import argparse import os import shutil import sys from time import time # isort: off import torch import tensorrt as trt from tensorrt_llm.builder import Builder # isort: on from PIL import Image from torchvision import transforms from transformers import (AutoConfig, AutoModelForCausalLM, AutoProcessor, Blip2ForConditionalGeneration, Blip2Processor, FuyuForCausalLM, FuyuProcessor, LlavaForConditionalGeneration, NougatProcessor, Pix2StructForConditionalGeneration, VisionEncoderDecoderModel) def parse_arguments(): parser = argparse.ArgumentParser() parser.add_argument('--model_type', type=str, default=None, choices=[ 'opt-2.7b', 'opt-6.7b', 'flan-t5-xl', 'flan-t5-xxl', 'llava', 'vila', 'nougat', 'cogvlm', 'fuyu', 'pix2struct' ], help="Model type") parser.add_argument('--model_path', type=str, default=None, help="Huggingface repo or local directory with weights") parser.add_argument('--vila_path', type=str, default=None, help="Path to VILA source code directory") parser.add_argument('--output_dir', type=str, default=None, 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") return parser.parse_args() class VisionEngineBuilder: def __init__(self, args): args.device = torch.device( "cuda") if torch.cuda.is_available() else "cpu" if args.output_dir is None: args.output_dir = 'visual_engines/%s' % ( args.model_path.split('/')[-1]) if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) self.args = args def build(self): args = self.args if 'opt' in args.model_type or 't5' in args.model_type: build_blip2_engine(args) elif args.model_type == 'pix2struct': build_pix2struct_engine(args) elif args.model_type == 'llava': build_llava_engine(args) elif args.model_type == 'vila': assert args.vila_path is not None, "Please clone and provide VILA source code path" build_vila_engine(args) elif args.model_type == 'nougat': build_nougat_engine(args) elif args.model_type == 'cogvlm': build_cogvlm_engine(args) elif args.model_type == 'fuyu': build_fuyu_engine(args) else: raise RuntimeError(f"Invalid model type {args.model_type}") def export_visual_wrapper_onnx(visual_wrapper, input, output_dir, input_names=['input'], dynamic_axes={'input': { 0: 'batch' }}): logger.log(trt.Logger.INFO, "Exporting onnx") os.makedirs(f'{output_dir}/onnx', exist_ok=True) torch.onnx.export(visual_wrapper, input, f'{output_dir}/onnx/visual_encoder.onnx', opset_version=17, input_names=input_names, output_names=['output'], dynamic_axes=dynamic_axes) def build_trt_engine(model_type, input_sizes, output_dir, max_batch_size, dtype=torch.float16): part_name = 'visual_encoder' onnx_file = '%s/onnx/%s.onnx' % (output_dir, part_name) engine_file = '%s/%s.engine' % (output_dir, part_name) config_file = '%s/%s' % (output_dir, "config.json") 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_wrapper = Builder().create_builder_config( precision="float16" if dtype == torch.float16 else "bfloat16", model_type=model_type) config = config_wrapper.trt_builder_config parser = trt.OnnxParser(network, logger) with open(onnx_file, 'rb') as model: if not parser.parse(model.read(), os.path.abspath(onnx_file)): 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 inputT = network.get_input(0) # input sizes can be a list of ints (e.g., [3, H, W]) when inputs are images, # or a list of three int lists (e.g., [[1, 1, 2700], [1, 500, 2700], [1, 4096, 2700]]). assert isinstance(input_sizes, list), "input_sizes must be a list" if isinstance(input_sizes[0], int): logger.log(trt.Logger.INFO, f"Processed input sizes {input_sizes}") inputT.shape = [nBS, *input_sizes] min_size = opt_size = max_size = input_sizes elif len(input_sizes) == 3 and isinstance(input_sizes[0], list): min_size, opt_size, max_size = input_sizes logger.log( trt.Logger.INFO, f"Processed min/opt/max input sizes {min_size}/{opt_size}/{max_size}" ) else: raise ValueError(f"invalid input sizes: {input_sizes}") profile.set_shape(inputT.name, [nMinBS, *min_size], [nOptBS, *opt_size], [nMaxBS, *max_size]) if model_type == "pix2struct": inputT = network.get_input(1) P = input_sizes[0] # Number of patches inputT.shape = [nBS, P] profile.set_shape(inputT.name, [nMinBS, P], [nOptBS, P], [nMaxBS, P]) 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) Builder.save_config(config_wrapper, config_file) def build_blip2_engine(args): model_type = 'Salesforce/blip2-' + args.model_type processor = Blip2Processor.from_pretrained(model_type) 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, vision_model, qformer, projector, query_tokens): super().__init__() self.vision_model = vision_model self.qformer = qformer self.projector = projector self.query_tokens = 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) model = Blip2ForConditionalGeneration.from_pretrained( model_type, torch_dtype=torch.float16) wrapper = Blip2VisionWrapper(model.vision_model, model.qformer, model.language_projection, model.query_tokens) wrapper.to(args.device) export_visual_wrapper_onnx(wrapper, image, args.output_dir) build_trt_engine( model_type, [image.shape[1], image.shape[2], image.shape[3]], # [3, H, W] args.output_dir, args.max_batch_size) def build_pix2struct_engine(args): processor = AutoProcessor.from_pretrained(args.model_path) raw_image = Image.new('RGB', [10, 10]) # dummy image dtype = torch.float16 inputs = processor(text="dummy", images=raw_image, return_tensors="pt") image = inputs['flattened_patches'].to(args.device, dtype) attention_mask = inputs['attention_mask'].to(args.device, torch.int) class pix2structVisionWrapper(torch.nn.Module): def __init__(self, encoder): super().