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from typing import Optional, Tuple, Union
import torch
from torch import nn
import torch.nn.functional as F
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.utils import logging
from .modeling_qwen import QWenModel, QWenLMHeadModel
SUPPORT_CUDA = torch.cuda.is_available()
SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported()
SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7
logger = logging.get_logger(__name__)
class MonkeyModel(QWenModel):
def __init__(self, config):
super().__init__(config)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: 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,
images: Optional[torch.FloatTensor] = None,
):
if past_key_values is None:
bs, n_patchs, _, _, _ = images.shape # (bs, 5, C, H, W)
feats = self.visual(images.flatten(0, 1)).unflatten(0, sizes=(bs, n_patchs)) # (bs, 5, seq_len, d_hidden)
images = feats.flatten(1, 2) # (bs, 5*seq_len, d_hidden)
else:
images = None
return super().forward(input_ids,
past_key_values,
attention_mask,
token_type_ids,
position_ids,
head_mask,inputs_embeds,
encoder_hidden_states,
encoder_attention_mask,
use_cache,
output_attentions,
output_hidden_states,
return_dict,
images)
class MonkeyLMHeadModel(QWenLMHeadModel):
_keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"]
_keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"]
def __init__(self, config):
super().__init__(config)
assert (
config.bf16 + config.fp16 + config.fp32 <= 1
), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true"
autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0
if autoset_precision:
if SUPPORT_BF16:
logger.warn(
"The model is automatically converting to bf16 for faster inference. "
"If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
)
config.bf16 = True
elif SUPPORT_FP16:
logger.warn(
"The model is automatically converting to fp16 for faster inference. "
"If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
)
config.fp16 = True
else:
config.fp32 = True
if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16:
logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".")
if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16:
logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster")
if config.fp32:
if SUPPORT_BF16:
logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".")
elif SUPPORT_FP16:
logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".")
self.transformer = MonkeyModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
if config.bf16:
self.transformer.bfloat16()
self.lm_head.bfloat16()
if config.fp16:
self.transformer.half()
self.lm_head.half()
# self.post_init()
def _reset_parameters(self):
self.linkin._reset_parameters()
self.det_neck._reset_parameters()
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
images: Optional[torch.FloatTensor] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
images=images,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# shift_logits = lm_logits[..., 1282:-1, :].contiguous()
# shift_labels = labels[..., 1283:].contiguous()
# loss = F.cross_entropy(
# shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
# )
lm_logits = lm_logits[..., :-1, :]
labels = labels[..., 1:]
lm_logits = lm_logits[labels != -100]
labels = labels[labels != -100]
loss = F.cross_entropy(
lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)
)
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
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