import os from typing import List, Optional, Tuple import onnxruntime as onnxrt import requests import torch from PIL import Image from transformers import AutoConfig, AutoProcessor, GenerationConfig, PreTrainedModel from transformers.generation import GenerationMixin from transformers.modeling_outputs import BaseModelOutput, CausalLMOutputWithPast from optimum.utils import NormalizedConfigManager os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" device = torch.device("cpu") model_name = "llava-1.5-7b-hf/" processor = AutoProcessor.from_pretrained(model_name) config = AutoConfig.from_pretrained(model_name) prompt = "\nUSER: What's the content of the image?\nASSISTANT:" url = "https://www.ilankelman.org/stopsigns/australia.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(text=prompt, images=image, return_tensors="pt") class ORTModel(torch.nn.Module): def __init__(self, path, config): super().__init__() self._device = device self.config = config self.session = onnxrt.InferenceSession(path, providers=["CPUExecutionProvider"]) self.input_names = {input_key.name: idx for idx, input_key in enumerate(self.session.get_inputs())} self.output_names = {output_key.name: idx for idx, output_key in enumerate(self.session.get_outputs())} class ORTEncoder(ORTModel): def forward( self, input_ids: torch.FloatTensor, pixel_values: torch.FloatTensor, attention_mask: torch.LongTensor, **kwargs, ) -> BaseModelOutput: onnx_inputs = { "input_ids": input_ids.cpu().detach().numpy(), "pixel_values": pixel_values.cpu().detach().numpy(), "attention_mask": attention_mask.cpu().detach().numpy(), } # Run inference outputs = self.session.run(None, onnx_inputs) for i, output in enumerate(outputs): outputs[i] = torch.from_numpy(output).to(self._device) return ( outputs[self.output_names["inputs_embeds"]], outputs[self.output_names["decoder_attention_mask"]], outputs[self.output_names["position_ids"]], ) class ORTDecoderProcessor(ORTModel): def forward( self, input_ids: torch.FloatTensor, attention_mask: torch.LongTensor, past_key_value: torch.FloatTensor, **kwargs, ) -> BaseModelOutput: onnx_inputs = { "input_ids": input_ids.cpu().detach().numpy(), "attention_mask": attention_mask.cpu().detach().numpy(), "past_key_values.0.key": past_key_value.cpu().detach().numpy(), } # Run inference outputs = self.session.run(None, onnx_inputs) for i, output in enumerate(outputs): outputs[i] = torch.from_numpy(output).to(self._device) return ( outputs[self.output_names["inputs_embeds"]], outputs[self.output_names["decoder_attention_mask"]], outputs[self.output_names["position_ids"]], ) class ORTDecoder(ORTModel): def __init__(self, path, config): super().__init__(path, config) self.normalized_config = NormalizedConfigManager.get_normalized_config_class(config.text_config.model_type)( config.text_config ) self.generation_config = GenerationConfig.from_model_config(config) self.key_value_input_names = [key for key in self.input_names if (".key" in key) or (".value" in key)] self.key_value_output_names = [key for key in self.output_names if (".key" in key) or (".value" in key)] self.num_pkv = 2 def prepare_pkv(self, batch_size: int): if self.config.text_config.model_type in {"mistral", "llama"}: num_attention_heads = self.normalized_config.num_key_value_heads else: num_attention_heads = self.normalized_config.num_attention_heads embed_size_per_head = self.normalized_config.hidden_size // self.normalized_config.num_attention_heads shape = (batch_size, num_attention_heads, 0, embed_size_per_head) key_or_value = torch.zeros(shape, dtype=torch.float32) past_key_values = tuple(key_or_value for _ in range(len(self.key_value_input_names))) return past_key_values def forward( self, attention_mask: torch.LongTensor, position_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, past_key_values: Tuple[Tuple[torch.FloatTensor]] = None, ) -> CausalLMOutputWithPast: onnx_inputs = { "attention_mask": attention_mask.cpu().detach().numpy(), "position_ids": position_ids.cpu().detach().numpy(), "inputs_embeds": inputs_embeds.cpu().detach().numpy(), } if past_key_values is None: past_key_values = self.prepare_pkv(inputs_embeds.shape[0]) else: past_key_values = tuple( past_key_value for pkv_per_layer in past_key_values for past_key_value in pkv_per_layer ) for input_name, past_key_value in zip(self.key_value_input_names, past_key_values): onnx_inputs[input_name] = past_key_value.cpu().detach().numpy() # Run inference outputs = self.session.run(None, onnx_inputs) logits = torch.from_numpy(outputs[self.output_names["logits"]]) past_key_values = tuple( torch.from_numpy(outputs[self.output_names[key]]) for key in self.key_value_output_names ) past_key_values = tuple( past_key_values[i : i + self.num_pkv] for i in range(0, len(past_key_values), self.num_pkv) ) return CausalLMOutputWithPast(logits=logits, past_key_values=past_key_values) class ORTModelForLLava(PreTrainedModel, GenerationMixin): def __init__(self, *args, **kwargs): config = AutoConfig.from_pretrained(model_name) super().__init__(config) self.config = config self._device = device self.vision_tower = ORTEncoder(model_name + "encoder_model.onnx", config) self.language_model = ORTDecoder(model_name + "decoder_model.onnx", config) self.decoder_input_processor = ORTDecoderProcessor(model_name + "decoder_input_processor_model.onnx", config) def forward( self, input_ids: torch.LongTensor = None, pixel_values: torch.FloatTensor = None, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, **kwargs, ) -> CausalLMOutputWithPast: if past_key_values is None: inputs_embeds, attention_mask, position_ids = self.vision_tower( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, ) else: inputs_embeds, attention_mask, position_ids = self.decoder_input_processor( input_ids=input_ids, attention_mask=attention_mask, past_key_value=past_key_values[0][0][:, :, :, 0], ) # Decode decoder_outputs = self.language_model( attention_mask=attention_mask, position_ids=position_ids, inputs_embeds=inputs_embeds, past_key_values=past_key_values, ) return decoder_outputs def can_generate(self): return True def prepare_inputs_for_generation( self, input_ids, past_key_values=None, inputs_embeds=None, pixel_values=None, attention_mask=None, **kwargs ): if past_key_values is not None: cache_length = past_length = past_key_values[0][0].shape[2] if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] elif past_length < input_ids.shape[1]: input_ids = input_ids[:, past_length:] elif self.config.image_token_index in input_ids: input_ids = input_ids[:, input_ids.shape[1] - 1 :] if cache_length < past_length and attention_mask is not None: attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :] model_inputs = {"input_ids": input_ids} model_inputs.update( { "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, "pixel_values": pixel_values, } ) return model_inputs @property def device(self) -> torch.device: return self._device @device.setter def device(self, value: torch.device): self._device = value def to(self, device): self.device = device return self model = ORTModelForLLava() generated_ids = model.generate(**inputs, max_length=30) out = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] print(out)