src/model/model_cut_layer.py

from typing import Dict
import torch
import torch.distributed as dist
from torch import nn, Tensor
from transformers import PreTrainedModel, AutoModelForCausalLM, AutoConfig
from peft import LoraConfig, get_peft_model, PeftModel
from src.model.processor import QWEN2_5_VL_TOKENSELECTION
from src.arguments import ModelArguments, TrainingArguments
from src.model.processor import LLAVA_NEXT, QWEN2_VL, PHI3V, get_backbone_name, print_master, QWEN2_5_VL, \
    backbone2model, QWEN2_VL_TOKENSELECTION, QWEN2_5_VL_TOKENSELECTION, E5_V

from src.arguments import ModelArguments
from src.model.processor import LLAVA_NEXT, QWEN2_VL, PHI3V, get_backbone_name, print_master, QWEN2_5_VL, INTERNVIDEO2, \
    QWEN2_VL_TOKENSELECTION, backbone2model, GME, VLM_IMAGE_TOKENS, LamRA, LamRA_QWEN2_5, COLPALI
from src.model.baseline_backbone.colpali import ColPali
from src.model.baseline_backbone.gme.gme_inference import GmeQwen2VL
from src.model.baseline_backbone.lamra.lamra_inference import LamRAQwen2VL
from src.model.baseline_backbone.lamra.lamra_qwen25_inference import LamRAQwen25VL
from src.model.baseline_backbone.phi3_v.modeling_phi3_v import Phi3VForCausalLM
from src.model.baseline_backbone.llava_next import LlavaNextForConditionalGeneration

from transformers import modeling_utils
if not hasattr(modeling_utils, "ALL_PARALLEL_STYLES") or modeling_utils.ALL_PARALLEL_STYLES is None:
    modeling_utils.ALL_PARALLEL_STYLES = ["tp", "none", "colwise", 'rowwise']

from contextlib import contextmanager

class MMEBModel(nn.Module):
    TRANSFORMER_CLS = AutoModelForCausalLM

    def __init__(self,
                 encoder: PreTrainedModel,
                 pooling: str = 'last',
                 normalize: bool = False,
                 temperature: float = 0.02,
                 ):
        super().__init__()
        self.config = encoder.config
        self.encoder = encoder
        self.pooling = pooling
        self.normalize = normalize
        self.temperature = temperature
        self.cross_entropy = nn.CrossEntropyLoss(reduction='mean')
        self.is_ddp = dist.is_initialized()
        if self.is_ddp:
            self.process_rank = dist.get_rank()
            self.world_size = dist.get_world_size()
        self.qry_lm_layers = 16  # e.g., 16 代表只跑前16层
        self.tgt_lm_layers = 16  # None 或 0 表示不裁层（全层）
        # NEW: 最近一次底模 forward 的 image token 布尔掩码（[B, L]）
        self._last_image_token_bool_masks = None

    def set_inference_layers(self, qry_layers: int | None = None, tgt_layers: int | None = None):
        """设置推理阶段每侧使用的LM层数：qry用qry_layers层、cand用tgt_layers层。None代表不裁层。"""
        self.qry_lm_layers = qry_layers
        self.tgt_lm_layers = tgt_layers

    def _locate_lm_layers(self, enc: nn.Module):
        """
        返回(decoder容器, 'layers')，用于访问 LM 的 ModuleList。
        兼容常见Qwen/LLaMA路径：model.language_model.layers / model.model.layers / model.layers / transformer.layers
        """
        candidates = [
            ("model", "language_model", "layers"),
            ("model", "model", "layers"),
            ("model", "layers"),
            ("language_model", "layers"),
            ("transformer", "layers"),
        ]
        for path in candidates:
            obj = enc
            ok = True
            for p in path:
                if hasattr(obj, p):
                    obj = getattr(obj, p)
                else:
                    ok = False
                    break
            if ok and isinstance(obj, nn.ModuleList):
                # 返回父对象与属性名 'layers'
                parent = enc
                for p in path[:-1]:
                    parent = getattr(parent, p)
                return parent, path[-1]
        return None, None

