cogagent-chat-hf / modeling_cogagent.py

Update modeling_cogagent.py

9ca995d 5 months ago

No virus

42.5 kB

	"""largely copy from llama and adapt for CogAgent"""
	import warnings
	from typing import TYPE_CHECKING, Optional, Tuple, List, Union, Literal, Dict, Any

	import math
	import torch
	from torch import nn
	from torch.nn import CrossEntropyLoss
	from torchvision import transforms
	from einops import rearrange

	from transformers import PreTrainedModel, PreTrainedTokenizer
	from transformers.utils.logging import get_logger
	from transformers.activations import ACT2FN
	from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast

	from .configuration_cogagent import CogAgentConfig
	# from .util import FastRotaryEmbedding
	from torch.nn import functional as F
	from .visual import EVA2CLIPModel
	from .cross_visual import CrossVisionModel

	if TYPE_CHECKING:
	from transformers.utils import ModelOutput

	logger = get_logger(__name__)

	LANGUAGE_TOKEN_TYPE = 0
	VISION_TOKEN_TYPE = 1


	# Copied from transformers.models.bart.modeling_bart._make_causal_mask
	def _make_causal_mask(
	input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
	):
	"""
	Make causal mask used for bi-directional self-attention.
	"""
	bsz, tgt_len = input_ids_shape
	mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
	mask_cond = torch.arange(mask.size(-1), device=device)
	mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
	mask = mask.to(dtype)

	if past_key_values_length > 0:
	mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
	return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


	# Copied from transformers.models.bart.modeling_bart._expand_mask
	def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
	"""
	Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
	"""
	bsz, src_len = mask.size()
	tgt_len = tgt_len if tgt_len is not None else src_len

	expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

	inverted_mask = 1.0 - expanded_mask

	return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)


	class RMSNorm(nn.Module):
	def __init__(self, hidden_size, eps=1e-6):
	super().__init__()
	self.weight = nn.Parameter(torch.ones(hidden_size))
	self.variance_epsilon = eps

	def forward(self, hidden_states):
	input_dtype = hidden_states.dtype
	hidden_states = hidden_states.to(torch.float32)
	variance = hidden_states.pow(2).mean(-1, keepdim=True)
	hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
	return (self.weight * hidden_states).to(input_dtype)


	class MLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.hidden_size = config.hidden_size
	self.intermediate_size = config.intermediate_size
	self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
	self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
	self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
	self.act_fn = ACT2FN[config.hidden_act]

	def forward(self, x):
	down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
	return down_proj


	def get_expert_mask(token_type_ids: "torch.LongTensor(B, L)") -> "[torch.BoolTensor(B, L), torch.BoolTensor(B, L)]":
	vision_token_mask = torch.zeros_like(token_type_ids, dtype=torch.bool)
	vision_token_mask[:, :-1] = (token_type_ids[:, :-1] == VISION_TOKEN_TYPE) & (token_type_ids[:, 1:] == VISION_TOKEN_TYPE)
	language_token_mask = ~vision_token_mask
	return vision_token_mask, language_token_mask


	class VisionExpertMLP(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.language_mlp = MLP(config)
	self.vision_mlp = MLP(config)

	def forward(self, hidden_states: "torch.Tensor(B, L, D)", token_type_ids: "torch.LongTensor(B, L)"):
	output = torch.empty(hidden_states.shape, dtype=hidden_states.dtype, device=hidden_states.device)
	vision_token_mask, language_token_mask = get_expert_mask(token_type_ids)
	output[vision_token_mask] = self.vision_mlp(hidden_states[vision_token_mask])
	output[language_token_mask] = self.language_mlp(hidden_states[language_token_mask])
	return output


