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# 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.model.vlm_backbone.qwen2_vl.modeling_qwen2_vl import Qwen2VLForConditionalGeneration as _Qwen2VLForConditionalGeneration_src
# from src.arguments 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_cut_layer 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():
# if isinstance(output, torch.Tensor):
# token_info["vision_tokens"] = output.shape[0] # For visual features, usually (batch_size, num_tokens, hidden_dim)
# elif hasattr(output, 'last_hidden_state'):
# token_info["vision_tokens"] = output.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)}")
# # 使用从 'src' 路径导入的 Qwen2VLForConditionalGeneration 进行检查
# if isinstance(model.encoder, _Qwen2VLForConditionalGeneration_src):
# # print_master("Detected MMEBModel structure, registering hooks on model.encoder's sub-modules.")
# core_model = model.encoder
# else:
# print_master(f"WARNING: model.encoder is not an instance of _Qwen2VLForConditionalGeneration_src. Its type is {type(model.encoder)}. Hooks will be registered on top-level model if applicable.")
# 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)
# 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()
# 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_all = 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_indices, 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()
############################################################################################
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.model.vlm_backbone.qwen2_vl.modeling_qwen2_vl_train_tokrnpooling import Qwen2VLForConditionalGeneration as _Qwen2VLForConditionalGeneration_src
from src.arguments 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_cut_layer import MMEBModel
from src.model.processor import get_backbone_name, load_processor, COLPALI
from src.utils import batch_to_device, print_rank, print_master
import math
def get_env_mid_layer():
v = os.environ.get("MID_LM_LAYER", "").strip()
if v == "" or v.lower() in {"none", "null"}:
return None
try:
return int(v)
except:
logger.warning(f"Invalid MID_LM_LAYER={v}, ignore.")
return None
def get_env_eval_layers():
"""
解析环境变量 LM_LAYERS（优先）或兼容旧的 MID_LM_LAYER。
- LM_LAYERS 示例："4,8,12,last"；可包含 'last'/'none'/'null'/'-1' 表示最后一层(None)。
- 若未设置 LM_LAYERS，则回落到旧逻辑：MID_LM_LAYER=None -> [None]；否则 [mid, None]
返回: list[ int | None ]，例如 [4, 8, 12, None]；None 代表最后一层。
"""
v = os.environ.get("LM_LAYERS", None)
if v is not None:
v = v.strip()
if v:
toks = [t.strip() for t in v.split(',') if t.strip() != ""]
layers = []
for tok in toks:
tl = tok.lower()
if tl in {"last", "none", "null", "-1"}:
layers.append(None)
else:
try:
val = int(tok)
if val > 0:
layers.append(val)
else:
logger.warning(f"Ignoring non-positive layer '{tok}' in LM_LAYERS.")
except Exception:
logger.warning(f"Invalid token '{tok}' in LM_LAYERS; must be int or 'last'/'none'.")