    @contextmanager
    def _limit_lm_layers(self, enc: nn.Module, keep_layers: int | None):
        """
        临时把 enc 的 LM layers 截断为前 keep_layers 层；退出时恢复。
        keep_layers 为 None/0/负数 -> 不裁层。
        """
        if not keep_layers or keep_layers <= 0:
            yield
            return
        root, attr = self._locate_lm_layers(enc)
        if root is None:
            # 找不到LM层，直接忽略
            yield
            return
        full: nn.ModuleList = getattr(root, attr)
        try:
            # 注意：这里复用相同的子模块对象，不复制权重；仅替换列表视图，显存不额外膨胀
            setattr(root, attr, nn.ModuleList(list(full[:keep_layers])))
            yield
        finally:
            setattr(root, attr, full)
    
    # def encode_input(self, input):
    # def encode_input(self, input, compression_rate=None):
    def encode_input(self, input, side: str | None = None):
        max_layers = None
        if side == "qry":
            max_layers = self.qry_lm_layers
        elif side == "tgt":
            max_layers = self.tgt_lm_layers

        with self._limit_lm_layers(self.encoder, max_layers):
            if getattr(self, "model_backbone", None) == INTERNVIDEO2:
                if "input_ids" in input.keys():
                    # text side
                    text_output = self.encoder.get_text_encoder()(
                        input["input_ids"],
                        attention_mask=input["attention_mask"],
                        return_dict=True,
                        mode="text",
                    )
                    text_embeds = text_output.last_hidden_state
                    pooled_text_embeds = text_embeds[:, 0]
                    pooled_output = self.encoder.text_proj(pooled_text_embeds)
                    pooled_output /= pooled_output.norm(dim=-1, keepdim=True)
                    return pooled_output
                else:
                    _, vfeat = self.encoder.encode_vision(input["pixel_values"], test=True)
                    vfeat = self.encoder.vision_proj(vfeat)
                    vfeat /= vfeat.norm(dim=-1, keepdim=True)
                    return vfeat
            elif getattr(self, "model_backbone", None) in [GME, LamRA, LamRA_QWEN2_5]:
                # pooled_output = self.encoder(**input, return_dict=True, output_hidden_states=True)
                texts = [text.replace(VLM_IMAGE_TOKENS[QWEN2_VL] + '\n', '') for text in input["texts"]] # we are actually passing video queries so this should not happen
                images = []
                for imgs in input['images']:
                    # if multi images are given, select the middle frame only
                    if isinstance(imgs, list):
                        imgs = imgs[len(imgs) // 2]
                        assert not isinstance(imgs, list) # make sure we have extracted the middle frame and it is no longer a list
                        images.append(imgs)
                    else:
                        images.append(imgs)
                pooled_output = self.encoder.get_fused_embeddings(texts=texts, images=images)
                return pooled_output
            elif getattr(self, "model_backbone", None) == COLPALI:
                pooled_output = self.encoder(**input, return_dict=True, output_hidden_states=True)
                return pooled_output
            elif getattr(self, "model_backbone", None) == LLAVA_NEXT:
                input['pixel_values'] = input['pixel_values'].squeeze(dim=1)
                input['image_sizes'] = input['image_sizes'].squeeze(dim=1)
                hidden_states = self.encoder(**input, return_dict=True, output_hidden_states=True)
                hidden_states = hidden_states.hidden_states[-1]
                pooled_output = self._pooling(hidden_states, input['attention_mask'])
                return pooled_output
            else:
                # hidden_states = self.encoder(**input, return_dict=True, output_hidden_states=True)
                # # hidden_states = self.encoder(**input, compression_rate=compression_rate, return_dict=True, output_hidden_states=True)
                # hidden_states = hidden_states.hidden_states[-1]
                # pooled_output = self._pooling(hidden_states, input['attention_mask'])
                # return pooled_output
                # hidden_states = outputs.hidden_states
                # pooled_from_layer20 = self._pooling(hidden_states[20], input["attention_mask"])
                # pooled_from_last = self._pooling(hidden_states[-1], input["attention_mask"])
                # return {
                #     "layer20": pooled_from_layer20,
                #     "last": pooled_from_last,
                # }
                outputs = self.encoder(**input, return_dict=True, output_hidden_states=True)
                img_masks = getattr(outputs, "image_token_bool_masks", None)
                if img_masks is None:
                    try:
                        img_masks = outputs.get("image_token_bool_masks", None)
                    except Exception:
                        pass
                self._last_image_token_bool_masks = img_masks

