co-instruct / visual_encoder.py

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	import math
	from typing import Any, Optional, Tuple, Union

	from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, BaseModelOutputWithPastAndCrossAttentions
	from transformers.modeling_utils import PreTrainedModel
	from transformers.pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer

	import numpy as np
	import torch
	import torch.nn as nn
	import torch.utils.checkpoint
	from icecream import ic

	def get_abs_pos(abs_pos, tgt_size):
	# abs_pos: L, C
	# tgt_size: M
	# return: M, C
	src_size = int(math.sqrt(abs_pos.size(0)))
	tgt_size = int(math.sqrt(tgt_size))
	dtype = abs_pos.dtype

	if src_size != tgt_size:
	return F.interpolate(
	abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
	size=(tgt_size, tgt_size),
	mode="bicubic",
	align_corners=False,
	).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype)
	else:
	return abs_pos

	# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
	def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
	"""
	grid_size: int of the grid height and width
	return:
	pos_embed: [grid_sizegrid_size, embed_dim] or [1+grid_sizegrid_size, embed_dim] (w/ or w/o cls_token)
	"""
	grid_h = np.arange(grid_size, dtype=np.float32)
	grid_w = np.arange(grid_size, dtype=np.float32)
	grid = np.meshgrid(grid_w, grid_h) # here w goes first
	grid = np.stack(grid, axis=0)

	grid = grid.reshape([2, 1, grid_size, grid_size])
	pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
	if cls_token:
	pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
	return pos_embed


	def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
	assert embed_dim % 2 == 0

	# use half of dimensions to encode grid_h
	emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
	emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)

	emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
	return emb


	def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
	"""
	embed_dim: output dimension for each position
	pos: a list of positions to be encoded: size (M,)
	out: (M, D)
	"""
	assert embed_dim % 2 == 0
	omega = np.arange(embed_dim // 2, dtype=np.float32)
	omega /= embed_dim / 2.
	omega = 1. / 10000**omega # (D/2,)

	pos = pos.reshape(-1) # (M,)
	out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product

	emb_sin = np.sin(out) # (M, D/2)
	emb_cos = np.cos(out) # (M, D/2)

	emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
	return emb



	class MplugOwlVisionEmbeddings(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.image_size = config.image_size
	self.patch_size = config.patch_size

	self.cls_token = nn.Parameter(torch.randn(1, 1, self.hidden_size))

	self.patch_embed = nn.Conv2d(
	in_channels=3,
	out_channels=self.hidden_size,
	kernel_size=self.patch_size,
	stride=self.patch_size,
	bias=False,
	)

	self.num_patches = (self.image_size // self.patch_size) ** 2

	self.position_embedding = nn.Parameter(torch.randn(1, self.num_patches + 1, self.hidden_size))

	self.pre_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)

	def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
	batch_size = pixel_values.size(0)
	image_embeds = self.patch_embed(pixel_values)
	image_embeds = image_embeds.flatten(2).transpose(1, 2)

	class_embeds = self.cls_token.expand(batch_size, 1, -1).to(image_embeds.dtype)
	embeddings = torch.cat([class_embeds, image_embeds], dim=1)
	embeddings = embeddings + self.position_embedding[:, : embeddings.size(1)].to(image_embeds.dtype)
	embeddings = self.pre_layernorm(embeddings)
	return embeddings



	class MplugOwlVisionAttention(nn.Module):
	"""Multi-headed attention from 'Attention Is All You Need' paper"""

	def __init__(self, config):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.hidden_size // self.num_heads
	if self.head_dim * self.num_heads != self.hidden_size:
	raise ValueError(
	f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
	f" {self.num_heads})."
	)
	self.scale = self.head_dim**-0.5
	self.dropout = nn.Dropout(config.attention_dropout)

	self.query_key_value = nn.Linear(self.hidden_size, 3 * self.hidden_size)
	self.dense = nn.Linear(self.hidden_size, self.hidden_size)

	def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
	return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

	def forward(
	self,
	hidden_states: torch.Tensor,
	head_mask: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	"""Input shape: Batch x Time x Channel"""

	bsz, seq_len, embed_dim = hidden_states.size()