# 去重但保持顺序
seen = set()
uniq = []
for l in layers:
key = -1 if l is None else l
if key in seen:
continue
seen.add(key)
uniq.append(l)
if not uniq:
return [None]
return uniq
else:
# 兼容旧逻辑
mid = get_env_mid_layer()
return [None] if mid is None else [mid, None]
def make_layer_tag(keep_layers: int | None):
return f"layer{keep_layers}" if keep_layers and keep_layers > 0 else "layerlast"
def dot_sim(a: np.ndarray, b: np.ndarray) -> np.ndarray:
# a: [Nq, D], b: [Nc, D], both L2-normalized already if normalize=true
return a @ b.T
def build_score_details(qid: int, cand_ids: list, score_vec: np.ndarray, ranked_indices: np.ndarray):
return {
"qid": int(qid),
"cand_scores": [
{"cand_id": str(cand_ids[i]), "score": float(score_vec[i])}
for i in ranked_indices
]
}
def top1_top2_margin(score_vec: np.ndarray) -> float:
if len(score_vec) < 2:
return float("inf") # 只有一个候选时视作极大margin
top2 = np.partition(score_vec, -2)[-2:]
top2.sort()
return float(top2[-1] - top2[-2])
def simulate_early_exit_by_margin(
sims_mid: list[dict], sims_last: list[dict], labels: list[list[str]], metrics_to_report: list[str],
taus: list[float], rank_global: bool
):
"""
sims_mid / sims_last: 每个query一个dict: {cand_id: score}
labels: 每个query的正样本cand_id列表
返回：不同tau下的覆盖率、指标
"""
assert len(sims_mid) == len(sims_last) == len(labels)
N = len(labels)
results = []
from src.eval_utils.metrics import RankingMetrics
metrics = RankingMetrics(metrics_to_report)
# 预构造 用于metrics.evaluate 的pred_dict
def to_pred_dicts(use_mid_mask: list[bool]) -> list[dict]:
pred_dicts = []
for qid in range(N):
sims_use = sims_mid[qid] if use_mid_mask[qid] else sims_last[qid]
# 排序
ranked = sorted(sims_use.items(), key=lambda x: -x[1])
pred_dicts.append({
"prediction": [cid for cid, _ in ranked],
"label": labels[qid],
"rel_scores": None
})
return pred_dicts
# 计算中间层margin
margins = []
for qid in range(N):
# 取前两大分数的margin
if len(sims_mid[qid]) == 0:
margins.append(0.0)
continue
scores = np.array(list(sims_mid[qid].values()), dtype=np.float32)
margins.append(top1_top2_margin(scores))
margins = np.array(margins, dtype=np.float32)
for tau in taus:
use_mid_mask = (margins >= tau).tolist()
pred_dicts = to_pred_dicts(use_mid_mask)
score_dict = metrics.evaluate(pred_dicts)
coverage = float(np.mean(use_mid_mask)) # 早停覆盖率
results.append({
"tau": tau,
"coverage": coverage,
**score_dict
})
return results
def top1_top2_margin_from_array(score_vec: np.ndarray) -> float:
if score_vec is None or len(score_vec) == 0:
return 0.0
if len(score_vec) == 1:
return float('inf')
# 取前两大
top2 = np.partition(score_vec, -2)[-2:]
top2.sort()
return float(top2[-1] - top2[-2])
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():
if isinstance(output, torch.Tensor):
token_info["vision_tokens"] = output.shape[0] # For visual features, usually (batch_size, num_tokens, hidden_dim)
elif hasattr(output, 'last_hidden_state'):
token_info["vision_tokens"] = output.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)}")
# 使用从 'src' 路径导入的 Qwen2VLForConditionalGeneration 进行检查
if isinstance(model.encoder, _Qwen2VLForConditionalGeneration_src):
# print_master("Detected MMEBModel structure, registering hooks on model.encoder's sub-modules.")
core_model = model.encoder
else:
print_master(f"WARNING: model.encoder is not an instance of _Qwen2VLForConditionalGeneration_src. Its type is {type(model.encoder)}. Hooks will be registered on top-level model if applicable.")
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)
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():
# ----------------------- Distributed init -----------------------
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
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()}")
# 兼容 torchrun 参数
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)
# ----------------------- Parse args -----------------------
parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
model_args: ModelArguments
data_args: DataArguments
training_args: TrainingArguments
os.makedirs(data_args.encode_output_path, exist_ok=True)
# 支持多层评测（优先 LM_LAYERS，兼容 MID_LM_LAYER）
layers_to_eval = get_env_eval_layers()
print_master(f"Eval layers (qry/tgt): {layers_to_eval}")
# ----------------------- 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}')
# 仅 rank0 下载，其他rank等待缓存
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}...")