                hs = outputs.hidden_states
                if isinstance(hs, (list, tuple)):
                    last_hidden = hs[-1]            # [B, L, D]
                else:
                    last_hidden = hs                 # [B, L, D]（如果底模走了 glimpse 路径）
                attn = getattr(outputs, "attention_mask", None)
                # print('attn:', attn)
                # exit()
                if attn is None:
                    attn = input["attention_mask"]

                pooled_output = self._pooling(last_hidden, attn)
                return pooled_output

    def _pooling(self, last_hidden_state, attention_mask):
        if self.pooling == 'last' or self.pooling == 'eos':
            left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
            batch_size = last_hidden_state.shape[0]
            if left_padding:
                # Get the vectors at the last position
                reps = last_hidden_state[torch.arange(batch_size), -1, :]
            else:
                # Calculate last 1 position in the original tensor
                eos_indices = attention_mask.sum(dim=1) - 1
                # Get the vectors at the last 1 position of each attention mask
                reps = last_hidden_state[
                    torch.arange(batch_size, device=last_hidden_state.device), eos_indices]
        else:
            raise NotImplementedError
        if self.normalize:
            reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
        return reps

    @classmethod
    def build(cls, model_args: ModelArguments, **kwargs):
        config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
        variant = getattr(config, "backbone_variant", None)
        if variant == "layerprune":
            model_backbone = "QWEN2_VL_LayerPrune"
        else:
            model_backbone = get_backbone_name(hf_config=config)
        print_master(f'Loading backbone [{model_backbone}] from {model_args.model_name}')
        # Loading the base model
        if model_backbone == PHI3V:
            config._attn_implementation = "eager"
            config.padding_side = "right"
            config.use_cache = False
            base_model = Phi3VForCausalLM.from_pretrained(
                model_args.model_name,
                config=config,
                torch_dtype=torch.bfloat16,
                low_cpu_mem_usage=True,
            )
        elif model_backbone == LLAVA_NEXT:
            config.use_cache = False
            config.padding_side = "left"
            base_model = LlavaNextForConditionalGeneration.from_pretrained(
                model_args.model_name,
                config=config,
                torch_dtype=torch.bfloat16,
                low_cpu_mem_usage=True,
            )
        elif model_backbone in [QWEN2_VL, QWEN2_5_VL]:
            config._attn_implementation = "flash_attention_2"
            config.padding_side = "left"
            config.use_cache = False
            base_model = backbone2model[model_backbone].from_pretrained(
                model_args.model_name,
                config=config,
                torch_dtype=torch.bfloat16,
                low_cpu_mem_usage=True,
            )
        elif model_backbone in ["QWEN2_VL_LayerPrune"]:
            config._attn_implementation = "flash_attention_2"
            config.padding_side = "left"
            config.use_cache = False
            base_model = backbone2model[model_backbone].from_pretrained(
                model_args.model_name,
                config=config,
                torch_dtype=torch.bfloat16,
                low_cpu_mem_usage=True,
            )
        elif model_backbone in [QWEN2_VL_TOKENSELECTION, QWEN2_5_VL_TOKENSELECTION]:
            config._attn_implementation = "flash_attention_2"
            config.padding_side = "left"
            config.use_cache = False

