eval_test_time_vision_compression.py

import datetime
import logging
import json
import random
import time
import numpy as np
import os
import pickle
import sys
import torch
import torch.distributed as dist
import torch.nn.functional as F
import yaml
import transformers

from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import HfArgumentParser, AutoConfig, AutoTokenizer
from datasets import Dataset, concatenate_datasets
from datasets.distributed import split_dataset_by_node

from src.arguments_vision_compression import ModelArguments, DataArguments, TrainingArguments
from src.data.collator.eval_collator import MultimodalEvalDataCollator
from src.data.eval_dataset.base_eval_dataset import AutoEvalPairDataset, generate_cand_dataset
from src.eval_utils.metrics import RankingMetrics
from src.model.model_vision_compression import MMEBModel
from src.model.processor import get_backbone_name, load_processor, COLPALI
from src.utils import batch_to_device, print_rank, print_master

logging.basicConfig(level=logging.INFO, format='[%(asctime)s] %(levelname)s [%(name)s:%(lineno)s] %(message)s')
logger = logging.getLogger(__name__)

# --- Global Dictionaries for Hooks (will be cleared before each encode_embeddings call) ---
timing_info = {}
token_info = {
    "vision_tokens": 0,
    "text_input_tokens": 0,  # Refers to the original text token count
    "text_output_tokens": 0, # Not directly applicable here as we are encoding, not generating. Will be 0.
    "total_llm_input_tokens": 0, # Refers to the total tokens LLM receives (visual + formatted text)
}

# --- Hook Functions Definition ---
def timing_pre_hook(module, input):
    module_id = id(module)
    if module_id not in timing_info:
        timing_info[module_id] = []
    timing_info[module_id].append((time.time(), 'pre', module.__class__.__name__))

def timing_post_hook(module, input, output):
    module_id = id(module)
    if module_id not in timing_info:
        # print(f"Warning: No pre-hook data for module {module.__class__.__name__} ({module_id})")
        return

    timing_info[module_id].append((time.time(), 'post', module.__class__.__name__))

    # Collect vision token count (only from Vision Transformer module's post hook)
    module_name = module.__class__.__name__
    if "vision" in module_name.lower() and "transformer" in module_name.lower():
        out = output
        # VisionZip 可能返回 tuple: (hidden_states, attn_mean, attn_key)
        if isinstance(out, (tuple, list)) and len(out) > 0:
            out = out[0]

        if torch.is_tensor(out):
            # Qwen2.5-VL 的视觉 tower常见输出是 [T, D]（batch 内 concat），这里记录总 token 数
            if out.dim() == 2:
                token_info["vision_tokens"] = out.shape[0]
            elif out.dim() == 3:
                token_info["vision_tokens"] = out.shape[1]
        elif hasattr(out, "last_hidden_state") and torch.is_tensor(out.last_hidden_state):
            token_info["vision_tokens"] = out.last_hidden_state.shape[1]

def register_model_hooks(model):
    registered_modules = []
    
    core_model = model
    # print_master(f"DEBUG: Initial model type in register_model_hooks: {type(model)}")
    
    if hasattr(model, 'encoder') and model.encoder is not None:
        print_master(f"DEBUG: model has 'encoder' attribute. Type of model.encoder: {type(model.encoder)}")
    else:
        print_master("WARNING: Model structure does not have an 'encoder' attribute. Registering hooks directly on top-level modules.")

    # Vision module
    if hasattr(core_model, 'visual') and core_model.visual is not None:
        vision_module = core_model.visual
        vision_module.register_forward_pre_hook(timing_pre_hook)
        vision_module.register_forward_hook(timing_post_hook)
        registered_modules.append(vision_module)
        print_master(f"Registered hooks for vision module: {vision_module.__class__.__name__}")
    else:
        print_master(f"WARNING: No 'visual' attribute found on core_model ({type(core_model)}).")


    # Merger module (if inside visual) - it's part of the vision component
    if hasattr(core_model, 'visual') and hasattr(core_model.visual, 'merger') and core_model.visual.merger is not None:
        merger_module = core_model.visual.merger
        merger_module.register_forward_pre_hook(timing_pre_hook)
        merger_module.register_forward_hook(timing_post_hook)
        registered_modules.append(merger_module)
        print_master(f"Registered hooks for merger module: {merger_module.__class__.__name__}")
    else:
        print_master(f"WARNING: No 'merger' attribute found on core_model.visual ({type(getattr(core_model, 'visual', 'N/A'))}).")

    # Language model body
    if hasattr(core_model, 'model') and core_model.model is not None:
        llm_main_module = core_model.model
        llm_main_module.register_forward_pre_hook(timing_pre_hook)
        llm_main_module.register_forward_hook(timing_post_hook)
        registered_modules.append(llm_main_module)
        print_master(f"Registered hooks for LLM main module: {llm_main_module.__class__.__name__}")
    else:
        print_master(f"WARNING: No 'model' attribute found on core_model ({type(core_model)}).")