if torch.distributed.is_initialized():
torch.distributed.barrier()
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)
# 确保“最后一层”时不裁层（避免类里默认20层的坑）
model.set_inference_layers(qry_layers=None, tgt_layers=None)
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()):
if dist.is_initialized():
dist.barrier()
print_master(f"\n--- Evaluating {dataset_name} ---")
# 根据 data_basedir 修正路径
if data_args.data_basedir is not None:
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])
# 构建数据集
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
if dist.is_initialized():
world_size = dist.get_world_size()
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
# 路径索引
saved_paths = {} # {(side, tag): path}
# --------- 针对每个层设置（中间层/最后一层）分别编码与保存 ---------
for keep_layers in layers_to_eval:
tag = make_layer_tag(keep_layers)
print_master(f"[{dataset_name}] Start encoding for tag={tag} (keep_layers={keep_layers})")
# 设置模型层数
model.set_inference_layers(qry_layers=keep_layers, tgt_layers=keep_layers)
# 路径
query_embed_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_{tag}")
cand_embed_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_tgt_{tag}")
dataset_info_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_info.jsonl")
query_inference_stats_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_inference_stats_{tag}.json")
cand_inference_stats_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_cand_inference_stats_{tag}.json")
qry_img_masks_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_img_token_masks_{tag}.jsonl")
cand_img_masks_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_cand_img_token_masks_{tag}.jsonl")
saved_paths[("qry", tag)] = query_embed_path
saved_paths[("tgt", tag)] = cand_embed_path
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)
# 动态累计统计
def init_total_stats():
return {
"total_inference_time_seconds": 0.0,
"module_inference_times": {}, # 动态模块名 -> {"total": float, "count": int}
"token_counts": {
"visual_tokens": 0,
"language_input_tokens_raw": 0,
"llm_total_input_tokens": 0,
"language_output_tokens": 0,
},
"data_point_count": 0
}
def accumulate_stats(total_stats, batch_stats):
batch_size = batch_stats["batch_size"]
total_stats["total_inference_time_seconds"] += batch_stats["total_inference_time_seconds"]
# 模块时间
for mname, mstats in batch_stats["module_inference_times"].items():
if mname not in total_stats["module_inference_times"]:
total_stats["module_inference_times"][mname] = {"total": 0.0, "count": 0}
total_stats["module_inference_times"][mname]["total"] += mstats.get("total", 0.0)
total_stats["module_inference_times"][mname]["count"] += mstats.get("count", 0)
# token 统计（按样本乘 batch_size 再累积）
total_stats["token_counts"]["visual_tokens"] += batch_stats["token_counts"]["visual_tokens"] * batch_size
total_stats["token_counts"]["language_input_tokens_raw"] += batch_stats["token_counts"]["language_input_tokens_raw"] * batch_size
total_stats["token_counts"]["llm_total_input_tokens"] += batch_stats["token_counts"]["llm_total_input_tokens"] * batch_size
total_stats["token_counts"]["language_output_tokens"] += batch_stats["token_counts"]["language_output_tokens"] * batch_size
total_stats["data_point_count"] += batch_size
def finalize_and_save_stats(total_stats, out_path, task_name, encode_side):
if local_rank != 0:
return
if total_stats["data_point_count"] <= 0:
print_master(f"No data processed for {task_name} [{encode_side}], skip saving stats.")
