src/model/model_analysis.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']


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()

    def encode_input(self, input):
        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 = hidden_states.hidden_states[-1]
            pooled_output = self._pooling(hidden_states, input['attention_mask'])
            return pooled_output

    def encode_all_layers(self, input):
        """
        Extract pooled embeddings from ALL layers for analysis.
        Returns: tensor of shape [num_layers, batch_size, hidden_dim]
        """
        # Ensure we get hidden states
        input_kwargs = {"return_dict": True, "output_hidden_states": True}
        
        # 1. Forward pass based on backbone type
        if getattr(self, "model_backbone", None) == INTERNVIDEO2:
            # InternVideo logic (simplified for analysis, might need adjustment if used)
            if "input_ids" in input.keys():
                output = self.encoder.get_text_encoder()(
                    input["input_ids"], attention_mask=input["attention_mask"], **input_kwargs
                )
                hidden_states = output.hidden_states
            else:
                # Vision part logic is complex in InternVideo, skipping for generic VLM focus
                raise NotImplementedError("Layer-wise analysis for InternVideo vision not implemented yet.")
        
        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)
            output = self.encoder(**input, **input_kwargs)
            hidden_states = output.hidden_states
            
        else:
            # Generic HF models (Qwen2-VL, Phi3V, etc.)
            output = self.encoder(**input, **input_kwargs)
            hidden_states = output.hidden_states

        # 2. Iterate through all layers and pool them
        # hidden_states is a tuple of (L+1) tensors. We usually skip the embedding layer (index 0) or keep it.
        # Let's keep indices 1 to end (Transformer layers).
        
        all_layer_embeddings = []
        # Check if attention_mask is present for pooling
        attn_mask = input.get('attention_mask', None)
        
        # Iterate over layers (skipping the initial embedding layer usually at idx 0)
        # Qwen/Llama hidden_states usually includes input embeddings at 0.
        start_idx = 1 if len(hidden_states) > 1 else 0
        
        for i in range(start_idx, len(hidden_states)):
            layer_state = hidden_states[i] # [Batch, Seq, Dim]
            
            # Use the existing _pooling method logic manually
            # We assume _pooling takes (hidden_state, attention_mask)
            pooled = self._pooling(layer_state, attn_mask)
            
            if self.normalize:
                pooled = torch.nn.functional.normalize(pooled, p=2, dim=-1)
                
            all_layer_embeddings.append(pooled)

        # Stack to [Layers, Batch, Dim]
        return torch.stack(all_layer_embeddings)

    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)
        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_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 = self.encode_input(qry) if qry else None  # (bsz_per_device, dim)
        tgt_reps = self.encode_input(tgt) if tgt else None # (bsz_per_device, dim)

        if qry_reps is None or tgt_reps is None:
            return {"qry_reps": qry_reps, "tgt_reps": tgt_reps}

        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))