Add deepseek-ai/Janus-Pro-7B to eval queue

deepseek-ai/Janus-Pro-7B_eval_request_False_float16_Original.json

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+{"model": "deepseek-ai/Janus-Pro-7B", "model_api_url": "", "model_api_key": "", "model_api_name": "", "base_model": "", "revision": "main", "precision": "float16", "private": false, "weight_type": "Original", "status": "PENDING", "submitted_time": "2025-02-11T08:15:48Z", "model_type": "\ud83d\udfe2 : pretrained", "params": 0, "runsh": "#!/bin/bash\ncurrent_file=\"$0\"\ncurrent_dir=\"$(dirname \"$current_file\")\"\nSERVER_IP=$1\nSERVER_PORT=$2\nPYTHONPATH=$current_dir:$PYTHONPATH  accelerate launch $current_dir/model_adapter.py  --server_ip $SERVER_IP --server_port $SERVER_PORT \"${@:3}\" --cfg $current_dir/meta.json\n", "adapter": "import time\n\nfrom flagevalmm.server import ServerDataset\nfrom flagevalmm.models.base_model_adapter import BaseModelAdapter\nfrom flagevalmm.server.utils import (\n    parse_args,\n    default_collate_fn,\n    process_images_symbol,\n    load_pil_image,\n)\nfrom typing import Dict, Any\n\nimport torch\nfrom transformers import AutoModelForCausalLM\nfrom janus.models import MultiModalityCausalLM, VLChatProcessor\nfrom janus.utils.io import load_pil_images\n\n\nclass CustomDataset(ServerDataset):\n    def __getitem__(self, index):\n        data = self.get_data(index)\n        qs, idx = process_images_symbol(\n            data[\"question\"], dst_pattern=\"<image_placeholder>\"\n        )\n        question_id = data[\"question_id\"]\n        img_path = data[\"img_path\"]\n        image_list, idx = load_pil_image(\n            img_path, idx, reqiures_img=True, reduplicate=False\n        )\n\n        return question_id, qs, image_list\n\n\nclass ModelAdapter(BaseModelAdapter):\n    def model_init(self, task_info: Dict):\n        ckpt_path = task_info[\"model_path\"]\n\n        torch.set_grad_enabled(False)\n        with accelerator.main_process_first():\n            vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)\n            tokenizer = vl_chat_processor.tokenizer\n\n            vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(\n                model_path, trust_remote_code=True\n            )\n            vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()\n        model = accelerator.prepare_model(vl_gpt, evaluation_mode=True)\n        if hasattr(model, \"module\"):\n            model = model.module\n\n    def build_message(\n        self,\n        query: str,\n        image_paths=[],\n    ) -> str:\n        messages = [\n            {\n                \"role\": \"<|User|>\",\n                \"content\": f\"<image_placeholder>\\n{question}\",\n                \"images\": image_paths,\n            },\n            {\"role\": \"<|Assistant|>\", \"content\": \"\"},\n        ]\n        return messages\n\n    def run_one_task(self, task_name: str, meta_info: Dict[str, Any]):\n        results = []\n        cnt = 0\n\n        data_loader = self.create_data_loader(\n            CustomDataset,\n            task_name,\n            collate_fn=default_collate_fn,\n            batch_size=1,\n            num_workers=2,\n        )\n        for question_id, question, images in data_loader:\n            if cnt == 1:\n                start_time = time.perf_counter()\n            cnt += 1\n            messages = self.build_message(question[0], images[0])\n            pil_images = load_pil_images(messages)\n            prepare_inputs = self.vl_chat_processor(\n                conversations=messages, images=pil_images, force_batchify=True\n            ).to(self.model.device)\n\n            inputs_embeds = self.model.prepare_inputs_embeds(**prepare_inputs)\n\n            # run the model to get the response\n            outputs = self.model.language_model.generate(\n                inputs_embeds=inputs_embeds,\n                attention_mask=prepare_inputs.attention_mask,\n                pad_token_id=self.tokenizer.eos_token_id,\n                bos_token_id=self.tokenizer.bos_token_id,\n                eos_token_id=self.tokenizer.eos_token_id,\n                max_new_tokens=4096,\n                do_sample=False,\n                use_cache=True,\n            )\n\n            response = self.tokenizer.decode(\n                outputs[0].cpu().tolist(), skip_special_tokens=True\n            )\n\n            self.accelerator.print(f\"{question[0]}\\n{response}\\n\\n\")\n            results.append(\n                {\n                    \"question_id\": question_id[0],\n                    \"answer\": response.strip(),\n                    \"prompt\": question[0],\n                }\n            )\n        rank = self.accelerator.state.local_process_index\n\n        # save results for the rank\n        self.save_result(results, meta_info, rank=rank)\n        self.accelerator.wait_for_everyone()\n\n        if self.accelerator.is_main_process:\n            correct_num = self.collect_results_and_save(meta_info)\n            total_time = time.perf_counter() - start_time\n            print(\n                f\"Total time: {total_time}\\nAverage time:{total_time / cnt}\\nResults_collect number: {correct_num}\"\n            )\n\n        print(\"rank\", rank, \"finished\")\n\n\nif __name__ == \"__main__\":\n    args = parse_args()\n    model_adapter = ModelAdapter(\n        server_ip=args.server_ip,\n        server_port=args.server_port,\n        timeout=args.timeout,\n        extra_cfg=args.cfg,\n    )\n    model_adapter.run()\n"}