return
final_stats = {
"task_name": task_name,
"encode_side": encode_side,
"data_point_count": total_stats["data_point_count"],
"inference_times": {
"total_inference_time_seconds": total_stats["total_inference_time_seconds"],
"avg_inference_time_per_item_seconds": total_stats["total_inference_time_seconds"] / max(1, total_stats["data_point_count"]),
"module_average_times_per_call": {},
"module_total_times_seconds": {},
"module_calls_count": {},
},
"token_counts": {
"total_visual_tokens": total_stats["token_counts"]["visual_tokens"],
"avg_visual_tokens_per_item": total_stats["token_counts"]["visual_tokens"] / max(1, total_stats["data_point_count"]),
"total_language_input_tokens_raw": total_stats["token_counts"]["language_input_tokens_raw"],
"avg_language_input_tokens_raw_per_item": total_stats["token_counts"]["language_input_tokens_raw"] / max(1, total_stats["data_point_count"]),
"total_llm_total_input_tokens": total_stats["token_counts"]["llm_total_input_tokens"],
"avg_llm_total_input_tokens_per_item": total_stats["token_counts"]["llm_total_input_tokens"] / max(1, total_stats["data_point_count"]),
"total_language_output_tokens": total_stats["token_counts"]["language_output_tokens"],
"avg_language_output_tokens_per_item": total_stats["token_counts"]["language_output_tokens"] / max(1, total_stats["data_point_count"]),
}
}
for mname, mstats in total_stats["module_inference_times"].items():
total = mstats.get("total", 0.0)
count = mstats.get("count", 0)
final_stats["inference_times"]["module_total_times_seconds"][mname] = total
final_stats["inference_times"]["module_calls_count"][mname] = count
final_stats["inference_times"]["module_average_times_per_call"][mname] = (total / count) if count > 0 else 0.0
with open(out_path, 'w', encoding='utf-8') as f:
json.dump(final_stats, f, ensure_ascii=False, indent=4)
print_master(f"[{task_name}] {encode_side} inference statistics saved to: {out_path}")
# ------- Encode queries -------
if do_query:
print_master(f"[{tag}] 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
)
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[{tag}] for {dataset_name}"
)
# 截断到真实长度
query_embeds = query_embeds[:len(full_eval_qry_dataset)]
gt_infos = gt_infos[:len(full_eval_qry_dataset)]
qry_img_masks = qry_img_masks[:len(full_eval_qry_dataset)]
# 累计统计并保存
qry_total_stats = init_total_stats()
for bs in qry_batch_stats:
accumulate_stats(qry_total_stats, bs)
if local_rank == 0:
with open(query_embed_path, 'wb') as f:
pickle.dump(query_embeds, f)
# dataset_info 只需写一次；若第一次就写
if not os.path.exists(dataset_info_path):
with open(dataset_info_path, 'w') as f:
for info in gt_infos:
f.write(json.dumps(info) + '\n')
# 保存 masks
with open(qry_img_masks_path, 'w', encoding='utf-8') as f:
for i, m in enumerate(qry_img_masks):
f.write(json.dumps({"index": i, "mask": m}, ensure_ascii=False) + "\n")
print_master(f"Saved query embeddings to {query_embed_path}")
print_master(f"Saved query image token masks to {qry_img_masks_path}")
finalize_and_save_stats(qry_total_stats, query_inference_stats_path, dataset_name, f"query[{tag}]")
if dist.is_initialized():
dist.barrier()
# ------- Encode candidates -------
if do_cand:
print_master(f"[{tag}] 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
)
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[{tag}] for {dataset_name}"
)
cand_embeds = cand_embeds[:len(full_eval_cand_dataset)]
all_cand_ids = all_cand_ids[:len(full_eval_cand_dataset)]
cand_img_masks = cand_img_masks[:len(full_eval_cand_dataset)]
cand_total_stats = init_total_stats()
for bs in cand_batch_stats:
accumulate_stats(cand_total_stats, bs)
if local_rank == 0:
cand_embed_dict = {cid: emb for cid, emb in zip(all_cand_ids, cand_embeds)}
with open(cand_embed_path, 'wb') as f:
pickle.dump(cand_embed_dict, f)
with open(cand_img_masks_path, 'w', encoding='utf-8') as f:
