defmodule Bumblebee.Text.Bart do
alias Bumblebee.Shared
options =
[
vocab_size: [
default: 50265,
doc: """
the vocabulary size of the token embedding. This corresponds to the number of distinct
tokens that can be represented in model input and output
"""
],
max_positions: [
default: 1024,
doc: """
the vocabulary size of the position embedding. This corresponds to the maximum sequence
length that this model can process. Typically this is set to a large value just in case,
such as 512, 1024 or 2048
"""
],
hidden_size: [
default: 1024,
doc: "the dimensionality of hidden layers"
],
encoder_num_blocks: [
default: 12,
doc: "the number of Transformer blocks in the encoder"
],
decoder_num_blocks: [
default: 12,
doc: "the number of Transformer blocks in the decoder"
],
encoder_num_attention_heads: [
default: 16,
doc: "the number of attention heads for each attention layer in the encoder"
],
decoder_num_attention_heads: [
default: 16,
doc: "the number of attention heads for each attention layer in the decoder"
],
encoder_intermediate_size: [
default: 4096,
docs:
"the dimensionality of the intermediate layer in the transformer feed-forward network (FFN) in the encoder"
],
decoder_intermediate_size: [
default: 4096,
docs:
"the dimensionality of the intermediate layer in the transformer feed-forward network (FFN) in the decoder"
],
scale_embedding: [
default: false,
doc: "whether to scale embeddings by dividing by the square root of `:hidden_size`"
],
activation: [
default: :gelu,
doc: "the activation function"
],
dropout_rate: [
default: 0.1,
doc: "the dropout rate for encoder and decoder"
],
attention_dropout_rate: [
default: 0.0,
doc: "the dropout rate for attention weights"
],
activation_dropout_rate: [
default: 0.0,
doc: "the dropout rate for activations inside fully connected layers"
],
classifier_dropout_rate: [
default: 0.0,
doc: "the dropout rate for the classification head"
],
initializer_scale: [
default: 0.02,
doc:
"the standard deviation of the normal initializer used for initializing kernel parameters"
]
] ++
Shared.common_options([
:output_hidden_states,
:output_attentions,
:num_labels,
:id_to_label
]) ++
Shared.token_options(
eos_token_id: 2,
decoder_start_token_id: 2
)
@moduledoc """
BART model family.
## Architectures
* `:base` - plain BART without any head on top
* `:for_causal_language_modeling` - BART with a language modeling
head. The head returns logits for each token in the original
sequence
* `:for_conditional_generation` - BART with a language modeling
head. The head returns logits for each token in the original
sequence
* `:for_sequence_classification` - BART with a sequence
classification head. The head returns logits corresponding to
possible classes
* `:for_question_answering` - BART with a span classification head.
The head returns logits for the span start and end positions
## Inputs
* `"input_ids"` - `{batch_size, sequence_length}`
Indices of input sequence tokens in the vocabulary.
* `"attention_mask"` - `{batch_size, sequence_length}`
Mask indicating which tokens to attend to. This is used to ignore
padding tokens, which are added when processing a batch of sequences
with different length.
* `"position_ids"` - `{batch_size, sequence_length}`
Indices of positions of each input sequence tokens in the position
embeddings.
* `"attention_head_mask"` - `{encoder_num_blocks, encoder_num_attention_heads}`
Mask to nullify selected heads of the self-attention blocks in
the encoder.
* `"input_embeddings"` - `{batch_size, sequence_length, hidden_size}`
Embedded representation of `"input_ids"`, which can be specified
for more control over how `"input_ids"` are embedded than the
model's internal embedding lookup. If `"input_embeddings"` are present,
then `"input_ids"` will be ignored.
* `"decoder_input_ids"` - `{batch_size, target_sequence_length}`
Indices of decoder input sequence tokens in the vocabulary. If not
present and `"input_ids"` is, it will be generated by shifting
each token in `"input_ids"` to the right once.