	def attention_fn(
	query_layer: "torch.tensor(B, H, L, HD)",
	key_layer: "torch.tensor(B, H, L, HD)",
	value_layer: "torch.tensor(B, H, L, HD)",
	attention_mask: "torch.tensor(B, H, L, HD)",
	*,
	scaling_attention_score: bool = True,
	attention_dropout: nn.Module = None
	):
	attention_mask_bool = (attention_mask == 0)
	is_low_triangle = (attention_mask_bool == torch.ones_like(attention_mask_bool, dtype=torch.float).tril()).all()
	is_full = (attention_mask_bool > 0).all()
	if not (int(torch.__version__.split('.')[0]) >= 2):
	warnings.warn("It's recommended to use torch2.0 or higher.")
	if int(torch.__version__.split('.')[0]) >= 2 and scaling_attention_score and (is_full or is_low_triangle):
	dropout_p = 0. if attention_dropout is None or not attention_dropout.training else attention_dropout.p
	return torch.nn.functional.scaled_dot_product_attention(
	query_layer, key_layer, value_layer,
	attn_mask=None,
	dropout_p=dropout_p,
	is_causal=not is_full
	)
	else:
	if scaling_attention_score:
	query_layer = query_layer / math.sqrt(query_layer.shape[-1])
	attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
	attention_scores = attention_scores + attention_mask
	attention_scores = nn.functional.softmax(attention_scores, dim=-1, dtype=torch.float32).to(query_layer.dtype)
	if attention_dropout is not None:
	attention_scores = attention_dropout(attention_scores)
	context_layer = torch.matmul(attention_scores, value_layer)
	return context_layer

	class RotaryEmbedding(torch.nn.Module):
	def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
	super().__init__()

	self.dim = dim
	self.max_position_embeddings = max_position_embeddings
	self.base = base
	inv_freq = self._compute_inv_freq(device)
	self.register_buffer("inv_freq", inv_freq)
	self.max_seq_len_cached = 0

	def _compute_inv_freq(self, device=None):
	return 1.0 / (
	self.base
	** (torch.arange(0, self.dim, 2, device=device) / self.dim)
	)

	def _set_cos_sin_cache(self, seq_len, device, dtype):
	self.max_seq_len_cached = seq_len
	t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

	freqs = torch.einsum("i,j->ij", t, self.inv_freq)
	# Different from paper, but it uses a different permutation in order to obtain the same calculation
	emb = torch.cat((freqs, freqs), dim=-1)
	self.register_buffer("cos_cached", emb.cos()[:, None, :].to(dtype), persistent=False)
	self.register_buffer("sin_cached", emb.sin()[:, None, :].to(dtype), persistent=False)

	def forward(self, x, seq_len):
	# x: [bs, num_attention_heads, seq_len, head_size]
	if seq_len > self.max_seq_len_cached:
	self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

	return (
	self.cos_cached[:seq_len, ...].to(dtype=x.dtype),
	self.sin_cached[:seq_len, ...].to(dtype=x.dtype),
	)


	def rotate_half(x):
	x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
	return torch.cat((-x2, x1), dim=x1.ndim - 1)


	def apply_rotary_pos_emb_index_bhs(q, k, cos, sin, position_id):
	# batch_size, num_head, seq_len, hidden_size
	cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(1), \
	F.embedding(position_id, sin.squeeze(1)).unsqueeze(1)
	q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
	return q, k

	class VisionExpertAttention(nn.Module):
	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
	self.max_position_embeddings = config.max_position_embeddings

	self.rotary_emb = RotaryEmbedding(self.head_dim)
	self.vision_expert_query_key_value = nn.Linear(self.hidden_size, self.hidden_size * 3, bias=False)
	self.vision_expert_dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
	self.language_expert_query_key_value = nn.Linear(self.hidden_size, self.hidden_size * 3, bias=False)
	self.language_expert_dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False)

	def _transpose_for_scores(self, tensor):
	"""Transpose a 3D tensor [B, L, H*HD] into a 4D tensor with size [B H L HD]."""
	new_tensor_shape = tensor.size()[:-1] + (self.num_heads, self.head_dim)
	tensor = tensor.view(*new_tensor_shape)
	return tensor.permute(0, 2, 1, 3)

	def forward(
	self,
	hidden_states: torch.Tensor,
	token_type_ids: torch.LongTensor,
	position_ids: torch.LongTensor,
	attention_mask: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	bsz, q_len, _ = hidden_states.size()
	vision_token_mask, language_token_mask = get_expert_mask(token_type_ids)

	shape = list(hidden_states.shape)
	shape[-1] = shape[-1] * 3
	mixed_raw_layer = torch.empty(shape, dtype=hidden_states.dtype, device=hidden_states.device)
	mixed_raw_layer[vision_token_mask] = self.vision_expert_query_key_value(hidden_states[vision_token_mask])
	mixed_raw_layer[language_token_mask] = self.language_expert_query_key_value(hidden_states[language_token_mask])