    # LM Head
    if hasattr(core_model, 'lm_head') and core_model.lm_head is not None:
        lm_head_module = core_model.lm_head
        lm_head_module.register_forward_pre_hook(timing_pre_hook)
        lm_head_module.register_forward_hook(timing_post_hook)
        registered_modules.append(lm_head_module)
        print_master(f"Registered hooks for LM head module: {lm_head_module.__class__.__name__}")
    else:
        print_master(f"WARNING: No 'lm_head' attribute found on core_model ({type(core_model)}).")


    if not registered_modules:
        print_master("Warning: No major modules found for hook registration. Check model architecture.")
    return registered_modules


def pad_dataset_to_divisible(dataset, world_size):
    num_samples = len(dataset)
    if num_samples % world_size == 0:
        return dataset, num_samples

    num_to_add = world_size - (num_samples % world_size)
    padded_size = num_samples + num_to_add

    padding_data = dataset.select([i % len(dataset) for i in range(num_to_add)])
    padded_dataset = concatenate_datasets([dataset, padding_data])
    return padded_dataset, padded_size

def encode_embeddings(
    model: MMEBModel,
    loader: DataLoader,
    training_args: TrainingArguments,
    model_args: ModelArguments,
    full_dataset: Dataset,
    encode_side: str,
    description: str = "Encoding"
) -> tuple[np.ndarray, list, list, list]:  # CHANGED: + list for img_token_masks
    """
    Encodes embeddings for a given dataset using the model, handling both standard and
    late-interaction models in a DDP-safe manner.
    Returns:
        - embeddings: np.ndarray
        - infos_or_ids: list
        - batch_stats_list: list
        - img_token_masks: list[None | list[bool]]  # NEW
    """
    local_rank = dist.get_rank() if dist.is_initialized() else 0
    world_size = dist.get_world_size() if dist.is_initialized() else 1

    # Check if the model is a late-interaction type
    is_late_interaction = (model_args.model_backbone == COLPALI)

    local_embeds = []
    local_gt_infos = []
    local_max_len = 0
    
    # --- New: List to store statistics for each batch ---
    batch_stats_list = []

    # --- NEW: Collect image token masks locally ---
    local_img_token_masks = []  # 每个样本一个元素：None 或 [bool, ...]

    model.eval()

    # Register hooks for the model once per encode_embeddings call
    registered_hooks = register_model_hooks(model)

    # --- NEW: helpers to取mask并序列化 ---
    def _search_key(obj, key: str):
        # 递归搜索 dict/list/tuple，找到指定 key
        if isinstance(obj, dict):
            if key in obj:
                return obj[key]
            for v in obj.values():
                r = _search_key(v, key)
                if r is not None:
                    return r
        elif isinstance(obj, (list, tuple)):
            for v in obj:
                r = _search_key(v, key)
                if r is not None:
                    return r
        return None

    def _to_serializable_mask_list(mask_list, batch_size: int):
        # 将模型返回的 mask（list/tensor/ndarray/None）转成 [None | list[bool]] * B
        if mask_list is None:
            return [None] * batch_size

        out = []
        if isinstance(mask_list, (list, tuple)):
            for m in mask_list:
                if m is None:
                    out.append(None)
                elif torch.is_tensor(m):
                    out.append(m.detach().cpu().tolist())
                elif isinstance(m, np.ndarray):
                    out.append(m.tolist())
                else:
                    # already python list/bool
                    out.append(m)
        elif torch.is_tensor(mask_list):
            # 若是 2D 张量（B, L），直接 tolist() -> list[list[bool/int]]
            out = mask_list.detach().cpu().tolist()
        elif isinstance(mask_list, np.ndarray):
            out = mask_list.tolist()
        else:
            # 未知类型，保守返回 None 占位
            out = [None] * batch_size

        # 长度对齐 batch_size
        if isinstance(out, list):
            if len(out) < batch_size:
                out = out + [None] * (batch_size - len(out))
            elif len(out) > batch_size:
                out = out[:batch_size]
        return out

    with torch.no_grad():
        for inputs, dataset_info in tqdm(loader, desc=f"{description} (rank {local_rank})", disable=local_rank > 0):
            # --- Reset statistics for each inference pass ---
            timing_info.clear()
            token_info["vision_tokens"] = 0
            token_info["text_input_tokens"] = 0
            token_info["text_output_tokens"] = 0
            token_info["total_llm_input_tokens"] = 0

            inputs = batch_to_device(inputs, training_args.device)
            current_batch_size = inputs['input_ids'].shape[0] if 'input_ids' in inputs and inputs['input_ids'] is not None else 1

            with torch.autocast(enabled=True, dtype=torch.bfloat16, device_type="cuda"):
                start_inference_time = time.time()
                if encode_side == "qry":
                    output = model(qry=inputs)
                    # torch.set_printoptions(threshold=10000)
                    # print('output:', output)
                    # exit()
                    reps = output["qry_reps"].detach()
                    local_gt_infos.extend(dataset_info)
                else:
                    output = model(tgt=inputs)
                    reps = output["tgt_reps"].detach()
                    local_gt_infos.extend([info["cand_name"] for info in dataset_info])
                end_inference_time = time.time()

            # --- NEW: 提取并保存本 batch 的 image_token_bool_masks ---
            # 期望 MMEBModel 的 output 中直接或间接包含 'image_token_bool_masks'
            img_masks_raw = None
            if isinstance(output, dict):
                img_masks_raw = _search_key(output, "image_token_bool_masks")
            # 可选：若你在 MMEBModel 上挂了属性，也可以尝试读取
            if img_masks_raw is None and hasattr(model, "image_token_bool_masks"):
                img_masks_raw = getattr(model, "image_token_bool_masks")

            img_masks_serializable = _to_serializable_mask_list(img_masks_raw, current_batch_size)
            local_img_token_masks.extend(img_masks_serializable)

