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from typing import Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from transformers.modeling_flax_outputs import (
FlaxBaseModelOutputWithPooling,
FlaxMaskedLMOutput,
FlaxSequenceClassifierOutput,
)
from transformers.models.bert.modeling_flax_bert import (
FlaxBertEncoder,
FlaxBertOnlyMLMHead,
FlaxBertPooler,
FlaxPreTrainedModel,
)
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
from .configuration_clip_vision_bert import CLIPVisionBertConfig
class FlaxCLIPVisionBertEmbeddings(nn.Module):
config: CLIPVisionBertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
bert_config = self.config.bert_config
clip_vision_config = self.config.clip_vision_config
self.word_embeddings = nn.Embed(
bert_config.vocab_size,
bert_config.hidden_size,
embedding_init=jax.nn.initializers.normal(
stddev=bert_config.initializer_range
),
dtype=self.dtype,
)
self.position_embeddings = nn.Embed(
bert_config.max_position_embeddings,
bert_config.hidden_size,
embedding_init=jax.nn.initializers.normal(
stddev=bert_config.initializer_range
),
dtype=self.dtype,
)
self.token_type_embeddings = nn.Embed(
bert_config.type_vocab_size,
bert_config.hidden_size,
embedding_init=jax.nn.initializers.normal(
stddev=bert_config.initializer_range
),
dtype=self.dtype,
)
self.clip_vision_module = FlaxCLIPVisionModule(
clip_vision_config, dtype=self.dtype
)
self.visual_projection = nn.Dense(
bert_config.hidden_size,
dtype=self.dtype,
kernel_init=jax.nn.initializers.normal(
bert_config.initializer_range, self.dtype
),
)
self.visual_position_embeddings = nn.Embed(
bert_config.max_position_embeddings,
bert_config.hidden_size,
embedding_init=jax.nn.initializers.normal(
stddev=bert_config.initializer_range
),
dtype=self.dtype,
)
self.visual_token_type_embeddings = nn.Embed(
bert_config.type_vocab_size,
bert_config.hidden_size,
embedding_init=jax.nn.initializers.normal(
stddev=bert_config.initializer_range
),
dtype=self.dtype,
)
self.LayerNorm = nn.LayerNorm(
epsilon=bert_config.layer_norm_eps, dtype=self.dtype
)
self.dropout = nn.Dropout(rate=bert_config.hidden_dropout_prob)
def __call__(
self,
input_ids,
token_type_ids,
position_ids,
pixel_values,
visual_token_type_ids,
visual_position_ids,
deterministic: bool = True,
):
# Embed
inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
position_embeds = self.position_embeddings(position_ids.astype("i4"))
token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4"))
# Sum all embeddings
word_embeddings = inputs_embeds + token_type_embeddings + position_embeds
# Visual Embed
visual_inputs_embeds = self.clip_vision_module(pixel_values=pixel_values)[0]
visual_inputs_embeds = self.visual_projection(visual_inputs_embeds)
visual_token_type_embeddings = self.visual_token_type_embeddings(
visual_token_type_ids.astype("i4")
)
visual_position_embeds = self.visual_position_embeddings(
visual_position_ids.astype("i4")
)
# Sum all visual embeddings
visual_embeddings = (
visual_inputs_embeds + visual_token_type_embeddings + visual_position_embeds
)
# Concat
hidden_states = jnp.concatenate((word_embeddings, visual_embeddings), axis=1)
# Layer Norm
hidden_states = self.LayerNorm(hidden_states)
hidden_states = self.dropout(hidden_states, deterministic=deterministic)
return hidden_states
class FlaxCLIPVisionBertModule(nn.Module):
config: CLIPVisionBertConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
add_pooling_layer: bool = True
def setup(self):
self.embeddings = FlaxCLIPVisionBertEmbeddings(self.config, dtype=self.dtype)
self.encoder = FlaxBertEncoder(self.config.bert_config, dtype=self.dtype)
self.pooler = FlaxBertPooler(self.config.bert_config, dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
pixel_values,
visual_attention_mask,
visual_token_type_ids,
visual_position_ids,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
hidden_states = self.embeddings(
input_ids,
token_type_ids,
position_ids,
pixel_values,
visual_token_type_ids,
visual_position_ids,
deterministic=deterministic,
)
combined_attention_mask = jnp.concatenate(
(attention_mask, visual_attention_mask), axis=1
)
outputs = self.encoder(