* `"decoder_attention_mask"` - `{batch_size, target_sequence_length}`
Mask indicating which decoder tokens to attend to. This is used
to ignore padding tokens, which are added when processing a batch
of sequences with different length.
* `"decoder_position_ids"` - `{batch_size, target_sequence_length}`
Indices of positions of each decoder input sequence tokens in
the position embeddings.
* `"decoder_attention_head_mask"` - `{decoder_num_blocks, decoder_num_attention_heads}`
Mask to nullify selected heads of the self-attention blocks in
the decoder.
* `"decoder_input_embeddings"` - `{batch_size, sequence_length, hidden_size}`
Embedded representation of `"decoder_input_ids"`, which can be
specified for more control over how `"decoder_input_ids"` are
embedded than the model's internal embedding lookup. If
`"decoder_input_embeddings"` are present, then `"decoder_input_ids"`
will be ignored.
* `"encoder_hidden_state"` - `{batch_size, sequence_length, hidden_size}`
Last hidden state output from the encoder. This hidden state is
used in cross-attention blocks in the decoder. If specified, the
model will skip the encoding process and use this value directly
for cross-attentions in the decoder.
* `"cross_attention_head_mask"` - `{decoder_num_blocks, decoder_num_attention_heads}`
Mask to nullify selected heads of the cross-attention blocks in
the decoder with shape.
* `"cache"`
A container with cached layer results used to speed up sequential
decoding (autoregression). With cache, certain hidden states are
taken from the cache, rather than recomputed on every decoding
pass. The cache should be treated as opaque and initialized with
`Bumblebee.Text.Generation.init_cache/4`.
### Exceptions
The `:for_causal_language_modeling` model is just the decoder part and
accepts the following inputs instead: `"input_ids"`, `"attention_mask"`,
`"position_ids"`, `"attention_head_mask"`, `"input_embeddings"`, `"encoder_hidden_state"`,
`"encoder_attention_mask"`, `"cross_attention_head_mask"`, `"cache"`.
## Configuration
#{Shared.options_doc(options)}
"""
defstruct [architecture: :base] ++ Shared.option_defaults(options)
@behaviour Bumblebee.ModelSpec
@behaviour Bumblebee.Configurable
@behaviour Bumblebee.Text.Generation
import Bumblebee.Utils.Model, only: [join: 2]
alias Bumblebee.Layers
@impl true
def architectures(),
do: [
:base,
:for_causal_language_modeling,
:for_conditional_generation,
:for_sequence_classification,
:for_question_answering
]
@impl true
def config(spec, opts) do
spec
|> Shared.put_config_attrs(opts)
|> Shared.validate_label_options()
end
@impl true
def input_template(_spec) do
%{
"input_ids" => Nx.template({1, 1}, :u32)
}
end
@impl true
def model(%__MODULE__{architecture: :base} = spec) do
inputs = encoder_decoder_inputs(spec)
inputs
|> core(spec)
|> Layers.output()
end
def model(%__MODULE__{architecture: :for_conditional_generation} = spec) do
inputs = encoder_decoder_inputs(spec)
outputs = core(inputs, spec)
logits =
outputs.hidden_state
|> language_modeling_head(spec, name: "language_modeling_head")
|> Axon.bias(name: "language_modeling_head.logits_bias", bias_initializer: :zeros)
Layers.output(%{
logits: logits,
decoder_hidden_states: outputs.decoder_hidden_states,
decoder_attentions: outputs.decoder_attentions,
cross_attentions: outputs.cross_attentions,
encoder_hidden_state: outputs.encoder_hidden_state,
encoder_hidden_states: outputs.encoder_hidden_states,