            # --- Update total LLM input tokens after the model call ---
            if 'input_ids' in inputs and inputs['input_ids'] is not None:
                token_info["total_llm_input_tokens"] = inputs['input_ids'].shape[1]
                token_info["text_input_tokens"] = token_info["total_llm_input_tokens"] - token_info["vision_tokens"]
                token_info["text_input_tokens"] = max(0, token_info["text_input_tokens"])

            # --- Collect and Store Batch Statistics ---
            batch_inference_time = end_inference_time - start_inference_time
            
            current_batch_stats = {
                "batch_size": current_batch_size,
                "total_inference_time_seconds": batch_inference_time,
                "module_inference_times": {},
                "token_counts": {
                    "visual_tokens": token_info["vision_tokens"],
                    "language_input_tokens_raw": token_info["text_input_tokens"],
                    "llm_total_input_tokens": token_info["total_llm_input_tokens"],
                    "language_output_tokens": token_info["text_output_tokens"],
                }
            }

            # Calculate and store module timings for the current batch
            for module_obj in registered_hooks:
                module_id = id(module_obj)
                module_name = module_obj.__class__.__name__
                times = timing_info.get(module_id, [])
                durations = []
                pre_times = {} 
                for t, event_type, _ in times:
                    if event_type == 'pre':
                        pre_times[module_id] = t
                    elif event_type == 'post' and module_id in pre_times:
                        duration = t - pre_times.pop(module_id)
                        durations.append(duration)
                
                if durations:
                    current_batch_stats["module_inference_times"][module_name] = {
                        "total": sum(durations),
                        "count": len(durations),
                        "avg": sum(durations) / len(durations)
                    }
                else:
                    current_batch_stats["module_inference_times"][module_name] = {
                        "total": 0.0,
                        "count": 0,
                        "avg": 0.0
                    }
            
            batch_stats_list.append(current_batch_stats)

            # --- Debug prints (optional) ---
            print_rank(f"\n--- Inference Statistics for {encode_side} batch (Rank {local_rank}) ---")
            print_rank(f"Batch Inference took: {batch_inference_time:.4f} seconds")
            print_rank("--- Module Inference Timing Statistics ---")
            for module_name, stats in current_batch_stats["module_inference_times"].items():
                print_rank(f"**{module_name}**: Total: {stats['total']:.6f}s, Count: {stats['count']}, Avg: {stats['avg']:.6f}s")
            print_rank("--- Token Count Statistics ---")
            print_rank(f"**视觉 token 数量**: {current_batch_stats['token_counts']['visual_tokens']}")
            print_rank(f"**语言输入 token 数量 (仅原始文本)**: {current_batch_stats['token_counts']['language_input_tokens_raw']}")
            print_rank(f"**LLM总输入 token 数量 (包含视觉 + 格式化文本)**: {current_batch_stats['token_counts']['llm_total_input_tokens']}")
            print_rank(f"**语言输出 token 数量**: {current_batch_stats['token_counts']['language_output_tokens']}")

            if is_late_interaction and reps.dim() == 3:
                local_max_len = max(local_max_len, reps.shape[1])

            local_embeds.append(reps)

    if not local_embeds:
        # Handle cases where a rank gets no data
        return np.array([]), [], [], []  # CHANGED: 4个返回值

    # === DDP Synchronization and Padding for Late-Interaction Models ===
    if is_late_interaction:
        if dist.is_initialized():
            # 1: global max length
            local_max_len_tensor = torch.tensor(local_max_len, device=training_args.device)
            dist.all_reduce(local_max_len_tensor, op=dist.ReduceOp.MAX)
            global_max_len = local_max_len_tensor.item()
        else:
            global_max_len = local_max_len

        # 2: pad to global max length
        padded_embeds = []
        for reps_batch in local_embeds:
            if reps_batch.dim() == 3:
                B, L, H = reps_batch.shape
                padding_size = global_max_len - L
                padded_batch = F.pad(reps_batch, (0, 0, 0, padding_size), "constant", 0)
                padded_embeds.append(padded_batch)
            else:
                padded_embeds.append(reps_batch)

        embeds_tensor = torch.cat(padded_embeds, dim=0).contiguous()
    else:
        embeds_tensor = torch.cat(local_embeds, dim=0).contiguous()

    # === Gather embeddings and keys from all ranks ===
    if dist.is_initialized() and full_dataset.num_rows >= world_size:
        print_master(f"Gathering {encode_side} embeddings across all ranks...")

        # tensor gather
        output_shape = list(embeds_tensor.shape)
        output_shape[0] = full_dataset.num_rows
        embeds_tensor = embeds_tensor.to(training_args.device)
        gathered_embeds_tensor = torch.empty(output_shape, dtype=embeds_tensor.dtype, device=training_args.device)
        dist.all_gather_into_tensor(gathered_embeds_tensor, embeds_tensor)
        final_embeddings = gathered_embeds_tensor.cpu().float().numpy()

        # object gather for infos and stats
        gathered_gt_infos = [None for _ in range(world_size)]
        dist.all_gather_object(gathered_gt_infos, local_gt_infos)
        all_gt_infos = [key for rank_keys in gathered_gt_infos for key in rank_keys]

        gathered_batch_stats = [None for _ in range(world_size)]
        dist.all_gather_object(gathered_batch_stats, batch_stats_list)
        all_batch_stats = [stats for rank_stats in gathered_batch_stats for stats in rank_stats]