hidden_states,
combined_attention_mask,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
pooled = self.pooler(hidden_states) if self.add_pooling_layer else None
if not return_dict:
# if pooled is None, don't return it
if pooled is None:
return (hidden_states,) + outputs[1:]
return (hidden_states, pooled) + outputs[1:]
return FlaxBaseModelOutputWithPooling(
last_hidden_state=hidden_states,
pooler_output=pooled,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class FlaxCLIPVisionBertModel(FlaxPreTrainedModel):
config_class = CLIPVisionBertConfig
module_class = FlaxCLIPVisionBertModule
def __init__(
self,
config: CLIPVisionBertConfig,
input_shape: Tuple = None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
**kwargs,
):
if input_shape is None:
input_shape = (
(1, 1),
(
1,
config.clip_vision_config.image_size,
config.clip_vision_config.image_size,
3,
),
(
1,
(
config.clip_vision_config.image_size
// config.clip_vision_config.patch_size
)
** 2
+ 1,
),
)
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(
config, module, input_shape=input_shape, seed=seed, dtype=dtype
)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
# init input tensors
textual_input_shape = input_shape[0]
input_ids = jnp.zeros(textual_input_shape, dtype="i4")
token_type_ids = jnp.zeros_like(input_ids)
position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), textual_input_shape
)
attention_mask = jnp.ones_like(input_ids)
pixel_values = jax.random.normal(rng, input_shape[1])
visual_attention_mask = jnp.ones(input_shape[2])
visual_token_type_ids = jnp.ones(input_shape[2])
visual_position_ids = jnp.broadcast_to(
jnp.zeros(jnp.atleast_2d(visual_token_type_ids).shape[-1]), input_shape[2]
)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
return self.module.init(
rngs,
input_ids,
attention_mask,
token_type_ids,
position_ids,
pixel_values,
visual_attention_mask,
visual_token_type_ids,
visual_position_ids,
return_dict=False,
)["params"]
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
pixel_values=None,
visual_attention_mask=None,
visual_token_type_ids=None,
visual_position_ids=None,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.bert_config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.bert_config.output_hidden_states
)
return_dict = (
return_dict
if return_dict is not None
else self.config.bert_config.return_dict
)
# pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # Don't need this for torch permuted input
visual_sequence_length = (
pixel_values.shape[0],
(
self.config.clip_vision_config.image_size
// self.config.clip_vision_config.patch_size
)
** 2
+ 1,
)
# init input tensors if not passed
if token_type_ids is None:
token_type_ids = jnp.zeros_like(input_ids)
if position_ids is None:
position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape
)
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
if visual_token_type_ids is None:
visual_token_type_ids = jnp.ones(visual_sequence_length)
if visual_position_ids is None:
visual_position_ids = jnp.broadcast_to(
jnp.atleast_2d(visual_token_type_ids).shape[-1], visual_sequence_length
)
if visual_attention_mask is None:
visual_attention_mask = jnp.ones(visual_sequence_length)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
return self.module.apply(
{"params": params or self.params},
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
jnp.array(token_type_ids, dtype="i4"),
jnp.array(position_ids, dtype="i4"),
jnp.array(pixel_values, dtype=jnp.float32),
jnp.array(visual_attention_mask, dtype="i4"),
jnp.array(visual_token_type_ids, dtype="i4"),
jnp.array(visual_position_ids, dtype="i4"),
not train,
output_attentions,
output_hidden_states,
return_dict,
rngs=rngs,
)
@classmethod
def from_bert_clip_vision_pretrained(
cls,
bert_model_name_or_path: str = None,
clip_vision_model_name_or_path: str = None,
*model_args,
**kwargs,
) -> FlaxPreTrainedModel:
kwargs_bert = {
argument[len("bert_") :]: value
for argument, value in kwargs.items()
if argument.startswith("text_")
}
kwargs_clip_vision = {
argument[len("clip_vision_") :]: value
for argument, value in kwargs.items()
if argument.startswith("vision_")