__init__() self.encoder = encoder def forward(self, image, attention_mask): vision_x = self.encoder.embeddings(image) img_features = self.encoder.encoder(vision_x, attention_mask=attention_mask) img_features = self.encoder.layernorm(img_features[0]) return img_features model = Pix2StructForConditionalGeneration.from_pretrained( args.model_path, torch_dtype=dtype) wrapper = pix2structVisionWrapper(model.encoder.to(args.device)) # input shape: batch size, number of patches, hidden dimension # attention mask shape: batch size, number of patches # The number of image patches can vary depending on the image size, but it typically # falls within a relatively narrow range. To improve performance, we can avoid using # dynamic axis for the input patches and instead use a fixed number of patches along # with an attention mask. export_visual_wrapper_onnx(wrapper, (image, attention_mask), args.output_dir, input_names=['input', 'attention_mask'], dynamic_axes={ 'input': { 0: 'batch' }, 'attention_mask': { 0: 'batch' } }) build_trt_engine( args.model_type, [image.shape[1], image.shape[2]], # Number of Patches, Hidden Dimension args.output_dir, args.max_batch_size, torch.bfloat16) 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) wrapper = LlavaVisionWrapper(model.vision_tower.to(args.device), model.multi_modal_projector.to(args.device), model.config.vision_feature_layer) export_visual_wrapper_onnx(wrapper, image, args.output_dir) build_trt_engine( args.model_type, [image.shape[1], image.shape[2], image.shape[3]], # [3, H, W] args.output_dir, args.max_batch_size) def build_vila_engine(args): # Note: VILA model is not in public HF model zoo yet. We need to explicitly import from the git repo sys.path.append(args.vila_path) from llava.model import LlavaLlamaForCausalLM model = LlavaLlamaForCausalLM.from_pretrained(args.model_path, torch_dtype=torch.float16) vision_tower = model.get_vision_tower() image_processor = vision_tower.image_processor raw_image = Image.new('RGB', [10, 10]) # dummy image image = image_processor(images=raw_image, return_tensors="pt")['pixel_values'].to( args.device, torch.float16) class VilaVisionWrapper(torch.nn.Module): def __init__(self, tower, projector): super().__init__() self.tower = tower self.projector = projector def forward(self, image): features = self.tower(image) return self.projector(features) model = LlavaLlamaForCausalLM.from_pretrained(args.model_path, torch_dtype=torch.float16) wrapper = VilaVisionWrapper( model.get_model().get_vision_tower().to(args.device), model.get_model().mm_projector.to(args.device)) export_visual_wrapper_onnx(wrapper, image, args.output_dir) build_trt_engine( args.model_type, [image.shape[1], image.shape[2], image.shape[3]], # [3, H, W] 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( args.model_type, [image.shape[1], image.shape[2], image.shape[3]], # [3, H, W] args.output_dir, args.max_batch_size) def build_cogvlm_engine(args): raw_image = Image.new('RGB', [10, 10]) # dummy image hf_config = AutoConfig.from_pretrained(args.model_path, trust_remote_code=True) image_size = hf_config.vision_config['image_size'] dtype = hf_config.torch_dtype transform = transforms.Compose([ transforms.Resize((image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC), transforms.ToTensor(), transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), ]) image = transform(raw_image).unsqueeze(0).to(args.device, dtype) class CogVlmVisionWrapper(torch.nn.Module): def __init__(self, encoder): super().__init__() self.encoder = encoder def forward(self, image): return self.encoder(image) cogvlm = AutoModelForCausalLM.from_pretrained(args.model_path, torch_dtype=dtype, trust_remote_code=True) vit_encoder = cogvlm.model.vision.to(args.device).eval() wrapper = CogVlmVisionWrapper(vit_encoder) export_visual_wrapper_onnx(wrapper, image, args.output_dir) build_trt_engine( args.model_type, [image.shape[1], image.shape[2], image.shape[3]], # [3, H, W] args.output_dir, args.max_batch_size, dtype) def build_fuyu_engine(args): processor = FuyuProcessor.from_pretrained(args.model_path) raw_image = Image.new('RGB', [10, 10]) image = processor(text="dummy", images=raw_image, return_tensors="pt")['image_patches'][0].to( args.device, torch.float16).unsqueeze(0) class FuyuEncoderWrapper(torch.nn.Module): def __init__(self, linear): super().__init__() self.linear = linear.to(torch.float16) def forward(self, patches): return self.linear(patches).flatten(0, 1) model = FuyuForCausalLM.from_pretrained(args.model_path, torch_dtype=torch.float16) vision_encoder = model.vision_embed_tokens wrapper = FuyuEncoderWrapper(vision_encoder).to(args.device) export_visual_wrapper_onnx(wrapper, image, args.output_dir, dynamic_axes={'input': { 0: 'batch', 2: 'patch' }}) build_trt_engine( args.model_type, # [nImgs, nImgPatches, nDims] # nImgs is always one since each query has exactly one image # nImgPatches depends on image size (patch size: 30x30) # nDims is 30x30x3=2700 (patch size x color channels) [[1, 1, 2700], [1, 500, 2700], [1, 4096, 2700]], args.output_dir, args.max_batch_size) if __name__ == '__main__': logger = trt.Logger(trt.Logger.INFO) args = parse_arguments() builder = VisionEngineBuilder(args) builder.build()