        # --- NEW: gather masks ---
        gathered_masks = [None for _ in range(world_size)]
        dist.all_gather_object(gathered_masks, local_img_token_masks)
        all_img_token_masks = [m for rank_list in gathered_masks for m in rank_list]
    else:
        all_gt_infos = local_gt_infos
        final_embeddings = embeds_tensor.cpu().float().numpy()
        all_batch_stats = batch_stats_list
        all_img_token_masks = local_img_token_masks  # NEW

    return final_embeddings, all_gt_infos, all_batch_stats, all_img_token_masks  # CHANGED


def main():
    if "RANK" in os.environ and dist.is_available() and not dist.is_initialized():
        dist.init_process_group(backend="nccl", timeout=datetime.timedelta(minutes=60))
    local_rank = dist.get_rank() if dist.is_initialized() else 0
    world_size = dist.get_world_size() if dist.is_initialized() else 1
    # DEBUG PRINTS for Distributed Setup
    print_master("Distributed init debug info:")
    print_master(f"RANK: {os.environ.get('RANK')}")
    print_master(f"LOCAL_RANK: {os.environ.get('LOCAL_RANK')}")
    print_master(f"WORLD_SIZE: {os.environ.get('WORLD_SIZE')}")
    print_master(f"MASTER_ADDR: {os.environ.get('MASTER_ADDR')}")
    print_master(f"MASTER_PORT: {os.environ.get('MASTER_PORT')}")
    if dist.is_initialized():
        print_rank(f"dist.get_rank(): {dist.get_rank()}")
        print_rank(f"dist.get_world_size(): {dist.get_world_size()}")

    for arg in sys.argv:
        if arg.startswith("--local-rank="):
            rank = arg.split("=")[1]
            sys.argv.remove(arg)
            sys.argv.append('--local_rank')
            sys.argv.append(rank)
    parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    if not hasattr(model_args, "vision_compression") or model_args.vision_compression is None:
        model_args.vision_compression = "token_pooling"
    model_args: ModelArguments
    data_args: DataArguments
    training_args: TrainingArguments
    os.makedirs(data_args.encode_output_path, exist_ok=True)

    # --- Model Loading ---
    hf_config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
    if not getattr(model_args, "model_backbone", None):
        model_backbone = get_backbone_name(hf_config=hf_config, model_type=model_args.model_type)
        setattr(model_args, 'model_backbone', model_backbone)
        setattr(training_args, 'model_backbone', model_backbone)
    print_master(f'Model Backbone: {model_args.model_backbone}')
    # --- DDP-Safe Model Loading ---
    # Step 1: Only the master process (rank 0) downloads the model.
    if local_rank == 0:
        processor = load_processor(model_args, data_args)
        model = MMEBModel.load(model_args, is_trainable=False, processor=processor)
        print_master(f"[rank=0] Loading the model from Huggingface: {model_args.model_name}...")
    # Step 2: All processes wait here. The non-master processes will pause
    # until the master process (rank 0) finishes downloading and exits this barrier.
    if torch.distributed.is_initialized():
        torch.distributed.barrier()
    # Step 3: Now that the model is cached, the non-master processes load it from the local cache.
    if local_rank != 0:
        print_rank(f"Loading the model from cache...")
        processor = load_processor(model_args, data_args)
        time.sleep(random.randint(2 * local_rank, 3 * local_rank))
        model = MMEBModel.load(model_args, is_trainable=False, processor=processor)
    model.eval()
    model = model.to(training_args.device, dtype=torch.bfloat16)
    with open(data_args.dataset_config, 'r') as yaml_file:
        dataset_configs = yaml.safe_load(yaml_file)


    # --- Main Evaluation Loop ---
    for dataset_idx, (dataset_name, task_config) in enumerate(dataset_configs.items()):
        # Initialize task-level statistics accumulators for QUERY
        query_total_stats = {
            "total_inference_time_seconds": 0.0,
            "module_inference_times": {
                "Qwen2VisionTransformerPretrainedModel": {"total": 0.0, "count": 0},
                "PatchMerger": {"total": 0.0, "count": 0},
                "Qwen2VLModel": {"total": 0.0, "count": 0},
                "Linear": {"total": 0.0, "count": 0},
            },
            "token_counts": {
                "visual_tokens": 0,
                "language_input_tokens_raw": 0,
                "llm_total_input_tokens": 0,
                "language_output_tokens": 0,
            },
            "data_point_count": 0 # Number of image-text pairs processed
        }

        # Initialize task-level statistics accumulators for CANDIDATE
        cand_total_stats = {
            "total_inference_time_seconds": 0.0,
            "module_inference_times": {
                "Qwen2VisionTransformerPretrainedModel": {"total": 0.0, "count": 0},
                "PatchMerger": {"total": 0.0, "count": 0},
                "Qwen2VLModel": {"total": 0.0, "count": 0},
                "Linear": {"total": 0.0, "count": 0},
            },
            "token_counts": {
                "visual_tokens": 0,
                "language_input_tokens_raw": 0,
                "llm_total_input_tokens": 0,
                "language_output_tokens": 0,
            },
            "data_point_count": 0 # Number of image-text pairs processed
        }

        if dist.is_initialized():
            dist.barrier()
        print_master(f"\n--- Evaluating {dataset_name} ---")

        query_embed_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry")
        cand_embed_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_tgt")
        dataset_info_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_info.jsonl")
        