for cid, m in zip(all_cand_ids, cand_img_masks):
f.write(json.dumps({"cand_id": str(cid), "mask": m}, ensure_ascii=False) + "\n")
print_master(f"Saved candidate embeddings to {cand_embed_path}")
print_master(f"Saved candidate image token masks to {cand_img_masks_path}")
finalize_and_save_stats(cand_total_stats, cand_inference_stats_path, dataset_name, f"candidate[{tag}]")
if dist.is_initialized():
dist.barrier()
# --------- Scoring per layer + combined + early-exit curve ---------
if local_rank == 0:
dataset_info_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_info.jsonl")
gt_infos = [json.loads(l) for l in open(dataset_info_path)]
rank_against_all_candidates = task_config.get("eval_type", "global") == "global"
metrics_to_report = task_config.get("metrics", ["hit", "ndcg", "precision", "recall", "f1", "map", "mrr"])
layer_tags = [make_layer_tag(l) for l in layers_to_eval]
sims_by_layer = {} # tag -> list[ dict(cand_id->score) ]
for tag in layer_tags:
query_embed_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_qry_{tag}")
cand_embed_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_tgt_{tag}")
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)
pred_dicts = []
score_detail_dicts = []
sims_for_exit = []
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 isinstance(qry_embeds, np.ndarray) and 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()
else:
sim_matrix = np.dot(qry_embeds, cand_embeds.T)
ranked_all = np.argsort(-sim_matrix, axis=1)
for qid, gt_info in tqdm(enumerate(gt_infos), total=len(gt_infos), desc=f"[{tag}] scoring(all) {dataset_name}"):
ranked_indices = ranked_all[qid]
rel_docids = gt_info["label_name"] if isinstance(gt_info["label_name"], list) else [gt_info["label_name"]]
rel_scores = gt_info.get("rel_scores")
pred_dicts.append({
"prediction": [cand_keys[i] for i in ranked_indices],
"label": rel_docids,
"rel_scores": rel_scores,
})
score_detail_dicts.append(build_score_details(qid, cand_keys, sim_matrix[qid], ranked_indices))
sims_for_exit.append({cand_keys[i]: float(sim_matrix[qid][i]) for i in range(len(cand_keys))})
else:
# 非全局：每个query用 gt_info["cand_names"] 的子集进行评分
for qid, (qry_embed, gt_info) in tqdm(enumerate(zip(qry_embeds, gt_infos)), total=len(gt_infos), desc=f"[{tag}] scoring(local) {dataset_name}"):
cand_ids_local = gt_info["cand_names"]
cand_embeds = np.stack([cand_embed_dict[key] for key in cand_ids_local])
if isinstance(qry_embeds, np.ndarray) and qry_embeds.ndim == 3:
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]
else:
sim_vec = np.dot(qry_embed, cand_embeds.T)
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.get("rel_scores")
pred_dicts.append({
"prediction": [cand_ids_local[i] for i in ranked_indices],
"label": rel_docids,
"rel_scores": rel_scores,
})
score_detail_dicts.append(build_score_details(qid, cand_ids_local, sim_vec, ranked_indices))
sims_for_exit.append({cid: float(s) for cid, s in zip(cand_ids_local, sim_vec.tolist())})
# 保存每层指标与详情
layer_score_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_score_{tag}.json")
layer_pred_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_pred_{tag}.jsonl")
layer_detail_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_score_details_{tag}.jsonl")
metrics = RankingMetrics(metrics_to_report)
score_dict = metrics.evaluate(pred_dicts)
score_dict["num_pred"] = len(pred_dicts)
score_dict["num_data"] = len(gt_infos)
with open(layer_score_path, "w") as f:
json.dump(score_dict, f, indent=4)
with open(layer_pred_path, "w") as f:
for pred in pred_dicts:
f.write(json.dumps(pred) + '\n')
with open(layer_detail_path, "w") as f:
for detail in score_detail_dicts:
f.write(json.dumps(detail) + "\n")
print_master(f"[{dataset_name}] {tag} score: " + json.dumps({k: (f"{v:.4f}" if isinstance(v, (int, float)) else v) for k, v in score_dict.items()}))