	mixed_qkv = self.query_key_value(hidden_states)

	mixed_qkv = mixed_qkv.reshape(bsz, seq_len, self.num_heads, 3, embed_dim // self.num_heads).permute(
	3, 0, 2, 1, 4
	) # [3, b, np, sq, hn]
	query_states, key_states, value_states = (
	mixed_qkv[0],
	mixed_qkv[1],
	mixed_qkv[2],
	)
	# if self.config.use_flash_attn and flash_attn_func is not None:
	if False:
	# [b*sq, np, hn]
	query_states = query_states.permute(0, 2, 1, 3).contiguous()
	query_states = query_states.view(query_states.size(0) * query_states.size(1), query_states.size(2), -1)

	key_states = key_states.permute(0, 2, 1, 3).contiguous()
	key_states = key_states.view(key_states.size(0) * key_states.size(1), key_states.size(2), -1)

	value_states = value_states.permute(0, 2, 1, 3).contiguous()
	value_states = value_states.view(value_states.size(0) * value_states.size(1), value_states.size(2), -1)

	cu_seqlens = torch.arange(
	0, (bsz + 1) * seq_len, step=seq_len, dtype=torch.int32, device=query_states.device
	)

	context_layer = flash_attn_func(
	query_states,
	key_states,
	value_states,
	cu_seqlens,
	cu_seqlens,
	seq_len,
	seq_len,
	self.dropout if self.training else 0.0,
	softmax_scale=self.scale,
	causal=False,
	return_attn_probs=False,
	)
	# [b*sq, np, hn] => [b, sq, np, hn]
	context_layer = context_layer.view(bsz, seq_len, context_layer.size(1), context_layer.size(2))
	else:
	# Take the dot product between "query" and "key" to get the raw attention scores.
	attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2))

	attention_scores = attention_scores * self.scale

	# Normalize the attention scores to probabilities.
	attention_probs = torch.softmax(attention_scores, dim=-1)

	# This is actually dropping out entire tokens to attend to, which might
	# seem a bit unusual, but is taken from the original Transformer paper.
	attention_probs = self.dropout(attention_probs)

	# Mask heads if we want to
	if head_mask is not None:
	attention_probs = attention_probs * head_mask

	context_layer = torch.matmul(attention_probs, value_states).permute(0, 2, 1, 3)

	new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size,)
	context_layer = context_layer.reshape(new_context_layer_shape)

	output = self.dense(context_layer)

	outputs = (output, attention_probs) if output_attentions else (output, None)

	return outputs


	class QuickGELU(nn.Module):
	def forward(self, x: torch.Tensor):
	return x * torch.sigmoid(1.702 * x)


	class MplugOwlMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.activation_fn = QuickGELU()
	self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
	self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.fc1(hidden_states)
	hidden_states = self.activation_fn(hidden_states)
	hidden_states = self.fc2(hidden_states)
	return hidden_states


	class MplugOwlVisionEncoderLayer(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.hidden_size = config.hidden_size
	self.self_attn = MplugOwlVisionAttention(config)
	self.input_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)
	self.mlp = MplugOwlMLP(config)
	self.post_attention_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: torch.Tensor,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.FloatTensor]:
	"""
	Args:
	hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
	attention_mask (`torch.FloatTensor`): attention mask of size
	`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
	`(config.encoder_attention_heads,)`.
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	"""
	residual = hidden_states

	hidden_states = self.input_layernorm(hidden_states)
	hidden_states, attn_weights = self.self_attn(
	hidden_states=hidden_states,
	head_mask=attention_mask,
	output_attentions=output_attentions,
	)
	hidden_states = hidden_states + residual
	residual = hidden_states
	hidden_states = self.post_attention_layernorm(hidden_states)
	hidden_states = self.mlp(hidden_states)

	hidden_states = hidden_states + residual

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (attn_weights,)

	return outputs


	class MplugOwlVisionEncoder(nn.Module):
	"""
	Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
	[`MplugOwlVisionEncoderLayer`].