        # New: Define distinct paths for query and candidate inference statistics output
        query_inference_stats_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_inference_stats.json")
        cand_inference_stats_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_cand_inference_stats.json")


        do_query = not os.path.exists(query_embed_path) or not os.path.exists(dataset_info_path)
        do_cand = not os.path.exists(cand_embed_path)

        if do_query or do_cand:
            if data_args.data_basedir is not None:
                # Construct full paths for data files if --data_basedir is provided
                for key in ["image_root", "video_root", "frame_root", "clip_root", "data_path"]:
                    if data_args.data_basedir and task_config.get(key):
                        task_config[key] = os.path.join(data_args.data_basedir, task_config[key])

            full_eval_qry_dataset, corpus = AutoEvalPairDataset.instantiate(model_args=model_args, data_args=data_args, **task_config)
            full_eval_cand_dataset = generate_cand_dataset(full_eval_qry_dataset, corpus)
            eval_qry_dataset, eval_cand_dataset = full_eval_qry_dataset, full_eval_cand_dataset
            # Pad datasets to be divisible by world_size before splitting
            if dist.is_initialized():
                padded_qry_dataset, _ = pad_dataset_to_divisible(full_eval_qry_dataset, world_size)
                padded_cand_dataset, _ = pad_dataset_to_divisible(full_eval_cand_dataset, world_size)
                eval_qry_dataset = split_dataset_by_node(padded_qry_dataset, rank=local_rank, world_size=world_size)
                eval_cand_dataset = split_dataset_by_node(padded_cand_dataset, rank=local_rank, world_size=world_size)
            else:
                padded_qry_dataset, padded_cand_dataset = full_eval_qry_dataset, full_eval_cand_dataset

        # --- 1. Compute Query Embeddings ---
        if do_query:
            print_master("Encoding queries...")
            eval_qry_collator = MultimodalEvalDataCollator(processor, model_args, data_args, "qry")
            eval_qry_loader = DataLoader(eval_qry_dataset, batch_size=training_args.per_device_eval_batch_size, collate_fn=eval_qry_collator, num_workers=training_args.dataloader_num_workers)
            
            # Modified: capture batch_stats_list
            query_embeds, gt_infos, qry_batch_stats, qry_img_masks = encode_embeddings(model, eval_qry_loader, training_args, model_args, padded_qry_dataset, encode_side="qry", description=f"Queries for {dataset_name}")
            
            # Accumulate query statistics
            for batch_stat in qry_batch_stats:
                batch_size = batch_stat["batch_size"]
                query_total_stats["total_inference_time_seconds"] += batch_stat["total_inference_time_seconds"]
                for module_name, module_stats in batch_stat["module_inference_times"].items():
                    if module_name in query_total_stats["module_inference_times"]:
                        query_total_stats["module_inference_times"][module_name]["total"] += module_stats["total"]
                        query_total_stats["module_inference_times"][module_name]["count"] += module_stats["count"]
                
                query_total_stats["token_counts"]["visual_tokens"] += batch_stat["token_counts"]["visual_tokens"] * batch_size
                query_total_stats["token_counts"]["language_input_tokens_raw"] += batch_stat["token_counts"]["language_input_tokens_raw"] * batch_size
                query_total_stats["token_counts"]["llm_total_input_tokens"] += batch_stat["token_counts"]["llm_total_input_tokens"] * batch_size
                query_total_stats["token_counts"]["language_output_tokens"] += batch_stat["token_counts"]["language_output_tokens"] * batch_size
                
                query_total_stats["data_point_count"] += batch_size # Accumulate the number of processed items

            query_embeds = query_embeds[:len(full_eval_qry_dataset)]
            gt_infos = gt_infos[:len(full_eval_qry_dataset)]
            if local_rank == 0:
                with open(query_embed_path, 'wb') as f:
                    pickle.dump(query_embeds, f)
                with open(dataset_info_path, 'w') as f:
                    for info in gt_infos:
                        f.write(json.dumps(info) + '\n')
                print_master(f"Saved query embeddings to {query_embed_path}")

                qry_img_masks_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_img_token_masks.jsonl")
                with open(qry_img_masks_path, 'w', encoding='utf-8') as f:
                    for i, m in enumerate(qry_img_masks[:len(full_eval_qry_dataset)]):
                        f.write(json.dumps({"index": i, "mask": m}, ensure_ascii=False) + "\n")
                print_master(f"Saved query image token masks to {qry_img_masks_path}")
                