            from .utils import parse_layer_type
            lm_qwen_layer = 28
            vis_qwen_layer = 32
            lm_skip_layer = parse_layer_type(model_args.lm_skip_layer, lm_qwen_layer)
            vis_skip_layer = parse_layer_type(model_args.vis_skip_layer, vis_qwen_layer)

            base_model = backbone2model[model_backbone].from_pretrained(
                model_args.model_name,
                config=config,
                torch_dtype=torch.bfloat16,
                low_cpu_mem_usage=True,
                lm_skip_layer=lm_skip_layer,
                vis_skip_layer=vis_skip_layer,
            )
        else:
            config.use_cache = False
            base_model = cls.TRANSFORMER_CLS.from_pretrained(
                model_args.model_name, **kwargs, config=config,
                attn_implementation="flash_attention_2",
                torch_dtype=torch.bfloat16,
                trust_remote_code=True)

        if model_args.lora:
            print_master(f'Loading lora adapter from {base_model}')
            lora_config = LoraConfig(
                r=model_args.lora_r,
                lora_alpha=model_args.lora_alpha,
                target_modules=model_args.lora_target_modules.split(','),
                lora_dropout=model_args.lora_dropout,
                init_lora_weights="gaussian",
                use_dora=True,
                inference_mode=False
            )
            lora_model = get_peft_model(base_model, lora_config)
            model = cls(
                encoder=lora_model,
                pooling=model_args.pooling,
                normalize=model_args.normalize,
                temperature=model_args.temperature
            )
        else:
            model = cls(
                encoder=base_model,
                pooling=model_args.pooling,
                normalize=model_args.normalize,
                temperature=model_args.temperature
            )
        return model


    @classmethod
    def load(cls, model_args: ModelArguments, is_trainable=True, **kwargs):
        # Loading the base model
        model_name_or_path = model_args.checkpoint_path if model_args.checkpoint_path else model_args.model_name
        config = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
        if not hasattr(model_args, "model_backbone") or not model_args.model_backbone:
            model_backbone = get_backbone_name(hf_config=config, model_type=model_args.model_type)
            setattr(model_args, 'model_backbone', model_backbone)
        print_master(f'Loading backbone [{model_args.model_backbone}] from {model_name_or_path}')
        if model_args.model_backbone in {LLAVA_NEXT, QWEN2_VL, QWEN2_5_VL, QWEN2_VL_TOKENSELECTION, QWEN2_5_VL_TOKENSELECTION, E5_V}:
            config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
            config._attn_implementation = "flash_attention_2"
            config.vision_config._attn_implementation = "flash_attention_2"
            base_model = backbone2model[model_args.model_backbone].from_pretrained(
                model_args.model_name,
                torch_dtype=torch.bfloat16,
                low_cpu_mem_usage=True,
                config=config
            )
        elif model_args.model_backbone == PHI3V:
            config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
            config.use_cache = False
            config.padding_side = "right"
            base_model = Phi3VForCausalLM.from_pretrained(model_args.model_name, **kwargs, config=config,
                                                          torch_dtype=torch.bfloat16, trust_remote_code=True)
            base_model.padding_side = "right"
        elif model_args.model_backbone == INTERNVIDEO2:
            print_master(f'Loading backbone [{model_args.model_backbone}] from {"src/model/vlm_backbone/internvideo2/"}')
            config = AutoConfig.from_pretrained("src/model/vlm_backbone/internvideo2/",
                                                trust_remote_code=True)
            base_model = backbone2model[model_args.model_backbone].from_pretrained("src/model/vlm_backbone/internvideo2/", config=config,
                                                                                   trust_remote_code=True)
        elif model_args.model_backbone == GME:
            base_model = GmeQwen2VL(model_args.model_name, processor=kwargs['processor'])
            setattr(base_model, 'config', config)
        elif model_args.model_backbone == LamRA:
            base_model = LamRAQwen2VL(model_args.model_name)
            setattr(base_model, 'config', config)
        elif model_args.model_backbone == LamRA_QWEN2_5:
            base_model = LamRAQwen25VL(model_args.model_name)
            setattr(base_model, 'config', config)
        elif model_args.model_backbone == COLPALI:
            base_model = ColPali.from_pretrained(model_args.model_name)
            setattr(base_model, 'config', config)
        else:
            # Loading external base model from HF
            config = AutoConfig.from_pretrained(model_args.model_name, trust_remote_code=True)
            config.use_cache = False
            base_model = cls.TRANSFORMER_CLS.from_pretrained(
                model_name_or_path, **kwargs, config=config,
                torch_dtype=torch.bfloat16,
                trust_remote_code=True)

