class_embedding"),
initializer: Axon.Initializers.normal()
)
input_embeddings = Layers.concatenate_embeddings([class_embedding, patch_embeddings])
num_patches = div(spec.image_size, spec.patch_size) ** 2
num_positions = num_patches + 1
position_ids = position_ids(num_positions)
position_embeddings =
Axon.embedding(position_ids, num_patches + 1, spec.hidden_size,
name: join(name, "position_embedding")
)
Axon.add(input_embeddings, position_embeddings)
end
defp patch_embedding(pixel_values, spec, opts) do
name = opts[:name]
pixel_values
|> Axon.conv(spec.hidden_size,
kernel_size: spec.patch_size,
strides: spec.patch_size,
padding: :valid,
kernel_initializer: Axon.Initializers.normal(),
use_bias: false,
name: name
)
|> Axon.reshape({:batch, :auto, spec.hidden_size}, name: join(name, "reshape"))
end
defp position_ids(num_position_ids) do
Axon.layer(
fn _opts -> Nx.iota({1, num_position_ids}) end,
[],
op_name: :position_ids
)
end
defp encoder(embeddings, spec, opts) do
name = opts[:name]
Layers.Transformer.blocks(embeddings,
num_blocks: spec.num_blocks,
num_attention_heads: spec.num_attention_heads,
hidden_size: spec.hidden_size,
kernel_initializer: Axon.Initializers.normal(scale: 0.01),
dropout_rate: 0.0,
attention_dropout_rate: spec.attention_dropout_rate,
layer_norm: [
epsilon: spec.layer_norm_epsilon
],
ffn: [
intermediate_size: spec.intermediate_size,
activation: spec.activation
],
block_type: :norm_first,
output_hidden_states: spec.output_hidden_states,
output_attentions: spec.output_attentions,
name: join(name, "blocks")
)
end
defimpl Bumblebee.HuggingFace.Transformers.Config do
# Support loading from the entire Clip configuration
def load(spec, %{"model_type" => "clip", "vision_config" => data}) do
load(spec, data)
end
def load(spec, data) do
import Shared.Converters
opts =
convert!(data,
image_size: {"image_size", number()},
patch_size: {"patch_size", number()},
hidden_size: {"hidden_size", number()},
num_blocks: {"num_hidden_layers", number()},
num_attention_heads: {"num_attention_heads", number()},
intermediate_size: {"intermediate_size", number()},
activation: {"hidden_act", atom()},
attention_dropout_rate: {"attention_dropout", number()},
layer_norm_epsilon: {"layer_norm_eps", number()}
) ++ Shared.common_options_from_transformers(data, spec)
@for.config(spec, opts)
end
end
defimpl Bumblebee.HuggingFace.Transformers.Model do
def params_mapping(_spec) do
%{
"embedder.patch_embedding" => "vision_model.embeddings.patch_embedding",
"embedder.class_embedding" => %{
"embeddings" => {
[{"vision_model.embeddings", "class_embedding"}],
fn [value] -> Nx.new_axis(value, 0) end
}
},
"embedder.position_embedding" => "vision_model.embeddings.position_embedding",
"encoder.blocks.{n}.self_attention_norm" => "vision_model.encoder.layers.{n}.layer_norm1",
"encoder.blocks.{n}.self_attention.query" =>
"vision_model.encoder.layers.{n}.self_attn.q_proj",
"encoder.blocks.{n}.self_attention.key" =>
"vision_model.encoder.layers.{n}.self_attn.k_proj",
"encoder.blocks.{n}.self_attention.value" =>
"vision_model.encoder.layers.{n}.self_attn.v_proj",
"encoder.blocks.{n}.self_attention.output" =>
"vision_model.encoder.layers.{n}.self_attn.out_proj",
"encoder.blocks.{n}.ffn.intermediate" => "vision_model.encoder.layers.{n}.mlp.fc1",
"encoder.blocks.{n}.ffn.output" => "vision_model.encoder.layers.{n}.mlp.fc2",
"encoder.blocks.{n}.output_norm" => "vision_model.encoder.layers.{n}.layer_norm2",
"pre_norm" => "vision_model.pre_layrnorm",
"post_norm" => "vision_model.post_layernorm"
}
end
end
end