                # Save query-specific inference statistics
                if query_total_stats["data_point_count"] > 0:
                    final_query_stats = {
                        "task_name": dataset_name,
                        "encode_side": "query",
                        "data_point_count": query_total_stats["data_point_count"],
                        "inference_times": {
                            "total_inference_time_seconds": query_total_stats["total_inference_time_seconds"],
                            "avg_inference_time_per_item_seconds": query_total_stats["total_inference_time_seconds"] / query_total_stats["data_point_count"],
                            "module_average_times_per_call": {},
                            "module_total_times_seconds": {},
                            "module_calls_count": {},
                        },
                        "token_counts": {
                            "total_visual_tokens": query_total_stats["token_counts"]["visual_tokens"],
                            "avg_visual_tokens_per_item": query_total_stats["token_counts"]["visual_tokens"] / query_total_stats["data_point_count"],
                            "total_language_input_tokens_raw": query_total_stats["token_counts"]["language_input_tokens_raw"],
                            "avg_language_input_tokens_raw_per_item": query_total_stats["token_counts"]["language_input_tokens_raw"] / query_total_stats["data_point_count"],
                            "total_llm_total_input_tokens": query_total_stats["token_counts"]["llm_total_input_tokens"],
                            "avg_llm_total_input_tokens_per_item": query_total_stats["token_counts"]["llm_total_input_tokens"] / query_total_stats["data_point_count"],
                            "total_language_output_tokens": query_total_stats["token_counts"]["language_output_tokens"],
                            "avg_language_output_tokens_per_item": query_total_stats["token_counts"]["language_output_tokens"] / query_total_stats["data_point_count"],
                        }
                    }
                    for module_name, stats in query_total_stats["module_inference_times"].items():
                        final_query_stats["inference_times"]["module_total_times_seconds"][module_name] = stats["total"]
                        final_query_stats["inference_times"]["module_calls_count"][module_name] = stats["count"]
                        if stats["count"] > 0:
                            final_query_stats["inference_times"]["module_average_times_per_call"][module_name] = stats["total"] / stats["count"]
                        else:
                            final_query_stats["inference_times"]["module_average_times_per_call"][module_name] = 0.0

                    with open(query_inference_stats_path, 'w', encoding='utf-8') as f:
                        json.dump(final_query_stats, f, ensure_ascii=False, indent=4)
                    print_master(f"Query inference statistics for {dataset_name} saved to: {query_inference_stats_path}")
                else:
                    print_master(f"No query data processed for {dataset_name}, skipping query inference statistics output.")

            if dist.is_initialized():
                dist.barrier()


        # --- 2. Compute Candidate Embeddings ---
        if do_cand:
            print_master("Encoding candidates...")
            eval_cand_collator = MultimodalEvalDataCollator(processor, model_args, data_args, "cand")
            eval_cand_loader = DataLoader(eval_cand_dataset, batch_size=training_args.per_device_eval_batch_size, collate_fn=eval_cand_collator, num_workers=training_args.dataloader_num_workers)

            # Modified: capture batch_stats_list
            cand_embeds, all_cand_ids, cand_batch_stats, cand_img_masks = encode_embeddings(model, eval_cand_loader, training_args, model_args, padded_cand_dataset, encode_side="cand", description=f"Candidates for {dataset_name}")
            
            # Accumulate candidate statistics (similar logic as query)
            for batch_stat in cand_batch_stats:
                batch_size = batch_stat["batch_size"]
                cand_total_stats["total_inference_time_seconds"] += batch_stat["total_inference_time_seconds"]
                for module_name, module_stats in batch_stat["module_inference_times"].items():
                    if module_name in cand_total_stats["module_inference_times"]:
                        cand_total_stats["module_inference_times"][module_name]["total"] += module_stats["total"]
                        cand_total_stats["module_inference_times"][module_name]["count"] += module_stats["count"]
                
                cand_total_stats["token_counts"]["visual_tokens"] += batch_stat["token_counts"]["visual_tokens"] * batch_size
                cand_total_stats["token_counts"]["language_input_tokens_raw"] += batch_stat["token_counts"]["language_input_tokens_raw"] * batch_size
                cand_total_stats["token_counts"]["llm_total_input_tokens"] += batch_stat["token_counts"]["llm_total_input_tokens"] * batch_size
                cand_total_stats["token_counts"]["language_output_tokens"] += batch_stat["token_counts"]["language_output_tokens"] * batch_size
                
                cand_total_stats["data_point_count"] += batch_size # Accumulate the number of processed items

            cand_embeds = cand_embeds[:len(full_eval_cand_dataset)]
            all_cand_ids = all_cand_ids[:len(full_eval_cand_dataset)]

            if local_rank == 0:
                cand_embed_dict = {cand_id: embed for cand_id, embed in zip(all_cand_ids, cand_embeds)}
                with open(cand_embed_path, 'wb') as f: pickle.dump(cand_embed_dict, f)
                print_master(f"Saved candidate embeddings to {cand_embed_path}")

                cand_img_masks_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_cand_img_token_masks.jsonl")
                with open(cand_img_masks_path, 'w', encoding='utf-8') as f:
                    for cid, m in zip(all_cand_ids[:len(full_eval_cand_dataset)], cand_img_masks[:len(full_eval_cand_dataset)]):
                        f.write(json.dumps({"cand_id": str(cid), "mask": m}, ensure_ascii=False) + "\n")
                print_master(f"Saved candidate image token masks to {cand_img_masks_path}")