        # Building the model on top of the base
        if model_args.lora:
            print_master(f'Loading LoRA from {model_name_or_path}')
            lora_config = LoraConfig.from_pretrained(model_name_or_path)
            lora_model = PeftModel.from_pretrained(base_model, model_name_or_path, config=lora_config, is_trainable=is_trainable)
            lora_model.load_adapter(model_name_or_path, lora_model.active_adapter, is_trainable=is_trainable)
            if not is_trainable:
                lora_model = lora_model.merge_and_unload()
            model = cls(
                encoder=lora_model,
                pooling=model_args.pooling,
                normalize=model_args.normalize,
                temperature=model_args.temperature
            )
        else:
            model = cls(
                encoder=base_model,
                pooling=model_args.pooling,
                normalize=model_args.normalize,
                temperature=model_args.temperature
            )

        model.model_backbone = model_args.model_backbone
        return model

    def save(self, output_dir: str):
        self.encoder.save_pretrained(output_dir)

    def forward(self, qry: Dict[str, Tensor] = None, tgt: Dict[str, Tensor] = None, *args, **kwargs):
        qry_reps, tgt_reps = None, None

        if qry is not None:
            qry_reps = self.encode_input(qry, side="qry")   # 仅qry侧用裁层
        if tgt is not None:
            tgt_reps = self.encode_input(tgt, side="tgt")   # cand侧保持全层（或按你设置）

        # 只编码一侧时，按你之前的返回约定原样返回
        if qry_reps is None or tgt_reps is None:
            # return {"qry_reps": qry_reps, "tgt_reps": tgt_reps}
            out = {"qry_reps": qry_reps, "tgt_reps": tgt_reps}
            # NEW: 透传最近一次底模 forward 的图像 token 布尔掩码
            img_masks = getattr(self, "_last_image_token_bool_masks", None)
            if img_masks is not None:
                out["image_token_bool_masks"] = img_masks
            return out

        if self.is_ddp:
            all_qry_reps = self._dist_gather_tensor(qry_reps)
            all_tgt_reps = self._dist_gather_tensor(tgt_reps)
        else:
            all_qry_reps = qry_reps
            all_tgt_reps = tgt_reps

        scores = self.compute_similarity(all_qry_reps, all_tgt_reps)
        scores = scores.view(all_qry_reps.size(0), -1)
        target = torch.arange(scores.size(0), device=scores.device, dtype=torch.long)
        target = target * (all_qry_reps.size(0) // all_tgt_reps.size(0))
        loss = self.cross_entropy(scores / self.temperature, target)
        if self.is_ddp:
            loss = loss * self.world_size

        return loss

    def _dist_gather_tensor(self, t: Tensor):
        t = t.contiguous()
        all_tensors = [torch.empty_like(t) for _ in range(self.world_size)]
        dist.all_gather(all_tensors, t)
        all_tensors[self.process_rank] = t
        all_tensors = torch.cat(all_tensors, dim=0)
        return all_tensors

    def compute_similarity(self, q_reps, p_reps):
        return torch.matmul(q_reps, p_reps.transpose(0, 1))