	Args:
	config (`MplugOwlVisionConfig`):
	The corresponding vision configuration for the `MplugOwlEncoder`.
	"""

	def __init__(self, config):
	super().__init__()
	self.config = config
	self.layers = nn.ModuleList([MplugOwlVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
	self.gradient_checkpointing = True

	def forward(
	self,
	inputs_embeds,
	attention_mask: Optional[torch.Tensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutput]:
	r"""
	Args:
	inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
	Embedded representation of the inputs. Should be float, not int tokens.
	attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, optional):
	Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.

	[What are attention masks?](../glossary#attention-mask)
	output_attentions (`bool`, optional):
	Whether or not to return the attentions tensors of all attention layers. See `attentions` under
	returned tensors for more detail.
	output_hidden_states (`bool`, optional):
	Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
	for more detail.
	return_dict (`bool`, optional):
	Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
	"""
	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

	encoder_states = () if output_hidden_states else None
	all_attentions = () if output_attentions else None

	hidden_states = inputs_embeds
	for idx, encoder_layer in enumerate(self.layers):
	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)
	if self.gradient_checkpointing and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(encoder_layer),
	hidden_states,
	attention_mask,
	)
	else:
	layer_outputs = encoder_layer(
	hidden_states,
	attention_mask,
	output_attentions=output_attentions,
	)

	hidden_states = layer_outputs[0]

	if output_attentions:
	all_attentions = all_attentions + (layer_outputs[1],)

	if output_hidden_states:
	encoder_states = encoder_states + (hidden_states,)

	if not return_dict:
	return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
	return BaseModelOutput(
	last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
	)


	class MplugOwlVisionModel(PreTrainedModel):
	main_input_name = "pixel_values"
	_no_split_modules = ["MplugOwlVisionEncoderLayer"]

	def __init__(self, config):
	super().__init__(config)
	self.config = config
	self.hidden_size = config.hidden_size

	self.embeddings = MplugOwlVisionEmbeddings(config)
	self.encoder = MplugOwlVisionEncoder(config)
	self.post_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps)

	self.post_init()


	def forward(
	self,
	pixel_values: Optional[torch.FloatTensor] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPooling]:
	r"""
	Returns:

	"""
	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

	if pixel_values is None:
	raise ValueError("You have to specify pixel_values")

	hidden_states = self.embeddings(pixel_values)

	encoder_outputs = self.encoder(
	inputs_embeds=hidden_states,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	last_hidden_state = encoder_outputs[0]
	last_hidden_state = self.post_layernorm(last_hidden_state)

	pooled_output = last_hidden_state[:, 0, :]
	pooled_output = self.post_layernorm(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,
	)

	def get_input_embeddings(self):
	return self.embeddings


	class MplugOwlVisualAbstractorMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	in_features = config.hidden_size
	self.act = nn.SiLU()

	self.w1 = nn.Linear(in_features, config.intermediate_size)
	self.w2 = nn.Linear(config.intermediate_size, in_features)
	self.w3 = nn.Linear(in_features, config.intermediate_size)
	self.ffn_ln = nn.LayerNorm(config.intermediate_size, eps=config.layer_norm_eps)

	def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
	hidden_states = self.act(self.w1(hidden_states)) * self.w3(hidden_states)
	hidden_states = self.ffn_ln(hidden_states)
	hidden_states = self.w2(hidden_states)
	return hidden_states


	class MplugOwlVisualAbstractorMultiHeadAttention(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	if config.hidden_size % config.num_attention_heads != 0:
	raise ValueError(
	"The hidden size (%d) is not a multiple of the number of attention heads (%d)"
	% (config.hidden_size, config.num_attention_heads)
	)

	self.num_attention_heads = config.num_attention_heads
	self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
	self.all_head_size = self.num_attention_heads * self.attention_head_size

	self.query = nn.Linear(config.hidden_size, self.all_head_size)
	self.key = nn.Linear(config.encoder_hidden_size, self.all_head_size)
	self.value = nn.Linear(config.encoder_hidden_size, self.all_head_size)

	self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
	self.save_attention = False