                # Save candidate-specific inference statistics
                if cand_total_stats["data_point_count"] > 0:
                    final_cand_stats = {
                        "task_name": dataset_name,
                        "encode_side": "candidate",
                        "data_point_count": cand_total_stats["data_point_count"],
                        "inference_times": {
                            "total_inference_time_seconds": cand_total_stats["total_inference_time_seconds"],
                            "avg_inference_time_per_item_seconds": cand_total_stats["total_inference_time_seconds"] / cand_total_stats["data_point_count"],
                            "module_average_times_per_call": {},
                            "module_total_times_seconds": {},
                            "module_calls_count": {},
                        },
                        "token_counts": {
                            "total_visual_tokens": cand_total_stats["token_counts"]["visual_tokens"],
                            "avg_visual_tokens_per_item": cand_total_stats["token_counts"]["visual_tokens"] / cand_total_stats["data_point_count"],
                            "total_language_input_tokens_raw": cand_total_stats["token_counts"]["language_input_tokens_raw"],
                            "avg_language_input_tokens_raw_per_item": cand_total_stats["token_counts"]["language_input_tokens_raw"] / cand_total_stats["data_point_count"],
                            "total_llm_total_input_tokens": cand_total_stats["token_counts"]["llm_total_input_tokens"],
                            "avg_llm_total_input_tokens_per_item": cand_total_stats["token_counts"]["llm_total_input_tokens"] / cand_total_stats["data_point_count"],
                            "total_language_output_tokens": cand_total_stats["token_counts"]["language_output_tokens"],
                            "avg_language_output_tokens_per_item": cand_total_stats["token_counts"]["language_output_tokens"] / cand_total_stats["data_point_count"],
                        }
                    }
                    for module_name, stats in cand_total_stats["module_inference_times"].items():
                        final_cand_stats["inference_times"]["module_total_times_seconds"][module_name] = stats["total"]
                        final_cand_stats["inference_times"]["module_calls_count"][module_name] = stats["count"]
                        if stats["count"] > 0:
                            final_cand_stats["inference_times"]["module_average_times_per_call"][module_name] = stats["total"] / stats["count"]
                        else:
                            final_cand_stats["inference_times"]["module_average_times_per_call"][module_name] = 0.0

                    with open(cand_inference_stats_path, 'w', encoding='utf-8') as f:
                        json.dump(final_cand_stats, f, ensure_ascii=False, indent=4)
                    print_master(f"Candidate inference statistics for {dataset_name} saved to: {cand_inference_stats_path}")
                else:
                    print_master(f"No candidate data processed for {dataset_name}, skipping candidate inference statistics output.")

        if dist.is_initialized():
            dist.barrier()

        # --- 3. Compute Scores (on master rank only) ---
        score_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_score.json")
        ####################################################################################
        pred_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_pred.jsonl")
        score_detail_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_score_details.jsonl")  # 新文件，存相似度分数
        def append_score_detail(score_detail_list, qid, ranked_indices, score_vector, cand_ids, labels):
            """追加一个 query 的候选分数详情"""
            score_detail_list.append({
                "qid": int(qid),
                "cand_scores": [
                    {"cand_id": str(cand_ids[i]), "score": float(score_vector[i])}
                    for i in ranked_indices
                ],
                "label": labels
            })
        ####################################################################################
        if local_rank == 0:
            if os.path.exists(score_path):
                try:
                    with open(score_path, "r") as f:
                        score_dict = json.load(f)
                    print_master(f"Score of {dataset_name} (loaded from previous run): {score_path}")
                    formatted = {k: f"{v:.4f}" for k, v in score_dict.items()}
                    print_master(formatted)
                    # No `continue` here, as we want to ensure other files are processed/generated
                except Exception as e:
                    print_master(f"Failed to load score for {dataset_name}, proceeding to recompute. Error: {e}")
            # Proceed with score computation if not loaded or failed to load
            with open(query_embed_path, 'rb') as f: qry_embeds = pickle.load(f)
            with open(cand_embed_path, 'rb') as f: cand_embed_dict = pickle.load(f)
            gt_infos = [json.loads(l) for l in open(dataset_info_path)]
            pred_dicts = []
            score_detail_dicts = []###################################

            rank_against_all_candidates = task_config.get("eval_type", "global") == "global"
            # if rank_against_all_candidates:
            #     cand_keys = list(cand_embed_dict.keys())
            #     cand_embeds = np.stack([cand_embed_dict[key] for key in cand_keys])
            #     # Handle late-interaction scoring
            #     if qry_embeds.ndim == 3: # Query: [N_q, L_q, H] | Candidate: [N_c, L_c, H]
            #         qry_embed = torch.from_numpy(qry_embeds)
            #         cand_embeds = [torch.from_numpy(np.array(t)) for t in cand_embeds]
            #         scores = processor.score(qry_embed, cand_embeds, batch_size=64)  # use ColPali score function
            #         ranked_candids = torch.argsort(-scores, dim=1).cpu().numpy().tolist()
            #         scores = scores.cpu().numpy()
            #     else: # Dense
            #         cosine_scores = np.dot(qry_embeds, cand_embeds.T)
            #         ranked_candids = np.argsort(-cosine_scores, axis=1)
            #####################################################
            if rank_against_all_candidates:
                cand_keys = list(cand_embed_dict.keys())
                cand_embeds = np.stack([cand_embed_dict[key] for key in cand_keys])

                if qry_embeds.ndim == 3:  # Late-interaction
                    qry_embed_t = torch.from_numpy(qry_embeds)
                    cand_embeds_t = [torch.from_numpy(np.array(t)) for t in cand_embeds]
                    sim_matrix = processor.score(qry_embed_t, cand_embeds_t, batch_size=64).cpu().numpy()  # [N_q, N_c]
                else:  # Dense
                    sim_matrix = np.dot(qry_embeds, cand_embeds.T)  # [N_q, N_c]