encoder_attentions: outputs.encoder_attentions,
cache: outputs.cache
})
end
def model(%__MODULE__{architecture: :for_sequence_classification} = spec) do
inputs = encoder_decoder_inputs(spec)
outputs = core(inputs, spec)
sentence_representation =
Axon.layer(
fn input_ids, hidden_state, _opts ->
eos_mask = Nx.equal(input_ids, spec.eos_token_id)
eos_idx = Nx.argmax(eos_mask, tie_break: :high, axis: 1)
Bumblebee.Utils.Nx.batched_take(hidden_state, eos_idx)
end,
[inputs["input_ids"], outputs.hidden_state]
)
logits =
sentence_representation
|> Axon.dropout(rate: spec.classifier_dropout_rate)
|> Axon.dense(spec.hidden_size,
kernel_initializer: kernel_initializer(spec),
name: "sequence_classification_head.dense"
)
|> Axon.activation(:tanh)
|> Axon.dropout(rate: spec.classifier_dropout_rate)
|> Axon.dense(spec.num_labels,
kernel_initializer: kernel_initializer(spec),
name: "sequence_classification_head.output"
)
Layers.output(%{
logits: logits,
decoder_hidden_states: outputs.decoder_hidden_states,
decoder_attentions: outputs.decoder_attentions,
cross_attentions: outputs.cross_attentions,
encoder_hidden_state: outputs.encoder_hidden_state,
encoder_hidden_states: outputs.encoder_hidden_states,
encoder_attentions: outputs.encoder_attentions
})
end
def model(%__MODULE__{architecture: :for_question_answering} = spec) do
inputs = encoder_decoder_inputs(spec)
outputs = core(inputs, spec)
logits =
Axon.dense(outputs.hidden_state, 2,
kernel_initializer: kernel_initializer(spec),
name: "question_answering_head.output"
)
{start_logits, end_logits} = Layers.split_pair(logits)
Layers.output(%{
start_logits: start_logits,
end_logits: end_logits,
decoder_hidden_states: outputs.decoder_hidden_states,
decoder_attentions: outputs.decoder_attentions,
cross_attentions: outputs.cross_attentions,
encoder_hidden_state: outputs.encoder_hidden_state,
encoder_hidden_states: outputs.encoder_hidden_states,
encoder_attentions: outputs.encoder_attentions
})
end
def model(%__MODULE__{architecture: :for_causal_language_modeling} = spec) do
shape = {nil, nil}
hidden_shape = {nil, nil, spec.hidden_size}
decoder_attention_head_mask_shape =
{spec.decoder_num_blocks, spec.decoder_num_attention_heads}
inputs =
Bumblebee.Utils.Model.inputs_to_map([
Axon.input("input_ids", optional: true, shape: shape),
Axon.input("attention_mask", optional: true, shape: shape),
Axon.input("position_ids", optional: true, shape: shape),
Axon.input("attention_head_mask",
optional: true,
shape: decoder_attention_head_mask_shape
),
Axon.input("input_embeddings", optional: true, shape: hidden_shape),
Axon.input("encoder_hidden_state", optional: true, shape: hidden_shape),
Axon.input("encoder_attention_mask", optional: true, shape: shape),
Axon.input("cross_attention_head_mask",
optional: true,
shape: decoder_attention_head_mask_shape
),
Axon.input("cache", optional: true)
])
embeddings =
embedder(inputs["input_ids"], inputs["position_ids"], inputs["input_embeddings"], spec,
name: "decoder_embedder"
)
outputs =
decoder(
embeddings,
inputs["attention_mask"],
inputs["attention_head_mask"],
inputs["encoder_hidden_state"],
inputs["encoder_attention_mask"],
inputs["cross_attention_head_mask"],
inputs["cache"],
spec,
name: "decoder"
)
logits = language_modeling_head(outputs.hidden_state, spec, name: "language_modeling_head")
Layers.output(%{
logits: logits,
hidden_states: outputs.hidden_states,
attentions: outputs.attentions,