}
# remove text, vision kwargs from kwargs
for key in kwargs_bert.keys():
del kwargs["bert_" + key]
for key in kwargs_clip_vision.keys():
del kwargs["clip_vision_" + key]
# Load and initialize the text and vision model
bert_model = kwargs_bert.pop("model", None)
if bert_model is None:
assert (
bert_model_name_or_path is not None
), "If `model` is not defined as an argument, a `bert_model_name_or_path` has to be defined"
from transformers import FlaxBertModel
if "config" not in kwargs_bert:
from transformers import BertConfig
bert_config = BertConfig.from_pretrained(bert_model_name_or_path)
kwargs_bert["config"] = bert_config
bert_model = FlaxBertModel.from_pretrained(
bert_model_name_or_path, *model_args, from_pt=True, **kwargs_bert
)
clip_vision_model = kwargs_clip_vision.pop("model", None)
if clip_vision_model is None:
assert (
clip_vision_model_name_or_path is not None
), "If `model` is not defined as an argument, a `clip_vision_model_name_or_path` has to be defined"
from transformers import FlaxCLIPVisionModel
if "config" not in kwargs_clip_vision:
from transformers import CLIPVisionConfig
clip_vision_config = CLIPVisionConfig.from_pretrained(
clip_vision_model_name_or_path
)
kwargs_clip_vision["config"] = clip_vision_config
clip_vision_model = FlaxCLIPVisionModel.from_pretrained(
clip_vision_model_name_or_path, *model_args, **kwargs_clip_vision
)
# instantiate config with corresponding kwargs
dtype = kwargs.pop("dtype", jnp.float32)
config = CLIPVisionBertConfig.from_bert_clip_vision_configs(
bert_model.config, clip_vision_model.config, **kwargs
)
# init model
model = cls(config, *model_args, dtype=dtype, **kwargs)
for key in model.params.keys():
if key != "embeddings":
model.params[key] = bert_model.params[key]
else:
model.params["embeddings"][
"clip_vision_module"
] = clip_vision_model.params
for sub_key in bert_model.params[key]:
model.params[key][sub_key] = bert_model.params[key][sub_key]
return model
# flax_model = FlaxCLIPVisionBertModel.from_bert_clip_vision_pretrained('bert-base-uncased', 'openai/clip-vit-base-patch32', seed=0, dtype=jnp.float32)
# outputs = flax_model(input_ids, attention_mask,token_type_ids, position_ids, pixel_values, visual_attention_mask, visual_token_type_ids, visual_position_ids, output_hidden_states=True)
class FlaxCLIPVisionBertForMaskedLMModule(nn.Module):
config: CLIPVisionBertConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.model = FlaxCLIPVisionBertModule(
config=self.config, add_pooling_layer=False, dtype=self.dtype
)
self.cls = FlaxBertOnlyMLMHead(config=self.config.bert_config, dtype=self.dtype)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
pixel_values,
visual_attention_mask,
visual_token_type_ids,
visual_position_ids,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.model(
input_ids,
attention_mask,
token_type_ids,
position_ids,
pixel_values,
visual_attention_mask,
visual_token_type_ids,
visual_position_ids,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
if self.config.bert_config.tie_word_embeddings:
shared_embedding = self.model.variables["params"]["embeddings"][
"word_embeddings"
]["embedding"]
else:
shared_embedding = None
# Compute the prediction scores
logits = self.cls(hidden_states, shared_embedding=shared_embedding)
if not return_dict:
return (logits,) + outputs[1:]
return FlaxMaskedLMOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class FlaxCLIPVisionBertForMaskedLM(FlaxPreTrainedModel):
config_class = CLIPVisionBertConfig
module_class = FlaxCLIPVisionBertForMaskedLMModule
def __init__(
self,
config: CLIPVisionBertConfig,
input_shape: Tuple = None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
**kwargs,
):
if input_shape is None:
input_shape = (
(1, 1),
(
1,
config.clip_vision_config.image_size,
config.clip_vision_config.image_size,
3,
),
(
1,
(
config.clip_vision_config.image_size
// config.clip_vision_config.patch_size
)
** 2
+ 1,
),
)
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(
config, module, input_shape=input_shape, seed=seed, dtype=dtype
)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
# init input tensors
textual_input_shape = input_shape[0]
