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from transformers import (
PretrainedConfig,
PreTrainedModel
)
from torch.nn import CrossEntropyLoss
from transformers.models.gpt_bigcode.modeling_gpt_bigcode import CausalLMOutputWithCrossAttentions
from typing import Optional, Tuple, Union
import torch
from transformers.processing_utils import ProcessorMixin
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode, pad
from transformers.feature_extraction_sequence_utils import BatchFeature
from transformers import AutoProcessor
class SimpleStarVectorProcessor(ProcessorMixin):
attributes = ["tokenizer"] # Only include tokenizer in attributes
valid_kwargs = ["size", "mean", "std"] # Add other parameters as valid kwargs
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(self,
tokenizer=None, # Make tokenizer the first argument
size=224,
mean=None,
std=None,
**kwargs,
):
if mean is None:
mean = (0.48145466, 0.4578275, 0.40821073)
if std is None:
std = (0.26862954, 0.26130258, 0.27577711)
# Store these as instance variables
self.mean = mean
self.std = std
self.size = size
self.normalize = transforms.Normalize(mean=mean, std=std)
self.transform = transforms.Compose([
transforms.Lambda(lambda img: img.convert("RGB") if img.mode == "RGBA" else img),
transforms.Lambda(lambda img: self._pad_to_square(img)),
transforms.Resize(size, interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
self.normalize
])
# Initialize parent class with tokenizer
super().__init__(tokenizer=tokenizer)
def __call__(self, images=None, text=None, **kwargs) -> BatchFeature:
"""
Process images and/or text inputs.
Args:
images: Optional image input(s)
text: Optional text input(s)
**kwargs: Additional arguments
"""
if images is None and text is None:
raise ValueError("You have to specify at least one of `images` or `text`.")
image_inputs = {}
if images is not None:
if isinstance(images, (list, tuple)):
images_ = [self.transform(img) for img in images]
else:
images_ = self.transform(images)
image_inputs = {"pixel_values": images_}
text_inputs = {}
if text is not None:
text_inputs = self.tokenizer(text, **kwargs)
return BatchFeature(data={**text_inputs, **image_inputs})
AutoProcessor.register(SimpleStarVectorProcessor, SimpleStarVectorProcessor)
class StarVectorConfig(PretrainedConfig):
model_type = "starvector"
def __init__(
self,
starcoder_model_name: str = "bigcode/starcoderbase-1b",
image_encoder_type: str = "clip",
adapter_norm: str = "layer_norm",
image_size: int = 224,
max_length: int = 8192,
max_length_train: int = 8192,
use_flash_attn: bool = True,
use_cache: bool = True,
num_attention_heads: int = 16,
num_hidden_layers: int = 24,
vocab_size: int = 49152,
hidden_size: int = 2048,
num_kv_heads: int = 4,
torch_dtype: str = "bfloat16",
**kwargs,
):
kwargs["torch_dtype"] = torch_dtype
self.starcoder_model_name = starcoder_model_name
self.image_encoder_type = image_encoder_type
self.adapter_norm = adapter_norm
self.image_size = image_size
self.max_length = max_length
self.max_length_train = max_length_train
self.use_flash_attn = use_flash_attn
self.use_cache = use_cache
self.num_attention_heads = num_attention_heads
self.num_hidden_layers = num_hidden_layers
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_kv_heads = num_kv_heads
super().__init__(**kwargs)
class StarVectorForCausalLM(PreTrainedModel):
config_class = StarVectorConfig
_no_split_modules = []
def __init__(self, config: StarVectorConfig, **kwargs):
super().__init__(config)
starcoder_model_name = config.starcoder_model_name
if 'starcoder2' in starcoder_model_name:
from starvector.model.models.starvector_v2 import StarVectorStarCoder2
self.model = StarVectorStarCoder2(config=config, **kwargs)
else:
from starvector.model.models.starvector_v1 import StarVectorStarCoder
self.model = StarVectorStarCoder(config=config, **kwargs)
@property
def supports_gradient_checkpointing(self):
# If the underlying transformer (e.g., the one in StarCoderModel)
# supports gradient checkpointing, delegate to it.
if hasattr(self.model, 'svg_transformer'):
return getattr(self.model.svg_transformer, 'supports_gradient_checkpointing', False)
return False
def gradient_checkpointing_enable(self):
# Optionally, forward this call to the internal transformer.
if hasattr(self.model, 'svg_transformer') and hasattr(self.model.svg_transformer, 'gradient_checkpointing_enable'):
self.model.svg_transformer.gradient_checkpointing_enable()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
num_logits_to_keep: int = 0,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model.svg_transformer.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,
)
hidden_states = transformer_outputs[0]
# If GRPO requested only the last tokens, slice accordingly.
if num_logits_to_keep > 0:
lm_logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
else:
lm_logits = self.lm_head(hidden_states)
# lm_logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous().to(shift_logits.device)
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
cross_attentions=transformer_outputs.cross_attentions,
)
# def forward(self, batch):
# return self.model(batch)
def generate_im2svg(self, batch, **kwargs):
return self.model.generate_im2svg(batch, **kwargs)
def generate_im2text(self, batch, **kwargs):
return self.model.generate_im2text(batch, **kwargs)
def process_images(self, images):
return self.model.image_encoder.process_images(images)
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