cross_attentions: outputs.cross_attentions,
cache: outputs.cache
})
end
defp encoder_decoder_inputs(spec) do
shape = {nil, nil}
hidden_shape = {nil, nil, spec.hidden_size}
encoder_attention_head_mask_shape =
{spec.encoder_num_blocks, spec.encoder_num_attention_heads}
decoder_attention_head_mask_shape =
{spec.decoder_num_blocks, spec.decoder_num_attention_heads}
Bumblebee.Utils.Model.inputs_to_map([
Axon.input("input_ids", optional: true, shape: shape),
Axon.input("attention_mask", optional: true, shape: shape),
Axon.input("position_ids", optional: true, shape: shape),
Axon.input("attention_head_mask", optional: true, shape: encoder_attention_head_mask_shape),
Axon.input("input_embeddings", optional: true, shape: hidden_shape),
Axon.input("decoder_input_ids", optional: true, shape: shape),
Axon.input("decoder_attention_mask", optional: true, shape: shape),
Axon.input("decoder_position_ids", optional: true, shape: shape),
Axon.input("decoder_attention_head_mask",
optional: true,
shape: decoder_attention_head_mask_shape
),
Axon.input("decoder_input_embeddings", optional: true, shape: hidden_shape),
Axon.input("encoder_hidden_state", optional: true, shape: hidden_shape),
Axon.input("cross_attention_head_mask",
optional: true,
shape: decoder_attention_head_mask_shape
),
Axon.input("cache", optional: true)
])
end
@impl true
def init_cache(spec, batch_size, max_length, inputs) do
encoder_sequence_length =
if encoder_hidden_state = inputs["encoder_hidden_state"] do
Nx.axis_size(encoder_hidden_state, 1)
end
Layers.Decoder.init_cache(batch_size, max_length,
hidden_size: spec.hidden_size,
decoder_num_attention_heads: spec.decoder_num_attention_heads,
encoder_num_attention_heads: spec.encoder_num_attention_heads,
decoder_num_blocks: spec.decoder_num_blocks,
encoder_sequence_length: encoder_sequence_length
)
end
@impl true
def traverse_cache(_spec, cache, fun) do
Layers.Decoder.traverse_cache(cache, fun)
end
defp core(inputs, spec) do
encoder_outputs =
Layers.if_present inputs["encoder_hidden_state"] do
%{
hidden_state: inputs["encoder_hidden_state"],
hidden_states: Layers.none(),
attentions: Layers.none()
}
else
embeddings =
embedder(inputs["input_ids"], inputs["position_ids"], inputs["input_embeddings"], spec,
name: "encoder_embedder"
)
embeddings
|> encoder(inputs["attention_mask"], inputs["attention_head_mask"], spec, name: "encoder")
|> Map.take([:hidden_state, :hidden_states, :attentions])
end
decoder_input_ids =
Layers.default inputs["decoder_input_ids"] do
Layers.shift_tokens_right(inputs["input_ids"], spec.decoder_start_token_id)
end
embeddings =
embedder(
decoder_input_ids,
inputs["decoder_position_ids"],
inputs["decoder_input_embeddings"],
spec,
name: "decoder_embedder"
)
decoder_outputs =
decoder(
embeddings,
inputs["decoder_attention_mask"],
inputs["decoder_attention_head_mask"],
encoder_outputs.hidden_state,
inputs["attention_mask"],
inputs["cross_attention_head_mask"],
inputs["cache"],
spec,
name: "decoder"
)
%{
hidden_state: decoder_outputs.hidden_state,
decoder_hidden_states: decoder_outputs.hidden_states,
decoder_attentions: decoder_outputs.attentions,
cross_attentions: decoder_outputs.cross_attentions,
cache: decoder_outputs.cache,
encoder_hidden_state: encoder_outputs.hidden_state,
encoder_hidden_states: encoder_outputs.hidden_states,
encoder_attentions: encoder_outputs.attentions
}
end
defp embedder(input_ids, position_ids, input_embeddings, spec, opts) do
name = opts[:name]