input_ids = jnp.zeros(textual_input_shape, dtype="i4")
token_type_ids = jnp.zeros_like(input_ids)
position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), textual_input_shape
)
attention_mask = jnp.ones_like(input_ids)
pixel_values = jax.random.normal(rng, input_shape[1])
visual_attention_mask = jnp.ones(input_shape[2])
visual_token_type_ids = jnp.ones(input_shape[2])
visual_position_ids = jnp.broadcast_to(
jnp.zeros(jnp.atleast_2d(visual_token_type_ids).shape[-1]), input_shape[2]
)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
return self.module.init(
rngs,
input_ids,
attention_mask,
token_type_ids,
position_ids,
pixel_values,
visual_attention_mask,
visual_token_type_ids,
visual_position_ids,
return_dict=False,
)["params"]
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
pixel_values=None,
visual_attention_mask=None,
visual_token_type_ids=None,
visual_position_ids=None,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.bert_config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.bert_config.output_hidden_states
)
return_dict = (
return_dict
if return_dict is not None
else self.config.bert_config.return_dict
)
# pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
# init input tensors if not passed
if token_type_ids is None:
token_type_ids = jnp.zeros_like(input_ids)
if position_ids is None:
position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape
)
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
visual_sequence_length = (
pixel_values.shape[0],
(
self.config.clip_vision_config.image_size
// self.config.clip_vision_config.patch_size
)
** 2
+ 1,
)
if visual_token_type_ids is None:
visual_token_type_ids = jnp.ones(visual_sequence_length)
if visual_position_ids is None:
visual_position_ids = jnp.broadcast_to(
jnp.atleast_2d(jnp.ones(visual_sequence_length)).shape[-1],
(visual_sequence_length),
)
if visual_attention_mask is None:
visual_attention_mask = jnp.ones(visual_sequence_length)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
return self.module.apply(
{"params": params or self.params},
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
jnp.array(token_type_ids, dtype="i4"),
jnp.array(position_ids, dtype="i4"),
jnp.array(pixel_values, dtype=jnp.float32),
jnp.array(visual_attention_mask, dtype="i4"),
jnp.array(visual_token_type_ids, dtype="i4"),
jnp.array(visual_position_ids, dtype="i4"),
not train,
output_attentions,
output_hidden_states,
return_dict,
rngs=rngs,
)
@classmethod
def from_pretrained(cls, *args, **kwargs):
# At the moment fast initialization is not supported
# for composite models
# kwargs["_fast_init"] = False
return super().from_pretrained(*args, **kwargs)
@classmethod
def from_clip_vision_bert_pretrained(
cls,
clip_vision_model_name_or_path: str = None,
bert_model_name_or_path: str = None,
*model_args,
**kwargs,
) -> FlaxPreTrainedModel:
kwargs_bert = {
argument[len("bert_") :]: value
for argument, value in kwargs.items()
if argument.startswith("text_")
}
kwargs_clip_vision = {
argument[len("clip_vision_") :]: value
for argument, value in kwargs.items()
if argument.startswith("vision_")
}
# remove text, vision kwargs from kwargs
for key in kwargs_bert.keys():
del kwargs["bert_" + key]
for key in kwargs_clip_vision.keys():
del kwargs["clip_vision_" + key]
# Load and initialize the text and vision model
bert_model = kwargs_bert.pop("model", None)
if bert_model is None:
assert (
bert_model_name_or_path is not None
), "If `model` is not defined as an argument, a `bert_model_name_or_path` has to be defined"
from transformers import FlaxBertForMaskedLM
if "config" not in kwargs_bert:
from transformers import BertConfig
bert_config = BertConfig.from_pretrained(bert_model_name_or_path)
kwargs_bert["config"] = bert_config
bert_model = FlaxBertForMaskedLM.from_pretrained(
bert_model_name_or_path, *model_args, from_pt=True, **kwargs_bert
)
clip_vision_model = kwargs_clip_vision.pop("model", None)
if clip_vision_model is None:
assert (
clip_vision_model_name_or_path is not None
), "If `model` is not defined as an argument, a `clip_vision_model_name_or_path` has to be defined"