	# self.q_pos_embed = nn.Parameter(
	# torch.from_numpy(get_1d_sincos_pos_embed_from_grid(config.hidden_size, np.arange(config.num_learnable_queries, dtype=np.float32))).float()
	# ).requires_grad_(False)
	# grids = config.grid_size
	# self.k_pos_embed = nn.Parameter(
	# torch.from_numpy(get_2d_sincos_pos_embed(config.hidden_size, grids, cls_token=True)).float()
	# ).requires_grad_(False)
	grids = config.grid_size
	self.register_buffer(
	'q_pos_embed',
	torch.from_numpy(get_1d_sincos_pos_embed_from_grid(config.hidden_size, np.arange(config.num_learnable_queries, dtype=np.float32))).float()
	)
	self.register_buffer(
	'k_pos_embed',
	torch.from_numpy(get_2d_sincos_pos_embed(config.hidden_size, grids, cls_token=True)).float()
	)


	def save_attn_gradients(self, attn_gradients):
	self.attn_gradients = attn_gradients

	def get_attn_gradients(self):
	return self.attn_gradients

	def save_attention_map(self, attention_map):
	self.attention_map = attention_map

	def get_attention_map(self):
	return self.attention_map

	def transpose_for_scores(self, x):
	new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
	x = x.view(*new_x_shape)
	return x.permute(0, 2, 1, 3)

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_value=None,
	output_attentions=False,
	):
	# If this is instantiated as a cross-attention module, the keys
	# and values come from an encoder; the attention mask needs to be
	# such that the encoder's padding tokens are not attended to.

	qk_pos_embed = torch.cat([self.q_pos_embed, self.k_pos_embed], dim = 0).unsqueeze(0).to(dtype=hidden_states.dtype)

	key_layer = self.transpose_for_scores(self.key(encoder_hidden_states + qk_pos_embed))
	value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
	attention_mask = encoder_attention_mask

	mixed_query_layer = self.query(hidden_states + self.q_pos_embed.unsqueeze(0).to(dtype=hidden_states.dtype))

	query_layer = self.transpose_for_scores(mixed_query_layer)

	past_key_value = (key_layer, value_layer)

	# Take the dot product between "query" and "key" to get the raw attention scores.
	attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

	attention_scores = attention_scores / math.sqrt(self.attention_head_size)

	if attention_mask is not None:
	# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
	attention_scores = attention_scores + attention_mask

	# Normalize the attention scores to probabilities.
	attention_probs = nn.Softmax(dim=-1)(attention_scores)

	if self.save_attention:
	self.save_attention_map(attention_probs)
	attention_probs.register_hook(self.save_attn_gradients)

	# This is actually dropping out entire tokens to attend to, which might
	# seem a bit unusual, but is taken from the original Transformer paper.
	attention_probs_dropped = self.dropout(attention_probs)

	# Mask heads if we want to
	if head_mask is not None:
	attention_probs_dropped = attention_probs_dropped * head_mask

	context_layer = torch.matmul(attention_probs_dropped, value_layer)

	context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
	new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
	context_layer = context_layer.view(*new_context_layer_shape)

	outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)

	outputs = outputs + (past_key_value,)
	return outputs


	class MplugOwlVisualAbstractorCrossOutput(nn.Module):
	def __init__(self, config):
	super().__init__()
	dim = config.hidden_size
	self.out_proj = nn.Linear(dim, dim, bias=True)
	self.norm2 = nn.LayerNorm(dim)
	self.mlp = MplugOwlVisualAbstractorMLP(config)

	def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
	input_tensor = input_tensor + self.out_proj(hidden_states)
	input_tensor = input_tensor + self.mlp(self.norm2(input_tensor))
	return input_tensor


	class MplugOwlVisualAbstractorAttention(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.attention = MplugOwlVisualAbstractorMultiHeadAttention(config)
	self.output = MplugOwlVisualAbstractorCrossOutput(config)
	self.pruned_heads = set()
	self.norm1 = nn.LayerNorm(config.hidden_size)
	self.normk = nn.LayerNorm(config.hidden_size)

	def prune_heads(self, heads):
	if len(heads) == 0:
	return
	heads, index = find_pruneable_heads_and_indices(
	heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
	)

