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"""largely copy from llama and adapt for cogvlm"""
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 functional as F
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_cogvlm import CogVLMConfig
from .visual import EVA2CLIPModel
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 CogVLMDecoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = VisionExpertAttention(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)
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: Optional[bool] = False,
use_cache: Optional[bool] = False,
print_values = False,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
if print_values:
print("Hidden states before self attention:", hidden_states[0,:3,:3])
# 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,
output_attentions=output_attentions,
use_cache=use_cache,
)
if print_values:
print("Hidden states after self attention:", hidden_states[0,:3,:3])
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states, token_type_ids=token_type_ids)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs # type: ignore
class CogVLMPreTrainedModel(PreTrainedModel):
config_class = CogVLMConfig
base_model_prefix = "model"
supports_gradient_checkpointing = False
_no_split_modules = ["CogVLMDecoderLayer", "TransformerLayer"]
_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 CogVLMModel(CogVLMPreTrainedModel):
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([CogVLMDecoderLayer(config) for _ in range(config.num_hidden_layers)])
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.vision = EVA2CLIPModel(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 forward(
self,
input_ids: torch.LongTensor = None,
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,
) -> 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:
pass # 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)
images_features = rearrange(images_features, 'b n d -> (b n) d')
images_features = images_features.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
print("First values of text embeddings:", inputs_embeds[0, :3, :3])
print("First values of images_features:", images_features[0, :3, :3])
inputs_embeds = inputs_embeds.index_put([token_type_ids == VISION_TOKEN_TYPE], images_features)
print("First values of inputs_embeds after index_put:", inputs_embeds[0, :3, :3])
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)
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,
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,
)
def llm_forward(
self,
input_ids: torch.LongTensor = None,
token_type_ids: 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,
) -> Union[Tuple, BaseModelOutputWithPast]:
"""largely copy from llama forward and adapt for cogvlm 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()
print("Input ids:", input_ids)
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
)
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,)
if idx in [0, 1, 2]:
print(f"Hidden states before layer {idx}", hidden_states[0,:3,:3])
print(f"Mean of hidden states before layer {idx}", hidden_states.mean())
past_key_value = past_key_values[idx] if past_key_values is not None else None
layer_outputs = decoder_layer(
hidden_states,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
print_values=idx in [0, 1, 2],
)
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 _history_to_prompt(signal_type, history, query):
if signal_type == 'base':
return query
elif signal_type == 'vqa':
answer_format = 'Short answer:'
elif signal_type == 'chat':
answer_format = 'Answer:'
else:
assert False, f"Unknown signal type {signal_type}"
prompt = ''
for i, (old_query, response) in enumerate(history):
prompt += 'Question: ' + old_query + " {} ".format(answer_format) + response + "\n"
prompt += 'Question: {} {}'.format(query, answer_format)
return prompt
class CogVLMForCausalLM(CogVLMPreTrainedModel):
_auto_class = "AutoModelForCausalLM"
def __init__(self, config):
super().__init__(config)
self.model = CogVLMModel(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,
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,
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, 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,
"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,
model_inputs: Optional[Dict[str, Any]] = None,
) -> 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']
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:
# 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(images[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,
}
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