	query_states, key_states, value_states = torch.split(mixed_raw_layer, self.hidden_size, dim=-1)
	query_states = self._transpose_for_scores(query_states) # B, H, L, HD
	key_states = self._transpose_for_scores(key_states) # B, H, L, HD
	value_states = self._transpose_for_scores(value_states) # B, H, L, HD

	kv_seq_len = key_states.shape[-2]
	if past_key_value is not None:
	kv_seq_len += past_key_value[0].shape[-2]

	cos, sin = self.rotary_emb(value_states, seq_len=position_ids.max() + 1)
	query_states, key_states = apply_rotary_pos_emb_index_bhs(query_states, key_states, cos, sin, position_ids)

	if past_key_value is not None:
	key_states = torch.cat([past_key_value[0], key_states], dim=2)
	value_states = torch.cat([past_key_value[1], value_states], dim=2)

	past_key_value = (key_states, value_states) if use_cache else None

	context_layer = attention_fn(
	query_layer=query_states, key_layer=key_states, value_layer=value_states, attention_mask=attention_mask,
	scaling_attention_score=True, attention_dropout=None)
	if context_layer.size() != (bsz, self.num_heads, q_len, self.head_dim):
	raise ValueError(
	f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
	f" {context_layer.size()}"
	)
	context_layer = context_layer.transpose(1, 2).contiguous().reshape(bsz, q_len, self.hidden_size)

	attn_output = torch.empty(context_layer.shape, dtype=hidden_states.dtype, device=hidden_states.device)
	attn_output[vision_token_mask] = self.vision_expert_dense(context_layer[vision_token_mask])
	attn_output[language_token_mask] = self.language_expert_dense(context_layer[language_token_mask])

	if output_attentions:
	warnings.warn("output_attentions is not implemented.")

	return attn_output, None, past_key_value

	class CrossAttention(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.config = config
	self.hidden_size = config.hidden_size
	self.cross_hidden_size = config.cross_hidden_size
	self.cross_compute_hidden_size = config.cross_compute_hidden_size
	self.num_heads = config.num_attention_heads
	self.head_dim = self.hidden_size // self.num_heads
	self.cross_head_dim = self.cross_compute_hidden_size // self.num_heads
	self.max_position_embeddings = config.max_position_embeddings

	self.query = nn.Linear(self.hidden_size, self.cross_compute_hidden_size, bias=False)
	self.key_value = nn.Linear(self.cross_hidden_size, self.cross_compute_hidden_size * 2, bias=False)
	self.dense = nn.Linear(self.cross_compute_hidden_size, self.hidden_size, bias=False)

	def _transpose_for_scores(self, tensor):
	"""Transpose a 3D tensor [B, L, H*HD] into a 4D tensor with size [B H L HD]."""
	new_tensor_shape = tensor.size()[:-1] + (self.num_heads, self.cross_head_dim)
	tensor = tensor.view(*new_tensor_shape)
	return tensor.permute(0, 2, 1, 3)

	def forward(
	self,
	hidden_states: torch.Tensor,
	encoder_outputs: torch.LongTensor,
	attention_mask: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: bool = False,
	use_cache: bool = False,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
	bsz, q_len, _ = hidden_states.size()

	shape = list(hidden_states.shape)
	shape[-1] = shape[-1] * 3

	mixed_query_layer = self.query(hidden_states)
	if past_key_value is None:
	mixed_x_layer = self.key_value(encoder_outputs)
	mixed_key_layer, mixed_value_layer = torch.split(mixed_x_layer, self.cross_compute_hidden_size, dim=-1)
	key_states = self._transpose_for_scores(mixed_key_layer) # B, H, L, HD
	value_states = self._transpose_for_scores(mixed_value_layer) # B, H, L, HD
	else:
	key_states, value_states = past_key_value

	query_states = self._transpose_for_scores(mixed_query_layer) # B, H, L, HD

	past_key_value = (key_states, value_states) if use_cache else None

	context_layer = attention_fn(
	query_layer=query_states, key_layer=key_states, value_layer=value_states, attention_mask=attention_mask,
	scaling_attention_score=True, attention_dropout=None)
	if context_layer.size() != (bsz, self.num_heads, q_len, self.cross_head_dim):
	raise ValueError(
	f"`cross_attn_output` should be of size {(bsz, self.num_heads, q_len, self.cross_head_dim)}, but is"
	f" {context_layer.size()}"
	)
	context_layer = context_layer.transpose(1, 2).contiguous().reshape(bsz, q_len, self.cross_hidden_size)

	attn_output = self.dense(context_layer)

	if output_attentions:
	warnings.warn("output_attentions is not implemented.")