sims_by_layer[tag] = sims_for_exit
# 合并对比文件 + 早停曲线（仅在存在中间层时）
if len(layer_tags) == 2 and "layerlast" in layer_tags:
mid_tag = [t for t in layer_tags if t != "layerlast"][0]
last_tag = "layerlast"
# 合并详情：每个query包含 mid/last 的cand_scores、top1、margin
combined_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_score_details_both_layers.jsonl")
with open(combined_path, "w", encoding='utf-8') as f:
for qid in range(len(gt_infos)):
sims_mid = sims_by_layer[mid_tag][qid]
sims_last = sims_by_layer[last_tag][qid]
def top1_cid(sims: dict):
return max(sims.items(), key=lambda x: x[1])[0] if sims else None
def margin_of(sims: dict):
vals = np.array(list(sims.values()), dtype=np.float32)
return top1_top2_margin_from_array(vals)
row = {
"qid": int(qid),
"label": gt_infos[qid]["label_name"] if isinstance(gt_infos[qid]["label_name"], list) else [gt_infos[qid]["label_name"]],
"mid": {
"top1": top1_cid(sims_mid),
"margin": margin_of(sims_mid),
"cand_scores": sims_mid
},
"last": {
"top1": top1_cid(sims_last),
"margin": margin_of(sims_last),
"cand_scores": sims_last
}
}
f.write(json.dumps(row, ensure_ascii=False) + "\n")
print_master(f"[{dataset_name}] combined details saved to {combined_path}")
# 早停曲线（margin 阈值）
taus = [round(x, 3) for x in np.linspace(0.0, 0.6, 31).tolist()]
labels = [
gi["label_name"] if isinstance(gi["label_name"], list) else [gi["label_name"]]
for gi in gt_infos
]
exit_curve = simulate_early_exit_by_margin(
sims_by_layer[mid_tag], sims_by_layer[last_tag], labels, metrics_to_report, taus, rank_against_all_candidates
)
curve_path = os.path.join(data_args.encode_output_path, f"{dataset_name}_early_exit_curve_margin.json")
with open(curve_path, "w") as f:
json.dump(exit_curve, f, indent=4)
print_master(f"[{dataset_name}] early-exit curve saved to {curve_path}")
# # 合并对比文件 + 早停曲线（与 last 对比），如果存在 last
# last_tag = "layerlast" if "layerlast" in layer_tags else None
# if last_tag is not None:
# # 准备 labels 一次即可
# labels = [
# gi["label_name"] if isinstance(gi["label_name"], list) else [gi["label_name"]]
# for gi in gt_infos
# ]
# taus = [round(x, 3) for x in np.linspace(0.0, 0.6, 31).tolist()]
# # 对每个中间层分别与 last 做对比
# for mid_tag in [t for t in layer_tags if t != last_tag]:
# # 合并详情：每个query包含 mid/last 的cand_scores、top1、margin
# combined_path = os.path.join(
# data_args.encode_output_path,
# f"{dataset_name}_score_details_{mid_tag}_vs_last.jsonl"
# )
# with open(combined_path, "w", encoding="utf-8") as f:
# for qid in range(len(gt_infos)):
# sims_mid = sims_by_layer[mid_tag][qid]
# sims_last = sims_by_layer[last_tag][qid]
# def top1_cid(sims: dict):
# return max(sims.items(), key=lambda x: x[1])[0] if sims else None
# def margin_of(sims: dict):
# vals = np.array(list(sims.values()), dtype=np.float32)
# return top1_top2_margin_from_array(vals)
# row = {
# "qid": int(qid),
# "label": labels[qid],
# "mid": {
# "top1": top1_cid(sims_mid),
# "margin": margin_of(sims_mid),
# "cand_scores": sims_mid
# },
# "last": {
# "top1": top1_cid(sims_last),
# "margin": margin_of(sims_last),
# "cand_scores": sims_last
# }
# }
# f.write(json.dumps(row, ensure_ascii=False) + "\n")
# print_master(f"[{dataset_name}] combined details saved to {combined_path} (mid={mid_tag} vs last)")
# # 早停曲线（margin 阈值）
# exit_curve = simulate_early_exit_by_margin(
# sims_by_layer[mid_tag], sims_by_layer[last_tag], labels, metrics_to_report, taus, rank_against_all_candidates
# )
# curve_path = os.path.join(
# data_args.encode_output_path,
# f"{dataset_name}_early_exit_curve_margin_{mid_tag}_vs_last.json"
# )
# with open(curve_path, "w") as f:
# json.dump(exit_curve, f, indent=4)
# print_master(f"[{dataset_name}] early-exit curve saved to {curve_path} (mid={mid_tag} vs last)")
if dist.is_initialized():
dist.barrier()
if __name__ == '__main__':
main() |