from transformers import FlaxCLIPVisionModel
if "config" not in kwargs_clip_vision:
from transformers import CLIPVisionConfig
clip_vision_config = CLIPVisionConfig.from_pretrained(
clip_vision_model_name_or_path
)
kwargs_clip_vision["config"] = clip_vision_config
clip_vision_model = FlaxCLIPVisionModel.from_pretrained(
clip_vision_model_name_or_path, *model_args, **kwargs_clip_vision
)
# instantiate config with corresponding kwargs
dtype = kwargs.pop("dtype", jnp.float32)
config = CLIPVisionBertConfig.from_clip_vision_bert_configs(
clip_vision_model.config, bert_model.config, **kwargs
)
# init model
model = cls(config, *model_args, dtype=dtype, **kwargs)
model.params["cls"] = bert_model.params["cls"]
for key in model.params["model"].keys():
if key != "embeddings":
model.params["model"][key] = bert_model.params["bert"][key]
else:
model.params["model"]["embeddings"][
"clip_vision_module"
] = clip_vision_model.params
for sub_key in bert_model.params["bert"][key]:
model.params["model"][key][sub_key] = bert_model.params["bert"][
key
][sub_key]
return model
class FlaxCLIPVisionBertForSequenceClassificationModule(nn.Module):
config: CLIPVisionBertConfig
dtype: jnp.dtype = jnp.float32
num_labels: int = 3129 # TODO: Remove this hard-coding!
def setup(self):
self.model = FlaxCLIPVisionBertModule(config=self.config, dtype=self.dtype)
self.dropout = nn.Dropout(rate=self.config.bert_config.hidden_dropout_prob)
self.classifier = nn.Dense(
self.num_labels,
dtype=self.dtype,
)
def __call__(
self,
input_ids,
attention_mask,
token_type_ids,
position_ids,
pixel_values,
visual_attention_mask,
visual_token_type_ids,
visual_position_ids,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# Model
outputs = self.model(
input_ids,
attention_mask,
token_type_ids,
position_ids,
pixel_values,
visual_attention_mask,
visual_token_type_ids,
visual_position_ids,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output, deterministic=deterministic)
logits = self.classifier(pooled_output)
if not return_dict:
return (logits,) + outputs[2:]
return FlaxSequenceClassifierOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class FlaxCLIPVisionBertForSequenceClassification(FlaxPreTrainedModel):
config_class = CLIPVisionBertConfig
module_class = FlaxCLIPVisionBertForSequenceClassificationModule
def __init__(
self,
config: CLIPVisionBertConfig,
input_shape: Tuple = None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
**kwargs,
):
if input_shape is None:
input_shape = (
(1, 1),
(
1,
config.clip_vision_config.image_size,
config.clip_vision_config.image_size,
3,
),
(
1,
(
config.clip_vision_config.image_size
// config.clip_vision_config.patch_size
)
** 2
+ 1,
),
)
module = self.module_class(config=config, dtype=dtype, **kwargs)
super().__init__(
config, module, input_shape=input_shape, seed=seed, dtype=dtype
)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
# init input tensors
textual_input_shape = input_shape[0]
input_ids = jnp.zeros(textual_input_shape, dtype="i4")
token_type_ids = jnp.zeros_like(input_ids)
position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), textual_input_shape
)
attention_mask = jnp.ones_like(input_ids)
pixel_values = jax.random.normal(rng, input_shape[1])
visual_attention_mask = jnp.ones(input_shape[2])
visual_token_type_ids = jnp.ones(input_shape[2])
visual_position_ids = jnp.broadcast_to(
jnp.zeros(jnp.atleast_2d(visual_token_type_ids).shape[-1]), input_shape[2]
)
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
return self.module.init(
rngs,
input_ids,
attention_mask,
token_type_ids,
position_ids,
pixel_values,
visual_attention_mask,
visual_token_type_ids,
visual_position_ids,
return_dict=False,
)["params"]
def __call__(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
position_ids=None,
pixel_values=None,
visual_attention_mask=None,
visual_token_type_ids=None,
visual_position_ids=None,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.bert_config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.bert_config.output_hidden_states
)
return_dict = (
return_dict
if return_dict is not None
else self.config.bert_config.return_dict
)
# pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
# init input tensors if not passed
if token_type_ids is None:
token_type_ids = jnp.zeros_like(input_ids)
if position_ids is None:
position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape
)
if attention_mask is None:
attention_mask = jnp.ones_like(input_ids)
visual_sequence_length = (
pixel_values.shape[0],
(
self.config.clip_vision_config.image_size
// self.config.clip_vision_config.patch_size
)
** 2
+ 1,
)
if visual_token_type_ids is None:
visual_token_type_ids = jnp.ones(visual_sequence_length)
if visual_position_ids is None:
visual_position_ids = jnp.broadcast_to(
jnp.atleast_2d(jnp.ones(visual_sequence_length)).shape[-1],
(visual_sequence_length),
)
if visual_attention_mask is None:
visual_attention_mask = jnp.ones(visual_sequence_length)
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
return self.module.apply(
{"params": params or self.params},
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
jnp.array(token_type_ids, dtype="i4"),
jnp.array(position_ids, dtype="i4"),
jnp.array(pixel_values, dtype=jnp.float32),
jnp.array(visual_attention_mask, dtype="i4"),
jnp.array(visual_token_type_ids, dtype="i4"),
jnp.array(visual_position_ids, dtype="i4"),
not train,
output_attentions,
output_hidden_states,
return_dict,
rngs=rngs,
)
@classmethod
def from_pretrained(cls, *args, **kwargs):
# At the moment fast initialization is not supported
# for composite models
# kwargs["_fast_init"] = False
return super().from_pretrained(*args, **kwargs)
@classmethod
def from_clip_vision_bert_pretrained(
cls,
clip_vision_model_name_or_path: str = None,
bert_model_name_or_path: str = None,
*model_args,
**kwargs,
) -> FlaxPreTrainedModel:
kwargs_bert = {
argument[len("bert_") :]: value
for argument, value in kwargs.items()
if argument.startswith("bert_")
}
kwargs_clip_vision = {
argument[len("clip_vision_") :]: value
for argument, value in kwargs.items()
if argument.startswith("clip_vision_")
}
# remove text, vision kwargs from kwargs
for key in kwargs_bert.keys():
del kwargs["bert_" + key]
for key in kwargs_clip_vision.keys():
del kwargs["clip_vision_" + key]
# Load and initialize the text and vision model
bert_model = kwargs_bert.pop("model", None)
if bert_model is None:
assert (
bert_model_name_or_path is not None
), "If `model` is not defined as an argument, a `bert_model_name_or_path` has to be defined"
from transformers import FlaxBertForSequenceClassification
if "config" not in kwargs_bert:
from transformers import BertConfig
bert_config = BertConfig.from_pretrained(bert_model_name_or_path)
kwargs_bert["config"] = bert_config
bert_model = FlaxBertForSequenceClassification.from_pretrained(
bert_model_name_or_path, *model_args, from_pt=True, **kwargs_bert
)
clip_vision_model = kwargs_clip_vision.pop("model", None)
if clip_vision_model is None:
assert (
clip_vision_model_name_or_path is not None
), "If `model` is not defined as an argument, a `clip_vision_model_name_or_path` has to be defined"
from transformers import FlaxCLIPVisionModel
if "config" not in kwargs_clip_vision:
from transformers import CLIPVisionConfig
clip_vision_config = CLIPVisionConfig.from_pretrained(
clip_vision_model_name_or_path
)
kwargs_clip_vision["config"] = clip_vision_config
clip_vision_model = FlaxCLIPVisionModel.from_pretrained(
clip_vision_model_name_or_path, *model_args, **kwargs_clip_vision
)
# instantiate config with corresponding kwargs
dtype = kwargs.pop("dtype", jnp.float32)
config = CLIPVisionBertConfig.from_clip_vision_bert_configs(
clip_vision_model.config, bert_model.config, **kwargs
)
# init model
model = cls(config, *model_args, dtype=dtype, **kwargs)
# model.params["classifier"] = bert_model.params["classifier"]
for key in model.params["model"].keys():
if key != "embeddings":
model.params["model"][key] = bert_model.params["bert"][key]
else:
model.params["model"]["embeddings"][
"clip_vision_module"
] = clip_vision_model.params
for sub_key in bert_model.params["bert"][key]:
model.params["model"][key][sub_key] = bert_model.params["bert"][
key
][sub_key]
return model
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