	return attn_output, None, past_key_value

	class CogAgentDecoderLayer(nn.Module):
	def __init__(self, config):
	super().__init__()
	self.hidden_size = config.hidden_size
	self.self_attn = VisionExpertAttention(config=config)
	self.cross_attn = CrossAttention(config=config)
	self.mlp = VisionExpertMLP(config)
	self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
	self.post_cross_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

	def forward(
	self,
	hidden_states: torch.Tensor,
	encoder_outputs: torch.Tensor,
	token_type_ids: torch.LongTensor,
	position_ids: torch.LongTensor,
	attention_mask: Optional[torch.Tensor] = None,
	cross_attention_mask: Optional[torch.Tensor] = None,
	past_key_value: Optional[Tuple[torch.Tensor]] = None,
	output_attentions: Optional[bool] = False,
	use_cache: Optional[bool] = False,
	) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
	residual = hidden_states

	hidden_states = self.input_layernorm(hidden_states)

	# Self Attention
	hidden_states, self_attn_weights, present_key_value = self.self_attn(
	hidden_states=hidden_states,
	token_type_ids=token_type_ids,
	position_ids=position_ids,
	attention_mask=attention_mask,
	past_key_value=past_key_value[:2] if past_key_value is not None else None,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)
	hidden_states = residual + hidden_states

	cross_input = self.post_cross_attention_layernorm(hidden_states)
	# Fully Connected
	attention_output, self_cross_attn_weights, present_cross_key_value = self.cross_attn(
	hidden_states=cross_input,
	encoder_outputs=encoder_outputs,
	attention_mask=cross_attention_mask,
	past_key_value=past_key_value[-2:] if past_key_value is not None else None,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)
	hidden_states = hidden_states + attention_output
	mlp_input = self.post_attention_layernorm(hidden_states)
	mlp_output = self.mlp(mlp_input, token_type_ids=token_type_ids)
	hidden_states = mlp_output + hidden_states

	outputs = (hidden_states,)

	if output_attentions:
	outputs += (self_attn_weights,)

	if use_cache:
	outputs += (present_key_value+present_cross_key_value,)

	return outputs # type: ignore


	class CogAgentPreTrainedModel(PreTrainedModel):
	config_class = CogAgentConfig
	base_model_prefix = "model"
	supports_gradient_checkpointing = False
	_no_split_modules = ["CogAgentDecoderLayer", 'TransformerLayer', 'Block']
	_skip_keys_device_placement = "past_key_values"

	def _init_weights(self, module):
	std = self.config.initializer_range
	if isinstance(module, nn.Linear):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.bias is not None:
	module.bias.data.zero_()
	elif isinstance(module, nn.Embedding):
	module.weight.data.normal_(mean=0.0, std=std)
	if module.padding_idx is not None:
	module.weight.data[module.padding_idx].zero_()


	def is_empty(images_list: Optional[List[List[torch.Tensor]]]):
	if images_list is None or len(images_list) == 0:
	return True
	for image_list in images_list:
	if len(image_list):
	return False
	return True