	# Prune linear layers
	self.attention.query = prune_linear_layer(self.attention.query, index)
	self.attention.key = prune_linear_layer(self.attention.key, index)
	self.attention.value = prune_linear_layer(self.attention.value, index)
	self.output.dense = prune_linear_layer(self.output.out_proj, index, dim=1)

	# Update hyper params and store pruned heads
	self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
	self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
	self.pruned_heads = self.pruned_heads.union(heads)

	def forward(
	self,
	hidden_states: torch.Tensor,
	attention_mask: Optional[torch.FloatTensor] = None,
	head_mask: Optional[torch.FloatTensor] = None,
	encoder_hidden_states: Optional[torch.FloatTensor] = None,
	encoder_attention_mask: Optional[torch.FloatTensor] = None,
	past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
	output_attentions: Optional[bool] = False,
	) -> Tuple[torch.Tensor]:
	# HACK we apply norm on q and k
	hidden_states = self.norm1(hidden_states)
	encoder_hidden_states = self.normk(encoder_hidden_states)
	encoder_hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1)
	encoder_attention_mask = torch.cat([attention_mask, encoder_attention_mask], dim=-1)
	self_outputs = self.attention(
	hidden_states,
	attention_mask,
	head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	past_key_value,
	output_attentions,
	)
	attention_output = self.output(self_outputs[0], hidden_states)
	# add attentions if we output them
	outputs = (attention_output,) + self_outputs[1:]
	return outputs


	class MplugOwlVisualAbstractorLayer(nn.Module):
	def __init__(self, config, layer_idx):
	super().__init__()
	self.chunk_size_feed_forward = config.chunk_size_feed_forward
	self.seq_len_dim = 1

	self.layer_idx = layer_idx

	self.crossattention = MplugOwlVisualAbstractorAttention(config)
	self.has_cross_attention = True

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	output_attentions=False,
	):
	if encoder_hidden_states is None:
	raise ValueError("encoder_hidden_states must be given for cross-attention layers")
	cross_attention_outputs = self.crossattention(
	hidden_states,
	attention_mask,
	head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	output_attentions=output_attentions,
	)
	query_attention_output = cross_attention_outputs[0]

	outputs = (query_attention_output,)
	return outputs


	class MplugOwlVisualAbstractorEncoder(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.layers = nn.ModuleList(
	[MplugOwlVisualAbstractorLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
	)
	self.gradient_checkpointing = True

	def forward(
	self,
	hidden_states,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_values=None,
	output_attentions=False,
	output_hidden_states=False,
	return_dict=True,
	):
	all_hidden_states = () if output_hidden_states else None

	for i in range(self.config.num_hidden_layers):
	layer_module = self.layers[i]
	if output_hidden_states:
	all_hidden_states = all_hidden_states + (hidden_states,)

	layer_head_mask = head_mask[i] if head_mask is not None else None
	past_key_value = past_key_values[i] if past_key_values is not None else None

	if getattr(self.config, "gradient_checkpointing", False) and self.training:

	def create_custom_forward(module):
	def custom_forward(*inputs):
	return module(*inputs, past_key_value, output_attentions)

	return custom_forward

	layer_outputs = torch.utils.checkpoint.checkpoint(
	create_custom_forward(layer_module),
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	)
	else:
	layer_outputs = layer_module(
	hidden_states,
	attention_mask,
	layer_head_mask,
	encoder_hidden_states,
	encoder_attention_mask,
	output_attentions,
	)

	hidden_states = layer_outputs[0]

	return BaseModelOutput(
	last_hidden_state=hidden_states,
	)


	class MplugOwlVisualAbstractorModel(PreTrainedModel):
	_no_split_modules = ["MplugOwlVisualAbstractorLayer"]
	def __init__(self, config, language_hidden_size):
	super().__init__(config)
	self.config = config

	self.encoder = MplugOwlVisualAbstractorEncoder(config)
	self.visual_fc = torch.nn.Linear(config.hidden_size, language_hidden_size)
	self.query_embeds = torch.nn.Parameter(torch.randn(1, config.num_learnable_queries, config.hidden_size))
	self.vit_eos = torch.nn.Parameter(torch.randn(1, 1, language_hidden_size))

	self.post_init()

	def _prune_heads(self, heads_to_prune):
	"""
	Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
	class PreTrainedModel
	"""
	for layer, heads in heads_to_prune.items():
	self.encoder.layer[layer].attention.prune_heads(heads)

	def get_extended_attention_mask(
	self,
	attention_mask: torch.Tensor,
	input_shape: Tuple[int],
	device: torch.device,
	) -> torch.Tensor:
	"""
	Makes broadcastable attention and causal masks so that future and masked tokens are ignored.

