File size: 2,484 Bytes
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import torch
from transformers import GenerationMixin, LogitsProcessorList, StoppingCriteriaList
from transformers.generation.utils import GenerationConfig, GenerateOutput
from transformers.utils import ModelOutput
class TSGenerationMixin(GenerationMixin):
@torch.no_grad()
def generate(
self,
inputs: Optional[torch.Tensor] = None,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
synced_gpus: Optional[bool] = None,
assistant_model: Optional["PreTrainedModel"] = None,
streamer: Optional["BaseStreamer"] = None,
negative_prompt_ids: Optional[torch.Tensor] = None,
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
revin: Optional[bool] = True,
num_samples: Optional[int] = 1,
max_output_length: Optional[int] = 96,
inference_patch_len: Optional[int] = 48,
**kwargs,
) -> Union[GenerateOutput, torch.Tensor]:
if len(inputs.shape) != 2:
raise ValueError('Input shape must be: [batch_size, seq_len]')
if revin:
means = inputs.mean(dim=-1, keepdim=True)
stdev = inputs.std(dim=-1, keepdim=True, unbiased=False) + 1e-5
inputs = (inputs - means) / stdev
model_inputs = {
"input_ids": inputs,
"max_output_length": max_output_length,
"revin": False,
"num_samples": num_samples,
"inference_patch_len": inference_patch_len,
}
outputs = self(**model_inputs)
predictions = outputs.logits
if revin:
stdev = stdev.unsqueeze(1).repeat(1, num_samples, 1)
means = means.unsqueeze(1).repeat(1, num_samples, 1)
predictions = (predictions * stdev) + means
return predictions
def _update_model_kwargs_for_generation(
self,
outputs: ModelOutput,
model_kwargs: Dict[str, Any],
horizon_length: int = 1,
is_encoder_decoder: bool = False,
standardize_cache_format: bool = False,
) -> Dict[str, Any]:
return model_kwargs
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