	def build_position_ids(x: "torch.BoolTensor(B, L)", attention_mask: Optional["torch.BoolTensor(B, L)"] = None) -> "torch.LongTensor(B, L)":
	if attention_mask is not None:
	tmp = x.clone()
	tmp[~(attention_mask.bool())] = -1
	else:
	tmp = x.clone()
	# image boi eoi token as LANGUAGE_TOKEN_TYPE
	is_boi_eoi = torch.zeros_like(x, dtype=torch.bool)
	is_boi_eoi[:, 1:] \|= (tmp[:, 1:] == VISION_TOKEN_TYPE) & (tmp[:, :-1] == LANGUAGE_TOKEN_TYPE)
	is_boi_eoi[:, 0] \|= (tmp[:, 0] == VISION_TOKEN_TYPE)
	is_boi_eoi[:, :-1] \|= (tmp[:, :-1] == VISION_TOKEN_TYPE) & (tmp[:, 1:] == LANGUAGE_TOKEN_TYPE)
	is_boi_eoi[:, -1] \|= (tmp[:, -1] == VISION_TOKEN_TYPE)
	tmp[is_boi_eoi] = LANGUAGE_TOKEN_TYPE
	# final position ids
	y = torch.zeros_like(x, dtype=torch.long)
	y[:, 1:] = (tmp[:, 1:] == LANGUAGE_TOKEN_TYPE) \| ((tmp[:, 1:] == VISION_TOKEN_TYPE) & (tmp[:, :-1] == LANGUAGE_TOKEN_TYPE))
	y = y.cumsum(dim=-1)
	return y


	class CogAgentModel(CogAgentPreTrainedModel):
	def __init__(self, config):
	super().__init__(config)
	self.padding_idx = config.pad_token_id
	self.vocab_size = config.vocab_size

	self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
	self.layers = nn.ModuleList([CogAgentDecoderLayer(config) for _ in range(config.num_hidden_layers)])
	self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

	self.vision = EVA2CLIPModel(config)
	self.cross_vision = CrossVisionModel(config)

	self.gradient_checkpointing = False
	# Initialize weights and apply final processing
	self.post_init()

	def encode_images(self, images: List[List[torch.Tensor]]) -> torch.Tensor:
	images_list, images = images, []

	images = []
	for image_list in images_list:
	for image in image_list:
	images.append(image)

	images = torch.stack(images)
	images_features = self.vision(images)
	return images_features

	def encode_cross_images(self, images: List[List[torch.Tensor]]) -> torch.Tensor:
	images_list, images = images, []

	images = []
	for image_list in images_list:
	for image in image_list:
	images.append(image)

	images = torch.stack(images)
	encoder_outputs = self.cross_vision(images)
	return encoder_outputs

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	images: List[List[torch.Tensor]] = None,
	cross_images: List[List[torch.Tensor]] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	cross_attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPast]:
	"""take care of image_encode, token_type_ids, position_ids and (attention_mask = None is fine)"""

	if past_key_values is not None:
	encoder_outputs = None
	# generate mode with past_key_values. the image features are already mapped
	else:
	# not allow for inputs_embeds, because we want to process image feature
	assert input_ids is not None and inputs_embeds is None, f"{input_ids} {inputs_embeds}"
	if not is_empty(images): # multi-modality
	assert token_type_ids is not None, f"multi-modality requires `token_type_ids`!"
	assert len(input_ids) == len(images), f"{len(input_ids)} {len(images)}"
	inputs_embeds = self.embed_tokens(input_ids)
	images_features = self.encode_images(images)
	encoder_outputs = self.encode_cross_images(cross_images)
	images_features = rearrange(images_features, 'b n d -> (b n) d')
	images_features = images_features.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
	inputs_embeds = inputs_embeds.index_put([token_type_ids == VISION_TOKEN_TYPE], images_features)
	else: # single-modality
	if token_type_ids is None:
	token_type_ids = torch.ones_like(input_ids, dtype=torch.long, device=input_ids.device) * LANGUAGE_TOKEN_TYPE
	assert not (token_type_ids == VISION_TOKEN_TYPE).any(), f"{(token_type_ids == VISION_TOKEN_TYPE).sum()}"
	inputs_embeds = self.embed_tokens(input_ids)
	encoder_outputs = None

	if position_ids is None:
	position_ids = build_position_ids(token_type_ids, attention_mask)
	input_ids = None

	return self.llm_forward(
	input_ids=input_ids,
	encoder_outputs=encoder_outputs,
	token_type_ids=token_type_ids,
	attention_mask=attention_mask,
	cross_attention_mask=cross_attention_mask,
	position_ids=position_ids,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	def llm_forward(
	self,
	input_ids: torch.LongTensor = None,
	encoder_outputs: torch.LongTensor = None,
	token_type_ids: torch.LongTensor = None,
	attention_mask: Optional[torch.Tensor] = None,
	cross_attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	) -> Union[Tuple, BaseModelOutputWithPast]:
	"""largely copy from llama forward and adapt for CogAgent with `token_type_ids`"""
	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
	)
	use_cache = use_cache if use_cache is not None else self.config.use_cache

	return_dict = return_dict if return_dict is not None else self.config.use_return_dict