                ranked_candids = np.argsort(-sim_matrix, axis=1)
            #########################################################
                for qid, (ranked_candid, gt_info) in tqdm(enumerate(zip(ranked_candids, gt_infos)), desc=f"Calculating scores for {dataset_name}"):
                    rel_docids = gt_info["label_name"] if isinstance(gt_info["label_name"], list) else [gt_info["label_name"]]
                    rel_scores = gt_info["rel_scores"] if "rel_scores" in gt_info else None
                    assert rel_scores is None or len(rel_docids) == len(rel_scores)
                    pred_dicts.append({
                        "prediction": [cand_keys[i] for i in ranked_candid],
                        "label": rel_docids,
                        "rel_scores": rel_scores,
                    })
                    ################################# 新增：详细相似度字典
                    append_score_detail(score_detail_dicts, qid, ranked_candid, sim_matrix[qid], cand_keys, rel_docids)
                    ########################################
            # else:
            #     for qid, (qry_embed, gt_info) in tqdm(enumerate(zip(qry_embeds, gt_infos)), desc=f"Calculating scores for {dataset_name}"):
            #         cand_embeds = np.stack([cand_embed_dict[key] for key in gt_info["cand_names"]])
            #         if qry_embeds.ndim == 3: # Query: [N_q, L_q, H] | Candidate: [N_c, L_c, H]
            #             qry_embed = torch.from_numpy(np.array(qry_embed)).unsqueeze(0)
            #             cand_embeds = [torch.from_numpy(np.array(t)) for t in cand_embeds]
            #             scores = processor.score(qry_embed, cand_embeds, batch_size=1024)  # use ColPali score function
            #             ranked_candids = torch.argsort(-scores, dim=1).cpu().numpy().tolist()[0]
            #         else:
            #             cosine_score = np.dot(qry_embed, cand_embeds.T)
            #             ranked_candids = np.argsort(-cosine_score)
            #         rel_docids = gt_info["label_name"] if isinstance(gt_info["label_name"], list) else [gt_info["label_name"]]
            #         rel_scores = gt_info["rel_scores"] if "rel_scores" in gt_info else None

            #         assert rel_scores is None or len(rel_docids) == len(rel_scores)
            #         pred_dicts.append({
            #             "prediction": [gt_info["cand_names"][i] for i in ranked_candids],
            #             "label": rel_docids,
            #             "rel_scores": rel_scores,
            #         })
            #######################################################################
            else:  # 非全局
                for qid, (qry_embed, gt_info) in tqdm(enumerate(zip(qry_embeds, gt_infos)), desc=f"Calculating scores for {dataset_name}"):
                    cand_ids_local = gt_info["cand_names"]
                    cand_embeds = np.stack([cand_embed_dict[key] for key in cand_ids_local])

                    if qry_embeds.ndim == 3:  # Late-interaction
                        qry_embed_t = torch.from_numpy(np.array(qry_embed)).unsqueeze(0)  # [1, Lq, H]
                        cand_embeds_t = [torch.from_numpy(np.array(t)) for t in cand_embeds]
                        sim_vec = processor.score(qry_embed_t, cand_embeds_t, batch_size=1024).cpu().numpy()[0]  # [N_c]
                    else:  # Dense
                        sim_vec = np.dot(qry_embed, cand_embeds.T)  # [N_c]

                    ranked_indices = np.argsort(-sim_vec)
                    rel_docids = gt_info["label_name"] if isinstance(gt_info["label_name"], list) else [gt_info["label_name"]]
                    rel_scores = gt_info["rel_scores"] if "rel_scores" in gt_info else None
                    assert rel_scores is None or len(rel_docids) == len(rel_scores)

                    pred_dicts.append({
                        "prediction": [cand_ids_local[i] for i in ranked_indices],
                        "label": rel_docids,
                        "rel_scores": rel_scores,
                    })

                    # 新增：分数详情
                    append_score_detail(score_detail_dicts, qid, ranked_indices, sim_vec, cand_ids_local, rel_docids)

            ########################################## 保存预测和分数
            with open(score_detail_path, "w") as f:  # 新增
                for detail in score_detail_dicts:
                    f.write(json.dumps(detail) + '\n')
            print_master(f"Detailed score file saved to: {score_detail_path}")

            metrics_to_report = task_config["metrics"] if task_config.get("metrics", None) is not None else ["hit", "ndcg", "precision", "recall", "f1", "map", "mrr"]
            metrics = RankingMetrics(metrics_to_report)
            score_dict = metrics.evaluate(pred_dicts)
            formatted = {k: f"{v:.4f}" for k, v in score_dict.items()}
            score_dict["num_pred"] = len(pred_dicts)
            score_dict["num_data"] = len(gt_infos)
            print_master(f"Score of {dataset_name}:")
            print_master(formatted)
            print_master(f"Outputting final score to: {score_path}")
            with open(score_path, "w") as f:
                json.dump(score_dict, f, indent=4)
            with open(pred_path, "w") as f:
                for pred in pred_dicts:
                    f.write(json.dumps(pred) + '\n')
            ####################################################################
            score_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_score.json")
            pred_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_pred.jsonl")

            metrics_to_report = task_config["metrics"] if task_config.get("metrics", None) is not None else ["hit", "ndcg", "precision", "recall", "f1", "map", "mrr"]
            metrics = RankingMetrics(metrics_to_report)
            score_dict = metrics.evaluate(pred_dicts)
            formatted = {k: f"{v:.4f}" for k, v in score_dict.items()}
            score_dict["num_pred"] = len(pred_dicts)
            score_dict["num_data"] = len(gt_infos)
            print_master(f"Score of {dataset_name}:")
            print_master(formatted)
            print_master(f"Outputting final score to: {score_path}")
            with open(score_path, "w") as f:
                json.dump(score_dict, f, indent=4)
            with open(pred_path, "w") as f:
                for pred in pred_dicts:
                    f.write(json.dumps(pred) + '\n')            


if __name__ == '__main__':
    main()