	Arguments:
	attention_mask (`torch.Tensor`):
	Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
	input_shape (`Tuple[int]`):
	The shape of the input to the model.
	device: (`torch.device`):
	The device of the input to the model.

	Returns:
	`torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`.
	"""
	# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
	# ourselves in which case we just need to make it broadcastable to all heads.
	if attention_mask.dim() == 3:
	extended_attention_mask = attention_mask[:, None, :, :]
	elif attention_mask.dim() == 2:
	# Provided a padding mask of dimensions [batch_size, seq_length]
	# - the model is an encoder, so make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
	extended_attention_mask = attention_mask[:, None, None, :]
	else:
	raise ValueError(
	"Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
	input_shape, attention_mask.shape
	)
	)

	# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
	# masked positions, this operation will create a tensor which is 0.0 for
	# positions we want to attend and -10000.0 for masked positions.
	# Since we are adding it to the raw scores before the softmax, this is
	# effectively the same as removing these entirely.
	extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
	extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
	return extended_attention_mask

	def forward(
	self,
	attention_mask=None,
	head_mask=None,
	encoder_hidden_states=None,
	encoder_attention_mask=None,
	past_key_values=None,
	output_attentions=None,
	output_hidden_states=None,
	return_dict=None,
	):
	r"""
	encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, `optional`):
	Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
	the model is configured as a decoder.
	encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, `optional`):
	Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
	the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
	- 1 for tokens that are not masked,
	- 0 for tokens that are masked.
	past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of:
	shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and
	value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are
	used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key
	value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape
	`(batch_size, sequence_length)`.
	"""
	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

	query_embeds = self.query_embeds.repeat(encoder_hidden_states.shape[0], 1, 1)
	embedding_output = query_embeds
	input_shape = embedding_output.size()[:-1]
	batch_size, seq_length = input_shape
	device = embedding_output.device

	# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
	# ourselves in which case we just need to make it broadcastable to all heads.
	if attention_mask is None:
	attention_mask = torch.ones(
	(query_embeds.shape[0], query_embeds.shape[1]), dtype=torch.long, device=query_embeds.device
	)
	extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape, device)

	# If a 2D or 3D attention mask is provided for the cross-attention
	# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
	if encoder_hidden_states is not None:
	if type(encoder_hidden_states) == list:
	encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size()
	else:
	(
	encoder_batch_size,
	encoder_sequence_length,
	_,
	) = encoder_hidden_states.size()
	encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)

	if type(encoder_attention_mask) == list:
	encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask]
	elif encoder_attention_mask is None:
	encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
	encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
	else:
	encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
	else:
	encoder_extended_attention_mask = None

	# Prepare head mask if needed
	# 1.0 in head_mask indicate we keep the head
	# attention_probs has shape bsz x n_heads x N x N
	# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
	# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
	head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

	encoder_outputs = self.encoder(
	embedding_output,
	attention_mask=extended_attention_mask,
	head_mask=head_mask,
	encoder_hidden_states=encoder_hidden_states,
	encoder_attention_mask=encoder_extended_attention_mask,
	past_key_values=past_key_values,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)
	sequence_output = encoder_outputs[0]
	pooled_output = sequence_output[:, 0, :]

	sequence_output = self.visual_fc(sequence_output)
	sequence_output = torch.cat([sequence_output, self.vit_eos.repeat(sequence_output.shape[0], 1, 1)], dim=1)

	return BaseModelOutputWithPooling(
	last_hidden_state=sequence_output,
	pooler_output=pooled_output,
	hidden_states=encoder_outputs.hidden_states,
	)