	# retrieve input_ids and inputs_embeds
	if input_ids is not None and inputs_embeds is not None:
	raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
	elif input_ids is not None:
	batch_size, seq_length = input_ids.shape
	elif inputs_embeds is not None:
	batch_size, seq_length, _ = inputs_embeds.shape
	else:
	raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

	seq_length_with_past = seq_length
	past_key_values_length = 0

	if past_key_values is not None:
	past_key_values_length = past_key_values[0][0].shape[2]
	seq_length_with_past = seq_length_with_past + past_key_values_length

	if position_ids is None:
	device = input_ids.device if input_ids is not None else inputs_embeds.device
	position_ids = torch.arange(
	past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
	)
	position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
	else:
	position_ids = position_ids.view(-1, seq_length).long()

	if inputs_embeds is None:
	inputs_embeds = self.embed_tokens(input_ids)
	# embed positions
	if attention_mask is None:
	attention_mask = torch.ones(
	(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
	)
	if cross_attention_mask is None:
	cross_attention_mask = torch.ones(
	(batch_size, 1), dtype=torch.bool, device=inputs_embeds.device
	)
	attention_mask = self._prepare_decoder_attention_mask(
	attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
	)

	hidden_states = inputs_embeds

	# decoder layers
	all_hidden_states = () if output_hidden_states else None
	all_self_attns = () if output_attentions else None
	next_decoder_cache = () if use_cache else None

	for idx, decoder_layer in enumerate(self.layers):
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	past_key_value = past_key_values[idx] if past_key_values is not None else None
	layer_outputs = decoder_layer(
	hidden_states,
	encoder_outputs=encoder_outputs,
	token_type_ids=token_type_ids,
	attention_mask=attention_mask,
	cross_attention_mask=cross_attention_mask,
	position_ids=position_ids,
	past_key_value=past_key_value,
	output_attentions=output_attentions,
	use_cache=use_cache,
	)
	hidden_states = layer_outputs[0]

	if use_cache:
	next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)

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

	hidden_states = self.norm(hidden_states)

	# add hidden states from the last decoder layer
	if output_hidden_states:
	all_hidden_states += (hidden_states,)

	next_cache = next_decoder_cache if use_cache else None
	if not return_dict:
	return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
	return BaseModelOutputWithPast(
	last_hidden_state=hidden_states,
	past_key_values=next_cache,
	hidden_states=all_hidden_states,
	attentions=all_self_attns,
	)

	def get_input_embeddings(self):
	return self.embed_tokens

	def set_input_embeddings(self, value):
	self.embed_tokens = value

	# noinspection PyMethodMayBeStatic
	# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
	def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
	# create causal mask
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	combined_attention_mask = None
	if input_shape[-1] > 1:
	combined_attention_mask = _make_causal_mask(
	input_shape,
	inputs_embeds.dtype,
	device=inputs_embeds.device,
	past_key_values_length=past_key_values_length,
	)

	if attention_mask is not None:
	# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
	expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
	inputs_embeds.device
	)
	combined_attention_mask = (
	expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
	)

	return combined_attention_mask

	def vqa_history_to_prompt(history, query):
	# Only support single round chat in vqa mode
	prompt = "<EOI>Question: "
	# for i, (old_query, response) in enumerate(history):
	# prompt += old_query + " Short answer: " + response + " Question: "
	prompt += query + " Short answer:"
	return prompt

	def chat_old_history_to_prompt(history, query):
	prompt = "<EOI>Question: "
	for i, (old_query, response) in enumerate(history):
	prompt += old_query + " Answer: " + response + "\nQuestion: "
	prompt += query + " Answer:"
	return prompt

	def chat_history_to_prompt(history, query):
	prompt = " [INST] "
	for i, (old_query, response) in enumerate(history):
	prompt += old_query + " [/INST] " + response + " [INST] "
	prompt += query + " [/INST] "
	return prompt