input_embeddings =
Layers.default input_embeddings do
token_embedding(input_ids, spec, name: join(name, "token_embedding"))
end
position_ids =
Layers.default position_ids do
Layers.default_position_ids(input_embeddings)
end
position_embeddings =
position_embedding(position_ids, spec, name: join(name, "position_embedding"))
Axon.add([input_embeddings, position_embeddings])
|> Axon.layer_norm(epsilon: 1.0e-5, name: join(name, "norm"))
|> Axon.dropout(rate: spec.dropout_rate)
end
defp token_embedding(input_ids, spec, opts) do
name = opts[:name]
input_embeddings =
Axon.embedding(input_ids, spec.vocab_size, spec.hidden_size,
kernel_initializer: kernel_initializer(spec),
name: name
)
if spec.scale_embedding do
Axon.nx(input_embeddings, fn x -> Nx.multiply(x, Nx.sqrt(spec.hidden_size)) end)
else
input_embeddings
end
end
defp position_embedding(position_ids, spec, opts) do
name = opts[:name]
# For BART we need to offset the embeddings
offset = 2
position_ids
|> Axon.add(Axon.constant(Nx.tensor(offset)))
|> Axon.embedding(spec.max_positions + offset, spec.hidden_size, name: name)
end
defp encoder(hidden_state, attention_mask, attention_head_mask, spec, opts) do
name = opts[:name]
Layers.Transformer.blocks(hidden_state,
attention_mask: attention_mask,
attention_head_mask: attention_head_mask,
num_blocks: spec.encoder_num_blocks,
num_attention_heads: spec.encoder_num_attention_heads,
hidden_size: spec.hidden_size,
kernel_initializer: kernel_initializer(spec),
dropout_rate: spec.dropout_rate,
attention_dropout_rate: spec.attention_dropout_rate,
layer_norm: [
epsilon: 1.0e-5
],
ffn: [
intermediate_size: spec.encoder_intermediate_size,
activation: spec.activation
],
output_hidden_states: spec.output_hidden_states,
output_attentions: spec.output_attentions,
name: join(name, "blocks")
)
end
defp decoder(
hidden_state,
attention_mask,
attention_head_mask,
encoder_hidden_state,
encoder_attention_mask,
cross_attention_head_mask,
cache,
spec,
opts
) do
name = opts[:name]
Layers.Transformer.blocks(hidden_state,
attention_mask: attention_mask,
attention_head_mask: attention_head_mask,
cross_hidden_state: encoder_hidden_state,
cross_attention_mask: encoder_attention_mask,
cross_attention_head_mask: cross_attention_head_mask,
cache: cache,
causal: true,
num_blocks: spec.decoder_num_blocks,
num_attention_heads: spec.decoder_num_attention_heads,
hidden_size: spec.hidden_size,
kernel_initializer: kernel_initializer(spec),
dropout_rate: spec.dropout_rate,
attention_dropout_rate: spec.attention_dropout_rate,
layer_norm: [
epsilon: 1.0e-5
],
ffn: [
intermediate_size: spec.decoder_intermediate_size,
activation: spec.activation
],
output_hidden_states: spec.output_hidden_states,
output_attentions: spec.output_attentions,
name: join(name, "blocks")
)
end
defp language_modeling_head(hidden_state, spec, opts) do
name = opts[:name]
# TODO: Tie lm-head to word embedding as a spec option
Layers.dense_transposed(hidden_state, spec.vocab_size,
kernel_initializer: kernel_initializer(spec),
name: join(name, "output")
)
end
defp kernel_initializer(spec) do
Axon.Initializers.normal(scale: spec.initializer_scale)
end
defimpl Bumblebee.HuggingFace.Transformers.Config do
def load(spec, data) do
import Shared.Converters
opts =
convert!(data,
vocab_size: {"vocab_size", number()},
max_positions: {"max_position_embeddings", number()},
hidden_size: {"d_model", number()},
encoder_num_blocks: {"encoder_layers", number()},
