import argparse import os import shutil from time import time # isort: off import torch import tensorrt as trt # isort: on from PIL import Image from transformers import (AutoProcessor, Blip2ForConditionalGeneration, Blip2Processor, LlavaForConditionalGeneration, NougatProcessor, VisionEncoderDecoderModel) def export_visual_wrapper_onnx(visual_wrapper, image, output_dir): logger.log(trt.Logger.INFO, "Exporting onnx") os.mkdir(f'{output_dir}/onnx') torch.onnx.export(visual_wrapper, image, f'{output_dir}/onnx/visual_encoder.onnx', opset_version=17, input_names=['input'], output_names=['output'], dynamic_axes={'input': { 0: 'batch' }}) def build_trt_engine(img_height, img_width, output_dir, max_batch_size): part_name = 'visual_encoder' onnx_file = '%s/onnx/%s.onnx' % (output_dir, part_name) engine_file = '%s/%s_fp16.engine' % (output_dir, part_name) logger.log(trt.Logger.INFO, "Building TRT engine for %s" % part_name) builder = trt.Builder(logger) network = builder.create_network( 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) profile = builder.create_optimization_profile() config = builder.create_builder_config() config.set_flag(trt.BuilderFlag.FP16) parser = trt.OnnxParser(network, logger) with open(onnx_file, 'rb') as model: if not parser.parse(model.read(), "/".join(onnx_file.split("/"))): logger.log(trt.Logger.ERROR, "Failed parsing %s" % onnx_file) for error in range(parser.num_errors): logger.log(trt.Logger.ERROR, parser.get_error(error)) logger.log(trt.Logger.INFO, "Succeeded parsing %s" % onnx_file) # Delete onnx files since we don't need them now shutil.rmtree(f'{output_dir}/onnx') nBS = -1 nMinBS = 1 nOptBS = max(nMinBS, int(max_batch_size / 2)) nMaxBS = max_batch_size logger.log(trt.Logger.INFO, f"Processed image dims {img_height}x{img_width}") H, W = img_height, img_width inputT = network.get_input(0) inputT.shape = [nBS, 3, H, W] profile.set_shape(inputT.name, [nMinBS, 3, H, W], [nOptBS, 3, H, W], [nMaxBS, 3, H, W]) config.add_optimization_profile(profile) t0 = time() engine_string = builder.build_serialized_network(network, config) t1 = time() if engine_string is None: raise RuntimeError("Failed building %s" % (engine_file)) else: logger.log(trt.Logger.INFO, "Succeeded building %s in %d s" % (engine_file, t1 - t0)) with open(engine_file, 'wb') as f: f.write(engine_string) def build_blip2_engine(args): model_type = 'Salesforce/blip2-' + args.model_name processor = Blip2Processor.from_pretrained(model_type) model = Blip2ForConditionalGeneration.from_pretrained( model_type, torch_dtype=torch.float16) model.to(args.device) raw_image = Image.new('RGB', [10, 10]) # dummy image prompt = "Question: what is this? Answer:" inputs = processor(raw_image, prompt, return_tensors="pt").to(args.device, torch.float16) image = inputs['pixel_values'] class Blip2VisionWrapper(torch.nn.Module): def __init__(self, model): super().__init__() self.vision_model = model.vision_model self.qformer = model.qformer self.projector = model.language_projection self.query_tokens = model.query_tokens def forward(self, image): features = self.vision_model(image)[0] qformer_output = self.qformer(query_embeds=self.query_tokens, encoder_hidden_states=features, return_dict=True) return self.projector(qformer_output.last_hidden_state) wrapper = Blip2VisionWrapper(model) export_visual_wrapper_onnx(wrapper, image, args.output_dir) build_trt_engine(image.shape[2], image.shape[3], args.output_dir, args.max_batch_size) def build_llava_engine(args): processor = AutoProcessor.from_pretrained(args.model_path) raw_image = Image.new('RGB', [10, 10]) # dummy image image = processor(text="dummy", images=raw_image, return_tensors="pt")['pixel_values'].to( args.device, torch.float16) class LlavaVisionWrapper(torch.nn.Module): def __init__(self, tower, projector, feature_layer): super().__init__() self.tower = tower self.projector = projector self.feature_layer = feature_layer def forward(self, image): all_hidden_states = self.tower( image, output_hidden_states=True).hidden_states features = all_hidden_states[self.feature_layer][:, 1:] return self.projector(features) model = LlavaForConditionalGeneration.from_pretrained( args.model_path, torch_dtype=torch.float16) model.to(args.device) wrapper = LlavaVisionWrapper(model.vision_tower, model.multi_modal_projector, model.config.vision_feature_layer) export_visual_wrapper_onnx(wrapper, image, args.output_dir) build_trt_engine(image.shape[2], image.shape[3], args.output_dir, args.max_batch_size) def build_nougat_engine(args): processor = NougatProcessor.from_pretrained(args.model_path) raw_image = Image.new('RGB', [10, 10]) # dummy image image = processor(raw_image, return_tensors="pt")['pixel_values'].to( args.device, torch.float16) class SwinEncoderWrapper(torch.nn.Module): def __init__(self, encoder): super().__init__() self.encoder = encoder def forward(self, image): return self.encoder(image).last_hidden_state model = VisionEncoderDecoderModel.from_pretrained(args.model_path, torch_dtype=torch.float16) swin_encoder = model.get_encoder().to(args.device) wrapper = SwinEncoderWrapper(swin_encoder) export_visual_wrapper_onnx(wrapper, image, args.output_dir) build_trt_engine(image.shape[2], image.shape[3], args.output_dir, args.max_batch_size) if __name__ == '__main__': logger = trt.Logger(trt.Logger.ERROR) parser = argparse.ArgumentParser() parser.add_argument('--model_name', type=str, default=None, help="Model name") parser.add_argument('--model_path', type=str, default=None, help="Huggingface repo or local directory with weights") parser.add_argument('--output_dir', type=str, default='visual_engines', help="Directory where visual TRT engines are saved") parser.add_argument('--max_batch_size', type=int, default=4, help="Maximum batch size for input images") args = parser.parse_args() args.device = torch.device("cuda") if torch.cuda.is_available() else "cpu" args.output_dir = args.output_dir + "/" + args.model_name if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) if args.model_name in ['opt-2.7b', 'flan-t5-xl']: build_blip2_engine(args) elif 'llava' in args.model_name: build_llava_engine(args) elif 'nougat' in args.model_name: build_nougat_engine(args) else: raise RuntimeError(f"Invalid model name {args.model_name}")