	def base_history_to_prompt(history, query):
	prompt = query
	return prompt


	_history_to_prompt = {
	"base": base_history_to_prompt,
	"chat": chat_history_to_prompt,
	"chat_old": chat_old_history_to_prompt,
	"vqa": vqa_history_to_prompt
	}


	class CogAgentForCausalLM(CogAgentPreTrainedModel):
	_auto_class = "AutoModelForCausalLM"

	def __init__(self, config):
	super().__init__(config)
	self.model = CogAgentModel(config)
	self.vocab_size = config.vocab_size
	self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

	# Initialize weights and apply final processing
	self.post_init()

	def get_input_embeddings(self):
	return self.model.embed_tokens

	def set_input_embeddings(self, value):
	self.model.embed_tokens = value

	def get_output_embeddings(self):
	return self.lm_head

	def set_output_embeddings(self, new_embeddings):
	self.lm_head = new_embeddings

	def set_decoder(self, decoder):
	self.model = decoder

	def get_decoder(self):
	return self.model

	def forward(
	self,
	input_ids: torch.LongTensor = None,
	images: List[List[torch.Tensor]] = None,
	cross_images: List[List[torch.Tensor]] = None,
	token_type_ids: Optional[torch.LongTensor] = None,
	attention_mask: Optional[torch.Tensor] = None,
	position_ids: Optional[torch.LongTensor] = None,
	past_key_values: Optional[List[torch.FloatTensor]] = None,
	inputs_embeds: Optional[torch.FloatTensor] = None,
	use_cache: Optional[bool] = None,
	output_attentions: Optional[bool] = None,
	output_hidden_states: Optional[bool] = None,
	return_dict: Optional[bool] = None,
	labels: Optional[torch.LongTensor] = None,
	) -> Union[Tuple, CausalLMOutputWithPast]:
	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

	# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
	outputs = self.model(
	input_ids=input_ids,
	images=images,
	cross_images=cross_images,
	token_type_ids=token_type_ids,
	attention_mask=attention_mask,
	position_ids=position_ids,
	past_key_values=past_key_values,
	inputs_embeds=inputs_embeds,
	use_cache=use_cache,
	output_attentions=output_attentions,
	output_hidden_states=output_hidden_states,
	return_dict=return_dict,
	)

	hidden_states = outputs[0]
	logits = self.lm_head(hidden_states)
	logits = logits.float()

	loss = None
	if labels is not None:
	# Shift so that tokens < n predict n
	shift_logits = logits[..., :-1, :].contiguous()
	shift_labels = labels[..., 1:].contiguous()
	# Flatten the tokens
	loss_fct = CrossEntropyLoss()
	shift_logits = shift_logits.view(-1, self.config.vocab_size)
	shift_labels = shift_labels.view(-1)
	# Enable model parallelism
	shift_labels = shift_labels.to(shift_logits.device)
	loss = loss_fct(shift_logits, shift_labels)

	if not return_dict:
	output = (logits,) + outputs[1:]
	return (loss,) + output if loss is not None else output

	return CausalLMOutputWithPast(
	loss=loss,
	logits=logits,
	past_key_values=outputs.past_key_values,
	hidden_states=outputs.hidden_states,
	attentions=outputs.attentions,
	)

	def _prepare_attention_mask_for_generation(
	self,
	inputs: torch.Tensor,
	pad_token_id: Optional[int],
	eos_token_id: Optional[Union[int, List[int]]],
	) -> torch.LongTensor:
	return torch.ones(inputs.shape[:2], dtype=torch.long, device=inputs.device) # type: ignore

	def prepare_inputs_for_generation(
	self, input_ids, token_type_ids, images=None, cross_images=None, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
	):
	# build position_ids if needed
	position_ids = kwargs.get("position_ids", None)
	if position_ids is None:
	position_ids = build_position_ids(token_type_ids, attention_mask)

	if past_key_values:
	input_ids = input_ids[:, -1:]
	token_type_ids = token_type_ids[:, -1:]
	position_ids = position_ids[:, -1:]

	# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
	if inputs_embeds is not None and past_key_values is None:
	model_inputs = {"inputs_embeds": inputs_embeds}
	else:
	model_inputs = {"input_ids": input_ids}

	model_inputs.update(
	{
	"token_type_ids": token_type_ids,
	"images": images,
	"cross_images": cross_images,
	"position_ids": position_ids,
	"past_key_values": past_key_values,
	"use_cache": kwargs.get("use_cache"),
	"attention_mask": attention_mask,
	}
	)
	return model_inputs

	def _update_model_kwargs_for_generation(
	self,
	outputs: "ModelOutput",
	model_kwargs: Dict[str, Any],
	is_encoder_decoder: bool = False,
	standardize_cache_format: bool = False,
	) -> Dict[str, Any]:
	# update past_key_values
	model_kwargs["past_key_values"] = self._extract_past_from_model_output(
	outputs, standardize_cache_format=standardize_cache_format
	)
	if getattr(outputs, "state", None) is not None:
	model_kwargs["state"] = outputs.state

	# update token_type_ids with last value
	if "token_type_ids" in model_kwargs:
	token_type_ids = model_kwargs["token_type_ids"]
	new_token_type_ids = torch.ones(size=(token_type_ids.shape[0], 1), dtype=token_type_ids.dtype, device=token_type_ids.device) * LANGUAGE_TOKEN_TYPE
	model_kwargs["token_type_ids"] = torch.cat([token_type_ids, new_token_type_ids], dim=-1)

	if not is_encoder_decoder:
	# update attention mask
	if "attention_mask" in model_kwargs:
	attention_mask = model_kwargs["attention_mask"]
	model_kwargs["attention_mask"] = torch.cat(
	[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
	)
	else:
	# update decoder attention mask
	if "decoder_attention_mask" in model_kwargs:
	decoder_attention_mask = model_kwargs["decoder_attention_mask"]
	model_kwargs["decoder_attention_mask"] = torch.cat(
	[decoder_attention_mask, decoder_attention_mask.new_ones((decoder_attention_mask.shape[0], 1))],
	dim=-1,
	)

	return model_kwargs

	def _reorder_cache(self, past_key_values, beam_idx):
	reordered_past = ()
	for layer_past in past_key_values:
	reordered_past += (
	tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
	)
	return reordered_past

	def build_conversation_input_ids(
	self,
	tokenizer: "PreTrainedTokenizer",
	*,
	query: str,
	history: Optional[List[Tuple[str, str]]] = None,
	images: Optional[List["PIL.Image"]] = None,
	template_version: Optional[Literal["base", "chat", "vqa"]] = None,
	):
	image_size: int = self.config.vision_config['image_size']
	cross_image_size: int = self.config.cross_image_size
	patch_size: int = self.config.vision_config['patch_size']
	template_version = template_version or self.config.template_version
	assert images is None or len(images) <= 1, f"not support multi images by now."
	history = history or []
	text = _history_to_prompt[template_version](history, query)

	input_ids = [tokenizer.bos_token_id]
	token_type_ids = [LANGUAGE_TOKEN_TYPE]
	if images is not None and len(images) == 1:
	ori = images
	# vision
	transform = transforms.Compose(
	[
	transforms.Resize(
	(image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC
	),
	transforms.ToTensor(),
	transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
	]
	)
	images = [transform(ori[0])]
	cross_transform = transforms.Compose(
	[
	transforms.Resize(
	(cross_image_size, cross_image_size), interpolation=transforms.InterpolationMode.BICUBIC
	),
	transforms.ToTensor(),
	transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
	]
	)
	cross_images = [cross_transform(ori[0])]
	# language
	vision_token_num = (image_size // patch_size) * (image_size // patch_size) + 2
	input_ids += [tokenizer.pad_token_id] * vision_token_num
	token_type_ids += [VISION_TOKEN_TYPE] * vision_token_num
	text_ids = tokenizer.encode(text, add_special_tokens=False)

	input_ids += text_ids
	token_type_ids += [LANGUAGE_TOKEN_TYPE] * len(text_ids)
	attention_mask = [1] * len(input_ids)

	return {
	'input_ids': torch.tensor(input_ids, dtype=torch.long),
	'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),
	'attention_mask': torch.tensor(attention_mask, dtype=torch.long),
	'images': images,
	'cross_images': cross_images
	}