decoder_num_blocks: {"decoder_layers", number()},
encoder_num_attention_heads: {"encoder_attention_heads", number()},
decoder_num_attention_heads: {"decoder_attention_heads", number()},
encoder_intermediate_size: {"encoder_ffn_dim", number()},
decoder_intermediate_size: {"decoder_ffn_dim", number()},
scale_embedding: {"scale_embedding", boolean()},
activation: {"activation_function", activation()},
dropout_rate: {"dropout", number()},
attention_dropout_rate: {"attention_dropout", number()},
activation_dropout_rate: {"activation_dropout", number()},
classifier_dropout_rate: {"classifier_dropout", number()},
initializer_scale: {"init_std", 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
%{
"encoder_embedder.token_embedding" => "model.encoder.embed_tokens",
"encoder_embedder.position_embedding" => "model.encoder.embed_positions",
"encoder_embedder.norm" => "model.encoder.layernorm_embedding",
"encoder.blocks.{n}.self_attention.query" => "model.encoder.layers.{n}.self_attn.q_proj",
"encoder.blocks.{n}.self_attention.key" => "model.encoder.layers.{n}.self_attn.k_proj",
"encoder.blocks.{n}.self_attention.value" => "model.encoder.layers.{n}.self_attn.v_proj",
"encoder.blocks.{n}.self_attention.output" =>
"model.encoder.layers.{n}.self_attn.out_proj",
"encoder.blocks.{n}.self_attention_norm" =>
"model.encoder.layers.{n}.self_attn_layer_norm",
"encoder.blocks.{n}.ffn.intermediate" => "model.encoder.layers.{n}.fc1",
"encoder.blocks.{n}.ffn.output" => "model.encoder.layers.{n}.fc2",
"encoder.blocks.{n}.output_norm" => "model.encoder.layers.{n}.final_layer_norm",
"decoder_embedder.token_embedding" => "model.decoder.embed_tokens",
"decoder_embedder.position_embedding" => "model.decoder.embed_positions",
"decoder_embedder.norm" => "model.decoder.layernorm_embedding",
"decoder.blocks.{n}.self_attention.query" => "model.decoder.layers.{n}.self_attn.q_proj",
"decoder.blocks.{n}.self_attention.key" => "model.decoder.layers.{n}.self_attn.k_proj",
"decoder.blocks.{n}.self_attention.value" => "model.decoder.layers.{n}.self_attn.v_proj",
"decoder.blocks.{n}.self_attention.output" =>
"model.decoder.layers.{n}.self_attn.out_proj",
"decoder.blocks.{n}.self_attention_norm" =>
"model.decoder.layers.{n}.self_attn_layer_norm",
"decoder.blocks.{n}.cross_attention.query" =>
"model.decoder.layers.{n}.encoder_attn.q_proj",
"decoder.blocks.{n}.cross_attention.key" =>
"model.decoder.layers.{n}.encoder_attn.k_proj",
"decoder.blocks.{n}.cross_attention.value" =>
"model.decoder.layers.{n}.encoder_attn.v_proj",
"decoder.blocks.{n}.cross_attention.output" =>
"model.decoder.layers.{n}.encoder_attn.out_proj",
"decoder.blocks.{n}.cross_attention_norm" =>
"model.decoder.layers.{n}.encoder_attn_layer_norm",
"decoder.blocks.{n}.ffn.intermediate" => "model.decoder.layers.{n}.fc1",
"decoder.blocks.{n}.ffn.output" => "model.decoder.layers.{n}.fc2",
"decoder.blocks.{n}.output_norm" => "model.decoder.layers.{n}.final_layer_norm",
"language_modeling_head.output" =>
case spec.architecture do
:for_causal_language_modeling -> "lm_head"
_other -> "model.shared"
end,
"language_modeling_head.logits_bias" => %{
"bias" => {[{"model", "final_logits_bias"}], fn [value] -> Nx.squeeze(value) end}
},
"sequence_classification_head.dense" => "classification_head.dense",
"sequence_classification_head.output" => "classification_head.out_proj",
"question_answering_head.output" => "qa_outputs"
}
end
end
end
